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Introduction: Influenza virus infections are a major global health problem. Influenza can result in mild/moderate disease or progress to more severe disease, leading to high morbidity and mortality. Severity is thought to be primarily driven by immunopathology, but predicting which individuals are at a higher risk of being hospitalized warrants investigation into host genetics and the molecular signatures of the host response during influenza infections., Methods: Here, we performed transcriptome and genotype analysis in healthy controls and patients exhibiting mild/moderate or severe influenza (ICU patients). A unique aspect of our study was the genotyping of all participants, which allowed us to assign ethnicities based on genetic variation and assess whether the variation was correlated with expression levels., Results: We identified 169 differentially expressed genes and related molecular pathways between patients in the ICU and those who were not in the ICU. The transcriptome/genotype association analysis identified 871 genes associated to a genetic variant and 39 genes distinct between African-Americans and Caucasians. We also investigated the effects of age and sex and found only a few discernible gene effects in our cohort., Discussion: Together, our results highlight select risk factors that may contribute to an increased risk of ICU admission for influenza-infected patients. This should help to develop better diagnostic tools based on molecular signatures, in addition to a better understanding of the biological processes in the host response to influenza., Competing Interests: During the conduct of this study, ET and CW were co-founders of Predigen, Inc. and held equity in Biomeme, Inc. ET is currently employed by and holds equity in Danaher Corp. ET and CW have patents pending or granted for Methods to Diagnose and Treat Acute Respiratory Infections; Gene Expression Signatures Useful to Predict or Diagnose Sepsis and Methods of Using the Same; Methods for Characterizing Infections and Methods for Developing Tests for the Same; and Systems and methods for determining status, type, severity, and/or risk of infection. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be constructed as a potential conflict of interest., (Copyright © 2024 Schughart, Smith, Tsalik, Threlkeld, Sellers, Fischer, Schreiber, Lücke, Cornberg, Debarry, Woods, McClain and Heise.)

Background: Pneumonia due to typical bacterial, atypical bacterial and viral pathogens can be difficult to clinically differentiate. Host response-based diagnostics are emerging as a complementary diagnostic strategy to pathogen detection., Methods: We used murine models of typical bacterial, atypical bacterial and viral pneumonia to develop diagnostic signatures and understand the host's response to these types of infections. Mice were intranasally inoculated with Streptococcus pneumoniae , Mycoplasma pneumoniae , influenza or saline as a control. Peripheral blood gene expression analysis was performed at multiple time points. Differentially expressed genes were used to perform gene set enrichment analysis and generate diagnostic signatures. These murine-derived signatures were externally validated in silico using human gene expression data. The response to S. pneumoniae was the most rapid and robust., Results: Mice infected with M. pneumoniae had a delayed response more similar to influenza-infected animals. Diagnostic signatures for the three types of infection had 0.94-1.00 area under the receiver operator curve (auROC). Validation in five human gene expression datasets revealed auROC of 0.82-0.96., Discussion: This study identified discrete host responses to typical bacterial, atypical bacterial and viral aetiologies of pneumonia in mice. These signatures validated well in humans, highlighting the conserved nature of the host response to these pathogen classes., Competing Interests: Competing interests: None declared., (© Author(s) (or their employer(s)) 2024. Re-use permitted under CC BY-NC. No commercial re-use. See rights and permissions. Published by BMJ.)

Background: Plasma microbial cell-free DNA (mcfDNA) sequencing can establish the etiology of multiple infectious syndromes by identifying microbial DNA in plasma. However, data are needed to define the clinical scenarios where this tool offers the highest clinical benefit., Methods: We conducted a prospective multicenter observational study that evaluated the impact of plasma mcfDNA sequencing compared with usual care testing among adults with hematologic malignancies. This is a secondary analysis of an expanded cohort that evaluated the clinical utility of plasma mcfDNA sequencing across prespecified and adjudicated outcomes. We examined the percentage of participants for whom plasma mcfDNA sequencing identified a probable cause of pneumonia or clinically relevant nonpneumonia infection. We then assessed potential changes in antimicrobial therapy based on plasma mcfDNA sequencing results and the potential for early mcfDNA testing to avoid bronchoscopy and its associated adverse events., Results: Of 223 participants, at least 1 microbial detection by plasma mcfDNA sequencing was adjudicated as a probable cause of pneumonia in 57 (25.6%) and a clinically relevant nonpneumonia infection in 88 (39.5%). A probable cause of pneumonia was exclusively identified by plasma mcfDNA sequencing in 23 (10.3%) participants. Antimicrobial therapy would have changed for 41 (18.4%) participants had plasma mcfDNA results been available in real time. Among the 57 participants with a probable cause of pneumonia identified by plasma mcfDNA sequencing, bronchoscopy identified no additional probable cause of pneumonia in 52 (91.2%)., Conclusions: Plasma mcfDNA sequencing could improve management of both pneumonia and other concurrent infections in immunocompromised patients with suspected pneumonia., Competing Interests: Potential conflicts of interest. Deng B. Madut reports receiving consulting fees from Karius. Roy F. Chemaly reports being a scientific advisor or consultant for ADMA Biologics, Pulmotec, Janssen, Merck/MSD, Partner Therapeutics, Takeda, Shinoigi, Genentech, Astellas, Adagio Therapeutics, Oxford Immunotec, Qiagen, Karius, Moderna, and Ansun Pharmaceuticals and received research grants paid to his institution from NIH/NCI, Merck/MSD, Karius, AiCuris, Ansun Pharmaceuticals, Takeda, Genentech, Oxford Immunotec, Freestyle, and Eurofins-Viracor. Sanjeet Dadwal reports research grants paid to his institution from Karius, Merck, Gilead, Ansun Biopharma, Allovir. He has served on advisory boards for Merck, Takeda, Allovir, and Aseptiscope, Inc. He is on speakers’ bureaus of Merck, Astellas, and Takeda and holds stock options in Aseptiscope, Inc. Joshua A. Hill reports research grants paid to his institution from Karius and consulting for Karius. He reports no other relevant conflicts of interest. Yeon Joo Lee has served as an investigator for Astellas, Karius, AiCuris, and Scynexis and has received research grant support from Merck & Co. Inc. Ghady Haidar is a recipient of research grants from Allovir, Karius, and AstraZeneca and also serves on the scientific advisory boards of Karius and AstraZeneca. Alfred Luk reports no relevant conflicts of interest. Alexander Drelick reports no relevant conflicts of interest. Peter V. Chin-Hong reports no relevant conflicts of interest. Esther Benamu reports no relevant conflicts of interest. Fareed Khawaja reports an honorarium from Medscape and research support from Eurofins-Viracor. Deepa Nanayakkara reports no relevant conflicts of interest. Genovefa A. Papanicolaou reports research support from MSD and Takeda/Shire and consulting and other fees from AlloVir, Amplyx, SLC Behring, Cidara, MSD, Takeda, and Octapharma. Catherine Butkus Small reports research grant support from NIH, GlaxoSmithKline, ViiV, Abbott, Merck, Gilead, Chimerix, Shire/Takeda, Schering-Plough, Ablynx, Janssen, Ansun Biopharma, and Karyopharm Therapeutics. Monica Fung reports research grant support from NIH. Michelle A. Barron reports no relevant conflicts of interest. Thomas Davis reports no relevant conflicts of interest. Micah T. McClain reports being a consultant for UpToDate and a co-inventor of patents on Methods to Detect and Treat Acute Respiratory Infections. Eileen K. Maziarz reports no relevant conflicts of interest. Armando D. Bedoya reports no relevant conflicts of interest. Daniel L. Gilstrap reports no relevant conflicts of interest. Jamie L. Todd reports advisory board participation for Sanofi, Natera, Theravance, and Altavant Sciences (now Enzyvant Therapeutics), in addition to grants to the institution from Boehringer-Ingelheim, AstraZeneca, and CareDx. Christina E. Barkauskas reports no relevant conflicts of interest. Madeleine R. Heldman reports receiving consulting fees from Karius and Medacorp. Robert Bigelow reports stock holdings in Merck, Johnson & Johnson, COVIDien, Pfizer, Sanofi, McKesson, and Viatris and consulting fees from Elixir. Jeffrey D. Leimberger reports no relevant conflicts of interest. Ephraim L. Tsalik reports personal fees from Biomeme, stock options in Danaher, and being an employee of Danaher Corp. Olivia Wolf reports no relevant conflicts of interest. Mona Mughar reports being an employee of Karius Inc., Redwood City, California, and may own stock options. Constance Lau reports being an employee of Karius Inc., Redwood City, California, and may own stock options. Nicholas Noll reports being an employee of Karius Inc., Redwood City, California, and may own stock options. Desiree Hollemon reports being a former employee of Karius Inc., Redwood City, California. Radha Duttagupta reports being a former employee of Karius Inc., Redwood City, California. Daniel S. Lupu reports being an employee of Karius Inc., Redwood City, California, and may own stock options. Sivan Bercovici reports being an employee of Karius Inc., Redwood City, California, and may own stock options. Bradley A. Perkins reports being an employee of Karius Inc., Redwood City, California, and may own stock options. Timothy A. Blauwkamp reports being an employee of Karius Inc., Redwood City, California, and may own stock options. Vance G. Fowler Jr. reports personal fees from Novartis, Debiopharm, Genentech, Achaogen, Affinium, Medicines Co., MedImmune, Bayer, Basilea, Affinergy, Janssen, Contrafect, Regeneron, Destiny, Amphliphi Biosciences, Integrated Biotherapeutics, C3J, Armata, Valanbio, Akagera, Aridis, and Roche; grants from NIH, MedImmune, Allergan, Pfizer, Advanced Liquid Logics, Theravance, Novartis, Merck, Medical Biosurfaces, Locus, Affinergy, Contrafect, Karius, Genentech, Regeneron, Deep Blue, Basilea, and Janssen; royalties from UpToDate; stock options from Valanbio and ArcBio; honoraria from the Infectious Diseases Society of America for his service as Associate Editor of Clinical Infectious Diseases; and a pending patent on sepsis diagnostics. Thomas L. Holland reports being an advisor and consultant to Aridis, Basilea Pharmaceutica, Karius, and Lysovant and royalties from UpToDate. Stephen P. Bergin reports being a consultant to C3J Therapeutics., (© The Author(s) 2024. Published by Oxford University Press on behalf of Infectious Diseases Society of America.)

Background: Sepsis from infection is a global health priority and clinical trials have failed to deliver effective therapeutic interventions. To address complicating heterogeneity in sepsis pathobiology, and improve outcomes, promising precision medicine approaches are helping identify disease endotypes, however, they require a more complete definition of sepsis subgroups., Methods: Here, we use RNA sequencing from peripheral blood to interrogate the host response to sepsis from participants in a global observational study carried out in West Africa, Southeast Asia, and North America (N = 494)., Results: We identify four sepsis subtypes differentiated by 28-day mortality. A low mortality immunocompetent group is specified by features that describe the adaptive immune system. In contrast, the three high mortality groups show elevated clinical severity consistent with multiple organ dysfunction. The immunosuppressed group members show signs of a dysfunctional immune response, the acute-inflammation group is set apart by molecular features of the innate immune response, while the immunometabolic group is characterized by metabolic pathways such as heme biosynthesis., Conclusions: Our analysis reveals details of molecular endotypes in sepsis that support immunotherapeutic interventions and identifies biomarkers that predict outcomes in these groups., (© 2024. The Author(s).)

