Metabolic Dynamics and Prediction of Gestational Age and Time to Delivery in Pregnant Women.

Metabolism during pregnancy is a dynamic and precisely programmed process, the failure of which can bring devastating consequences to the mother and fetus. To define a high-resolution temporal profile of metabolites during healthy pregnancy, we analyzed the untargeted metabolome of 784 weekly blood samples from 30 pregnant women. Broad changes and a highly choreographed profile were revealed: 4,995 metabolic features (of 9,651 total), 460 annotated compounds (of 687 total), and 34 human metabolic pathways (of 48 total) were significantly changed during pregnancy. Using linear models, we built a metabolic clock with five metabolites that time gestational age in high accordance with ultrasound (R = 0.92). Furthermore, two to three metabolites can identify when labor occurs (time to delivery within two, four, and eight weeks, AUROC ≥ 0.85). Our study represents a weekly characterization of the human pregnancy metabolome, providing a high-resolution landscape for understanding pregnancy with potential clinical utilities.

Structure and topography of the synaptic V-ATPase-synaptophysin complex.

Synaptic vesicles are organelles with a precisely defined protein and lipid composition1,2, yet the molecular mechanisms for the biogenesis of synaptic vesicles are mainly unknown. Here we discovered a well-defined interface between the synaptic vesicle V-ATPase and synaptophysin by in situ cryo-electron tomography and single-particle cryo-electron microscopy of functional synaptic vesicles isolated from mouse brains3. The synaptic vesicle V-ATPase is an ATP-dependent proton pump that establishes the proton gradient across the synaptic vesicle, which in turn drives the uptake of neurotransmitters4,5. Synaptophysin6 and its paralogues synaptoporin7 and synaptogyrin8 belong to a family of abundant synaptic vesicle proteins whose function is still unclear. We performed structural and functional studies of synaptophysin-knockout mice, confirming the identity of synaptophysin as an interaction partner with the V-ATPase. Although there is little change in the conformation of the V-ATPase upon interaction with synaptophysin, the presence of synaptophysin in synaptic vesicles profoundly affects the copy number of V-ATPases. This effect on the topography of synaptic vesicles suggests that synaptophysin assists in their biogenesis. In support of this model, we observed that synaptophysin-knockout mice exhibit severe seizure susceptibility, suggesting an imbalance of neurotransmitter release as a physiological consequence of the absence of synaptophysin.

Generalized reporter score-based enrichment analysis for omics data.

Enrichment analysis contextualizes biological features in pathways to facilitate a systematic understanding of high-dimensional data and is widely used in biomedical research. The emerging reporter score-based analysis (RSA) method shows more promising sensitivity, as it relies on P-values instead of raw values of features. However, RSA cannot be directly applied to multi-group and longitudinal experimental designs and is often misused due to the lack of a proper tool. Here, we propose the Generalized Reporter Score-based Analysis (GRSA) method for multi-group and longitudinal omics data. A comparison with other popular enrichment analysis methods demonstrated that GRSA had increased sensitivity across multiple benchmark datasets. We applied GRSA to microbiome, transcriptome and metabolome data and discovered new biological insights in omics studies. Finally, we demonstrated the application of GRSA beyond functional enrichment using a taxonomy database. We implemented GRSA in an R package, ReporterScore, integrating with a powerful visualization module and updatable pathway databases, which is available on the Comprehensive R Archive Network (https://cran.r-project.org/web/packages/ReporterScore). We believe that the ReporterScore package will be a valuable asset for broad biomedical research fields.

Precision environmental health monitoring by longitudinal exposome and multi-omics profiling.

Conventional environmental health studies have primarily focused on limited environmental stressors at the population level, which lacks the power to dissect the complexity and heterogeneity of individualized environmental exposures. Here, as a pilot case study, we integrated deep-profiled longitudinal personal exposome and internal multi-omics to systematically investigate how the exposome shapes a single individual's phenome. We annotated thousands of chemical and biological components in the personal exposome cloud and found they were significantly correlated with thousands of internal biomolecules, which was further cross-validated using corresponding clinical data. Our results showed that agrochemicals and fungi predominated in the highly diverse and dynamic personal exposome, and the biomolecules and pathways related to the individual's immune system, kidney, and liver were highly associated with the personal external exposome. Overall, this data-driven longitudinal monitoring study shows the potential dynamic interactions between the personal exposome and internal multi-omics, as well as the impact of the exposome on precision health by producing abundant testable hypotheses.

Deep learning-based pseudo-mass spectrometry imaging analysis for precision medicine.

