Anal fistula is a common proctological disease, but the thorough mechanisms of the anal fistula formation are still unclear. An increasing number of studies have revealed the crucial role of gut microbiota in intestinal diseases. We used 16S rRNA gene sequencing to analyze the intestinal microbiome in order to determine whether there are differences in the microbiome between anal fistula patients and healthy individuals. The microbiome samples were extracted by repeatedly wiping the rectal wall with intestinal swab. Before this operation, the whole intestine of all participants was irrigated and the score of the Boston bowel preparation scale reached 9. The biodiversity of gut microbiome of rectum revealed significant difference between anal fistula patients and healthy individuals. 36 discriminative taxa were identified by LEfSe analysis between two groups. At the phylum level, Synergistetes was enriched in anal fistula patients, while Proteobacteria was higher in healthy individuals. We also found that at the genus level, Blautia, Faecalibacterium, Ruminococcus, Coprococcus, Bacteroides, Clostridium, Megamonas and Anaerotruncus were highly enriched in anal fistula patients, while the microbiome of healthy individuals was enriched with Peptoniphilus and Corynebacterium. Spearman correlations showed the extensive and close association among genera and species. Finally, a diagnostic prediction model was constructed by random forest classifier, and the area under curve (AUC) reached 0.990. This study gave an important hint for analyzing gut microbiome of rectum in anal fistula patient.Keypoints.We use the 16S rRNA gene sequencing to test the microbiome samples extracted from the intestinal swab. This is the first study to explore the gut microbiome of rectum using this workflow. We also found the distinct gut microbiome of rectum differences between anal fistula patients and healthy individuals.
Abstract Background Kashin‐Beck Disease (KBD) is a chronic, endemic osteoarthropathy. Inositol 1,4,5‐triphosphate receptor type 2 ( ITPR2 ) gene is involved in chondrocytes. We speculated that single‐nucleotide polymorphisms (SNPs) in ITPR2 gene may have an association with KBD in Tibetan. Methods To prove this hypothesis, a total of eight SNPs (rs1049376, rs11048526, rs11048556, rs11048585, rs16931011, rs10842759, rs2230372, and rs7134213) were selected, and genotyped in 316 KBD patients and 320 controls. The association between ITPR2 variants and KBD risk was assessed by logistic regression analysis. Results The study identified significant association ( p = 0.019) between KBD and a novel locus, ITPR2 SNP rs11048526 (OR = 1.49, 95% CI = 1.07–2.08). The variant A/G genotype frequency in rs11048526 had a significantly increased risk of KBD in co‐dominant model (OR = 3.70, 95% CI = 1.26–10.89, p = 0.018), dominant model (OR = 3.56, 95% CI = 1.22–10.40, p = 0.020), log‐additive model (OR = 3.00, 95% CI = 1.12–8.00, p = 0.029) after adjusted for age and gender. Conclusion The results indicate a potential association between ITPR2 and KBD risk in Tibetan. Further work is required to confirm these results and explore the mechanisms of these effects.
Abstract Enhancer RNAs (eRNAs) are a subclass of long noncoding RNAs (lncRNAs) that have a wide effect in human tumors. However, the systematic analysis of potential functions of eRNAs‐related genes (eRGs) in colon cancer (CC) remains unexplored. In this study, a total of 8231 eRGs including 6236 protein‐coding genes and 1995 lncRNAs were identified in CC based on the multiple resources. These eRGs showed higher expression level and stability compared to other genes. What's more, the functions of these eRGs were closely related to cancer. Then a prognostic prediction model with 12 eRGs signatures were obtained for colon adenocarcinoma (COAD) patients. ROC curves showed the AUCs were 0.81, 0.77, and 0.78 for 1‐, 3‐, and 5‐year survival prediction, respectively. And the prognostic model also manifested good performance in the validation datasets. Besides, the expression levels of two prognostic signatures, TMEM220 and LRRN2 , were verified to be significantly lower in CC tissues than in adjacent noncancerous tissues ( p < .05). Finally, the distinct molecular features were characterized between the high‐ and low‐risk group through multiomics analysis including DNA mutation and methylation. Our results show eRGs signatures based prognostic model has high accuracy and may provide innovative biomarkers in COAD.
Abstract Enterovirus A71 (EV-A71), Coxsackievirus A16 (CV-A16) and CV-A10 are the major causative agents of hand, foot and mouth disease (HFMD). The conformational epitopes play a vital role in monitoring the antigenic evolution, predicting dominant strains and preparing vaccines. In this study, we employed a Bioinformatics-based algorithm to predict the conformational epitopes of EV-A71 and CV-A16 and compared with that of CV-A10. Prediction results revealed that the distribution patterns of conformational epitopes of EV-A71 and CV-A16 were similar to that of CV-A10 and their epitopes likewise consisted of three sites: site 1 (on the “north rim” of the canyon around the fivefold vertex), site 2 (on the “puff”) and site 3 (one part was in the “knob” and the other was near the threefold vertex). The reported epitopes highly overlapped with our predicted epitopes indicating the predicted results were reliable. These data suggested that three-site distribution pattern may be the basic distribution role of epitopes on the enteroviruses capsids. Our prediction results of EV-A71 and CV-A16 can provide essential information for monitoring the antigenic evolution of enterovirus.
Abstract Growing evidence supports the transcription of enhancer RNAs (eRNAs) and their important roles in gene regulation. However, their interactions with other biomolecules and their corresponding functionality remain poorly understood. In an attempt to facilitate mechanistic research, this study presents eRNA-IDO, the first integrative computational platform for the identification, interactome discovery, and functional annotation of human eRNAs. eRNA-IDO comprises two modules: eRNA-ID and eRNA-Anno. Functionally, eRNA-ID can identify eRNAs from de novo assembled transcriptomes. eRNA-ID includes eight kinds of enhancer makers, enabling users to customize enhancer regions flexibly and conveniently. In addition, eRNA-Anno provides cell-/tissue-specific functional annotation for both new and known eRNAs by analyzing the eRNA interactome from prebuilt or user-defined networks between eRNAs and protein-coding genes. The prebuilt networks include the Genotype-Tissue Expression (GTEx)-based co-expression networks in normal tissues, The Cancer Genome Atlas (TCGA)-based co-expression networks in cancer tissues, and omics-based eRNA-centric regulatory networks. eRNA-IDO can facilitate research on the biogenesis and functions of eRNAs. The eRNA-IDO server is freely available at http://bioinfo.szbl.ac.cn/eRNA_IDO/.
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