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Microbial carbon use efficiency (CUE) affects the fate and storage of carbon in terrestrial ecosystems, but its global importance remains uncertain. Accurately modeling and predicting CUE on a global scale is challenging due to inconsistencies in measurement techniques and the complex interactions of climatic, edaphic, and biological factors across scales. The link between microbial CUE and soil organic carbon relies on the stabilization of microbial necromass within soil aggregates or its association with minerals, necessitating an integration of microbial and stabilization processes in modeling approaches. In this perspective, we propose a comprehensive framework that integrates diverse data sources, ranging from genomic information to traditional soil carbon assessments, to refine carbon cycle models by incorporating variations in CUE, thereby enhancing our understanding of the microbial contribution to carbon cycling.

Juvenile myelomonocytic leukemia (JMML) is a hematologic malignancy of young children caused by mutations that increase Ras signaling output. Hematopoietic stem cell transplantation (HSCT) is a potentially curative treatment, but patients with relapsed or refractory (advanced) disease have dismal outcomes. This phase II trial evaluated the safety and efficacy of trametinib, an oral MEK1/2 inhibitor, in patients with advanced JMML. Ten infants and children were enrolled, and the objective response rate was 50%. Four patients with refractory disease proceeded to HSCT after receiving trametinib. Three additional patients completed all 12 cycles permitted on study and continue to receive off-protocol trametinib without HSCT. The remaining three patients had progressive disease with two demonstrating molecular evolution by the end of cycle 2. Transcriptomic and proteomic analyses provided novel insights into the mechanisms of response and resistance to trametinib in JMML. ClinicalTrials.gov Identifier: NCT03190915. Significance: Trametinib was safe and effective in young children with relapsed or refractory JMML, a lethal disease with poor survival rates. Seven of 10 patients completed the maximum 12 cycles of therapy or used trametinib as a bridge to HSCT and are alive with a median follow-up of 24 months.

Methane is a powerful greenhouse gas, more potent than carbon dioxide, and emitted from a variety of natural sources including wetlands, permafrost, mammalian guts and termites. As increases in global temperatures continue to break records, quantifying the magnitudes of key methane sources has never been more pertinent. Over the last 40 years, the contribution of termites to the global methane budget has been subject to much debate. The most recent estimates of termite emissions range between 9 and 15 Tg CH4 year-1, approximately 4% of emissions from natural sources (excluding wetlands). However, we argue that the current approach for estimating termite contributions to the global methane budget is flawed. Key parameters, namely termite methane emissions from soil, deadwood, living tree stems, epigeal mounds and arboreal nests, are largely ignored in global estimates. This omission occurs because data are lacking and research objectives, crucially, neglect variation in termite ecology. Furthermore, inconsistencies in data collection methods prohibit the pooling of data required to compute global estimates. Here, we summarise the advances made over the last 40 years and illustrate how different aspects of termite ecology can influence the termite contribution to global methane emissions. Additionally, we highlight technological advances that may help researchers investigate termite methane emissions on a larger scale. Finally, we consider dynamic feedback mechanisms of climate warming and land-use change on termite methane emissions. We conclude that ultimately the global contribution of termites to atmospheric methane remains unknown and thus present an alternative framework for estimating their emissions. To significantly improve estimates, we outline outstanding questions to guide future research efforts.

Microbes are essential for the functioning of all ecosystems, and as global warming and anthropogenic pollution threaten ecosystems, it is critical to understand how microbes respond to these changes. We investigated the climate response of Sphingomonas, a widespread gram-negative bacterial genus, during an 18-month microbial community reciprocal transplant experiment across a Southern California climate gradient. We hypothesized that after 18 months, the transplanted Sphingomonas clade and functional composition would correspond with site conditions and reflect the Sphingomonas composition of native communities. We extracted Sphingomonas sequences from metagenomic data across the gradient and assessed their clade and functional composition. Representatives of at least 12 major Sphingomonas clades were found at varying relative abundances along the climate gradient, and transplanted Sphingomonas clade composition shifted after 18 months. Site had a significant effect (PERMANOVA; P < 0.001) on the distribution of both Sphingomonas functional (R2 = 0.465) and clade composition (R2 = 0.400), suggesting that Sphingomonas composition depends on climate parameters. Additionally, for both Sphingomonas clade and functional composition, ordinations revealed that the transplanted communities shifted closer to the native Sphingomonas composition of the grassland site compared with the site they were transplanted into. Overall, our results indicate that climate and substrate collectively determine Sphingomonas clade and functional composition.IMPORTANCESphingomonas is the most abundant gram-negative bacterial genus in litter-degrading microbial communities of desert, grassland, shrubland, and forest ecosystems in Southern California. We aimed to determine whether Sphingomonas responds to climate change in the same way as gram-positive bacteria and whole bacterial communities in these ecosystems. Within Sphingomonas, both clade composition and functional genes shifted in response to climate and litter chemistry, supporting the idea that bacteria respond similarly to climate at different scales of genetic variation. This understanding of how microbes respond to perturbation across scales may aid in future predictions of microbial responses to climate change.

Although substantial advances in predicting the ecological impacts of global change have been made, predictions of the evolutionary impacts have lagged behind. In soil ecosystems, microbes act as the primary energetic drivers of carbon cycling; however, microbes are also capable of evolving on timescales comparable to rates of global change. Given the importance of soil ecosystems in global carbon cycling, we assess the potential impact of microbial evolution on carbon-climate feedbacks in this system. We begin by reviewing the current state of knowledge concerning microbial evolution in response to global change and its specific effect on soil carbon dynamics. Through this integration, we synthesize a roadmap detailing how to integrate microbial evolution into ecosystem biogeochemical models. Specifically, we highlight the importance of microscale mechanistic soil carbon models, including choosing an appropriate evolutionary model (e.g., adaptive dynamics, quantitative genetics), validating model predictions with 'omics' and experimental data, scaling microbial adaptations to ecosystem level processes, and validating with ecosystem-scale measurements. The proposed steps will require significant investment of scientific resources and might require 10-20 years to be fully implemented. However, through the application of multi-scale integrated approaches, we will advance the integration of microbial evolution into predictive understanding of ecosystems, providing clarity on its role and impact within the broader context of environmental change.

