Blood molecular markers associated with COVID-19 immunopathology and multi-organ damage

Integrative proteomic analysis reveals the potential diagnostic marker and drug target for the Type-2 diabetes mellitus

Objective

The escalating prevalence of Type-2 diabetes mellitus (T2DM) poses a significant global health challenge. Utilizing integrative proteomic analysis, this study aimed to identify a panel of potential protein markers for T2DM, enhancing diagnostic accuracy and paving the way for personalized treatment strategies.

Methods

Proteome profiles from two independent cohorts were integrated: cohort 1 composed of 10 T2DM patients and 10 healthy controls (HC), and cohort 2 comprising 87 T2DM patients and 60 healthy controls. Differential expression analysis, functional enrichment analysis, receiver operating characteristic (ROC) analysis, and classification error matrix analysis were employed.

Results

Comparative proteomic analysis identified the differential expressed proteins (DEPs) and changes in biological pathways associated with T2DM. Further combined analysis refined a group of protein panel (including CA1, S100A6, and DDT), which were significantly increased in T2DM in both two cohorts. ROC analysis revealed the area under curve (AUC) values of 0.94 for CA1, 0.87 for S100A6, and 0.97 for DDT; the combined model achieved an AUC reaching 1. Classification error matrix analysis demonstrated the combined model could reach an accuracy of 1 and 0.875 in the 60% training set and 40% testing set.

Conclusions

This study incorporates different cohorts of T2DM, and refines the potential markers for T2DM with high accuracy, offering more reliable markers for clinical translation.

Supplementary Information

The online version contains supplementary material available at 10.1007/s40200-025-01562-3.

Metabolomic characteristics and related pathways in patients with different severity of COVID-19: a systematic review and meta-analysis

Background

Despite advances in metabolomic research on COVID-19, existing studies have small sample sizes and few have comprehensively described the metabolic characteristics of patients with COVID-19 at each stage. In this systematic review, we aimed to summarise the similarities and differences of biomarkers in patients with COVID-19 of different severity and describe their metabolic characteristics at different stages.

Methods

We retrieved studies from PubMed, Embase, Web of Science, and the Cochrane Library published by October 2022. We performed a meta-analysis on untargeted and targeted metabolomics research data, using the ratio of means as the effect size. We compared changes in metabolite levels between patients with varying severity and controls and investigated sources of heterogeneity through subgroup analyses and meta-regression analysis.

Results

We included 22 cohorts from 21 studies, comprising 2421 participants, including COVID-19 patients of varying severity and healthy controls. We conducted meta-analysis and heterogeneity analysis on the 1058 metabolites included in the study. The results indicated that, compared to the healthy control group, 23 biomarkers were associated with mild cases (P < 0.05), 3 biomarkers with moderate cases (P < 0.05), and 37 biomarkers with severe cases (P < 0.05). Pathway enrichment analysis revealed significant disturbances in amino acid metabolism, aminoacyl-tRNA biosynthesis, primary bile acid biosynthesis, pantothenate and CoA biosynthesis, the tricarboxylic acid cycle, taurine and hypotaurine metabolism, and nitrogen metabolism in patients with mild, moderate, and severe disease. Additionally, we found that each severity stage exhibited unique metabolic patterns (all P < 0.05) and that the degree of metabolic dysregulation progressively worsened with increasing disease severity (P < 0.05).

Conclusions

The results of our meta-analysis indicate the similarities and differences of biomarkers and metabolic characteristics of patients with different severity in COVID-19, thereby providing new pathways for the study of pathogenesis, the development precise treatment, and the formulation of comprehensive strategies.

Registration

PROSPERO: CRD42022369937.

Metabolomic profile of severe COVID-19 and a signature predictive of progression towards severe disease status: a prospective cohort study (METCOVID)

Profound metabolomic alterations occur during COVID-19. Early identification of the subset of hospitalised COVID-19 patients at risk of developing severe disease is critical for optimal resource utilization and prompt treatment. This work explores the metabolomic profile of hospitalised adult COVID-19 patients with severe disease, and establishes a predictive signature for disease progression. Within 48 hours of admission, serum samples were collected from 148 hospitalised patients for nuclear magnetic resonance (NMR) spectroscopy. Lipoprotein profiling was performed using the 1H-NMR-based Liposcale test, while low molecular weight metabolites were analysed using one-dimensional Carr-Purcell-Meiboom-Gill pulse spectroscopy and an adaptation of the Dolphin method for lipophilic extracts. Severe COVID-19, per WHO's Clinical Progression Scale, was characterized by altered lipoprotein distribution, elevated signals of glyc-A and glyc-B, a shift towards a catabolic state with elevated levels of branched-chain amino acids, and accumulation of ketone bodies. Furthermore, COVID-19 patients initially presenting with moderate disease but progressing to severe stages exhibited a distinct metabolic signature. Our multivariate model demonstrated a cross-validated AUC of 0.82 and 72% predictive accuracy for severity progression. NMR spectroscopy-based metabolomic profiling enables the identification of moderate COVID-19 patients at risk of disease progression, aiding in resource allocation and early intervention.

Bullatine A suppresses glioma cell growth by targeting SIRT6

Gliomas are the most common primary tumors of the nervous system, which is generally treated using adjuvant chemotherapy following surgical resection. However, patient survival time is still short, and there is currently no successful treatment for highly malignant gliomas. Bullatine A (BLA) is a diterpenoid alkaloid of the genus Aconitum which antirheumatic and anti-inflammatory pharmacological properties. The effects of BLA on gliomas have not yet been elucidated. In this study, we investigated the effects of BLA on human brain malignant glioblastoma cells. Our results showed that BLA inhibited the proliferation of U87MG and U251 cells in a dose-dependent manner and decreased their survival rate. BLA dose-dependently induced apoptosis in U87MG cells, upregulated the expression of cleaved caspase-9, cleaved caspase-3 pro-apoptotic protein, and Bax protein, and downregulated the expression of Bcl-2 anti-apoptotic protein. Moreover, BLA dose-dependently induced U87MG and U251 cell cycle arrest in the G2/M phase, and downregulated the expression of p-ERK and Myc proteins. Further, BLA significantly inhibited the acetylation of histones H3K9 and H3K56, and upregulated the expression of the protein deacetylase SIRT6. Mechanistic studies revealed that the effect of BLA on inducing apoptosis and inhibiting the proliferation of glioma cells was blocked by SIRT6 knockout. In summary, our study indicated that BLA is a potential therapeutic agent for glioma that targets SIRT6 to inhibit glioma cell proliferation and induce apoptosis.

Targeted and untargeted cross-sectional study for sex-specific identification of plasma biomarkers of COVID-19 severity

Coronavirus disease 2019 is a highly contagious respiratory illness caused by the coronavirus SARS-CoV-2. Symptoms can range from mild to severe and typically appear 2-14 days after virus exposure. While vaccination has significantly reduced the incidence of severe complications, strategies for the identification of new biomarkers to assess disease severity remains a critical area of research. Severity biomarkers are essential for personalizing treatment strategies and improving patient outcomes. This study aimed to identify sex-specific biomarkers for COVID-19 severity in a Chilean population (n = 123 female, n = 115 male), categorized as control, mild, moderate, or severe. Data were collected using clinical biochemistry parameters and mass spectrometry-based metabolomics and lipidomics to detect alterations in plasma cytokines, metabolites, and lipid profiles related to disease severity. Principal component analysis (PCA) and orthogonal partial least squares discriminant analysis (OPLS-DA) were performed to select significant characteristic features for each group. The results revealed distinct biomarkers for males and females. In males, COVID-19 severity of was associated with inflammation parameters, triglycerides content, and phospholipids profiles. For females, liver damage parameters, triglycerides content, cholesterol derivatives, and phosphatidylcholine were identified as severity biomarkers. For both sexes, most of the biomarker combinations evaluated got areas under the ROC curve greater than 0.8 and low prediction errors. These findings suggest that sex-specific biomarkers can help differentiate the levels of COVID-19 severity, potentially aiding in the development of tailored treatment approaches.

Diagnosis, Severity, and Prognosis from Potential Biomarkers of COVID-19 in Urine: A Review of Clinical and Omics Results

The COVID-19 pandemic, caused by the SARS-CoV-2 virus, has spurred an extraordinary scientific effort to better understand the disease's pathophysiology and develop diagnostic and prognostic tools to guide more precise and effective clinical management. Among the biological samples analyzed for biomarker identification, urine stands out due to its low risk of infection, non-invasive collection, and suitability for frequent, large-volume sampling. Integrating data from omics studies with standard biochemical analyses offers a deeper and more comprehensive understanding of COVID-19. This review aims to provide a detailed summary of studies published to date that have applied omics and clinical analyses on urine samples to identify potential biomarkers for COVID-19. In July 2024, an advanced search was conducted in Web of Science using the query: "covid* (Topic) AND urine (Topic) AND metabol* (Topic)". The search included results published up to 14 October 2024. The studies retrieved from this digital search were evaluated through a two-step screening process: first by reviewing titles and abstracts for eligibility, and then by retrieving and assessing the full texts of articles that met the specific criteria. The initial search retrieved 913 studies, of which 45 articles were ultimately included in this review. The most robust biomarkers identified include kynurenine, neopterin, total proteins, red blood cells, ACE2, citric acid, ketone bodies, hypoxanthine, amino acids, and glucose. The biological causes underlying these alterations reflect the multisystemic impact of COVID-19, highlighting key processes such as systemic inflammation, renal dysfunction, critical hypoxia, and metabolic stress.

