Prevalence of long COVID symptoms in Haryana, India: a cross-sectional follow-up study

Background

Emerging research indicates growing concern over long COVID globally, although there have been limited studies that estimate population burden. We aimed to estimate the burden of long COVID in three districts of Haryana, India, using an opportunity to link a seroprevalence study to follow-up survey of symptoms associated with long COVID.

Methods

We used a population-based seroprevalence survey for COVID-19 conducted in September 2021 across Haryana, India. Adults from three purposively selected districts (Rohtak, Gurugram, and Jhajjar) were eligible to participate; 2205 of 3213 consented to participate in a survey on health status. Trained investigators administered a structured questionnaire that included demographic characteristics, self-reported symptoms of illness in the last six months before the survey, mental health, and history of COVID-19.

Findings

Unadjusted regression estimates indicated positive correlations between symptomatic complaints and COVID-19 exposure, suggesting lingering effects of COVID-19 in this population. The overall physical morbidity index was higher among those who tested positive for COVID-19, as was the incidence of new cases. However, both morbidity and incidence became statistically insignificant after adjustment for multiple comparisons. Cough emerged as the only statistically significant individual persistent symptom. Sex-stratified analyses indicated significant estimates only for physical morbidity in women.

Interpretation

This study is one of the first from India that uses a large population-based sample to examine longer term repercussions of COVID infections. The burden of long COVID should primarily be addressed in clinical settings, where specialised treatment for individual cases continues to evolve. Our analyses also provide insight into the size and nature of studies required to assess the population-level burden of long COVID.

Funding

This paper was produced under the auspices of the Lancet COVID 19 Commission India Task Force, which was supported financially by the Reliance Foundation. The Lancet COVID 19 Commission was set up in July 2020 and submitted its final report by October 2022. This report by the India Task Force was prepared during the same period.

Transcriptomic and Proteomic Characterization of the Immune Response to Elective Spinal Reconstructive Surgery: Impact of Aging and Comparison with Traumatic Injury Response

BACKGROUND

Major surgery triggers trauma-like stress responses linked to age, surgery duration, and blood loss, resembling polytrauma. This similarity suggests elective surgery as a surrogate model for studying polytrauma immune responses. We investigated stress responses across age groups and compared them with those of polytrauma patients.

STUDY DESIGN

Patients undergoing major spinal reconstruction surgery were divided into older (age >65 years, n = 5) and young (age 18 to 39 years, n = 6) groups. A comparison group consisted of matched trauma patients (n = 8). Blood samples were collected before, during, and after surgery. Bone marrow mononuclear cells and peripheral blood mononuclear cells were analyzed using cellular indexing of transcriptomes and epitopes sequencing or single-cell RNA sequencing. Plasma was subjected to dual-platform proteomic analysis (SomaLogic and O-link).

RESULTS

Response to polytrauma was highest within 4 hours. By comparison, the response to surgery was highest at 24 hours. Both insults triggered significant changes in cluster of differentiation 14 monocytes, with increased inflammation and lower major histocompatibility complex-class 2 expression. Older patient's cluster of differentiation 14 monocytes displayed higher inflammation and less major histocompatibility complex-class 2 suppression; a trend was also seen in bone marrow mononuclear cells. Although natural killer cells were markedly activated after polytrauma, they were suppressed after surgery, especially in older patients. In plasma, innate immunity proteins dominated at 24 hours, shifting to adaptive immunity proteins by 6 weeks with heightened inflammation in older patients. Senescence-associated secretory phenotype proteins were higher in older patients at baseline and further elevated during and after surgery.

CONCLUSIONS

Although both major surgery and polytrauma initiate immune and stress responses, substantial differences exist in timing and cellular profiles, suggesting major elective surgery is not a suitable surrogate for the polytrauma response. Nonetheless, distinct responses in young vs older patients highlight the utility of elective spinal in studying patient-specific factors affecting outcomes after major elective surgery.

SARS-CoV-2 mRNA Vaccines Induce Greater Complement Activation and Decreased Viremia and Nef Antibodies in Men With HIV-1

BACKGROUND

Immune dysregulation in people with human immunodeficiency virus-1 (PWH) persists despite potent antiretroviral therapy and, consequently, PWH tend to have lower immune responses to licensed vaccines. However, limited information is available about the impact of mRNA vaccines in PWH. This study details the immunologic responses to severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) mRNA vaccines in PWH and their impact on HIV-1.

METHODS

We quantified anti-S immunoglobulin G (IgG) binding and neutralization of 3 SARS-CoV-2 variants of concern and complement activation in blood from virally suppressed men with HIV-1 (MWH) and men without HIV-1 (MWOH), and the characteristics that may impact the vaccine immune responses. We also studied antibody levels against HIV-1 proteins and HIV-1 plasma RNA.

RESULTS

MWH had lower anti-S IgG binding and neutralizing antibodies against the 3 variants compared to MWOH. MWH also produced anti-S1 antibodies with a 10-fold greater ability to activate complement and exhibited higher C3a blood levels than MWOH. MWH had decreased residual HIV-1 plasma viremia and anti-Nef IgG approximately 100 days after immunization.

CONCLUSIONS

MWH respond to SARS-CoV-2 mRNA vaccines with lower antibody titers and with greater activation of complement, while exhibiting a decrease in HIV-1 viremia and anti-Nef antibodies. These results suggest an important role of complement activation mediating protection in MWH.

Population-level comparisons of gene regulatory networks modeled on high-throughput single-cell transcriptomics data

Single-cell technologies enable high-resolution studies of phenotype-defining molecular mechanisms. However, data sparsity and cellular heterogeneity make modeling biological variability across single-cell samples difficult. Here we present SCORPION, a tool that uses a message-passing algorithm to reconstruct comparable gene regulatory networks from single-cell/nuclei RNA-sequencing data that are suitable for population-level comparisons by leveraging the same baseline priors. Using synthetic data, we found that SCORPION outperformed 12 existing gene regulatory network reconstruction techniques. Using supervised experiments, we show that SCORPION can accurately identify differences in regulatory networks between wild-type and transcription factor-perturbed cells. We demonstrate SCORPION's scalability to population-level analyses using a single-cell RNA-sequencing atlas containing 200,436 cells from colorectal cancer and adjacent healthy tissues. The differences between tumor regions detected by SCORPION are consistent across multiple cohorts as well as with our understanding of disease progression, and elucidate phenotypic regulators that may impact patient survival.

SLIDE: Significant Latent Factor Interaction Discovery and Exploration across biological domains

Modern multiomic technologies can generate deep multiscale profiles. However, differences in data modalities, multicollinearity of the data, and large numbers of irrelevant features make analyses and integration of high-dimensional omic datasets challenging. Here we present Significant Latent Factor Interaction Discovery and Exploration (SLIDE), a first-in-class interpretable machine learning technique for identifying significant interacting latent factors underlying outcomes of interest from high-dimensional omic datasets. SLIDE makes no assumptions regarding data-generating mechanisms, comes with theoretical guarantees regarding identifiability of the latent factors/corresponding inference, and has rigorous false discovery rate control. Using SLIDE on single-cell and spatial omic datasets, we uncovered significant interacting latent factors underlying a range of molecular, cellular and organismal phenotypes. SLIDE outperforms/performs at least as well as a wide range of state-of-the-art approaches, including other latent factor approaches. More importantly, it provides biological inference beyond prediction that other methods do not afford. Thus, SLIDE is a versatile engine for biological discovery from modern multiomic datasets.

Impact of the COVID-19 pandemic on quality of tuberculosis care in private facilities in Bandung, Indonesia: a repeated cross-sectional standardized patients study

BACKGROUND

Indonesia has the second highest incidence of tuberculosis in the world. While 74% of people with tuberculosis in Indonesia first accessed the private health sector when seeking care for their symptoms, only 18% of tuberculosis notifications originate in the private sector. Little is known about the impact of the COVID-19 pandemic on the private sector. Using unannounced standardized patient visits to private providers, we aimed to measure quality of tuberculosis care during the COVID-19 pandemic.

