Transitions of blood immune endotypes and improved outcome by anakinra in COVID-19 pneumonia: an analysis of the SAVE-MORE randomized controlled trial.

BACKGROUND: Endotype classification may guide immunomodulatory management of patients with bacterial and viral sepsis. We aimed to identify immune endotypes and transitions associated with response to anakinra (human interleukin 1 receptor antagonist) in participants in the SAVE-MORE trial. METHODS: Adult patients hospitalized with radiological findings of PCR-confirmed severe pneumonia caused by SARS-CoV-2 and plasma-soluble urokinase plasminogen activator receptor levels of ≥ 6 ng/ml in the SAVE-MORE trial (NCT04680949) were characterized at baseline and days 4 and 7 of treatment using a previously defined 33-messenger RNA classifier to assign an immunological endotype in blood. Endpoints were changes in endotypes and progression to severe respiratory failure (SRF) associated with anakinra treatment. RESULTS: At baseline, 23.2% of 393 patients were designated as inflammopathic, 41.1% as adaptive, and 35.7% as coagulopathic. Only 23.9% were designated as the same endotype at days 4 and 7 compared to baseline, while all other patients transitioned between endotypes. Anakinra-treated patients were more likely to remain in the adaptive endotype during 7-day treatment (24.4% vs. 9.9%; p < 0.001). Anakinra also protected patients with coagulopathic endotype at day 7 against SRF compared to placebo (27.8% vs. 55.9%; p = 0.013). CONCLUSION: We identify an association between endotypes defined using blood transcriptome and anakinra therapy for COVID-19 pneumonia, with anakinra-treated patients shifting toward endotypes associated with a better outcome, mainly the adaptive endotype. Trial registration ClinicalTrials.gov, NCT04680949, December 23, 2020.

Monocyte state 1 (MS1) cells in critically ill patients with sepsis or non-infectious conditions: association with disease course and host response.

BACKGROUND: Sepsis is a life-threatening condition arising from an aberrant host response to infection. Recent single-cell RNA sequencing investigations identified an immature bone-marrow-derived CD14+ monocyte phenotype with immune suppressive properties termed "monocyte state 1" (MS1) in patients with sepsis. Our objective was to determine the association of MS1 cell profiles with disease presentation, outcomes, and host response characteristics. METHODS: We used the transcriptome deconvolution method (CIBERSORTx) to estimate the percentage of MS1 cells from blood RNA profiles of patients with sepsis admitted to the intensive care unit (ICU). We compared these profiles to ICU patients without infection and to healthy controls. Host response dysregulation was further studied by gene co-expression network and gene set enrichment analyses of blood leukocytes, and measurement of 15 plasma biomarkers indicative of pathways implicated in sepsis pathogenesis. RESULTS: Sepsis patients (n = 332) were divided into three equally-sized groups based on their MS1 cell levels (low, intermediate, and high). MS1 groups did not differ in demographics or comorbidities. The intermediate and high MS1 groups presented with higher disease severity and more often had shock. MS1 cell abundance did not differ between survivors and non-survivors, or between patients who did or did not acquire a secondary infection. Higher MS1 cell percentages were associated with downregulation of lymphocyte-related and interferon response genes in blood leukocytes, with concurrent upregulation of inflammatory response pathways, including tumor necrosis factor signaling via nuclear factor-κB. Previously described sepsis host response transcriptomic subtypes showed different MS1 cell abundances, and MS1 cell percentages positively correlated with the "quantitative sepsis response signature" and "molecular degree of perturbation" scores. Plasma biomarker levels, indicative of inflammation, endothelial cell activation, and coagulation activation, were largely similar between MS1 groups. In ICU patients without infection (n = 215), MS1 cell percentages and their relation with disease severity, shock, and host response dysregulation were highly similar to those in sepsis patients. CONCLUSIONS: High MS1 cell percentages are associated with increased disease severity and shock in critically ill patients with sepsis or a non-infectious condition. High MS1 cell abundance likely indicates broad immune dysregulation, entailing not only immunosuppression but also anomalies reflecting exaggerated inflammatory responses.

