Comprehensive investigation of pathway enrichment methods for functional interpretation of LC-MS global metabolomics data.

BACKGROUND: Global or untargeted metabolomics is widely used to comprehensively investigate metabolic profiles under various pathophysiological conditions such as inflammations, infections, responses to exposures or interactions with microbial communities. However, biological interpretation of global metabolomics data remains a daunting task. Recent years have seen growing applications of pathway enrichment analysis based on putative annotations of liquid chromatography coupled with mass spectrometry (LC-MS) peaks for functional interpretation of LC-MS-based global metabolomics data. However, due to intricate peak-metabolite and metabolite-pathway relationships, considerable variations are observed among results obtained using different approaches. There is an urgent need to benchmark these approaches to inform the best practices. RESULTS: We have conducted a benchmark study of common peak annotation approaches and pathway enrichment methods in current metabolomics studies. Representative approaches, including three peak annotation methods and four enrichment methods, were selected and benchmarked under different scenarios. Based on the results, we have provided a set of recommendations regarding peak annotation, ranking metrics and feature selection. The overall better performance was obtained for the mummichog approach. We have observed that a ~30% annotation rate is sufficient to achieve high recall (~90% based on mummichog), and using semi-annotated data improves functional interpretation. Based on the current platforms and enrichment methods, we further propose an identifiability index to indicate the possibility of a pathway being reliably identified. Finally, we evaluated all methods using 11 COVID-19 and 8 inflammatory bowel diseases (IBD) global metabolomics datasets.

mGWAS-Explorer: Linking SNPs, Genes, Metabolites, and Diseases for Functional Insights.

Tens of thousands of single-nucleotide polymorphisms (SNPs) have been identified to be significantly associated with metabolite abundance in over 65 genome-wide association studies with metabolomics (mGWAS) to date. Obtaining mechanistic or functional insights from these associations for translational applications has become a key research area in the mGWAS community. Here, we introduce mGWAS-Explorer, a user-friendly web-based platform to help connect SNPs, metabolites, genes, and their known disease associations via powerful network visual analytics. The application of the mGWAS-Explorer was demonstrated using a COVID-19 and a type 2 diabetes case studies.

Clinical Value of COVID-19 Chest Radiography and High-Resolution CT Examination.

BACKGROUND: Novel coronavirus disease 2019 (COVID-19) pneumonia remains a matter of concern. Chest CT findings of COVID-19 pneumonia have been reported widely, while there is relatively rare research on chest X-ray (CXR). OBJECTIVE: The study was aimed to compare the CXR and chest CT findings of patients with confirmed COVID-19 infection and to explore their respective clinical values. METHODS: 28 inpatients with COVID-19 pneumonia who underwent both CXR and CT were included. The pulmonary manifestations of the lesions were recorded. Ground-glass opacity (GGO), consolidation, and fibrosis were quantified in CXR and chest CT separately. Consistency was analyzed using Fleiss' kappa and intraclass correlation coefficient. The stages of the disease in CXR and chest CT were evaluated. RESULTS: Approximately 67.9% (19/28) of subjects had abnormal findings on CXR. The common manifestations in CXR were ground-glass opacities (GGO) (100%, 19/19) and consolidation (68.4%, 13/19). 92.9% (26/28) of patients had abnormal manifestations on CT. The common manifestations in CT were GGO (88.5%, 23/26), consolidation (69.2%, 18/26), reticular opacity (69.2%, 18/26) and nodule (46.2%, 12/26). Among the abnormalities between CXR and CT, only consolidation was consistent (κ=0.510). GGO (ICC=0.501) and consolidation (ICC=0.431) scores were consistent in CXR and chest CT. The results of staging were the same in 14 cases, most of them were in stage I and stage II. While in other cases with inconsistent results, CT was more advanced in the disease stage than CXR, mainly stage III and stage IV. CONCLUSION: CXR is helpful to observe the change of the pulmonary lesions in patients with confirmed COVID-19 pneumonia. CT can be used for early diagnosis and staging of lesions.

Comprehensive Meta-Analysis of COVID-19 Global Metabolomics Datasets.

The novel coronavirus SARS-CoV-2 has spread across the world since 2019, causing a global pandemic. The pathogenesis of the viral infection and the associated clinical presentations depend primarily on host factors such as age and immunity, rather than the viral load or its genetic variations. A growing number of omics studies have been conducted to characterize the host immune and metabolic responses underlying the disease progression. Meta-analyses of these datasets have great potential to identify robust molecular signatures to inform clinical care and to facilitate therapeutics development. In this study, we performed a comprehensive meta-analysis of publicly available global metabolomics datasets obtained from three countries (United States, China and Brazil). To overcome high heterogeneity inherent in these datasets, we have (a) implemented a computational pipeline to perform consistent raw spectra processing; (b) conducted meta-analyses at pathway levels instead of individual feature levels; and (c) performed visual data mining on consistent patterns of change between disease severities for individual studies. Our analyses have yielded several key metabolic signatures characterizing disease progression and clinical outcomes. Their biological interpretations were discussed within the context of the current literature. To the best of our knowledge, this is the first comprehensive meta-analysis of global metabolomics datasets of COVID-19.

High-resolution Chest CT Features and Clinical Characteristics of Patients Infected with COVID-19 in Jiangsu, China.

BACKGROUND: A pneumonia associated with the coronavirus disease 2019 (COVID-19) recently emerged in China. It was recognized as a global health hazard. METHODS: 234 inpatients with COVID-19 were included. Detailed clinical data, chest HRCT basic performances and certain signs were recorded Ground-glass opacity (GGO), consolidation, fibrosis and air trapping were quantified. Both clinical types and CT stages were evaluated. RESULTS: Most patients (approximately 90%) were classified as common type and with epidemiologic history. Fever and cough were main symptoms. Chest CT showed abnormal attenuation in bilateral multiple lung lobes, distributed in the lower and/or periphery of the lungs (94.98%), with multiple shapes. GGO and vascular enhancement sign were most frequent seen, followed by interlobular septal thickening and air bronchus sign as well as consolidation, fibrosis and air trapping. There were significant differences in most of CT signs between different stage groups. The SpO2 and OI were decreased in stage IV, and the CT score of consolidation, fibrosis and air trapping was significantly lower in stage I (P<0.05). A weak relevance was between the fibrosis score and the value of PaO2 and SpO2 (P<0.05). CONCLUSIONS: Clinical performances of patients with COVID-19, mostly with epidemiologic history and typical symptoms, were critical valuable in the diagnosis of the COVID-19. While chest HRCT provided the distribution, shape, attenuation and extent of lung lesions, as well as some typical CT signs of COVID-19 pneumonia.