SARS-CoV-2 variants evolve convergent strategies to remodel the host response.

SARS-CoV-2 variants of concern (VOCs) emerged during the COVID-19 pandemic. Here, we used unbiased systems approaches to study the host-selective forces driving VOC evolution. We discovered that VOCs evolved convergent strategies to remodel the host by modulating viral RNA and protein levels, altering viral and host protein phosphorylation, and rewiring virus-host protein-protein interactions. Integrative computational analyses revealed that although Alpha, Beta, Gamma, and Delta ultimately converged to suppress interferon-stimulated genes (ISGs), Omicron BA.1 did not. ISG suppression correlated with the expression of viral innate immune antagonist proteins, including Orf6, N, and Orf9b, which we mapped to specific mutations. Later Omicron subvariants BA.4 and BA.5 more potently suppressed innate immunity than early subvariant BA.1, which correlated with Orf6 levels, although muted in BA.4 by a mutation that disrupts the Orf6-nuclear pore interaction. Our findings suggest that SARS-CoV-2 convergent evolution overcame human adaptive and innate immune barriers, laying the groundwork to tackle future pandemics.

The Global Phosphorylation Landscape of SARS-CoV-2 Infection.

The causative agent of the coronavirus disease 2019 (COVID-19) pandemic, severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), has infected millions and killed hundreds of thousands of people worldwide, highlighting an urgent need to develop antiviral therapies. Here we present a quantitative mass spectrometry-based phosphoproteomics survey of SARS-CoV-2 infection in Vero E6 cells, revealing dramatic rewiring of phosphorylation on host and viral proteins. SARS-CoV-2 infection promoted casein kinase II (CK2) and p38 MAPK activation, production of diverse cytokines, and shutdown of mitotic kinases, resulting in cell cycle arrest. Infection also stimulated a marked induction of CK2-containing filopodial protrusions possessing budding viral particles. Eighty-seven drugs and compounds were identified by mapping global phosphorylation profiles to dysregulated kinases and pathways. We found pharmacologic inhibition of the p38, CK2, CDK, AXL, and PIKFYVE kinases to possess antiviral efficacy, representing potential COVID-19 therapies.

Evolution of enhanced innate immune evasion by SARS-CoV-2.

The emergence of SARS-CoV-2 variants of concern suggests viral adaptation to enhance human-to-human transmission1,2. Although much effort has focused on the characterization of changes in the spike protein in variants of concern, mutations outside of spike are likely to contribute to adaptation. Here, using unbiased abundance proteomics, phosphoproteomics, RNA sequencing and viral replication assays, we show that isolates of the Alpha (B.1.1.7) variant3 suppress innate immune responses in airway epithelial cells more effectively than first-wave isolates. We found that the Alpha variant has markedly increased subgenomic RNA and protein levels of the nucleocapsid protein (N), Orf9b and Orf6-all known innate immune antagonists. Expression of Orf9b alone suppressed the innate immune response through interaction with TOM70, a mitochondrial protein that is required for activation of the RNA-sensing adaptor MAVS. Moreover, the activity of Orf9b and its association with TOM70 was regulated by phosphorylation. We propose that more effective innate immune suppression, through enhanced expression of specific viral antagonist proteins, increases the likelihood of successful transmission of the Alpha variant, and may increase in vivo replication and duration of infection4. The importance of mutations outside the spike coding region in the adaptation of SARS-CoV-2 to humans is underscored by the observation that similar mutations exist in the N and Orf9b regulatory regions of the Delta and Omicron variants.

A SARS-CoV-2 protein interaction map reveals targets for drug repurposing.

A newly described coronavirus named severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), which is the causative agent of coronavirus disease 2019 (COVID-19), has infected over 2.3 million people, led to the death of more than 160,000 individuals and caused worldwide social and economic disruption1,2. There are no antiviral drugs with proven clinical efficacy for the treatment of COVID-19, nor are there any vaccines that prevent infection with SARS-CoV-2, and efforts to develop drugs and vaccines are hampered by the limited knowledge of the molecular details of how SARS-CoV-2 infects cells. Here we cloned, tagged and expressed 26 of the 29 SARS-CoV-2 proteins in human cells and identified the human proteins that physically associated with each of the SARS-CoV-2 proteins using affinity-purification mass spectrometry, identifying 332 high-confidence protein-protein interactions between SARS-CoV-2 and human proteins. Among these, we identify 66 druggable human proteins or host factors targeted by 69 compounds (of which, 29 drugs are approved by the US Food and Drug Administration, 12 are in clinical trials and 28 are preclinical compounds). We screened a subset of these in multiple viral assays and found two sets of pharmacological agents that displayed antiviral activity: inhibitors of mRNA translation and predicted regulators of the sigma-1 and sigma-2 receptors. Further studies of these host-factor-targeting agents, including their combination with drugs that directly target viral enzymes, could lead to a therapeutic regimen to treat COVID-19.

