Identification of Differentially Expressed Genes with Multivariate Outlier Analysis

TORC1-signalling is down-regulated in Saccharomyces cerevisiae hsp30Δ cells by SNF1-dependent mechanisms

Hsp30 is a plasma membrane localized heat shock protein in Saccharomyces cerevisiae whose expression is induced by numerous environmental stressors. Elucidation of its mechanism of action has remained elusive primarily because hsp30Δ cells do not show a strong phenotype. To identify cellular functions associated with Hsp30, we thus compared the transcriptome of BY4741hsp30Δ with that of its wild type counterpart. Our studies indicate down-regulation of the target of rapamycin complex 1 (TORC1)-dependent gene-expression programme in hsp30Δ cells. We further show that TORC1-signalling through its effectors (Sch9 and Tap42) was down-regulated in the deletion strain. Specifically, (a) phosphorylation levels of Sch9 were lower and nuclear exclusion of Rim15 (Sch9-downstream function) was overridden in hsp30Δ cells, (b) membrane association of Tor1 and Tap42 was lower in hsp30Δ cells, and (c) Tap42-downstream functions were abrogated in the deletion strain. Furthermore, transcription factors Rtg1, Rtg3, Gat1, and Gln3 were localized in the nucleus of the hsp30Δ as observed upon inactivation of TORC1. Studies aimed at determining how TORC1-signalling is down-regulated in hsp30Δ cells indicated that total reducing sugar levels were lower and ADP:ATP ratio was higher in hsp30Δ cells -conditions known to activate the Snf1 kinase and consequently to the inactivation of TORC1. We thus determined if TORC1-signalling could be restored in hsp30Δ cells upon the deletion of SNF1. Sch9 phosphorylation levels (TORC1-signalling) was restored to wild type levels in hsp30Δsnf1Δ cells. TORC1-signalling is thus down-regulated in hsp30Δ cells by SNF1-dependent mechanisms. A probable role for Hsp30 is discussed.

On the role of extrinsic noise in microRNA-mediated bimodal gene expression

Several studies highlighted the relevance of extrinsic noise in shaping cell decision making and differentiation in molecular networks. Bimodal distributions of gene expression levels provide experimental evidence of phenotypic differentiation, where the modes of the distribution often correspond to different physiological states of the system. We theoretically address the presence of bimodal phenotypes in the context of microRNA (miRNA)-mediated regulation. MiRNAs are small noncoding RNA molecules that downregulate the expression of their target mRNAs. The nature of this interaction is titrative and induces a threshold effect: below a given target transcription rate almost no mRNAs are free and available for translation. We investigate the effect of extrinsic noise on the system by introducing a fluctuating miRNA-transcription rate. We find that the presence of extrinsic noise favours the presence of bimodal target distributions which can be observed for a wider range of parameters compared to the case with intrinsic noise only and for lower miRNA-target interaction strength. Our results suggest that combining threshold-inducing interactions with extrinsic noise provides a simple and robust mechanism for obtaining bimodal populations without requiring fine tuning. Furthermore, we characterise the protein distribution's dependence on protein half-life.

Demonstration of sequential adaptation strategy for developing salt tolerance in bacteria for wastewater treatment: a study using Escherichia coli as model

A wastewater isolate identified as Escherichia coli HPC781 was adapted for high salt concentration through sequential transfers in Luria Broth (LB). The cells were grown in LB with 5% sodium chloride (NaCl) and were analyzed for the acquired salt resistance network through gene expression profiles. Microarray studies revealed TCA, glyoxylate shunt and acetyl Co-A metabolism as key nodes for stress combat to arrive at compromised physiology. It also proposed that the cells were receiving signals from salt environment via OmpR-EnvZ two component systems and stress dependent general regulatory protein rpoH and rpoE. The salt adapted culture, when challenged with wastewater having additional 5% salt showed growth. The work represents a tactic to adjust biochemical network towards stress and reveals its applicability via real-time PCR measurement of genes in wastewater. The study proposes that the recycled biomass with an adaptation strategy could be applied for treatment of wastewater with high salt levels.

DigOut: viewing differential expression genes as outliers

With regards to well-replicated two-conditional microarray datasets, the selection of differentially expressed (DE) genes is a well-studied computational topic, but for multi-conditional microarray datasets with limited or no replication, the same task is not properly addressed by previous studies. This paper adopts multivariate outlier analysis to analyze replication-lacking multi-conditional microarray datasets, finding that it performs significantly better than the widely used limit fold change (LFC) model in a simulated comparative experiment. Compared with the LFC model, the multivariate outlier analysis also demonstrates improved stability against sample variations in a series of manipulated real expression datasets. The reanalysis of a real non-replicated multi-conditional expression dataset series leads to satisfactory results. In conclusion, a multivariate outlier analysis algorithm, like DigOut, is particularly useful for selecting DE genes from non-replicated multi-conditional gene expression dataset.

Bimodal gene expression patterns in breast cancer

We identified a set of genes with an unexpected bimodal distribution among breast cancer patients in multiple studies. The property of bimodality seems to be common, as these genes were found on multiple microarray platforms and in studies with different end-points and patient cohorts. Bimodal genes tend to cluster into small groups of four to six genes with synchronised expression within the group (but not between the groups), which makes them good candidates for robust conditional descriptors. The groups tend to form concise network modules underlying their function in cancerogenesis of breast neoplasms.

