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Ecologists seek general explanations for the dramatic variation in species abundances in space and time. An increasingly popular solution is to predict species distributions, dynamics, and responses to environmental change based on easily measured anatomical and morphological traits. Trait-based approaches assume that simple functional traits influence fitness and life history evolution, but rigorous tests of this assumption are lacking, because they require quantitative information about the full lifecycles of many species representing different life histories. Here, we link a global traits database with empirical matrix population models for 222 species and report strong relationships between functional traits and plant life histories. Species with large seeds, long-lived leaves, or dense wood have slow life histories, with mean fitness (i.e., population growth rates) more strongly influenced by survival than by growth or fecundity, compared with fast life history species with small seeds, short-lived leaves, or soft wood. In contrast to measures of demographic contributions to fitness based on whole lifecycles, analyses focused on raw demographic rates may underestimate the strength of association between traits and mean fitness. Our results help establish the physiological basis for plant life history evolution and show the potential for trait-based approaches in population dynamics.

Understanding how biotic mechanisms confer stability in variable environments is a fundamental quest in ecology, and one that is becoming increasingly urgent with global change. Several mechanisms, notably a portfolio effect associated with species richness, compensatory dynamics generated by negative species covariance and selection for stable dominant species populations can increase the stability of the overall community. While the importance of these mechanisms is debated, few studies have contrasted their importance in an environmental context. We analyzed nine long-term data sets of grassland species composition to investigate how two key environmental factors, precipitation amount and variability, may directly influence community stability and how they may indirectly influence stability via biotic mechanisms. We found that the importance of stability mechanisms varied along the environmental gradient: strong negative species covariance occurred in sites characterized by high precipitation variability, whereas portfolio effects increased in sites with high mean annual precipitation. Instead of questioning whether compensatory dynamics are important in nature, our findings suggest that debate should widen to include several stability mechanisms and how these mechanisms vary in importance across environmental gradients.

Climate gradients shape spatial variation in the richness and composition of plant communities. Given future predicted changes in climate means and variability, and likely regional variation in the magnitudes of these changes, it is important to determine how temporal variation in climate influences temporal variation in plant community structure. Here, we evaluated how species richness, turnover, and composition of grassland plant communities responded to interannual variation in precipitation by synthesizing long-term data from grasslands across the United States. We found that mean annual precipitation (MAP) was a positive predictor of species richness across sites, but a positive temporal relationship between annual precipitation and richness was only evident within two sites with low MAP. We also found higher average rates of species turnover in dry sites that in turn had a high proportion of annual species, although interannual rates of species turnover were surprisingly high across all locations. Annual species were less abundant than perennial species at nearly all sites, and our analysis showed that the probability of a species being lost or gained from one year to the next increased with decreasing species abundance. Bray-Curtis dissimilarity from one year to the next, a measure of species composition change that is influenced mainly by abundant species, was insensitive to precipitation at all sites. These results suggest that the richness and turnover patterns we observed were driven primarily by rare species, which comprise the majority of the local species pools at these grassland sites. These findings are consistent with the idea that short-lived and less abundant species are more sensitive to interannual climate variability than longer-lived and more abundant species. We conclude that, among grassland ecosystems, xeric grasslands are likely to exhibit the greatest responsiveness of community composition (richness and turnover) to predicted future increases in interannual precipitation variability. Over the long term, species composition may shift to reflect spatial patterns of mean precipitation; however, perennial-dominated systems will be buffered against rising interannual variation, while systems that have a large number of rare, annual species will show the greatest temporal variability in species composition in response to rising interannual variability in precipitation.

Expected increases in interannual precipitation variability due to climate change will lead to increases in the variability of primary production, with potentially important consequences for natural resource management. Previous work has suggested that various biotic and abiotic processes might amplify or buffer variation in production in response to variation in precipitation. In particular, production to rain variability ratios (PRVR), the coefficient of variation of production divided by the coefficient of variation of precipitation, indicate that production is often relatively more variable than precipitation. We used 37 long-term data sets from grasslands across the globe to test how future increases in precipitation variability might alter the variability of aboveground net primary production (ANPP). We demonstrate that PRVR is not a useful metric: it is predicted by a site's precipitation-production relationship and a PRVR greater than 1 need not imply that increases in the variability of ANPP will be disproportionately greater than increases in precipitation variability. Instead, it is the form of the precipitation-ANPP relationship that determines how increases in precipitation variability will impact ANPP variability. We fit linear, lag effect, and nonlinear precipitation-ANPP relationships to each data set. At most sites, the precipitation-ANPP relationship is weakly nonlinear, though the lag effect model, incorporating previous year ANPP, performed best at several sites. To test whether the three models project differences in the response of ANPP to future increases in precipitation variability, we directly perturbed the observed precipitation time series and quantified the results of this perturbation on ANPP variability. Under simple linear or lag effect models, relative increases in ANPP variability were always equal to the relative increases in precipitation variability. When we modeled ANPP as a nonlinear, saturating function of precipitation, projected increases in ANPP variability were disproportionately high, with production dropping more in dry years than it increases in wet years. In six cases, increases in ANPP variability were twice as large as increases in precipitation variability. Based on Akaike model weights, a 5% increase in precipitation variability would cause a 6.3% increase in ANPP variability on average.

