Coenzyme A restriction as a factor underlying pre-eclampsia and other antenatal conditions.

Pre-eclampsia is the most common pregnancy complication affecting 1 in 20 pregnancies, characterized by high blood pressure and signs of organ damage, most often to the liver and kidneys. Metabolic network analysis of published lipidomic data points to a shortage of Coenzyme A (CoA). Gene-expression profile data reveal alterations to many areas of metabolism and, crucially, to conflicting cellular regulatory mechanisms arising from the overproduction of signalling lipids driven by CoA limitation. Adverse feedback loops appear, forming sphingosine-1-phosphate (a cause of hypertension, hypoxia and inflammation), cytotoxic isoketovaleric acid (inducing acidosis and organ damage) and a thrombogenic lysophosphatidyl serine. These also induce mitochondrial and oxidative stress, leading to untimely apoptosis, which is possibly the cause of CoA restriction. This work provides a molecular basis for the signs of pre-eclampsia, why other conditions are risk factors and what might be done to treat and reduce the risk of this and related diseases.

Identification of Key Pathways and Genes in SARS-CoV-2 Infecting Human Intestines by Bioinformatics Analysis

COVID-19 is a serious infectious disease that has recently swept the world, and research on its causative virus, SARS-CoV-2, remains insufficient. Therefore, this study uses bioinformatics analysis techniques to explore the human digestive tract diseases that may be caused by SARS-CoV-2 infection. The gene expression profile data set, numbered GSE149312, is from the Gene Expression Omnibus (GEO) database and is divided into a 24-h group and a 60-h group. R software is used to analyze and screen out differentially expressed genes (DEGs) and then gene ontology (GO) term and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses are performed. In KEGG, the pathway of non-alcoholic fatty liver disease exists in both the 24-h group and 60-h group. STRING is used to establish a protein–protein interaction (PPI) network, and Cytoscape is then used to visualize the PPI and define the top 12 genes of the node as the hub genes. Through verification, nine statistically significant hub genes are identified: AKT1, TIMP1, NOTCH, CCNA2, RRM2, TTK, BUB1B, KIF20A, and PLK1. In conclusion, the results of this study can provide a certain direction and basis for follow-up studies of SARS-CoV-2 infection of the human digestive tract and provide new insights for the prevention and treatment of diseases caused by SARS-CoV-2.

Quantifying Intratumor Heterogeneity by Key Genes Selected Using Concrete Autoencoder

The tumor cell population in a cancer tissue has distinct molecular characteristics and exhibits different phenotypes, thus, resulting in different subpopulations. This phenomenon is known as Intratumor Heterogeneity (ITH), which is a major contributor in drug resistance, poor prognosis, etc. Therefore, quantifying the levels of ITH in cancer patients is essential and there are many algorithms which do so in different ways, using different types of omics data. DEPTH (Deviating gene Expression Profiling Tumor Heterogeneity) is the latest algorithm that uses transcriptomic data to evaluate the ITH score. It shows promising performance, has strong similarity with six other algorithms, and has advantage over two algorithms that uses same type of data (tITH, sITH). However, it has a major drawback that it uses expression values of all the genes (~20K genes) in quantifying ITH levels. We hypothesize that a subset of key genes is sufficient to quantify the ITH level for a tumor. To prove our hypothesis, we developed a deep learning-based computational framework using unsupervised Concrete Autoencoder (CAE) to select a set of cancer-specific key genes that can be used to evaluate the ITH score. For experiment, we used gene expression profile data of tumor cohorts of breast, kidney and lung cancer from TCGA repository. We selected three sets of key genes, each set related to breast, kidney, and lung tumor cohorts, using multi-run CAE. For the three cancers stated and three molecular subtypes of lung cancer, we calculated ITH level using all genes and key genes selected by CAE and performed a side-by-side comparison. It was found that similar conclusions can be reached for survival and prognostic outcomes based on ITH scores derived from all genes and the sets of key genes. Additionally, for subtypes of lung cancer, the comparative distribution of ITH scores derived from all genes and key genes remains similar. Based on these observations, it can be stated that, a subset of key genes, instead of all genes, is sufficient for ITH quantification. Our results also showed that many of the key genes are prognostically significant, which can be used as possible therapeutic targets.

