Enhancer deregulation inTET2-mutant clonal hematopoiesis is associated with increased COVID-19 severity and mortality

Background: COVID-19 causes significant morbidity and mortality, albeit with considerable heterogeneity among affected individuals. It remains unclear which host factors determine disease severity and survival. Given the propensity of clonal hematopoiesis (CH) to promote inflammation in healthy individuals, we investigated its effect on COVID-19 outcomes. Method(s): We performed a multi-omics interrogation of the genome, epigenome, transcriptome, and proteome of peripheral blood mononuclear cells from COVID-19 patients (n=227). We obtained clinical data, laboratory studies, and survival outcomes. We determined CH status and TET2-related DNA methylation. We performed single-cell proteogenomics to understand clonal composition in relation to cell phenotype. We interrogated single-cell gene expression in isolation and in conjunction with DNA accessibility. We integrated these multi-omics data to understand the effect of CH on clonal composition, gene expression, methylation of cis-regulatory elements, and lineage commitment in COVID-19 patients. We performed shRNA knockdowns to validate the effect of one candidate transcription factor in myeloid cell lines. Result(s): The presence of CH was strongly associated with COVID-19 severity and all-cause mortality, independent of age (HR 3.48, 95% CI 1.45-8.36, p=0.005). Differential methylation of promoters and enhancers was prevalent in TET2-mutant, but not DNMT3A-mutant CH. TET2- mutant CH was associated with enhanced classical/intermediate monocytosis and single-cell proteogenomics confirmed an enrichment of TET2 mutations in these cell types. We identified celltype specific gene expression changes associated with TET2 mutations in 102,072 single cells (n=34). Single-cell RNA-seq confirmed the skewing of hematopoiesis towards classical and intermediate monocytes and demonstrated the downregulation of EGR1 (a transcription factor important for monocyte differentiation) along with up-regulation of the lncRNA MALAT1 in monocytes. Combined scRNA-/scATAC-seq in 43,160 single cells (n=18) confirmed the skewing of hematopoiesis and up-regulation of MALAT1 in monocytes along with decreased accessibility of EGR1 motifs in known cis-regulatory elements. Using myeloid cell lines for functional validation, shRNA knockdowns of EGR1 confirmed the up-regulation of MALAT1 (in comparison to wildtype controls). Conclusion(s): CH is an independent prognostic factor in COVID-19 and skews hematopoiesis towards monocytosis. TET2-mutant CH is characterized by differential methylation and accessibility of enhancers binding myeloid transcriptions factors including EGR1. The ensuing loss of EGR1 expression in monocytes causes MALAT1 overexpression, a factor known to promote monocyte differentiation and inflammation. These data provide a mechanistic insight to the adverse prognostic impact of CH in COVID-19.

Integrative multi-omics approach for identifying molecular signatures and pathways and deriving and validating molecular scores for COVID-19 severity and status.

BACKGROUND: There is still more to learn about the pathobiology of COVID-19. A multi-omic approach offers a holistic view to better understand the mechanisms of COVID-19. We used state-of-the-art statistical learning methods to integrate genomics, metabolomics, proteomics, and lipidomics data obtained from 123 patients experiencing COVID-19 or COVID-19-like symptoms for the purpose of identifying molecular signatures and corresponding pathways associated with the disease. RESULTS: We constructed and validated molecular scores and evaluated their utility beyond clinical factors known to impact disease status and severity. We identified inflammation- and immune response-related pathways, and other pathways, providing insights into possible consequences of the disease. CONCLUSIONS: The molecular scores we derived were strongly associated with disease status and severity and can be used to identify individuals at a higher risk for developing severe disease. These findings have the potential to provide further, and needed, insights into why certain individuals develop worse outcomes.

Systems genetics identifies miRNA-mediated regulation of host response in COVID-19.