METHODS

A cross-sectional study was conducted using standardized patients in Bandung City, West Java, Indonesia. Ten standardized patients completed 292 visits with private providers between 9 July 2021 and 21 January 2022, wherein standardized patients presented a presumptive tuberculosis case. Results were compared to standardized patients surveys conducted in the same geographical area before the onset of COVID-19.

RESULTS

Overall, 35% (95% confidence interval (CI): 29.2-40.4%) of visits were managed correctly according to national tuberculosis guidelines. There were no significant differences in the clinical management of presumptive tuberculosis patients before and during the COVID-19 pandemic, apart from an increase in temperature checks (adjusted odds ratio (aOR): 8.05, 95% CI: 2.96-21.9, p < 0.001) and a decrease in throat examinations (aOR 0.16, 95% CI: 0.06-0.41, p = 0.002) conducted during the pandemic.

CONCLUSIONS

Results indicate that providers successfully identify tuberculosis in their patients yet do not manage them according to national guidelines. There were no major changes found in quality of tuberculosis care due to the COVID-19 pandemic. As tuberculosis notifications have declined in Indonesia due to the COVID-19 pandemic, there remains an urgent need to increase private provider engagement in Indonesia and improve quality of care.

Care pathways of individuals with tuberculosis before and during the COVID-19 pandemic in Bandung, Indonesia

The COVID-19 pandemic is thought to have undone years' worth of progress in the fight against tuberculosis (TB). For instance, in Indonesia, a high TB burden country, TB case notifications decreased by 14% and treatment coverage decreased by 47% during COVID-19. We sought to better understand the impact of COVID-19 on TB case detection using two cross-sectional surveys conducted before (2018) and after the onset of the pandemic (2021). These surveys allowed us to quantify the delays that individuals with TB who eventually received treatment at private providers faced while trying to access care for their illness, their journey to obtain a diagnosis, the encounters individuals had with healthcare providers before a TB diagnosis, and the factors associated with patient delay and the total number of provider encounters. We found some worsening of care seeking pathways on multiple dimensions. Median patient delay increased from 28 days (IQR: 10, 31) to 32 days (IQR: 14, 90) and the median number of encounters increased from 5 (IQR: 4, 8) to 7 (IQR: 5, 10), but doctor and treatment delays remained relatively unchanged. Employed individuals experienced shorter delays compared to unemployed individuals (adjusted medians: -20.13, CI -39.14, -1.12) while individuals whose initial consult was in the private hospitals experienced less encounters compared to those visiting public providers, private primary care providers, and informal providers (-4.29 encounters, CI -6.76, -1.81). Patients who visited the healthcare providers >6 times experienced longer total delay compared to those with less than 6 visits (adjusted medians: 59.40, 95% CI: 35.04, 83.77). Our findings suggest the need to ramp up awareness programs to reduce patient delay and strengthen private provide engagement in the country, particularly in the primary care sector.

PRMT blockade induces defective DNA replication stress response and synergizes with PARP inhibition

Multiple cancers exhibit aberrant protein arginine methylation by both type I arginine methyltransferases, predominately protein arginine methyltransferase 1 (PRMT1) and to a lesser extent PRMT4, and by type II PRMTs, predominately PRMT5. Here, we perform targeted proteomics following inhibition of PRMT1, PRMT4, and PRMT5 across 12 cancer cell lines. We find that inhibition of type I and II PRMTs suppresses phosphorylated and total ATR in cancer cells. Loss of ATR from PRMT inhibition results in defective DNA replication stress response activation, including from PARP inhibitors. Inhibition of type I and II PRMTs is synergistic with PARP inhibition regardless of homologous recombination function, but type I PRMT inhibition is more toxic to non-malignant cells. Finally, we demonstrate that the combination of PARP and PRMT5 inhibition improves survival in both BRCA-mutant and wild-type patient-derived xenografts without toxicity. Taken together, these results demonstrate that PRMT5 inhibition may be a well-tolerated approach to sensitize tumors to PARP inhibition.

From bench to bedside via bytes: Multi-omic immunoprofiling and integration using machine learning and network approaches

A significant surge in research endeavors leverages the vast potential of high-throughput omic technology platforms for broad profiling of biological responses to vaccines and cutting-edge immunotherapies and stem-cell therapies under development. These profiles capture different aspects of core regulatory and functional processes at different scales of resolution from molecular and cellular to organismal. Systems approaches capture the complex and intricate interplay between these layers and scales. Here, we summarize experimental data modalities, for characterizing the genome, epigenome, transcriptome, proteome, metabolome, and antibody-ome, that enable us to generate large-scale immune profiles. We also discuss machine learning and network approaches that are commonly used to analyze and integrate these modalities, to gain insights into correlates and mechanisms of natural and vaccine-mediated immunity as well as therapy-induced immunomodulation.

An Automated Brain Metastasis Detection and Segmentation System from MRI with a Large Multi-Institutional Dataset

PURPOSE/OBJECTIVE(S)

Developments of automated systems for brain metastasis (BM) detection and segmentation from MRI for assisting early detection and stereotactic radiosurgery (SRS) have been reported but most based upon relatively small datasets from single institutes. This work aims to develop and evaluate a system using a large multi-institutional dataset, and to improve both identification of small/subtle BMs and segmentation accuracy of large BMs.

MATERIALS/METHODS

A 3D U-Net system was trained and evaluated to detect and segment intraparenchymal BMs with a size > 2mm using 1856 MRI volumes from 1791 patients treated with SRS from seven institutions (1539 volumes for training, 183 for validation, and 134 for testing). All patients had 3D post-Gd T1w MRI scans pre-SRS. Gross tumor volumes (GTVs) of BMs for SRS were curated by each institute first. Then, additional efforts were spent to create GTVs for the untreated and/or uncontoured BMs, including central reviews by two radiologists, to improve accuracy of ground truth. The training dataset was augmented with synthetic BMs of 3773 MRIs using a 3D generative pipeline. Our system consists of two U-Nets with one using small 3D patches dedicated for detecting small BMs and another using large 3D patches for segmenting large BMs, and a random-forest based fusion module for combining the two network outputs. The first U-Net was trained with 3D patches containing at least one BM < 0.1 cm3. For detection performance, we measured BM-level sensitivity and case-level false-positive (FP) rate. For segmentation performance, we measured BM-level Dice similarity coefficient (DSC) and 95-percentile Hausdorff distance (HD95). We also stratified performances based upon BM sizes.

RESULTS

For 739 BMs in the 134 testing cases, the overall lesion-level sensitivity was 0.870 with an average case-level FP of 1.34±1.92 (95% CI: 1.02-1.67). The sensitivity was >0.969 for the BMs >0.1 cm3, but dropped to 0.755 for the BMs < 0.1 cm3 (Table 1). The average DSC and HD95 for all detected BMs were 0.786 and 1.35mm. The worse performance for BMs > 20 cm3 was caused by a case with 83 cm3 GTV and artifacts in the MRI volume.

CONCLUSION

We achieved excellent detection sensitivity and segmentation accuracy for BMs > 0.1 cm3, and promising performance for small BMs (<0.1cm3) with a controlled FP rate using a large multi-institutional dataset. Clinical utility for assisting early detection and SRS planning will be investigated. Table 1: Per-lesion detection and segmentation performance stratified by individual BM size. N is the number of BMs in each category.

Cell Type-Specific Biomarkers of Systemic Sclerosis Disease Severity Capture Cell-Intrinsic and Cell-Extrinsic Circuits

OBJECTIVE

Systemic sclerosis (SSc) is a multifactorial autoimmune fibrotic disorder involving complex rewiring of cell-intrinsic and cell-extrinsic signaling coexpression networks involving a range of cell types. However, the rewired circuits as well as corresponding cell-cell interactions remain poorly understood. To address this, we used a predictive machine learning framework to analyze single-cell RNA-sequencing data from 24 SSc patients across the severity spectrum as quantified by the modified Rodnan skin score (MRSS).

