Biology subjects

Weissman, J. L.

Publications and source records attributed to Weissman, J. L..

12 recordsLinked to original sources

bioRxiv2024

Emergent Metabolic Niches for Marine Heterotrophs

Ocean microbial communities are made up of thousands of diverse taxa whose metabolic demands set the rates of both biomass production and degradation. Thus, these microscopic organisms play a critical role in ecosystem dynamics, global carbon cycling, and climate. While we have frameworks for relating phytoplankton diversity to rates of carbon fixation, our knowledge of how variations in heterotrophic microbial populations drive changes in carbon cycling is in its infancy. Here, we leverage global metagenomic datasets and metabolic models to identify a set of metabolic niches with distinct growth strategies. These groupings provide a simplifying framework for describing microbial communities in different oceanographic regions and for understanding how heterotrophic microbial populations function. This framework, predicated directly on metabolic capability rather than taxonomy, enables us to tractably link heterotrophic diversity directly to biogeochemical rates in large scale ecosystem models.

ecology↗

bioRxiv2024

Predictable functional biogeography of marine microbial heterotrophs

Heterotrophic bacteria and archaea ( heteroprokaryotes) drive global carbon cycling, but how to quantitatively organize their functional complexity remains unclear. We generated a global-scale understanding of marine heteroprokaryotic functional biogeography by synthesizing genetic sequencing data with a mechanistic marine ecosystem model. We incorporated heteroprokaryotic diversity into the trait-based model along two axes: substrate lability and growth strategy. Using genetic sequences along three ocean transects, we compiled 21 heteroprokaryotic guilds and estimated their degree of optimization for rapid growth (copiotrophy). Data and model consistency indicated that gradients in grazing and substrate lability predominantly set biogeographical patterns, and identified deep-ocean slow copiotrophs whose ecological interactions control the surface accumulation of dissolved organic carbon.

ecology↗

bioRxiv2022

Symbiotic diazotrophic UCYN-A strains co-occurred with El Ni&ntildeo, relaxed upwelling, and varied eukaryotes over 10 years off Southern California Bight

Biological nitrogen fixation, the conversion of N2 gas into a more bioavailable form, is vital to sustaining marine primary production. Studies have shifted beyond traditionally studied tropical diazotrophs. Candidatus Atelocyanobacterium thalassa (or UCYN-A) has emerged as a research focal point due to its streamlined metabolism, intimate partnership with a haptophyte host, and broad distribution. Here, we explore the abiotic factors that govern UCYN-As presence at the San Pedro Ocean Time-series (SPOT), its partner fidelity, and statistical interactions with non-symbiotic eukaryotes. 16S and 18S rRNA sequences were amplified by "universal primers" from monthly samples and resolved into Amplicon Sequence Variants, allowing us to observe multiple UCYN-A symbioses. UCYN-A1 relative abundances increased following the 2015-2016 El Nino event. When this "open ocean ecotype" was present, coastal upwelling ceased, and Ekman transport brought tropical waters into the region. Network analyses reveal all strains of UCYN-A co-occur with dinoflagellates including Lepidodinium, a potential predator, and parasitic Syndiniales. UCYN-A2 appeared to pair with multiple hosts and was not tightly coupled to its predominate host, while UCYN-A1 maintained a strong host-symbiont relationship. These biological relationships are particularly important to study in the context of climate change, which will alter UCYN-A distribution patterns both locally and globally.

microbiology↗

bioRxiv2022

Benchmarking community-wide estimates of growth potential from metagenomes using codon usage statistics

Trait inference from mixed-species assemblages is a central problem in microbial ecology. Frequently, sequencing information from an environment is available, but phenotypic measurements from individual community members are not. With the increasing availability of molecular data for microbial communities, bioinformatic approaches that map metagenome to (meta)phenotype are needed. Recently, we developed a tool, gRodon, that enables the prediction of the maximum growth rate of an organism from genomic data on the basis of codon usage patterns. Our work and that of other groups suggests that such predictors can be applied to mixed-species communities in order to derive estimates of the average community-wide maximum growth rate. Here, we present an improved maximum growth rate predictor designed for metagenomes that corrects a persistent GC bias in the original gRodon model for metagenomic prediction. We benchmark this predictor with simulated metagenomic datasets to show that it has superior performance on mixed-species communities relative to earlier models. We go on to provide guidance on data pre-processing, and show that calling genes from assembled contigs rather than directly from reads dramatically improves performance. Finally, we apply our predictor to large-scale metagenomic datasets from marine and human microbiomes to illustrate how community-wide growth prediction can be a powerful approach for hypothesis-generation. Altogether, we provide an updated tool with clear guidelines for users about the uses and pitfalls of metagenomic prediction of the average community-wide maximal growth rate.

