Co-expression networks uncover regulation of splicing and transcription markers of disease.
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ABSTRACT:
SUBMITTER: Zhang P
PROVIDER: S-EPMC8764752 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:Gene co-expression networks capture biologically important patterns in gene expression data, enabling functional analyses of genes, discovery of biomarkers, and interpretation of genetic variants. Most network analyses to date have been limited to assessing correlation between total gene expression levels in a single tissue or small sets of tissues. Here, we built networks that additionally capture the regulation of relative isoform abundance and splicing, along with tissue-specific connections unique to each of a diverse set of tissues. We used the Genotype-Tissue Expression (GTEx) project v6 RNA sequencing data across 50 tissues and 449 individuals. First, we developed a framework called Transcriptome-Wide Networks (TWNs) for combining total expression and relative isoform levels into a single sparse network, capturing the interplay between the regulation of splicing and transcription. We built TWNs for 16 tissues and found that hubs in these networks were strongly enriched for splicing and RNA binding genes, demonstrating their utility in unraveling regulation of splicing in the human transcriptome. Next, we used a Bayesian biclustering model that identifies network edges unique to a single tissue to reconstruct Tissue-Specific Networks (TSNs) for 26 distinct tissues and 10 groups of related tissues. Finally, we found genetic variants associated with pairs of adjacent nodes in our networks, supporting the estimated network structures and identifying 20 genetic variants with distant regulatory impact on transcription and splicing. Our networks provide an improved understanding of the complex relationships of the human transcriptome across tissues.
| S-EPMC5668942 | biostudies-literature
Project description:Transcriptional factors (TFs) and microRNAs (miRNAs) have been recognized as 2 classes of principal gene regulators that may be responsible for genome coexpression changes observed in schizophrenia (SZ).This study aims to (1) identify differentially coexpressed genes (DCGs) in 3 mRNA expression microarray datasets; (2) explore potential interactions among the DCGs, and differentially expressed miRNAs identified in our dataset composed of early-onset SZ patients and healthy controls; (3) validate expression levels of some key transcripts; and (4) explore the druggability of DCGs using the curated database.We detected a differential coexpression network associated with SZ and found that 9 out of the 12 regulators were replicated in either of the 2 other datasets. Leveraging the differentially expressed miRNAs identified in our previous dataset, we constructed a miRNA-TF-gene network relevant to SZ, including an EGR1-miR-124-3p-SKIL feed-forward loop. Our real-time quantitative PCR analysis indicated the overexpression of miR-124-3p, the under expression of SKIL and EGR1 in the blood of SZ patients compared with controls, and the direction of change of miR-124-3p and SKIL mRNA levels in SZ cases were reversed after a 12-week treatment cycle. Our druggability analysis revealed that many of these genes have the potential to be drug targets.Together, our results suggest that coexpression network abnormalities driven by combinatorial and interactive action from TFs and miRNAs may contribute to the development of SZ and be relevant to the clinical treatment of the disease.
| S-EPMC4903044 | biostudies-literature
Project description:BackgroundAlternative splicing is a ubiquitous gene regulatory mechanism that dramatically increases the complexity of the proteome. However, the mechanism for regulating alternative splicing is poorly understood, and study of coordinated splicing regulation has been limited to individual cases. To study genome-wide splicing regulation, we integrate many human RNA-seq datasets to identify splicing module, which we define as a set of cassette exons co-regulated by the same splicing factors.ResultsWe have designed a tensor-based approach to identify co-splicing clusters that appear frequently across multiple conditions, thus very likely to represent splicing modules - a unit in the splicing regulatory network. In particular, we model each RNA-seq dataset as a co-splicing network, where the nodes represent exons and the edges are weighted by the correlations between exon inclusion rate profiles. We apply our tensor-based method to the 38 co-splicing networks derived from human RNA-seq datasets and indentify an atlas of frequent co-splicing clusters. We demonstrate that these identified clusters represent potential splicing modules by validating against four biological knowledge databases. The likelihood that a frequent co-splicing cluster is biologically meaningful increases with its recurrence across multiple datasets, highlighting the importance of the integrative approach.ConclusionsCo-splicing clusters reveal novel functional groups which cannot be identified by co-expression clusters, particularly they can grant new insights into functions associated with post-transcriptional regulation, and the same exons can dynamically participate in different pathways depending on different conditions and different other exons that are co-spliced. We propose that by identifying splicing module, a unit in the splicing regulatory network can serve as an important step to decipher the splicing code.
