Investigating Ethical Tradeoffs in Crisis Standards of Care through Simulation of Ventilator Allocation Protocols.

Introduction: Arguments over the appropriate Crisis Standards of Care (CSC) for public health emergencies often assume that there is a tradeoff between saving the most lives, saving the most life-years, and preventing racial disparities. However, these assumptions have rarely been explored empirically. To quantitatively characterize possible ethical tradeoffs, we aimed to simulate the implementation of five proposed CSC protocols for rationing ventilators in the context of the COVID-19 pandemic. Methods: A Monte Carlo simulation was used to estimate the number of lives saved and life-years saved by implementing clinical acuity-, comorbidity- and age-based CSC protocols under different shortage conditions. This model was populated with patient data from 3707 adult admissions requiring ventilator support in a New York hospital system between April 2020 and May 2021. To estimate lives and life-years saved by each protocol, we determined survival to discharge and estimated remaining life expectancy for each admission. Results: The simulation demonstrated stronger performance for age- and comorbidity-sensitive protocols. For a capacity of 1 bed per 2 patients, ranking by age bands saves approximately 28.7 lives and 3408 life-years per thousand patients, while ranking by Sequential Organ Failure Assessment (SOFA) bands saved the fewest lives (13.2) and life-years (416). For all protocols, we observed a positive correlation between lives saved and life-years saved. For all protocols except lottery and the banded SOFA, significant disparities in lives saved and life-years saved were noted between White non-Hispanic, Black non-Hispanic, and Hispanic sub-populations. Conclusion: While there is significant variance in the number of lives saved and life-years saved, we did not find a tradeoff between saving the most lives and saving the most life-years. Moreover, concerns about racial discrimination in triage protocols require thinking carefully about the tradeoff between enforcing equality of survival rates and maximizing the lives saved in each sub-population.

Ventilation during COVID-19 in a school for students with intellectual and developmental disabilities (IDD).

BACKGROUND: This study examined the correlation of classroom ventilation (air exchanges per hour (ACH)) and exposure to CO2 ≥1,000 ppm with the incidence of SARS-CoV-2 over a 20-month period in a specialized school for students with intellectual and developmental disabilities (IDD). These students were at a higher risk of respiratory infection from SARS-CoV-2 due to challenges in tolerating mitigation measures (e.g. masking). One in-school measure proposed to help mitigate the risk of SARS-CoV-2 infection in schools is increased ventilation. METHODS: We established a community-engaged research partnership between the University of Rochester and the Mary Cariola Center school for students with IDD. Ambient CO2 levels were measured in 100 school rooms, and air changes per hour (ACH) were calculated. The number of SARS-CoV-2 cases for each room was collected over 20 months. RESULTS: 97% of rooms had an estimated ACH ≤4.0, with 7% having CO2 levels ≥2,000 ppm for up to 3 hours per school day. A statistically significant correlation was found between the time that a room had CO2 levels ≥1,000 ppm and SARS-CoV-2 PCR tests normalized to room occupancy, accounting for 43% of the variance. No statistically significant correlation was found for room ACH and per-room SARS-CoV-2 cases. Rooms with ventilation systems using MERV-13 filters had lower SARS-CoV-2-positive PCR counts. These findings led to ongoing efforts to upgrade the ventilation systems in this community-engaged research project. CONCLUSIONS: There was a statistically significant correlation between the total time of room CO2 concentrations ≥1,000 and SARS-CoV-2 cases in an IDD school. Merv-13 filters appear to decrease the incidence of SARS-CoV-2 infection. This research partnership identified areas for improving in-school ventilation.

Outpatient COVID-19 convalescent plasma recipient antibody thresholds correlated to reduced hospitalizations within a randomized trial.

