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Gamma-d-glutamyl-l-tryptophan (SCV-07) demonstrated an overall efficacy signal in ameliorating oral mucositis (OM) in a clinical trial of head and neck cancer patients. However, not all SCV-07-treated subjects responded positively. Here we determined if specific gene clusters could discriminate between subjects who responded to SCV-07 and those who did not. Microarrays were done using peripheral blood RNA obtained at screening and on the last day of radiation from 28 subjects enrolled in the SCV-07 trial. An analytical technique was applied that relied on learned Bayesian networks to identify gene clusters which discriminated between individuals who received SCV-07 and those who received placebo, and which differentiated subjects for whom SCV-07 was an effective OM intervention from those for whom it was not. We identified 107 genes that discriminated SCV-07 responders from non-responders using four models and applied Akaike Information Criteria (AIC) and Bayes Factor (BF) analysis to evaluate predictive accuracy. AIC were superior to BF: the accuracy of predicting placebo vs. treatment was 78% using BF, but 91% using the AIC score. Our ability to differentiate responders from non-responders using the AIC score was dramatic and ranged from 93% to 100% depending on the dataset that was evaluated. Predictive Bayesian networks were identified and functional cluster analyses were performed. A specific 10 gene cluster was a critical contributor to the predictability of the dataset. Our results demonstrate proof of concept in which the application of a genomics-based analytical paradigm was capable of discriminating responders and non-responders for an OM intervention. [ABSTRACT FROM AUTHOR]

Abstract Background Identification of expression quantitative trait loci (eQTLs) is an emerging area in genomic study. The task requires an integrated analysis of genome-wide single nucleotide polymorphism (SNP) data and gene expression data, raising a new computational challenge due to the tremendous size of data. Results We develop a method to identify eQTLs. The method represents eQTLs as information flux between genetic variants and transcripts. We use information theory to simultaneously interrogate SNP and gene expression data, resulting in a Transcriptional Information Map (TIM) which captures the network of transcriptional information that links genetic variations, gene expression and regulatory mechanisms. These maps are able to identify both cis- and trans- regulating eQTLs. The application on a dataset of leukemia patients identifies eQTLs in the regions of the GART, PCP4, DSCAM, and RIPK4 genes that regulate ADAMTS1, a known leukemia correlate. Conclusions The information theory approach presented in this paper is able to infer the dependence networks between SNPs and transcripts, which in turn can identify cis- and trans-eQTLs. The application of our method to the leukemia study explains how genetic variants and gene expression are linked to leukemia.

Alzheimer's disease (AD) is an age-dependent neurodegenerative disease characterized by extracellular Amyloid Aβ peptide (Aβ) deposition and intracellular Tau protein aggregation. Glia, especially microglia and astrocytes are core participants during the progression of AD and these cells are the mediators of Aβ clearance and degradation. The microbiota-gut-brain axis (MGBA) is a complex interactive network between the gut and brain involved in neurodegeneration. MGBA affects the function of glia in the central nervous system (CNS), and microbial metabolites regulate the communication between astrocytes and microglia; however, whether such communication is part of AD pathophysiology remains unknown. One of the potential links in bilateral gut-brain communication is tryptophan (Trp) metabolism. The microbiota-originated Trp and its metabolites enter the CNS to control microglial activation, and the activated microglia subsequently affect astrocyte functions. The present review highlights the role of MGBA in AD pathology, especially the roles of Trp per se and its metabolism as a part of the gut microbiota and brain communications. We (i) discuss the roles of Trp derivatives in microglia-astrocyte crosstalk from a bioinformatics perspective, (ii) describe the role of glia polarization in the microglia-astrocyte crosstalk and AD pathology, and (iii) summarize the potential of Trp metabolism as a therapeutic target. Finally, we review the role of Trp in AD from the perspective of the gut-brain axis and microglia, as well as astrocyte crosstalk, to inspire the discovery of novel AD therapeutics.

