Large-Scale Multi-omic Analysis of COVID-19 Severity

Proteomic analysis of lung responses to SARS-CoV-2 infection in aged non-human primates: clinical and research relevance

With devastating health and socioeconomic impact worldwide, much work is left to understand the Coronavirus Disease 2019 (COVID-19), with emphasis in the severely affected elderly population. Here, we present a proteomics study of lung tissue obtained from aged vs. young rhesus macaques (Macaca mulatta) and olive baboons (Papio Anubis) infected with severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Using age as a variable, we identified common proteomic profiles in the lungs of aged infected non-human primates (NHPs), including key regulators of immune function, as well as cell and tissue remodeling, and discuss the potential clinical relevance of such parameters. Further, we identified key differences in proteomic profiles between both NHP species, and compared those to what is known about SARS-CoV-2 in humans. Finally, we explored the translatability of these animal models in the context of aging and the human presentation of the COVID-19.

OmicPredict: a framework for omics data prediction using ANOVA-Firefly algorithm for feature selection

High-throughput technologies and machine learning (ML), when applied to a huge pool of medical data such as omics data, result in efficient analysis. Recent research aims to apply and develop ML models to predict a disease well in time using available omics datasets. The present work proposed a framework, 'OmicPredict', deploying a hybrid feature selection method and deep neural network (DNN) model to predict multiple diseases using omics data. The hybrid feature selection method is developed using the Analysis of Variance (ANOVA) technique and firefly algorithm. The OmicPredict framework is applied to three case studies, Alzheimer's disease, Breast cancer, and Coronavirus disease 2019 (COVID-19). In the case study of Alzheimer's disease, the framework predicts patients using GSE33000 and GSE44770 dataset. In the case study of Breast cancer, the framework predicts human epidermal growth factor receptor 2 (HER2) subtype status using Molecular Taxonomy of Breast Cancer International Consortium (METABRIC) dataset. In the case study of COVID-19, the framework performs patients' classification using GSE157103 dataset. The experimental results show that DNN model achieved an Area Under Curve (AUC) score of 0.949 for the Alzheimer's (GSE33000 and GSE44770) dataset. Furthermore, it achieved an AUC score of 0.987 and 0.989 for breast cancer (METABRIC) and COVID-19 (GSE157103) datasets, respectively, outperforming Random Forest, Naïve Bayes models, and the existing research.

Micro Scale Chromatography of Human Plasma Proteins for Nano LC-ESI-MS/MS

Organic precipitation of proteins with acetonitrile demonstrated complete protein recovery and improved chromatography of human plasma proteins. The separation of 25 μL of human plasma into 22 fractions on a QA SAX resin facilitated more effective protein discovery despite the limited sample size. Micro chromatography of plasma proteins over quaternary amine (QA) strong anion exchange (SAX) resins performed best, followed by diethylaminoethyl (DEAE), heparin (HEP), carboxymethyl cellulose (CMC), and propyl sulfate (PS) resins. Two independent statistical methods, Monte Carlo comparison with random MS/MS spectra and the rigorous X!TANDEM goodness of fit algorithm protein p-values corrected to false discovery rate q-values (q≤ 0.01) that agreed on at least 12,000 plasma proteins, each represented by at least three fully tryptic corrected peptide observations. There was qualitative agreement on 9,393 protein/gene symbols between the linear quadrupole versus orbital ion trap but also quantitative agreement with a highly significant linear regression relationship between log observation frequency (F value 4,173, p-value 2.2e-16). The use of a QA resin showed nearly perfect replication of all the proteins that were also found using DEAE-, HEP-, CMC-, and PS-based chromatographic methods combined and together estimated the size of the size of the plasma proteome as ≥12,000 gene symbols.

Trypsin Digestion Conditions of Human Plasma for Observation of Peptides and Proteins from Tandem Mass Spectrometry

Previous meta-analysis indicated that plasma or serum proteome groups using various experimental conditions detected different peptides from the same plasma proteins, which is strong evidence for the veracity of blood fluid LC-ESI-MS/MS but also evidences that the trypsin digestion step is a key source of variation in plasma proteomics. Agreement between different digestion conditions and MS/MS algorithms may serve as an independent confirmation of the validity of the LC-ESI-MS/MS analysis of plasma peptides. Plasma contains a high percentage of albumin held together by multiple disulfide bonds; hence, reduction and/or alkylation may greatly enhance the digestion efficiency of albumin. Plasma proteins were precipitated in 90% acetonitrile, collected over quaternary amine resin, and eluted in NaCl prior to digestion treatments. To determine the effect of trypsin digestion methods, the plasma proteins were digested in 600 mM urea and 5% acetonitrile with trypsin alone, or reduced with 2 mM DTT followed by trypsin, or DTT followed by 15 mM iodoacetamide and then trypsin. The resulting peptides were analyzed by LC-ESI-MS/MS with a linear quadrupole ion trap (LIT). The MS/MS spectra were directly fit to peptides by the X!TANDEM and SEQUEST algorithms. Blank noise injections served as the analytical control, and 30 million random MS/MS served as the statistical control. Digesting human plasma with DTT reduction, or reduction and alkylation, resulted in a dramatic increase in the number and observation frequency of albumin peptides. In contrast, digestion with trypsin alone suppressed the observation of albumin, and instead, many low abundance plasma and cellular proteins showed higher observation frequency. Digestion with trypsin alone increased the observation frequency of APOC1, ACAN, ATRN, CPB2, GP2, GPX3, HBA1, PAPD5, PKD1, and many cellular proteins. After correction against noise and random controls, SEQUEST showed good agreement with the true positive plasma proteins identified by X!TANDEM and resulted in an R-squared of 0.5238 with an F-statistic of 10,930 on 9,935 protein gene symbols with a p-value < 2.2e-16. Digestion of plasma with trypsin alone avoids the complete digestion of albumin and permits the enhanced detection of some other cellular proteins from plasma. Different digestion approaches were complimentary and together resulted in a more comprehensive plasma proteome. The protein FDR q-values, the modest effect of background and Monte Carlo correction, and the significant STRING analysis were all consistent with the high fidelity of the rigorous X!TANDEM algorithm. In contrast, SEQUEST required significant correction against noise and statistical controls and selection of high cross correlation (XCorr) scores to show good agreement with X!TANDEM. There was qualitative and quantitative agreement between plasma proteins digested without alkylation from the orbital ion trap (OIT) versus the LIT instrument that showed highly significant regression against the X!TANDEM OIT monoisotopic results, those from heavy isotopes and other masses from X!TANDEM, and with those from MaxQuant. There was significant qualitative and quantitative agreement between the complementary digestion conditions consistent with the good fidelity of plasma analysis by LC-ESI-MS/MS with a sensitive linear ion trap.

Identification and evaluation of candidate COVID-19 critical genes and medicinal drugs related to plasma cells

The ongoing COVID-19 pandemic, caused by the SARS-CoV-2 virus, represents one of the most significant global health crises in recent history. Despite extensive research into the immune mechanisms and therapeutic options for COVID-19, there remains a paucity of studies focusing on plasma cells. In this study, we utilized the DESeq2 package to identify differentially expressed genes (DEGs) between COVID-19 patients and controls using datasets GSE157103 and GSE152641. We employed the xCell algorithm to perform immune infiltration analyses, revealing notably elevated levels of plasma cells in COVID-19 patients compared to healthy individuals. Subsequently, we applied the Weighted Gene Co-expression Network Analysis (WGCNA) algorithm to identify COVID-19 related plasma cell module genes. Further, positive cluster biomarker genes for plasma cells were extracted from single-cell RNA sequencing data (GSE171524), leading to the identification of 122 shared genes implicated in critical biological processes such as cell cycle regulation and viral infection pathways. We constructed a robust protein-protein interaction (PPI) network comprising 89 genes using Cytoscape, and identified 20 hub genes through cytoHubba. These genes were validated in external datasets (GSE152418 and GSE179627). Additionally, we identified three potential small molecules (GSK-1070916, BRD-K89997465, and idarubicin) that target key hub genes in the network, suggesting a novel therapeutic approach. These compounds were characterized by their ability to down-regulate AURKB, KIF11, and TOP2A effectively, as evidenced by their low free binding energies determined through computational analyses using cMAP and AutoDock. This study marks the first comprehensive exploration of plasma cells' role in COVID-19, offering new insights and potential therapeutic targets. It underscores the importance of a systematic approach to understanding and treating COVID-19, expanding the current body of knowledge and providing a foundation for future research.

Unraveling the pathogenic interplay between SARS-CoV-2 and polycystic ovary syndrome using bioinformatics and experimental validation

The prevalence of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) among polycystic ovary syndrome (PCOS) is significantly higher than in the general population. However, the mechanisms underlying this remain obscure. This study aimed to explore the mechanisms by identifying the genetic signature of SARS-CoV-2 infection in PCOS. In the present study, a total of 27 common differentially expressed genes (DEGs) were selected for subsequent analyses. Functional analyses showed that immunity and hormone-related pathways collectively participated in the development and progression of PCOS and SARS-CoV-2 infection. Under these, 7 significant hub genes were identified, including S100A9, MMP9, TLR2, THBD, ITGB2, ICAM1, and CD86 by using the algorithm in Cytoscape. Furthermore, hub gene expression was confirmed in the validation set, PCOS clinical samples, and mouse model. Immune microenvironment analysis with the CIBERSORTx database demonstrated that the hub genes were significantly correlated with T cells, dendritic cells, mast cells, B cells, NK cells, and eosinophils and positively correlated with immune scores. Among the hub genes, S100A9, MMP9, THBD, ITGB2, CD86, and ICAM1 demonstrated potential as possible diagnostic markers for COVID-19 and PCOS. In addition, we established the interaction networks of ovary-specific genes, transcription factors, miRNAs, drugs, and chemical compounds with hub genes with NetworkAnalyst. This work uncovered the common pathogenesis and genetic signature of PCOS and SARS-CoV-2 infection, which might provide a theoretical basis and innovative ideas for further mechanistic research and drug discovery of the comorbidity of the two diseases.

