Type 2 immunity is controlled by IL-4/IL-13 expression in hematopoietic non-eosinophil cells of the innate immune system

Nippostrongylus brasiliensis infection and ovalbumin-induced allergic lung pathology are highly interleukin (IL)-4/IL-13 dependent, but the contributions of IL-4/IL-13 from adaptive (T helper [Th]2 cells) and innate (eosinophil, basophils, and mast cells) immune cells remain unknown. Although required for immunoglobulin (Ig)E induction, IL-4/IL-13 from Th2 cells was not required for worm expulsion, tissue inflammation, or airway hyperreactivity. In contrast, innate hematopoietic cell–derived IL-4/IL-13 was dispensable for Th2 cell differentiation in lymph nodes but required for effector cell recruitment and tissue responses. Eosinophils were not required for primary immune responses. Thus, components of type 2 immunity mediated by IL-4/IL-13 are partitioned between T cell–dependent IgE and an innate non-eosinophil tissue component, suggesting new strategies for interventions in allergic immunity

Latent gammaherpesvirus 68 infection induces distinct transcriptional changes in different organs

Previous studies identified a role for latent herpesvirus infection in cross-protection against infection and exacerbation of chronic inflammatory diseases. Here, we identified more than 500 genes differentially expressed in spleens, livers, or brains of mice latently infected with gammaherpesvirus 68 and found that distinct sets of genes linked to different pathways were altered in the spleen compared to those in the liver. Several of the most differentially expressed latency-specific genes (e.g., the gamma interferon [IFN-γ], Cxcl9, and Ccl5 genes) are associated with known latency-specific phenotypes. Chronic herpesvirus infection, therefore, significantly alters the transcriptional status of host organs. We speculate that such changes may influence host physiology, the status of the immune system, and disease susceptibility

Pervasive transcription of a herpesvirus genome generates functionally important RNAs

ABSTRACT Pervasive transcription is observed in a wide range of organisms, including humans, mice, and viruses, but the functional significance of the resulting transcripts remains uncertain. Current genetic approaches are often limited by their emphasis on protein-coding open reading frames (ORFs). We previously identified extensive pervasive transcription from the murine gammaherpesvirus 68 (MHV68) genome outside known ORFs and antisense to known genes (termed expressed genomic regions [EGRs]). Similar antisense transcripts have been identified in many other herpesviruses, including Kaposi’s sarcoma-associated herpesvirus and human and murine cytomegalovirus. Despite their prevalence, whether these RNAs have any functional importance in the viral life cycle is unknown, and one interpretation is that these are merely “noise” generated by functionally unimportant transcriptional events. To determine whether pervasive transcription of a herpesvirus genome generates RNA molecules that are functionally important, we used a strand-specific functional approach to target transcripts from thirteen EGRs in MHV68. We found that targeting transcripts from six EGRs reduced viral protein expression, proving that pervasive transcription can generate functionally important RNAs. We characterized transcripts emanating from EGRs 26 and 27 in detail using several methods, including RNA sequencing, and identified several novel polyadenylated transcripts that were enriched in the nuclei of infected cells. These data provide the first evidence of the functional importance of regions of pervasive transcription emanating from MHV68 EGRs. Therefore, studies utilizing mutation of a herpesvirus genome must account for possible effects on RNAs generated by pervasive transcription. IMPORTANCE The fact that pervasive transcription produces functionally important RNAs has profound implications for design and interpretation of genetic studies in herpesviruses, since such studies often involve mutating both strands of the genome. This is a common potential problem; for example, a conservative estimate is that there are an additional 73,000 nucleotides transcribed antisense to annotated ORFs from the 119,450-bp MHV68 genome. Recognizing the importance of considering the function of each strand of the viral genome independently, we used strand-specific approaches to identify six regions of the genome encoding transcripts that promoted viral protein expression. For two of these regions, we mapped novel transcripts and determined that targeting transcripts from these regions reduced viral replication and the expression of other viral genes. This is the first description of a function for these RNAs and suggests that novel transcripts emanating from regions of pervasive transcription are critical for the viral life cycle

KG-COVID-19: A Framework to Produce Customized Knowledge Graphs for COVID-19 Response.

Integrated, up-to-date data about SARS-CoV-2 and COVID-19 is crucial for the ongoing response to the COVID-19 pandemic by the biomedical research community. While rich biological knowledge exists for SARS-CoV-2 and related viruses (SARS-CoV, MERS-CoV), integrating this knowledge is difficult and time-consuming, since much of it is in siloed databases or in textual format. Furthermore, the data required by the research community vary drastically for different tasks; the optimal data for a machine learning task, for example, is much different from the data used to populate a browsable user interface for clinicians. To address these challenges, we created KG-COVID-19, a flexible framework that ingests and integrates heterogeneous biomedical data to produce knowledge graphs (KGs), and applied it to create a KG for COVID-19 response. This KG framework also can be applied to other problems in which siloed biomedical data must be quickly integrated for different research applications, including future pandemics

NSAID use and clinical outcomes in COVID-19 patients: a 38-center retrospective cohort study.