METHODS

We used a least absolute shrinkage and selection operator (LASSO)-based predictive machine learning approach on the single-cell RNA-sequencing data set to identify predictive biomarkers of SSc severity, both across and within cell types. The use of L1 regularization helps prevent overfitting on high-dimensional data. Correlation network analyses were coupled to the LASSO model to identify cell-intrinsic and cell-extrinsic co-correlates of the identified biomarkers of SSc severity.

RESULTS

We found that the uncovered cell type-specific predictive biomarkers of MRSS included previously implicated genes in fibroblast and myeloid cell subsets (e.g., SFPR2+ fibroblasts and monocytes), as well as novel gene biomarkers of MRSS, especially in keratinocytes. Correlation network analyses revealed novel cross-talk between immune pathways and implicated keratinocytes in addition to fibroblast and myeloid cells as key cell types involved in SSc pathogenesis. We then validated the uncovered association of key gene expression and protein markers in keratinocytes, KRT6A and S100A8, with SSc skin disease severity.

CONCLUSION

Our global systems analyses reveal previously uncharacterized cell-intrinsic and cell-extrinsic signaling coexpression networks underlying SSc severity that involve keratinocytes, myeloid cells, and fibroblasts.

A systematic review and knowledge mapping on ICT-based remote and automatic COVID-19 patient monitoring and care

BACKGROUND

e-Health has played a crucial role during the COVID-19 pandemic in primary health care. e-Health is the cost-effective and secure use of Information and Communication Technologies (ICTs) to support health and health-related fields. Various stakeholders worldwide use ICTs, including individuals, non-profit organizations, health practitioners, and governments. As a result of the COVID-19 pandemic, ICT has improved the quality of healthcare, the exchange of information, training of healthcare professionals and patients, and facilitated the relationship between patients and healthcare providers. This study systematically reviews the literature on ICT-based automatic and remote monitoring methods, as well as different ICT techniques used in the care of COVID-19-infected patients.

OBJECTIVE

The purpose of this systematic literature review is to identify the e-Health methods, associated ICTs, method implementation strategies, information collection techniques, advantages, and disadvantages of remote and automatic patient monitoring and care in COVID-19 pandemic.

METHODS

The search included primary studies that were published between January 2020 and June 2022 in scientific and electronic databases, such as EBSCOhost, Scopus, ACM, Nature, SpringerLink, IEEE Xplore, MEDLINE, Google Scholar, JMIR, Web of Science, Science Direct, and PubMed. In this review, the findings from the included publications are presented and elaborated according to the identified research questions. Evidence-based systematic reviews and meta-analyses were conducted using the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) framework. Additionally, we improved the review process using the Rayyan tool and the Scale for the Assessment of Narrative Review Articles (SANRA). Among the eligibility criteria were methodological rigor, conceptual clarity, and useful implementation of ICTs in e-Health for remote and automatic monitoring of COVID-19 patients.

RESULTS

Our initial search identified 664 potential studies; 102 were assessed for eligibility in the pre-final stage and 65 articles were used in the final review with the inclusion and exclusion criteria. The review identified the following eHealth methods-Telemedicine, Mobile Health (mHealth), and Telehealth. The associated ICTs are Wearable Body Sensors, Artificial Intelligence (AI) algorithms, Internet-of-Things, or Internet-of-Medical-Things (IoT or IoMT), Biometric Monitoring Technologies (BioMeTs), and Bluetooth-enabled (BLE) home health monitoring devices. Spatial or positional data, personal and individual health, and wellness data, including vital signs, symptoms, biomedical images and signals, and lifestyle data are examples of information that is managed by ICTs. Different AI and IoT methods have opened new possibilities for automatic and remote patient monitoring with associated advantages and weaknesses. Our findings were represented in a structured manner using a semantic knowledge graph (e.g., ontology model).

CONCLUSIONS

Various e-Health methods, related remote monitoring technologies, different approaches, information categories, the adoption of ICT tools for an automatic remote patient monitoring (RPM), advantages and limitations of RMTs in the COVID-19 case are discussed in this review. The use of e-Health during the COVID-19 pandemic illustrates the constraints and possibilities of using ICTs. ICTs are not merely an external tool to achieve definite remote and automatic health monitoring goals; instead, they are embedded in contexts. Therefore, the importance of the mutual design process between ICT and society during the global health crisis has been observed from a social informatics perspective. A global health crisis can be observed as an information crisis (e.g., insufficient information, unreliable information, and inaccessible information); however, this review shows the influence of ICTs on COVID-19 patients' health monitoring and related information collection techniques.

Temporal dynamics and genomic programming of plasma cell fates

Affinity-matured plasma cells (PCs) of varying lifespans are generated through a germinal center (GC) response. The developmental dynamics and genomic programs of antigen-specific PC precursors remain to be elucidated. Using a model antigen, we demonstrate biphasic generation of PC precursors, with those generating long-lived bone marrow PCs preferentially produced in the late phase of GC response. Clonal tracing using scRNA-seq+BCR-seq in spleen and bone marrow compartments, coupled with adoptive transfer experiments, reveal a novel PC transition state that gives rise to functionally competent PC precursors. The latter undergo clonal expansion, dependent on inducible expression of TIGIT. We propose a model for the proliferation and programming of precursors of long-lived PCs, based on extended antigen encounters followed by reduced antigen availability.

The family doctor: health, kin testing and primary care in Patna, India

Private primary care providers are usually the first site where afflictions come under institutional view. In the context of poverty, the relationship between illness and care is more complex than a simple division of responsibilities between various actors-with care given by kin, and diagnosis and treatment being the purview of providers. Since patients would often visit the provider with family members, providers are attuned to the patients' web of kinship. Providers would take patients' kinship arrangements into account when prescribing diagnostic tests and treatments. This paper terms this aspect of the clinical encounter as 'kin testing' to refer to situations/clinical encounters when providers take into consideration that care provided by kin was conditional. 'Kin testing' allowed providers to manage the episode of illness that had brought the patient to the clinic by relying on clinical judgment rather than confirmed laboratory tests. Furthermore, since complaints of poor health also were an idiom to communicate kin neglect, providers had to also discern how to negotiate diagnoses and treatments. Kinship determined whether the afflicted bodies brought to the clinics were diagnosed, whether medicines reached the body, and adherence maintained. The providers' actions make visible the difference that kinship made in how health is imagined in the clinic and in standardized protocols. Focusing on primary care clinics in Patna, India, we contribute to research that shows that kinship determines care and management of illnesses at home by showing that relatedness of patients gets folded in the clinic by providers as well.

The gut protist Tritrichomonas arnold restrains virus-mediated loss of oral tolerance by modulating dietary antigen-presenting dendritic cells

Loss of oral tolerance (LOT) to gluten, driven by dendritic cell (DC) priming of gluten-specific T helper 1 (Th1) cell immune responses, is a hallmark of celiac disease (CeD) and can be triggered by enteric viral infections. Whether certain commensals can moderate virus-mediated LOT remains elusive. Here, using a mouse model of virus-mediated LOT, we discovered that the gut-colonizing protist Tritrichomonas (T.) arnold promotes oral tolerance and protects against reovirus- and murine norovirus-mediated LOT, independent of the microbiota. Protection was not attributable to antiviral host responses or T. arnold-mediated innate type 2 immunity. Mechanistically, T. arnold directly restrained the proinflammatory program in dietary antigen-presenting DCs, subsequently limiting Th1 and promoting regulatory T cell responses. Finally, analysis of fecal microbiomes showed that T. arnold-related Parabasalid strains are underrepresented in human CeD patients. Altogether, these findings will motivate further exploration of oral-tolerance-promoting protists in CeD and other immune-mediated food sensitivities.

