The human gut microbiome is known to be associated with various human disorders, but a major challenge is to go beyond association studies and elucidate causalities. Mathematical modeling of the ...human gut microbiome at a genome scale is a useful tool to decipher microbe-microbe, diet-microbe and microbe-host interactions. Here, we describe the CASINO (Community And Systems-level INteractive Optimization) toolbox, a comprehensive computational platform for analysis of microbial communities through metabolic modeling. We first validated the toolbox by simulating and testing the performance of single bacteria and whole communities in vitro. Focusing on metabolic interactions between the diet, gut microbiota, and host metabolism, we demonstrated the predictive power of the toolbox in a diet-intervention study of 45 obese and overweight individuals and validated our predictions by fecal and blood metabolomics data. Thus, modeling could quantitatively describe altered fecal and serum amino acid levels in response to diet intervention.
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•Community And Systems-level INteractive Optimization toolbox•Modeling the effect of diet and gene richness on the gut microbiota•Revealing altered amino acid and SCFA levels after diet interventions
Shoaie et al. describe a computational platform designed to elucidate the complex metabolic interactions between gut microbes, host, and diet. The model predictions are validated in humans and reveal how microbial gene richness and diet affect gut microbiota composition, as well as amino acid and SCFA levels.
Massive data produced due to the advent of next-generation sequencing (NGS) technology is widely used for biological researches and medical diagnosis. The crucial step in NGS analysis is read ...alignment or mapping which is computationally intensive and complex. The mapping bias tends to affect the downstream analysis, including detection of polymorphisms. In order to provide guidelines to the biologist for suitable selection of aligners; we have evaluated and benchmarked 5 different aligners (BWA, Bowtie2, NovoAlign, Smalt and Stampy) and their mapping bias based on characteristics of 5 microbial genomes. Two million simulated read pairs of various sizes (36bp, 50bp, 72bp, 100bp, 125bp, 150bp, 200bp, 250bp and 300bp) were aligned. Specific alignment features such as sensitivity of mapping, percentage of properly paired reads, alignment time and effect of tandem repeats on incorrectly mapped reads were evaluated. BWA showed faster alignment followed by Bowtie2 and Smalt. NovoAlign and Stampy were comparatively slower. Most of the aligners showed high sensitivity towards long reads (>100bp) mapping. On the other hand NovoAlign showed higher sensitivity towards both short reads (36bp, 50bp, 72bp) and long reads (>100bp) mappings; It also showed higher sensitivity towards mapping a complex genome like Plasmodium falciparum. The percentage of properly paired reads aligned by NovoAlign, BWA and Stampy were markedly higher. None of the aligners outperforms the others in the benchmark, however the aligners perform differently with genome characteristics. We expect that the results from this study will be useful for the end user to choose aligner, thus enhance the accuracy of read mapping.
•Evaluation and assessment of read-mapping by multiple Next-generation sequencing aligners based on genome-wide characteristics•The five widely used aligners BWA, Bowtie2, NovoAlign, Smalt and Stampy were evaluate their performance on different five microbial genomes, which have diverse genome characteristics. The result form the benchmarking was reported as a guideline for the end user to choose an appropriate aligner enhancing the accuracy of read mapping•The performance of the all aligners are all good at the read length at 100 or above. BWA aligner were out performed in computational time.
There is growing interest in the metabolic interplay between the gut microbiome and host metabolism. Taxonomic and functional profiling of the gut microbiome by next-generation sequencing (NGS) has ...unveiled substantial richness and diversity. However, the mechanisms underlying interactions between diet, gut microbiome and host metabolism are still poorly understood. Genome-scale metabolic modeling (GSMM) is an emerging approach that has been increasingly applied to infer diet⁻microbiome, microbe⁻microbe and host⁻microbe interactions under physiological conditions. GSMM can, for example, be applied to estimate the metabolic capabilities of microbes in the gut. Here, we discuss how meta-omics datasets such as shotgun metagenomics, can be processed and integrated to develop large-scale, condition-specific, personalized microbiota models in healthy and disease states. Furthermore, we summarize various tools and resources available for metagenomic data processing and GSMM, highlighting the experimental approaches needed to validate the model predictions.