Derivation, validation, and transcriptomic assessment of pediatric septic shock phenotypes identified through latent profile analyses: Results from a prospective multi-center observational cohort.

Background: Sepsis poses a grave threat, especially among children, but treatments are limited due to clinical and biological heterogeneity among patients. Thus, there is an urgent need for precise subclassification of patients to guide therapeutic interventions. Methods: We used clinical, laboratory, and biomarker data from a prospective multi-center pediatric septic shock cohort to derive phenotypes using latent profile analyses. Thereafter, we trained a support vector machine model to assign phenotypes in a hold-out validation set. We tested interactions between phenotypes and common sepsis therapies on clinical outcomes and conducted transcriptomic analyses to better understand the phenotype-specific biology. Finally, we compared whether newly identified phenotypes overlapped with established gene-expression endotypes and tested the utility of an integrated subclassification scheme. Findings: Among 1,071 patients included, we identified two phenotypes which we named 'inflamed' (19.5%) and an 'uninflamed' phenotype (80.5%). The 'inflamed' phenotype had an over 4-fold risk of 28-day mortality relative to those 'uninflamed'. Transcriptomic analysis revealed overexpression of genes implicated in the innate immune response and suggested an overabundance of developing neutrophils, pro-T/NK cells, and NK cells among those 'inflamed'. There was no significant overlap between endotypes and phenotypes. However, an integrated subclassification scheme demonstrated varying survival probabilities when comparing endophenotypes. Interpretation: Our research underscores the reproducibility of latent profile analyses to identify clinical and biologically informative pediatric septic shock phenotypes with high prognostic relevance. Pending validation, an integrated subclassification scheme, reflective of the different facets of the host response, holds promise to inform targeted intervention among those critically ill.

A machine learning classifier using 33 host immune response mRNAs accurately distinguishes viral and non-viral acute respiratory illnesses in nasal swab samples.

BACKGROUND: Viral acute respiratory illnesses (viral ARIs) contribute significantly to human morbidity and mortality worldwide, but their successful treatment requires timely diagnosis of viral etiology, which is complicated by overlap in clinical presentation with the non-viral ARIs. Multiple pandemics in the twenty-first century to date have further highlighted the unmet need for effective monitoring of clinically relevant emerging viruses. Recent studies have identified conserved host response to viral infections in the blood. METHODS: We hypothesize that a similarly conserved host response in nasal samples can be utilized for diagnosis and to rule out viral infection in symptomatic patients when current diagnostic tests are negative. Using a multi-cohort analysis framework, we analyzed 1555 nasal samples across 10 independent cohorts dividing them into training and validation. RESULTS: Using six of the datasets for training, we identified 119 genes that are consistently differentially expressed in viral ARI patients (N = 236) compared to healthy controls (N = 146) and further down-selected 33 genes for classifier development. The resulting locked logistic regression-based classifier using the 33-mRNAs had AUC of 0.94 and 0.89 in the six training and four validation datasets, respectively. Furthermore, we found that although trained on healthy controls only, in the four validation datasets, the 33-mRNA classifier distinguished viral ARI from both healthy or non-viral ARI samples with > 80% specificity and sensitivity, irrespective of age, viral type, and viral load. Single-cell RNA-sequencing data showed that the 33-mRNA signature is dominated by macrophages and neutrophils in nasal samples. CONCLUSION: This proof-of-concept signature has potential to be adapted as a clinical point-of-care test ('RespVerity') to improve the diagnosis of viral ARIs.

Defining critical illness using immunological endotypes in patients with and without sepsis: a cohort study.

