23.2: Genomic Approaches- The DNA Microarray - Biology

23.2: Genomic Approaches- The DNA Microarray - Biology

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Traditionally, when cellular levels of a protein were known to change in response to a chemical effector, molecular studies focused on control of the transcription of its gene. These studies often revealed that the control of gene expression was at the level of transcription, turning a gene on or off through interactions of transcription factors with DNA. However, protein levels are also controlled post-transcriptionally, by regulating the rate of mRNA translation or degradation. Studies of transcriptional and posttranscriptional regulation mechanisms are seminal to our understanding of how the correct protein is made in the right amounts at the right time.

We may have suspected, but now know that control of gene expression and cellular responses can be more complex than increasing or decreasing the transcription of a single gene or translation of a single protein. Whole genome sequences and new techniques make possible the study of the expression of virtually all genes in a cell at the same time, a field of investigation called genomics. Genomic studies reveal networks of regulated genes that must be understood to more fully explain the developmental and physiological changes in an organism. When you can ‘see’ all of the RNAs being transcribed from active genes in a cell, you are looking at a cell’s transcriptome. By analogy to genomics, transcriptomics defines studies of ‘webs’ of interactive RNAs. Again, by analogy to genomics and transcriptomics, the broad study of active and inactive proteins in cells or tissues, how they are modified (processed) before use and how they interact is called proteomics. The technologies applied to proteomic studies include protein microarrays, immunochemical techniques and others uniquely suited to protein analysis (click Proteomics Techniques-Wikipedia for more information). Protein Microarrays are increasingly being used to identify protein-protein interactions, as well as the different states of proteins under different cellular conditions. Read even more about these exciting developments and their impact on basic and clinical research at Protein Microarrays from ncbi.

Finally think about this: creating a proteomic library analogous to a genomic library would seem a daunting prospect. But efforts are underway. Check out A stab at mapping the Human Proteome for original research leading to the sampling of a tissue-specific human proteome, and click Strategies for Approaching the Proteome for more general information. Let’s look at some uses of DNA microarrays. This technology involves ‘spotting’ DNA (e.g., cloned DNA from a genomic or cDNA library, PCR products, oligonucleotides…) on a glass slide, or chip. In the language of microarray analysis, the slides are the probes. Spotting a chip is a robotic process. Because the DNA spots are microscopic, a cellspecific transcriptome (cDNA library) can fit on a single chip. A small genome microarray might also fit on a single chip, while larger genomes might need several slides. A primary use of DNA microarrays is transcriptional profiling. A genomic microarray can probe a mixture of fluorescently tagged target cDNAs made from mRNAs, in order to identify many (if not all) of the genes expressed in the cells at a given moment (i.e., its transcriptome). cDNA microarray probes can also probe quantitative differences in gene expression in cells or tissues during normal differentiation or in response to chemical signals. They are also valuable for genotyping, (i.e. characterizing the genes in an organism). Microarrays are so sensitive that they can even distinguish between two genes or regions of DNA that differ by a single nucleotide. Click Single Nucleotide Polymorphisms, or SNPs to learn more. In the microarray below, each colored spot (red, yellow, green) is a different fluorescently tagged molecule hybridizing to target sequences on the microarray. In the fluorescence microscope, the spots fluoresce different colors in response to UV light.

With quantitative microarray methods, the brightness (intensity) of the signal from each probe can be measured. In this way, we can compare the relative amounts of cDNA (and thus, different RNAs) in the transcriptome of different tissues or resulting from different tissue treatments. A table of different applications of microarrays (adapted from Wikipedia) is shown on the next page.

Application of TechnologySynopsis
Gene Expression ProfilingIn a transcription (mRNA or gene expression) profiling experiment the expression levels of thousands of genes are simultaneously monitored to study the effects of certain treatments, diseases, and developmental stages on gene expression.
Comparative genomic hybridizationAssessing genome content in different cells or closely related organisms, where one organism’s genome is the probe for a target genome from a different species.
GeneIDSmall microarrays to check IDs of organisms in food and feed for genetically modified organisms (GMOs), mycoplasmas in cell culture, or pathogens for disease detection. These detection protocols often combine PCR and microarray technology.
CHIP; Chromatin immunoprecipitationDNA sequences bound to a particular protein can be isolated by immunoprecipitating the protein. The fragments can be hybridized to a microarray (such as a tiling array) allowing the determination of protein binding site occupancy throughout the genome.
DamIDAnalogously to ChIP, genomic regions bound by a protein of interest can be isolated and used to probe a microarray to determine binding site occupancy. Unlike ChIP, DamID does not require antibodies but makes use of adenine methylation near the protein's binding sites to selectively amplify those regions, introduced by expressing minute amounts of protein of interest fused to bacterial DNA adenine methyltransferase.
SNP detectionIdentifying single nucleotide polymorphism among alleles within or between populations. Some microarray applications make use of SNP detection, including Genotyping, forensic analysis, measuring predisposition to disease, identifying drug-candidates, evaluating germline mutations in individuals or somatic mutations in cancers, assessing loss of heterozygosity, or genetic linkage analysis.
Alternative splicing protectionAn exon junction array design uses probes specific to the expected or potential splice sites of predicted exons for a gene. It is of intermediate density, or coverage, to a typical gene expression array (with 1-3 probes per gene) and a genomic tiling array (with hundreds or thousands of probes per gene). It is used to assay the expression of alternative splice forms of a gene. Exon arrays have a different design, employing probes designed to detect each individual exon for known or predicted genes, and can be used for detecting different splicing isoforms
Tiling arrayGenome tiling arrays consist of overlapping probes designed to densely represent a genomic region of interest, sometimes as large as an entire human chromosome. The purpose is to empirically detect expression of transcripts or alternatively spliced forms which may not have been previously known or predicted.

The Power of Microarrays.

If you like world records, check out the salamander with the largest genome, 10X bigger than our own: The HUGE Axolotl Genome. What do they do with all that DNA? And can our current technologies figure it out? For the original report, click on the following link: here.


Frederick D. Park , . Tannishtha Reya , in Basic Science Methods for Clinical Researchers , 2017

Sensitivity, Specificity, and Dependence on Known Sequences

Microarrays are both less sensitive and less specific than RNA-Seq. The range of microarray probe coverage is entirely dependent on prior sequence knowledge, whereas RNA-Seq is not biased or limited by such information since it provides a comprehensive view of the entire transcriptome. And so, even though high-density microarrays are narrowing the gap, microarrays may still miss novel transcripts or isoforms that can be detected by RNA-Seq. In addition, SNPs found by RNA-Seq may be both novel and more informative since they are expressed in RNA transcripts, whereas microarrays only detect known SNPs in genomic DNA. Another disadvantage of microarrays is that as new sequence data becomes available, earlier microarray data may become obsolete. A set of samples would need to be run anew with a newer microarray that covers the new sequence, whereas the more comprehensive RNA-Seq data would remain relevant. RNA-Seq can also distinguish host from parasite transcripts and examine transcripts of any organism without prior sequence knowledge. This is especially useful for bacteria, which have large variations in genomic sequence even within a single strain.