Soil carbon (C) responses to environmental change represent a major source of uncertainty in the global C cycle. Feedbacks between soil C stocks and climate drivers could impact atmospheric CO2 levels, further altering the climate. Here, we assessed the reliability of Earth system model (ESM) predictions of soil C change using the Coupled Model Intercomparison Project phases 5 and 6 (CMIP5 and CMIP6). ESMs predicted global soil C gains under the high emission scenario, with soils taking up 43.9 Pg (95% CI: 9.2–78.5 Pg) C on average during the 21st century. The variation in global soil C change declined significantly from CMIP5 (with average of 48.4 Pg [95% CI: 2.0–94.9 Pg] C) to CMIP6 models (with average of 39.3 Pg [95% CI: 23.9–54.7 Pg] C). For some models, a small C increase in all biomes contributed to this convergence. For other models, offsetting responses between cold and warm biomes contributed to convergence. Although soil C predictions appeared to converge in CMIP6, the dominant processes driving soil C change at global or biome scales differed among models and in many cases between earlier and later versions of the same model. Random Forest models, for soil carbon dynamics, accounted for more than 63% variation of the global soil C change predicted by CMIP5 ESMs, but only 36% for CMIP6 models. Although most CMIP6 models apparently agree on increased soil C storage during the 21st century, this consensus obscures substantial model disagreement on the mechanisms underlying soil C response, calling into question the reliability of model predictions.

Anthropogenic climate change has increased the frequency of drought, wildfire, and invasions of non-native species. Although high-severity fires linked to drought can inhibit recovery of native vegetation in forested ecosystems, it remains unclear how drought impacts the recovery of other plant communities following wildfire. We leveraged an existing rainfall manipulation experiment to test the hypothesis that reduced precipitation, fuel load, and fire severity convert plant community composition from native shrubs to invasive grasses in a Southern California coastal sage scrub system. We measured community composition before and after the 2020 Silverado wildfire in plots with three rainfall treatments. Drought reduced fuel load and vegetation cover, which reduced fire severity. Native shrubs had greater prefire cover in added water plots compared to reduced water plots. Native cover was lower and invasive cover was higher in postfire reduced water plots compared to postfire added and ambient water plots. Our results demonstrate the importance of fuel load on fire severity and plant community composition on an ecosystem scale. Management should focus on reducing fire frequency and removing invasive species to maintain the resilience of coastal sage scrub communities facing drought. In these communities, controlled burns are not recommended as they promote invasive plants.

Understanding microbial adaptation is crucial for predicting how soil carbon dynamics and global biogeochemical cycles will respond to climate change. This study employs the DEMENT model of microbial decomposition, along with empirical mutation and dispersal rates, to explore the roles of mutation and dispersal in the adaptation of soil microbial populations to shifts in litter chemistry, changes that are anticipated with climate-driven vegetation dynamics. Following a change in litter chemistry, mutation generally allows for a higher rate of litter decomposition than dispersal, especially when dispersal predominantly introduces genotypes already present in the population. These findings challenge the common idea that mutation rates are too low to affect ecosystem processes on ecological timescales. These results demonstrate that evolutionary processes, such as mutation, can help maintain ecosystem functioning as the climate changes.

Deadwood is an important yet understudied carbon pool in tropical ecosystems. Deadwood degradation to CO2 through decomposer (microbial, termite) activities is driven by wood moisture and temperature, which are in turn strongly influenced by local climate. Thus, climate data could be used to predict CO2 fluxes from decaying wood. Given the increasing availability of gridded climate data, this link would allow for the rapid estimation of deadwood-related CO2 fluxes from tropical ecosystems worldwide. In this study, we adapted a mechanistic fuel moisture model that uses weather variables (e.g., air temperature, precipitation, solar radiation) to simulate wood moisture and temperature along a rainfall gradient in Queensland, Australia. We then developed a Bayesian statistical relationship between wood moisture and temperature and CO2 flux from pine (Pinus radiata) blocks and combined this relationship with our simulations to predict CO2 fluxes from deadwood at 1 h temporal resolution. We compared our pine-based simulations to the moisture-CO2 relationships from stems of native tree species deployed at the wettest and driest sites. Finally, we integrated fluxes over time to estimate the amount of carbon entering the atmosphere and compared these estimates to measured mass loss in pines and native stems. Our statistical model showed a positive relationship between CO2 fluxes and wood moisture and temperature. Comparing cumulative CO2 with wood mass loss, we observed that carbon from deadwood decomposition is mainly released as CO2 regardless of the precipitation regime. At the dry savanna, only about 20 % of the wood mass loss was decomposed within 48 months, compared to almost 100 % at the wet rainforest, suggesting longer residence times of deadwood compared to wetter sites. However, the amount of carbon released in situ as CO2 is lower when wood blocks are attacked by termites, especially at drier sites. These results highlight the important but understudied role of termites in the breakdown of deadwood in dry climates. Additionally, mass loss-flux relationships of decaying native stems deviated from those of pine blocks. Our results indicate that wood moisture and temperature are necessary but not sufficient for predicting CO2 fluxes from deadwood degradation. Other factors, such as wood traits (wood quality, chemical composition, and stoichiometry) and biotic processes, should be considered in future modeling efforts.

Soil microbiomes are heterogeneous, complex microbial communities. Metagenomic analysis is generating vast amounts of data, creating immense challenges in sequence assembly and analysis. Although advances in technology have resulted in the ability to easily collect large amounts of sequence data, soil samples containing thousands of unique taxa are often poorly characterized. These challenges reduce the usefulness of genome-resolved metagenomic (GRM) analysis seen in other fields of microbiology, such as the creation of high quality metagenomic assembled genomes and the adoption of genome scale modeling approaches. The absence of these resources restricts the scale of future research, limiting hypothesis generation and the predictive modeling of microbial communities. Creating publicly available databases of soil MAGs, similar to databases produced for other microbiomes, has the potential to transform scientific insights about soil microbiomes without requiring the computational resources and domain expertise for assembly and binning.

The global population consumes more seafood from aquaculture today than from capture fisheries and although the aquaculture industry continues to grow, both seafood sectors will continue to be important to the global food supply into the future. As farming continues to expand into ocean systems, understanding how wild populations and fisheries will interact with farms will be increasingly important to informing sustainable ocean planning and management. Using a spatially explicit population and fishing model we simulate several impacts from ocean aquaculture (i.e., aggregation, protection from fishing, and impacts on fitness) to evaluate the mechanisms underlying interactions between aquaculture, wild populations and fisheries. We find that aggregation of species to farms can increase the benefits of protection from fishing that a farm provides and can have greater impacts on more mobile species. Splitting total farm area into smaller farms can benefit fishery catches, whereas larger farms can provide greater ecological benefits through conservation of wild populations. Our results provide clear lessons on how to design and co-manage expanding ocean aquaculture along with wild capture ecosystem management to benefit fisheries or conservation objectives.