BACKGROUNDCOVID-19 convalescent plasma (CCP) virus-specific antibody levels that translate into recipient posttransfusion antibody levels sufficient to prevent disease progression are not defined.METHODSThis secondary analysis correlated donor and recipient antibody levels to hospitalization risk among unvaccinated, seronegative CCP recipients within the outpatient, double-blind, randomized clinical trial that compared CCP to control plasma. The majority of COVID-19 CCP arm hospitalizations (15/17, 88%) occurred in this unvaccinated, seronegative subgroup. A functional cutoff to delineate recipient high versus low posttransfusion antibody levels was established by 2 methods: (i) analyzing virus neutralization-equivalent anti-Spike receptor-binding domain immunoglobulin G (anti-S-RBD IgG) responses in donors or (ii) receiver operating characteristic (ROC) curve analysis.RESULTSSARS-CoV-2 anti-S-RBD IgG antibody was volume diluted 21.3-fold into posttransfusion seronegative recipients from matched donor units. Virus-specific antibody delivered was approximately 1.2 mg. The high-antibody recipients transfused early (symptom onset within 5 days) had no hospitalizations. A CCP-recipient analysis for antibody thresholds correlated to reduced hospitalizations found a statistical significant association between early transfusion and high antibodies versus all other CCP recipients (or control plasma), with antibody cutoffs established by both methods-donor-based virus neutralization cutoffs in posttransfusion recipients (0/85 [0%] versus 15/276 [5.6%]; P = 0.03) or ROC-based cutoff (0/94 [0%] versus 15/267 [5.4%]; P = 0.01).CONCLUSIONIn unvaccinated, seronegative CCP recipients, early transfusion of plasma units in the upper 30% of study donors' antibody levels reduced outpatient hospitalizations. High antibody level plasma units, given early, should be reserved for therapeutic use.TRIAL REGISTRATIONClinicalTrials.gov NCT04373460.FUNDINGDepartment of Defense (W911QY2090012); Defense Health Agency; Bloomberg Philanthropies; the State of Maryland; NIH (3R01AI152078-01S1, U24TR001609-S3, 1K23HL151826NIH); the Mental Wellness Foundation; the Moriah Fund; Octapharma; the Healthnetwork Foundation; the Shear Family Foundation; the NorthShore Research Institute; and the Rice Foundation.

Discrete-state models identify pathway specific B cell states across diseases and infections at single-cell resolution.

Oxygen (O2) regulated pathways modulate B cell activation, migration and proliferation during infection, vaccination, and other diseases. Modeling these pathways in health and disease is critical to understand B cell states and ways to mediate them. To characterize B cells by their activation of O2 regulated pathways we develop pathway specific discrete state models using previously published single-cell RNA-sequencing (scRNA-seq) datasets from isolated B cells. Specifically, Single Cell Boolean Omics Network Invariant-Time Analysis (scBONITA) was used to infer logic gates for known pathway topologies. The simplest inferred set of logic gates that maximized the number of "OR" interactions between genes was used to simulate B cell networks involved in oxygen sensing until they reached steady network states (attractors). By focusing on the attractors that best represented sequenced cells, we identified genes critical in determining pathway specific cellular states that corresponded to diseased and healthy B cell phenotypes. Specifically, we investigate the transendothelial migration, regulation of actin cytoskeleton, HIF1A, and Citrate Cycle pathways. Our analysis revealed attractors that resembled the state of B cell exhaustion in HIV+ patients as well as attractors that promoted anerobic metabolism, angiogenesis, and tumorigenesis in breast cancer patients, which were eliminated after neoadjuvant chemotherapy (NACT). Finally, we investigated the attractors to which the Azimuth-annotated B cells mapped and found that attractors resembling B cells from HIV+ patients encompassed a significantly larger number of atypical memory B cells than HIV- attractors. Meanwhile, attractors resembling B cells from breast cancer patients post NACT encompassed a reduced number of atypical memory B cells compared to pre-NACT attractors.

Ventilation during COVID-19 in a school for students with intellectual and developmental disabilities (IDD).

