Differential metabolomics analysis allows characterization of diversity of metabolite networks between males and females

The use of ecological analytical tools as an unconventional approach for untargeted metabolomics data analysis: the case of Cecropia obtusifolia and its adaptive responses to nitrate starvation

Plant metabolomics studies haves revealed new bioactive compounds. However, like other omics disciplines, the generated data are not fully exploited, mainly because the commonly performed analyses focus on elucidating the presence/absence of distinctive metabolites (and/or their precursors) and not on providing a holistic view of metabolomic changes and their participation in organismal adaptation to biotic and abiotic stress conditions. Therefore, spectral libraries generated from Cecropia obtusifolia cell suspension cultures in a previous study were considered as a case study and were reanalyzed herein. These libraries were obtained from a time-course experiment under nitrate starvation conditions using both electrospray ionization modes. The applied methodology included the use of ecological analytical tools in a systematic four-step process, including a population analysis of metabolite α diversity, richness, and evenness (i); a chemometrics analysis to identify discriminant groups (ii); differential metabolic marker identification (iii); and enrichment analyses and annotation of active metabolic pathways enriched by differential metabolites (iv). Our species α diversity results referring to the diversity of metabolites represented by mass-to-charge ratio (m/z) values detected at a specific retention time (rt) (an uncommon way to analyze untargeted metabolomic data) suggest that the metabolome is dynamic and is modulated by abiotic stress. A total of 147 and 371 m/z_rt pairs was identified as differential markers responsive to nitrate starvation in ESI^- and ESI⁺ modes, respectively. Subsequent enrichment analysis showed a high degree of completeness of biosynthetic pathways such as those of brassinosteroids, flavonoids, and phenylpropanoids.

Systemic inflammation and metabolic disturbances underlie inpatient mortality among ill children with severe malnutrition

Children admitted to hospital with an acute illness and concurrent severe malnutrition [complicated severe malnutrition (CSM)] have a high risk of dying. The biological processes underlying their mortality are poorly understood. In this case-control study nested within a multicenter randomized controlled trial among children with CSM in Kenya and Malawi, we found that blood metabolomic and proteomic profiles robustly differentiated children who died (n = 92) from those who survived (n = 92). Fatalities were characterized by increased energetic substrates (tricarboxylic acid cycle metabolites), microbial metabolites (e.g., propionate and isobutyrate), acute phase proteins (e.g., calprotectin and C-reactive protein), and inflammatory markers (e.g., interleukin-8 and tumor necrosis factor-α). These perturbations indicated disruptions in mitochondria-related bioenergetic pathways and sepsis-like responses. This study identified specific biomolecular disturbances associated with CSM mortality, revealing that systemic inflammation and bioenergetic deficits are targetable pathophysiological processes for improving survival of this vulnerable population.

Path-level interpretation of Gaussian graphical models using the pair-path subscore

BACKGROUND : Construction of networks from cross-sectional biological data is increasingly common. Many recent methods have been based on Gaussian graphical modeling, and prioritize estimation of conditional pairwise dependencies among nodes in the network. However, challenges remain on how specific paths through the resultant network contribute to overall 'network-level' correlations. For biological applications, understanding these relationships is particularly relevant for parsing structural information contained in complex subnetworks. RESULTS: We propose the pair-path subscore (PPS), a method for interpreting Gaussian graphical models at the level of individual network paths. The scoring is based on the relative importance of such paths in determining the Pearson correlation between their terminal nodes. PPS is validated using human metabolomics data from the Hyperglycemia and adverse pregnancy outcome (HAPO) study, with observations confirming well-documented biological relationships among the metabolites. We also highlight how the PPS can be used in an exploratory fashion to generate new biological hypotheses. Our method is implemented in the R package pps, available at https://github.com/nathan-gill/pps . CONCLUSIONS: The PPS can be used to probe network structure on a finer scale by investigating which paths in a potentially intricate topology contribute most substantially to marginal behavior. Adding PPS to the network analysis toolkit may enable researchers to ask new questions about the relationships among nodes in network data.

Personal health records sharing scheme based on attribute based signcryption with data integrity verifiable

The distribution of personal health records (PHRs) via a cloud server is a promising platform as it reduces the cost of data maintenance. Nevertheless, the cloud server is semi-trusted and can expose the patients’ PHRs to unauthorized third parties for financial gains or compromise the query result. Therefore, ensuring the integrity of the query results and privacy of PHRs as well as realizing fine-grained access control are critical key issues when PHRs are shared via cloud computing. Hence, we propose new personal health records sharing scheme with verifiable data integrity based on B+ tree data structure and attribute-based signcryption scheme to achieve data privacy, query result integrity, unforgeability, blind keyword search, and fine-grained access control.

