Peer-reviewed Publication: MDPI Vaccines (September 9 2022)

Preprint: OSF Preprints (December 2 2021)

Featured in: Washington Post · Reuters · USA Today · New York Times · WSJ · The Scientist · Medscape · Bloomberg · Daily Mail · CNN · The Hill · Fortune · Euronews · Economic Times · Financial Times

AJ Venkatakrishnan Praveen Anand Patrick J Lenehan Rohit Suratekar Bharathwaj Raghunathan Michiel J.M. Niesen Venky Soun… more

The emergence of a heavily mutated SARS-CoV-2 variant (Omicron; Pango lineage B.1.1.529 and BA sublineages) and its rapid spread to over 75 countries raised a global public health alarm. Characterizing the mutational profile of Omicron is necessary to interpret its clinical phenotypes which are shared with or distinctive from those of other SARS-CoV-2 variants. We compared the mutations of the initially circulating Omicron variant (now known as BA.1) with prior variants of concern (Alpha, Beta, Gamma, and Delta), variants of interest (Lambda, Mu, Eta, Iota, and Kappa), and ~1500 SARS-CoV-2 lineages constituting ~5.8 million SARS-CoV-2 genomes. Omicron’s Spike protein harbors 26 amino acid mutations (23 substitutions, 2 deletions, and 1 insertion) that are distinct compared to other variants of concern. While the substitution and deletion mutations appeared in previous SARS-CoV-2 lineages, the insertion mutation (ins214EPE) was not previously observed in any other SARS-CoV-2 lineage. Here, we consider and discuss various mechanisms through which the nucleotide sequence encoding for ins214EPE could have been acquired, including local duplication, polymerase slippage, and template switching. Although we are not able to definitively determine the mechanism, we highlight the plausibility of template switching. Analysis of the homology of the inserted nucleotide sequence and flanking regions suggests that this template-switching event could have involved the genomes of SARS-CoV-2 variants (e.g., the B.1.1 strain), other human coronaviruses that infect the same host cells as SARS-CoV-2 (e.g., HCoV-OC43 or HCoV-229E), or a human transcript expressed in a host cell that was infected by the Omicron precursor.

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

Preprint: medRxiv (August 15 2022)

Featured in: News Medical Life Sciences

Mihika Nadig, Michiel Niesen, Patrick J Lenehan, Vineet Agarwal, Jason Ross, Sankar Ardhanari, Aiveliagaram J Venkatakri… more

Omicron sub-lineages such as BA2.12.1 and BA5 have breached prior infection-induced immunity and vaccine-induced immunity. This capacity of Omicron to reinfect patients calls for a characterization of vaccination, infection, and reinfection patterns. We analyzed de-identified longitudinal electronic health records for 389,746 individuals (88,679 fully-vaccinated, 184,205 boosted, 73,184 with prior infection) across a multi-state health system. Compared to individuals with only full vaccination, the rates of SARS-CoV-2 infections in the Omicron era were reduced for individuals with additional prior infection (1.4 to 1.8-fold reduced, depending on vaccine status) or booster vaccination (1.3 to 2.0-fold reduced). Although prior infection was associated with lower incidence of SARS-CoV-2 infection, we found that the relative risk (RR) of infections for individuals with prior infection has increased during Omicron. During October, 2021, RR was 0.11 [0.10-0.13, 95% CI] while during May, 2022, it increased to 0.57 [0.46-0.68, 95% CI], suggesting an increase in reinfections with Omicron. Furthermore, we found that time since prior infection is associated with risk of reinfection, providing evidence of waning immunity. Prior infections before June, 2021, were associated with marginal reduction in risk of infection (eg., RR = 0.80 [0.68-0.90] for prior infection during January, 2021), while recent prior infections were associated with significant reduction in risk (eg., RR = 0.24 [0.20-0.29, 95% CI] for prior infection during November, 2021). Despite an observed increase in reinfections and vaccine breakthrough infections, our findings emphasize the protective effect of natural and vaccine immunity, with prior infection providing ~6 months of protection from reinfection.

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

Peer-reviewed Publication: Lancet: Digital Health (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)