Background: This study examined the correlation of classroom ventilation (air exchanges per hour (ACH)) and exposure to CO2 ≥1,000 ppm with the incidence of SARS-CoV-2 over a 20-month period in a specialized school for students with intellectual and developmental disabilities (IDD). These students were at a higher risk of respiratory infection from SARS-CoV-2 due to challenges in tolerating mitigation measures (e.g. masking). One in-school measure proposed to help mitigate the risk of SARS-CoV-2 infection in schools is increased ventilation. Methods: We established a community-engaged research partnership between the University of Rochester and the Mary Cariola Center school for students with IDD. Ambient CO2 levels were measured in 100 school rooms, and air changes per hour (ACH) were calculated. The number of SARS-CoV-2 cases for each room was collected over 20 months. Results: 97% of rooms had an estimated ACH ≤4.0, with 7% having CO2 levels ≥2,000 ppm for up to 3 hours per school day. A statistically significant correlation was found between the time that a room had CO2 levels ≥1,000 ppm and SARS-CoV-2 PCR tests normalized to room occupancy, accounting for 43% of the variance. No statistically significant correlation was found for room ACH and per-room SARS-CoV-2 cases. Rooms with ventilation systems using MERV-13 filters had lower SARS-CoV-2-positive PCR counts. These findings led to ongoing efforts to upgrade the ventilation systems in this community-engaged research project. Conclusions: There was a statistically significant correlation between the total time of room CO2 concentrations ≥1,000 and SARS-CoV-2 cases in an IDD school. Merv-13 filters appear to decrease the incidence of SARS-CoV-2 infection. This research partnership identified areas for improving in-school ventilation.

Utilizing the Un-Meeting model to advance innovative translational and team science.

Advances in translational science require innovative solutions, and engagement of productive transdisciplinary teams play a critical role. While various forms of scientific meetings have long provided venues for sharing scientific findings and generating new collaborations, many conferences lack opportunities for active discussions. We describe the use of an Un-Meeting to foster innovative translational science teams through engaged discussions across multidisciplinary groups addressing a shared theme. The Un-Meeting was delivered by the University of Rochester Center for Leading Innovation and Collaboration, the national coordinating center for the National Institutes of Health Clinical and Translational Science Awards (CTSA) program. This pilot CTSA program Un-Meeting focused on engaging translational scientists, policy-makers, community members, advocates, and public health professionals to address the opioid crisis. The participant-driven format leveraged lightning talks, attendee-led idea generation, and extensive breakout discussions to foster multidisciplinary networking. Results indicated participation by a broad set of attendees and a high level of networking during the meeting. These results, coupled with the growth of the Un-Meeting across the CTSA Consortium, provide practices and models to potentially advance team and translational science. While future work will further assess the impact of Un-Meetings, this format presents a promising approach to enhance translational science.

Transfusion reactions associated with COVID-19 convalescent plasma in outpatient clinical trials.

BACKGROUND: COVID-19 convalescent plasma (CCP) is an important therapeutic option for outpatients at high risk of hospitalization from SARS-CoV-2 infection. We assessed the safety of outpatient CCP transfusions administered during clinical trials. STUDY DESIGN AND METHODS: We analyzed data pertaining to transfusion-related reactions from two randomized controlled trials in the U.S. that evaluated the efficacy of CCP versus control plasma in various ambulatory settings. Multivariable logistic regression was used to assess whether CCP was associated with transfusion reactions, after adjusting for potential confounders. RESULTS: The combined study reported 79/1351 (5.9%) adverse events during the transfusion visit, with the majority 62/1351 (4.6%) characterized by mild, allergic-type findings of urticaria, and/or pruritus consistent with minor allergic transfusion reactions; the other reported events were attributed to the patients' underlying disease, COVID-19, or vasovagal in nature. We found no difference in the likelihood of allergic transfusion reactions between those receiving CCP versus control plasma (adjusted odds ratio [AOR], 0.75; 95% CI, 0.43-1.31). Risk of urticaria and/or pruritus increased with a pre-existing diagnosis of asthma (AOR, 2.33; 95% CI, 1.16-4.67). We did not observe any CCP-attributed antibody disease enhancement in participants with COVID-19 or increased risk of infection. There were no life-threatening severe transfusion reactions and no patients required hospitalization related to transfusion-associated complications. DISCUSSION: Outpatient plasma administration was safely performed for nearly 1400 participants. CCP is a safe therapeutic option for outpatients at risk of hospitalization from COVID-19.