Liquid chromatography-mass spectrometry (LC-MS)-based untargeted metabolomics provides systematic profiling of metabolic. Yet, its applications in precision medicine (disease diagnosis) have been limited by several challenges, including metabolite identification, information loss and low reproducibility. Here, we present the deep-learning-based Pseudo-Mass Spectrometry Imaging (deepPseudoMSI) project (https://www.deeppseudomsi.org/), which converts LC-MS raw data to pseudo-MS images and then processes them by deep learning for precision medicine, such as disease diagnosis. Extensive tests based on real data demonstrated the superiority of deepPseudoMSI over traditional approaches and the capacity of our method to achieve an accurate individualized diagnosis. Our framework lays the foundation for future metabolic-based precision medicine.

massDatabase: utilities for the operation of the public compound and pathway database.

SUMMARY: One of the major challenges in liquid chromatography coupled to mass spectrometry data is converting many metabolic feature entries to biological function information, such as metabolite annotation and pathway enrichment, which are based on the compound and pathway databases. Multiple online databases have been developed. However, no tool has been developed for operating all these databases for biological analysis. Therefore, we developed massDatabase, an R package that operates the online public databases and combines with other tools for streamlined compound annotation and pathway enrichment. massDatabase is a flexible, simple and powerful tool that can be installed on all platforms, allowing the users to leverage all the online public databases for biological function mining. A detailed tutorial and a case study are provided in the Supplementary Material. AVAILABILITY AND IMPLEMENTATION: https://massdatabase.tidymass.org/. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

metID: an R package for automatable compound annotation for LC-MS-based data.

SUMMARY: Accurate and efficient compound annotation is a long-standing challenge for LC-MS-based data (e.g. untargeted metabolomics and exposomics). Substantial efforts have been devoted to overcoming this obstacle, whereas current tools are limited by the sources of spectral information used (in-house and public databases) and are not automated and streamlined. Therefore, we developed metID, an R package that combines information from all major databases for comprehensive and streamlined compound annotation. metID is a flexible, simple and powerful tool that can be installed on all platforms, allowing the compound annotation process to be fully automatic and reproducible. A detailed tutorial and a case study are provided in Supplementary Materials. AVAILABILITY AND IMPLEMENTATION: https://jaspershen.github.io/metID. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

The mediation effect of serum metabolites on the relationship between long-term smoking exposure and esophageal squamous cell carcinoma.

BACKGROUND: Long-term smoking exposure will increase the risk of esophageal squamous cell carcinoma (ESCC), whereas the mechanism is still unclear. We conducted a cross-sectional study to explore whether serum metabolites mediate the occurrence of ESCC caused by cigarette smoking. METHODS: Serum metabolic profiles and lifestyle information of 464 participants were analyzed. Multiple logistic regression was used to estimate adjusted odds ratios (ORs) and 95% confidence intervals (CIs) of smoking exposure to ESCC risk. High-dimensional mediation analysis and univariate mediation analysis were performed to screen potential intermediate metabolites of smoking exposure for ESCC. RESULTS: Ever smoking was associated with a 3.11-fold increase of ESCC risk (OR = 3.11, 95% CI 1.63-6.05), and for each cigarette-years increase in smoking index, ESCC risk increased by 56% (OR = 1.56, 95% CI 1.18-2.13). A total of 5 metabolites were screened as mediators by high-dimensional mediation analysis. In addition, glutamine, histidine, and cholic acid were further proved existing mediation effects according to univariate mediation analysis. And the proportions of mediation of histidine and glutamine were 40.47 and 30.00%, respectively. The mediation effect of cholic acid was 8.98% according to the analysis of smoking index. CONCLUSIONS: Our findings suggest that cigarette smoking contributed to incident ESCC, which may be mediated by glutamine, histidine and cholic acid.

MetFlow: an interactive and integrated workflow for metabolomics data cleaning and differential metabolite discovery.

SUMMARY: Mass spectrometry-based metabolomics aims to profile the metabolic changes in biological systems and identify differential metabolites related to physiological phenotypes and aberrant activities. However, many confounding factors during data acquisition complicate metabolomics data, which is characterized by high dimensionality, uncertain degrees of missing and zero values, nonlinearity, unwanted variations and non-normality. Therefore, prior to differential metabolite discovery analysis, various types of data cleaning such as batch alignment, missing value imputation, data normalization and scaling are essentially required for data post-processing. Here, we developed an interactive web server, namely, MetFlow, to provide an integrated and comprehensive workflow for metabolomics data cleaning and differential metabolite discovery. AVAILABILITY AND IMPLEMENTATION: The MetFlow is freely available on http://metflow.zhulab.cn/. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

LipidIMMS Analyzer: integrating multi-dimensional information to support lipid identification in ion mobility-mass spectrometry based lipidomics.

