Probabilities of spurious connections in gene networks: Application to expression time series

Motivation: The reconstruction of gene networks from gene expression microarrays is gaining popularity as methods improve and as more data become available. The reliability of such networks could be judged by the probability that a connection between genes is spurious, resulting from chance fluctuations rather than from a true biological relationship. Results: Unlike the false discovery rate and positive false discovery rate, the decisive false discovery rate (dFDR) is exactly equal to a conditional probability without assuming independence or the randomness of hypothesis truth values. This property is useful not only in the common application to the detection of differential gene expression, but also in determining the probability of a spurious connection in a reconstructed gene network. Estimators of the dFDR can estimate each of three probabilities: 1. The probability that two genes that appear to be associated with each other lack such association. 2. The probability that a time ordering observed for two associated genes is misleading. 3. The probability that a time ordering observed for two genes is misleading, either because they are not associated or because they are associated without a lag in time. The first probability applies to both static and dynamic gene networks, and the other two only apply to dynamic gene networks. Availability: Cross-platform software for network reconstruction, probability estimation, and plotting is free from http://www.davidbickel.com as R functions and a Java application.Comment: Like q-bio.GN/0404032, this was rejected in March 2004 because it was submitted to the math archive. The only modification is a corrected reference to q-bio.GN/0404032, which was not modified at al

Key inflammatory pathway activations in the MCI stage of Alzheimer's disease

OBJECTIVE: To determine the key inflammatory pathways that are activated in the peripheral and CNS compartments at the mild cognitive impairment (MCI) stage of Alzheimer's disease (AD). METHODS: A cross-sectional study of patients with clinical and biomarker characteristics consistent with MCI-AD in a discovery cohort, with replication in the Alzheimer's Disease Neuroimaging Initiative (ADNI) cohort. Inflammatory analytes were measured in the CSF and plasma with the same validated multiplex analyte platform in both cohorts and correlated with AD biomarkers (CSF Aβ42, total tau (t-tau), phosphorylated tau (p-tau) to identify key inflammatory pathway activations. The pathways were additionally validated by evaluating genes related to all analytes in coexpression networks of brain tissue transcriptome from an autopsy confirmed AD cohort to interrogate if the same pathway activations were conserved in the brain tissue gene modules. RESULTS: Analytes of the tumor necrosis factor (TNF) signaling pathway (KEGG ID:4668) in the CSF and plasma best correlated with CSF t-tau and p-tau levels, and analytes of the complement and coagulation pathway (KEGG ID:4610) best correlated with CSF Aβ42 levels. The top inflammatory signaling pathways of significance were conserved in the peripheral and the CNS compartments. They were also confirmed to be enriched in AD brain transcriptome gene clusters. INTERPRETATION: A cell-protective rather than a proinflammatory analyte profile predominates in the CSF in relation to neurodegeneration markers among MCI-AD patients. Analytes from the TNF signaling and the complement and coagulation pathways are relevant in evaluating disease severity at the MCI stage of AD

FoxP2 isoforms delineate spatiotemporal transcriptional networks for vocal learning in the zebra finch.

Human speech is one of the few examples of vocal learning among mammals yet ~half of avian species exhibit this ability. Its neurogenetic basis is largely unknown beyond a shared requirement for FoxP2 in both humans and zebra finches. We manipulated FoxP2 isoforms in Area X, a song-specific region of the avian striatopallidum analogous to human anterior striatum, during a critical period for song development. We delineate, for the first time, unique contributions of each isoform to vocal learning. Weighted gene coexpression network analysis of RNA-seq data revealed gene modules correlated to singing, learning, or vocal variability. Coexpression related to singing was found in juvenile and adult Area X whereas coexpression correlated to learning was unique to juveniles. The confluence of learning and singing coexpression in juvenile Area X may underscore molecular processes that drive vocal learning in young zebra finches and, by analogy, humans

DiffCoEx: a simple and sensitive method to find differentially coexpressed gene modules

Background: Large microarray datasets have enabled gene regulation to be studied through coexpression analysis. While numerous methods have been developed for identifying differentially expressed genes between two conditions, the field of differential coexpression analysis is still relatively new. More specifically, there is so far no sensitive and untargeted method to identify gene modules (also known as gene sets or clusters) that are differentially coexpressed between two conditions. Here, sensitive and untargeted means that the method should be able to construct de novo modules by grouping genes based on shared, but subtle, differential correlation patterns. Results: We present DiffCoEx, a novel method for identifying correlation pattern changes, which builds on the commonly used Weighted Gene Coexpression Network Analysis (WGCNA) framework for coexpression analysis. We demonstrate its usefulness by identifying biologically relevant, differentially coexpressed modules in a rat cancer dataset. Conclusions: DiffCoEx is a simple and sensitive method to identify gene coexpression differences between multiple conditions