BACKGROUND: Sepsis is a heterogenous syndrome with limited therapeutic options. Identifying immunological endotypes through gene expression patterns in septic patients may lead to targeted interventions. We investigated whether patients admitted to a surgical intensive care unit (ICU) with sepsis and with high risk of mortality express similar endotypes to non-septic, but still critically ill patients using two multiplex transcriptomic metrics obtained both on admission to a surgical ICU and at set intervals. METHODS: We analyzed transcriptomic data from 522 patients in two single-site, prospective, observational cohorts admitted to surgical ICUs over a 5-year period ending in July 2020. Using an FDA-cleared analytical platform (nCounter FLEX®, NanoString, Inc.), we assessed a previously validated 29-messenger RNA transcriptomic classifier for likelihood of 30-day mortality (IMX-SEV-3) and a 33-messenger RNA transcriptomic endotype classifier. Clinical outcomes included all-cause mortality, development of chronic critical illness, and secondary infections. Univariate and multivariate analyses were performed to assess for true effect and confounding. RESULTS: Sepsis was associated with a significantly higher predicted and actual hospital mortality. At enrollment, the predominant endotype for both septic and non-septic patients was adaptive, though with significantly different distributions. Inflammopathic and coagulopathic septic patients, as well as inflammopathic non-septic patients, showed significantly higher frequencies of secondary infections compared to those with adaptive endotypes (p < 0.01). Endotypes changed during ICU hospitalization in 57.5% of patients. Patients who remained adaptive had overall better prognosis, while those who remained inflammopathic or coagulopathic had worse overall outcomes. For severity metrics, patients admitted with sepsis and a high predicted likelihood of mortality showed an inflammopathic (49.6%) endotype and had higher rates of cumulative adverse outcomes (67.4%). Patients at low mortality risk, whether septic or non-septic, almost uniformly presented with an adaptive endotype (100% and 93.4%, respectively). CONCLUSION: Critically ill surgical patients express different and evolving immunological endotypes depending upon both their sepsis status and severity of their clinical course. Future studies will elucidate whether endotyping critically ill, septic patients can identify individuals for targeted therapeutic interventions to improve patient management and outcomes.

Defining critical illness using immunological endotypes in patients with and without of sepsis: A cohort study.

Background: Sepsis is a heterogenous syndrome with limited therapeutic options. Identifying characteristic gene expression patterns, or endotypes, in septic patients may lead to targeted interventions. We investigated whether patients admitted to a surgical ICU with sepsis and with high risk of mortality express similar endotypes to non-septic, but still critically ill patients using two multiplex transcriptomic metrics obtained both on admission to a surgical intensive care unit (ICU) and at set intervals. Methods: We analyzed transcriptomic data from 522 patients in two single-site, prospective, observational cohorts admitted to surgical ICUs over a 5-year period ending in July 2020 . Using an FDA-cleared analytical platform (nCounter FLEX ® , NanoString, Inc.), we assessed a previously validated 29-messenger RNA transcriptomic classifier for likelihood of 30-day mortality (IMX-SEV-3) and a 33-messenger RNA transcriptomic endotype classifier. Clinical outcomes included all-cause (in-hospital, 30-, 90-day) mortality, development of chronic critical illness (CCI), and secondary infections. Univariate and multivariate analyses were performed to assess for true effect and confounding. Results: Sepsis was associated with a significantly higher predicted and actual hospital mortality. At enrollment, the predominant endotype for both septic and non-septic patients was adaptive , though with significantly different distributions. Inflammopathic and coagulopathic septic patients, as well as inflammopathic non-septic patients, showed significantly higher frequencies of secondary infections compared to those with adaptive endotypes (p<0.01). Endotypes changed during ICU hospitalization in 57.5% of patients. Patients who remained adaptive had overall better prognosis, while those who remained inflammopathic or coagulopathic had worse overall outcomes. For severity metrics, patients admitted with sepsis and a high predicted likelihood of mortality showed an inflammopathic (49.6%) endotype and had higher rates of cumulative adverse outcomes (67.4%). Patients at low mortality risk, whether septic or non-septic, almost uniformly presented with an adaptive endotype (100% and 93.4%, respectively). Conclusion : Critically ill surgical patients express different and evolving immunological endotypes depending upon both their sepsis status and severity of their clinical course. Future studies will elucidate whether endotyping critically ill, septic patients can identify individuals for targeted therapeutic interventions to improve patient management and outcomes.