Utilization and Impact of Symptomatic and Exposure SARS-CoV-2 Testing in K-12 Schools.

OBJECTIVES: The Centers for Disease Control and Prevention recommend that schools can offer severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) diagnostic (on-demand) testing for students and staff with coronavirus disease 2019 symptoms or exposures. Data related to the uptake, implementation, and effect of school-associated on-demand diagnostic testing have not been described. METHODS: The Rapid Acceleration of Diagnostics Underserved Populations Return to School program provided resources to researchers to implement on-demand SARS-CoV-2 testing in schools. This study describes the strategies used and uptake among the different testing programs. Risk of positivity was compared for symptomatic and exposure testing during the δ and ο variant periods. We estimated the number of school absence days saved with school-based diagnostic testing. RESULTS: Of the 16 eligible programs, 7 provided school-based on-demand testing. The number of persons that participated in these testing programs is 8281, with 4134 (49.9%) receiving >1 test during the school year. Risk of positivity was higher for symptomatic testing compared with exposure testing and higher during the ο variant predominant period compared with the δ variant predominant period. Overall, access to testing saved an estimated 13 806 absent school days. CONCLUSIONS: School-based on-demand SARS-CoV-2 testing was used throughout the school year, and nearly half the participants accessed testing on more than 1 occasion. Future studies should work to understand participant preferences around school-based testing and how these strategies can be used both during and outside of pandemics.

Building School-Academic Partnerships to Implement COVID-19 Testing in Underserved Populations.

OBJECTIVE: In April 2021, the US government made substantial investments in students' safe return to school by providing resources for school-based coronavirus disease 2019 (COVID-19) mitigation strategies, including COVID-19 diagnostic testing. However, testing uptake and access among vulnerable children and children with medical complexities remained unclear. METHODS: The Rapid Acceleration of Diagnostics Underserved Populations program was established by the National Institutes of Health to implement and evaluate COVID-19 testing programs in underserved populations. Researchers partnered with schools to implement COVID-19 testing programs. The authors of this study evaluated COVID-19 testing program implementation and enrollment and sought to determine key implementation strategies. A modified Nominal Group Technique was used to survey program leads to identify and rank testing strategies to provide a consensus of high-priority strategies for infectious disease testing in schools for vulnerable children and children with medical complexities. RESULTS: Among the 11 programs responding to the survey, 4 (36%) included prekindergarten and early care education, 8 (73%) worked with socioeconomically disadvantaged populations, and 4 focused on children with developmental disabilities. A total of 81 916 COVID-19 tests were performed. "Adapting testing strategies to meet the needs, preferences, and changing guidelines," "holding regular meetings with school leadership and staff," and "assessing and responding to community needs" were identified as key implementation strategies by program leads. CONCLUSIONS: School-academic partnerships helped provide COVID-19 testing in vulnerable children and children with medical complexities using approaches that met the needs of these populations. Additional work is needed to develop best practices for in-school infectious disease testing in all children.

Clinical and Analytic Accuracy of Simultaneously Acquired Hemoglobin Measurements: A Multi-Institution Cohort Study to Minimize Redundant Laboratory Usage.