Models estimating decomposition rates of dead wood across space and time are mainly based on studies carried out in temperate zones where microbes are dominant drivers of decomposition. However, most dead wood biomass is found in tropical ecosystems, where termites are also important wood consumers. Given the dependence of microbial decomposition on moisture with termite decomposition thought to be more resilient to dry conditions, the relative importance of these decomposition agents is expected to shift along gradients in precipitation that affect wood moisture. Here, we investigated the relative roles of microbes and termites in wood decomposition across precipitation gradients in space, time and with a simulated drought experiment in tropical Australia. We deployed mesh bags with non-native pine wood blocks, allowing termite access to half the bags. Bags were collected every 6 months (end of wet and dry seasons) over a 4-year period across five sites along a rainfall gradient (ranging from savanna to wet sclerophyll to rainforest) and within a simulated drought experiment at the wettest site. We expected microbial decomposition to proceed faster in wet conditions with greater relative influence of termites in dry conditions. Consistent with expectations, microbial-mediated wood decomposition was slowest in dry savanna sites, dry seasons and simulated drought conditions. Wood blocks discovered by termites decomposed 16–36% faster than blocks undiscovered by termites regardless of precipitation levels. Concurrently, termites were 10 times more likely to discover wood in dry savanna compared with wet rainforest sites, compensating for slow microbial decomposition in savannas. For wood discovered by termites, seasonality and drought did not significantly affect decomposition rates. Taken together, we found that spatial and seasonal variation in precipitation is important in shaping wood decomposition rates as driven by termites and microbes, although these different gradients do not equally impact decomposition agents. As we better understand how climate change will affect precipitation regimes across the tropics, our results can improve predictions of how wood decomposition agents will shift with potential for altering carbon fluxes. Read the free Plain Language Summary for this article on the Journal blog.

Changes in substrate quality driven by climate, land use, or other forms of global change may represent a strong selective force on microbial communities. Invasion of new taxa into a community through dispersal, evolution, or recolonization could impact the outcome of this environmental selection. Here, we simulated substrate change with a trait-based model of microbial litter decomposition (DEMENTpy) to assess the legacy effects of past substrate quality and the impact of selection by a new substrate on community decomposition activity. Simulations were run with different levels of invasion, including invasion from communities long-adapted to the new substrate. Legacy effects were evident with substrate change for native communities differing in composition. Protein was the only substrate that exerted a strong enough selective force to affect community composition. Legacy effects disappeared when invaders came from substrates similar to the new substrate. Together, our simulations demonstrate that substrate quality changes associated with global change can lead to legacy effects on substrate degradation. In decomposing plant litter, such legacy effects can occur if substrate inputs shift to higher protein content and if invasion is low.

Woody biomass is a large carbon store in terrestrial ecosystems. In calculating biomass, tree stems are assumed to be solid structures. However, decomposer agents such as microbes and insects target stem heartwood, causing internal wood decay which is poorly quantified. We investigated internal stem damage across five sites in tropical Australia along a precipitation gradient. We estimated the amount of internal aboveground biomass damaged in living trees and measured four potential stem damage predictors: wood density, stem diameter, annual precipitation, and termite pressure (measured as termite damage in downed deadwood). Stem damage increased with increasing diameter, wood density, and termite pressure and decreased with increasing precipitation. High wood density stems sustained less damage in wet sites and more damage in dry sites, likely a result of shifting decomposer communities and their differing responses to changes in tree species and wood traits across sites. Incorporating stem damage reduced aboveground biomass estimates by > 30% in Australian savannas, compared to only 3% in rainforests. Accurate estimates of carbon storage across woody plant communities are critical for understanding the global carbon budget. Future biomass estimates should consider stem damage in concert with the effects of changes in decomposer communities and abiotic conditions.

Background: Juvenile myelomonocytic leukemia (JMML) is an aggressive myeloproliferative neoplasm of infants and toddlers. Upfront therapies typically include high-dose cytarabine or azacitidine, but the only definitive treatment is hematopoietic stem cell transplantation (HSCT). While HSCT cures ~50% of patients, the prognosis is dismal for those who relapse. In the absence of a second HSCT, patients who relapse have a 2-year overall survival of ~10%. JMML is initiated by germline and somatic driver mutations in NF1, KRAS, NRAS, PTPN11, and CBL. These mutations converge on Ras signaling, leading to elevated levels of active Ras-GTP in specific cell lineages. Genetically engineered mouse (GEM) models accurately model key molecular, biologic, and biochemical features of JMML. Preclinical trials of the allosteric MEK inhibitors in Kras and Nf1 mutant mice demonstrated dramatic phenotypic responses with reduction in white blood cell counts, resolution of splenomegaly, and reversion to normal erythropoiesis. Based on the promising efficacy signal in GEM models, we evaluated trametinib, an orally bioavailable allosteric inhibitor of MEK1/2, in a prospective clinical trial in children with relapsed or refractory JMML to determine the overall response rate to trametinib. Results: Ten infants and children with JMML (median age 23.6 months) were enrolled and all were evaluable for safety and efficacy. Patients received age-adjusted dosing of trametinib for 28-day cycles and could remain on study for up to 12 cycles in the absence of disease progression or toxicity. A clonal Ras pathway mutation was confirmed in the blood and/or bone marrow of all patients. The objective response rate was 50% (two complete and three partial clinical responses). Four patients proceeded to HSCT after receiving protocol therapy and remain alive in complete remission with undetectable levels of the Ras pathway mutation identified at enrollment. Three additional patients completed 12 cycles of trametinib and continue to receive off-protocol therapy 6-24 months later with no change in the variant allele frequency of the underlying Ras pathway mutation. The remaining three patients had progressive disease with two demonstrating molecular evolution by the end of cycle 2. Paired pre- and post-trametinib RNASeq and proteomic analyses confirmed on-target biochemical effects of trametinib with down-regulation of both Ras/MAPK pathway related gene expression and MEK1/2 kinase activity, respectively. To gain deeper insight into how trametinib might affect distinct populations of hematopoietic cells, we generated single cell RNASeq data before and after treatment. The most prominent finding was a reduction in the proportions of classical and non-classical monocytes and broad downregulation of immune-related pathways in all cell populations. However, the downregulation of MAPK signaling genes was confined to specific cell types including, macrophages, classical monocytes, and granulocyte-monocyte progenitors, which uniquely displayed downregulated KRAS-related signatures following treatment. High DNA methylation and the presence of >1 mutation at enrollment correlated with lack of response to trametinib. Conclusions: We conducted an open label, phase 2 trial of trametinib in children with relapsed or refractory JMML. This is the first completed study of a MEK inhibitor in any hematologic malignancy in children. The trial met its primary objective and demonstrated a 50% objective response rate with 70% of patients bridging to a successful HSCT or completing the maximum 12 cycles permitted on study. The three patients who completed 12 cycles continue to receive trametinib off study for as long as 2 years. Although limited by a small number of patients, the long-term survival, and correlative molecular analyses from this trial raise provocative questions about whether certain patients with JMML might be spared the long-term adverse health risks of genotoxic HSCT conditioning regimens based on favorable molecular characteristics at diagnosis. This hypothesis will now be tested in a national, risk-stratified therapeutic trial (NCT05849662) of trametinib in combination with azacitidine in newly diagnosed patients. The authors would like to acknowledge funding and support from: NCTN Operations Center Grant (U10CA180886), NCTN Statistics & Data Center Grant (U10CA180899), and the St. Baldrick's Foundation.

