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At least 55 records · Page 3

Machine learning enables identification of an alternative yeast galactose utilization pathway

How genomic differences contribute to phenotypic differences is a major question in biology. The recently characterized genomes, isolation environments, and qualitative patterns of growth on 122 sources and conditions of 1,154 strains from 1,049 fungal species (nearly all known) in the yeast subphylum Saccharomycotina provide a powerful, yet complex, dataset for addressing this question. We used a random forest algorithm trained on these genomic, metabolic, and environmental data to predict growth on several carbon sources with high accuracy. Known structural genes involved in assimilation of these sources and presence/absence patterns of growth in other sources were important features contributing to prediction accuracy. By further examining growth on galactose, we found that it can be predicted with high accuracy from either genomic (92.2%) or growth data (82.6%) but not from isolation environment data (65.6%). Prediction accuracy was even higher (93.3%) when we combined genomic and growth data. After the GALactose utilization genes, the most important feature for predicting growth on galactose was growth on galactitol, raising the hypothesis that several species in two orders, Serinales and Pichiales (containing the emerging pathogen Candida auris and the genus Ogataea, respectively), have an alternative galactose utilization pathway because they lack the GAL genes. Growth and biochemical assays confirmed that several of these species utilize galactose through an alternative oxidoreductive D-galactose pathway, rather than the canonical GAL pathway. Machine learning approaches are powerful for investigating the evolution of the yeast genotype–phenotype map, and their application will uncover novel biology, even in well-studied traits.

59 BASIC BIOLOGICAL SCIENCES↗

Kingdoms in turmoil

How should the world's living organisms be classified? Into how many kingdoms should they be grouped? Scientists have been grappling with these questions since the time of Aristotle, drawing on a broad base of biological characteristics for clues. The fossil record, visible traits of living organisms and, more recently, results from cell biology have all shaped theories of biological classification. But last year a new and controversial concept emerged: a classification of life based solely on molecular traits. The focal point of the controversy is a tree of life, or "phylogeny", devised by Carl Woese of the University of Illinois, Otto Kandler of the University of Munich and Mark Wheelis of the University of California. The tree is unusual because, unlike all previous schemes, it is constructed solely from biochemical data such as DNA sequences rather than a range of different organism characteristics. But that is not all. The scheme also challenges the idea that life on Earth is best divided into five kingdoms, with the main split being between bacteria and all other organisms. Woese and his colleagues create three main groupings by dividing the bacteria in two and unifying all other organisms.

NASA Program Exobiology↗

Environmental factors have a greater influence on photosynthetic capacity in C 4 plants than biochemical subtypes or growth forms

Our understanding of how photosynthetic capacity varies among C 4 species and across growth and measurement conditions remains limited. We collated 1696 CO 2 response curves of net CO 2 assimilation rate (A/C i curves) from C 4 species grown and measured at various environmental conditions and used these data to estimate the apparent maximum carboxylation activity of phosphoenolpyruvate carboxylase (V pmaxA ) and CO 2 -saturated net photosynthetic rate (A max ), two key parameters describing photosynthetic capacity. We examined how V pmaxA and A max vary with species-specific traits, growth and measurement conditions.

59 BASIC BIOLOGICAL SCIENCES↗

Emergence of Orchestrated and Dynamic Metabolism of Saccharomyces cerevisiae

Microbial metabolism is a fundamental cellular process that involves many biochemical events and is distinguished by its emergent properties. While the molecular details of individual reactions have been increasingly elucidated, it is not well understood how these reactions are quantitatively orchestrated to produce collective cellular behaviors. Here we developed a coarse-grained, systems, and dynamic mathematical framework, which integrates metabolic reactions with signal transduction and gene regulation to dissect the emergent metabolic traits of Saccharomyces cerevisiae. Our framework mechanistically captures a set of characteristic cellular behaviors, including the Crabtree effect, diauxic shift, diauxic lag time, and differential growth under nutrient-altered environments. It also allows modular expansion for zooming in on specific pathways for detailed metabolic profiles. Furthermore, this study provides a systems mathematical framework for yeast metabolic behaviors, providing insights into yeast physiology and metabolic engineering.

