Systems biology and omics technology

B A S I C  L E V E L

In the last decades the rapid progress of the molecular biology and the achievements in this scientific area is directly related to the advances in the computer sciences and the development of new software tools.

Contents

 

Systems Biology and Omics Technologies: The Big Picture

In the last decades the rapid progress of the molecular biology and the achievements in this scientific area is directly related to the advances in the computer sciences and the development of new software tools. The application of different omics-related technologies (genomics, epigenomics, transcriptomic, proteomics, metabolomics, etc.) leads to the accumulation of versatile biological data and their combined interpretation open new possibilities for the scientists to move from studying isolated biological molecules towards a broad analysis of large sets of biological molecules. The biological sciences have become big-data sciences which are of growing importance for solving problems concerning human health and environment. The big data challenges are not only their size but also their increasing complexity.

Systems biology of the cell: from single-omics to multi-omics experiments data

Systems biology has emerged as a new interdisciplinary study field which has gained a tremendous attention in the last few years. Although the term “Systems biology” has been used in various ways, it is generally understood to describe research that combines biology with seemingly disparate disciplines such as physics, biochemistry, engineering, biostatistics, mathematics, computer science, bioinformatics and others. The main purpose of this cross-disciplinary field is to obtain a highly parallel view of the biological systems at the molecular level and understand how they function as a whole instead as a sum of parts. By clarifying the molecular mechanisms and processes at different system levels in the organism or a cell, it could be predicted how these systems change over time and under varying conditions. The great amount of information obtained on molecular bases of cell physiology and organization could provide solutions concerning diseases, toxicities, therapies, drug discovery, etc.

Systems biology has launched as a distinct discipline in 1950s with the work of the systems theorists Mihajlo Mesarovic and Ludwig von Bertalanffy on the general systems theory. The main concept of this theory is that the dynamics of any system is a result of the relationships between its separate units which determining its function. At that time, by thoroughly studying enzymes and kinetics of enzyme reactions, biochemists, followers of the system theory, tried to examine the behavior of the biochemical pathways as a network instead as a sum of its parts. Moving away from the biochemical area, Reinhart Heinrich developed theoretical approaches for the description and quantitative investigation of signaling pathways and developed a metabolic control theory. The need for an integrated approach for a more detailed study and understanding of the complex biological processes became evident.

Systems biology is highly dependent on the biological information obtained by molecular biology and / or individual omics approaches, but often they are largely hypothesis-driven or reductionist. In the reductionist approach the functional properties of the individual molecular components in the complex biological systems are studied. After the completion of the Human Genome Project the modern science has developed beyond the gene-centered view of the earlier genomic era. This period designated as a postgenomic era is characterized by major changes in the scientific research conduction and results interpretation and according to the Bloom this is the end of the “naïve reductionism”. Nevertheless, that these classical methods alone cannot provide a complete understanding of living organisms, they will continue to be an essential element of all biological research. In this context, Systems biology has caused the fundamental change in traditional approaches. It uses a new hypothesis-generating holistic approach, focuses on the study of subsystems, which allows deeper understanding of the whole process. The galactose utilization (GAL) pathway of yeast Saccharomyces cerevisiae is one example of the application of Systems biology approach for rereading and interpretation of the results obtained by the reductionist approach (one gene/one protein at a time). On the basis of experimental data obtained from protein and RNA levels analyses as well as protein–protein and protein–DNA interactions and their integration into a single model, new hypothesis was provided on the regulation of GAL pathway, which was experimentally verified afterward.

Through the use of computational and mathematical tools a large amount of experimental data is collected and integrated by Systems biology for revealing unknown patterns and hypothesis generation. This is an important strategy to gain new insights of the biological systems and also help in the experimental design.

The systems-level approach of the biological systems is aimed at clarifying the following three issues in the context of molecular network: i) what are the individual components of the system; ii) how do they work separately? and iii) how do these components work together to accomplish a task? (3). In the context of molecular networks, the basic purpose of the Systems biology can be summarized as follows: (i) an understanding of the structure of all the components of a cell / organism up to molecular level, (ii) the ability to predict the future state of the cell / organism under a normal environment, iii) the ability to predict the output responses for a given input stimulus, and iv) the ability to estimate the changes in system behavior upon perturbation of the components or the environment.

The term “systems” in Systems biology defines different span of complexity ranging from two macromolecules that interact to perform a particular task to whole organisms (Fig.1).

Thus, Systems biology shares a common scientific goal with the discipline of physiology which is dedicated to study the integrated function of the entire complex biological systems. Organisms are much more than the sum of their parts and the complexity of physiological processes cannot be understood simply by studying how the components work in isolation. For example, genes encode the primary structure of the cellular proteins that in turn carry out specific functions supporting cell metabolism and physiology and organism development. At the same time, the proteins in each cell do not act in isolation but in a complex network whose are important to specify phenotypes. Most biological processes are highly dynamic and often involve more than one type of molecules. At the same time one phenotype may be conditioned by several different molecular and epigenetic mechanisms and one type of molecule may be involved in different phenotypes. Actually, even in the same organism, a protein may play different roles in different cells; or signal pathway effectors may induce various differentiation programs in different cell lineages. Moreover, in multicellular organisms, single cells do not have an existence independent from the whole organism, they are ontogenetically linked.

Regardless of their common goal, Systems biology and physiology utilize different tools and experimental approaches, which leads to obtaining a different set of experimental data. A cross-disciplinary Systems biology is a modern rapidly evolving discipline due to the fact that it uses a variety of methods and tools including large-scale functional genomics and other omics technologies, bioinformatics and computer modeling, which are not exploited by the physiologists. The role of computational biology in Systems biology is to process and analyze massive amounts of empirical data produced by different omics levels which in turn leads to biological knowledge discovery and generation of research hypothesis. The application of in silico and simulation-based analyses give opportunities to make predictions which further are confirmed by experimental assays. The rapid advance of information technologies including also enhancement of public web-databases of biological information also support the development of omics studies and respectively lead to the progress of systems biology.
In order to comprehensively study biological processes, it is critical to understand how the separate biological layers (genome, epigenome, transcriptome, proteome, metabolome, and ionome) interconnect with one another in a cellular system and how the flow of biological information takes place. The combination of different omics analyses employing multi-omics approach is required to design a precise picture of living organisms. Transcriptomics, proteomics, and metabolomics data can answer key biological questions regarding the expression of transcripts, proteins, and metabolites, independently, but a systematic multi-omics integration can comprehensively assimilate, annotate, and model these large data sets.

Different branches of omics – challenges to combine biological information

The transfer of genetic information in biological systems is realized from DNA to mRNA to protein and this is stated as the Central dogma in molecular biology. The three main processes in each cell are replication, transcription and translation. Their constant flow ensures the maintenance and conversion of the genetic information, encoded in DNA into gene products, which are either RNAs or proteins, depending on the gene. Replication is a process of a cell`s DNA duplication and it is the basis for biological inheritance. It is carried out by the enzyme DNA polymerase that copies a single parental double-stranded DNA molecule into two daughter double-stranded DNA molecules. The enzyme RNA polymerase creates an RNA molecule from DNA and that process is known as transcription. The newly synthesized RNA molecule is complementary to a gene-encoding stretch of DNA. Translation makes protein from mRNA. The ribosome generates a polypeptide chain of amino acids using mRNA as a template. The polypeptide chain folds up to become a protein. In eukaryotic cells, or those cells that have a nucleus, replication and transcription take place within the nucleus while translation takes place outside of the nucleus in cytoplasm. In prokaryotic cells, or those cells that do not have a nucleus, all three processes occur in the cytoplasm. The organisms` phenotype is determined by that information transfer paradigm. Biologists have studied these “omes” for years in the form of genomics, transcriptomics and proteomics. The data from these experimental approaches are complemented by epigenomics and metabolomics that have recently been used to solve specific problems concerning many functions of an organism. The rapid development and advance in “omics” technologies determine the progressive expand of the volume of information that can be gathered in individual studies. Moreover, the current high throughput nature of these techniques has increased accessibility to this information in terms of time and cost. Many researchers are placed in a situation where they can collect several omics data sets on the same experimental samples. In aim to obtain more comprehensive conclusions on biological processes these data sets must be integrated by multi-omics approach and analyzed as a holistic system (Fig. 2).


The term “Omics” derived from a Greek word and the addition of thе suffix -ome to cellular molecules, such as gene, transcript, protein, metabolite, gives meaning to “whole,” “all,” or “complete.” The different layers of the cell, consisting of DNA and modifications (Genome, Epigenome), RNA and protein content (Transcriptome, Proteome), small molecules (Metabolome, Lipidome) and elemental composition (measured as ‘Ionome’), can be analysed by omic technologies. The combination of omic layers in a multi-ome dataset are integrated through robust systems biology which is able to reveal inter-layer mechanisms and interactions as well as the function of cell populations’ tissues, organs, and the whole organism. Omics approaches comprise a larger number of measurements per endpoint and although the number of parameters measured per analysis is increased, the number of replicates is decreased. This on one hand is due to the super estimation of methods since it is considered that more measurements would compensate a small number of samples and on the other hand is due to the cost and time of omics experiments.

The single-omic disciplines aimed at studying specific biological issues without requiring a prior understanding of the biological bases involved. Depending on the type of biomolecule they primarily focus on in a specific biological sample, omics technologies are divided into: genomics (genome / gene), metagenomics (genomes recovered directly from environmental samples), epigenomics (supporting structure of genome, including protein and RNA binders, alternative DNA structures, and chemical modifications on DNA), transcriptomics (mRNA), proteomics (peptides / proteins), metabolomics (metabolites), lipidomics (lipids) glycomes (carbohydrate and sugars), ionomics (ions).

Although none of the current omic technologies is perfect, some of them succeed to provide more comprehensive picture of the biological layer they aim to study than others. This fact is due not only to differences in the state of technological developments, but rather of the differences in the in chemical and physical complexity of each biological level.

Genomics

Genomics is the systematic study of all of an organism’s genes (the genome), including interactions of those genes with each other and with the organism`s environment. The genome is the basal biological layer in the cell and represents the total DNA of a cell or organism. Deoxyribonucleic acid (DNA) is the chemical compound that contains all the necessary genetic information needed to develop and direct the activities of nearly all living organisms. DNA molecules are made of two twisting, paired strands, often referred to as a double helix. Each DNA strand is made of four nucleotide bases – adenine (A), thymine (T), guanine (G), and cytosine (C) that pair specifically on opposite strands: A always pairs with a T; a C always pairs with a G. A sequence of three adjacent nucleotides (codon) encodes for a specific amino acid during protein synthesis or translation. The nucleotides` order along the DNA molecules determines the genetic code. The genetic code is universal because it is the same among all organisms and it is also degenerate because 64 codons encode only 22 amino acids. With its four-letter language, DNA contains the information needed to build an entire organism. A gene represents the unit of DNA that carries codes for making a specific protein or a set of proteins. On the basis of the intrinsic complementarity of the nucleotide bases in DNA, it has become possible to rapidly sequencing a huge number of genomes at a relatively low cost. From the efficiently sequenced genomes predictions about RNA and protein sequences could be made which is combined in the multi-omic approaches. The digital form of the DNA sequences as a kind of biological omic information could be easily stored in biological databases and shared between scientists all over the world. The sequencing of the first whole genome of the bacterium Haemophilus influenza in 1995 made a revolution in molecular biology. The big volume of sequences data was produced which was beyond being completely interpreted. To decipher relevant genetic information from the background genetic material was almost an impossible task. To overcome this, more detailed biological information was required. Information on the transcription of the genetic material and subsequent production of proteins was necessary.

An essential research direction in the field of Systems biology is the functional genomics. This discipline develops and exploit large-scale and high-throughput methodologies in aim to define and analyze gene function at a global level. It is very important for developing a systems level understanding of a biological process to identify the genes, and the proteins they encode which work together to give rise to that process. Functional genomics is an integrative scientific field which combines multiple large-scale datasets in attempts to generate insights into gene function. At first, genes were analyzed individually, but the advance of technologies in recent years make possible the expression of thousands of genes to be analyzed simultaneously. This large-scale analysis of gene function is called DNA microarrays technology (Fig. 3).

DNA microarrays measure differences in DNA sequence between individuals and this technology can provide information for the function of uncharacterized genes and also can reveal clusters of interacting genes that give rise to a biological process of interest. Microarray analyses can also provide insights into mechanisms of gene regulation, evolution and the etiology of disease. For example, the microarray data analysis could reveal abnormalities such as chromosomal insertions and deletions or abnormal chromosomal numbers in a process called comparative genomic hybridization. The most common variations in DNA sequences between people are single nucleotide polymorphisms (SNPs), in which one nucleotide is substituted for another; this may have functional significance if the change results in a codon for a different amino acid. They are of particular interest when linked with diseases with a genetic determination. Single nucleotide polymorphism profiling also has a role in pharmacogenomics in exploring individual patient responses to drugs.

Epigenomics

The epigenetic modifications such as DNA methylation, histone modifications, 2D and 3D analysis of chromatin structure and non-coding RNA are studied by the methods of epigenomics. This -omics direction focuses on the analysis of overall epigenetic changes which provides important information regarding mechanisms and function of gene regulation across many genes in a cell or organism. Scientists have understood that the individual`s phenotype is not controlled only by the genome but also by the changes in regulation of gene activities. Genetic experiments in humans and animals have proved that, in addition to the DNA sequence, epigenetic marks may be transmitted from parent to offspring via the gametes and influence the phenotype of offspring.
Epigenomics defines the modifications in the regulation of gene activities that act without, or independently of, changes in gene sequences. Some definitions confine epigenetics/epigenomics to modifications of the phenotype without changes of the DNA sequence that are transmitted to the next generations. The epigenetic modifications are chemical modifications, which are not coded by the genome and they coordinate how and when genes are expressed. The epigenomics explores heritable, reversible modifications of DNA and chromatin that do not influence the primary nucleotide sequences. While the term epigenomics would describe the analysis of epigenetic changes across many genes in a cell or throughout an entire organism, epigenetics centers on processes that regulate how and when specific genes are turned on and turned off. Several factors are known to affect the epigenetic regulation: 1) Nutrition (dietary factors); 2) Environmental factors; 3) Radiation exposure; 4) Infectious agents; 5) Immunological factors; 6) Genetic factors; 7) Toxic agents; 8) Mutagens.
Versatile detection methods are applied for analysis of epigenetics modifications in the cell. DNA methylation are analyzed by digestion assays and bisulfite sequencing of DNA. In digestion assays, the genomic DNA is fragmented with methylation-sensitive and methylation-insensitive endonucleases. Methylation-sensitive restriction enzymes cleave only unmethylated DNA and leave methylated DNA fragments undigested. The fragmented DNA can be analyzed by sequencing or microarray, and thus the methylation sites are mapped. The disadvantage of this method is that it only studies the DNA sequences near the targets of chosen restriction enzymes, and is usually used to characterize only global levels of DNA methylation instead of identifying methylated DNA at the single residue level. Resolution is improved by using the method of bisulfite sequencing. This strand-specific method is used to convert unmethylated cytosine to uracil, whereas methylated cytosine residues remain unaffected. The resulting DNA is amplified in PCR and can be analyzed by sequencing of the regions of interest. The analysis can also be done using MALDI-TOF mass spectrometry or microarrays. DNA methylation can also be studied through methods based on the principle of affinity chromatography. The sample containing fragmented DNA is loaded to a column with bound methyl-binding domain (MBD) of MeCP2, specific for methylated DNA. The methylated DNA fractions are eluted out and analyzed with genome-wide techniques such as MBDCap-seq/MethylCap-seq. Another approach is immunoprecipitation of methylated DNA (MeDIP), which is based on the specific binding of antibodies to the methylated cytosine (5mC) in DNA. This method is also used for the analysis of hydroxylated methylcytosine (5hmC). Purified fragments are analyzed by PCR, sequencing or microarray.
A widely used technique for detection of histone modifications is the immunoprecipitation of chromatin (ChIP). It is used for identifying local posttranslational modifications of the histone tails and for monitoring changes in the modifications in response to different stimuli. Antibodies against specific histone modifications (such as trimethylation of histone H3 lysine K27) are used for immunoprecipitation of that chromatin regions that have these modifications. ChIP can also be applied to study the binding of transcription factors and enzymes to the chromatin, using a specific antibody against the factor of interest. The associated chromatin regions can be analyzed either by PCR to detect specific loci or deep sequencing for a more global view, as well as by using microarray (ChIP-chip), but in the last case the resolution of data is weaker compared to sequencing. During the last decade the basic ChIP method has been developed which leads to the emergence of other diverse ChIP methods such as μChIP-seq for low, micro-scale sample amount analysis, and the modern single-molecule real-time sequencing (SMRT; “third generation sequencing”) of ChIP-samples.

