Gene Expression Analysis Methods on Microarray Data a A Review

In recent years a new type of experiments are changing the way that biologists and other specialists analyze many problems. These are called high throughput experiments and the main difference with those that were performed some years ago is mainly in the quantity of the data obtained from them. Thanks to the technology known generically as microarrays, it is possible to study nowadays in a single experiment the behavior of all the genes of an organism under different conditions. The data generated by these experiments may consist from thousands to millions of variables and they pose many challenges to the scientists who have to analyze them. Many of these are of statistical nature and will be the center of this review. There are many types of microarrays which have been developed to answer different biological questions and some of them will be explained later. For the sake of simplicity we start with the most well known ones: expression microarrays

Whole-transciptome analysis of [psi+] budding yeast via cDNA microarrays

Introduction: Prions of yeast present a novel analytical challenge in terms of both initial characterization and in vitro manipulation as models for human disease research. Presently, few robust analysis strategies have been successfully implemented which enable the efficient study of prion behavior in vivo. This study sought to evaluate the utilization of conventional dual-channel cDNA microarrays for the surveillance of transcriptomic regulation patterns by the [PSI+] yeast prion relative to an identical prion deficient yeast variant, [psi-]. Methods: A data analysis and normalization workflow strategy was developed and applied to cDNA array images, yielded quality-regulated expression ratios for a subset of genes exhibiting statistical congruence across multiple experimental repetitions and nested hybridization events. The significant gene list was analyzed using classical analytical approaches including several clustering-based methods and singular value decomposition. To add biological meaning to the differential expression data in hand, functional annotation using the Gene Ontology as well as several pathway-mapping approaches was conducted. Finally, the expression patterns observed were queried against all publicly curated microarray data performed using S. cerevisiae in order to discover similar expression behavior across a vast array of experimental conditions. Results: These data collectively implicate a low-level of overall genomic regulation as a result of the [PSI+] state, where the maximum statistically significant degree of differential expression was less than ±1 Log2(FC) in all cases. Notwithstanding, the [PSI+] differential expression was localized to several specific classes of structural elements and cellular functions, implying under homeostatic conditions significant up or down regulation is likely unnecessary but possible in those specific systems if environmental conditions warranted. As a result of these findings additional work pertaining to this system should include controlled insult to both yeast variants of differing environmental properties to promote a potential [PSI+] regulatory response coupled with co-surveillance of these conditions using transcriptomic and proteomic analysis methodologies

Determination of N-Linked Glycosylation Changes in Hepatocellular Carcinoma and the Associated Glycoproteins for Enhanced Biomarker Discovery and Therapeutic Targets

With hepatocellular carcinoma (HCC) remaining as the fifth most common cancer in the world, causing more than 700,000 deaths annually, the need for reliable, early stage diagnoses and preventive treatments is crucial. While serum glycoproteins are hepatic in origin, making them excellent targets for HCC biomarkers, they can originate from both cancerous and non-cancerous regions and direct analysis of cancerous tissue itself is lacking. To counteract this, I hypothesized that direct tissue analysis combined with proteomic analysis could be utilized to identify more potential targets specific to HCC for early detection. This was done with a primary focus on glycosylation—as most clinically approved biomarkers are glycoproteins—and examined direct tissue glycomics in conjunction with glycoproteomic techniques through two specific aims: 1) Determining patterns of N-linked glycan changes in HCC tissue using MALDI imaging mass spectrometry to compare to previously published serum changes and 2) identifying glycopeptides containing changes in observed patterns of N- linked glycans in HCC samples using a targeted glycoproteomic approach. In Aim 1, HCC tissue was examined using MALDI imaging mass spectrometry to v verify changes in glycosylation via direct tissue analysis. Here, it was found that increased branching and fucosylation were directly associated with the cancerous tissue when compared to normal or cirrhotic. To further identify changes in glycosylation, two methods (one novel and one adapted for imaging) were implemented on tissue to further classify N-linked glycan isoforms through linkage analysis, specifically for sialic acids and core fucose. Again, it was shown that core fucose is most directly related to HCC tissue, thus confirming serum findings in the literature. For Aim 2, the novel method of determining core fucosylation was used in conjunction with glycoproteomic techniques to further elucidate the core fucosylated glycoproteins of interest. With the tag left behind following the enzymatic cleavage, targeted glycoproteomics was used to determine glycoproteins of interest while eliminating some biases inherent in the method, such as low ionization efficiencies for more complex N-glycans. This work outlines the first in-depth analysis of HCC tissue specifically regarding N- glycan changes, a novel application to determine N-glycan isoforms, and the application of these methods for glycoproteomic enhancement. With these findings, new trends in glycosylation related to the disease state could be further uncovered, as well as provide new biomarker candidates or therapeutic targets for future studies