59 BASIC BIOLOGICAL SCIENCES↗

Xylem-dwelling pathogen unaffected by local xylem vessel network properties in grapevines ( Vitis spp.)

Abstract Background and aims Xylella fastidiosa (Xf) is the xylem-dwelling bacterium associated with Pierce’s disease (PD), which causes mortality in agriculturally important species, such as grapevine (Vitis vinifera). The development of PD symptoms in grapevines depends on the ability of Xf to produce cell-wall-degrading enzymes to break up intervessel pit membranes and systematically spread through the xylem vessel network. Our objective here was to investigate whether PD resistance could be mechanistically linked to xylem vessel network local connectivity. Methods We used high-resolution X-ray micro-computed tomography (microCT) imaging to identify and describe the type, area and spatial distribution of intervessel connections for six different grapevine genotypes from three genetic backgrounds, with varying resistance to PD (four PD resistant and two PD susceptible). Key results Our results suggest that PD resistance is unlikely to derive from local xylem network connectivity. The intervessel pit area (Ai) varied from 0.07 ± 0.01 mm2 mm−3 in Lenoir to 0.17 ± 0.03 mm2 mm−3 in Blanc do Bois, both PD resistant. Intervessel contact fraction (Cp) was not statically significant, but the two PD-susceptible genotypes, Syrah (0.056 ± 0.015) and Chardonnay (0.041 ± 0.013), were among the most highly connected vessel networks. Neither Ai nor Cp explained differences in PD resistance among the six genotypes. Bayesian re-analysis of our data shows moderate evidence against the effects of the traits analysed: Ai (BF01 = 4.88), mean vessel density (4.86), relay diameter (4.30), relay density (3.31) and solitary vessel proportion (3.19). Conclusions Our results show that radial and tangential xylem network connectivity is highly conserved within the six different Vitis genotypes we sampled. The way that Xf traverses the vessel network may limit the importance of local network properties to its spread and may confer greater importance on host biochemical responses.

Plant Sciences↗

Phylogenetically diverse wild plant species use common biochemical strategies to thrive in the Atacama Desert

The best ideotypes are under mounting pressure due to increased aridity. Understanding the conserved molecular mechanisms that evolve in wild plants adapted to harsh environments is crucial in developing new strategies for agriculture. Yet our knowledge of such mechanisms in wild species is scant. We performed metabolic pathway reconstruction using transcriptome information from 32 Atacama and phylogenetically related species that do not live in Atacama (sister species). We analyzed reaction enrichment to understand the commonalities and differences of Atacama plants. To gain insights into the mechanisms that ensure survival, we compared expressed gene isoform numbers and gene expression patterns between the annotated biochemical reactions from 32 Atacama and sister species. We found biochemical convergences characterized by reactions enriched in at least 50% of the Atacama species, pointing to potential advantages against drought and nitrogen starvation, for instance. Furthermore, these findings suggest that the adaptation in the Atacama Desert may result in part from shared genetic legacies governing the expression of key metabolic pathways to face harsh conditions. Enriched reactions corresponded to ubiquitous compounds common to extreme and agronomic species and were congruent with our previous metabolomic analyses. Convergent adaptive traits offer promising candidates for improving abiotic stress resilience in crop species.

59 BASIC BIOLOGICAL SCIENCES↗

Lost and Found: Rediscovering Microbiome-Associated Phenotypes that Reshape Agricultural Sustainability

Modern agriculture faces an urgent need to improve nutrient use efficiency while reducing environmental impacts. Here, we show that ancestral traits controlling rhizosphere microbiome functions can be reintroduced into elite maize through targeted teosinte introgressions. Using near-isogenic lines, we mapped microbiome-associated phenotypes (MAPs) derived from teosinte that suppress nitrification and denitrification—key microbial processes contributing to nitrogen loss. These introgressions altered root exudate chemistry, resulting in distinct microbial assemblies and enhanced nitrogen retention. We identified candidate loci and metabolites responsible for suppressive activity and demonstrated their functional effects in vitro. Our findings reveal a genetic and biochemical basis for rewilding microbiome-mediated ecosystem services in crops, offering a scalable path toward sustainable nutrient management in global agriculture. ---- These maize root exduate metabolomics data are a subset of this larger project and make up a phenotyping for candidate lines.