Microarrays are also inherently less sensitive and specific than RNA-Seq because they rely upon nucleic acid hybridization. Although probe sequences are selected for good specificity to a target sequence, a probe may not be completely specific to a single target. Also, fluorescent dyes can differentially affect hybridization. Given this fundamental difference, one might say that microarray data versus RNA-Seq data are “analog” versus “digital,” respectively. Microarrays can only provide relative expression by comparing probe intensities. They do not provide absolute quantitation of transcript expression such as RNA-Seq. In addition, microarrays have less potential dynamic range due to nonlinear hybridization kinetics at either extreme of abundance—probes can be saturated by high abundance targets and give little signal above background noise with very low abundance targets. With increased depth of sequencing, RNA-Seq offers much better sensitivity and signal-to-noise ratio for low abundance transcripts.

What is a DNA microarray used for?

When they were first introduced, DNA microarrays were used only as a research tool. Scientists continue today to conduct large-scale population studies - for example, to determine how often individuals with a particular mutation actually develop breast cancer, or to identify the changes in gene sequences that are most often associated with particular diseases. This has become possible because, just as is the case for computer chips, very large numbers of 'features' can be put on microarray chips, representing a very large portion of the human genome.

Microarrays can also be used to study the extent to which certain genes are turned on or off in cells and tissues. In this case, instead of isolating DNA from the samples, RNA (which is a transcript of the DNA) is isolated and measured.

Today, DNA microarrays are used in clinical diagnostic tests for some diseases. Sometimes they are also used to determine which drugs might be best prescribed for particular individuals, because genes determine how our bodies handle the chemistry related to those drugs.With the advent of new DNA sequencing technologies, some of the tests for which microarrays were used in the past now use DNA sequencing instead. But microarray tests still tend to be less expensive than sequencing, so they may be used for very large studies, as well as for some clinical tests.

When they were first introduced, DNA microarrays were used only as a research tool. Scientists continue today to conduct large-scale population studies - for example, to determine how often individuals with a particular mutation actually develop breast cancer, or to identify the changes in gene sequences that are most often associated with particular diseases. This has become possible because, just as is the case for computer chips, very large numbers of 'features' can be put on microarray chips, representing a very large portion of the human genome.

Microarrays can also be used to study the extent to which certain genes are turned on or off in cells and tissues. In this case, instead of isolating DNA from the samples, RNA (which is a transcript of the DNA) is isolated and measured.

Today, DNA microarrays are used in clinical diagnostic tests for some diseases. Sometimes they are also used to determine which drugs might be best prescribed for particular individuals, because genes determine how our bodies handle the chemistry related to those drugs.With the advent of new DNA sequencing technologies, some of the tests for which microarrays were used in the past now use DNA sequencing instead. But microarray tests still tend to be less expensive than sequencing, so they may be used for very large studies, as well as for some clinical tests.

Chromatin immunoprecipitation and microarray-based analysis of protein location

Genome-wide location analysis, also known as ChIP-Chip, combines chromatin immunoprecipitation and DNA microarray analysis to identify protein-DNA interactions that occur in living cells. Protein-DNA interactions are captured in vivo by chemical crosslinking. Cell lysis, DNA fragmentation and immunoaffinity purification of the desired protein will co-purify DNA fragments that are associated with that protein. The enriched DNA population is then labeled, combined with a differentially labeled reference sample and applied to DNA microarrays to detect enriched signals. Various computational and bioinformatic approaches are then applied to normalize the enriched and reference channels, to connect signals to the portions of the genome that are represented on the DNA microarrays, to provide confidence metrics and to generate maps of protein-genome occupancy. Here, we describe the experimental protocols that we use from crosslinking of cells to hybridization of labeled material, together with insights into the aspects of these protocols that influence the results. These protocols require approximately 1 week to complete once sufficient numbers of cells have been obtained, and have been used to produce robust, high-quality ChIP-chip results in many different cell and tissue types.


A sample timeline for the…

A sample timeline for the ChIP-Chip protocol. Individual steps are shown in white…

Results of varying degrees of…

Results of varying degrees of sonication on fragment size. A total of 2…

2% agarose gel showing an example of input DNA, DNA after LMPCR amplification…

Samples of hybridized arrays and…

Samples of hybridized arrays and scatterplots. ( a ) A portion of a…

Examples of data from an array that are processed to identify genomic regions…

Genome content and gene sequencing

- but human genome has 20,000 genes. yeast is 6,000 genes. so not that much diff in genes in a single cell organism.

- A lot of our dna in our genome doesnt encode protein = junk dna accumulated by transposition and evolution

- some of these smaller genomes must replicate more efficiently, not an issue for us. our genome is more tolerant to having these extra bits of dna, not completely clear

- genome size does not always equate with organism complexity

- the 500 bases that you get out are referred to as a read. An output of a single dna seq reaction

Fed funded is the 1st.
- 1st method: used in sequence the first human genome, relied on the cloning of DNA in microbial cells and employed the Sanger dideoxy sequencing technique. Traditional WGS.

- 2nd method: cell-free, high throughput.

-1st, Generate genomic library: collection of
genomic DNA fragments (from restriction
enzyme digestion) cloned into vectors
(plasmids, artificial chromosomes)
collectively representing the entire genome

here we're generating a genomic lib (get dna, cut it and clone it into a vector). vector is going to be like a plasmid, dna that can replicate by itself autonomously, but also put it in a bacteria and replicate. dna cloned into vectors (plasmids 6 or 7 kb only) and artificial chromosomes can hold longer pieces of dna). so you generate library that would represent all the dna seq of an organism.

- go about seq individual clones

- take inserts and sequence the ends of them. just determine the end sequence. cause if yoru goal is to determine overlap, so the most imp seq for that is the ends. once you have determined which ones overlap. and then go back and seq them fully. but if yoru able to determine the ends. then you have ordered patterns, to combine them into full.

- use paired end reads. you can have multiple inserts.
you know the seq of this vector in red. so its easy to design this vector based on seq of vector. so you can design these primers. then you can gain some new seq, in green, blue, light green orange.

- for euk, there are repeats, tandem repeat is longer than max seq read. no way to bridge gap sometimes they align with wrong reads.

