Factors associated with resistance to SARS-CoV-2 infection discovered using large-scale medical record data and machine learning.

There have been over 621 million cases of COVID-19 worldwide with over 6.5 million deaths. Despite the high secondary attack rate of COVID-19 in shared households, some exposed individuals do not contract the virus. In addition, little is known about whether the occurrence of COVID-19 resistance differs among people by health characteristics as stored in the electronic health records (EHR). In this retrospective analysis, we develop a statistical model to predict COVID-19 resistance in 8,536 individuals with prior COVID-19 exposure using demographics, diagnostic codes, outpatient medication orders, and count of Elixhauser comorbidities in EHR data from the COVID-19 Precision Medicine Platform Registry. Cluster analyses identified 5 patterns of diagnostic codes that distinguished resistant from non-resistant patients in our study population. In addition, our models showed modest performance in predicting COVID-19 resistance (best performing model AUROC = 0.61). Monte Carlo simulations conducted indicated that the AUROC results are statistically significant (p < 0.001) for the testing set. We hope to validate the features found to be associated with resistance/non-resistance through more advanced association studies.

Tandem phage-display for the identification of non-overlapping binding pairs of recombinant affinity reagents.

The 'sandwich' binding format, which uses two reagents that can bind simultaneously to a given analyte, is the gold standard in diagnostics and many biochemical techniques. One of the bottlenecks in creating a sandwich assay is identifying pairs of reagents that bind non-competitively to the target. To bridge this gap, we invented Megaprimer Shuffling for Tandem Affinity Reagents (MegaSTAR) to identify non-competitive binding pairs of recombinant affinity reagents through phage-display. The key innovation in MegaSTAR is the construction of a tandem library, in which two reagents are randomly-displayed on the phage surface. This is accomplished by using a pool of 300-nucleotide long 'megaprimers', which code for previously-selected reagents, to prime second strand synthesis of a single-stranded DNA template and generate millions of pair-wise combinations. The tandem library is then affinity selected to isolate pairs that both reagents contribute to binding the target. As a proof-of-concept, we used MegaSTAR to identify pairs of fibronectin type III monobodies for three human proteins. For each target, we could identify between five and fifteen unique pairs and successfully used a single pair in a sandwich assay. MegaSTAR is a versatile tool for generating sandwich ELISA-grade and bispecific reagents.

Streamlining the Pipeline for Generation of Recombinant Affinity Reagents by Integrating the Affinity Maturation Step.

Often when generating recombinant affinity reagents to a target, one singles out an individual binder, constructs a secondary library of variants, and affinity selects a tighter or more specific binder. To enhance the throughput of this general approach, we have developed a more integrated strategy where the "affinity maturation" step is part of the phage-display pipeline, rather than a follow-on process. In our new schema, we perform two rounds of affinity selection, followed by error-prone PCR on the pools of recovered clones, generation of secondary libraries, and three additional rounds of affinity selection, under conditions of off-rate competition. We demonstrate the utility of this approach by generating low nanomolar fibronectin type III (FN3) monobodies to five human proteins: ubiquitin-conjugating enzyme E2 R1 (CDC34), COP9 signalosome complex subunit 5 (COPS5), mitogen-activated protein kinase kinase 5 (MAP2K5), Splicing factor 3A subunit 1 (SF3A1) and ubiquitin carboxyl-terminal hydrolase 11 (USP11). The affinities of the resulting monobodies are typically in the single-digit nanomolar range. We demonstrate the utility of two binders by pulling down the targets from a spiked lysate of HeLa cells. This integrated approach should be applicable to directed evolution of any phage-displayed affinity reagent scaffold.