The life history strategies of soil microbes determine their metabolic potential and their response to environmental changes. Yet these strategies remain poorly understood. Here we use shotgun metagenomes from terrestrial biomes to characterize overarching covariations of the genomic traits that capture dominant life history strategies in bacterial communities. The emerging patterns show a triangle of life history strategies shaped by two trait dimensions, supporting previous theoretical and isolate-based studies. The first dimension ranges from streamlined genomes with simple metabolisms to larger genomes and expanded metabolic capacities. As metabolic capacities expand, bacterial communities increasingly differentiate along a second dimension that reflects a trade-off between increasing capacities for environmental responsiveness or for nutrient recycling. Random forest analyses show that soil pH, C:N ratio and precipitation patterns together drive the dominant life history strategy of soil bacterial communities and their biogeographic distribution. Our findings provide a trait-based framework to compare life history strategies of soil bacteria.

The review provides an overview of the current status of the solvent-producing clostridia. The origin and development of industrial clostridial species, as well as the history of the industrial Acetone Butanol Ethanol fermentation process, is reexamined, and the recent resurgence of interest in the production of biobutanol is reviewed. Over 300 fully sequenced genomes for solvent-producing and closely related clostridial species are currently available in public databases. These include 270 genomes sourced from the David Jones culture collection. These genomes were allocated arbitrary DJ codes, and a conversion table to identify the species and strains has now been provided. The expanded genomic database facilitated new comparative genomic and phylogenetic analysis. A synopsis of the common features, molecular taxonomy, and phylogeny of solvent-producing clostridia and the application of comparative phylogenomics are evaluated. A survey and analysis of resident prophages in solvent-producing clostridia are discussed, and the discovery, occurrence, and role of novel R-type tailocins are reported. Prophage genomes with R-type tailocin-like features were detected in all 12 species investigated. The widespread occurrence of tailocins in Gram-negative species is well documented; this survey has indicated that they may also be widespread in clostridia.

Microorganisms are the primary engines of biogeochemical processes and foundational to the provisioning of ecosystem services to human society. Free-living microbial communities (microbiomes) and their functioning are now known to be highly sensitive to environmental change. Given microorganisms' capacity for rapid evolution, evolutionary processes could play a role in this response. Currently, however, few models of biogeochemical processes explicitly consider how microbial evolution will affect biogeochemical responses to environmental change. Here, we propose a conceptual framework for explicitly integrating evolution into microbiome-functioning relationships. We consider how microbiomes respond simultaneously to environmental change via four interrelated processes that affect overall microbiome functioning (physiological acclimation, demography, dispersal and evolution). Recent evidence in both the laboratory and the field suggests that ecological and evolutionary dynamics occur simultaneously within microbiomes; however, the implications for biogeochemistry under environmental change will depend on the timescales over which these processes contribute to a microbiome's response. Over the long term, evolution may play an increasingly important role for microbially driven biogeochemical responses to environmental change, particularly to conditions without recent historical precedent.

Black yeasts are polyextremotolerant fungi that contain high amounts of melanin in their cell wall and maintain a primar yeast form. These fungi grow in xeric, nutrient depletes environments which implies that they require highly flexible metabolisms and have been suggested to contain the ability to form lichen-like mutualisms with nearby algae and bacteria. However, the exact ecological niche and interactions between these fungi and their surrounding community are not well understood. We have isolated 2 novel black yeasts from the genus Exophiala that were recovered from dryland biological soil crusts. Despite notable differences in colony and cellular morphology, both fungi appear to be members of the same species, which has been named Exophiala viscosa (i.e. E. viscosa JF 03-3 Goopy and E. viscosa JF 03-4F Slimy). A combination of whole genome sequencing, phenotypic experiments, and melanin regulation experiments have been performed on these isolates to fully characterize these fungi and help decipher their fundamental niche within the biological soil crust consortium. Our results reveal that E. viscosa is capable of utilizing a wide variety of carbon and nitrogen sources potentially derived from symbiotic microbes, can withstand many forms of abiotic stresses, and excretes melanin which can potentially provide ultraviolet resistance to the biological soil crust community. Besides the identification of a novel species within the genus Exophiala, our study also provides new insight into the regulation of melanin production in polyextremotolerant fungi.

Cutaneous neurofibromas (cNFs) are benign tumors of the skin that affect >95% of adults with neurofibromatosis type 1. Despite their benign histology, cNFs can significantly impact QOL due to disfigurement, pain, and pruritus. There are no approved therapies for cNFs. Existing treatments are limited to surgery or laser-based treatments that have had mixed success and cannot be readily applied to a large number of tumors. We review cNF treatment options that are currently available and under investigation, discuss the regulatory considerations specific to cNFs, and propose strategies to improve cNF clinical trial design and standardize clinical trial endpoints.

Droughts are becoming more frequent and intense with climate change. As plants and microbes respond to drought, there may be consequences for the vast stocks of organic carbon stored in soils. If microbes sustain their activity under drought, soils could lose carbon, especially if inputs from plants decline. Empirical and theoretical studies reveal multiple mechanisms of microbial drought resistance, including tolerance and avoidance. Physiological responses allow microbes to acclimate to drought within minutes to days. Along with dispersal, shifts in community composition could allow microbiomes to maintain functioning despite drought. Microbes might also adapt to drier conditions through evolutionary processes. Together, these mechanisms could result in soil carbon losses larger than currently anticipated under climate change.