- soln was to make use of pairs of sequence reads from opposite ends of the genomic inserts in the same clone, paired end reads.

- trying to find the gap between the two contigs. since distance is known of the clones, then you know how long the missing gap.

ultimately hoping to generate a single ordered pattern of dna frag with the understanding of where you have gaps between your frag. do this for a large number of dna frag and you can build up so much. but you cant build up the entire million bases for a chromosome from just doing this. you will have some gaps along the way some of the dna wont be represented in the library or sequencing rxn was working perfectly.

one of the ways to go back then is to take seq contigs. these are the slightly longer sequences (contigs). look at the ends. say you have a gap between them. these ends dont overlap. either generate another dna lib or look at lib again. some dna that would span that region.
if you were to find that, some dna frag in the lib. if you were able to sequence that you could fill the gap
ordered patterning of dna frag contigs and gaps are called scaffold. scaffolds are maps that you generate, not a physical thing as much as it is an ordering.

for examp, contig a is on chromosome 1 and contig b is also there and you know theres a 2 kb gap = scaffold.

- an alternative to bridging sequence gaps through use of genomic libraries

- take cdna, and just seq the ends. then generate tags. your not interested in the full seq. just want to know which genes are transcribed. info from ends is enough to know if this is gene x or gal4 or w/e.

- A database of nt or AA sequences is searched for sequences similar to a "query" sequence (e.g., putative gene)

- idea is that there are databases that have been generated of dna seq and protein seq. if you have dna and do a conceptional translation (take chunks of seq in groups of three (codons), consider translated amino acid and string amino acids together and search against a database with known protein sequences).

- You could see if there is matching with stretch of dna, if it is actually protein coding. Blast is an alignment tool, it helps you do the analysis. Computational tool, allow you to identify related nt or AA seq.

- you can take nt and compare it against other dna seq. a lot more powerful to do analysis on aa not nt. less options with amino acids, so more alignment power. a lot easier to compare protein seq.

- so you would take data base, enter in some unknown seq. do translation of it automatically and search against iyou can take nt and compare it against other dna seq. a lot more powerful to do analysis on aa not nt. less options with amino acids, so more alignment power. a lot easier to compare protein seq. gives back a number, with expectancy that the match you got occurs by random.
- putatitive gene is simialr to known gene in another organisms = ortholog.
lets say you do blast search, find strong match. dna seq you find is prob is conserved. putative gene in your seq is similar to known seq in another organism, its called an ortholog. you found a gene that is similar/same in same organism (duplication events) - thats called a paralog not an ortholog.

reason its imp is if you find an ortholog, infer something abt the function of the sequences, encoded proteins should function similarly
if you find seq translates protein in the same organism, cant infer anything abt function - may have same function, may not. one of them accumulates mutation (functionally useful). thats how protein has different function. cell has one function protein, but with duplicate with changes, maybe some same function, but encoded protein will have diff function as well.

The long-term selection of microorganisms or populations under laboratory conditions to model simple evolutionary scenarios.

The identification of a genomic variant, the actual state of which is not known until further analysis.

In the context of microarrays, DNA probe refers to the DNA oligonucleotide, PCR product or genomic clone that is attached to a microarray in order to probe a labelled genomic DNA sample that is added in solution. In the context of Southern blotting, DNA probe refers to the labelled DNA oligonucleotide that is added in solution to probe the genomic DNA sample that is immobilized on a membrane.

The use of masks to selectively deprotect nascent oligonucleotides using light, allowing the parallel synthesis of millions of probes.

The use of print cartridge heads to deposit one of the four DNA bases at a probe site on the microarray.

Fluorescent in situ hybridization

(FISH). A technique in which a fluorescently labelled DNA probe is used to detect a particular chromosome or gene using fluorescence microscopy.

A procedure in which the products of a PCR reaction are measured by monitoring the signal that is produced by a fluorescent dye, which accumulates during each PCR cycle.

The Tm (melting temperature) of an oligonucleotide is the temperature at which 50% of the duplex strands are separated.

Mutations that suppress, or alleviate, the phenotypic effect of another mutation.

The number of different DNA sequences in a genome, originally measured by the rate of re-association of heat-denatured DNA.

Determination of the sequence at both ends of a fragment of DNA of known size.

The determination of the exact DNA sequence by comparison with a known reference.

Statistical tests that assume an underlying distribution, which is usually Gaussian. The term Gaussian describes a continuous probability distribution that is symmetrical around a defined mean value, the shape of which is determined by the variance.

(ChIP). Fractionation of DNA that is bound to a protein of interest by means of an antibody.

The use of methods that include an internal reference so that the ratio between sample and control is the metric of interest.


Cardiac hypertrophy is a common pathological response to a number of cardiovascular diseases such as hypertension, ischemic heart disease, valvular diseases, and endocrine disorders. Cardiac hypertrophy often leads to heart failure in humans and is a major determinant of mortality and morbidity in cardiovascular diseases. miRNAs are important regulators for the differentiation and growth of cardiac cells, and it is therefore reasonable to hypothesize that miRNAs play important roles in cardiac hypertrophy and heart failure.

Almost simultaneously, three independent groups (including the current author) reported dramatic results in the miRNA expression signature of mouse hearts that were made hypertrophic by either aortic binding or expression of activated calcineurin (24, 101, 115) (Table 1). It should be noted that miRNAs are aberrantly expressed in hypertrophic hearts in both animal models, and these results were confirmed by in vitro studies of cardiac myocytes with hypertrophy (24, 101, 111, 115). Furthermore, overexpression of some miRNAs that are upregulated in hypertrophic hearts induces cardiac myocyte hypertrophy, whereas overexpression of some miRNAs that are downregulated in hypertrophic hearts prevents cardiac myocyte hypertrophy. On the other hand, inhibition of miR-21, an miRNA that is upregulated in the hypertrophic animal and human hearts, inhibits hypertrophic hearts in vitro (24). The role of miR-21 was further confirmed by another group (111). In vivo, overexpression of miR-195, a miRNA that is upregulated in hypertrophic hearts, is sufficient to induce cardiac hypertrophy (115), while a gene mutation or “decoy” approach has confirmed the role of miR-208 and miR-133 in cardiomyocyte hypertrophy (20, 114). Taken together, these findings demonstrate that multiple miRNAs are involved in cardiac hypertrophy and that modulating one aberrantly expressed miRNA is sufficient to modulate the hypertrophy. However, the molecular mechanisms responsible for individual miRNA-mediated effects on cardiac hypertrophy are unclear.