RNA-Seq analysis of human heart tissue reveals SARS-CoV-2 infection and inappropriate activation of the TNF-NF-κB pathway in cardiomyocytes

The negative impact of SARS-CoV-2 virus infection on cardiovascular disease (CVD) patients is well established. This research article explores the cellular pathways involved in underlying heart diseases after infection. The systemic inflammatory response to SARS-CoV-2 infection likely exacerbates this increased cardiovascular risk; however, whether the virus directly infects cardiomyocytes remains unknown due to limited multi-omics data. While public transcriptome data exists for COVID-19 infection in different cell types (including cardiomyocytes), infection times vary between studies. We used available RNA-seq data from human heart tissue to delineate SARS-CoV-2 infection and heart failure aetiology specific gene expression signatures. A total of fifty-four samples from four studies were analysed. Our aim was to investigate specific transcriptome changes occurring in cardiac tissue with SARS-CoV-2 infection compared to non-infected controls. Our data establish that SARS-CoV-2 infects cardiomyocytes by the TNF-NF-κB pathway, potentially triggering acute cardiovascular complications and increasing the long-term cardiovascular risk in COVID-19 patients.

CRTAC1 has a Compact β-propeller-TTR Core Stabilized by Potassium Ions

Cartilage acidic protein-1 (CRTAC1) is a secreted glycoprotein with roles in development, function and repair of the nervous system. It is linked to ischemic stroke, osteoarthritis and (long) COVID outcomes, and has suppressive activity in carcinoma and bladder cancer. Structural characterization of CRTAC1 has been complicated by its tendency to form disulfide-linked aggregates. Here, we show that CRTAC1 is stabilized by potassium ions. Using x-ray crystallography, we determined the structure of CRTAC1 to 1.6 Å. This reveals that the protein consists of a three-domain fold, including a previously-unreported compact β-propeller-TTR combination, in which an extended loop of the TTR plugs the β-propeller core. Electron density is observed for ten bound ions: six calcium, three potassium and one sodium. Low potassium ion concentrations lead to changes in tryptophan environment and exposure of two buried free cysteines located on a β-blade and in the β-propeller-plugging TTR loop. Mutating the two free cysteines to serines prevents covalent intermolecular interactions, but not aggregation, in absence of potassium ions. The potassium ion binding sites are located between the blades of the β-propeller, explaining their importance for the stability of the CRTAC1 fold. Despite varying in sequence, the three potassium ion binding sites are structurally similar and conserved features of the CRTAC protein family. These insights into the stability and structure of CRTAC1 provide a basis for further work into the function of CRTAC1 in health and disease.

Primate-specific BTN3A2 protects against SARS-CoV-2 infection by interacting with and reducing ACE2

BACKGROUND

Coronavirus disease 2019 (COVID-19) is an immune-related disorder caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). The complete pathogenesis of the virus remains to be determined. Unraveling the molecular mechanisms governing SARS-CoV-2 interactions with host cells is crucial for the formulation of effective prophylactic measures and the advancement of COVID-19 therapeutics.

METHODS

We analyzed human lung single-cell RNA sequencing dataset to discern the association of butyrophilin subfamily 3 member A2 (BTN3A2) expression with COVID-19. The BTN3A2 gene edited cell lines and transgenic mice were infected by live SARS-CoV-2 in a biosafety level 3 (BSL-3) laboratory. Immunoprecipitation, flow cytometry, biolayer interferometry and competition ELISA assays were performed in BTN3A2 gene edited cells. We performed quantitative real-time PCR, histological and/or immunohistochemical analyses for tissue samples from mice with or without SARS-CoV-2 infection.

FINDINGS

The BTN3A2 mRNA level was correlated with COVID-19 severity. BTN3A2 expression was predominantly identified in epithelial cells, elevated in pathological epithelial cells from COVID-19 patients and co-occurred with ACE2 expression in the same lung cell subtypes. BTN3A2 targeted the early stage of the viral life cycle by inhibiting SARS-CoV-2 attachment through interactions with the receptor-binding domain (RBD) of the Spike protein and ACE2. BTN3A2 inhibited ACE2-mediated SARS-CoV-2 infection by reducing ACE2 in vitro and in vivo.

INTERPRETATION

These results reveal a key role of BTN3A2 in the fight against COVID-19. Identifying potential monoclonal antibodies which mimic BTN3A2 may facilitate disruption of SARS-CoV-2 infection, providing a therapeutic avenue for COVID-19.

FUNDING

This study was supported by the National Natural Science Foundation of China (32070569, U1902215, and 32371017), the CAS "Light of West China" Program, and Yunnan Province (202305AH340006).

Exploring COVID-19 Pandemic Disparities with Transcriptomic Meta-analysis from the Perspective of Personalized Medicine

Infection with SARS-CoV2, which is responsible for COVID-19, can lead to differences in disease development, severity and mortality rates depending on gender, age or the presence of certain diseases. Considering that existing studies ignore these differences, this study aims to uncover potential differences attributable to gender, age and source of sampling as well as viral load using bioinformatics and multi-omics approaches. Differential gene expression analyses were used to analyse the phenotypic differences between SARS-CoV-2 patients and controls at the mRNA level. Pathway enrichment analyses were performed at the gene set level to identify the activated pathways corresponding to the differences in the samples. Drug repurposing analysis was performed at the protein level, focusing on host-mediated drug candidates to uncover potential therapeutic differences. Significant differences (i.e. the number of differentially expressed genes and their characteristics) were observed for COVID-19 at the mRNA level depending on the sample source, gender and age of the samples. The results of the pathway enrichment show that SARS-CoV-2 can be combated more effectively in the respiratory tract than in the blood samples. Taking into account the different sample sources and their characteristics, different drug candidates were identified. Evaluating disease prediction, prevention and/or treatment strategies from a personalised perspective is crucial. In this study, we not only evaluated the differences in COVID-19 from a personalised perspective, but also provided valuable data for further experimental and clinical efforts. Our findings could shed light on potential pandemics.

Comprehensive Overview of Bottom-Up Proteomics Using Mass Spectrometry

Proteomics is the large scale study of protein structure and function from biological systems through protein identification and quantification. "Shotgun proteomics" or "bottom-up proteomics" is the prevailing strategy, in which proteins are hydrolyzed into peptides that are analyzed by mass spectrometry. Proteomics studies can be applied to diverse studies ranging from simple protein identification to studies of proteoforms, protein-protein interactions, protein structural alterations, absolute and relative protein quantification, post-translational modifications, and protein stability. To enable this range of different experiments, there are diverse strategies for proteome analysis. The nuances of how proteomic workflows differ may be challenging to understand for new practitioners. Here, we provide a comprehensive overview of different proteomics methods. We cover from biochemistry basics and protein extraction to biological interpretation and orthogonal validation. We expect this Review will serve as a handbook for researchers who are new to the field of bottom-up proteomics.

The Biology and Biochemistry of Kynurenic Acid, a Potential Nutraceutical with Multiple Biological Effects

Kynurenic acid (KYNA) is an antioxidant degradation product of tryptophan that has been shown to have a variety of cytoprotective, neuroprotective and neuronal signalling properties. However, mammalian transporters and receptors display micromolar binding constants; these are consistent with its typically micromolar tissue concentrations but far above its serum/plasma concentration (normally tens of nanomolar), suggesting large gaps in our knowledge of its transport and mechanisms of action, in that the main influx transporters characterized to date are equilibrative, not concentrative. In addition, it is a substrate of a known anion efflux pump (ABCC4), whose in vivo activity is largely unknown. Exogeneous addition of L-tryptophan or L-kynurenine leads to the production of KYNA but also to that of many other co-metabolites (including some such as 3-hydroxy-L-kynurenine and quinolinic acid that may be toxic). With the exception of chestnut honey, KYNA exists at relatively low levels in natural foodstuffs. However, its bioavailability is reasonable, and as the terminal element of an irreversible reaction of most tryptophan degradation pathways, it might be added exogenously without disturbing upstream metabolism significantly. Many examples, which we review, show that it has valuable bioactivity. Given the above, we review its potential utility as a nutraceutical, finding it significantly worthy of further study and development.

