Carlson, E. E. | bioRxiv Science

bioRxiv2018

Streptomyces volatile compounds influence exploration and microbial community dynamics by altering iron availability

Bacteria and fungi produce a wide array of volatile organic compounds (VOCs), and these can act as infochemicals or as competitive tools. Recent work has shown that the VOC trimethylamine (TMA) can promote a new form of Streptomyces growth, termed exploration. Here, we report that TMA also serves to alter nutrient availability in the area surrounding exploring cultures: TMA dramatically increases the environmental pH, and in doing so, reduces iron availability. This, in turn, compromised the growth of other soil bacteria and fungi. In contrast, Streptomyces thrives in these iron-depleted niches by secreting a suite of differentially modified siderophores, and by upregulating genes associated with siderophore uptake. Further reducing iron levels by siderophore piracy, limiting siderophore uptake, or growing cultures in the presence of iron chelators, unexpectedly enhanced exploration. Our work reveals a new role for VOCs in modulating iron levels in the environment, and implies a critical role for VOCs in modulating the behaviour of microbes and the makeup of their communities.

microbiology↗

bioRxiv2017

PANOPLY: A computational method for identification of promising drugs for a patient based on multidimensional data

PurposeThe majority of cancer patients receive treatments that are minimally informed by omics data. We propose a precision medicine computational framework (PANOPLY: Precision cancer genomic report: single sample inventory) to identify and prioritize drug targets and cancer therapy regimens.\n\nMethodsThe PANOPLY approach integrates clinical data with germline and somatic features obtained from multi-omics platforms, and apply machine learning, and network analysis approaches in the context of the individual patient and matched controls. The PANOPLY workflow employs four steps (i) selection of matched controls to the case of interest (ii) identification of case-specific genomic events (iii) identification of suitable drugs using the driver-gene network and random forest analyses and (iv) provide an integrated multi-omics case report of the patient with prioritization of anti-cancer drugs.\n\nResultsThe PANOPLY workflow can be executed on a stand-alone virtual machine and is also available for download as an R package. We applied the method to an institutional breast cancer neoadjuvant chemotherapy study which collected clinical and genomic data as well as patient-derived xenografts (PDXs) to investigate the prioritization offered by PANOPLY. In a chemotherapy-resistant PDX model, we found that that the prioritized drug, olaparib, was more effective than placebo in treating the tumor (P < 0.05). We also applied PANOPLY to in-house and publicly accessible multi-omics tumor datasets with therapeutic response or survival data available.\n\nConclusionPANOPLY shows promise as a means to prioritize drugs based on clinical and multi-omics data for an individual cancer patient. Additional studies are needed to confirm this approach.

bioinformatics↗