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Thompson, K. J.

Publications and source records attributed to Thompson, K. J..

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The Metabolic Capability and Phylogenetic Diversity of Mono Lake During a Bloom of the Eukaryotic Phototroph Picocystis strain ML

Algal blooms in lakes are often associated with anthropogenic eutrophication; however, they can occur naturally. In Spring of 2016 Mono Lake, a hyperalkaline lake in California, was near the height of a rare bloom of the algae Picocystis strain ML and at the apex of a multi-year long drought. These conditions presented a unique sampling opportunity to investigate microbiological dynamics during an intense natural bloom. We conducted a comprehensive molecular analysis along a depth transect near the center of the lake from surface to 25 m depth during June 2016. Across sampled depths, rRNA gene sequencing revealed that Picocystis associated chloroplast were found at 40-50 % relative abundance, greater than values recorded previously. Despite the presence of the photosynthetic oxygenic algal genus Picocystis, oxygen declined below detectible limits below 15 m depth, corresponding with an increase in microorganisms known to be anaerobic. In contrast to previously sampled years, metagenomic and metatranscriptomic data suggested a loss of sulfate reducing microorganisms throughout the lakes water column. Gene transcripts associated with Photosystem I and II were expressed at both 2 m and 25 m, suggesting that limited oxygen production may occur at extremely low light levels at depth within the lake. Oxygenic photosynthesis under low light conditions, in the absence of potential grazing by the brine shrimp Artemia, may allow for a cryptic redox cycle to occur in an otherwise anoxic setting at depth in the lake with the following effects: enhanced productivity, reduced grazing pressure on Picocystis, and an exacerbation of bloom.\n\nIMPORTANCEMono Lake, California provides habitat to a unique ecological community that is heavily stressed due to recent human water diversions and a period of extended drought. To date, no baseline information exists about Mono Lake to understand how the microbial community responds to drought, bloom, and what genetic functions are lost in the water column. While previously identified anaerobic members of the microbial community disappear from the water column during drought and bloom, sediment samples suggest these microorganisms seek refuge at lake bottom or in the subsurface. Thus, the sediments may represent a type of seed bank which could restore the microbial community as a bloom subsides. Our work also sheds light on the activity of the halotolerant algae Picocystis strain ML during a bloom at Mono Lake, its ability to potentially produce oxygen via photosynthesis even under extreme low-light conditions, and how the remainder of the microbial community responds.

microbiology

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