Favela, Alonso [School of Plant Sciences, Universi↗

Lost and found: Rediscovering microbiome-associated phenotypes that reshape agricultural sustainability

Modern agriculture faces an urgent need to improve nutrient use efficiency while reducing environmental impacts. Here, we show that ancestral traits controlling rhizosphere microbiome functions can be reintroduced into elite maize through targeted teosinte introgressions. Using near-isogenic lines, we mapped microbiome-associated phenotypes (MAPs) derived from teosinte that suppress nitrification and denitrification—key microbial processes contributing to nitrogen loss. These introgressions altered root exudate chemistry, resulting in distinct microbial assemblies and enhanced nitrogen retention. We identified candidate loci and exudate metabolites responsible for suppressive activity and demonstrated their functional effects in vitro. These findings reveal a genetic and biochemical basis for rewilding microbiome-mediated ecosystem services in crops, offering a scalable path toward sustainable nutrient management in global agriculture.

60 APPLIED LIFE SCIENCES↗

METABOLIC: high-throughput profiling of microbial genomes for functional traits, metabolism, biogeochemistry, and community-scale functional networks

Background Advances in microbiome science are being driven in large part due to our ability to study and infer microbial ecology from genomes reconstructed from mixed microbial communities using metagenomics and single-cell genomics. Such omics-based techniques allow us to read genomic blueprints of microorganisms, decipher their functional capacities and activities, and reconstruct their roles in biogeochemical processes. Currently available tools for analyses of genomic data can annotate and depict metabolic functions to some extent; however, no standardized approaches are currently available for the comprehensive characterization of metabolic predictions, metabolite exchanges, microbial interactions, and microbial contributions to biogeochemical cycling. Results We present METABOLIC (METabolic And BiogeOchemistry anaLyses In miCrobes), a scalable software to advance microbial ecology and biogeochemistry studies using genomes at the resolution of individual organisms and/or microbial communities. The genome-scale workflow includes annotation of microbial genomes, motif validation of biochemically validated conserved protein residues, metabolic pathway analyses, and calculation of contributions to individual biogeochemical transformations and cycles. The community-scale workflow supplements genome-scale analyses with determination of genome abundance in the microbiome, potential microbial metabolic handoffs and metabolite exchange, reconstruction of functional networks, and determination of microbial contributions to biogeochemical cycles. METABOLIC can take input genomes from isolates, metagenome-assembled genomes, or single-cell genomes. Results are presented in the form of tables for metabolism and a variety of visualizations including biogeochemical cycling potential, representation of sequential metabolic transformations, community-scale microbial functional networks using a newly defined metric “MW-score” (metabolic weight score), and metabolic Sankey diagrams. METABOLIC takes ~ 3 h with 40 CPU threads to process ~ 100 genomes and corresponding metagenomic reads within which the most compute-demanding part of hmmsearch takes ~ 45 min, while it takes ~ 5 h to complete hmmsearch for ~ 3600 genomes. Tests of accuracy, robustness, and consistency suggest METABOLIC provides better performance compared to other software and online servers. To highlight the utility and versatility of METABOLIC, we demonstrate its capabilities on diverse metagenomic datasets from the marine subsurface, terrestrial subsurface, meadow soil, deep sea, freshwater lakes, wastewater, and the human gut. Conclusion METABOLIC enables the consistent and reproducible study of microbial community ecology and biogeochemistry using a foundation of genome-informed microbial metabolism, and will advance the integration of uncultivated organisms into metabolic and biogeochemical models. METABOLIC is written in Perl and R and is freely available under GPLv3 at https://github.com/AnantharamanLab/METABOLIC.