In addition to causing an increase in mean temperatures, climate change is also altering precipitation regimes across the globe. General circulation models project both latitude-dependent changes in precipitation mean and increases in precipitation variability. These changes in water availability will impact terrestrial primary productivity, the fixation of carbon dioxide into organic matter by plants. In my thesis, I addressed the following three questions: (1) What will be the relative effect of changes in the mean and standard deviation of annual precipitation on mean annual primary production? (2) Which ecosystems will be the most sensitive to changes in precipitation? (3) Will increases in production variability be disproportionately greater than increases in precipitation variability? I gathered 58 time series of annual precipitation and aboveground net primary production (ANPP) from long-term ecological study sites across the globe. I quantified the sensitivity of ANPP at each site to changes in precipitation mean and variance. My results indicated that mean ANPP is about 40 times more sensitive to changes in precipitation mean than to changes in precipitation variance. I showed that semi-arid ecosystems such as shortgrass steppe in Colorado or typical steppe in Inner Mongolia may be the most sensitive to changes in precipitation mean. At these sites and several others, a 1% change in mean precipitation may result in a change in ANPP that is greater than 1%. To address how increases in interannual precipitation variability will impact the variability of ANPP, I perturbed the variability of observed precipitation time series and evaluated the impact of this perturbation on predicted ANPP variability. I found that different assumptions about the precipitation-ANPP relationship had different implications for how increases in precipitation variability will impact ANPP variability. Increases in ANPP variability were always directly proportional to increases in precipitation variability when ANPP was modeled as a simple linear or a lagged function of precipitation. However, when ANPP was modeled as a nonlinear, saturating function of precipitation, increases in ANPP variability were disproportionately low compared to increases in precipitation variability during wet years but disproportionately high during dry years. My thesis addresses an existing research gap regarding the long-term impact of increases in interannual precipitation variability on key ecosystem functioning. I showed that increases in precipitation variability will have negligible impacts on ANPP mean and have disproportionately large impacts on ANPP variability only when ANPP is a concave down, nonlinear function of precipitation. My work also demonstrates the importance of the precipitation-ANPP relationship in determining the magnitude of impacts to ANPP caused by changes in precipitation. Finally, my thesis highlights the potential for considerable changes in ANPP variability due to increases in precipitation variability.

The ongoing COVID-19 pandemic has caused an unprecedented global health crisis. Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is the causative agent of COVID-19. Subversion of host protein synthesis is a common strategy that pathogenic viruses use to replicate and propagate in their host. In this study, we show that SARS-CoV-2 is able to shut down host protein synthesis and that SARS-CoV-2 nonstructural protein NSP14 exerts this activity. We show that the translation inhibition activity of NSP14 is conserved in human coronaviruses. NSP14 is required for virus replication through contribution of its exoribonuclease (ExoN) and N7-methyltransferase (N7-MTase) activities. Mutations in the ExoN or N7-MTase active sites of SARS-CoV-2 NSP14 abolish its translation inhibition activity. In addition, we show that the formation of NSP14−NSP10 complex enhances translation inhibition executed by NSP14. Consequently, the translational shutdown by NSP14 abolishes the type I interferon (IFN-I)-dependent induction of interferon-stimulated genes (ISGs). Together, we find that SARS-CoV-2 shuts down host innate immune responses via a translation inhibitor, providing insights into the pathogenesis of SARS-CoV-2.

