Potential Pathogenicity Determinants Identified from Structural Proteomics of SARS-CoV and SARS-CoV-2.

Despite SARS-CoV and SARS-CoV-2 being equipped with highly similar protein arsenals, the corresponding zoonoses have spread among humans at extremely different rates. The specific characteristics of these viruses that led to such distinct outcomes remain unclear. Here, we apply proteome-wide comparative structural analysis aiming to identify the unique molecular elements in the SARS-CoV-2 proteome that may explain the differing consequences. By combining protein modeling and molecular dynamics simulations, we suggest nonconservative substitutions in functional regions of the spike glycoprotein (S), nsp1, and nsp3 that are contributing to differences in virulence. Particularly, we explain why the substitutions at the receptor-binding domain of S affect the structure-dynamics behavior in complexes with putative host receptors. Conservation of functional protein regions within the two taxa is also noteworthy. We suggest that the highly conserved main protease, nsp5, of SARS-CoV and SARS-CoV-2 is part of their mechanism of circumventing the host interferon antiviral response. Overall, most substitutions occur on the protein surfaces and may be modulating their antigenic properties and interactions with other macromolecules. Our results imply that the striking difference in the pervasiveness of SARS-CoV-2 and SARS-CoV among humans seems to significantly derive from molecular features that modulate the efficiency of viral particles in entering the host cells and blocking the host immune response.

Genotype to phenotype: Diet-by-mitochondrial DNA haplotype interactions drive metabolic flexibility and organismal fitness.

Diet may be modified seasonally or by biogeographic, demographic or cultural shifts. It can differentially influence mitochondrial bioenergetics, retrograde signalling to the nuclear genome, and anterograde signalling to mitochondria. All these interactions have the potential to alter the frequencies of mtDNA haplotypes (mitotypes) in nature and may impact human health. In a model laboratory system, we fed four diets varying in Protein: Carbohydrate (P:C) ratio (1:2, 1:4, 1:8 and 1:16 P:C) to four homoplasmic Drosophila melanogaster mitotypes (nuclear genome standardised) and assayed their frequency in population cages. When fed a high protein 1:2 P:C diet, the frequency of flies harbouring Alstonville mtDNA increased. In contrast, when fed the high carbohydrate 1:16 P:C food the incidence of flies harbouring Dahomey mtDNA increased. This result, driven by differences in larval development, was generalisable to the replacement of the laboratory diet with fruits having high and low P:C ratios, perturbation of the nuclear genome and changes to the microbiome. Structural modelling and cellular assays suggested a V161L mutation in the ND4 subunit of complex I of Dahomey mtDNA was mildly deleterious, reduced mitochondrial functions, increased oxidative stress and resulted in an increase in larval development time on the 1:2 P:C diet. The 1:16 P:C diet triggered a cascade of changes in both mitotypes. In Dahomey larvae, increased feeding fuelled increased ß-oxidation and the partial bypass of the complex I mutation. Conversely, Alstonville larvae upregulated genes involved with oxidative phosphorylation, increased glycogen metabolism and they were more physically active. We hypothesise that the increased physical activity diverted energy from growth and cell division and thereby slowed development. These data further question the use of mtDNA as an assumed neutral marker in evolutionary and population genetic studies. Moreover, if humans respond similarly, we posit that individuals with specific mtDNA variations may differentially metabolise carbohydrates, which has implications for a variety of diseases including cardiovascular disease, obesity, and perhaps Parkinson's Disease.

Mitochondrial variation in small brown planthoppers linked to multiple traits and probably reflecting a complex evolutionary trajectory.