Background: Pneumonia is a common cause of morbidity and mortality, yet a causative pathogen is identified in a minority of cases. Plasma microbial cell-free DNA sequencing may improve diagnostic yield in immunocompromised patients with pneumonia., Methods: In this prospective, multicenter, observational study of immunocompromised adults undergoing bronchoscopy to establish a pneumonia etiology, plasma microbial cell-free DNA sequencing was compared to standardized usual care testing. Pneumonia etiology was adjudicated by a blinded independent committee. The primary outcome, additive diagnostic value, was assessed in the Per Protocol population (patients with complete testing results and no major protocol deviations) and defined as the percent of patients with an etiology of pneumonia exclusively identified by plasma microbial cell-free DNA sequencing. Clinical additive diagnostic value was assessed in the Per Protocol subgroup with negative usual care testing., Results: Of 257 patients, 173 met Per Protocol criteria. A pneumonia etiology was identified by usual care in 52/173 (30.1%), plasma microbial cell-free DNA sequencing in 49/173 (28.3%) and the combination of both in 73/173 (42.2%) patients. Plasma microbial cell-free DNA sequencing exclusively identified an etiology of pneumonia in 21/173 patients (additive diagnostic value 12.1%, 95% confidence interval [CI], 7.7% to 18.0%, P < .001). In the Per Protocol subgroup with negative usual care testing, plasma microbial cell-free DNA sequencing identified a pneumonia etiology in 21/121 patients (clinical additive diagnostic value 17.4%, 95% CI, 11.1% to 25.3%)., Conclusions: Non-invasive plasma microbial cell-free DNA sequencing significantly increased diagnostic yield in immunocompromised patients with pneumonia undergoing bronchoscopy and extensive microbiologic and molecular testing., Clinical Trials Registration: NCT04047719., Competing Interests: Potential conflicts of interest. S. P. B. reports being a consultant to C3J Therapeutics. R. F. C. reports being a scientific advisor or consultant for ADMA Biologics, Pulmotec, Janssen, Merck/MSD, Partner Therapeutics, Takeda, Shinoigi, Genentech, Astellas, Adagio Therapeutics, Oxford Immunotec, Qiagen, Karius, and Ansun Pharmaceuticals and received research grants paid to his institution from National Institutes of Health (NIH)/National Cancer Institute (NCI), Merck/Merck, Sharp & Dohme (MSD), Karius, AiCuris, Ansun Pharmaceuticals, Takeda, Genentech, Oxford Immunotec, Freestyle, and Eurofins-Viracor, and reports payment or honoraria as a speaker for Qiagen and Viracor-Eurofins. S. D. reports research grants paid to his institution from Karius, Merck, Gilead, Ansun Biopharma, Allovir, Geovax. He has served on advisory boards of Merck, Takeda, Allovir, and Aseptiscope, Inc. He is on speaker's bureau of Merck, Astellas and Takeda, and reports payment or honoraria from Viracor for educational event at ID week 2022 and from MJH for cytomegalovirus (CMV) symposium at American Society of Hematology (ASH) 2022. He has stock options in Aseptiscope, Inc, Matinas Biopharma, and Cidara Therapeutics. He reports consulting fees from Merck and Allovir; support for attending meetings and/or travel from American Society of Transplantation and Cellular Therapy—Tandem 2022 and 2023 (registration credit), ID week 2022—Infectious Diseases Society of America (IDSA) (partial support—airfare), and Immunocompromised Host Society (ICHS) 2022 (air fare and hotel expenses); Patent # 9416395, Assigned to City of Hope. Inventors—Markus Kalkum, Karine Bagramyan, Diana Diaz-Arevalo, James I. Ito, Sanjeet S. Dadwal (assigned to City of Hope) and US Provisional Patent Application no. 63/067,855: Inventors—Markus Kalkum, Daniel Roeth, Sanjeet S. Dadwal (assigned to City of Hope); a role as Chair of Transplant Infectious Disease Special Interest Group of American Society of Transplant and Cellular Therapy (ASTCT). J. A. H. reports research grants paid to his institution from Karius and consulting for Karius and support for travel to advisory board meeting from Karius. Y. J. L. has served as an investigator for Astellas, Karius, AiCuris, and Scynexis and has received research grant support from Merck & Co Inc. G. H. is a recipient of research grants from Allovir, Karius, and AstraZeneca and also serves on the scientific advisory boards of and receives consulting fees from Karius and AstraZeneca, and reports grants or contracts from NIH; payment or honoraria for lectures, presentations, speakers bureaus, manuscript writing or educational events from MDOutlook, PeerView Medical Education, International AIDS Society. A. L. reports consulting fees from Karius. F. K. reports honorarium from Medscape and research support from Eurofins-Viracor. G. A. P. reports research support from MSD and Takeda/Shire and consulting and other fees from AlloVir, Amplyx, SLC Behring, Cidara, MSD, Takeda and Octapharma. C. B. S. reports research grant support from NIH, GlaxoSmithKline, ViiV, Abbott, Merck, Gilead, Chimerix, Shire/Takeda, Schering-Plough, Ablynx, Janssen, Ansun Biopharma, and Karyopharm Therapeutics; participation on Janssen Respiratory Viruses Advisory Board and Merck Scientific Input Engagement (SIE) Panel for Molnupirivir. M. F. reports research grant support from NIH, and research funding (UCSF PI Fung) from Scynexis and Vatic, Ltd. M. T. M. reports being a consultant for UpToDate and Karius and a co-inventor of patents on Methods to Detect and Treat Acute Respiratory Infections. E. K. M. reports consulting fees from Karius Inc as a Clinical Events Committee member/reviewer—received payments as clinical adjudicator for another Karius project. D. B. M. reports consulting fees from Karius Inc.. A. D. B . reports payment or honoraria from Novartis for lecture on Sepsis Watch, AI program developed at Duke. J. L. T. reports advisory board participation for Sanofi, Natera, Theravance and Altavant Sciences (now Enzyvant Therapeutics) in addition to grants to the institution from Boehringer-Ingelheim, AstraZeneca, Sanofi, NIH, Cystic Fibrosis Foundation and CareDx; and consulting fees from Sanofi. C. E. B. reports grants or contracts from American Lung Association and NIH (National Heart, Lung, and Blood Institute [NHLBI] and National Institute of Allergy and Infectious Diseases [NIAID] funded projects—not related to this project); participation as member of a Data and Safety Monitoring Board (DSMB) for VASSPR—an ICU-based trial with no relationship to this project. R. B. reports stock holdings in Merck, Johnson & Johnson, COVIDien, Pfizer, Sanofi, McKesson, and Viatris and consulting fees from Elixir. E. L. T. reports personal fees from Biomeme and Predigen, stock options in Danaher, and being an employee of Danaher Corp. O, W. reports support for attending meetings and/or travel from Karius (as Clinical Events Classification [CEC] Project Leader initiated and attended meetings with Karius for the PICKUP study); a role as CEC Project Leader coordinated CEC committee meetings within Duke Clinical Research Institute (DCRI) for the PICKUP trial. M. M. reports being an employee of Karius Inc, Redwood City, California, and may own stock options. D. H. reports being a former employee of Karius Inc, Redwood City, California, and a patent issued for related work while at Karius as employee (WO-2020106987-A1—Detection and Prediction of Infectious Disease). R. D. reports being a former employee of Karius Inc, Redwood City, California, and is owner of Karius stock options. D. S. L. reports being an employee of Karius Inc, Redwood City, California, and may own stock options. S. B. reports being an employee of Karius Inc, Redwood City, California, and may own stock options, and reports support for attending meetings and/or travel and patents planned, issued, or pending with Karius Inc. B, A. P. reports being an employee of Karius Inc, Redwood City, California, patents planned, issued or pending with Karius Inc, and may own stock options. T. A. B. reports being an employee of and having a leadership or fiduciary role with Karius Inc, Redwood City, California, and may own stock options. V. G. F. reports personal fees from Novartis, Debiopharm, Genentech, Achaogen, Affinium, Medicines Co., MedImmune, Bayer, Basilea, Affinergy, Janssen, Contrafect, Regeneron, Destiny, Amphliphi Biosciences, Integrated Biotherapeutics; C3J, Armata, Valanbio; Akagera, Aridis, Roche, grants from NIH, MedImmune, Allergan, Pfizer, Advanced Liquid Logics, Theravance, Novartis, Merck; Medical Biosurfaces; Locus; Affinergy; Contrafect; Karius; Genentech, Regeneron, Deep Blue, Basilea, Janssen; royalties from UpToDate, stock options from Valanbio and ArcBio, honoraria from IDSA for his service as Associate Editor of Clinical Infectious Diseases, and a patent sepsis diagnostics pending. T. L. H. reports being an advisor and consultant to Aridis, Basilea Pharmaceutica, Karius, and Lysovant; royalties from UpToDate; reports grants or contracts with Karius (adjudication committee) and with NIH; reports consulting fees from Pfizer and Affinivax; participation on DSMB for platform trial for SNAP Trial; and on advisory board for Basilea. All other authors report no potential conflicts. All authors have submitted the ICMJE Form for Disclosure of Potential Conflicts of Interest. Conflicts that the editors consider relevant to the content of the manuscript have been disclosed., (© The Author(s) 2023. Published by Oxford University Press on behalf of Infectious Diseases Society of America.)

Background: Index-cluster studies may help characterize the spread of communicable infections in the presymptomatic state. We describe a prospective index-cluster sampling strategy (ICSS) to detect presymptomatic respiratory viral illness and its implementation in a college population., Methods: We enrolled an annual cohort of first-year undergraduates who completed daily electronic symptom diaries to identify index cases (ICs) with respiratory illness. Investigators then selected 5-10 potentially exposed, asymptomatic close contacts (CCs) who were geographically co-located to follow for infections. Symptoms and nasopharyngeal samples were collected for 5 days. Logistic regression model-based predictions for proportions of self-reported illness were compared graphically for the whole cohort sampling group and the CC group., Results: We enrolled 1379 participants between 2009 and 2015, including 288 ICs and 882 CCs. The median number of CCs per IC was 6 (interquartile range, 3-8). Among the 882 CCs, 111 (13%) developed acute respiratory illnesses. Viral etiology testing in 246 ICs (85%) and 719 CCs (82%) identified a pathogen in 57% of ICs and 15% of CCs. Among those with detectable virus, rhinovirus was the most common (IC: 18%; CC: 6%) followed by coxsackievirus/echovirus (IC: 11%; CC: 4%). Among 106 CCs with a detected virus, only 18% had the same virus as their associated IC. Graphically, CCs did not have a higher frequency of self-reported illness relative to the whole cohort sampling group., Conclusions: Establishing clusters by geographic proximity did not enrich for cases of viral transmission, suggesting that ICSS may be a less effective strategy to detect spread of respiratory infection., Competing Interests: Potential conflicts of interest. M. T. M. reports grants from the Defense Advanced Research Projects Agency (DARPA) and the National Institutes of Health (NIH), and has a patent pending on “Methods to diagnose and treat acute respiratory infections.” T. W. B. reports grants from DARPA and NIH; reports owning equity in and serving as a consultant for Biomeme; and has a patent pending on Methods to diagnose and treat acute respiratory infections. E. L. T. reports consultancy fees and equity from Biomeme; has patents pending on Biomarkers for the molecular classification of bacterial infection and Methods to diagnose and treat acute respiratory infections; and is currently an employee of Danaher Diagnostics. C. W. W. and G. S. G. have patents pending on Molecular classification of bacterial infection and gene expression signatures useful to predict or diagnose sepsis and methods of using the same, and have patents issued on Methods to diagnose and treat acute respiratory disease and Methods of identifying infectious disease and assays for identifying infectious disease. C. W. W. reports owning equity in and consulting for Biomeme; reports grants from DARPA, NIH, Antibacterial Resistance Leadership Group, and Sanofi; and has received consultancy fees from bioMérieux, Roche, Biofire, Giner, and Biomeme. All other authors report no potential conflicts., (© The Author(s) 2024. Published by Oxford University Press on behalf of Infectious Diseases Society of America.)

Diagnostic limitations challenge management of clinically indistinguishable acute infectious illness globally. Gene expression classification models show great promise distinguishing causes of fever. We generated transcriptional data for a 294-participant (USA, Sri Lanka) discovery cohort with adjudicated viral or bacterial infections of diverse etiology or non-infectious disease mimics. We then derived and cross-validated gene expression classifiers including: 1) a single model to distinguish bacterial vs. viral (Global Fever-Bacterial/Viral [GF-B/V]) and 2) a two-model system to discriminate bacterial and viral in the context of noninfection (Global Fever-Bacterial/Viral/Non-infectious [GF-B/V/N]). We then translated to a multiplex RT-PCR assay and independent validation involved 101 participants (USA, Sri Lanka, Australia, Cambodia, Tanzania). The GF-B/V model discriminated bacterial from viral infection in the discovery cohort an area under the receiver operator curve (AUROC) of 0.93. Validation in an independent cohort demonstrated the GF-B/V model had an AUROC of 0.84 (95% CI 0.76-0.90) with overall accuracy of 81.6% (95% CI 72.7-88.5). Performance did not vary with age, demographics, or site. Host transcriptional response diagnostics distinguish bacterial and viral illness across global sites with diverse endemic pathogens., (© 2023. The Author(s).)

