DNA MICROARRAYS

Book (in French) published in 2003 at the occasion of the first exhibition staging exclusively biomedia art works at the national art center Le Lieu Unique in Nantes/France. Hauser Jens (ed.): L'Art Biotech. Nantes/Trézélan, 2003.

The publication has not been available anymore since 2008; this PDF is made accessible with the agreement and the kind support of the Filigranes publishing house, until an augmented new edition will be be available. Great thanks to Filigranes and Patrick Le Bescont! https://www.filigranes.com

Sequencing projects arising from high-throughput technologies including those of sequencing DNA microarray allowed measuring simultaneously the expression levels of millions of genes of a biological sample as well as to annotate and to identify the role (function) of those genes. Consequently, to better manage and organize this significant amount of information, bioinformatics approaches have been developed. These approaches provide a representation and a more 'relevant' integration of data in order to test and validate the researchers' hypothesis. In this context, this article describes and discusses some techniques used for the functional analysis of gene expression data.

INTRODUCTION
The set of all RNA molecules, including mRNA, rRNA, tRNA, and other non-coding RNA transcribed in one cell or a
population of cells is known as transcriptome. GeneChip DNA microarray can be used for identification of genes in
multiple regions within a bacterial genome, or it can be applied to samples consisting of different genomes [1].
Transcriptional profiles generated by GeneChip analysis of bacteria have been used to investigate differential gene
expression in response to antimicrobial agents [2, 3]. Methicillin Resistant Staphylococcus aureus (MRSA) is one of the
most important bacterial pathogens that infect human and animal [4]. Its ability of resistance to all currently available
antibiotics over the last two decades lead to search for new and save potential natural antimicrobial activity against MRSA
[5]. Natural chloramphenicol (CAP) and one type of diketopiperazine known as cyclo (L-Pro-L-Val) were isolated from
endophytic Streptomyces Sp., SUK 25 which exhibits a high degree of potency against MRSA. In this study we determined
the impact of exposure of MRSA to subinhibitory concentration (4 µg/ mL) of CAP and cyclo (L-Pro-L-Val) on the gene
transcriptional profile of MRSA ATCC 43300 at one time point (45 mins) using three biological replication set of
Affymetrix S. aureus GeneChip TM.

DNA microarrays are frequently used to study transcriptome regulation in a wide variety of organisms. Although they are an invaluable tool for the acquisition of large scale dataset in plant systems biology, a number of surprising results and unanticipated complications are often encountered that illustrate the limitations and potential pitfalls of this technology. In this chapter we will present examples of real world studies from two classes of microarray experiments that were designed to (i) identify target genes for transcriptional regulators and (ii) to characterize complex expression patterns to reveal unexpected dependencies within transcriptional networks.

Introduction: Early detection of emerging influenza virus variant is a key factor in the WHO influenza Global strategies for prevention and control. Rapid, accurate, inexpensive and portable detection systems are needed for influenza virus diagnosis and surveillance. Such a detection system should easily identify all the subtypes of influenza virus. Degenerate primers and probes designed from evolutionally conserved regions for known influenza A viruses present the best way to identify unknown subtypes of influenza A virus by polymerase chain reaction PCR and array techniques. The isothermal reactions, Nucleic Acid Sequencing Based Amplification (NASBA) and Loop-mediated isothermal Amplification (LAMP) possess great potential for influenza A virus detection especially in developing countries. However, multiplex real-time (rT) or quantitative (q) polymerase chain reaction (qPCR) remains a rapid, accurate and time-saving technique used for influenza virus detection. Aim: This manuscript explained the principles of nucleic acid amplification techniques commonly used in developing countries. Methods: Literature search was done in NCBI PUBMED, PUBMED Central and Google Scholar using words and phrases including “Influenza- molecular diagnosis, NAAT”, Molecular techniques/ methods, PCR, qPCR, NASBA, LAMP, and DNA microarray. Results: The underlining principles and basic processes involved in the application of nucleic acid amplification techniques for the detection and epidemiological surveillance of influenza virus were identified and grouped under PCR (RT-PCR and qRT-PCR) and Non-PCR (LCR, pyrosequencing, NASBA, LAMP and DNA microarray) amplifications. Conclusion: It is hoped that by understanding the techniques and basic principles of Nucleic acid amplifications, less expensive, and more convenient protocols for influenza virus detection and surveillance can be developed

