Differential variability and correlation of gene expression identifies key genes involved in neuronal differentiation

ScHGSC-IGDC: Identifying genes with differential correlations of high-grade serous ovarian cancer based on single-cell RNA sequencing analysis

Due to the high heterogeneity of ovarian cancer (OC), it occupies the main cause of cancer-related death among women. As the most aggressive and frequent subtype of OC, high-grade serous cancer (HGSC) represents around 70 % of all patients. With the booming progress of single-cell RNA sequencing (scRNA-seq), unique and subtle changes among different cell states have been identified including novel risk genes and pathways. Here, our present study aims to identify differentially correlated core genes between normal and tumor status through HGSC scRNA-seq data analysis. R package high-dimension Weighted Gene Co-expression Network Analysis (hdWGCNA) was implemented for building gene interaction networks based on HGSC scRNA-seq data. DiffCorr was integrated for identifying differentially correlated genes between tumor and their adjacent normal counterparts. Software Cytoscape was implemented for constructing and visualizing biological networks. Real-time qPCR (RT-qPCR) was utilized to confirm expression pattern of new genes. We introduced ScHGSC-IGDC (Identifying Genes with Differential Correlations of HGSC based on scRNA-seq analysis), an in silico framework for identifying core genes in the development of HGSC. We detected thirty-four modules in the network. Scores of new genes with opposite correlations with others such as NDUFS5, TMSB4X, SERPINE2 and ITPR2 were identified. Further survival and literature validation emphasized their great values in the HGSC management. Meanwhile, RT-qPCR verified expression pattern of NDUFS5, TMSB4X, SERPINE2 and ITPR2 in human OC cell lines and tissues. Our research offered novel perspectives on the gene modulatory mechanisms from single cell resolution, guiding network based algorithms in cancer etiology field.

clrDV: a differential variability test for RNA-Seq data based on the skew-normal distribution

Background

Pathological conditions may result in certain genes having expression variance that differs markedly from that of the control. Finding such genes from gene expression data can provide invaluable candidates for therapeutic intervention. Under the dominant paradigm for modeling RNA-Seq gene counts using the negative binomial model, tests of differential variability are challenging to develop, owing to dependence of the variance on the mean.

Methods

Here, we describe clrDV, a statistical method for detecting genes that show differential variability between two populations. We present the skew-normal distribution for modeling gene-wise null distribution of centered log-ratio transformation of compositional RNA-seq data.

Results

Simulation results show that clrDV has false discovery rate and probability of Type II error that are on par with or superior to existing methodologies. In addition, its run time is faster than its closest competitors, and remains relatively constant for increasing sample size per group. Analysis of a large neurodegenerative disease RNA-Seq dataset using clrDV successfully recovers multiple gene candidates that have been reported to be associated with Alzheimer's disease.

On the Use of Heterogeneous Stock Mice to Map Transcriptomes Associated With Excessive Ethanol Consumption

We and many others have noted the advantages of using heterogeneous (HS) animals to map genes and gene networks associated with both behavioral and non-behavioral phenotypes. Importantly, genetically complex Mus musculus crosses provide substantially increased resolution to examine old and new relationships between gene expression and behavior. Here we report on data obtained from two HS populations: the HS/NPT derived from eight inbred laboratory mouse strains and the HS-CC derived from the eight collaborative cross inbred mouse strains that includes three wild-derived strains. Our work has focused on the genes and gene networks associated with risk for excessive ethanol consumption, individual variation in ethanol consumption and the consequences, including escalation, of long-term ethanol consumption. Background data on the development of HS mice is provided, including advantages for the detection of expression quantitative trait loci. Examples are also provided of using HS animals to probe the genes associated with ethanol preference and binge ethanol consumption.

Identification of Key Genes With Differential Correlations in Lung Adenocarcinoma

Background

With the advent of large-scale molecular profiling, an increasing number of oncogenic drivers contributing to precise medicine and reshaping classification of lung adenocarcinoma (LUAD) have been identified. However, only a minority of patients archived improved outcome under current standard therapies because of the dynamic mutational spectrum, which required expanding susceptible gene libraries. Accumulating evidence has witnessed that understanding gene regulatory networks as well as their changing processes was helpful in identifying core genes which acted as master regulators during carcinogenesis. The present study aimed at identifying key genes with differential correlations between normal and tumor status.

Methods

Weighted gene co-expression network analysis (WGCNA) was employed to build a gene interaction network using the expression profile of LUAD from The Cancer Genome Atlas (TCGA). R package DiffCorr was implemented for the identification of differential correlations between tumor and adjacent normal tissues. STRING and Cytoscape were used for the construction and visualization of biological networks.

Results

A total of 176 modules were detected in the network, among which yellow and medium orchid modules showed the most significant associations with LUAD. Then genes in these two modules were further chosen to evaluate their differential correlations. Finally, dozens of novel genes with opposite correlations including ATP13A4-AS1, HIGD1B, DAP3, and ISG20L2 were identified. Further biological and survival analyses highlighted their potential values in the diagnosis and treatment of LUAD. Moreover, real-time qPCR confirmed the expression patterns of ATP13A4-AS1, HIGD1B, DAP3, and ISG20L2 in LUAD tissues and cell lines.

