No abstract

Single-cell sequencing promotes our understanding of the heterogeneity of cellular populations, including the haplotypes and genomic variability among different generation of cells. Whole-genome amplification is crucial to generate sufficient DNA fragments for single-cell sequencing projects. Using sequencing data from single sperms, we quantitatively compare two prevailing amplification methods that extensively applied in single-cell sequencing, multiple displacement amplification (MDA) and multiple annealing and looping-based amplification cycles (MALBAC). Our results show that MALBAC, as a combination of modified MDA and tweaked PCR, has a higher level of uniformity, specificity and reproducibility.

Type 2 Diabetes Mellitus (T2DM) and obesity have become increasingly prevalent in recent years. Recent studies have focused on identifying causal variations or candidate genes for obesity and T2DM via analysis of expression quantitative trait loci (eQTL) within a single tissue. T2DM and obesity are affected by comprehensive sets of genes in multiple tissues. In the current study, gene expression levels in multiple human tissues from GEO datasets were analyzed, and 21 candidate genes displaying high percentages of differential expression were filtered out. Specifically, DENND1B, LYN, MRPL30, POC1B, PRKCB, RP4-655J12.3, HIBADH, and TMBIM4 were identified from the T2DM-control study, and BCAT1, BMP2K, CSRNP2, MYNN, NCKAP5L, SAP30BP, SLC35B4, SP1, BAP1, GRB14, HSP90AB1, ITGA5, and TOMM5 were identified from the obesity-control study. The majority of these genes are known to be involved in T2DM and obesity. Therefore, analysis of gene expression in various tissues using GEO datasets may be an effective and feasible method to determine novel or causal genes associated with T2DM and obesity.

Integrated single-cell RNA sequencing technologies reveal a high-resolution immune landscape of colorectal primary tumors and liver metastasis, identifying major immune cell types and distinct cell functional states of T and B cells as well as predictions of complex cell-cell interactions.

Non-human primates have emerged as an important resource for the study of human disease and evolution. The characterization of genomic variation between and within non-human primate species could advance the development of genetically defined non-human primate disease models. However, non-human primate specific reagents that would expedite such research, such as exon-capture tools, are lacking. We evaluated the efficiency of using a human exome capture design for the selective enrichment of exonic regions of non-human primates. We compared the exon sequence recovery in nine chimpanzees, two crab-eating macaques and eight Japanese macaques. Over 91% of the target regions were captured in the non-human primate samples, although the specificity of the capture decreased as evolutionary divergence from humans increased. Both intra-specific and inter-specific DNA variants were identified; Sanger-based resequencing validated 85.4% of 41 randomly selected SNPs. Among the short indels identified, a majority (54.6%–77.3%) of the variants resulted in a change of 3 base pairs, consistent with expectations for a selection against frame shift mutations. Taken together, these findings indicate that use of a human design exon-capture array can provide efficient enrichment of non-human primate gene regions. Accordingly, use of the human exon-capture methods provides an attractive, cost-effective approach for the comparative analysis of non-human primate genomes, including gene-based DNA variant discovery.

As research focusing on the colorectal cancer fecal microbiome using shotgun sequencing continues, increasing evidence has supported correlations between colorectal carcinomas (CRCs) and fecal microbiome dysbiosis. However, large-scale on-site and off-site (surrounding adjacent) tissue microbiome characterization of CRC was underrepresented. Here, considering each taxon as a feature, we demonstrate a machine learning-based method to investigate tissue microbial differences among CRC, colorectal adenoma (CRA), and healthy control groups using 16S rRNA data sets retrieved from 15 studies. A total of 2,099 samples were included and analyzed in case-control comparisons. Multiple methods, including differential abundance analysis, random forest classification, cooccurrence network analysis, and Dirichlet multinomial mixture analysis, were conducted to investigate the microbial signatures. We showed that the dysbiosis of the off-site tissue of colonic cancer was distinctive and predictive. The AUCs (areas under the curve) were 80.7%, 96.0%, and 95.8% for CRC versus healthy control random forest models using stool, tissue, and adjacent tissue samples and 69.9%, 91.5%, and 89.5% for the corresponding CRA models, respectively. We also found that the microbiota ecologies of the surrounding adjacent tissues of CRC and CRA were similar to their on-site counterparts according to network analysis. Furthermore, based on the enterotyping of tissue samples, the cohort-specific microbial signature might be the crux in addressing classification generalization problems. Despite cohort heterogeneity, the dysbiosis of lesion-adjacent tissues might provide us with further perspectives in demonstrating the role of the microbiota in colorectal cancer tumorigenesis. IMPORTANCE Turbulent fecal and tissue microbiome dysbiosis of colorectal carcinoma and adenoma has been identified, and some taxa have been proven to be carcinogenic. However, the microbiomes of surrounding adjacent tissues of colonic cancerous tissues were seldom investigated uniformly on a large scale. Here, we characterize the microbiome signatures and dysbiosis of various colonic cancer sample groups. We found a high correlation between colorectal carcinoma adjacent tissue microbiomes and their on-site counterparts. We also discovered that the microbiome dysbiosis in adjacent tissues could discriminate colorectal carcinomas from healthy controls effectively. These results extend our knowledge on the microbial profile of colorectal cancer tissues and highlight microbiota dysbiosis in the surrounding tissues. They also suggest that microbial feature variations of cancerous lesion-adjacent tissues might help to reveal the microbial etiology of colonic cancer and could ultimately be applied for diagnostic and screening purposes.

Parkinson's disease (PD) is the second most common neurodegenerative disease, and mild cognitive impairment (MCI) is a well-established risk factor for the development of dementia in PD. A growing body of evidence suggests that low expression of glucocerebrosidase (GBA) promotes the transmission of α-synuclein (α-Syn) interpolymers and the progression of PD. However, how GBA mutations affect the pathogenesis of PD via abnormal aggregation of α-Syn is unclear, and no clinically valid PD-MCI genetic markers have been identified. Here, we first located a GBA eQTL, rs12411216, by analysing DHS, eQTL SNP, and transcription factor binding site data using the UCSC database. Subsequently, we found that rs12411216 was significantly associated with PD-MCI (P < 0.05) in 306 PD patients by genotyping. In exploring the relationship between rs12411216 and GBA expression, the SNP was found to be associated with GBA expression in 50 PD patients through qPCR verification. In a further CRISPR/Cas9-mediated genome editing module, the SNP was identified to cause a decrease in GBA expression, weaken enzymatic activity and enhance the abnormal aggregation of α-Syn in SH-SY5Y cells. Additionally, using an electrophoretic mobility shift assay, we confirmed that the binding efficiency of transcription factor E2F4 was affected by the rs12411216 SNP. In conclusion, our results showed that rs12411216 regulated GBA expression, supporting its potential role as a PD-MCI genetic biomarker and highlighting novel mechanisms underlying Parkinson's disease.

The product of transmembrane and coiled-coil domains 1 (TMCO1) gene is a member of DUF841 superfamily of several eukaryotic proteins with unknown function. The partial DNA sequence of porcine TMCO1 was first cloned with a pig 567 bp ORF encoding 188 amino acids. By tissues expression analysis, the TMCO1 was found highly expressed in the liver, kidney and heart. The porcine TMCO1 protein was subsequently demonstrated to localize in the mitochondrion by confocal fluorescence microscopy. This data provides an important basis for conducing further studies on the functions and regulatory mechanisms underlying the role of TMCO1 gene.