This review revealed a new mechanism for gene regulation through
“gene silencing” at the transcriptional level (TGS) or at the post -transcriptional level (PTGS), which play a key role in many essential
cellular processes. Today dsRNA is used as a powerful tool to
experimentally elucidate the function of essentially any gene in a
cell. The immense impact of the discovery of RNA interference
(RNAi) on biomedical research and its novel medical applications in
the future are reviewed in this article, with particular stress on
therapeutic applications of radio -labeled antisense oligonucleotides
(RASONs) for diagnosis and treatment of various  cancers and
neurodegenerative diseases by “gene silencing”. Antisense
oligonucleotides (ASONs) can also modulate alternative splicing
which 74% of all human genes undergo.  Epigenetic changes affect
chromatin structure and thus regulate processes such as transcription,
X-chromosome inactivation, allele-specific expression of imprinted
genes, and inactivation of tumor suppressor genes. Treatment with
inhibitors of DNA methylation and histone deacetylation can
reactivate epigenetically silenced genes and has been shown to
restore normal gene function. In cancer cells, this results in
expression of tumor suppressor genes and other regulatory functions,
inducing growth arrest and apoptosis.

Aging is one of the chief biomedical problems of the 21st century. After decades of basic research on biogerontology
(the science of aging), the aging process still remains an enigma. Although hundreds of "theories" on aging have
been formulated and many fundamental insights about age-related changes and genetic as well as environmental
interventions that change the pace of aging have been discovered, the actual why and how we age remain enigmatic.
In the post-genomic era there is an exponential increase in data. As a consequence it is a challenge to utilize all
information based on it and derive meaningful knowledge about biological phenomena. No individual scientist, no
group, nor consortium is capable of keeping up within their own field and are overwhelmed by the explosion of data
increase. Machine learning applied on biological data has the potential to solve this and cause a paradigm shift from
hypothesis-driven research (which predominates biological research including biogerontology) to data-driven
research.
This dissertation addresses this problem. In particular it proposes and executes the use of machine learning on
current existing data to predict drivers of aging (and therefore helps to distinguish causes from consequences),
interventions to counteract aging, and specific hypotheses to fill in research gaps that require experimental
validation.
The objective of this project is therefore to build computational models that are based on data relevant to the
phenomenon of aging and to predict as many of its aspects and dimensions as possible (thus elucidate their relations
to each other). For converting between and sorting within dimensions which are relevant to aging, different machine
learning models are evaluated. Ones models are built, it can be determined how much they can explain different
aspects of aging. Those models will also be capable of specifying which features are most relevant for prediction (in
both classification or regression). It is possible to train models that incorporate age-related changes based on
transcriptomic, proteomic, metabolomic, epigenomic as well as morphological data and their combinations. Machine
learning is further used to convert between and within them.
This work focuses on three types of predictors. Subsequently, discoveries are made with the statistical and learning
algorithms. The first model (lifespan predictor) is trained on predicting the lifespan based on genotype, environment
and combinations thereof. It is useful for predicting lifespan extending interventions on the population level. The
second model (age predictor) is trained on predicting the age given features measured on individuals. This is useful
for identifying biomarkers of aging and to determine the effects of interventions on the level of individuals. The third
model predicts functions/regulations of biological entities in regard to the aging process based on heterogeneous data
such as ontologies and diverse omics including time-series gene expression profiles (which can be visualized as
plots), and linked data. It is used to understand the role of genes and proteins as well as perhaps other entities such as
small molecules including lipids and other metabolites. Functions of proteins, which are still unknown, especially
those involved in yeast lipid metabolism and its regulation, can be predicted.
For this purpose we use primarily yeast as model organism as well as data on humans. Other biomedical model
organisms might be added if found beneficial.
The novel aspects of this research are for instance that 1) aging is investigated systematically in an unbiased
data-driven approach, 2) lifespan is predicted as continuous values, 3) age is predicted by combining multiple omics
data, 4) functions and regulations of biological entities like genes are predicted with high confidence from
heterogeneous data sources.
This thesis discovered that genetics is the most important feature of lifespan determination. Phenotypic features
related to lipid and membranes such as vacuolar morphology and autophagy activity are important for lifespan
determination according to the best performing models. A age predictor based on transcriptomics and proteomics can
highly accurately determine the age. It selected features are associated with both translation and lipid metabolism.
Among the top selected features are transcripts of genes when deleted exhibit abnormal vacuolar morphology as well
as targets of Opi1. Opi1 itself and its regulators were found to be differentially regulated post-transcriptional or
post-translational. Lastly, a function predictor for genes was created that achieved exceptional accuracy of
classifying aging genes. It learned for instance that piecemeal autophagy of the nucleus is strongly predictive for
aging-suppressor genes while cytoplasmic translation is strongly predictive for gerontogenes