Multisite validation of a host response signature for predicting likelihood of bacterial and viral infections in patients with suspected influenza.

BACKGROUND: Indiscriminate use of antimicrobials and antimicrobial resistance is a public health threat. IMX-BVN-1, a 29-host mRNA classifier, provides two separate scores that predict likelihoods of bacterial and viral infections in patients with suspected acute infections. We validated the performance of IMX-BVN-1 in adults attending acute health care settings with suspected influenza. METHOD: We amplified 29-host response genes in RNA extracted from blood by NanoString nCounter. IMX-BVN-1 calculated two scores to predict probabilities of bacterial and viral infections. Results were compared against the infection status (no infection; highly probable/possible infection; confirmed infection) determined by clinical adjudication. RESULTS: Amongst 602 adult patients (74.9% ED, 16.9% ICU, 8.1% outpatients), 7.6% showed in-hospital mortality and 15.5% immunosuppression. Median IMX-BVN-1 bacterial and viral scores were higher in patients with confirmed bacterial (0.27) and viral (0.62) infections than in those without bacterial (0.08) or viral (0.21) infection, respectively. The AUROC distinguishing bacterial from nonbacterial illness was 0.81 and 0.87 when distinguishing viral from nonviral illness. The bacterial top quartile's positive likelihood ratio (LR) was 4.38 with a rule-in specificity of 88%; the bacterial bottom quartile's negative LR was 0.13 with a rule-out sensitivity of 96%. Similarly, the viral top quartile showed an infinite LR with rule-in specificity of 100%; the viral bottom quartile had a LR of 0.22 and a rule-out sensitivity of 85%. CONCLUSION: IMX-BVN-1 showed high accuracy for differentiating bacterial and viral infections from noninfectious illness in patients with suspected influenza. Clinical utility of IMX-BVN will be validated following integration into a point of care system.

A robust host-response-based signature distinguishes bacterial and viral infections across diverse global populations.

Limited sensitivity and specificity of current diagnostics lead to the erroneous prescription of antibiotics. Host-response-based diagnostics could address these challenges. However, using 4,200 samples across 69 blood transcriptome datasets from 20 countries from patients with bacterial or viral infections representing a broad spectrum of biological, clinical, and technical heterogeneity, we show current host-response-based gene signatures have lower accuracy to distinguish intracellular bacterial infections from viral infections than extracellular bacterial infections. Using these 69 datasets, we identify an 8-gene signature to distinguish intracellular or extracellular bacterial infections from viral infections with an area under the receiver operating characteristic curve (AUROC) > 0.91 (85.9% specificity and 90.2% sensitivity). In prospective cohorts from Nepal and Laos, the 8-gene classifier distinguished bacterial infections from viral infections with an AUROC of 0.94 (87.9% specificity and 91% sensitivity). The 8-gene signature meets the target product profile proposed by the World Health Organization and others for distinguishing bacterial and viral infections.

A 29-MRNA HOST RESPONSE WHOLE-BLOOD SIGNATURE IMPROVES PREDICTION OF 28-DAY MORTALITY AND 7-DAY INTENSIVE CARE UNIT CARE IN ADULTS PRESENTING TO THE EMERGENCY DEPARTMENT WITH SUSPECTED ACUTE INFECTION AND/OR SEPSIS.