OBJECTIVES: Frequent diagnostic blood sampling contributes to anemia among critically ill children. Reducing duplicative hemoglobin testing while maintaining clinical accuracy can improve patient care efficacy. The objective of this study was to determine the analytical and clinical accuracy of simultaneously acquired hemoglobin measurements with different methods. DESIGN: Retrospective cohort study. SETTING: Two U.S. children's hospitals. PATIENTS: Children (< 18 yr old) admitted to the PICU. INTERVENTIONS: None. MEASUREMENTS AND MAIN RESULTS: We identified hemoglobin results from complete blood count (CBC) panels paired with blood gas (BG) panels and point-of-care (POC) devices. We estimated analytic accuracy by comparing hemoglobin distributions, correlation coefficients, and Bland-Altman bias. We measured clinical accuracy with error grid analysis and defined mismatch zones as low, medium, or high risk-based on deviance from unity and risk of therapeutic error. We calculated pairwise agreement to a binary decision to transfuse based on a hemoglobin value. Our cohort includes 49,004 ICU admissions from 29,926 patients, resulting in 85,757 CBC-BG hemoglobin pairs. BG hemoglobin was significantly higher (mean bias, 0.43-0.58 g/dL) than CBC hemoglobin with similar Pearson correlation ( R2 ) (0.90-0.91). POC hemoglobin was also significantly higher, but of lower magnitude (mean bias, 0.14 g/dL). Error grid analysis revealed only 78 (< 0.1%) CBC-BG hemoglobin pairs in the high-risk zone. For CBC-BG hemoglobin pairs, at a BG hemoglobin cutoff of greater than 8.0 g/dL, the "number needed to miss" a CBC hemoglobin less than 7 g/dL was 275 and 474 at each institution, respectively. CONCLUSIONS: In this pragmatic two-institution cohort of greater than 29,000 patients, we show similar clinical and analytic accuracy of CBC and BG hemoglobin. Although BG hemoglobin values are higher than CBC hemoglobin values, the small magnitude is unlikely to be clinically significant. Application of these findings may reduce duplicative testing and decrease anemia among critically ill children.

Outpatient COVID-19 convalescent plasma recipient antibody thresholds correlated to reduced hospitalizations within a randomized trial.

BACKGROUND: The COVID-19 convalescent plasma (CCP) viral specific antibody levels that translate into recipient post-transfusion antibody levels sufficient to prevent disease progression is not defined. METHODS: This secondary analysis correlated donor and recipient antibody levels to hospitalization risk among unvaccinated, seronegative CCP recipients within the outpatient, double blind, randomized clinical trial that compared CCP to control plasma. The majority of COVID-19 CCP arm hospitalizations (15/17, 88%) occurred in this unvaccinated, seronegative subgroup. A functional cutoff to delineate recipient high versus low post-transfusion antibody levels was established by two methods: 1) analyzing virus neutralization-equivalent anti-S-RBD IgG responses in donors or 2) receiver operating characteristic (ROC) analysis. RESULTS: SARS-CoV-2 anti-S-RBD IgG antibody was diluted by a factor of 21.3 into post-transfusion seronegative recipients from matched donor units. Viral specific antibody delivered approximated 1.2 mg. The high antibody recipients transfused early (symptom onset within 5 days) had no hospitalizations. A CCP recipient analysis for antibody thresholds correlated to reduced hospitalizations found a significant association with Fisher's exact test between early and high antibodies versus all other CCP recipients (or control plasma) with antibody cutoffs established by both methods-donor virus neutralization-based cutoff: (0/85; 0% versus 15/276; 5.6%) p=0.03 or ROC based cutoff: (0/94; 0% versus 15/267; 5.4%) p=0.01. CONCLUSION: In unvaccinated, seronegative CCP recipients, early transfusion of plasma units corresponding to the upper 30% of all study donors reduced outpatient hospitalizations. These high antibody level plasma units, given early, should be reserved for therapeutic use.Trial registration: NCT04373460. FUNDING: Defense Health Agency and others.

Supervised learning applied to classifying fallers versus non-fallers among older adults with cancer.