| S-EPMC3403501 | biostudies-literature
Project description:Mammalian genomes are pervasively transcribed to produce thousands of long non-coding RNAs (lncRNAs). A few of these lncRNAs have been shown to recruit regulatory complexes through RNA-protein interactions to influence the expression of nearby genes, and it has been suggested that many other lncRNAs can also act as local regulators. Such local functions could explain the observation that lncRNA expression is often correlated with the expression of nearby genes. However, these correlations have been challenging to dissect and could alternatively result from processes that are not mediated by the lncRNA transcripts themselves. For example, some gene promoters have been proposed to have dual functions as enhancers, and the process of transcription itself may contribute to gene regulation by recruiting activating factors or remodelling nucleosomes. Here we use genetic manipulation in mouse cell lines to dissect 12 genomic loci that produce lncRNAs and find that 5 of these loci influence the expression of a neighbouring gene in cis. Notably, none of these effects requires the specific lncRNA transcripts themselves and instead involves general processes associated with their production, including enhancer-like activity of gene promoters, the process of transcription, and the splicing of the transcript. Furthermore, such effects are not limited to lncRNA loci: we find that four out of six protein-coding loci also influence the expression of a neighbour. These results demonstrate that cross-talk among neighbouring genes is a prevalent phenomenon that can involve multiple mechanisms and cis-regulatory signals, including a role for RNA splice sites. These mechanisms may explain the function and evolution of some genomic loci that produce lncRNAs and broadly contribute to the regulation of both coding and non-coding genes.
| S-EPMC6853796 | biostudies-literature
Project description:BackgroundCurrent microRNA (miRNA) research in progress has engendered rapid accumulation of expression data evolving from microarray experiments. Such experiments are generally performed over different tissues belonging to a specific species of metazoan. For disease diagnosis, microarray probes are also prepared with tissues taken from similar organs of different candidates of an organism. Expression data of miRNAs are frequently mapped to co-expression networks to study the functions of miRNAs, their regulation on genes and to explore the complex regulatory network that might exist between Transcription Factors (TFs), genes and miRNAs. These directions of research relating miRNAs are still not fully explored, and therefore, construction of reliable and compatible methods for mining miRNA co-expression networks has become an emerging area. This paper introduces a novel method for mining the miRNA co-expression networks in order to obtain co-expressed miRNAs under the hypothesis that these might be regulated by common TFs.ResultsThree co-expression networks, configured from one patient-specific, one tissue-specific and a stem cell-based miRNA expression data, are studied for analyzing the proposed methodology. A novel compactness measure is introduced. The results establish the statistical significance of the sets of miRNAs evolved and the efficacy of the self-pruning phase employed by the proposed method. All these datasets yield similar network patterns and produce coherent groups of miRNAs. The existence of common TFs, regulating these groups of miRNAs, is empirically tested. The results found are very promising. A novel visual validation method is also proposed that reflects the homogeneity as well as statistical properties of the grouped miRNAs. This visual validation method provides a promising and statistically significant graphical tool for expression analysis.ConclusionA heuristic mining methodology that resembles a clustering motivation is proposed in this paper. However, there remains a basic difference between the mining method and a clustering approach. The heuristic approach can produce priority modules (PM) from an miRNA co-expression network, by employing a self-pruning phase, which are analyzed for statistical and biological significance. The mining algorithm minimizes the space/time complexity of the analysis, and also handles noise in the data. In addition, the mining method reveals promising results in the unsupervised analysis of TF-miRNA regulation.