Epigenetic modifications can also be studied by methods identifying the active regions of chromatin. In the DNase-seq method, the enzyme DNase I is utilized to digest the DNA that is not protected by nucleosome structure, so the regions that are sensitive to DNase are associated with active genes. Sequencing, microarray or Southern Blot techniques can be used for the results analysis and interpretation. The open chromatin can be detected also by the formaldehyde-assisted isolation of regulatory elements (FAIRE), which result in the isolation of open, nucleosome-depleted chromatin regions. The FAIRE method is based on the cross-linking between formaldehyde and DNA, histones and other proteins associated with it. The DNA is sonicated to be fragmented and after that isolated with phenol-chloroform extraction. Only DNA that is not bound by nucleosomes and associated proteins remains in the aqueous phase in the extraction, thus resulting in the isolation of the open and active regions of the genome. The isolated fragments can be analyzed again with different methods, such as PCR, microarray, and sequencing. Chromatin can also be studied by chromatin conformation capture (3C) technique that identifies the chromatin regions that are physically associated together, such as promoters with enhancers. 3C is often analyzed by PCR, but nowadays also by deep sequencing of the interactions globally (Hi-C).

Noncoding RNAs (ncRNAs) act as epigenetic modifiers to strongly regulate gene expression. Their aberrant expression mainly in the form of microRNAs (miRNAs) and long noncoding RNAs, may modify gene expression and trigger complicated immune disorders. The analysis of the RNA component of epigenetics most often is done on a genome-wide scale using next-generation sequencing methods. The changes in ncRNA and mRNA can be characterized by deep sequencing. The expression of RNAs can be analyzed also by quantitative Polymerase Chain Reaction (qPCR). Quantitative PCR also known as real-time PCR is a laboratory technique in molecular biology, by which the amount of the PCR product can be determined in real-time, and is very useful for investigating gene expression.

Transcriptomics

When genes are expressed, the genetic information stored in DNA is transferred to RNA (ribonucleic acid). RNAs are important macromolecules, composed of linear chains of nucleotides (Fig. 4), which are produced by the cellular process of transcription. RNAs perform diverse cellular and biological functions as either serve as templates for protein synthesis, or play critical catalytic and regulatory roles. In transcription the genetic information is transferred from DNA to mRNA. This process is carried out by an enzyme RNA polymerase. Several classes of RNAs exist in cells (messenger RNAs (mRNA), transfer RNAs (tRNA), ribosomal RNA (rRNA), small nuclear RNA (snRNA), small nucleolar RNA (snoRNA), short interfering RNA (siRNAs), micro RNA (miRNAs), long non-coding RNA and pseudogenes), but those which take part in protein synthesis are messenger RNA (mRNA), transfer RNA (tRNA) and ribosomal RNA. Messenger RNA (mRNA) is a single-stranded molecule that mediate the transfer of genetic information to the ribosomes where the proteins are synthesized. In eukaryotes, each gene is transcribed to yield a single mRNA, whereas in prokaryotes, a single mRNA molecule may carry the genetic information from several genes; that is, several protein coding regions. A linear correspondence exists between the base sequence of a gene and the amino acid sequence of a polypeptide. Each group of three consecutive nucleotides encode the location of a particular amino acid in a protein molecule and each such triplet of bases is called a codon. Codons are translated into sequences of amino acids by ribosomes (which themselves consist of proteins and rRNA), tRNA, and helper proteins called translation factors.

The term “transcriptome” is widely used to designate the complete set of all the ribonucleic acid (RNA) molecules in a cell, tissue, or organism. The transcriptome reflects the molecular activity in cells, the genes that are actively expressed at any given moment. It encompasses all forms of RNAs molecules including protein coding, non-protein coding, alternatively spliced, alternatively polyadenylated, alternatively initiated, sense, antisense, and RNA-edited transcripts. Respectively, transcriptomics studies all types of transcripts within a cell or an organism, including mRNAs, miRNAs and different types of long noncoding RNAs (lncRNAs). Transcriptomics covers everything relating to RNAs such as their transcription and expression levels, functions, locations, trafficking, and degradation. It also includes the structures of transcripts and their parent genes with regard to start sites, 5′ and 3′ end sequences, splicing patterns, and posttranscriptional modifications.

The major studies of transcriptomics are aimed at:

  • characterization of different states of cells (i.e. development stages), tissues or cell cycle phases by expression patterns;
  • investigation of the molecular mechanisms underlying a phenotype;
  • identifications of biomarkers differently expressed between the diseased state and healthy state;
  • differentiation of disease stages or subtypes (e.g. cancer stages);
  • setting up the causative relationship between genetic variants and gene expression patterns to illuminate the etiology of diseases.

During the last three decades the technological advance has revolutionized transcriptome profiling and redefined what is possible to investigate. Integration of transcriptomic data with other omics is giving an increasingly integrated view of cellular complexities facilitating holistic approaches to biomedical research.

The major techniques applied for transcriptome study are:

  • Expressed sequence tag (EST)-based methods
  • SAGE
  • Hybridization-based microarray
  • Real-time PCR
  • NGS-based RNA-sequencing (RNA-seq) methods,
  • RNA interference
  • Bioinformatics tools for transcriptomes analysis.

The selection of the technique is dependent on cost effectiveness, sensitivity, high throughput, and minimal concentration of starting RNA. The methodology involves RNA isolation, purification, quantification, cDNA library construction, and high-throughput sequencing.

  • Expressed sequence tag (EST)-based methods – Expressed sequence tags (ESTs) are relatively short DNA sequences (usually 200–300 nucleotides) usually generated from the 3′ ends of cDNA clones from which PCR primers can be derived and used to detect the presence of the specific coding sequence in genomic DNA. The sequencing of the ESTs gives an overview on the expression level of the gene. As more and more EST data have become publicly available, the usage of ESTs has expanded to other areas, such as in silico genetic marker discovery, in silico gene discovery, construction of gene models, alternative splicing prediction, genome annotation, expression profiling, and comparative genomics. In comparison with whole genome sequencing, EST technology is simpler and less costly, especially in the case of large genomes. There are EST databases such as dbEST (NCBI EST) (https://www.ncbi.nlm.nih.gov/genbank/dbest/) that contain sequence data and other information on “single-pass” cDNA sequences, or “Expressed Sequence Tags”, from a number of organisms and thus serve as a reference for the expression profile of an organism.
  • Serial Analysis of Gene Expression (SAGE) is a transcriptomic technique used by scientists to produce a snapshot of the messenger RNA population in a sample of interest in the form of small tags that correspond to fragments of those transcripts. This technique is advantageous over EST because only short “tags” of about only 15 bases are sequenced. The short fragments generated are then joined together and sequenced. A pool of cDNA can be subjected to high-throughput NGS known as RNA-seq for quantification, discovery of novel ESTs, and profiling of RNAs.
  • DNA chip, gene chip, biochip, or microarray is a collection of DNA, cDNA, oligonucleotides spots attached to a solid support such as glass or silicon chip. Through this hybridization-based method the expression levels of thousands of genes are monitored simultaneously. The major restriction of this technology is that genome sequence information is a prerequisite and also higher background inherent of hybridization technique.
  • Quantitative real-time PCR (qRT-PCR) is a type of PCR for reliable quantification of low-abundance mRNA or low-copy transcripts. The major advantages of this technique are its high sensitivity, better reproducibility, and wide dynamic quantification range. It facilitates gene expressions and regulation studies even in a single cell based on its exponential amplification ability. The availability of diverse types of fluorescence monitoring system attached with the PCR resulted in its popularity for gene-expression studies. The problems of nonspecific amplification, formation of primer-dimers are some of the limitations of qRT-PCR.
  • RNA-sequencing is one of the advanced high-throughput technology for transcriptomics. RNA-seq, also known as whole-transcriptome shotgun sequencing, utilizes NGS tools. The advantages of RNA-seq are that it does not rely on the availability of the genome sequence, has no upper quantification limits, shows high reproducibility, and possesses a large dynamic detection range.

The progress of transcriptomics led to a number of biological discoveries. This omic technology laid the foundation of the first real multi-omic studies, based on the comparisons between DNA sequence and mRNA expression. Nowadays, transcriptional analysis remains more frequently employed by most biologists since the data obtained are still more easily analyzed and shared than the more ‘downstream omics’ such as proteomics and metabolomics. More recently, transcriptomics is enjoying a second revival, as it is in many cases applicable to single cells.

Proteomics

Proteins command cellular structure and activity, provide the mechanisms for signaling between cells and tissues, and catalyze chemical reactions that support metabolism. The protein structure dictates the function (or dysfunction). Proteins can be the base cause of diseases (such as Alzheimer’s or Huntington’s disease), and they can be used to cure it (e.g., antibodies are used as therapeutics against viral and bacterial infections). The set of all expressed proteins in a biological system under a specific, defined conditions is known as proteome. The word “proteome” is a combination of protein and genome and was coined by Mark Wilkins in 1994.

The main aim of proteomics is a large-scale experimental analysis of the structure and function of this entire set of proteins produced by a living organism. The term “proteomics” first appeared in 1997 and referring to a core technology in systems biology approaches which studies the proteome. The function of cells is dependent on the proteins that are present in the intra- and intercellular space and their abundance. The synthesis of proteins in the cell is based on mRNA precursors but it is impossible to predict the abundance of specific proteins only based on analysis of gene expression. The reason for this is that native proteins undergo post-translational modifications (PTMs) or alterations in response to changes in the environment. The proteome is a dynamic reflection of both genes and the environment and is a valuable source for biomarker`s discovery because proteins are most likely to be ubiquitously affected in disease and disease response. The complete characterization of all proteins has been the goal of proteomics since its initiation almost 25 years ago. It tends to do more than merely identify proteins potentially present in a sample, but also to assess protein abundance, localization, posttranslational modifications, isoforms, and molecular interactions. Proteomics aims to study the flow of biological information through protein pathways and networks, with the eventual aim of understanding the functional relevance of proteins. This requires the development of technologies that can detect a wide range of proteins in samples from different origins. Various technologies are used in proteomics and most often they are applied in combination, for example one- or two-dimensional gel electrophoresis with mass spectrometry (MS) or liquid chromatography and MS.

The conventional techniques for isolation and purification of proteins are chromatography based such as ion exchange chromatography (IEC), hydroxyapatite, size exclusion chromatography (SEC) and affinity chromatography. For analysis of selective proteins, enzyme-linked immunosorbent assay (ELISA) and western blotting can be used. These techniques may be applied for analysis of individual proteins but they cannot define protein expression level. Sodium dodecyl sulfate-polyacrylamide gel electrophoresis (SDS-PAGE), two-dimensional gel electrophoresis (2-DE) and two-dimensional differential gel electrophoresis (2D-DIGE) techniques are used for separation of complex protein samples.

  • Mass spectrometry (MS) enables the analysis of proteomes and usually is the preferred method for identifying proteins present in biological systems. The three primary applications of MS to proteomics are: cataloging protein expression, defining protein interactions, and identifying sites of protein modification. Mass spectrometry measures the mass-to-charge ratio (m/z) of gas-phase ions. Mass spectrometers consist of an ion source that converts analyte molecules into gas-phase ions, a mass analyzer that separates ionized analytes based on m/z ratio, and a detector that records the number of ions at each m/z value. The development of electrospray ionization (ESI) and matrix-assisted laser desorption/ionization (MALDI), the two soft ionization techniques capable of ionizing peptides or proteins, revolutionized protein analysis using MS. The mass analyzer is central to MS technology. For proteomics research, four types of mass analyzers are commonly used: quadrupole (Q), ion trap (quadrupole ion trap, QIT; linear ion trap, LIT or LTQ), time-of-flight (TOF) mass analyzer, and Fourier-transform ion cyclotron resonance (FTICR) mass analyzer. They vary in their physical principles and analytical performance. “Hybrid” instruments have been designed to combine the capabilities of different mass analyzers and include the Q-Q-Q, Q-Q-LIT, Q-TOF, TOF-TOF, and LTQ-FTICR.
  • Tandem mass spectrometry is additional experimental procedure which is also known as MS/MS or MS2. This is a key technique for protein or peptide sequencing and PTM analysis where two or more mass analyzer are coupled. The molecules of a given sample are ionized and the first spectrometer (designated MS1) separates these ions by their mass-to-charge ratio (often given as m/z or m/Q). Ions of a particular m/z-ratio coming from MS1 are selected and then made to split into smaller fragment ions, e.g. by collision-induced dissociation (CID), electron-capture dissociation (ECD), electron-transfer dissociation (ETD), ion-molecule reaction, or photo dissociation. These fragments are then introduced into the second mass spectrometer (MS2), which in turn separates the fragments by their m/z-ratio and detects them. The fragmentation step makes it possible to identify and separate ions that have very similar m/z-ratios in regular mass spectrometers. The complexity of the biological systems requires that the proteome be separated before analysis. Both gel chromatography- and liquid chromatography-based separations have proven useful in this regard. Typically, after these extensive separations, proteins are characterized by MS analysis of either intact protein (top–down) or enzymatically digested protein peptides (bottom–up). Protein identifications are made by comparing measured masses of intact proteins (top–down) or digested protein peptides (bottom–up) to calculated masses obtained from genome data.
  • Isobaric Taq Labelling (ITL) is a commonly used method for quantification of proteins. In proteomics the quantification of the protein abundance is an important focus. Protein expression levels represent the balance between translation and degradation of proteins in cells. It is therefore assumed that the abundance of a specific protein is related to its role in cell function. ITL provides opportunity for simultaneous identification and quantification of proteins from multiple samples in a single analysis. To measure proteins` quantity in a given sample, peptides are labeled with chemical tags that have the same structure and nominal mass, but vary in the distribution of heavy isotopes in their structure. These tags, commonly referred to as tandem mass tags, are designed so that the mass tag is cleaved at a specific linker region upon higher-energy collisional-induced dissociation (HCD) during tandem mass spectrometry yielding reporter ions of different masses. Protein quantitation is accomplished by comparing the intensities of the reporter ions in the MS/MS spectra. Other quantitative techniques are ICAT labelling and Stable Isotopic Labeling with Amino Acids in Cell Culture (SILAC). The ICAT has also expanded the range of proteins that can be analyzed and permits the accurate quantification and sequence identification of proteins from complex mixtures. SILAC is an MS-based approach for quantitative proteomics that depends on metabolic labeling of whole cellular proteome. The proteomes of different cells grown in cell culture are labeled with “light” or “heavy” form of amino acids and differentiated through MS.
  • X-ray crystallography and nuclear magnetic resonance (NMR) spectroscopy are two major high-throughput techniques that provide three-dimensional (3D) structure of protein that might be helpful to understand its biological function. With the support of high-throughput technologies, a huge volume of proteomics data is collected. Bioinformatics databases are established to handle enormous quantity of data and its storage. Various bioinformatics tools are developed for 3D structure prediction, protein domain and motif analysis, rapid analysis of protein–protein interaction and data analysis of MS. The alignment tools are helpful for sequence and structure alignment to discover the evolutionary relationship. Proteome analysis provides the complete depiction of structural and functional information of cell as well as the response mechanism of cell against various types of stress and drugs using single or multiple proteomics techniques.

