Generalized gene co-expression analysis via subspace clustering using low-rank representation

BACKGROUND: Gene Co-expression Network Analysis (GCNA) helps identify gene modules with potential biological functions and has become a popular method in bioinformatics and biomedical research. However, most current GCNA algorithms use correlation to build gene co-expression networks and identify modules with highly correlated genes. There is a need to look beyond correlation and identify gene modules using other similarity measures for finding novel biologically meaningful modules. RESULTS: We propose a new generalized gene co-expression analysis algorithm via subspace clustering that can identify biologically meaningful gene co-expression modules with genes that are not all highly correlated. We use low-rank representation to construct gene co-expression networks and local maximal quasi-clique merger to identify gene co-expression modules. We applied our method on three large microarray datasets and a single-cell RNA sequencing dataset. We demonstrate that our method can identify gene modules with different biological functions than current GCNA methods and find gene modules with prognostic values. CONCLUSIONS: The presented method takes advantage of subspace clustering to generate gene co-expression networks rather than using correlation as the similarity measure between genes. Our generalized GCNA method can provide new insights from gene expression datasets and serve as a complement to current GCNA algorithms

Revisiting Date and Party Hubs: Novel Approaches to Role Assignment in Protein Interaction Networks

The idea of 'date' and 'party' hubs has been influential in the study of protein-protein interaction networks. Date hubs display low co-expression with their partners, whilst party hubs have high co-expression. It was proposed that party hubs are local coordinators whereas date hubs are global connectors. Here we show that the reported importance of date hubs to network connectivity can in fact be attributed to a tiny subset of them. Crucially, these few, extremely central, hubs do not display particularly low expression correlation, undermining the idea of a link between this quantity and hub function. The date/party distinction was originally motivated by an approximately bimodal distribution of hub co-expression; we show that this feature is not always robust to methodological changes. Additionally, topological properties of hubs do not in general correlate with co-expression. Thus, we suggest that a date/party dichotomy is not meaningful and it might be more useful to conceive of roles for protein-protein interactions rather than individual proteins. We find significant correlations between interaction centrality and the functional similarity of the interacting proteins.Comment: 27 pages, 5 main figures, 4 supplementary figure

Genome-wide co-expression analysis in multiple tissues

Expression quantitative trait loci (eQTLs) represent genetic control points of gene expression, and can be categorized as cis- and trans-acting, reflecting local and distant regulation of gene expression respectively. Although there is evidence of co-regulation within clusters of trans-eQTLs, the extent of co-expression patterns and their relationship with the genotypes at eQTLs are not fully understood. We have mapped thousands of cis- and trans-eQTLs in four tissues (fat, kidney, adrenal and left ventricle) in a large panel of rat recombinant inbred (RI) strains. Here we investigate the genome-wide correlation structure in expression levels of eQTL transcripts and underlying genotypes to elucidate the nature of co-regulation within cis- and trans-eQTL datasets. Across the four tissues, we consistently found statistically significant correlations of cis-regulated gene expression to be rare (<0.9% of all pairs tested). Most (>80%) of the observed significant correlations of cis-regulated gene expression are explained by correlation of the underlying genotypes. In comparison, co-expression of trans-regulated gene expression is more common, with significant correlation ranging from 2.9%-14.9% of all pairs of trans-eQTL transcripts. We observed a total of 81 trans-eQTL clusters (hot-spots), defined as consisting of > or =10 eQTLs linked to a common region, with very high levels of correlation between trans-regulated transcripts (77.2-90.2%). Moreover, functional analysis of large trans-eQTL clusters (> or =30 eQTLs) revealed significant functional enrichment among genes comprising 80% of the large clusters. The results of this genome-wide co-expression study show the effects of the eQTL genotypes on the observed patterns of correlation, and suggest that functional relatedness between genes underlying trans-eQTLs is reflected in the degree of co-expression observed in trans-eQTL clusters. Our results demonstrate the power of an integrative, systematic approach to the analysis of a large gene expression dataset to uncover underlying structure, and inform future eQTL studies

Dopamine perturbation of gene co-expression networks reveals differential response in schizophrenia for translational machinery.