Novel pattern recognition approaches for transcriptomics data analysis

We proposed a family of methods for transcriptomics and genomics data analysis based on multi-level thresholding approach, such as OMTG for sub-grid and spot detection in DNA microarrays, and OMT for detecting significant regions based on next generation sequencing data. Extensive experiments on real-life datasets and a comparison to other methods show that the proposed methods perform these tasks fully automatically and with a very high degree of accuracy. Moreover, unlike previous methods, the proposed approaches can be used in various types of transcriptome analysis problems such as microarray image gridding with different resolutions and spot sizes as well as finding the interacting regions of DNA with a protein of interest using ChIP-Seq data without any need for parameter adjustment. We also developed constrained multi-level thresholding (CMT), an algorithm used to detect enriched regions on ChIP-Seq data with the ability of targeting regions within a specific range. We show that CMT has higher accuracy in detecting enriched regions (peaks) by objectively assessing its performance relative to other previously proposed peak finders. This is shown by testing three algorithms on the well-known FoxA1 Data set, four transcription factors (with a total of six antibodies) for Drosophila melanogaster and the H3K4ac antibody dataset. Finally, we propose a tree-based approach that conducts gene selection and builds a classifier simultaneously, in order to select the minimal number of genes that would reliably predict a given breast cancer subtype. Our results support that this modified approach to gene selection yields a small subset of genes that can predict subtypes with greater than 95%overall accuracy. In addition to providing a valuable list of targets for diagnostic purposes, the gene ontologies of the selected genes suggest that these methods have isolated a number of potential genes involved in breast cancer biology, etiology and potentially novel therapeutics

Evaluating cell-mimicking giant unilamellar vesicles as simplified biological models using single molecule methods

One of the main drivers within the field of bottom-up synthetic biology is to develop artificial chemical machines, perhaps even living systems, that have programmable functionality. Bottom-up methods take an engineering approach to biology, with multiple downstream applications. Such applications that require very specific quantities of product e.g., antibodies, drug, or vaccines, necessitate the design of vesicles with total and precise control of a vesicle’s molecular machinery. There exists a wide range of toolkits to produce and engineer artificial cells with an ever-increasing array of functionality and capability. However, little attention has been given to the quality control of vesicle production; so, to date, there is a paucity of techniques that are able to measure their molecular constituents precisely upon formation and with absolute quantification. The work outlined here presents the development of a microfluidic-based methodology that is able to characterise artificial cells at the single vesicle level with single-molecule resolution. High content fluorescence microscopy was used to assess the properties of Giant unilamellar vesicles (GUVs) and their statistics at the population level. Since this technique is semi-quantitative, the relative concentration of protein across the GUV population was determined however the absolute concentration of protein within each GUV was not. To overcome this, a lab-on-a-chip approach was taken to determine the precise number of biomolecules within each GUV and across the population of GUVs. The pulldown-5 array single-molecule high-throughput (PASH) chip was developed and capable of determining the encapsulation efficiency of protein within GUVs produced by phase transfer of an inverted emulsion. Using this approach, it was possible to determine the variability of the encapsulation efficiency between GUVs across a population as well as between different populations while testing batch-to-batch variation. The encapsulation efficiency measured to be 11.4 ± 6.2% across all tested parameters. This has consequences for the use of GUVs as precise biological models as well as for their development in a variety of biotherapeutic applications. To determine whether GUVs with specific concentrations of biomolecules could be produced by phase transfer, changes in the concentration of the seeding materials and reagents were investigated; while inefficiencies in encapsulation could be overcome, the variation in concentration could not. The results presented in this thesis help to understand the limitation of the phase transfer technique applied to systems biology research. In biological systems, measuring changes in gene expression at the transcriptomic and proteomic level is important. When used to develop simplified models of protein expression, these results indicate that vesicles produced using phase transfer may only be confidently used to produce 10-fold changes in encapsulant protein. It is possible to distinguish different GUV populations with a precision down to a two-fold change in encapsulant protein but with significant population overlap. The understanding of the limitations of encapsulation efficiency provides insight into the potential relevance of such systems for a variety of therapeutic applications such as smart drug delivery.Open Acces

BIOMOLECULE INSPIRED DATA SCIENCE

BIOMOLECULE INSPIRED DATA SCIENC

Regulation of metamorphosis and the evolution of life cycles: insights from the common moon jelly Aurelia aurita