INTRODUCTION: Supervised machine learning approaches are increasingly used to analyze clinical data, including in geriatric oncology. This study presents a machine learning approach to understand falls in a cohort of older adults with advanced cancer starting chemotherapy, including fall prediction and identification of contributing factors. MATERIALS AND METHODS: This secondary analysis of prospectively collected data from the GAP 70+ Trial (NCT02054741; PI: Mohile) enrolled patients aged ≥70 with advanced cancer and ≥ 1 geriatric assessment domain impairment who planned to start a new cancer treatment regimen. Of ≥2000 baseline variables ("features") collected, 73 were selected based on clinical judgment. Machine learning models to predict falls at three months were developed, optimized, and tested using data from 522 patients. A custom data preprocessing pipeline was implemented to prepare data for analysis. Both undersampling and oversampling techniques were applied to balance the outcome measure. Ensemble feature selection was applied to identify and select the most relevant features. Four models (logistic regression [LR], k-nearest neighbor [kNN], random forest [RF], and MultiLayer Perceptron [MLP]) were trained and subsequently tested on a holdout set. Receiver operating characteristic (ROC) curves were generated and area under the curve (AUC) was calculated for each model. SHapley Additive exPlanations (SHAP) values were utilized to further understand individual feature contributions to observed predictions. RESULTS: Based on the ensemble feature selection algorithm, the top eight features were selected for inclusion in the final models. Selected features aligned with clinical intuition and prior literature. The LR, kNN, and RF models performed equivalently well in predicting falls in the test set, with AUC values 0.66-0.67, and the MLP model showed AUC 0.75. Ensemble feature selection resulted in improved AUC values compared to using LASSO alone. SHAP values, a model-agnostic technique, revealed logical associations between selected features and model predictions. DISCUSSION: Machine learning techniques can augment hypothesis-driven research, including in older adults for whom randomized trial data are limited. Interpretable machine learning is particularly important, as understanding which features impact predictions is a critical aspect of decision-making and intervention. Clinicians should understand the philosophy, strengths, and limitations of a machine learning approach applied to patient data.

An evaluation of statistical differential analysis methods in single-cell RNA-seq data.

Background: Single-cell RNA Sequencing is gaining popularity in recent years. Compared to bulk RNA-Seq, single-cell RNA Sequencing allows the gene expression being measured within individual cells instead of mean gene expression levels across all cells in the sample. Thus, cell-to-cell variation of gene expressions could be examined. Gene differential expression analysis remains the major purpose in most single-cell RNA sequencing experiments and many methods have been developed in recent years to conduct gene differential expression analysis for single-cell RNA sequencing data. Results: Through simulation studies and real data examples, we evaluated the performance of five open-source popular methods used for gene differential expression analysis in single-cell RNA sequencing data. The five methods included DEsingle (Zero-inflated negative binomial model), Linnorm (Empirical Bayes method on transformed count data using the limma package), monocle (An approximate Chi-Square likelihood ratio test), MAST (A generalized linear hurdle model), and DESeq2 (A generalized linear model with empirical Bayes approach and also commonly used for bulk RNA sequencing differential express analyses). We assessed the false discovery rate (FDR) control, sensitivity, specificity, accuracy, and area under the receiver operating characteristics (AUROC) curve for all five methods under different sample sizes, distribution assumptions, and proportions of zeros in the data. Conclusions: We found the MAST method performed the best among the five methods compared with the largest AUROC values across all tested sample sizes and different proportion of truly differential expressed genes, when the data followed negative binomial distributions. When the sample size increased to 100 in each group, the MAST method showed the best performance with the highest AUROC regardless of the data distributions. If the excess zeros were first filtered out before the gene differential analyses, the DESingle, Linnorm, and DESeq2 performed relatively better than the MAST and the monocle methods with higher AUROC values.

Executable Network Models of Integrated Multiomics Data.

Multiomics profiling provides a holistic picture of a condition being examined and captures the complexity of signaling events, beginning from the original cause (environmental or genetic), to downstream functional changes at multiple molecular layers. Pathway enrichment analysis has been used with multiomics data sets to characterize signaling mechanisms. However, technical and biological variability between these layered data limit an integrative computational analyses. We present a Boolean network-based method, multiomics Boolean Omics Network Invariant-Time Analysis (mBONITA), to integrate omics data sets that quantify multiple molecular layers. mBONITA utilizes prior knowledge networks to perform topology-based pathway analysis. In addition, mBONITA identifies genes that are consistently modulated across molecular measurements by combining observed fold-changes and variance, with a measure of node (i.e., gene or protein) influence over signaling, and a measure of the strength of evidence for that gene across data sets. We used mBONITA to integrate multiomics data sets from RAMOS B cells treated with the immunosuppressant drug cyclosporine A under varying O2 tensions to identify pathways involved in hypoxia-mediated chemotaxis. We compare mBONITA's performance with 6 other pathway analysis methods designed for multiomics data and show that mBONITA identifies a set of pathways with evidence of modulation across all omics layers. mBONITA is freely available at https://github.com/Thakar-Lab/mBONITA.