The increasing capabilities of AI pose new risks and vulnerabilities for organizations and decision makers. Several trustworthy AI frameworks have been created by U.S. federal agencies and international organizations to outline the principles to which AI systems must adhere for their use to be considered responsible. Different trustworthy AI frameworks reflect the priorities and perspectives of different stakeholders, and there is no consensus on a single framework yet. We evaluate the leading frameworks and provide a holistic perspective on trustworthy AI values, allowing federal agencies to create agency-specific trustworthy AI strategies that account for unique institutional needs and priorities. We apply this approach to the Department of Veterans Affairs, an entity with largest health care system in US. Further, we contextualize our framework from the perspective of the federal government on how to leverage existing trustworthy AI frameworks to develop a set of guiding principles that can provide the foundation for an agency to design, develop, acquire, and use AI systems in a manner that simultaneously fosters trust and confidence and meets the requirements of established laws and regulations., Competing Interests: The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Background and Objective: To develop the COVid Veteran (COVet) score for clinical deterioration in Veterans hospitalized with COVID-19 and further validate this model in both Veteran and non-Veteran samples. No such score has been derived and validated while incorporating a Veteran sample., Derivation Cohort: Adults (age ≥ 18 yr) hospitalized outside the ICU with a diagnosis of COVID-19 for model development to the Veterans Health Administration (VHA) (n = 80 hospitals)., Validation Cohort: External validation occurred in a VHA cohort of 34 hospitals, as well as six non-Veteran health systems for further external validation (n = 21 hospitals) between 2020 and 2023., Prediction Model: eXtreme Gradient Boosting machine learning methods were used, and performance was assessed using the area under the receiver operating characteristic curve and compared with the National Early Warning Score (NEWS). The primary outcome was transfer to the ICU or death within 24 hours of each new variable observation. Model predictor variables included demographics, vital signs, structured flowsheet data, and laboratory values., Results: A total of 96,908 admissions occurred during the study period, of which 59,897 were in the Veteran sample and 37,011 were in the non-Veteran sample. During external validation in the Veteran sample, the model demonstrated excellent discrimination, with an area under the receiver operating characteristic curve of 0.88. This was significantly higher than NEWS (0.79; p < 0.01). In the non-Veteran sample, the model also demonstrated excellent discrimination (0.86 vs. 0.79 for NEWS; p < 0.01). The top three variables of importance were eosinophil percentage, mean oxygen saturation in the prior 24-hour period, and worst mental status in the prior 24-hour period., Conclusions: We used machine learning methods to develop and validate a highly accurate early warning score in both Veterans and non-Veterans hospitalized with COVID-19. The model could lead to earlier identification and therapy, which may improve outcomes., Competing Interests: Dr. Churpek and Alexandra Spicer have funding from the Department of Defense: Peer Reviewed Medical Research Program (PRMRP) W81XWH-21-1-0009. Drs. Churpek and Edelson both have patents for electronic Cardiac Arrest Risk Triage (eCART), a non-Veteran deterioration analytic. Dr. Edelson has ownership interest in Quant HC and is president/cofounder of AgileMD with licensing agreements with Philips Healthcare and EarlySense. The remaining authors have disclosed that they do not have any potential conflicts of interest.

Clinical trials are critical to many medical advances; however, recruiting patients remains a persistent obstacle. Automated clinical trial matching could expedite recruitment across all trial phases. We detail our initial efforts towards automating the matching process by linking realistic synthetic electronic health records to clinical trial eligibility criteria using natural language processing methods. We also demonstrate how the Sørensen-Dice Index can be adapted to quantify match quality between a patient and a clinical trial., (©2024 AMIA - All rights reserved.)

High-resolution whole slide image scans of histopathology slides have been widely used in recent years for prediction in cancer. However, in some cases, clinical informatics practitioners may only have access to low-resolution snapshots of histopathology slides, not high-resolution scans. We evaluated strategies for training neural network prognostic models in non-small cell lung cancer (NSCLC) based on low-resolution snapshots, using data from the Veterans Affairs Precision Oncology Data Repository. We compared strategies without transfer learning, with transfer learning from general domain images, and with transfer learning from publicly available high-resolution histopathology scans. We found transfer learning from high-resolution scans achieved significantly better performance than other strategies. Our contribution provides a foundation for future development of prognostic models in NSCLC that incorporate data from low-resolution pathology slide snapshots alongside known clinical predictors.