To elucidate host response elements that define impending decompensation during SARS-CoV-2 infection, we enrolled subjects hospitalized with COVID-19 who were matched for disease severity and comorbidities at the time of admission. We performed combined single-cell RNA sequencing (scRNA-seq) and single-cell assay for transposase-accessible chromatin using sequencing (scATAC-seq) on peripheral blood mononuclear cells (PBMCs) at admission and compared subjects who improved from their moderate disease with those who later clinically decompensated and required invasive mechanical ventilation or died. Chromatin accessibility and transcriptomic immune profiles were markedly altered between the two groups, with strong signals in CD4 + T cells, inflammatory T cells, dendritic cells, and NK cells. Multiomic signature scores at admission were tightly associated with future clinical deterioration (auROC 1.0). Epigenetic and transcriptional changes in PBMCs reveal early, broad immune dysregulation before typical clinical signs of decompensation are apparent and thus may act as biomarkers to predict future severity in COVID-19., Competing Interests: M.T.M., E.L.T., T.W.B., and C.W.W. have patents pending on Molecular Methods to Diagnose and Treat Respiratory Infections.

Research on the COVID-19 pandemic revealed a disproportionate burden of COVID-19 infection and death among underserved populations and exposed low rates of SARS-CoV-2 testing in these communities. A landmark National Institutes of Health (NIH) funding initiative, the Rapid Acceleration of Diagnostics-Underserved Populations (RADx-UP) program, was developed to address the research gap in understanding the adoption of COVID-19 testing in underserved populations. This program is the single largest investment in health disparities and community-engaged research in the history of the NIH. The RADx-UP Testing Core (TC) provides community-based investigators with essential scientific expertise and guidance on COVID-19 diagnostics. This commentary describes the first 2 years of the TC's experience, highlighting the challenges faced and insights gained to safely and effectively deploy large-scale diagnostics for community-initiated research in underserved populations during a pandemic. The success of RADx-UP shows that community-based research to increase access and uptake of testing among underserved populations can be accomplished during a pandemic with tools, resources, and multidisciplinary expertise provided by a centralized testing-specific coordinating center. We developed adaptive tools to support individual testing strategies and frameworks for these diverse studies and ensured continuous monitoring of testing strategies and use of study data. In a rapidly evolving setting of tremendous uncertainty, the TC provided essential and real-time technical expertise to support safe, effective, and adaptive testing. The lessons learned go beyond this pandemic and can serve as a framework for rapid deployment of testing in response to future crises, especially when populations are affected inequitably., Competing Interests: The authors declare a conflict of interest. Ephraim Tsalik has been a consultant for Biomeme, Inc.; has a patent pending for Methods to Diagnose and Treat Acute Respiratory Infections (US20180245154A1); and is currently employed by Danaher Corp. Michael Cohen-Wolkowiez receives support for research from the NIH [1U24-MD016258]; National Institute of Allergy and Infectious Diseases [HHSN272201500006I, 1K24-AI143971]; U.S. Food and Drug Administration [5U18-FD006298]; and industry for drug development in adults and children. Cathy A Petti receives consulting fees from Abbott Molecular and Rapid Diagnostics. Christopher Woods reports consulting fees from Arena Pharmaceuticals, BioFire, FHI Clinical, Giner, Karius, and SeLux Diagnostics. All other authors report no conflicts of interests.

Optimal management of lower respiratory tract infection relies on distinguishing infectious from noninfectious etiologies and identifying the microbiologic cause if applicable. This process is complicated by overlapping clinical symptoms and the colonizing lung microbiota. In a recent issue of the JCI, Mick, Tsitsiklis, and colleagues apply RNA-Seq to tracheal aspirates from critically ill children and demonstrate how integration of the host response with microbial identification results in a harmonious and accurate diagnostic classifier. Though promising, there are numerous barriers to realizing a combined host and pathogen diagnostic.

Background: Lower respiratory tract infections are frequently treated with antibiotics, despite a viral cause in many cases. It remains unknown whether low procalcitonin concentrations can identify patients with lower respiratory tract infection who are unlikely to benefit from antibiotics. We aimed to compare the efficacy and safety of azithromycin versus placebo to treat lower respiratory tract infections in patients with low procalcitonin., Methods: We conducted a randomised, placebo-controlled, double-blind, non-inferiority trial at five health centres in the USA. Adults aged 18 years or older with clinically suspected non-pneumonia lower respiratory tract infection and symptom duration from 24 h to 28 days were eligible for enrolment. Participants with a procalcitonin concentration of 0·25 ng/mL or less were randomly assigned (1:1), in blocks of four with stratification by site, to receive over-encapsulated oral azithromycin 250 mg or matching placebo (two capsules on day 1 followed by one capsule daily for 4 days). Participants, non-study clinical providers, investigators, and study coordinators were masked to treatment allocation. The primary outcome was efficacy of azithromycin versus placebo in terms of clinical improvement at day 5 in the intention-to-treat population. The non-inferiority margin was -12·5%. Solicited adverse events (abdominal pain, vomiting, diarrhoea, allergic reaction, or yeast infections) were recorded as a secondary outcome. This trial is registered with ClinicalTrials.gov, NCT03341273., Findings: Between Dec 8, 2017, and March 9, 2020, 691 patients were assessed for eligibility and 499 were enrolled and randomly assigned to receive azithromycin (n=249) or placebo (n=250). Clinical improvement at day 5 was observed in 148 (63%, 95% CI 54 to 71) of 238 participants with full data in the placebo group and 155 (69%, 61 to 77) of 227 participants with full data in the azithromycin group in the intention-to-treat analysis (between-group difference -6%, 95% CI -15 to 2). The 95% CI for the difference did not meet the non-inferiority margin. Solicited adverse events and the severity of solicited adverse events were not significantly different between groups at day 5, except for increased abdominal pain associated with azithromycin (47 [23%, 95% CI 18 to 29] of 204 participants) compared with placebo (35 [16%, 12 to 21] of 221; between-group difference -7% [95% CI -15 to 0]; p=0·066)., Interpretation: Placebo was not non-inferior to azithromycin in terms of clinical improvement at day 5 in adults with lower respiratory tract infection and a low procalcitonin concentration. After accounting for both the rates of clinical improvement and solicited adverse events at day 5, it is unclear whether antibiotics are indicated for patients with lower respiratory tract infection and a low procalcitonin concentration., Funding: National Institute of Allergy and Infectious Diseases, bioMérieux., Competing Interests: Declaration of interests ELT and CWW report consulting for Biomeme. ELT, CWW, and MTM have a patent pending for Methods to Diagnose and Treat Acute Respiratory Infections (US20180245154A1). Following completion of the study, ELT became an employee of Danaher Diagnostics. EBW is a principal investigator for Pfizer-funded studies of COVID-19 vaccine, a co-investigator for a vaccine study funded by Moderna, and a member of an advisory board for Vaxcyte. NGR serves as a safety consultant for the Emmes Corporation and ICON-I, and reports research funds from Pfizer, Merck, Sanofi, Quidel, and Lilly. All other authors declare no competing interests., (Copyright © 2023 Elsevier Ltd. All rights reserved.)

Lack of a gold standard can present a challenge for evaluation of diagnostic test accuracy of some infectious diseases tests, particularly when the test's accuracy potentially exceeds that of its predecessors. This approach may measure agreement with an imperfect reference, rather than correctness, because the right answer is unknown. Solutions consist of multitest comparators, including those that involve a test under evaluation if multiple new tests are being evaluated together, using latent class modeling, and clinically adjudicated reference standards. Clinically adjudicated reference standards may be considered as comparator methods when no predefined test or composite of tests is sufficiently accurate; they emulate clinical practice in that multiple data pieces are clinically assessed together., Competing Interests: Potential conflicts of interest. V. G. F. reports personal fees from Novartis, Novadigm, Durata, Debiopharm, Genentech, Achaogen, Affinium, Medicines Co., Cerexa, Tetraphase, Trius, MedImmune, Bayer, Theravance, Basilea, Affinergy, Janssen, xBiotech, Contrafect, Regeneron, Basilea, Destiny, Amphliphi Biosciences, Integrated Biotherapeutics, C3J, Armata, Valanbio, Akagera, Aridis, Roche, and Pfizer; grants from the National Institutes of Health (NIH), MedImmune, Cerexa/Forest/Actavis/Allergan, Pfizer, Advanced Liquid Logics, Theravance, Novartis, Cubist/Merck, Medical Biosurfaces, Locus, Affinergy, Contrafect, Karius, Genentech, Regeneron, Basilea, Janssen, from Green Cross, Cubist, Cerexa, Durata, Theravance, and Debiopharm. Royalties from UpToDate. V. G. F. also reports support for attending meetings and/or travel from Contrafect to present phase 2 data at 2019 ECCMID; patents planned, issued, or pending (Sepsis Diagnostics); a role as Associate Editor, Clinical Infectious Diseases (2017–2022); and stock or stock options from ArcBio and Valanbio. R. P. reports grants from ContraFect, TenNor Therapeutics Limited, and BioFire. R. P. is a consultant to Curetis, PathoQuest, Selux Diagnostics, 1928 Diagnostics, PhAST, Torus Biosystems, Day Zero Diagnostics, Mammoth Biosciences, and Qvella; monies are paid to Mayo Clinic. Mayo Clinic and R. P. have a relationship with Pathogenomix. R. P. has research supported by Adaptive Phage Therapeutics. Mayo Clinic has a royalty-bearing know-how agreement and equity in Adaptive Phage Therapeutics. R. P. is also a consultant to Netflix, Abbott Laboratories, and CARB-X. In addition, R. P. has a patent on Bordetella pertussis/parapertussis polymerase chain reaction issued, a patent on a device/method for sonication with royalties paid by Samsung to Mayo Clinic, and a patent on an antibiofilm substance issued. R. P. receives honoraria from the NBME, Up-to-Date, and the Infectious Diseases Board Review Course. E. L. T. reports grants or contracts from NIAID, DTRA, Sanofi, NIGMS, DARPA, Karius, Department of Veterans Affairs, and Department of Defense. E. L. T. has patents issued or pending for methods for characterizing infections and methods for developing tests for the same; host based molecular signatures of human infection with severe acute respiratory syndrome coronavirus 2; transcriptional signature for candidemia, gene expression signature useful to predict or diagnose sepsis and methods of using the same; methods to diagnose and treat acute respiratory infections; and biomarkers for the molecular classification of bacterial infection; E. L. T. is an employee of Danaher Corp and previously received consulting fees from Biomeme and has stock or stock options for Biomeme and Danaher. S. R. E. reports grant support from NIH; a contract with DeGruyter; royalties from Taylor & Francis; honoraria from the Analgesic, Anesthetic, and Addiction Clinical Trial Translations, Innovations, Opportunities, and Networks (ACTTION); meeting support from the Food and Drug Administration, Deming Conference on Applied Statistics, Clinical Trial Transformation Initiative, Council for International Organizations of Medical Sciences, International Chinese Statistical Association Applied Statistics Symposium, Antimicrobial Resistance and Stewardship Conference; unpaid board membership from the American Statistical Association, Society for Clinical Trials, Frontier Science Foundation; consulting fees from Genentech, AstraZeneca, Takeda, Microbiotix, Johnson & Johnson, Endologix, ChemoCentryx, Becton Dickenson, Atricure, Roivant, Neovasc, Nobel Pharma, Horizon, International Drug Development Institute, and SVB Leerink; data and safety monitoring board or advisory board service for NIH, Breast International Group, University of Pennsylvania, Washington University, Duke University, Roche, Pfizer, Takeda, Akouos, Apellis, Teva, Vir, DayOneBio, Alexion, Tracon, Rakuten, Abbvie, GSK, Eli Lilly, Nuvelution, Clover, FHI Clinical, Lung Biotech, SAB Biopharm, and Advantagene/Candel. S. B. D. reports research funding related to coronavirus disease 2019 (COVID-19) from Gilead, Regeneron, Shinogi, and the NIH and non–COVID-19-related consulting and research funding from Genentech, Basilea, Shinogi, J + J, and the NIH; support for travel from IDSA, and patents issued (WO2006116688A3). All other authors report no potential conflicts. All authors have submitted the ICMJE Form for Disclosure of Potential Conflicts of Interest. Conflicts that the editors consider relevant to the content of the manuscript have been disclosed., (© The Author(s) 2022. Published by Oxford University Press on behalf of Infectious Diseases Society of America. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.)

