Australian researchers report on findings from a functional analysis of cancer drug resistance genes in cell lines and tumor xenograft models in mice. Using a CRISPR-Cas9 gene editing-based knockout screening in cancer cells, the team tallied the genes and pathways targeted by cancer treatments, while unearthing 10 genes related to resistance to multiple drugs. In follow up experiments in mouse models, for example, they found saw a rise in chemotherapy resistance and dip in prognoses when the expression of a gene called RDD1 was dialed down. "Together these data, complemented by existing drug sensitivity information and CRISPR-Cas9 knockout data, may help to shape personalized therapies, instruct future drug development, and guide the design of molecularly optimized combination treatment for cancer patients," they conclude.
A University of North Carolina at Chapel Hill-led team outlines a transcriptome-wide association study (TWAS) framework focused on teasing out relationships between germline variants with cancer in breast cancer cases spanning diverse human populations, including populations with particularly poor survival outcomes. Starting with an expression quantitative trait locus analysis centered on more than 400 breast cancer-related genes, the researchers trained germline variant-informed tumor gene expression prediction models, demonstrating the predictive differences that arise in such models depending on individuals' ancestry and tumor subtype. "We show that carefully implemented and thoroughly validated TWAS is an efficient approach for understanding the genetics underpinning breast cancer outcomes in diverse populations," they write.
Finally, researchers from China, the US, and Italy profile the microbial communities found within airborne particulate matter in the city of Beijing over a six month timeframe. The team collected 106 airborne particulate samples in late 2012 and early 2013, using shotgun metagenomic sequencing to identify reads from bacteria, eukaryotes, archaea, and viruses in these "megacity" microbiomes over time in relation to air quality and other environmental factors. The sequence data also revealed the sets of antibiotic resistance and detoxification genes that were present in the particulate matter microbiomes, among other findings. "Our work provides further evidence for potential environmental and mammalian sources of microbes associated with urban airborne particulate matter," the authors write, "and demonstrates differences between pollution levels that could be associated with potential health risks."