The dopaminergic hypothesis of schizophrenia (SZ) postulates that positive symptoms of SZ, in particular psychosis, are due to disturbed neurotransmission via the dopamine (DA) receptor D2 (DRD2). However, DA is a reactive molecule that yields various oxidative species, and thus has important non-receptor-mediated effects, with empirical evidence of cellular toxicity and neurodegeneration. Here we examine non-receptor-mediated effects of DA on gene co-expression networks and its potential role in SZ pathology. Transcriptomic profiles were measured by RNA-seq in B-cell transformed lymphoblastoid cell lines from 514 SZ cases and 690 controls, both before and after exposure to DA ex vivo (100 μM). Gene co-expression modules were identified using Weighted Gene Co-expression Network Analysis for both baseline and DA-stimulated conditions, with each module characterized for biological function and tested for association with SZ status and SNPs from a genome-wide panel. We identified seven co-expression modules under baseline, of which six were preserved in DA-stimulated data. One module shows significantly increased association with SZ after DA perturbation (baseline: P = 0.023; DA-stimulated: P = 7.8 × 10-5; ΔAIC = -10.5) and is highly enriched for genes related to ribosomal proteins and translation (FDR = 4 × 10-141), mitochondrial oxidative phosphorylation, and neurodegeneration. SNP association testing revealed tentative QTLs underlying module co-expression, notably at FASTKD2 (top P = 2.8 × 10-6), a gene involved in mitochondrial translation. These results substantiate the role of translational machinery in SZ pathogenesis, providing insights into a possible dopaminergic mechanism disrupting mitochondrial function, and demonstrates the utility of disease-relevant functional perturbation in the study of complex genetic etiologies

Differential coupling of G protein alpha subunits to seven-helix receptors expressed in Xenopus oocytes

Xenopus oocytes were used to examine the coupling of the serotonin 1c (5HT1c) and thyrotropin-releasing hormone (TRH) receptors to both endogenous and heterologously expressed G protein alpha subunits. Expression of either G protein-coupled receptor resulted in agonist- induced, Ca(2+)-activated Cl- currents that were measured using a two- electrode voltage clamp. 5HT-induced Cl- currents were reduced 80% by incubating the injected oocytes with pertussis toxin (PTX) and inhibited 50-65% by injection of antisense oligonucleotides to the PTX- sensitive Go alpha subunit. TRH-induced Cl- currents were reduced only 20% by PTX treatment but were inhibited 60% by injection of antisense oligonucleotides to the PTX-insensitive Gq alpha subunit. Injection of antisense oligonucleotides to a novel Xenopus phospholipase C-beta inhibited the 5HT1c (and Go)-induced Cl- current with little effect on the TRH (and Gq)-induced current. These results suggest that receptor- activated Go and Gq interact with different effectors, most likely different isoforms of phospholipase C-beta. Co-expression of each receptor with seven different mammalian G protein alpha subunit cRNAs (Goa, Gob, Gq, G11, Gs, Golf, and Gt) was also examined. Co-expression of either receptor with the first four of these G alpha subunits resulted in a maximum 4-6-fold increase in Cl- currents; the increase depended on the amount of G alpha subunit cRNA injected. This increase was blocked by PTX for G alpha oa and G alpha ob co-expression but not for G alpha q or G alpha 11 co-expression. Co-expression of either receptor with Gs, Golf, or Gt had no effect on Ca(2+)-activated Cl- currents; furthermore, co-expression with Gs or Golf also failed to reveal 5HT- or TRH-induced changes in adenylyl cyclase as assessed by activation of the cystic fibrosis transmembrane conductance regulator Cl- channel. These results indicate that in oocytes, the 5HT1c and TRH receptors do the following: 1) preferentially couple to PTX-sensitive (Go) and PTX-insensitive (Gq) G proteins and that these G proteins act on different effectors, 2) couple within the same cell type to several different heterologously expressed G protein alpha subunits to activate the oocyte's endogenous Cl- current, and 3) fail to couple to G protein alpha subunits that activate cAMP or phosphodiesterase

2A - the "go-to" technology for transgene co-expression

In order to co-express multiple genes for biotechnological and biomedical applications, several approaches have been used with varying degrees of success. Currently, internal ribosome entry site (IRES) elements and “self-cleaving” 2A peptides are the most widely used. The length of the IRES can be prohibitive and IRES-dependent translation of the second open reading frame is often significantly reduced. 2A peptides have gained in popularity due to their small size and ability to consistently produce discrete proteins at an equal level. Here, we promote the use of these sequences as the “go-to” technology for co-expression of multiple proteins.Publisher PDFPeer reviewe

Spectral analysis of Gene co-expression network of Zebrafish

We analyze the gene expression data of Zebrafish under the combined framework of complex networks and random matrix theory. The nearest neighbor spacing distribution of the corresponding matrix spectra follows random matrix predictions of Gaussian orthogonal statistics. Based on the eigenvector analysis we can divide the spectra into two parts, first part for which the eigenvector localization properties match with the random matrix theory predictions, and the second part for which they show deviation from the theory and hence are useful to understand the system dependent properties. Spectra with the localized eigenvectors can be characterized into three groups based on the eigenvalues. We explore the position of localized nodes from these different categories. Using an overlap measure, we find that the top contributing nodes in the different groups carry distinguished structural features. Furthermore, the top contributing nodes of the different localized eigenvectors corresponding to the lower eigenvalue regime form different densely connected structure well separated from each other. Preliminary biological interpretation of the genes, associated with the top contributing nodes in the localized eigenvectors, suggests that the genes corresponding to same vector share common features.Comment: 6 pages, four figures (accepted in EPL