Repertoire-scale measures of antigen binding.

Antibodies and T cell receptors (TCRs) are the fundamental building blocks of adaptive immunity. Repertoire-scale functionality derives from their epitope-binding properties, just as macroscopic properties like temperature derive from microscopic molecular properties. However, most approaches to repertoire-scale measurement, including sequence diversity and entropy, are not based on antibody or TCR function in this way. Thus, they potentially overlook key features of immunological function. Here we present a framework that describes repertoires in terms of the epitope-binding properties of their constituent antibodies and TCRs, based on analysis of thousands of antibody-antigen and TCR-peptide-major-histocompatibility-complex binding interactions and over 400 high-throughput repertoires. We show that repertoires consist of loose overlapping classes of antibodies and TCRs with similar binding properties. We demonstrate the potential of this framework to distinguish specific responses vs. bystander activation in influenza vaccinees, stratify cytomegalovirus (CMV)-infected cohorts, and identify potential immunological "super-agers." Classes add a valuable dimension to the assessment of immune function.

Universal PCR for bacteria, mycobacteria, and fungi: a 10-year retrospective review of clinical indications and patient outcomes.

IMPORTANCE: Our work provides a retrospective analysis of universal PCR orders for bacteria, mycobacteria, and fungi across our institution across a 10-year period. We assessed the positivity rates for this diagnostic tool by test type and specimen type and, critically, studied whether and how the results influenced the outcomes from treatment change, to readmission, to death.

Autism gene Ube3a and seizures impair sociability by repressing VTA Cbln1.

Maternally inherited 15q11-13 chromosomal triplications cause a frequent and highly penetrant type of autism linked to increased gene dosages of UBE3A, which encodes a ubiquitin ligase with transcriptional co-regulatory functions. Here, using in vivo mouse genetics, we show that increasing UBE3A in the nucleus downregulates the glutamatergic synapse organizer Cbln1, which is needed for sociability in mice. Epileptic seizures also repress Cbln1 and are found to expose sociability impairments in mice with asymptomatic increases in UBE3A. This Ube3a-seizure synergy maps to glutamate neurons of the midbrain ventral tegmental area (VTA), where Cbln1 deletions impair sociability and weaken glutamatergic transmission. We provide preclinical evidence that viral-vector-based chemogenetic activation of, or restoration of Cbln1 in, VTA glutamatergic neurons reverses the sociability deficits induced by Ube3a and/or seizures. Our results suggest that gene and seizure interactions in VTA glutamatergic neurons impair sociability by downregulating Cbln1, a key node in the expanding protein interaction network of autism genes.

The Limit of Detection Matters: The Case for Benchmarking Severe Acute Respiratory Syndrome Coronavirus 2 Testing.

BACKGROUND: Resolving the coronavirus disease 2019 (COVID-19) pandemic requires diagnostic testing to determine which individuals are infected with severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). The current gold standard is to perform reverse-transcription polymerase chain reaction (PCR) on nasopharyngeal samples. Best-in-class assays demonstrate a limit of detection (LoD) of approximately 100 copies of viral RNA per milliliter of transport media. However, LoDs of currently approved assays vary over 10,000-fold. Assays with higher LoDs will miss infected patients. However, the relative clinical sensitivity of these assays remains unknown. METHODS: Here we model the clinical sensitivities of assays based on their LoD. Cycle threshold (Ct) values were obtained from 4700 first-time positive patients using the Abbott RealTime SARS-CoV-2 Emergency Use Authorization test. We derived viral loads from Ct based on PCR principles and empiric analysis. A sliding scale relationship for predicting clinical sensitivity was developed from analysis of viral load distribution relative to assay LoD. RESULTS: Ct values were reliably repeatable over short time testing windows, providing support for use as a tool to estimate viral load. Viral load was found to be relatively evenly distributed across log10 bins of incremental viral load. Based on these data, each 10-fold increase in LoD is expected to lower assay sensitivity by approximately 13%. CONCLUSIONS: The assay LoD meaningfully impacts clinical performance of SARS-CoV-2 tests. The highest LoDs on the market will miss a majority of infected patients. Assays should therefore be benchmarked against a universal standard to allow cross-comparison of SARS-CoV-2 detection methods.