Transcriptome-based Gene Networks for Systems-level Analysis of Plant Gene Functions

Present day genomic technologies are evolving at an unprecedented rate, allowing interrogation of cellular activities with increasing breadth and depth. However, we know very little about how the genome functions and what the identified genes do. The lack of functional annotations of genes greatly limits the post-analytical interpretation of new high throughput genomic datasets. For plant biologists, the problem is much severe. Less than 50% of all the identified genes in the model plant Arabidopsis thaliana, and only about 20% of all genes in the crop model Oryza sativa have some aspects of their functions assigned. Therefore, there is an urgent need to develop innovative methods to predict and expand on the currently available functional annotations of plant genes. With open-access catching the ‘pulse’ of modern day molecular research, an integration of the copious amount of transcriptome datasets allows rapid prediction of gene functions in specific biological contexts, which provide added evidence over traditional homology-based functional inference. The main goal of this dissertation was to develop data analysis strategies and tools broadly applicable in systems biology research. Two user friendly interactive web applications are presented: The Rice Regulatory Network (RRN) captures an abiotic-stress conditioned gene regulatory network designed to facilitate the identification of transcription factor targets during induction of various environmental stresses. The Arabidopsis Seed Active Network (SANe) is a transcriptional regulatory network that encapsulates various aspects of seed formation, including embryogenesis, endosperm development and seed-coat formation. Further, an edge-set enrichment analysis algorithm is proposed that uses network density as a parameter to estimate the gain or loss in correlation of pathways between two conditionally independent coexpression networks

Divergent allometric trajectories in gene expression and coexpression produce species differences in sympatrically speciating Midas cichlid fish

The mechanisms of speciation without geographic isolation (i.e., sympatric speciation) remain debated. This is due in part to the fact that the genomic landscape that could promote or hinder species divergence in the presence of gene flow is still largely unknown. However, intensive research is now centered on understanding the genetic architecture of adaptive traits associated with this process as well as how gene expression might affect these traits. Here, using RNAseq data, we investigated gene expression of sympatrically speciating benthic and limnetic Neotropical cichlid fishes at two developmental stages. First, we identified groups of co-expressed genes (modules) at each stage. While there are a few large and well-preserved modules, most of the other modules are not preserved across life stages. Second, we show that later in development more and larger co-expression modules are associated with divergence between benthic and limnetic fish compared to the earlier life stage. This divergence between benthic and limnetic fish in co-expression mirrors divergence in overall expression between benthic and limnetic fish, which is more pronounced later in life. Our results reveal that already at one-day post-hatch benthic and limnetic fish diverge in (co)expression, and that this divergence becomes more substantial when fish are free-swimming but still unlikely to have divergent swimming and feeding habits. More importantly, our study describes how the co-expression of several genes through development, as opposed to individual genes, is associated with benthic-limnetic species differences, and how two morphogenetic trajectories diverge as fish grow older.publishe

Shared activity patterns arising at genetic susceptibility loci reveal underlying genomic and cellular architecture of human disease

<div><p>Genetic variants underlying complex traits, including disease susceptibility, are enriched within the transcriptional regulatory elements, promoters and enhancers. There is emerging evidence that regulatory elements associated with particular traits or diseases share similar patterns of transcriptional activity. Accordingly, shared transcriptional activity (coexpression) may help prioritise loci associated with a given trait, and help to identify underlying biological processes. Using cap analysis of gene expression (CAGE) profiles of promoter- and enhancer-derived RNAs across 1824 human samples, we have analysed coexpression of RNAs originating from trait-associated regulatory regions using a novel quantitative method (network density analysis; NDA). For most traits studied, phenotype-associated variants in regulatory regions were linked to tightly-coexpressed networks that are likely to share important functional characteristics. Coexpression provides a new signal, independent of phenotype association, to enable fine mapping of causative variants. The NDA coexpression approach identifies new genetic variants associated with specific traits, including an association between the regulation of the OCT1 cation transporter and genetic variants underlying circulating cholesterol levels. NDA strongly implicates particular cell types and tissues in disease pathogenesis. For example, distinct groupings of disease-associated regulatory regions implicate two distinct biological processes in the pathogenesis of ulcerative colitis; a further two separate processes are implicated in Crohn’s disease. Thus, our functional analysis of genetic predisposition to disease defines new distinct disease endotypes. We predict that patients with a preponderance of susceptibility variants in each group are likely to respond differently to pharmacological therapy. Together, these findings enable a deeper biological understanding of the causal basis of complex traits.</p></div