Lipid and metabolite correlation networks specific to clinical and biochemical covariate show differences associated with sexual dimorphism in a cohort of nonagenarians

This study defines and estimates the metabolite-lipidic component association networks constructed from an array of 20 metabolites and 114 lipids identified and quantified via NMR spectroscopy in the serum of a cohort of 355 Italian nonagenarians and ultra-nonagenarian. Metabolite-lipid association networks were built for men and women and related to an array of 101 clinical and biochemical parameters, including the presence of diseases, bio-humoral parameters, familiarity diseases, drugs treatments, and risk factors. Different connectivity patterns were observed in lipids, branched chains amino acids, alanine, and ketone bodies, suggesting their association with the sex-related and sex-clinical condition-related intrinsic metabolic changes. Furthermore, our results demonstrate, using a holistic system biology approach, that the characterization of metabolic structures and their dynamic inter-connections is a promising tool to shed light on the dimorphic pathophysiological mechanisms of aging at the molecular level.

Network Analysis in Systems Epidemiology

Traditional epidemiological studies have identified a number of risk factors for various diseases using regression-based methods that examine the association between an exposure and an outcome (i.e., one-to-one correspondences). One of the major limitations of this approach is the "black-box" aspect of the analysis, in the sense that this approach cannot fully explain complex relationships such as biological pathways. With high-throughput data in current epidemiology, comprehensive analyses are needed. The network approach can help to integrate multi-omics data, visualize their interactions or relationships, and make inferences in the context of biological mechanisms. This review aims to introduce network analysis for systems epidemiology, its procedures, and how to interpret its findings.

Metabolomic Profiling Reveals Sex Specific Associations with Chronic Obstructive Pulmonary Disease and Emphysema

Susceptibility and progression of lung disease, as well as response to treatment, often differ by sex, yet the metabolic mechanisms driving these sex-specific differences are still poorly understood. Women with chronic obstructive pulmonary disease (COPD) have less emphysema and more small airway disease on average than men, though these differences become less pronounced with more severe airflow limitation. While small studies of targeted metabolites have identified compounds differing by sex and COPD status, the sex-specific effect of COPD on systemic metabolism has yet to be interrogated. Significant sex differences were observed in 9 of the 11 modules identified in COPDGene. Sex-specific associations by COPD status and emphysema were observed in 3 modules for each phenotype. Sex stratified individual metabolite associations with COPD demonstrated male-specific associations in sphingomyelins and female-specific associations in acyl carnitines and phosphatidylethanolamines. There was high preservation of module assignments in SPIROMICS (SubPopulations and InteRmediate Outcome Measures In COPD Study) and similar female-specific shift in acyl carnitines. Several COPD associated metabolites differed by sex. Acyl carnitines and sphingomyelins demonstrate sex-specific abundances and may represent important metabolic signatures of sex differences in COPD. Accurately characterizing the sex-specific molecular differences in COPD is vital for personalized diagnostics and therapeutics.

Characterization of Microbial Dynamics and Volatile Metabolome Changes During Fermentation of Chambourcin Hybrid Grapes From Two Pennsylvania Regions

Microbial diversity present on grapes in wineries, and throughout fermentation has been associated with important metabolites for final wine quality. Although microbiome-metabolome associations have been well characterized and could be used as indicators of wine quality, the impact of regionality on the microbiome and metabolome is not well known. Additionally, studies between microbiome and metabolome have been conducted on single species grape such as Vitis vinifera instead of other species and interspecific hybrids. Although the Pennsylvania wine industry is relatively young compared to California, the industry has been experiencing rapid growth over the past decade and is expected to continue to grow in the future. Pennsylvania's climate of cold winters and high levels of rainfall throughout the growing season favors cultivation of interspecific hybrid grapes such as Vitis ssp. Chambourcin, one of the most commonly grown hybrid varieties in the state. Chambourcin is a prime candidate for studying the impact of regionality on microbiome-metabolome interactions as interspecific hybrid varieties could shape the future of winemaking. Here, we identify for the first time the regional distribution of microbial communities and their interactions with volatile metabolome during fermentation (0-20 days) by integrating high throughput Illumina sequencing (16S and ITS) and headspace-solid phase microextraction-gas chromatography-mass spectrometry. Analyzing 88 samples from nine wineries in the Central and East Pennsylvania regions, we observed high microbial diversity during early stages of fermentation (1-4 days) where non-Saccharomyces yeasts such as Starmerella and Aureobasidium and non-Oenococcus bacteria, Sphingomonas, likely contribute to microbial terroir to the resulting wines. Furthermore, key differentiators between two regions in Pennsylvania, as identified by LEfSe analysis, include the fungal genera Cladosporium and Kazachstania and the bacterial genera Lactococcus and Microbacterium. Moreover, 29 volatile fermentation metabolites were discriminated significantly (variable importance in projection > 1) between the two regions as shown by Partial Least Squares-Discriminant Analysis. Finally, Spearman's correlation identified regional differences of microbial-metabolite associations throughout fermentation that could be used for targeted microbiome manipulation to improve wine quality and preserve regionality. In summary, these results demonstrate the microbial signatures during fermentation and differential microorganisms and metabolites further support impact of regionality on Chambourcin wines in Pennsylvania.