Stroke is the leading cause of death and disability worldwide. Novel and effective therapies for ischemic stroke are urgently needed. Here, we report that melatonin receptor 1A (MT1) agonist ramelteon is a neuroprotective drug candidate as demonstrated by comprehensive experimental models of ischemic stroke, including a middle cerebral artery occlusion (MCAO) mouse model of cerebral ischemia in vivo, organotypic hippocampal slice cultures ex vivo, and cultured neurons in vitro; the neuroprotective effects of ramelteon are diminished in MT1-knockout (KO) mice and MT1-KO cultured neurons. For the first time, we report that the MT1 receptor is significantly depleted in the brain of MCAO mice, and ramelteon treatment significantly recovers the brain MT1 losses in MCAO mice, which is further explained by the Connectivity Map L1000 bioinformatic analysis that shows gene-expression signatures of MCAO mice are negatively connected to melatonin receptor agonist like Ramelteon. We demonstrate that ramelteon improves the cerebral blood flow signals in ischemic stroke that is potentially mediated, at least, partly by mechanisms of activating endothelial nitric oxide synthase. Our results also show that the neuroprotection of ramelteon counteracts reactive oxygen species-induced oxidative stress and activates the nuclear factor erythroid 2-related factor 2/heme oxygenase-1 pathway. Ramelteon inhibits the mitochondrial and autophagic death pathways in MCAO mice and cultured neurons, consistent with gene set enrichment analysis from a bioinformatics perspective angle. Our data suggest that Ramelteon is a potential neuroprotective drug candidate, and MT1 is the neuroprotective target for ischemic stroke, which provides new insights into stroke therapy. MT1-KO mice and cultured neurons may provide animal and cellular models of accelerated ischemic damage and neuronal cell death., (© 2023 John Wiley & Sons A/S. Published by John Wiley & Sons Ltd.)

In order to address a long standing challenge for internal medicine physicians we developed artificial intelligence (AI) models to identify patients at risk of increased mortality. After querying 2,425 records of patients transferred from non-intensive care units to intensive care units from the Veteran Affairs Corporate Data Warehouse (CDW), we created two datasets. The former used 22 independent variables that included "Length of Hospital Stay" and "Days to Intensive Care Transfer," and the latter lacked these two variables. Since these two variables are unknown at the time of admission, the second set is more clinically relevant. We trained 16 machine learning models using both datasets. The best-performing models were fine-tuned and evaluated. The LightGBM model achieved the best results for both datasets. The model trained with 22 variables achieved a Receiver Operating Characteristics Curve-Area Under the Curve (ROC-AUC) of 0.89 and an accuracy of 0.72, with a sensitivity of 0.97 and a specificity of 0.68. The model trained with 20 variables achieved a ROC-AUC of 0.86 and an accuracy of 0.71, with a sensitivity of 0.94 and a specificity of 0.67. The top features for the former model included "Total length of Stay," "Admit to ICU Transfer Days," and "Lymphocyte Next Lab Value." For the latter model, the top features included "Lymphocyte First Lab Value," "Hemoglobin First Lab Value," and "Hemoglobin Next Lab Value." Our clinically relevant predictive mortality model can assist providers in optimizing resource utilization when managing large caseloads, particularly during shift changes., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2023 Lezama, Alterovitz, Jakey, Kraus, Kim and Borkowski.)

Objective: Enabling clinicians to formulate individualized clinical management strategies from the sea of molecular data remains a fundamentally important but daunting task. Here, we describe efforts towards a new paradigm in genomics-electronic health record (HER) integration, using a standardized suite of FHIR Genomics Operations that encapsulates the complexity of molecular data so that precision medicine solution developers can focus on building applications., Materials and Methods: FHIR Genomics Operations essentially "wrap" a genomics data repository, presenting a uniform interface to applications. More importantly, operations encapsulate the complexity of data within a repository and normalize redundant data representations-particularly relevant in genomics, where a tremendous amount of raw data exists in often-complex non-FHIR formats., Results: Fifteen FHIR Genomics Operations have been developed, designed to support a wide range of clinical scenarios, such as variant discovery; clinical trial matching; hereditary condition and pharmacogenomic screening; and variant reanalysis. Operations are being matured through the HL7 balloting process, connectathons, pilots, and the HL7 FHIR Accelerator program., Discussion: Next-generation sequencing can identify thousands to millions of variants, whose clinical significance can change over time as our knowledge evolves. To manage such a large volume of dynamic and complex data, new models of genomics-EHR integration are needed. Qualitative observations to date suggest that freeing application developers from the need to understand the nuances of genomic data, and instead base applications on standardized APIs can not only accelerate integration but also dramatically expand the applications of Omic data in driving precision care at scale for all., (© The Author(s) 2022. Published by Oxford University Press on behalf of the American Medical Informatics Association.)

