Biology subjects

Kelly, L.

Publications and source records attributed to Kelly, L..

3 recordsLinked to original sources

bioRxiv2019

Bacterial Hyperswarming as a Protective Response to Intestinal Stress

Bacterial swarming, a collective movement on a surface, has rarely been associated with human pathophysiology. Here, we report for the first time that bacterial swarmers are associated with protection against intestinal inflammation. We show that bacterial swarmers are highly predictive of intestinal stress in mice and humans. We isolated a novel Enterobacter swarming strain, SM3, from mouse feces. SM3 and other known commensal swarmers contrast to their respective swarming-deficient, but swimming-competent isogenic strains abrogated intestinal inflammation in mice. Treatment of colitic mice with SM3, but not its mutants, enriched beneficial fecal anaerobes belonging to the family, Bacteroidales S24-7. We observed SM3 swarming associated pathways in the in vivo fecal metatranscriptomes. In vitro growth of S24-7 was enriched in presence of SM3 or its mutants conjecturing that bacterial swarming in vivo might influence SM3s access to S24-7 in the intestines. Overall, our work identifies a new paradigm in which intestinal stress allows for the emergence of swarming bacteria, which can counterintuitively heal intestinal inflammation.

microbiology↗

bioRxiv2019

Multiclass Disease Classification from Microbial Whole-Community Metagenomes using Graph Convolutional Neural Networks.

There is a wealth of information contained within ones microbiome regarding their physiology and environment, and this is a promising avenue for developing non-invasive diagnostic tools. Here, we utilize 5643 aggregated, annotated whole-community metagenomes from 19 different diseases to implement the first multiclass microbiome disease classifier of this scale. We compared three different machine learning models: random forests, deep neural nets, and a novel graph convolutional architecture which exploits the graph structure of phylogenetic trees as its input. We show that the graph convolutional model outperforms deep neural nets in terms of accuracy (achieving 75% average test-set accuracy), receiver-operator-characteristics (92.1% average AUC), and precision-recall (50% average AUPR). Additionally, the convolutional nets performance complements that of the random forest, achieving similar accuracy but better receiver-operator-characteristics and lower area under precision-recall. Lastly, we are able to achieve over 90% average top-3 accuracy across all of our models. Together, these results indicate that there are predictive, disease specific signatures across microbiomes which could potentially be used for diagnostic purposes.

bioinformatics↗

bioRxiv2019

Inferring the quasipotential landscape of microbial ecosystems with topological data analysis

Microbiome dynamics influence the health and functioning of human physiology and the environment and are driven in part by interactions between large numbers of microbial taxa, making large-scale prediction and modeling a challenge. Here, using topological data analysis, we identify states and dynamical features relevant to macroscopic processes.We show that gut disease processes and marine geochemical events are associated with transitions between community states, defined as topological features of the data density. We find a reproducible two-state succession during recovery from cholera in the gut microbiomes of multiple patients, evidence of dynamic stability in the gut microbiome of a healthy human after experiencing diarrhea during travel, and periodic state transitions in a marine Prochlorococcus community driven by water column cycling. Our approach bridges small-scale fluctuations in microbiome composition and large-scale changes in phenotype without details of underlying mechanisms, and provides a novel assessment of microbiome stability and its relation to human and environmental health.

systems biology↗