The major techniques used in the proteomics research are given in the Fig. 5.

Metabolomics

The metabolome was first described by Oliver and colleagues in 1998, during their pioneering work on yeast metabolism, as the complete complement of small molecules in a biological system or fluid. It includes all the small molecules such as lipids, amino acids, fatty acids, carbohydrates and vitamins which are known as metabolites and are produced as a result of cellular metabolism. They participate in the metabolic processes in the cells by interacting with other biological molecules following metabolic pathways. The metabolites` status in the biological systems is highly variable and time-dependent and changes in levels of key metabolites are observed due to the genetic, environmental, nutritional and other factors.

Metabolomics (metabolome analysis) is the most recently introduced strategy among the Omics that systematically identify and quantify the metabolites present in a cell, tissue, organ, biofluids, or organism at a specific point of time. Methods used in metabolomics aim to measure low molecular weight compounds (metabolites) with different physical characteristics such as compound polarity, functional groups and structural similarity. On the basis of these properties the metabolome is divided into subsets of various metabolites and for their investigation analytical procedures optimized for each type of molecule is applied in metabolomics.

The metabolites are products of biochemical reactions which are carried out with the participation of proteins of the proteome (Fig. 6). Their concentrations are dependent on the interactions of other processes (transcription, translation, cellular signaling, etc.), they have been investigated as reporters for metabolism in PD. This in turn determines the biological structure and function of the final phenotype of the organism. Therefore, changes in gene expression, proteins` function and the environment directly affect the metabolites` concentration in a biological system. The metabolome is composed of a relatively small number (around 5000), but different types of metabolites which make the metabolome more physically and chemically complex than genome, transcriptome and proteome. Moreover, the genome, transcriptome and proteome consist of compounds which are part of the metabolome. In addition, the components of the metabolome are highly conserved between organisms in comparison to genome, transcriptome and proteome which is why it is considered that the metabolome is evolutionary the oldest part of the cell.

Metabolomics is the most appropriate strategy for determination of disease-associated biomarkers. Comprehensive study of metabolites is a desirable tool for diagnosing disease, identifying new therapeutic targets, and enabling appropriate treatments. The analyses of small numbers of metabolites are used in diseases` diagnostics for decades, for example, the development of blood glucose test strips in the 1950s to test for diabetes or quantifying phenylalanine in newborns to screen for phenylketonuria.

Metabolomics analyses can be grouped into two main approaches: targeted and untargeted metabolomics. In targeted analysis the metabolites which are determined are known and represent specific pathway(s) or class(es) of molecules. On the contrary, the untargeted metabolomics aims to quantify and identify as many metabolites as possible. Targeted metabolomics refers to absolute quantification (nM or mg/ml) and uses an internal standard and semi quantitative or quantitative analysis to detect known compounds related to specific pathways. The untargeted approach measures all the present metabolites in a sample and apply relative quantification (fold change) and comparison between samples. The key role that metabolomics plays in multi-omics integration and system modelling is due to the fact that it can be quantitative. Systems modeling cannot be performed without accurate values or accurate concentrations as inputs and, likewise, systems models cannot be easily verified without accurate, quantitative concentrations as outputs. Metabolomics can deliver both (quantitative input and output data), making it extremely valuable to systems modelers.

Due to the metabolome`s enormous chemical complexity, it cannot be studied comprehensively by a single technology. The earliest application of metabolomics dates back to 1970s when gas chromatography–mass spectrometry was used for metabolite profiling of clinical urine samples. Nicholson and others in the 1980s made a profile of clinical samples by the application of nuclear magnetic resonance spectroscopy. Mass spectrometry coupled with both liquid chromatography and capillary electrophoresis are also applied in metabolomics to complement the analytical techniques available. Some of the analytical instruments utilized for the purposes of metabolomics research are being applied frequently and routinely while others have specific roles and are applied less frequently.

  • Gas chromatography – Mass spectrometry (GC-MS) – Among the commonly used methods in metabolomics is GC-MS. As the name implies, GC-MS unifies two techniques to form a single method for analyzing mixtures of chemicals. Gas chromatography separates the components of a mixture and mass spectroscopy characterizes each of the components individually. By combining the two techniques, each metabolite in a sample can be both qualitatively and quantitatively evaluate. This chromatographic technique is used to study metabolites which have a low boiling point and which will be present in the gas phase at the temperature range 50-350°C. These metabolites can have a low boiling point in their biologically native form or the boiling point of a metabolite can be decreased through a chemical alteration, also known as chemical derivatisation. The sample containing metabolites is introduced (injected) into a mobile phase, which in the gas chromatography is an inert gas such as helium. The mobile phase carries the sample mixture through what is referred to as a stationary phase. The metabolites are separated on the basis of their adsorption to the stationary phase. The stationary phase is usually contained in a glass or stainless steel column and represents a chemical that can selectively adsorb components in a sample mixture. By changing characteristics of the mobile phase and the stationary phase, different mixtures of chemicals can be separated. As the individual metabolites elute from the GC column, they enter the electron ionization (mass spec) detector. There, they are bombarded with a stream of high-energy electrons (70 eV) which causing them to break apart into fragments. These fragments can be large or small pieces of the original molecules. The mass spectrum obtained for a given chemical compound is basically the same every time. Therefore, the mass spectrum is a fingerprint for the molecule. This fingerprint can be used to dentify the compound.
  • Liquid Chromatography-Mass Spectrometry (LC-MS) – The main difference between GC-MS and LC-MS is that in liquid chromatography (LC), the mobile phase is a solvent. The LC separates mixture of metabolites which are in liquid form, usually contains methanol, acetonitrile and water. By using different packing of columns (different stationary phase) with high efficiency small amount of complex mixture can be separated. The columns used in HPLC are consists of Octadecyl (C18), Octyl (C8), Cyano, Amino, Phenyl packing’s and generally their length is about 50mm to 300mm. The columns are used on the basis of nature of compounds to be separated. This liquid containing mixture of components is transferred into the ion source of mass spectrometer where the process of ionization takes place and droplets carry an excess of positive or negative electric charge are formed. The ionization could be achieved by the use of different types of ionization sources and interfaces. After ionization the ions are transferred into mass Analyser where the separation of ions are done according to their mass to charge (m/z) ratio.
  • Capillary Electrophoresis-Mass Spectrometry (CE-MS) – this analytical technique is also called capillary zone electrophoresis-mass spectrometry. CE-MS involves the separation of ionic species in the liquid phase via the application of high voltages and is usually coupled to an electrospray mass spectrometry. Capillary electrophoresis separates metabolites based on their electrophoretic mobility in a liquid electrolyte solution operating in an electric field. Electrophoretic mobility is dependent on the charge and size of the metabolite and so separation of metabolites with different sizes and/or charges is possible. All metabolites have to be charged to allow any mobility to occur. In GC-MS, LC-MS and CE-MS there is a separation of metabolites before their detection with mass spectrometry. This peculiarity provides the ability to detect metabolites at low concentrations, commonly nanomoles/litre (nM/L) or micromoles/litre (µM/L).
  • Nuclear magnetic resonance (NMR) spectroscopy is the most widely used analytical instruments in metabolomics research together with Mass spectrometry. NMR spectroscopy applies the magnetic properties of atomic nuclei in a metabolite. Only some atoms are NMR active and include 1H, 13C and 31P; proton (1H). The technique operates by placing a liquid sample in a small internal diameter tube (for example a 5 mm tube), or occasionally a piece of tissue is studied directly using a special sample holder. The sample is pulsed with a range of radio frequencies covering all possible energies required for exciting the selected type of nuclei. The nuclei absorb energy at different radio frequencies depending on their chemical environment and then the release of this energy is measured, forming a free induction decay (FID). This FID is converted from a time domain data set to a frequency domain – using a Fourier transformation – and an NMR spectrum is constructed as the chemical shift (effectively the absorption energy) plotted against peak intensity.
  • Vibrational spectroscopy techniques such as Fourier transform–infrared (FT-IR) spectroscopy and Raman spectroscopy are applied for analysis of the metabolic changes in biological samples. The techniques` principle is based on the ultraviolet or infrared light transmission through a sample, or sometimes reflection of the light from the sample, before its detection. The approaches predominantly measure the vibrations and rotations of bonds related to different chemical functional groups resulting from the interaction of the sample with the ultraviolet or infrared light. These techniques usually lack the specificity to detect each metabolite separately but instead specific parts of the molecule absorb the ultraviolet or infrared light at specific wavelengths. For example, in FT-IR, C-H stretching vibrations characteristic of fatty acid chains are observed between the wavenumber range 3100 – 2800 cm-1 and the region between 1800 and 1500 cm-1 is dominated by amide I and amide II bands indicating the predominance of either alpha helix or beta sheet structures.

In the field of metabolomics, many hundreds of samples are routinely analyzed, and a minimum of several hundreds of metabolites are usually detected. The data obtained from the detection of metabolites in a given biological samples is followed by further analysis using statistical multivariate methods (chemometrics) to extract biological, physiological and clinically relevant information.

Advantages and disadvantages of omics technologies

The implementation of omics technologies and the integration of omics data has been realized for a broad range of research areas, including food and nutrition science, systems microbiology, analysis of microbiomes, genotype–phenotype interactions, systems biology, natural product discovery and disease biology. Recently omics-based approaches have been significantly improved with the addition of novel concepts such as exposome/exposomics (role of the environment in human diseases), adductomics (study of compounds that bind DNA and cause damage and mutations), volatilomics (study of volatile organic compounds to the metabolomics/lipidomics analysis), nutrigenomics (study of how foods affect our genes), etc. However, omics technologies are aimed at primarily four omics research fields – genomics, transcriptomics, proteomics and metabolomics. The traditional research fields like genetics, pharmacogenetics and toxicogenetics which do not apply the omics concept, provide the static sequences of genes and proteins. On the contrary, the omics technologies provide simultaneously measurement of the number of proteins, genes, metabolites and enables to get large scale data in a short time. The collective analysis of the biological processes in a biological system provided by all omics is an advantage to previous traditional methods in better understanding of the whole picture of the biological system. Although large-scale omics data are becoming more accessible, the integration of real multi-omics data is still very challenging. This is due to fact that many of the specific analycal tools and experimental designs that are conventionally used for individual omics disciplines (genomics, transcriptomics, and proteomics) are not sufficiently well-suited to allow reliable comparisons and integration across multiple omics disciplines. For example, methods for sample collection and storage and the quantity and type of biological samples required in genomics studies are often not compatible with metabolomics, proteomics or transcriptomics. During collecting data, there are many problems such as data heterogeneity, small sample size in comparison to many parameters, confirmation and interpretation of data due to many interactions in biological system and deficient information on those systems. Almost in all omics experiments hundreds to thousands of target molecules and variables are measured (metabolites, proteins, genes, transcripts and SNPs). This requires the investigation of sufficient number of biological samples in aim to provide statistically reliable results. Moreover, the multi-omics data must be generated from the same set of samples to permit the direct comparison under the same conditions. However, this is not always possible due to limitations in sample biomass, sample access or financial resources. In single omics experiments, larger sample sizes are often required to overcome this problem. In addition, the integrated multi-omics data must be analyzed as single data sets before being deposited into omics-specific databases in order to make it publicly available. These issues emphasize that for the high-quality large scale-omics studies is necessary to : 1) plan an experiment properly, 2) collect, prepare, and store biological samples attentively, 3) collect quantitative multi-omics data and associated meta-data thoughtfully, 4) utilize proper tools for integration and interpretation of the data.

Some of the advantages and disadvantages of the main omics technologies are summarized in Table 1.

Omics technology Advantage Disadvantage
Genomics • Analysis of the complete genome
• Studying gene polymorphism among individuals
• Through SNPs identification a valuable information for early diagnosis and treatment of disease is provided
• Easy to implement lab techniques
• Performed genome analysis is not enough to predict the final biological effect of DNA due to epigenetics, post-transcriptional and posttranslational changes.
• Require specific and expensive lab equipment
Epigenomics Provide knowledge about regulation of gene expression • It is difficult to relate the obtained epigenetic data with gene expression, because the transcription can be affected by multiple processes
• Not every methylated region can be detected with the techniques applied.
Transcriptomics • Study of the complete set of mRNA (transcriptome)
• Identification of the major pathways involved in drug response and toxicity.
• Easy to implement lab techniques
• Protein expression is influenced by post-translational changes which lead to incorrect data
• Require expensive and specific equipment
Proteomics • Study of the complete set of proteins in a biological system
• Detection of unknown and unexpected proteins
• Expensive equipment and time-consuming procedures not applicable to entire proteome
• Some proteins are difficultly separated and purified
• MS application and interpretation of data require specially trained staff
Metabolomics • Provides adequate data about modifications in the metabolic processes in the cell
• Endogenous metabolites are less than genes, transcripts and proteins, so fewer data have to be interpreted
• Detecting of diseases` biomarkers
• Relatively low numbers of metabolites (a few thousand), as can be measured
• Specific, expensive equipment
• The use of MS and NMR require specially trained staff

Multi-omics datasets can provide a greater depth of understanding in certain scenarios, but this is not without cost. These studies frequently are based on a large numbers of comparisons, correct data type, relevant statistical analyses, specific equipment and skilled personnel and considerable investment of time and money. When designing an experiment it must be taken into account what types of omics data can and should be integrated to obtain powerful biological insights of the system being studied. The application of high throughput omics platforms is not always necessary to answer the research question. The results obtained from the traditional techniques, such as quantitative polymerase chain reaction (qPCR), enzyme-linked immunosorbent assay (ELISA), immunohistochemistry (IHC), sometimes may be enough to provide understanding for biological mechanisms. These techniques often complement the findings from a larger omics study as they are applied to verify the significant molecule identified from omics data is a true positive result.