A transcriptome-based risk model in sepsis enables prognostic prediction and drug repositioning

Septic management presented a tremendous challenge due to heterogeneous host responses. We aimed to develop a risk model for early septic stratification based on transcriptomic signature. Here, we combined genes OLAH, LY96, HPGD, and ABLIM1 into a prognostic risk score model, which demonstrated exceptional performance in septic diagnosis (AUC = 0.99-1.00) and prognosis (AUC = 0.61-0.70), outperforming that of Mars and SRS endotypes. Also, the model unveiled immunosuppressive status in high-risk patients and a poor response to hydrocortisone in low-risk individuals. Single-cell transcriptome analysis further elucidated expression patterns and effects of the four genes across immune cell types, illustrating integrated host responses reflected by this model. Upon distinct transcriptional profiles of risk subgroups, we identified fenretinide and meloxicam as therapeutic agents, which significantly improved survival in septic mice models. Our study introduced a risk model that optimized risk stratification and drug repurposing of sepsis, thereby offering a comprehensive management approach.

Metabonomic Biomarkers of Plaque Burden and Instability in Patients With Coronary Atherosclerotic Disease After Moderate Lipid-Lowering Therapy

BACKGROUND

Contemporary risk assessment in patients with coronary atherosclerotic disease (CAD) often relies on invasive angiography. However, we aimed to explore the potential of metabolomic biomarkers in reflecting residual risk in patients with CAD after moderate lipid-lowering therapy.

METHODS AND RESULTS

We analyzed serum metabolomic profile among 2560 patients with newly diagnosed CAD undergoing moderate lipid-lowering therapy, through nuclear magnetic resonance spectroscopy and quantified 175 metabolites, predominantly lipoproteins and their components. CAD severity was evaluated using Gensini score for plaque burden and circulating cardiac troponin T levels for plaque instability. The association of metabolites with CAD severity was examined using multivariate linear regression, and the underlying potential causality was explored using a 2-sample Mendelian randomization approach. Two composite metabolomic indices were constructed to reflect CAD severity using least absolute shrinkage and selection operator linear regression, and their associations with risk of major adverse cardiac events during a median follow-up of 3.8 years were evaluated using Cox models. Our investigation revealed that triglycerides and apolipoprotein B in low-density lipoprotein particles displayed stronger associations with CAD severity compared with the clinically used low-density lipoprotein cholesterol marker. In large high-density lipoprotein, components like cholesterol, cholesterol esters, triglyceride, apolipoprotein A1/A2 showed inverse associations with CAD severity. Certain metabolites, including apolipoprotein B and dihydrothymine, showed a putative causal link with Gensini score. Notably, per standard deviation increase in Gensini score-based metabolomic index was associated with 14.8% higher major adverse cardiac event risk (hazard ratio, 1.148 [95% CI, 1.018-1.295]) independent of demographic factors, medication use, and disease status.

CONCLUSIONS

Our findings highlight the potential of nuclear magnetic resonance-based metabolomics in identifying novel biomarkers of plaque burden and instability. Metabolites related to plaque burden may facilitate noninvasive assessment of CAD prognosis.

Serum proteome reveals distinctive molecular features of H7N9- and SARS-CoV-2-infected patients

The coronavirus disease 2019 (COVID-19) pandemic has reminded us of human infections with the H7N9 virus and has raised questions related to the clinical and molecular pathophysiological diversity between the two diseases. Here, we performed a proteomic approach on sera samples from patients with H7N9-virus or SARS-CoV-2-virus infection and healthy controls. Compared to SARS-CoV-2, H7N9-virus infection caused elevated neutrophil concentrations, T cell exhaustion, and increased cytokine/interleukin secretion. Cell-type deconvolution and temporal analysis revealed that T cells and neutrophils could regulate the core immunological trajectory and influence the prognosis of patients with severe H7N9-virus infection. Elevated tissue-enhanced proteins combined with alterations of clinical biochemical indexes suggested that H7N9 infection induced more severe inflammatory organ injury and dysfunction in the liver and intestine. Further mechanical analysis revealed that the high concentration of neutrophils might impact the intestinal enterocyte cells through cytokine-receptor interaction, leading to intestinal damage in patients with H7N9-virus infection.

Multi-omic molecular characterization and diagnostic biomarkers for occult hepatitis B infection and HBsAg-positive hepatitis B infection

Background

The pathological and physiological characteristics between HBsAg-positive HBV infection and occult hepatitis B infection (OBI) are currently unclear. This study aimed to explore the immune microenvironment in the peripheral circulation of OBI patients through integration of proteomic and metabolomic sequencing, and to identify molecular biomarkers for clinical diagnosis of HBsAg-positive HBV and OBI.

Methods

This research involved collection of plasma from 20 patients with OBI (negative for HBsAg but positive for HBV DNA, with HBV DNA levels < 200 IU/mL), 20 patients with HBsAg-positive HBV infection, and 10 healthy individuals. Mass spectrometry-based detection was used to analyze the proteome, while nuclear magnetic resonance spectroscopy was employed to study the metabolomic phenotypes. Differential molecule analysis, pathway enrichment and functional annotation, as well as weighted correlation network analysis (WGCNA), were conducted to uncover the characteristics of HBV-related liver disease. Prognostic biomarkers were identified using machine learning algorithms, and their validity was confirmed in a larger cohort using enzyme linked immunosorbent assay (ELISA).

Results

HBsAg-positive HBV individuals showed higher ALT levels (p=0.010) when compared to OBI patients. The influence of HBV infection on metabolic functions and inflammation was evident through the analysis of distinct metabolic pathways in HBsAg-positive HBV and OBI groups. Tissue tracing demonstrated a connection between Kupffer cells and HBsAg-positive HBV infection, as well as between hepatocytes and OBI. Immune profiling revealed the correlation between CD4 Tem cells, memory B cells and OBI, enabling a rapid response to infection reactivation through cytokine secretion and antibody production. A machine learning-constructed and significantly expressed molecule-based diagnostic model effectively differentiated HBsAg-positive and OBI groups (AUC values > 0.8). ELISA assay confirmed the elevation of FGB and FGG in OBI samples, suggesting their potential as biomarkers for distinguishing OBI from HBsAg-positive infection.

Conclusions

The immune microenvironment and metabolic status of HBsAg-positive HBV patients and OBI patients vary significantly. The machine learning-based diagnostic model described herein displayed impressive classification accuracy, presenting a non-invasive means of differentiating between OBI and HBsAg-positive HBV infections.

Circulating metabolome in relation to cognitive impairment: a community-based cohort of older adults

The role of circulating metabolome in cognitive impairment is inconclusive, and whether the associations are in the severity-dependent manner remains unclear. We aimed to identify plasma metabolites associated with cognitive impairment and evaluate the added predictive capacity of metabolite biomarkers on incident cognitive impairment beyond traditional risk factors. In the Rugao Longevity and Ageing Study (RuLAS), plasma metabolome was profiled by nuclear magnetic resonance spectroscopy. Participants were classified into the cognitively normal, moderately impaired, and severely impaired groups according to their performance in two objective cognitive tests. A two-step strategy of cross-sectional discovery followed by prospective validation was applied. In the discovery stage, we included 1643 participants (age: 78.9 ± 4.5 years) and conducted multinomial logistic regression. In the validation stage, we matched 68 incident cases of cognitive impairment (moderately-to-severely impaired) during the 2-year follow-up with 204 cognitively normal controls by age and sex at a 1:3 ratio, and conducted conditional logistic regression. We identified 28 out of 78 metabolites cross-sectionally related to severely impaired cognition, among which IDL particle number, ApoB in IDL, leucine, and valine were prospectively associated with 28%, 28%, 29%, and 33% lower risk of developing cognitive impairment, respectively. Incorporating 13 metabolite biomarkers selected through Lasso regression into the traditional risk factors-based prediction model substantially improved the ability to predict incident cognitive impairment (AUROC: 0.839 vs. 0.703, P < 0.001; AUPRC: 0.705 vs. 0.405, P < 0.001). This study identified specific plasma metabolites related to cognitive impairment. Incorporation of specific metabolites substantially improved the prediction performance for cognitive impairment.

