Cosplicing network analysis of mammalian brain RNA-Seq data utilizing WGCNA and Mantel correlations

The spectrum of pre-mRNA splicing in autism

Disruptions in spatiotemporal gene expression can result in atypical brain function. Specifically, autism spectrum disorder (ASD) is characterized by abnormalities in pre-mRNA splicing. Abnormal splicing patterns have been identified in the brains of individuals with ASD, and mutations in splicing factors have been found to contribute to neurodevelopmental delays associated with ASD. Here we review studies that shed light on the importance of splicing observed in ASD and that explored the intricate relationship between splicing factors and ASD, revealing how disruptions in pre-mRNA splicing may underlie ASD pathogenesis. We provide an overview of the research regarding all splicing factors associated with ASD and place a special emphasis on five specific splicing factors-HNRNPH2, NOVA2, WBP4, SRRM2, and RBFOX1-known to impact the splicing of ASD-related genes. In the discussion of the molecular mechanisms influenced by these splicing factors, we lay the groundwork for a deeper understanding of ASD's complex etiology. Finally, we discuss the potential benefit of unraveling the connection between splicing and ASD for the development of more precise diagnostic tools and targeted therapeutic interventions. This article is categorized under: RNA in Disease and Development > RNA in Disease RNA Evolution and Genomics > RNA and Ribonucleoprotein Evolution RNA Evolution and Genomics > Computational Analyses of RNA RNA-Based Catalysis > RNA Catalysis in Splicing and Translation.

Construction of a molecular regulatory network related to fat deposition by multi-tissue transcriptome sequencing of Jiaxian red cattle

Intramuscular fat (IMF) refers to the fat that accumulates between muscle bundles or within muscle cells, whose content significantly impacts the taste, tenderness, and flavor of meat products, making it a crucial economic characteristic in livestock production. However, the intricate mechanisms governing IMF deposition, involving non-coding RNAs (ncRNAs), genes, and complex regulatory networks, remain largely enigmatic. Identifying adipose tissue-specific genes and ncRNAs is paramount to unravel these molecular mysteries. This study, conducted on Jiaxian red cattle, harnessed whole transcriptome sequencing to unearth the nuances of circRNAs and miRNAs across seven distinct tissues. The interplay of these ncRNAs was assessed through differential expression analysis and network analysis. These findings are not only pivotal in unveiling the intricacies of fat deposition mechanisms but also lay a robust foundation for future research, setting the stage for enhancing IMF content in Jiaxian red cattle breeding.

Identifying stage-associated hub genes in bladder cancer via weighted gene co-expression network and robust rank aggregation analyses

BACKGROUND

Bladder cancer (BC) is among the most frequent cancers globally. Although substantial efforts have been put to understand its pathogenesis, its underlying molecular mechanisms have not been fully elucidated.

METHODS

The robust rank aggregation approach was adopted to integrate 4 eligible bladder urothelial carcinoma microarray datasets from the Gene Expression Omnibus. Differentially expressed gene sets were identified between tumor samples and equivalent healthy samples. We constructed gene co-expression networks using weighted gene co-expression network to explore the alleged relationship between BC clinical characteristics and gene sets, as well as to identify hub genes. We also incorporated the weighted gene co-expression network and robust rank aggregation to screen differentially expressed genes.

RESULTS

CDH11, COL6A3, EDNRA, and SERPINF1 were selected from the key module and validated. Based on the results, significant downregulation of the hub genes occurred during the early stages of BC. Moreover, receiver operating characteristics curves and Kaplan-Meier plots showed that the genes exhibited favorable diagnostic and prognostic value for BC. Based on gene set enrichment analysis for single hub gene, all the genes were closely linked to BC cell proliferation.

CONCLUSIONS

These results offer unique insight into the pathogenesis of BC and recognize CDH11, COL6A3, EDNRA, and SERPINF1 as potential biomarkers with diagnostic and prognostic roles in BC.

Bridging the splicing gap in human genetics with long-read RNA sequencing: finding the protein isoform drivers of disease

Aberrant splicing underlies many human diseases, including cancer, cardiovascular diseases and neurological disorders. Genome-wide mapping of splicing quantitative trait loci (sQTLs) has shown that genetic regulation of alternative splicing is widespread. However, identification of the corresponding isoform or protein products associated with disease-associated sQTLs is challenging with short-read RNA-seq, which cannot precisely characterize full-length transcript isoforms. Furthermore, contemporary sQTL interpretation often relies on reference transcript annotations, which are incomplete. Solutions to these issues may be found through integration of newly emerging long-read sequencing technologies. Long-read sequencing offers the capability to sequence full-length mRNA transcripts and, in some cases, to link sQTLs to transcript isoforms containing disease-relevant protein alterations. Here, we provide an overview of sQTL mapping approaches, the use of long-read sequencing to characterize sQTL effects on isoforms, the linkage of RNA isoforms to protein-level functions and comment on future directions in the field. Based on recent progress, long-read RNA sequencing promises to be part of the human disease genetics toolkit to discover and treat protein isoforms causing rare and complex diseases.

Improved biomarker discovery through a plot twist in transcriptomic data analysis

Background

Transcriptomic analysis is crucial for understanding the functional elements of the genome, with the classic method consisting of screening transcriptomics datasets for differentially expressed genes (DEGs). Additionally, since 2005, weighted gene co-expression network analysis (WGCNA) has emerged as a powerful method to explore relationships between genes. However, an approach combining both methods, i.e., filtering the transcriptome dataset by DEGs or other criteria, followed by WGCNA (DEGs + WGCNA), has become common. This is of concern because such approach can affect the resulting underlying architecture of the network under analysis and lead to wrong conclusions. Here, we explore a plot twist to transcriptome data analysis: applying WGCNA to exploit entire datasets without affecting the topology of the network, followed with the strength and relative simplicity of DEG analysis (WGCNA + DEGs). We tested WGCNA + DEGs against DEGs + WGCNA to publicly available transcriptomics data in one of the most transcriptomically complex tissues and delicate processes: vertebrate gonads undergoing sex differentiation. We further validate the general applicability of our approach through analysis of datasets from three distinct model systems: European sea bass, mouse, and human.

Results

In all cases, WGCNA + DEGs clearly outperformed DEGs + WGCNA. First, the network model fit and node connectivity measures and other network statistics improved. The gene lists filtered by each method were different, the number of modules associated with the trait of interest and key genes retained increased, and GO terms of biological processes provided a more nuanced representation of the biological question under consideration. Lastly, WGCNA + DEGs facilitated biomarker discovery.

Conclusions

We propose that building a co-expression network from an entire dataset, and only thereafter filtering by DEGs, should be the method to use in transcriptomic studies, regardless of biological system, species, or question being considered.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12915-022-01398-w.

Establishment of tumor protein p53 mutation-based prognostic signatures for acute myeloid leukemia

PURPOSE

The tumor protein p53 gene (TP53) mutations are associated with poor prognosis of patients with acute myeloid leukemia (AML). This study aimed to establish TP53 mutation-based prognostic risk signatures.