Test: LO1- Basic level

Welcome to your LO1B: Systems Biology and Omics Technologies: The Big Picture

References

  • Altaf-Ul-Amin M, Afendi FM, Kiboi SK, Kanaya S. 2014. Systems Biology in the Context of Big Data and Networks, BioMed Res Int, 1:11.
  • Aslam B, Basit M, Nisar MA, Khurshid M, Rasool MH. 2017. Proteomics: Technologies and Their Applications. J Chromatogr Sci, 55(2).
  • Benavente L, Goldberg A, Myburg H, Chiera J, Zapata M & van Zyl L. 2015. The application of systems biology to biomanufacturing. Pharm. Bioprocess, 3(4):341–355.
  • Bertolaso M, Giuliani A, de Gara L. 2010. Systems Biology Reveals Biology of systems. Wiley Periodicals, Inc., 16 (6).
  • Bloom FE. 2001. What does it all mean to you? J Neurosci, 21:8304–8305.
  • Clish CB. 2015. Metabolomics: an emerging but powerful tool for precision medicine. Cold Spring Harb Mol Case Stud, 1: a000588
  • Coorssen JR. 2013. Proteomics. Brenner’s Encyclopedia of Genetics (Second Edition), p. 508-510.
  • Dos Santos EAF, Santa Cruz EC, Ribeiro HC, Barbosa LD, Zandonadi FS, Sussulini A. 2020. Multi-omics: An Opportunity to Dive into Systems Biology. Braz J Anal Chem, 7(29): 18-44.
  • Haas R, Zelezniak A, Iacovacci J, Kamrad S, Townsend S, Ralser M. 2017. Designing and interpreting ‘multi-omic’ experiments that may change our understanding of biology. Curr Opin Syst Biol, 6:37–45.
  • Han X, Aslanian A, Yates JR. 2008. Mass Spectrometry for Proteomics. Curr Opin Chem Biol, 12(5): 483–490.
  • Horgan RP, Kenny LC. 2011. SAC review ‘Omic’technologies: genomics, transcriptomics, proteomics and metabolomics. Obstet Gynecol, 13:189–195
  • Ideker T, Thorsson V, Ranish JA, ChristmasR, Buhler J, Eng R, Bumgarner JK, Goodlett DR, Aebersold R, Hood L. 2001. Integrated genomic and proteomic analyses of a systematically perturbed metabolic network. Science, 292:929–934.
  • Ishii N, Tomita M. 2009. Multi-Omics Data-Driven Systems Biology of E. coli. In: Lee S.Y. (eds) Systems Biology and Biotechnology of Escherichia coli. Springer, Dordrecht.
  • Kalavacharla V, (Kal), Subramani M, Ayyappan V, et al. 2017. Plant Epigenomics. Handbook of Epigenetics, 245–258.
  • Karahalil B. 2016. Overview of Systems Biology and Omics Technologies. Curr Med Chem, 23:1-10.
  • Karamperis K, Wadge S, Koromina M. 2020. Genetic testing. Applied Genomics and Public Health (Translational and Applied Genomics), Elsevier, 189-207.
  • Laurence P. 2015. The case of the gene: Postgenomics between modernity and postmodernity. EMBO Reports, 16 (7): 777–781.
  • Liang KH. 2013. Transcriptomics. Bioinformatics for Biomedical Science and Clinical Applications. Woodhead Publishing, p. 49-82.
  • Lin, S., Fang, L., Li, C., Liu, G. 2019. Epigenetics and heritable phenotypic variations in livestock. In: Tollefsbol T. (editor). Transgenerational Epigenetics. 2nd edition. Amsterdam, Netherlands: Elsevier, p. 283-313
  • Milward EA, Shahandeh A, Heidari M, Johnstone DM, Daneshi N, Hondermarck H. 2016. Transcriptomics. Encyclopedia of Cell Biology. Elsevier, The Netherland, 4: 160-165.
  • O’Donnell ST, Ross RP, Stanton C. 2020. The Progress of Multi-Omics Technologies: Determining Function in Lactic Acid Bacteria Using a Systems Level Approach. Front. Microbiol, 10: 1-17.
  • Pereira C, Adamec BJ. 2019. Metabolome Analysis. Encyclopedia of Bioinformatics and Computational Biology, 3:463-475
  • Pinu FR, Beale DJ, Paten AM, Kouremenos K, et al. 2019. Systems Biology and Multi-Omics Integration: Viewpoints from the Metabolomics Research Community. Metabolites, 9(76).
  • Potters, G. 2010. Systems Biology of the Cell. Nature Education, 3(9):33.
  • Pratima NA, Gadikar R. 2018. Liquid Chromatography-Mass Spectrometry and Its Applications: A Brief Review. Arc Org Inorg Chem Sci, 1(1): 26-34.
  • Shah TR, Ambikanandan M. 2011. Proteomics. Challenges in Delivery of Therapeutic Genomics and Proteomics. Elsevier, p. 387-427.
  • Strange K. 2005. The end of “naïve reductionism”: rise of systems biology or renaissance of physiology? Am J Physiol Cell Physiol, 288: C968–C974.
  • Turunen TA, … Ylä-Herttuala S. 2018. Epigenomics. Encyclopedia of Cardiovascular Research and Medicine, p. 258-265.
  • Vlaanderen J, Moore LE, Smith MT, et al. 2010. Application of omics technologies in occupational and environmental health research: current status and projections. Occup Environ Med, 67: 136-143.
  • Yadav D, Tanveer A, Malviya N, Yadav S. 2018. Overview and Principles of Bioengineering: The Drivers of Omics Technologies and Bio-Engineering Towards Improving Quality of Life, p.3-23
  • Yang X. 2020. Multitissue Multiomics Systems Biology to Dissect Complex Diseases. Trends Mol Med, 26 (8).
  • A brief guide to genomics. https://www.genome.gov/about-genomics/fact-sheets/A-Brief-Guide-to-Genomics
  • Analytical Techniques applied in Metabolomics. https://www.futurelearn.com/info/courses/metabolomics/0/steps/10710
  • Boundless biology. https://courses.lumenlearning.com/boundless-biology/chapter/the-genetic-code/
  • Altaf-Ul-Amin M, Afendi FM, Kiboi SK, Kanaya S. 2014. Systems Biology in the Context of Big Data and Networks, BioMed Res Int, 1:11.
  • Aslam B, Basit M, Nisar MA, Khurshid M, Rasool MH. 2017. Proteomics: Technologies and Their Applications. J Chromatogr Sci, 55(2).
  • Benavente L, Goldberg A, Myburg H, Chiera J, Zapata M & van Zyl L. 2015. The application of systems biology to biomanufacturing. Pharm. Bioprocess, 3(4):341–355.
  • Bertolaso M, Giuliani A, de Gara L. 2010. Systems Biology Reveals Biology of systems. Wiley Periodicals, Inc., 16 (6).
  • Bloom FE. 2001. What does it all mean to you? J Neurosci, 21:8304–8305.
  • Clish CB. 2015. Metabolomics: an emerging but powerful tool for precision medicine. Cold Spring Harb Mol Case Stud, 1: a000588
  • Coorssen JR. 2013. Proteomics. Brenner’s Encyclopedia of Genetics (Second Edition), p. 508-510.
  • Dos Santos EAF, Santa Cruz EC, Ribeiro HC, Barbosa LD, Zandonadi FS, Sussulini A. 2020. Multi-omics: An Opportunity to Dive into Systems Biology. Braz J Anal Chem, 7(29): 18-44.
  • Haas R, Zelezniak A, Iacovacci J, Kamrad S, Townsend S, Ralser M. 2017. Designing and interpreting ‘multi-omic’ experiments that may change our understanding of biology. Curr Opin Syst Biol, 6:37–45.
  • Han X, Aslanian A, Yates JR. 2008. Mass Spectrometry for Proteomics. Curr Opin Chem Biol, 12(5): 483–490.
  • Horgan RP, Kenny LC. 2011. SAC review ‘Omic’technologies: genomics, transcriptomics, proteomics and metabolomics. Obstet Gynecol, 13:189–195
  • Ideker T, Thorsson V, Ranish JA, ChristmasR, Buhler J, Eng R, Bumgarner JK, Goodlett DR, Aebersold R, Hood L. 2001. Integrated genomic and proteomic analyses of a systematically perturbed metabolic network. Science, 292:929–934.
  • Ishii N, Tomita M. 2009. Multi-Omics Data-Driven Systems Biology of E. coli. In: Lee S.Y. (eds) Systems Biology and Biotechnology of Escherichia coli. Springer, Dordrecht.
  • Kalavacharla V, (Kal), Subramani M, Ayyappan V, et al. 2017. Plant Epigenomics. Handbook of Epigenetics, 245–258.
  • Karahalil B. 2016. Overview of Systems Biology and Omics Technologies. Curr Med Chem, 23:1-10.
  • Karamperis K, Wadge S, Koromina M. 2020. Genetic testing. Applied Genomics and Public Health (Translational and Applied Genomics), Elsevier, 189-207.
  • Laurence P. 2015. The case of the gene: Postgenomics between modernity and postmodernity. EMBO Reports, 16 (7): 777–781.
  • Liang KH. 2013. Transcriptomics. Bioinformatics for Biomedical Science and Clinical Applications. Woodhead Publishing, p. 49-82.
  • Lin, S., Fang, L., Li, C., Liu, G. 2019. Epigenetics and heritable phenotypic variations in livestock. In: Tollefsbol T. (editor). Transgenerational Epigenetics. 2nd edition. Amsterdam, Netherlands: Elsevier, p. 283-313
  • Milward EA, Shahandeh A, Heidari M, Johnstone DM, Daneshi N, Hondermarck H. 2016. Transcriptomics. Encyclopedia of Cell Biology. Elsevier, The Netherland, 4: 160-165.
  • O’Donnell ST, Ross RP, Stanton C. 2020. The Progress of Multi-Omics Technologies: Determining Function in Lactic Acid Bacteria Using a Systems Level Approach. Front. Microbiol, 10: 1-17.
  • Pereira C, Adamec BJ. 2019. Metabolome Analysis. Encyclopedia of Bioinformatics and Computational Biology, 3:463-475
  • Pinu FR, Beale DJ, Paten AM, Kouremenos K, et al. 2019. Systems Biology and Multi-Omics Integration: Viewpoints from the Metabolomics Research Community. Metabolites, 9(76).
  • Potters, G. 2010. Systems Biology of the Cell. Nature Education, 3(9):33.
  • Pratima NA, Gadikar R. 2018. Liquid Chromatography-Mass Spectrometry and Its Applications: A Brief Review. Arc Org Inorg Chem Sci, 1(1): 26-34.
  • Shah TR, Ambikanandan M. 2011. Proteomics. Challenges in Delivery of Therapeutic Genomics and Proteomics. Elsevier, p. 387-427.
  • Strange K. 2005. The end of “naïve reductionism”: rise of systems biology or renaissance of physiology? Am J Physiol Cell Physiol, 288: C968–C974.
  • Turunen TA, … Ylä-Herttuala S. 2018. Epigenomics. Encyclopedia of Cardiovascular Research and Medicine, p. 258-265.
  • Vlaanderen J, Moore LE, Smith MT, et al. 2010. Application of omics technologies in occupational and environmental health research: current status and projections. Occup Environ Med, 67: 136-143.
  • Yadav D, Tanveer A, Malviya N, Yadav S. 2018. Overview and Principles of Bioengineering: The Drivers of Omics Technologies and Bio-Engineering Towards Improving Quality of Life, p.3-23
  • Yang X. 2020. Multitissue Multiomics Systems Biology to Dissect Complex Diseases. Trends Mol Med, 26 (8).
  • A brief guide to genomics. https://www.genome.gov/about-genomics/fact-sheets/A-Brief-Guide-to-Genomics
  • Analytical Techniques applied in Metabolomics. https://www.futurelearn.com/info/courses/metabolomics/0/steps/10710
  • Boundless biology. https://courses.lumenlearning.com/boundless-biology/chapter/the-genetic-code/

Systems biology and omics technology

A D V A N C E D     L E V E L

Advances in omics technologies — such as genomics, transcriptomics, proteomics and metabolomics — have begun to enable medicine at an extraordinarily detailed molecular level. The rapidly decreasing costs of high-throughput sequencing and other massively parallel technologies, such as mass spectrometry, are enabling their use in clinical research and clinical practice.

Contents

 

OMICS technologies towards improving the quality of life

OMICS Technologies and Molecular Medicine

Advances in omics technologies — such as genomics, transcriptomics, proteomics and metabolomics — have begun to enable medicine at an extraordinarily detailed molecular level. The rapidly decreasing costs of high-throughput sequencing and other massively parallel technologies, such as mass spectrometry, are enabling their use in clinical research and clinical practice. Exome and genome sequencing are already being used to aid diagnoses, particularly of rare diseases, to inform cancer treatment and progression and, in early efforts, to create predictive models of disease in healthy individuals. Numerous research efforts and companies are focusing on genome-wide profiles of genetic, gene expression and other omics data, such as the microbiome, as biomarkers for disease. These techniques have also been applied in identifying risk loci for disease, defining precise pathophysiology of the disease, as well as performing research in Occupational Environmental Health (OEH).

Ideally, different technologies would be combined both to help diagnose disease and to create a holistic picture of human phenotypes and disease. However, implementation of multi-omics data introduces new informatics and interpretation challenges. What are the ways in which integrative omics can impact medicine by helping to manage health, as well as diagnose and treat disease? In fact, to improve the treatment outcomes and reduce adverse events that matter to both the clinician and patient one of the perspective solutions is to develop the personalized medicine – customized medical treatment created to fit individuals’ characteristics, needs and unique molecular and genetic profile (Fig. 1).

OMICs technologies in diagnostics

Prenatal diagnostics

Nearly 10% of pediatric diseases and 20% of infant deaths are due to Mendelian diseases. Genetic defects can be a major threat to the baby’s health. To overcome this issue, last developments in the omics technologies are employed in a person’s life even before birth. Prenatal screening has become widespread in many countries for examining not only abnormalities of a fetus but also the possible risks of diseases or abnormal conditions after birth. When a genetic predisposition exists, such as cystic fibrosis or muscular dystrophy, consulting a medical specialist or genetic diagnosis company become of great importance. The latest innovations in molecular and cellular biology allowed various genetic diagnoses for fetuses. Till recently, different invasive technologies have been applied such as amniocentesis and chorionic villus sampling. Although the procedures have been improved, there is still risk of miscarriage and serious side effects. One of the innovative attempts to avoid the side effects of conventional invasive technologies is to isolate and analyze fetal nucleated cells from maternal blood.

Unfortunately, the number of fetal cells that can be isolated is about 3–5 cells per 30 ml of maternal blood. The huge jump in the field of non‐invasive prenatal testing (NIPT) has been made after the advancement of omics technologies and their combination with various experimental approaches. The use of DNA amplification technology combined with NGS allowed the direct detection of the fetal DNA in maternal blood. Currently, such approach is used to determine the sex and Rh blood type, and to analyze chromosomal aneuploidies. The cell‐free fetal DNA is typically released in maternal blood during the process of apoptosis of placental cells. The fragmented cell‐free fetal DNA is ∼200 bp long and has a half‐life of ∼16 min and can be detected in maternal plasma from early pregnancy (5–7 weeks). In addition to fetal DNA, fetal RNAs can also be used for NIPT. For example, fetal trisomy 21 can be diagnosed by checking the allelic ratio of PLAC4 mRNA, produced from chromosome 21 in the placenta. If a baby receives heterogeneous alleles from the parents, an unequal allelic ratio (2 : 1) suggests trisomy.

Integrating fetal genomic, transcriptomic, proteomic, metabolomic, and epigenetic data will help pushing forward the prenatal genetic diagnostics. In this way, any genetic disorder could be identified early in the pregnancy by applying non‐invasive approaches. However, some concerns occur regarding raise of serious ethical issues possibilities when advanced genomic technology are used. Therefore, further studies on the impact of new technologies on society, such as possible discrimination based on genetic information, undetermined harmful effects of nanotechnologies on humans and nature, and copyright infringements due to the development of information technology should be considered (see LO8).

Lab-on-a-Chip Technology in diagnostics

Rapidly after the development of micro-electro-mechanical systems (MEMS), the potential use of these miniaturized platforms for various applications has been revealed. During the past few decades, interest in biological or biomedical MEMS (BioMEMS) has been significantly increased and it has found widespread applications in a various area life science including diagnostics, therapeutics, drug delivery, biosensors and tissue engineering. These integrated systems are also known as “lab-on-a-chip” (LOC) or “micro-total analysis systems” (μTAS) and provide a solid way for miniaturization, integration, automation, and parallelization of chemical processes.

Lab-on-a-chip devices integrate multiple laboratory functions on a single chip (from few square millimeters to a few square centimeters in size). These platforms provide complex chemical and/or biological analyses which can offer cheaper, faster, controllable and higher performance of bio-chemical assays at a very small scale when compared with conventional laboratory tests. They are capable of handling extremely small fluid volumes down to less than a few picoliters (few micro-droplets of whole blood, plasma, saliva, tear, urine or sweat). This fact is very important in many clinical trials where usually very small volumes of patient samples are available. Moreover, the automation with eliminating the human interfering parameters can increase the reliability in the analysis.
LOC has been used in different applications, such as: DNA extraction and purification, PCR, qPCR, molecular detection, electrophoresis, etc.