Objectives: We evaluated the performance of commonly used sepsis screening tools across prospective sepsis cohorts in the USA, Cambodia and Ghana., Design: Prospective cohort studies., Setting and Participants: From 2014 to 2021, participants with two or more SIRS (Systemic Inflammatory Response Syndrome) criteria and suspected infection were enrolled in emergency departments and medical wards at hospitals in Cambodia and Ghana and hospitalised participants with suspected infection were enrolled in the USA. Cox proportional hazards regression was performed, and Harrell's C-statistic calculated to determine 28-day mortality prediction performance of the quick Sequential Organ Failure Assessment (qSOFA) score ≥2, SIRS score ≥3, National Early Warning Score (NEWS) ≥5, Modified Early Warning Score (MEWS) ≥5 or Universal Vital Assessment (UVA) score ≥2. Screening tools were compared with baseline risk (age and sex) with the Wald test., Results: The cohorts included 567 participants (42.9% women) including 187 participants from Kumasi, Ghana, 200 participants from Takeo, Cambodia and 180 participants from Durham, North Carolina in the USA. The pooled mortality was 16.4% at 28 days. The mortality prediction accuracy increased from baseline risk with the MEWS (C-statistic: 0.63, 95% CI 0.58 to 0.68; p=0.002), NEWS (C-statistic: 0.68; 95% CI 0.64 to 0.73; p<0.001), qSOFA (C-statistic: 0.70, 95% CI 0.64 to 0.75; p<0.001), UVA score (C-statistic: 0.73, 95% CI 0.69 to 0.78; p<0.001), but not with SIRS (0.60; 95% CI 0.54 to 0.65; p=0.13). Within individual cohorts, only the UVA score in Ghana performed better than baseline risk (C-statistic: 0.77; 95% CI 0.71 to 0.83; p<0.001)., Conclusions: Among the cohorts, MEWS, NEWS, qSOFA and UVA scores performed better than baseline risk, largely driven by accuracy improvements in Ghana, while SIRS scores did not improve prognostication accuracy. Prognostication scores should be validated within the target population prior to clinical use., Competing Interests: Competing interests: ET has held equity and consulted for Predigen and Biomeme, and he is an employee of Danaher Diagnostics. All other authors declared no competing interests., (© Author(s) (or their employer(s)) 2023. Re-use permitted under CC BY-NC. No commercial re-use. See rights and permissions. Published by BMJ.)

Assays detecting blood transcriptome changes are studied for infectious disease diagnosis. Blood-based RNA alternative splicing (AS) events, which have not been well characterized in pathogen infection, have potential normalization and assay platform stability advantages over gene expression for diagnosis. Here, we present a computational framework for developing AS diagnostic biomarkers. Leveraging a large prospective cohort of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection and whole-blood RNA sequencing (RNA-seq) data, we identify a major functional AS program switch upon viral infection. Using an independent cohort, we demonstrate the improved accuracy of AS biomarkers for SARS-CoV-2 diagnosis compared with six reported transcriptome signatures. We then optimize a subset of AS-based biomarkers to develop microfluidic PCR diagnostic assays. This assay achieves nearly perfect test accuracy (61/62 = 98.4%) using a naive principal component classifier, significantly more accurate than a gene expression PCR assay in the same cohort. Therefore, our RNA splicing computational framework enables a promising avenue for host-response diagnosis of infection., Competing Interests: A provisional patent application on this work has been submitted, and the Icahn School of Medicine at Mount Sinai and Princeton University are in discussions about licensing the technology. Z.Z., O.G.T., and S.C.S. are co-inventors of this technology and may benefit from any licensing.

The identification of a COVID-19 host response signature in blood can increase the understanding of SARS-CoV-2 pathogenesis and improve diagnostic tools. Applying a multi-objective optimization framework to both massive public and new multi-omics data, we identified a COVID-19 signature regulated at both transcriptional and epigenetic levels. We validated the signature's robustness in multiple independent COVID-19 cohorts. Using public data from 8,630 subjects and 53 conditions, we demonstrated no cross-reactivity with other viral and bacterial infections, COVID-19 comorbidities, or confounders. In contrast, previously reported COVID-19 signatures were associated with significant cross-reactivity. The signature's interpretation, based on cell-type deconvolution and single-cell data analysis, revealed prominent yet complementary roles for plasmablasts and memory T cells. Although the signal from plasmablasts mediated COVID-19 detection, the signal from memory T cells controlled against cross-reactivity with other viral infections. This framework identified a robust, interpretable COVID-19 signature and is broadly applicable in other disease contexts. A record of this paper's transparent peer review process is included in the supplemental information., Competing Interests: Declaration of interests Icahn School of Medicine at Mount Sinai has submitted a provisional patent related to this work. A.C., D.G.C., S.C.S., S.H.K., and E.Z. are inventors of the technology filed through ISMMS related to this manuscript. S.H.K. receives consulting fees from Peraton. C.W.W. is a founder of Predigen, Inc, and consultant and Chief Medical Officer of Biomeme. C.W.W. also consults for Arena Pharmaceuticals, Biofire, Karius, and FHI Clinical. T.W.B. is a consultant for and equity owner in Biomeme. E.L.T. is an employee and shareholder of Danaher Corp. M.T.M., E.L.T., R.H., T.W.B., and C.W.W. are co-inventors of technology filed under patent application “Methods to Detect and Treat SARS-COV2 (COVID19) Infection.” O.G.T. is on the advisory board of Cell Systems., (Copyright © 2022 Elsevier Inc. All rights reserved.)

Objectives: Sepsis causes significant mortality. However, most patients who die of sepsis do not present with severe infection, hampering efforts to deliver early, aggressive therapy. It is also known that the host gene expression response to infection precedes clinical illness. This study seeks to develop transcriptomic models to predict progression to sepsis or shock within 72 hours of hospitalization and to validate previously identified transcriptomic signatures in the prediction of 28-day mortality., Design: Retrospective differential gene expression analysis and predictive modeling using RNA sequencing data., Patients: Two hundred seventy-seven patients enrolled at four large academic medical centers; all with clinically adjudicated infection were considered for inclusion in this study., Measurements and Main Results: Sepsis progression was defined as an increase in Sepsis 3 category within 72 hours. Transcriptomic data were generated using RNAseq of whole blood. Least absolute shrinkage and selection operator modeling was used to identify predictive signatures for various measures of disease progression. Four previously identified gene signatures were tested for their ability to predict 28-day mortality. There were no significant differentially expressed genes in 136 subjects with worsened Sepsis 3 category compared with 141 nonprogressor controls. There were 1,178 differentially expressed genes identified when sepsis progression was defined as ICU admission or 28-day mortality. A model based on these genes predicted progression with an area under the curve of 0.71. Validation of previously identified gene signatures to predict sepsis mortality revealed area under the receiver operating characteristic values of 0.70-0.75 and no significant difference between signatures., Conclusions: Host gene expression was unable to predict sepsis progression when defined by an increase in Sepsis-3 category, suggesting this definition is not a useful framework for transcriptomic prediction methods. However, there was a differential response when progression was defined as ICU admission or death. Validation of previously described signatures predicted 28-day mortality with insufficient accuracy to offer meaningful clinical utility., Competing Interests: Dr. Fiorino’s institution received funding from the U.S. Defense Advanced Research Projects Agency (DARPA) (N6600109-C2082) and the National Institute of Allergy and Infectious Diseases (NIAID) (UM1AI104681); she received funding from the Eugene A. Stead Scholarship, Duke University School of Medicine. Drs. Fiorino, Burke, McClain, Woods, and Tsalik received support for article research from the National Institutes of Health (NIH). Dr. Burke’s institution received funding from DARPA and NIAID. Drs. Burke, Ginsburg, Woods, and Tsalik received funding from Biomeme. Dr. McClain’s institution received funding from the NIH. Dr. Woods’ institution received funding from Janssen; he received funding from IDbyDNA, Vavarian, Nordic, Roche, Molecular Karius, Sanofi, the American Society of Microbiology, and FHI360Clinical. Drs. Henao, Ginsburg, Woods, and Tsalik have filed a patent for a Gene Expression Signatures Useful to Predict or Diagnose Sepsis and Methods of Using the Same. Dr. Tsalik’s institution received funding from Predigen; he received funding from Predigen. The remaining authors have disclosed that they do not have any potential conflicts of interest., (Copyright © 2022 by the Society of Critical Care Medicine and Wolters Kluwer Health, Inc. All Rights Reserved.)

Purpose: Several cases of symptomatic reinfection with severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) after full recovery from a prior episode have been reported. As reinfection has become an increasingly common phenomenon, an improved understanding of the risk factors for reinfection and the character and duration of the serological responses to infection and vaccination is critical for managing the coronavirus disease 2019 (COVID-19) pandemic., Methods: We described four cases of SARS-CoV-2 reinfection in individuals representing a spectrum of healthy and immunocompromised states, including (1) a healthy 41-year-old pediatrician, (2) an immunocompromised 31-year-old with granulomatosis with polyangiitis, (3) a healthy 26-year-old pregnant woman, and (4) a 50-year-old with hypertension and hyperlipidemia. We performed confirmatory quantitative reverse transcription-polymerase chain reaction and qualitative immunoglobulin M and quantitative IgG testing on all available patient samples to confirm the presence of infection and serological response to infection., Results: Our analysis showed that patients 1 and 2, a healthy and an immunocompromised patient, both failed to mount a robust serologic response to the initial infection. In contrast, patients 3 and 4, with minimal comorbid disease, both mounted a strong serological response to their initial infection, but were still susceptible to reinfection., Conclusion: Repeat episodes of COVID-19 are capable of occurring in patients regardless of the presence of known risk factors for infection or level of serological response to infection, although this did not trigger critical illness in any instance., Competing Interests: The authors declare no conflicts of interest., (© 2022 The Authors. Health Science Reports published by Wiley Periodicals LLC.)

SARS-CoV-2 infection triggers profound and variable immune responses in human hosts. Chromatin remodeling has been observed in individuals severely ill or convalescing with COVID-19, but chromatin remodeling early in disease prior to anti-spike protein IgG seroconversion has not been defined. We performed the Assay for Transposase-Accessible Chromatin using sequencing (ATAC-seq) and RNA-seq on peripheral blood mononuclear cells (PBMCs) from outpatients with mild or moderate symptom severity at different stages of clinical illness. Early in the disease course prior to IgG seroconversion, modifications in chromatin accessibility associated with mild or moderate symptoms were already robust and included severity-associated changes in accessibility of genes in interleukin signaling, regulation of cell differentiation and cell morphology. Furthermore, single-cell analyses revealed evolution of the chromatin accessibility landscape and transcription factor motif accessibility for individual PBMC cell types over time. The most extensive remodeling occurred in CD14+ monocytes, where sub-populations with distinct chromatin accessibility profiles were observed prior to seroconversion. Mild symptom severity was marked by upregulation of classical antiviral pathways, including those regulating IRF1 and IRF7, whereas in moderate disease, these classical antiviral signals diminished, suggesting dysregulated and less effective responses. Together, these observations offer novel insight into the epigenome of early mild SARS-CoV-2 infection and suggest that detection of chromatin remodeling in early disease may offer promise for a new class of diagnostic tools for COVID-19., (© 2022. The Author(s).)