Cooperation under Pressure: Lessons from the COVID-19 Swab Crisis.

The early months of the COVID-19 pandemic were marked by a desperate need for nasopharyngeal swabs to test for SARS-CoV-2, with demand far outstripping supply. April marked the anniversary of an unprecedented nationwide multibusiness/multihospital partnership that successfully met this need, a fitting occasion to review lessons learned. Here, I briefly recount the key events, constraints, and thought processes surrounding the effort in order to better inform responses to future crises. Overall, the experience was a strong validation of Joy's Law and illustrated the utility of recognizing temptations to avoid, in order to reap the rewards of cooperation. I conclude by summarizing lessons learned.

Nasal Swab Performance by Collection Timing, Procedure, and Method of Transport for Patients with SARS-CoV-2.

The urgent need for large-scale diagnostic testing for SARS-CoV-2 has prompted interest in sample collection methods of sufficient sensitivity to replace nasopharynx (NP) sampling. Nasal swab samples are an attractive alternative; however, previous studies have disagreed over how nasal sampling performs relative to NP sampling. Here, we compared nasal versus NP specimens collected by health care workers in a cohort of individuals clinically suspected of COVID-19 as well as SARS-CoV-2 reverse transcription (RT)-PCR-positive outpatients undergoing follow-up. We compared subjects being seen for initial evaluation versus follow-up, two different nasal swab collection protocols, and three different transport conditions, including traditional viral transport media (VTM) and dry swabs, on 307 total study participants. We compared categorical results and viral loads to those from standard NP swabs collected at the same time from the same patients. All testing was performed by RT-PCR on the Abbott SARS-CoV-2 RealTime emergency use authorization (EUA) (limit of detection [LoD], 100 copies viral genomic RNA/ml transport medium). We found low concordance overall, with Cohen's kappa (κ) of 0.49, with high concordance only for subjects with very high viral loads. We found medium concordance for testing at initial presentation (κ = 0.68) and very low concordance for follow-up testing (κ = 0.27). Finally, we show that previous reports of high concordance may have resulted from measurement using assays with sensitivity of ≥1,000 copies/ml. These findings suggest nasal-swab testing be used for situations in which viral load is expected to be high, as we demonstrate that nasal swab testing is likely to miss patients with low viral loads.

Open Development and Clinical Validation of Multiple 3D-Printed Nasopharyngeal Collection Swabs: Rapid Resolution of a Critical COVID-19 Testing Bottleneck.

The pandemic caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has caused a severe international shortage of the nasopharyngeal swabs that are required for collection of optimal specimens, creating a critical bottleneck blocking clinical laboratories' ability to perform high-sensitivity virological testing for SARS-CoV-2. To address this crisis, we designed and executed an innovative, cooperative, rapid-response translational-research program that brought together health care workers, manufacturers, and scientists to emergently develop and clinically validate new swabs for immediate mass production by 3D printing. We performed a multistep preclinical evaluation of 160 swab designs and 48 materials from 24 companies, laboratories, and individuals, and we shared results and other feedback via a public data repository (http://github.com/rarnaout/Covidswab/). We validated four prototypes through an institutional review board (IRB)-approved clinical trial that involved 276 outpatient volunteers who presented to our hospital's drive-through testing center with symptoms suspicious for COVID-19. Each participant was swabbed with a reference swab (the control) and a prototype, and SARS-CoV-2 reverse transcriptase PCR (RT-PCR) results were compared. All prototypes displayed excellent concordance with the control (κ = 0.85 to 0.89). Cycle threshold (CT ) values were not significantly different between each prototype and the control, supporting the new swabs' noninferiority (Mann-Whitney U [MWU] test, P > 0.05). Study staff preferred one of the prototypes over the others and preferred the control swab overall. The total time elapsed between identification of the problem and validation of the first prototype was 22 days. Contact information for ordering can be found at http://printedswabs.org Our experience holds lessons for the rapid development, validation, and deployment of new technology for this pandemic and beyond.