SUMMARY: Ion mobility-mass spectrometry (IM-MS) has showed great application potential for lipidomics. However, IM-MS based lipidomics is significantly restricted by the available software for lipid structural identification. Here, we developed a software tool, namely, LipidIMMS Analyzer, to support the accurate identification of lipids in IM-MS. For the first time, the software incorporates a large-scale database covering over 260 000 lipids and four-dimensional structural information for each lipid [i.e. m/z, retention time (RT), collision cross-section (CCS) and MS/MS spectra]. Therefore, multi-dimensional information can be readily integrated to support lipid identifications, and significantly improve the coverage and confidence of identification. Currently, the software supports different IM-MS instruments and data acquisition approaches. AVAILABILITY AND IMPLEMENTATION: The software is freely available at: http://imms.zhulab.cn/LipidIMMS/. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Development of a Correlative Strategy To Discover Colorectal Tumor Tissue Derived Metabolite Biomarkers in Plasma Using Untargeted Metabolomics.

The metabolic profiling of biofluids using untargeted metabolomics provides a promising choice to discover metabolite biomarkers for clinical cancer diagnosis. However, metabolite biomarkers discovered in biofluids may not necessarily reflect the pathological status of tumor tissue, which makes these biomarkers difficult to reproduce. In this study, we developed a new analysis strategy by integrating the univariate and multivariate correlation analysis approach to discover tumor tissue derived (TTD) metabolites in plasma samples. Specifically, untargeted metabolomics was first used to profile a set of paired tissue and plasma samples from 34 colorectal cancer (CRC) patients. Next, univariate correlation analysis was used to select correlative metabolite pairs between tissue and plasma, and a random forest regression model was utilized to define 243 TTD metabolites in plasma samples. The TTD metabolites in CRC plasma were demonstrated to accurately reflect the pathological status of tumor tissue and have great potential for metabolite biomarker discovery. Accordingly, we conducted a clinical study using a set of 146 plasma samples from CRC patients and gender-matched polyp controls to discover metabolite biomarkers from TTD metabolites. As a result, eight metabolites were selected as potential biomarkers for CRC diagnosis with high sensitivity and specificity. For CRC patients after surgery, the survival risk score defined by metabolite biomarkers also performed well in predicting overall survival time ( p = 0.022) and progression-free survival time ( p = 0.002). In conclusion, we developed a new analysis strategy which effectively discovers tumor tissue related metabolite biomarkers in plasma for cancer diagnosis and prognosis.

LipidCCS: Prediction of Collision Cross-Section Values for Lipids with High Precision To Support Ion Mobility-Mass Spectrometry-Based Lipidomics.

The use of collision cross-section (CCS) values derived from ion mobility-mass spectrometry (IM-MS) has been proven to facilitate lipid identifications. Its utility is restricted by the limited availability of CCS values. Recently, the machine-learning algorithm-based prediction (e.g., MetCCS) is reported to generate CCS values in a large-scale. However, the prediction precision is not sufficient to differentiate lipids due to their high structural similarities and subtle differences on CCS values. To address this challenge, we developed a new approach, namely, LipidCCS, to precisely predict lipid CCS values. In LipidCCS, a set of molecular descriptors were optimized using bioinformatic approaches to comprehensively describe the subtle structure differences for lipids. The use of optimized molecular descriptors together with a large set of standard CCS values for lipids (458 in total) to build the prediction model significantly improved the precision. The prediction precision of LipidCCS was externally validated with median relative errors (MRE) of ∼1% using independent data sets across different instruments (Agilent DTIM-MS and Waters TWIM-MS) and laboratories. We also demonstrated that the improved precision in the predicted LipidCCS database (15 646 lipids and 63 434 CCS values in total) could effectively reduce false-positive identifications of lipids. Common users can freely access our LipidCCS web server for the following: (1) the prediction of lipid CCS values directly from SMILES structure; (2) database search; and (3) lipid match and identification. We believe LipidCCS will be a valuable tool to support IM-MS-based lipidomics. The web server is freely available on the Internet ( http://www.metabolomics-shanghai.org/LipidCCS/ ).

Large-Scale Prediction of Collision Cross-Section Values for Metabolites in Ion Mobility-Mass Spectrometry.