Optimization of glycopeptide enrichment techniques for the identification of clinical biomarkers

INTRODUCTION

The identification and characterization of glycopeptides through LC-MS/MS and advanced enrichment techniques are crucial for advancing clinical glycoproteomics, significantly impacting the discovery of disease biomarkers and therapeutic targets. Despite progress in enrichment methods like Lectin Affinity Chromatography (LAC), Hydrophilic Interaction Liquid Chromatography (HILIC), and Electrostatic Repulsion Hydrophilic Interaction Chromatography (ERLIC), issues with specificity, efficiency, and scalability remain, impeding thorough analysis of complex glycosylation patterns crucial for disease understanding.

AREAS COVERED

This review explores the current challenges and innovative solutions in glycopeptide enrichment and mass spectrometry analysis, highlighting the importance of novel materials and computational advances for improving sensitivity and specificity. It outlines the potential future directions of these technologies in clinical glycoproteomics, emphasizing their transformative impact on medical diagnostics and therapeutic strategies.

EXPERT OPINION

The application of innovative materials such as Metal-Organic Frameworks (MOFs), Covalent Organic Frameworks (COFs), functional nanomaterials, and online enrichment shows promise in addressing challenges associated with glycoproteomics analysis by providing more selective and robust enrichment platforms. Moreover, the integration of artificial intelligence and machine learning is revolutionizing glycoproteomics by enhancing the processing and interpretation of extensive data from LC-MS/MS, boosting biomarker discovery, and improving predictive accuracy, thus supporting personalized medicine.

Metabolite profiles of distinct obesity phenotypes integrating impacts of altitude and their association with diet and metabolic disorders in Tibetans

OBJECTIVE

Improved understanding of metabolic obesity phenotypes holds great promise for personalized strategies to combat obesity and its co-morbidities. Such investigation is however lacking in Tibetans with unique living environments and lifestyle in the highlands. Effects of altitude on heterogeneous metabolic obesity phenotypes remain unexplored.

METHODS

We defined metabolic obesity phenotypes i.e., metabolically healthy/unhealthy and obesity/normal weight in Tibetans (n = 1204) living at 2800 m in the suburb or over 4000 m in pastoral areas. 129 lipoprotein parameters and 25 low-molecular-weight metabolites were quantified and their associations with each phenotype were assessed using logistic regression models adjusting for potential confounders. The metabolic BMI (mBMI) was generated using a machine learning strategy and its relationship with prevalence of obesity co-morbidities and dietary exposures were investigated.

RESULTS

Ultrahigh altitude positively associated with the metabolically healthy and non-obese phenotype and had a tendency towards a negative association with metabolically unhealthy phenotype. Phenotype-specific associations were found for 107 metabolites (e.g., lipoprotein subclasses, N-acetyl-glycoproteins, amino acids, fatty acids and lactate, p < 0.05), among which 55 were manipulated by altitude. The mBMI showed consistent yet more pronounced associations with cardiometabolic outcomes than BMI. The ORs for diabetes, prediabetes and hypertriglyceridemia were reduced in individuals residing at ultrahigh altitude compared to those residing at high altitude. The mBMI mediated the negative association between pastoral diet and prevalence of prediabetes, hypertension and hypertriglyceridemia, respectively.

CONCLUSIONS

We found metabolite markers representing distinct obesity phenotypes associated with obesity co-morbidities and the modification effect of altitude, deciphering mechanisms underlying protective effect of ultrahigh altitude and the pastoral diet on metabolic health.

STAVER: a standardized benchmark dataset-based algorithm for effective variation reduction in large-scale DIA-MS data

Mass spectrometry (MS)-based proteomics has become instrumental in comprehensively investigating complex biological systems. Data-independent acquisition (DIA)-MS, utilizing hybrid spectral library search strategies, allows for the simultaneous quantification of thousands of proteins, showing promise in enhancing protein identification and quantification precision. However, low-quality profiles can considerably undermine quantitative precision, resulting in inaccurate protein quantification. To tackle this challenge, we introduced STAVER, a novel algorithm that leverages standardized benchmark datasets to reduce non-biological variation in large-scale DIA-MS analyses. By eliminating unwanted noise in MS signals, STAVER significantly improved protein quantification precision, especially in hybrid spectral library searches. Moreover, we validated STAVER's robustness and applicability across multiple large-scale DIA datasets, demonstrating significantly enhanced precision and reproducibility of protein quantification. STAVER offers an innovative and effective approach for enhancing the quality of large-scale DIA proteomic data, facilitating cross-platform and cross-laboratory comparative analyses. This advancement significantly enhances the consistency and reliability of findings in clinical research. The complete package is available at https://github.com/Ran485/STAVER.

Association Between COVID-19 and Neurological Diseases: Evidence from Large-Scale Mendelian Randomization Analysis and Single-Cell RNA Sequencing Analysis

Observational studies have suggested that SARS-CoV-2 infection increases the risk of neurological diseases, but it remains unclear whether the association is causal. The present study aims to evaluate the causal relationships between SARS-CoV-2 infections and neurological diseases and analyzes the potential routes of SARS-CoV-2 entry at the cellular level. We performed Mendelian randomization (MR) analysis with CAUSE method to investigate causal relationship of SARS-CoV-2 infections with neurological diseases. Then, we conducted single-cell RNA sequencing (scRNA-seq) analysis to obtain evidence of potential neuroinvasion routes by measuring SARS-CoV-2 receptor expression in specific cell subtypes. Fast gene set enrichment analysis (fGSEA) was further performed to assess the pathogenesis of related diseases. The results showed that the COVID-19 is causally associated with manic (delta_elpd, - 0.1300, Z-score: - 2.4; P = 0.0082) and epilepsy (delta_elpd: - 2.20, Z-score: - 1.80; P = 0.038). However, no significant effects were observed for COVID-19 on other traits. Moreover, there are 23 cell subtypes identified through the scRNA-seq transcriptomics data of epilepsy, and SARS-CoV-2 receptor TTYH2 was found to be specifically expressed in oligodendrocyte and astrocyte cell subtypes. Furthermore, fGSEA analysis showed that the cell subtypes with receptor-specific expression was related to methylation of lysine 27 on histone H3 (H3K27ME3), neuronal system, aging brain, neurogenesis, and neuron projection. In summary, this study shows causal links between SARS-CoV-2 infections and neurological disorders such as epilepsy and manic, supported by MR and scRNA-seq analysis. These results should be considered in further studies and public health measures on COVID-19 and neurological diseases.

Gut microbial features and circulating metabolomic signatures of frailty in older adults

Frailty, a multidimensional indicator of suboptimal aging, reflects cumulative declines across multiple physiological systems. Although age-related changes have been reported in gut microbiota, their role in healthy aging remains unclear. In this study, we calculated frailty index (FI) from 33 health-related items to reflect the overall health status of 1,821 older adults (62-96 years, 55% female) and conducted multi-omics analysis using gut metagenomic sequencing data and plasma metabolomic data. We identified 18 microbial species and 17 metabolites shifted along with frailty severity, with stronger links observed in females. The associations of nine species, including various Clostridium species and Faecalibacterium prausnitzii, with FI were reproducible in two external populations. Plasma glycerol levels, white blood cell count and kidney function partially mediated these associations. A composite microbial score derived from FI significantly predicted 2-year mortality (adjusted hazard ratio across extreme quartiles, 2.86; 95% confidence interval, 1.38-5.93), highlighting the potential of microbiota-based strategies for risk stratification in older adults.

Mass spectrometric approaches in discovering lipid biomarkers for COVID-19 by lipidomics: Future challenges and perspectives

Coronavirus disease 2019 (COVID-19) has emerged as a global health threat and has rapidly spread worldwide. Significant changes in the lipid profile before and after COVID-19 confirmed the significance of lipid metabolism in regulating the response to viral infection. Therefore, understanding the role of lipid metabolism may facilitate the development of new therapeutics for COVID-19. Owing to their high sensitivity and accuracy, mass spectrometry (MS)-based methods are widely used for rapidly identifying and quantifying of thousands of lipid species present in a small amount of sample. To enhance the capabilities of MS for the qualitative and quantitative analysis of lipids, different platforms have been combined to cover a wide range of lipidomes with high sensitivity, specificity, and accuracy. Currently, MS-based technologies are being established as efficient methods for discovering potential diagnostic biomarkers for COVID-19 and related diseases. As the lipidome of the host cell is drastically affected by the viral replication process, investigating lipid profile alterations in patients with COVID-19 and targeting lipid metabolism pathways are considered to be crucial steps in host-directed drug targeting to develop better therapeutic strategies. This review summarizes various MS-based strategies that have been developed for lipidomic analyzes and biomarker discoveries to combat COVID-19 by integrating various other potential approaches using different human samples. Furthermore, this review discusses the challenges in using MS technologies and future perspectives in terms of drug discovery and diagnosis of COVID-19.