More recently, the roles of miRNAs in human cardiac hypertrophy and heart failure have been elucidated in several clinical studies (76, 115, 126). Northern blot analysis of the hypertrophy-regulated miRNAs in idiopathic, end-stage, failing human hearts shows that the expression of miR-24, miR-125b, miR-195, miR-199a, and miR-214 is significantly increased compared with control hearts (115). Forty-three out of 87 detected miRNAs are aberrantly expressed in hearts with ischemic cardiomyopathy, dilated cardiomyopathy, or aortic stenosis (87), indicating that miRNAs are indeed involved in the pathophysiology of human cardiac hypertrophy and heart failure.

Neointimal lesion formation is a common pathological lesion found in diverse cardiovascular diseases such as atherosclerosis, coronary heart diseases, postangioplasty restenosis, and transplantation arteriopathy. Using microarray analysis and a well-established neointimal formation model, we determined the miRNA expression profile in the vascular wall with neointimal lesion formation (24). Compared with normal, uninjured arteries, microarray analysis demonstrated that aberrant miRNA expression is a characteristic of vascular walls after angioplasty. Those miRNAs that are highly expressed in the rat carotid artery and are more than onefold upregulated or 50% downregulated after angioplasty were further verified by qRT-PCR and/or Northern blot analysis (24). Modulating an aberrantly overexpressed miRNA, miR-21, via antisense-mediated knockdown has a significantly negative effect on neointimal lesion formation in rat artery after angioplasty (Fig. 2). These results indicate that miRNAs are important regulators in the development of proliferative vascular diseases (Table 1).

Fig. 2.Downregulation of miR-21 decreases neointimal lesion formation in rat carotid artery after angioplasty. Representative hematoxylin-eosin-stained photomicrographs of rat carotid arteries from vehicle-, miR-21 antisense oligonucleotide (2′OMe-miR-21)-, and control oligonucleotide (2′OMe-EGFP)-treated groups at 14 days after angioplasty. Reproduced with permission from Cir Res (56).

Cardiac arrhythmias in the setting of ischemic heart disease remain a serious health problem because of their sudden and unpredictable nature and their potentially grave consequences. In a rat model of myocardial infarction and in human heart with coronary heart disease, the muscle-specific miRNA, miR-1, was significantly upregulated in ischemic heart tissue (126). To further determine the role of miR-1 in arrhythmogenesis, both gain-of-function and loss-of-function approaches were applied to enhance or inhibit miR-1 expression in the infarcted myocardium. The results show that injection of mature miR-1 exacerbates arrhythmogenesis, whereas elimination of miR-1 by an antisense inhibitor suppresses arrhythmias. The results indicate that miR-1 has proarrhythmic, as well as arrhythmogenic effects (126). Silencing the genes for the ion channels GJA1 and KCNJ2 verified that these proteins are important players in the miR-1-mediated arrhythmogenic effect (126) (Table 1).

miR-133 expression is upregulated (123) in diabetic rabbit heart. The ether-a-go-go-related gene (ERG), a long QT syndrome gene encoding a key K + channel (IKr) in cardiac cells, was confirmed to be a target for miR-133 (123). Delivery of exogenous miR-133 into the rabbit myocytes and cell lines produces posttranscriptional repression of ERG, thereby downregulating ERG protein levels without altering its transcript level, subsequently causing substantial depression of IKr, an effect that is abrogated by the miR-133 antisense inhibitor (123). Thus, depression of IKr via repression of ERG by miR-133 may contribute to the slowing of myocyte repolarization and, thereby, QT prolongation and the associated arrhythmias in diabetic hearts (Table 1).

In cardiac cells, KCNQ1 assembles with KCNE1 and forms a channel complex constituting the slow delayed rectifier current IKs. Expression of KCNQ1 and KCNE1 is regionally heterogeneous and changes with pathological states of the heart however, the molecular mechanisms responsible for these changes are unclear. Recently, one study has characterized KCNQ1 and KCNE1 as targets of the muscle-specific miRNAs, miR-133 and miR-1, respectively (124). The heterogeneous expression of miR-1 and miR-133 offers an explanation for the well-recognized regional differences in expression of KCNQ1 and KCNE1 and for the disparity between the levels of their mRNA and protein in each region (124).

HCN2 and HCN4 are two important cardiac pacemaker channel proteins that control rhythmic activity of the heart. One recent study has demonstrated that HCN2 mRNA is a target of miR-1 and miR-133 and that HCN4 mRNA is a target of miR-1 (122). To explore the possibility of using miRNAs in a gene-specific manner, the authors of this study developed two new therapeutic approaches, which were the gene-specific miRNA mimic and miRNA-masking antisense approaches. Their results demonstrate that gene-specific miRNA mimics, which are 22-nt RNAs designed to target the 3′-UTRs of HCN2 and HCN4, are efficient in abrogating the expression and function of HCN2 and HCN4. Meanwhile, the microRNA-masking antisense, based on the miR-1 and miR-133 target sites in the 3′-UTRs of HCN2 and HCN4, markedly enhance HCN2 and HCN4 expression and function. Thus, these two therapeutic approaches based on the principles of action of miRNAs could provide novel gene therapy strategies for cardiac arrhythmias (122).

Microarray Milestones

Edwin Southern files UK patent applications for in situ synthesized, oligo-nucleotide microarrays

Stephen Fodor and colleagues publish photolithographic array fabrication method

Undeterred by NIH naysayers, Patrick Brown develops spotted arrays

Affymax begets Affymetrix

Mark Schena publishes first use of microarrays for gene expression analysis

Edwin Southern founds Oxford Gene Technologies

First human gene expression microarray study published

Affymetrix releases its first catalog GeneChip microarray, for HIV, in April

Stanford researchers publish the first whole-genome microarray study, of yeast

Brown's lab develops CLUSTER, a statistical tool for microarray data analysis red and green "thermal plots" start popping up everywhere

Todd Golub and colleagues use microarrays to classify cancers, sparking widespread interest in clinical applications

Affymetrix spins off Perlegen, to sequence multiple human genomes and identify genetic variation using arrays

The Microarray Gene Expression Data Society develops MIAME standard for the collection and reporting of microarray data

Joseph DeRisi uses a microarray to identify the SARS virus

Affymetrix, Applied Biosystems, and Agilent Technologies individually array human genome on a single chip

Roche releases Amplichip CYP450, the first FDA-approved microarray for diagnostic purposes

Ekland EH, Fidock DA: Advances in understanding the genetic basis of antimalarial drug resistance. Curr Opin Microbiol. 2007, 10: 363-370. 10.1016/j.mib.2007.07.007.