PATIENTS AND METHODS

The transcriptomes and clinical characteristics of AML patients were acquired from The Cancer Genome Atlas database, including 11 TP53-mutant samples and 114 TP53-wildtype samples. Differentially expressed mRNAs and long non-coding RNAs (lncRNA) in TP53-mutant samples were identified. Weighted gene correlation network analysis was performed to generate survival-associated co-expression modules. LASSO regression analysis was conducted to build mRNA- and lncRNA-based prognostic risk signatures. Kaplan-Meier curve analysis and multivariate regression analysis were carried out to assess the prognostic values of the risk signatures. Receiver operating characteristic (ROC) analysis was performed to evaluate the accuracy of the signatures.

RESULTS

Based on the co-expression modules, a 5-mRNA risk signature and a 13-lncRNA risk signature were constructed to predict the overall survival for AML patients. Kaplan-Meier curves revealed that the high-risk patients had significantly shorter overall survival than the low-risk patients. ROC analysis yielded 1-, 3-, and 5-year AUCs of 0.681, 0.783, and 0.827 for mRNA signature and 0.85, 0.835, and 0.908 for lncRNA signature. Multivariate regression analysis revealed that both mRNA (HR = 1.45, P< 0.001) and lncRNA (HR = 1.19, P< 0.001) risk scores were independent prognostic factors for AML patients.

CONCLUSION

We provided a potential patients stratification tool for AML prognosis prediction and management, which established by effective TP53 mutation-related gene signatures.

Gene Co-Expression Network Analysis Reveals the Correlation Patterns Among Genes in Different Temperature Stress Adaptation of Manila Clam

The Manila clam (Ruditapes philippinarum) is one of the most important aquaculture species and widely distributed along the coasts of China, Japan, and Korea. Due to its wide distribution, it can tolerate a wide range of temperature. Studying the gene expression profiles of clam gills had found differentially expressed genes (DEGs) and pathway involved in temperature stress tolerance. A systematic study of cellular response to temperature stress may provide insights into the mechanism of acquired tolerance. Here, weighted gene co-expression network analysis (WGCNA) was carried out using RNA-seq data from gill transcriptome in response to high and low temperature stress. There are a total 32 gene modules, of which 18 gene modules were identified as temperature-related modules. Blue module was one significantly correlated with temperature which was associated with cellular metabolism, apoptosis pathway, ER stress, and others.

miR-155-5p regulates hypoxia-induced pulmonary artery smooth muscle cell function by targeting PYGL

Pulmonary arterial hypertension (PAH) is a cardiovascular disease that has high incidence and causes massive deaths. miR-155-5p/PYGL pathway was revealed to play a crucial role in PAH by weighted gene co-expression network analysis (WGCNA). The potential mechanism of miR-155-5p in regulating hypoxia-induced pulmonary artery smooth muscle cell (PASMC) function was analyzed through in vitro experiments. Hypoxia treatment stimulated the proliferation of PASMCs and increased the expression of vascular endothelial growth factor (VEGF) and hypoxia-inducible factor-1α (HIF-1α). At the same time, revealed by qRT-PCR and western blot, the level of miR-155-5p was raised, and the level of PYGL was decreased in hypoxia-induced PASMCs. Through CCK-8 assay, transwell assay and flow cytometry, it was revealed that miR-155-5p inhibitor remarkably inhibited the cell proliferation and migration and decreased the proportion of hypoxia-stimulated PASMCs in S and G2/M phases. Dual-luciferase reporter system was subsequently applied to validate the straight regulation of miR-155-5p on PYGL based on the analysis of online database. Furthermore, siPYGL was revealed to reverse the influence of miR-155-5p inhibitor on hypoxia-induced PASMCs. These outcomes indicate that the increased level of miR-155-5p in hypoxia-stimulated PASMCs could enhance the cell proliferation, cell migration, and cell cycle progression by targeting PYGL directly. This study may supply novel treatment strategies for PAH.Abbreviations: PH, pulmonary hypertension; PAH, pulmonary arterial hypertension; WGCNA, weighted gene co-expression network analysis; PASMCs, pulmonary artery smooth muscle cells; VEGF, vascular endothelial growth factor; HIF-1α, hypoxia-inducible factor-1α; SMCs, smooth muscle cells; DEGs, differentially expressed genes; GEO, Gene Expression Omnibus; GO, Gene Ontology; KEGG, Kyoto Encyclopedia of Genes and Genomes; FBS, fetal bovine serum; OD, optical density; BCA, bicinchoninic acid; PVDF, polyvinylidene fluoride; PBS, phosphate-buffered saline; BP, biological process; MF, molecular function; CC, cell component.

Spatial Transcriptomic Analysis Using R-Based Computational Machine Learning Reveals the Genetic Profile of Yang or Yin Deficiency Syndrome in Chinese Medicine Theory

Objectives

Yang and Yin are two main concepts responsible for harmonious balance reflecting health conditions based on Chinese medicine theory. Of note, deficiency of either Yang or Yin is associated with disease susceptibility. In this study, we aim to clarify the molecular feature of Yang and Yin deficiency by reanalyzing a transcriptomic data set retrieved from the GEO database using R-based machine learning analyses, which lays a foundation for medical diagnosis, prevention, and treatment of unbalanced Yang or Yin.

Methods

Besides conventional methods for target mining, we took the advantage of spatial transcriptomic analysis using R-based machine learning approaches to elucidate molecular profiles of Yin and Yang deficiency by reanalyzing an RNA-Seq data set (GSE87474) in the GEO focusing on peripheral blood mononuclear cells (PBMCs). The add-on functions in R including GEOquery, DESeq2, WGCNA (target identification with a scale-free topological assumption), Scatterplot3d, Tidyverse, and UpsetR were used. For information in the selected GEO data set, PBMCs representing 20,740 expressed genes were collected from subjects with Yang or Yin deficiency (n = 12 each), based on Chinese medicine-related diagnostic criteria.

Results

The symptomatic gene targets for Yang deficiency (KAT2B, NFKB2, CREBBP, GTF2H3) or Yin deficiency (JUNB, JUND, NGLY1, TNF, RAF1, PPP1R15A) were potentially discovered. CREBBP was identified as a shared key contributive gene regulating either the Yang or Yin deficiency group. The intrinsic molecular characteristics of these specific genes could link with clinical observations of Yang/Yin deficiency, in which Yang deficiency is associated with immune dysfunction tendency and energy deregulation, while Yin deficiency mainly contains oxidative stress, dysfunction of the immune system, and abnormal lipid/protein metabolism.

Conclusion

Our study provides representative gene targets and modules for supporting clinical traits of Yang or Yin deficiency in Chinese medicine theory, which is beneficial for promoting the modernization of Chinese medicine theory. Besides, R-based machine learning approaches adopted in this study might be further applied for investigating the underlying genetic polymorphisms related to Chinese medicine theory.

