Peer-reviewed Publication: The Lancet (July 11 2022)

Seul Kee Byeon, Anil K Madugundu, Kishore Garapati, Madan Gopal Ramarajan, Mayank Saraswat, Praveen Kumar-M, Travis Hugh… more

COVID-19 is a multi-system disorder with high variability in clinical outcomes among patients who are admitted to hospital. Although some cytokines such as interleukin (IL)-6 are believed to be associated with severity, there are no early biomarkers that can reliably predict patients who are more likely to have adverse outcomes. Thus, it is crucial to discover predictive markers of serious complications. In this retrospective cohort study, we analysed samples from 455 participants with COVID-19 who had had a positive SARS-CoV-2 RT-PCR result between April 14, 2020, and Dec 1, 2020 and who had visited one of three Mayo Clinic sites in the USA (Minnesota, Arizona, or Florida) in the same period. These participants were assigned to three subgroups depending on disease severity as defined by the WHO ordinal scale of clinical improvement (outpatient, severe, or critical). Our control cohort comprised of 182 anonymised age-matched and sex-matched plasma samples that were available from the Mayo Clinic Biorepository and banked before the COVID-19 pandemic. We did a deep profiling of circulatory cytokines and other proteins, lipids, and metabolites from both cohorts. Most patient samples were collected before, or around the time of, hospital admission, representing ideal samples for predictive biomarker discovery. We used proximity extension assays to quantify cytokines and circulatory proteins and tandem mass spectrometry to measure lipids and metabolites. Biomarker discovery was done by applying an AutoGluon-tabular classifier to a multiomics dataset, producing a stacked ensemble of cutting-edge machine learning algorithms. Global proteomics and glycoproteomics on a subset of patient samples with matched pre-COVID-19 plasma samples was also done.

Correspondence to: Akhilesh Pandey (pandey.akhilesh@mayo.edu)

Peer-reviewed Publication: PNAS Nexus (July 04 2022)

Preprint: medRxiv (February 05 2022)

Featured in: National Geographic

Colin Pawlowski, Eli Silvert, John C. O'Horo, Patrick J. Lenehan, Douglas W Challener, Esteban Gnass, Karthik Murugadoss… more

Background: Case reports of patients infected with COVID-19 and influenza virus ("flurona") have raised questions around the prevalence and clinical significance of these reports. Methods: Epidemiological data from the HHS Protect Public Data Hub was analyzed to show trends in SARS-CoV-2 and influenza co-infection-related hospitalizations in the United States in relation to SARS-CoV-2 and influenza strain data from NCBI Virus and FluView. In addition, we retrospectively analyzed all cases of PCR-confirmed SARS-CoV-2 across the Mayo Clinic Enterprise from January 2020 to January 2022 and identified cases of influenza co-infections within two weeks of PCR-positive diagnosis date. Using a cohort from the Mayo Clinic with joint PCR testing data, we estimated the expected number of co-infection cases given the background prevalences of COVID-19 and influenza during the Wuhan (Original), Alpha, Delta, and Omicron waves of the pandemic. Findings: Considering data from all states of the United States using HHS Protect Public Data Hub, hospitalizations due to influenza co-infection with SARS-CoV-2 were seen to be highest in January 2022 compared to all previous months during the COVID-19 pandemic. Among 171,639 SARS-CoV-2-positive cases analyzed at Mayo Clinic between January 2020 and January 2022, only 73 cases of influenza co-infection were observed. Identified coinfected patients were relatively young (mean age: 28.4 years), predominantly male, and had few comorbidities. During the Delta era (June 16, 2021 to December 13, 2021), there were 9 lab-confirmed co-infection cases observed compared to 13.9 expected cases (95% CI: [12.7, 15.2]), and during the Omicron era (December 14, 2021 to January 17, 2022), there were 54 lab-confirmed co-infection cases compared to 80.9 expected cases (95% CI: [76.6, 85.1]). Conclusions: Reported co-infections of SARS-CoV-2 and influenza are rare. These co-infections have occurred throughout the COVID-19 pandemic and their prevalence can be explained by background rates of COVID-19 and influenza infection. Preliminary assessment of longitudinal EHR data suggests that most co-infections so far have been observed among relatively young and healthy patients. Further analysis is needed to assess the outcomes of "flurona" among subpopulations with risk factors for severe COVID-19 such as older age, obesity, and immunocompromised status. Significance Statement: Reports of COVID-19 and influenza co-infections ("flurona") have raised concern in recent months as both COVID-19 and influenza cases have increased to significant levels in the US. Here, we analyze trends in co-infection cases over the course of the pandemic to show that these co-infection cases are expected given the background prevalences of COVID-19 and influenza independently. In addition, from an initial analysis of these co-infection cases which have been observed at the Mayo Clinic, we find that these co-infection cases are extremely rare and have mostly been observed in relatively young, healthy patients.