A multiplex DNA microarray chip was developed for simultaneous identification of nine genetically modified organisms (GMOs), five plant species and three GMO screening elements, i.e. the 35S promoter, the nos terminator and the nptII gene. The chips also include several controls, such as that for the possible presence of CaMV. The on-chip detection was performed directly with PCR amplified products. Particular emphasis was placed on the reduction of the number of PCR reactions required and on the number of primers present per amplification tube. The targets were biotin labelled and the arrays were detected using a colorimetric methodology. Specificity was provided by specific capture probes designed for each GMO and for the common screening elements. The sensitivity of the assay was tested by experiments carried out in five different laboratories. The limit of detection was lower than 0.3% GMO for all tests and in general around 0.1% for most GMOs. The chip detection system complies with the requirements of current EU regulations and other countries where thresholds are established for the labelling of GMO.

The continuously increasing concentration of atmospheric CO2 has considerably altered ecosystem functioning. However, few studies have examined the long-term (i.e. over a decade) effect of elevated CO2 on soil microbial communities. Using 16S rRNA gene amplicons and a GeoChip microarray, we investigated soil microbial communities from a Californian annual grassland after 14 years of experimentally elevated CO2 (275 ppm higher than ambient). Both taxonomic and functional gene compositions of the soil microbial community were modified by elevated CO2. There was decrease in relative abundance for taxa with higher ribosomal RNA operon (rrn) copy number under elevated CO2, which is a functional trait that responds positively to resource availability in culture. In contrast, taxa with lower rrn copy number were increased by elevated CO2. As a consequence, the abundance-weighted average rrn copy number of significantly changed OTUs declined from 2.27 at ambient CO2 to 2.01 at elevated CO2. The nitrogen (N) fixation gene nifH and the ammonium-oxidizing gene amoA significantly decreased under elevated CO2 by 12.6% and 6.1%, respectively. Concomitantly, nitrifying enzyme activity decreased by 48.3% under elevated CO2, albeit this change was not significant. There was also a substantial but insignificant decrease in available soil N, with both nitrate (NO3−) (−27.4%) and ammonium (NH4+) (−15.4%) declining. Further, a large number of microbial genes related to carbon (C) degradation were also affected by elevated CO2, whereas those related to C fixation remained largely unchanged. The overall changes in microbial communities and soil N pools induced by long-term elevated CO2 suggest constrained microbial N decomposition, thereby slowing the potential maximum growth rate of the microbial community.

Data mining is defined as the process of extracting or mining knowledge from vast and large database. Data mining is an interdisciplinary field that brings together techniques from machine learning, pattern recognition, statistics, databases, and visualization to address the issue of information extraction from large databases. Bioinformatics is defined as the science of organizing and analyzing the biological data. Microarray technology helps biologists for monitoring expression of thousands of genes in a single experiment on a small chip. Microarray is also called as DNA chip, gene chip, or biochip is used to analyze the gene expression profiles. Fuzzy Logic is defined as a multivalued logic that provides the intermediate values to be defined between conventional evaluations like true or false, yes or no, high or low, etc.In this paper, a type 2 fuzzy logic approach is used in microarray gene expression data to convert the numerical values into fuzzy terms. After fuzzification, the fuzzy association patterns are discovered. A framework is proposed to cluster microarray gene data based on fuzzy association patterns. Then the proposed type 2 fuzzy approach is compared with traditional clustering algorithms.

With the growing interest in clinical interventions that involve medical devices, the role for new biomaterials in modern medicine is currently expanding at a phenomenal rate. Failure of most implant materials stems from an inability to predict and control biological phenomena, such as protein adsorption and cell interaction, resulting in an inappropriate host response to the materials. Contemporary advances in biological investigation are starting to shift focus in the biomaterials field, in particular with the advent of high-throughput methodologies for gene and protein expression profiling. Here, we examine the role that emerging transcriptomic and proteomic technologies could play in relation to biomaterial development and usage. Moreover, a number of studies are highlighted which have utilized such approaches in order to try to create a deeper understanding of cell–biomaterial interactions and, hence, improve our ability to predict and control the biocompatibility of new materials.