LOC for DNA Extraction and Purification: Often for diagnostic assays nucleic acid have to be purified from cells. The process of DNA extraction generally includes two steps: cell lysis by disrupting cellular and nucleus membrane, and DNA purification from membrane lipids, proteins, and RNA. Cell lysis on LOC devices can be achieved through several types of lysis methods: chemical, thermal, ultrasonic, electrical, mechanical or some other type of lysis. Thew well known DNA purification methods through extraction columns, using the DNA adsorption on silica beads under certain buffer conditions, are also adaptable to microfluidic techniques.

LOC Devices for PCR, qPCR, and Molecular Detection: After DNA extraction, the most frequent analysis is the PCR. The importance of PCR in genomic analysis led to the development of numerous LOC devices. Miniaturization of volume and the high surface to volume ratio defines rapid thermal transfer and more accurate PCR. This analysis requires a post-analysis and amplicons’ size detection, which generally is carried out by electrophoresis. With the introduction of LOCs, DNA electrophoresis was one of the first molecular processes that was integrated on a chip. This miniaturization diminishes even more the overall process time and reduces the reagent consumption.

qPCR is another technique that was modified to LOC devices. Using ultra-fast pressure controller and fluorescence reader, ultra-fast qPCR microfluidic system had been developed for the molecular detection of diseases like Anthrax and Ebola. The detection efficiency is identical to commercial systems and results are achievable in less than 8 minutes being 7-15 times faster than traditionally used ones. Other advanced field is digital microfluidics that deals with emulsion and droplets within LOC devices. Ultra-low quantity of DNA can be captured within droplets, and detection limits can be enhanced with one copy number detection within LOC droplet qPCR.

Genomic Application: Due to the numerous sequencing projects, such as the Human Genome Project, the field of genomics gained a tremendous attention in recent years. Next-generation sequencing techniquess of DNA which are currently under development include sequencing-by-hybridization (SBH), nanopore sequencing, and sequencing-by synthesis (SBS), the latter of which includes many different DNA polymerase dependent strategies. Using LOC approach allows the drastic reduction in cost and duration of high-throughput sequencing.

When studying the complex DNA samples expression an integration of multiple biosensors in connection with DNA microarrays is required. The most essential characteristics of these LOCs are miniaturization, speed, and precision. This technology offers vast possibilities for rapid multiplex analysis of nucleic acid samples, including the diagnosis of genetic diseases, detection of infectious agents, measurements of differential gene expression, drug screening, or forensic analysis.

Biochemical Applications: LOC strategy is also applicable in coupling enzymatic and immunological assays on a single-channel microfluidic device. Typical example is the simultaneous measurement of glucose and insulin. Key for the effective realization of such glucose/insulin monitoring is the integration of relevant sample pretreatment/cleanup procedures essential for whole-blood analysis. This innovative LOC approach can be applicable to the incorporation of other analyzes and additional sample handling steps, as is desired for creating miniaturized clinical instruments.

Proteomics: Proteomics is one of the great scientific challenges in the post-genome era. LOC devices are useful for creating advanced techniquess to answer complex analytical problems, such as proteome profiling. LOC devices could be applied in four key areas of proteomics: chemical processing, sample preconcentration and cleanup, chemical separations, and interfaces with mass MS. LOC being a miniaturized device could be used for separation and detection of proteins. This process is characterized with less reagent consumption, easy operation, and very fast analysis. Another benefit is that the data from the LOC devices can easily be compared to those obtained using 2D-PAGE. In order to quantitate proteins a LOC MS protein profiling device is developed. Some authors describe LOC devices that are used for direct infusion into the mass spectrometer, including Capillary Electrophoresis (CE) separation, on-chip sample digestion, and infusion or CE before MS analysis. Researchers use electrospray ionization MS and focus on the interface design between LOC and the mass spectrometer.

Biosensors: For detecting target analytes LOC biosensors are also developed. Such devices intricately link a biological recognition element with a physical transducer converting the biorecognition event into an electrical signal. There are two types of LOC biosensors: bioaffinity and biocatalytic devices. Bioaffinity devices are based on selective binding of the target analyte to a surface-enclosed ligand partner (e.g., antibody, oligonucleotide). For example, in hybridization biosensors, there are immobilized single-stranded (ss) DNA probes onto the transducer surface. When a duplex is formed it can be detected following the association of an appropriate hybridization indicator. On the other hand, in biocatalytic devices, an immobilized enzyme is used for recognizing the target substrate. For example, sensor strips with immobilized glucose oxidase for monitoring of diabetes (for more information see LO3).

Cell Research: Using LOC devices a small-scale experiment with cells could also be performed. The single cell is positioned within the microchannel in a predetermined location, making its handling and manipulation easy. The microsystem allows handling small volumes of fluid containing small quantities of analyte. LOC systems also provide precise control of the local environment around the cell, monitoring the physical or chemical nature of the surface to which the cell adheres, the local pH, and temperature. When the cell is to be exposed to a different type of stimuli, numerous and often complex fluid-handling components can be used. If the cell is a subject of further analysis, lysis and necessary analytical methods could also be introduced onto the same platform. Thus, sample loss or dilution is avoiding, which would inevitably occur if multiple devices were used.

Drug Development: The industry needs for development of new drugs and to predict their potential behavior in animals and cells triggers other analytical applications of LOC systems: e.g., analytical systems to monitor and optimize the production of protein drugs such as therapeutic antibodies; assays based on primary human cells that could predict performance in human clinical trials and pothers. These LOC systems are proven to be highly reproducible, easily manipulated, and could be used routinely.

OMICs analysis for revealing genetic architecture of common disease

Identifying genetic markers that are associated with a specific disease feature is key issue when estimating the disease risk. As the genetic information of a individual remains mostly unchanged, the mutated sequences can be reliable markers for certain diseases. In this regard one of the important applications of omics technology is the genome‐wide association studies (GWASs). They can identify common genetic variants, generally single nucleotide polymorphisms (SNPs), associated with certain disease features. In general, SNP information is collected from two groups, a patient group and a healthy control group. Then it is statistically analyzed, and the SNPs associated with the specific disease characteristics of the corresponding patient group re identified.

For example, variations in the apolipoprotein E (APOE) gene are a well‐known genetic factor that is correlated with the late‐onset Alzheimer’s disease (LOAD). After the assessment of 502,627 SNPs in 1086 AD patients, it was demonstrated that rs4420638 on chromosome 19 is the strongest marker differentiating AD patients from the control group. Other important success is achieved during investigation of hereditary cancers. BRCA1 and BRCA2 genes are one of the most important genetic markers for hereditary breast and ovarian cancers. These genes encode tumor suppressor proteins, which help repairing damaged DNA through homology directed repair. Some mutations in BRCA1 and/or BRCA2 significantly raise the risk of developing breast and/or ovarian cancers. For BRCA1, two mutations were detected: 185delAG and 5382insC. Approximately 55–65% of women who inherit one of the harmful BRCA1 mutations and around 45% of women who carry the BRCA2 mutation have developed breast cancer by the age of 70. Numerous other disease markers such as autosomal‐dominant colorectal cancer, Li—Fraumeni multi‐cancer syndrome, mucolipidosis IV, and hypertrophic cardiomyopathy, have also been successfully identified using NGS.
However, for the most common diseases such as diabetes, obesity, schizophrenia and autism, a combination of multiple genetic and environmental factors is responsible. A growing number of evidence indicates that genetic markers commonly explain only a part of the total trait variation. One of the missing crucial factors are the epigenetic variations. Various epigenetic modifications have been reported to be associated with the early stage of different cancers (abnormal growth of a tumor). Moreover, it is well known that colorectal cancer is more frequently identified in men than in women. That is why it has been suggested that estrogen may help suppress colorectal cancer. In addition, the epigenetic profiles of adjacent mucosa, and non‐adjacent mucosa samples of 95 colorectal cancer patients have revealed that the promoter of the MGMT, a DNA repair gene, was frequently methylated. Up to now, thousands of genomic loci have been identified and associated with various human diseases. However, the difficulties arise when these genes have to be characterized in the context of the molecular pathophysiology and the corresponding interacting genes and pathways.

Application of OMICs approach in disease treatment

The main aim of personalized medicine is to decide on the most effective treatment option for an individual. In this regard, the simultaneous quantification of multiple protein markers can play an important role in subtyping tumors and selecting optimized therapies for each subtype. High‐throughput, single cell‐based protein quantification techniques, such as CyTOF, can be excellent alternatives for immunohistochemistry‐based methods.

For example, in the last decade a new approach for treating cancers has emerged — allowing the patient’s immune system combat his or her cancer, called adoptive cell transfer (ACT). Glioblastoma multiforme (GBM) is the most frequent and aggressive type of malignant brain tumor. Although GBM has been treated with surgical removal, radiation treatment, and chemotherapy methods, there have been no noticeable effects. Over the past 20 years, numerous clinical trials have been performed to use the immune system to cure cancers such as GBM. Among numerous ACT approaches, particular attention has been paid on the dendritic cell (DC)‐ and T cell‐based vaccines. They can penetrate and induce anti‐tumoral response in the brain and show great potential for effectively killing various cancers including the malignant and advanced types. Autologous DCs, obtained from tumor tissue or in the form of a tumor lysate of a patient, can be grown in vitro in the presence of tumor‐associated antigens (TAAs), a method called ‘pulsing’. The pulsed, autologous DCs are reintroduced into the patient to stimulate specific, cell‐mediated, anti‐tumoral cytotoxicity against GBM and other malignant cancers. More revolutionary approaches, e.g. engineering specific immune cells using zinc‐finger nucleases (ZFNs) and CRISPR technology, have also been attempted.

Every year, thousands of patients undergo a transplantation of organ or hematopoietic stem cells. However, mortality among such patients remains very high. A standard procedure for matching donors with recipients involves human leukocyte antigen (HLA) typing. However, it becomes clear that non-HLA factors can also considerably affect prognosis and development of graft-versus-host disease (GVHD). In order to overcome this, many omics applications can be used to determine optimal donor–recipient matches, as well as to examine different markers of rejection. For example, free DNA sequencing can be used to assess the severity of organ rejection as well as to simultaneously detect the eventual presence of viral DNA as a marker of infection. Additional omics data, such as RNA or protein expression, may also be utilized to evaluate the donor–recipient compatibility. Thus, integration between several omics’ technologies may be used as a useful tool for transplant biology.

Furthermore, the microbiome has been associated with many common human diseases. Whereas causality is simple in genomic data, where DNA influences phenotypes, it is more difficult to unravel whether microbiome composition is a cause or effect of disease. Nevertheless, it is evident that patients with inflammatory bowel disease, type 2 diabetes and obesity, have different microbiome profiles from those of healthy controls. In addition, the microbiome has a great influence on immune function, which in some cases has been putatively causally related to disease in animal models. As our understanding of the microbiome advances, integrative assessment of this and other omics technologies data will help for our better understanding of human disease. Recently, it has been shown that human genetic profiles influence overall gut microbiota composition. Additionally, interactions between human genetics and microbiomes have been shown to influence disease manifestation. Similarly, metabolic signaling between hosts and their microbiomes has become an area of active research, and there is increasing evidence that a number of metabolites secreted from gut bacteria may play a role in human disease. Therefore, it is obvious that integrated analysis across genome, metabolome, microbiome and other omics profiles will provide means for managing health and combating disease.

OMICs technologies for disease prevention – future prospects

Other important goal of personalized medicine is to prevent the development of various diseases before their onset. Current preventive treatments are limited to surgery — removing a tissue or an organ if a person possesses relevant cancer markers. However, this approach is not applicable to diseases like Alzheimer, Huntington’s disease, and congenital muscular dystrophy. One of the recent approaches to address such issues is the nutrigenomics‐based diet treatment. Omics profiling can be an effective approach in detecting large-scale or pathway-level changes, can show patient-specific trends and add statistical support through repeated measurements.

Application of OMICS Technologies in Toxicology

The use of omics tools to evaluate environmental health status is becoming more and more important in environmental management and risk assessment. Omics permit connecting molecular events and adverse effects with biological levels of organization relevant for risk assessment schemes. The analysis of the RNA transcripts level or cellular proteomics have already gained particular interest. These studies helped in better understanding the interactions between the stress factors and the organism, but also to unravel the modes of action (MoA) of various toxic substances. Therefore, from a toxicological point of view, omics techniques allow to effectively and accurately generate important information on substance-induced molecular perturbations in cells and tissues that are associated with adverse outcomes.

Cellular regulatory pathways involve a range of different (bio)molecules, which are characterized with different physicochemical properties and display complex non-linear interactions. A single-omics technique can measure biomolecules of a specific type, RNAs or proteins, and frequently these are not even the entirety of biomolecules of one type but a smaller subset of them. For example, the detection of short and long RNAs, or short peptides and proteins requires different transcriptomic or proteomic approaches. Only the use of multi-omics, also known as cross-omics approaches, permits to directly identify a significant fraction of a pathways’ response to chemical exposure. Thereof, utilizing multi-omics strategies for toxicological research questions has greater importance.
Such system toxicology approach, integrating classical toxicology with quantitative analysis of large molecular networks and functional changes occurring across multiple levels of biological organization, is promising in analyzing the full spectrum of xenobiotics toxicity. A systems toxicology approach includes the following elements:

  • Absorption and distribution of the xenobiotic within the biological system.
  • Transformation of the xenobiotic and generation of toxic and/or non-toxic intermediates.
  • Interaction of the xenobiotic and/or its products with the cellular targets.
  • Modifications in the cellular molecules including genes, proteins, and lipids.
  • Structural manifestations of target organ toxicity.
  • Functional manifestations of toxicity, and
  • Elimination of the xenobiotic and/or its intermediates from the biological system.

Toxicological studies via OMICs analysis

For better understanding the cellular response to chemical exposure, studies should be focused on different levels: transcriptomics, proteomics, phosphoproteomics, and metabolomics. Epigenomics should be considered as well for specific cases.

Transcriptomics. Transcriptomics main aim is the comprehensive detection of RNAa in the cell. A pathway response in transcriptomics is primarily detected via a known set of target genes that are found differentially expressed. The techniques applied to determine changes in gene expression in response to exposure to a xenobiotic include: northern hybridization, quantitative real time PCR (QRT-PCR), subtractive hybridization, serial analysis of gene expression, microarray analysis, and next generation sequencing. Depending on the purpose of the study, one or more of these techniques may be applied to define either partial or global expression profiles in the tested biological sample. However, the most commonly used gene expression profiling techniques are QRT-PCR, microarray analysis, and NGS. If the aim is to investigate the expression profile of a single or a limited number of transcripts, QRT-PCR analysis is the method of choice. QRT-PCR analysis is a relatively simple technique that includes the reverse transcription of mRNAs followed by PCR amplification of the resulting cDNAs using primers specific for the targeted gene as well as for one or more house-keeping genes. The amount of the PCR amplified gene products are than quantified. As QRT-PCR can accurately determine the expression of an individual or limited number of transcripts, it has some limitations from a system toxicology perspective. In order to study the systems-level toxicity of a given xenobiotic, it is necessary to employ a high throughput techniques that is capable of determining expression levels of the entire transcriptome. That is why the microarray and next generation sequencing are the most popular global transcriptome analysis techniques applied in toxicology research.