Rationale: Idiopathic pulmonary fibrosis (IPF) is a rare, irreversible, and progressive disease of the lungs. Common genetic variants, in addition to nongenetic factors, have been consistently associated with IPF. Rare variants identified by candidate gene, family-based, and exome studies have also been reported to associate with IPF. However, the extent to which rare variants, genome-wide, may contribute to the risk of IPF remains unknown. Objectives: We used whole-genome sequencing to investigate the role of rare variants, genome-wide, on IPF risk. Methods: As part of the Trans-Omics for Precision Medicine Program, we sequenced 2,180 cases of IPF. Association testing focused on the aggregated effect of rare variants (minor allele frequency ⩽0.01) within genes or regions. We also identified individual rare variants that are influential within genes and estimated the heritability of IPF on the basis of rare and common variants. Measurements and Main Results: Rare variants in both TERT and RTEL1 were significantly associated with IPF. A single rare variant in each of the TERT and RTEL1 genes was found to consistently influence the aggregated test statistics. There was no significant evidence of association with other previously reported rare variants. The SNP heritability of IPF was estimated to be 32% (SE = 3%). Conclusions: Rare variants within the TERT and RTEL1 genes and well-established common variants have the largest contribution to IPF risk overall. Efforts in risk profiling or the development of therapies for IPF that focus on TERT, RTEL1, common variants, and environmental risk factors are likely to have the largest impact on this complex disease.

BackgroundEpithelial remodeling is a histopathologic feature of chronic inflammatory airway diseases including chronic rhinosinusitis (CRS). Cell-type shifts and their relationship to CRS endotypes and severity are incompletely described.ObjectiveWe sought to understand the relationship of epithelial cell remodeling to inflammatory endotypes and disease outcomes in CRS.MethodsUsing cell-type transcriptional signatures derived from epithelial single-cell sequencing, we analyzed bulk RNA-sequencing data from sinus epithelial brushings obtained from patients with CRS with and without nasal polyps in comparison to healthy controls.ResultsThe airway epithelium in nasal polyposis displayed increased tuft cell transcripts and decreased ciliated cell transcripts along with an IL-13 activation signature. In contrast, CRS without polyps showed an IL-17 activation signature. IL-13 activation scores were associated with increased tuft cell, goblet cell, and mast cell scores and decreased ciliated cell scores. Furthermore, the IL-13 score was strongly associated with a previously reported activated ("polyp") tuft cell score and a prostaglandin E2 activation signature. The Lund-Mackay score, a computed tomographic metric of sinus opacification, correlated positively with activated tuft cell, mast cell, prostaglandin E2, and IL-13 signatures and negatively with ciliated cell transcriptional signatures.ConclusionsThese results demonstrate that cell-type alterations and prostaglandin E2 stimulation are key components of IL-13-induced epithelial remodeling in nasal polyposis, whereas IL-17 signaling is more prominent in CRS without polyps, and that clinical severity correlates with the degree of IL-13-driven epithelial remodeling.

Variation in decay rates across woody species is a key uncertainty in predicting the fate of carbon stored in deadwood, especially in the tropics. Quantifying the relative contributions of biotic decay agents, particularly microbes and termites, under different climates and across species with diverse wood traits could help explain this variation. To fill this knowledge gap, we deployed woody stems from 16 plant species native to either rainforest (n = 10) or savanna (n = 6) in northeast Australia, with and without termite access. For comparison, we also deployed standardized, non-native pine blocks at both sites. We hypothesized that termites would increase rates of deadwood decay under conditions that limit microbial activity. Specifically, termite contributions to wood decay should be greater under dry conditions and in wood species with traits that constrain microbial decomposers. Termite discovery of stems was surprisingly low with only 17.6% and 22.6% of accessible native stems discovered in the rainforest and savanna respectively. Contrary to our hypothesis, stems discovered by termites decomposed faster only in the rainforest. Termites discovered and decayed pine blocks at higher rates than native stems in both the rainforest and savanna. We found significant variation in termite discovery and microbial decay rates across native wood species within the same site. Although wood traits explained 85% of the variation in microbial decay, they did not explain termite-driven decay. For stems undiscovered by termites, decay rates were greater in species with higher wood nutrient concentrations and syringyl:guiacyl lignin ratios but lower carbon concentrations and wood densities. Synthesis. Ecosystem-scale predictions of deadwood turnover and carbon storage should account for the impact of wood traits on decomposer communities. In tropical Australia, termite-driven decay was lower than expected for native wood on the ground. Even if termites are present, they may not always increase decomposition rates of fallen native wood in tropical forests. Our study shows how the drivers of wood decay differ between Australian tropical rainforest and savanna; further research should test whether such differences apply world-wide.

Despite much progress, image processing remains a significant bottleneck for high-throughput analysis of microscopy data. One popular platform for single-cell time-lapse imaging is the mother machine, which enables long-term tracking of microbial cells under precisely controlled growth conditions. While several mother machine image analysis pipelines have been developed in the past several years, adoption by a non-expert audience remains a challenge. To fill this gap, we implemented our own software, MM3, as a plugin for the multidimensional image viewer napari. napari-MM3 is a complete and modular image analysis pipeline for mother machine data, which takes advantage of the high-level interactivity of napari. Here, we give an overview of napari-MM3 and test it against several well-designed and widely-used image analysis pipelines, including BACMMAN and DeLTA. In addition, the rapid adoption and widespread popularity of deep-learning methods by the scientific community raises an important question: to what extent can users trust the results generated by such "black box" methods? We explicitly demonstrate "What You Put Is What You Get" (WYPIWYG); i.e., the image analysis results can reflect the user bias encoded in the training dataset. Finally, while the primary purpose of this work is to introduce the image analysis software that we have developed over a decade in our lab, we also provide useful information for those who want to implement mother-machine-based high-throughput imaging and image analysis methods in their research. This includes our guiding principles and best practices to ensure transparency and reproducible results.

ObjectiveThis randomized clinical trial tested the effectiveness of an addiction-based digital weight-loss intervention, focusing on withdrawal/abstinence from self-identified problem foods, snacking and excessive amounts at meals, and discomfort displacement, with and without coaching, compared to an in-person, multi-disciplinary, care model among adolescents with obesity. We hypothesized that the digital intervention with coaching would yield greater weight loss and lower delivery burden than the standard clinical arm, and greater participant engagement than the digital arm without coaching.MethodsAdolescents were randomized to app intervention, with or without coaching, or in-person multidisciplinary obesity intervention for 6 months. The primary outcome was change in %BMIp95 at weeks 12 and 24. A mixed-effects linear regression model was used to assess the association between change in %BMIp95 and intervention arm. We were also interested in assessing delivery burden, participant engagement and evaluating the relationships between weight change and demographic characteristics, mood, executive function and eating behaviours.ResultsAll adolescents (n = 161; BMI ≥95th%, age 16 ± 2.5 year; 47% Hispanic, 65% female, 59% publicly insured) lost weight over 24-weeks (-1.29%, [-1.82, -0.76], p