Do medical facilities also help advance improvements in socio-economic outcomes? We focus on Veterans, a vulnerable group over the COVID-19 pandemic who have access to a comprehensive healthcare network, and the receipt of funds from the Paycheck Protection Program (PPP) between April and June as a source of variation. First, we find that Veterans received 3.5% more loans and 6.8% larger loans than their counterparts (p < 0.01), controlling for a wide array of zipcode characteristics. Second, we develop models to predict the number of PPP loans awarded to Veterans, finding that the inclusion of local VA medical center characteristics adds almost as much explanatory power as the industry and occupational composition in an area and even more than the education, race, and age distribution combined. Our results suggest that VA medical centers can play an important role in helping Veterans thrive even beyond addressing their direct medical needs., Competing Interests: Gil and Christos completed this research through their roles on the National Artificial Intelligence Institute at the Department of Veterans Affairs. J. D. completed this research during his undergraduate studies at Stanford University., (Copyright: This is an open access article, free of all copyright, and may be freely reproduced, distributed, transmitted, modified, built upon, or otherwise used by anyone for any lawful purpose. The work is made available under the Creative Commons CC0 public domain dedication.)

Background: COVID-19 has been observed to be associated with venous and arterial thrombosis. The inflammatory disease prolongs hospitalization, and preexisting comorbidities can intensity the thrombotic burden in patients with COVID-19. However, venous thromboembolism, arterial thrombosis, and other vascular complications may go unnoticed in critical care settings. Early risk stratification is paramount in the COVID-19 patient population for proactive monitoring of thrombotic complications., Objective: The aim of this exploratory research was to characterize thrombotic complication risk factors associated with COVID-19 using information from electronic health record (EHR) and insurance claims databases. The goal is to develop an approach for analysis using real-world data evidence that can be generalized to characterize thrombotic complications and additional conditions in other clinical settings as well, such as pneumonia or acute respiratory distress syndrome in COVID-19 patients or in the intensive care unit., Methods: We extracted deidentified patient data from the insurance claims database IBM MarketScan, and formulated hypotheses on thrombotic complications in patients with COVID-19 with respect to patient demographic and clinical factors using logistic regression. The hypotheses were then verified with analysis of deidentified patient data from the Research Patient Data Registry (RPDR) Mass General Brigham (MGB) patient EHR database. Data were analyzed according to odds ratios, 95% CIs, and P values., Results: The analysis identified significant predictors (P<.001) for thrombotic complications in 184,831 COVID-19 patients out of the millions of records from IBM MarketScan and the MGB RPDR. With respect to age groups, patients 60 years and older had higher odds (4.866 in MarketScan and 6.357 in RPDR) to have thrombotic complications than those under 60 years old. In terms of gender, men were more likely (odds ratio of 1.245 in MarketScan and 1.693 in RPDR) to have thrombotic complications than women. Among the preexisting comorbidities, patients with heart disease, cerebrovascular diseases, hypertension, and personal history of thrombosis all had significantly higher odds of developing a thrombotic complication. Cancer and obesity were also associated with odds>1. The results from RPDR validated the IBM MarketScan findings, as they were largely consistent and afford mutual enrichment., Conclusions: The analysis approach adopted in this study can work across heterogeneous databases from diverse organizations and thus facilitates collaboration. Searching through millions of patient records, the analysis helped to identify factors influencing a phenotype. Use of thrombotic complications in COVID-19 patients represents only a case study; however, the same design can be used across other disease areas by extracting corresponding disease-specific patient data from available databases., (©Bedda Rosario, Andrew Zhang, Mehool Patel, Amol Rajmane, Ning Xie, Dilhan Weeraratne, Gil Alterovitz. Originally published in the Journal of Medical Internet Research (https://www.jmir.org), 21.10.2022.)