Recent advancements in omics technologies have generated a wealth of biological data. Integrating these data within mathematical models is essential to fully leverage their potential. Genome-scale ...metabolic models (GEMs) provide a robust framework for studying complex biological systems. GEMs have significantly contributed to our understanding of human metabolism, including the intrinsic relationship between the gut microbiome and the host metabolism. In this review, we highlight the contributions of GEMs and discuss the critical challenges that must be overcome to ensure their reproducibility and enhance their prediction accuracy, particularly in the context of precision medicine. We also explore the role of machine learning in addressing these challenges within GEMs. The integration of omics data with GEMs has the potential to lead to new insights, and to advance our understanding of molecular mechanisms in human health and disease.
Human peripheral blood mononuclear cells (PBMCs) are the key drivers of the immune responses. These cells undergo activation, proliferation and differentiation into various subsets. During these ...processes they initiate metabolic reprogramming, which is coordinated by specific gene and protein activities. PBMCs as a model system have been widely used to study metabolic and autoimmune diseases. Herein we review various omics and systems-based approaches such as transcriptomics, epigenomics, proteomics, and metabolomics as applied to PBMCs, particularly T helper subsets, that unveiled disease markers and the underlying mechanisms. We also discuss and emphasize several aspects of T cell metabolic modeling in healthy and disease states using genome-scale metabolic models.
Aims/hypothesis
Previous metabolomics studies suggest that type 1 diabetes is preceded by specific metabolic disturbances. The aim of this study was to investigate whether distinct metabolic patterns ...occur in peripheral blood mononuclear cells (PBMCs) of children who later develop pancreatic beta cell autoimmunity or overt type 1 diabetes.
Methods
In a longitudinal cohort setting, PBMC metabolomic analysis was applied in children who (1) progressed to type 1 diabetes (PT1D,
n
= 34), (2) seroconverted to ≥1 islet autoantibody without progressing to type 1 diabetes (P1Ab,
n
= 27) or (3) remained autoantibody negative during follow-up (CTRL,
n
= 10).
Results
During the first year of life, levels of most lipids and polar metabolites were lower in the PT1D and P1Ab groups compared with the CTRL group. Pathway over-representation analysis suggested alanine, aspartate, glutamate, glycerophospholipid and sphingolipid metabolism were over-represented in PT1D. Genome-scale metabolic models of PBMCs during type 1 diabetes progression were developed by using publicly available transcriptomics data and constrained with metabolomics data from our study. Metabolic modelling confirmed altered ceramide pathways, known to play an important role in immune regulation, as specifically associated with type 1 diabetes progression.
Conclusions/interpretation
Our data suggest that systemic dysregulation of lipid metabolism, as observed in plasma, may impact the metabolism and function of immune cells during progression to overt type 1 diabetes.
Data availability
The GEMs for PBMCs have been submitted to BioModels (
www.ebi.ac.uk/biomodels/
), under accession number MODEL1905270001. The metabolomics datasets and the clinical metadata generated in this study were submitted to MetaboLights (
https://www.ebi.ac.uk/metabolights/
), under accession number MTBLS1015.
Various studies aiming to elucidate the role of the gut microbiome-metabolome co-axis in health and disease have primarily focused on water-soluble polar metabolites, whilst non-polar microbial ...lipids have received less attention. The concept of microbiota-dependent lipid biotransformation is over a century old. However, only recently, several studies have shown how microbial lipids alter intestinal and circulating lipid concentrations in the host, thus impacting human lipid homeostasis. There is emerging evidence that gut microbial communities play a particularly significant role in the regulation of host cholesterol and sphingolipid homeostasis. Here, we review and discuss recent research focusing on microbe-host-lipid co-metabolism. We also discuss the interplay of human gut microbiota and molecular lipids entering host systemic circulation, and its role in health and disease.