The microarray technique represents a major breakthrough analyses in the post-human genome sequencing era. Like Southern hybridization technique, the microarray approach is grounded on the ability of DNA molecules to bind or hybridize in a nucleotide sequence-specific manner. Microarray can be considered as a high throughput northern blot hybridization in which almost all the genes that are expressed in a biological sample at a given time can be detected simultaneously. Therefore, it allows to detect small differences in the global gene expression profiles in a given biological sample in response to xenobiotic(s) exposure. The microarray technique involves the isolation of high quality RNA, reverse transcription of the mRNA to synthesize cDNA, cRNA synthesis and labeling to generate “probes”, hybridization of the “probes” with “targets” present on the microarray, detection of the signal intensity of the hybridized probe-target complex, and analysis of the resulting data to determine the levels of expression of the genes of interest. Microarray-based analysis has been used in different areas such as environmental toxicology, drug development, and occupational toxicology. The data obtained provides a snapshot of all the genes whose expression levels are affected in response to xenobiotic exposure.

Next generation sequencing (NGS) is the most recently developed global transcriptome analysis. This technique can be applied to determine a global gene expression profile in RNA obtained from different biological samples. The key steps involved in NGS are RNA isolation, removal of abundant RNA molecules such as ribosomal RNA and globin RNA, preparation of sequencing libraries, PCR amplification, and sequencing of the libraries. The resulting data is analyzed, and the quantity of the individual transcripts is determined.

Proteomics: Transcriptomics is often the first choice for systems-level investigations as numerous well-established measurement methods have been developed during the years. However, protein alterations can be considered to be closer to the relevant functional impact of a studied stimulus. Although mRNA and protein expression are tightly related through translation, their correlation is limited. The mRNA transcript levels only explain about 50% of the variation of protein levels. This is due to the additional levels of protein regulation including their rate of translation and degradation. Moreover, the regulation of protein activity does not stop at its expression level but is often further controlled through posttranslational modifications. From toxicology point of view such examples for important post-transcriptional regulation include: the tight regulation of p53 and hypoxia-inducible factor (HIF) protein-levels and their rapid post-transcriptional stabilization, e.g., upon DNA damage and hypoxic conditions; the regulation of several cellular stress responses (e.g., oxidative stress) at the level of protein translation; and the regulation of cellular stress response through protein phosphorylation cascades. The most often applied proteomics analyzes being of key importance for toxicology include: 2D polyacrylamide gel electrophoresis, gel-free liquid chromatography, mass spectrometry and analysis of the posttranslational modifications.

2D polyacrylamide gel electrophoresis (2DGE) is used to study perturbations on the proteome based on changes in protein expression. The 2DGE technique relies on the separation of proteins based on their pH (charge) as well as their size. It has the capability to separate and visualize up to 2000 proteins in one gel. The first dimension, which is known as isoelectric focusing (IEF) separates the proteins by their isoelectric point (pI). The second dimension further separates the proteins by their mass. State-of-the-art image acquisition and analysis software allow the comparison of control and treated samples helping to identify the differentially regulated proteins by their relative intensity. A variant of 2DGE is difference gel electrophoresis (DIGE) which is based on labeling of proteins with fluorescent cyanine dyes (Cy2, Cy3 and Cy5). Although 2DGE is a powerful tool to identify many proteins, the approach has its limitations. The main constraint is that not all proteins can be separated by IEF, such as membrane, basic, small (< 10 kDa) and large (> 100 kDa) proteins. The second limitation is that less abundant proteins are often masked by the abundant proteins in the mixture.

Other frequently used techniques are the gel-free liquid chromatography mass spectrometry (LC MS/MS). The LC approach employs the differences in the physiochemical properties of proteins and peptides, i.e., size, charge, and hydrophobicity. 2D-LC can be applied to fractionate protein mixtures on two columns with different physiochemical properties and maximize the separation of proteins and peptides in complex mixtures. Mass spectrometry is widely believed to be the key technology platform for toxicoproteomics. It main advantages include unsurpassed sensitivity, improved speed and the ability to produce high throughput datasets. Due to the high precision of MS, in tissues and biological matrices can be detected peptides in the femtomolar (10− 15) to attomolar (10− 18) range. This is extremely beneficial in comparative analysis where simultaneous analysis of control and treated samples are carried out. Thus, comprehensive understanding of how stimuli affect the proteome, and the subsequent identification of potential biomarkers can be achieved.

As system biology requires accurate quantification of a particular set of peptides/proteins across multiple samples, targeted approaches for biomarker quantification have been also developed. Such technique is the selected reaction monitoring (SRM). SRM measures peptides produced by the enzymatic digestion of the proteome in triple quadrupole MS. An SRM-based proteomic experiment starts with the selection of a list of target proteins, derived from preceding experiments or data search such as a pathway map or literature. This step is followed by 1) selection of the proteotypic target peptides that optimally and uniquely represent the protein target, 2) selection of a set of suitable SRM transitions for each target peptide, 3) detection of the selected peptide transitions in a sample, 4) optimization of SRM assay parameters, and 5) application of the assays to the detection and quantification of the proteins/peptides. The major advantages of the SRM technique are: 1) possibilities to monitor tens to hundreds of proteins during the same run, 2) possibilities for absolute and relative quantification, 3) highly data reproducibility, and 4) yielding absolute molecular specificity. However, some limitations of this technique also exist: 1) only a limited number of measurable proteins can be included in the same run, and 2) even with its high sensitivity it cannot detect all the proteins present in an organism.

Another MS-based targeted approach known as parallel reaction monitoring (PRM) has also been employed. It is centered on the use of next-generation, quadrupole-equipped high-resolution and accurate mass instruments (mainly the Orbitrap MS system). This technique is closely related to SRM but allows for the measurement of all fragmentation products of a given peptide in parallel.

Other key approach is the analysis of the posttranslational modifications (PTMs) occurred due to the toxic exposure. They represent an essential mechanism for regulating the cellular proteome. PTMs are chemical modifications that have an important role in regulating activity, localization and interactions of cellular biomolecules. The identification and characterization of proteins and their PTM sites are very important to the biochemical understanding of the PTM pathways. This knowledge provides deeper insights into the possible regulation of the cellular physiology triggered by PTM. Some examples of PTMs include phosphorylation, glycosylation, ubiquitination, nitrosylation, methylation, acetylation, lipidation and proteolysis. During the past decade, MS-based proteomics has proven to be a powerful tool for the identification and mapping of PTMs. Moreover, the MS-based approaches took great advantage from the progress in MS instrumentation making the analysis more sensitive, accurate and with higher resolution for the detection of less abundant proteins.
Metabolomics: The metabolome is different to the other omics analysis, as it is dealing with a collection of chemically highly heterogeneous molecules. The metabolome is typically defined as the complete balance of all small molecule metabolites ( < 1500Da ) found in a specific cell, organ or organism. Thanks to initiatives like the Human Metabolome Project, the endogenous metabolome of human and animal model organisms has been mapped to a large extent. Metabolome measurements employ either nuclear magnetic resonance (NMR)-based detection or gas or liquid chromatography hyphenated to MS. NMR based approaches have the capacity to quantify the studied metabolites and constitute the gold standard for the structural elucidation of unknown metabolites. MS-based approaches surpass NMR in terms of sensitivity and can be run in an untargeted or targeted manner.

Metabolomics differs from proteomics and transcriptomics in several aspects:
(i) detects the response at very different levels of a pathway, and
(ii) simultaneously capturing molecules of a pathway that respond on extremely different time scales.

The progression of metabolomics has made it routinely and highly useful in many toxicology-related studies. It has remarkable potential in screening drug-induced cellular or organ toxicity. Rapid assessment of biological samples, together with mathematical and statistical analysis (chemometrics) of obtained data serves as a powerful tool for drug safety assessment. For example, if a drug or chemical substance can cause hepatic toxicity by inducing cellular oxidative stress, metabolomics analysis of the drug-exposed biological sample can provide direct information about metabolites that are markers of the oxidative stress. It can also identify metabolites that are considered as known biomarkers of hepatic injury, thus suggesting hepatic pathology. In addition, metabolomics analysis can also provide information about metabolites that are indirectly associated with toxicity.

Metabolomics has several advantages over conventional clinical pathology assessment. For instance, a study examining an unknown compound has shown that this compound increases serum cholesterol levels. Upon the utilization of metabolomics, it has been observed that, apart from cholesterol, the same compound also raises several phytosterols, indicating that the higher cholesterol is a result of the sterol absorption in the gut. Such findings clearly indicate that metabolomics has a much higher potential in terms of identifying exact toxicological endpoints.

Epigenomics. Main focus of epigenomics is to study the chemical modifications of the DNA and proteins organizing the three-dimensional structure of genomic DNA. Nowadays, DNA methylation is assessed by a modified genome sequencing approach (Methylome-seq). However, this approach still requires high coverage associated with still significant sequencing costs. That is why the targeted microarray-based approaches are most frequently used. DNA methylation includes two different chemical modifications with distinct functional consequences—5-methylcytosine and 5-hydroxy-methylcytosine. Histone modifications are analyzed using Chromatin-immuno-precipitation method applying antibodies specific for the targeted modification followed by sequencing (ChIP-seq). Typically, to get a comprehensive picture, several modifications need to be detected. That requires performance of several ChIP-seq analysis simultaneously. As ChIP-seq needs more sample material than the other epigenomic approaches, such a strategy may be hard to implement in toxicological studies. ATAC-seq is an alternative approach that does not investigate chemical modifications directly, but rather one of the modifications’ main consequence, DNA accessibility.
However, knowledge on the involvement of regulatory pathways and specific epigenetic modifications is still scarce. Epigenomics is thus of limited value when a pathway-based data integration approach is used. However, epigenomic data have proven highly valuable for trans-generational studies or when trying to predict long-term effects of chemical exposure based on omics data of short-term exposure.

OMICs applications for toxicology risk assessment and regulatory submissions

In the last decades, ‘omics technologies have been extensively used in research and it was demonstrated that they are capable to provide a profound insight into the biochemistry and physiology of the cell and any harmful effect of xenobiotics. This has led to an enthusiastic approval by research toxicologists. Hopes were expressed that ‘omics technologies would provide the tools to identify a set of biomarkers of adverse effects and their modes-of-action. Thus, the prediction of human effects during substance hazard assessment will be improved and a contribution will be made to the development of alternative methods to animal testing. Despite this, the translation of ‘omics into the regulatory domain remains at best cautious.
The European Centre for Ecotoxicology and Toxicology of Chemicals (ECETOC) has, therefore, organized a series of workshops to evaluate the possibilities and challenges of omics techniques in chemical risk assessment. The most recent workshop concluded that omics approaches contribute to answering relevant questions in risk assessment including:

(i) the classification of substances and definition of similarity,
(ii) the elucidation of the mode of action of substances, and
(iii) the identification of species-specific effects and the demonstration of human health relevance.

However, a need was identified to increase the reproducibility of omics data acquisition and data analysis, as well as to define the best practices. Furthermore, the regulatory use of any test method is closely related to the specific legal framework that is relevant for the substance under investigation. Considering the REACH Regulation (EP and Council of the EU, 2006), “omics-based data could potentially be used for regulatory submissions”. To use omics in REACH dossiers, the REACH Regulation lists different requirements: specific standard information that cover specific physico-chemical, toxicological and ecotoxicological limits. These data are evaluated during hazard and risk assessment, and they are analyzed to determine risk management alternatives, respectively. The application and integration of omics tools may be useful in different levels of regulatory hazard identification and assessment contributing to:

  1. Classification and labelling (C&L) of substances.
  2. Weight-of-evidence (WoE) approaches to elucidate the MoA of the substance under investigation.
  3. Substantiation of chemical similarity.
  4. Determination of points-of-departure (PoDs) for hazard assessment.
  5. Demonstration of species-specific effects and human health relevance.

The fast development of omics technologies pose several challenges for facilitating their use for hazard assessment, particularly from the regulatory submission standpoint. Sets of ‘big data’ must be condensed applying complex approaches and applying specific knowledge to obtain information that is pertinent for hazard assessment. Moreover, knowledge in this rapidly evolving area is not necessarily familiar to many researchers working in the regulatory settings. Accordingly, the lack of regulatory approval of omics technologies is not only linked to a lack of best practices frameworks and quality criteria, but also to a lack of confidence in the analysis and the level of uncertainty with respect to what constitutes enough data. Scientists and regulators have to first gain experience with the data obtained from this new technology and then to build confidence in its applicability.

Environmental OMICS and Biodiversity

Generally, most of the environmental studies comprise culturing of isolated microorganisms or amplifying and sequencing conserved genes. However, some difficulties exist in understanding the complexity of large numbers of various microorganisms in an environment. This led to the development of new techniques allowing to enrich specific microorganisms for upstream analysis, such as single-cell isolation and analyses. Advanced tools in metagenomics and single-cell genomics (SCG) are paving the way to new methods of studying and understanding our environment.

Early environmental studies of organism diversity required samples cultivation to increase DNA quantity before genomic libraries to be constructed. At the time, sequencing of 16S and 18S rRNA was the best option available to define environmental diversity. However, it became clear that majority of sample communities represents uncultivable species. Fortunately, polymerase chain reaction (PCR) of rRNA allows access to both cultivable and uncultivable sample diversity. Because PCR can be performed directly on environmental samples without cloning, it can be utilized to amplify targeted genes directly from the environment.

Importance of sequencing both noncoding rRNA and protein coding genes is growing, with specific interest in reconstructing bacterial genomes. A comprehensive sampling of all genes from all organisms existing in a complex sample is achievable via shotgun metagenomic sequencing. Using this method make possible to evaluate bacterial diversity and to detect the abundance of microorganisms in a specific environment. Moreover, it allows the reconstitution of complete genomes of uncultivable microorganisms. In addition to the discovery of new archaeal, bacterial, protozoan, algal, fungal, and eukaryotic species, this technique is crucian for the reconstruction of viral genomes. This was a critical advancement taking into account that viruses lack a shared universal phylogenetic marker such as bacterial 16S rRNA or eukaryotic 18S rRNA.

Another approach is the metatranscriptome approach which rather than using genomic DNA (gDNA) involves the collection of the entire RNA community. The latest is used to construct cDNA libraries that are sequenced. Although RNA is less stable than DNA, it gives a snapshot of the current state of the community by revealing up and down regulated genes. To date, one of the most important metatranscriptome studies are performed for the marine water communities.

Single-cell genomics

Metagenomic sequencing is a helpful tool for understanding environmental communities. However, assembling gene catalogs and composite genomes can be challenging. Furthermore, it is difficult to distinguish between genes that originate from the same or different organisms in genomes that are already assembled (Fig. 3). Therefore, there is considerable interest in developing a system to understand the organization of discovered genes and pathways within genomes.

The challenge of sample heterogeneity could be partially solved via using two techniques – scaling-up and deep-sequencing. Different approaches are required to compare metagenomic samples collected from different environments and to reveal the composition and organization of microorganism genomes in the environment. One of the approaches is to enrich the culture of a specific organisms based on their specific environmental functions. This type of targeted-metagenomic approach permits for the development of composite microbial genomes. Additionally, to the uncultivability of many organisms, other problems occur when faster growing microorganisms outgrow slower ones, subsequently introducing additional biases.

Hence, instead of culturing, a complex sample can be enriched for a target cell population using fluorescence-activated cell sorting (FACS). Using this approach helps sort cells on the basis of their shape, size, and density. Alternatively, specific organisms of interest can be enriched by handling a small number of cells that possess specific function in the environment. With the development of whole genome amplification (WGA) techniques (e.g., multiple displacement amplification (MDA) or rolling circle amplification (RCA)), the gDNA of a restricted number of cells can be amplified and sequenced. However, WGA techniques create potential deviations because the abundance of specific sequences vary and therefore are not equally amplified. Hence, gDNA amplification of defined populations with a restricted number of cells compromises the quantitative analysis of metagenomics.