RYR2 mutation in non-small cell lung cancer prolongs survival via down-regulation of DKK1 and up-regulation of GS1-115G20.1: A weighted gene Co-expression network analysis and risk prognostic models

RYR2 mutation is clinically frequent in non-small cell lung cancer (NSCLC) with its function being elusive. We downloaded lung squamous cell carcinoma and lung adenocarcinoma samples from the TCGA database, split the samples into RYR2 mutant group (n = 337) and RYR2 wild group (n = 634), and established Kaplan-Meier curves. The results showed that RYR2 mutant group lived longer than the wild group (p = 0.027). Weighted gene co-expression network analysis (WGCNA) of differentially expressed genes (DEGs) yielded prognosis-related genes. Five mRNAs and 10 lncRNAs were selected to build survival prognostic models with other clinical features. The AUCs of 2 models are 0.622 and 0.565 for predicting survival at 3 years. Among these genes, the AUCs of DKK1 and GS1-115G20.1 expression levels were 0.607 and 0.560, respectively, which predicted the 3-year survival rate of NSCLC sufferers. GSEA identified an association of high DKK1 expression with TP53, MTOR, and VEGF expression. Several target miRNAs interacting with GS1-115G20.1 were observed to show the relationship with the phenotype, treatment, and survival of NSCLC. NSCLC patients with RYR2 mutation may obtain better prognosis by down-regulating DKK1 and up-regulating GS1-115G20.1.

Dual-Organ Transcriptomic Analysis of Rainbow Trout Infected With Ichthyophthirius multifiliis Through Co-Expression and Machine Learning

Ichthyophthirius multifiliis is a major pathogen that causes a high mortality rate in trout farms. However, systemic responses to the pathogen and its interactions with multiple organs during the course of infection have not been well described. In this study, dual-organ transcriptomic responses in the liver and head kidney and hemato-serological indexes were profiled under I. multifiliis infection and recovery to investigate systemic immuno-physiological characteristics. Several strategies for massive transcriptomic interpretation, such as differentially expressed genes (DEGs), Poisson linear discriminant (PLDA), and weighted gene co-expression network analysis (WGCNA) models were used to investigate the featured genes/pathways while minimizing the disadvantages of individual methods. During the course of infection, 6,097 and 2,931 DEGs were identified in the head kidney and liver, respectively. Markers of protein processing in the endoplasmic reticulum, oxidative phosphorylation, and the proteasome were highly expressed. Likewise, simultaneous ferroptosis and cellular reconstruction was observed, which is strongly linked to multiple organ dysfunction. In contrast, pathways relevant to cellular replication were up-regulated in only the head kidney, while endocytosis- and phagosome-related pathways were notably expressed in the liver. Moreover, interestingly, most immune-relevant pathways (e.g., leukocyte trans-endothelial migration, Fc gamma R-mediated phagocytosis) were highly activated in the liver, but the same pathways in the head kidney were down-regulated. These conflicting results from different organs suggest that interpretation of co-expression among organs is crucial for profiling of systemic responses during infection. The dual-organ transcriptomics approaches presented in this study will greatly contribute to our understanding of multi-organ interactions under I. multifiliis infection from a broader perspective.

Identification of a tumor microenvironment-related seven-gene signature for predicting prognosis in bladder cancer

Background

Accumulating evidences demonstrated tumor microenvironment (TME) of bladder cancer (BLCA) may play a pivotal role in modulating tumorigenesis, progression, and alteration of biological features. Currently we aimed to establish a prognostic model based on TME-related gene expression for guiding clinical management of BLCA.

Methods

We employed ESTIMATE algorithm to evaluate TME cell infiltration in BLCA. The RNA-Seq data from The Cancer Genome Atlas (TCGA) database was used to screen out differentially expressed genes (DEGs). Underlying relationship between co-expression modules and TME was investigated via Weighted gene co-expression network analysis (WGCNA). COX regression and the least absolute shrinkage and selection operator (LASSO) analysis were applied for screening prognostic hub gene and establishing a risk predictive model. BLCA specimens and adjacent tissues from patients were obtained from patients. Bladder cancer (T24, EJ-m3) and bladder uroepithelial cell line (SVHUC1) were used for genes validation. qRT-PCR was employed to validate genes mRNA level in tissues and cell lines.

Results

365 BLCA samples and 19 adjacent normal samples were selected for identifying DEGs. 2141 DEGs were identified and used to construct co-expression network. Four modules (magenta, brown, yellow, purple) were regarded as TME regulatory modules through WGCNA and GO analysis. Furthermore, seven hub genes (ACAP1, ADAMTS9, TAP1, IFIT3, FBN1, FSTL1, COL6A2) were screened out to establish a risk predictive model via COX and LASSO regression. Survival analysis and ROC curve analysis indicated our predictive model had good performance on evaluating patients prognosis in different subgroup of BLCA. qRT-PCR result showed upregulation of ACAP1, IFIT3, TAP1 and downregulation of ADAMTS9, COL6A2, FSTL1,FBN1 in BLCA specimens and cell lines.

Conclusions

Our study firstly integrated multiple TME-related genes to set up a risk predictive model. This model could accurately predict BLCA progression and prognosis, which offers clinical implication for risk stratification, immunotherapy drug screen and therapeutic decision.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12885-021-08447-7.

Identification of Hub Genes and MicroRNAs Associated With Idiopathic Pulmonary Arterial Hypertension by Integrated Bioinformatics Analyses

Objective

The aim of this study is the identification of hub genes associated with idiopathic pulmonary arterial hypertension (IPAH).

Materials and Methods

GSE15197 gene expression data was downloaded from the Gene Expression Omnibus (GEO) database. Differentially expressed genes (DEGs) were identified by screening IPAH patients and controls. The 5,000 genes with the greatest variances were analyzed using a weighted gene co-expression network analysis (WGCNA). Modules with the strongest correlation with IPAH were chosen, followed by a functional enrichment analysis. Protein–protein interaction (PPI) networks were constructed to identify hub gene candidates using calculated degrees. Real hub genes were found from the overlap of DEGs and candidate hub genes. microRNAs (miRNAs) targeting real hub genes were found by screening miRNet 2.0. The most important IPAH miRNAs were identified.

Results

There were 4,395 DEGs identified. WGCNA indicated that green and brown modules associated most strongly with IPAH. Functional enrichment analysis showed that green and brown module genes were mainly involved in protein digestion and absorption and proteoglycans in cancer, respectively. The top ten candidate hub genes in green and brown modules were identified, respectively. After overlapping with DEGs, 11 real hub genes were identified: EP300, MMP2, CDH2, CDK2, GNG10, ALB, SMC2, DHX15, CUL3, BTBD1, and LTN1. These genes were expressed with significant differences in IPAH versus controls, indicating a high diagnostic ability. The miRNA–gene network showed that hsa-mir-1-3p could associate with IPAH.

Conclusion

EP300, MMP2, CDH2, CDK2, GNG10, ALB, SMC2, DHX15, CUL3, BTBD1, and LTN1 may play essential roles in IPAH. Predicted miRNA hsa-mir-1-3p could regulate gene expression in IPAH. Such hub genes may contribute to the pathology and progression in IPAH, providing potential diagnostic and therapeutic opportunities for IPAH patients.