Correspondence to: Venky Soundararajan (venky@nference.net) and Andrew Badley (badley.andrew@mayo.edu)

Peer-reviewed Publication: PNAS Nexus (July 4 2022)

Preprint: medRxiv (Jun 1 2022)

Featured in: Reuters · News Medical Life Sciences

Michiel JM Niesen, Karthik Murugadoss, Patrick J Lenehan, Aron Marchler-Bauer, Jiyao Wang, Ryan Connor, James Brister, A… more

The COVID-19 pandemic has seen the persistent emergence of immune-evasive SARS-CoV-2 variants under the selection pressure of natural and vaccination-acquired immunity. However, it is currently challenging to quantify how immunologically distinct a new variant is compared to all the prior variants to which a population has been exposed. Here we define Distinctiveness of SARS-CoV-2 sequences based on a proteome-wide comparison with all prior sequences from the same geographical region. We observe a correlation between Distinctiveness relative to contemporary sequences and future change in prevalence of a newly circulating lineage (Pearson r = 0.75), suggesting that the Distinctiveness of emergent SARS-CoV-2 lineages is associated with their competitive fitness. By assessing the Delta variant in India versus Brazil, we show that the same lineage can have different Distinctiveness-contributing positions in different geographical regions, depending on the other variants that previously circulated in those regions. More broadly, we analyze 944 combinations of geographic regions and time windows to demonstrate that the average Distinctiveness of a lineage in a country/time window is predictive of a greater than 20 percentage point future increase in infection prevalence after 56 days with an ROC AUC of 0.89. Finally, we find that positions that constitute known SARS-CoV-2 epitopes contribute disproportionately (20-fold higher than the average position) to Distinctiveness. Overall, this study suggests that real-time assessment of new SARS-CoV-2 variants in the context of prior regional herd exposure via Distinctiveness can augment genomic surveillance efforts.

Correspondence to: Venky Soundararajan (venky@nference.net)

Peer-reviewed Publication: PNAS Nexus (June 8 2022)

Preprint: medRxiv (September 7 2021)

Featured in: Nature · Star Tribune

Arjun Puranik, Patrick J Lenehan, John C O'Horo, Colin Pawlowski, Michiel J M Niesen, Abinash Virk, Melanie D Swift, Wal… more

COVID-19 vaccines are effective, but breakthrough infections have been increasingly reported. We conducted a test-negative case-control study to assess the durability of protection after full vaccination with BNT162b2 against polymerase chain reaction (PCR)-confirmed symptomatic SARS-CoV-2 infection, in a national medical practice from January 2021 through January 2022. We fit conditional logistic regression (CLR) models stratified on residential county and calendar time of testing to assess the association between time elapsed since vaccination and the odds of symptomatic infection or non-COVID-19 hospitalization (negative control), adjusted for several covariates. There were 5,985 symptomatic individuals with a positive test after full vaccination with BNT162b2 (cases) and 32,728 negative tests contributed by 27,753 symptomatic individuals after full vaccination (controls). The adjusted odds of symptomatic infection were higher 250 days after full vaccination versus at the date of full vaccination (Odds Ratio [OR]: 3.62, 95% CI: 2.52 to 5.20). The odds of infection were still lower 285 days after the first BNT162b2 dose as compared to 4 days after the first dose (OR: 0.50, 95% CI: 0.37 to 0.67), when immune protection approximates the unvaccinated status. Low rates of COVID-19 associated hospitalization or death in this cohort precluded analyses of these severe outcomes. The odds of non-COVID-19 associated hospitalization (negative control) decreased with time since vaccination, suggesting a possible underestimation of waning protection by this approach due to confounding factors. In summary, BNT162b2 strongly protected against symptomatic SARS-CoV-2 infection for at least 8 months after full vaccination, but the degree of protection waned significantly over this period.

Correspondence to: Venky Soundararajan (venky@nference.net)

Peer-reviewed Publication: PNAS Nexus (May 20 2022)

Arjun Puranik, Patrick J Lenehan, John C O'Horo, Colin Pawlowski, Abinash Virk, Melanie D Swift, Walter Kremers, A J Ven… more

COVID-19 vaccines are effective, but breakthrough infections have been increasingly reported. We conducted a test-negative case-control study to assess the durability of protection against symptomatic infection after vaccination with mRNA-1273. We fit conditional logistic regression (CLR) models stratified on residential county and calendar date of SARS-CoV-2 PCR testing to assess the association between the time elapsed since vaccination and the odds of symptomatic infection, adjusted for several covariates. There were 2364 symptomatic individuals who had a positive SARS-CoV-2 PCR test after full vaccination with mRNA-1273 (“cases”) and 12949 symptomatic individuals who contributed 15087 negative tests after full vaccination (“controls”). The odds of symptomatic infection were significantly higher 250 days after full vaccination compared to the date of full vaccination (Odds Ratio [OR]: 2.47, 95% confidence interval [CI]: 1.19–5.13). The odds of non-COVID-19 associated hospitalization and non-COVID-19 pneumonia (negative control outcomes) remained relatively stable over the same time interval (Day 250 ORNon-COVID Hospitalization: 0.68, 95% CI: 0.47–1.0; Day 250 ORNon-COVID Pneumonia: 1.11, 95% CI: 0.24–5.2). The odds of symptomatic infection remained significantly lower almost 300 days after the first mRNA-1273 dose as compared to 4 days after the first dose, when immune protection approximates the unvaccinated state (OR: 0.26, 95% CI: 0.17–0.39). Low rates of COVID-19 associated hospitalization or death in this cohort precluded analyses of these severe outcomes. In summary, mRNA-1273 robustly protected against symptomatic SARS-CoV-2 infection at least eight months after full vaccination, but the degree of protection waned over this time period.

Correspondence to: Venky Soundararajan (venky@nference.net) Andrew D Badley (Badley.Andrew@mayo.edu)