Gene expression profile in obesity and type 2 diabetes mellitus

Obesity is an important component of metabolic syndrome X and predisposes to the development of type 2 diabetes mellitus. The incidence of obesity, type 2 diabetes mellitus and metabolic syndrome X is increasing, and the cause(s) for this increasing incidence is not clear. Although genetics could play an important role in the higher prevalence of these diseases, it is not clear how genetic factors interact with environmental and dietary factors to increase their incidence. We performed gene expression profile in subjects with obesity and type 2 diabetes mellitus with and without family history of these diseases. It was noted that genes involved in carbohydrate, lipid and amino acid metabolism pathways, glycan of biosynthesis, metabolism of cofactors and vitamin pathways, ubiquitin mediated proteolysis, signal transduction pathways, neuroactive ligand-receptor interaction, nervous system pathways, neurodegenerative disorders pathways are upregulated in obesity compared to healthy subjects. In contrast genes involved in cell adhesion molecules, cytokine-cytokine receptor interaction, insulin signaling and immune system pathways are downregulated in obese. Genes involved in signal transduction, regulation of actin cytoskeleton, antigen processing and presentation, complement and coagulation cascades, axon guidance and neurodegenerative disorders pathways are upregulated in subjects with type 2 diabetes with family history of diabetes compared to those who are diabetic but with no family history. Genes involved in oxidative phosphorylation, immune, nervous system, and metabolic disorders pathways are upregulated in those with diabetes with family history of diabetes compared to those with diabetes but with no family history. In contrast, genes involved in lipid and amino acid pathways, ubiquitin mediated proteolysis, signal transduction, insulin signaling and PPAR signaling pathways are downregulated in subjects with diabetes with family history of diabetes. It was noted that genes involved in inflammatory pathway are differentially expressed both in obesity and type 2 diabetes. These results suggest that genes concerned with carbohydrate, lipid and amino acid metabolic pathways, neuronal function and inflammation play a significant role in the pathobiology of obesity and type 2 diabetes.

Quantitative mass spectrometry in proteomics: a critical review

The quantification of differences between two or more physiological states of a biological system is among the most important but also most challenging technical tasks in proteomics. In addition to the classical methods of differential protein gel or blot staining by dyes and fluorophores, mass-spectrometry-based quantification methods have gained increasing popularity over the past five years. Most of these methods employ differential stable isotope labeling to create a specific mass tag that can be recognized by a mass spectrometer and at the same time provide the basis for quantification. These mass tags can be introduced into proteins or peptides (i) metabolically, (ii) by chemical means, (iii) enzymatically, or (iv) provided by spiked synthetic peptide standards. In contrast, label-free quantification approaches aim to correlate the mass spectrometric signal of intact proteolytic peptides or the number of peptide sequencing events with the relative or absolute protein quantity directly. In this review, we critically examine the more commonly used quantitative mass spectrometry methods for their individual merits and discuss challenges in arriving at meaningful interpretations of quantitative proteomic data.

Bystander effect: biological endpoints and microarray analysis

In cell populations exposed to ionizing radiation, the biological effects occur in a much larger proportion of cells than are estimated to be traversed by radiation. It has been suggested that irradiated cells are capable of providing signals to the neighboring unirradiated cells resulting in damage to these cells. This phenomenon is termed the bystander effect. The bystander effect induces persistent, long-term, transmissible changes that result in delayed death and neoplastic transformation. Because the bystander effect is relevant to carcinogenesis, it could have significant implications for risk estimation for radiation exposure. The nature of the bystander effect signal and how it impacts the unirradiated cells remains to be elucidated. Examination of the changes in gene expression could provide clues to understanding the bystander effect and could define the signaling pathways involved in sustaining damage to these cells. The microarray technology serves as a tool to gain insight into the molecular pathways leading to bystander effect. Using medium from irradiated normal human diploid lung fibroblasts as a model system we examined gene expression alterations in bystander cells. The microarray data revealed that the radiation-induced gene expression profile in irradiated cells is different from unirradiated bystander cells suggesting that the pathways leading to biological effects in the bystander cells are different from the directly irradiated cells. The genes known to be responsive to ionizing radiation were observed in irradiated cells. Several genes were upregulated in cells receiving media from irradiated cells. Surprisingly no genes were found to be downregulated in these cells. A number of genes belonging to extracellular signaling, growth factors and several receptors were identified in bystander cells. Interestingly 15 genes involved in the cell communication processes were found to be upregulated. The induction of receptors and the cell communication processes in bystander cells receiving media from irradiated cells supports the active involvement of these processes in inducing bystander effect.

PACK: Profile Analysis using Clustering and Kurtosis to find molecular classifiers in cancer

MOTIVATION

Elucidating the molecular taxonomy of cancers and finding biological and clinical markers from microarray experiments is problematic due to the large number of variables being measured. Feature selection methods that can identify relevant classifiers or that can remove likely false positives prior to supervised analysis are therefore desirable.

RESULTS

We present a novel feature selection procedure based on a mixture model and a non-gaussianity measure of a gene's expression profile. The method can be used to find genes that define either small outlier subgroups or major subdivisions, depending on the sign of kurtosis. The method can also be used as a filtering step, prior to supervised analysis, in order to reduce the false discovery rate. We validate our methodology using six independent datasets by rediscovering major classifiers in ER negative and ER positive breast cancer and in prostate cancer. Furthermore, our method finds two novel subtypes within the basal subgroup of ER negative breast tumours, associated with apoptotic and immune response functions respectively, and with statistically different clinical outcome.

AVAILABILITY

An R-function pack that implements the methods used here has been added to vabayelMix, available from (www.cran.r-project.org).

CONTACT

aet21@cam.ac.uk

SUPPLEMENTARY INFORMATION

Supplementary information is available at Bioinformatics online.