Discovery of Kerivoula kachinensis and a validity of K. titania (Chiroptera: Vespertilionidae) in China

In April 2019, 15 (10♂, 5♀) Kerivoula bats were collected by harp traps from Xishuangbanna, Yunnan Province, China. External and craniodental examination, multivariate statistical analyses and molecular phylogenetic inference (CoI, Cytb and Rag2 gene markers) indicated they are Kerivoula kachinensis and Kerivoula titania, respectively. Former represents a new chiropteran record from China, while the latter is a valid occurrence of K. titania in this region because recent study indicate a misidentification of “K. titania” in Guangdong, Guangxi and Hainan, China. All specimens are presently preserved at Key Laboratory of Conservation and Application in Biodiversity of South China in Guangzhou University, Guangzhou, China. Nowadays, four woolly bats occur in China including, Kerivoula furva, K. kachinensis, Kerivoula picta and K. titania, whilst there is a risk of underestimation the actual species diversity in China region when comparing those of neighboring region such as Vietnam. Supports for field survey need to be continued in future.

Improved Genomic Identification, Clustering, and Serotyping of Shiga Toxin-Producing Escherichia coli Using Cluster/Serotype-Specific Gene Markers

Shiga toxin-producing Escherichia coli (STEC) have more than 470 serotypes. The well-known STEC O157:H7 serotype is a leading cause of STEC infections in humans. However, the incidence of non-O157:H7 STEC serotypes associated with foodborne outbreaks and human infections has increased in recent years. Current detection and serotyping assays are focusing on O157 and top six (“Big six”) non-O157 STEC serogroups. In this study, we performed phylogenetic analysis of nearly 41,000 publicly available STEC genomes representing 460 different STEC serotypes and identified 19 major and 229 minor STEC clusters. STEC cluster-specific gene markers were then identified through comparative genomic analysis. We further identified serotype-specific gene markers for the top 10 most frequent non-O157:H7 STEC serotypes. The cluster or serotype specific gene markers had 99.54% accuracy and more than 97.25% specificity when tested using 38,534 STEC and 14,216 non-STEC E. coli genomes, respectively. In addition, we developed a freely available in silico serotyping pipeline named STECFinder that combined these robust gene markers with established E. coli serotype specific O and H antigen genes and stx genes for accurate identification, cluster determination and serotyping of STEC. STECFinder can assign 99.85% and 99.83% of 38,534 STEC isolates to STEC clusters using assembled genomes and Illumina reads respectively and can simultaneously predict stx subtypes and STEC serotypes. Using shotgun metagenomic sequencing reads of STEC spiked food samples from a published study, we demonstrated that STECFinder can detect the spiked STEC serotypes, accurately. The cluster/serotype-specific gene markers could also be adapted for culture independent typing, facilitating rapid STEC typing. STECFinder is available as an installable package (https://github.com/LanLab/STECFinder) and will be useful for in silico STEC cluster identification and serotyping using genome data.

Membrane tension sensing molecule-FNBP1 is a prognostic biomarker related to immune infiltration in BRCA, LUAD and STAD

Background Formin-binding protein 1/17 (FNBP1/FBP17), as a membrane-bound protein, is wildly expressed in eukaryotic cells and performs a critical role in tumor tumorigenesis and progression. However, the relationship between FNBP1 and immune infiltrating cells, prognostic value in patients still require comprehensive understanding. We purposed to explore the correlations of FNBP1 expression, prognosis and immune infiltration levels in various cancers. Method The expression and survival data of FNBP1 were collected from Oncomine, TIMER, GEPIA, Kaplan–Meier Plotter and PrognoScan databases. Correlations between FNBP1 and immune infiltrates were analyzed in TIMER and GEPIA databases. Results Compared with normal tissues, FNBP1 is significantly differentially expressed in a variety of tumor tissues. FNBP1 has significant and complex effects on the prognosis of kinds of cancers. High-expression was obviously correlated with better prognosis in breast carcinoma and lung adenocarcinoma, while worse prognosis in stomach adenocarcinoma. Besides, FNBP1 had a correlation with various immune infiltrating cells and diverse immune gene markers in breast invasive carcinoma (BRCA), lung adenocarcinoma (LUAD), and stomach adenocarcinoma (STAD). FNBP1 was also positively correlated with the adjustment of CD8+ cells, T cells, M2 macrophage, neutrophils, monocyte, Th1 cells, T regulatory cells (Treg) and Tumor-associated macrophages (TAMs). The expression level of FNBP1 is closely positively correlated with the expression level of multiple immune checkpoints in the three cancers. In addition, FNBP1 is significantly positively correlated with the expression levels of a variety of immunosuppressive molecules. Conclusion Our findings reveal FNBP1 can serve as a significant biomarker to influence the prognosis and the immune infiltrating levels in different cancers. The differential expression of FNBP1 might not only contribute to the judgment of metastatic and non-metastatic tumors but also in the immune escape by upregulating the expression of immune checkpoints.