BACKGROUND: Individuals infected with SARS-CoV-2 vary greatly in their disease severity, ranging from asymptomatic infection to severe disease. The regulation of gene expression is an important mechanism in the host immune response and can modulate the outcome of the disease. miRNAs play important roles in post-transcriptional regulation with consequences on downstream molecular and cellular host immune response processes. The nature and magnitude of miRNA perturbations associated with blood phenotypes and intensive care unit (ICU) admission in COVID-19 are poorly understood. RESULTS: We combined multi-omics profiling-genotyping, miRNA and RNA expression, measured at the time of hospital admission soon after the onset of COVID-19 symptoms-with phenotypes from electronic health records to understand how miRNA expression contributes to variation in disease severity in a diverse cohort of 259 unvaccinated patients in Abu Dhabi, United Arab Emirates. We analyzed 62 clinical variables and expression levels of 632 miRNAs measured at admission and identified 97 miRNAs associated with 8 blood phenotypes significantly associated with later ICU admission. Integrative miRNA-mRNA cross-correlation analysis identified multiple miRNA-mRNA-blood endophenotype associations and revealed the effect of miR-143-3p on neutrophil count mediated by the expression of its target gene BCL2. We report 168 significant cis-miRNA expression quantitative trait loci, 57 of which implicate miRNAs associated with either ICU admission or a blood endophenotype. CONCLUSIONS: This systems genetics study has given rise to a genomic picture of the architecture of whole blood miRNAs in unvaccinated COVID-19 patients and pinpoints post-transcriptional regulation as a potential mechanism that impacts blood traits underlying COVID-19 severity. The results also highlight the impact of host genetic regulatory control of miRNA expression in early stages of COVID-19 disease.

A machine learning analysis to evaluate the outcome measures in inflammatory myopathies.

OBJECTIVE: To assess the long-term outcome in patients with Idiopathic Inflammatory Myopathies (IIM), focusing on damage and activity disease indexes using artificial intelligence (AI). BACKGROUND: IIM are a group of rare diseases characterized by involvement of different organs in addition to the musculoskeletal. Machine Learning analyses large amounts of information, using different algorithms, decision-making processes and self-learning neural networks. METHODS: We evaluate the long-term outcome of 103 patients with IIM, diagnosed on 2017 EULAR/ACR criteria. We considered different parameters, including clinical manifestations and organ involvement, number and type of treatments, serum creatine kinase levels, muscle strength (MMT8 score), disease activity (MITAX score), disability (HAQ-DI score), disease damage (MDI score), and physician and patient global assessment (PGA). The data collected were analysed, applying, with R, supervised ML algorithms such as lasso, ridge, elastic net, classification, and regression trees (CART), random forest and support vector machines (SVM) to find the factors that best predict disease outcome. RESULTS AND CONCLUSION: Using artificial intelligence algorithms we identified the parameters that best correlate with the disease outcome in IIM. The best result was on MMT8 at follow-up, predicted by a CART regression tree algorithm. MITAX was predicted based on clinical features such as the presence of RP-ILD and skin involvement. A good predictive capacity was also demonstrated on damage scores: MDI and HAQ-DI. In the future Machine Learning will allow us to identify the strengths or weaknesses of the composite disease activity and damage scores, to validate new criteria or to implement classification criteria.

Systems analysis of human innate immunity in COVID-19.

Recent developments in sequencing technologies, the computer and data sciences, as well as increasingly high-throughput immunological measurements have made it possible to derive holistic views on pathophysiological processes of disease and treatment effects directly in humans. We and others have illustrated that incredibly predictive data for immune cell function can be generated by single cell multi-omics (SCMO) technologies and that these technologies are perfectly suited to dissect pathophysiological processes in a new disease such as COVID-19, triggered by SARS-CoV-2 infection. Systems level interrogation not only revealed the different disease endotypes, highlighted the differential dynamics in context of disease severity, and pointed towards global immune deviation across the different arms of the immune system, but was already instrumental to better define long COVID phenotypes, suggest promising biomarkers for disease and therapy outcome predictions and explains treatment responses for the widely used corticosteroids. As we identified SCMO to be the most informative technologies in the vest to better understand COVID-19, we propose to routinely include such single cell level analysis in all future clinical trials and cohorts addressing diseases with an immunological component.