The mechanism of geniposide in patients with COVID-19 and atherosclerosis: A pharmacological and bioinformatics analysis

In patients with severe acute respiratory syndrome coronavirus 2 (which causes coronavirus disease 2019 [COVID-19]), oxidative stress (OS) is associated with disease severity and death. OS is also involved in the pathogenesis of atherosclerosis (AS). Previous studies have shown that geniposide has anti-inflammatory and anti-viral properties, and can protect cells against OS. However, the potential target(s) of geniposide in patients with COVID-19 and AS, as well as the mechanism it uses, are unclear. We combined pharmacology and bioinformatics analysis to obtain geniposide against COVID-19/AS targets, and build protein-protein interaction network to filter hub genes. The hub genes were performed an enrichment analysis by ClueGO, including Gene Ontology and KEGG. The Enrichr database and the target microRNAs (miRNAs) of hub genes were predicted through the MiRTarBase via Enrichr. The common miRNAs were used to construct the miRNAs-mRNAs regulated network, and the miRNAs' function was evaluated by mirPath v3.0 software. Two hundred forty-seven targets of geniposide were identified in patients with COVID-19/AS comorbidity by observing the overlap between the genes modulated by geniposide, COVID-19, and AS. A protein-protein interaction network of geniposide in patients with COVID-19/AS was constructed, and 27 hub genes were identified. The results of enrichment analysis suggested that geniposide may be involved in regulating the OS via the FoxO signaling pathway. MiRNA-mRNA network revealed that hsa-miR-34a-5p may play an important role in the therapeutic mechanism of geniposide in COVID-19/AS patients. Our study found that geniposide represents a promising therapy for patients with COVID-19 and AS comorbidity. Furthermore, the target genes and miRNAs that we identified may aid the development of new treatment strategies against COVID-19/AS.

Gut microbiota dysbiosis is associated with altered tryptophan metabolism and dysregulated inflammatory response in COVID-19

The clinical course of COVID-19 is variable and often unpredictable. To test the hypothesis that disease progression and inflammatory responses associate with alterations in the microbiome and metabolome, we analyzed metagenome, metabolome, cytokine, and transcriptome profiles of repeated samples from hospitalized COVID-19 patients and uninfected controls, and leveraged clinical information and post-hoc confounder analysis. Severe COVID-19 was associated with a depletion of beneficial intestinal microbes, whereas oropharyngeal microbiota disturbance was mainly linked to antibiotic use. COVID-19 severity was also associated with enhanced plasma concentrations of kynurenine and reduced levels of several other tryptophan metabolites, lysophosphatidylcholines, and secondary bile acids. Moreover, reduced concentrations of various tryptophan metabolites were associated with depletion of Faecalibacterium, and tryptophan decrease and kynurenine increase were linked to enhanced production of inflammatory cytokines. Collectively, our study identifies correlated microbiome and metabolome alterations as a potential contributor to inflammatory dysregulation in severe COVID-19.

Blood DNA methylation in post-acute sequelae of COVID-19 (PASC): a prospective cohort study

BACKGROUND

DNA methylation integrates environmental signals with transcriptional programs. COVID-19 infection induces changes in the host methylome. While post-acute sequelae of COVID-19 (PASC) is a long-term complication of acute illness, its association with DNA methylation is unknown. No universal blood marker of PASC, superseding single organ dysfunctions, has yet been identified.

METHODS

In this single centre prospective cohort study, PASC, post-COVID without PASC, and healthy participants were enrolled to investigate their symptoms association with peripheral blood DNA methylation data generated with state-of-the-art whole genome sequencing. PASC-induced quality-of-life deterioration was scored with a validated instrument, SF-36. Analyses were conducted to identify potential functional roles of differentially methylated loci, and machine learning algorithms were used to resolve PASC severity.

FINDINGS

103 patients with PASC (22.3% male, 77.7% female), 15 patients with previous COVID-19 infection but no PASC (40.0% male, 60.0% female), and 27 healthy volunteers (48.1% male, 51.9% female) were enrolled. Whole genome methylation sequencing revealed 39 differentially methylated regions (DMRs) specific to PASC, each harbouring an average of 15 consecutive positions, that differentiate patients with PASC from the two control groups. Motif analyses of PASC-regulated DMRs identify binding domains for transcription factors regulating circadian rhythm and others. Some DMRs annotated to protein coding genes were associated with changes of RNA expression. Machine learning support vector algorithm and random forest hierarchical clustering reveal 28 unique differentially methylated positions (DMPs) in the genome discriminating patients with better and worse quality of life.

INTERPRETATION

Blood DNA methylation levels identify PASC, stratify PASC severity, and suggest that DNA motifs are targeted by circadian rhythm-regulating pathways in PASC.

FUNDING

This project has been funded by the following agencies: NIH-AI173035 (A. Jaitovich and R. Alisch); and NIH-AG066179 (R. Alisch).

A T-Cell-Derived 3-Gene Signature Distinguishes SARS-CoV-2 from Common Respiratory Viruses

Research on the host responses to respiratory viruses could help develop effective interventions and therapies against the current and future pandemics from the host perspective. To explore the pathogenesis that distinguishes SARS-CoV-2 infections from other respiratory viruses, we performed a multi-cohort analysis with integrated bioinformatics and machine learning. We collected 3730 blood samples from both asymptomatic and symptomatic individuals infected with SARS-CoV-2, seasonal human coronavirus (sHCoVs), influenza virus (IFV), respiratory syncytial virus (RSV), or human rhinovirus (HRV) across 15 cohorts. First, we identified an enhanced cellular immune response but limited interferon activities in SARS-CoV-2 infection, especially in asymptomatic cases. Second, we identified a SARS-CoV-2-specific 3-gene signature (CLSPN, RBBP6, CCDC91) that was predominantly expressed by T cells, could distinguish SARS-CoV-2 infection, including Omicron, from other common respiratory viruses regardless of symptoms, and was predictive of SARS-CoV-2 infection before detectable viral RNA on RT-PCR testing in a longitude follow-up study. Thereafter, a user-friendly online tool, based on datasets collected here, was developed for querying a gene of interest across multiple viral infections. Our results not only identify a unique host response to the viral pathogenesis in SARS-CoV-2 but also provide insights into developing effective tools against viral pandemics from the host perspective.

The Multi-Omic Approach to Newborn Screening: Opportunities and Challenges

Newborn screening programs have seen significant evolution since their initial implementation more than 60 years ago, with the primary goal of detecting treatable conditions within the earliest possible timeframe to ensure the optimal treatment and outcomes for the newborn. New technologies have driven the expansion of screening programs to cover additional conditions. In the current era, the breadth of screened conditions could be further expanded by integrating omic technologies such as untargeted metabolomics and genomics. Genomic screening could offer opportunities for lifelong care beyond the newborn period. For genomic newborn screening to be effective and ready for routine adoption, it must overcome barriers such as implementation cost, public acceptability, and scalability. Metabolomics approaches, on the other hand, can offer insight into disease phenotypes and could be used to identify known and novel biomarkers of disease. Given recent advances in metabolomic technologies, alongside advances in genomics including whole-genome sequencing, the combination of complementary multi-omic approaches may provide an exciting opportunity to leverage the best of both approaches and overcome their respective limitations. These techniques are described, along with the current outlook on multi-omic-based NBS research.

Plasma taurine level is linked to symptom burden and clinical outcomes in post-COVID condition

BACKGROUND

A subset of individuals (10-20%) experience post-COVID condition (PCC) subsequent to initial SARS-CoV-2 infection, which lacks effective treatment. PCC carries a substantial global burden associated with negative economic and health impacts. This study aims to evaluate the association between plasma taurine levels with self-reported symptoms and adverse clinical outcomes in patients with PCC.

METHODS AND FINDINGS

We analyzed the plasma proteome and metabolome of 117 individuals during their acute COVID-19 hospitalization and at the convalescence phase six-month post infection. Findings were compared with 28 age and sex-matched healthy controls. Plasma taurine levels were negatively associated with PCC symptoms and correlated with markers of inflammation, tryptophan metabolism, and gut dysbiosis. Stratifying patients based on the trajectories of plasma taurine levels during six-month follow-up revealed a significant association with adverse clinical events. Increase in taurine levels during the transition to convalescence were associated with a reduction in adverse events independent of comorbidities and acute COVID-19 severity. In a multivariate analysis, increased plasma taurine level between acute and convalescence phase was associated with marked protection from adverse clinical events with a hazard ratio of 0.13 (95% CI: 0.05-0.35; p<0.001).

CONCLUSIONS

Taurine emerges as a promising predictive biomarker and potential therapeutic target in PCC. Taurine supplementation has already demonstrated clinical benefits in various diseases and warrants exploration in large-scale clinical trials for alleviating PCC.

Differentiation between descending thoracic aortic diseases using machine learning and plasma proteomic signatures

BACKGROUND

Descending thoracic aortic aneurysms and dissections can go undetected until severe and catastrophic, and few clinical indices exist to screen for aneurysms or predict risk of dissection.

METHODS

This study generated a plasma proteomic dataset from 75 patients with descending type B dissection (Type B) and 62 patients with descending thoracic aortic aneurysm (DTAA). Standard statistical approaches were compared to supervised machine learning (ML) algorithms to distinguish Type B from DTAA cases. Quantitatively similar proteins were clustered based on linkage distance from hierarchical clustering and ML models were trained with uncorrelated protein lists across various linkage distances with hyperparameter optimization using fivefold cross validation. Permutation importance (PI) was used for ranking the most important predictor proteins of ML classification between disease states and the proteins among the top 10 PI protein groups were submitted for pathway analysis.

RESULTS

Of the 1,549 peptides and 198 proteins used in this study, no peptides and only one protein, hemopexin (HPX), were significantly different at an adjusted p < 0.01 between Type B and DTAA cases. The highest performing model on the training set (Support Vector Classifier) and its corresponding linkage distance (0.5) were used for evaluation of the test set, yielding a precision-recall area under the curve of 0.7 to classify between Type B from DTAA cases. The five proteins with the highest PI scores were immunoglobulin heavy variable 6-1 (IGHV6-1), lecithin-cholesterol acyltransferase (LCAT), coagulation factor 12 (F12), HPX, and immunoglobulin heavy variable 4-4 (IGHV4-4). All proteins from the top 10 most important groups generated the following significantly enriched pathways in the plasma of Type B versus DTAA patients: complement activation, humoral immune response, and blood coagulation.