HIV-1 virological synapse formation enhances infection spread by dysregulating Aurora Kinase B.

HIV-1 spreads efficiently through direct cell-to-cell transmission at virological synapses (VSs) formed by interactions between HIV-1 envelope proteins (Env) on the surface of infected cells and CD4 receptors on uninfected target cells. Env-CD4 interactions bring the infected and uninfected cellular membranes into close proximity and induce transport of viral and cellular factors to the VS for efficient virion assembly and HIV-1 transmission. Using novel, cell-specific stable isotope labeling and quantitative mass spectrometric proteomics, we identified extensive changes in the levels and phosphorylation states of proteins in HIV-1 infected producer cells upon mixing with CD4+ target cells under conditions inducing VS formation. These coculture-induced alterations involved multiple cellular pathways including transcription, TCR signaling and, unexpectedly, cell cycle regulation, and were dominated by Env-dependent responses. We confirmed the proteomic results using inhibitors targeting regulatory kinases and phosphatases in selected pathways identified by our proteomic analysis. Strikingly, inhibiting the key mitotic regulator Aurora kinase B (AURKB) in HIV-1 infected cells significantly increased HIV activity in cell-to-cell fusion and transmission but had little effect on cell-free infection. Consistent with this, we found that AURKB regulates the fusogenic activity of HIV-1 Env. In the Jurkat T cell line and primary T cells, HIV-1 Env:CD4 interaction also dramatically induced cell cycle-independent AURKB relocalization to the centromere, and this signaling required the long (150 aa) cytoplasmic C-terminal domain (CTD) of Env. These results imply that cytoplasmic/plasma membrane AURKB restricts HIV-1 envelope fusion, and that this restriction is overcome by Env CTD-induced AURKB relocalization. Taken together, our data reveal a new signaling pathway regulating HIV-1 cell-to-cell transmission and potential new avenues for therapeutic intervention through targeting the Env CTD and AURKB activity.

Comparative Analysis of T-Cell Spatial Proteomics and the Influence of HIV Expression.

As systems biology approaches to virology have become more tractable, highly studied viruses such as HIV can now be analyzed in new unbiased ways, including spatial proteomics. We employed here a differential centrifugation protocol to fractionate Jurkat T cells for proteomic analysis by mass spectrometry; these cells contain inducible HIV-1 genomes, enabling us to look for changes in the spatial proteome induced by viral gene expression. Using these proteomics data, we evaluated the merits of several reported machine learning pipelines for classification of the spatial proteome and identification of protein translocations. From these analyses, we found that classifier performance in this system was organelle dependent, with Bayesian t-augmented Gaussian mixture modeling outperforming support vector machine learning for mitochondrial and endoplasmic reticulum proteins but underperforming on cytosolic, nuclear, and plasma membrane proteins by QSep analysis. We also observed a generally higher performance for protein translocation identification using a Bayesian model, Bayesian analysis of differential localization experiments, on row-normalized data. Comparative Bayesian analysis of differential localization experiment analysis of cells induced to express the WT viral genome versus cells induced to express a genome unable to express the accessory protein Nef identified known Nef-dependent interactors such as T-cell receptor signaling components and coatomer complex. Finally, we found that support vector machine classification showed higher consistency and was less sensitive to HIV-dependent noise. These findings illustrate important considerations for studies of the spatial proteome following viral infection or viral gene expression and provide a reference for future studies of HIV-gene-dropout viruses.

Neighborhood Predictors of Poor Prenatal Care and Well-Child Visit Attendance.