The rapid development of metabolomics has significantly advanced health and disease related research. However, metabolite identification remains a major analytical challenge for untargeted metabolomics. While the use of collision cross-section (CCS) values obtained in ion mobility-mass spectrometry (IM-MS) effectively increases identification confidence of metabolites, it is restricted by the limited number of available CCS values for metabolites. Here, we demonstrated the use of a machine-learning algorithm called support vector regression (SVR) to develop a prediction method that utilized 14 common molecular descriptors to predict CCS values for metabolites. In this work, we first experimentally measured CCS values (ΩN2) of ∼400 metabolites in nitrogen buffer gas and used these values as training data to optimize the prediction method. The high prediction precision of this method was externally validated using an independent set of metabolites with a median relative error (MRE) of ∼3%, better than conventional theoretical calculation. Using the SVR based prediction method, a large-scale predicted CCS database was generated for 35 203 metabolites in the Human Metabolome Database (HMDB). For each metabolite, five different ion adducts in positive and negative modes were predicted, accounting for 176 015 CCS values in total. Finally, improved metabolite identification accuracy was demonstrated using real biological samples. Conclusively, our results proved that the SVR based prediction method can accurately predict nitrogen CCS values (ΩN2) of metabolites from molecular descriptors and effectively improve identification accuracy and efficiency in untargeted metabolomics. The predicted CCS database, namely, MetCCS, is freely available on the Internet.

Intramyocardial transplantation of cardiac telocytes decreases myocardial infarction and improves post-infarcted cardiac function in rats.

The midterm effects of cardiac telocytes (CTs) transplantation on myocardial infarction (MI) and the cellular mechanisms involved in the beneficial effects of CTs transplantation are not understood. In the present study, we have revealed that transplantation of CTs was able to significantly decrease the infarct size and improved cardiac function 14 weeks after MI. It has established that CT transplantation exerted a protective effect on the myocardium and this was maintained for at least 14 weeks. The cellular mechanism behind this beneficial effect on MI was partially attributed to increased cardiac angiogenesis, improved reconstruction of the CT network and decreased myocardial fibrosis. These combined effects decreased the infarct size, improved the reconstruction of the LV and enhanced myocardial function in MI. Our findings suggest that CTs could be considered as a potential cell source for therapeutic use to improve cardiac repair and function following MI, used either alone or in tandem with stem cells.

Exploring the underlying molecular mechanisms of acute myocardial infarction after SARS-CoV-2 infection.

An increase in acute myocardial infarction (AMI)-related deaths has been reported during the COVID-19 pandemic. Despite evidence suggesting the association between severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection and AMI, the underlying mechanisms remain unclear. Here, we integrated mRNA and microRNA expression profiles related to SARS-CoV-2 infection and AMI from public databases. We then performed transcriptomic analysis using bioinformatics and systems biology approaches to explore the potential molecular mechanisms of SARS-CoV-2 infection affects AMI. First, twenty-one common differentially expressed genes (DEGs) were identified from SARS-CoV-2 infection and AMI patients in endothelial cells datasets and then we performed functional analysis to predict the roles of these DEGs. The functional analysis emphasized that the endothelial cell response to cytokine stimulus due to excessive inflammation was essential in these two diseases. Importantly, the tumor necrosis factor and interleukin-17 signaling pathways appeared to be integral factors in this mechanism. Interestingly, most of these common genes were also upregulated in transcriptomic datasets of SARS-CoV-2-infected cardiomyocytes, suggesting that these genes may be shared in cardiac- and vascular-related injuries. We subsequently built a protein-protein interaction network and extracted hub genes and essential modules from this network. At the transcriptional and post-transcriptional levels, regulatory networks with common DEGs were also constructed, and some key regulator signatures were further identified and validated. In summary, our research revealed that a highly activated inflammatory response in patients with COVID-19 might be a crucial factor for susceptibility to AMI and we identified some candidate genes and regulators that could be used as biomarkers or potential therapeutic targets.

Multi-omics microsampling for the profiling of lifestyle-associated changes in health.

Current healthcare practices are reactive and use limited physiological and clinical information, often collected months or years apart. Moreover, the discovery and profiling of blood biomarkers in clinical and research settings are constrained by geographical barriers, the cost and inconvenience of in-clinic venepuncture, low sampling frequency and the low depth of molecular measurements. Here we describe a strategy for the frequent capture and analysis of thousands of metabolites, lipids, cytokines and proteins in 10 µl of blood alongside physiological information from wearable sensors. We show the advantages of such frequent and dense multi-omics microsampling in two applications: the assessment of the reactions to a complex mixture of dietary interventions, to discover individualized inflammatory and metabolic responses; and deep individualized profiling, to reveal large-scale molecular fluctuations as well as thousands of molecular relationships associated with intra-day physiological variations (in heart rate, for example) and with the levels of clinical biomarkers (specifically, glucose and cortisol) and of physical activity. Combining wearables and multi-omics microsampling for frequent and scalable omics may facilitate dynamic health profiling and biomarker discovery.