Urinary tract infections (UTIs) are among the most common bacterial infections in the United States and are a major driver of antibiotic use, both appropriate and inappropriate, across healthcare settings. Novel UTI diagnostics are a strategy that might enable better UTI treatment. Members of the Antibacterial Resistance Leadership Group Laboratory Center and the Infectious Diseases Society of America Diagnostics Committee convened to envision ideal future UTI diagnostics, with a view towards improving delivery of healthcare, patient outcomes and experiences, and antibiotic use, addressing which types of UTI diagnostics are needed and how companies might approach development of novel UTI diagnostics., (© The Author(s) 2021. Published by Oxford University Press for the Infectious Diseases Society of America. All rights reserved. For permissions, e-mail: journals.permissions@oup.com.)

Importance: Bacterial and viral causes of acute respiratory illness (ARI) are difficult to clinically distinguish, resulting in the inappropriate use of antibacterial therapy. The use of a host gene expression-based test that is able to discriminate bacterial from viral infection in less than 1 hour may improve care and antimicrobial stewardship., Objective: To validate the host response bacterial/viral (HR-B/V) test and assess its ability to accurately differentiate bacterial from viral infection among patients with ARI., Design, Setting, and Participants: This prospective multicenter diagnostic study enrolled 755 children and adults with febrile ARI of 7 or fewer days' duration from 10 US emergency departments. Participants were enrolled from October 3, 2014, to September 1, 2019, followed by additional enrollment of patients with COVID-19 from March 20 to December 3, 2020. Clinical adjudication of enrolled participants identified 616 individuals as having bacterial or viral infection. The primary analysis cohort included 334 participants with high-confidence reference adjudications (based on adjudicator concordance and the presence of an identified pathogen confirmed by microbiological testing). A secondary analysis of the entire cohort of 616 participants included cases with low-confidence reference adjudications (based on adjudicator discordance or the absence of an identified pathogen in microbiological testing). Thirty-three participants with COVID-19 were included post hoc., Interventions: The HR-B/V test quantified the expression of 45 host messenger RNAs in approximately 45 minutes to derive a probability of bacterial infection., Main Outcomes and Measures: Performance characteristics for the HR-B/V test compared with clinical adjudication were reported as either bacterial or viral infection or categorized into 4 likelihood groups (viral very likely [probability score <0.19], viral likely [probability score of 0.19-0.40], bacterial likely [probability score of 0.41-0.73], and bacterial very likely [probability score >0.73]) and compared with procalcitonin measurement., Results: Among 755 enrolled participants, the median age was 26 years (IQR, 16-52 years); 360 participants (47.7%) were female, and 395 (52.3%) were male. A total of 13 participants (1.7%) were American Indian, 13 (1.7%) were Asian, 368 (48.7%) were Black, 131 (17.4%) were Hispanic, 3 (0.4%) were Native Hawaiian or Pacific Islander, 297 (39.3%) were White, and 60 (7.9%) were of unspecified race and/or ethnicity. In the primary analysis involving 334 participants, the HR-B/V test had sensitivity of 89.8% (95% CI, 77.8%-96.2%), specificity of 82.1% (95% CI, 77.4%-86.6%), and a negative predictive value (NPV) of 97.9% (95% CI, 95.3%-99.1%) for bacterial infection. In comparison, the sensitivity of procalcitonin measurement was 28.6% (95% CI, 16.2%-40.9%; P < .001), the specificity was 87.0% (95% CI, 82.7%-90.7%; P = .006), and the NPV was 87.6% (95% CI, 85.5%-89.5%; P < .001). When stratified into likelihood groups, the HR-B/V test had an NPV of 98.9% (95% CI, 96.1%-100%) for bacterial infection in the viral very likely group and a positive predictive value of 63.4% (95% CI, 47.2%-77.9%) for bacterial infection in the bacterial very likely group. The HR-B/V test correctly identified 30 of 33 participants (90.9%) with acute COVID-19 as having a viral infection., Conclusions and Relevance: In this study, the HR-B/V test accurately discriminated bacterial from viral infection among patients with febrile ARI and was superior to procalcitonin measurement. The findings suggest that an accurate point-of-need host response test with high NPV may offer an opportunity to improve antibiotic stewardship and patient outcomes.

Background: Measuring host gene expression is a promising diagnostic strategy to discriminate bacterial and viral infections. Multiple signatures of varying size, complexity, and target populations have been described. However, there is little information to indicate how the performance of various published signatures compare to one another., Methods: This systematic comparison of host gene expression signatures evaluated the performance of 28 signatures, validating them in 4589 subjects from 51 publicly available datasets. Thirteen COVID-specific datasets with 1416 subjects were included in a separate analysis. Individual signature performance was evaluated using the area under the receiving operating characteristic curve (AUC) value. Overall signature performance was evaluated using median AUCs and accuracies., Results: Signature performance varied widely, with median AUCs ranging from 0.55 to 0.96 for bacterial classification and 0.69-0.97 for viral classification. Signature size varied (1-398 genes), with smaller signatures generally performing more poorly (P < 0.04). Viral infection was easier to diagnose than bacterial infection (84% vs. 79% overall accuracy, respectively; P < .001). Host gene expression classifiers performed more poorly in some pediatric populations (3 months-1 year and 2-11 years) compared to the adult population for both bacterial infection (73% and 70% vs. 82%, respectively; P < .001) and viral infection (80% and 79% vs. 88%, respectively; P < .001). We did not observe classification differences based on illness severity as defined by ICU admission for bacterial or viral infections. The median AUC across all signatures for COVID-19 classification was 0.80 compared to 0.83 for viral classification in the same datasets., Conclusions: In this systematic comparison of 28 host gene expression signatures, we observed differences based on a signature's size and characteristics of the validation population, including age and infection type. However, populations used for signature discovery did not impact performance, underscoring the redundancy among many of these signatures. Furthermore, differential performance in specific populations may only be observable through this type of large-scale validation., (© 2022. The Author(s).)

Objectives: Compare three host response strategies to distinguish bacterial and viral etiologies of acute respiratory illness (ARI)., Methods: In this observational cohort study, procalcitonin, a 3-protein panel (CRP, IP-10, TRAIL), and a host gene expression mRNA panel were measured in 286 subjects with ARI from four emergency departments. Multinomial logistic regression and leave-one-out cross validation were used to evaluate the protein and mRNA tests., Results: The mRNA panel performed better than alternative strategies to identify bacterial infection: AUC 0.93 vs. 0.83 for the protein panel and 0.84 for procalcitonin (P<0.02 for each comparison). This corresponded to a sensitivity and specificity of 92% and 83% for the mRNA panel, 81% and 73% for the protein panel, and 68% and 87% for procalcitonin, respectively. A model utilizing all three strategies was the same as mRNA alone. For the diagnosis of viral infection, the AUC was 0.93 for mRNA and 0.84 for the protein panel (p<0.05). This corresponded to a sensitivity and specificity of 89% and 82% for the mRNA panel, and 85% and 62% for the protein panel, respectively., Conclusions: A gene expression signature was the most accurate host response strategy for classifying subjects with bacterial, viral, or non-infectious ARI., Competing Interests: ELT, RH, MTM, GSG, TWB, and CWW have filed for a patent for Methods to Diagnose and Treat Acute Respiratory Infections (WO 2017/004390 A1).

Background: Difficulty discriminating bacterial from viral infections drives antibacterial misuse. Host gene expression tests discriminate bacterial and viral etiologies, but their clinical utility has not been evaluated., Methods: Host gene expression and procalcitonin levels were measured in 582 emergency department participants with suspected infection. We also recorded clinician diagnosis and clinician-recommended treatment. These 4 diagnostic strategies were compared with clinical adjudication as the reference. To estimate the clinical impact of host gene expression, we calculated the change in overall Net Benefit (∆NB; the difference in Net Benefit comparing 1 diagnostic strategy with a reference) across a range of prevalence estimates while factoring in the clinical significance of false-positive and -negative errors., Results: Gene expression correctly classified bacterial, viral, or noninfectious illness in 74.1% of subjects, similar to the other strategies. Clinical diagnosis and clinician-recommended treatment revealed a bias toward overdiagnosis of bacterial infection resulting in high sensitivity (92.6% and 94.5%, respectively) but poor specificity (67.2% and 58.8%, respectively), resulting in a 33.3% rate of inappropriate antibacterial use. Gene expression offered a more balanced sensitivity (79.0%) and specificity (80.7%), which corresponded to a statistically significant improvement in average weighted accuracy (79.9% vs 71.5% for procalcitonin and 76.3% for clinician-recommended treatment; P <.0001 for both). Consequently, host gene expression had greater Net Benefit in diagnosing bacterial infection than clinician-recommended treatment (∆NB=6.4%) and procalcitonin (∆NB=17.4%)., Conclusions: Host gene expression-based tests to distinguish bacterial and viral infection can facilitate appropriate treatment, improving patient outcomes and mitigating the antibacterial resistance crisis., (Published by Oxford University Press on behalf of Infectious Diseases Society of America 2021.)

Viruses cause a wide spectrum of clinical disease, the majority being acute respiratory infections (ARI). In most cases, ARI symptoms are similar for different viruses although severity can be variable. The objective of this study was to understand the shared and unique elements of the host transcriptional response to different viral pathogens. We identified 162 subjects in the US and Sri Lanka with infections due to influenza, enterovirus/rhinovirus, human metapneumovirus, dengue virus, cytomegalovirus, Epstein Barr Virus, or adenovirus. Our dataset allowed us to identify common pathways at the molecular level as well as virus-specific differences in the host immune response. Conserved elements of the host response to these viral infections highlighted the importance of interferon pathway activation. However, the magnitude of the responses varied between pathogens. We also identified virus-specific responses to influenza, enterovirus/rhinovirus, and dengue infections. Influenza-specific differentially expressed genes (DEG) revealed up-regulation of pathways related to viral defense and down-regulation of pathways related to T cell and neutrophil responses. Functional analysis of entero/rhinovirus-specific DEGs revealed up-regulation of pathways for neutrophil activation, negative regulation of immune response, and p38MAPK cascade and down-regulation of virus defenses and complement activation. Functional analysis of dengue-specific up-regulated DEGs showed enrichment of pathways for DNA replication and cell division whereas down-regulated DEGs were mainly associated with erythrocyte and myeloid cell homeostasis, reactive oxygen and peroxide metabolic processes. In conclusion, our study will contribute to a better understanding of molecular mechanisms to viral infections in humans and the identification of biomarkers to distinguish different types of viral infections., Competing Interests: ELT, GSG, CWW, and TWB consult for and hold equity in Biomeme, Inc. The remaining 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 © 2021 Tsalik, Fiorino, Aqeel, Liu, Henao, Ko, Burke, Reller, Bodinayake, Nagahawatte, Arachchi, Devasiri, Kurukulasooriya, McClain, Woods, Ginsburg, Tillekeratne and Schughart.)

