Shade from neighboring plants limits light for photosynthesis; as a consequence, plants have a variety of strategies to avoid canopy shade and compete with their neighbors for light. Collectively the response to foliar shade is called the shade avoidance syndrome (SAS). The SAS includes elongation of a variety of organs, acceleration of flowering time, and additional physiological responses, which are seen throughout the plant life cycle. However, current mechanistic knowledge is mainly limited to shade-induced elongation of seedlings. Here we use phenotypic profiling of seedling, leaf, and flowering time traits to untangle complex SAS networks. We used over-representation analysis (ORA) of shade-responsive genes, combined with previous annotation, to logically select 59 known and candidate novel mutants for phenotyping. Our analysis reveals shared and separate pathways for each shade avoidance response. In particular, auxin pathway components were required for shade avoidance responses in hypocotyl, petiole, and flowering time, whereas jasmonic acid pathway components were only required for petiole and flowering time responses. Our phenotypic profiling allowed discovery of seventeen novel shade avoidance mutants. Our results demonstrate that logical selection of mutants increased success of phenotypic profiling to dissect complex traits and discover novel components.

Herein we provide a living summary of the data generated during the COVID Moonshot project focused on the development of SARS-CoV-2 main protease (Mpro) inhibitors. Our approach uniquely combines crowdsourced medicinal chemistry insights with high throughput crystallography, exascale computational chemistry infrastructure for simulations, and machine learning in triaging designs and predicting synthetic routes. This manuscript describes our methodologies leading to both covalent and non-covalent inhibitors displaying protease IC50 values under 150 nM and viral inhibition under 5 uM in multiple different viral replication assays. Furthermore, we provide over 200 crystal structures of fragment-like and lead-like molecules in complex with the main protease. Over 1000 synthesized and ordered compounds are also reported with the corresponding activity in Mpro enzymatic assays using two different experimental setups. The data referenced in this document will be continually updated to reflect the current experimental progress of the COVID Moonshot project, and serves as a citable reference for ensuing publications. All of the generated data is open to other researchers who may find it of use.

Significant improvements have been made to the OPLS-AA force field for modeling RNA. New torsional potentials were optimized based on DFT scans at the ωB97X-D/6-311++G(d,p) level for potential energy surfaces of the backbone α and γ dihedral angles. In combination with previously reported improvements for the sugar puckering and glycosidic torsion terms, the new force field was validated through diverse molecular dynamics simulations for RNAs in aqueous solution. Results for dinucleotides and tetranucleotides revealed both accurate reproduction of 3 J couplings from NMR and the avoidance of several unphysical states observed with other force fields. Simulations of larger systems with noncanonical motifs showed significant structural improvements over the previous OPLS-AA parameters. The new force field, OPLSAA/M, is expected to perform competitively with other recent RNA force fields and to be compatible with OPLS-AA models for proteins and small molecules.

Gene conversion is the nonreciprocal exchange of genetic material between homologous chromosomes. Multiple lines of evidence from a variety of taxa strongly suggest that gene conversion events are biased toward GC-bearing alleles. However, in Drosophila, the data have largely been indirect and unclear, with some studies supporting the predictions of a GC-biased gene conversion model and other data showing contradictory findings. Here, we test whether gene conversion events are GC-biased in Drosophila melanogaster using whole-genome polymorphism and divergence data. Our results provide no support for GC-biased gene conversion and thus suggest that this process is unlikely to significantly contribute to patterns of polymorphism and divergence in this system.

Machine learning methods may have the potential to significantly accelerate drug discovery.However, the increasing rate of new methodological approaches being published in the literature raises the fundamental question of how models should be benchmarked and validated. We reanalyze the data generated by a recently published large-scale comparison of machine learning models for bioactivity prediction and arrive at a somewhat different conclusion. We show that the performance of support vector machines is competitive with that of deep learning methods. Additionally, using a series of numerical experiments, we question the relevance of area under the receiver operating characteristic curve as a metric in virtual screening, and instead suggest that area under the precision-recall curve should be used in conjunction with the receiver operating characteristic. Our numerical experiments also highlight challenges in estimating the uncertainty in model performance via scaffold-split nested cross validation. * aal44@cam.ac.uk