Identification of Hub Biomarkers and Immune-Related Pathways Participating in the Progression of Antineutrophil Cytoplasmic Antibody-Associated Glomerulonephritis

BackgroundAntineutrophil cytoplasmic antibody (ANCA)-associated vasculitis (AAV) is a systemic autoimmune disease that generally induces the progression of rapidly progressive glomerulonephritis (GN). The purpose of this study was to identify key biomarkers and immune-related pathways involved in the progression of ANCA-associated GN (ANCA-GN) and their relationship with immune cell infiltration.MethodsGene microarray data were downloaded from the Gene Expression Omnibus (GEO). Hub markers for ANCA-GN were mined based on differential expression analysis, weighted gene co-expression network analysis (WGCNA) and lasso regression, followed by Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG) and Gene Set Enrichment Analysis (GSEA) of the differential genes. The infiltration levels of 28 immune cells in the expression profile and their relationship to hub gene markers were analysed using single-sample GSEA (ssGSEA). In addition, the accuracy of the hub markers in diagnosing ANCA-GN was subsequently evaluated using the receiver operating characteristic curve (ROC).ResultsA total of 651 differential genes were screened. Twelve co-expression modules were obtained via WGCNA; of which, one hub module (black module) had the highest correlation with ANCA-GN. A total of 66 intersecting genes were acquired by combining differential genes. Five hub genes were subsequently obtained by lasso analysis as potential biomarkers for ANCA-GN. The immune infiltration results revealed the most significant relationship among monocytes, CD4+ T cells and CD8+ T cells. ROC curve analysis demonstrated a prime diagnostic value of the five hub genes. According to the functional enrichment analysis of the differential genes, hub genes were mainly enhanced in immune- and inflammation-related pathways.ConclusionB cells and monocytes were closely associated with the pathogenesis of ANCA-GN. Hub genes (CYP3A5, SLC12A3, BGN, TAPBP and TMEM184B) may be involved in the progression of ANCA-GN through immune-related signal pathways.

High-Throughput Muscle Fiber Typing From RNA Sequencing Data

BACKGROUND: Skeletal muscle fiber type distribution has implications for human health, muscle function and performance. This knowledge has been gathered using labor-intensive and costly methodology that limited these studies. Here we present a method based on muscle tissue RNA sequencing data (totRNAseq) to estimate the distribution of skeletal muscle fiber types from frozen human samples, allowing for a larger number of individuals to be tested.METHODS: By using single-nuclei RNA sequencing (snRNAseq) data as a reference, cluster expression signatures were produced by averaging gene expression of cluster gene markers and then applying these to totRNAseq data and inferring muscle fiber nuclei type via linear matrix decomposition. This estimate was then compared with fiber type distribution measured by ATPase staining or myosin heavy chain protein isoform distribution of 62 muscle samples in two independent cohorts (n = 39 and 22).RESULTS: The correlation between the sequencing-based method and the other two were rATPas = 0.65 [0.46 – 0.84], [95% CI] and rmyosin = 0.80 [0.71 – 0.89], with p = 7.96 x 10-6 and 8.06 x 10-6 respectively. The deconvolution inference of fiber type composition was accurate even for very low totRNAseq sequencing depths, i.e., down to an average of ~5.000 paired-end reads.CONCLUSIONS: This new method (https://github.com/OlaHanssonLab/PredictFiberType) consequently allows for measurement of fiber type distribution of a larger number of samples using totRNAseq in a cost and labor-efficient way. For the first time, it is now feasible to study the association between fiber type distribution and e.g. health outcomes in large well-powered studies.