While it has been proposed in several taxa that the mitochondrial genome is associated with adaptive evolution to different climatic conditions, making links between mitochondrial haplotypes and organismal phenotypes remains a challenge. Mitonuclear discordance occurs in the small brown planthopper (SBPH), Laodelphax striatellus, with one mitochondrial haplogroup (HGI) more common in the cold climate region of China relative to another form (HGII) despite strong nuclear gene flow, providing a promising model to investigate climatic adaptation of mitochondrial genomes. We hypothesized that cold adaptation through HGI may be involved, and considered mitogenome evolution, population genetic analyses, and bioassays to test this hypothesis. In contrast to our hypothesis, chill-coma recovery tests and population genetic tests of selection both pointed to HGII being involved in cold adaptation. Phylogenetic analyses revealed that HGII is nested within HGI, and has three nonsynonymous changes in ND2, ND5 and CYTB in comparison to HGI. These molecular changes likely increased mtDNA copy number, cold tolerance and fecundity of SBPH, particularly through a function-altering amino acid change involving M114T in ND2. Nuclear background also influenced fecundity and chill recovery (i.e., mitonuclear epistasis) and protein modelling indicates possible nuclear interactions for the two nonsynonymous changes in ND2 and CYTB. The high occurrence frequency of HGI in the cold climate region of China remains unexplained, but several possible reasons are discussed. Overall, our study points to a link between mtDNA variation and organismal-level evolution and suggests a possible role of mitonuclear interactions in maintaining mtDNA diversity.

Exogenous Factors May Differentially Influence the Selective Costs of mtDNA Mutations.

In this review, we provide evidence to suggest that the cost of specific mtDNA mutations can be influenced by exogenous factors. We focus on macronutrient-mitochondrial DNA interactions as factors that may differentially influence the consequences of a change as mitochondria must be flexible in its utilization of dietary proteins, carbohydrates, and fats. To understand this fundamental dynamic, we briefly discuss the energy processing pathways in mitochondria. Next, we explore the mitochondrial functions that are initiated during energy deficiency or when cells encounter cellular stress. We consider the anterograde response (nuclear control of mitochondrial function) and the retrograde response (nuclear changes in response to mitochondrial signaling) and how this mito-nuclear crosstalk may be influenced by exogenous factors such as temperature and diet. Finally, we employ Complex I of the mitochondrial electron transport system as a case study and discuss the potential role of the dietary macronutrient ratio as a strong selective force that may shape the frequencies of mitotypes in populations and species. We conclude that this underexplored field likely has implications in the fundamental disciplines of evolutionary biology and quantitative genetics and the more biomedical fields of nutrigenomics and pharmacogenomics.

Novel Mito-Nuclear Combinations Facilitate the Global Invasion of a Major Agricultural Crop Pest.

A fundamental understanding of the underlying mechanisms involved in biological invasions is crucial to developing effective risk assessment and control measures against invasive species. The fall armyworm (FAW), Spodoptera frugiperda, is a highly invasive pest that has rapidly spread from its native Americas into much of the Eastern Hemisphere, with a highly homogeneous nuclear genetic background. However, the exact mechanism behind its rapid introduction and propagation remains unclear. Here, a systematic investigation is conducted into the population dynamics of FAW in China from 2019 to 2021 and found that FAW individuals carrying "rice" mitochondria (FAW-mR) are more prevalent (>98%) than that with "corn" mitochondria (FAW-mC) at the initial stage of the invasion and in newly-occupied non-overwintering areas. Further fitness experiments show that the two hybrid-strains of FAW exhibit different adaptions in the new environment in China, and this may have been facilitated by amino acid changes in mitochondrial-encoded proteins. FAW-mR used increases energy metabolism, faster wing-beat frequencies, and lower wing loadings to drive greater flight performance and subsequent rapid colonization of new habitats. In contrast, FAW-mC individuals adapt with more relaxed mitochondria and shuttle energetics into maternal investment, observed as faster development rate and higher fecundity. The presence of two different mitochondria types within FAW has the potential to significantly expand the range of damage and enhance competitive advantage. Overall, the study describes a novel invasion mechanism displayed by the FAW population that facilitates its expansion and establishment in new environments.

Exploring the Hawaiian Ala Wai Watershed with Zebrafish.