ImportanceThe effectiveness of fluvoxamine to shorten symptom duration or prevent hospitalization among outpatients with mild to moderate symptomatic COVID-19 is unclear.ObjectiveTo evaluate the efficacy of low-dose fluvoxamine (50 mg twice daily) for 10 days compared with placebo for the treatment of mild to moderate COVID-19 in the US.Design, Setting, and ParticipantsThe ongoing Accelerating COVID-19 Therapeutic Interventions and Vaccines (ACTIV-6) platform randomized clinical trial was designed to test repurposed medications in outpatients with mild to moderate COVID-19. A total of 1288 participants aged 30 years or older with test-confirmed SARS-CoV-2 infection and experiencing 2 or more symptoms of acute COVID-19 for 7 days or less were enrolled between August 6, 2021, and May 27, 2022, at 91 sites in the US.InterventionsParticipants were randomized to receive 50 mg of fluvoxamine twice daily for 10 days or placebo.Main Outcomes and MeasuresThe primary outcome was time to sustained recovery (defined as the third day of 3 consecutive days without symptoms). There were 7 secondary outcomes, including a composite outcome of hospitalization, urgent care visit, emergency department visit, or death through day 28.ResultsAmong 1331 participants who were randomized (median age, 47 years [IQR, 38-57 years]; 57% were women; and 67% reported receiving ≥2 doses of a SARS-CoV-2 vaccine), 1288 completed the trial (674 in the fluvoxamine group and 614 in the placebo group). The median time to sustained recovery was 12 days (IQR, 11-14 days) in the fluvoxamine group and 13 days (IQR, 12-13 days) in the placebo group (hazard ratio [HR], 0.96 [95% credible interval, 0.86-1.06], posterior P = .21 for the probability of benefit [determined by an HR >1]). For the composite outcome, 26 participants (3.9%) in the fluvoxamine group were hospitalized, had an urgent care visit, had an emergency department visit, or died compared with 23 participants (3.8%) in the placebo group (HR, 1.1 [95% credible interval, 0.5-1.8], posterior P = .35 for the probability of benefit [determined by an HR <1]). One participant in the fluvoxamine group and 2 participants in the placebo group were hospitalized; no deaths occurred in either group. Adverse events were uncommon in both groups.Conclusions and RelevanceAmong outpatients with mild to moderate COVID-19, treatment with 50 mg of fluvoxamine twice daily for 10 days, compared with placebo, did not improve time to sustained recovery. These findings do not support the use of fluvoxamine at this dose and duration in patients with mild to moderate COVID-19.Trial RegistrationClinicalTrials.gov Identifier: NCT04885530

Meiotic recombination is a genetic process that is critical for proper chromosome segregation in many organisms. Despite being fundamental for organismal fitness, rates of crossing over vary greatly between taxa. Both genetic and environmental factors contribute to phenotypic variation in crossover frequency, as do genotype–environment interactions. Here, we test the hypothesis that maternal age influences rates of crossing over in a genotypic-specific manner. Using classical genetic techniques, we estimated rates of crossing over for individual Drosophila melanogaster females from five strains over their lifetime from a single mating event. We find that both age and genetic background significantly contribute to observed variation in recombination frequency, as do genotype–age interactions. We further find differences in the effect of age on recombination frequency in the two genomic regions surveyed. Our results highlight the complexity of recombination rate variation and reveal a new role of genotype by maternal age interactions in mediating recombination rate.

Oxidative stress alters cell viability, from microorganism irradiation sensitivity to human aging and neurodegeneration. Deleterious effects of protein carbonylation by reactive oxygen species (ROS) make understanding molecular properties determining ROS susceptibility essential. The radiation-resistant bacterium Deinococcus radiodurans accumulates less carbonylation than sensitive organisms, making it a key model for deciphering properties governing oxidative stress resistance. We integrated shotgun redox proteomics, structural systems biology, and machine learning to resolve properties determining protein damage by c-irradiation in Escherichia coli and D. radiodurans at multiple scales. Local accessibility, charge, and lysine enrichment accurately predict ROS susceptibility. Lysine, methionine, and cysteine usage also contribute to ROS resistance of the D. radiodurans proteome. Our model predicts proteome maintenance machinery, and proteins protecting against ROS are more resistant in D. radiodurans. Our findings substantiate that protein-intrinsic protection impacts oxidative stress resistance, identifying causal molecular properties.