Stability and heterogeneity in the antimicrobiota reactivity of human milk-derived immunoglobulin A

Immunoglobulin A (IgA) is secreted into breast milk and is critical for both protecting against enteric pathogens and shaping the infant intestinal microbiota. The efficacy of breast milk-derived maternal IgA (BrmIgA) is dependent upon its specificity; however, heterogeneity in BrmIgA binding ability to the infant microbiota is not known. Using a flow cytometric array, we analyzed the reactivity of BrmIgA against bacteria common to the infant microbiota and discovered substantial heterogeneity between all donors, independent of preterm or term delivery. Surprisingly, we also observed intradonor variability in the BrmIgA response to closely related bacterial isolates. Conversely, longitudinal analysis showed that the antibacterial BrmIgA reactivity was relatively stable through time, even between sequential infants, indicating that mammary gland IgA responses are durable. Together, our study demonstrates that the antibacterial BrmIgA reactivity displays interindividual heterogeneity but intraindividual stability. These findings have important implications for how breast milk shapes the development of the preterm infant microbiota and protects against necrotizing enterocolitis.

High-dimensional proteomics identifies organ injury patterns associated with outcomes in human trauma

INTRODUCTION

Severe traumatic injury with shock can lead to direct and indirect organ injury; however, tissue-specific biomarkers are limited in clinical panels. We used proteomic and metabolomic databases to identify organ injury patterns after severe injury in humans.

METHODS

Plasma samples (times 0, 24, and 72 hours after arrival to trauma center) from injured patients enrolled in two randomized prehospital trials were subjected to multiplexed proteomics (SomaLogic Inc., Boulder, CO). Patients were categorized by outcome: nonresolvers (died >72 hours or required ≥7 days of critical care), resolvers (survived to 30 days and required <7 days of critical care), and low Injury Severity Score (ISS) controls. Established tissue-specific biomarkers were identified through a literature review and cross-referenced with tissue specificity from the Human Protein Atlas. Untargeted plasma metabolomics (Metabolon Inc., Durham, NC), inflammatory mediators, and endothelial damage markers were correlated with injury biomarkers. Kruskal-Wallis/Mann-Whitney U tests with false discovery rate correction assessed differences in biomarker expression across outcome groups (significance; p < 0.1).

RESULTS

Of 142 patients, 78 were nonresolvers (median ISS, 30), 34 were resolvers (median ISS, 22), and 30 were low ISS controls (median ISS, 1). A broad release of tissue-specific damage markers was observed at admission; this was greater in nonresolvers. By 72 hours, nine cardiac, three liver, eight neurologic, and three pulmonary proteins remained significantly elevated in nonresolvers compared with resolvers. Cardiac damage biomarkers showed the greatest elevations at 72 hours in nonresolvers and had significant positive correlations with proinflammatory mediators and endothelial damage markers. Nonresolvers had lower concentrations of fatty acid metabolites compared with resolvers, particularly acyl carnitines and cholines.

CONCLUSION

We identified an immediate release of tissue-specific biomarkers with sustained elevation in the liver, pulmonary, neurologic, and especially cardiac injury biomarkers in patients with complex clinical courses after severe injury. The persistent myocardial injury in nonresolvers may be due to a combination of factors including metabolic stress, inflammation, and endotheliopathy.

Do private providers initiate anti-tuberculosis therapy on the basis of chest radiographs? A standardised patient study in urban India

Background

The initiation of anti-tuberculosis treatment (ATT) based on results of WHO-approved microbiological diagnostics is an important marker of quality tuberculosis (TB) care. Evidence suggests that other diagnostic processes leading to treatment initiation may be preferred in high TB incidence settings. This study examines whether private providers start anti-TB therapy on the basis of chest radiography (CXR) and clinical examinations.

Methods

This study uses the standardized patient (SP) methodology to generate accurate and unbiased estimates of private sector, primary care provider practice when a patient presents a standardized TB case scenario with an abnormal CXR. Using multivariate log-binomial and linear regressions with standard errors clustered at the provider level, we analyzed 795 SP visits conducted over three data collection waves from 2014 to 2020 in two Indian cities. Data were inverse-probability-weighted based on the study sampling strategy, resulting in city-wave-representative results.

Findings

Amongst SPs who presented to a provider with an abnormal CXR, 25% (95% CI: 21-28%) visits resulted in ideal management, defined as the provider prescribing a microbiological test and not offering a concurrent prescription for a corticosteroid or antibiotic (including anti-TB medications). In contrast, 23% (95% CI: 19-26%) of 795 visits were prescribed anti-TB medications. Of 795 visits, 13% (95% CI: 10-16%) resulted in anti-TB treatment prescriptions/dispensation and an order for confirmatory microbiological testing.

Interpretation

One in five SPs presenting with abnormal CXR were prescribed ATT by private providers. This study contributes novel insights to empiric treatment prevalence based on CXR abnormality. Further work is needed to understand how providers make trade-offs between existing diagnostic practices, new technologies, profits, clinical outcomes, and the market dynamics with laboratories.

Funding

This study was funded by the Bill & Melinda Gates Foundation (grant OPP1091843), and the Knowledge for Change Program at The World Bank.

Use of standardised patients to assess tuberculosis case management by private pharmacies in Patna, India: A repeat cross-sectional study

As the first point of care for many healthcare seekers, private pharmacies play an important role in tuberculosis (TB) care. However, previous studies in India have showed that private pharmacies commonly dispense symptomatic treatments and broad-spectrum antibiotics over-the-counter (OTC), rather than referring patients for TB testing. Such inappropriate management by pharmacies can delaye TB diagnosis. We assessed medical advice and OTC drug dispensing practices of pharmacists for standardized patients presenting with classic symptoms of pulmonary TB (case 1) and for those with sputum smear positive pulmonary TB (case 2), and examined how practices have changed over time in an urban Indian site. We examined how and whether private pharmacies improved practices for TB in 2019 compared to a baseline study conducted in 2015 in the city of Patna, using the same survey sampling techniques and study staff. The proportion of patient-pharmacist interactions that resulted in correct or ideal management, as well as the proportion of interactions resulting in antibiotic, quinolone, and corticosteroid are presented, with standard errors clustered at the provider level. To assess the difference in case management and the use of drugs across the two cases by round, a difference in difference (DiD) model was employed. A total of 936 SP interactions were completed over both rounds of survey. Our results indicate that across both rounds of data collection, 331 of 936 (35%; 95% CI: 32-38%) of interactions were correctly managed. At baseline, 215 of 500 (43%; 95% CI: 39-47%) of interactions were correctly managed whereas 116 of 436 (27%; 95% CI: 23-31%) were correctly managed in the second round of data collection. Ideal management, where in addition to a referral, patients were not prescribed any potentially harmful medications, was seen in 275 of 936 (29%; 95% CI: 27-32%) of interactions overall, with 194 of 500 (39%; 95% CI: 35-43%) of interactions at baseline and 81 of 436 (19%; 95% CI: 15-22%) in round 2. No private pharmacy dispensed anti-TB medications without a prescription. On average, the difference in correct case management between case 1 vs. case 2 dropped by 20 percent points from baseline to the second round of data collection. Similarly, ideal case management decreased by 26 percentage points between rounds. This is in contrast with the dispensation of medicines, which had the opposite effect between rounds; the difference in dispensation of quinolones between case 1 and case 2 increased by 14 percentage points, as did corticosteroids by 9 percentage points, antibiotics by 25 percentage points and medicines generally by 30 percentage points. Our standardised patient study provides valuable insights into how private pharmacies in an Indian city changed their management of patients with TB symptoms or with confirmed TB over a 5-year period. We saw that overall, private pharmacy performance has weakened over time. However, no OTC dispensation of anti-TB medications occurred in either survey round. As the first point of contact for many care seekers, continued and sustained efforts to engage with Indian private pharmacies should be prioritized.

Antibodies against the Ebola virus soluble glycoprotein are associated with long-term vaccine-mediated protection of non-human primates

The 2013 Ebola epidemic in Central and West Africa heralded the emergence of wide-spread, highly pathogenic viruses. The successful recombinant vector vaccine against Ebola (rVSVΔG-ZEBOV-GP) will limit future outbreaks, but identifying mechanisms of protection is essential to protect the most vulnerable. Vaccine-induced antibodies are key determinants of vaccine efficacy, yet the mechanism by which vaccine-induced antibodies prevent Ebola infection remains elusive. Here, we exploit a break in long-term vaccine efficacy in non-human primates to identify predictors of protection. Using unbiased humoral profiling that captures neutralization and Fc-mediated functions, we find that antibodies specific for soluble glycoprotein (sGP) drive neutrophil-mediated phagocytosis and predict vaccine-mediated protection. Similarly, we show that protective sGP-specific monoclonal antibodies have elevated neutrophil-mediated phagocytic activity compared with non-protective antibodies, highlighting the importance of sGP in vaccine protection and monoclonal antibody therapeutics against Ebola virus.

