Biology subjects

Hoffert, M.

Publications and source records attributed to Hoffert, M..

5 recordsLinked to original sources

bioRxiv2026

Predicting optimal growth temperatures of bacteria using learned structural information from a single protein

Temperature is a fundamental determinant of bacterial physiology and ecology. Optimal growth temperature (OGT) is highly variable across species, contributing to differences in where and when species are most likely to thrive. Although the OGTs for most bacteria remain unknown, the increasing availability of genomes from uncultivated and cultivated taxa has made it advantageous to build genomic, cultivation-independent models to infer OGT. However, pre-existing genomic models often lack the generalizability and mechanistic grounding required for robust inferences of OGT. We propose a novel framework for predicting bacterial OGT which uses learned protein structural signatures of thermal adaptation. We hypothesize that biophysical tradeoffs which dictate enzymatic functions across variable temperatures provide a more robust empirical basis for OGT prediction than broad genomic features. Our OGT-predicting model, ROSEATE, is based on a single gene, adenylate kinase (ADK), that encodes for a ubiquitous enzyme essential for energy homeostasis. ROSEATE uses high-dimensional latent space encoding via MSA Transformer, a protein language model which embeds ADKs in a manner which preserves biophysical information about embedded proteins. We show that the accuracy of the ROSEATE model is on par with other genome-based models, has a high degree of phylogenetic generalizability, and the ESM embeddings effectively capture key temperature-adaptive enzyme characteristics derived from AlphaFold structures. Because ROSEATE is based on analyses of a single ubiquitous protein, it can be used with metagenomic data to infer the community-level variation in bacterial OGTs. We demonstrate this feature of ROSEATE by reconstructing ADK sequences from over 500 environmental and host-associated metagenomes, successfully distinguishing community-wide thermal preferences across diverse habitats, from polar oceans to mammalian guts. By transitioning from genomic proxies to informationally dense protein structural features, this work provides an efficient, interpretable tool for predicting bacterial OGTs across taxa and whole communities. Author SummaryThe temperature preferences of bacteria are key to determining where species are most likely to grow and how bacterial communities may respond to changes in temperature regimes. Unfortunately, the optimal growth temperatures of most bacteria, including a broad diversity of bacteria found in many host-associated and environmental systems, currently remain unknown as many bacterial species cannot be grown or studied in a laboratory. While we now have genomic data for many bacteria, using these data to infer optimal temperatures for bacterial growth has remained a persistent challenge. We developed and validated a novel approach to predict bacterial temperature preferences. When heated, proteins often unfold, becoming nonfunctional. To adapt to warmer environments, organisms evolve more stable proteins which resist denaturing at high temperatures. Instead of analyzing a bacterias entire genome, our approach uses a protein language model to quantify stability-enhancing changes in a single protein found across all bacteria. We found that this single-protein approach can be used to effectively predict the optimal growth temperatures of individual bacterial species and even whole bacterial communities. By changing how we use genomic information to predict temperature preferences, our framework provides a scalable blueprint for predicting other important bacterial traits from protein structure information.

bioinformatics↗

bioRxiv2026

Moss Transplants in the Tundra Reveal Host-Specific Microbiomes and Nitrogen Fixation Responses

In tundra ecosystems, moss-associated microbes are a major source of new nitrogen, yet the relative contributions of environment, host identity, and microbiome composition to variation in nitrogen-fixation rates are difficult to disentangle. To test how environmental change alters moss microbiomes and nitrogen-fixation rates, we used a one-year reciprocal transplant experiment between two Alaskan tundra sites that differ by 5{degrees}C in mean annual temperature. Intact moss cores containing one of three moss species, Hylocomium splendens, Aulacomnium turgidum, and Pleurozium schreberi, were transplanted between sites or returned to their home site. After one year, we quantified nitrogen-fixation rates using 15N incubation and characterized bacterial communities using 16S rRNA gene amplicon sequencing. H. splendens showed consistently low nitrogen-fixation rates with little transplant response, whereas P. schreberi and A. turgidum home and transplant tundra cores generally exhibited higher rates at the cooler, more northern site regardless of origin. In contrast, bacterial community structure changed little following transplantation, with composition driven primarily by moss species. Only in cyanobacteria and some heterotrophic bacterial lineages did we find subtle ASV-level changes. The absence of an association between microbial composition and nitrogen fixation, together with the heterogeneity among moss species, suggests that over short timescales, host physiology and microenvironment play a larger role in the variation of nitrogen-fixation rates than community turnover. The fact that short-term shifts in moss-associated nitrogen-fixation rate are driven primarily by host species identity, rather than microbiome restructuring, has important implications for near-term predictions of nitrogen inputs under Arctic climate change.

