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Chronic pain is a common problem, with more than one-fifth of adult Americans reporting pain daily or on most days. It adversely affects quality of life and imposes substantial personal and economic costs. Efforts to treat chronic pain using opioids played a central role in precipitating the opioid crisis. Despite an estimated heritability of 25–50%, the genetic architecture of chronic pain is not well characterized, in part because studies have largely been limited to samples of European ancestry. To help address this knowledge gap, we conducted a cross-ancestry meta-analysis of pain intensity in 598,339 participants in the Million Veteran Program, which identified 125 independent genetic loci, 82 of which are novel. Pain intensity was genetically correlated with other pain phenotypes, level of substance use and substance use disorders, other psychiatric traits, education level, and cognitive traits. Integration of the GWAS findings with functional genomics data shows enrichment for putatively causal genes (n = 142) and proteins (n = 14) expressed in brain tissues, specifically in GABAergic neurons. Drug repurposing analysis identified anticonvulsants, beta-blockers, and calcium-channel blockers, among other drug groups, as having potential analgesic effects. Our results provide insights into key molecular contributors to the experience of pain and highlight attractive drug targets.

Stilbenes are developmentally induced metabolites in Scots pine heartwood where they have important role in protecting wood from decaying fungi. The stilbene pathway is also stress inducible, and ultraviolet (UV)-C radiation was among the first discovered artificial stress activators of the pathway. Many conditions that activate the pathway are known, but the specific transcriptional regulators and the biosynthetic enzyme responsible for activating the stilbene precursor cinnamate in the pathway are unknown.Here we describe the first large-scale transcriptomic analysis of pine needles in response to UV-C exposure and treatment with translational inhibitor cycloheximide, both activating the transcription of stilbene pathway genes, to uncover the candidates for the transcriptional regulation of the pathway and the cinnamate activating CoA ligase. We show that the regulation of pine stilbene pathway has shared features with grapevine stilbene pathway. In both species, the stilbene pathway was transcriptionally activated after UV-C treatment, protein phosphatase inhibitors and plant hormones ethylene and jasmonate.The pine stilbene synthase promoter retains its inducibility when transformed in plants that normally do not synthesize stilbenes. Pine stilbene synthase promoter was able to activate the expression of a reporter gene in Arabidopsis in response to UV-C exposure and phosphatase inhibitors, but not as response to plant hormone treatment. This indicates that stilbene synthase gene regulation occurs both via ancient stress-response pathway(s) but also via species specific regulators. With transcriptomic approach we identified candidate enzymes for cinnamate acting CoA ligase and transcription factors regulating the pathway.

openaire: EC/H2020/648925/EU//BHIVE Funding Information: We thank Kuisma Littunen and Kim Kataja who assisted with substrate preparation and Nelly Monties who assisted with enzyme production. We also thank Professor A. McGuigan (University of Toronto) for helpful discussions. Funding Information: We thank the following granting agencies for their financial support: the European Research Council (ERC) (Consolidator Grant no. BHIVE – 648925) (MM, EJ, VL and ERM); Jenny and Antti Wihuri Foundation (Centre for Young Synbio Scientists) (TK); Academy of Finland Postdoctoral Researcher Grant No. 331853 (TP); The French national research agency (anr) Grant No. ANR-18-CES43-DECO (TE and CD). Publisher Copyright: © 2022, The Author(s). Background : Substrate accessibility remains a key limitation to the efficient enzymatic deconstruction of lignocellulosic biomass. Limited substrate accessibility is often addressed by increasing enzyme loading, which increases process and product costs. Alternatively, considerable efforts are underway world-wide to identify amorphogenesis-inducing proteins and protein domains that increase the accessibility of carbohydrate-active enzymes to targeted lignocellulose components. Results : We established a three-dimensional assay, PACER (plant cell wall model for the analysis of non-catalytic and enzymatic responses), that enables analysis of enzyme migration through defined lignocellulose composites. A cellulose/azo-xylan composite was made to demonstrate the PACER concept and then used to test the migration and activity of multiple xylanolytic enzymes. In addition to non-catalytic domains of xylanases, the potential of loosenin-like proteins to boost xylanase migration through cellulose/azo-xylan composites was observed. Conclusions : The PACER assay is inexpensive and parallelizable, suitable for screening proteins for ability to increase enzyme accessibility to lignocellulose substrates. Using the PACER assay, we visualized the impact of xylan-binding modules and loosenin-like proteins on xylanase mobility and access to targeted substrates. Given the flexibility to use different composite materials, the PACER assay presents a versatile platform to study impacts of lignocellulose components on enzyme access to targeted substrates.