59 BASIC BIOLOGICAL SCIENCES↗

Water chemistry in flume channel and hyporheic zone (i.e., porewater) associated with: “Rethinking Aerobic Respiration in the Hyporheic Zone Under Variation in Carbon and Nitrogen Stoichiometry”

Dissolved oxygen (DO), total organic carbon (TOC), total nitrogen (TN), molecular data for organic matter, and biochemical reactions for surface water and porewater (i.e., hyporheic zone) collected from a water recirculating flume located at the University of Texas, Austin. The flume contained real river water from Lower Colorado River(Austin, TX) and clean sand. Hyporheic exchange in the flume was induced through The study aims to understand relationships between aerobic metabolism of organic matter and molecular characteristics of organic matter, such as thermodynamic signature and nitrogen content, through the extent of the hyporheic zone at 10 cm- resolution, and through time. During the experiment, organic matter (dry leaves) was added to the flume and removed after 24 hours. The water samples were collected before the addition of leaves, at the time of removal of leaves, and at hour 72. The water samples were analyzed using ultrahigh resolution Fourier transform ion cyclotron resonance mass spectrometry (FTICR-MS) and total organic carbon (TOC) and total nitrogen (TN) analysis. Dissolved oxygen content throughout the surface water and the hyporheic zone of the flume was measured with a large planar optode. This data package is associated with the publication ’ Rethinking Aerobic Respiration in the Hyporheic Zone Under Variation in Carbon and Nitrogen Stoichiometry’ published in Environmental Science and Technology (Turețcaia et al., 2023 https://doi.org/10.1021/acs.est.3c04765). The dataset is comprised of five folders (1) Diss_O2_pic, (2) input_files (3) output_files; (4) python_code; and (5) R_code . Diss_O2_pic contains siximages of dissolved oxygen distribution in a bedform at hours 0, 24, and 72 of the experiment conducted in a large recirculation flume. Images are in separate R and G channels (i.e., RGB). The input_files contains (1) a csv file with FTICR peaks identified within each sample, (2) a csv file with molecular information pertinent to FTICR data with Gibbs free energy calculations adjusted for environmental temperature, (3) a csv file containing concentrations of non-purgeable organic carbon measured throughout the experiment , (4) a csv file containing concentrations of total nitrogen measured throughout the experiment, (5) a csv file containing total biochemical reactions (i.e., transformations) identified in the dataset, (6) a csv containing transformation profiles, and (7) a csv file containing transformations with formulas, and (8) a jpg file with schematic representation of locations for sample collection. The output_files contains (1) and xlsx file containing percent biochemical reactions containing nitrogen identified across all 39 sample, (2) a csv file of merged FTICR data and molecular information files, (3) a csv files containing average Gibbs free energy within sampling domains and at each sampling location, (4) a csv file with average concentrations of dissolved oxygen across sampling locations at hour 0, (5) a csv file with average concentrations of dissolved oxygen across sampling locations at hour 24, (6) a csv file with average concentrations of dissolved oxygen across sampling locations at hour 72, (7) a csv file with percent chemical classes identified across sampling locations at hour 0, (8) a csv file with percent chemical classes identified across sampling locations at hour 24, (9) a csv file with percent chemical classes identified across sampling locations at hour 72, and (10) a csv file containing percent nitrogen containing biochemical reactions identified across sampling locations at hours 0, 24, and 72. The python_code contains seven ipynb files which are Jupyter Notebooks used for data analysis and figures generation. The R_code contains 3 R files with R code used for data analysis and figures generation. This data package contains the processed data used in the associated manuscript. This data has not been previously published.

54 ENVIRONMENTAL SCIENCES↗

Linking metabolic phenotypes to pathogenic traits among “ Candidatus Liberibacter asiaticus” and its hosts

Candidatus Liberibacter asiaticus (CLas) has been associated with Huanglongbing, a lethal vector-borne disease affecting citrus crops worldwide. While comparative genomics has provided preliminary insights into the metabolic capabilities of this uncultured microorganism, a comprehensive functional characterization is currently lacking. Here, we reconstructed and manually curated genome-scale metabolic models for the six CLas strains A4, FL17, gxpsy, Ishi-1, psy62, and YCPsy, in addition to a model of the closest related culturable microorganism, L. crescens BT-1. Predictions about nutrient requirements and changes in growth phenotypes of CLas were confirmed using in vitro hairy root-based assays, while the L. crescens BT-1 model was validated using cultivation assays. Host-dependent metabolic phenotypes were revealed using expression data obtained from CLas-infected citrus trees and from the CLas-harboring psyllid Diaphorina citri Kuwayama. These results identified conserved and unique metabolic traits, as well as strain-specific interactions between CLas and its hosts, laying the foundation for the development of model-driven Huanglongbing management strategies.

