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Nucleic Acids Research Papers on Metabolic Metabolite, Pediatric Xenograft, Small Molecule Tools

A team from the US, Norway, and the Netherlands presents a computational tool for gleaning microbial traits from microbiome data, particularly pertaining to metabolic features found in the bugs. The researchers applied their "Distilled and refined annotation of metabolism" (DRAM) software to in silico soil microbial community collections, available gut metagenomic sequence collections, where they found that the computational framework could classify microbes based on their carbohydrate metabolism and other features. They note that a version of the tool known as DRAM-v picks up auxiliary metabolic genes encoded by viral members of the community, providing complementary curation clues. "Together," the authors report, "DRAM and DRAM-v provide critical metabolic profiling capabilities that decipher mechanisms underpinning microbiome function."

University of Texas Health at San Antonio researchers describe the "Patient-derived xenograft (PDX) for childhood cancer therapeutics," or PCAT, database and the rationale behind it. "Distinct from previously reported PDX portals, PCAT is focused on pediatric cancer models and provides intuitive interfaces for querying and data mining," they write. The team notes that PCAT currently contains gene expression, mutation, copy number, preclinical, and/or other data for more than 300 pediatric cancer PDX models, including data generated for a project known as TARGET project, along with a preponderance of samples stemming from patients with Hispanic ethnicity.

Investigators at the University of Colorado outline a CRISPR-Cas13a-based gene editing approach designed to assay for small molecules. The "SHERLOCK-based profiling of in vitro transcription," or SPRINT, platform uses fluorescent reporters to track transcriptional regulators with the help of a Cas13a RNase enzyme that can cleave labeled RNA transcripts produced in vitro, the team explains, noting that "fluorogenic output can be measured to assess transcriptional output." Using the SPRINT strategy, the authors took a look at eight small molecule compounds ranging from amino acid metabolites to tetracycline antibiotics. "We demonstrate that the SPRINT method is easily adaptable to the detection of diverse classes of compounds and can be used in a rapid, high-throughput manner," they write, "and represents a significant advance in CRISPR-based biosensing technology."

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