Stability and heterogeneity in the anti-microbiota reactivity of human milk-derived Immunoglobulin A

Immunoglobulin A (IgA) is secreted into breast milk and is critical to both protecting against enteric pathogens and shaping the infant intestinal microbiota. The efficacy of breast milk-derived maternal IgA (BrmIgA) is dependent upon its specificity, however heterogeneity in BrmIgA binding ability to the infant microbiota is not known. Using a flow cytometric array, we analyzed the reactivity of BrmIgA against bacteria common to the infant microbiota and discovered substantial heterogeneity between all donors, independent of preterm or term delivery. We also observed intra-donor variability in the BrmIgA response to closely related bacterial isolates. Conversely, longitudinal analysis showed that the anti-bacterial BrmIgA reactivity was relatively stable through time, even between sequential infants, indicating that mammary gland IgA responses are durable. Together, our study demonstrates that the anti-bacterial BrmIgA reactivity displays inter-individual heterogeneity but intra-individual stability. These findings have important implications for how breast milk shapes the development of the infant microbiota and protects against Necrotizing Enterocolitis.

SUMMARY

We analyze the ability of breast milk-derived Immunoglobulin A (IgA) antibodies to bind the infant intestinal microbiota. We discover that each mother secretes into their breast milk a distinct set of IgA antibodies that are stably maintained over time.

Landscape of MicroRNA Regulatory Network Architecture and Functional Rerouting in Cancer

Somatic mutations are a major source of cancer development, and many driver mutations have been identified in protein coding regions. However, the function of mutations located in miRNA and their target binding sites throughout the human genome remains largely unknown. Here, we built detailed cancer-specific miRNA regulatory networks across 30 cancer types to systematically analyze the effect of mutations in miRNAs and their target sites in 3' untranslated region (3' UTR), coding sequence (CDS), and 5' UTR regions. A total of 3,518,261 mutations from 9,819 samples were mapped to miRNA-gene interactions (mGI). Mutations in miRNAs showed a mutually exclusive pattern with mutations in their target genes in almost all cancer types. A linear regression method identified 148 candidate driver mutations that can significantly perturb miRNA regulatory networks. Driver mutations in 3'UTRs played their roles by altering RNA binding energy and the expression of target genes. Finally, mutated driver gene targets in 3' UTRs were significantly downregulated in cancer and functioned as tumor suppressors during cancer progression, suggesting potential miRNA candidates with significant clinical implications. A user-friendly, open-access web portal (mGI-map) was developed to facilitate further use of this data resource. Together, these results will facilitate novel noncoding biomarker identification and therapeutic drug design targeting the miRNA regulatory networks.

SIGNIFICANCE

A detailed miRNA-gene interaction map reveals extensive miRNA-mediated gene regulatory networks with mutation-induced perturbations across multiple cancers, serving as a resource for noncoding biomarker discovery and drug development.

Tuberculosis service disruptions and adaptations during the first year of the COVID-19 pandemic in the private health sector of two urban settings in Nigeria-A mixed methods study

Nigeria has the second largest share of undiagnosed TB cases in the world and a large private health sector estimated to be the point of initial care-seeking for 67% of TB patients. There is evidence that COVID-19 restrictions disrupted private healthcare provision, but insufficient data on how private healthcare provision changed as a result of the pandemic. We conducted qualitative interviews and a survey to assess the impact of the pandemic, and government response on private healthcare provision, and the disruptions providers experienced, particularly for TB services. Using mixed methods, we targeted policymakers, and a network of clinical facilities, laboratories, community pharmacies, and medicine vendors in Kano and Lagos, Nigeria. We interviewed 11 policymakers, surveyed participants in 2,412 private facilities. Most (n = 1,676, 70%) facilities remained open during the initial lockdown period, and most (n = 1,667, 69%) offered TB screening. TB notifications dipped during the lockdown periods but quickly recovered. Clinical facilities reported disruptions in availability of medical supplies, staff, required renovations, patient volume and income. Few private providers (n = 119, 11% in Kano; n = 323, 25% in Lagos) offered any COVID-19 screening up to the time of the survey, as these were only available in designated facilities. These findings aligned with the interviews as policymakers reported a gradual return to pre-COVID services after initial disruptions and diversion of resources to the pandemic response. Our results show that COVID-19 and control measures had a temporary impact on private sector TB care. Although some facilities saw decreases in TB notifications, private facilities continued to provide care for individuals with TB who otherwise might have been unable to seek care in the public sector. Our findings highlight resilience in the private sector as they recovered fairly quickly from pandemic-related disruptions, and the important role private providers can play in supporting TB control efforts.

Determining asthma endotypes and outcomes: Complementing existing clinical practice with modern machine learning

There is unprecedented opportunity to use machine learning to integrate high-dimensional molecular data with clinical characteristics to accurately diagnose and manage disease. Asthma is a complex and heterogeneous disease and cannot be solely explained by an aberrant type 2 (T2) immune response. Available and emerging multi-omics datasets of asthma show dysregulation of different biological pathways including those linked to T2 mechanisms. While T2-directed biologics have been life changing for many patients, they have not proven effective for many others despite similar biomarker profiles. Thus, there is a great need to close this gap to understand asthma heterogeneity, which can be achieved by harnessing and integrating the rich multi-omics asthma datasets and the corresponding clinical data. This article presents a compendium of machine learning approaches that can be utilized to bridge the gap between predictive biomarkers and actual causal signatures that are validated in clinical trials to ultimately establish true asthma endotypes.

Lipidomic signatures align with inflammatory patterns and outcomes in critical illness

Alterations in lipid metabolism have the potential to be markers as well as drivers of pathobiology of acute critical illness. Here, we took advantage of the temporal precision offered by trauma as a common cause of critical illness to identify the dynamic patterns in the circulating lipidome in critically ill humans. The major findings include an early loss of all classes of circulating lipids followed by a delayed and selective lipogenesis in patients destined to remain critically ill. The previously reported survival benefit of early thawed plasma administration was associated with preserved lipid levels that related to favorable changes in coagulation and inflammation biomarkers in causal modelling. Phosphatidylethanolamines (PE) were elevated in patients with persistent critical illness and PE levels were prognostic for worse outcomes not only in trauma but also severe COVID-19 patients. Here we show selective rise in systemic PE as a common prognostic feature of critical illness.

Coping strategies and barriers to coping in climate- vulnerable Bangladesh: a qualitative study

 

With the mental wellbeing of billions of people at risk due to climate change, more research is required to better understand mental health and psychological implications of climate vulnerability. This research contributes to understanding how people in climate vulnerable populations psychologically cope with stress with crucial implications for adaptation efforts. We conducted n = 60 qualitative in-depth interviews with men and women in Bhola, Bangladesh to elicit the lived experiences of a climate vulnerable population. We analysed data following the tenets of Grounded Theory. Through our inductive analysis, we found coping strategies where participants highlighted what they did when encountering stress, such as ‘Resignation’ or ‘Help Seeking'. Barriers to coping were, among others, limited ‘Efficacy', ‘Time’ or ‘Stigma'. We categorized coping strategies with barriers as high-barrier coping strategies and, those without reported barriers, as low- barrier coping strategies. High-barriers restricted participants - especially women - in their coping efforts and led them to using low-barrier coping strategies. Some low-barrier coping strategies can be interpreted as maladaptive if used frequently, as they are unhealthy and draw upon resources needed to adapt for the future. Maladaptive coping strategies can thus impede long-term adaptation by reducing motivation and the ability and willingness to act. To enable adaptive coping, we recommend lifting the barriers to coping through community-led interventions where community workers create platforms for sharing problems and knowledge, such as group support meetings. Sharing and discussing could strengthen efficacy and open new opportunities for functional, adaptive coping. As the negative impacts of climate change will be felt globally with more intensity and frequency, enabling adaptive coping and removing barriers to coping in frontline communities will be essential to supporting physical and mental wellbeing.