59 BASIC BIOLOGICAL SCIENCES↗

Retrospective analysis of biochemical limitations to photosynthesis in 49 species: C 4 crops appear still adapted to pre‐industrial atmospheric [ CO 2 ]

Abstract Leaf CO 2 uptake ( A ) in C 4 photosynthesis is limited by the maximum apparent rate of PEPc carboxylation ( V pmax ) at low intercellular [CO 2 ] ( c i ) with a sharp transition to a c i ‐saturated rate ( V max ) due to co‐limitation by ribulose‐1:5‐bisphosphate carboxylase/oxygenase (Rubisco) and regeneration of PEP. The response of A to c i has been widely used to determine these two parameters. V max and V pmax depend on different enzymes but draw on a shared pool of leaf resources, such that resource distribution is optimized, and A maximized, when V max and V pmax are co‐limiting. We collected published A / c i curves in 49 C 4 species and assessed variation in photosynthetic traits between phylogenetic groups, and as a function of atmospheric [CO 2 ]. The balance of V max ‐ V pmax varied among evolutionary lineages and C 4 subtypes. Operating A was strongly V max ‐limited, such that re‐allocation of resources from V pmax towards V max was predicted to improve A by 12% in C 4 crops. This would not require additional inputs but rather altered partitioning of existing leaf nutrients, resulting in increased water and nutrient‐use efficiency. Optimal partitioning was achieved only in plants grown at pre‐industrial atmospheric [CO 2 ], suggesting C 4 crops have not adjusted to the rapid increase in atmospheric [CO 2 ] of the past few decades.

Pignon, Charles P.↗

Transgenic plants with increased calcium stores

The present invention provides transgenic plants over-expressing a transgene encoding a calcium-binding protein or peptide (CaBP). Preferably, the CaBP is a calcium storage protein and over-expression thereof does not have undue adverse effects on calcium homeostasis or biochemical pathways that are regulated by calcium. In preferred embodiments, the CaBP is calreticulin (CRT) or calsequestrin. In more preferred embodiments, the CaBP is the C-domain of CRT, a fragment of the C-domain, or multimers of the foregoing. In other preferred embodiments, the CaBP is localized to the endoplasmic reticulum by operatively associating the transgene encoding the CaBP with an endoplasmic reticulum localization peptide. Alternatively, the CaBP is targeted to any other sub-cellular compartment that permits the calcium to be stored in a form that is biologically available to the plant. Also provided are methods of producing plants with desirable phenotypic traits by transformation of the plant with a transgene encoding a CaBP. Such phenotypic traits include increased calcium storage, enhanced resistance to calcium-limiting conditions, enhanced growth and viability, increased disease and stress resistance, enhanced flower and fruit production, reduced senescence, and a decreased need for fertilizer production. Further provided are plants with enhanced nutritional value as human food or animal feed.

Wyatt, Sarah↗

Ancestral archaea expanded the genetic code with pyrrolysine

The pyrrolysyl-tRNA synthetase (PylRS) facilitates the cotranslational installation of the 22nd amino acid pyrrolysine. Owing to its tolerance for diverse amino acid substrates, and its orthogonality in multiple organisms, PylRS has emerged as a major route to install noncanonical amino acids into proteins in living cells. Recently, a novel class of PylRS enzymes was identified in a subset of methanogenic archaea. Enzymes within this class (ΔPylSn) lack the N-terminal tRNA-binding domain that is widely conserved amongst PylRS enzymes, yet remain active and orthogonal in bacteria and eukaryotes. In this study, we use biochemical and in vivo UAG-readthrough assays to characterize the aminoacylation efficiency and substrate spectrum of a ΔPylSn class PylRS from the archaeon Candidatus Methanomethylophilus alvus. We show that, compared with the full-length enzyme from Methanosarcina mazei, the Ca. M. alvus PylRS displays reduced aminoacylation efficiency but an expanded amino acid substrate spectrum. To gain insight into the evolution of ΔPylSn enzymes, we performed molecular phylogeny using 156 PylRS and 105 pyrrolysine tRNA (tRNA Pyl ) sequences from diverse archaea and bacteria. This analysis suggests that the PylRS•tRNA Pyl pair diverged before the evolution of the three domains of life, placing an early limit on the evolution of the Pyl-decoding trait. Furthermore, our results document the coevolutionary history of PylRS and tRNA Pyl and reveal the emergence of tRNA Pyl sequences with unique A73 and U73 discriminator bases. The orthogonality of these tRNA Pyl species with the more common G73-containing tRNA Pyl will enable future efforts to engineer PylRS systems for further genetic code expansion.