The Ala Wai Canal is an artificial waterway in the tourist district of Waikiki in Honolulu, HI. Originally built to collect runoff from industrial, residential, and green spaces dedicated to recreation, the Ala Wai Canal has since experienced potent levels of toxicity due to this runoff entering the watershed and making it hazardous for both marine life and humans at current concentration, including Danio rerio (zebrafish). A community of learners at educations levels from high school to postbaccalaureate from Oahu, HI was connected through the Consortium for Increasing Research and Collaborative Learning Experiences (CIRCLE) distance research program. This team conducted research with an Investigator and team from Mayo Clinic in Rochester, MN, with the Ala Wai Canal as its primary subject. Through CIRCLE, research trainees sent two 32 oz bottles of Ala Wai- acquired water to a partnered laboratory at the Mayo Clinic in which zebrafish embryos were observed at differing concentrations of the sampled water against a variety of developmental and behavioral assays. Research trainees also created atlases of developmental outcomes in zebrafish following exposure to environmental toxins and tables of potential pesticide contaminants to enable the identification of the substances linked to structural defects and enhanced stress during Ala Wai water exposure experiments.

Structural variants identified using non-Mendelian inheritance patterns advance the mechanistic understanding of autism spectrum disorder.

The heritability of autism spectrum disorder (ASD), based on 680,000 families and five countries, is estimated to be nearly 80%, yet heritability reported from SNP-based studies are consistently lower, and few significant loci have been identified with genome-wide association studies. This gap in genomic information may reside in rare variants, interaction among variants (epistasis), or cryptic structural variation (SV) and may provide mechanisms that underlie ASD. Here we use a method to identify potential SVs based on non-Mendelian inheritance patterns in pedigrees using parent-child genotypes from ASD families and demonstrate that they are enriched in ASD-risk genes. Most are in non-coding genic space and are over-represented in expression quantitative trait loci, suggesting that they affect gene regulation, which we confirm with their overlap of differentially expressed genes in postmortem brain tissue of ASD individuals. We then identify an SV in the GRIK2 gene that alters RNA splicing and a regulatory region of the ACMSD gene in the kynurenine pathway as significantly associated with a non-verbal ASD phenotype, supporting our hypothesis that these currently excluded loci can provide a clearer mechanistic understanding of ASD. Finally, we use an explainable artificial intelligence approach to define subgroups demonstrating their use in the context of precision medicine.

Using iterative random forest to find geospatial environmental and Sociodemographic predictors of suicide attempts.

Introduction: Despite a recent global decrease in suicide rates, death by suicide has increased in the United States. It is therefore imperative to identify the risk factors associated with suicide attempts to combat this growing epidemic. In this study, we aim to identify potential risk factors of suicide attempt using geospatial features in an Artificial intelligence framework. Methods: We use iterative Random Forest, an explainable artificial intelligence method, to predict suicide attempts using data from the Million Veteran Program. This cohort incorporated 405,540 patients with 391,409 controls and 14,131 attempts. Our predictive model incorporates multiple climatic features at ZIP-code-level geospatial resolution. We additionally consider demographic features from the American Community Survey as well as the number of firearms and alcohol vendors per 10,000 people to assess the contributions of proximal environment, access to means, and restraint decrease to suicide attempts. In total 1,784 features were included in the predictive model. Results: Our results show that geographic areas with higher concentrations of married males living with spouses are predictive of lower rates of suicide attempts, whereas geographic areas where males are more likely to live alone and to rent housing are predictive of higher rates of suicide attempts. We also identified climatic features that were associated with suicide attempt risk by age group. Additionally, we observed that firearms and alcohol vendors were associated with increased risk for suicide attempts irrespective of the age group examined, but that their effects were small in comparison to the top features. Discussion: Taken together, our findings highlight the importance of social determinants and environmental factors in understanding suicide risk among veterans.

Plasma proteomics of SARS-CoV-2 infection and severity reveals impact on Alzheimer's and coronary disease pathways.