bioinformatics↗

bioRxiv2021

NetGAM: Using generalized additive models to improve the predictive power of ecological network analyses constructed using time-series data

Ecological network analyses are used to identify potential biotic interactions between microorganisms from species abundance data. These analyses are often carried out using time-series data; however, time-series networks have unique statistical challenges. Time-dependent species abundance data can lead to species co-occurrence patterns that are not a result of direct, biotic associations and may therefore result in inaccurate network predictions. Here, we describe a generalize additive model (GAM)-based data transformation that removes time-series signals from species abundance data prior to running network analyses. Validation of the transformation was carried out by generating mock, time-series datasets, with an underlying covariance structure, running network analyses on these datasets with and without our GAM transformation, and comparing the network outputs to the known covariance structure of the simulated data. The results revealed that seasonal abundance patterns substantially decreased the accuracy of the inferred networks. Additionally, the GAM transformation increased the F1 score of inferred ecological networks on average and improved the ability of network inference methods to capture important features of network structure. This study underscores the importance of considering temporal features when carrying out network analyses and describes a simple, effective tool that can be used to improve results.

ecology↗

bioRxiv2021

Estimating the maximal growth rates of eukaryotic microbes from cultures and metagenomes via codon usage patterns

Microbial eukaryotes are ubiquitous in the environment and play important roles in key ecosystem processes, including accounting for a significant portion of global primary production. Yet, our tools for assessing the functional capabilities of eukaryotic microbes in the environment are quite limited because many microbes have yet to be grown in culture. Maximum growth rate is a fundamental parameter of microbial lifestyle that reveals important information about an organisms functional role in a community. We developed and validated a genomic estimator of maximum growth rate for eukaryotic microbes, enabling the assessment of growth potential for organisms and communities directly in the environment. We produced a database of over 700 maximum growth rate predictions from genomes, transcriptomes, and metagenome-assembled genomes. By comparing the maximal growth rates of existing culture collections with environmentally-derived genomes we found that, unlike for prokaryotes, culture collections of microbial eukaryotes are only minimally biased in terms of growth potential. We then extended our tool to make community-wide estimates of growth potential from over 500 marine metagenomes, mapping growth potential across the global oceans. We found that prokaryotic and eukaryotic communities have highly correlated growth potentials near the ocean surface, but there is no correlation in their genomic potentials deeper in the water column. This suggests that fast growing eukaryotes and prokaryotes thrive under similar conditions at the ocean surface, but that there is a decoupling of these communities as resources become scarce deeper in the water column.

microbiology↗

bioRxiv2020

Immune Lag Is a Major Cost of Prokaryotic Adaptive Immunity During Viral Outbreaks

CRISPR-Cas adaptive immune systems enable bacteria and archaea to efficiently respond to viral pathogens by creating a genomic record of previous encounters. These systems are broadly distributed across prokaryotic taxa, yet are surprisingly absent in a majority of organisms, suggesting that the benefits of adaptive immunity frequently do not outweigh the costs. Here, combining experiments and models, we show that a delayed immune response which allows viruses to transiently redirect cellular resources to reproduction, which we call "immune lag", is extremely costly during viral outbreaks, even to completely immune hosts. Critically, the costs of lag are only revealed by examining the early, transient dynamics of a host-virus system occurring immediately after viral challenge. Lag is a basic parameter of microbial defense, relevant to all intracellular, post-infection antiviral defense systems, that has to-date been largely ignored by theoretical and experimental treatments of host-phage systems.

microbiology↗

bioRxiv2020

Estimating maximal microbial growth rates from cultures, metagenomes, and single cells via codon usage patterns

Maximal growth rate is a basic parameter of microbial lifestyle that varies over several orders of magnitude, with doubling times ranging from a matter of minutes to multiple days. Growth rates are typically measured using laboratory culture experiments. Yet, we lack sufficient understanding of the physiology of most microbes to design appropriate culture conditions for them, severely limiting our ability to assess the global diversity of microbial growth rates. Genomic estimators of maximal growth rate provide a practical solution to survey the distribution of microbial growth potential, regardless of cultivation status. We developed an improved maximal growth rate estimator, and implement this estimator in an easy-to-use R package (gRodon), which outperforms the state-of-the-art growth estimator in multiple settings, including in a community context where we implement a novel species abundance correction for metagenomes. Additionally, we estimate maximal growth rates from over 200,000 genomes, metagenome-assembled genomes, and single-cell amplified genomes to survey growth potential across the range of prokaryotic diversity. We provide these compiled maximal growth rates in a publicly-available database (EGGO), which we use to illustrate how culture collections show a strong bias towards organisms capable of rapid growth. We demonstrate how this database can be used to propagate maximal growth rate predictions to organisms for which we lack genomic information, on the basis of 16S rRNA sequence alone. Finally, we observe a bias in growth predictions for extremely slow-growing organisms, ultimately leading us to suggest a novel evolutionary definition of oligotrophy based on the selective regime an organism occupies. SignificanceDespite the wide perception that microbes have rapid growth rates, many environments like seawater and soil are often dominated by microorganisms that can only grow very slowly. Our knowledge about growth is necessarily biased towards easily culturable organisms, which turn out to be those that tend to grow fast, because microbial growth rates have traditionally been measured using lab growth experiments. But how are potential growth rates distributed in nature? We developed a tool to predict maximum growth rate from an organisms genome sequence (gRodon). We predicted the growth rates of over 200,000 organisms and compiled these predictions in a publicly-available database (EGGO), which illustrates how current collections of cultured microbes are strongly biased towards fast-growing organisms.

