Patterns of co-expression for protein complexes by size in Saccharomyces cerevisiae

Many successful functional studies by gene expression profiling in the literature have led to the perception that profile similarity is likely to imply functional association. But how true is the converse of the above statement? Do functionally associated genes tend to be co-regulated at the transcription level? In this paper, we focus on a set of well-validated yeast protein complexes provided by Munich Information Center for Protein Sequences (MIPS). Using four well-known large-scale microarray expression data sets, we computed the correlations between genes from the same complex. We then analyzed the relationship between the distribution of correlations and the complex size (the number of genes in a protein complex). We found that except for a few large protein complexes, such as mitochondrial ribosomal and cytoplasmic ribosomal proteins, the correlations are on the average not much higher than that from a pair of randomly selected genes. The global impact of large complexes on the expression of other genes in the genome is also studied. Our result also showed that the expression of over 85% of the genes are affected by six large complexes: the cytoplasmic ribosomal complex, mitochondrial ribosomal complex, proteasome complex, F0/F1 ATP synthase (complex V) (size 18), rRNA splicing (size 24) and H+- transporting ATPase, vacular (size 15)

Trait-trait dynamic interaction: 2D-trait eQTL mapping for genetic variation study

BackgroundMany studies have shown that the abundance level of gene expression is heritable. Analogous to the traditional genetic study, most researchers treat the expression of one gene as a quantitative trait and map it to expression quantitative trait loci (eQTL). This is 1D-trait mapping. 1D-trait mapping ignores the trait-trait interaction completely, which is a major shortcoming.ResultsTo overcome this limitation, we study the expression of a pair of genes and treat the variation in their co-expression pattern as a two dimensional quantitative trait. We develop a method to find gene pairs, whose co-expression patterns, including both signs and strengths, are mediated by genetic variations and map these 2D-traits to the corresponding genetic loci. We report several applications by combining 1D-trait mapping with 2D-trait mapping, including the contribution of genetic variations to the perturbations in the regulatory mechanisms of yeast metabolic pathways.ConclusionOur approach of 2D-trait mapping provides a novel and effective way to connect the genetic variation with higher order biological modules via gene expression profiles

Patterns of co-expression for protein complexes by size in Saccharomyces cerevisiae

Many successful functional studies by gene expression profiling in the literature have led to the perception that profile similarity is likely to imply functional association. But how true is the converse of the above statement? Do functionally associated genes tend to be co-regulated at the transcription level? In this paper, we focus on a set of well-validated yeast protein complexes provided by Munich Information Center for Protein Sequences (MIPS). Using four well-known large-scale microarray expression data sets, we computed the correlations between genes from the same complex. We then analyzed the relationship between the distribution of correlations and the complex size (the number of genes in a protein complex). We found that except for a few large protein complexes, such as mitochondrial ribosomal and cytoplasmic ribosomal proteins, the correlations are on the average not much higher than that from a pair of randomly selected genes. The global impact of large complexes on the expression of other genes in the genome is also studied. Our result also showed that the expression of over 85% of the genes are affected by six large complexes: the cytoplasmic ribosomal complex, mitochondrial ribosomal complex, proteasome complex, F0/F1 ATP synthase (complex V) (size 18), rRNA splicing (size 24) and H+- transporting ATPase, vacular (size 15)

Genome-wide expression links the electron transfer pathway of Shewanella oneidensis to chemotaxis

<p>Abstract</p> <p>Background</p> <p>By coupling the oxidation of organic substrates to a broad range of terminal electron acceptors (such as nitrate, metals and radionuclides), <it>Shewanella oneidensis </it>MR-1 has the ability to produce current in microbial fuel cells (MFCs). <it>omcA</it>, <it>mtrA</it>, <it>omcB </it>(also known as <it>mtrC</it>), <it>mtrB</it>, and <it>gspF </it>are some known genes of <it>S. oneidensis </it>MR-1 that participate in the process of electron transfer. How does the cell coordinate the expression of these genes? To shed light on this problem, we obtain the gene expression datasets of MR-1 that are recently public-accessible in Gene Expression Omnibus. We utilize the novel statistical method, liquid association (LA), to investigate the complex pattern of gene regulation.</p> <p>Results</p> <p>Through a web of information obtained by our data analysis, a network of transcriptional regulatory relationship between chemotaxis and electron transfer pathways is revealed, highlighting the important roles of the chemotaxis gene <it>cheA-1</it>, the magnesium transporter gene <it>mgtE-1</it>, and a triheme <it>c</it>-type cytochrome gene SO4572.</p> <p>Conclusion</p> <p>We found previously unknown relationship between chemotaxis and electron transfer using LA system. The study has the potential of helping researchers to overcome the intrinsic metabolic limitation of the microorganisms for improving power density output of an MFC.</p

Trait-trait dynamic interaction: 2D-trait eQTL mapping for genetic variation study