Identification of proteins dysregulated by COVID-19 infection is critically important for better understanding of its pathophysiology, building prognostic models, and identifying new targets. Plasma proteomic profiling of 4,301 proteins was performed in two independent datasets and tested for the association for three COVID-19 outcomes (infection, ventilation, and death). We identified 1,449 proteins consistently associated in both datasets with any of these three outcomes. We subsequently created highly accurate models that distinctively predict infection, ventilation, and death. These proteins were enriched in specific biological processes including cytokine signaling, Alzheimer's disease, and coronary artery disease. Mendelian randomization and gene network analyses identified eight causal proteins and 141 highly connected hub proteins including 35 with known drug targets. Our findings provide distinctive prognostic biomarkers for two severe COVID-19 outcomes, reveal their relationship to Alzheimer's disease and coronary artery disease, and identify potential therapeutic targets for COVID-19 outcomes.

Response to comment on 'SARS-CoV-2 suppresses anticoagulant and fibrinolytic gene expression in the lung'.

Early in the SARS-CoV-2 pandemic, we compared transcriptome data from hospitalized COVID-19 patients and control patients without COVID-19. We found changes in procoagulant and fibrinolytic gene expression in the lungs of COVID-19 patients (Mast et al., 2021). These findings have been challenged based on issues with the samples (Fitzgerald and Jamieson, 2022). We have revisited our previous analyses in the light of this challenge and find that these new analyses support our original conclusions.

Structural and functional characterization of NEMO cleavage by SARS-CoV-2 3CLpro.

In addition to its essential role in viral polyprotein processing, the SARS-CoV-2 3C-like protease (3CLpro) can cleave human immune signaling proteins, like NF-κB Essential Modulator (NEMO) and deregulate the host immune response. Here, in vitro assays show that SARS-CoV-2 3CLpro cleaves NEMO with fine-tuned efficiency. Analysis of the 2.50 Å resolution crystal structure of 3CLpro C145S bound to NEMO226-234 reveals subsites that tolerate a range of viral and host substrates through main chain hydrogen bonds while also enforcing specificity using side chain hydrogen bonds and hydrophobic contacts. Machine learning- and physics-based computational methods predict that variation in key binding residues of 3CLpro-NEMO helps explain the high fitness of SARS-CoV-2 in humans. We posit that cleavage of NEMO is an important piece of information to be accounted for, in the pathology of COVID-19.

Antiviral Strategies Against SARS-CoV-2: A Systems Biology Approach.

The unprecedented scientific achievements in combating the COVID-19 pandemic reflect a global response informed by unprecedented access to data. We now have the ability to rapidly generate a diversity of information on an emerging pathogen and, by using high-performance computing and a systems biology approach, we can mine this wealth of information to understand the complexities of viral pathogenesis and contagion like never before. These efforts will aid in the development of vaccines, antiviral medications, and inform policymakers and clinicians. Here we detail computational protocols developed as SARS-CoV-2 began to spread across the globe. They include pathogen detection, comparative structural proteomics, evolutionary adaptation analysis via network and artificial intelligence methodologies, and multiomic integration. These protocols constitute a core framework on which to build a systems-level infrastructure that can be quickly brought to bear on future pathogens before they evolve into pandemic proportions.

NF-κB perturbation reveals unique immunomodulatory functions in Prx1+ fibroblasts that promote development of atopic dermatitis.

Skin is composed of diverse cell populations that cooperatively maintain homeostasis. Up-regulation of the nuclear factor κB (NF-κB) pathway may lead to the development of chronic inflammatory disorders of the skin, but its role during the early events remains unclear. Through analysis of single-cell RNA sequencing data via iterative random forest leave one out prediction, an explainable artificial intelligence method, we identified an immunoregulatory role for a unique paired related homeobox-1 (Prx1)+ fibroblast subpopulation. Disruption of Ikkb-NF-κB under homeostatic conditions in these fibroblasts paradoxically induced skin inflammation due to the overexpression of C-C motif chemokine ligand 11 (CCL11; or eotaxin-1) characterized by eosinophil infiltration and a subsequent TH2 immune response. Because the inflammatory phenotype resembled that seen in human atopic dermatitis (AD), we examined human AD skin samples and found that human AD fibroblasts also overexpressed CCL11 and that perturbation of Ikkb-NF-κB in primary human dermal fibroblasts up-regulated CCL11. Monoclonal antibody treatment against CCL11 was effective in reducing the eosinophilia and TH2 inflammation in a mouse model. Together, the murine model and human AD specimens point to dysregulated Prx1+ fibroblasts as a previously unrecognized etiologic factor that may contribute to the pathogenesis of AD and suggest that targeting CCL11 may be a way to treat AD-like skin lesions.