BACKGROUND: Non-steroidal anti-inflammatory drugs (NSAIDs) are commonly used to reduce pain, fever, and inflammation but have been associated with complications in community-acquired pneumonia. Observations shortly after the start of the COVID-19 pandemic in 2020 suggested that ibuprofen was associated with an increased risk of adverse events in COVID-19 patients, but subsequent observational studies failed to demonstrate increased risk and in one case showed reduced risk associated with NSAID use. METHODS: A 38-center retrospective cohort study was performed that leveraged the harmonized, high-granularity electronic health record data of the National COVID Cohort Collaborative. A propensity-matched cohort of 19,746 COVID-19 inpatients was constructed by matching cases (treated with NSAIDs at the time of admission) and 19,746 controls (not treated) from 857,061 patients with COVID-19 available for analysis. The primary outcome of interest was COVID-19 severity in hospitalized patients, which was classified as: moderate, severe, or mortality/hospice. Secondary outcomes were acute kidney injury (AKI), extracorporeal membrane oxygenation (ECMO), invasive ventilation, and all-cause mortality at any time following COVID-19 diagnosis. RESULTS: Logistic regression showed that NSAID use was not associated with increased COVID-19 severity (OR: 0.57 95% CI: 0.53-0.61). Analysis of secondary outcomes using logistic regression showed that NSAID use was not associated with increased risk of all-cause mortality (OR 0.51 95% CI: 0.47-0.56), invasive ventilation (OR: 0.59 95% CI: 0.55-0.64), AKI (OR: 0.67 95% CI: 0.63-0.72), or ECMO (OR: 0.51 95% CI: 0.36-0.7). In contrast, the odds ratios indicate reduced risk of these outcomes, but our quantitative bias analysis showed E-values of between 1.9 and 3.3 for these associations, indicating that comparatively weak or moderate confounder associations could explain away the observed associations. CONCLUSIONS: Study interpretation is limited by the observational design. Recording of NSAID use may have been incomplete. Our study demonstrates that NSAID use is not associated with increased COVID-19 severity, all-cause mortality, invasive ventilation, AKI, or ECMO in COVID-19 inpatients. A conservative interpretation in light of the quantitative bias analysis is that there is no evidence that NSAID use is associated with risk of increased severity or the other measured outcomes. Our results confirm and extend analogous findings in previous observational studies using a large cohort of patients drawn from 38 centers in a nationally representative multicenter database

A method for comparing multiple imputation techniques: A case study on the U.S. national COVID cohort collaborative.

Healthcare datasets obtained from Electronic Health Records have proven to be extremely useful for assessing associations between patients’ predictors and outcomes of interest. However, these datasets often suffer from missing values in a high proportion of cases, whose removal may introduce severe bias. Several multiple imputation algorithms have been proposed to attempt to recover the missing information under an assumed missingness mechanism. Each algorithm presents strengths and weaknesses, and there is currently no consensus on which multiple imputation algorithm works best in a given scenario. Furthermore, the selection of each algorithm’s pa- rameters and data-related modeling choices are also both crucial and challenging

KG-Hub-building and exchanging biological knowledge graphs.

MOTIVATION: Knowledge graphs (KGs) are a powerful approach for integrating heterogeneous data and making inferences in biology and many other domains, but a coherent solution for constructing, exchanging, and facilitating the downstream use of KGs is lacking. RESULTS: Here we present KG-Hub, a platform that enables standardized construction, exchange, and reuse of KGs. Features include a simple, modular extract-transform-load pattern for producing graphs compliant with Biolink Model (a high-level data model for standardizing biological data), easy integration of any OBO (Open Biological and Biomedical Ontologies) ontology, cached downloads of upstream data sources, versioned and automatically updated builds with stable URLs, web-browsable storage of KG artifacts on cloud infrastructure, and easy reuse of transformed subgraphs across projects. Current KG-Hub projects span use cases including COVID-19 research, drug repurposing, microbial-environmental interactions, and rare disease research. KG-Hub is equipped with tooling to easily analyze and manipulate KGs. KG-Hub is also tightly integrated with graph machine learning (ML) tools which allow automated graph ML, including node embeddings and training of models for link prediction and node classification. AVAILABILITY AND IMPLEMENTATION: https://kghub.org

Characterizing Long COVID: Deep Phenotype of a Complex Condition.

BACKGROUND: Numerous publications describe the clinical manifestations of post-acute sequelae of SARS-CoV-2 (PASC or long COVID ), but they are difficult to integrate because of heterogeneous methods and the lack of a standard for denoting the many phenotypic manifestations. Patient-led studies are of particular importance for understanding the natural history of COVID-19, but integration is hampered because they often use different terms to describe the same symptom or condition. This significant disparity in patient versus clinical characterization motivated the proposed ontological approach to specifying manifestations, which will improve capture and integration of future long COVID studies. METHODS: The Human Phenotype Ontology (HPO) is a widely used standard for exchange and analysis of phenotypic abnormalities in human disease but has not yet been applied to the analysis of COVID-19. FINDINGS: We identified 303 articles published before April 29, 2021, curated 59 relevant manuscripts that described clinical manifestations in 81 cohorts three weeks or more following acute COVID-19, and mapped 287 unique clinical findings to HPO terms. We present layperson synonyms and definitions that can be used to link patient self-report questionnaires to standard medical terminology. Long COVID clinical manifestations are not assessed consistently across studies, and most manifestations have been reported with a wide range of synonyms by different authors. Across at least 10 cohorts, authors reported 31 unique clinical features corresponding to HPO terms; the most commonly reported feature was Fatigue (median 45.1%) and the least commonly reported was Nausea (median 3.9%), but the reported percentages varied widely between studies. INTERPRETATION: Translating long COVID manifestations into computable HPO terms will improve analysis, data capture, and classification of long COVID patients. If researchers, clinicians, and patients share a common language, then studies can be compared/pooled more effectively. Furthermore, mapping lay terminology to HPO will help patients assist clinicians and researchers in creating phenotypic characterizations that are computationally accessible, thereby improving the stratification, diagnosis, and treatment of long COVID. FUNDING: U24TR002306; UL1TR001439; P30AG024832; GBMF4552; R01HG010067; UL1TR002535; K23HL128909; UL1TR002389; K99GM145411