59 BASIC BIOLOGICAL SCIENCES↗

Overexpression of REDUCED WALL ACETYLATION C increases xylan acetylation and biomass recalcitrance in Populus

Abstract Plant lignocellulosic biomass, i.e. secondary cell walls of plants, is a vital alternative source for bioenergy. However, the acetylation of xylan in secondary cell walls impedes the conversion of biomass to biofuels. Previous studies have shown that REDUCED WALL ACETYLATION (RWA) proteins are directly involved in the acetylation of xylan but the regulatory mechanism of RWAs is not fully understood. In this study, we demonstrate that overexpression of a Populus trichocarpa PtRWA-C gene increases the level of xylan acetylation and increases the lignin content and S/G ratio, ultimately yielding poplar woody biomass with reduced saccharification efficiency. Furthermore, through gene coexpression network and expression quantitative trait loci (eQTL) analysis, we found that PtRWA-C was regulated not only by the secondary cell wall hierarchical regulatory network but also by an AP2 family transcription factor HARDY (HRD). Specifically, HRD activates PtRWA-C expression by directly binding to the PtRWA-C promoter, which is also the cis-eQTL for PtRWA-C. Taken together, our findings provide insights into the functional roles of PtRWA-C in xylan acetylation and consequently saccharification and shed light on synthetic biology approaches to manipulate this gene and alter cell wall properties. These findings have substantial implications for genetic engineering of woody species, which could be used as a sustainable source of biofuels, valuable biochemicals, and biomaterials.

59 BASIC BIOLOGICAL SCIENCES↗

High foliar K and P resorption efficiencies in old-growth tropical forests growing on nutrient-poor soils

Resorption is the active withdrawal of nutrients before leaf abscission. This mechanism represents an important strategy to maintain efficient nutrient cycling; however, resorption is poorly characterized in old-growth tropical forests growing in nutrientpoor soils. We investigated nutrient resorption from leaves in 39 tree species in two tropical forests on the Guiana Shield, French Guiana, to investigate whether resorption efficiencies varied with soil nutrient, seasonality, and species traits. The stocks of P in leaves, litter, and soil were low at both sites, indicating potential P limitation of the forests. Accordingly, mean resorption efficiencies were higher for P (35.9%) and potassium (K; 44.6%) than for nitrogen (N; 10.3%). K resorption was higher in the wet (70.2%) than in the dry (41.7%) season. P resorption increased slightly with decreasing total soil P; and N and P resorptions were positively related to their foliar concentrations. We conclude that nutrient resorption is a key plant nutrition strategy in these old-growth tropical forests, that trees with high foliar nutrient concentration reabsorb more nutrient, and that nutrients resorption in leaves, except P, are quite decoupled from nutrients in the soil. Seasonality and biochemical limitation played a role in the resorption of nutrients in leaves, but species-specific requirements obscured general tendencies at stand and ecosystem level.

59 BASIC BIOLOGICAL SCIENCES↗

A trait-based framework for linking microbial communities with carbon transformations under precipitation change