Plasma proteomics of SARS-CoV-2 infection and severity reveals impact on Alzheimer and coronary disease pathways.

Identification of the plasma proteomic changes of Coronavirus disease 2019 (COVID-19) is essential to understanding the pathophysiology of the disease and developing predictive models and novel therapeutics. We performed plasma deep proteomic profiling from 332 COVID-19 patients and 150 controls and pursued replication in an independent cohort (297 cases and 76 controls) to find potential biomarkers and causal proteins for three COVID-19 outcomes (infection, ventilation, and death). We identified and replicated 1,449 proteins associated with any of the three outcomes (841 for infection, 833 for ventilation, and 253 for death) that can be query on a web portal ( https://covid.proteomics.wustl.edu/ ). Using those proteins and machine learning approached we created and validated specific prediction models for ventilation (AUC>0.91), death (AUC>0.95) and either outcome (AUC>0.80). These proteins were also enriched in specific biological processes, including immune and cytokine signaling (FDR ≤ 3.72×10 -14 ), Alzheimer's disease (FDR ≤ 5.46×10 -10 ) and coronary artery disease (FDR ≤ 4.64×10 -2 ). Mendelian randomization using pQTL as instrumental variants nominated BCAT2 and GOLM1 as a causal proteins for COVID-19. Causal gene network analyses identified 141 highly connected key proteins, of which 35 have known drug targets with FDA-approved compounds. Our findings provide distinctive prognostic biomarkers for two severe COVID-19 outcomes (ventilation and death), reveal their relationship to Alzheimer's disease and coronary artery disease, and identify potential therapeutic targets for COVID-19 outcomes.

SARS-CoV-2 suppresses anticoagulant and fibrinolytic gene expression in the lung.

Extensive fibrin deposition in the lungs and altered levels of circulating blood coagulation proteins in COVID-19 patients imply local derangement of pathways that limit fibrin formation and/or promote its clearance. We examined transcriptional profiles of bronchoalveolar lavage fluid (BALF) samples to identify molecular mechanisms underlying these coagulopathies. mRNA levels for regulators of the kallikrein-kinin (C1-inhibitor), coagulation (thrombomodulin, endothelial protein C receptor), and fibrinolytic (urokinase and urokinase receptor) pathways were significantly reduced in COVID-19 patients. While transcripts for several coagulation proteins were increased, those encoding tissue factor, the protein that initiates coagulation and whose expression is frequently increased in inflammatory disorders, were not increased in BALF from COVID-19 patients. Our analysis implicates enhanced propagation of coagulation and decreased fibrinolysis as drivers of the coagulopathy in the lungs of COVID-19 patients.

A k-mer based approach for classifying viruses without taxonomy identifies viral associations in human autism and plant microbiomes.