ABSTRACT: Background: Risk stratification of emergency department patients with suspected acute infections and/or suspected sepsis remains challenging. We prospectively validated a 29-messenger RNA host response classifier for predicting severity in these patients. Methods: We enrolled adults presenting with suspected acute infections and at least one vital sign abnormality to six emergency departments in Greece. Twenty-nine target host RNAs were quantified on NanoString nCounter and analyzed with the Inflammatix Severity 2 (IMX-SEV-2) classifier to determine risk scores as low, moderate, and high severity. Performance of IMX-SEV-2 for prediction of 28-day mortality was compared with that of lactate, procalcitonin, and quick sequential organ failure assessment (qSOFA). Results: A total of 397 individuals were enrolled; 38 individuals (9.6%) died within 28 days. Inflammatix Severity 2 classifier predicted 28-day mortality with an area under the receiver operator characteristics curve of 0.82 (95% confidence interval [CI], 0.74-0.90) compared with lactate, 0.66 (95% CI, 0.54-0.77); procalcitonin, 0.67 (95% CI, 0.57-0.78); and qSOFA, 0.81 (95% CI, 0.72-0.89). Combining qSOFA with IMX-SEV-2 improved prognostic accuracy from 0.81 to 0.89 (95% CI, 0.82-0.96). The high-severity (rule-in) interpretation band of IMX-SEV-2 demonstrated 96.9% specificity for predicting 28-day mortality, whereas the low-severity (rule-out) band had a sensitivity of 78.9%. Similarly, IMX-SEV-2 alone accurately predicted the need for day-7 intensive care unit care and further boosted overall accuracy when combined with qSOFA. Conclusions: Inflammatix Severity 2 classifier predicted 28-day mortality and 7-day intensive care unit care with high accuracy and boosted the accuracy of clinical scores when used in combination.

Evaluation of a Multivalent Transcriptomic Metric for Diagnosing Surgical Sepsis and Estimating Mortality Among Critically Ill Patients.

Importance: Rapid and accurate discrimination of sepsis and its potential severity currently require multiple assays with slow processing times that are often inconclusive in discerning sepsis from sterile inflammation. Objective: To analyze a whole-blood, multivalent, host-messenger RNA expression metric for estimating the likelihood of bacterial infection and 30-day mortality and compare performance of the metric with that of other diagnostic and prognostic biomarkers and clinical parameters. Design, Setting, and Participants: This prospective diagnostic and prognostic study was performed in the surgical intensive care unit (ICU) of a single, academic health science center. The analysis included 200 critically ill adult patients admitted with suspected sepsis (cohort A) or those at high risk for developing sepsis (cohort B) between July 1, 2020, and July 30, 2021. Exposures: Whole-blood sample measurements of a custom 29-messenger RNA transcriptomic metric classifier for likelihood of bacterial infection (IMX-BVN-3) or 30-day mortality (severity) (IMX-SEV-3) in a clinical-diagnostic laboratory setting using an analysis platform (510[k]-cleared nCounter FLEX; NanoString, Inc), compared with measurement of procalcitonin and interleukin 6 (IL-6) plasma levels, and maximum 24-hour sequential organ failure assessment (SOFA) scores. Main Outcomes and Measures: Estimated sepsis and 30-day mortality performance. Results: Among the 200 patients included (124 men [62.0%] and 76 women [38.0%]; median age, 62.5 [IQR, 47.0-72.0] years), the IMX-BVN-3 bacterial infection classifier had an area under the receiver operating characteristics curve (AUROC) of 0.84 (95% CI, 0.77-0.90) for discriminating bacterial infection at ICU admission, similar to procalcitonin (0.85 [95% CI, 0.79-0.90]; P = .79) and significantly better than IL-6 (0.67 [95% CI, 0.58-0.75]; P < .001). For estimating 30-day mortality, the IMX-SEV-3 metric had an AUROC of 0.81 (95% CI, 0.66-0.95), which was significantly better than IL-6 levels (0.57 [95% CI, 0.37-0.77]; P = .006), marginally better than procalcitonin levels (0.65 [95% CI, 0.50-0.79]; P = .06), and similar to the SOFA score (0.76 [95% CI, 0.62-0.91]; P = .48). Combining IMX-BVN-3 and IMX-SEV-3 with procalcitonin or IL-6 levels or SOFA scores did not significantly improve performance. Among patients with sepsis, IMX-BVN-3 scores decreased over time, reflecting the resolution of sepsis. In 11 individuals at high risk (cohort B) who subsequently developed sepsis during their hospital course, IMX-BVN-3 bacterial infection scores did not decline over time and peaked on the day of documented infection. Conclusions and Relevance: In this diagnostic and prognostic study, a novel, multivalent, transcriptomic metric accurately estimated the presence of bacterial infection and risk for 30-day mortality in patients admitted to a surgical ICU. The performance of this single transcriptomic metric was equivalent to or better than multiple alternative diagnostic and prognostic metrics when measured at admission and provided additional information when measured over time.