Ringwald P: Susceptibility of Plasmodium falciparum to antimalarial drugs: Report on global monitoring: 1996-2004. 2005, Geneva, Switzerland: World Health Organization

Richie TL: High road, low road? Choices and challenges on the pathway to a malaria vaccine. Parasitology. 2006, 133: S113-S144. 10.1017/S0031182006001843.

Thera MA, Doumbo OK, Coulibaly D, Diallo DA, Kone AK, Guindo AB, Traore K, Dicko A, Sagara I, Sissoko MS, Baby M, Sissoko M, Diarra I, Niangaly A, Dolo A, Daou M, Diawara SI, Heppner DG, Stewart VA, Angov E, Bergmann-Leitner ES, Lanar DE, Dutta S, Soisson L, Diggs CL, Leach A, Owusu A, Dubois M-C, Cohen J, Nixon JN, et al: Safety and immunogenicity of an AMA-1 malaria vaccine in Malian adults: results of a phase 1 randomized controlled trial. PLoS ONE. 2008, 3: e1465-10.1371/journal.pone.0001465.

Weisman JL, Liou AP, Shelat AA, Cohen FE, Kiplin Guy R, DeRisi JL: Searching for new antimalarial therapeutics amongst known drugs. Chem Biol Drug Des. 2006, 67: 409-416. 10.1111/j.1747-0285.2006.00391.x.

Chong CR, Chen X, Shi L, Liu JO, Sullivan DJ: A clinical drug library screen identifies astemizole as an antimalarial agent. Nat Chem Biol. 2006, 2: 415-416. 10.1038/nchembio806.

Plouffe D, Brinker A, McNamara C, Henson K, Kato N, Kuhen K, Nagle A, Adrian F, Matzen JT, Anderson P, Nam T-g, Gray NS, Chatterjee A, Janes J, Yan SF, Trager R, Caldwell JS, Schultz PG, Zhou Y, Winzeler EA: In silico activity profiling reveals the mechanism of action of antimalarials discovered in a high-throughput screen. Proc Natl Acad Sci USA. 2008, 105: 9059-9064. 10.1073/pnas.0802982105.

Wellems TE, Panton LJ, Gluzman IY, do Rosario VE, Gwadz RW, Walker-Jonah A, Krogstad DJ: Chloroquine resistance not linked to mdr-like genes in a Plasmodium falciparum cross. Nature. 1990, 345: 253-255. 10.1038/345253a0.

Fidock DA, Nomura T, Talley AK, Cooper RA, Dzekunov SM, Ferdig MT, Ursos LMB, bir Singh Sidhu A, Naudé B, Deitsch KW, Su X-z, Wootton JC, Roepe PD, Wellems TE: Mutations in the P. falciparum digestive vacuole transmembrane protein PfCRT and evidence for their role in chloroquine resistance. Mol Cell. 2000, 6: 861-871. 10.1016/S1097-2765(05)00077-8.

Sidhu ABS, Verdier-Pinard D, Fidock DA: Chloroquine resistance in Plasmodium falciparum malaria parasites conferred by pfcrt mutations. Science. 2002, 298: 210-213. 10.1126/science.1074045.

Hunt P, Afonso A, Creasey A, Culleton R, Sidhu ABS, Logan J, Valderramos SG, McNae I, Cheesman S, Rosario Vd, Carter R, Fidock DA, Cravo P: Gene encoding a deubiquitinating enzyme is mutated in artesunate- and chloroquine-resistant rodent malaria parasites&sect. Mol Microbiol. 2007, 65: 27-40. 10.1111/j.1365-2958.2007.05753.x.

Volkman SK, Sabeti PC, DeCaprio D, Neafsey DE, Schaffner SF, Milner DA, Daily JP, Sarr O, Ndiaye D, Ndir O, Mboup S, Duraisingh MT, Lukens A, Derr A, Stange-Thomann N, Waggoner S, Onofrio R, Ziaugra L, Mauceli E, Gnerre S, Jaffe DB, Zainoun J, Wiegand RC, Birren BW, Hartl DL, Galagan JE, Lander ES, Wirth DF: A genome-wide map of diversity in Plasmodium falciparum. Nat Genet. 2007, 39: 113-119. 10.1038/ng1930.

Jeffares DC, Pain A, Berry A, Cox AV, Stalker J, Ingle CE, Thomas A, Quail MA, Siebenthall K, Uhlemann A-C, Kyes S, Krishna S, Newbold C, Dermitzakis ET, Berriman M: Genome variation and evolution of the malaria parasite Plasmodium falciparum. Nat Genet. 2007, 39: 120-125. 10.1038/ng1931.

Mu J, Awadalla P, Duan J, McGee KM, Keebler J, Seydel K, McVean GAT, Su X-z: Genome-wide variation and identification of vaccine targets in the Plasmodium falciparum genome. Nat Genet. 2007, 39: 126-130. 10.1038/ng1924.

Wootton JC, Feng X, Ferdig MT, Cooper RA, Mu J, Baruch DI, Magill AJ, Su X-z: Genetic diversity and chloroquine selective sweeps in Plasmodium falciparum. Nature. 2002, 418: 320-323. 10.1038/nature00813.

Price RN, Uhlemann A-C, Brockman A, McGready R, Ashley E, Phaipun L, Patel R, Laing K, Looareesuwan S, White NJ, Nosten F, Krishna S: Mefloquine resistance in Plasmodium falciparum and increased pfmdr1 gene copy number. Lancet. 2004, 364: 438-447. 10.1016/S0140-6736(04)16767-6.

Sidhu ABS, Uhlemann A-C, Valderramos SG, Valderramos J-C, Krishna S, Fidock DA: Decreasing pfmdr1 copy number in Plasmodium falciparum malaria heightens susceptibility to mefloquine, lumefantrine, halofantrine, quinine, and artemisinin. J Infect Dis. 2006, 194: 528-535. 10.1086/507115.

Jiang H, Joy D, Furuya T, Su Xz: Current understanding of the molecular basis of chloroquine-resistance in Plasmodium falciparum. J Postgrad Med. 2006, 52: 271-276.

Reed MB, Saliba KJ, Caruana SR, Kirk K, Cowman AF: Pgh1 modulates sensitivity and resistance to multiple antimalarials in Plasmodium falciparum. Nature. 2000, 403: 906-909. 10.1038/35002615.

Gregson A, Plowe CV: Mechanisms of resistance of malaria parasites to antifolates. Pharmacol Rev. 2005, 57: 117-145. 10.1124/pr.57.1.4.

Sidhu ABS, Sun Q, Nkrumah LJ, Dunne MW, Sacchettini JC, Fidock DA: In vitro efficacy, resistance selection, and structural modeling studies implicate the malarial parasite apicoplast as the target of azithromycin. J Biol Chem. 2007, 282: 2494-2504. 10.1074/jbc.M608615200.