Bioinformatics and molecular biology tools for diagnosis, prevention, treatment and prognosis of COVID-19

Since December 2019, a new form of Severe Acute Respiratory Syndrome (SARS) has emerged worldwide, caused by SARS coronavirus 2 (SARS-CoV-2). This disease was called COVID-19 and was declared a pandemic by the World Health Organization in March 2020. Symptoms can vary from a common cold to severe pneumonia, hypoxemia, respiratory distress, and death. During this period of world stress, the medical and scientific community were able to acquire information and generate scientific data at unprecedented speed, to better understand the disease and facilitate vaccines and therapeutics development. Notably, bioinformatics tools were instrumental in decoding the viral genome and identifying critical targets for COVID-19 diagnosis and therapeutics. Through the integration of omics data, bioinformatics has also improved our understanding of disease pathogenesis and virus-host interactions, facilitating the development of targeted treatments and vaccines. Furthermore, molecular biology techniques have accelerated the design of sensitive diagnostic tests and the characterization of immune responses, paving the way for precision medicine approaches in treating COVID-19. Our analysis highlights the indispensable contributions of bioinformatics and molecular biology to the global effort against COVID-19. In this review, we aim to revise the COVID-19 features, diagnostic, prevention, treatment options, and how molecular biology, modern bioinformatic tools, and collaborations have helped combat this pandemic. An integrative literature review was performed, searching articles on several sites, including PUBMED and Google Scholar indexed in referenced databases, prioritizing articles from the last 3 years. The lessons learned from this COVID-19 pandemic will place the world in a much better position to respond to future pandemics.

Prolonged lymphopenia and prognoses among inpatients with different respiratory virus infections: A retrospective cohort study

Background

Lymphopenia is common in respiratory viral infection. However, no studies elucidated the impact of prolonged lymphopenia on worse outcome in the way of quantitative risk.

Methods

Adult patients with laboratory-confirmed respiratory virus infection (influenza, SARS-CoV-2, and other viruses) between January 1st, 2016, and February 1st, 2023 were enrolled in this retrospective cohort study. Serial data of laboratory examination during hospitalization were acquired. The primary outcome was in-hospital all-cause death, and all information was obtained from the electronic medical records system. Legendre orthogonal polynomials (LOP), restricted cubic splines, and multivariable logistic regression were performed.

Results

Finally, 2388 inpatients were involved in this study, including 436 patients with influenza, 1397 with SARS-CoV-2, and 319 with other respiratory virus infections. After being adjusted for age, corticosteroids, chronic kidney disease, chronic respiratory disease, cardiovascular disease, lymphopenia on admission and length of hospital stay, prolonged lymphopenia was significantly associated with death in influenza (OR 7.20, 95 % CI 2.27-22.77, p = 0. 0008 for lasting for 3-7 days; OR 17.80, 95 % CI 5.21-60.82, p < 0.0001 for lasting for more than 7 days) and SARS-CoV-2 (OR 3.07, 95 % CI 1.89-5.01, p < 0.0001 for lasting for 3-7 days; OR 6.28, 95 % CI 3.53-11.18, p < 0.0001 for lasting for more than 7 days), compared with a transient lymphopenia of 1-2 days, while no significant association was found in other respiratory viruses. Prolonged lymphopenia was also associated with multi-organ damage in influenza and SARS-CoV-2 infections.

Conclusions

Prolonged lymphopenia was significantly associated with worse clinical prognoses in influenza and SARS-CoV-2 infections, but not in other respiratory virus infections.

Restoring T and B cell generation in X-linked severe combined immunodeficiency mice through hematopoietic stem cells adenine base editing

Base editing of hematopoietic stem/progenitor cells (HSPCs) is an attractive strategy for treating immunohematologic diseases. However, the feasibility of using adenine-base-edited HSPCs for treating X-linked severe combined immunodeficiency (SCID-X1), the influence of dose-response relationships on immune cell generation, and the potential risks have not been demonstrated in vivo. Here, a humanized SCID-X1 mouse model was established, and 86.67% ± 2.52% (n = 3) of mouse hematopoietic stem cell (HSC) pathogenic mutations were corrected, with no single-guide-RNA (sgRNA)-dependent off-target effects detected. Analysis of peripheral blood over 16 weeks post-transplantation in mice with different immunodeficiency backgrounds revealed efficient immune cell generation following transplantation of different amounts of modified HSCs. Therefore, a large-scale infusion of gene-corrected HSCs within a safe range can achieve rapid, stable, and durable immune cell regeneration. Tissue-section staining further demonstrated the restoration of immune organ tissue structures, with no tumor formation in multiple organs. Collectively, these data suggest that base-edited HSCs are a potential therapeutic approach for SCID-X1 and that a threshold infusion dose of gene-corrected cells is required for immune cell regeneration. This study lays a theoretical foundation for the clinical application of base-edited HSCs in treating SCID-X1.

Innate Immunity in Protection and Pathogenesis During Coronavirus Infections and COVID-19

The COVID-19 pandemic was caused by the recently emerged β-coronavirus SARS-CoV-2. SARS-CoV-2 has had a catastrophic impact, resulting in nearly 7 million fatalities worldwide to date. The innate immune system is the first line of defense against infections, including the detection and response to SARS-CoV-2. Here, we discuss the innate immune mechanisms that sense coronaviruses, with a focus on SARS-CoV-2 infection and how these protective responses can become detrimental in severe cases of COVID-19, contributing to cytokine storm, inflammation, long-COVID, and other complications. We also highlight the complex cross talk among cytokines and the cellular components of the innate immune system, which can aid in viral clearance but also contribute to inflammatory cell death, cytokine storm, and organ damage in severe COVID-19 pathogenesis. Furthermore, we discuss how SARS-CoV-2 evades key protective innate immune mechanisms to enhance its virulence and pathogenicity, as well as how innate immunity can be therapeutically targeted as part of the vaccination and treatment strategy. Overall, we highlight how a comprehensive understanding of innate immune mechanisms has been crucial in the fight against SARS-CoV-2 infections and the development of novel host-directed immunotherapeutic strategies for various diseases.

Hussein S, Abdalhabib EK, Nabi SU. The critical impacts of cytokine storms in respiratory disorders

Cytokine storm (CS) refers to the spontaneous dysregulated and hyper-activated inflammatory reaction occurring in various clinical conditions, ranging from microbial infection to end-stage organ failure. Recently the novel coronavirus involved in COVID-19 (Coronavirus disease-19) caused by SARS-CoV-2 (Severe Acute Respiratory Syndrome Coronavirus 2) has been associated with the pathological phenomenon of CS in critically ill patients. Furthermore, critically ill patients suffering from CS are likely to have a grave prognosis and a higher case fatality rate. Pathologically CS is manifested as hyper-immune activation and is clinically manifested as multiple organ failure. An in-depth understanding of the etiology of CS will enable the discovery of not just disease risk factors of CS but also therapeutic approaches to modulate the immune response and improve outcomes in patients with respiratory diseases having CS in the pathogenic pathway. Owing to the grave consequences of CS in various diseases, this phenomenon has attracted the attention of researchers and clinicians throughout the globe. So in the present manuscript, we have attempted to discuss CS and its ramifications in COVID-19 and other respiratory diseases, as well as prospective treatment approaches and biomarkers of the cytokine storm. Furthermore, we have attempted to provide in-depth insight into CS from both a prophylactic and therapeutic point of view. In addition, we have included recent findings of CS in respiratory diseases reported from different parts of the world, which are based on expert opinion, clinical case-control research, experimental research, and a case-controlled cohort approach.

Plasma proteome profiling reveals dynamic of cholesterol marker after dual blocker therapy

Dual blocker therapy (DBT) has the enhanced antitumor benefits than the monotherapy. Yet, few effective biomarkers are developed to monitor the therapy response. Herein, we investigate the DBT longitudinal plasma proteome profiling including 113 longitudinal samples from 22 patients who received anti-PD1 and anti-CTLA4 DBT therapy. The results show the immune response and cholesterol metabolism are upregulated after the first DBT cycle. Notably, the cholesterol metabolism is activated in the disease non-progressive group (DNP) during the therapy. Correspondingly, the clinical indicator prealbumin (PA), free triiodothyronine (FT3) and triiodothyronine (T3) show significantly positive association with the cholesterol metabolism. Furthermore, by integrating proteome and radiology approach, we observe the high-density lipoprotein partial remodeling are activated in DNP group and identify a candidate biomarker APOC3 that can reflect DBT response. Above, we establish a machine learning model to predict the DBT response and the model performance is validated by an independent cohort with balanced accuracy is 0.96. Thus, the plasma proteome profiling strategy evaluates the alteration of cholesterol metabolism and identifies a panel of biomarkers in DBT.