Viruses are an underrepresented taxa in the study and identification of microbiome constituents; however, they play an essential role in health, microbiome regulation, and transfer of genetic material. Only a few thousand viruses have been isolated, sequenced, and assigned a taxonomy, which limits the ability to identify and quantify viruses in the microbiome. Additionally, the vast diversity of viruses represents a challenge for classification, not only in constructing a viral taxonomy, but also in identifying similarities between a virus' genotype and its phenotype. However, the diversity of viral sequences can be leveraged to classify their sequences in metagenomic and metatranscriptomic samples, even if they do not have a taxonomy. To identify and quantify viruses in transcriptomic and genomic samples, we developed a dynamic programming algorithm for creating a classification tree out of 715,672 metagenome viruses. To create the classification tree, we clustered proportional similarity scores generated from the k-mer profiles of each of the metagenome viruses to create a database of metagenomic viruses. The resulting Kraken2 database of the metagenomic viruses can be found here: https://www.osti.gov/biblio/1615774 and is compatible with Kraken2. We then integrated the viral classification database with databases created with genomes from NCBI for use with ParaKraken (a parallelized version of Kraken provided in Supplemental Zip 1), a metagenomic/transcriptomic classifier. To illustrate the breadth of our utility for classifying metagenome viruses, we analyzed data from a plant metagenome study identifying genotypic and compartment specific differences between two Populus genotypes in three different compartments. We also identified a significant increase in abundance of eight viral sequences in post mortem brains in a human metatranscriptome study comparing Autism Spectrum Disorder patients and controls. We also show the potential accuracy for classifying viruses by utilizing both the JGI and NCBI viral databases to identify the uniqueness of viral sequences. Finally, we validate the accuracy of viral classification with NCBI databases containing viruses with taxonomy to identify pathogenic viruses in known COVID-19 and cassava brown streak virus infection samples. Our method represents the compulsory first step in better understanding the role of viruses in the microbiome by allowing for a more complete identification of sequences without taxonomy. Better classification of viruses will improve identifying associations between viruses and their hosts as well as viruses and other microbiome members. Despite the lack of taxonomy, this database of metagenomic viruses can be used with any tool that utilizes a taxonomy, such as Kraken, for accurate classification of viruses.

Structural and functional characterization of NEMO cleavage by SARS-CoV-2 3CLpro.

In addition to its essential role in viral polyprotein processing, the SARS-CoV-2 3C-like (3CLpro) protease can cleave human immune signaling proteins, like NF-κB Essential Modulator (NEMO) and deregulate the host immune response. Here, in vitro assays show that SARS-CoV-2 3CLpro cleaves NEMO with fine-tuned efficiency. Analysis of the 2.14 Å resolution crystal structure of 3CLpro C145S bound to NEMO 226-235 reveals subsites that tolerate a range of viral and host substrates through main chain hydrogen bonds while also enforcing specificity using side chain hydrogen bonds and hydrophobic contacts. Machine learning- and physics-based computational methods predict that variation in key binding residues of 3CLpro- NEMO helps explain the high fitness of SARS-CoV-2 in humans. We posit that cleavage of NEMO is an important piece of information to be accounted for in the pathology of COVID-19.

A mechanistic model and therapeutic interventions for COVID-19 involving a RAS-mediated bradykinin storm.

Neither the disease mechanism nor treatments for COVID-19 are currently known. Here, we present a novel molecular mechanism for COVID-19 that provides therapeutic intervention points that can be addressed with existing FDA-approved pharmaceuticals. The entry point for the virus is ACE2, which is a component of the counteracting hypotensive axis of RAS. Bradykinin is a potent part of the vasopressor system that induces hypotension and vasodilation and is degraded by ACE and enhanced by the angiotensin1-9 produced by ACE2. Here, we perform a new analysis on gene expression data from cells in bronchoalveolar lavage fluid (BALF) from COVID-19 patients that were used to sequence the virus. Comparison with BALF from controls identifies a critical imbalance in RAS represented by decreased expression of ACE in combination with increases in ACE2, renin, angiotensin, key RAS receptors, kinogen and many kallikrein enzymes that activate it, and both bradykinin receptors. This very atypical pattern of the RAS is predicted to elevate bradykinin levels in multiple tissues and systems that will likely cause increases in vascular dilation, vascular permeability and hypotension. These bradykinin-driven outcomes explain many of the symptoms being observed in COVID-19.