Kidgell C, Volkman SK, Daily J, Borevitz JO, Plouffe D, Zhou Y, Johnson JR, Le Roch KG, Sarr O, Ndir O, Mboup S, Batalov S, Wirth DF, Winzeler EA: A systematic map of genetic variation in Plasmodium falciparum. PLoS Pathog. 2006, 2: e57-10.1371/journal.ppat.0020057.

Nair S, Miller B, Barends M, Jaidee A, Patel J, Mayxay M, Newton P, Nosten Fo, Ferdig MT, Anderson TJC: Adaptive copy number evolution in malaria parasites. PLoS Genetics. 2008, 4: e1000243-10.1371/journal.pgen.1000243.

Gardner MJ, Hall N, Fung E, White O, Berriman M, Hyman RW, Carlton JM, Pain A, Nelson KE, Bowman S, Paulsen IT, James K, Eisen JA, Rutherford K, Salzberg SL, Craig A, Kyes S, Chan M-S, Nene V, Shallom SJ, Suh B, Peterson J, Angiuoli S, Pertea M, Allen J, Selengut J, Haft D, Mather MW, Vaidya AB, Martin DMA, et al: Genome sequence of the human malaria parasite Plasmodium falciparum. Nature. 2002, 419: 498-511. 10.1038/nature01097.

Gresham D, Ruderfer DM, Pratt SC, Schacherer J, Dunham MJ, Botstein D, Kruglyak L: Genome-wide detection of polymorphisms at nucleotide resolution with a single DNA microarray. Science. 2006, 311: 1932-1936. 10.1126/science.1123726.

Jiang H, Yi M, Mu J, Zhang L, Ivens A, Klimczak L, Huyen Y, Stephens R, Su X-z: Detection of genome-wide polymorphisms in the AT-rich Plasmodium falciparum genome using a high-density microarray. BMC Genomics. 2008, 9: 398-10.1186/1471-2164-9-398.

Bolstad BM, Irizarry RA, Astrand M, Speed TP: A comparison of normalization methods for high density oligonucleotide array data based on variance and bias. Bioinformatics. 2003, 19: 185-193. 10.1093/bioinformatics/19.2.185.

Zhou Y, Abagyan R: Match-only integral distribution (MOID) algorithm for high-density oligonucleotide array analysis. BMC Bioinformatics. 2002, 3: 3-10.1186/1471-2105-3-3.

Cowman AF, Galatis D, Thompson JK: Selection for mefloquine resistance in Plasmodium falciparum is linked to amplification of the pfmdr1 gene and cross-resistance to halofantrine and quinine. Proc Natl Acad Sci USA. 1994, 91: 1143-1147. 10.1073/pnas.91.3.1143.

Ferreira ID, Rosário VE, Cravo PV: Real-time quantitative PCR with SYBR Green I detection for estimating copy numbers of nine drug resistance candidate genes in Plasmodium falciparum. Malar J. 2006, 5: 1-10.1186/1475-2875-5-1.

Nair S, Nash D, Sudimack D, Jaidee A, Barends M, Uhlemann A-C, Krishna S, Nosten F, Anderson TJC: Recurrent gene amplification and soft selective sweeps during evolution of multidrug resistance in malaria parasites. Mol Biol Evol. 2007, 24: 562-573. 10.1093/molbev/msl185.

Carret CK, Horrocks P, Konfortov B, Winzeler E, Qureshi M, Newbold C, Ivens A: Microarray-based comparative genomic analyses of the human malaria parasite Plasmodium falciparum using Affymetrix arrays. Mol Biochem Parasitol. 2005, 144: 177-186. 10.1016/j.molbiopara.2005.08.010.

Nkrumah LJ, Muhle RA, Moura PA, Ghosh P, Hatfull GF, Jacobs WR, Fidock DA: Efficient site-specific integration in Plasmodium falciparum chromosomes mediated by mycobacteriophage Bxb1 integrase. Nat Methods. 2006, 3: 615-621. 10.1038/nmeth904.

Jomaa H, Wiesner J, Sanderbrand S, Altincicek B, Weidemeyer C, Hintz M, uuml , rbachova I, Eberl M, Zeidler J, Lichtenthaler HK, Soldati D, Beck E: Inhibitors of the nonmevalonate pathway of isoprenoid biosynthesis as antimalarial drugs. Science. 1999, 285: 1573-1576. 10.1126/science.285.5433.1573.

Rohmer M: The discovery of a mevalonate-independent pathway for isoprenoid biosynthesis in bacteria, algae and higher plants. Nat Prod Rep. 1999, 16: 565-574. 10.1039/a709175c.

Borrmann S, Adegnika A, Matsiegui P-B, Issifou S, Schindler A, Mawili-Mboumba D, Baranek T, Wiesner J, Jomaa H, Kremsner P: Fosmidomycin-clindamycin for Plasmodium falciparum infections in African children. J Infect Dis. 2004, 189: 901-908. 10.1086/381785.

Borrmann S, Issifou S, Esser G, Adegnika A, Ramharter M, Matsiegui P-B, Oyakhirome S, Mawili-Mboumba D, Missinou M, Kun JJ, Jomaa H, Kremsner P: Fosmidomycin-clindamycin for the treatment of Plasmodium falciparum malaria. J Infect Dis. 2004, 190: 1534-1540. 10.1086/424603.

Borrmann S, Adegnika AA, Moussavou F, Oyakhirome S, Esser G, Matsiegui P-B, Ramharter M, Lundgren I, Kombila M, Issifou S, Hutchinson D, Wiesner J, Jomaa H, Kremsner PG: Short-course regimens of artesunate-fosmidomycin in treatment of uncomplicated Plasmodium falciparum malaria. Antimicrob Agents Chemother. 2005, 49: 3749-3754. 10.1128/AAC.49.9.3749-3754.2005.

Cassera MB, Gozzo FC, D'Alexandri FL, Merino EF, del Portillo HA, Peres VJ, Almeida IC, Eberlin MN, Wunderlich G, Wiesner J, Jomaa H, Kimura EA, Katzin AM: The methylerythritol phosphate pathway is functionally active in all intraerythrocytic stages of Plasmodium falciparum. J Biol Chem. 2004, 279: 51749-51759. 10.1074/jbc.M408360200.

Cassera MB, Merino EF, Peres VJ, Kimura EA, Wunderlich G, Katzin AM: Effect of fosmidomycin on metabolic and transcript profiles of the methylerythritol phosphate pathway in Plasmodium falciparum. Mem Inst Oswaldo Cruz. 2007, 102: 377-384. 10.1590/S0074-02762007000300019.