CONCLUSIONS

We conclude that ML may be useful in differentiating the plasma proteome of highly similar disease states that would otherwise not be distinguishable using statistics, and, in such cases, ML may enable prioritizing important proteins for model prediction.

Extraction of naturally occurring peptides versus the tryptic digestion of proteins from fetal versus adult bovine serum for LC-ESI-MS/MS

The naturally occurring peptides and digested proteins of fetal versus adult bovine serum were compared by LC-ESI-MS/MS after correction against noise from blank injections and random MS/MS spectra as statistical controls. Serum peptides were extracted by differential precipitation with mixtures of acetonitrile and water. Serum proteins were separated by partition chromatography over quaternary amine resin followed by tryptic digestion. The rigorous X!TANDEM goodness of fit algorithm that has a low error rate as demonstrated by low FDR q-values (q ≤ 0.01) showed qualitative and quantitative agreement with the SEQUEST cross correlation algorithm on 12,052 protein gene symbols. Tryptic digestion provided a quantitative identification of the serum proteins where observation frequency reflected known high abundance. In contrast, the naturally occurring peptides reflected the cleavage of common serum proteins such as C4A, C3, FGB, HPX, A2M but also proteins in lower concentration such as F13A1, IK, collagens and protocadherins. Proteins associated with cellular growth and development such as actins (ACT), ribosomal proteins like Ribosomal protein S6 (RPS6), synthetic enzymes and extracellular matrix factors were enriched in fetal calf serum. In contrast to the large literature from cord blood, IgG light chains were absent from fetal serum as observed by LC-ESI-MS/MS and confirmed by ELISA.

Comparative analysis of integrative classification methods for multi-omics data

Recent advances in sequencing, mass spectrometry, and cytometry technologies have enabled researchers to collect multiple 'omics data types from a single sample. These large datasets have led to a growing consensus that a holistic approach is needed to identify new candidate biomarkers and unveil mechanisms underlying disease etiology, a key to precision medicine. While many reviews and benchmarks have been conducted on unsupervised approaches, their supervised counterparts have received less attention in the literature and no gold standard has emerged yet. In this work, we present a thorough comparison of a selection of six methods, representative of the main families of intermediate integrative approaches (matrix factorization, multiple kernel methods, ensemble learning, and graph-based methods). As non-integrative control, random forest was performed on concatenated and separated data types. Methods were evaluated for classification performance on both simulated and real-world datasets, the latter being carefully selected to cover different medical applications (infectious diseases, oncology, and vaccines) and data modalities. A total of 15 simulation scenarios were designed from the real-world datasets to explore a large and realistic parameter space (e.g. sample size, dimensionality, class imbalance, effect size). On real data, the method comparison showed that integrative approaches performed better or equally well than their non-integrative counterpart. By contrast, DIABLO and the four random forest alternatives outperform the others across the majority of simulation scenarios. The strengths and limitations of these methods are discussed in detail as well as guidelines for future applications.

SCARLET (Supplemental Citicoline Administration to Reduce Lung injury Efficacy Trial): study protocol for a single-site, double-blinded, placebo-controlled, and randomized Phase 1/2 trial of i.v. citicoline (CDP-choline) in hospitalized SARS CoV-2-infected patients with hypoxemic acute respiratory failure

BACKGROUND

The SARS CoV-2 pandemic has resulted in more than 1.1 million deaths in the USA alone. Therapeutic options for critically ill patients with COVID-19 are limited. Prior studies showed that post-infection treatment of influenza A virus-infected mice with the liponucleotide CDP-choline, which is an essential precursor for de novo phosphatidylcholine synthesis, improved gas exchange and reduced pulmonary inflammation without altering viral replication. In unpublished studies, we found that treatment of SARS CoV-2-infected K18-hACE2-transgenic mice with CDP-choline prevented development of hypoxemia. We hypothesize that administration of citicoline (the pharmaceutical form of CDP-choline) will be safe in hospitalized SARS CoV-2-infected patients with hypoxemic acute respiratory failure (HARF) and that we will obtain preliminary evidence of clinical benefit to support a larger Phase 3 trial using one or more citicoline doses.

METHODS

We will conduct a single-site, double-blinded, placebo-controlled, and randomized Phase 1/2 dose-ranging and safety study of Somazina® citicoline solution for injection in consented adults of any sex, gender, age, or ethnicity hospitalized for SARS CoV-2-associated HARF. The trial is named "SCARLET" (Supplemental Citicoline Administration to Reduce Lung injury Efficacy Trial). We hypothesize that SCARLET will show that i.v. citicoline is safe at one or more of three doses (0.5, 2.5, or 5 mg/kg, every 12 h for 5 days) in hospitalized SARS CoV-2-infected patients with HARF (20 per dose) and provide preliminary evidence that i.v. citicoline improves pulmonary outcomes in this population. The primary efficacy outcome will be the SpO2:FiO2 ratio on study day 3. Exploratory outcomes include Sequential Organ Failure Assessment (SOFA) scores, dead space ventilation index, and lung compliance. Citicoline effects on a panel of COVID-relevant lung and blood biomarkers will also be determined.

DISCUSSION

Citicoline has many characteristics that would be advantageous to any candidate COVID-19 therapeutic, including safety, low-cost, favorable chemical characteristics, and potentially pathogen-agnostic efficacy. Successful demonstration that citicoline is beneficial in severely ill patients with SARS CoV-2-induced HARF could transform management of severely ill COVID patients.

TRIAL REGISTRATION

The trial was registered at www.

CLINICALTRIALS

gov on 5/31/2023 (NCT05881135).

TRIAL STATUS

Currently enrolling.

Conventional and unconventional T-cell responses contribute to the prediction of clinical outcome and causative bacterial pathogen in sepsis patients

Sepsis is characterized by a dysfunctional host response to infection culminating in life-threatening organ failure that requires complex patient management and rapid intervention. Timely diagnosis of the underlying cause of sepsis is crucial, and identifying those at risk of complications and death is imperative for triaging treatment and resource allocation. Here, we explored the potential of explainable machine learning models to predict mortality and causative pathogen in sepsis patients. By using a modelling pipeline employing multiple feature selection algorithms, we demonstrate the feasibility of identifying integrative patterns from clinical parameters, plasma biomarkers, and extensive phenotyping of blood immune cells. While no single variable had sufficient predictive power, models that combined five and more features showed a macro area under the curve (AUC) of 0.85 to predict 90-day mortality after sepsis diagnosis, and a macro AUC of 0.86 to discriminate between Gram-positive and Gram-negative bacterial infections. Parameters associated with the cellular immune response contributed the most to models predictive of 90-day mortality, most notably, the proportion of T cells among PBMCs, together with expression of CXCR3 by CD4+ T cells and CD25 by mucosal-associated invariant T (MAIT) cells. Frequencies of Vδ2+ γδ T cells had the most profound impact on the prediction of Gram-negative infections, alongside other T-cell-related variables and total neutrophil count. Overall, our findings highlight the added value of measuring the proportion and activation patterns of conventional and unconventional T cells in the blood of sepsis patients in combination with other immunological, biochemical, and clinical parameters.

Vitamin D regulates COVID-19 associated severity by suppressing the NLRP3 inflammasome pathway

BACKGROUND

The role of vitamin D3 (VitD3) in modulating innate and adaptive immunity has been reported in different disease contexts. Since the start of the coronavirus disease-2019 (COVID-19) pandemic, the role of VitD3 has been highlighted in many correlational and observational studies. However, the exact mechanisms of action are not well identified. One of the mechanisms via which VitD3 modulates innate immunity is by regulating the NLRP3-inflammasome pathway, being a main underlying cause of SARS-CoV-2-induced hyperinflammation.

AIMS AND MAIN METHODS

Blood specimens of severe COVID-19 patients with or without VitD3 treatment were collected during their stay in the intensive care unit and patients were followed up for 29 days. qPCR, western blot, and ELISA were done to investigate the mechanism of action of VitD3 on the NLRP3 inflammasome activation.

KEY FINDINGS

We here report the ability of VitD3 to downregulate the NLRP3-inflammsome pathway in severe COVID-19 patients. Lower inflammasome pathway activation was observed with significantly lower gene and protein expression of NLRP3, cleaved caspase-1, ASC and IL-1β among severe COVID-19 patients treated with VitD3. The reduction of the inflammasome pathway was associated with a reduction in disease severity markers and enhancement of type I IFN pathway.

SIGNIFICANCE

Our data reveals an important anti-inflammatory effect of VitD3 during SARS-CoV-2 infection. Further investigations are warranted to better characterize the ability of VitD3 to control disease pathogenesis and prevent progression to severe states. This will allow for a more efficient use of a low cost and accessible treatment like VitD3.