Metabolic alterations in meningioma reflect the clinical course

Background

Meningiomas are common brain tumours that are usually defined by benign clinical course. However, some meningiomas undergo a malignant transformation and recur within a short time period regardless of their World Health Organization (WHO) grade. The current study aimed to identify potential markers that can discriminate between benign and malignant meningioma courses.

Methods

We profiled the metabolites from 43 patients with low- and high-grade meningiomas. Tumour specimens were analyzed by nuclear magnetic resonance analysis; 270 metabolites were identified and clustered with the AutoPipe algorithm.

Results

We observed two distinct clusters marked by alterations in glycine/serine and choline/tryptophan metabolism. Glycine/serine cluster showed significantly lower WHO grades and proliferation rates. Also progression-free survival was significantly longer in the glycine/serine cluster.

Conclusion

Our findings suggest that alterations in glycine/serine metabolism are associated with lower proliferation and more recurrent tumours. Altered choline/tryptophan metabolism was associated with increases proliferation, and recurrence. Our results suggest that tumour malignancy can be reflected by metabolic alterations, which may support histological classifications to predict the clinical outcome of patients with meningiomas.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12885-021-07887-5.

Enhanced Understanding of Molecular Interactions and Function Underlying Pain Processes Through Networks of Transcript Isoforms, Genes, and Gene Families

Introduction

Molecular networks based on the abundance of mRNA at the gene level and pathway networks that relate families or groups of paralog genes have supported the understanding of interactions between molecules. However, multiple molecular mechanisms underlying health and behavior, such as pain signal processing, are modulated by the abundances of the transcript isoforms that originate from alternative splicing, in addition to gene abundances. Alternative splice variants of growth factors, ion channels, and G-protein-coupled receptors can code for proteoforms that can have different effects on pain and nociception. Therefore, networks inferred using abundance from more agglomerative molecular units (eg, gene family, or gene) have limitations in capturing interactions at a more granular level (eg, gene, or transcript isoform, respectively) do not account for changes in the abundance at the transcript isoform level.

Objective

The objective of this study was to evaluate the relative benefits of network inference using abundance patterns at various aggregate levels.

Methods

Sparse networks were inferred using Gaussian Markov random fields and a novel aggregation criterion was used to aggregate network edges. The relative advantages of network aggregation were evaluated on two molecular systems that have different dimensions and connectivity, circadian rhythm and Toll-like receptor pathways, using RNA-sequencing data from mice representing two pain level groups, opioid-induced hyperalgesia and control, and two central nervous system regions, the nucleus accumbens and the trigeminal ganglia.

Results

The inferred networks were benchmarked against the Kyoto Encyclopedia of Genes and Genomes reference pathways using multiple criteria. Networks inferred using more granular information performed better than networks inferred using more aggregate information. The advantage of granular inference varied with the pathway and data set used.

Discussion

The differences in inferred network structure between data sets highlight the differences in OIH effect between central nervous system regions. Our findings suggest that inference of networks using alternative splicing variants can offer complementary insights into the relationship between genes and gene paralog groups.

(Differential) Co-Expression Analysis of Gene Expression: A Survey of Best Practices

Analysis of gene expression data is widely used in transcriptomic studies to understand functions of molecules inside a cell and interactions among molecules. Differential co-expression analysis studies diseases and phenotypic variations by finding modules of genes whose co-expression patterns vary across conditions. We review the best practices in gene expression data analysis in terms of analysis of (differential) co-expression, co-expression network, differential networking, and differential connectivity considering both microarray and RNA-seq data along with comparisons. We highlight hurdles in RNA-seq data analysis using methods developed for microarrays. We include discussion of necessary tools for gene expression analysis throughout the paper. In addition, we shed light on scRNA-seq data analysis by including preprocessing and scRNA-seq in co-expression analysis along with useful tools specific to scRNA-seq. To get insights, biological interpretation and functional profiling is included. Finally, we provide guidelines for the analyst, along with research issues and challenges which should be addressed.

High Expression of the SH3TC2-DT/SH3TC2 Gene Pair Associated With FLT3 Mutation and Poor Survival in Acute Myeloid Leukemia: An Integrated TCGA Analysis

Fms-like tyrosine kinase 3 (FLT3) mutation is one of the most common mutations in acute myeloid leukemia (AML). However, the effect of FLT3 mutation on survival is currently still controversial and the leukemogenic mechanisms are still under further investigation. The aim of our study is to identify differentially expressed genes (DEGs) in FLT3-mutant AML and to find crucial DEGs whose expression level is related to prognosis for further analysis. By mining the TCGA-LAML dataset, 619 differentially expressed lncRNAs (DElncRNAs) and 1,428 differentially expressed mRNAs (DEmRNAs) were identified between FLT3-mutant and FLT3-wildtype samples. Through weighted gene correlation network analysis (WGCNA) and the following Cox proportional hazards regression analysis, we constructed the prognostic risk models to identify the hub DElncRNAs and DEmRNAs associated with AML prognosis. The presence of both SH3TC2 divergent transcript (SH3TC2-DT) and SH3TC2 in respective prognostic risk models promotes us to further study the significance of this gene pair in AML. SH3TC2-DT and SH3TC2 were identified to be coordinately high expressed in FLT3-mutant AML samples. High expression of this gene pair was associated with poor survival. Using logistic regression analysis, we found that high SH3TC2-DT/SH3TC2 expression was associated with FLT3 mutation, high WBC count, and intermediate cytogenetic and molecular–genetic risk. AML with SH3TC2-DT/SH3TC2 high expression showed enrichment of transcripts associated with stemness, quiescence, and leukemogenesis. Our study suggests that the SH3TC2-DT/SH3TC2 gene pair may be a possible biomarker to further optimize AML prognosis and may function in stemness or quiescence of FLT3-mutant leukemic stem cells (LSCs).

Integrated analysis of the proteome and transcriptome in a MCAO mouse model revealed the molecular landscape during stroke progression

•

DIA proteomics was applied to MCAO mice detection for the first time.

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Proteomics and bioinformatics revealed relationship between stroke process and immunity, especially inflammation.

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C3, Apoa4 and S100a9 were highlighted as a marker or drug targets for stroke.

Strokes usually results in long-term disability and death, and they occur worldwide. Recently, increased research on both on the physiopathological mechanisms and the transcriptome during stroke progression, have highlighted the relationship between stroke progression and immunity, with a special focus on inflammation. Here, we applied proteome analysis to a middle carotid artery occlusion (MCAO) mouse model at 0 h, 6 h, 12 h and 24 h, in which proteome profiling was performed with 23 samples, and 41 differentially expressed proteins (DEPs) were identified. Bioinformatics studies on our data revealed the importance of the immune response and particularly identified the inflammatory response, cytokine- cytokine receptor interactions, the innate immune response and reactive oxygen species (ROS) during stroke progression. In addition, we compared our data with multiple gene expression omnibus (GEO) datasets with and without a time series, in which similar pathways were identified, and three proteins, C3, Apoa4 and S100a9, were highlighted as markers or drug targets for stroke; these three proteins were significantly upregulated in the MCAO model, both in our proteomic data and in the GEO database.