Key messages

• Barriers to adaptive coping strategies can lead people to using maladaptive low-barrier coping strategies, which draw upon resources needed for long-term adaptation.

• Enabling adaptive coping by lifting barriers to coping in climate vulnerable populations is crucial to strengthen adaptation efforts.

Tuberculosis diagnosis and management in the public versus private sector: a standardised patients study in Mumbai, India

BACKGROUND

There are few rigorous studies comparing quality of tuberculosis (TB) care in public versus private sectors.

METHODS

We used standardised patients (SPs) to measure technical quality and patient experience in a sample of private and public facilities in Mumbai.

RESULTS

SPs presented a 'classic, suspected TB' scenario and a 'recurrence or drug-resistance' scenario. In the private sector, SPs completed 643 interactions. In the public sector, 164 interactions. Outcomes included indicators of correct management, medication use and client experience. Public providers used microbiological testing (typically, microscopy) more frequently, in 123 of 164 (75%; 95% CI 68% to 81%) vs 223 of 644 interactions (35%; 95% CI 31% to 38%) in the private sector. Private providers were more likely to order chest X-rays, in 556 of 639 interactions (86%; 95% CI 84% to 89%). According to national TB guidelines, we found higher proportions of correct management in the public sector (75% vs 35%; (adjusted) difference 35 percentage points (pp); 95% CI 25 to 46). If X-rays were considered acceptable for the first case but drug-susceptibility testing was required for the second case, the private sector correctly managed a slightly higher proportion of interactions (67% vs 51%; adjusted difference 16 pp; 95% CI 7 to 25). Broad-spectrum antibiotics were used in 76% (95% CI 66% to 84%) of the interactions in public hospitals, and 61% (95% CI 58% to 65%) in private facilities. Costs in the private clinics averaged rupees INR 512 (95% CI 485 to 539); public facilities charged INR 10. Private providers spent more time with patients (4.4 min vs 2.4 min; adjusted difference 2.0 min; 95% CI 1.2 to 2.9) and asked a greater share of relevant questions (29% vs 43%; adjusted difference 13.7 pp; 95% CI 8.2 to 19.3).

CONCLUSIONS

While the public providers did a better job of adhering to national TB guidelines (especially microbiological testing) and offered less expensive care, private sector providers did better on client experience.

High Dimensional Multiomics Reveals Unique Characteristics of Early Plasma Administration in Polytrauma Patients With TBI

OBJECTIVES

The authors sought to identify causal factors that explain the selective benefit of prehospital administration of thawed plasma (TP) in traumatic brain injury (TBI) patients using mediation analysis of a multiomic database.

BACKGROUND

The Prehospital Air Medical Plasma (PAMPer) Trial showed that patients with TBI and a pronounced systemic response to injury [defined as endotype 2 (E2)], have a survival benefit from prehospital administration of TP. An interrogation of high dimensional proteomics, lipidomics and metabolomics previously demonstrated unique patterns in circulating biomarkers in patients receiving prehospital TP, suggesting that a deeper analysis could reveal causal features specific to TBI patients.

METHODS

A novel proteomic database (SomaLogic Inc., aptamer-based assay, 7K platform) was generated using admission blood samples from a subset of patients (n=149) from the PAMPer Trial. This proteomic dataset was combined with previously reported metabolomic and lipidomic datasets from these same patients. A 2-step analysis was performed to identify factors that promote survival in E2-TBI patients who had received early TP. First, features were selected using both linear and multivariate-latent-factor regression analyses. Then, the selected features were entered into the causal mediation analysis.

RESULTS

Causal mediation analysis of observable features identified 16 proteins and 41 lipids with a high proportion of mediated effect (>50%) to explain the survival benefit of early TP in E2-TBI patients. The multivariate latent-factor regression analyses also uncovered 5 latent clusters of features with a proportion effect >30%, many in common with the observable features. Among the observable and latent features were protease inhibitors known to inhibit activated protein C and block fibrinolysis (SERPINA5 and CPB2), a clotting factor (factor XI), as well as proteins involved in lipid transport and metabolism (APOE3 and sPLA(2)-XIIA).

CONCLUSIONS

These findings suggest that severely injured patients with TBI process exogenous plasma differently than those without TBI. The beneficial effects of early TP in E2-TBI patients may be the result of improved blood clotting and the effect of brain protective factors independent of coagulation.

Multi-Omic Admission-Based Prognostic Biomarkers Identified by Machine Learning Algorithms Predict Patient Recovery and 30-Day Survival in Trauma Patients

Admission-based circulating biomarkers for the prediction of outcomes in trauma patients could be useful for clinical decision support. It is unknown which molecular classes of biomolecules can contribute biomarkers to predictive modeling. Here, we analyzed a large multi-omic database of over 8500 markers (proteomics, metabolomics, and lipidomics) to identify prognostic biomarkers in the circulating compartment for adverse outcomes, including mortality and slow recovery, in severely injured trauma patients. Admission plasma samples from patients (n = 129) enrolled in the Prehospital Air Medical Plasma (PAMPer) trial were analyzed using mass spectrometry (metabolomics and lipidomics) and aptamer-based (proteomics) assays. Biomarkers were selected via Least Absolute Shrinkage and Selection Operator (LASSO) regression modeling and machine learning analysis. A combination of five proteins from the proteomic layer was best at discriminating resolvers from non-resolvers from critical illness with an Area Under the Receiver Operating Characteristic curve (AUC) of 0.74, while 26 multi-omic features predicted 30-day survival with an AUC of 0.77. Patients with traumatic brain injury as part of their injury complex had a unique subset of features that predicted 30-day survival. Our findings indicate that multi-omic analyses can identify novel admission-based prognostic biomarkers for outcomes in trauma patients. Unique biomarker discovery also has the potential to provide biologic insights.

Adaptations to the first wave of the COVID-19 pandemic by private sector tuberculosis care providers in India

Background

India’s dominant private healthcare sector is the destination for 60–85% of initial tuberculosis care-seeking. The COVID-19 pandemic in India drastically affected TB case notifications in the first half of 2020. In this survey, we assessed the impact of the first wave of COVID-19 in India on private providers, and changes they adopted in their practice due to the pandemic.

Methods

The Joint Effort for Elimination of TB (JEET) is a nationwide Global Fund project implemented across 406 districts in 23 states to extend quality TB services to patients seeking care in private sector. We conducted a rapid survey of 11% (2,750) of active providers engaged under JEET’s intense Patient Provider Support Agency (PPSA) model across 15 Indian states in Q1 (February–March) of 2021. Providers were contacted in person or telephonically, and consenting participants were interviewed using a web-based survey tool. Responses from participants were elicited on their practice before COVID-19, during the 2020 lockdowns (March–April 2020) and currently (Q1 2021). Data were adjusted for survey design and non-response, and results were summarised using descriptive statistics and logistic regression.

Results

Of the 2,750 providers sampled, 2,011 consented and were surveyed (73 % response). Nearly 50 % were between 30 and 45 years of age, and 51 % were from Uttar Pradesh, Maharashtra and Gujarat. Seventy percent of providers reported reduced daily out-patient numbers in Q1 2021 compared to pre-COVID times. During the lockdown, 898 (40 %) of providers said their facilities were closed, while 323 (11 %) offered limited services including teleconsultation. In Q1 2021, 88 % of provider facilities were fully open, with 10 % providing adjusted services, and 4 % using teleconsultation. Only 2 % remained completely closed. Majority of the providers (92 %) reported not experiencing any delays in TB testing in Q1 2021 compared to pre-COVID times. Only 6 % reported raising costs at their clinic, mostly to cover personal protective equipment (PPE) and other infection control measures, although 60–90 % implemented various infection control measures. Thirty-three percent of TB providers were ordering COVID-19 testing, in addition to TB testing.