Longitudinal profiling of the microbiome at four body sites reveals core stability and individualized dynamics during health and disease.

To understand the dynamic interplay between the human microbiome and host during health and disease, we analyzed the microbial composition, temporal dynamics, and associations with host multi-omics, immune, and clinical markers of microbiomes from four body sites in 86 participants over 6 years. We found that microbiome stability and individuality are body-site specific and heavily influenced by the host. The stool and oral microbiome are more stable than the skin and nasal microbiomes, possibly due to their interaction with the host and environment. We identify individual-specific and commonly shared bacterial taxa, with individualized taxa showing greater stability. Interestingly, microbiome dynamics correlate across body sites, suggesting systemic dynamics influenced by host-microbial-environment interactions. Notably, insulin-resistant individuals show altered microbial stability and associations among microbiome, molecular markers, and clinical features, suggesting their disrupted interaction in metabolic disease. Our study offers comprehensive views of multi-site microbial dynamics and their relationship with host health and disease.

Longitudinal profiling of the microbiome at four body sites reveals core stability and individualized dynamics during health and disease.

To understand dynamic interplay between the human microbiome and host during health and disease, we analyzed the microbial composition, temporal dynamics, and associations with host multi-omics, immune and clinical markers of microbiomes from four body sites in 86 participants over six years. We found that microbiome stability and individuality are body-site-specific and heavily influenced by the host. The stool and oral microbiome were more stable than the skin and nasal microbiomes, possibly due to their interaction with the host and environment. Also, we identified individual-specific and commonly shared bacterial taxa, with individualized taxa showing greater stability. Interestingly, microbiome dynamics correlated across body sites, suggesting systemic coordination influenced by host-microbial-environment interactions. Notably, insulin-resistant individuals showed altered microbial stability and associations between microbiome, molecular markers, and clinical features, suggesting their disrupted interaction in metabolic disease. Our study offers comprehensive views of multi-site microbial dynamics and their relationship with host health and disease. Study Highlights: The stability of the human microbiome varies among individuals and body sites.Highly individualized microbial genera are more stable over time.At each of the four body sites, systematic interactions between the environment, the host and bacteria can be detected.Individuals with insulin resistance have lower microbiome stability, a more diversified skin microbiome, and significantly altered host-microbiome interactions.

Cardiac telocytes were decreased during myocardial infarction and their therapeutic effects for ischaemic heart in rat.

Recently, cardiac telocytes were found in the myocardium. However, the functional role of cardiac telocytes and possible changes in the cardiac telocyte population during myocardial infarction in the myocardium are not known. In this study, the role of the recently identified cardiac telocytes in myocardial infarction (MI) was investigated. Cardiac telocytes were distributed longitudinally and within the cross network of the myocardium, which was impaired during MI. Cardiac telocytes in the infarction zone were undetectable from approximately 4 days to 4 weeks after an experimental coronary occlusion was used to induce MI. Although cardiac telocytes in the non-ischaemic area of the ischaemic heart experienced cell death, the cell density increased approximately 2 weeks after experimental coronary occlusion. The cell density was then maintained at a level similar to that observed 1-4 days after left anterior descending coronary artery (LAD)-ligation, but was still lower than normal after 2 weeks. We also found that simultaneous transplantation of cardiac telocytes in the infarcted and border zones of the heart decreased the infarction size and improved myocardial function. These data indicate that cardiac telocytes, their secreted factors and microvesicles, and the microenvironment may be structurally and functionally important for maintenance of the physiological integrity of the myocardium. Rebuilding the cardiac telocyte network in the infarcted zone following MI may be beneficial for functional regeneration of the infarcted myocardium.

Organism-wide, cell-type-specific secretome mapping of exercise training in mice.

There is a significant interest in identifying blood-borne factors that mediate tissue crosstalk and function as molecular effectors of physical activity. Although past studies have focused on an individual molecule or cell type, the organism-wide secretome response to physical activity has not been evaluated. Here, we use a cell-type-specific proteomic approach to generate a 21-cell-type, 10-tissue map of exercise training-regulated secretomes in mice. Our dataset identifies >200 exercise training-regulated cell-type-secreted protein pairs, the majority of which have not been previously reported. Pdgfra-cre-labeled secretomes were the most responsive to exercise training. Finally, we show anti-obesity, anti-diabetic, and exercise performance-enhancing activities for proteoforms of intracellular carboxylesterases whose secretion from the liver is induced by exercise training.