Identification of genes and functional coexpression modules closely related to ulcerative colitis by gene datasets analysis

Background

Ulcerative colitis is a type of inflammatory bowel disease posing a great threat to the public health worldwide. Previously, gene expression studies of mucosal colonic biopsies have provided some insight into the pathophysiological mechanisms in ulcerative colitis; however, the exact pathogenesis is unclear. The purpose of this study is to identify the most related genes and pathways of UC by bioinformatics, so as to reveal the core of the pathogenesis.

Methods

Genome-wide gene expression datasets involving ulcerative colitis patients were collected from gene expression omnibus database. To identify most close genes, an integrated analysis of gene expression signature was performed by employing robust rank aggregation method. We used weighted gene co-expression network analysis to explore the functional modules involved in ulcerative colitis pathogenesis. Besides, biological process and pathways analysis of co-expression modules were figured out by gene ontology enrichment analysis using Metascape.

Results

A total of 328 ulcerative colitis patients and 138 healthy controls were from 14 datasets. The 150 most significant differentially expressed genes are likely to include causative genes of disease, and further studies are needed to demonstrate this. Seven main functional modules were identified, which pathway enrichment analysis indicated were associated with many biological processes. Pathways such as ‘extracellular matrix, immune inflammatory response, cell cycle, material metabolism’ are consistent with the core mechanism of ulcerative colitis. However, ‘defense response to virus’ and ‘herpes simplex infection’ suggest that viral infection is one of the aetiological agents. Besides, ‘Signaling by Receptor Tyrosine Kinases’ and ‘pathway in cancer’ provide new clues for the study of the risk and process of ulcerative colitis cancerization.

Transcriptional Networks of Microglia in Alzheimer's Disease and Insights into Pathogenesis

Microglia, the main immune cells of the central nervous system, are increasingly implicated in Alzheimer's disease (AD). Manifold transcriptomic studies in the brain have not only highlighted microglia's role in AD pathogenesis, but also mapped crucial pathological processes and identified new therapeutic targets. An important component of many of these transcriptomic studies is the investigation of gene expression networks in AD brain, which has provided important new insights into how coordinated gene regulatory programs in microglia (and other cell types) underlie AD pathogenesis. Given the rapid technological advancements in transcriptional profiling, spanning from microarrays to single-cell RNA sequencing (scRNA-seq), tools used for mapping gene expression networks have evolved to keep pace with the unique features of each transcriptomic platform. In this article, we review the trajectory of transcriptomic network analyses in AD from brain to microglia, highlighting the corresponding methodological developments. Lastly, we discuss examples of how transcriptional network analysis provides new insights into AD mechanisms and pathogenesis.

Regional Analysis of the Brain Transcriptome in Mice Bred for High and Low Methamphetamine Consumption

Transcriptome profiling can broadly characterize drug effects and risk for addiction in the absence of drug exposure. Modern large-scale molecular methods, including RNA-sequencing (RNA-Seq), have been extensively applied to alcohol-related disease traits, but rarely to risk for methamphetamine (MA) addiction. We used RNA-Seq data from selectively bred mice with high or low risk for voluntary MA intake to construct coexpression and cosplicing networks for differential risk. Three brain reward circuitry regions were explored, the nucleus accumbens (NAc), prefrontal cortex (PFC), and ventral midbrain (VMB). With respect to differential gene expression and wiring, the VMB was more strongly affected than either the PFC or NAc. Coexpression network connectivity was higher in the low MA drinking line than in the high MA drinking line in the VMB, oppositely affected in the NAc, and little impacted in the PFC. Gene modules protected from the effects of selection may help to eliminate certain mechanisms from significant involvement in risk for MA intake. One such module was enriched in genes with dopamine-associated annotations. Overall, the data suggest that mitochondrial function and glutamate-mediated synaptic plasticity have key roles in the outcomes of selective breeding for high versus low levels of MA intake.

Bladder cancer stage-associated hub genes revealed by WGCNA co-expression network analysis

Background

Bladder cancer was a malignant disease in patients, our research aimed at discovering the possible biomarkers for the diseases.

Results

The gene chip GSE31684, including 93samples, was downloaded from the GEO datasets and co-expression network was constructed by the data. Molecular complex detection(MCODE) was used to identify hub genes. The most significant cluster including 16 genes: CDH11, COL3A1, COL6A3, COL5A1, AEBP1, COL1A2, NTM, COL11A1, THBS2, COL8A1, COL1A1, BGN, MMP2, PXDN, THY1, and TGFB1I1 was identified. After annotated by BiNGO, they were suggested associated with collagen fibril organization and blood vessel development. In addition, the Kaplan Meier curves were obtained by UALCAN. The high expression of THY1, AEBP1, CDH11, COL1A1, COL1A2, COL11A1, MMP2, PXDN, BGN, COL5A1, COL8A1, and TGFB1I1 indicated poor prognosis of the patients(P < 0.05). Finally, we examined genes’ expression between low and high tumor stage by the Wilcoxon test(P < 0.05), TGFB1I1 was excluded.

Conclusion

THY1, AEBP1, CDH11, COL1A1, COL1A2, COL11A1, MMP2, PXDN, BGN, COL5A1, COL8A1 associated with the tumor stage as well as tumor patients’ prognosis. COL5A1, COL8A1(P < 0.01) may serve as therapeutic targets for the disease.

Assessment of complementarity of WGCNA and NERI results for identification of modules associated to schizophrenia spectrum disorders

Psychiatric disorders involve both changes in multiple genes as well different types of variations. As such, gene co-expression networks allowed the comparison of different stages and parts of the brain contributing to an integrated view of genetic variation. Two methods based on co-expression networks presents appealing results: Weighted Gene Correlation Network Analysis (WGCNA) and Network-Medicine Relative Importance (NERI). By selecting two different gene expression databases related to schizophrenia, we evaluated the biological modules selected by both WGCNA and NERI along these databases as well combining both WGCNA and NERI results (WGCNA-NERI). Also we conducted a enrichment analysis for the identification of partial biological function of each result (as well a replication analysis). To appraise the accuracy of whether both algorithms (as well our approach, WGCNA-NERI) were pointing to genes related to schizophrenia and its complex genetic architecture we conducted the MSET analysis, based on a reference gene list of schizophrenia database (SZDB) related to DNA Methylation, Exome, GWAS as well as copy number variation mutation studies. The WGCNA results were more associated with inflammatory pathways and immune system response; NERI obtained genes related with cellular regulation, embryological pathways e cellular growth factors. Only NERI were able to provide a statistical meaningful results to the MSET analysis (for Methylation and de novo mutations data). However, combining WGCNA and NERI provided a much more larger overlap in these two categories and additionally on Transcriptome database. Our study suggests that using both methods in combination is better for establishing a group of modules and pathways related to a complex disease than using each method individually. NERI is available at: https://bitbucket.org/sergionery/neri.