Multi-omic longitudinal study reveals immune correlates of clinical course among hospitalized COVID-19 patients.

The IMPACC cohort, composed of >1,000 hospitalized COVID-19 participants, contains five illness trajectory groups (TGs) during acute infection (first 28 days), ranging from milder (TG1-3) to more severe disease course (TG4) and death (TG5). Here, we report deep immunophenotyping, profiling of >15,000 longitudinal blood and nasal samples from 540 participants of the IMPACC cohort, using 14 distinct assays. These unbiased analyses identify cellular and molecular signatures present within 72 h of hospital admission that distinguish moderate from severe and fatal COVID-19 disease. Importantly, cellular and molecular states also distinguish participants with more severe disease that recover or stabilize within 28 days from those that progress to fatal outcomes (TG4 vs. TG5). Furthermore, our longitudinal design reveals that these biologic states display distinct temporal patterns associated with clinical outcomes. Characterizing host immune responses in relation to heterogeneity in disease course may inform clinical prognosis and opportunities for intervention.

The need for integrated research autopsies in the era of precision oral medicine.

BACKGROUND: Autopsy has benefited the practice of medicine for centuries; however, its use to advance the practice of oral health care is relatively limited. In the era of precision oral medicine, the research autopsy is poised to play an important role in understanding oral-systemic health, including infectious disease, autoimmunity, craniofacial genetics, and cancer. TYPES OF STUDIES REVIEWED: The authors reviewed relevant articles that used medical and dental research autopsies to summarize the advantages of minimally invasive autopsies of dental, oral, and craniofacial tissues and to outline practices for supporting research autopsies of the oral and craniofacial complex. RESULTS: The authors provide a historical summary of research autopsy in dentistry and provide a perspective on the value of autopsies for high-resolution multiomic studies to benefit precision oral medicine. As the promise of high-resolution multiomics is being realized, there is a need to integrate the oral and craniofacial complex into the practice of autopsy in medicine. Furthermore, the collaboration of autopsy centers with researchers will accelerate the understanding of dental, oral, and craniofacial tissues as part of the whole body. CONCLUSIONS: Autopsies must integrate oral and craniofacial tissues as part of biobanking procedures. As new technologies allow for high-resolution, multimodal phenotyping of human samples, using optimized sampling procedures will allow for unprecedented understanding of common and rare dental, oral, and craniofacial diseases in the future. PRACTICAL IMPLICATIONS: The COVID-19 pandemic highlighted the oral cavity as a site for viral infection and transmission potential; this was only discovered via clinical autopsies. The realization of the integrated autopsy's value in full body health initiatives will benefit patients across the globe.

Metabolomics meets systems immunology.

Metabolic processes play a critical role in immune regulation. Metabolomics is the systematic analysis of small molecules (metabolites) in organisms or biological samples, providing an opportunity to comprehensively study interactions between metabolism and immunity in physiology and disease. Integrating metabolomics into systems immunology allows the exploration of the interactions of multilayered features in the biological system and the molecular regulatory mechanism of these features. Here, we provide an overview on recent technological developments of metabolomic applications in immunological research. To begin, two widely used metabolomics approaches are compared: targeted and untargeted metabolomics. Then, we provide a comprehensive overview of the analysis workflow and the computational tools available, including sample preparation, raw spectra data preprocessing, data processing, statistical analysis, and interpretation. Third, we describe how to integrate metabolomics with other omics approaches in immunological studies using available tools. Finally, we discuss new developments in metabolomics and its prospects for immunology research. This review provides guidance to researchers using metabolomics and multiomics in immunity research, thus facilitating the application of systems immunology to disease research.