Predicting clinical risk is an important part of healthcare and can inform decisions about treatments, preventive interventions, and provision of extra services. The field of predictive models has been revolutionized over the past two decades by electronic health record data; the ability to link such data with other demographic, socioeconomic, and geographic information; the availability of high-capacity computing; and new machine learning and artificial intelligence methods for extracting insights from complex datasets. These advances have produced a new generation of computerized predictive models, but debate continues about their development, reporting, validation, evaluation, and implementation. In this review we reflect on more than 10 years of experience at the Veterans Health Administration, the largest integrated healthcare system in the United States, in developing, testing, and implementing such models at scale. We report lessons from the implementation of national risk prediction models and suggest an agenda for research.

Objective: The Genomic Medicine Working Group of the National Advisory Council for Human Genome Research virtually hosted its 13th genomic medicine meeting titled "Developing a Clinical Genomic Informatics Research Agenda". The meeting's goal was to articulate a research strategy to develop Genomics-based Clinical Informatics Tools and Resources (GCIT) to improve the detection, treatment, and reporting of genetic disorders in clinical settings., Materials and Methods: Experts from government agencies, the private sector, and academia in genomic medicine and clinical informatics were invited to address the meeting's goals. Invitees were also asked to complete a survey to assess important considerations needed to develop a genomic-based clinical informatics research strategy., Results: Outcomes from the meeting included identifying short-term research needs, such as designing and implementing standards-based interfaces between laboratory information systems and electronic health records, as well as long-term projects, such as identifying and addressing barriers related to the establishment and implementation of genomic data exchange systems that, in turn, the research community could help address., Discussion: Discussions centered on identifying gaps and barriers that impede the use of GCIT in genomic medicine. Emergent themes from the meeting included developing an implementation science framework, defining a value proposition for all stakeholders, fostering engagement with patients and partners to develop applications under patient control, promoting the use of relevant clinical workflows in research, and lowering related barriers to regulatory processes. Another key theme was recognizing pervasive biases in data and information systems, algorithms, access, value, and knowledge repositories and identifying ways to resolve them., (Published by Oxford University Press on behalf of the American Medical Informatics Association 2022. This work is written by US Government employees and is in the public domain in the US.)

Background: COVID-19 caused by SARS-CoV-2 has infected 219 million individuals at the time of writing of this paper. A large volume of research findings from observational studies about disease interactions with COVID-19 is being produced almost daily, making it difficult for physicians to keep track of the latest information on COVID-19's effect on patients with certain pre-existing conditions., Objective: In this paper, we describe the creation of a clinical decision support tool, the SMART COVID Navigator, a web application to assist clinicians in treating patients with COVID-19. Our application allows clinicians to access a patient's electronic health records and identify disease interactions from a large set of observational research studies that affect the severity and fatality due to COVID-19., Methods: The SMART COVID Navigator takes a 2-pronged approach to clinical decision support. The first part is a connection to electronic health record servers, allowing the application to access a patient's medical conditions. The second is accessing data sets with information from various observational studies to determine the latest research findings about COVID-19 outcomes for patients with certain medical conditions. By connecting these 2 data sources, users can see how a patient's medical history will affect their COVID-19 outcomes., Results: The SMART COVID Navigator aggregates patient health information from multiple Fast Healthcare Interoperability Resources-enabled electronic health record systems. This allows physicians to see a comprehensive view of patient health records. The application accesses 2 data sets of over 1100 research studies to provide information on the fatality and severity of COVID-19 for several pre-existing conditions. We also analyzed the results of the collected studies to determine which medical conditions result in an increased chance of severity and fatality of COVID-19 progression. We found that certain conditions result in a higher likelihood of severity and fatality probabilities. We also analyze various cancer tissues and find that the probabilities for fatality vary greatly depending on the tissue being examined., Conclusions: The SMART COVID Navigator allows physicians to predict the fatality and severity of COVID-19 progression given a particular patient's medical conditions. This can allow physicians to determine how aggressively to treat patients infected with COVID-19 and to prioritize different patients for treatment considering their prior medical conditions., (©Varun Suraj, Catherine Del Vecchio Fitz, Laura B Kleiman, Suresh K Bhavnani, Chinmay Jani, Surbhi Shah, Rana R McKay, Jeremy Warner, Gil Alterovitz. Originally published in the Journal of Medical Internet Research (https://www.jmir.org), 18.02.2022.)