When pitch adds to volume: coregulation of transcript diversity predicts gene function

Background

Genes corregulate their overall transcript volumes to perform their physiological functions. However, it is unknown if they additionally coregulate their transcript diversities. We studied the reliability, consistency and functional associations of co-splicing correlations of genes of interest, across two independent studies, multiple tissues and two statistical methods. We thoroughly investigated the reproducibility of co-splicing correlations of APP, the candidate gene of Azheimer’s disease (AD). We then studied how co-splicing correlations in different tissues contributed to predict functional interactions of three other genes and finally computed co-splicing frequency for 17 thousand genes across 52 human tissues.

Results

We replicated co-splicing correlations between APP and 5 AD-related genes and reproduced expected enrichment of APP co-splicing in synaptic vesicle cycle and proteosome pathways. We observed novel associations for tissue vulnerability to disease with enrichment in APP co-splicing, co-expression and epistasis in AD. APP co-splicing was the strongest predictor and replicated between studies. We confirmed known gene interactions of PRPF8 and GRIA1 in testis and brain cortex, and observed a novel interaction of FGFR2, in breast and prostate, modulated by cancer risk-variants. We produced a co-splicing map across 52 human tissues to help predict the function of over 17 thousand genes.

Conclusions

We show that coregulation of transcript diversities provides novel biological insights in gene physiology and helps to interpret GWAS results. Co-splicing correlations are reliable and frequent and should be further pursued to help predict gene function. Our results additionally support current AD interventions aiming at the ubiquitin proteosome pathway but unveil the need to consider transcript diversity in addition to volume to assess treatment response and susceptibility to the disease.

Electronic supplementary material

The online version of this article (10.1186/s12864-018-5263-z) contains supplementary material, which is available to authorized users.

Cross-species molecular dissection across alcohol behavioral domains

This review summarizes the proceedings of a symposium presented at the "Alcoholism and Stress: A Framework for Future Treatment Strategies" conference held in Volterra, Italy on May 9-12, 2017. Psychiatric diseases, including alcohol-use disorders (AUDs), are influenced through complex interactions of genes, neurobiological pathways, and environmental influences. A better understanding of the common neurobiological mechanisms underlying an AUD necessitates an integrative approach, involving a systematic assessment of diverse species and phenotype measures. As part of the World Congress on Stress and Alcoholism, this symposium provided a detailed account of current strategies to identify mechanisms underlying the development and progression of AUDs. Dr. Sean Farris discussed the integration and organization of transcriptome and postmortem human brain data to identify brain regional- and cell type-specific differences related to excessive alcohol consumption that are conserved across species. Dr. Brien Riley presented the results of a genome-wide association study of DSM-IV alcohol dependence; although replication of genetic associations with alcohol phenotypes in humans remains challenging, model organism studies show that COL6A3, KLF12, and RYR3 affect behavioral responses to ethanol, and provide substantial evidence for their role in human alcohol-related traits. Dr. Rob Williams expanded upon the systematic characterization of extensive genetic-genomic resources for quantifying and clarifying phenotypes across species that are relevant to precision medicine in human disease. The symposium concluded with Dr. Robert Hitzemann's description of transcriptome studies in a mouse model selectively bred for high alcohol ("binge-like") consumption and a non-human primate model of long-term alcohol consumption. Together, the different components of this session provided an overview of systems-based approaches that are pioneering the experimental prioritization and validation of novel genes and gene networks linked with a range of behavioral phenotypes associated with stress and AUDs.

Variation among intact tissue samples reveals the core transcriptional features of human CNS cell classes

It is widely assumed that cells must be physically isolated to study their molecular profiles. However, intact tissue samples naturally exhibit variation in cellular composition, which drives covariation of cell-class-specific molecular features. By analyzing transcriptional covariation in 7,221 intact CNS samples from 840 neurotypical individuals, representing billions of cells, we reveal the core transcriptional identities of major CNS cell classes in humans. By modeling intact CNS transcriptomes as a function of variation in cellular composition, we identify cell-class-specific transcriptional differences in Alzheimer's disease, among brain regions, and between species. Among these, we show that PMP2 is expressed by human but not mouse astrocytes and significantly increases mouse astrocyte size upon ectopic expression in vivo, causing them to more closely resemble their human counterparts. Our work is available as an online resource ( http://oldhamlab.ctec.ucsf.edu/ ) and provides a generalizable strategy for determining the core molecular features of cellular identity in intact biological systems.

A random forest classifier predicts recurrence risk in patients with ovarian cancer

Ovarian cancer (OC) is associated with a poor prognosis due to difficulties in early detection. The aims of the present study were to construct a recurrence risk prediction model and to reveal important OC genes or pathways. RNA sequencing data was obtained for 307 OC samples, and the corresponding clinical data were downloaded from The Cancer Genome Atlas database. Additionally, two validation datasets, GSE44104 (20 recurrent and 40 non-recurrent OC samples) and GSE49997 (204 OC samples), were obtained from the Gene Expression Omnibus database. Differentially expressed genes were screened using the differential expression via distance synthesis algorithm, followed by gene ontology enrichment analysis and weighted gene coexpression network analysis (WGCNA). Furthermore, subnetwork analysis was conducted for the protein-protein interaction (PPI) network using the BioNet package. Finally, a random forest classifier was constructed based on the subnetwork nodes, and its reliability was validated using the GSE44104 and GSE49997 validation datasets. A total of 44 upregulated and 117 downregulated genes were identified in the recurrent samples. Enrichment analysis indicated that cytochrome P450 family 17 subfamily A member 1 (CYP17A1) was associated with ‘positive regulation of steroid hormone biosynthetic processes’. WGCNA identified turquoise and grey modules that were significantly correlated with status and prognosis. A significant PPI subnetwork containing 16 nodes was also identified, including: Transcription factor GATA-4; fibroblast growth factor 9; aromatase; 3β-hydroxysteroid dehydrogenase/δ5-4-isomerase type 2; corticosteroid 11β-dehydrogenase isozyme 1; CYP17A1; pituitary homeobox 2; left-right determination factor 1; homeobox protein ARX; estrogen receptor β; steroidogenic factor 1; forkhead box protein L2; myocardin; steroidogenic acute regulatory protein mitochondrial; vesicular inhibitory amino acid transporter; and twist-related protein 1. A random forest classifier was constructed using the subnetwork nodes as feature genes, which exhibited a 92% true positive rate when classifying recurrent and non-recurrent OC samples. The classifying efficiency of the random forest classifier was validated using the two other independent datasets. Overall, 44 upregulated and 117 downregulated genes associated with OC recurrence were identified. Furthermore, the 16 subnetwork node genes that were identified may be important molecules in OC recurrence.

Gene co-expression analysis for functional classification and gene-disease predictions

Gene co-expression networks can be used to associate genes of unknown function with biological processes, to prioritize candidate disease genes or to discern transcriptional regulatory programmes. With recent advances in transcriptomics and next-generation sequencing, co-expression networks constructed from RNA sequencing data also enable the inference of functions and disease associations for non-coding genes and splice variants. Although gene co-expression networks typically do not provide information about causality, emerging methods for differential co-expression analysis are enabling the identification of regulatory genes underlying various phenotypes. Here, we introduce and guide researchers through a (differential) co-expression analysis. We provide an overview of methods and tools used to create and analyse co-expression networks constructed from gene expression data, and we explain how these can be used to identify genes with a regulatory role in disease. Furthermore, we discuss the integration of other data types with co-expression networks and offer future perspectives of co-expression analysis.