Sex differences in non-alcoholic fatty liver disease: hints for future management of the disease

Non-alcoholic fatty liver disease (NAFLD) remains a major cause of chronic liver disease worldwide. Despite extensive studies, the heterogeneity of the risk factors as well as different disease mechanisms complicate the goals toward effective diagnosis and management. Recently, it has been shown that sex differences play a role in the prevalence and progression of NAFLD. In vitro, in vivo, and clinical studies revealed that the lower prevalence of NAFLD in premenopausal as compared to postmenopausal women and men is mainly due to the protective effects of estrogen and body fat distribution. It has been also described that males and females present differential pathogenic features in terms of biochemical profiles and histological characteristics. However, the exact molecular mechanisms for the gender differences that exist in the pathogenesis of NAFLD are still elusive. Lipogenesis, oxidative stress, and inflammation play a key role in the progression of NAFLD. For NAFLD, only a few studies characterized these mechanisms at the molecular level. Therefore, we aim to review the reported differential molecular mechanisms that trigger such different pathogenesis in both sexes. Differences in lipid metabolism, glucose homeostasis, oxidative stress, inflammation, and fibrosis were discussed based on the evidence reported in recent publications. In conclusion, with this review, we hope to provide a new perspective for the development of future practice guidelines as well as a new avenue for the management of the disease.

Acyl ethanolamides in Diabetes and Diabetic Nephropathy: Novel targets from untargeted plasma metabolomic profiles of South Asian Indian men

The pathophysiology of diabetic nephropathy (DN) in type 2 diabetes (T2D) patients is minimally understood. We compared untargeted high-resolution accurate mass (HRAM) orbitrap-based plasma metabolomic profiles of 31 T2D-DN (with estimated glomerular filtration rate ≤80 mL/min/1.73 m²), 29 T2D and 30 normal glucose tolerance (NGT) Indian men. Of the 939 plasma metabolites that were differentially abundant amongst the NGT, T2D and T2D-DN (ANOVA, False Discovery Rate – FDR adjusted p-value < 0.05), 48 were associated with T2D irrespective of the renal function of the subjects. Acyl ethanolamides and acetylcholine were decreased while monoacylglycerols (MAGs) and cortisol were elevated in both T2D and T2D-DN. Sixteen metabolites, including amino acid metabolites Imidazolelactate and N-Acetylornithine, changed significantly between NGT, T2D and T2D-DN. 192 metabolites were specifically dysregulated in T2D-DN (ratio ≥2 or ≤0.5 between T2D-DN and T2D, similar abundance in NGT and T2D). These included increased levels of multiple acylcarnitine and amino acid metabolites. We observed a significant dysregulation of amino acid and fatty acid metabolism in South Asian Indian male T2D-DN subjects. Unique to this study, we report a reduction in acyl ethanolamide levels in both T2D and T2D-DN males. Those with dysregulation in acyl ethanolamides, which are endogenous agonists of GPR119, are likely to exhibit improved glycemic control with GPR119 agonists.

Clinical and lifestyle related factors influencing whole blood metabolite levels - A comparative analysis of three large cohorts

Objective

Human blood metabolites are influenced by a number of lifestyle and environmental factors. Identification of these factors and the proper quantification of their relevance provides insights into human biological and metabolic disease processes, is key for standardized translation of metabolite biomarkers into clinical applications, and is a prerequisite for comparability of data between studies. However, so far only limited data exist from large and well-phenotyped human cohorts and current methods for analysis do not fully account for the characteristics of these data. The primary aim of this study was to identify, quantify and compare the impact of a comprehensive set of clinical and lifestyle related factors on metabolite levels in three large human cohorts. To achieve this goal, we improve current methodology by developing a principled analysis approach, which could be translated to other cohorts and metabolite panels.

Methods

63 Metabolites (amino acids, acylcarnitines) were quantified by liquid chromatography tandem mass spectrometry in three cohorts (total N = 16,222). Supported by a simulation study evaluating various analytical approaches, we developed an analysis pipeline including preprocessing, identification, and quantification of factors affecting metabolite levels. We comprehensively identified uni- and multivariable metabolite associations considering 29 environmental and clinical factors and performed metabolic pathway enrichment and network analyses.

Results

Inverse normal transformation of batch corrected and outlier removed metabolite levels accompanied by linear regression analysis proved to be the best suited method to deal with the metabolite data. Association analyses revealed numerous uni- and multivariable significant associations. 15 of the analyzed 29 factors explained >1% of variance for at least one of the metabolites. Strongest factors are application of steroid hormones, reticulocytes, waist-to-hip ratio, sex, haematocrit, and age. Effect sizes of factors are comparable across studies.

Conclusions

We introduced a principled approach for the analysis of MS data allowing identification, and quantification of effects of clinical and lifestyle factors with metabolite levels. We detected a number of known and novel associations broadening our understanding of the regulation of the human metabolome. The large heterogeneity observed between cohorts could almost completely be explained by differences in the distribution of influencing factors emphasizing the necessity of a proper confounder analysis when interpreting metabolite associations.

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Amino-acids and acylcarnitines analyzed in three studies with >16,000 individuals.

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Develop a generic and adaptable bioinformatics workflow.

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Analysis of the impact of 29 clinical and life-style factors on blood metabolites.

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Analysis of network between factors and metabolites.

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Comparison of results between studies.