microbiology↗

bioRxiv2025

A cross-environment comparison of nontuberculous mycobacterial diversity

Nontuberculous mycobacteria (NTM) are a group of environmental bacteria that encompass nearly 200 described species, some of which can cause chronic pulmonary infection in humans. What makes these infections unique is that they are environmentally acquired, yet there remains a limited understanding of how different environments contribute to potential pathogen exposure. Here, we use new and existing marker gene datasets to compare the amounts and types of NTM across three environments known to harbor mycobacteria, surface waters, soil, and household plumbing biofilms, to better understand potential pathogen occurrence in each environment. We used 16S rRNA gene sequencing, in tandem with mycobacterial-specific marker gene sequencing to characterize variation in the relative abundances of the genus Mycobacterium and specific mycobacterial taxa across the three environments, with a focus on a clinically significant NTM. We found that household plumbing biofilms contained both the highest relative abundance of the genus Mycobacterium (on average, 13.7% of bacteria were members of the genus), as well as the highest occurrence of clinically relevant species detected (Mycobacterium avium, Mycobacterium abscessus), compared to surface waters and soil. Although mycobacteria are ubiquitous across many different environments, mycobacterial diversity is highly variable between environments with clinically relevant species largely restricted to household plumbing biofilms, information that is critical for understanding the ecology and epidemiology of NTM disease. ImportanceNontuberculous mycobacteria, or NTM, are a diverse group of bacteria within the genus Mycobacterium that are common in many environments. While most members of the genus pose little threat to human health, a handful of species, namely the Mycobacterium avium complex, M. abscessus, and M. mucogenicum, can cause severe and prolonged lung infections. These environmentally acquired infections are on the rise in the United States and around the world, yet we still dont have a good understanding of which environment types pose the greatest risk of infection to susceptible populations. Our study used cultivation independent approaches to identify the specific NTM taxa found in over 1,000 samples from three potentially important environmental reservoirs - surface waters, soils, and household plumbing systems, to determine which of these environments are most likely to harbor NTM of clinical significance. Our results highlight the high degree of variability in the types of NTM taxa detected in different environments (including extensive novel diversity within the genus) and show that household plumbing biofilms are likely the most important reservoir and subsequent route of transmission for clinically significant NTM.

microbiology↗

bioRxiv2023

Building a genome-based understanding of bacterial pH preferences

The environmental preferences of many microbes remain undetermined. This is the case for bacterial pH preferences, which can be difficult to predict a priori despite the importance of pH as a factor structuring bacterial communities in many systems. We compiled data on bacterial distributions from five datasets spanning pH gradients in soil and freshwater systems (1470 samples in total), quantified the pH preferences of bacterial taxa across these datasets, and compiled genomic data from representative bacterial taxa. While taxonomic and phylogenetic information were generally poor predictors of bacterial pH preferences, we identified genes consistently associated with pH preference across environments. We then developed and validated a machine learning model to estimate bacterial pH preferences from genomic information alone, a model which could aid in the selection of microbial inoculants, improve species distribution models, or help design effective cultivation strategies. More generally, we demonstrate the value of combining biogeographic and genomic data to infer and predict the environmental preferences of diverse bacterial taxa.

ecology↗

bioRxiv2021

Strain-level variation influences Methanothermococcus fitness in marine hydrothermal systems

Hydrogenotrophic methanogens are ubiquitous chemoautotrophic archaea inhabiting globally distributed deep-sea hydrothermal vent ecosystems and associated subseafloor niches within the rocky subseafloor, yet little is known about how they adapt and diversify in these habitats. To determine genomic variation and selection pressure within methanogenic populations at vents, we examined five Methanothermococcus single cell amplified genomes (SAGs) in conjunction with 15 metagenomes and 10 metatranscriptomes from venting fluids at two geochemically distinct hydrothermal vent fields on the Mid-Cayman Rise in the Caribbean Sea. We observed that some Methanothermococcus strains and their transcripts were more abundant than others in individual vent sites, and among the genes differentiating the Methanothermococcus SAGs were those related to nitrogen fixation and the CRISPR/Cas immune system. SAGs possessing these genes were less abundant than those missing that genomic region. Overall, patterns in nucleotide variation indicated that the population dynamics of Methanothermococcus were not governed by clonal expansions or selective sweeps, at least in the habitats and sampling times included in this study. Together, our results show that although specific lineages of Methanothermococcus co-exist in these habitats, some outcompete others, and possession of accessory metabolic functions does not necessarily provide a fitness advantage in these habitats in all conditions. This work highlights the power of combining single-cell, metagenomic, and metatranscriptomic datasets to determine how evolution shapes microbial abundance and diversity in hydrothermal vent ecosystems.

microbiology↗