Conclusion

Our study provided new insights into the gene regulatory mechanisms during transition from normal to tumor, pioneering a network-based algorithm in the application of tumor etiology.

RNA-Seq Analysis of Genetic and Transcriptome Network Effects of Dual-Trait Selection for Ethanol Preference and Withdrawal Using SOT and NOT Genetic Models

BACKGROUND

Genetic factors significantly affect alcohol consumption and vulnerability to withdrawal. Furthermore, some genetic models showing predisposition to severe withdrawal are also predisposed to low ethanol (EtOH) consumption and vice versa, even when tested independently in naïve animals.

METHODS

Beginning with a C57BL/6J × DBA/2J F2 intercross founder population, animals were simultaneously selectively bred for both high alcohol consumption and low acute withdrawal (SOT line), or vice versa (NOT line). Using randomly chosen fourth selected generation (S4) mice (N = 18-22/sex/line), RNA-Seq was employed to assess genome-wide gene expression in ventral striatum. The MegaMUGA array was used to detect genome-wide genotypic differences. Differential gene expression and the weighted gene co-expression network analysis were implemented as described elsewhere (Genes Brain Behav 16, 2017, 462).

RESULTS

The new selection of the SOT and NOT lines was similar to that reported previously (Alcohol Clin Exp Res 38, 2014, 2915). One thousand eight hundred and sixteen transcripts were detected as differentially expressed between the lines. For genes more highly expressed in the SOT line, there was enrichment in genes associated with cell adhesion, synapse organization, and postsynaptic membrane. The genes with a cell adhesion annotation included 23 protocadherins, Mpdz and Dlg2. Genes with a postsynaptic membrane annotation included Gabrb3, Gphn, Grid1, Grin2b, Grin2c, and Grm3. The genes more highly expressed in the NOT line were enriched in a network module (red) with annotations associated with mitochondrial function. Several of these genes were module hub nodes, and these included Nedd8, Guk1, Elof1, Ndufa8, and Atp6v1f.

CONCLUSIONS

Marked effects of selection on gene expression were detected. The NOT line was characterized by higher expression of hub nodes associated with mitochondrial function. Genes more highly expressed in the SOT aligned with previous findings, for example, Colville and colleagues (Genes Brain Behav 16, 2017, 462) that both high EtOH preference and consumption are associated with effects on cell adhesion and glutamate synaptic plasticity.

Identification of an early cell fate regulator by detecting dynamics in transcriptional heterogeneity and co-regulation during astrocyte differentiation

There are an increasing number of reports that characterize the temporal behavior of gene expression at the single-cell level during cell differentiation. Despite accumulation of data describing the heterogeneity of biological responses, the dynamics of gene expression heterogeneity and its regulation during the differentiation process have not been studied systematically. To understand transcriptional heterogeneity during astrocyte differentiation, we analyzed single-cell transcriptional data from cells representing the different stages of astrocyte differentiation. When we compared the transcriptional variability of co-expressed genes between the undifferentiated and differentiated states, we found that there was significant increase in transcriptional variability in the undifferentiated state. The genes showing large changes in both "variability" and "correlation" between neural stem cells (NSCs) and astrocytes were found to be functionally involved in astrocyte differentiation. We determined that these genes are potentially regulated by Ascl1, a previously known oscillatory gene in NSCs. Pharmacological blockade of Ntsr2, which is transcriptionally co-regulated with Ascl1, showed that Ntsr2 may play an important role in the differentiation from NSCs to astrocytes. This study shows the importance of characterizing transcriptional heterogeneity and rearrangement of the co-regulation network between different cell states. It also highlights the potential for identifying novel regulators of cell differentiation that will further increase our understanding of the molecular mechanisms underlying the differentiation process.

Gender-Specific Effects of Selection for Drinking in the Dark on the Network Roles of Coding and Noncoding RNAs

BACKGROUND

Transcriptional differences between heterogeneous stock mice and high drinking-in-the-dark selected mouse lines have previously been described based on microarray technology coupled with network-based analysis. The network changes were reproducible in 2 independent selections and largely confined to 2 distinct network modules; in contrast, differential expression appeared more specific to each selected line. This study extends these results by utilizing RNA-Seq technology, allowing evaluation of the relationship between genetic risk and transcription of noncoding RNA (ncRNA); we additionally evaluate sex-specific transcriptional effects of selection.

METHODS

Naïve mice (N = 24/group and sex) were utilized for gene expression analysis in the ventral striatum; the transcriptome was sequenced with the Illumina HiSeq platform. Differential gene expression and the weighted gene co-expression network analysis were implemented largely as described elsewhere, resulting in the identification of genes that change expression level or (co)variance structure.

RESULTS

Across both sexes, we detect selection effects on the extracellular matrix and synaptic signaling, although the identity of individual genes varies. A majority of nc RNAs cluster in a single module of relatively low density in both the male and female network. The most strongly differentially expressed transcript in both sexes was Gm22513, a small nuclear RNA with unknown function. Associated with selection, we also found a number of network hubs that change edge strength and connectivity. At the individual gene level, there are many sex-specific effects; however, at the annotation level, results are more concordant.

CONCLUSIONS

In addition to demonstrating sex-specific effects of selection on the transcriptome, the data point to the involvement of extracellular matrix genes as being associated with the binge drinking phenotype.

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.