Presepsin as a prognostic biomarker in COVID-19 patients: combining clinical scoring systems and laboratory inflammatory markers for outcome prediction

BACKGROUND

There is still limited research on the prognostic value of Presepsin as a biomarker for predicting the outcome of COVID-19 patients. Additionally, research on the combined predictive value of Presepsin with clinical scoring systems and inflammation markers for disease prognosis is lacking.

METHODS

A total of 226 COVID-19 patients admitted to Beijing Youan Hospital's emergency department from May to November 2022 were screened. Demographic information, laboratory measurements, and blood samples for Presepsin levels were collected upon admission. The predictive value of Presepsin, clinical scoring systems, and inflammation markers for 28-day mortality was analyzed.

RESULTS

A total of 190 patients were analyzed, 83 (43.7%) were mild, 61 (32.1%) were moderate, and 46 (24.2%) were severe/critically ill. 23 (12.1%) patients died within 28 days. The Presepsin levels in severe/critical patients were significantly higher compared to moderate and mild patients (p < 0.001). Presepsin showed significant predictive value for 28-day mortality in COVID-19 patients, with an area under the ROC curve of 0.828 (95% CI: 0.737-0.920). Clinical scoring systems and inflammation markers also played a significant role in predicting 28-day outcomes. After Cox regression adjustment, Presepsin, qSOFA, NEWS2, PSI, CURB-65, CRP, NLR, CAR, and LCR were identified as independent predictors of 28-day mortality in COVID-19 patients (all p-values < 0.05). Combining Presepsin with clinical scoring systems and inflammation markers further enhanced the predictive value for patient prognosis.

CONCLUSION

Presepsin is a favorable indicator for the prognosis of COVID-19 patients, and its combination with clinical scoring systems and inflammation markers improved prognostic assessment.

Multi-omics analysis reveals COVID-19 vaccine induced attenuation of inflammatory responses during breakthrough disease

The immune mechanisms mediating COVID-19 vaccine attenuation of COVID-19 remain undescribed. We conducted comprehensive analyses detailing immune responses to SARS-CoV-2 virus in blood post-vaccination with ChAdOx1 nCoV-19 or a placebo. Samples from randomised placebo-controlled trials (NCT04324606 and NCT04400838) were taken at baseline, onset of COVID-19-like symptoms, and 7 days later, confirming COVID-19 using nucleic amplification test (NAAT test) via real-time PCR (RT-PCR). Serum cytokines were measured with multiplexed immunoassays. The transcriptome was analysed with long, short and small RNA sequencing. We found attenuation of RNA inflammatory signatures in ChAdOx1 nCoV-19 compared with placebo vaccinees and reduced levels of serum proteins associated with COVID-19 severity. KREMEN1, a putative alternative SARS-CoV-2 receptor, was downregulated in placebo compared with ChAdOx1 nCoV-19 vaccinees. Vaccination ameliorates reductions in cell counts across leukocyte populations and platelets noted at COVID-19 onset, without inducing potentially deleterious Th2-skewed immune responses. Multi-omics integration links a global reduction in miRNA expression at COVID-19 onset to increased pro-inflammatory responses at the mRNA level. This study reveals insights into the role of COVID-19 vaccines in mitigating disease severity by abrogating pro-inflammatory responses associated with severe COVID-19, affirming vaccine-mediated benefit in breakthrough infection, and highlighting the importance of clinically relevant endpoints in vaccine evaluation.

Pathophysiological, immunological, and inflammatory features of long COVID

The COVID-19 pandemic continues to cause severe global disruption, resulting in significant excess mortality, overwhelming healthcare systems, and imposing substantial social and economic burdens on nations. While most of the attention and therapeutic efforts have concentrated on the acute phase of the disease, a notable proportion of survivors experience persistent symptoms post-infection clearance. This diverse set of symptoms, loosely categorized as long COVID, presents a potential additional public health crisis. It is estimated that 1 in 5 COVID-19 survivors exhibit clinical manifestations consistent with long COVID. Despite this prevalence, the mechanisms and pathophysiology of long COVID remain poorly understood. Alarmingly, evidence suggests that a significant proportion of cases within this clinical condition develop debilitating or disabling symptoms. Hence, urgent priority should be given to further studies on this condition to equip global public health systems for its management. This review provides an overview of available information on this emerging clinical condition, focusing on the affected individuals' epidemiology, pathophysiological mechanisms, and immunological and inflammatory profiles.

Meta-analysis of the serum/plasma proteome identifies significant associations between COVID-19 with Alzheimer's/Parkinson's diseases

In recent years, we have seen the widespread devastations and serious health complications manifested by COVID-19 globally. Although we have effectively controlled the pandemic, uncertainties persist regarding its potential long-term effects, including prolonged neurological issues. To gain comprehensive insights, we conducted a meta-analysis of mass spectrometry-based proteomics data retrieved from different studies with a total of 538 COVID-19 patients and 523 healthy controls. The meta-analysis revealed that top-enriched pathways were associated with neurological disorders, including Alzheimer's (AD) and Parkinson's disease (PD). Further analysis confirmed a direct correlation in the expression patterns of 24 proteins involved in Alzheimer's and 23 proteins in Parkinson's disease with COVID-19. Protein-protein interaction network and cluster analysis identified SNCA as a hub protein, a known biomarker for Parkinson's disease, in both AD and PD. To the best of our knowledge, this is the first meta-analysis study providing proteomic profiling evidence linking COVID-19 to neurological complications.

A novel endogenous retention-index for minimizing retention-time variations in metabolomic analysis with reversed-phase ultrahigh-performance liquid-chromatography and mass spectrometry

Consistent retention time (tR) of metabolites is vital for identification in metabolomic analysis with ultrahigh-performance liquid-chromatography (UPLC). To minimize inter-experimental tR variations from the reversed-phase UPLC-MS, we developed an endogenous retention-index (endoRI) using in-sample straight-chain acylcarnitines with different chain-length (LC, C0-C26) without additives. The endoRI-corrections reduced the tR variations caused by the combined changes of mobile phases, gradients, flow-rates, elution time, columns and temperature from up to 5.1 min-0.2 min for most metabolites in a model metabolome consisting of 91 metabolites and multiple biological matrices including human serum, plasma, fecal, urine, A549 cells and rabbit liver extracts. The endoRI-corrections also reduced the inter-batch and inter-platform tR variations from 1.5 min to 0.15 min for 95 % of detected features in the above biological samples. We further established a quantitative model between tR and LC for predicting tR values of acylcarnitines when absent in samples. This makes it possible to compare metabolites' tR from different tR databases and the UPLC-based metabolomic data from different batches.

Longitudinal plasma proteome profiling reveals the diversity of biomarkers for diagnosis and cetuximab therapy response of colorectal cancer

Cetuximab therapy is the major treatment for colorectal cancer (CRC), but drug resistance limits its effectiveness. Here, we perform longitudinal and deep proteomic profiling of 641 plasma samples originated from 147 CRC patients (CRCs) undergoing cetuximab therapy with multi-course treatment, and 90 healthy controls (HCs). COL12A1, THBS2, S100A8, and S100A9 are screened as potential proteins to distinguish CRCs from HCs both in plasma and tissue validation cohorts. We identify the potential biomarkers (RRAS2, MMP8, FBLN1, RPTOR, and IMPDH2) for the initial response prediction. In a longitudinal setting, we identify two clusters with distinct fluctuations and construct the model with high accuracy to predict the longitudinal response, further validated in the independent cohort. This study reveals the heterogeneity of different biomarkers for tumor diagnosis, the initial and longitudinal response prediction respectively in the first course and multi-course cetuximab treatment, may ultimately be useful in monitoring and intervention strategies for CRC.

Gene expression profiling of host lipid metabolism in SARS-CoV-2 infected patients: a systematic review and integrated bioinformatics analysis

BACKGROUND

The Coronavirus disease 2019 (COVID-19) pandemic occurred due to the dispersion of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Severe symptoms can be observed in COVID-19 patients with lipid-related comorbidities such as obesity and diabetes. Yet, the extensive molecular mechanisms of how SARS-CoV-2 causes dysregulation of lipid metabolism remain unknown.

METHODS

Here, an advanced search of articles was conducted using PubMed, Scopus, EBSCOhost, and Web of Science databases using terms from Medical Subject Heading (MeSH) like SARS-CoV-2, lipid metabolism and transcriptomic as the keywords. From 428 retrieved studies, only clinical studies using next-generation sequencing as a gene expression method in COVID-19 patients were accepted. Study design, study population, sample type, the method for gene expression and differentially expressed genes (DEGs) were extracted from the five included studies. The DEGs obtained from the studies were pooled and analyzed using the bioinformatics software package, DAVID, to determine the enriched pathways. The DEGs involved in lipid metabolic pathways were selected and further analyzed using STRING and Cytoscape through visualization by protein-protein interaction (PPI) network complex.