Metabolic predictors of COVID-19 mortality and severity: a survival analysis

Introduction

The global healthcare burden of COVID-19 pandemic has been unprecedented with a high mortality. Metabolomics, a powerful technique, has been increasingly utilized to study the host response to infections and to understand the progression of multi-system disorders such as COVID-19. Analysis of the host metabolites in response to SARS-CoV-2 infection can provide a snapshot of the endogenous metabolic landscape of the host and its role in shaping the interaction with SARS-CoV-2. Disease severity and consequently the clinical outcomes may be associated with a metabolic imbalance related to amino acids, lipids, and energy-generating pathways. Hence, the host metabolome can help predict potential clinical risks and outcomes.

Methods

In this prospective study, using a targeted metabolomics approach, we studied the metabolic signature in 154 COVID-19 patients (males=138, age range 48-69 yrs) and related it to disease severity and mortality. Blood plasma concentrations of metabolites were quantified through LC-MS using MxP Quant 500 kit, which has a coverage of 630 metabolites from 26 biochemical classes including distinct classes of lipids and small organic molecules. We then employed Kaplan-Meier survival analysis to investigate the correlation between various metabolic markers, disease severity and patient outcomes.

Results

A comparison of survival outcomes between individuals with high levels of various metabolites (amino acids, tryptophan, kynurenine, serotonin, creatine, SDMA, ADMA, 1-MH and carnitine palmitoyltransferase 1 and 2 enzymes) and those with low levels revealed statistically significant differences in survival outcomes. We further used four key metabolic markers (tryptophan, kynurenine, asymmetric dimethylarginine, and 1-Methylhistidine) to develop a COVID-19 mortality risk model through the application of multiple machine-learning methods.

Conclusions

Metabolomics analysis revealed distinct metabolic signatures among different severity groups, reflecting discernible alterations in amino acid levels and perturbations in tryptophan metabolism. Notably, critical patients exhibited higher levels of short chain acylcarnitines, concomitant with higher concentrations of SDMA, ADMA, and 1-MH in severe cases and non-survivors. Conversely, levels of 3-methylhistidine were lower in this context.

Integrated longitudinal multiomics study identifies immune programs associated with acute COVID-19 severity and mortality

BACKGROUNDPatients hospitalized for COVID-19 exhibit diverse clinical outcomes, with outcomes for some individuals diverging over time even though their initial disease severity appears similar to that of other patients. A systematic evaluation of molecular and cellular profiles over the full disease course can link immune programs and their coordination with progression heterogeneity.METHODSWe performed deep immunophenotyping and conducted longitudinal multiomics modeling, integrating 10 assays for 1,152 Immunophenotyping Assessment in a COVID-19 Cohort (IMPACC) study participants and identifying several immune cascades that were significant drivers of differential clinical outcomes.RESULTSIncreasing disease severity was driven by a temporal pattern that began with the early upregulation of immunosuppressive metabolites and then elevated levels of inflammatory cytokines, signatures of coagulation, formation of neutrophil extracellular traps, and T cell functional dysregulation. A second immune cascade, predictive of 28-day mortality among critically ill patients, was characterized by reduced total plasma Igs and B cells and dysregulated IFN responsiveness. We demonstrated that the balance disruption between IFN-stimulated genes and IFN inhibitors is a crucial biomarker of COVID-19 mortality, potentially contributing to failure of viral clearance in patients with fatal illness.CONCLUSIONOur longitudinal multiomics profiling study revealed temporal coordination across diverse omics that potentially explain the disease progression, providing insights that can inform the targeted development of therapies for patients hospitalized with COVID-19, especially those who are critically ill.TRIAL REGISTRATIONClinicalTrials.gov NCT04378777.FUNDINGNIH (5R01AI135803-03, 5U19AI118608-04, 5U19AI128910-04, 4U19AI090023-11, 4U19AI118610-06, R01AI145835-01A1S1, 5U19AI062629-17, 5U19AI057229-17, 5U19AI125357-05, 5U19AI128913-03, 3U19AI077439-13, 5U54AI142766-03, 5R01AI104870-07, 3U19AI089992-09, 3U19AI128913-03, and 5T32DA018926-18); NIAID, NIH (3U19AI1289130, U19AI128913-04S1, and R01AI122220); and National Science Foundation (DMS2310836).

Metabolic signatures of acute respiratory distress syndrome: COVID versus non-COVID

Acute respiratory distress syndrome (ARDS) is a fatal pulmonary disorder characterized by severe hypoxia and inflammation. ARDS is commonly triggered by systemic and pulmonary infections, with bacteria and viruses. Notable pathogens include Pseudomonas aeruginosa, Streptococcus aureus, Enterobacter species, coronaviruses, influenza viruses, and herpesviruses. COVID-19 ARDS represents the latest etiological phenotype of the disease. The pathogenesis of ARDS caused by bacteria and viruses exhibits variations in host immune responses and lung mesenchymal injury. We postulate that the systemic and pulmonary metabolomics profiles of ARDS induced by COVID-19 pathogens may exhibit distinctions compared with those induced by other infectious agents. This review aims to compare metabolic signatures in blood and lung specimens specifically within the context of ARDS. Both prevalent and phenotype-specific metabolomic signatures, including but not limited to glycolysis, ketone body production, lipid oxidation, and dysregulation of the kynurenine pathways, were thoroughly examined in this review. The distinctions in metabolic signatures between COVID-19 and non-COVID ARDS have the potential to reveal new biomarkers, elucidate pathogenic mechanisms, identify druggable targets, and facilitate differential diagnosis in the future.

Deep IDA: a deep learning approach for integrative discriminant analysis of multi-omics data with feature ranking-an application to COVID-19

Motivation

Many diseases are complex heterogeneous conditions that affect multiple organs in the body and depend on the interplay between several factors that include molecular and environmental factors, requiring a holistic approach to better understand disease pathobiology. Most existing methods for integrating data from multiple sources and classifying individuals into one of multiple classes or disease groups have mainly focused on linear relationships despite the complexity of these relationships. On the other hand, methods for nonlinear association and classification studies are limited in their ability to identify variables to aid in our understanding of the complexity of the disease or can be applied to only two data types.

Results

We propose Deep Integrative Discriminant Analysis (IDA), a deep learning method to learn complex nonlinear transformations of two or more views such that resulting projections have maximum association and maximum separation. Further, we propose a feature ranking approach based on ensemble learning for interpretable results. We test Deep IDA on both simulated data and two large real-world datasets, including RNA sequencing, metabolomics, and proteomics data pertaining to COVID-19 severity. We identified signatures that better discriminated COVID-19 patient groups, and related to neurological conditions, cancer, and metabolic diseases, corroborating current research findings and heightening the need to study the post sequelae effects of COVID-19 to devise effective treatments and to improve patient care.

Availability and implementation

Our algorithms are implemented in PyTorch and available at: https://github.com/JiuzhouW/DeepIDA.

Comprehensive analysis of immunogenic cell death-related gene and construction of prediction model based on WGCNA and multiple machine learning in severe COVID-19

The death of coronavirus disease 2019 (COVID-19) is primarily due to from critically ill patients, especially from ARDS complications caused by SARS-CoV-2. Therefore, it is essential to contribute an in-depth understanding of the pathogenesis of the disease and to identify biomarkers for predicting critically ill patients at the molecular level. Immunogenic cell death (ICD), as a specific variant of regulatory cell death driven by stress, can induce adaptive immune responses against cell death antigens in the host. Studies have confirmed that both innate and adaptive immune pathways are involved in the pathogenesis of SARS-CoV-2 infection. However, the role of ICD in the pathogenesis of severe COVID-19 has rarely been explored. In this study, we systematically evaluated the role of ICD-related genes in COVID-19. We conducted consensus clustering, immune infiltration analysis, and functional enrichment analysis based on ICD differentially expressed genes. The results showed that immune infiltration characteristics were altered in severe and non-severe COVID-19. In addition, we used multiple machine learning methods to screen for five risk genes (KLF5, NSUN7, APH1B, GRB10 and CD4), which are used to predict COVID-19 severity. Finally, we constructed a nomogram to predict the risk of severe COVID-19 based on the classification and recognition model, and validated the model with external data sets. This study provides a valuable direction for the exploration of the pathogenesis and progress of COVID-19, and helps in the early identification of severe cases of COVID-19 to reduce mortality.

Plasma Proteomics Elucidated a Protein Signature in COVID-19 Patients with Comorbidities and Early-Diagnosis Biomarkers

Despite great scientific efforts, deep understanding of coronavirus-19 disease (COVID-19) immunopathology and clinical biomarkers remains a challenge. Pre-existing comorbidities increase the mortality rate and aggravate the exacerbated immune response against the severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) infection, which can result in more severe symptoms as well as long-COVID and post-COVID complications. In this study, we applied proteomics analysis of plasma samples from 28 patients with SARS-CoV-2, with and without pre-existing comorbidities, as well as their corresponding controls to determine the systemic protein changes caused by the SARS-CoV-2 infection. As a result, the protein signature shared amongst COVID-19 patients with comorbidities was revealed to be characterized by alterations in the coagulation and complement pathways, acute-phase response proteins, tissue damage and remodeling, as well as cholesterol metabolism. These altered proteins may play a relevant role in COVID-19 pathophysiology. Moreover, several novel potential biomarkers for early diagnosis of the SARS-CoV-2 infection were detected, such as increased levels of keratin K22E, extracellular matrix protein-1 (ECM1), and acute-phase response protein α-2-antiplasmin (A2AP). Importantly, elevated A2AP may contribute to persistent clotting complications associated with the long-COVID syndrome in patients with comorbidities. This study provides new insights into COVID-19 pathogenesis and proposes novel potential biomarkers for early diagnosis that could be facilitated for clinical application by further validation studies.