Antibody repertoire deep sequencing reveals antigen-independent selection in maturing B cells.

Antibody repertoires are known to be shaped by selection for antigen binding. Unexpectedly, we now show that selection also acts on a non-antigen-binding antibody region: the heavy-chain variable (VH)-encoded "elbow" between variable and constant domains. By sequencing 2.8 million recombined heavy-chain genes from immature and mature B-cell subsets in mice, we demonstrate a striking gradient in VH gene use as pre-B cells mature into follicular and then into marginal zone B cells. Cells whose antibodies use VH genes that encode a more flexible elbow are more likely to mature. This effect is distinct from, and exceeds in magnitude, previously described maturation-associated changes in heavy-chain complementarity determining region 3, a key antigen-binding region, which arise from junctional diversity rather than differential VH gene use. Thus, deep sequencing reveals a previously unidentified mode of B-cell selection.

ct2vl: A Robust Public Resource for Converting SARS-CoV-2 Ct Values to Viral Loads.

The amount of SARS-CoV-2 in a sample is often measured using Ct values. However, the same Ct value may correspond to different viral loads on different platforms and assays, making them difficult to compare from study to study. To address this problem, we developed ct2vl, a Python package that converts Ct values to viral loads for any RT-qPCR assay/platform. The method is novel in that it is based on determining the maximum PCR replication efficiency, as opposed to fitting a sigmoid (S-shaped) curve relating signal to cycle number. We calibrated ct2vl on two FDA-approved platforms and validated its performance using reference-standard material, including sensitivity analysis. We found that ct2vl-predicted viral loads were highly accurate across five orders of magnitude, with 1.6-fold median error (for comparison, viral loads in clinical samples vary over 10 orders of magnitude). The package has 100% test coverage. We describe installation and usage both from the Unix command-line and from interactive Python environments. ct2vl is freely available via the Python Package Index (PyPI). It facilitates conversion of Ct values to viral loads for clinical investigators, basic researchers, and test developers for any RT-qPCR platform. It thus facilitates comparison among the many quantitative studies of SARS-CoV-2 by helping render observations in a natural, universal unit of measure.

SARS-CoV-2 live virus culture and sample freeze-thaw stability.

The effect of freeze-thaw on SARS-CoV-2 viral viability is not well established. We isolated virus from 31 split clinical samples cultured fresh or after a 7- or 17/18-day freeze. We found that freeze-thaw did not significantly affect viral culture isolation. Therefore, frozen samples may be used to assess SARS-CoV-2 infectiousness.

Case report and mini-review: Sarcina ventriculi in the stomach of an 80-year-old female.

Sarcina ventriculi, also known as Zymosarcina ventriculi and, incorrectly, as Clostridium ventriculi, is rarely encountered in clinical settings. A patient with a complicated gastrointestinal (GI) history, who was acutely presenting with small-bowel obstruction, was found to be colonized by S. ventriculi. The distinctive morphology of this species, with large Gram-variable cocci (up to 3 µm) arranged in two-by-two cuboid clusters reaching up to 20 µm, was key in identifying this bacterium in a stomach biopsy specimen. Sarcina ventriculi appears to be ubiquitously found in nature, and related bacterial species can cause GI-related disease in various animals. Clinical manifestations in humans are broad and often related to other underlying comorbidities. Isolation of S. ventriculi in the laboratory requires anaerobic culture on select media but its absence from standard MALDI-TOF databases complicates identification. Susceptibility data do not exist, so empiric treatment is the only option for this rare pathogen.

Predicting the cost and pace of pharmacogenomic advances: an evidence-based study.

