Jared Kehe

Interspecies interactions shape the structure and function of microbial communities. In particular, positive, growth-promoting interactions can substantially affect the diversity and productivity of natural and engineered communities. However, the prevalence of positive interactions and the conditions in which they occur are not well understood. To address this knowledge gap, we used kChip, an ultrahigh-throughput coculture platform, to measure 180,408 interactions among 20 soil bacteria across… Show more

Interspecies interactions shape the structure and function of microbial communities. In particular, positive, growth-promoting interactions can substantially affect the diversity and productivity of natural and engineered communities. However, the prevalence of positive interactions and the conditions in which they occur are not well understood. To address this knowledge gap, we used kChip, an ultrahigh-throughput coculture platform, to measure 180,408 interactions among 20 soil bacteria across 40 carbon environments. We find that positive interactions, often described to be rare, occur commonly and primarily as parasitisms between strains that differ in their carbon consumption profiles. Notably, nongrowing strains are almost always promoted by strongly growing strains (85%), suggesting a simple positive interaction–mediated approach for cultivation, microbiome engineering, and microbial consortium design. Show less

Microbial communities have many applications, but current experimental strategies to investigate their behavior are limited by the combinatorial complexity of interactions between species. Here, we introduce a platform to automatically construct and test synthetic communities of microbes from a set of input species at a scale of ∼100,000 communities per day. As a first demonstration, we discovered specific compositions of bacteria isolated from local soil that promote the growth of a model… Show more

Microbial communities have many applications, but current experimental strategies to investigate their behavior are limited by the combinatorial complexity of interactions between species. Here, we introduce a platform to automatically construct and test synthetic communities of microbes from a set of input species at a scale of ∼100,000 communities per day. As a first demonstration, we discovered specific compositions of bacteria isolated from local soil that promote the growth of a model plant symbiont. More broadly, our platform can be adopted for the discovery of microbial consortia with many useful properties, such as suppression of pathogens or degradation of recalcitrant substrates for use in biofuel production or environmental remediation. Show less

The conventional single-agent approach to therapeutics is challenged by the redundancy and feedback in biological networks. The use of multiple drugs in combination might improve desired functional outcomes while reducing toxicity and overcoming drug resistance. However, the complexity and resources required to test many combinations have slowed discovery efforts. Here, we introduce a screening platform that automatically constructs combinations of drugs from nanoliter-scale droplets, greatly… Show more

The conventional single-agent approach to therapeutics is challenged by the redundancy and feedback in biological networks. The use of multiple drugs in combination might improve desired functional outcomes while reducing toxicity and overcoming drug resistance. However, the complexity and resources required to test many combinations have slowed discovery efforts. Here, we introduce a screening platform that automatically constructs combinations of drugs from nanoliter-scale droplets, greatly simplifying screening logistics and reducing resource consumption. We applied our platform to discover drug pairs that act synergistically against the model pathogen Escherichia coli. Our platform can be further developed to support many types of phenotypic assays in a variety of disease models. Show less

Human mesenchymal stem cells (hMSCs) receive differentiation cues from a number of stimuli, including extracellular matrix (ECM) stiffness. The pathways used to sense stiffness and other physical cues are just now being understood and include proteins within focal adhesions. To rapidly advance the pace of discovery for novel mechanosensitive proteins, we employed a combination of in silico and high throughput in vitro methods to analyze 47 different focal adhesion proteins for cryptic kinase… Show more

Human mesenchymal stem cells (hMSCs) receive differentiation cues from a number of stimuli, including extracellular matrix (ECM) stiffness. The pathways used to sense stiffness and other physical cues are just now being understood and include proteins within focal adhesions. To rapidly advance the pace of discovery for novel mechanosensitive proteins, we employed a combination of in silico and high throughput in vitro methods to analyze 47 different focal adhesion proteins for cryptic kinase binding sites. High content imaging of hMSCs treated with small interfering RNAs for the top 6 candidate proteins showed novel effects on both osteogenic and myogenic differentiation; Vinculin and SORBS1 were necessary for stiffness-mediated myogenic and osteogenic differentiation, respectively. Both of these proteins bound to MAPK1 (also known as ERK2), suggesting that it plays a context-specific role in mechanosensing for each lineage; validation for these sites was performed. This high throughput system, while specifically built to analyze stiffness-mediated stem cell differentiation, can be expanded to other physical cues to more broadly assess mechanical signaling and increase the pace of sensor discovery. Show less

Course Reader (BENG 140A: Bioengineering Physiology, UC San Diego) providing an introduction to physiology in a question-answer format with supplemental information designed for engineering students. 212 pages.