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Файл:Ординатура / Хирургия / Библиотека им академика М.И. Перельмана / Книга_5864_Библиотеки_им_академика_М_И_Перельмана.pdf
Introduction to Pharmaceutical Biotechnology, Volume 2 (Second Edition)
gene at a time. Among potential branches of functional genomics, the field of
transcriptomics helps in predicting and understanding the exact functions of genes
and their respective products. High-throughput screening of gene transcripts in
biological systems, through the development of the necessary bioinformatics
analytical tools, has allowed scale-up of experiments classically performed with
single genes to identify genetic variation at large scales. Functional genomics can be
theoretically further categorized into gene-driven (dependent on genomic data to
identify, clone and express genes in a genome) and phenotype-driven approaches
(dependent on phenotypes from random mutation screens or naturally occurring
variants to recognize and characterize genes for the phenotype, without having any
previous information of the basic molecular mechanism or function). The most
important aspect of functional genomics is that it explores the functional arrangement of all genomes, in particualr parts that are external to any coding gene
sequences which play an important role in gene expression. The concept of the
epigenome covers all the molecules and proteins which can affect DNA function,
usually by turning genes on or off. Studies related with this field usually cover factors
responsible for controlling development and differentiation against external
changes. During this process epigenetic tags are made in the form of DNA
methylation and covalent modifications of histones to determine the arrangements
of cell- and tissue-specific gene expression, as described in figure 8.9. Epigenetic
readers also help in determining gene expression. In certain cases, small RNA
molecules are used to guarantee sequence specificity.
Overall, these studies of epigenetic modifications explain the forms of epigenetic
‘flavors’ (i.e., the existence of chromatin in different states). This means the presence of
chromatin in heterochromatin (inactive, repressed) and euchromatin (potentially
active), decides the tags and reads which may further decide flavors. In addition
functional genomic studies also cover the complex organization of RNA molecules in
the genome, i.e., well characterized coding RNA and non-coding RNAs. Functional
genomic investigations include highly complex genome and proteome information
which requires the development of potential computational and hardware tools. This
discipline gave birth to an independent discipline called bioinformatics. Recent
developments in bioinformatics have allowed collaboration between different disciplines, resulting in further new disciplines ending with the suffix ‘-omics’, e.g.
proteomics, genomics and metabolomics. All these disciplines have the common
objective of computational analysis of sophisticated and vast information at each and
every biological level; starting from the molecular level, i.e., genes and molecules, to
cells, tissues and organs, and finally to the study of whole-body systems.
8.6.1 Gene expression profiling
As mentioned above, functional genomics is defined as the branch of genomics that
predicts the functions of genes and gene products by high-throughput screening of
gene transcripts in a biological system. Functional genomics has numerous applications in clinical medicine [22]. Functional genomics is a broad approach that
allows the concurrent examination of mRNA levels for the complete human
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Figure 8.9. Epigenetic tags and reads.
transcriptome from as few as 1000 cells. Currently, functional genomics is employed
to categorize the development of disease and survival in response to traumatic and
burn injuries, sepsis and visceral ischemia, and reperfusion injury, as well as to
designate patterns of gene expression in reaction to erratic microbial pathogens. Due
to the number of new bioinformatics techniques emerging, functional genomics is
providing a foundation to reveal the fundamental complexity of the biological
response to a range of inflammatory diseases and is offering new methods for their
exploration [23]. Functional genomics has now been developed as a standard tool in
inflammation research to undo basic biological processes.
Expression profiling is a discipline that deals with the extent of expression of
multiple genes at the same time, i.e., it attempts to determine the complete
picture of cellular function at a molecular level. This can be achieved using certain
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tools, e.g. DNA microarrays, which help in measuring the relative expression of
multiple genes and their correlation with already identified target genes. The
microarray is an advanced tool to determine genetic expression by evaluating the
actual amount of mRNA present in the sample. In addition to exploring the role of
existing genes and their relevant association, microarrays also include genes with
currently unknown functions. This can provide the prospect of new gene discovery,
which in particular plays an important role in functional genomics, e.g. microarray
based analysis of previously unknown genes selectively expressed in T-helper 2 type
lymphocytes can offer new targets for asthma [24]. In addition, by using comprehensive arrays it is now possible to carry out deletion mapping to recognize selective
areas of chromosomes whose genes are not present in a large microarray.