The Pharma Proteomics Project is a precompetitive biopharmaceutical consortium characterizing the plasma proteomic profiles of 54,219 UK Biobank participants. Here we provide a detailed summary of this initiative, including technical and biological validations, insights into proteomic disease signatures, and prediction modelling for various demographic and health indicators. We present comprehensive protein quantitative trait locus (pQTL) mapping of 2,923 proteins that identifies 14,287 primary genetic associations, of which 81% are previously undescribed, alongside ancestry-specific pQTL mapping in non-European individuals. The study provides an updated characterization of the genetic architecture of the plasma proteome, contextualized with projected pQTL discovery rates as sample sizes and proteomic assay coverages increase over time. We offer extensive insights into trans pQTLs across multiple biological domains, highlight genetic influences on ligand–receptor interactions and pathway perturbations across a diverse collection of cytokines and complement networks, and illustrate long-range epistatic effects of ABO blood group and FUT2 secretor status on proteins with gastrointestinal tissue-enriched expression. We demonstrate the utility of these data for drug discovery by extending the genetic proxied effects of protein targets, such as PCSK9, on additional endpoints, and disentangle specific genes and proteins perturbed at loci associated with COVID-19 susceptibility. This public–private partnership provides the scientific community with an open-access proteomics resource of considerable breadth and depth to help to elucidate the biological mechanisms underlying proteo-genomic discoveries and accelerate the development of biomarkers, predictive models and therapeutics 1 . The Pharma Proteomics Project generates the largest open-access plasma proteomics dataset to date, offering insights into trans protein quantitative trait loci across multiple biological domains, and highlighting genetic influences on ligand–receptor interactions and pathway perturbations across a diverse collection of cytokines and complement networks.

Prime editors have been delivered using DNA or RNA vectors. Here we demonstrate prime editing with purified ribonucleoprotein complexes. We introduced somatic mutations in zebrafish embryos with frequencies as high as 30% and demonstrate germline transmission. We also observed unintended insertions, deletions and prime editing guide RNA (pegRNA) scaffold incorporations. In HEK293T and primary human T cells, prime editing with purified ribonucleoprotein complexes introduced desired edits with frequencies of up to 21 and 7.5%, respectively. Prime editors are delivered as ribonucleoproteins to zebrafish embryos and human primary cells.

Flaviviruses continue to emerge as global health threats. There are currently no Food and Drug Administration (FDA) approved antiviral treatments for flaviviral infections. Therefore, there is a pressing need to identify host and viral factors that can be targeted for effective therapeutic intervention. Type I interferon (IFN-I) production in response to microbial products is one of the host’s first line of defense against invading pathogens. Cytidine/uridine monophosphate kinase 2 (CMPK2) is a type I interferon-stimulated gene (ISG) that exerts antiviral effects. However, the molecular mechanism by which CMPK2 inhibits viral replication is unclear. Here, we report that CMPK2 expression restricts Zika virus (ZIKV) replication by specifically inhibiting viral translation and that IFN-I- induced CMPK2 contributes significantly to the overall antiviral response against ZIKV. We demonstrate that expression of CMPK2 results in a significant decrease in the replication of other pathogenic flaviviruses including dengue virus (DENV-2), Kunjin virus (KUNV) and yellow fever virus (YFV). Importantly, we determine that the N-terminal domain (NTD) of CMPK2, which lacks kinase activity, is sufficient to restrict viral translation. Thus, its kinase function is not required for CMPK2’s antiviral activity. Furthermore, we identify seven conserved cysteine residues within the NTD as critical for CMPK2 antiviral activity. Thus, these residues may form an unknown functional site in the NTD of CMPK2 contributing to its antiviral function. Finally, we show that mitochondrial localization of CMPK2 is required for its antiviral effects. Given its broad antiviral activity against flaviviruses, CMPK2 is a promising potential pan-flavivirus inhibitor.

Major histocompatibility complex class I (MHC-I) molecules, which are dimers of a glycosylated polymorphic transmembrane heavy chain and the small-protein β₂-microglobulin (β₂m), bind peptides in the endoplasmic reticulum that are generated by the cytosolic turnover of cellular proteins. In virus-infected cells, these peptides may include those derived from viral proteins. Peptide-MHC-I complexes then traffic through the secretory pathway and are displayed at the cell surface where those containing viral peptides can be detected by CD8⁺ T lymphocytes that kill infected cells. Many viruses enhance their in vivo survival by encoding genes that down-regulate MHC-I expression to avoid CD8⁺ T cell recognition. Here, we report that two accessory proteins encoded by SARS-CoV-2, the causative agent of the ongoing COVID-19 pandemic, down-regulate MHC-I expression using distinct mechanisms. First, ORF3a, a viroporin, reduces the global trafficking of proteins, including MHC-I, through the secretory pathway. The second, ORF7a, interacts specifically with the MHC-I heavy chain, acting as a molecular mimic of β₂m to inhibit its association. This slows the exit of properly assembled MHC-I molecules from the endoplasmic reticulum. We demonstrate that ORF7a reduces antigen presentation by the human MHC-I allele HLA-A*02:01. Thus, both ORF3a and ORF7a act post-translationally in the secretory pathway to lower surface MHC-I expression, with ORF7a exhibiting a specific mechanism that allows immune evasion by SARS-CoV-2.