BACKGROUND: Adverse outcomes associated with prescription drug use are common and costly. Many adverse outcomes can be avoided through pharmacogenomics: choosing and dosing of existing drugs according to a person's genomic variants. Finding and validating associations between outcomes and genomic variants and developing guidelines for avoiding drug-related adverse outcomes will require further research; however, no data-driven estimates yet exist for the time or money required for completing this research. METHODS: We identified examples of associations between adverse outcomes and genomic variants. We used these examples to estimate the time and money required to identify and confirm other associations, including the cost of failures, and to develop and validate pharmacogenomic dosing guidelines for them. We built a Monte Carlo model to estimate the time and financial costs required to cut the overall rate of drug-related adverse outcomes by meaningful amounts. We analyzed the model's predictions for a broad range of assumptions. RESULTS AND CONCLUSIONS: Our model projected that the development of guidelines capable of cutting overall drug-related adverse outcomes by 25%-50% with current approaches will require investment of single-digit billions of dollars and take 20 years. The model forecasts a pump-priming phase of 5-7 years, which would require expenditures of hundreds of millions of dollars, with little apparent return on investment. The single most important parameter was the extent to which genomic variants cause adverse outcomes. The size of the labor force was not a limiting factor. A "50 000 Pharmacogenomes Project" could speed progress. Our approach provides a template for other areas of genomic research.

ENRICHing medical imaging training sets enables more efficient machine learning.

OBJECTIVE: Deep learning (DL) has been applied in proofs of concept across biomedical imaging, including across modalities and medical specialties. Labeled data are critical to training and testing DL models, but human expert labelers are limited. In addition, DL traditionally requires copious training data, which is computationally expensive to process and iterate over. Consequently, it is useful to prioritize using those images that are most likely to improve a model's performance, a practice known as instance selection. The challenge is determining how best to prioritize. It is natural to prefer straightforward, robust, quantitative metrics as the basis for prioritization for instance selection. However, in current practice, such metrics are not tailored to, and almost never used for, image datasets. MATERIALS AND METHODS: To address this problem, we introduce ENRICH-Eliminate Noise and Redundancy for Imaging Challenges-a customizable method that prioritizes images based on how much diversity each image adds to the training set. RESULTS: First, we show that medical datasets are special in that in general each image adds less diversity than in nonmedical datasets. Next, we demonstrate that ENRICH achieves nearly maximal performance on classification and segmentation tasks on several medical image datasets using only a fraction of the available images and without up-front data labeling. ENRICH outperforms random image selection, the negative control. Finally, we show that ENRICH can also be used to identify errors and outliers in imaging datasets. CONCLUSIONS: ENRICH is a simple, computationally efficient method for prioritizing images for expert labeling and use in DL.

Scaling Monte-Carlo-Based Inference on Antibody and TCR Repertoires.

Previously, it has been shown that maximum-entropy models of immune-repertoire sequence can be used to determine a person's vaccination status. However, this approach has the drawback of requiring a computationally intensive method to compute each model's partition function (Z), the normalization constant required for calculating the probability that the model will generate a given sequence. Specifically, the method required generating approximately 1010 sequences via Monte-Carlo simulations for each model. This is impractical for large numbers of models. Here we propose an alternative method that requires estimating Z this way for only a few models: it then uses these expensive estimates to estimate Z more efficiently for the remaining models. We demonstrate that this new method enables the generation of accurate estimates for 27 models using only three expensive estimates, thereby reducing the computational cost by an order of magnitude. Importantly, this gain in efficiency is achieved with only minimal impact on classification accuracy. Thus, this new method enables larger-scale investigations in computational immunology and represents a useful contribution to energy-based modeling more generally.

Sars-Cov-2 antigen tests predict infectivity based on viral culture: comparison of antigen, PCR viral load, and viral culture testing on a large sample cohort.