Redefining critical illness.

Research and practice in critical care medicine have long been defined by syndromes, which, despite being clinically recognizable entities, are, in fact, loose amalgams of heterogeneous states that may respond differently to therapy. Mounting translational evidence-supported by research on respiratory failure due to severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection-suggests that the current syndrome-based framework of critical illness should be reconsidered. Here we discuss recent findings from basic science and clinical research in critical care and explore how these might inform a new conceptual model of critical illness. De-emphasizing syndromes, we focus on the underlying biological changes that underpin critical illness states and that may be amenable to treatment. We hypothesize that such an approach will accelerate critical care research, leading to a richer understanding of the pathobiology of critical illness and of the key determinants of patient outcomes. This, in turn, will support the design of more effective clinical trials and inform a more precise and more effective practice at the bedside.

Prospective validation of a transcriptomic severity classifier among patients with suspected acute infection and sepsis in the emergency department.

BACKGROUND AND IMPORTANCE: mRNA-based host response signatures have been reported to improve sepsis diagnostics. Meanwhile, prognostic markers for the rapid and accurate prediction of severity in patients with suspected acute infections and sepsis remain an unmet need. IMX-SEV-2 is a 29-host-mRNA classifier designed to predict disease severity in patients with acute infection or sepsis. OBJECTIVE: Validation of the host-mRNA infection severity classifier IMX-SEV-2. DESIGN, SETTINGS AND PARTICIPANTS: Prospective, observational, convenience cohort of emergency department (ED) patients with suspected acute infections. OUTCOME MEASURES AND ANALYSIS: Whole blood RNA tubes were analyzed using independently trained and validated composite target genes (IMX-SEV-2). IMX-SEV-2-generated risk scores for severity were compared to the patient outcomes in-hospital mortality and 72-h multiorgan failure. MAIN RESULTS: Of the 312 eligible patients, 22 (7.1%) died in hospital and 58 (18.6%) experienced multiorgan failure within 72 h of presentation. For predicting in-hospital mortality, IMX-SEV-2 had a significantly higher area under the receiver operating characteristic (AUROC) of 0.84 [95% confidence intervals (CI), 0.76-0.93] compared to 0.76 (0.64-0.87) for lactate, 0.68 (0.57-0.79) for quick Sequential Organ Failure Assessment (qSOFA) and 0.75 (0.65-0.85) for National Early Warning Score 2 (NEWS2), ( P = 0.015, 0.001 and 0.013, respectively). For identifying and predicting 72-h multiorgan failure, the AUROC of IMX-SEV-2 was 0.76 (0.68-0.83), not significantly different from lactate (0.73, 0.65-0.81), qSOFA (0.77, 0.70-0.83) or NEWS2 (0.81, 0.75-0.86). CONCLUSION: The IMX-SEV-2 classifier showed a superior prediction of in-hospital mortality compared to biomarkers and clinical scores among ED patients with suspected infections. No improvement for predicting multiorgan failure was found compared to established scores or biomarkers. Identifying patients with a high risk of mortality or multiorgan failure may improve patient outcomes, resource utilization and guide therapy decision-making.

Diagnostic Host Gene Expression Analysis by Quantitative Reverse Transcription Loop-Mediated Isothermal Amplification to Discriminate between Bacterial and Viral Infections.