Transcriptome-wide association study identifies susceptibility genes for rheumatoid arthritis

Objective To identify rheumatoid arthritis (RA)-associated susceptibility genes and pathways through integrating genome-wide association study (GWAS) and gene expression profile data. Methods A transcriptome-wide association study (TWAS) was conducted by the FUSION software for RA considering EBV-transformed lymphocytes (EL), transformed fibroblasts (TF), peripheral blood (NBL), and whole blood (YBL). GWAS summary data was driven from a large-scale GWAS, involving 5539 autoantibody-positive RA patients and 20,169 controls. The TWAS-identified genes were further validated using the mRNA expression profiles and made a functional exploration. Results TWAS identified 692 genes with PTWAS values < 0.05 for RA. CRIPAK (PEL = 0.01293, PTF = 0.00038, PNBL = 0.02839, PYBL = 0.0978), MUT (PEL = 0.00377, PTF = 0.00076, PNBL = 0.00778, PYBL = 0.00096), FOXRED1 (PEL = 0.03834, PTF = 0.01120, PNBL = 0.01280, PYBL = 0.00583), and EBPL (PEL = 0.00806, PTF = 0.03761, PNBL = 0.03540, PYBL = 0.04254) were collectively expressed in all the four tissues/cells. Eighteen genes, including ANXA5, AP4B1, ATIC (PTWAS = 0.0113, downregulated expression), C12orf65, CMAH, PDHB, RUNX3 (PTWAS = 0.0346, downregulated expression), SBF1, SH2B3, STK38, TMEM43, XPNPEP1, KIAA1530, NUFIP2, PPP2R3C, RAB24, STX6, and TLR5 (PTWAS = 0.04665, upregulated expression), were validated with integrative analysis of TWAS and mRNA expression profiles. TWAS-identified genes functionally involved in endoplasmic reticulum organization, regulation of cytokine production, TNF signaling pathway, immune response-regulating signaling pathway, regulation of autophagy, etc. Conclusion We identified multiple candidate genes and pathways, providing novel clues for the genetic mechanism of RA.

Immunological analysis and differential genes screening of venous thromboembolism

Purpose To explore the pathogenesis of venous thromboembolism (VTE) and provide bioinformatics basis for the prevention and treatment of VTE. Methods The R software was used to obtain the gene expression profile data of GSE19151, combining with the CIBERSORT database, obtain immune cells and differentially expressed genes (DEGs) of blood samples of VTE patients and normal control, and analyze DEGs for GO analysis and KEGG pathway enrichment analysis. Then, the protein-protein interaction (PPI) network was constructed by using the STRING database, the key genes (hub genes) and immune differential genes were screened by Cytoscape software, and the transcription factors (TFs) regulating hub genes and immune differential genes were analyzed by the NetworkAnalyst database. Results Compared with the normal group, monocytes and resting mast cells were significantly expressed in the VTE group, while regulatory T cells were significantly lower. Ribosomes were closely related to the occurrence of VTE. 10 hub genes and immune differential genes were highly expressed in VTE. MYC, SOX2, XRN2, E2F1, SPI1, CREM and CREB1 can regulate the expressions of hub genes and immune differential genes. Conclusions Ribosomal protein family genes are most relevant to the occurrence and development of VTE, and the immune differential genes may be the key molecules of VTE, which provides new ideas for further explore the pathogenesis of VTE.

Immunological analysis and differential genes screening of venous thromboembolism

Purpose: To explore the pathogenesis of venous thromboembolism (VTE) and provide bioinformatics basis for the prevention and treatment of VTE. Methods: The R software was used to obtain the gene expression profile data of GSE19151, combining with the CIBERSORT database, obtain immune cells and differentially expressed genes (DEGs) of blood samples of VTE patients and normal control, and analyze DEGs for GO analysis and KEGG pathway enrichment analysis. Then, the protein-protein interaction (PPI) network was constructed by using the STRING database, the key genes (hub genes) and immune differential genes were screened by Cytoscape software, and the transcription factors (TFs) regulating hub genes and immune differential genes were analyzed by the NetworkAnalyst database. Results: Compared with the normal group, monocytes and resting mast cells were significantly expressed in the VTE group, while regulatory T cells were significantly lower. Ribosomes were closely related to the occurrence of VTE. 10 hub genes and immune differential genes were highly expressed in VTE. MYC, SOX2, XRN2, E2F1, SPI1, CREM and CREB1 can regulate the expressions of hub genes and immune differential genes. Conclusions: Ribosomal protein family genes are most relevant to the occurrence and development of VTE, and the immune differential genes may be the key molecules of VTE, which provides new ideas for further explore the pathogenesis of VTE.

