Biology subjects

Kobel, C. M.

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

3 recordsLinked to original sources

bioRxiv2024

Protozoal populations drive system-wide variation in the rumen microbiome

While rapid progress has been made to characterize the bacterial and archaeal populations of the rumen microbiome, insight into how they interact with keystone protozoal species remains elusive. Here, we reveal two distinct rumen community types (RCT-A and RCT-B) that are not strongly associated with host phenotype nor genotype but instead linked to protozoal community patterns. We leveraged a series of multi-omic datasets to show that the dominant Epidinium spp. in animals with RCT-B employ a plethora of fiber-degrading enzymes that present enriched Prevotella spp. a favorable carbon landscape to forage upon. Conversely, animals with RCT-A, dominated by genera Isotricha and Entodinium, harbor a more even distribution of fiber, protein, and amino acid metabolizers, reflected by higher detection of metabolites from both protozoal and bacterial activity. We reveal microbiome variation across key protozoal and bacterial populations is interlinked, which should act as an important consideration for future development of microbiome-based technologies.

microbiology↗

bioRxiv2024

CompareM2 is a genomes-to-report pipeline for comparing microbial genomes

Here, we present CompareM2, a genomes-to-report pipeline for comparative analysis of bacterial and archaeal genomes derived from isolates and metagenomic assemblies. CompareM2 is easy to install and operate, and integrates community-adopted tools to perform genome quality control and annotation, taxonomic and functional predictions, as well as comparative analyses of core- and pan-genome partitions and phylogenetic relations. The central results generated via the CompareM2 workflow are emphasized in a portable dynamic report document. CompareM2 is free software and welcomes modifications and pull requests from the community on its Git repository at https://github.com/cmkobel/comparem2.

bioinformatics↗

bioRxiv2024

Predicting microbial genome-scale metabolic networks directly from 16S rRNA gene sequences

Genome-scale metabolic models are key biotechnology tools that can predict metabolic capabilities and growth for an organism. In particular, these models have become indispensable for metabolic analysis of microbial species and communities such as the gut microbiomes of humans and other animals. Accurate microbial models can be built automatically from genomes, but many microbes have only been observed through sequencing of marker genes such as 16S rRNA and thus remain inaccessible to genome-scale modeling. To extend the scope of genome-scale metabolic models to microbes that lack genomic information, we trained an artificial neural network to build microbial models from numeric representations of 16S rRNA gene sequences. Specifically, we built models and extracted 16S rRNA gene sequences from more than 15,000 reference and representative microbial genomes, computed multiple sequence alignments and large language model embeddings for the 16S rRNA gene sequences, and trained the neural network to predict metabolic reaction probabilities from sequences, alignments, or embeddings. Training was fast on a single graphics processing unit and trained networks predicted reaction probabilities accurately for unseen archaeal and bacterial sequences and species. This makes it possible to reconstruct microbial genome-scale metabolic networks from any 16S rRNA gene sequence and enables simulation of metabolism and growth for all observed microbial life.

systems biology↗