Latest advancements in single-genome amplification techniques enable SCG using physical cell separation (usually by FACS on 96-well plates), cell lysis, and WGA. An amplified genome from a single cell can then be sequenced. While metagenomics is accomplished by collecting the microorganism community and extracting and sequencing total DNA, SCG separates and studies individual cells from the microorganism community. Obtained sequencing data provides quantitative information on genomic variability in microorganism populations. Gene insertions, deletions, duplications, and genome rearrangements can be studied on a single-cell level. In this way the complex metabolic pathways can be analyzed for an individual cell.

Single-cell isolation and processing

Studying the genome and its regulation at the population level is done by analyzing millions of cells at once. However, such investigations do not allow for a view of the heterogeneity in biological systems, nor do they characterize the current state of the population. That is why the SCG is gaining more and more popularity for studying both cultivable and especially uncultivable microorganisms. The process to obtain, multiply, and sequence genetic material from either live or dead cells is relatively simple. First the cell needs to be isolated and lysed and then the released genetic material is amplified and the genomic library can be constructed.

One of the approaches for single cell isolation is the micromanipulation. Cells of interest are identified, isolated, and examined microscopically. Suitable cells are isolated using a micropipette, laser source, optical tweezers, or by real-time microfluidic flow. Using microfluidics, a desired phenotype correlating to a specific genome can be selected. In addition, cells can be easily observed before capture.

Other approach is the random encapsulation. Cells are randomly selected by serial dilution. The diluted sample can then be transferred into microwells, or the cells can be encapsulated by microdroplets. Using this technique, individual cells can be separated, processed, and subjected to genomic analysis.
Flow cytometry is the most popular procedure of random encapsulation. FACS allows the isolation of cells possessing specific criteria, such as the size, shape, color, or even presence of specific nucleic acids or activities, using fluorescent dyes (Fig. 4). One of the main advantages of FACS is its high throughput, high sorting speed, and ability to distinguish live cells. Moreover, FACS can detect the presence of cells in a droplet, reject empty droplets, and transferred the cells with desired properties into multiwell / microtiter plates. Then cells can be lysed, and genomes can be amplified in single wells.

Other possibility for single cell isolation is the microdroplet technology (Fig. 4). Microdroplets are the emulsion formed when cells in the water phase are vigorously mixed with oil. Oil droplets encapsulate cells in the water phase. Originally, PCR performed on an emulsion was thought to be much more specific than analysis of cells in the water phase alone. It was suggested that the yields of reaction products are greater and fewer chimeric byproducts are formed. Moreover, the droplets formed enclosed a limited number of template molecules (ideally only one). Latest developments in microfluidics permit to control the droplet size (nanoliter or even the picoliter), providing aqueous uniform (monodisperse) droplets in oil. One of the main advantages of the two-phase system of microdroplets over FACS is the possibility to handle single cells as separated units. Each droplet act as an independent well where cells can be grown and later screened for the products expressed. Released gDNA can be effectively amplified in microdroplets and used for sequencing-library preparation. Bacterial, yeast, plant, insect, and mammalian cells can be easily studied using microdroplet technology on agarose or other microgels. Even multicellular organisms can be encapsulated and grown in a droplet. Hypothetically, it is possible to measure any secreted molecule for which a fluorescently labeled ligand exists. Furthermore, the activities of various cell proteins can be monitored.

Approaches for studying various cells

Living cells can generally be classified into bacteria, archaea, and eukaryotes. Although viruses are not considered “living,” they constitute an important part of life science studies. Various single-cell analysis exists that are suitable for different organisms.

Viruses are present in practically every environment. They are the most abundant and diverse biological entities. However, to isolate single viruse and sequence its genome, a suitable cultivable virus-host system must first be established. For example, eukaryotic algae are a host to an incredible diversity of viruses. However, using both FACS and single-droplet systems, viruses can theoretically be isolated without dependence on the viral-host system.

Archaea and bacteria have a similar cell structure. Only differences in cell composition and organization set these two domains apart. Bacterial cells are ordinarily 0.5–5.0 μm in diameter, while archaea can be larger and reach 15 μm in diameter. Basically, all methods described above can be used to separate and process single archaeal and bacterial cells. However, during FACS high currents may slow the growth of these organisms.

Eukaryotes differentiate from other life domains by their membrane bound nucleus and membrane-bound organelles. They may be multicellular or single-celled organisms. Cell size typically range from about 10–100 μm or they are ten-times larger than bacteria. Dilution, FACS, and droplet sorting are all appropriate methods for isolation of eukaryotic cells.

Multicellular organisms can theoretically also be isolated and processed by various methods. FACS for example can easily sort multicellular organisms. If incubated in droplets for a sufficient amount of time, single-celled organisms may synthesize a protein or ligand and have the opportunity to multiply, potentially creating a multicellular structure.

Molecular studies of our environment and ecology gain more and more knowledge for living communities. With the development of molecular tools, especially sequencing machines, it became possible to sequence whole communities from selected environments. Recent advancements in single-cell technologies facilitate the isolation and amplification of genomic material from single cells and allow structuring of numerous sequence-based libraries. Furthermore, data obtained from SCG supplements data obtained by metagenomics.

Omics Approaches in Industrial Biotechnology and Bioprocess Engineering

Biotechnology utilizes biological processes, organisms, or systems to generate products and technologies that are improving human lives. The use of biological systems to produce bioproducts of commercial importance is a key component of the biotechnology industry. This biotechnological approach has found application in different areas: energy, material, pharmaceutical, food, agriculture, and cosmetic industries. The bioproducts made from biomanufacturing processes are usually metabolites and proteins, which are obtained from cells, tissues, and organs. The biological systems synthetizing these bioproducts can be natural or modified by genetic engineering, metabolic engineering, synthetic biology, and protein engineering. “Omics” technologies have great importance for biotechnology and metabolic engineering helping in characterizing and understanding metabolic networks. The significant amount of knowledge acquired from omics-driven experiments can be applied in the development of biotechnological tools and the advancement of metabolic engineering. This enables the manipulation of complex biological systems toward creating robust industrial biomanufacturing strategies.

Omics-Guided Biotechnology

“Omics” tools are used more and more in the development of biotechnological processes and the production of many vital products. Application of “omics” technologies in characterizing and understanding biological systems has permitted to select and predict phenotypes, which facilitates the optimization of biotechnological processes toward enhanced production (in quality and quantity) of commercially relevant products (Figure 5).

Production of Biofuels and Bioproducts

Microbial production of natural compounds represents an attractive and more sustainable alternative to traditional petrochemicals. Its implementation has led to a growing catalog of natural products and high-value chemicals. For instant, the use of lignocellulosic biomass represents an economical approach to generate biofuels and bioproducts. However, to achieve stable conversion of low-cost raw material into value-added products at industrial levels needs systematic engineering plans. The Design-Build-Test-Learn (DBTL) cycle is becoming an increasingly implemented approach for metabolic engineering experiments. It represents a systematic and effective tool to force development efforts in biofuels and bio-based products. The DBTL cycle uses in silico approach to Design and Build genetic constructs in microbial hosts. Next, the information obtained from “omics” technologies, during the Test phase of the cycle, is transferred on to Learning processes (Figure 6). What is Learned is then fed back to new design cycles to achieve further strain development and optimization. Thus, a rapid optimization of microbial strains producers of any chemical compound of interest is facilitating. The weakest point in the DBTL cycle workflow is the Learning process since mathematical models are only as good as their assumptions. Therefore, both high quality and large “omics” data sets are required to improve training models, ensuring increased accuracy and reliability of the Learning process.

Often in the DBTL cycle genomic sequence information is the traditional approach utilized in the initial stages of a study. For example, barcode sequencing (Bar-seq) (a method using a short section of DNA from a specific gene or genes) can be used to study population dynamics of Saccharomyces cerevisiae deletion libraries during bioreactor cultivation, allowing the identification of factors that impact the diversity of a mutant pool. Single cell sequencing guided approach can be used to identify key mutated promotors for adjusting expression levels, thereby facilitating the dynamic regulation of microbial growth. Proteomics-guided approaches have been employed to engineer polyketide biosynthesis platforms for in vitro production of adipic acid in yeast. Additionally, metabolomics permits the assessment of pathway flux, carbon source utilization, and cofactor imbalance, which all contribute to the identification of pathway bottlenecks. Such metabolomics guided approach is already used for the characterization of the cannabinoid production in engineered S. cerevisiae. Cannabinoid analogs have been identified produced by several promiscuous pathway genes. Furthermore, application of metabolomics helped the design and optimization of a novel isopentenyl diphosphate-bypass mevalonate pathway in E. coli for C5 alcohol production. Recently, some authors have utilized the DBTL cycle to engineer Rhodosporidium toruloides, an oleaginous yeast species growing on lignocellulosic materials and producing the diterpene ent-kaurene, a potential therapeutic, exhibiting antimicrobial, anti-inflammatory, cardiovascular, diuretic, anti-HIV, and cytotoxic effects.

Agricultural and Food Biotechnology

Recent innovations in agricultural biotechnology have led to new plant varieties, engineered by recombinant DNA technology and better responding to market demands and environmental challenges. In fact, “omics” tools in agricultural biotechnology have been used to enhance desirable phenotypic characteristics (e.g., color, taste, drought tolerance, pesticide resistance, etc.). “Omics” plays role not only in improving crop quality, consistency, and productivity, but also in the development of food crops with enhanced nutritional composition. Moreover, omics-driven systems biology helps in understanding the interactions between the “OMEs” and provide links between genes and specific characteristics.

In arable land soil is more susceptible to loss of structure, organic matter, minerals, and erosion. Thus, efforts are being made, via agricultural biotechnology, to provide a constant supply of nutrients essential to the growth of crops. An integral part of this approach is the use of biofertilizers. These are preparations containing specialized microbial inoculants that can fix, mobilize, solubilize, or decompose nutrient sources. Biofertilizers are generally applied through seed or soil and enhance nutrient uptake by plants. Widespread application of this approach, however, has been hampered by fluctuating responses of microbial inoculants across fields and crops. As a result, there is an pressing need to better understand the mechanisms underlying the interdependencies between soil microbial communities and host plant productivity. Recent genomics and exo-metabolomics studies showed that specific rhizosphere bacteria have a natural preference for certain aromatic organic acids exuded by plants. This fact suggested that plant exudation characteristics and microbial substrate uptake traits interact and form specific patterns of microbial community assembly. Furthermore, the application of genomics and transcriptomics to the study of phosphate uptake by various microalgae revealed a range of Pi transporters having specific expression patterns in relation to the availability of P. Currently, “omics” approaches are being used to study complex rhizospheric intercommunications, which is crucial to the development of new biofertilizers, thus promoting stable plant growth, better crop productivity and yield.

In the related field of food biotechnology, application of transcriptomics and metabolomics demonstrated that Bacillus pumilus LZP02 promote the growth of rice roots by enhancing carbohydrate metabolism and phenylpropanoid biosynthesis. Further, the application of “omics” in starch bioengineering provides better understanding on the most important enzymes for its biosynthesis. This facilitates the prediction on how starch-related phenotypes can be modified and ensures further progress in the research field of rice starch biotechnology. “Omics” also help solving issues related to food quality and traceability, to protect the origin of food, and discover biomarkers of potential food safety problems. The advance investigation of wine microbiome has a great influence for wine industry helping in better understanding of factors transforming grapes to wine, including flavor and aroma. “Omics” characterization of the complex relationships between these microorganisms, the substrate and environment, is essential to shaping wine production. Finally, combining “omics” technologies with genome editing of food microorganisms can be exploited to generate improved probiotic strains, develop innovative bio-therapeutics and alter microbial community structure in food matrices.

“OMICs” Technologies and COVID-19

The coronavirus disease 2019 (COVID-19) ia characterized by the Severe acute respiratory syndrome coronavirus 2 [i.e., SARS-CoV-2, which binds to the ACE2 receptor in the lung and other organs]. Due to its worldwide spread a global pandemic has been announced and much of the world’s economy has been slowed. Up to April 2021 there are more than 138 million confirmed cases and 2.5 million confirmed deaths globally (https://www.worldometers.info/coronavirus/). Thus, there is a urgent need for an effective countermeasure to mitigate the spread of the pandemic.

Therefore, efforts are ongoing to fast-track the development and production of safe and effective vaccines against SARS-CoV-2. Preceding knowledge of SARS and Middle East respiratory syndrome (MERS) has facilitated targeting the spike protein as the viral antigen (via the ACE2 receptor). Furthermore, the genome sequencing of the SARS-CoV-2 in January 2020 made it possible to accelerate the development of next generation mRNA and vaccine platforms that encode for the antigen. Once injected into a host, the mRNA, encapsulated into lipid nano-particles, remains in the cytoplasm. When DNA is used, encased in an attenuated adenovirus vector, it enters the nucleus. The host cell translates these genetic materials into the spike protein. It settles on the surface of the cell and provokes an adaptive immune response mediated by T cells (e.g., CD4+ and CD8+) and B cells (i.e., antibodies). These vaccines were reported to be effective against SARS-CoV-2 in recent clinical trials, which underlines the significance of genomics to this new era of vaccine development.

Since the beginning of the outbreak of SARS-CoV-2 a protein interaction was created map and using a proteomics-based approach targets for drug repurposing was revealed. By applying proteomics analysis 26 of the 29 SARS-CoV-2 proteins were cloned, affinity tagged and expressed in human cells and the associated proteins were identified. A total of 66 human proteins or host factors were discovered as potential drug targets of 69 compounds. Two sets of these pharmacological agents showed antiviral activity. Moreover, computational immunoproteomics studies were performed supporting the lab-based investigations and allowing the evaluation of diagnostic products specificity, the forecast of potential vaccines adverse effects and the reduction of animal models utilization.
Furthermore, recently a method was developed based on metabolomics that is able to distinguish COVID-19 patients from healthy controls via the analysis of 10 plasma metabolites. Additionally, the data from lipidomics study suggests that monosialodihexosyl ganglioside enriched exosomes could be involved in pathological processes. Proteomics and metabolomics investigations in COVID-19 patient sera revealed that SARS-CoV-2 infection causes metabolic dysregulation of macrophage and lipid metabolism, platelet degranulation, complement system pathways, and massive metabolic suppression. The analysis of plasma metabolomic signatures showed similar profile to those described for sepsis syndrome. Furthermore, transcriptomics results showed upregulation of genes related to oxidative phosphorylation both in peripheral mononuclear leukocytes and bronchoalveolar lavage fluid. All this information suggests a critical role of mitochondrial activity during SARS-CoV-2 infection. Understanding the clinical presentation of COVID-19 as well as metabolomic, proteomic, and genetic profiles could help in discovering specific diagnostic, prognostic, and predictive biomarkers, ensuring the development of more successful medical therapy. Moreover, differentiating metabolic biomarkers of severe vs. mild disease states in the lung during respiratory infections could lead to the discovery of novel therapeutics that modulate symptom and disease severity.

Nutri-omics Research

More than a decade has passed since the idea of nutrigenomics was first introduced. Nutrigenomics, also referred to as nutritional genomics, nutritional omics, or nutri-omics, can be defined as an area of food and nutrition research making use of profound analyses of molecules or other physical phenomena.
Considering the complexity of the human body and its various interactions with food, it is conceivable that holistic analyses of food- body interactions are an important prerequisite for better understanding the effect of dietary components. The main assumption is that the combination of different omics platforms will provide a deeper insight into the influence of food components and the mechanism of their actions.