Imperatively Hidden Objects/Factors (IHO/F) refers to relevant items, which we cannot perceive and measure yet. Nevertheless, they have an impact on our predicted outcome. We have created model that allows for the integration and discovery for the still unknown. If researchers would take the time to understand it, they'd design their experimental studies in a much different way because their scientific objective would shift toward feature discovery to enhance feature selection to make even better predictions with supervised machine learning algorithms.

Please click here http://singledrug.com/media/Temporal_Analysis_of_Aging_description.pdf to see it.

I plan to add the following:

I found Osh6 at a very high rank of lifespan extending genes.  That is why I want to take all the genes that are part of the same molecular function as Osh6 and run an expression similarity analysis on SPELL (Serial Pattern of Expression Levels Locator) (see spell.yeastgenome.org/). 

I hope to find genes of unknown functions that could be involved in the same molecular function.  If I find this, I'll validate with my time series plots and TFBS similarity analysis.  That way I hope to predict the function of an unknown gene in lipid metabolism.  I will also try the opposite approach, i.e. I'll take genes of unknown function and look for a group of similarly expressed genes, which belong to the same GO term.

I added 19 pages to the second chapter.  I was able to further improve the accuracy of my yeast lifespan predicting machine learning algorithm by adding more features about lipids.  My last draft had an average prediction error of +/-5 replications.  Now it is less than +/-3 replication for the 50th percentile of the replicative lifespan.  To my knowledge nobody has published any research on about using machine learning and feature selection to predict the replicative age.

If you have any more ideas how I could make my dissertation look better, I'd be very thankful for any new feasible ideas and inspirations.  I have mainly been using Python and R for all my analysis.

I found that ribosomal RNA and proteins are most closely follow the cell cycle and that if knocked out the yeast's lifespan can be extended significantly.  I want to use the different cyclical periodically recurring and highly fluctuating gene expression oscillation pattern to discover the functions of genes we have not yet discovered.  I am interested in this because a friend of mine showed me a cancer dataset that looked very similar.  Since cancer is very similar to the yeast in that it keeps constantly dividing and hence its age can be expressed in replication we could use the same guilt-by-association approach to infer the functions or at least the way their expression is controlled by TFs from genes with similar temporal expression patterns of genes with already known functions.

When analyzing the time series plots of the 62 yeast chaperons I discovered that their expression plot trajectories can easily be clustered into 7 expression patterns, which are consisted with the 7 classes of yeast chaperons.  If I had only taken the average centroid of all 62 chaperons I would have been misled because using this approach could not be applied to potentially discover more new currently still unknown chaperons because no unknown chaperon would have time series trajectories, which would highly correlate with the average chaperon expression pattern of all 7 classes since their in-group expression pattern is very homogeneous but between the 7 different chaperon classes the expression pattern is very different.

I intended to elucidate the different ways by which we age by a very similar clustering algorithm, which I have developed based on time series curves over three subsequent yeast cell cycle consisting of 36 mRNA measures each about 25 minutes apart; thus, allowing to measure very steep mRNA expression fluctuation periods by manifold within the only 90 minutes of the cell cycle because even very brief but rapid expression changes that last not much longer than 1/12th of a cell cycle, of which I found surprisingly many.

Background: Significant gaps remain regarding the pathomechanisms underlying the autoimmune response in vitiligo (VL), where the loss of self-tolerance leads to the targeted killing of melanocytes. Specifically, there is incomplete information regarding alterations in the systemic environment that are relevant to the disease state.

The objective is to understand how sterol transport is affected by caloric restriction (CR) and aging.  Sterol synthesis generally increases as cells age.  Yet, despite this increase, the sterol content of selected organelle membranes, such as lysosomal membranes, declines with age and adversely affects cellular functions. We are interested in a better understanding of changes in sterol transport to membranes of vacuoles (the yeast counterparts of mammalian lysosomes) in response to anti-aging and pro-aging manipulations.

The information of gene expression obtained from databases, have made possible the extraction and analysis of data related with several molecular processes involving not only in brain homeostasis but its disruption in some neuropathologies; principally in Down syndrome and the Alzheimer disease. To correlate the levels of transcription of 19 genes located in the Down Syndrome Critical Region (DSCR) with their expression in several substructures of normal human brain. There were obtained expression profiles of 19 DSCR genes in 42 brain substructures, from gene expression values available at the database of the human brain of the Brain Atlas of the Allen Institute for Brain Sciences", (http://human.brain-map.org/). The co-expression patterns of DSCR genes in brain were calculated by using multivariate statistical methods. Highest levels of gene expression were registered at caudate nucleus, nucleus accumbens and putamen among central areas of cerebral cortex. Increased expression...