MOINER: A Novel Multiomics Early Integration Framework for Biomedical Classification and Biomarker Discovery

In the context of precision medicine, multiomics data integration provides a comprehensive understanding of underlying biological processes and is critical for disease diagnosis and biomarker discovery. One commonly used integration method is early integration through concatenation of multiple dimensionally reduced omics matrices due to its simplicity and ease of implementation. However, this approach is seriously limited by information loss and lack of latent feature interaction. Herein, a novel multiomics early integration framework (MOINER) based on information enhancement and image representation learning is thus presented to address the challenges. MOINER employs the self-attention mechanism to capture the intrinsic correlations of omics-features, which make it significantly outperform the existing state-of-the-art methods for multiomics data integration. Moreover, visualizing the attention embedding and identifying potential biomarkers offer interpretable insights into the prediction results. All source codes and model for MOINER are freely available https://github.com/idrblab/MOINER.

Integrated analysis of RNA-seq datasets reveals novel targets and regulators of COVID-19 severity

During the COVID-19 pandemic, RNA-seq datasets were produced to investigate the virus-host relationship. However, much of these data remains underexplored. To improve the search for molecular targets and biomarkers, we performed an integrated analysis of multiple RNA-seq datasets, expanding the cohort and including patients from different countries, encompassing severe and mild COVID-19 patients. Our analysis revealed that severe COVID-19 patients exhibit overexpression of genes coding for proteins of extracellular exosomes, endomembrane system, and neutrophil granules (e.g., S100A9, LY96, and RAB1B), which may play an essential role in the cellular response to infection. Concurrently, these patients exhibit down-regulation of genes encoding components of the T cell receptor complex and nucleolus, including TP53, IL2RB, and NCL Finally, SPI1 may emerge as a central transcriptional factor associated with the up-regulated genes, whereas TP53, MYC, and MAX were associated with the down-regulated genes during COVID-19. This study identified targets and transcriptional factors, lighting on the molecular pathophysiology of syndrome coronavirus 2 infection.

Multi-Omics Profiling Reveals Phenotypic and Functional Heterogeneity of Neutrophils in COVID-19

Accumulating evidence has revealed unexpected phenotypic heterogeneity and diverse functions of neutrophils in several diseases. Coronavirus disease (COVID-19) can alter the leukocyte phenotype based on disease severity, including neutrophil activation in severe cases. However, the plasticity of neutrophil phenotypes and their relative impact on COVID-19 pathogenesis has not been well addressed. This study aimed to identify and validate the heterogeneity of neutrophils in COVID-19 and evaluate the functions of each subpopulation. We analyzed public single-cell RNA-seq, bulk RNA-seq, and proteome data from healthy donors and patients with COVID-19 to investigate neutrophil subpopulations and their response to disease pathogenesis. We identified eight neutrophil subtypes: pro-neutrophil, pre-neutrophil, immature neutrophil, and five mature neutrophil subpopulations. The subtypes exhibited distinct features, including diverse activation signatures and multiple enriched pathways. The pro-neutrophil subtype was associated with severe and fatal disease, while the pre-neutrophil subtype was particularly abundant in mild/moderate disease. One of the mature neutrophil subtypes showed consistently large fractions in patients with different disease severity. Bulk RNA-seq dataset analyses using a cellular deconvolution approach validated the relative abundances of neutrophil subtypes and the expansion of pro-neutrophils in severe COVID-19 patients. Cell-cell communication analysis revealed representative ligand-receptor interactions among the identified neutrophil subtypes. Further investigation into transcription factors and differential protein abundance revealed the regulatory network differences between healthy donors and patients with severe COVID-19. Overall, we demonstrated the complex interactions among heterogeneous neutrophil subtypes and other blood cell types during COVID-19 disease. Our work has great value in terms of both clinical and public health as it furthers our understanding of the phenotypic and functional heterogeneity of neutrophils and other cell populations in multiple diseases.

PANoptosis, an indicator of COVID-19 severity and outcomes

Coronavirus disease 2019 (COVID-19) has been wreaking havoc for 3 years. PANoptosis, a distinct and physiologically relevant inflammatory programmed cell death, perpetuates cytokine storm and multi-organ injuries in COVID-19. Although PANoptosis performs indispensable roles in host defense, further investigation is needed to elucidate the exact processes through which PANoptosis modulates immunological responses and prognosis in COVID-19. This study conducted a bioinformatics analysis of online single-cell RNA sequence (scRNA-seq) and bulk RNA-seq datasets to explore the potential of PANoptosis as an indicator of COVID-19 severity. The degree of PANoptosis in bronchoalveolar lavage fluid (BALF) and peripheral blood mononuclear cells (PBMC) indicated the severity of COVID-19. Single-cell transcriptomics identified pro-inflammatory monocytes as one of the primary sites of PANoptosis in COVID-19. The study subsequently demonstrated the immune and metabolic characteristics of this group of pro-inflammatory monocytes. In addition, the analysis illustrated that dexamethasone was likely to alleviate inflammation in COVID-19 by mitigating PANoptosis. Finally, the study showed that the PANoptosis-related genes could predict the intensive care unit admission (ICU) and outcomes of COVID-19 patients who are hospitalized.

Systems-wide view of host-pathogen interactions across COVID-19 severities using integrated omics analysis

The mechanisms explaining the variability in COVID-19 clinical manifestations (mild, moderate, and severe) are not fully understood. To identify key gene expression markers linked to disease severity, we employed an integrated approach, combining host-pathogen protein-protein interaction data and viral-induced host gene expression data. We analyzed an RNA-seq dataset from peripheral blood mononuclear cells across 12 projects representing the spectrum of disease severity. We identified genes showing differential expression across mild, moderate, and severe conditions. Enrichment analysis of the pathways in host proteins targeted by each of the SARS-CoV-2 proteins revealed a strong association with processes related to ribosomal biogenesis, translation, and translocation. Interestingly, most of these pathways and associated cellular machinery, including ribosomal biogenesis, ribosomal proteins, and translation, were upregulated in mild conditions but downregulated in severe cases. This suggests that COVID-19 exhibits a paradoxical host response, boosting host/viral translation in mild cases but slowing it in severe cases.

Identification of biomarkers and pathways for the SARS-CoV-2 infections in obstructive sleep apnea patients based on machine learning and proteomic analysis

BACKGROUND

The prevalence of obstructive sleep apnea (OSA) was found to be higher in individuals following COVID-19 infection. However, the intricate mechanisms that underscore this concomitance remain partially elucidated. The aim of this study was to delve deeper into the molecular mechanisms that underpin this comorbidity.

METHODS

We acquired gene expression profiles for COVID-19 (GSE157103) and OSA (GSE75097) from the Gene Expression Omnibus (GEO) database. Upon identifying shared feature genes between OSA and COVID-19 utilizing LASSO, Random forest and Support vector machines algorithms, we advanced to functional annotation, analysis of protein-protein interaction networks, module construction, and identification of pivotal genes. Furthermore, we established regulatory networks encompassing transcription factor (TF)-gene and TF-miRNA interactions, and searched for promising drug targets. Subsequently, the expression levels of pivotal genes were validated through proteomics data from COVID-19 cases.

RESULTS

Fourteen feature genes shared between OSA and COVID-19 were selected for further investigation. Through functional annotation, it was indicated that metabolic pathways play a role in the pathogenesis of both disorders. Subsequently, employing the cytoHubba plugin, ten hub genes were recognized, namely TP53, CCND1, MDM2, RB1, HIF1A, EP300, STAT3, CDK2, HSP90AA1, and PPARG. The finding of proteomics unveiled a substantial augmentation in the expression level of HSP90AA1 in COVID-19 patient samples, especially in severe conditions.

CONCLUSIONS

Our investigation illuminate a mutual pathogenic mechanism that underlies both OSA and COVID-19, which may provide novel perspectives for future investigations into the underlying mechanisms.

Consequences of COVID-19 on Adipose Tissue Signatures

Since the emergence of coronavirus disease-19 (COVID-19) in 2019, it has been crucial to investigate the causes of severe cases, particularly the higher rates of hospitalization and mortality in individuals with obesity. Previous findings suggest that adipocytes may play a role in adverse COVID-19 outcomes in people with obesity. The impact of COVID-19 vaccination and infection on adipose tissue (AT) is currently unclear. We therefore analyzed 27 paired biopsies of visceral and subcutaneous AT from donors of the Leipzig Obesity BioBank that have been categorized into three groups (1: no infection/no vaccination; 2: no infection but vaccinated; 3: infected and vaccinated) based on COVID-19 antibodies to spike (indicating vaccination) and/or nucleocapsid proteins. We provide additional insights into the impact of COVID-19 on AT biology through a comprehensive histological transcriptome and serum proteome analysis. This study demonstrates that COVID-19 infection is associated with smaller average adipocyte size. The impact of infection on gene expression was significantly more pronounced in subcutaneous than in visceral AT and mainly due to immune system-related processes. Serum proteome analysis revealed the effects of the infection on circulating adiponectin, interleukin 6 (IL-6), and carbonic anhydrase 5A (CA5A), which are all related to obesity and blood glucose abnormalities.

Children with severe neurological symptoms associated with SARS-CoV-2 infection during Omicron pandemic in China

BACKGROUND

To analyze the clinical characteristics and outcomes of children with severe neurological symptoms associated with the severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) infection during the Omicron pandemic in China.