To adapt, 82% of survey providers implemented social distancing and increased timing between appointments and 83% started conducting temperature checks, with variation by state and provider type, while 89% adopted additional sanitation measures in their facilities. Furthermore, 62% of providers started using PPE, and 13% made physical changes (air filters, isolation of patient areas) to their clinic to prevent infection. Seventy percent of providers stated that infection control measures could decrease TB transmission.

Conclusion

Although COVID-19 restrictions resulted in significant declines in patient turn-out at private facilities, our analysis showed that most providers were open and costs for TB care remained mostly the same in Q1 2021. As result of the COVID-19 pandemic, several positive strategies have been adapted by the private sector TB care providers. Since the subsequent COVID-19 waves were more severe or widespread, additional work is needed to assess the impact of the pandemic on the private health sector.

A supervised take on dimensionality reduction via hybrid subset selection

Amouzgar et al. present HSS-LDA, a supervised dimensionality reduction approach for single-cell data that outperforms existing unsupervised techniques. They couple hybrid subset selection to linear discriminant analysis and identify interpretable linear combinations of predictors that best separate predefined biological groups.

Antibodies targeting conserved non-canonical antigens and endemic coronaviruses associate with favorable outcomes in severe COVID-19

While there have been extensive analyses characterizing cellular and humoral responses across the severity spectrum in COVID-19, outcome predictors within severe COVID-19 remain less comprehensively elucidated. Furthermore, properties of antibodies (Abs) directed against viral antigens beyond spike and their associations with disease outcomes remain poorly defined. We perform deep molecular profiling of Abs directed against a wide range of antigenic specificities in severe COVID-19 patients. The profiles included canonical (spike [S], receptor-binding domain [RBD], and nucleocapsid [N]) and non-canonical (orf3a, orf8, nsp3, nsp13, and membrane [M]) antigenic specificities. Notably, multivariate Ab profiles directed against canonical or non-canonical antigens are equally discriminative of survival in severe COVID-19. Intriguingly, pre-pandemic healthy controls have cross-reactive Abs directed against nsp13, a protein conserved across coronaviruses. Consistent with these findings, a model built on Ab profiles for endemic coronavirus antigens also predicts COVID-19 outcome. Our results suggest the importance of studying Abs targeting non-canonical severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) and endemic coronavirus antigens in COVID-19.

Mutability Patterns Across the Spike Glycoprotein Reveal the Diverging and Lineage-specific Evolutionary Space of SARS-CoV-2

Mutations in the spike glycoprotein of SARS-CoV-2 allow the virus to probe the sequence space in search of higher-fitness states. New sublineages of SARS-CoV-2 variants-of-concern (VOCs) continuously emerge with such mutations. Interestingly, the sites of mutation in these sublineages vary between the VOCs. Whether such differences reflect the random nature of mutation appearance or distinct evolutionary spaces of spike in the VOCs is unclear. Here we show that each position of spike has a lineage-specific likelihood for mutations to appear and dominate descendent sublineages. This likelihood can be accurately estimated from the lineage-specific mutational profile of spike at a protein-wide level. The mutability environment of each position, including adjacent sites on the protein structure and neighboring sites on the network of comutability, accurately forecast changes in descendent sublineages. Mapping of imminent changes within the VOCs can contribute to the design of immunogens and therapeutics that address future forms of SARS-CoV-2.

Autoreactive CD8+ T cells are restrained by an exhaustion-like program that is maintained by LAG3

Impaired chronic viral and tumor clearance has been attributed to CD8⁺ T cell exhaustion, a differentiation state in which T cells have reduced and altered effector function that can be partially reversed upon blockade of inhibitory receptors. The role of the exhaustion program and transcriptional networks that control CD8⁺ T cell function and fate in autoimmunity is not clear. Here we show that intra-islet CD8⁺ T cells phenotypically, transcriptionally, epigenetically and metabolically possess features of canonically exhausted T cells, yet maintain important differences. This 'restrained' phenotype can be perturbed and disease accelerated by CD8⁺ T cell-restricted deletion of the inhibitory receptor lymphocyte activating gene 3 (LAG3). Mechanistically, LAG3-deficient CD8⁺ T cells have enhanced effector-like functions, trafficking to the islets, and have a diminished exhausted phenotype, highlighting a physiological role for an exhaustion program in limiting autoimmunity and implicating LAG3 as a target for autoimmune therapy.

Essential Regression: A generalizable framework for inferring causal latent factors from multi-omic datasets

High-dimensional cellular and molecular profiling of biological samples highlights the need for analytical approaches that can integrate multi-omic datasets to generate prioritized causal inferences. Current methods are limited by high dimensionality of the combined datasets, the differences in their data distributions, and their integration to infer causal relationships. Here, we present Essential Regression (ER), a novel latent-factor-regression-based interpretable machine-learning approach that addresses these problems by identifying latent factors and their likely cause-effect relationships with system-wide outcomes/properties of interest. ER can integrate many multi-omic datasets without structural or distributional assumptions regarding the data. It outperforms a range of state-of-the-art methods in terms of prediction. ER can be coupled with probabilistic graphical modeling, thereby strengthening the causal inferences. The utility of ER is demonstrated using multi-omic system immunology datasets to generate and validate novel cellular and molecular inferences in a wide range of contexts including immunosenescence and immune dysregulation.

A network-based approach to identify expression modules underlying rejection in pediatric liver transplantation

Selecting the right immunosuppressant to ensure rejection-free outcomes poses unique challenges in pediatric liver transplant (LT) recipients. A molecular predictor can comprehensively address these challenges. Currently, there are no well-validated blood-based biomarkers for pediatric LT recipients before or after LT. Here, we discover and validate separate pre- and post-LT transcriptomic signatures of rejection. Using an integrative machine learning approach, we combine transcriptomics data with the reference high-quality human protein interactome to identify network module signatures, which underlie rejection. Unlike gene signatures, our approach is inherently multivariate and more robust to replication and captures the structure of the underlying network, encapsulating additive effects. We also identify, in an individual-specific manner, signatures that can be targeted by current anti-rejection drugs and other drugs that can be repurposed. Our approach can enable personalized adjustment of drug regimens for the dominant targetable pathways before and after LT in children.

Functional reprogramming of monocytes in acute and convalescent severe COVID-19 patients

Severe COVID-19 disease is associated with dysregulation of the myeloid compartment during acute infection. Survivors frequently experience long-lasting sequelae, but little is known about the eventual persistence of this immune alteration. Herein, we evaluated TLR-induced cytokine responses in a cohort of mild to critical patients during acute or convalescent phases (n = 97). In the acute phase, we observed impaired cytokine production by monocytes in the patients with the most severe COVID-19. This capacity was globally restored in convalescent patients. However, we observed increased responsiveness to TLR1/2 ligation in patients who recovered from severe disease, indicating that these cells display distinct functional properties at the different stages of the disease. In patients with acute severe COVID-19, we identified a specific transcriptomic and epigenomic state in monocytes that can account for their functional refractoriness. The molecular profile of monocytes from recovering patients was distinct and characterized by increased chromatin accessibility at activating protein 1 (AP1) and MAF loci. These results demonstrate that severe COVID-19 infection has a profound impact on the differentiation status and function of circulating monocytes, during both the acute and the convalescent phases, in a completely distinct manner. This could have important implications for our understanding of short- and long-term COVID-19–related morbidity.

People critically ill with COVID-19 exhibit peripheral immune profiles predictive of mortality and reflective of SARS-CoV-2 lung viral burden

Despite extensive analyses, there remains an urgent need to delineate immune cell states that contribute to mortality in people critically ill with COVID-19. Here, we present high-dimensional profiling of blood and respiratory samples from people with severe COVID-19 to examine the association between cell-linked molecular features and mortality outcomes. Peripheral transcriptional profiles by single-cell RNA sequencing (RNA-seq)-based deconvolution of immune states are associated with COVID-19 mortality. Further, persistently high levels of an interferon signaling module in monocytes over time lead to subsequent concerted upregulation of inflammatory cytokines. SARS-CoV-2-infected myeloid cells in the lower respiratory tract upregulate CXCL10, leading to a higher risk of death. Our analysis suggests a pivotal role for viral-infected myeloid cells and protracted interferon signaling in severe COVID-19.