PURPOSE: Women and children continue to miss preventive visits. Which neighborhood factors predict inadequate prenatal care (PNC) and well-child visit (WCV) attendance remain unclear. DESCRIPTION: In a retrospective case-control study at Virginia Commonwealth University Health System, mothers with less than 50% adherence or initiation after 5 months gestation were eligible as cases and those with ≥ 80% adherence and initiation before 5 months were eligible as controls. Children in the lowest quintile of adherence were eligible as cases and those with ≥ 80% of adherence were eligible as controls. Cases and controls were randomly selected at a 1:2 ratio and matched on birth month. Covariates were derived from the 2018 American Community Survey. A hotspot was defined as a zip code tabulation area (ZCTA) with a proportion of controls less than 0.66. ZCTAs with fewer than 5 individuals were excluded. Weighted quantile regression was used to determine which covariates were most associated with inadequate attendance. ASSESSMENT: We identified 38 and 35 ZCTAs for the PNC and WCV analyses, respectively. Five of 11 hotspots for WCV were also hotspots for PNC. Education and income predicted 51% and 34% of the variation in missed PNCs, respectively; language, education and transportation difficulties explained 33%, 29%, and 17% of the variation in missed WCVs, respectively. Higher proportions of Black residents lived in hotspots of inadequate PCV and WCV attendance. CONCLUSION: Neighborhood-level factors performed well in predicting inadequate PCV and WCV attendance. The disproportionate impact impact of inadequate PCV and WCV in neighborhoods where higher proportions of Black people lived highlights the potential influence of systemic racism and segregation on healthcare utilization.

Patients' Progress and Confidence Addressing Root Causes of Poor Health in Primary Care.

Context: Patients with multiple chronic conditions (MCC) may have unmet behavioral, mental, and social needs which can be difficult to address in primary care. Care planning provides a framework for patients to be screened, collaborate on a care plan, and access a patient navigator who can support them achieving their personal health goals. Objective: To compare patients' progress and confidence in addressing personal care plans for different topics. Study Design and Analysis: Clinician level randomized control trial and descriptive analyses. Dataset: My Own Health Report (MOHR) study and navigator field notes. Population Studied: As part of a randomized controlled trial (RCT) to evaluate a feasible approach to patient care planning, 24 clinicians from 12 practices in the Virginia Ambulatory Care Outcomes Research Network (ACORN) in the Greater Richmond metro and the Northern Virginia areas participated in a care planning intervention. 91 patients in the intervention arm received support from a patient navigator for making and working on a goal. We focused on patients with uncontrolled chronic conditions that have complex needs. Intervention/Instrument: Community-clinical linkage support and navigator field notes in My Own Health Report (MOHR). Outcome Measures: We determined confidence and progress ratings (ranked by patients on 1-10 point scale), health risk assessment responses, and care plan topics selected by patients. Results: Patients feel more confident addressing nutrition than weight loss (mean = 8.07 vs 6.31, p=0.0031). Patients tended to report better prior progress on nutrition care plans (mean = 3.80) than physical activity (mean =2.95, p=0.0024) and weight loss (mean=2.93, p=0.004). Conclusions: Helping patients create care plans on topics they feel most comfortable addressing may better address root causes of poor health associated with chronic conditions. Connecting them with a patient navigator for the short-term may have long-term benefits for patients and care teams.

A network-based comparative framework to study conservation and divergence of proteomes in plant phylogenies.

Comparative functional genomics offers a powerful approach to study species evolution. To date, the majority of these studies have focused on the transcriptome in mammalian and yeast phylogenies. Here, we present a novel multi-species proteomic dataset and a computational pipeline to systematically compare the protein levels across multiple plant species. Globally we find that protein levels diverge according to phylogenetic distance but is more constrained than the mRNA level. Module-level comparative analysis of groups of proteins shows that proteins that are more highly expressed tend to be more conserved. To interpret the evolutionary patterns of conservation and divergence, we develop a novel network-based integrative analysis pipeline that combines publicly available transcriptomic datasets to define co-expression modules. Our analysis pipeline can be used to relate the changes in protein levels to different species-specific phenotypic traits. We present a case study with the rhizobia-legume symbiosis process that supports the role of autophagy in this symbiotic association.

Data-Independent Acquisition Protease-Multiplexing Enables Increased Proteome Sequence Coverage Across Multiple Fragmentation Modes.

The use of multiple proteases has been shown to increase protein sequence coverage in proteomics experiments; however, due to the additional analysis time required, it has not been widely adopted in routine data-dependent acquisition (DDA) proteomic workflows. Alternatively, data-independent acquisition (DIA) has the potential to analyze multiplexed samples from different protease digests, but has been primarily optimized for fragmenting tryptic peptides. Here we evaluate a DIA multiplexing approach that combines three proteolytic digests (Trypsin, AspN, and GluC) into a single sample. We first optimize data acquisition conditions for each protease individually with both the canonical DIA fragmentation mode (beam type CID), as well as resonance excitation CID, to determine optimal consensus conditions across proteases. Next, we demonstrate that application of these conditions to a protease-multiplexed sample of human peptides results in similar protein identifications and quantitative performance as compared to trypsin alone, but enables up to a 63% increase in peptide detections, and a 45% increase in nonredundant amino acid detections. Nontryptic peptides enabled noncanonical protein isoform determination and resulted in 100% sequence coverage for numerous proteins, suggesting the utility of this approach in applications where sequence coverage is critical, such as protein isoform analysis.