Life histories of ancient marine invertebrates offer insight into the evolution of life cycles. The archetypal metagenetic life cycle of cnidarians alternates between sessile asexual polyps and pelagic medusa. Transition from one life form to another is triggered by environmental signals, but the molecular cascades involved in these drastic morphological and physiological changes remain unknown. Here I show in the moon jelly Aurelia aurita that the molecular machinery controlling transition of the sessile polyp into a free-swimming jellyfish consists of two counterparts. One is conserved and relies on the retinoic acid signalling which is responsible for the initiation of metamorphosis. The second, novel part, is based on secreted proteins which are strongly up-regulated prior to metamorphosis in response to seasonal temperature changes. One of these secreted proteins is a temperature sensitive "timer" and a precursor of the metamorphosis hormone. Polyp to jellyfish transition is accompanied by the dramatic changes in morphology. In my thesis, I have identified genes which are responsible for this transformation. Stage specific genes were selected and their expression patterns were examined. I also functionally analyzed several conserved signalling pathways by chemical interference. The data indicate that two taxonomically restricted genes and retinoic acid, hedgehog, Wnt and TGFβ signalling pathways in cooperation with ETS transcription factor play the central role in Aurelia segmentation and the following ephyra morphogenesis. These findings uncover the molecular framework controlling the life cycle regulation of a basal metazoan and imply that the Urmetazoan ancestors of currently living cnidarians possessed a medusoid stage, which has been secondarily lost in corals and sea anemones.Die Lebensentwicklung mariner Invertebraten ermöglicht einen Einblick in die Evolution des Lebenszyklus. Der archetypische metagenetische Generationswechsel findet bei Cnidarian zwischen dem sessilen asexuellen Polypen und der pelagischen Meduse statt. Die Umwandlung von einer Lebensform zur anderen wird durch Umweltsignale induziert, doch sind die molekularen Signalwege, die an diesem drastischen morphologischen und physiologischen Wechsel beteiligt sind, unbekannt. In dieser Arbeit zeige ich, dass in der Ohrenqualle Aurelia aurita der molekulare Mechanismus, der den Wechsel vom sessilen Polypen zur freischwimmenden Meduse kontrolliert, aus zwei Gegenspielern besteht. Der eine ist konserviert und beruht auf dem Retinolsäure-Signalweg, welcher für die Inhibierung der Metamorphose verantwortlich ist. Der andere basiert auf sekretierten Proteinen, die als Antwort auf saisonale Temperaturwechsel vor Beginn der Metamorphose stark hochreguliert werden. Eins dieser sekretierten Proteine ist ein temperatursensitiver „Wecker“ und ein Vorläufer des Metamorphosehormons. Der Übergang vom Polyp zur Qualle wird durch einen dramatischen Wechel der Morphologie begleitet. In meiner Arbeit0 habe ich Gene identifiziert, die für diese Transformation verantwortlich sind. Es wurden stadiumspezifische Gene ausgewählt und ihre Expressionsmuster untersucht. Außerdem habe ich verschiedene konservierte Signalwege mittels chemischer Interferenz funktionell untersucht. Die Daten deuten daraufhin, dass zwei taxonomisch beschränkte Gene und der Retinolsäure-, der hedgehog-, der Wnt- und der TGFβ-Signalweg in Zusammenarbeit mit dem ETS-Transkriptionsfaktor die zentrale Rolle in der Aurelia-Segmentation und der anschließenden Ephyra-Morphogenese spielen. Diese Ergebnisse decken die molekulare Grundstruktur auf, welche den Lebenszyklus basaler Metazoa kontrolliert. Des Weiteren implizieren sie, dass die Urmetazoan-Vorfahren der heute lebenden Cnidaria ein Medusenstadium besaßen, welches in Korallen und Seeanemonen sekundär verloren ging

The histone deacetylase inhibiting drug Entinostat induces lipid accumulation in differentiated HepaRG cells

Dietary overload of toxic, free metabolic intermediates leads to disrupted insulin signalling and fatty liver disease. However, it was recently reported that this pathway might not be universal: depletion of histone deacetylase (HDAC) enhances insulin sensitivity alongside hepatic lipid accumulation in mice, but the mechanistic role of microscopic lipid structure in this effect remains unclear. Here we study the effect of Entinostat, a synthetic HDAC inhibitor undergoing clinical trials, on hepatic lipid metabolism in the paradigmatic HepaRG liver cell line. Specifically, we statistically quantify lipid droplet morphology at single cell level utilizing label-free microscopy, coherent anti-Stokes Raman scattering, supported by gene expression. We observe Entinostat efficiently rerouting carbohydrates and free-fatty acids into lipid droplets, upregulating lipid coat protein gene Plin4, and relocating droplets nearer to the nucleus. Our results demonstrate the power of Entinostat to promote lipid synthesis and storage, allowing reduced systemic sugar levels and sequestration of toxic metabolites within protected protein-coated droplets, suggesting a potential therapeutic strategy for diseases such as diabetes and metabolic syndrome

Effect of space conditions on neuronal plasticity and connectivity

Looking for opportunities to explore new frontiers and developing new technologies have always been in the nature of mankind. In 1957, the first rocket in space opened a new era for space traveling towards other planets. Concomitantly, a wide range of concerns related to human health risks that could occur during spaceflight was raised. Up to now, a large number of experiments has been performed to determine the biological effects of space conditions on human health, in order to develop appropriate countermeasures. However, extensive investigations still need to be performed before considering long-term spaceflight towards other planets such as Mars. Since the first human space flight, it has been observed that in weightlessness conditions, equilibrium sense organs can send misleading inputs to the central nervous system which is forced to develop new strategies and adapt to adequately translate these messages. Furthermore, cosmic radiations are known to induce oxidative stress as well as genomic damages. In this thesis, we studied concomitant microgravity and radiation exposures as models for space conditions and developed various methods to analyse their specific and combined effects on in vitro neuronal network models. In vitro primary neuronal network cultures were established and exposed to simulated space conditions to investigate neuronal network remodelling (plasticity and connectivity) as well as genomic damage/repair dynamics. This work was performed to address questions on neuronal network disorders occurring during spaceflights and, in the future, to develop strategies against these effects