Sets of DNA strands that satisfy combinatorial and thermodynamic properties play
an important role in various approaches to biomolecular computations, nano structure
design, molecular tagging, and DNA microarrays. The problem of designing
such sets of DNA strands appears to be computationally hard.
This thesis introduces new algorithms for design of DNA strand sets that satisfy
any of several combinatorial and thermodynamic constraints, which aim to maximize
desired hybridization between strands and their complements, while minimizing
undesired cross-hybridizations. To heuristically search for good strand sets
for bio-computing applications, our algorithms use a conflict-driven stochastic local
search approach, which is known to be effective in solving comparable search
problems.
We describe new and improved thermodynamic measures of the quality of
strand sets. With respect to these measures of quality, our algorithms consistently
find, within reasonable time, sets that are significantly better than previously published
sets in the literature. We also present a detailed analysis and selection of
heuristics for improving the quality of DNA strand selection criteria with direct
applications in microarray probe design.

Major gaps remain regarding pathogenetic mechanisms underlying clinical heterogeneity in lupus erythematosus (LE). As systemic changes are likely to underlie skin specific manifestation, we analyzed global gene expression in peripheral blood of a small cohort of chronic cutaneous LE (CCLE) patients and healthy individuals. Unbiased hierarchical clustering distinguished patients from controls revealing a "disease" based signature. Functional annotation of the differentially expressed genes (DEGs) highlight enrichment of interferon related immune response and apoptosis signatures, along with other key pathways. There is a 26% overlap of the blood and lesional skin transcriptional profile from a previous analysis by our group. We identified four transcriptional "hot spots" at chromosomal regions harboring statistically increased numbers of DEGs which offer prioritized potential loci for downstream fine mapping studies in the search for CCLE specific susceptibility l...

Introduction: Early detection of emerging influenza virus variant is a key factor in the WHO influenza Global strategies for prevention and control. Rapid, accurate, inexpensive and portable detection systems are needed for influenza virus diagnosis and surveillance. Such a detection system should easily identify all the subtypes of influenza virus. Degenerate primers and probes designed from evolutionally conserved regions for known influenza A viruses present the best way to identify unknown subtypes of influenza A virus by polymerase chain reaction PCR and array techniques. The isothermal reactions, Nucleic Acid Sequencing Based Amplification (NASBA) and Loop-mediated isothermal Amplification (LAMP) possess great potential for influenza A virus detection especially in developing countries. However, multiplex real-time (rT) or quantitative (q) polymerase chain reaction (qPCR) remains a rapid, accurate and time-saving technique used for influenza virus detection. Aim: This manuscri...

The information of gene expression obtained from databases, have made possible the extraction and analysis of data related with several molecular processes involving not only in brain homeostasis but its disruption in some neuropathologies; principally in Down syndrome and the Alzheimer disease. To correlate the levels of transcription of 19 genes located in the Down Syndrome Critical Region (DSCR) with their expression in several substructures of normal human brain. There were obtained expression profiles of 19 DSCR genes in 42 brain substructures, from gene expression values available at the database of the human brain of the Brain Atlas of the Allen Institute for Brain Sciences", (http://human.brain-map.org/). The co-expression patterns of DSCR genes in brain were calculated by using multivariate statistical methods. Highest levels of gene expression were registered at caudate nucleus, nucleus accumbens and putamen among central areas of cerebral cortex. Increased expression...

Understanding how mesenchymal cells arise from epithelial cells could have a strong impact in unveiling mechanisms of epithelial cell plasticity underlying kidney regeneration and repair. In primary human tubular epithelial cells (HUTEC) under different TGF beta 1 concentrations we had observed epithelial-to-mesenchymal transition (EMT) but not epithelial-myofibroblast transdifferentiation. We hypothesized that the process triggered by TGFbeta 1 could be a dedifferentiation event. The purpose of this study is to comprehensively delineate genetic programs associated with TGF beta 1-driven EMT in our in vitro model using gene expression profile on large-scale oligonucleotide microarrays. In HUTEC under TGF beta 1 stimulus, 977 genes were found differentially expressed. Thirty genes were identified whose expression depended directly on TGF beta 1 concentration. By mapping the differentially expressed genes in the Human Interactome Map using Cytoscape software, we identified a single sc...