Researchers from the University of Cape Town in South Africa found that the functional potential of the cervicovaginal microbiome (CVM) is influenced by microbial diversity and bacterial vaginosis, but not by HPV infection. As they report in PLOS One, the researchers used the PICRUSt bioinformatics tool to predict the functional metagenomic profiles of the CVMs of a cohort of 75 African women. From this analysis, they found that the predicted functional metagenome content largely correlated with microbial taxonomic diversity and bacterial vaginosis. In addition, they found that transport systems, such as ABC transporters, and transcription factors were enriched in diverse CVMs. "Such differentially abundant functional categories in CVM of women with and without microbial diversity, BV, and HR-HPV infection may have diagnostic, therapeutic, and prognostic applications," the researchers add.
A team from the US and Germany report in PLOS Biology on glycosylation patterns within the model archaeon Haloferax volcanii. They analyzed glycoproteomic datasets from both wild-type and mutant Hfx. volcanii strains in combination with data from the Archaeal Proteome Project to find that N- glycosylation influences colony morphology and cell shape, in addition to its known role in motility. They further found that different N-glycosylation pathways can glycosylate the same sites. "In conclusion, the extensive and complex N-glycoproteome of Hfx. volcanii that we revealed here shines new light on a variety of cellular functions," the researchers write. "While the specific effects of N-glycosylation on the various identified proteins remain to be studied in detail, their identification represents an essential first step in analyzing the roles of N-glycosylation in archaea."
A team from the Harbin Institute of Technology in China presents a new approach to block imputation for dropout events within single-cell data. As they write in PLOS Computational Biology, the researchers note that the low number of mRNA reads obtained can lead to drop outs, affecting downstream analyses. They present a statistical method dubbed SDImpute, for Single-cell RNA-seq Dropout Imputation, to recover single-cell RNA-seq data from cell-level and gene-level information. When they then performed analyses like clustering and differential gene expression analysis on simulated and real single-cell RNA-seq data, they found SDImpute improved upon other imputation approaches. "We hope that SDImpute will be beneficial to researchers to identify mechanisms underlying some biological processes by analysis of the scRNA-seq data," they add.