First Impressions Matter: Immune Imprinting and Antibody Cross-Reactivity in Influenza and SARS-CoV-2.

Many rigorous studies have shown that early childhood infections leave a lasting imprint on the immune system. The understanding of this phenomenon has expanded significantly since 1960, when Dr. Thomas Francis Jr first coined the term "original antigenic sin", to account for all previous pathogen exposures, rather than only the first. Now more commonly referred to as "immune imprinting", this effect most often focuses on how memory B-cell responses are shaped by prior antigen exposure, and the resultant antibodies produced after subsequent exposure to antigenically similar pathogens. Although imprinting was originally observed within the context of influenza viral infection, it has since been applied to the pandemic coronavirus SARS-CoV-2. To fully comprehend how imprinting affects the evolution of antibody responses, it is necessary to compare responses elicited by pathogenic strains that are both antigenically similar and dissimilar to strains encountered previously. To accomplish this, we must be able to measure the antigenic distance between strains, which can be easily accomplished using data from multidimensional immunological assays. The knowledge of imprinting, combined with antigenic distance measures, may allow for improvements in vaccine design and development for both influenza and SARS-CoV-2 viruses.

Anamnestic broadly reactive antibodies induced by H7N9 virus more efficiently bind to seasonal H3N2 strains.

The very first influenza virus exposure in a human during infancy is known to imprint the host immune system. However, it is unclear how the memory B cells that first target virus epitopes affect antibody response to the stalk of hemagglutinin (HA) domain of influenza virus. Our study is designed to measure the cross-reactivity of antibodies induced by inactivated H7N9 virus using isolated human peripheral blood B cells. Most of the participants displayed higher levels of plasma IgG against the seasonal strains A/Vic11 and A/Cali09 than those binding to historical outbreak A/HK68 and A/PR8. H3 stalk-binding antibodies were detected in plasma at a 1:5000 dilution in 12 of 13 donors, H1 stalk-binding antibodies in all donors, indicating the existence of H3 and H1 stalk-reactive memory B cells. A moderate to high level of broadly cross-reactive antibodies was induced in memory B cells from all donors after in vitro stimulation of B cells with H7N9 virus. H3 stalk-binding antibodies were also detected in most subjects, with cross-reactivity to H1 and H7 stalk domains. The stalk-reactive antibodies bound to five H3 strains spanning 45 years and different H1, H2, H3, H5, H6, H7, H9 and B strains. Interestingly, H1- and H3-reactive IgG were much higher than H7-binding antibodies after 6 days of H7N9 stimulation. Our results demonstrate that HA stalk-reactive antibodies induced by H7N9 viruses more efficiently bound to yearly circulating both H3N2 and H1N1 strains than the boosting strain, indicating that HA stalk immunological imprint can be extended across currently circulating strains or vaccines.

An evaluation of RNA-seq differential analysis methods.

RNA-seq is a high-throughput sequencing technology widely used for gene transcript discovery and quantification under different biological or biomedical conditions. A fundamental research question in most RNA-seq experiments is the identification of differentially expressed genes among experimental conditions or sample groups. Numerous statistical methods for RNA-seq differential analysis have been proposed since the emergence of the RNA-seq assay. To evaluate popular differential analysis methods used in the open source R and Bioconductor packages, we conducted multiple simulation studies to compare the performance of eight RNA-seq differential analysis methods used in RNA-seq data analysis (edgeR, DESeq, DESeq2, baySeq, EBSeq, NOISeq, SAMSeq, Voom). The comparisons were across different scenarios with either equal or unequal library sizes, different distribution assumptions and sample sizes. We measured performance using false discovery rate (FDR) control, power, and stability. No significant differences were observed for FDR control, power, or stability across methods, whether with equal or unequal library sizes. For RNA-seq count data with negative binomial distribution, when sample size is 3 in each group, EBSeq performed better than the other methods as indicated by FDR control, power, and stability. When sample sizes increase to 6 or 12 in each group, DESeq2 performed slightly better than other methods. All methods have improved performance when sample size increases to 12 in each group except DESeq. For RNA-seq count data with log-normal distribution, both DESeq and DESeq2 methods performed better than other methods in terms of FDR control, power, and stability across all sample sizes. Real RNA-seq experimental data were also used to compare the total number of discoveries and stability of discoveries for each method. For RNA-seq data analysis, the EBSeq method is recommended for studies with sample size as small as 3 in each group, and the DESeq2 method is recommended for sample size of 6 or higher in each group when the data follow the negative binomial distribution. Both DESeq and DESeq2 methods are recommended when the data follow the log-normal distribution.