RESULTS

The analysis identified nine remarkable clusters from the PPI complex, where cluster 1 showed the highest molecular interaction score. Three potential candidate genes (PPARG, IFITM3 and APOBEC3G) were pointed out from the integrated bioinformatics analysis in this systematic review and were chosen due to their significant role in regulating lipid metabolism. These candidate genes were significantly involved in enriched lipid metabolic pathways, mainly in regulating lipid homeostasis affecting the pathogenicity of SARS-CoV-2, specifically in mechanisms of viral entry and viral replication in COVID-19 patients.

CONCLUSIONS

Taken together, our findings in this systematic review highlight the affected lipid-metabolic pathways along with the affected genes upon SARS-CoV-2 invasion, which could be a potential target for new therapeutic strategies study in the future.

Rapid quantification of 50 fatty acids in small amounts of biological samples for population molecular phenotyping

Efficient quantification of fatty-acid (FA) composition (fatty-acidome) in biological samples is crucial for understanding physiology and pathophysiology in large population cohorts. Here, we report a rapid GC-FID/MS method for simultaneous quantification of all FAs in numerous biological matrices. Within eight minutes, this method enabled simultaneous quantification of 50 FAs as fatty-acid methyl esters (FAMEs) in femtomole levels following the efficient transformation of FAs in all lipids including FFAs, cholesterol-esters, glycerides, phospholipids and sphingolipids. The method showed satisfactory inter-day and intra-day precision, stability and linearity (R2 > 0.994) within a concentration range of 2-3 orders of magnitude. FAs were then quantified in typical multiple biological matrices including human biofluids (urine, plasma) and cells, animal intestinal content and tissue samples. We also established a quantitative structure-retention relationship (QSRR) for analytes to accurately predict their retention time and aid their reliable identification. We further developed a novel no-additive retention index (NARI) with endogenous FAMEs reducing inter-batch variations to 15 seconds; such NARI performed better than the alkanes-based classical RI, making meta-analysis possible for data obtained from different batches and platforms. Collectively, this provides an inexpensive high-throughput analytical system for quantitative phenotyping of all FAs in 8-minutes multiple biological matrices in large cohort studies of pathophysiological effects.

Multi-omics Approach in Kidney Transplant: Lessons Learned from COVID-19 Pandemic

Purpose of Review

Multi-omics approach has advanced our knowledge on transplantation-associated clinical outcomes, such as acute rejection and infection, and emerging omics data are becoming available in kidney transplant and COVID-19. Herein, we discuss updated findings of multi-omics data on kidney transplant outcomes, as well as COVID-19 and kidney transplant.

Recent Findings

Transcriptomics, proteomics, and metabolomics revealed various inflammation pathways associated with kidney transplantation-related outcomes and COVID-19. Although multi-omics data on kidney transplant and COVID-19 is limited, activation of innate immune pathways and suppression of adaptive immune pathways were observed in the active phase of COVID-19 in kidney transplant recipients.

Summary

Multi-omics analysis has led us to a deeper exploration and a more comprehensive understanding of key biological pathways in complex clinical settings, such as kidney transplantation and COVID-19. Future multi-omics analysis leveraging multi-center biobank collaborative will further advance our knowledge on the precise immunological responses to allograft and emerging pathogens.

MetaboliteCOVID: A manually curated database of metabolite markers for COVID-19

In the realm of unraveling COVID-19's intricacies, numerous metabolomic investigations have been conducted to discern the unique metabolic traits exhibited within infected patients. These endeavors have yielded a substantial reservoir of potential data pertaining to metabolic biomarkers linked to the virus. Despite these strides, a comprehensive and meticulously structured database housing these crucial biomarkers remains absent. In this study, we developed MetaboliteCOVID, a manually curated database of COVID-19-related metabolite markers. The database currently comprises 665 manually selected entries of significantly altered metabolites associated with early diagnosis, disease severity, prognosis, and drug response in COVID-19, encompassing 337 metabolites. Additionally, the database offers a user-friendly interface, containing abundant information for querying, browsing, and analyzing COVID-19-related abnormal metabolites in different body fluids. In summary, we believe that this database will effectively facilitate research on the functions and mechanisms of COVID-19-related metabolic biomarkers, thereby advancing both basic and clinical research on COVID-19. MetaboliteCOVID is free available at: https://cellknowledge.com.cn/MetaboliteCOVID.

SARS-CoV-2 envelope protein induces necroptosis and mediates inflammatory response in lung and colon cells through receptor interacting protein kinase 1

SARS-CoV-2 Envelope protein (E) is one of the crucial components in virus assembly and pathogenesis. The current study investigated its role in the SARS-CoV-2-mediated cell death and inflammation in lung and gastrointestinal epithelium and its effect on the gastrointestinal-lung axis. We observed that transfection of E protein increases the lysosomal pH and induces inflammation in the cell. The study utilizing Ethidium bromide/Acridine orange and Hoechst/Propidium iodide staining demonstrated necrotic cell death in E protein transfected cells. Our study revealed the role of the necroptotic marker RIPK1 in cell death. Additionally, inhibition of RIPK1 by its specific inhibitor Nec-1s exhibits recovery from cell death and inflammation manifested by reduced phosphorylation of NFκB. The E-transfected cells' conditioned media induced inflammation with differential expression of inflammatory markers compared to direct transfection in the gastrointestinal-lung axis. In conclusion, SARS-CoV-2 E mediates inflammation and necroptosis through RIPK1, and the E-expressing cells' secretion can modulate the gastrointestinal-lung axis. Based on the data of the present study, we believe that during severe COVID-19, necroptosis is an alternate mechanism of cell death besides ferroptosis, especially when the disease is not associated with drastic increase in serum ferritin.

Phenome-wide association study and precision medicine of cardiovascular diseases in the post-COVID-19 era

SARS-CoV-2 infection causes injuries of not only the lungs but also the heart and endothelial cells in vasculature of multiple organs, and induces systemic inflammation and immune over-reactions, which makes COVID-19 a disease phenome that simultaneously affects multiple systems. Cardiovascular diseases (CVD) are intrinsic risk and causative factors for severe COVID-19 comorbidities and death. The wide-spread infection and reinfection of SARS-CoV-2 variants and the long-COVID may become a new common threat to human health and propose unprecedented impact on the risk factors, pathophysiology, and pharmacology of many diseases including CVD for a long time. COVID-19 has highlighted the urgent demand for precision medicine which needs new knowledge network to innovate disease taxonomy for more precise diagnosis, therapy, and prevention of disease. A deeper understanding of CVD in the setting of COVID-19 phenome requires a paradigm shift from the current phenotypic study that focuses on the virus or individual symptoms to phenomics of COVID-19 that addresses the inter-connectedness of clinical phenotypes, i.e., clinical phenome. Here, we summarize the CVD manifestations in the full clinical spectrum of COVID-19, and the phenome-wide association study of CVD interrelated to COVID-19. We discuss the underlying biology for CVD in the COVID-19 phenome and the concept of precision medicine with new phenomic taxonomy that addresses the overall pathophysiological responses of the body to the SARS-CoV-2 infection. We also briefly discuss the unique taxonomy of disease as Zheng-hou patterns in traditional Chinese medicine, and their potential implications in precision medicine of CVD in the post-COVID-19 era.

Integrative proteomics and metabolomics study reveal enhanced immune responses by COVID-19 vaccine booster shot against Omicron SARS-CoV-2 infection

Since its outbreak in late 2021, the Omicron variant of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has been widely reported to be able to evade neutralizing antibodies, becoming more transmissible while causing milder symptoms than previous SARS-CoV-2 strains. Understanding the underlying molecular changes of Omicron SARS-CoV-2 infection and corresponding host responses are important to the control of Omicron COVID-19 pandemic. In this study, we report an integrative proteomics and metabolomics investigation of serum samples from 80 COVID-19 patients infected with Omicron SARS-CoV-2, as well as 160 control serum samples from 80 healthy individuals and 80 patients who had flu-like symptoms but were negative for SARS-CoV-2 infection. The multiomics results indicated that Omicron SARS-CoV-2 infection caused significant changes to host serum proteome and metabolome comparing to the healthy controls and patients who had flu-like symptoms without COVID-19. Protein and metabolite changes also pointed to liver dysfunctions and potential damage to other host organs by Omicron SARS-CoV-2 infection. The Omicron COVID-19 patients could be roughly divided into two subgroups based on their proteome differences. Interestingly, the subgroup who mostly had received full vaccination with booster shot had fewer coughing symptom, changed sphingomyelin lipid metabolism, and stronger immune responses including higher numbers of lymphocytes, monocytes, neutrophils, and upregulated proteins related to CD4+ T cells, CD8+ effector memory T cells (Tem), and conventional dendritic cells, revealing beneficial effects of full COVID-19 vaccination against Omicron SARS-CoV-2 infection through molecular changes.