Tuberculosis drug resistance continues to threaten global health but the underline molecular mechanisms are not clear. Ethambutol (EMB), one of the well-known first - line drugs in tuberculosis treatment is, unfortunately, not free from drug resistance problems. Genomic studies have shown that some genetic mutations in Mycobacterium tuberculosis (Mtb) EmbR, and EmbC/A/B genes cause EMB resistance. EmbR-PknH pair controls embC/A/B operon, which encodes EmbC/A/B genes, and EMB interacts with EmbA/B proteins. However, the EmbR binding site on PknH was unknown. We conducted molecular simulation on the EmbR- peptides binding structures and discovered phosphorylated PknH 273-280 (N'-HEALS P DPD-C') makes β strand with the EmbR FHA domain, as β-MoRF (MoRF; molecular recognition feature) does at its binding site. Hydrogen bond number analysis also supported the peptides' β-MoRF forming activity at the EmbR FHA domain. Also, we discovered that previously known phosphorylation residues might have their chronological order according to the phosphorylation status. The discovery validated that Mtb PknH 273-280 (N'-HEALSDPD-C') has reliable EmbR binding affinity. This approach is revolutionary in the computer-aided drug discovery field, because it is the first trial to discover the protein-protein interaction site, and find binding partner in nature from this site., Competing Interests: This manuscript has not been published and is not under consideration for publication elsewhere. We have no conflicts of interest to disclose, all authors read the manuscript and agreed to submit to Signal Transduction and Targeted Therapy., (© 2021 The Authors.)

Objective: In many cases, genetic testing labs provide their test reports as portable document format files or scanned images, which limits the availability of the contained information to advanced informatics solutions, such as automated clinical decision support systems. One of the promising standards that aims to address this limitation is Health Level Seven International (HL7) Fast Healthcare Interoperability Resources Clinical Genomics Implementation Guide-Release 1 (FHIR CG IG STU1). This study aims to identify various data content of some genetic lab test reports and map them to FHIR CG IG specification to assess its coverage and to provide some suggestions for standard development and implementation., Materials and Methods: We analyzed sample reports of 4 genetic tests and relevant professional reporting guidelines to identify their key data elements (KDEs) that were then mapped to FHIR CG IG., Results: We identified 36 common KDEs among the analyzed genetic test reports, in addition to other unique KDEs for each genetic test. Relevant suggestions were made to guide the standard implementation and development., Discussion and Conclusion: The FHIR CG IG covers the majority of the identified KDEs. However, we suggested some FHIR extensions that might better represent some KDEs. These extensions may be relevant to FHIR implementations or future FHIR updates.The FHIR CG IG is an excellent step toward the interoperability of genetic lab test reports. However, it is a work-in-progress that needs informative and continuous input from the clinical genetics' community, specifically professional organizations, systems implementers, and genetic knowledgebase providers., (© The Author(s) 2021. Published by Oxford University Press on behalf of the American Medical Informatics Association. All rights reserved. For permissions, please email: journals.permissions@oup.com.)

Maximizing the personal, public, research, and clinical value of genomic information will require the reliable exchange of genetic variation data. We report here the Variation Representation Specification (VRS, pronounced "verse"), an extensible framework for the computable representation of variation that complements contemporary human-readable and flat file standards for genomic variation representation. VRS provides semantically precise representations of variation and leverages this design to enable federated identification of biomolecular variation with globally consistent and unique computed identifiers. The VRS framework includes a terminology and information model, machine-readable schema, data sharing conventions, and a reference implementation, each of which is intended to be broadly useful and freely available for community use. VRS was developed by a partnership among national information resource providers, public initiatives, and diagnostic testing laboratories under the auspices of the Global Alliance for Genomics and Health (GA4GH)., Competing Interests: DECLARATION OF INTERESTS H.L.R. is a member of the advisory board for Cell Genomics.