Ribacke U, Mok BW, Wirta V, Normark J, Lundeberg J, Kironde F, Egwang TG, Nilsson P, Wahlgren M: Genome wide gene amplifications and deletions in Plasmodium falciparum. Mol Biochem Parasitol. 2007, 155: 33-44. 10.1016/j.molbiopara.2007.05.005.

Iafrate AJ, Feuk L, Rivera MN, Listewnik ML, Donahoe PK, Qi Y, Scherer SW, Lee C: Detection of large-scale variation in the human genome. Nat Genet. 2004, 36: 949-951. 10.1038/ng1416.

Sebat J, Lakshmi B, Troge J, Alexander J, Young J, Lundin P, Maner S, Massa H, Walker M, Chi M, Navin N, Lucito R, Healy J, Hicks J, Ye K, Reiner A, Gilliam TC, Trask B, Patterson N, Zetterberg A, Wigler M: Large-scale copy number polymorphism in the human genome. Science. 2004, 305: 525-528. 10.1126/science.1098918.

Redon R, Ishikawa S, Fitch KR, Feuk L, Perry GH, Andrews TD, Fiegler H, Shapero MH, Carson AR, Chen W, Cho EK, Dallaire S, Freeman JL, Gonzalez JR, Gratacos M, Huang J, Kalaitzopoulos D, Komura D, MacDonald JR, Marshall CR, Mei R, Montgomery L, Nishimura K, Okamura K, Shen F, Somerville MJ, Tchinda J, Valsesia A, Woodwark C, Yang F, et al: Global variation in copy number in the human genome. Nature. 2006, 444: 444-454. 10.1038/nature05329.

Sebat J: Major changes in our DNA lead to major changes in our thinking. Nat Genet. 2007, 39: S3-S5. 10.1038/ng2095.

Ganesan K, Ponmee N, Jiang L, Fowble JW, White J, Kamchonwongpaisan S, Yuthavong Y, Wilairat P, Rathod PK: A genetically hard-wired metabolic transcriptome in Plasmodium falciparum fails to mount protective responses to lethal antifolates. PLoS Pathog. 2008, 4: e1000214-10.1371/journal.ppat.1000214.

Shendure J, Porreca GJ, Reppas NB, Lin X, McCutcheon JP, Rosenbaum AM, Wang MD, Zhang K, Mitra RD, Church GM: Accurate multiplex polony sequencing of an evolved bacterial genome. Science. 2005, 309: 1728-1732. 10.1126/science.1117389.

Bekal S, Craig J, Hudson M, Niblack T, Domier L, Lambert K: Genomic DNA sequence comparison between two inbred soybean cyst nematode biotypes facilitated by massively parallel 454 micro-bead sequencing. Mol Genet Genomics. 2008, 279: 535-543. 10.1007/s00438-008-0331-8.

Hillier LW, Marth GT, Quinlan AR, Dooling D, Fewell G, Barnett D, Fox P, Glasscock JI, Hickenbotham M, Huang W, Magrini VJ, Richt RJ, Sander SN, Stewart DA, Stromberg M, Tsung EF, Wylie T, Schedl T, Wilson RK, Mardis ER: Whole-genome sequencing and variant discovery in C. elegans. Nat Methods. 2008, 5: 183-188. 10.1038/nmeth.1179.

Trager W, Jenson JB: Cultivation of malarial parasites. Nature. 1978, 273: 621-622. 10.1038/273621a0.

Winzeler EA, Richards DR, Conway AR, Goldstein AL, Kalman S, McCullough MJ, McCusker JH, Stevens DA, Wodicka L, Lockhart DJ, Davis RW: Direct allelic variation scanning of the yeast genome. Science. 1998, 281: 1194-1197. 10.1126/science.281.5380.1194.

Goodyer ID, Taraschi TF: Plasmodium falciparum: a simple, rapid method for detecting parasite clones in microtiter plates. Exp Parasitol. 1997, 86: 158-160. 10.1006/expr.1997.4156.

Desjardins RE, Pamplin CL, von Bredow J, Barry KG, Canfield CJ: Kinetics of a new antimalarial, mefloquine. Clin Pharmacol Ther. 1979, 26: 372-379.

Fidock DA, Wellems TE: Transformation with human dihydrofolate reductase renders malaria parasites insensitive to WR99210 but does not affect the intrinsic activity of proguanil. Proc Natl Acad Sci USA. 1997, 94: 10931-10936. 10.1073/pnas.94.20.10931.

PlasmoDB: The Plasmodium Genome Resource. []


Determination of procaryotic species and the degree of their relationship is a great challenge for microbiologists. In the last 50 years, many different molecular methods, including whole-genome DNA–DNA hybridization, SSU rRNA sequencing, multiple locus sequencing of protein encoding genes (for example, gyrB, recA) and average nucleotide identity, have been proposed for delineating bacterial species. Although the SSU rRNA gene-based method is a valuable, convenient and rapid tool for the determination of the phylogenetic relationships among different microorganisms, it provides poor resolution at the species and subspecies levels (Yamamoto and Harayama, 1998). Also, many procaryotic species have virtually identical SSU rRNA gene sequences but only have 25% DNA similarity (Stackebrandt and Goebel, 1994). Thus, the SSU rRNA analysis method is not a valid approach for determining species/strain relationships. Protein-coding genes could provide high resolution for species/strain determination, but the difficulty in using sets of protein coding genes for phylogenetic evaluation lies in selecting appropriate gene targets and designing amplification primers useful for large sets of microorganisms (Harayama and Kasai, 2006). In addition, phylogenetic analyses based on complete microbial genome sequences are possible, but the likelihood that all the sequenced genomes needed for comparison will be available is not feasible (Brutlag, 1998). Finally, whole-genome sequence analysis is a powerful approach for resolving the major problems of evolution, phylogeny and systematics of living organisms, but its use in general taxonomic studies is not currently practical (Tourova, 2000). Such analyses will require larger genomic data sets and more carefully designed sampling of natural populations (Konstantinidis and Tiedje, 2007). The average nucleotide identity of the shared genes between two strains was proposed to be a robust approach to determine genetic relatedness among different strains (Konstantinidis and Tiedje, 2004). The average nucleotide identity value of 94% corresponded to the traditional 70% DNA–DNA reassociation standard of the current species definition. Although the average nucleotide identity approach is simple, it still relies on the availability of whole-genome sequences and hence it will have a limited use. Nevertheless, whole-genome DNA–DNA hybridization is still considered to be the cornerstone for bacterial species determination and will have to be used to circumscribe procaryotic species (Rossello-Mora, 2006).