The integrative metabolomic-transcriptomic landscape of glioblastome multiforme

The purpose of this study was to map the landscape of metabolic-transcriptional alterations in glioblastoma multiforme. Omic-datasets were acquired by metabolic profiling (1D-NMR spectroscopy n=33 Patient) and transcriptomic profiling (n=48 Patients). Both datasets were analyzed by integrative network modeling. The computed model concluded in four different metabolic-transcriptomic signatures containing: oligodendrocytic differentiation, cell-cycle functions, immune response and hypoxia. These clusters were found being distinguished by individual metabolism and distinct transcriptional programs. The study highlighted the association between metabolism and hallmarks of oncogenic signaling such as cell-cycle alterations, immune escape mechanism and other cancer pathway alterations. In conclusion, this study showed the strong influence of metabolic alterations in the wide scope of oncogenic transcriptional alterations.

Comprehensive analysis of PD-L1 expression in glioblastoma multiforme

Glioblastoma multiforme are highly malignant brain tumours with frequent genetic and epigenetic alterations. The poor clinical outcome of these tumours necessitates the development of new treatment options. Immunotherapies for glioblastoma multiforme including PD1/PD-L1 inhibition are currently tested in ongoing clinical trials. The purpose of this study was to investigate the molecular background of PD-L1 expression in glioblastoma multiforme and to find associated pathway activation and genetic alterations. We show that PD-L1 is up-regulated in IDH1/2 wildtype glioblastoma multiforme compared to lower-grade gliomas. In addition, a strong association of PD-L1 with the mesenchymal expression subgroup was observed. Consistent with that, NF1 mutation and corresponding activation of the MAPK pathway was strongly connected to PD-L1 expression. Our findings may explain different response to PD-L1 inhibition of patients in ongoing trials and may help to select patients that may profit of immunotherapy in the future.

Microenvironment-Derived Regulation of HIF Signaling Drives Transcriptional Heterogeneity in Glioblastoma Multiforme

The evolving and highly heterogeneous nature of malignant brain tumors underlies their limited response to therapy and poor prognosis. In addition to genetic alterations, highly dynamic processes, such as transcriptional and metabolic reprogramming, play an important role in the development of tumor heterogeneity. The current study reports an adaptive mechanism in which the metabolic environment of malignant glioma drives transcriptional reprogramming. Multiregional analysis of a glioblastoma patient biopsy revealed a metabolic landscape marked by varying stages of hypoxia and creatine enrichment. Creatine treatment and metabolism was further shown to promote a synergistic effect through upregulation of the glycine cleavage system and chemical regulation of prolyl-hydroxylase domain. Consequently, creatine maintained a reduction of reactive oxygen species and change of the α-ketoglutarate/succinate ratio, leading to an inhibition of HIF signaling in primary tumor cell lines. These effects shifted the transcriptional pattern toward a proneural subtype and reduced the rate of cell migration and invasion in vitroImplications: Transcriptional subclasses of glioblastoma multiforme are heterogeneously distributed within the same tumor. This study uncovered a regulatory function of the tumor microenvironment by metabolism-driven transcriptional reprogramming in infiltrating glioma cells. Mol Cancer Res; 16(4); 655-68. ©2018 AACR.

On the relationships in rhesus macaques between chronic ethanol consumption and the brain transcriptome

This is the first description of the relationship between chronic ethanol self-administration and the brain transcriptome in a non-human primate (rhesus macaque). Thirty-one male animals self-administered ethanol on a daily basis for over 12 months. Gene transcription was quantified with RNA-Seq in the central nucleus of the amygdala (CeA) and cortical Area 32. We constructed coexpression and cosplicing networks, and we identified areas of preservation and areas of differentiation between regions and network types. Correlations between intake and transcription included largely distinct gene sets and annotation categories across brain regions and between expression and splicing; positive and negative correlations were also associated with distinct annotation groups. Membrane, synaptic and splicing annotation categories were over-represented in the modules (gene clusters) enriched in positive correlations (CeA); our cosplicing analysis further identified the genes affected only at the exon inclusion level. In the CeA coexpression network, we identified Rab6b, Cdk18 and Igsf21 among the intake-correlated hubs, while in the Area 32, we identified a distinct hub set that included Ppp3r1 and Myeov2. Overall, the data illustrate that excessive ethanol self-administration is associated with broad expression and splicing mechanisms that involve membrane and synapse genes.

Co-expression networks reveal the tissue-specific regulation of transcription and splicing

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.

Reduced expression of BMP3 contributes to the development of pulmonary fibrosis and predicts the unfavorable prognosis in IIP patients

Idiopathic pulmonary fibrosis (IPF) and idiopathic nonspecific interstitial pneumonia (INSIP) are two related diseases involving varying degrees of pulmonary fibrosis with no effective cure. Bone morphogenetic protein 3 (BMP3) is a member of the transforming growth factor-β (TGF-β) super-family, which has not been implicated in pulmonary fibrosis previously. In this study, we aimed to investigate the potential role of BMP3 playing in pulmonary fibrosis from clinical diagnosis to molecular signaling regulation. RNA sequencing was performed to explore the potential biomarker of IIP patients. The expression of BMP3 was evaluated in 83 cases of IPF and INSIP by immunohistochemistry. The function of BMP3 was investigated in both fibroblast cells and a bleomycin-induced murine pulmonary fibrosis model. The clinical relevance of BMP3 expression were analyzed in 47 IIP patients, which were included in 83 cases and possess more than five-year follow-up data. Both RNA-sequencing and immunohistochemistry staining revealed that BMP3 was significantly down-regulated in lung tissues of patients with IPF and INSIP. Consistently, lower expression of BMP3 also was found in pulmonary fibrotic tissues of bleomycin-induced mice model. Up-regulation of BMP3 prevented pulmonary fibrosis processing through inhibiting cellular proliferation of fibroblasts as well as TGF-β1 signal transduction. Finally, the relatively higher expression of BMP3 in IPF patients was associated with less/worse mortality. Intravenous injection of recombinant BMP3. Taken together, our results suggested that the low expression level of BMP3 may indicate the unfavorable prognosis of IPF patients, targeting BMP3 may represent a novel potential therapeutic method for pulmonary fibrosis management.

Molecular differences between cerebral blood volume and vessel size in glioblastoma multiforme

The purpose of this study was to investigate the molecular background of cerebral blood volume (CBV) and vessel size (VS) of capillaries in glioblastoma multiforme (GBM). Both parameters are derived from extended perfusion MR imaging.A prospective case study including 21 patients (median age 66 years, 10 females) was performed. Before operation, CBV and VS of contrast enhancing tumor were assessed. Tissue was sampled from the assessed areas under neuronavigation control. After RNA extraction, transcriptional data was analyzed by Weighted Gene Co-Expression Network Analysis (WGCNA) and split into modules based on its network affiliations. Gene Set Enrichment Analysis (GSEA) identified biological functions or pathways of the genetic modules. These were applied on 484 GBM samples of the TCGA database.Ten modules were highly correlated to CBV and VS. One module was exclusively associated to VS and highly correlated to hypoxia, another one exclusively to CBV showing strong enrichments in the Epithelial Growth Factor (EGF) pathway and Epithelial-to-Mesenchymal-Transition (EMT). Moreover, patients with increased CBV and VS predominantly showed a mesenchymal gene-expression, a finding that could be corroborated by TCGA data.In conclusion, CBV and VS mirror different genetic pathways and reflect certain molecular subclasses of GBM.