Morphological, Cellular, and Molecular Basis of Brain Infection in COVID-19 Patients and Its Association to Psychiatric Symptoms

Background: Although increasing evidence confirms neuropsychiatric manifestations associated mainly with severe COVID-19 infection, long-term neuropsychiatric dysfunction (recently characterized as part of "long COVID-19" syndrome) has been frequently observed after mild infection. Method(s): We performed a broad translational investigation, employing brain imaging and cognitive tests in 81 living COVID-19 patients (mildly infected individuals) as well as flow cytometry, respirometry, microscopy, proteomics, and metabolomics in postmortem brain samples, and in preclinical in vitro and ex vivo models. Result(s): We observed orbitofrontal cortical atrophy, neurocognitive impairment, excessive fatigue and anxiety symptoms in living individuals. Postmortem brain tissue from 26 individuals who died of COVID-19 revealed histopathological signs of brain damage. Five individuals out of the 26 exhibited foci of SARS- CoV-2 infection and replication, particularly in astrocytes. Supporting the hypothesis of astrocyte infection, neural stem cell-derived human astrocytes in vitro are susceptible to SARS-CoV-2 infection through a non-canonical mechanism that involves spike-NRP1 interaction. SARS-CoV-2-infected astrocytes manifested changes in energy metabolism and in key proteins and metabolites used to fuel neurons, as well as in the biogenesis of neurotransmitters. Moreover, human astrocyte infection elicits a secretory phenotype that significantly reduces neuronal viability. Conclusion(s): Our data support the model in which COVID-19 alter cortical thickness, promoting psychiatric symptoms. In addition, SARS-CoV-2 is able to reach the brain, infects astrocytes, and consequently, leads to neuronal death or dysfunction. These deregulated processes could contribute to the structural and functional alterations seen in the brains of COVID-19 patients. Funding Source: Sao Paulo Research Foundation (FAPESP) Keywords: COVID-19, Anxiety, Astrocytes, Multi-omics, Brain Magnetic Resonance Imaging (MRI)Copyright © 2023

Harnessing Deep Learning for Omics in an Era of COVID-19.

Omics data are multidimensional, heterogeneous, and high throughput. Robust computational methods and machine learning (ML)-based models offer new prospects to accelerate the data-to-knowledge trajectory. Deep learning (DL) is a powerful subset of ML inspired by brain structure and has created unprecedented momentum in bioinformatics and computational biology research. This article provides an overview of the current DL models applied to multi-omics data for both the beginner and the expert user. Additionally, COVID-19 will continue to impact planetary health as a pandemic and an endemic disease, with genomic and multi-omic pathophysiology. DL offers, therefore, new ways of harnessing systems biology research on COVID-19 diagnostics and therapeutics. Herein, we discuss, first, the statistical ML algorithms and essential deep architectures. Then, we review DL applications in multi-omics data analysis and their intersection with COVID-19. Finally, challenges and several promising directions are highlighted going forward in the current era of COVID-19.

Gene function and cell surface protein association analysis based on single-cell multiomics data.

Single-cell transcriptomics provides researchers with a powerful tool to resolve the transcriptome heterogeneity of individual cells. However, this method falls short in revealing cellular heterogeneity at the protein level. Previous single-cell multiomics studies have focused on data integration rather than exploiting the full potential of multiomics data. Here we introduce a new analysis framework, gene function and protein association (GFPA), that mines reliable associations between gene function and cell surface protein from single-cell multimodal data. Applying GFPA to human peripheral blood mononuclear cells (PBMCs), we observe an association of epithelial mesenchymal transition (EMT) with the CD99 protein in CD4 T cells, which is consistent with previous findings. Our results show that GFPA is reliable across multiple cell subtypes and PBMC samples. The GFPA python packages and detailed tutorials are freely available at https://github.com/studentiz/GFPA.

Multi-omics analysis reveals genomic, clinical and immunological features of SARS-CoV-2 virus target genes in pan-cancer.

