Biology subjects

Clark, C. M.

Publications and source records attributed to Clark, C. M..

3 recordsLinked to original sources

bioRxiv2024

Creating and leveraging bespoke large-scale knowledge graphs for comparative genomics and multi-omics drug discovery with SocialGene

The rapid expansion of multi-omics data has transformed biological research, offering unprecedented opportunities to explore complex genomic relationships across diverse organisms. However, the vast volume and heterogeneity of these datasets presents significant challenges for analyses. Here we introduce SocialGene, a comprehensive software suite designed to collect, analyze, and organize multi-omics data into structured knowledge graphs, with the ability to handle small projects to repository-scale analyses. Originally developed to enhance genome mining for natural product drug discovery, SocialGene has been effective across various applications, including functional genomics, evolutionary studies, and systems biology. SocialGenes concerted Python and Nextflow libraries streamline data ingestion, manipulation, aggregation, and analysis, culminating in a custom Neo4j database. The software not only facilitates the exploration of genomic synteny but also provides a foundational knowledge graph supporting the integration of additional diverse datasets and the development of advanced search engines and analyses. This manuscript introduces some of SocialGenes capabilities through brief case studies including targeted genome mining for drug discovery, accelerated searches for similar and distantly related biosynthetic gene clusters in biobank-available organisms, integration of chemical and analytical data, and more. SocialGene is free, open-source, MIT-licensed, designed for adaptability and extension, and available from github.com/socialgene.

bioinformatics↗

bioRxiv2021

Relationship between bacterial phylotype and specialized metabolite production in the culturable microbiome of two freshwater sponges

Microbial drug discovery programs rely heavily on accessing bacterial diversity from the environment to acquire new specialized metabolite (SM) lead compounds for the therapeutic pipeline. Therefore, knowledge of how certain bacterial taxa are distributed in nature, in addition to the degree of variation of SM production within those taxa, is critical to informing these front-end discovery efforts and making the overall sample collection and bacterial library creation process more efficient. In the current study we employed MALDI-TOF mass spectrometry and the bioinformatics pipeline IDBac to analyze diversity within phylotype groupings and SM profiles of hundreds of bacterial isolates from two Eunapius fragilis freshwater sponges, collected 1.5 km apart. We demonstrated that within two sponge samples of the same species, the culturable bacterial populations contained significant overlap in approximate genus-level phylotypes but mostly non-overlapping populations of isolates when grouped lower than the level of genus. Further, correlations between bacterial phylotype and SM production varied at the species level and below, suggesting SM distribution within bacterial taxa must be analyzed on a case-by-case basis. Our results suggest that two E. fragilis freshwater sponges collected in similar environments can exhibit large culturable diversity on a species-level scale, thus researchers should scrutinize the isolates with analyses that take both phylogeny and SM production into account in order to optimize the chemical space entering into a downstream bacterial library.

microbiology↗

bioRxiv2017

Coupling MALDI-TOF mass spectrometry protein and specialized metabolite analyses to rapidly discriminate bacterial function

For decades, researchers have lacked the ability to rapidly correlate microbial identity with bacterial metabolism. Since specialized metabolites are critical to bacterial function and survival in the environment, we designed a data acquisition and bioinformatics technique (IDBac) that utilizes in situ matrix-assisted laser desorption/ionization time-of-flight mass spectrometry (MALDI-TOF MS) to analyze protein and specialized metabolite spectra of single bacterial colonies from agar plates. We demonstrated the power of our approach by discriminating between two Bacillus subtilis colonies in under 30 minutes, which differ by a single genomic mutation, solely on the basis of their differential ability to produce cyclic peptide antibiotics surfactin and plipastatin. Next, we employed our IDBac technique to detect subtle intra-species differences in the production of metal scavenging acyl-desferrioxamines in a group of eight freshwater Micromonospora isolates that share >99% sequence similarity in the 16S rRNA gene. Finally, we employed our method to simultaneously extract protein and specialized metabolite MS profiles from unidentified species of Lake Michigan sponge-associated bacteria cultivated on an agar plate. In just 3 hours, we created hierarchical protein MS groupings of 11 environmental isolates (10 MS replicates each, for a total of 110 samples) that accurately mirrored phylogenetic groupings. We further distinguished isolates within these groupings, which share nearly identical 16S rRNA gene sequence identity, based on inter- and intra-species differences in specialized metabolite production. To our knowledge, IDBac is the first attempt to couple in situ MS analyses of protein content and specialized metabolite production to allow the distinction of closely related bacterial colonies.\n\nSignificanceMass spectrometry is a powerful technique that has been used to identify bacteria via protein content, and to assess bacterial function in an environment via analysis of specialized metabolites. However, until now these analyses have operated independently, and this has resulted in the inability to rapidly connect bacterial phylogenetic identity with patterns of specialized metabolism. To bridge this gap, we designed a MALDI-TOF mass spectrometry data acquisition and bioinformatics pipeline (IDBac) to discriminate both intact protein and specialized metabolite spectra directly from bacterial cells grown on agar. To our knowledge, this is the first technique that organizes bacteria into highly similar phylogenetic groups and allows for comparison of metabolic differences of hundreds of isolates in just a few hours.

microbiology↗