Mass spectrometry-based protein-protein interaction networks for the study of human diseases.

A better understanding of the molecular mechanisms underlying disease is key for expediting the development of novel therapeutic interventions. Disease mechanisms are often mediated by interactions between proteins. Insights into the physical rewiring of protein-protein interactions in response to mutations, pathological conditions, or pathogen infection can advance our understanding of disease etiology, progression, and pathogenesis and can lead to the identification of potential druggable targets. Advances in quantitative mass spectrometry (MS)-based approaches have allowed unbiased mapping of these disease-mediated changes in protein-protein interactions on a global scale. Here, we review MS techniques that have been instrumental for the identification of protein-protein interactions at a system-level, and we discuss the challenges associated with these methodologies as well as novel MS advancements that aim to address these challenges. An overview of examples from diverse disease contexts illustrates the potential of MS-based protein-protein interaction mapping approaches for revealing disease mechanisms, pinpointing new therapeutic targets, and eventually moving toward personalized applications.

Identifying bright spot communities: Socioecological, workforce, and healthcare delivery factors influencing opioid mortality.

Context: There were 50,000 U.S. opioid overdose deaths in 2019. Millions suffer from opioid addiction. Identifying protective factors for low community opioid mortality may have important implications for addressing the opioid epidemic. This study was funded through the Virginia (VA) Department of Medical Assistance Services (DMAS) through a SUPPORT Act Grant. Objective: To identify "Bright Spot" communities in Virginia with protective factors associated with reduced opioid mortality and morbidity. Study Design: Ecologic study. Dataset: Virginia All Payer Claims Database (APCD), Virginia Department of Health (VDH) statewide medical examiner registry, and American Community Survey (ACS). Time Period: 2016-2019; 2019 data cited here. Population Studied: APCD includes VA residents with medical claims through commercial, Medicaid, and Medicare coverage. VDH data includes fatal drug overdoses. ACS surveys all VA residents. Outcome Measures: Primary outcome: fatal opioid overdoses. Secondary outcomes: emergency room visits for overdoses and opioid-related diagnoses, outpatient diagnoses for opioid-related disorder, prescription rate for opioids, and prescription rate for buprenorphine. Results: Opioid mortality was associated with higher rates of community poverty (r=.38, p<.0001) and disability (r=.52, r<.0001). Opioid mortality was associated with inequality, with higher Gini index associated with higher opioid mortality (r=.23, p<.0001). A higher percentage of black residents was associated with increased fatal opioid overdoses (r=.37, p<.0001) and ED visits for overdoses (r=.30, p<.0001). A higher percentage of white residents correlated with increased outpatient visits for opioid use disorder (r=.24, p<.0001) and higher rates of buprenorphine (r=.34, p<.0001) and opioid prescriptions (r=.31, p <.0001). Conclusions: These findings suggest significant racial disparities in opioid outcomes. Communities with a higher percentage of black residents are more likely to have higher opioid mortality and a lower rate of outpatient treatment. This association may be affected by the time period used in the analysis (2015-2019), as nationally there has been an increasing rate of synthetic opioid deaths in Black communities. These measures have been incorporated into a multivariate analysis to identify Bright Spot communities, which will be discussed during the presentation.

Community, Social, and Facility Factors and Long-stay Antipsychotic Use.

OBJECTIVES: Compare Virginia nursing homes in the top- and bottom-quintiles of antipsychotic use for variation in community, social, and facility factors. METHODS: 2018 CMS data ascertained Virginia nursing homes in the top and bottom quintiles for antipsychotic use. The Virginia Health Department provided social determinant of health (SDOH) statistics for each facility's county/city while claims identified facility demographics. Chi square and independent two-sample t-tests compared quintiles for regional, social, and demographic differences. RESULTS: Quintiles averaged 3000 residents and 56 facilities. Facilities with the lowest rates of antipsychotic use were more likely to be privately owned and had fewer African-American and minority residents and more white residents. All 18 SDOH statistics were superior for the communities of facilities with the lowest antipsychotic rates. Nine of these differences were statistically significant, including the aggregated "Health Opportunity Index." CONCLUSIONS: The antipsychotic prevalence rate for facilities in the top-quintile of antipsychotic use is fivefold the bottom-quintile's rate. Antipsychotic prescribing in nursing homes is associated with regional, demographic, and social factors not addressed by existing antipsychotic reduction measures, with vulnerable populations at greatest risk. CLINICAL IMPLICATIONS: The efficacy of measures aimed at curbing long-stay antipsychotic prescribing could be improved by addressing SDOH including economic opportunities.