Droughts are common throughout the world. As the climate changes, droughts may become more frequent and intense. Still, scientists are uncertain about how drought will affect the natural world, particularly the bacteria, fungi, and other microbes that live in soils. These tiny life forms are crucial because they control the Earth’s flows of carbon and essential nutrients. Researchers at the University of California, Irvine, and Lawrence Berkeley National Laboratory teamed up to study how the microbiome, or collection of bacteria and fungi in the soil, deals with drought. Since 2007, the researchers have used shelters with retractable roofs to prevent nearly half of normal rainfall from reaching grass and shrub ecosystems, and their soil microbiomes, in Southern California. The study team discovered that microbes have some clever tricks up their sleeve for surviving drought. When growing on dead grass as a food source, microbes ramp up production of specialized chemicals called osmolytes that keep their cells from drying out. But microbes growing on dead shrubs face another problem. Unlike grass, shrub leaves are a lousy food source. To digest and consume shrub leaves, microbes have to exude more enzymes, which act like biochemical chef knives that chop complex leaf molecules into bite-sized pieces. Carbon is the coin of the microbial realm, earned via enzyme action or slurping up dead plant juices. Microbes growing under normal conditions on tasty dead grass have it easy: they can spend most of their carbon currency on growth. With drought, life gets harder as microbes need to pay up for osmolytes and ramp down their growth. It gets worse with shrub leaves because microbes have to multi-task among growth, enzyme secretion, and osmolyte production. When drought hits, microbes on shrub leaves forgo the osmolytes, probably because losing the carbon revenue from enzyme investment would be a deal-breaker for survival. The next question tackled by the researchers asked how the genes controlling microbial lifestyles sort out across the tree of life. Most microbiologists thought these lifestyles would correspond to rather large branches on the tree. But the study team found that in fact, very closely related microbes differ in important and interesting ways. For example, bacteria that have nearly identical housekeeping genes respond distinctively to warming, rainfall, and plant chemistry. As a result, soil microbiomes are much richer in diversity than originally thought. And studying microbial diversity in much greater detail could open up many more possibilities for how microbial life deals with changing environmental conditions. Trying to understand microbial life without the fine details of genetic diversity is like trying to stream Netflix on a dial-up connection. By looking across the landscape, the researchers revealed that microbial diversity is absolutely critical for maintaining the planet’s flows of carbon and nutrients. The study team designed a new technology—microbial cages—for transplanting intact microbiomes. With the cages, the researchers could move microbiomes to novel environments and compare their ability to cycle carbon and nutrients. In some cases, performance tailed off when microbiomes found themselves in a new environment, but in other cases, performance rivaled or even exceeded that of the resident microbiome. And even the low performers eventually caught up to the native microbiomes if given sufficient time. These findings mean that microbiomes—at least in Southern California—may be resilient to climate shifts due to a high diversity of microbial lifestyles. Coping with heat and drought may literally be in their DNA. The last piece of the research puzzle, and a “Holy Grail” for microbial ecologists, is to forecast the behavior of diverse microbiomes. To meet this challenge, the study team developed new theory and computer models. For the first time, these models account for hundreds of different microbes and how their intricate lifestyles cope with drought. The models are starting to connect the tiniest microbes with the global cycles that sustain Earth’s farms, fields, and forests. With that connection, it will be easier for society to plan for a world with more droughts and other climate disruptions.

54 ENVIRONMENTAL SCIENCES↗

Finding the missing pieces: filling gaps that impede the translation of omics data into models

High-throughput omics technologies such as DNA sequencing have made the sequencing and computational assembly of microbial genomes recovered from the environment relatively routine. Computational inference of the protein products encoded by these genomes, and the associated biochemical functions, should enable the accurate prediction and modeling of microbial metabolism, organismal interactions, and ecosystem processes. However, a lack of scalable, probabilistic protein annotation tools limits the full potential of modeling for understanding the metabolism and biogeochemical cycles of microbial communities. Our approach to improve inference of protein annotations and metabolic models relied on learning from and emulating expert manual curation, leveraging software engineering and data science best practices to scale up the throughput and accuracy of annotations and metabolic model construction, building software to objectively evaluate different annotation strategies, and more closely linking the protein annotation and metabolic model inference process. Outcomes of this research include several improved or new computational tools, including DRAM (Distilled and Refined Annotation of Metabolism) for annotating microbial genomes with protein function and metabolic traits, CAMPER (Curated Annotations for Microbial Polyphenol Enzymes and Reactions) for annotating key polyphenol metabolisms, EC-Bench for comprehensive and unbiased benchmarking of annotation tools, and several apps available via the DOE Systems Biology Knowledgebase (KBase) for building genome-scale metabolic models. We demonstrate that these tools allow us to scalably annotate and understand thousands of genomes for microbial communities from a variety of systems and test cases, including rivers, thawing permafrost, and gut microbiomes. All of these computational tools are available as open-source software, with most broadly and easily accessible to the scientific community via KBase apps.

59 BASIC BIOLOGICAL SCIENCES↗