BackgroundMany studies have shown that the abundance level of gene expression is heritable. Analogous to the traditional genetic study, most researchers treat the expression of one gene as a quantitative trait and map it to expression quantitative trait loci (eQTL). This is 1D-trait mapping. 1D-trait mapping ignores the trait-trait interaction completely, which is a major shortcoming.ResultsTo overcome this limitation, we study the expression of a pair of genes and treat the variation in their co-expression pattern as a two dimensional quantitative trait. We develop a method to find gene pairs, whose co-expression patterns, including both signs and strengths, are mediated by genetic variations and map these 2D-traits to the corresponding genetic loci. We report several applications by combining 1D-trait mapping with 2D-trait mapping, including the contribution of genetic variations to the perturbations in the regulatory mechanisms of yeast metabolic pathways.ConclusionOur approach of 2D-trait mapping provides a novel and effective way to connect the genetic variation with higher order biological modules via gene expression profiles

A method for analyzing censored survival phenotype with gene expression data

<p>Abstract</p> <p>Background</p> <p>Survival time is an important clinical trait for many disease studies. Previous works have shown certain relationship between patients' gene expression profiles and survival time. However, due to the censoring effects of survival time and the high dimensionality of gene expression data, effective and unbiased selection of a gene expression signature to predict survival probabilities requires further study.</p> <p>Method</p> <p>We propose a method for an integrated study of survival time and gene expression. This method can be summarized as a two-step procedure: in the first step, a moderate number of genes are pre-selected using correlation or liquid association (LA). Imputation and transformation methods are employed for the correlation/LA calculation. In the second step, the dimension of the predictors is further reduced using the modified sliced inverse regression for censored data (censorSIR).</p> <p>Results</p> <p>The new method is tested via both simulated and real data. For the real data application, we employed a set of 295 breast cancer patients and found a linear combination of 22 gene expression profiles that are significantly correlated with patients' survival rate.</p> <p>Conclusion</p> <p>By an appropriate combination of feature selection and dimension reduction, we find a method of identifying gene expression signatures which is effective for survival prediction.</p

Tissue-specific gene expression templates for accurate molecular characterization of the normal physiological states of multiple human tissues with implication in development and cancer studies

<p>Abstract</p> <p>Background</p> <p>To elucidate the molecular complications in many complex diseases, we argue for the priority to construct a model representing the normal physiological state of a cell/tissue.</p> <p>Results</p> <p>By analyzing three independent microarray datasets on normal human tissues, we established a quantitative molecular model GET, which consists of 24 tissue-specific <it>G</it>ene <it>E</it>xpression <it>T</it>emplates constructed from a set of 56 genes, for predicting 24 distinct tissue types under disease-free condition. 99.2% correctness was reached when a large-scale validation was performed on 61 new datasets to test the tissue-prediction power of GET. Network analysis based on molecular interactions suggests a potential role of these 56 genes in tissue differentiation and carcinogenesis.</p> <p>Applying GET to transcriptomic datasets produced from tissue development studies the results correlated well with developmental stages. Cancerous tissues and cell lines yielded significantly lower correlation with GET than the normal tissues. GET distinguished melanoma from normal skin tissue or benign skin tumor with 96% sensitivity and 89% specificity.</p> <p>Conclusions</p> <p>These results strongly suggest that a normal tissue or cell may uphold its normal functioning and morphology by maintaining specific chemical stoichiometry among genes. The state of stoichiometry can be depicted by a compact set of representative genes such as the 56 genes obtained here. A significant deviation from normal stoichiometry may result in malfunction or abnormal growth of the cells.</p

Clustered Genomic Alterations in Chromosome 7p Dictate Outcomes and Targeted Treatment Responses of Lung Adenocarcinoma with Egfr-Activating Mutations

Purpose Although epidermal growth factor receptor (EGFR) tyrosine kinase inhibitors (TKIs) have been proven more effective for patients with lung adenocarcinoma with EGFR- activating mutation rather than wild type, the former group still includes approximately 30% nonresponders. The molecular basis of this substantial response heterogeneity is unknown. Our purpose was to seek molecular aberrations contributing to disease progression at the genome-wide level and identify the prognostic signature unique to patients with EGFR-activating mutation. Patients and Methods We first investigated the molecular differences between tumors with EGFR-activating mutation and wild-type tumors by conducting high-density array comparative genomic hybridization on a collection of 138 adenocarcinoma tissues. We then used an independent group of 114 patients to validate the clinical relevance of copy-number alterations (CNAs) in predicting overall and disease-free survival. Finally, focusing on 23 patients with EGFR mutation receiving EGFR-TKI treatment, we investigated the association between CNAs and response to EGFR-TKIs. Results We identified chromosome regions with differential CNAs between tumors with EGFR- activating mutation and wild-type tumors and found the aberration sites to cluster highly on chromosome 7p. A cluster of six representative chromosome 7p genes predicted overall and disease-free survival for patients with EGFR-activating mutation but not for those with wild type. Importantly, simultaneous presence of more genes with increased CNAs in this cluster correlated with less favorable response to EGFR -TKIs in patients with EGFR-activating mutation. Conclusion Our results shed light on why responses to EGFR-TKIs are heterogeneous among patients with EGFR- activating mutation. They may lead to better patient management in this population