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.

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.

Despite a global decrease in suicide rates in recent years, death by suicide has increased in the United States. It is therefore imperative to identify the risk factors associated with suicide attempts in order to combat this growing epidemic. In this study, we use an explainable-artificial intelligence method, iterative Random Forest, to predict suicide attempts using data from the Million Veteran Program. Our predictive model incorporates multiple environmental variables (e.g., elevation, light wavelength absorbance, temperature, humidity, etc) at ZIP code-level geospatial resolution. We additionally consider demographic variables from the American Community Survey as well as the number of firearms and alcohol vendors per 10,000 people in order to assess the contributions of proximal environment, access to means, and restraint decrease to suicide attempts. 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. Taken together, our findings highlight the importance of social determinants and environmental factors in understanding suicide risk among veterans.

Genome-wide association studies (GWAS) identify genetic variants underlying complex traits but are limited by stringent genome-wide significance thresholds. Here we dramatically relax GWAS stringency by orders of magnitude and apply GRIN (Gene set Refinement through Interacting Networks), which increases confidence in the expanded gene set by retaining genes strongly connected by biological networks from diverse lines of evidence. From multiple GWAS summary statistics of suicide attempt, a complex psychiatric phenotype, GRIN identified additional genes that replicated across independent cohorts and retained genes that were more biologically interrelated despite a relaxed significance threshold. We present a conceptual model of how these retained genes interact through neurobiological pathways to influence suicidal behavior and identify existing drugs associated with these pathways that would not have been identified under traditional GWAS thresholds. We demonstrate that GRIN is a useful community resource for improving the signal to noise ratio of GWAS results.

The SARS-CoV-2 pandemic has entered an alarming new phase with the emergence of the variants of concern (VOC), P.1, B.1.351, and B.1.1.7, in late 2020, and B.1.427, B.1.429, and B.1.617, in 2021. Substitutions in the spike glycoprotein (S), such as Asn501Tyr and Glu484Lys, are likely key in several VOC. However, Asn501Tyr circulated for months in earlier strains and Glu484Lys is not found in B.1.1.7, indicating that they do not fully explain those fast-spreading variants. Here we use a computational systems biology approach to process more than 900,000 SARS-CoV-2 genomes, map their spatiotemporal relationships, and identify lineage-defining mutations followed by structural analyses that reveal their critical attributes. Comparisons to earlier dominant mutations and protein structural analyses indicate that increased transmission is promoted by epistasis, i.e., the combination of functionally complementary mutations in S and in other regions of the SARS-CoV-2 proteome. We report that the VOC have in common mutations in non-S proteins involved in immune-antagonism and replication performance, such as the nonstructural proteins 6 and 13, suggesting convergent evolution of the virus. Critically, we propose that recombination events among divergent coinfecting haplotypes greatly accelerates the emergence of VOC by bringing together cooperative mutations and explaining the remarkably high mutation load of B.1.1.7. Therefore, extensive community distribution of SARS-CoV-2 increases the probability of future recombination events, further accelerating the evolution of the virus. This study reinforces the need for a global response to stop COVID-19 and future pandemics. Notice: This manuscript has been authored by UT-Battelle, LLC, under contract DE-AC05-00OR22725 with the US Department of Energy (DOE). The US government retains and the publisher, by accepting the article for publication, acknowledges that the US government retains a nonexclusive, paid-up, irrevocable, worldwide license to publish or reproduce the published form of this manuscript, or allow others to do so, for US government purposes. DOE will provide public access to these results of federally sponsored research in accordance with the DOE Public Access Plan (http://energy.gov/downloads/doe-public-access-plan). ”Nothing in Biology Makes Sense Except in the Light of Evolution” -Theodosius Dobzhansky

Heterobasidion parviporum Niemela & Korhonen is a necrotrophic fungal pathogen of Norway spruce (Picea abies). The H. parviporum genome encodes numerous necrotrophic small secreted proteins (SSP) which might be important for promoting and sustaining the disease development. However, their transcriptional dynamics and plant defense response during infection are largely unknown. In this study, we identified a necrotrophic SSP named HpSSP35.8 and its coding gene was highly expressed in the pre-symptomatic phase of the host (Norway spruce) infection. We explored the impact of HpSSP35.8 on non-host Nicotiana benthamiana using Agrobacterium-mediated transient expression system under visible spectrum RGB imaging and chlorophyll fluorescence imaging. The results showed that HpSSP35.8 triggered a form of SSP-associated programmed cell death, accompanied by a decrease in the plant photosynthetic activity. Defense-related genes including WRKY12, ethylene response factor (ERF1α) and a chitinase gene PR4 were up-regulated in both HpSSP35.8-N. benthamiana interaction and H. parviporum-Norway spruce pathosystem. This study also highlighted the potential to use the chlorophyll fluorescence imaging approach to monitor both the indirect effects of SSP and also for the selection of other potential effector-like protein candidates.