Atmospheric nitrogen (N) deposition is enriching soils with N across biomes. Soil N enrichment can increase plant productivity and affect microbial activity, thereby increasing soil organic carbon (SOC), but such responses vary across biomes. Drylands cover ~45% of Earth's land area and store ~33% of global SOC contained in the top 1 m of soil. Nitrogen fertilization could, therefore, disproportionately impact carbon (C) cycling, yet whether dryland SOC storage increases with N remains unclear. To understand how N enrichment may change SOC storage, we separated SOC into plant-derived, particulate organic C (POC), and largely microbially derived, mineral-associated organic C (MAOC) at four N deposition experimental sites in Southern California. Theory suggests that N enrichment increases the efficiency by which microbes build MAOC (C stabilization efficiency) if soil pH stays constant. But if soils acidify, a common response to N enrichment, then microbial biomass and enzymatic organic matter decay may decrease, increasing POC but not MAOC. We found that N enrichment had no effect on C fractions except for a decrease in MAOC at one site. Specifically, despite reported increases in plant biomass in three sites and decreases in microbial biomass and extracellular enzyme activities in two sites that acidified, POC did not increase. Furthermore, microbial C use and stabilization efficiency increased in a non-acidified site, but without increasing MAOC. Instead, MAOC decreased by 16% at one of the sites that acidified, likely because it lost 47% of the exchangeable calcium (Ca) relative to controls. Indeed, MAOC was strongly and positively affected by Ca, which directly and, through its positive effect on microbial biomass, explained 58% of variation in MAOC. Long-term effects of N fertilization on dryland SOC storage appear abiotic in nature, such that drylands where Ca-stabilization of SOC is prevalent and soils acidify, are most at risk for significant C loss.

IntroductionCognitive composite scales constructed by combining existing neuropsychometric tests are seeing wide application as endpoints for clinical trials and cohort studies of Alzheimer's disease (AD) predementia conditions. Preclinical Alzheimer's Cognitive Composite (PACC) scales are composite scores calculated as the sum of the component test scores weighted by the reciprocal of their standard deviations at the baseline visit. Reciprocal standard deviation is an arbitrary weighting in this context, and may be an inefficient utilization of the data contained in the component measures. Mathematically derived optimal composite weighting is a promising alternative.MethodsSample size projections using standard power calculation formulas were used to describe the relative performance of component measures and their composites when used as endpoints for clinical trials. Power calculations were informed by (n=1,333) amnestic mild cognitive impaired participants in the National Alzheimer's Coordinating Center (NACC) Uniform Data Set.ResultsA composite constructed using PACC reciprocal standard deviation weighting was both less sensitive to change than one of its component measures and less sensitive to change than its optimally weighted counterpart. In standard sample size calculations informed by NACC data, a clinical trial using the PACC weighting would require 38% more subjects than a composite calculated using optimal weighting.DiscussionThese findings illustrate how reciprocal standard deviation weighting can result in inefficient cognitive composites, and underscore the importance of component weights to the performance of composite scales. In the future, optimal weighting parameters informed by accumulating clinical trial data may improve the efficiency of clinical trials in AD.

DISCLOSURES: Funding for this summary was contributed by Blue Cross Blue Shield of MA, California Healthcare Foundation, The Patrick and Catherine Weldon Donaghue Medical Research Foundation, Arnold Ventures, and Kaiser Foundation Health Plan Inc., to the Institute for Clinical and Economic Review (ICER), an independent organization that evaluates the evidence on the value of health care interventions. ICER's annual policy summit is supported by dues from Aetna, America's Health Insurance Plans, AbbVie, Alnylam, AstraZeneca, Biogen, Blue Shield of CA, Boehringer Ingelheim, Cambia Health Services, CVS, Editas, Evolve Pharmacy Solutions, Express Scripts, Genentech/ Roche, GlaxoSmithKline, Harvard Pilgrim, Health Care Service Corporation, Health First, Health Partners, Humana, Johnson & Johnson (Janssen), Kaiser Permanente, LEO Pharma, Mallinckrodt, Merck, Novartis, National Pharmaceutical Council, Pfizer. Premera, Prime Therapeutics, Regeneron, Sanofi, Spark Therapeutics, Sun Life Financial, uniQure, and United Healthcare. Mr Nikitin, Ms McKenna, Ms Richardson, and Drs Rind and Pearson are employed by ICER. Through their affiliated institutions, Drs Makam, Carlson, and Suh received funding from ICER for the work described in this summary.

Spleen tyrosine kinase (SYK) is a previously unidentified therapeutic target that inhibits neutrophil and macrophage activation in coronavirus disease 2019 (COVID-19). Fostamatinib, a SYK inhibitor, was studied in a phase 2 placebo-controlled randomized clinical trial and was associated with improvements in many secondary end points related to efficacy. Here, we used a multiomic approach to evaluate cellular and soluble immune mediator responses of patients enrolled in this trial. We demonstrated that SYK inhibition was associated with reduced neutrophil activation, increased circulation of mature neutrophils (CD10+CD33-), and decreased circulation of low-density granulocytes and polymorphonuclear myeloid-derived suppressor cells (HLA-DR-CD33+CD11b-). SYK inhibition was also associated with normalization of transcriptional activity in circulating monocytes relative to healthy controls, an increase in frequency of circulating nonclassical and HLA-DRhi classical monocyte populations, and restoration of interferon responses. Together, these data suggest that SYK inhibition may mitigate proinflammatory myeloid cellular and soluble mediator responses thought to contribute to immunopathogenesis of severe COVID-19.

BackgroundDisparities in cancer outcomes persist for underserved populations; one important aspect of this is limited access to promising early phase clinical trials. To address this, the National Cancer Institute-funded Create Access to Targeted Cancer Therapy for Underserved Populations (CATCH-UP.2020) was created. We report the tools developed and accrual metrics of the initial year of CATCH-UP.2020 with a focus on racial, ethnic, geographic, and socioeconomically underserved populations.MethodsCATCH-UP.2020 is a P30 supplement awarded to 8 National Cancer Institute-designated cancer centers with existing resources to rapidly open and accrue to Experimental Therapeutics Clinical Trials Network (ETCTN) trials with emphasis on engaging patients from underserved populations. Sites used patient-based, community-based, investigator-based, and program-based tools to meet specific program goals.ResultsFrom September 2020 to August 2021, CATCH-UP.2020 sites opened 45 ETCTN trials. Weighted average trial activation time for the 7 sites reporting this was 107 days. In the initial year, sites enrolled 145 patients in CATCH-UP.2020 with 68 (46.9%) representing racial, ethnic, rural, and socioeconomically underserved populations using the broader definition of underserved encompassed in the grant charge. During the initial year of CATCH-UP.2020, a time impacted by the COVID-19 pandemic, 15.8% (66 of 417) and 21.4% (31 of 145) of patients enrolled to ETCTN trials at network and at CATCH-UP sites, respectively, were from racial and ethnic minority groups, a more limited definition of underserved for which comparable data are available.ConclusionTargeted funding accelerated activation and accrual of early phase trials and expanded access to this therapeutic option for underserved populations.