Abstract
Knowledge about in vivo effects of human circulating C-6 hydroxylated bile acids (BAs), also called muricholic acids, is sparse. It is unsettled if the gut microbiome might contribute to ...their biosynthesis. Here, we measured a range of serum BAs and related them to markers of human metabolic health and the gut microbiome. We examined 283 non-obese and obese Danish adults from the MetaHit study. Fasting concentrations of serum BAs were quantified using ultra-performance liquid chromatography-tandem mass-spectrometry. The gut microbiome was characterized with shotgun metagenomic sequencing and genome-scale metabolic modeling. We find that tauro- and glycohyocholic acid correlated inversely with body mass index (
P
= 4.1e-03,
P
= 1.9e-05, respectively), waist circumference (
P
= 0.017,
P
= 1.1e-04, respectively), body fat percentage (
P
= 2.5e-03,
P
= 2.3e-06, respectively), insulin resistance (
P
= 0.051,
P
= 4.6e-4, respectively), fasting concentrations of triglycerides (
P
= 0.06,
P
= 9.2e-4, respectively) and leptin (
P
= 0.067,
P
= 9.2e-4). Tauro- and glycohyocholic acids, and tauro-a-muricholic acid were directly linked with a distinct gut microbial community primarily composed of
Clostridia
species (
P
= 0.037,
P
= 0.013,
P
= 0.027, respectively). We conclude that serum conjugated C-6-hydroxylated BAs associate with measures of human metabolic health and gut communities of
Clostridia
species. The findings merit preclinical interventions and human feasibility studies to explore the therapeutic potential of these BAs in obesity and type 2 diabetes.
•Over last decade, increasing incidence of type 1 diabetes has stabilized in Finland.•Stabilization of disease incidence coincides with tighter regulation of PFAS.•High prenatal PFAS exposure ...associates with decreased postnatal serum phospholipids.•High prenatal PFAS exposure associates with appearance of islet autoantibodies.•Prenatal PFAS exposure contributes to increased postnatal risk of type 1 diabetes.
In the last decade, increasing incidence of type 1 diabetes (T1D) stabilized in Finland, a phenomenon that coincides with tighter regulation of perfluoroalkyl substances (PFAS). Here, we quantified PFAS to examine their effects, during pregnancy, on lipid and immune-related markers of T1D risk in children. In a mother-infant cohort (264 dyads), high PFAS exposure during pregnancy associated with decreased cord serum phospholipids and progression to T1D-associated islet autoantibodies in the offspring. This PFAS-lipid association appears exacerbated by increased human leukocyte antigen-conferred risk of T1D in infants. Exposure to a single PFAS compound or a mixture of organic pollutants in non-obese diabetic mice resulted in a lipid profile characterized by a similar decrease in phospholipids, a marked increase of lithocholic acid, and accelerated insulitis. Our findings suggest that PFAS exposure during pregnancy contributes to risk and pathogenesis of T1D in offspring.
Cancer progression is linked to gene-environment interactions that alter cellular homeostasis. The use of biomarkers as early indicators of disease manifestation and progression can substantially ...improve diagnosis and treatment. Large omics datasets generated by high-throughput profiling technologies, such as microarrays, RNA sequencing, whole-genome shotgun sequencing, nuclear magnetic resonance, and mass spectrometry, have enabled data-driven biomarker discoveries. The identification of differentially expressed traits as molecular markers has traditionally relied on statistical techniques that are often limited to linear parametric modeling. The heterogeneity, epigenetic changes, and high degree of polymorphism observed in oncogenes demand biomarker-assisted personalized medication schemes. Deep learning (DL), a major subunit of machine learning (ML), has been increasingly utilized in recent years to investigate various diseases. The combination of ML/DL approaches for performance optimization across multi-omics datasets produces robust ensemble-learning prediction models, which are becoming useful in precision medicine. This review focuses on the recent development of ML/DL methods to provide integrative solutions in discovering cancer-related biomarkers, and their utilization in precision medicine.
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