The Global Alliance for Genomics and Health (GA4GH) aims to accelerate biomedical advances by enabling the responsible sharing of clinical and genomic data through both harmonized data aggregation and federated approaches. The decreasing cost of genomic sequencing (along with other genome-wide molecular assays) and increasing evidence of its clinical utility will soon drive the generation of sequence data from tens of millions of humans, with increasing levels of diversity. In this perspective, we present the GA4GH strategies for addressing the major challenges of this data revolution. We describe the GA4GH organization, which is fueled by the development efforts of eight Work Streams and informed by the needs of 24 Driver Projects and other key stakeholders. We present the GA4GH suite of secure, interoperable technical standards and policy frameworks and review the current status of standards, their relevance to key domains of research and clinical care, and future plans of GA4GH. Broad international participation in building, adopting, and deploying GA4GH standards and frameworks will catalyze an unprecedented effort in data sharing that will be critical to advancing genomic medicine and ensuring that all populations can access its benefits.

Candida albicans is a life-threatening, opportunistic fungal pathogen with a high mortality rate, especially within the immunocompromised populations. Multidrug resistance combined with limited antifungal drugs even worsens the situation. Given the facts that the current drug discovery strategies fail to deliver sufficient antifungals for the emerging multidrug resistance, we urgently need to develop novel approaches. By systematically investigating what caused the different antifungal activity of rapamycin in RPMI 1640 and YPD, we discovered that peptide-like compounds can generate a hyper-synergistic antifungal effect with rapamycin on both azole-resistant and sensitive clinical C. albicans isolates. The minimum inhibitory concentration (MIC) of rapamycin reaches as low as 2.14 nM (2 -9 μg/mL), distinguishing this drug combination as a hyper-synergism by having a fractional inhibitory concentration (FIC) index ≤ 0.05 from the traditional defined synergism with an FIC index < 0.5. Further studies reveal that this hyper-synergism orthogonally targets the protein Tor1 and affects the TOR signaling pathway in C. albicans , very likely without crosstalk to the stress response, Ras/cAMP/PKA, or calcineurin signaling pathways. These results lead to a novel strategy of controlling drug resistant C. albicans infection in the immunocompromised populations. Instead of prophylactically administering other antifungals with undesirable side-effects for extended durations, we now only need to coadminister some nontoxic peptide additives. The novel antifungal strategy approached in this study not only provides a new therapeutic method to control fungal infections in rapamycin-taking immunocompromised patients but also mitigates the immunosuppressive side-effects of rapamycin, repurposing rapamycin as an antifungal agent with wide applications.

Clinical use of antimicrobials faces great challenges from the emergence of multidrug-resistant pathogens. The overexpression of drug efflux pumps is one of the major contributors to multidrug resistance (MDR). Reversing the function of drug efflux pumps is a promising approach to overcome MDR. In the life-threatening fungal pathogen Candida albicans, the major facilitator superfamily (MFS) transporter Mdr1p can excrete many structurally unrelated antifungals, leading to MDR. Here we report a counterintuitive case of reversing MDR in C. albicans by using a natural product berberine to hijack the overexpressed Mdr1p for its own importation. Moreover, we illustrate that the imported berberine accumulates in mitochondria and compromises the mitochondrial function by impairing mitochondrial membrane potential and mitochondrial Complex I. This results in the selective elimination of Mdr1p overexpressed C. albicans cells. Furthermore, we show that berberine treatment can prolong the mean survival time of mice with blood-borne dissemination of Mdr1p overexpressed multidrug-resistant candidiasis. This study provides a potential direction of novel anti-MDR drug discovery by screening for multidrug efflux pump converters., Competing Interests: Conflict of interest The authors declare that they have no conflict of interest., (Copyright © 2020 Science China Press. Published by Elsevier B.V. All rights reserved.)