Researchers at Albert Einstein College of Medicine, Fred Hutchinson Cancer Research Center, and elsewhere report on findings from a gut microbial community analysis focused on participants in the Hispanic Community Health Study/Study of Latinos. Using targeted 16S ribosomal RNA gene sequencing, the team profiled bacterial and fungal community members in fecal samples from almost 1,700 individuals between the ages of 18 and 74, identifying gut microbial features that varied alongside participants' socioeconomic features, migration history, or body mass index. Their analysis highlighted declining gut microbiome diversity in individuals who moved to the US when they were young, for example, and suggested that an enhanced ratio of Prevotella to Bacteroides bugs in more obese individuals — a pattern that contrasts with previous studies documenting a dip in the Prevotella to Bacteroides ratio in the gut microbiomes of obese individuals.
A team from Korea and the US describes a collection of patient-derived tumor cells established from gynecological cancer to track drug responses and other pharmacogenomic features. The set currently contains 139 tumor cells developed with samples from individuals with cervical, uterine/endometrial, or epithelial ovarian cancers, the researchers report, noting that somatic mutation profiles, copy number changes, gene expression, and responses to more than three dozen molecularly targeted drugs were assessed in a subset of the tumors with exome sequencing, transcriptome sequencing, and drug screening. With these data, the authors started unraveling tumor features coinciding with drug response, including potential markers for specific drug responses. "[O]ur results demonstrate the potential utility of rapid drug screening combined with genomic profiling for precision treatment of gynecologic cancers," they write.
Finally, an international team led by investigators at Iowa State University and Northeastern University shares findings from the third challenge done under the "Critical Assessment of Functional Annotation" (CAFA) initiative, a community challenge aimed at evaluating and enhancing computational strategies for protein function annotations. For the CAFA3 challenge, the investigators continued evaluating protein function prediction methods, bringing in experimental data from gene-function screens or assays done on Candida albicans, C. aeruginosa, and Drosophila melanogaster models, before comparing the methods that rose to the top during the first, second, and third stages of CAFA. "We conclude that while predictions of the molecular function and biological process annotations have slightly improved over time, those of the cellular components have not," the authors report, adding that "[t]erm-centric prediction of experimental annotations remains equally challenging."