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Biology subjects

Kaster, A. K.

Publications and source records attributed to Kaster, A. K..

2 recordsLinked to original sources

Temporal emergence of functional dark matter in microbial responses to PFAS revealed by materials-based cultivation

Microbial responses to xenobiotic compounds are difficult to resolve due to environmental complexity and limited functional annotation. Here, we establish a materials-based cultivation framework using macroporous elastomeric silicone foams (MESIF) to capture microbial adaptation across environmental contexts and timescales. Using glyphosate as a model compound and per- and polyfluoroalkyl substances (PFAS) as a poorly understood class, we show that responses differ depending on the availability of established metabolic pathways. Glyphosate exposure induced rapid, pathway-specific functional enrichment with minimal taxonomic change. In contrast, PFAS exposure did not yield consistent taxonomic or annotation-based signals but instead produced responses that emerged over time and were primarily detectable at protein and genome-resolved levels. LC-MS analyses revealed transformation dynamics, including formation of shorter-chain products. These responses were not explained by known degrader taxa but involved uncharacterized proteins and microbial populations, highlighting the extent of functional "dark matter" in microbial responses to persistent contaminants.

microbiology↗

Deciphering the Proteome of Escherichia coli K-12: Integrating Transcriptomics and Machine Learning to Annotate Hypothetical Proteins

Omics technologies have led to the discovery of a vast number of proteins that are expressed but have no functional annotation - so called hypothetical proteins (HPs). Even in the best-studied model organism Escherichia coli K-12, over 2% of the proteome remains uncharacterized. This knowledge gap becomes even worse when looking at microbial dark matter. However, knowing the functions of proteins is crucial for elucidating cellular and metabolic processes and harnessing biotechnological potentials. Here, we employed machine learning to decipher the transcriptional regulatory network of E. coli K-12, as well as other in silico tools to assign functions to uncharacterized HPs. We further provide experimental validation of in silico predicted functions for three HP-encoding genes (yhdN, yeaC and ydgH) as proof of concept, by analyzing growth patterns of deletion mutants compared to the wild type, as well as their transcriptional responses to specific conditions. This study demonstrates that the use of Big Omics Data in combination with Artificial Intelligence and experimental controls is a powerful approach to illuminate functional dark matter. Graphical Abstract O_FIG O_LINKSMALLFIG WIDTH=188 HEIGHT=200 SRC="FIGDIR/small/643886v2_ufig1.gif" ALT="Figure 1"> View larger version (32K): org.highwire.dtl.DTLVardef@9f5035org.highwire.dtl.DTLVardef@14ad641org.highwire.dtl.DTLVardef@385573org.highwire.dtl.DTLVardef@722780_HPS_FORMAT_FIGEXP M_FIG C_FIG

bioinformatics↗