Modeling metabolism of the human gut microbiome

Personalized gut microbial community modeling by leveraging genome-scale metabolic models and metagenomics

The impact of the gut microbiome on human health is increasingly recognized as dysbiosis has been found to be associated with a spectrum of diseases. Here, we review the databases of genome-scale metabolic models (GEMs), which have paved the way for investigations into the metabolic capabilities of gut microbes and their interspecies dynamics. We further discuss the strategies for developing community-level GEMs, which are crucial for understanding the complex interactions within microbial communities and between the microbiome and its host. Such GEMs can guide the design of synthetic microbial communities for disease treatment. Finally, we explore advances in personalized gut microbiome modeling. These advancements broaden our mechanistic understanding and hold promise for applications in precision medicine and therapeutic interventions.

Modelling the influence of vitamin D and probiotic supplementation on the microbiome and immune response

The intestinal microbiota play a critical role in human health and disease, maintaining metabolic and immune/inflammatory health, synthesizing essential vitamins and amino acids and maintaining intestinal barrier integrity. The aim of this paper is to develop a mathematical model to describe the complex interactions between the microbiota, vitamin D/vitamin D receptor (VDR) pathway, epithelial barrier and immune response in order to understand better the effects of supplementation with probiotics and vitamin D. This is motivated by emerging data indicating the beneficial effects of vitamin D and probiotics individually and when combined. We propose a system of ordinary differential equations determining the time evolution of intestinal bacterial populations, concentration of the VDR:1,25(OH)$_{2}$D complex in epithelial and immune cells, the epithelial barrier and the immune response. The model shows that administration of probiotics and/or vitamin D upregulates the VDR complex, which enhances barrier function and protects against intestinal inflammation. The model also suggests co-supplementation to be superior to individual supplements. We explore the effects of inflammation on the populations of commensal and pathogenic bacteria and the vitamin D/VDR pathway and discuss the value of gathering additional experimental data motivated by the modelling insights.

Serum Metabolites Relate to Mucosal and Transmural Inflammation in Paediatric Crohn Disease

BACKGROUND AND AIMS

We aimed to identify serum metabolites associated with mucosal and transmural inflammation in paediatric Crohn disease [pCD].

METHODS

In all, 56 pCD patients were included through a pre-planned sub-study of the multicentre, prospective, ImageKids cohort, designed to develop the Paediatric Inflammatory Crohn magnetic resonance enterography [MRE] Index [PICMI]. Children were included throughout their disease course when undergoing ileocolonoscopy and MRE and were followed for 18 months, when MRE was repeated. Serum metabolites were identified using liquid chromatography/mass spectroscopy. Outcomes included: PICMI, the simple endoscopic score [SES], faecal calprotectin [FCP], and C-reactive protein [CRP], to assess transmural, mucosal, and systemic inflammation, respectively. Random forest models were built by outcome. Maximum relevance minimum redundancy [mRMR] feature selection with a j-fold cross-validation scheme identified the best subset of features and hyperparameter settings.

RESULTS

Tryptophan and glutarylcarnitine were the top common mRMR metabolites linked to pCD inflammation. Random forest models established that amino acids and amines were among the most influential metabolites for predicting transmural and mucosal inflammation. Predictive models performed well, each with an area under the curve [AUC] > 70%. In addition, serum metabolites linked with pCD inflammation mainly related to perturbations in the citrate cycle [TCA cycle], aminoacyl-tRNA biosynthesis, tryptophan metabolism, butanoate metabolism, and tyrosine metabolism.

CONCLUSIONS

We extend on recent studies, observing differences in serum metabolites between healthy controls and Crohn disease patients, and suggest various associations of serum metabolites with transmural and mucosal inflammation. These metabolites could improve the understanding of pCD pathogenesis and assessment of disease severity.