A Putatively New Family of Alphaproteobacterial Chloromethane Degraders from a Deciduous Forest Soil Revealed by Stable Isotope Probing and Metagenomics

Background: Chloromethane (CH3 Cl) is the most abundant halogenated organic compound in the atmosphere and substantially responsible for the destruction of the stratospheric ozone layer. Since anthropogenic CH 3 Cl sources have become negligible with the application of the Montreal Protocol (1987), natural sources, such as vegetation and soils, have increased proportionally in the global budget. CH3 Cl-degrading methylotrophs occurring in soils might be an important and overlooked sink.Results & Conclusions: The objective of our study was to link the biotic CH3 Cl sink with the identity of active microorganisms and their biochemical pathways for CH3 Cl degradation in a deciduous forest soil. When tested in laboratory microcosms, biological CH3 Cl consumption occurred in leaf litter, senescent leaves, and organic and mineral soil horizons. Highest consumption rates, around 2 mmol CH3 Cl g -1 dry weight h -1 , were measured in organic soil and senescent leaves, suggesting that top soil layers are active (micro-)biological CH 3 Cl degradation compartments of forest ecosystems. The DNA of these [13C]-CH3 Cl-degrading microbial communities was labelled using stable isotope probing (SIP), and the corresponding taxa and their metabolic pathways studied using high-throughput metagenomics sequencing analysis. [ 13C]-labelled Metagenome-Assembled Genome closely related to the family Beijerinckiaceae may represent a new methylotroph family of Alphaproteobacteria, which is found in metagenome databases of forest soils samples worldwide. Gene markers of the only known pathway for aerobic CH3 Cl degradation, via the methyltransferase system encoded by the CH3 Cl utilisation genes (cmu), were undetected in the DNA-SIP metagenome data, suggesting that biological CH3 Cl sink in this deciduous forest soil operates by a cmu-independent metabolism.

Single-cell analysis of microglial transcriptomic diversity in subarachnoid hemorrhage

Background: Subarachnoid hemorrhage (SAH) is a severe stroke and the advanced treatment for SAH is still limited. Recent studies have shown that microglia-mediated neuroinflammation plays a critical role in the pathogenesis of SAH. Microglia can transform their states in response to central nervous system injury. However, the transcriptomic features of microglia remained unknown in SAH. Recent developed single-cell RNA sequencing (scRNA-seq) provides a possible way to solve this problem. Methods: Endovascular perforation (EVP) murine SAH model was established to reproduce experimental SAH. Microglia states are examined with immune staining and quantitate analysis. Post-SAH microglial single-cell suspension were harvest and sequenced using 10X scRNA-seq platform. Then, the detailed single-cell transcriptomic characterization of post-SAH microglia were analyzed with bioinformatics. Results: Transcriptional analysis revealed at least ten diverse microglial subgroups, including SAH-associated microglia (SAM), inflammatory-associated microglia (IAM) and proliferation-associated microglia (PAM), which all exhibit distinct marker gene expression patterns. Microglia subsets interaction reveals the functional relationship between elevated signaling pathways and microglial sub-populations in SAH. Receptor-ligand pair analysis revealed that complex inter-cellular interactions exist between the microglia subsets and other cell types, and indicated that microglia are important mediators of neuroinflammation after SAH. Integrated analysis with normal microglia further proved the existence of these microglia subpopulations and different gene markers associated with SAH were clarified. Conclusions: Collectively, we first report the single-cell transcriptome of post-SAH microglia and found specific biomarkers related to the neuroinflammation in SAH. These results enhanced our understanding of the pathological mechanisms of microglial response to SAH, and may guide future development of SAH monitoring methods and therapeutics.

Cellular Processes in Human Ovarian Follicles Are Regulated by Expression Profile of New Gene Markers—Clinical Approach

In the growing ovarian follicle, the maturing oocyte is accompanied by cumulus (CCs) and granulosa (GCs) cells. Currently, there remain many unanswered questions about the epithelial origin of these cells. Global and targeted gene transcript levels were assessed on 1, 7, 15, 30 days of culture for CCs and GCs. Detailed analysis of the genes belonging to epithelial cell-associated ontological groups allowed us to assess a total of 168 genes expressed in CCs (97 genes) and GCs (71 genes) during long-term in vitro culture. Expression changes of the analyzed genes allowed the identification of the group of genes: TGFBR3, PTGS2, PRKX, AHI1, and IL11, whose expression decreased the most and the group of ANXA3, DKK1, CCND1, STC1, CAV1, and SFRP4 genes, whose expression significantly increased. These genes’ expression indicates CCs and GCs epithelialization processes and their epithelial origin. Expression change analysis of genes involved in epithelization processes in GCs and CCs during their in vitro culture made it possible to describe the most significantly altered of the 11 genes. Detailed analysis of gene expression in these two cell populations at different time intervals confirms their ovarian surface epithelial origin. Furthermore, some gene expression profiles appear to have tumorigenic properties, suggesting that granulosa cells may play a role in cancerogenesis.