Functional genomics covers global expression profiling to study genes under
epigenetic and transcriptional regulation to further study several forms of coding
and non-coding RNA molecules. It is a branch that deals with the simultaneous
examination of the expression pattern, usually of all genes present in the genome at
the RNA level or at the protein level. In contrast to global expression profiling,
traditional approaches are more focused on a single gene, which can restrict access
to these complex interactions. Systematic investigation of gene expression profiling
helps in interpreting the transcriptomes of consecutive developmental stages. This
information is required to understand the developmental mechanism which can shed
light on conservation and diversification at the molecular level. Thus functional
genomics is directed towards studying global expression profiling at the RNA level
(by nucleic acid arrays or direct sequence analysis) or at the protein level (by 2DE
followed by mass spectrometry or protein arrays).
Global expression profiling is a valuable approach which can help in the
identification of those particular genes that play an important role in development,
and also help in understanding the complex behavior of a gene in a particular
environment against different levels of stress or any other stimuli which can cause
the onset of a disease, etc. Moreover, it also offers a unique method for characterizing cellular phenotypes and exploring novel drug targets which can help in
developing potential drugs.
8.6.2 Transcriptome, proteome and genomes
The transcriptome is the initial product of genome expression. Basically, it is an
assortment of RNA molecules obtained from those protein-coding genes whose
biological information is essential for the cell at a specific time [25]. These RNA
molecules further direct the production of the final product of genome expression,
the proteome, the cell’s repertoire of proteins, which stipulates the nature of the
biochemical reactions that the cell is able to carry out. The transcriptome is created
by the process known as transcription, in which individual genes are copied into
RNA molecules.
Transcriptomic studies are part of integrative genomics, which includes the set of
all RNA molecules from protein-coding (mRNA) to non-coding RNA, of a
complete organism or a particular cell. As the name suggests, the transcriptome
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Figure 8.10. General overview of transcriptomes.
encompasses the complete set of transcripts (the copies of DNA or RNA) in a
particular cell or tissue. Transcripts are complementary strands of DNA or RNA
synthesized by the genome (figure 8.10). Thus the whole objective of transcriptome
studies is to recognize genes differentially expressed under different environmental
conditions. This may help in understanding genes or their associated pathways. To
understand this more deeply we have to consider a eukaryotic system, in which
genetic expression of a single gene results in the synthesis of more than one type of
developed mRNA. This process of alternative splicing or differential splicing results
in a single gene coding for multiple proteins, i.e., mRNA is directed to synthesize
different proteins that may have different functions or properties. Alternative
splicing involves elimination of introns (nucleotide sequences) from the gene
(complementary RNA/DNA) and fixing exons together in the right direction to
yield the final functional mRNA product. The information which is not of any use,
i.e., non-coding sections of an RNA transcript, are removed and the coding sections
(the code for proteins) are joined together to form the functional product in the form
of mRNA. Alternative splicing can take place in different ways, but still results in
well-defined patterns. This information helps in understanding the whole human
genome project. For example, when a single human gene has undergone alternative
splicing to produce functional RNA to further produce three different proteins, you
can assume how many proteins the projected 35 000 human genes could create (the
estimated number is 105 000 different proteins, i.e., different in their properties and
functions). This diversity of proteins present inside the body helps in controlling the
whole metabolism, structure, etc. A popular example of alternative splicing is seen in
the case of the Drosophila gene Dscam [26]. Dscam is a (immunoglobulin superfamily) protein essential for the development of neuronal connections in Drosophila.