BACKGROUND: Early and accurate diagnosis of acute infections can help minimize the overprescription of antibiotics and improve patient outcomes. Discrimination between bacterial and viral etiologies in acute infection based on changes in host gene expression has been described. Unfortunately, established technologies used for gene expression profiling are typically expensive and slow, confounding integration into clinical workflows. Here we report the development of an ultra-rapid test system for host gene expression profiling from blood based on quantitative reverse transcription followed by loop-mediated isothermal amplification (qRT-LAMP). METHODS: We developed 10 messenger ribonucleic acid-specific assays based on qRT-LAMP targeting 7 informative biomarkers to discriminate viral from bacterial infections and 3 housekeeping reference genes. We optimized qRT-LAMP formulations to achieve a turnaround time of 12 min without sacrificing specificity or precision. The accuracy of the test system was verified utilizing blood samples from 57 patients and comparing qRT-LAMP results to profiles obtained using an orthogonal reference technology. RESULTS: We observed a Pearson coefficient of 0.90 between bacterial/viral metascores generated by qRT-LAMP and the reference technology. CONCLUSIONS: qRT-LAMP assays can provide sufficiently accurate gene expression profiling data to enable discrimination between bacterial and viral etiologies using an established set of biomarkers and a classification algorithm.

A robust gene expression signature for NASH in liver expression data.

Non-Alcoholic Fatty Liver Disease (NAFLD) is a progressive liver disease that affects up to 30% of worldwide population, of which up to 25% progress to Non-Alcoholic SteatoHepatitis (NASH), a severe form of the disease that involves inflammation and predisposes the patient to liver cirrhosis. Despite its epidemic proportions, there is no reliable diagnostics that generalizes to global patient population for distinguishing NASH from NAFLD. We performed a comprehensive multicohort analysis of publicly available transcriptome data of liver biopsies from Healthy Controls (HC), NAFLD and NASH patients. Altogether we analyzed 812 samples from 12 different datasets across 7 countries, encompassing real world patient heterogeneity. We used 7 datasets for discovery and 5 datasets were held-out for independent validation. Altogether we identified 130 genes significantly differentially expressed in NASH versus a mixed group of NAFLD and HC. We show that our signature is not driven by one particular group (NAFLD or HC) and reflects true biological signal. Using a forward search we were able to downselect to a parsimonious set of 19 mRNA signature with mean AUROC of 0.98 in discovery and 0.79 in independent validation. Methods for consistent diagnosis of NASH relative to NAFLD are urgently needed. We showed that gene expression data combined with advanced statistical methodology holds the potential to serve basis for development of such diagnostic tests for the unmet clinical need.

A 6-mRNA host response classifier in whole blood predicts outcomes in COVID-19 and other acute viral infections.

Predicting the severity of COVID-19 remains an unmet medical need. Our objective was to develop a blood-based host-gene-expression classifier for the severity of viral infections and validate it in independent data, including COVID-19. We developed a logistic regression-based classifier for the severity of viral infections and validated it in multiple viral infection settings including COVID-19. We used training data (N = 705) from 21 retrospective transcriptomic clinical studies of influenza and other viral illnesses looking at a preselected panel of host immune response messenger RNAs. We selected 6 host RNAs and trained logistic regression classifier with a cross-validation area under curve of 0.90 for predicting 30-day mortality in viral illnesses. Next, in 1417 samples across 21 independent retrospective cohorts the locked 6-RNA classifier had an area under curve of 0.94 for discriminating patients with severe vs. non-severe infection. Next, in independent cohorts of prospectively (N = 97) and retrospectively (N = 100) enrolled patients with confirmed COVID-19, the classifier had an area under curve of 0.89 and 0.87, respectively, for identifying patients with severe respiratory failure or 30-day mortality. Finally, we developed a loop-mediated isothermal gene expression assay for the 6-messenger-RNA panel to facilitate implementation as a rapid assay. With further study, the classifier could assist in the risk assessment of COVID-19 and other acute viral infections patients to determine severity and level of care, thereby improving patient management and reducing healthcare burden.

A Multi-mRNA Prognostic Signature for Anti-TNFα Therapy Response in Patients with Inflammatory Bowel Disease.