| S-EPMC2707367 | biostudies-literature
Project description:More and more transcription factors and their motifs have been reported and linked to specific gene expression levels. However, focusing only on transcription is not sufficient for mechanism research. Most genes, especially in eukaryotes, are alternatively spliced to different isoforms. Some of these isoforms increase the biodiversity of proteins. From this viewpoint, transcription and splicing are two of important mechanisms to modulate expression levels of isoforms. To integrate these two kinds of regulation, we built a linear regression model to select a subset of transcription factors and splicing factors for each co-expressed isoforms using least-angle regression approach. Then, we applied this method to investigate the mechanism of myelodysplastic syndromes (MDS), a precursor lesion of acute myeloid leukemia. Results suggested that expression levels of most isoforms were regulated by a set of selected regulatory factors. Some of the detected factors, such as EGR1 and STAT family, are highly correlated with progression of MDS. We discovered that the splicing factor SRSF11 experienced alternative splicing switch, and in turn induced different amino acid sequences between MDS and controls. This splicing switch causes two different splicing mechanisms. Polymerase Chain Reaction experiments also confirmed that one of its isoforms was over-expressed in MDS. We analyzed the regulatory networks constructed from the co-expressed isoforms and their regulatory factors in MDS. Many of these networks were enriched in the herpes simplex infection pathway which involves many splicing factors, and pathways in cancers and acute or chronic myeloid leukemia.
| S-EPMC3828332 | biostudies-literature
Project description:ObjectiveMany primary tumors have insufficient supply of molecular oxygen, called hypoxia. Hypoxia is one of the leading characteristics of solid tumors resulting in a higher risk of local failure and distant metastasis. It is quite necessary to investigate the hypoxia associated molecular hallmarks in breast cancer.Materials and methodsAccording to the published studies, we selected 13 hypoxia related gene expression signature to define the hypoxia status of breast cancer using ConsensusClusterPlus package based on the data from The Cancer Genome Atlas (TCGA). Subsequently, we characterized the infiltration of 24 immune cell types under different hypoxic conditions. Furthermore, the differentially expressed hypoxia associated microRNAs, mRNAs and related signaling pathways were analyzed and depicted. On this basis, a series of prognostic markers related to hypoxia were identified and ceRNA co-expression networks were constructed.ResultsTwo subgroups (cluster1 and cluster2) were identified and the 13 hypoxia related gene signature were all up-regulated in cluster1. Thus, we defined the cluster1 as "hypoxic subgroup" compared with cluster2. The infiltration of CD8+ T cell and CD4+ T cell were lower in cluster1 while the nTreg cell and iTreg cell were higher, indicating that there was immunosuppressive status in cluster1. We observed widespread hypoxia-associated dysregulation of microRNAs and mRNAs. Next, a risk signature for predicting prognosis of breast cancer patients was established based on 12 dysregulated hypoxia associated prognostic genes. Two microRNAs, hsa-miR-210-3p and hsa-miR-190b, with the most significant absolute logFC value were related to unfavorable and better prognosis, respectively. Several long non-coding RNAs were predicted to be microRNA targets and positively correlated with two selected mRNAs, CPEB2 and BCL11A. Predictions based on the SNHG16-hsa-miR-210-3p-CPEB2 and LINC00899/PSMG3-AS1/PAXIP-AS1-hsa-miR-190b-BCL11A ceRNA regulation networks indicated that the two genes might act as tumor suppressor and oncogene, respectively.ConclusionHypoxia plays an important role in the initiation and progression of breast cancer. Our research provides potential mechanisms into molecular-level understanding of tumor hypoxia.
