Aims We aimed to assess the association between gut bacterial biomarkers during early pregnancy and subsequent risk of gestational diabetes mellitus (GDM) in Chinese pregnant women. Methods Within the Tongji-Shuangliu Birth Cohort study, we conducted a nested case-control study among 201 incident GDM cases and 201 matched controls. Fecal samples were collected during early pregnancy (at 6-15 weeks), and GDM was diagnosed at 24-28 weeks of pregnancy. Community DNA isolated from fecal samples and V3-V4 region of 16S rRNA gene amplicon libraries were sequenced. Results In GDM cases versus controls, Rothia, Actinomyces, Bifidobacterium, Adlercreutzia, and Coriobacteriaceae, and Lachnospiraceae spp. were significantly reduced, while Enterobacteriaceae, Ruminococcaceae spp. and Veillonellaceae were over-represented. In addition, the abundance of Staphylococcus relative to Clostridium, Roseburia and Coriobacteriaceae as reference microorganisms were positively correlated with fasting blood glucose, 1-h and 2-h postprandial glucose levels. Adding microbial taxa to the base GDM prediction model with conventional risk factors increased the C-statistic significantly (P<0.001) from 0.69 to 0.75. Conclusions Gut microbiota during early pregnancy was associated with subsequent risk of GDM. Several beneficial and commensal gut microorganisms showed inverse relations with incident GDM, while opportunistic pathogenic members were related to higher risk of incident GDM and positively correlated with glucose levels on OGTT.

Genetic mutations are the major pathogenic factor of Autism Spectrum Disorder (ASD). In recent years, more and more ASD risk genes have been revealed, among which there are a group of transcriptional regulators. Considering the similarity of the core clinical phenotypes, it is possible that these different factors may regulate the expression levels of certain key targets. Identification of these targets could facilitate the understanding of the etiology and developing of novel diagnostic and therapeutic methods. Therefore, we performed integrated transcriptome analyses of RNA‐Seq and microarray data in multiple ASD mouse models and identified a number of common downstream genes in various brain regions, many of which are related to the structure and function of the synapse components or drug addiction. We then established protein–protein interaction networks of the overlapped targets and isolated the hub genes by 11 algorithms based on the topological structure of the networks, including Sdc4, Vegfa, and Cp in the Cortex‐Adult subgroup, Gria1 in the Cortex‐Juvenile subgroup, and Kdr, S1pr1, Ubc, Grm2, Grin2b, Nrxn1, Pdyn, Grin3a, Itgam, Grin2a, Gabra2, and Camk4 in the Hippocampus‐Adult subgroup, many of which have been associated with ASD in previous studies. Finally, we cross compared our results with human brain transcriptional data sets and verified several key candidates, which may play important role in the pathology process of ASD, including SDC4, CP, S1PR1, UBC, PDYN, GRIN2A, GABRA2, and CAMK4. In summary, by integrated bioinformatics analysis, we have identified a series of potentially important molecules for future ASD research. Autism Res 2020, 13: 352–368. © 2019 International Society for Autism Research, Wiley Periodicals, Inc. Lay Summary Abnormal transcriptional regulation accounts for a significant portion of Autism Spectrum Disorder. In this study, we performed transcriptome analyses of mouse models to identify common downstream targets of transcriptional regulators involved in ASD. We identified several recurrent target genes that are close related to the common pathological process of ASD, including SDC4, CP, S1PR1, UBC, PDYN, GRM2, NRXN1, GRIN3A, ITGAM, GRIN2A, GABRA2, and CAMK4. These results provide potentially important targets for understanding the molecular mechanism of ASD.

BackgroundNeuroblastoma is one of the most devastating forms of childhood cancer. Despite large amounts of attempts in precise survival prediction in neuroblastoma, the prediction efficacy remains to be improved.MethodsHere, we applied a deep-learning (DL) model with the attention mechanism to predict survivals in neuroblastoma. We utilized 2 groups of features separated from 172 genes, to train 2 deep neural networks and combined them by the attention mechanism.ResultsThis classifier could accurately predict survivals, with areas under the curve of receiver operating characteristic (ROC) curves and time-dependent ROC reaching 0.968 and 0.974 in the training set respectively. The accuracy of the model was further confirmed in a validation cohort. Importantly, the two feature groups were mapped to two groups of patients, which were prognostic in Kaplan-Meier curves. Biological analyses showed that they exhibited diverse molecular backgrounds which could be linked to the prognosis of the patients.ConclusionsIn this study, we applied artificial intelligence methods to improve the accuracy of neuroblastoma survival prediction based on gene expression and provide explanations for better understanding of the molecular mechanisms underlying neuroblastoma.

Malignant pleural mesothelioma (MPM) is a highly aggressive cancer with short survival time. Unbalanced competing endogenous RNAs (ceRNAs) have been shown to participate in the tumor pathogenesis and served as biomarkers for the clinical prognosis. However, the comprehensive analyses of the ceRNA network in the prognosis of MPM are still rarely reported. In this study, we obtained the transcriptome data of the MPM and the normal samples from TCGA, EGA, and GEO databases and identified the differentially expressed (DE) mRNAs, lncRNAs, and miRNAs. The functions of the prognostic genes and the overlapped DEmRNAs were further annotated by the multiple enrichment analyses. Then, the targeting relationships among lncRNA–miRNA and miRNA–mRNA were predicted and calculated, and a prognostic ceRNA regulatory network was established. We included the prognostic 73 mRNAs and 13 miRNAs and 26 lncRNAs into the ceRNA network. Moreover, 33 mRNAs, three miRNAs, and seven lncRNAs were finally associated with prognosis, and a model including seven mRNAs, two lincRNAs, and some clinical factors was finally established and validated by two independent cohorts, where CDK6 and SGMS1-AS1 were significant to be independent prognostic factors. In addition, the identified co-expressed modules associated with the prognosis were overrepresented in the ceRNA network. Multiple enrichment analyses showed the important roles of the extracellular matrix components and cell division dysfunction in the invasion of MPM potentially. In summary, the prognostic ceRNA network of MPM was established and analyzed for the first time and these findings shed light on the function of ceRNAs and revealed the potential prognostic and therapeutic biomarkers of MPM.

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