CIDer: A Statistical Framework for Interpreting Differences in CID and HCD Fragmentation.

Library searching is a powerful technique for detecting peptides using either data independent or data dependent acquisition. While both large-scale spectrum library curators and deep learning prediction approaches have focused on beam-type CID fragmentation (HCD), resonance CID fragmentation remains a popular technique. Here we demonstrate an approach to model the differences between HCD and CID spectra, and present a software tool, CIDer, for converting libraries between the two fragmentation methods. We demonstrate that just using a combination of simple linear models and basic principles of peptide fragmentation, we can explain up to 43% of the variation between ions fragmented by HCD and CID across an array of collision energy settings. We further show that in some circumstances, searching converted CID libraries can detect more peptides than searching existing CID libraries or libraries of machine learning predictions from FASTA databases. These results suggest that leveraging information in existing libraries by converting between HCD and CID libraries may be an effective interim solution while large-scale CID libraries are being developed.

Toward Comprehensive Plasma Proteomics by Orthogonal Protease Digestion.

Rapid and consistent protein identification across large clinical cohorts is an important goal for clinical proteomics. With the development of data-independent technologies (DIA/SWATH-MS), it is now possible to analyze hundreds of samples with great reproducibility and quantitative accuracy. However, this technology benefits from empirically derived spectral libraries that define the detectable set of peptides and proteins. Here, we apply a simple and accessible tip-based workflow for the generation of spectral libraries to provide a comprehensive overview on the plasma proteome in individuals with and without active tuberculosis (TB). To boost protein coverage, we utilized nonconventional proteases such as GluC and AspN together with the gold standard trypsin, identifying more than 30,000 peptides mapping to 3309 proteins. Application of this library to quantify plasma proteome differences in TB infection recovered more than 400 proteins in 50 min of MS acquisition, including diagnostic Mycobacterium tuberculosis (Mtb) proteins that have previously been detectable primarily by antibody-based assays and intracellular proteins not previously described to be in plasma.

Unreported Antipsychotic Use Increasing in Nursing Homes: The Impact of Quality-Measure Exclusions on the Percentage of Long-Stay Residents Who Got an Antipsychotic Medication Quality-Measure.

OBJECTIVE: Excluded from reporting to CMS's Percentage of long-stay residents who got an antipsychotic medication quality-measure are antipsychotics prescribed to nursing home patients with schizophrenia, Tourette's, or Huntington's. Over the 4 years following its 2012 debut, the quality-measure calculated a 27% reduction in inappropriate antipsychotic use but also an 18.3% increase in exclusion claims. This study evaluated the impact of these exclusions on the measure's findings. METHODS: Claims data for the years 2011-2016 retrospectively identified the prevalence of schizophrenia, Tourette's, and Huntington's in quarterly cohorts of Virginia long-stay residents prescribed antipsychotics. Annualized diagnoses in 2011 were compared with subsequent years using simple logistic regression. RESULTS: In 2016, 29% of the antipsychotics prescribed in Virginia nursing homes were to residents with diagnoses of schizophrenia, Tourette's, and Huntington's, a significant 32% increase from 2011. CONCLUSION: Almost 30% of the antipsychotics employed in Virginia nursing homes are excluded from CMS's long-stay antipsychotic quality-measure.

An overview of biological applications and fundamentals of new inlet and vacuum ionization technologies.