Objectives: Host gene expression signatures discriminate bacterial and viral infection but have not been translated to a clinical test platform. This study enrolled an independent cohort of patients to describe and validate a first-in-class host response bacterial/viral test., Design: Subjects were recruited from 2006 to 2016. Enrollment blood samples were collected in an RNA preservative and banked for later testing. The reference standard was an expert panel clinical adjudication, which was blinded to gene expression and procalcitonin results., Setting: Four U.S. emergency departments., Patients: Six-hundred twenty-three subjects with acute respiratory illness or suspected sepsis., Interventions: Forty-five-transcript signature measured on the BioFire FilmArray System (BioFire Diagnostics, Salt Lake City, UT) in ~45 minutes., Measurements and Main Results: Host response bacterial/viral test performance characteristics were evaluated in 623 participants (mean age 46 yr; 45% male) with bacterial infection, viral infection, coinfection, or noninfectious illness. Performance of the host response bacterial/viral test was compared with procalcitonin. The test provided independent probabilities of bacterial and viral infection in ~45 minutes. In the 213-subject training cohort, the host response bacterial/viral test had an area under the curve for bacterial infection of 0.90 (95% CI, 0.84-0.94) and 0.92 (95% CI, 0.87-0.95) for viral infection. Independent validation in 209 subjects revealed similar performance with an area under the curve of 0.85 (95% CI, 0.78-0.90) for bacterial infection and 0.91 (95% CI, 0.85-0.94) for viral infection. The test had 80.1% (95% CI, 73.7-85.4%) average weighted accuracy for bacterial infection and 86.8% (95% CI, 81.8-90.8%) for viral infection in this validation cohort. This was significantly better than 68.7% (95% CI, 62.4-75.4%) observed for procalcitonin (p < 0.001). An additional cohort of 201 subjects with indeterminate phenotypes (coinfection or microbiology-negative infections) revealed similar performance., Conclusions: The host response bacterial/viral measured using the BioFire System rapidly and accurately discriminated bacterial and viral infection better than procalcitonin, which can help support more appropriate antibiotic use., Competing Interests: Dr. Tsalik received in-kind support from BioFire Diagnostics by way of consumables and test instruments; received funding from Predigen, Inc.. BioFire, Inc. provided in-kind support for test development reagents used in this study. Drs. Tsalik, Henao, McClain, Ginsburg, Burke, and Woods disclosed filing for a patent pertaining to the signatures discussed in this study (WO 2017/004390 A1). Dr. Montgomery was an employee of BioFire Diagnostics, LLC. Dr. Nawrocki disclosed he has shares in BioMérieux. Dr. Lydon was supported by the Eugene A. Stead Scholarship from Duke University School of Medicine and the Infectious Diseases Society of America Medical Scholars Program. Drs. Tsalik, Ginsburg, and Woods disclosed that they are cofounders of Predigen, Inc. Drs. Tsalik, Ko, Petzold, Cairns, Kingsmore, Fowler, Ginsburg, Burke, and Woods received support for article research from the National Institutes of Health (NIH). Drs. Nawrocki and Hemmert received funding from BioFire Diagnostics, LLC.; and disclosed that they are employees of BioFire Diagnostics, LLC. Dr. Cairns is a consultant for BioMérieux, Inc. Dr. Ko’s institution received funding from the Antibiotic Resistance Leadership Group; disclosed the off-label product use of diagnostic tests. Dr. Petzold received support for article research from the Defense Advanced Research Projects Agency (DARPA) (NIH National Institute of Allergy and Infectious Diseases (NIAID) U01AI066569 and UM1AI104681 U.S. DARPA contract—N66001-09-C2082). Dr. Cairns’ institution received funding from the NIH (NIAID) and the DARPA; and received funding from BioMérieux. Dr. Kingsmore’s institution received funding from the NIH. Dr. Fowler received funding from the NIH, MedImmune, Allergan, Pfizer, Advanced Liquid Logics, Theravance, Novartis, Merck; Medical Biosurfaces; Locus; Affinergy; Contrafect; Karius; Genentech, Regeneron, Basilea, and Janssen; received funding from Basilea, Affinergy, Janssen, Basilea, Integrated Biotherapeutics; C3J, Armata, Valanbio; Akagera, Aridis, Novartis, Novadigm, Durata, Debiopharm, Genentech, Achaogen, Affinium, Medicines Co., Cerexa, Tetraphase, Trius, MedImmune, Bayer, Theravance, Basilea, Affinergy, Janssen, xBiotech, Contrafect, Regeneron, Destiny, UpToDate; Stock options Valanbio; a patent for sepsis diagnosis (US9850539B2). Dr. McClain disclosed he has patents pending on diagnostic signatures for respiratory infections. Dr. Crisp was an employee of BioFire Diagnostics and is currently an employee of BioMérieux, Inc. Dr. Ginsburg’s institution received funding from DARPA; received support for article research from the Bill & Melinda Gates Foundation. Dr. Burke is a consultant for and holds equity in Predigen, Inc. Dr. Burke’s institution received funding from the NIH; received funding from Predigen, Inc.; disclosed he is a coinventor on patents pending on Molecular Methods to Diagnose and Treat Respiratory Infections. Dr. Hemmert disclosed the off-label product use of BioFire FilmArray System. The remaining authors have disclosed that they do not have any potential conflicts of interest., (Copyright © 2021 by the Society of Critical Care Medicine and Wolters Kluwer Health, Inc. All Rights Reserved.)

Importance: Currently, there are no presymptomatic screening methods to identify individuals infected with a respiratory virus to prevent disease spread and to predict their trajectory for resource allocation., Objective: To evaluate the feasibility of using noninvasive, wrist-worn wearable biometric monitoring sensors to detect presymptomatic viral infection after exposure and predict infection severity in patients exposed to H1N1 influenza or human rhinovirus., Design, Setting, and Participants: The cohort H1N1 viral challenge study was conducted during 2018; data were collected from September 11, 2017, to May 4, 2018. The cohort rhinovirus challenge study was conducted during 2015; data were collected from September 14 to 21, 2015. A total of 39 adult participants were recruited for the H1N1 challenge study, and 24 adult participants were recruited for the rhinovirus challenge study. Exclusion criteria for both challenges included chronic respiratory illness and high levels of serum antibodies. Participants in the H1N1 challenge study were isolated in a clinic for a minimum of 8 days after inoculation. The rhinovirus challenge took place on a college campus, and participants were not isolated., Exposures: Participants in the H1N1 challenge study were inoculated via intranasal drops of diluted influenza A/California/03/09 (H1N1) virus with a mean count of 106 using the median tissue culture infectious dose (TCID50) assay. Participants in the rhinovirus challenge study were inoculated via intranasal drops of diluted human rhinovirus strain type 16 with a count of 100 using the TCID50 assay., Main Outcomes and Measures: The primary outcome measures included cross-validated performance metrics of random forest models to screen for presymptomatic infection and predict infection severity, including accuracy, precision, sensitivity, specificity, F1 score, and area under the receiver operating characteristic curve (AUC)., Results: A total of 31 participants with H1N1 (24 men [77.4%]; mean [SD] age, 34.7 [12.3] years) and 18 participants with rhinovirus (11 men [61.1%]; mean [SD] age, 21.7 [3.1] years) were included in the analysis after data preprocessing. Separate H1N1 and rhinovirus detection models, using only data on wearble devices as input, were able to distinguish between infection and noninfection with accuracies of up to 92% for H1N1 (90% precision, 90% sensitivity, 93% specificity, and 90% F1 score, 0.85 [95% CI, 0.70-1.00] AUC) and 88% for rhinovirus (100% precision, 78% sensitivity, 100% specificity, 88% F1 score, and 0.96 [95% CI, 0.85-1.00] AUC). The infection severity prediction model was able to distinguish between mild and moderate infection 24 hours prior to symptom onset with an accuracy of 90% for H1N1 (88% precision, 88% sensitivity, 92% specificity, 88% F1 score, and 0.88 [95% CI, 0.72-1.00] AUC) and 89% for rhinovirus (100% precision, 75% sensitivity, 100% specificity, 86% F1 score, and 0.95 [95% CI, 0.79-1.00] AUC)., Conclusions and Relevance: This cohort study suggests that the use of a noninvasive, wrist-worn wearable device to predict an individual's response to viral exposure prior to symptoms is feasible. Harnessing this technology would support early interventions to limit presymptomatic spread of viral respiratory infections, which is timely in the era of COVID-19.

In December 2019, the Antibacterial Resistance Leadership Group (ARLG) was awarded funding for another 7-year cycle to support a clinical research network on antibacterial resistance. ARLG 2.0 has 3 overarching research priorities: infections caused by antibiotic-resistant (AR) gram-negative bacteria, infections caused by AR gram-positive bacteria, and diagnostic tests to optimize use of antibiotics. To support the next generation of AR researchers, the ARLG offers 3 mentoring opportunities: the ARLG Fellowship, Early Stage Investigator seed grants, and the Trialists in Training Program. The purpose of this article is to update the scientific community on the progress made in the original funding period and to encourage submission of clinical research that addresses 1 or more of the research priority areas of ARLG 2.0., (© The Author(s) 2021. Published by Oxford University Press for the Infectious Diseases Society of America. All rights reserved. For permissions, e-mail: journals.permissions@oup.com.)

Background: Host gene expression has emerged as a complementary strategy to pathogen detection tests for the discrimination of bacterial and viral infection. The impact of immunocompromise on host-response tests remains unknown. We evaluated a host-response test discriminating bacterial, viral, and noninfectious conditions in immunocompromised subjects., Methods: An 81-gene signature was measured using real-time-polymerase chain reaction in subjects with immunocompromise (chemotherapy, solid-organ transplant, immunomodulatory agents, AIDS) with bacterial infection, viral infection, or noninfectious illness. A regularized logistic regression model trained in immunocompetent subjects was used to estimate the likelihood of each class in immunocompromised subjects., Results: Accuracy in the 136-subject immunocompetent training cohort was 84.6% for bacterial versus nonbacterial discrimination and 80.8% for viral versus nonviral discrimination. Model validation in 134 immunocompromised subjects showed overall accuracy of 73.9% for bacterial infection (P = .04 relative to immunocompetent subjects) and 75.4% for viral infection (P = .30). A scheme reporting results by quartile improved test utility. The highest probability quartile ruled-in bacterial and viral infection with 91.4% and 84.0% specificity, respectively. The lowest probability quartile ruled-out infection with 90.1% and 96.4% sensitivity for bacterial and viral infection, respectively. Performance was independent of the type or number of immunocompromising conditions., Conclusions: A host gene expression test discriminated bacterial, viral, and noninfectious etiologies at a lower overall accuracy in immunocompromised patients compared with immunocompetent patients, although this difference was only significant for bacterial infection classification. With modified interpretive criteria, a host-response strategy may offer clinically useful diagnostic information for patients with immunocompromise., (© The Author(s) 2021. Published by Oxford University Press for the Infectious Diseases Society of America. All rights reserved. For permissions, e-mail: journals.permissions@oup.com.)

Bacterial pneumonia is a major cause of morbidity and mortality worldwide despite the use of antibiotics, and novel therapies are urgently needed. Building on previous work, we aimed to 1 ) develop a baboon model of severe pneumococcal pneumonia and sepsis with organ dysfunction and 2 ) test the safety and efficacy of a novel extracorporeal blood filter to remove proinflammatory molecules and improve organ function. After a dose-finding pilot study, 12 animals were inoculated with Streptococcus pneumoniae [5 × 10 9 colony-forming units (CFU)], given ceftriaxone at 24 h after inoculation, and randomized to extracorporeal blood purification using a filter coated with surface-immobilized heparin sulfate ( n = 6) or sham treatment ( n = 6) for 4 h at 30 h after inoculation. For safety analysis, four uninfected animals also underwent purification. At 48 h, necropsy was performed. Inoculated animals developed severe pneumonia and septic shock. Compared with sham-treated animals, septic animals treated with purification displayed significantly less kidney injury, metabolic acidosis, hypoglycemia, and shock ( P < 0.05). Purification blocked the rise in peripheral blood S. pneumoniae DNA, attenuated bronchoalveolar lavage (BAL) CCL4, CCL2, and IL-18 levels, and reduced renal oxidative injury and classical NLRP3 inflammasome activation. Purification was safe in both uninfected and infected animals and produced no adverse effects. We demonstrate that heparin-based blood purification significantly attenuates levels of circulating S. pneumoniae DNA and BAL cytokines and is renal protective in baboons with severe pneumococcal pneumonia and septic shock. Purification was associated with less severe acute kidney injury, metabolic derangements, and shock. These results support future clinical studies in critically ill septic patients.