From insights to action: Enriching the clinical and translational research workforce by developing diverse and inclusive career programs.

Diversification of the Translational Science workforce is a strategic goal for the National Center for the Advancement of Translational Science (NCATS) program. NCATS has identified the development of translational science education, training, and support for a diverse translational science workforce as key to advancing the growing field of translational science. An annual mixed-methods assessment has been conducted on Common Metrics data submitted by over 60 Clinical & Translational Science Awards (CTSA) programs nationwide and includes metrics addressing recruitment and retention of scientists with particular attention to underrepresented persons and women. This article describes a methodology for the development of From Insights to Action, a resource for guiding program implementation and strategic planning to develop a diverse clinical and translational science workforce. This was informed by the Common Metrics Initiative process and constituted of findings from qualitative interviews of a subset of CTSAs that participated. The dissemination of this guide had several impacts, including providing structural foci for the CTSA Fall 2020 program meeting centered on Diversity, Equity, and Inclusion in translational science; addressing NCATS' goal of workforce diversity; and understanding the number of diverse graduates still engaged in research.

Cyclosporine A Modulates LSP1 Protein Levels in Human B Cells to Attenuate B Cell Migration at Low O2 Levels.

Coordinated migration of B cells within and between secondary lymphoid tissues is required for robust antibody responses to infection or vaccination. Secondary lymphoid tissues normally expose B cells to a low O2 (hypoxic) environment. Recently, we have shown that human B cell migration is modulated by an O2-dependent molecular switch, centrally controlled by the hypoxia-induced (transcription) factor-1α (HIF1A), which can be disrupted by the immunosuppressive calcineurin inhibitor, cyclosporine A (CyA). However, the mechanisms by which low O2 environments attenuate B cell migration remain poorly defined. Proteomics analysis has linked CXCR4 chemokine receptor signaling to cytoskeletal rearrangement. We now hypothesize that the pathways linking the O2 sensing molecular switch to chemokine receptor signaling and cytoskeletal rearrangement would likely contain phosphorylation events, which are typically missed in traditional transcriptomic and/or proteomic analyses. Hence, we have performed a comprehensive phosphoproteomics analysis of human B cells treated with CyA after engagement of the chemokine receptor CXCR4 with CXCL12. Statistical analysis of the separate and synergistic effects of CyA and CXCL12 revealed 116 proteins whose abundance is driven by a synergistic interaction between CyA and CXCL12. Further, we used our previously described algorithm BONITA to reveal a critical role for Lymphocyte Specific Protein 1 (LSP1) in cytoskeletal rearrangement. LSP1 is known to modulate neutrophil migration. Validating these modeling results, we show experimentally that LSP1 levels in B cells increase with low O2 exposure, and CyA treatment results in decreased LSP1 protein levels. This correlates with the increased chemotactic activity observed after CyA treatment. Lastly, we directly link LSP1 levels to chemotactic capacity, as shRNA knock-down of LSP1 results in significantly increased B cell chemotaxis at low O2 levels. These results directly link CyA to LSP1-dependent cytoskeletal regulation, demonstrating a previously unrecognized mechanism by which CyA modulates human B cell migration. Data are available via ProteomeXchange with identifier PXD036167.