Marine heatwaves are increasingly affecting marine ecosystems, with cascading impacts on coastal economies, communities, and food systems. Studies of heatwaves provide crucial insights into potential ecosystem shifts under future climate change and put fisheries social-ecological systems through “stress tests” that expose both vulnerabilities and resilience. The 2014–16 Northeast Pacific heatwave was the strongest and longest marine heatwave on record and resulted in profound ecological changes that impacted fisheries, fisheries management, and human livelihoods. Here, we synthesize the impacts of the 2014–2016 marine heatwave on US and Canada West Coast fisheries and extract key lessons for preparing global fisheries science, management, and industries for the future. We set the stage with a brief review of the impacts of the heatwave on marine ecosystems and the first systematic analysis of the economic impacts of these changes on commercial and recreational fisheries. We then examine ten key case studies that provide instructive examples of the complex and surprising challenges that heatwaves pose to fisheries social-ecological systems. These reveal important insights into improving the resilience of monitoring and management and increasing adaptive capacity to future stressors. Key recommendations include: (1) expanding monitoring to enhance mechanistic understanding, provide early warning signals, and improve predictions of impacts; (2) increasing the flexibility, adaptiveness, and inclusiveness of management where possible; (3) using simulation testing to help guide management decisions; and (4) enhancing the adaptive capacity of fishing communities by promoting engagement, flexibility, experimentation, and failsafes. These advancements are important as global fisheries prepare for a changing ocean.

Through their rooting profiles and water demands, plants affect the distribution of water in the soil profile. Simultaneously, soil water content controls plant development and interactions within and between plant communities. These plant-soil water feedbacks might vary across plant communities with different rooting depths and species composition. In semiarid environments, understanding these differences will be essential to predict how ecosystems will respond to drought, which may become more frequent and severe with climate change. In this study, we tested how plant-soil water feedbacks responded to drought in two contrasting ecosystem types—grassland and shrubland—in the coastal foothills of southern California. During years 5–8 of an ongoing precipitation manipulation experiment, we measured changes in plant communities and soil moisture up to 2 m depth. We observed different water use patterns in grassland and shrubland communities with distinct plant functional types and water use strategies. Drought treatment did not affect perennial, deep-rooted shrubs because they could access deep soil water pools. However, mid-rooted shrubs were sensitive to drought and experienced decreased productivity and die-off. As a result, water content actually increased with drought at soil depths from 50–150 cm. In grassland, biomass production by annual species, including annual grasses and forbs, declined with drought, resulting in lower water uptake from the surface soil layer. An opportunistic “live fast, die young“ life strategy allowed these species to recover quickly once water availability increased. Our results show how drought interacts with plant community composition to affect the soil water balance of semiarid ecosystems, information that could be integrated into global scale models.

Whether microbes show habitat preferences is a fundamental question in microbial ecology. If different microbial lineages have distinct traits, those lineages may occur more frequently in habitats where their traits are advantageous. Sphingomonas is an ideal bacterial clade in which to investigate how habitat preference relates to traits because these bacteria inhabit diverse environments and hosts. Here we downloaded 440 publicly available Sphingomonas genomes, assigned them to habitats based on isolation source, and examined their phylogenetic relationships. We sought to address whether: (1) there is a relationship between Sphingomonas habitat and phylogeny, and (2) whether there is a phylogenetic correlation between key, genome-based traits and habitat preference. We hypothesized that Sphingomonas strains from similar habitats would cluster together in phylogenetic clades, and key traits that improve fitness in specific environments should correlate with habitat. Genome-based traits were categorized into the Y-A-S trait-based framework for high growth yield, resource acquisition, and stress tolerance. We selected 252 high quality genomes and constructed a phylogenetic tree with 12 well-defined clades based on an alignment of 404 core genes. Sphingomonas strains from the same habitat clustered together within the same clades, and strains within clades shared similar clusters of accessory genes. Additionally, key genome-based trait frequencies varied across habitats. We conclude that Sphingomonas gene content reflects habitat preference. This knowledge of how environment and host relate to phylogeny may also help with future functional predictions about Sphingomonas and facilitate applications in bioremediation.

Studies have shown that women on the Alzheimer's disease (AD) continuum have more pathological tau in the brain and cerebrospinal fluid (CSF), than men. Some studies have found that higher levels of tau biomarkers are more strongly associated with clinical AD, cognitive decline and neurodegeneration in women than in men. Despite major developments in the use of plasma tau phosphorylated at threonine 181 (p-tau181) as an AD biomarker, it is unknown whether these sex differences apply to plasma p-tau181. In 1060 Alzheimer's Disease Neuroimaging Initiative (ADNI) participants (47% women, 73.8 ± 7.6 years old), we examined sex differences in plasma p-tau181 levels and their association with other biomarkers, cognitive decline and incident AD. Linear regressions tested for an effect of sex on plasma p-tau181 levels and for plasma p-tau181 × sex interactions on CSF p-tau181, as well as entorhinal cortex tau, cortical amyloid-β (Aβ) deposition, and brain glucose metabolism, quantified using PET imaging. Linear mixed effects models tested for a sex × baseline plasma p-tau181 interaction on change in cognition over time. Finally, Cox models tested for a sex × plasma p-tau181 interaction on the risk of AD dementia in participants who were free of dementia at baseline. Despite similar plasma p-tau181 levels between sexes, women had lower brain glucose metabolism, greater brain Aβ and entorhinal cortex tau deposition, higher CSF p-tau181 and faster cognitive decline in relation to higher baseline plasma p-tau181 levels compared with men. Among Aβ positive, dementia-free participants, women had higher rates of incident AD dementia associated with increasing baseline plasma p-tau181 levels, relative to men. Our results suggest that sex may impact the clinical interpretation of plasma p-tau181 concentrations. If replicated, these findings could have important implications for the use of plasma p-tau181 as an accessible AD biomarker and screening tool for preventive and therapeutic clinical trials.

