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Rodgers, R.

Publications and source records attributed to Rodgers, R..

2 recordsLinked to original sources

Microbiota produced indole metabolites disrupt host cell mitochondrial energy production and inhibit Cryptosporidium parvum growth

Cryptosporidiosis is a leading cause of life-threatening diarrhea in young children in resource-poor settings. Susceptibility rapidly declines with age, associated with changes in the microbiota. To explore microbial influences on susceptibility, we screened 85 microbiota- associated metabolites enriched in the adult gut for their effects on C. parvum growth in vitro. We identified eight inhibitory metabolites in three main classes: secondary bile salts/acids, a vitamin B6 precursor, and indoles. Growth restriction of C. parvum by indoles did not depend on the host aryl hydrocarbon receptor (AhR) pathway. Instead, treatment impaired host mitochondrial function and reduced total cellular ATP, as well as directly reduced the membrane potential in the parasite mitosome, a degenerate mitochondria. Oral administration of indoles, or reconstitution of the gut microbiota with indole producing bacteria, delayed life cycle progression of the parasite in vitro and reduced severity of C. parvum infection in mice. Collectively, these findings indicate that microbiota metabolites contribute to colonization resistance to Cryptosporidium infection.

microbiology↗

The evolution of splicing: transcriptome complexity and transcript distances implemented in TranD

Alternative splicing contributes to organismal complexity. Comparing transcripts between and within species is an important first step toward understanding questions about how evolution of transcript structure changes between species and contributes to sub-functionalization. These questions are confounded with issues of data quality and availability. The recent explosion of affordable long read sequencing of mRNA has considerably widened the ability to study transcriptional variation in non-model species. In this work, we develop a computational framework that uses nucleotide resolution distance metrics to compare transcript models for structural phenotypes: total transcript length, intron retention, donor/acceptor site variation, alternative exon cassettes, alternative 5/3 UTRs are each scored qualitatively and quantitatively in terms of number of nucleotides. For a single annotation file, all differences among transcripts within a gene are summarized and transcriptome-level complexity metrics: number of variable nucleotides, unique exons per gene, exons per transcript, and transcripts per gene are calculated. To compare two transcriptomes on the same co-ordinates, a weighted total distance between pairs of transcripts for the same gene is calculated. The weight function proposed has larger penalties for intron retention and exon skipping than alternative donor/acceptor sites. Minimum distances can be used to identify both transcript pairs and transcripts missing structural elements in either of the two annotations. This enables a broad range of functionality from comparing sister species to comparing different methods of building and summarizing transcriptomes. Importantly, the philosophy here is to output metrics, enabling others to explore the nucleotide-level distance metrics. Single transcriptome annotation summaries and pairwise comparisons are implemented in a new tool, TranD, distributed as a PyPi package and in the open-source web-based Galaxy (www.galaxyproject.org) platform.

genomics↗