The SARS-CoV-2 virus, also known as the severe acute respiratory syndrome coronavirus 2, has raised great threats to humans. The connection between the SARS-CoV-2 virus and cancer is currently unclear. In this study, we thus evaluated the multi-omics data from the Cancer Genome Atlas (TCGA) database utilizing genomic and transcriptomic techniques to fully identify the SARS-CoV-2 target genes (STGs) in tumor samples from 33 types of cancers. The expression of STGs was substantially linked with the immune infiltration and may be used to predict survival in cancer patients. STGs were also substantially associated with immunological infiltration, immune cells, and associated immune pathways. At the molecular level, the genomic changes of STGs were frequently related with carcinogenesis and patient survival. In addition, pathway analysis revealed that STGs were involved in the control of signaling pathways associated with cancer. The prognostic features and nomogram of clinical factors of STGs in cancers have been developed. Lastly, by mining the cancer drug sensitivity genomics database, a list of potential STG-targeting medicines was compiled. Collectively, this work demonstrated comprehensively the genomic alterations and clinical characteristics of STGs, which may offer new clues to explore the mechanisms on a molecular level between SARS-CoV-2 virus and cancers as well as provide new clinical guidance for cancer patients who are threatened by the COVID-19 epidemic.

Multiomic characterisation of the long-term sequelae of SARS survivors: a clinical observational study.

Background: We aimed to characterise the long-term health outcomes of survivors of severe acute respiratory syndrome (SARS) and determine their recovery status and possible immunological basis. Methods: We performed a clinical observational study on 14 health workers who survived SARS coronavirus infection between Apr 20, 2003 and Jun 6, 2003 in Haihe Hospital (Tianjin, China). Eighteen years after discharge, SARS survivors were interviewed using questionnaires on symptoms and quality of life, and received physical examination, laboratory tests, pulmonary function tests, arterial blood gas analysis, and chest imaging. Plasma samples were collected for metabolomic, proteomic, and single-cell transcriptomic analyses. The health outcomes were compared 18 and 12 years after discharge. Control individuals were also health workers from the same hospital but did not infect with SARS coronavirus. Findings: Fatigue was the most common symptom in SARS survivors 18 years after discharge, with osteoporosis and necrosis of the femoral head being the main sequelae. The respiratory function and hip function scores of the SARS survivors were significantly lower than those of the controls. Physical and social functioning at 18 years was improved compared to that after 12 years but still worse than the controls. Emotional and mental health were fully recovered. Lung lesions on CT scans remained consistent at 18 years, especially in the right upper lobe and left lower lobe lesions. Plasma multiomics analysis indicated an abnormal metabolism of amino acids and lipids, promoted host defense immune responses to bacteria and external stimuli, B-cell activation, and enhanced cytotoxicity of CD8+ T cells but impaired antigen presentation capacity of CD4+ T cells. Interpretation: Although health outcomes continued to improve, our study suggested that SARS survivors still suffered from physical fatigue, osteoporosis, and necrosis of the femoral head 18 years after discharge, possibly related to plasma metabolic disorders and immunological alterations. Funding: This study was funded by the Tianjin Haihe Hospital Science and Technology Fund (HHYY-202012) and Tianjin Key Medical Discipline (Specialty) Construction Project (TJYXZDXK-063B, TJYXZDXK-067C).

MTOR hypermethylation may associate with the susceptibility and survival of SARS-CoV-2 infections to lung adenocarcinoma patients based on multi-omics data and machine learning

Recent studies have shown that lung adenocarcinoma (LUAD) patients have a higher risk and worse prognosis of COVID-19 caused by SARS-CoV-2 compared to normal samples. Whereas, in addition to the receptor for SARS-CoV-2, other genes also deserve attention. In our study, we identified 19 differentially methylated genes (DMGs) that were co-upregulated in LUAD and COVID-19 samples. These 19 DMGs mainly regulated the immune-related and multiple viral infection signaling pathways. Gene Ontology and pathway enrichment analysis were applied with these genes. Then, 6 key DMGs (MTOR, ACE, IGF1, PTPRC, C3, and PTGS2) were identified by constructing and analyzing the protein-protein interaction (PPI) network. Besides, MTOR was significantly associated with 5 prognostic markers (CDO1, NEURL4, SMAP1, NPEPPS, IQCK) identified by survival analysis based on machine learning. In total, MTOR hypermethylation may be related to the susceptibility of LUAD patients to SARS-CoV-2 and the prognosis of LUAD patients suffering from COVID-19. © 2022 IEEE.