Huanglian Decoction Suppresses the Growth of Hepatocellular Carcinoma Cells by Reducing CCNB1 Expression

Background: Hepatocellular carcinoma (HCC) is one of the most prevalent cancers in human populations worldwide. Conversely, Huanglian Decoction is one of the most important Chinese medicine formulas, with the potential to treat cancer. Methods: To identify differentially expressed genes (DEG), we herein downloaded gene expression profile data from the TCGA (TCGA-LIHC) and GEO (GSE45436) databases. We obtained phytochemicals of the four constituent herbs of Huanglian Decoction from the traditional Chinese medicine systems pharmacology database and analysis platform (TCMSP). We also established a regulatory network of DEG and their drug target genes and subsequently analyzed key genes using bioinformatic approaches. Furthermore, we explored the effect of Huanglian Decoction by conducting in vitro experiments so as to verify the prediction. In particular, the CCNB1 gene was knockdown to verify the primary target of this Decoction. Results: Based on the results of network pharmacological analysis, we revealed that there are 5 bioactive compounds in Huanglian Decoction acting on HCC. In addition, our findings confirmed that CCNB1 was the primary key gene, which can be highly expressed in tumors and was significantly associated with a worse prognosis (P = 0.002) according to PPI network analysis of the target genes of these five compounds, as well as expression and prognosis analyses in tumors. We also noted that CCNB1 can be used as an independent prognostic indicator of HCC (P &lt; 0.01). Moreover, in vitro experimental results demonstrate that Huanglian Decoction can significantly inhibit the growth, migration, and invasion of HCC cells. Finally, further analysis showed that this Decoction may inhibit the growth of HCC cells by down-regulating the expression level of CCNB1.

Involvement of neutrophils in machineries underlying the rupture of intracranial aneurysms in rats

Subarachnoid hemorrhage due to rupture of an intracranial aneurysm has a quite poor prognosis after the onset of symptoms, despite the modern technical advances. Thus, the mechanisms underlying the rupture of lesions should be clarified. To this end, we obtained gene expression profile data and identified the neutrophil-related enriched terms in rupture-prone lesions using Gene Ontology analysis. Next, to validate the role of neutrophils in the rupture of lesions, granulocyte-colony stimulating factor (G-CSF) was administered to a rat model, in which more than half of induced lesions spontaneously ruptured, leading to subarachnoid hemorrhage. As a result, G-CSF treatment not only increased the number of infiltrating neutrophils, but also significantly facilitated the rupture of lesions. To clarify the mechanisms of how neutrophils facilitate this rupture, we used HL-60 cell line and found an enhanced collagenolytic activity, corresponding to matrix metalloproteinase 9 (MMP9), upon inflammatory stimuli. The immunohistochemical analyses revealed the accumulation of neutrophils around the site of rupture and the production of MMP9 from these cells in situ. Consistently, the collagenolytic activity of MMP9 could be detected in the lysate of ruptured lesions. These results suggest the crucial role of neutrophils to the rupture of intracranial aneurysms; implying neutrophils as a therapeutic or diagnostic target candidate.

Transcriptome-wide association study identifies susceptibility genes for rheumatoid arthritis

ObjectiveTo identify rheumatoid arthritis (RA) associated susceptibility genes and pathways through integrating genome-wide association study (GWAS) and gene expression profile data. MethodsA transcriptome-wide association study (TWAS) was conducted by the FUSION software for RA considering EBV-transformed lymphocytes (EL), transformed fibroblasts (TF), peripheral blood (NBL) and whole blood (YBL). GWAS summary data was driven from a large-scale GWAS, involving 5,539 autoantibody-positive RA patients and 20,169 controls. The TWAS-identified genes were further validated using the mRNA expression profiles and made a functional exploration. ResultsTWAS identified 692 genes with PTWAS values < 0.05 for RA. CRIPAK (PEL = 0.01293, PTF = 0.00038, PNBL = 0.02839, PYBL = 0.0978), MUT (PEL = 0.00377, PTF = 0.00076, PNBL = 0.00778, PYBL = 0.00096), FOXRED1 (PEL = 0.03834, PTF = 0.01120, PNBL = 0.01280, PYBL = 0.00583) and EBPL (PEL = 0.00806, PTF = 0.03761, PNBL = 0.03540, PYBL = 0.04254) were collectively expressed in all the four tissues/cells. 18 genes, including ANXA5, AP4B1, ATIC (PTWAS = 0.0113, down-regulated expression), C12orf65, CMAH, PDHB, RUNX3 (PTWAS = 0.0346, down-regulated expression), SBF1, SH2B3, STK38, TMEM43, XPNPEP1, KIAA1530, NUFIP2, PPP2R3C, RAB24, STX6, TLR5 (PTWAS = 0.04665, up-regulated expression), were validated with integrative analysis of TWAS and mRNA expression profiles. TWAS-identified genes functionally involved in endomembrane system organization, endoplasmic reticulum organization, regulation of cytokine production, TNF signaling pathway, etc. ConclusionWe identified multiple candidate genes and pathways, providing novel clues for the genetic mechanism of RA.