COVID-19 and cholesterol biosynthesis: Towards innovative decision support systems

With COVID-19 becoming endemic, there is a continuing need to find biomarkers characterizing the disease and aiding in patient stratification. We studied the relation between COVID-19 and cholesterol biosynthesis by comparing 10 intermediates of cholesterol biosynthesis during the hospitalization of 164 patients (admission, disease deterioration, discharge) admitted to the University Medical Center of Ljubljana. The concentrations of zymosterol, 24-dehydrolathosterol, desmosterol, and zymostenol were significantly altered in COVID-19 patients. We further developed a predictive model for disease severity based on clinical parameters alone and their combination with a subset of sterols. Our machine learning models applying 8 clinical parameters predicted disease severity with excellent accuracy (AUC = 0.96), showing substantial improvement over current clinical risk scores. After including sterols, model performance remained better than COVID-GRAM. This is the first study to examine cholesterol biosynthesis during COVID-19 and shows that a subset of cholesterol-related sterols is associated with the severity of COVID-19.

Role of OGDH in Atophagy-IRF3-IFN-β pathway during classical swine fever virus infection

Classical swine fever (CSF) is a severe infectious disease caused by the classical swine fever virus (CSFV) that poses significant challenges to the swine industry. α-ketoglutarate dehydrogenase (OGDH), the first rate-limiting enzyme of the tricarboxylic acid (TCA) cycle, catalyzes α-ketoglutarate (α-KG) to succinyl-CoA, playing a crucial role in glycometabolism. Our previous studies showed that CSFV disrupts the TCA cycle, resulting in α-KG accumulation. However, the interplay between CSFV and OGDH remains unclear. In this study, we found that CSFV significantly reduces OGDH protein levels and promotes α-KG secretion through OGDH in PK-15 cells. Furthermore, we observed CSFV C protein interacts with OGDH and revealed that CSFV utilizes NDP52/NBR1 to target OGDH protein degradation in the autophagy-lysosome pathway. We also unveiled that OGDH overexpression inhibits CSFV proliferation, whereas OGDH knockdown increases CSFV proliferation. Further investigation into the mechanisms of OGDH on CSFV replication revealed that OGDH regulates the AMPK-mTOR-autophagy pathway. Additionally, using the autophagy agonist/inhibitor, rapamycin/3-MA, we observed that OGDH modulates autophagy to regulate the IRF3-IFN-β network and CSFV replication. These findings shed light on the role of OGDH in CSFV infection and host metabolism, promoting the development of innovative strategies for combating CSFV and other viral infections via targeting metabolic pathways.

Metabolic crosstalk between skeletal muscle cells and liver through IRF4-FSTL1 in nonalcoholic steatohepatitis

Inter-organ crosstalk has gained increasing attention in recent times; however, the underlying mechanisms remain unclear. In this study, we elucidate an endocrine pathway that is regulated by skeletal muscle interferon regulatory factor (IRF) 4, which manipulates liver pathology. Skeletal muscle specific IRF4 knockout (F4MKO) mice exhibited ameliorated hepatic steatosis, inflammation, and fibrosis, without changes in body weight, when put on a nonalcoholic steatohepatitis (NASH) diet. Proteomics analysis results suggested that follistatin-like protein 1 (FSTL1) may constitute a link between muscles and the liver. Dual luciferase assays showed that IRF4 can transcriptionally regulate FSTL1. Further, inducing FSTL1 expression in the muscles of F4MKO mice is sufficient to restore liver pathology. In addition, co-culture experiments confirmed that FSTL1 plays a distinct role in various liver cell types via different receptors. Finally, we observed that the serum FSTL1 level is positively correlated with NASH progression in humans. These data indicate a signaling pathway involving IRF4-FSTL1-DIP2A/CD14, that links skeletal muscle cells to the liver in the pathogenesis of NASH.

Comprehensive proteomics and meta-analysis of COVID-19 host response

COVID-19 is characterised by systemic immunological perturbations in the human body, which can lead to multi-organ damage. Many of these processes are considered to be mediated by the blood. Therefore, to better understand the systemic host response to SARS-CoV-2 infection, we performed systematic analyses of the circulating, soluble proteins in the blood through global proteomics by mass-spectrometry (MS) proteomics. Here, we show that a large part of the soluble blood proteome is altered in COVID-19, among them elevated levels of interferon-induced and proteasomal proteins. Some proteins that have alternating levels in human cells after a SARS-CoV-2 infection in vitro and in different organs of COVID-19 patients are deregulated in the blood, suggesting shared infection-related changes.The availability of different public proteomic resources on soluble blood proteome alterations leaves uncertainty about the change of a given protein during COVID-19. Hence, we performed a systematic review and meta-analysis of MS global proteomics studies of soluble blood proteomes, including up to 1706 individuals (1039 COVID-19 patients), to provide concluding estimates for the alteration of 1517 soluble blood proteins in COVID-19. Finally, based on the meta-analysis we developed CoViMAPP, an open-access resource for effect sizes of alterations and diagnostic potential of soluble blood proteins in COVID-19, which is publicly available for the research, clinical, and academic community.

Meta-Analysis of COVID-19 Metabolomics Identifies Variations in Robustness of Biomarkers

The global COVID-19 pandemic resulted in widespread harms but also rapid advances in vaccine development, diagnostic testing, and treatment. As the disease moves to endemic status, the need to identify characteristic biomarkers of the disease for diagnostics or therapeutics has lessened, but lessons can still be learned to inform biomarker research in dealing with future pathogens. In this work, we test five sets of research-derived biomarkers against an independent targeted and quantitative Liquid Chromatography-Mass Spectrometry metabolomics dataset to evaluate how robustly these proposed panels would distinguish between COVID-19-positive and negative patients in a hospital setting. We further evaluate a crowdsourced panel comprising the COVID-19 metabolomics biomarkers most commonly mentioned in the literature between 2020 and 2023. The best-performing panel in the independent dataset-measured by F1 score (0.76) and AUROC (0.77)-included nine biomarkers: lactic acid, glutamate, aspartate, phenylalanine, β-alanine, ornithine, arachidonic acid, choline, and hypoxanthine. Panels comprising fewer metabolites performed less well, showing weaker statistical significance in the independent cohort than originally reported in their respective discovery studies. Whilst the studies reviewed here were small and may be subject to confounders, it is desirable that biomarker panels be resilient across cohorts if they are to find use in the clinic, highlighting the importance of assessing the robustness and reproducibility of metabolomics analyses in independent populations.

Plasma proteomic profiling discovers molecular features associated with upper tract urothelial carcinoma

Upper tract urothelial carcinoma (UTUC) is often diagnosed late and exhibits poor prognosis. Limited data are available on potential non-invasive biomarkers for disease monitoring. Here, we investigate the proteomic profile of plasma in 362 UTUC patients and 239 healthy controls. We present an integrated tissue-plasma proteomic approach to infer the signature proteins for identifying patients with muscle-invasive UTUC. We discover a protein panel that reflects lymph node metastasis, which is of interest in identifying UTUC patients with high risk and poor prognosis. We also identify a ten-protein classifier and establish a progression clock predicting progression-free survival of UTUC patients. Finally, we further validate the signature proteins by parallel reaction monitoring assay in an independent cohort. Collectively, this study portrays the plasma proteomic landscape of a UTUC cohort and provides a valuable resource for further biological and diagnostic research in UTUC.

Differential abundance of lipids and metabolites related to SARS-CoV-2 infection and susceptibility

The mechanisms driving SARS-CoV-2 susceptibility remain poorly understood, especially the factors determining why unvaccinated individuals remain uninfected despite high-risk exposures. To understand lipid and metabolite profiles related with COVID-19 susceptibility and disease progression. We collected samples from an exceptional group of unvaccinated healthcare workers heavily exposed to SARS-CoV-2 but not infected ('non-susceptible') and subjects who became infected during the follow-up ('susceptible'), including non-hospitalized and hospitalized patients with different disease severity providing samples at early disease stages. Then, we analyzed their plasma metabolomic profiles using mass spectrometry coupled with liquid and gas chromatography. We show specific lipids profiles and metabolites that could explain SARS-CoV-2 susceptibility and COVID-19 severity. More importantly, non-susceptible individuals show a unique lipidomic pattern characterized by the upregulation of most lipids, especially ceramides and sphingomyelin, which could be interpreted as markers of low susceptibility to SARS-CoV-2 infection. This study strengthens the findings of other researchers about the importance of studying lipid profiles as relevant markers of SARS-CoV-2 pathogenesis.

Development and validation of a predicted nomogram for mortality of COVID-19: a multicenter retrospective cohort study of 4,711 cases in multiethnic

Background

Coronavirus disease 2019 (COVID-19) is an infectious disease spreading rapidly worldwide. As it quickly spreads and can cause severe disease, early detection and treatment may reduce mortality. Therefore, the study aims to construct a risk model and a nomogram for predicting the mortality of COVID-19.