In late 2019, a new virus named SARS-CoV-2, which causes a disease in humans called COVID-19, emerged in China and quickly spread around the world. Many individuals infected with the virus develop only mild, symptoms including a cough, high temperature and loss of sense of smell; while others may develop no symptoms at all. However, some individuals develop much more severe, life-threatening symptoms affecting the lungs and other parts of the body including the heart and brain. SARS-CoV-2 uses a human enzyme called ACE2 like a 'Trojan Horse' to sneak into the cells of its host. ACE2 lowers blood pressure in the human body and works against another enzyme known as ACE (which has the opposite effect). Therefore, the body has to balance the levels of ACE and ACE2 to maintain a normal blood pressure. It remains unclear whether SARS-CoV-2 affects how ACE2 and ACE work. When COVID-19 first emerged, a team of researchers in China studied fluid and cells collected from the lungs of patients to help them identify the SARS-CoV-2 virus. Here, Garvin et al. analyzed the data collected in the previous work to investigate whether changes in how the body regulates blood pressure may contribute to the life-threatening symptoms of COVID-19. The analyses found that SARS-CoV-2 caused the levels of ACE in the lung cells to decrease, while the levels of ACE2 increased. This in turn increased the levels of a molecule known as bradykinin in the cells (referred to as a 'Bradykinin Storm'). . Previous studies have shown that bradykinin induces pain and causes blood vessels to expand and become leaky which will lead to swelling and inflammation of the surrounding tissue. In addition, the analyses found that production of a substance called hyaluronic acid was increased and the enzymes that could degrade it greatly decreased. Hyaluronic acid can absorb more than 1,000 times its own weight in water to form a hydrogel. The Bradykinin-Storm-induced leakage of fluid into the lungs combined with the excess hyaluronic acid would likely result in a Jello-like substance that is preventing oxygen uptake and carbon dioxide release in the lungs of severely affected COVID-19 patients. Therefore, the findings of Garvin et al. suggest that the Bradykinin Storm may be responsible for the more severe symptoms of COVID-19. Further experiments identified several existing medicinal drugs that have the potential to be re-purposed to treat the Bradykinin Storm. A possible next step would be to carry out clinical trials to assess how effective these drugs are in treating patients with COVID-19. In addition, understanding how SARS-Cov-2 affects the body will help researchers and clinicians identify individuals who are most at risk of developing life-threatening symptoms.

Can exascale computing and explainable artificial intelligence applied to plant biology deliver on the United Nations sustainable development goals?

Human population growth and accelerated climate change necessitate agricultural improvements using designer crop ideotypes (idealized plants that can grow in niche environments). Diverse and highly skilled research groups must integrate efforts to bridge the gaps needed to achieve international goals toward sustainable agriculture. Given the scale of global agricultural needs and the breadth of multiple types of omics data needed to optimize these efforts, explainable artificial intelligence (AI with a decipherable decision making process that provides a meaningful explanation to humans) and exascale computing (computers that can perform 1018 floating-point operations per second, or exaflops) are crucial. Accurate phenotyping and daily-resolution climatype associations are equally important for refining ideotype production to specific environments at various levels of granularity. We review advances toward tackling technological hurdles to solve multiple United Nations Sustainable Development Goals and discuss a vision to overcome gaps between research and policy.

Potentially adaptive SARS-CoV-2 mutations discovered with novel spatiotemporal and explainable AI models.

BACKGROUND: A mechanistic understanding of the spread of SARS-CoV-2 and diligent tracking of ongoing mutagenesis are of key importance to plan robust strategies for confining its transmission. Large numbers of available sequences and their dates of transmission provide an unprecedented opportunity to analyze evolutionary adaptation in novel ways. Addition of high-resolution structural information can reveal the functional basis of these processes at the molecular level. Integrated systems biology-directed analyses of these data layers afford valuable insights to build a global understanding of the COVID-19 pandemic. RESULTS: Here we identify globally distributed haplotypes from 15,789 SARS-CoV-2 genomes and model their success based on their duration, dispersal, and frequency in the host population. Our models identify mutations that are likely compensatory adaptive changes that allowed for rapid expansion of the virus. Functional predictions from structural analyses indicate that, contrary to previous reports, the Asp614Gly mutation in the spike glycoprotein (S) likely reduced transmission and the subsequent Pro323Leu mutation in the RNA-dependent RNA polymerase led to the precipitous spread of the virus. Our model also suggests that two mutations in the nsp13 helicase allowed for the adaptation of the virus to the Pacific Northwest of the USA. Finally, our explainable artificial intelligence algorithm identified a mutational hotspot in the sequence of S that also displays a signature of positive selection and may have implications for tissue or cell-specific expression of the virus. CONCLUSIONS: These results provide valuable insights for the development of drugs and surveillance strategies to combat the current and future pandemics.