BACKGROUND: Anti-TNF-alpha (anti-TNFα) therapies have transformed the care and management of inflammatory bowel disease (IBD). However, they are expensive and ineffective in greater than 50% of patients, and they increase the risk of infections, liver issues, arthritis, and lymphoma. With 1.6 million Americans suffering from IBD and global prevalence on the rise, there is a critical unmet need in the use of anti-TNFα therapies: a test for the likelihood of therapy response. Here, as a proof-of-concept, we present a multi-mRNA signature for predicting response to anti-TNFα treatment to improve the efficacy and cost-to-benefit ratio of these biologics. METHODS: We surveyed public data repositories and curated four transcriptomic datasets (n = 136) from colonic and ileal mucosal biopsies of IBD patients (pretreatment) who were subjected to anti-TNFα therapy and subsequently adjudicated for response. We applied a multicohort analysis with a leave-one-study-out (LOSO) approach, MetaIntegrator, to identify significant differentially expressed (DE) genes between responders and non-responders and then used a greedy forward search to identify a parsimonious gene signature. We then calculated an anti-TNFα response (ATR) score based on this parsimonious gene signature to predict responder status and assessed discriminatory performance via an area-under-receiver operating-characteristic curve (AUROC). RESULTS: We identified 324 significant DE genes between responders and non-responders. The greedy forward search yielded seven genes that robustly distinguish anti-TNFα responders from non-responders, with an AUROC of 0.88 (95% CI: 0.70-1). The Youden index yielded a mean sensitivity of 91%, mean specificity of 76%, and mean accuracy of 86%. CONCLUSIONS: Our findings suggest that there is a robust transcriptomic signature for predicting anti-TNFα response in mucosal biopsies from IBD patients prior to treatment initiation. This seven-gene signature should be further investigated for its potential to be translated into a predictive test for clinical use.

A Transcriptomic Severity Metric That Predicts Clinical Outcomes in Critically Ill Surgical Sepsis Patients.

OBJECTIVES: Clinically deployable methods for the rapid and accurate prediction of sepsis severity that could elicit a meaningful change in clinical practice are currently lacking. We evaluated a whole-blood, multiplex host-messenger RNA expression metric, Inflammatix-Severity-2, for identifying septic, hospitalized patients' likelihood of 30-day mortality, development of chronic critical illness, discharge disposition, and/or secondary infections. DESIGN: Retrospective, validation cohort analysis. SETTING: Single, academic health center ICU. PATIENTS: Three hundred thirty-five critically ill adult surgical patients with sepsis. INTERVENTIONS: None. MEASUREMENTS AND MAIN RESULTS: Whole blood was collected in PAXgene Blood RNA collection tubes at 24 hours after sepsis diagnosis and analyzed using a custom 29-messenger RNA classifier (Inflammatix-Severity-2) in a Clinical Laboratory Improvement Amendments certified diagnostic laboratory using the NanoString FLEX platform. Among patients meeting Sepsis-3 criteria, the Inflammatix-Severity-2 severity score was significantly better (p < 0.05) at predicting secondary infections (area under the receiver operating curve 0.71) and adverse clinical outcomes (area under the receiver operating curve 0.75) than C-reactive protein, absolute lymphocyte counts, total WBC count, age, and Charlson comorbidity index (and better, albeit nonsignificantly, than interleukin-6 and Acute Physiology and Chronic Health Evaluation II). Using multivariate logistic regression analysis, only combining the Charlson comorbidity index (area under the receiver operating curve 0.80) or Acute Physiology and Chronic Health Evaluation II (area under the receiver operating curve 0.81) with Inflammatix-Severity-2 significantly improved prediction of adverse clinical outcomes, and combining with the Charlson comorbidity index for predicting 30-day mortality (area under the receiver operating curve 0.79). CONCLUSIONS: The Inflammatix-Severity-2 severity score was superior at predicting secondary infections and overall adverse clinical outcomes compared with other common metrics. Combining a rapidly measured transcriptomic metric with clinical or physiologic indices offers the potential to optimize risk-based resource utilization and patient management adjustments that may improve outcomes in surgical sepsis. Hospitalized patients who are septic and present with an elevated IMX-SEV2 severity score and preexisting comorbidities may be ideal candidates for clinical interventions aimed at reducing the risk of secondary infections and adverse clinical outcomes.