OBJECTIVE: To define the relationship of SARS-CoV-2 antigen, viral load determined by RT-qPCR, and viral culture detection. Presumptively, viral culture can provide a surrogate measure for infectivity of sampled individuals and thereby inform how and where to most appropriately deploy antigen and nucleic acid amplification-based diagnostic testing modalities. METHODS: We compared the antigen testing results from three lateral flow and one microfluidics assay to viral culture detection and viral load determination performed in parallel in up to 189 nasopharyngeal swab samples positive for SARS-CoV-2. Sample viral loads, determined by RT-qPCR, were distributed across the range of viral load values observed in our testing population. RESULTS: Antigen tests were predictive of viral culture positivity, with the LumiraDx microfluidics method showing enhanced sensitivity (90%; 95% CI 83-94%) compared with the BD Veritor (74%, 95% CI 65-81%), CareStart (74%, 95% CI 65-81%) and Oscar Corona (74%, 95% CI 65-82%) lateral flow antigen tests. Antigen and viral culture positivity were also highly correlated with sample viral load, with areas under the receiver operator characteristic curves of 0.94 to 0.97 and 0.92, respectively. A viral load threshold of 100 000 copies/mL was 95% sensitive (95% CI, 90-98%) and 72% specific (95% CI, 60-81%) for predicting viral culture positivity. Adjusting for sample dilution inherent in our study design, sensitivities of antigen tests were ≥95% for detection of viral culture positive samples with viral loads >106 genome copies/mL, although specificity of antigen testing was imperfect. DISCUSSION: Antigen testing results and viral culture were correlated. For culture positive samples, the sensitivity of antigen tests was high at high viral loads that are likely associated with significant infectivity. Therefore, our data provides support for use of antigen testing in ruling out infectivity at the time of sampling.

$\textit{greylock}$: A Python Package for Measuring The Composition of Complex Datasets.

Machine-learning datasets are typically characterized by measuring their size and class balance. However, there exists a richer and potentially more useful set of measures, termed diversity measures, that incorporate elements' frequencies and between-element similarities. Although these have been available in the R and Julia programming languages for other applications, they have not been as readily available in Python, which is widely used for machine learning, and are not easily applied to machine-learning-sized datasets without special coding considerations. To address these issues, we developed $\textit{greylock}$, a Python package that calculates diversity measures and is tailored to large datasets. $\textit{greylock}$ can calculate any of the frequency-sensitive measures of Hill's D-number framework, and going beyond Hill, their similarity-sensitive counterparts (Greylock is a mountain). $\textit{greylock}$ also outputs measures that compare datasets (beta diversities). We first briefly review the D-number framework, illustrating how it incorporates elements' frequencies and between-element similarities. We then describe $\textit{greylock}$'s key features and usage. We end with several examples - immunomics, metagenomics, computational pathology, and medical imaging - illustrating $\textit{greylock}$'s applicability across a range of dataset types and fields.

Visualizing omicron: COVID-19 deaths vs. cases over time.

For most of the COVID-19 pandemic, the daily focus has been on the number of cases, and secondarily, deaths. The most recent wave was caused by the omicron variant, first identified at the end of 2021 and the dominant variant through the first part of 2022. South Africa, one of the first countries to experience and report data regarding omicron (variant 21.K), reported far fewer deaths, even as the number of reported cases rapidly eclipsed previous peaks. However, as the omicron wave has progressed, time series show that it has been markedly different from prior waves. To more readily visualize the dynamics of cases and deaths, it is natural to plot deaths per million against cases per million. Unlike the time-series plots of cases or deaths that have become daily features of pandemic updates during the pandemic, which have time as the x-axis, in a plot of deaths vs. cases, time is implicit, and is indicated in relation to the starting point. Here we present and briefly examine such plots from a number of countries and from the world as a whole, illustrating how they summarize features of the pandemic in ways that illustrate how, in most places, the omicron wave is very different from those that came before. Code for generating these plots for any country is provided in an automatically updating GitHub repository.