METHODS

This study used a questionnaire to obtain data from pediatric intensive care unit (PICU) centers in seven tertiary hospitals in Northeast China from December 1, 2022, to January 31, 2023.

RESULTS

A total of 255 patients were confirmed to have SARS-CoV-2 infection, and 45 patients (17.65 %) were included in this study. Of these, seven (15.6%) patients died, and the median time from admission to death was 35 h (IQR, 14-120 h). Twenty (52.6%) survivors experienced neurological sequelae. Patients with platelet counts lower than 100 × 109/L had a higher incidence of complications such as multiple organ dysfunction, mechanical ventilation rate, and mortality. Cranial magnetic resonance imaging (MRI) always reveals cerebral tissue edema, with some severe lesions forming a softening site.

CONCLUSION

Children infected with SARS-CoV-2 often exhibit severe neurological symptoms, and in some cases, they may rapidly develop malignant cerebral edema or herniation, leading to a fatal outcome. An early decrease in platelet count may associated with an unfavorable prognosis.

IMPACT

Since early December 2022, China has gradually adjusted its prevention and control policy of SARS-CoV-2; Omicron outbreaks have occurred in some areas for a relatively short period. Due to the differences in ethnicity, endemic strains and vaccination status, there was a little difference from what has been reported about children with SARS-CoV-2 infection with severe neurological symptoms in abroad. This is the first multicenter clinical study in children with nervous system involvement after acute SARS-CoV-2 infection in China, and helpful for pediatricians to have a more comprehensive understanding of the clinical symptoms and prognosis of such disease.

5-HT7R enhances neuroimmune resilience and alleviates meningitis by promoting CCR5 ubiquitination

INTRODUCTION

Excessive immune activation induces tissue damage during infection. Compared to external strategies to reconstruct immune homeostasis, host balancing ways remain largely unclear.

OBJECTIVES

Here we found a neuroimmune way that prevents infection-induced tissue damage.

METHODS

By FACS and histopathology analysis of brain Streptococcus pneumonia meningitis infection model and behavioral testing. Western blot, co-immunoprecipitation, and ubiquitination analyze the Fluoxetine initiate 5-HT7R-STUB1-CCR5 K48-linked ubiquitination degradation.

RESULTS

Fluoxetine, a selective serotonin reuptake inhibitor, or the agonist of serotonin receptor 5-HT7R, protects mice from meningitis by inhibiting CCR5-mediated excessive immune response and tissue damage. Mechanistically, the Fluoxetine-5-HT7R axis induces proteasome-dependent degradation of CCR5 via mTOR signaling, and then recruits STUB1, an E3 ubiquitin ligase, to initiate K48-linked polyubiquitination of CCR5 at K138 and K322, promotes its proteasomal degradation. STUB1 deficiency blocks 5-HT7R-mediated CCR5 degradation.

CONCLUSION

Our results reveal a neuroimmune pathway that balances anti-infection immunity via happiness neurotransmitter receptor and suggest the 5-HT7R-CCR5 axis as a potential target to promote neuroimmune resilience.

Complex changes in serum protein levels in COVID-19 convalescents

The COVID-19 pandemic, triggered by severe acute respiratory syndrome coronavirus 2, has affected millions of people worldwide. Much research has been dedicated to our understanding of COVID-19 disease heterogeneity and severity, but less is known about recovery associated changes. To address this gap in knowledge, we quantified the proteome from serum samples from 29 COVID-19 convalescents and 29 age-, race-, and sex-matched healthy controls. Samples were acquired within the first months of the pandemic. Many proteins from pathways known to change during acute COVID-19 illness, such as from the complement cascade, coagulation system, inflammation and adaptive immune system, had returned to levels seen in healthy controls. In comparison, we identified 22 and 15 proteins with significantly elevated and lowered levels, respectively, amongst COVID-19 convalescents compared to healthy controls. Some of the changes were similar to those observed for the acute phase of the disease, i.e. elevated levels of proteins from hemolysis, the adaptive immune systems, and inflammation. In contrast, some alterations opposed those in the acute phase, e.g. elevated levels of CETP and APOA1 which function in lipid/cholesterol metabolism, and decreased levels of proteins from the complement cascade (e.g. C1R, C1S, and VWF), the coagulation system (e.g. THBS1 and VWF), and the regulation of the actin cytoskeleton (e.g. PFN1 and CFL1) amongst COVID-19 convalescents. We speculate that some of these shifts might originate from a transient decrease in platelet counts upon recovery from the disease. Finally, we observed race-specific changes, e.g. with respect to immunoglobulins and proteins related to cholesterol metabolism.

Genomic, Proteomic, and Phenotypic Biomarkers of COVID-19 Severity: Protocol for a Retrospective Observational Study

BACKGROUND

Health organizations and countries around the world have found it difficult to control the spread of COVID-19. To minimize the future impact on the UK National Health Service and improve patient care, there is a pressing need to identify individuals who are at a higher risk of being hospitalized because of severe COVID-19. Early targeted work was successful in identifying angiotensin-converting enzyme-2 receptors and type II transmembrane serine protease dependency as drivers of severe infection. Although a targeted approach highlights key pathways, a multiomics approach will provide a clearer and more comprehensive picture of severe COVID-19 etiology and progression.

OBJECTIVE

The COVID-19 Response Study aims to carry out an integrated multiomics analysis to identify biomarkers in blood and saliva that could contribute to host susceptibility to SARS-CoV-2 and the development of severe COVID-19.

METHODS

The COVID-19 Response Study aims to recruit 1000 people who recovered from SARS-CoV-2 infection in both community and hospital settings on the island of Ireland. This protocol describes the retrospective observational study component carried out in Northern Ireland (NI; Cohort A); the Republic of Ireland cohort will be described separately. For all NI participants (n=519), SARS-CoV-2 infection has been confirmed by reverse transcription-quantitative polymerase chain reaction. A prospective Cohort B of 40 patients is also being followed up at 1, 3, 6, and 12 months postinfection to assess longitudinal symptom frequency and immune response. Data will be sourced from whole blood, saliva samples, and clinical data from the electronic care records, the general health questionnaire, and a 12-item general health questionnaire mental health survey. Saliva and blood samples were processed to extract DNA and RNA before whole-genome sequencing, RNA sequencing, DNA methylation analysis, microbiome analysis, 16S ribosomal RNA gene sequencing, and proteomic analysis were performed on the plasma. Multiomics data will be combined with clinical data to produce sensitive and specific prognostic models for severity risk.

RESULTS

An initial demographic and clinical profile of the NI Cohort A has been completed. A total of 249 hospitalized patients and 270 nonhospitalized patients were recruited, of whom 184 (64.3%) were female, and the mean age was 45.4 (SD 13) years. High levels of comorbidity were evident in the hospitalized cohort, with cardiovascular disease and metabolic and respiratory disorders being the most significant (P<.001), grouped according to the International Classification of Diseases 10 codes.

CONCLUSIONS

This study will provide a comprehensive opportunity to study the mechanisms of COVID-19 severity in recontactable participants.

INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID)

DERR1-10.2196/50733.

Gene set correlation enrichment analysis for interpreting and annotating gene expression profiles

Pathway analysis, including nontopology-based (non-TB) and topology-based (TB) methods, is widely used to interpret the biological phenomena underlying differences in expression data between two phenotypes. By considering dependencies and interactions between genes, TB methods usually perform better than non-TB methods in identifying pathways that include closely relevant or directly causative genes for a given phenotype. However, most TB methods may be limited by incomplete pathway data used as the reference network or by difficulties in selecting appropriate reference networks for different research topics. Here, we propose a gene set correlation enrichment analysis method, Gscore, based on an expression dataset-derived coexpression network to examine whether a differentially expressed gene (DEG) list (or each of its DEGs) is associated with a known gene set. Gscore is better able to identify target pathways in 89 human disease expression datasets than eight other state-of-the-art methods and offers insight into how disease-wide and pathway-wide associations reflect clinical outcomes. When applied to RNA-seq data from COVID-19-related cells and patient samples, Gscore provided a means for studying how DEGs are implicated in COVID-19-related pathways. In summary, Gscore offers a powerful analytical approach for annotating individual DEGs, DEG lists, and genome-wide expression profiles based on existing biological knowledge.

SARS-CoV-2 RNA stabilizes host mRNAs to elicit immunopathogenesis

SARS-CoV-2 RNA interacts with host factors to suppress interferon responses and simultaneously induces cytokine release to drive the development of severe coronavirus disease 2019 (COVID-19). However, how SARS-CoV-2 hijacks host RNAs to elicit such imbalanced immune responses remains elusive. Here, we analyzed SARS-CoV-2 RNA in situ structures and interactions in infected cells and patient lung samples using RIC-seq. We discovered that SARS-CoV-2 RNA forms 2,095 potential duplexes with the 3' UTRs of 205 host mRNAs to increase their stability by recruiting RNA-binding protein YBX3 in A549 cells. Disrupting the SARS-CoV-2-to-host RNA duplex or knocking down YBX3 decreased host mRNA stability and reduced viral replication. Among SARS-CoV-2-stabilized host targets, NFKBIZ was crucial for promoting cytokine production and reducing interferon responses, probably contributing to cytokine storm induction. Our study uncovers the crucial roles of RNA-RNA interactions in the immunopathogenesis of RNA viruses such as SARS-CoV-2 and provides valuable host targets for drug development.