Ecological interactions are not uniform across time and can vary with environmental conditions. Yet, interactions among species are often measured with short-term controlled experiments whose outcomes can depend greatly on the particular environmental conditions under which they are performed. As an alternative, we use empirical dynamic modeling to estimate species interactions across a wide range of environmental conditions directly from existing long-term monitoring data. In our case study from a southern California kelp forest, we test whether interactions between multiple kelp and sea urchin species can be reliably reconstructed from time-series data and whether those interactions vary predictably in strength and direction across observed fluctuations in temperature, disturbance, and low-frequency oceanographic regimes. We show that environmental context greatly alters the strength and direction of species interactions. In particular, the state of the North Pacific Gyre Oscillation seems to drive the competitive balance between kelp species, asserting bottom-up control on kelp ecosystem dynamics. We show the importance of specifically studying variation in interaction strength, rather than mean interaction outcomes, when trying to understand the dynamics of complex ecosystems. The significant context dependency in species interactions found in this study argues for a greater utilization of long-term data and empirical dynamic modeling in studies of the dynamics of other ecosystems.

Renal cell carcinomas present with locally advanced or metastatic disease in 25% of patients. Thermal ablation may be considered in selected patients with single-site or oligometastatic disease in selected patients. We describe single-session transarterial particle embolization with the assistance of a balloon-occlusion catheter and microwave ablation of a large hypervascular adrenal metastasis using cone beam CT and fluoroscopic XperGuide needle guidance.

PurposeThis article illustrates multiple atypical manifestations of ocular toxoplasmosis masquerading as acute retinal necrosis and vitreoretinal lymphoma.MethodsTwo case presentations are discussed, and the body of pertinent literature is reviewed and discussed.ResultsIn these cases, an extensive workup and attention to history lead to the correct diagnosis and management.ConclusionsAggressive cases of ocular toxoplasmosis may present in a variety of phenotypes that may mimic other vision- and potentially life-threatening conditions, particularly in a milieu of inadequate endogenous and exogenous antimicrobial defenses.

Most of the carbon (C) that enters the soil is broken down by the microbial community and either respired or stored in soil depending on the microbial allocation strategy. Changes in how the microbial community uses C can significantly affect soil C pool sizes, so new models have begun to explicitly represent microbial allocation. Most models use a parameter called carbon use efficiency (CUE) to represent microbial allocation, which partitions consumed C between respiration and growth. Here we compare a “Typical Microbial Model” with this representation of microbial allocation to two other models. One is the “Microbial Allocation Model” that represents CUE as an emergent property of the microbial community, explicitly modeling multiple processes involved in CUE. The second is the “Two-Pool Biomass Model” that similarly accounts for CUE as an emergent property but also represents the biomass using two C pools with different turnover times. We assessed the models’ relative ability to track a14C-glucose tracer pulse over three weeks through the extractable C pool, the microbial biomass, and respiration. We also used the 14C data to test how estimates of microbial CUE change during the incubation. Our results suggest that CUE estimates in soil are sensitive to incubation timing and are at no point stable. Isotopic data can best parameterize models when a time course of measurements is used. Our model comparison showed that the Two-Pool Biomass Model best fit our data. Using the Two-Pool Biomass model to represent microbial allocation is more biologically realistic and better matches the dynamics observed in our microbial C partitioning data. Mechanistic biogeochemical models should consider using a dynamic allocation scheme to represent CUE, rather than assuming a static value.

Bacterial communities in the organic leaf litter layer and bulk (mineral and organic) soil are sensitive to environmental change. However, despite close interactions between these communities, the leaf litter layer has historically been studied in isolation from the bulk soil. Whether bacterial response to environmental change is uniform throughout the surface soil remains unclear. Here, we simultaneously characterized how bacterial community composition in three surface soil layers (the leaf litter layer, 0–2 cm of bulk soil, and 0–10 cm of bulk soil) responded to a wildfire burning through a 13-year drought simulation in two adjacent ecosystems, a grassland and coastal sage scrubland. We found that bacterial communities in all three surface soil layers were distinct in composition and varied with drought, ecosystem type, and temporal variation. Moreover, the impact of these environmental changes on bacterial community composition decreased with depth in the surface soil. Bacterial response to drought was three-fold higher in the leaf litter layer than in the top 10 cm of bulk soil, with the drought treatment explaining 4.8% and 1.6% of the compositional variation, respectively. Wildfire altered bacterial composition in the leaf litter layer but not within the top 10 cm of bulk soil. Further, previous exposure of the bacterial communities in the leaf litter layer to drought did not influence its response to the wildfire. Thus, considering soil depth when assessing the impact of environmental conditions on the surface soil microbiome may improve predictions about the degree to which microbial communities, and therefore soil carbon, will respond to future environmental change.

Analysis of the genes retained in the minimized Mycoplasma JCVI-Syn3A genome established that systems that repair or preempt metabolite damage are essential to life. Several genes known to have such functions were identified and experimentally validated, including 5-formyltetrahydrofolate cycloligase, coenzyme A (CoA) disulfide reductase, and certain hydrolases. Furthermore, we discovered that an enigmatic YqeK hydrolase domain fused to NadD has a novel proofreading function in NAD synthesis and could double as a MutT-like sanitizing enzyme for the nucleotide pool. Finally, we combined metabolomics and cheminformatics approaches to extend the core metabolic map of JCVI-Syn3A to include promiscuous enzymatic reactions and spontaneous side reactions. This extension revealed that several key metabolite damage control systems remain to be identified in JCVI-Syn3A, such as that for methylglyoxal. IMPORTANCE Metabolite damage and repair mechanisms are being increasingly recognized. We present here compelling genetic and biochemical evidence for the universal importance of these mechanisms by demonstrating that stripping a genome down to its barest essentials leaves metabolite damage control systems in place. Furthermore, our metabolomic and cheminformatic results point to the existence of a network of metabolite damage and damage control reactions that extends far beyond the corners of it that have been characterized so far. In sum, there can be little room left to doubt that metabolite damage and the systems that counter it are mainstream metabolic processes that cannot be separated from life itself.

Purine nucleoside phosphorylase (PNP) enables the breakdown and recycling of guanine nucleosides. PNP insufficiency in humans is paradoxically associated with both immunodeficiency and autoimmunity, but the mechanistic basis for these outcomes is incompletely understood. Here, we identify two immune lineage-dependent consequences of PNP inactivation dictated by distinct gene interactions. During T cell development, PNP inactivation is synthetically lethal with downregulation of the dNTP triphosphohydrolase SAMHD1. This interaction requires deoxycytidine kinase activity and is antagonized by microenvironmental deoxycytidine. In B lymphocytes and macrophages, PNP regulates Toll-like receptor 7 signaling by controlling the levels of its (deoxy)guanosine nucleoside ligands. Overriding this regulatory mechanism promotes germinal center formation in the absence of exogenous antigen and accelerates disease in a mouse model of autoimmunity. This work reveals that one purine metabolism gene protects against immunodeficiency and autoimmunity via independent mechanisms operating in distinct immune lineages and identifies PNP as a potentially novel metabolic immune checkpoint.