By means of alternative splicing, Dscam can potentially develop 19 008 different
extracellular domains associated with one of two alternative transmembrane segments, resulting in 38 016 isoforms. All these isoforms share the same domain
structure, however, they encompass variable amino acid sequences within three Ig
domains in the extracellular region [27]. A gene identified in this study produced
approximately 40 000 different mRNAs. These functional mRNAs can be further
translated in different receptor proteins. After the determination of the complete
human genome sequence in 2001, it is now understood that only a small portion of
the human transcriptome is translated into proteins. The rest of the residual
transcripts have unidentified functions. It has been reported that a noticeable
increase in transcriptional complexity is connected with organization of the transcriptional units in the genome. There are genomic regions that are highly enriched
for transcripts which may be subjected to shared epigenetics regulatory control over
larger regions. This complexity is due to the production of a manifold of functional
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mRNAs from each RNA transcript, which makes them more vulnerable to
mutation. The multiple bioconversion steps, which involve the passage of large
quantities of information from one molecule to another, and further the multiple
copies made in the form of a functional message (mRNA) to synthesize different
proteins, involve cascades of molecular events where each time elements are expose
against different conditions, there is a chance of being mutated. The extent and
nature of transcriptome varies from tissue to tissue because no tissue will express all
of the genes. The genetic expression of the transcriptome always varies, which is why
each tissue has its own transcriptome to synthesize unique proteins. This process is
very selective in nature as genes expressed in a unique tissue will vary from those
present in another tissue. Consequently, based on this variation it is obvious to
discuss different transcriptomes, such as the human brain transcriptome, mouse liver
transcriptome, etc
8.6.3 DNA arrays: a potential genomic tool
DNA microarray techniques are primarily used to determine the transcriptional
levels of RNA transcripts derived from thousands of genes within a genome in a
single experiment (figure 8.11). By using this approach we can relate physiological
cell states to gene expression patterns for examining tumors, disease progression,
cellular response to stimuli and drug target identification. Currently DNA
Figure 8.11. Schematic representation of DNA arrays.
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Figure 8.12. DNA array representing DNA on a solid surface before and after hybridization.
microarrays are not limited to gene expression, as they are also being used to detect
the single nucleotide polymorphisms (SNPs) of the human genome (the Hap Map
project), aberrations in methylation patterns, alterations in gene copy-number,
alternative RNA splicing and pathogen detection [28].
DNA microarray or biochip techniques include synthesis of DNA sequences in
two- or three-dimensional format. These sequences are synthesized such that the
DNA sequences are covalently or non-covalently linked to the surface. In use, a
DNA array allows the hybridization of targets (labeled nucleic acids) to the probes
present on the array to evaluate the relative amount of nucleic acid present in a given
solution, as shown in figure 8.12.
Generally, during this process small sequences of nucleic acids are immobilized or
fixed on an appropriate solid support such as a glass chip, silicon chip or nylon
membrane.
DNA arrays are available in three types:
• Spotted arrays on glass.
• In situ synthesized arrays.
• Self-assembled arrays.
Spotted arrays on glass. Spotted DNA arrays allow high-density DNA arrays (10
6
–10
double-stranded DNA molecules with dense spots up to 5000 spots per cm2)tofixover
glass substrates [29]. Polylysine coated glass microscope slides are used for immobilizing
DNA and for spotting a robotic spotter can be utilized. This robotic spotter spots
various glass slide arrays with DNA from microtiter dishes. These small fragments of
DNA are derived from genomic libraries, in particular from cDNA clones or by PCR
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Figure 8.13. Spotting arrays. A glass fountain-pen-like structure (usually multiple pens are present) is allowed
to dip into solutions containing DNA and this is then deposited on glass slides.
amplification. This robotic spotter contains slotted pins which are quite similar to
fountain pens in design (figure 8.13). During this process a single dip is made in the DNA
solution, which can then be used over numerous slides. This robotic facility also allows
one to fluorescently label the samples for fluorescence based detection, which offers
greater sensitivity and a broad dynamic range, and can provide different colored labels,
which can further help in their detection after hybridization. Flourescent labels are also
cheaper than radioactive or chemilluminescent labels. Spotting is done in such a manner
that each individual spot or dot signifies a separate gene, i.e., the spots are not
characterized by repetition. Spotting can be studied using confocal scanning [30].