Discovery of common molecular signatures and drug repurposing for COVID-19/Asthma comorbidity: ACE2 and multi-partite networks

Angiotensin-converting enzyme 2 (ACE2) is identified as the functional receptor for severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), the causative agent of the ongoing global coronavirus disease-2019 (COVID-19) pandemic. This study aimed to elucidate potential therapeutic avenues by scrutinizing approved drugs through the identification of the genetic signature associated with SARS-CoV-2 infection in individuals with asthma. This exploration was conducted through an integrated analysis, encompassing interaction networks between the ACE2 receptor and common host (co-host) factors implicated in COVID-19/asthma comorbidity. The comprehensive analysis involved the identification of common differentially expressed genes (cDEGs) and hub-cDEGs, functional annotations, interaction networks, gene set variation analysis (GSVA), gene set enrichment analysis (GSEA), and module construction. Interaction networks were used to identify overlapping disease modules and potential drug targets. Computational biology and molecular docking analyzes were utilized to discern functional drug modules. Subsequently, the impact of the identified drugs on the expression of hub-cDEGs was experimentally validated using a mouse model. A total of 153 cDEGs or co-host factors associated with ACE2 were identified in the COVID-19 and asthma comorbidity. Among these, seven significant cDEGs and proteins - namely, HRAS, IFNG, JUN, CDH1, TLR4, ICAM1, and SCD-were recognized as pivotal host factors linked to ACE2. Regulatory network analysis of hub-cDEGs revealed eight top-ranked transcription factors (TFs) proteins and nine microRNAs as key regulatory factors operating at the transcriptional and post-transcriptional levels, respectively. Molecular docking simulations led to the proposal of 10 top-ranked repurposable drug molecules (Rapamycin, Ivermectin, Everolimus, Quercetin, Estradiol, Entrectinib, Nilotinib, Conivaptan, Radotinib, and Venetoclax) as potential treatment options for COVID-19 in individuals with comorbid asthma. Validation analysis demonstrated that Rapamycin effectively inhibited ICAM1 expression in the HDM-stimulated mice group (p < 0.01). This study unveils the common pathogenesis and genetic signature underlying asthma and SARS-CoV-2 infection, delineated by the interaction networks of ACE2-related host factors. These findings provide valuable insights for the design and discovery of drugs aimed at more effective therapeutics within the context of lung disease comorbidities.

Parameterization of asymmetric sigmoid functions in weighted gene co-expression network analysis

In most the biological contexts, examining gene expressions at the genomic level gives more accurate results than examining genes individually. It can improve understanding of the molecular mechanisms that cause molecular alterations. Weighted gene co-expression network analysis (WGCNA), which has recently been widely used to cluster transcriptomic datasets, implements a soft thresholding procedure using power function. However, these functions may sometimes exaggerate minor differences in expression correlations. We have previously proposed to use asymmetric sigmoid functions in soft thresholding as an alternative solution. However, the number of variables in asymmetric sigmoid functions may vary and parameterization can be problematic. In this study, we have introduced a systematic procedure for parameterizing asymmetric sigmoid function to ease using it as an alternative soft-thresholding solution in WGCNA. The efficiency of the employment was shown on four different COVID-19 datasets, on a yeast dataset, and on an E.Coli dataset. The results indicate that this approach provides biologically plausible associations for the resulting modules.

Transcriptome and machine learning analysis of the impact of COVID-19 on mitochondria and multiorgan damage

The effects of coronavirus disease 2019 (COVID-19) primarily concern the respiratory tract and lungs; however, studies have shown that all organs are susceptible to infection by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). COVID-19 may involve multiorgan damage from direct viral invasion through angiotensin-converting enzyme 2 (ACE2), through inflammatory cytokine storms, or through other secondary pathways. This study involved the analysis of publicly accessible transcriptome data from the Gene Expression Omnibus (GEO) database for identifying significant differentially expressed genes related to COVID-19 and an investigation relating to the pathways associated with mitochondrial, cardiac, hepatic, and renal toxicity in COVID-19. Significant differentially expressed genes were identified and ranked by statistical approaches, and the genes derived by biological meaning were ranked by feature importance; both were utilized as machine learning features for verification. Sample set selection for machine learning was based on the performance, sample size, imbalanced data state, and overfitting assessment. Machine learning served as a verification tool by facilitating the testing of biological hypotheses by incorporating gene list adjustment. A subsequent in-depth study for gene and pathway network analysis was conducted to explore whether COVID-19 is associated with cardiac, hepatic, and renal impairments via mitochondrial infection. The analysis showed that potential cardiac, hepatic, and renal impairments in COVID-19 are associated with ACE2, inflammatory cytokine storms, and mitochondrial pathways, suggesting potential medical interventions for COVID-19-induced multiorgan damage.

Hypoxia and Activation of Neutrophil Degranulation-Related Genes in the Peripheral Blood of COVID-19 Patients

Severe COVID-19 is characterized by systematic hyper-inflammation and subsequent damage to various organs. Therefore, it is critical to trace this cascade of hyper-inflammation. Blood transcriptome has been routinely utilized in the interrogation of host immune response in COVID-19 and other infectious conditions. In this study, consensus gene dysregulation in the blood was obtained from 13 independent transcriptome studies on COVID-19. Among the up-regulated genes, the most prominent functional categories were neutrophil degranulation and cell cycle, which is clearly different from the classical activation of interferon signaling pathway in seasonal flu. As for the potential upstream causal factors of the atypical gene dysregulation, systemic hypoxia was further examined because it is much more widely reported in COVID-19 than that in seasonal flu. It was found that both physiological and pathological hypoxia can induce activation of neutrophil degranulation-related genes in the blood. Furthermore, COVID-19 patients with different requirement for oxygen intervention showed distinctive levels of gene expression related to neutrophil degranulation in the whole blood, which was validated in isolated neutrophils. Thus, activation of neutrophil degranulation-related genes in the blood of COVID-19 could be partially attributed to hypoxia. Interestingly, similar pattern was also observed in H1N1 infection (the cause of Spanish flu) and several other severe respiratory viral infections. As for the molecular mechanism, both HIF-dependent and HIF-independent pathways have been examined. Since the activation of neutrophil degranulation-related genes is highly correlated with disease severity in COVID-19, early detection of hypoxia and active intervention may prevent further activation of neutrophil degranulation-related genes and other harmful downstream hyper-inflammation. This common mechanism is applicable to current and future pandemic as well as the severe form of common respiratory infection.

Multi-omic integration of microbiome data for identifying disease-associated modules

The human gut microbiome is a complex ecosystem with profound implications for health and disease. This recognition has led to a surge in multi-omic microbiome studies, employing various molecular assays to elucidate the microbiome's role in diseases across multiple functional layers. However, despite the clear value of these multi-omic datasets, rigorous integrative analysis of such data poses significant challenges, hindering a comprehensive understanding of microbiome-disease interactions. Perhaps most notably, multiple approaches, including univariate and multivariate analyses, as well as machine learning, have been applied to such data to identify disease-associated markers, namely, specific features (e.g., species, pathways, metabolites) that are significantly altered in disease state. These methods, however, often yield extensive lists of features associated with the disease without effectively capturing the multi-layered structure of multi-omic data or offering clear, interpretable hypotheses about underlying microbiome-disease mechanisms. Here, we address this challenge by introducing MintTea - an intermediate integration-based method for analyzing multi-omic microbiome data. MintTea combines a canonical correlation analysis (CCA) extension, consensus analysis, and an evaluation protocol to robustly identify disease-associated multi-omic modules. Each such module consists of a set of features from the various omics that both shift in concord, and collectively associate with the disease. Applying MintTea to diverse case-control cohorts with multi-omic data, we show that this framework is able to capture modules with high predictive power for disease, significant cross-omic correlations, and alignment with known microbiome-disease associations. For example, analyzing samples from a metabolic syndrome (MS) study, we found a MS-associated module comprising of a highly correlated cluster of serum glutamate- and TCA cycle-related metabolites, as well as bacterial species previously implicated in insulin resistance. In another cohort, we identified a module associated with late-stage colorectal cancer, featuring Peptostreptococcus and Gemella species and several fecal amino acids, in agreement with these species' reported role in the metabolism of these amino acids and their coordinated increase in abundance during disease development. Finally, comparing modules identified in different datasets, we detected multiple significant overlaps, suggesting common interactions between microbiome features. Combined, this work serves as a proof of concept for the potential benefits of advanced integration methods in generating integrated multi-omic hypotheses underlying microbiome-disease interactions and a promising avenue for researchers seeking systems-level insights into coherent mechanisms governing microbiome-related diseases.

mvlearnR and Shiny App for multiview learning

Summary

The package mvlearnR and accompanying Shiny App is intended for integrating data from multiple sources or views or modalities (e.g. genomics, proteomics, clinical, and demographic data). Most existing software packages for multiview learning are decentralized and offer limited capabilities, making it difficult for users to perform comprehensive integrative analysis. The new package wraps statistical and machine learning methods and graphical tools, providing a convenient and easy data integration workflow. For users with limited programming language, we provide a Shiny Application to facilitate data integration anywhere and on any device. The methods have potential to offer deeper insights into complex disease mechanisms.

Availability and implementation

mvlearnR is available from the following GitHub repository: https://github.com/lasandrall/mvlearnR. The web application is hosted on shinyapps.io and available at: https://multi-viewlearn.shinyapps.io/MultiView_Modeling/.