Methods

The original data of this study were from the article "Neurologic Syndromes Predict Higher In-Hospital Mortality in COVID-19." The database contained 4,711 multiethnic patients. In this secondary analysis, a statistical difference test was conducted for clinical demographics, clinical characteristics, and laboratory indexes. The least absolute shrinkage and selection operator (LASSO) and multivariate logistic regression analysis were applied to determine the independent predictors for the mortality of COVID-19. A nomogram was conducted and validated according to the independent predictors. The area under the curve (AUC), the calibration curve, and the decision curve analysis (DCA) were carried out to evaluate the nomogram.

Results

The mortality of COVID-19 is 24.4%. LASSO and multivariate logistic regression analysis suggested that risk factors for age, PCT, glucose, D-dimer, CRP, troponin, BUN, LOS, MAP, AST, temperature, O2Sats, platelets, Asian, and stroke were independent predictors of CTO. Using these independent predictors, a nomogram was constructed with good discrimination (0.860 in the C index) and internal validation (0.8479 in the C index), respectively. The calibration curves and the DCA showed a high degree of reliability and precision for this clinical prediction model.

Conclusion

An early warning model based on accessible variates from routine clinical tests to predict the mortality of COVID-19 were conducted. This nomogram can be conveniently used to facilitate identifying patients who might develop severe disease at an early stage of COVID-19. Further studies are warranted to validate the prognostic ability of the nomogram.

Identification of potential biomarkers to predict organ morbidity in COVID-19: A repository based proteomics perspective

SARS-CoV-2 causes substantial extrapulmonary manifestations in addition to pulmonary disease. Some of the major organs affected are cardiovascular, hematological and thrombotic, renal, neurological, and digestive systems. These types of muti-organ dysfunctions make it difficult and challenging for clinicians to manage and treat COVID-19 patients. The article focuses to identify potential protein biomarkers that can flag various organ systems affected in COVID-19. Publicly reposited high throughput proteomic data from human serum (HS), HEK293T/17 (HEK) and Vero E6 (VE) kidney cell culture were downloaded from ProteomeXchange consortium. The raw data was analyzed in Proteome Discoverer 2.4 to delineate the complete list of proteins in the three studies. These proteins were analyzed in Ingenuity Pathway Analysis (IPA) to associate them to various organ diseases. The shortlisted proteins were analyzed in MetaboAnalyst 5.0 to shortlist potential biomarker proteins. These were then assessed for disease-gene association in DisGeNET and validated by Protein-protein interactome (PPI) and functional enrichment studies (GO_BP, KEGG and Reactome pathways) in STRING. Protein profiling resulted in shortlisting 20 proteins in 7 organ systems. Of these 15 proteins showed at least 1.25-fold changes with a sensitivity and specificity of 70%. Association analysis further shortlisted 10 proteins with a potential association with 4 organ diseases. Validation studies established possible interacting networks and pathways affected, confirmingh the ability of 6 of these proteins to flag 4 different organ systems affected in COVID-19 disease. This study helps to establish a platform to seek protein signatures in different clinical phenotypes of COVID-19. The potential biomarker candidates that can flag organ systems involved are: (a) Vitamin K-dependent protein S and Antithrombin-III for hematological disorders; (b) Voltage-dependent anion-selective channel protein 1 for neurological disorders; (c) Filamin-A for cardiovascular disorder and, (d) Peptidyl-prolyl cis-trans isomerase A and Peptidyl-prolyl cis-trans isomerase FKBP1A for digestive disorders.

Integration of Omics Data and Network Models to Unveil Negative Aspects of SARS-CoV-2, from Pathogenic Mechanisms to Drug Repurposing

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) caused the COVID-19 health emergency, affecting and killing millions of people worldwide. Following SARS-CoV-2 infection, COVID-19 patients show a spectrum of symptoms ranging from asymptomatic to very severe manifestations. In particular, bronchial and pulmonary cells, involved at the initial stage, trigger a hyper-inflammation phase, damaging a wide range of organs, including the heart, brain, liver, intestine and kidney. Due to the urgent need for solutions to limit the virus' spread, most efforts were initially devoted to mapping outbreak trajectories and variant emergence, as well as to the rapid search for effective therapeutic strategies. Samples collected from hospitalized or dead COVID-19 patients from the early stages of pandemic have been analyzed over time, and to date they still represent an invaluable source of information to shed light on the molecular mechanisms underlying the organ/tissue damage, the knowledge of which could offer new opportunities for diagnostics and therapeutic designs. For these purposes, in combination with clinical data, omics profiles and network models play a key role providing a holistic view of the pathways, processes and functions most affected by viral infection. In fact, in addition to epidemiological purposes, networks are being increasingly adopted for the integration of multiomics data, and recently their use has expanded to the identification of drug targets or the repositioning of existing drugs. These topics will be covered here by exploring the landscape of SARS-CoV-2 survey-based studies using systems biology approaches derived from omics data, paying particular attention to those that have considered samples of human origin.

The Effects of Hospitalisation on the Serum Metabolome in COVID-19 Patients

COVID-19, a systemic multi-organ disease resulting from infection with severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), is known to result in a wide array of disease outcomes, ranging from asymptomatic to fatal. Despite persistent progress, there is a continued need for more accurate determinants of disease outcomes, including post-acute symptoms after COVID-19. In this study, we characterised the serum metabolomic changes due to hospitalisation and COVID-19 disease progression by mapping the serum metabolomic trajectories of 71 newly hospitalised moderate and severe patients in their first week after hospitalisation. These 71 patients were spread out over three hospitals in Switzerland, enabling us to meta-analyse the metabolomic trajectories and filter consistently changing metabolites. Additionally, we investigated differential metabolite-metabolite trajectories between fatal, severe, and moderate disease outcomes to find prognostic markers of disease severity. We found drastic changes in serum metabolite concentrations for 448 out of the 901 metabolites. These results included markers of hospitalisation, such as environmental exposures, dietary changes, and altered drug administration, but also possible markers of physiological functioning, including carboxyethyl-GABA and fibrinopeptides, which might be prognostic for worsening lung injury. Possible markers of disease progression included altered urea cycle metabolites and metabolites of the tricarboxylic acid (TCA) cycle, indicating a SARS-CoV-2-induced reprogramming of the host metabolism. Glycerophosphorylcholine was identified as a potential marker of disease severity. Taken together, this study describes the metabolome-wide changes due to hospitalisation and COVID-19 disease progression. Moreover, we propose a wide range of novel potential biomarkers for monitoring COVID-19 disease course, both dependent and independent of the severity.

Multisite and multitimepoint proteomics reveal that patent foramen ovale closure improves migraine and epilepsy by reducing right-to-left shunt-induced hypoxia

Patent foramen ovale (PFO) is a congenital defect in the partition between two atria, which may cause right-to-left shunt (RLS), leading to neurological chronic diseases with episodic manifestations (NCDEMs), such as migraine and epilepsy. However, whether PFO closure was effective in improving NCDEMs and the mechanism were unclear. Twenty-eight patients with migraine or epilepsy who underwent PFO closure were recruited. Notably, approximately half of patients received 50% or more reduction in seizure or headache attacks. Meanwhile, the postoperative blood oxygen partial pressure and oxygen saturation were elevated after PFO closure. Multisite (peripheral, right, and left atrial) and multitimepoint (before and after surgery) plasma proteomics from patients showed that the levels of free hemoglobin and cell adhesion molecules (CAMs) were significantly increased after PFO closure, which may be related to the relief of the hypoxic state. Furtherly, the omics data from multiple brain regions of mice revealed that a large number of proteins were differentially expressed in the occipital region in response to PFO, including redox molecules and CAMs, suggesting PFO-caused hypoxia may have great impacts on occipital region. Collectively, PFO may cause NCDEMs due to RLS-induced hypoxia, and PFO closure could prevent RLS to improve migraine and epilepsy.

Clinically informative microRNAs for SARS-CoV-2 infection

COVID-19 is a viral respiratory infection induced by the newly discovered coronavirus SARS-CoV-2. miRNA is an example of a strong and direct regulator of a gene's transcriptional activity. The interaction between miRNAs and their target molecules is responsible for homeostasis. Virus-derived and host-derived miRNAs are involved in the activity of hiding from immune system cells, inducing the inflammatory reaction through interplay with associated genes, during SARS-COV-2 infection. Interest in miRNAs has raised the comprehension of the machinery and pathophysiology of SARS-COV-2 infection. In this review, the effects and biological roles of miRNAs on SARS-CoV-2 pathogenicity and life cycle are described. The therapeutic potential of miRNAs against SARS-CoV-2 infection are also mentioned.