Towards precision medicine: Omics approach for COVID-19.

The coronavirus disease 2019 (COVID-19) pandemic had a devastating impact on human society. Beginning with genome surveillance of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), the development of omics technologies brought a clearer understanding of the complex SARS-CoV-2 and COVID-19. Here, we reviewed how omics, including genomics, proteomics, single-cell multi-omics, and clinical phenomics, play roles in answering biological and clinical questions about COVID-19. Large-scale sequencing and advanced analysis methods facilitate COVID-19 discovery from virus evolution and severity risk prediction to potential treatment identification. Omics would indicate precise and globalized prevention and medicine for the COVID-19 pandemic under the utilization of big data capability and phenotypes refinement. Furthermore, decoding the evolution rule of SARS-CoV-2 by deep learning models is promising to forecast new variants and achieve more precise data to predict future pandemics and prevent them on time.

Machine learning to analyse omic-data for COVID-19 diagnosis and prognosis.

BACKGROUND: With the global spread of COVID-19, the world has seen many patients, including many severe cases. The rapid development of machine learning (ML) has made significant disease diagnosis and prediction achievements. Current studies have confirmed that omics data at the host level can reflect the development process and prognosis of the disease. Since early diagnosis and effective treatment of severe COVID-19 patients remains challenging, this research aims to use omics data in different ML models for COVID-19 diagnosis and prognosis. We used several ML models on omics data of a large number of individuals to first predict whether patients are COVID-19 positive or negative, followed by the severity of the disease. RESULTS: On the COVID-19 diagnosis task, we got the best AUC of 0.99 with our multilayer perceptron model and the highest F1-score of 0.95 with our logistic regression (LR) model. For the severity prediction task, we achieved the highest accuracy of 0.76 with an LR model. Beyond classification and predictive modeling, our study founds ML models performed better on integrated multi-omics data, rather than single omics. By comparing top features from different omics dataset, we also found the robustness of our model, with a wider range of applicability in diverse dataset related to COVID-19. Additionally, we have found that omics-based models performed better than image or physiological feature-based models, proving the importance of the omics-based dataset for future model development. CONCLUSIONS: This study diagnoses COVID-19 positive cases and predicts accurate severity levels. It lowers the dependence on clinical data and professional judgment, by leveraging the utilization of state-of-the-art models. our model showed wider applicability across different omics dataset, which is highly transferable in other respiratory or similar diseases. Hospital and public health care mechanisms can optimize the distribution of medical resources and improve the robustness of the medical system.

Modeling and analyzing single-cell multimodal data with deep parametric inference.

The proliferation of single-cell multimodal sequencing technologies has enabled us to understand cellular heterogeneity with multiple views, providing novel and actionable biological insights into the disease-driving mechanisms. Here, we propose a comprehensive end-to-end single-cell multimodal analysis framework named Deep Parametric Inference (DPI). DPI transforms single-cell multimodal data into a multimodal parameter space by inferring individual modal parameters. Analysis of cord blood mononuclear cells (CBMC) reveals that the multimodal parameter space can characterize the heterogeneity of cells more comprehensively than individual modalities. Furthermore, comparisons with the state-of-the-art methods on multiple datasets show that DPI has superior performance. Additionally, DPI can reference and query cell types without batch effects. As a result, DPI can successfully analyze the progression of COVID-19 disease in peripheral blood mononuclear cells (PBMC). Notably, we further propose a cell state vector field and analyze the transformation pattern of bone marrow cells (BMC) states. In conclusion, DPI is a powerful single-cell multimodal analysis framework that can provide new biological insights into biomedical researchers. The python packages, datasets and user-friendly manuals of DPI are freely available at https://github.com/studentiz/dpi.