| S-EPMC7738636 | biostudies-literature
Project description:Genome wide association studies (GWAS) of human diseases have generally identified many loci associated with risk with relatively small effect sizes. The omnigenic model attempts to explain this observation by suggesting that diseases can be thought of as networks, where genes with direct involvement in disease-relevant biological pathways are named 'core genes', while peripheral genes influence disease risk via their interactions or regulatory effects on core genes. Here, we demonstrate a method for identifying candidate core genes solely from genes in or near disease-associated SNPs (GWAS hits) in conjunction with protein-protein interaction network data. Applied to 1,381 GWAS studies from 5 ancestries, we identify a total of 1,865 candidate core genes in 343 GWAS studies. Our analysis identifies several well-known disease-related genes that are not identified by GWAS, including BRCA1 in Breast Cancer, Amyloid Precursor Protein (APP) in Alzheimer's Disease, INS in A1C measurement and Type 2 Diabetes, and PCSK9 in LDL cholesterol, amongst others. Notably candidate core genes are preferentially enriched for disease relevance over GWAS hits and are enriched for both Clinvar pathogenic variants and known drug targets-consistent with the predictions of the omnigenic model. We subsequently use parent term annotations provided by the GWAS catalog, to merge related GWAS studies and identify candidate core genes in over-arching disease processes such as cancer-where we identify 109 candidate core genes.
| S-EPMC7390454 | biostudies-literature
Project description:Regulation of alternative splicing is controlled by pre-mRNA sequences (cis-elements) and trans-acting protein factors that bind them. The combinatorial interactions of multiple protein factors with the cis-elements surrounding a given alternative splicing event lead to an integrated splicing decision. The mechanism of multifactorial splicing regulation is poorly understood. Using a splicing-sensitive DNA microarray, we assayed 352 Caenorhabditis elegans alternative cassette exons for changes in embryonic splicing patterns between wild-type and 12 different strains carrying mutations in a splicing factor. We identified many alternative splicing events that are regulated by multiple splicing factors. Many splicing factors have the ability to behave as splicing repressors for some alternative cassette exons and as splicing activators for others. Unexpectedly, we found that the ability of a given alternative splicing factor to behave as an enhancer or repressor of a specific splicing event can change during development. Our observations that splicing factors can change their effects on a substrate during development support a model in which combinatorial effects of multiple factors, both constitutive and developmentally regulated ones, contribute to the overall splicing decision.
| S-EPMC3025544 | biostudies-literature
Project description:Background:Glioblastoma, the most aggressive primary brain tumor, is genetically heterogeneous. Alternative splicing (AS) plays a key role in numerous pathologies, including cancer. The objectives of our study were to determine whether aberrant AS could play a role in the malignant phenotype of glioma and to understand the mechanism underlying its aberrant regulation. Methods:We obtained surgical samples from patients with glioblastoma who underwent 5-aminolevulinic fluorescence-guided surgery. Biopsies were taken from the tumor center as well as from adjacent normal-appearing tissue. We used a global splicing array to identify candidate genes aberrantly spliced in these glioblastoma samples. Mechanistic and functional studies were performed to elucidate the role of our top candidate splice variant, BAF45d, in glioblastoma. Results:BAF45d is part of the switch/sucrose nonfermentable complex and plays a key role in the development of the CNS. The BAF45d/6A isoform is present in 85% of over 200 glioma samples that have been analyzed and contributes to the malignant glioma phenotype through the maintenance of an undifferentiated cellular state. We demonstrate that BAF45d splicing is mediated by polypyrimidine tract-binding protein 1 (PTBP1) and that BAF45d regulates PTBP1, uncovering a reciprocal interplay between RNA splicing regulation and transcription. Conclusions:Our data indicate that AS is a mechanism that contributes to the malignant phenotype of glioblastoma. Understanding the consequences of this biological process will uncover new therapeutic targets for this devastating disease.
| S-EPMC6007380 | biostudies-literature