Screening Potential Therapeutic Mechanisms of Houshiheisan Against Ischemic Stroke by LncRNAs and MRNAs Microarray Profiling

Background Long non-coding RNAs (lncRNAs) are substantial to wide varity of biological processes and pathogenesis processes of ischemic stroke. Houshiheisan (HSHS), a typical prescription of traditional Chinese medicine, has outstanding efficacy in treating stroke, although the comprehensive molecular mechanism of its therapeutic effect has remained obscure up to now. Methods In this work, we induced a ischemic stroke rat model by permanent middle cerebral artery occlusion (MCAO). The microarray expression profile of lncRNAs and mRNAs was used to investigate various possible roles and molecular mechanism of HSHS in treating MCAO rats. Results HSHS improved the neurological deficit and alleviated the pathological damage after cerebral ischemia. The Clariom D Assay (rat, Affymetrix) showed that 8128 mRNAs and 3022 lncRNAs differentially expressed between sham group and model group, and 868 mRNAs and 836 lncRNAs between HSHS and model groups. Among the three groups, the intersections (666 mRNAs and 288 lncRNAs) were chosen and analyzed. GO and KEGG analysis disclosed that the majority of overlapping mRNAs were enriched in Axon guidance, Autophagy, PI3K-AKT signaling pathway and mTOR signaling pathway. Pathway network, protein-protein network and molecular complex detection analysis identified significant hub genes, e.g., Rock2, Rps6kb1, Wnt4, IL-6 and so on. Furthermore, we explored dynamic interactions between the dysregulated lncRNAs and mRNAs by lncRNA-mRNA network analysis. Finally, qRT-PCR verified expressions of the critical differentially expressed lncRNAs and mRNAs within the three groups. Conclusion Our results indicate that these differentially expressed lncRNAs may affect pathological processes of ischemic stroke by regulating co-expressed mRNAs, providing novel insight in regarding lncRNAs’ involvement in the treatment of ischemic stroke.

Hallmarks of glycogene expression and glycosylation pathways in squamous and adenocarcinoma cervical cancer

Background Dysregulation of glycogene expression in cancer can lead to aberrant glycan expression, which can promote tumorigenesis. Cervical cancer (CC) displays an increased expression of glycogenes involved in sialylation and sialylated glycans. Here, we show a comprehensive analysis of glycogene expression in CC to identify glycogene expression signatures and the possible glycosylation pathways altered. Methods First, we performed a microarray expression assay to compare glycogene expression changes between normal and cervical cancer tissues. Second, we used 401 glycogenes to analyze glycogene expression in adenocarcinoma and squamous carcinoma from RNA-seq data at the cBioPortal for Cancer Genomics. Results The analysis of the microarray expression assay indicated that CC displayed an increase in glycogenes related to GPI-anchored biosynthesis and a decrease in genes associated with chondroitin and dermatan sulfate with respect to normal tissue. Also, the glycogene analysis of CC samples by the RNA-seq showed that the glycogenes involved in the chondroitin and dermatan sulfate pathway were downregulated. Interestingly the adenocarcinoma tumors displayed a unique glycogene expression signature compared to squamous cancer that shows heterogeneous glycogene expression divided into six types. Squamous carcinoma type 5 (SCC-5) showed increased expression of genes implicated in keratan and heparan sulfate synthesis, glycosaminoglycan degradation, ganglio, and globo glycosphingolipid synthesis was related to poorly differentiated tumors and poor survival. Squamous carcinoma type 6 (SCC-6) displayed an increased expression of genes involved in chondroitin/dermatan sulfate synthesis and lacto and neolacto glycosphingolipid synthesis and was associated with nonkeratinizing squamous cancer and good survival. In summary, our study showed that CC tumors are not a uniform entity, and their glycome signatures could be related to different clinicopathological characteristics.

Multicohort Analysis Identifies Monocyte Gene Signatures to Accurately Monitor Subset-Specific Changes in Human Diseases

Monocytes are crucial regulators of inflammation, and are characterized by three distinct subsets in humans, of which classical and non-classical are the most abundant. Different subsets carry out different functions and have been previously associated with multiple inflammatory conditions. Dissecting the contribution of different monocyte subsets to disease is currently limited by samples and cohorts, often resulting in underpowered studies and poor reproducibility. Publicly available transcriptome profiles provide an alternative source of data characterized by high statistical power and real-world heterogeneity. However, most transcriptome datasets profile bulk blood or tissue samples, requiring the use of in silico approaches to quantify changes in cell levels. Here, we integrated 853 publicly available microarray expression profiles of sorted human monocyte subsets from 45 independent studies to identify robust and parsimonious gene expression signatures, consisting of 10 genes specific to each subset. These signatures maintain their accuracy regardless of disease state in an independent cohort profiled by RNA-sequencing and are specific to their respective subset when compared to other immune cells from both myeloid and lymphoid lineages profiled across 6160 transcriptome profiles. Consequently, we show that these signatures can be used to quantify changes in monocyte subsets levels in expression profiles from patients in clinical trials. Finally, we show that proteins encoded by our signature genes can be used in cytometry-based assays to specifically sort monocyte subsets. Our results demonstrate the robustness, versatility, and utility of our computational approach and provide a framework for the discovery of new cellular markers.

Impact of gene annotation choice on the quantification of RNA-seq data

RNA sequencing is currently the method of choice for genome-wide profiling of gene expression. A popular approach to quantify expression levels of genes from RNA-seq data is to map reads to a reference genome and then count mapped reads to each gene. Gene annotation data, which include chromosomal coordinates of exons for tens of thousands of genes, are required for this quantification process. There are several major sources of gene annotations that can be used for quantification, such as Ensembl and RefSeq databases. However, there is very little understanding of the effect that the choice of annotation has on the accuracy of gene expression quantification in an RNA-seq analysis. In this paper, we present results from our comparison of Ensembl and RefSeq human annotations on their impact on gene expression quantification using a benchmark RNA-seq dataset generated by the SEquencing Quality Control (SEQC) consortium. We show that the use of RefSeq gene annotation models led to better quantification accuracy, based on the correlation with ground truths including expression data from $>$800 real-time PCR validated genes, known titration ratios of gene expression and microarray expression data. We also found that the recent expansion of the RefSeq annotation has led to a decrease in its annotation accuracy. Finally, we demonstrated that the RNA-seq quantification differences observed between different annotations were not affected by the use of different normalization methods.