Biology subjects

Friedman, J.

Publications and source records attributed to Friedman, J..

5 recordsLinked to original sources

bioRxiv2018

Mortality causes universal changes in microbial community composition

All organisms are sensitive to the abiotic environment, and a deteriorating environment can lead to extinction. However, survival in a multispecies community also depends upon inter-species interactions, and some species may even be favored by a harsh environment that impairs competitors. A deteriorating environment can thus cause surprising transitions in community composition. Here, we combine theory and laboratory microcosms to develop a predictive understanding of how simple multispecies communities change under added mortality, a parameter that represents environmental harshness. In order to explain changes in a multispecies microbial system across a mortality gradient, we examine its members pairwise interactions. We find that increasing mortality favors the faster grower, confirming a prediction of simple models. Furthermore, if the slower grower outcompetes the faster grower in environments with low or no added mortality, the competitive outcome can reverse as mortality increases. We find that this tradeoff between growth rate and competitive ability is indeed prevalent in our system, allowing for striking pairwise outcome changes that propagate up to multispecies communities. These results argue that a bottom-up approach can provide insight into how communities will change under stress.

ecology↗

bioRxiv2018

Rapid Whole Genome Sequencing Decreases Morbidity and Healthcare Cost of Hospitalized Infants

BACKGROUNDGenetic disorders are a leading cause of morbidity and mortality in infants. Rapid Whole Genome Sequencing (rWGS) can diagnose genetic disorders in time to change acute medical or surgical management (clinical utility) and improve outcomes in acutely ill infants.\n\nMETHODSRetrospective cohort study of acutely ill inpatient infants in a regional childrens hospital from July 2016-March 2017. Forty-two families received rWGS for etiologic diagnosis of genetic disorders. Probands received standard genetic testing as clinically indicated. Primary end-points were rate of diagnosis, clinical utility, and healthcare utilization. The latter was modelled in six infants by comparing actual utilization with matched historical controls and/or counterfactual utilization had rWGS been performed at different time points.\n\nFINDINGSThe diagnostic sensitivity was 43% (eighteen of 42 infants) for rWGS and 10% (four of 42 infants) for standard of care (P=.0005). The rate of clinical utility for rWGS (31%, thirteen of 42 infants) was significantly greater than for standard of care (2%, one of 42; P=.0015). Eleven (26%) infants with diagnostic rWGS avoided morbidity, one had 43% reduction in likelihood of mortality, and one started palliative care. In six of the eleven infants, the changes in management reduced inpatient cost by $800, 000 to $2,000,000.\n\nDISCUSSIONThese findings replicate a prior study of the clinical utility of rWGS in acutely ill inpatient infants, and demonstrate improved outcomes and net healthcare savings. rWGS merits consideration as a first tier test in this setting.

clinical trials↗

bioRxiv2017

Co-occurring soil bacteria exhibit a robust competitive hierarchy and lack of non-transitive interactions

Microbial communities are typically incredibly diverse, and this diversity is thought to play a key role in community function. However, explaining how this diversity can be maintained is a major challenge in ecology. Temporal fluctuations and spatial structure in the environment likely play a key role, but it has also been suggested that the structure of interactions within the community may act as a stabilizing force for species diversity. In particular, if competitive interactions are non-transitive as in the classic rock-paper-scissors game, they can contribute to the maintenance of species diversity; on the other hand, if they are predominantly hierarchical, any observed diversity must be maintained via other mechanisms. Here, we investigate the network of pairwise competitive interactions in a model community consisting of 20 strains of naturally co-occurring soil bacteria. We find that the interaction network is strongly hierarchical and lacks significant non-transitive motifs, a result that is robust across multiple environments. Moreover, in agreement with recently proposed community assembly rules, the full 20-strain competition resulted in extinction of all but three of the most highly competitive strains, indicating that higher order interactions do not play a major role in structuring this community. The lack of non-transitivity and higher order interactions in vitro indicates that other factors, such as temporal or spatial heterogeneity, must be at play in enabling these strains to coexist in nature.

ecology↗

bioRxiv2017

Deciphering Functional Redundancy in the Human Microbiome

Although the taxonomic composition of the human microbiome varies tremendously across individuals, its gene composition or functional capacity is highly conserved1-5---implying an ecological property known as functional redundancy. Such functional redundancy is thought to underlie the stability and resilience of the human microbiome6,7, but its origin is elusive. Here, we investigate the basis for functional redundancy in the human microbiome by analyzing its genomic content network --- a bipartite graph that links microbes to the genes in their genomes. We show that this network exhibits several topological features, such as highly nested structure and fat-tailed gene degree distribution, which favor high functional redundancy. To explain the origins of these topological features, we develop a simple genome evolution model that explicitly considers selection pressure, and the processes of gene gain and loss, and horizontal gene transfer. We find that moderate selection pressure and high horizontal gene transfer rate are necessary to generate genomic content networks with both highly nested structure and fat-tailed gene degree distribution, and consequently favor high functional redundancy. These findings provide insights into the relationships between structure and function in complex microbial communities. This work elucidates the potential ecological and evolutionary processes that create and maintain functional redundancy in the human microbiome and contribute to its resilience.

microbiology↗

bioRxiv2017

Mapping the ecological networks of microbial communities from steady-state data

Microbes form complex and dynamic ecosystems that play key roles in the health of the animals and plants with which they are associated. Such ecosystems are often represented by a directed, signed and weighted ecological network, where nodes represent microbial taxa and edges represent ecological interactions. Inferring the underlying ecological networks of microbial communities is a necessary step towards understanding their assembly rules and predicting their dynamical response to external stimuli. However, current methods for inferring such networks require assuming a particular population dynamics model, which is typically not known a priori. Moreover, those methods require fitting longitudinal abundance data, which is not readily available, and often does not contain the variation that is necessary for reliable inference. To overcome these limitations, here we develop a new method to map the ecological networks of microbial communities using steady-state data. Our method can qualitatively infer the inter-taxa interaction types or signs (positive, negative or neutral) without assuming any particular population dynamics model. Additionally, when the population dynamics is assumed to follow the classic Generalized Lotka-Volterra model, our method can quantitatively infer the inter-taxa interaction strengths and intrinsic growth rates. We systematically validate our method using simulated data, and then apply it to four experimental datasets of microbial communities. Our method offers a novel framework to infer microbial interactions and reconstruct ecological networks, and represents a key step towards reliable modeling of complex, real-world microbial communities, such as the human gut microbiota.

ecology↗