Genomics and nutrition

Human genes have different variants in the population. Having in mind that response to nutrition is a multigenic process, it is not surprising that non-related individuals may respond differently. They key hypothesis proposed in recent years stated that genetic variation underlies variation in nutrition-related disorders and risk for disease. In fact, nutrigenetics tries to explain how and to what extent nutrition-related traits and disorders are influenced by genetic variation. In other words, the key questions of nutrigenetics are:

  • Which genes are involved in determining a defined characteristic?
  • What is the functional identity of the variation by which people differ for this characteristic?
  • How can this knowledge be used for the benefit of the population?

Usually, researchers define ‘candidate genes’ for a characteristic or disorder and search in those genes for variation. This is described as the ‘hypothesis-driven’ approach because the selection of the genes is based on their presumed function. For example, the genes for lipoproteins are used as candidates for obesity and the genes for insulin signaling – as candidates for diabetes. Other attractive candidates in humans are the orthologous genes underlying animal models with a Mendelian segregation of a characteristic. A well-known example is the db/db mouse. It is a model for diabetes in which the gene for the leptin receptor was identified. The human homologue was then tested in persons with an impaired glucose tolerance. One variant (allele) of the gene was identified and was proven that it was associated with a significantly higher frequency of diabetes occurrence in comparison to the general population.

Other methods applied is related with studying the preferred segregation of an allele from parents to an affected child (transmission disequilibrium test) or to more affected siblings (sib-pair analysis). If a gene is found to be linked to the characteristic, it remains to be proven whether the associated or linked allele itself is the risk factor, or whether it could be used only as a marker for a nearby causative variation. If the genetic variation is linked with amino acid variation in the protein, a functional test could be developed and the allelic proteins can be compared for their enzymatic activity, DNA-binding affinity, etc.
Another approach to search for risk-conferring genes for a certain characteristic or disorder is the total genome scan. Hundreds of polymorphisms are detected with a random distribution across the genome but with a known chromosomal location. For every polymorphic site it is then identified whether an association exists between the allele and the characteristic. Using this approach risk loci can be identified for many human nutrition-related disorders like obesity and diabetes.

After the second world war in many Western countries, it was observed that the incidences of spina bifida gradually decreased. It was suggested that the improvement of the diet had brought forth this effect. A large epidemiologic study was started. The results obtained showed that the enrichment of the diet with vitamins was able to prevent the occurrence and recurrence of spina bifida in humans with more than 50%. Folic acid was discovered to be the active substance and now in many countries pre-conceptional folic acid supplementation is recommended as a general preventive measure. Simultaneously, it has become clear that concerning the risk for a child with spina bifida, the women in the population can be roughly divided into three groups: [i] not at risk even at a low folate intake, [b] at risk at low dietary folate but can be helped by increased folate intake, and [c] at risk despite extra folate intake. Genetic predisposition was presumed, and candidate genes taken from the folate metabolism were examined for genetic variation. This led to the detection of the 667C->T and 1298A->C alleles in the gene for methylenetetrahydrofolate reductase (MTHFR) as risk factors. However, these risk alleles are discovered to some extent in all three groups of women illustrating that genetic testing in the context of a personalized diet would be inadequate. Moreover, it became clear that not all the relative risk could be explained by these alleles. Therefore, the search for genetic variation in other genes is continued and new candidate genes may be found.

Currently, several hundreds of genes related to specific nutrition-related characteristics and disorders have already been identification via using genetic experiments in various populations and the investigation of animal and in vitro model systems. Finally, the nutrigenomics and nutrigenetics research should lead to the comprehensive understanding of the etiology of those characteristics and disorders with respect to the interacting genes, the dietary components, and the relative risk conveyed by both genetic variation and diet. This combined knowledge will permit the genetic profiling of every individual and thereby assess her/his risk for developing nutrition-related disorders. Based on this personal risk profile of genetic factors, a ‘personalized diet’ could then be proposed by which the onset of a disorder could be prevented or at least delayed. Already some companies offer genetic testing for alleles that were found to be associated with certain characteristics. For example, the ApoE allele is tested as a risk factor for cardiovascular disorders and an allele of the alcohol dehydrogenase gene is used to predict potential alcohol sensitivity and (ab)use. However, it is important to note that the presence of certain allele may be said to increase the risk for a disorder by 100%, while in absolute terms the risk would usually still be very small like an increase from 0.001 to 0.002. Additionally, testing only one genetic factor might give an incomplete or even a wrong view of the situation. Nowadays, we have no idea of all the genetic factors and how they interact. Till more information is gathered, the advice based on simple testing probably has to remain simple, sounding like “drink less alcohol” or “eat less saturated fat”.

Trascriptomics and nutrition

Transcriptomics is the most widely employed in food research because of the many advantages of the DNA microarray technology, including the comprehensiveness of the expression data, established protocols, and high reliability and reproducibility of the data. It has been also reported the alternations of global gene expression in response to different dietary changes such as nutritional deficiency, fasting, overeating, and ingestion of specific food factors. As one example of the performed attempts to acquire reference data a transcriptome analysis of the liver of rats treated with mild caloric restriction has been performed. When using animal experiments, it was shown that a modest reduction of food intake or altered intake pattern could be sufficient for occurrence of significant metabolic changes. Therefore, it is important to distinguish between the direct effects of the food component and the secondary effects caused by the change in eating behavior. When rats were fed a diet with 5 to 30 % less food than that consumed by an ad libitum-fed group for 1 week or 1 month it has been observed restriction – dependent changes in the expression level of cyp4a14. In fact, the cyp4a14 gene was induced by even a low level of caloric restriction. This data suggests that the gene can be used as a biomarker for the beneficial effects of food on energy metabolism.

Proteomics and nutrition

Many food and nutrition research studies have been carried out by using proteomics approaches. For instant, the effect of mild caloric restriction described above was also examined by proteomics. Nine significantly up-regulated proteins and nine down-regulated proteins have been detected after the proteome comparison of the liver of rats treated with 30% food restriction with control ones. Ten percent restriction caused the up-regulation of 9 proteins and the down-regulation of 2 proteins. An interesting discovery was the up-regulation of prohibitin, whose involvement in the regulation of longevity was recently revealed. This result suggests that prohibitin can be applied as an effective biomarker of the beneficial effects of food factors. Although the current stage of proteomics research is much less extensive compared with that of transcriptomics, the study described here together with other nutritional proteomics research findings indicates that proteomics is a highly promising tool for the discovery of biomarkers.

Matbolomics and nutrition

About ten thousand different types of major metabolites exist in animal bodies, while the number of proteins is thought to exceed 100,000. This feature of metabolites is expected to result in more comprehensive features of metabolomics analysis than proteomics ones. However, the analysis of metabolites is in fact fraught with difficulties and usually requires the use of sophisticated techniques and high skill level personnel. Another difficulty originates from the width of the metabolite’s abundance. Regardless of these obstacles, metabolomics is a powerful tool in food and nutrition science.

Many investigations showed that gut and serum metabolism is influenced by the change in gut microbiome composition. The gut microbiome and its alternation in relation to the diet are essential when evaluating dietary intervention trials with metabolomic end effects. The comparison between germ-free mice colonized by human baby flora and conventional mice showed the complexity of diet modifiable microbiome/metabolome covariation. In this study, the effect of the intestinal microbiome on plasma metabolites was studied. Data revealed that more than 10% of the plasma metabolome is directly dependent upon the microbiome. Some examples for microbial dependent compounds in plasma include cinammic acid, glycine conjugated compounds, hippuric acid, and other plasma metabolites. The gut microbiome also directly affects the host’s ability to metabolize lipids, carbohydrates, and proteins, and can carry out several phase II detoxification reactions. There is also evidence that gut microorganisms utilize nonnutritive phytochemicals. For example, several investigations demonstrated that levels of three microbiome-dependent diet-derived metabolites, choline, trimethylamine N-oxide, and betaine, could predict risk for cardiovascular disease in mice.

Nutrition and other OMICs

Many other omics platforms are also а focus of nutriomics research. It was shown that epigenetic alteration could be caused by the nutrition diet during fetal development and could affect the predisposition to lifestyle-related diseases in later life. For example, children of mothers who suffered over- or undernutrition during pregnancy have increased risk of developing obesity, diabetes, hypertension, cardiovascular diseases, etc. Many studies have proven the involvement of epigenetic modifications in such acquired susceptibility. Another promising target of omics approaches in food science is associated with the RNA transcripts that do not encode proteins. MicroRNA (miRNA) is a subtype of these non-coding RNAs. Precursors of miRNAs (pri-miRNA) are long products which are cleaved to yield mature miRNAs of 22 nucleotides in length. Mature miRNAs regulate the levels of mRNA degradation, mRNA translation, and even gene transcription. In the case of human cells, about 1,000 miRNAs are identified and are supposed to regulate the expression of more than half of the protein-coding genes. Considering the accumulating data supporting the key role of miRNA in the development of diseases and the maintenance of health, the information on the status of miRNAs is no doubt vital for the understanding of the interaction between food components and the body. Global miRNA analysis now can be easily performed by the use of commercial miRNA arrays.

With the introduction of new technologies and acquired knowledge, the number of fields in omics and their applications in various areas are rapidly increasing in the postgenomics era. Such emerging fields—including pharmacogenomics, toxicogenomics, regulomics, spliceomics, metagenomics, and environomics—present promising solutions to combat global challenges in biomedicine, agriculture, and the environment.

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References

  • Amer B, Baidoo EEK. 2021. Omics-Driven Biotechnology for Industrial Applications. Frontiers Bioeng. Biotech., 9, Art. 613307: 1-19.
  • Azizipour N, Avazpour R, Rosenzweig DH, Sawan M, Ajji A. 2020. Evolution of Biochip Technology: A Review from Lab-on-a-Chip to Organ-on-a-Chip. Micromachines, 11: 599.
  • Canzler S, · Schor J, · Busch W, et al. · 2020. Prospects and challenges of multi omics data integration in toxicology. Archives of Toxicology, 94:371–388.
  • Horgan RP, Kenny LC. 2011. ‘Omic’ technologies: genomics, transcriptomics, proteomics and metabolomics. The Obstetrician & Gynaecologist, 13:189–195.
  • Joseph P. 2017. Transcriptomics in toxicology. Food Chem Toxicol., 109(1): 650–662.
  • Karahalil B. 2016. Overview of Systems Biology and Omics Technologies. Curr. Med. Chem., 23, 1-10.
  • Karczewski KJ, Snyder MP. 2018. Integrative omics for health and disease. Nat Rev Genet., 19(5): 299–310.
  • Kato H, Takahashi S, Saito K. 2011. Omics and Integrated Omics for the Promotion of Food and Nutrition Science. J. Trad. Complement. Medicine, 1 (1): 25-30.
  • Kim DH, Kim YS, Son NI, Kang CK, Kim AR. 2017. Recent omics technologies and their emerging applications for personalized medicine. IET Syst. Biol.,11 (3): 87-98.
  • Kodzius R, Gojobori T. 2016. Single-cell technologies in environmental omics. Gene 576: 701–707.
  • Mariman ECM. 2006. Nutrigenomics and nutrigenetics: the ‘omics’ revolution in nutritional science. Biotechnol Appl Biochem., 44(3):119-28.
  • Mousumi D, Prasad GBKS, Bisen PS. 2010. Omics technology. In: Molecular Diagnostics: Promises and Possibilities, Dordrech Heidelberg London, Springer, pp 11-31.
  • Ryan EP, Heuberger AL, Broeckling CD, Borresen EC, Tillotson C, Prenni JE. 2013. Advances in Nutritional Metabolomics. Current Metabolomics, 1: 109-120.
  • Sauer UG, Deferme L, Gribaldo L, et al. 2017. The challenge of the application of ‘omics technologies in chemicals risk assessment: Background and outlook. Regulatory Toxicology and Pharmacology, 91: S14 – S26
  • Simões T, Novais SC, Natal-da-Luz T, et al. 2018. An integrative omics approach to unravel toxicity mechanisms of environmental chemicals: effects of a formulated herbicide. Scientific Reports 8:11376
  • Titz B, Elamin A, Martin F, et al. 2014. Proteomics for systems toxicology. Computational and Structural Biotechnology Journal, 11: 73–90.
  • Vlaanderen J, Moore LE, Smith MT, et al. 2010. Application of Omics Technologies in Occupational and Environmental Health Research; Current Status and Projections. Occup Environ Med., 67(2): 136–143.
  • Yılmaz B, Yılmaz F. 2018. Lab-on-a-Chip Technology and Its Applications. In: Omics Technologies and Bio-engineering: Towards Improving Quality of Life, pp. 145, Elsevier Inc.
  • Amer B, Baidoo EEK. 2021. Omics-Driven Biotechnology for Industrial Applications. Frontiers Bioeng. Biotech., 9, Art. 613307: 1-19.
  • Azizipour N, Avazpour R, Rosenzweig DH, Sawan M, Ajji A. 2020. Evolution of Biochip Technology: A Review from Lab-on-a-Chip to Organ-on-a-Chip. Micromachines, 11: 599.
  • Canzler S, · Schor J, · Busch W, et al. · 2020. Prospects and challenges of multi omics data integration in toxicology. Archives of Toxicology, 94:371–388.
  • Horgan RP, Kenny LC. 2011. ‘Omic’ technologies: genomics, transcriptomics, proteomics and metabolomics. The Obstetrician & Gynaecologist, 13:189–195.
  • Joseph P. 2017. Transcriptomics in toxicology. Food Chem Toxicol., 109(1): 650–662.
  • Karahalil B. 2016. Overview of Systems Biology and Omics Technologies. Curr. Med. Chem., 23, 1-10.
  • Karczewski KJ, Snyder MP. 2018. Integrative omics for health and disease. Nat Rev Genet., 19(5): 299–310.
  • Kato H, Takahashi S, Saito K. 2011. Omics and Integrated Omics for the Promotion of Food and Nutrition Science. J. Trad. Complement. Medicine, 1 (1): 25-30.
  • Kim DH, Kim YS, Son NI, Kang CK, Kim AR. 2017. Recent omics technologies and their emerging applications for personalized medicine. IET Syst. Biol.,11 (3): 87-98.
  • Kodzius R, Gojobori T. 2016. Single-cell technologies in environmental omics. Gene 576: 701–707.
  • Mariman ECM. 2006. Nutrigenomics and nutrigenetics: the ‘omics’ revolution in nutritional science. Biotechnol Appl Biochem., 44(3):119-28.
  • Mousumi D, Prasad GBKS, Bisen PS. 2010. Omics technology. In: Molecular Diagnostics: Promises and Possibilities, Dordrech Heidelberg London, Springer, pp 11-31.
  • Ryan EP, Heuberger AL, Broeckling CD, Borresen EC, Tillotson C, Prenni JE. 2013. Advances in Nutritional Metabolomics. Current Metabolomics, 1: 109-120.
  • Sauer UG, Deferme L, Gribaldo L, et al. 2017. The challenge of the application of ‘omics technologies in chemicals risk assessment: Background and outlook. Regulatory Toxicology and Pharmacology, 91: S14 – S26
  • Simões T, Novais SC, Natal-da-Luz T, et al. 2018. An integrative omics approach to unravel toxicity mechanisms of environmental chemicals: effects of a formulated herbicide. Scientific Reports 8:11376
  • Titz B, Elamin A, Martin F, et al. 2014. Proteomics for systems toxicology. Computational and Structural Biotechnology Journal, 11: 73–90.
  • Vlaanderen J, Moore LE, Smith MT, et al. 2010. Application of Omics Technologies in Occupational and Environmental Health Research; Current Status and Projections. Occup Environ Med., 67(2): 136–143.
  • Yılmaz B, Yılmaz F. 2018. Lab-on-a-Chip Technology and Its Applications. In: Omics Technologies and Bio-engineering: Towards Improving Quality of Life, pp. 145, Elsevier Inc.