Upper and lower respiratory tract correlates of protection against respiratory syncytial virus following vaccination of nonhuman primates

Respiratory syncytial virus (RSV) infection is a major cause of respiratory illness in infants and the elderly. Although several vaccines have been developed, none have succeeded in part due to our incomplete understanding of the correlates of immune protection. While both T cells and antibodies play a role, emerging data suggest that antibody-mediated mechanisms alone may be sufficient to provide protection. Therefore, to map the humoral correlates of immunity against RSV, antibody responses across six different vaccines were profiled in a highly controlled nonhuman primate-challenge model. Viral loads were monitored in both the upper and lower respiratory tracts, and machine learning was used to determine the vaccine platform-agnostic antibody features associated with protection. Upper respiratory control was associated with virus-specific IgA levels, neutralization, and complement activity, whereas lower respiratory control was associated with Fc-mediated effector mechanisms. These findings provide critical compartment-specific insights toward the rational development of future vaccines.

Simulated patients and their reality: An inquiry into theory and method

Simulated standardized patients (SSP) have emerged as close to a ‘gold standard’ for measuring the quality of clinical care. This method resolves problems of patient mix across healthcare providers and allows care to be benchmarked against preexisting standards. Nevertheless, SSPs are not real patients. How, then, should data from SSPs be considered relative to clinical observations with ‘real’ patients in a given health system?

Here, we reject the proposition that SSPs are direct substitutes for real patients and that the validity of SSP studies therefore relies on their ability to imitate real patients. Instead, we argue that the success of the SSP methodology lies in its counterfactual manipulations of the possibilities available to real careseekers – especially those paths not taken up by them – through which real responses can be elicited from real providers.

Using results from a unique pilot study where SSPs returned to providers for follow-ups when asked, we demonstrate that the SSP method works well to elicit responses from the provider through conditional manipulations of SSP behavior. At the same time, observational methods are better suited to understand what choices real people make, and how these can affect the direction of diagnosis and treatment. A combination of SSP and observational methods can thus help parse out how quality of care emerges for the “patient” as a shared history between care-seeking individuals and care providers.

•

We assess how simulated standardized patients (SSPs) compare to ‘real’ patients.

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SSPs elicit real responses from providers for different counterfactual patients.

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However, SSPs are not informative of observed variation in patient behavior.

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SSPs illustrate how providers behave with different types of patients.

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Observation studies can complement SSPs by studying patient responses.

Quantifying heterogeneity in SARS-CoV-2 transmission during the lockdown in India

The novel SARS-CoV-2 virus, as it manifested in India in April 2020, showed marked heterogeneity in its transmission. Here, we used data collected from contact tracing during the lockdown in response to the first wave of COVID-19 in Punjab, a major state in India, to quantify this heterogeneity, and to examine implications for transmission dynamics. We found evidence of heterogeneity acting at multiple levels: in the number of potentially infectious contacts per index case, and in the per-contact risk of infection. Incorporating these findings in simple mathematical models of disease transmission reveals that these heterogeneities act in combination to strongly influence transmission dynamics. Standard approaches, such as representing heterogeneity through secondary case distributions, could be biased by neglecting these underlying interactions between heterogeneities. We discuss implications for policy, and for more efficient contact tracing in resource-constrained settings such as India. Our results highlight how contact tracing, an important public health measure, can also provide important insights into epidemic spread and control.

Mechanisms of Impaired Lung Development and Ciliation in Mannosidase-1-Alpha-2 (Man1a2) Mutants

Background

Ciliary defects cause heterogenous phenotypes related to mutation burden which lead to impaired development. A previously reported homozygous deletion in the Man1a2 gene causes lethal respiratory failure in newborn pups and decreased lung ciliation compared with wild type (WT) pups. The effects of heterozygous mutation, and the potential for rescue are not known.

Purpose

We hypothesized that survival and lung ciliation, (a) would decrease progressively in Man1a2^+/− heterozygous and Man1a2^–/– null newborn pups compared with WT, and (b) could be enhanced by gestational treatment with N-Acetyl-cysteine (NAC), an antioxidant.

Methods

Man1a2^+/– adult mice were fed NAC or placebo from a week before breeding through gestation. Survival of newborn pups was monitored for 24 h. Lungs, liver and tails were harvested for morphology, genotyping, and transcriptional profiling.

Results

Survival (p = 0.0001, Kaplan-Meier) and percent lung ciliation (p = 0.0001, ANOVA) measured by frequency of Arl13b⁺ respiratory epithelial cells decreased progressively, as hypothesized. Compared with placebo, gestational NAC treatment enhanced (a) lung ciliation in pups with each genotype, (b) survival in heterozygous pups (p = 0.017) but not in WT or null pups. Whole transcriptome of lung but not liver demonstrated patterns of up- and down-regulated genes that were identical in living heterozygous and WT pups, and completely opposite to those in dead heterozygous and null pups. Systems biology analysis enabled reconstruction of protein interaction networks that yielded functionally relevant modules and their interactions. In these networks, the mutant Man1a2 enzyme contributes to abnormal synthesis of proteins essential for lung development. The associated unfolded protein, hypoxic and oxidative stress responses can be mitigated with NAC. Comparisons with the developing human fetal lung transcriptome show that NAC likely restores normal vascular and epithelial tube morphogenesis in Man1a2 mutant mice.

Conclusion

Survival and lung ciliation in the Man1a2 mutant mouse, and its improvement with N-Acetyl cysteine is genotype-dependent. NAC-mediated rescue depends on the central role for oxidative and hypoxic stress in regulating ciliary function and organogenesis during development.

Integrated multiplex network based approach for hub gene identification in oral cancer

Background: The incidence of Oral Cancer (OC) is high in Asian countries, which goes undetected at its early stage. The study of genetics, especially genetic networks holds great promise in this endeavor. Hub genes in a genetic network are prominent in regulating the whole network structure of genes. Thus identification of such genes related to specific cancer types can help in reducing the gap in OC prognosis. Methods: Traditional study of network biology is unable to decipher the inter-dependencies within and across diverse biological networks. Multiplex network provides a powerful representation of such systems and encodes much richer information than isolated networks. In this work, we focused on the entire multiplex structure of the genetic network integrating the gene expression profile and DNA methylation profile for OC. Further, hub genes were identified by considering their connectivity in the multiplex structure and the respective protein-protein interaction (PPI) network as well. Results: 46 hub genes were inferred in our approach with a high prediction accuracy (96%), outstanding Matthews coefficient correlation value (93%) and significant biological implications. Among them, genes PIK3CG, PIK3R5, MYH7, CDC20 and CCL4 were differentially expressed and predominantly enriched in molecular cascades specific to OC. Conclusions: The identified hub genes in this work carry ontological signatures specific to cancer, which may further facilitate improved understanding of the tumorigenesis process and the underlying molecular events. Result indicates the effectiveness of our integrated multiplex network approach for hub gene identification. This work puts an innovative research route for multi-omics biological data analysis.

New evidence on learning trajectories in a low-income setting

Using a unique longitudinal dataset collected from primary school students in Pakistan, we document four new facts about learning in low-income countries. First, children's test scores increase by 1.19 SD between Grades 3 and 6. Second, going to school is associated with greater learning. Children who dropout have the same test score gains prior to dropping out as those who do not but experience no improvements after dropping out. Third, there is significant variation in test score gains across students, but test scores converge over the primary schooling years. Students with initially low test scores gain more than those with initially high scores, even after accounting for mean reversion. Fourth, conditional on past test scores, household characteristics explain little of the variation in learning. In order to reconcile our findings with the literature, we introduce the concept of "fragile learning," where progression may be followed by stagnation or reversals. We discuss the implications of these results for several ongoing debates in the literature on education from Low- and Middle-Income Countries (LMICs).