Background: Candidemia is one of the most common nosocomial bloodstream infections in the United States, causing significant morbidity and mortality in hospitalized patients, but the breadth of the host response to Candida infections in human patients remains poorly defined., Methods: In order to better define the host response to Candida infection at the transcriptional level, we performed RNA sequencing on serial peripheral blood samples from 48 hospitalized patients with blood cultures positive for Candida species and compared them to patients with other acute viral, bacterial, and non-infectious illnesses. Regularized multinomial regression was utilized to develop pathogen class-specific gene expression classifiers., Results: Candidemia triggers a unique, robust, and conserved transcriptomic response in human hosts with 1641 genes differentially upregulated compared to healthy controls. Many of these genes corresponded to components of the immune response to fungal infection, heavily weighted toward neutrophil activation, heme biosynthesis, and T cell signaling. We developed pathogen class-specific classifiers from these unique signals capable of identifying and differentiating candidemia, viral, or bacterial infection across a variety of hosts with a high degree of accuracy (auROC 0.98 for candidemia, 0.99 for viral and bacterial infection). This classifier was validated on two separate human cohorts (auROC 0.88 for viral infection and 0.87 for bacterial infection in one cohort; auROC 0.97 in another cohort) and an in vitro model (auROC 0.94 for fungal infection, 0.96 for bacterial, and 0.90 for viral infection)., Conclusions: Transcriptional analysis of circulating leukocytes in patients with acute Candida infections defines novel aspects of the breadth of the human immune response during candidemia and suggests promising diagnostic approaches for simultaneously differentiating multiple types of clinical illnesses in at-risk, acutely ill patients.

Background: Sexual dimorphisms in immune responses contribute to coronavirus disease 2019 (COVID-19) outcomes, but the mechanisms governing this disparity remain incompletely understood., Methods: We carried out sex-balanced sampling of peripheral blood mononuclear cells from hospitalized and non-hospitalized individuals with confirmed COVID-19, uninfected close contacts, and healthy control individuals for 36-color flow cytometry and single-cell RNA sequencing., Findings: Our results revealed a pronounced reduction of circulating mucosal-associated invariant T (MAIT) cells in infected females. Integration of published COVID-19 airway tissue datasets suggests that this reduction represented a major wave of MAIT cell extravasation during early infection in females. Moreover, MAIT cells from females possessed an immunologically active gene signature, whereas cells from males were pro-apoptotic., Conclusions: Our findings uncover a female-specific protective MAIT cell profile, potentially shedding light on reduced COVID-19 susceptibility in females., Funding: This work was supported by NIH/NIAID (U01AI066569 and UM1AI104681), the Defense Advanced Projects Agency (DARPA; N66001-09-C-2082 and HR0011-17-2-0069), the Veterans Affairs Health System, and Virology Quality Assurance (VQA; 75N93019C00015). The content is solely the responsibility of the authors and does not necessarily represent the official view of the National Institutes of Health. COVID-19 samples were processed under Biosafety level 2 (BSL-2) with aerosol management enhancement or BSL-3 in the Duke Regional Biocontainment Laboratory, which received partial support for construction from NIH/NIAID (UC6AI058607)., Competing Interests: M.T.M. reports grants for biomarker diagnostics from the Defense Advanced Research Projects Agency (DARPA), National Institutes of Health (NIH), Sanofi, and the Department of Veterans Affairs. T.W.B. reports grants from DARPA and is a consultant for Predigen. M.T.M., T.W.B., E.L.T., and C.W.W. report pending patents on molecular methods to diagnose and treat respiratory infections. E.L.T. reports grants on biomarker diagnostics from DARPA and the NIH/Antibacterial Resistance Leadership Group (ARLG) and an ownership stake in Predigen. G.S.G. reports an ownership stake in Predigen. C.W.W. reports grants for biomarker diagnostics from DARPA, NIH/ARLG, Predigen, and Sanofi and has received consultancy fees from bioMerieux, Roche, Biofire, Giner, and Biomeme., (© 2021 Elsevier Inc.)

Background: While genome-wide associations studies (GWAS) have successfully elucidated the genetic architecture of complex human traits and diseases, understanding mechanisms that lead from genetic variation to pathophysiology remains an important challenge. Methods are needed to systematically bridge this crucial gap to facilitate experimental testing of hypotheses and translation to clinical utility., Results: Here, we leveraged cross-phenotype associations to identify traits with shared genetic architecture, using linkage disequilibrium (LD) information to accurately capture shared SNPs by proxy, and calculate significance of enrichment. This shared genetic architecture was examined across differing biological scales through incorporating data from catalogs of clinical, cellular, and molecular GWAS. We have created an interactive web database (interactive Cross-Phenotype Analysis of GWAS database (iCPAGdb)) to facilitate exploration and allow rapid analysis of user-uploaded GWAS summary statistics. This database revealed well-known relationships among phenotypes, as well as the generation of novel hypotheses to explain the pathophysiology of common diseases. Application of iCPAGdb to a recent GWAS of severe COVID-19 demonstrated unexpected overlap of GWAS signals between COVID-19 and human diseases, including with idiopathic pulmonary fibrosis driven by the DPP9 locus. Transcriptomics from peripheral blood of COVID-19 patients demonstrated that DPP9 was induced in SARS-CoV-2 compared to healthy controls or those with bacterial infection. Further investigation of cross-phenotype SNPs associated with both severe COVID-19 and other human traits demonstrated colocalization of the GWAS signal at the ABO locus with plasma protein levels of a reported receptor of SARS-CoV-2, CD209 (DC-SIGN). This finding points to a possible mechanism whereby glycosylation of CD209 by ABO may regulate COVID-19 disease severity., Conclusions: Thus, connecting genetically related traits across phenotypic scales links human diseases to molecular and cellular measurements that can reveal mechanisms and lead to novel biomarkers and therapeutic approaches. The iCPAGdb web portal is accessible at http://cpag.oit.duke.edu and the software code at https://github.com/tbalmat/iCPAGdb .

Sepsis is defined as a dysregulated host response to infection that leads to life-threatening acute organ dysfunction. It afflicts approximately 50 million people worldwide annually and is often deadly, even when evidence-based guidelines are applied promptly. Many randomized trials tested therapies for sepsis over the past 2 decades, but most have not proven beneficial. This may be because sepsis is a heterogeneous syndrome, characterized by a vast set of clinical and biologic features. Combinations of these features, however, may identify previously unrecognized groups, or "subclasses" with different risks of outcome and response to a given treatment. As efforts to identify sepsis subclasses become more common, many unanswered questions and challenges arise. These include: 1) the semantic underpinning of sepsis subclasses, 2) the conceptual goal of subclasses, 3) considerations about study design, data sources, and statistical methods, 4) the role of emerging data types, and 5) how to determine whether subclasses represent "truth." We discuss these challenges and present a framework for the broader study of sepsis subclasses. This framework is intended to aid in the understanding and interpretation of sepsis subclasses, provide a mechanism for explaining subclasses generated by different methodologic approaches, and guide clinicians in how to consider subclasses in bedside care., Competing Interests: Dr. DeMerle’s institution received funding from R35 GM119519-03 and T32HL007820. Dr. Calfee is supported in part by grants from the National Institutes of Health (NIH; HL140026). Dr. Carcillo is supported in part by grants from the National Institutes of Health (R01GM108618). Dr. DeMerle is supported in part by grants from the National Institutes of Health (T32HL007820). Dr. Angus received funding from Ferring Pharmaceuticals, Bristol-Myers Squibb, Bayer AG, and Alung Technologies. Drs. Angus, Brant, Carcillo, Chang, Dickson, Kennedy, Lindsell, Liu, Randolph, Thompson, Tsalik, Wong, and Seymour received support for article research from the NIH. Dr. Baillie received support for article research from Wellcome Trust/Charity Open Access Fund (COAF), and Research Councils UK. Dr. Calfee’s institution received funding from Roche/Genentech and Bayer, and she received funding from Roche/Genentech, Quark, CSL Behring, Bayer, Gen1e Life Sciences, and Vasomune. Drs. Carcillo’s and Seymour’s institutions received funding from the National Institute of General Medical Sciences. Drs. Chang’s, Lindsell’s, Liu’s, Randolph’s, Shapiro’s, and Wong’s institutions received funding from the NIH. Dr. Gordon’s institution received funding from the National Institute for Health Research (NIHR) Research Professorship (RP-2015-06-18), NIHR Imperial Biomedical Research Centre, GlaxoSmithKline, and Bristol Myers Squibb. Dr. Knight received support for article research from Wellcome Trust/COAF. Dr. Lindsell’s institution received funding from the Centers for Disease Control and Prevention (CDC), Department of Defense, Marcus Foundation, Entegrion, Endpoint Health, and bioMerieux, and he disclosed he is a coinventor on patents related to risk stratification in septic shock. Dr. Marshall received funding from AM Pharma, AKPA Pharma, and the Society of Critical Care Medicine (Critical Care Medicine Associate Editor). Dr. Randolph’s institution received funding from the CDC, and she received funding from La Jolla Pharma. Dr. Shapiro’s institution received funding from rapid pathogen screening, Baxter, and Inflammatix, and he received funding from Diagnostic Robotics. Dr. Sweeney received funding from Inflammatix. Dr. Thompson’s institution received funding from the National Heart, Lung, and Blood Institute, and he received funding from Bayer and Thetis. Dr. Tsalik disclosed that he is a founder and holds equity in Predigen; he receives salary support from the Durham VA Healthcare System and Duke University; and he has received salary support and/or grant funding (paid to his university) from the NIH, DARPA, DTRA, Karius, and Sanofi US. Dr. Wong disclosed that he and his institutions hold U.S. patents for sepsis biomarkers. Dr. Yende received funding from serving as consultant for expert testimony and he disclosed government work. Dr. Knight is supported by a Wellcome Trust Investigator Award (204969/Z/16/Z) and the NIHR Oxford Biomedical Research Centre. Dr. Lindsell was supported in part by grants from the National Institutes of Health (R35GM126943, R01HL149422), a research grant to VUMC from Endpoint Health, and is also listed as co-inventor on patents for endotyping and risk-stratification in pediatric septic shock. Dr. Liu is supported in part by grants from the National Institutes of Health (R35GM128672). Dr. Marshall is supported in part by grants from the Canadian Institutes of Health Research. Dr. Randolph is supported in part by grants from the National Institutes of Health (R21HD095228). Dr. Shankar-Hari is supported by the National Institute for Health Research Clinician Scientist Award (CS-2016-16-011). Dr. Wong is supported in part by grants from the National Institutes of Health (R35 GM126943). Dr. Sweeney is an employee of, and shareholder in, Inflammatix. The remaining authors have disclosed that they do not have any potential conflicts of interest., (Copyright © 2021 by the Society of Critical Care Medicine and Wolters Kluwer Health, Inc. All Rights Reserved.)

Background: Early and accurate identification of individuals with viral infections is crucial for clinical management and public health interventions. We aimed to assess the ability of transcriptomic biomarkers to identify naturally acquired respiratory viral infection before typical symptoms are present., Methods: In this index-cluster study, we prospectively recruited a cohort of undergraduate students (aged 18-25 years) at Duke University (Durham, NC, USA) over a period of 5 academic years. To identify index cases, we monitored students for the entire academic year, for the presence and severity of eight symptoms of respiratory tract infection using a daily web-based survey, with symptoms rated on a scale of 0-4. Index cases were defined as individuals who reported a 6-point increase in cumulative daily symptom score. Suspected index cases were visited by study staff to confirm the presence of reported symptoms of illness and to collect biospecimen samples. We then identified clusters of close contacts of index cases (ie, individuals who lived in close proximity to index cases, close friends, and partners) who were presumed to be at increased risk of developing symptomatic respiratory tract infection while under observation. We monitored each close contact for 5 days for symptoms and viral shedding and measured transcriptomic responses at each timepoint each day using a blood-based 36-gene RT-PCR assay., Findings: Between Sept 1, 2009, and April 10, 2015, we enrolled 1465 participants. Of 264 index cases with respiratory tract infection symptoms, 150 (57%) had a viral cause confirmed by RT-PCR. Of their 555 close contacts, 106 (19%) developed symptomatic respiratory tract infection with a proven viral cause during the observation window, of whom 60 (57%) had the same virus as their associated index case. Nine viruses were detected in total. The transcriptomic assay accurately predicted viral infection at the time of maximum symptom severity (mean area under the receiver operating characteristic curve [AUROC] 0·94 [95% CI 0·92-0·96]), as well as at 1 day (0·87 [95% CI 0·84-0·90]), 2 days (0·85 [0·82-0·88]), and 3 days (0·74 [0·71-0·77]) before peak illness, when symptoms were minimal or absent and 22 (62%) of 35 individuals, 25 (69%) of 36 individuals, and 24 (82%) of 29 individuals, respectively, had no detectable viral shedding., Interpretation: Transcriptional biomarkers accurately predict and diagnose infection across diverse viral causes and stages of disease and thus might prove useful for guiding the administration of early effective therapy, quarantine decisions, and other clinical and public health interventions in the setting of endemic and pandemic infectious diseases., Funding: US Defense Advanced Research Projects Agency., (Copyright © 2021 Elsevier Ltd. All rights reserved.)