The development and application of microarray-based genomic technology for microbial detection and community analysis have received a great deal of attention. Because of its high-density and high-throughput capacity, it is expected that microarray-based genomic technologies will revolutionize the detection, identification and characterization of microorganisms. Therefore, in this study, we have developed the CGA-based hybridization approach for determining species relationships. Experimental comparisons of the CGA hybridization-based results with available traditional DNA–DNA hybridization data, SSU rRNA and gyrB gene sequences and genome fingerprinting methods indicate that the CGA-based hybridization could be a useful alternative to the traditional whole-genome DNA–DNA hybridization approaches for determining procaryotic species relationships.

Overall, DNA similarities from the CGA-based hybridizations were comparable to those from various conventional whole-genome DNA–DNA hybridization approaches. When the actual values for genome relatedness were compared between different methods, the results from the CGA-based hybridization were more consistent to those from the S1 method than the membrane filter method (Goris et al., 1998), as indicated by smaller average differences (<15%) between the similarity values derived from CGA-based hybridization and S1 methods for A. tolulyticus strains. DNA similarities from the CGA-based hybridizations for reference strain P. stutzeri ATCC 17587 also matched well with the ΔTm values of the P. stuzeri strains by hydroxyapatite and/or dot filter method with the reference strain P. stutzeri ATCC 17591 (identical to 17587). However, DNA similarities from CGA-based hybridizations with multiple Pseudomonas strains were significantly lower than those from membrane filter methods, although strong linear relationships were observed. One possible explanation might be related to differences in hybridization stringency. Higher similarity values are expected if the hybridization is carried out at relatively low stringent conditions. The hybridization conditions may need to be optimized for different target microbial groups by considering GC content and genome size. It is also important to point out that DNA similarities determined by the traditional hybridization methods vary significantly among different methods (Goris et al., 1998).

Although significant relationships between the CGA hybridization-based similarities and those from SSU rRNA genes, gyrB genes or genomic fingerprinting were observed, the degree of correlations is considerably different. The correlations of the CGA hybridization-derived similarities to gyrB sequences are stronger than those to SSU rRNA sequences and genomic fingerprinting. It appears that the taxonomic resolution of the CGA-based hybridization is similar to or slightly higher than gyrB sequence analysis. While SSU rRNA sequence analysis can provide reliable information about species relationships at higher taxonomic levels (for example, genus or above), whole-genome DNA–DNA hybridizations are useful in providing insight into phylogenetic relationships at the species/strain levels. For example, the SSU rRNA gene sequence similarities among the A. tolulyticus strains tested in this study are all over 98%–100% however, the DNA similarities determined by both the CGA-based method and S1 method varied across a wide range (15.2%–93.9% for the CGA method, 18%–99% for S1 method) (Song et al., 1999). Owing to differences in resolving phylogenetic relationships, integrating CGA-based hybridization with SSU rRNA and gyrB gene sequence analysis could provide a reliable, rapid approach for delineating procaryotic species relationships.

Genome fingerprinting analysis is suitable for the elucidation of strain-level relationships (Versalovic et al., 1994), and was shown to be highly correlated to DNA–DNA reassociation values for xanthomonads (Rademaker et al., 2000). In this study, strong correlations between CGA hybridization-based similarities and the similarities derived from fingerprinting approaches were observed among closely related strains of P. stutzeri and A. tolulyticus, but not among distantly related species from Pseudomonas, Azoarcus or Shewanella genera. The genomic fingerprinting similarity values above 60% correlated well with CGA hybridization values for the tested P. stutzeri and A. tolulyticus strains. These results indicated that CGAs could provide meaningful insight into relationships between closely related strains. But the power of phylogeny to resolve relationships at the strain level will be lower using CGA-based hybridization than genome fingerprinting approaches. For instance, based on the CGA hybridization results, A. tolulyticus Td-3 was not separated from Td-19, and P. stutzeri DNSP21 (genetic group IV) could not be separated from P. stutzeri ATCC 11256 (genetic group I) (Figure 1), but they were well separated based on genome fingerprinting methods. Lower resolution was also observed for some Shewanella species (data not shown).

Compared to the traditional DNA–DNA reassociation approach, CGAs have several advantages for the determination of species relatedness, including high-throughput capacity, parallel analyses and quantitation. CGA hybridization differs from membrane filter-based hybridization approaches in that the non-porous surface has advantages of miniaturization, hybridization kinetics, sample volume, reagent absorption, signal detection approaches and reproducibility (Schena and Davis, 2000). The capability of accurate and precise miniaturization with robots on non-porous substrates with the use of fluorescence-based detection offers significant advantages. In addition, multiple pairwise comparisons can be done with smaller amounts of genomic DNA (that is, 1 μg). This is important for determining the relationships between procaryotic species that are difficult to cultivate. CGAs could provide a high-throughput means for rapid identification of microbial species/strains. Because of its high capacity, one can construct a CGA containing bacterial type strains plus appropriately related strains. By hybridizing genomic DNA from unknown strains with this type of microarray, one should be able to quickly and reliably identify unknown strains, provided a suitably related probe is on the array. Generally, SNRs for hybridizations with perfect match DNAs are significantly higher than those with mismatch DNAs from other strains of the same species (Wu et al., 2004). Thus, species identification can be achieved based on the differences in hybridization intensity. However, as a low level of cross-hybridization could occur among different strains, establishing appropriate SNR thresholds to differentiate self-hybridization, cross-hybridization and background hybridization among different strains should be useful. In addition, when using CGAs for species identification, lower stringent hybridization conditions (for example, 42 °C and 50% formamide) should be used first to ensure that good hybridization signals can be obtained for distantly related target species.

Microbial diversity is extremely high and the majority of microorganisms are as-yet uncultivable. This could be a limitation in using CGA-based hybridization to determine species relatedness of uncultured microorganisms. However, the CGA-based hybridization itself does not require culturing. With the recent advances in environmental genomics, high-molecular-weight DNA from uncultivated microorganisms could be accessed through bacterial artificial chromosomes or fosmid cloning. High-molecular-weight bacterial artificial chromosomes/fosmid clones could also be used to fabricate CGAs, thus allowing the determination of relationships of target strain/clones to the uncultivated components of a complex microbial community. Because the size of bacterial artificial chromosomes/fosmid clones is generally 50- to 200-fold less than that for an entire genome, it is expected that microarrays fabricated with high-molecular-weight bacterial artificial chromosomes/fosmid clones should have similar performance characteristics as CGAs.

Watch the video: DNA microarray (July 2022).


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