microbiology↗

bioRxiv2019

Potential link between selection for high GC content and repair of double strand breaks in prokaryotic genomes

Genomic GC content varies widely among microbes for reasons unknown. While mutation bias partially explains this variation, prokaryotes near-universally have a higher GC content than predicted solely by this bias. Debate surrounds the relative importance of the remaining explanations of selection versus biased gene conversion favoring GC alleles. Some environments (e.g. soils) are associated with a high genomic GC content of their inhabitants, which implies that this content may be a selective adaptation to particular habitats. Here, we report a novel association between the presence of the non-homologous end joining DNA doublestrand break repair pathway and GC content; this observation suggests that high GC content may be an adaptation to facilitate repair of double strand breaks when homologous recombination is not possible. We discuss potential mechanisms accounting for the observed association, and provide preliminary evidence that sites experiencing higher rates of doublestrand breaks are under selection for increased GC content relative to the genomic background.

microbiology↗

bioRxiv2018

Finding CRISPR’s Niche: Oxygen and Temperature Shape the Incidence of Adaptive Immunity

Bacteria and archaea are locked in a near-constant battle with their viral pathogens. Despite previous mechanistic characterization of numerous prokaryotic defense strategies, the underlying ecological drivers of different strategies remain largely unknown and predicting which species will take which strategies remains a challenge. Here, we focus on the CRISPR immune strategy and develop a phylogenetically-corrected machine learning approach to build a predictive model of CRISPR incidence using data on over 100 traits across over 2600 species. We discover a strong but hitherto-unknown negative interaction between CRISPR and aerobicity, which we hypothesize may result from interference between CRISPR associated proteins and non-homologous end-joining DNA repair due to oxidative stress. Our predictive model also quantitatively confirms previous observations of an association between CRISPR and temperature. Finally, we contrast the environmental associations of different CRISPR system types (I, II, III) and restriction modification systems, all of which act as intracellular immune systems.

microbiology↗

bioRxiv2017

Is having more than one CRISPR array adaptive?

Prokaryotes are under nearly constant attack by viral pathogens. To protect against this threat of infection, bacteria and archaea have evolved a wide array of defense mechanisms, singly and in combination. While immune diversity in a single organism likely reduces the chance of pathogen evolutionary escape, it remains puzzling why many prokaryotes also have multiple, seemingly redundant, copies of the same type of immune system. Here, we focus on the highly flexible CRISPR adaptive immune system, which is present in multiple copies in a surprising 28% of the prokaryotic genomes in RefSeq. We use a comparative genomics approach looking across all prokaryotes to demonstrate that, on average, organisms are under selection to maintain more than one CRISPR array. We hypothesize that a tradeoff between memory span and learning speed could select for both \"long-term memory\" and \"short-term memory\" CRISPR arrays, and we go on to develop a mathematical model to show that such a tradeoff could, in theory, lead to selection for multiple arrays.

evolutionary biology↗

bioRxiv2017

Immune Loss as a Driver of Coexistence During Host-Phage Coevolution

Bacteria and their viral pathogens face constant pressure for augmented immune and infective capabilities, respectively. Under this reciprocally imposed selective regime, we expect to see a runaway evolutionary arms race, ultimately leading to the extinction of one species. Despite this prediction, in many systems host and pathogen coexist with minimal coevolution even when well-mixed. Previous work explained this puzzling phenomenon by invoking fitness tradeoffs, which can diminish an arms race dynamic. Here we propose that the regular loss of immunity by the bacterial host can also produce host-phage coexistence. We pair a general model of immunity with an experimental and theoretical case study of the CRISPR-Cas immune system to contrast the behavior of tradeoff and loss mechanisms in well-mixed systems. We find that, while both mechanisms can produce stable coexistence, only immune loss does so robustly within realistic parameter ranges.

ecology↗