SARS-CoV-2 infection has been shown to trigger a wide spectrum of immune responses and clinical manifestations in human hosts. Here, we sought to elucidate novel aspects of the host response to SARS-CoV-2 infection through RNA sequencing of peripheral blood samples from 46 subjects with COVID-19 and directly comparing them to subjects with seasonal coronavirus, influenza, bacterial pneumonia, and healthy controls. Early SARS-CoV-2 infection triggers a powerful transcriptomic response in peripheral blood with conserved components that are heavily interferon-driven but also marked by indicators of early B-cell activation and antibody production. Interferon responses during SARS-CoV-2 infection demonstrate unique patterns of dysregulated expression compared to other infectious and healthy states. Heterogeneous activation of coagulation and fibrinolytic pathways are present in early COVID-19, as are IL1 and JAK/STAT signaling pathways, which persist into late disease. Classifiers based on differentially expressed genes accurately distinguished SARS-CoV-2 infection from other acute illnesses (auROC 0.95 [95% CI 0.92-0.98]). The transcriptome in peripheral blood reveals both diverse and conserved components of the immune response in COVID-19 and provides for potential biomarker-based approaches to diagnosis.

Background: Pharyngeal and rectal Neisseria gonorrhoeae and Chlamydia trachomatis play important roles in infection and antibacterial resistance transmission, but no US Food and Drug Administration (FDA)-cleared assays for detection at these sites existed prior to this study. The objective was to estimate performance of assays to detect those infections in pharyngeal and rectal specimens to support regulatory submission., Methods: We performed a cross-sectional, single-visit study of adults seeking sexually transmitted infection testing at 9 clinics in 7 states. We collected pharyngeal and rectal swabs from participants. The primary outcome was positive and negative percent agreement for detection of N. gonorrhoeae and C. trachomatis for 3 investigational assays compared to a composite reference. Secondary outcomes included positivity as well as positive and negative predictive values and likelihood ratios. Subgroup analyses included outcomes by symptom status and sex., Results: A total of 2598 participants (79% male) underwent testing. We observed N. gonorrhoeae positivity of 8.1% in the pharynx and 7.9% in the rectum and C. trachomatis positivity of 2.0% in the pharynx and 8.7% in the rectum. Positive percent agreement ranged from 84.8% to 96.5% for different anatomic site infection combinations, whereas negative percent agreement was 98.8% to 99.6%., Conclusions: This study utilized a Master Protocol to generate diagnostic performance data for multiple assays from different manufacturers in a single study population, which ultimately supported first-in-class FDA clearance for extragenital assays. We observed very good positive percent agreement when compared to a composite reference method for the detection of both pharyngeal and rectal N. gonorrhoeae and C. trachomatis., Clinical Trials Registration: NCT02870101., (© The Author(s) 2019. Published by Oxford University Press for the Infectious Diseases Society of America.)

Patient management relies on diagnostic information to identify appropriate treatment. Standard evaluations of diagnostic tests consist of estimating sensitivity, specificity, positive/negative predictive values, likelihood ratios, and accuracy. Although useful, these metrics do not convey the tests' clinical value, which is critical to informing decision-making. Full appreciation of the clinical impact of a diagnostic test requires analyses that integrate sensitivity and specificity, account for the disease prevalence within the population of test application, and account for the relative importance of specificity vs sensitivity by considering the clinical implications of false-positive and false-negative results. We developed average weighted accuracy (AWA), representing a pragmatic metric of diagnostic yield or global utility of a diagnostic test. AWA can be used to compare test alternatives, even across different studies. We apply the AWA methodology to evaluate a new diagnostic test developed in the Rapid Diagnostics in Categorizing Acute Lung Infections (RADICAL) study., (© The Author(s) 2019. Published by Oxford University Press for the Infectious Diseases Society of America. All rights reserved. For permissions, e-mail: journals.permissions@oup.com.)

Chlamydia trachomatis and Neisseria gonorrhoeae infections in the rectum and pharynx are important extragenital reservoirs of infection. Few assays approved by the US Food and Drug Administration are commercially available to diagnose pharyngeal or rectal infections. The current study reports on the analytical performance of the Abbott RealTime CT/NG assay, including the limit of detection, inclusivity, and analytical specificity for C. trachomatis and N. gonorrhoeae in rectal and pharyngeal specimens. The limit of detection was performed using known concentrations of organisms, elementary bodies per milliliter (EB/mL) for C. trachomatis and colony-forming units per milliliter (CFU/mL) for N. gonorrhoeae, in clinical rectal and pharyngeal swab matrices. Inclusivity was performed against 12 serovars of C. trachomatis and seven strains of N. gonorrhoeae. The analytical specificity was performed using 28 different bacteria and viruses. The limit of detection for C. trachomatis was 2.56 EB/mL in pharyngeal specimens and 12.8 EB/mL in rectal specimens. The limit of detection for N. gonorrhoeae was 0.0256 CFU/mL for both pharyngeal and rectal specimens. The inclusivity and analytical specificity were 100% for both rectal and pharyngeal specimens. These analytical performance data demonstrate that the Abbott CT/NG RealTime assay is an accurate, sensitive, and specific assay in rectal and pharyngeal specimens, supporting the potential of the assay for detection of rectal and pharyngeal C. trachomatis and N. gonorrhoeae infections., (Copyright © 2020 Association for Molecular Pathology and American Society for Investigative Pathology. Published by Elsevier Inc. All rights reserved.)

Background: Pathogen-based diagnostics for acute respiratory infection (ARI) have limited ability to detect etiology of illness. We previously showed that peripheral blood-based host gene expression classifiers accurately identify bacterial and viral ARI in cohorts of European and African descent. We determined classifier performance in a South Asian cohort., Methods: Patients ≥15 years with fever and respiratory symptoms were enrolled in Sri Lanka. Comprehensive pathogen-based testing was performed. Peripheral blood ribonucleic acid was sequenced and previously developed signatures were applied: a pan-viral classifier (viral vs nonviral) and an ARI classifier (bacterial vs viral vs noninfectious)., Results: Ribonucleic acid sequencing was performed in 79 subjects: 58 viral infections (36 influenza, 22 dengue) and 21 bacterial infections (10 leptospirosis, 11 scrub typhus). The pan-viral classifier had an overall classification accuracy of 95%. The ARI classifier had an overall classification accuracy of 94%, with sensitivity and specificity of 91% and 95%, respectively, for bacterial infection. The sensitivity and specificity of C-reactive protein (>10 mg/L) and procalcitonin (>0.25 ng/mL) for bacterial infection were 100% and 34%, and 100% and 41%, respectively., Conclusions: Previously derived gene expression classifiers had high predictive accuracy at distinguishing viral and bacterial infection in South Asian patients with ARI caused by typical and atypical pathogens., (© The Author(s) 2020. Published by Oxford University Press on behalf of Infectious Diseases Society of America.)

Objective: Distinguishing bacterial, viral, or other etiologies of acute illness is diagnostically challenging with significant implications for appropriate antimicrobial use. Host gene expression offers a promising approach, although no clinically useful test has been developed yet to accomplish this. Here, Qvella's FAST HR (Richmond Hill, Ontario, Canada) process was developed to quantify previously identified host gene expression signatures in whole blood in <45 minutes., Method: Whole blood was collected from 128 human subjects (mean age 47, range 18-88) with clinically adjudicated, microbiologically confirmed viral infection, bacterial infection, noninfectious illness, or healthy controls. Stabilized mRNA was released from cleaned and stabilized RNA-surfactant complexes using e-lysis, an electrical process providing a quantitative real-time reverse transcription polymerase chain reaction-ready sample. Threshold cycle values (C T ) for 10 host response targets were normalized to hypoxanthine phosphoribosyltransferase 1 expression, a control mRNA. The transcripts in the signature were specifically chosen to discriminate viral from nonviral infection (bacterial, noninfectious illness, or healthy). Classification accuracy was determined using cross-validated sparse logistic regression., Results: Reproducibility of mRNA quantification was within 1 cycle as compared to the difference seen between subjects with viral versus nonviral infection (up to 5.0 normalized C T difference). Classification of 128 subjects into viral or nonviral etiologies demonstrated 90.6% overall accuracy compared to 82.0% for procalcitonin ( P = .06). FAST HR achieved rapid and accurate measurement of the host response to viral infection in less than 45 minutes., Conclusions: These results demonstrate the ability to translate host gene expression signatures to clinical platforms for use in patients with suspected infection., Clinical Trials Registration: NCT00258869., (Published by Oxford University Press on behalf of Infectious Diseases Society of America 2019.)

Background: Antibiotic resistance is rising at disturbing rates and contributes to the deaths of millions of people yearly. Antibiotic resistant infections disproportionately affect those with immunocompromising conditions, chronic colonization, and frequent antibiotic use such as transplant patients or those with cystic fibrosis. However, clinicians lack the diagnostic tools to confidently diagnose and treat infections, leading to widespread use of empiric broad spectrum antimicrobials, often for prolonged duration., Case Presentation: A 22 year-old Caucasian female with cystic fibrosis received a bilateral orthotopic lung transplantation 5 months prior to the index hospitalization. She underwent routine surveillance bronchoscopy and was admitted for post-procedure fever. A clear cause of infection was not identified by routine methods. Imaging and bronchoscopic lung biopsy did not identify an infectious agent or rejection. She was treated with a prolonged course of antimicrobials targeting known colonizing organisms from prior bronchoalveolar lavage cultures (Pseudomonas, Staphylococcus aureus, and Aspergillus). However, we identified Stenotrophomonas maltophilia in two independent whole blood samples using direct-pathogen sequencing, which was not identified by other methods., Conclusions: This case represents a common clinical conundrum: identification of infection in a high-risk, complex patient. Here, direct-pathogen sequencing identified a pathogen that would not otherwise have been identified by common techniques. Had results been clinically available, treatment could have been customized, avoiding a prolonged course of broad spectrum antimicrobials that would only exacerbate resistance. Direct-pathogen sequencing is poised to fill a diagnostic gap for pathogen identification, allowing early identification and customization of treatment in a culture-independent, pathogen-agnostic manner.

Purpose: The inappropriate use of antimicrobials, especially in acute respiratory infections (ARIs), is largely driven by difficulty distinguishing bacterial, viral, and noninfectious etiologies of illness. A new frontier in infectious disease diagnostics looks to the host response for disease classification. This article examines how host response-based diagnostics for ARIs are being used in clinical practice, as well as new developments in the research pipeline., Methods: A limited search was conducted of the relevant literature, with emphasis placed on literature published in the last 5 years (2014-2019)., Findings: Advances are being made in all areas of host response-based diagnostics for ARIs. Specifically, there has been significant progress made in single protein biomarkers, as well as in various "omics" fields (including proteomics, metabolomics, and transcriptomics) and wearable technologies. There are many potential applications of a host response-based approach; a few key examples include the ability to discriminate bacterial and viral disease, presymptomatic diagnosis of infection, and pathogen-specific host response diagnostics, including modeling disease progression., Implications: As biomarker measurement technologies continue to improve, host response-based diagnostics will increasingly be translated to clinically available platforms that can generate a holistic characterization of an individual's health. This knowledge, in the hands of both patient and provider, can improve care for the individual patient and help fight rising rates of antibiotic resistance., (Copyright © 2019. Published by Elsevier Inc.)