RATIONALE: The developments of new ionization technologies based on processes previously unknown to mass spectrometry (MS) have gained significant momentum. Herein we address the importance of understanding these unique ionization processes, demonstrate the new capabilities currently unmet by other methods, and outline their considerable analytical potential. METHODS: The inlet and vacuum ionization methods of solvent-assisted ionization (SAI), matrix-assisted ionization (MAI), and laserspray ionization can be used with commercial and dedicated ion sources producing ions from atmospheric or vacuum conditions for analyses of a variety of materials including drugs, lipids, and proteins introduced from well plates, pipet tips and plate surfaces with and without a laser using solid or solvent matrices. Mass spectrometers from various vendors are employed. RESULTS: Results are presented highlighting strengths relative to ionization methods of electrospray ionization (ESI) and matrix-assisted laser desorption/ionization. We demonstrate the utility of multi-ionization platforms encompassing MAI, SAI, and ESI and enabling detection of what otherwise is missed, especially when directly analyzing mixtures. Unmatched robustness is achieved with dedicated vacuum MAI sources with mechanical introduction of the sample to the sub-atmospheric pressure (vacuum MAI). Simplicity and use of a wide array of matrices are attained using a conduit (inlet ionization), preferably heated, with sample introduction from atmospheric pressure. Tissue, whole blood, urine (including mouse, chicken, and human origin), bacteria strains and chemical on-probe reactions are analyzed directly and, especially in the case of vacuum ionization, without concern of carryover or instrument contamination. CONCLUSIONS: Examples are provided highlighting the exceptional analytical capabilities associated with the novel ionization processes in MS that reduce operational complexity while increasing speed and robustness, achieving mass spectra with low background for improved sensitivity, suggesting the potential of this simple ionization technology to drive MS into areas currently underserved, such as clinical and medical applications.

Building reliable and generalizable clerkship competency assessments: Impact of 'hawk-dove' correction.

PURPOSE: Systematic differences among raters' approaches to student assessment may result in leniency or stringency of assessment scores. This study examines the generalizability of medical student workplace-based competency assessments including the impact of rater-adjusted scores for leniency and stringency. METHODS: Data were collected from summative clerkship assessments completed for 204 students during 2017-2018 the clerkship at a single institution. Generalizability theory was used to explore variance attributed to different facets (rater, learner, item, and competency domain) through three unbalanced random-effects models by clerkship including applying assessor stringency-leniency adjustments. RESULTS: In the original assessments, only 4-8% of the variance was attributed to the student with the remainder being rater variance and error. Aggregating items to create a composite score increased variability attributable to the student (5-13% of variance). Applying a stringency-leniency ('hawk-dove') correction substantially increased the variance attributed to the student (14.8-17.8%) and reliability. Controlling for assessor leniency/stringency reduced measurement error, decreasing the number of assessments required for generalizability from 16-50 to 11-14. CONCLUSIONS: Similar to prior research, most of the variance in competency assessment scores was attributable to raters, with only a small proportion attributed to the student. Making stringency-leniency corrections using rater-adjusted scores improved the psychometric characteristics of assessment scores.

Practice facilitation to promote evidence-based screening and management of unhealthy alcohol use in primary care: a practice-level randomized controlled trial.

BACKGROUND: Unhealthy alcohol use is the third leading cause of preventable death in the United States. Evidence demonstrates that screening for unhealthy alcohol use and providing persons engaged in risky drinking with brief behavioral and counseling interventions improves health outcomes, collectively termed screening and brief interventions. Medication assisted therapy (MAT) is another effective method for treatment of moderate or severe alcohol use disorder. Yet, primary care clinicians are not regularly screening for or treating unhealthy alcohol use. METHODS AND ANALYSIS: We are initiating a clinic-level randomized controlled trial aimed to evaluate how primary care clinicians can impact unhealthy alcohol use through screening, counseling, and MAT. One hundred and 25 primary care practices in the Virginia Ambulatory Care Outcomes Research Network (ACORN) will be engaged; each will receive practice facilitation to promote screening, counseling, and MAT either at the beginning of the trial or at a 6-month control period start date. For each practice, the intervention includes provision of a practice facilitator, learning collaboratives with three practice champions, and clinic-wide information sessions. Clinics will be enrolled for 6-12 months. After completion of the intervention, we will conduct a mixed methods analysis to identify changes in screening rates, increase in provision of brief counseling and interventions as well as MAT, and the reduction of alcohol intake for patients after practices receive practice facilitation. DISCUSSION: This study offers a systematic process for dissemination and implementation of the evidence-based practice of screening, counseling, and treatment for unhealthy alcohol use. Practices will be asked to implement a process for screening, counseling, and treatment based on their practice characteristics, patient population, and workflow. We propose practice facilitation as a robust and feasible intervention to assist in making changes within the practice. We believe that the process can be replicated and used in a broad range of clinical settings; we anticipate this will be supported by our evaluation of this approach. TRIAL REGISTRATION: ClinicalTrials.gov, ClinicalTrials.gov Identifier: NCT04248023, Registered 5 February 2020.