The determinants of alternative RNA splicing in human cells

Alternative splicing represents an important level of the regulation of gene function in eukaryotic organisms. It plays a critical role in virtually every biological process within an organism, including regulation of cell division and cell death, differentiation of tissues in the embryo and the adult organism, as well as in cellular response to diverse environmental factors. In turn, studies of the last decade have shown that alternative splicing itself is controlled by different mechanisms. Unfortunately, there is no clear understanding of how these diverse mechanisms, or determinants, regulate and constrain the set of alternative RNA species produced from any particular gene in every cell of the human body. Here, we provide a consolidated overview of alternative splicing determinants including RNA-protein interactions, epigenetic regulation via chromatin remodeling, coupling of transcription-to-alternative splicing, effect of secondary structures in pre-RNA, and function of the RNA quality control systems. We also extensively and critically discuss some mechanistic insights on coordinated inclusion/exclusion of exons during the formation of mature RNA molecules. We conclude that the final structure of RNA is pre-determined by a complex interplay between cis- and trans-acting factors. Altogether, currently available empirical data significantly expand our understanding of the functioning of the alternative splicing machinery of cells in normal and pathological conditions. On the other hand, there are still many blind spots that require further deep investigations.

Integrative Diffusion-Weighted Imaging and Radiogenomic Network Analysis of Glioblastoma multiforme

In the past, changes of the Apparent Diffusion Coefficient in glioblastoma multiforme have been shown to be related to specific genes and described as being associated with survival. The purpose of this study was to investigate diffusion imaging parameters in combination with genome-wide expression data in order to obtain a comprehensive characterisation of the transcriptomic changes indicated by diffusion imaging parameters. Diffusion-weighted imaging, molecular and clinical data were collected prospectively in 21 patients. Before surgery, MRI diffusion metrics such as axial (AD), radial (RD), mean diffusivity (MD) and fractional anisotropy (FA) were assessed from the contrast enhancing tumour regions. Intraoperatively, tissue was sampled from the same areas using neuronavigation. Transcriptional data of the tissue samples was analysed by Weighted Gene Co-Expression Network Analysis (WGCNA) thus classifying genes into modules based on their network-based affiliations. Subsequent Gene Set Enrichment Analysis (GSEA) identified biological functions or pathways of the expression modules. Network analysis showed a strong association between FA and epithelial-to-mesenchymal-transition (EMT) pathway activation. Also, patients with high FA had a worse clinical outcome. MD correlated with neural function related genes and patients with high MD values had longer overall survival. In conclusion, FA and MD are associated with distinct molecular patterns and opposed clinical outcomes.

Effects of selection for ethanol preference on gene expression in the nucleus accumbens of HS-CC mice

Previous studies on changes in murine brain gene expression associated with the selection for ethanol preference have used F₂ intercross or heterogeneous stock (HS) founders, derived from standard laboratory strains. However, these populations represent only a small proportion of the genetic variance available in Mus musculus. To investigate a wider range of genetic diversity, we selected mice for ethanol preference using an HS derived from the eight strains of the collaborative cross. These HS mice were selectively bred (four generations) for high and low ethanol preference. The nucleus accumbens shell of naive S₄ mice was interrogated using RNA sequencing (RNA-Seq). Gene networks were constructed using the weighted gene coexpression network analysis assessing both coexpression and cosplicing. Selection targeted one of the network coexpression modules (greenyellow) that was significantly enriched in genes associated with receptor signaling activity including Chrna7, Grin2a, Htr2a and Oprd1. Connectivity in the module as measured by changes in the hub nodes was significantly reduced in the low preference line. Of particular interest was the observation that selection had marked effects on a large number of cell adhesion molecules, including cadherins and protocadherins. In addition, the coexpression data showed that selection had marked effects on long non-coding RNA hub nodes. Analysis of the cosplicing network data showed a significant effect of selection on a large cluster of Ras GTPase-binding genes including Cdkl5, Cyfip1, Ndrg1, Sod1 and Stxbp5. These data in part support the earlier observation that preference is linked to Ras/Mapk pathways.

Gene Co-Expression Network Analysis Reveals the Correlation Patterns Among Genes in Euryhaline Adaptation of Crassostrea gigas

The Pacific oyster Crassostrea gigas is a dominant aquaculture species in many intertidal zones throughout the Pacific and Atlantic Oceans and can tolerate a wide range of salinity. Studying the gene expression profiles of oyster gills had found differentially expressed genes (DEGs) involved in salinity tolerance. A systematic study of cellular response to salinity stress may provide insights into the mechanism of acquired salinity tolerance. Here, weighted gene co-expression network analysis (WGCNA) was carried out using RNA-seq data from gill transcriptome in response to different salinity. A total of 25,463 genes were parsed into 22 gene modules, of which 5 gene modules were identified as salinity-related modules. Brown module was the only one significantly correlated with salinity and free amino acids (FAAs) contents, which was associated with cellular metabolism, biosynthesis of amino acids, oxidation reduction, electron transport, nitrogen compound metabolism, and others. The enriched pathways in brown module were mainly about FAAs metabolism. The other four modules were significantly correlated with certain FAAs, and were over-represented in certain salinity. These results indicated that C. gigas triggered different FAAs in different salinity stress. This study represents the first RNA-seq gene network analysis in oysters responding to different salinity stresses. These results provide a systems-level framework to help understand the complexity of cellular process in response to osmotic stress and show the function and regulated genes of different FAAs at the molecular level.

Integrative Network-based Analysis of Magnetic Resonance Spectroscopy and Genome Wide Expression in Glioblastoma multiforme

The goal of this study was to identify correlations between metabolites from proton MR spectroscopy and genetic pathway activity in glioblastoma multiforme (GBM). Twenty patients with primary GBM were analysed by short echo-time chemical shift imaging and genome-wide expression analyses. Weighed Gene Co-Expression Analysis was used for an integrative analysis of imaging and genetic data. N-acetylaspartate, normalised to the contralateral healthy side (nNAA), was significantly correlated to oligodendrocytic and neural development. For normalised creatine (nCr), a group with low nCr was linked to the mesenchymal subtype, while high nCr could be assigned to the proneural subtype. Moreover, clustering of normalised glutamine and glutamate (nGlx) revealed two groups, one with high nGlx being attributed to the neural subtype, and one with low nGlx associated with the classical subtype. Hence, the metabolites nNAA, nCr, and nGlx correlate with a specific gene expression pattern reflecting the previously described subtypes of GBM. Moreover high nNAA was associated with better clinical prognosis, whereas patients with lower nNAA revealed a shorter progression-free survival (PFS).