In situ synthesized arrays. Fodor first suggested, in 1991, that single-stranded
oligonucleotides are synthesized in the presence of light on a solid substrate (the
process is called photolithography) [31]. Initially, ten amino acid peptides were
synthesized using di-nucleotides, and later 256 different octa-nucleotides were
synthesized. During 1995, Affymetrix array technology was utilized to determine
the variation in the reverse transcriptase and protease genes of the highly polymorphic HIV-1 genome and also to determine mutation in the human mitochondrial
genome. Affymetrix technology has been used to produce an extensive set of DNA
arrays for use in expression analysis [48, 49], genetic characterization and gene
sequencing [32]. In situ synthesized arrays can be synthesized by two approaches:
• Inkjet oligosynthesis methods.
• Photolithographic methods (e.g. Affymetrix).
Self-assembled arrays. Another method to construct arrays was initially suggested by
David Walt at Tufts University [33]. This technique was later licensed to Illumina (a
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Figure 8.14. Self-assembled arrays.
sequencing and array technology company in San Diego, CA). In this method small
DNA strands are synthesized on small polystryrene beads. Ultimately these DNA
coated beads are deposited on the ends of a fiber optic array (figure 8.14). The fibers
are engraved to provide a well which is larger than a single polystryrene bead. The
beads are optically encoded with different fluorophores so as to identify which
oligonucleotide was in which position on the array. This method is called an optical
sensor array.
Small single-stranded strands of oligonucleotide (20–25 base pairs) are synthesized and fixed (printed) over a solid matrix with the light-directed printing
technique known as photolithography, to form a printed oligonucleotide chip.
Oligonucleotides are directly synthesized over the matrix. To reduce the chance of
false positive outcomes, each fragment sequence is described by multiple oligonucleotides (20 non-overlapping oligonucleotides). A high density of oligonucleotides
over the matrix (64 000 cm
−2
) offers more accurate results for investigating gene
expression patterns. One of the most interesting applications of a microarray is to
establish the association between the gene expression patterns of two cell types (e.g.
cancerous tissue and normal tissue). Microarrays have potential applications in
transcriptomics, because the whole concept is based on the synthesis of complementary strands (cDNA) or probes from mRNA. For the development of probes,
targeted cells must first be identified to isolated mRNA, e.g. to study the genetic
variation between normal and cancerous cells mRNA from both is isolated.
However, since RNA is not very stable and can be quickly degraded, using reverse
transcriptase mRNA is converted into complementary DNA (cDNA). By using the
RNA molecule, an enzyme known as reverse transcriptase copies strand to
synthesize a new complementary template DNA. This newly formed cDNA is
labeled with suitable fluorescent dyes (fluors, for short). These labeled cDNA are
typically called probes. Labeling of target molecules (cDNA or cRNA) is s crucial
step in a microarray as it helps to determine the amount of mRNA indirectly
through the labeled molecules. In the case of investigating the genetic variation
between normal and cancerous cells, cDNA derived from both types of cells is
labeled with a different fluor that will yield fluorescence of different colors. Now, the
amount of cDNA from each sample signi fi es the amount of mRNA present in that
sample, i.e., the amount of cDNA in the probe will be comparable to that of the
mRNA in the cell. These cDNA are allowed to hybridize with cDNA probes.
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Each labeled cDNA molecule will hybridize with the DNA molecules of that dot of
the microarray to which it is complementary. Since at this stage some strands are
labeled and some are not, the strands utilized in hybridization can be identified by
their respective fluorescence pattern. So, because of the fluor existing on the cDNA
probe, these can be identified by their colored spots, however, those spots that do not
have complementary cDNA in the probe will not fluoresce and will appear as a
blank. Fluorescence intensity also helps in the identification of regions of blot paper
where hybrid strands are present. This will indirectly determine the relative amount
of mRNA present in the cell. To compare the genetic expression between cancerous
and normal cells, cDNA probes of both are used concurrently for hybridization
under same DNA array conditions. In such cases two different fluors are employed.
The information based on fluorescence imaging (spots) of the two fluors can be
accessed using suitable software. This software helps in establishing the relationship
between the intensities of the images and the amount of mRNA. The examination of
a microarray such as that described above will offer data on the following:
• The genes that are expressed in both normal and cancerous cells.