Image-based symptom scoring of plant diseases is a powerful tool for associating disease resistance with plant genotypes. Advancements in technology have enabled new imaging and image processing strategies for statistical analysis of time-course experiments. There are several tools available for analyzing symptoms on leaves and fruits of crop plants, but only a few are available for the model plant Arabidopsis thaliana (Arabidopsis). Arabidopsis and the model fungus Botrytis cinerea (Botrytis) comprise a potent model pathosystem for the identification of signaling pathways conferring immunity against this broad host-range necrotrophic fungus. Here, we present two strategies to assess severity and symptom progression of Botrytis infection over time in Arabidopsis leaves. Thus, a pixel classification strategy using color hue values from red-green-blue (RGB) images and a random forest algorithm was used to establish necrotic, chlorotic, and healthy leaf areas. Secondly, using chlorophyll fluorescence (ChlFl) imaging, the maximum quantum yield of photosystem II (Fv/Fm) was determined to define diseased areas and their proportion per total leaf area. Both RGB and ChlFl imaging strategies were employed to track disease progression over time. This has provided a robust and sensitive method for detecting sensitive or resistant genetic backgrounds. A full methodological workflow, from plant culture to data analysis, is described.

BackgroundA 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.ResultsHere 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.ConclusionsThese results provide valuable insights for the development of drugs and surveillance strategies to combat the current and future pandemics.

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.

Using a Systems Biology approach, we integrated genomic, transcriptomic, proteomic, and molecular structure information to provide a holistic understanding of the COVID-19 pandemic. The expression data analysis of the Renin Angiotensin System indicates mild nasal, oral or throat infections are likely and that the gastrointestinal tissues are a common primary target of SARS-CoV-2. Extreme symptoms in the lower respiratory system likely result from a secondary-infection possibly by a comorbidity-driven upregulation of ACE2 in the lung. The remarkable differences in expression of other RAS elements, the elimination of macrophages and the activation of cytokines in COVID-19 bronchoalveolar samples suggest that a functional immune deficiency is a critical outcome of COVID-19. We posit that using a non-respiratory system as a major pathway of infection is likely determining the unprecedented global spread of this coronavirus.One Sentence SummaryA Systems Approach Indicates Non-respiratory Pathways of Infection as Key for the COVID-19 Pandemic

Flowering time control integrates endogenous as well as environmental signals to promote flower development. The pathways and molecular networks involved are complex and integrate many modes of signal transduction. In plants ubiquitin mediated protein degradation pathway has been proposed to be as important mode of signaling as phosphorylation and transcription. To systematically study the role of ubiquitin signaling in the molecular regulation of flowering we have taken a genomic approach to identify flower related Ubiquitin Proteasome System components. As a large and versatile gene family the RING type ubiquitin E3 ligases were chosen as targets of the genomic screen. To this end the complete list of Arabidopsis RING E3 ligases were retrieved and verified in the Arabidopsis genome v11. Their differential expression was used for their categorization into flower organs or developmental stages. Known regulators of flowering time or floral organ development were identified in these categories through literature search and representative mutants for each category were purchased for functional characterization by growth and morphological phenotyping. To this end, a workflow was developed for high throughput phenotypic screening of growth, morphology and flowering of nearly a thousand Arabidopsis plants in one experimental round.

Seed quality is an important factor for seedling vigour as well as adult plant resilience. The key quality attributes are related to physical characteristics, physiological performance, genetic background and health status of the seeds. Many ways to address seed quality attributes have been developed and recently many of them have featured automated high throughput methods. In our study, we addressed two of the seed quality attributes, namely physiological performance and genetic background by analysing germination rates in our mutant collection. These mutants represent ubiquitin E3 ligases that transcriptionally respond to abscisic acid (ABA). This plant hormone is an important regulator of germination and seedling establishment. To facilitate in vitro germination screens of large seed collections a high throughput image-based assay was developed. As a read out of the germination on ABA treatment the cotyledon emergence was detected with top view chlorophyll fluorescence camera. By applying the ABA treatment during germination, RING-type ubiquitin E3 ligase mutants were identified, showing either resistant or sensitive responses to ABA. In conclusion, a scalable high throughput screen for in vitro germination assay was established that allowed fast screening of tens of mutants in a hormone supplemented media.