• The genes that are expressed in normal cells but are not expressed in
cancerous cells.
• Those genes that are expressed only in cancerous cells. This information would
allow the identification of cancerous cells, and also to devise suitable drugs to
selectively target such cells.
8.6.4 Gene function determination from sequence information
Scientists have synthesized a vast number of genome sequences from different
organisms. The available databases, such as GenBank at the NCBI, store many of
these sequences. These databases can be potentially utilized for studying comparative biology [34]. Not only do the databases store the genome sequences, but also
information about the function (if it is known) of the genes. By making comparisons
between known genes in the database, GenBank can be used to identify unknown
genes. In database, one program exclusively used for this purpose is BLAST (Basic
Local Alignment Search Tool). BLAST is a sequence similarity searching algorithms, which is based on the principle that if two sequences are similar then they are
possibly homologous (that is, they share a common evolutionary ancestor). By
means of this database, one can understand the function of an unknown gene by
discovering similar sequences of known genes and proteins. BLAST is an advanced
program, in that it searches at the nucleotide level, as well as making assessments at
the amino acid level, offering much greater sensitivity. Thus, if one is mainly
interested in the DNA sequence itself, it is better to hunt for genes using proteins. In
addition to whole proteins, similarity searches can find protein motifs. A motif is a
characteristic arrangement of amino acids, stored across many proteins, which offers
a particular function to the protein [35].
The primary objective of classical functional genomics is to regulate the function
of specific genes. Initial gene cloning is followed by mutation under in vitro
conditions and finally reincorporation of the mutated gene into the host organism
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to examine its expression or any other effect. This approach has its limitations, as the
process is complicated, slow and has a low success rate. The applications of a
combination of the experimental approach to functional genomics and highthroughput sequencing analysis are as follows:
• To identify the gene accountable for specific biological phenotypes and
diseases.
• To help in determining the function of gene.
• To facilitate the rapid identification of genes.
• To enable the discovery of genes that participate in other biological processes.
• To mutate every gene in the genome and accumulate the mutant strain to
create genome-wide mutant libraries.
The last point, establishing genome-wide mutant libraries in several organisms such
as bacteria, yeast, plants and mammals, is called mutational genomics. A mutational
genomics based library can be established using one of the following two
approaches.
The systematic approach. To establish a library, the systematic mutation of
individual genes in the genome at one time should be studied, which allows the
development of a bank of specific mutant strains. This can only be attained if the
complete genome sequence is known. In this method, homologous recombination
(which functions in the repair of DNA double-stranded breaks and inter-strand
crosslinks) is utilized to accommodate a selectable marker gene (which helps in the
selection of the targeted gene) inside the gene of interest. Usually, the insertion of the
gene of interest in a selectable marker disturbs the selectable marker function, which
can be further checked against the expression of normal selectable marker gene.
However, in contrast, here homologous recombination allows the selectable marker
to integrate into the gene of interest, which allows the dislocation of the targeted
gene. The whole process is called gene knockout. By using suitable sequences from
or around the target gene the selectable marker gene is flanked to attain homologous
recombination. So it is essential to recognize the sequences of the gene of interest
[36]. After the employment of suitable vectors, integration performed by homologous recombination may possibly occur up to 90% in prokaryotes. However, the
opposite is the case for plants and animals, the rate of integration by homologous
recombination is only 0.001% of the entire integration process. Homologous
recombination or integration in the case of embryonic stem cells derived from
mice has a rate up to 0.1%, which is much lower than that of yeast, which has 6200
genes and a rate of homologous integration of up to 90%. Knockout investigations
have allowed knockouts of almost 85% of yeast genes. The mutants produced by this
approach are studied to determine the functions of the missing genes. It is probable
that a similar mutagenesis procedure might be considered for Drosophila in the near
future.
The random approach. In contrast to the systematic approach, in random
integration the gene is randomly mutated and this can be functional for any species.
Random insertional mutagenesis can be achieved by the addition or deletion of
nucleotides from a target gene sequence. This type of insertion or deletion results in
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