Emerging tools and best practices for studying gut microbial community metabolism

The human gut microbiome vastly extends the set of metabolic reactions catalysed by our own cells, with far-reaching consequences for host health and disease. However, our knowledge of gut microbial metabolism relies on a handful of model organisms, limiting our ability to interpret and predict the metabolism of complex microbial communities. In this Perspective, we discuss emerging tools for analysing and modelling the metabolism of gut microorganisms and for linking microorganisms, pathways and metabolites at the ecosystem level, highlighting promising best practices for researchers. Continued progress in this area will also require infrastructure development to facilitate cross-disciplinary synthesis of scientific findings. Collectively, these efforts can enable a broader and deeper understanding of the workings of the gut ecosystem and open new possibilities for microbiome manipulation and therapy.

2'-Fucosyllactose helps butyrate producers outgrow competitors in infant gut microbiota simulations

A reduced capacity for butyrate production by the early infant gut microbiota is associated with negative health effects, such as inflammation and the development of allergies. Here, we develop new hypotheses on the effect of the prebiotic galacto-oligosaccharides (GOS) or 2'-fucosyllactose (2'-FL) on butyrate production by the infant gut microbiota using a multiscale, spatiotemporal mathematical model of the infant gut. The model simulates a community of cross-feeding gut bacteria in metabolic detail. It represents the community as a grid of bacterial populations that exchange metabolites, using 20 different subspecies-specific metabolic networks taken from the AGORA database. The simulations predict that both GOS and 2'-FL promote the growth of Bifidobacterium, whereas butyrate producing bacteria are only consistently abundant in the presence of propane-1,2-diol, a product of 2'-FL metabolism. In absence of prebiotics or in presence of only GOS, however, Bacteroides vulgatus and Cutibacterium acnes outcompete butyrate producers by consuming intermediate metabolites.

Gut microbiome-metabolome interactions predict host condition

BACKGROUND

The effect of microbes on their human host is often mediated through changes in metabolite concentrations. As such, multiple tools have been proposed to predict metabolite concentrations from microbial taxa frequencies. Such tools typically fail to capture the dependence of the microbiome-metabolite relation on the environment.

RESULTS

We propose to treat the microbiome-metabolome relation as the equilibrium of a complex interaction and to relate the host condition to a latent representation of the interaction between the log concentration of the metabolome and the log frequencies of the microbiome. We develop LOCATE (Latent variables Of miCrobiome And meTabolites rElations), a machine learning tool to predict the metabolite concentration from the microbiome composition and produce a latent representation of the interaction. This representation is then used to predict the host condition. LOCATE's accuracy in predicting the metabolome is higher than all current predictors. The metabolite concentration prediction accuracy significantly decreases cross datasets, and cross conditions, especially in 16S data. LOCATE's latent representation predicts the host condition better than either the microbiome or the metabolome. This representation is strongly correlated with host demographics. A significant improvement in accuracy (0.793 vs. 0.724 average accuracy) is obtained even with a small number of metabolite samples ([Formula: see text]).

CONCLUSION

These results suggest that a latent representation of the microbiome-metabolome interaction leads to a better association with the host condition than any of the two separated or the simple combination of the two. Video Abstract.

Microbial gatekeepers: unraveling the role of the gut microbiota enzyme DPP4 in diabetes management

Wang et al. identified dipeptidyl peptidase 4 (DPP4) as a gut microbe-derived enzyme that impacts on host glucose metabolism. They further introduced a novel therapeutic, daurisoline-d4 (Dau-d4), a selective microbial DPP4 (mDPP4) inhibitor that shows promise in improving glucose tolerance, highlighting the potential of therapies that target both host enzymes and gut microbial enzymes.

The interaction among Kluyveromyces marxianus G-Y4, Lacticaseibacillus paracasei GL1, and Lactobacillus helveticus SNA12 and signaling molecule AI-2 participate in the formation of biofilm

In this study, two strains of lactic acid bacteria (Lacticaseibacillus paracasei GL1 and Lactobacillus helveticus SNA12) and one yeast strain of Kluyveromyces marxianus G-Y4 (G-Y4) isolated from Tibetan kefir grains were co-cultured. It was found that the addition of G-Y4 could not only promote the growth of lactic acid bacteria, but also increase the release of metabolites (lactic acid, ethanol, and amino nitrogen). Furthermore, the addition of live cells and cell-free fermentation supernatant (CFS) of G-Y4 could increase the ability of biofilm formation. Morever, the surface characteristics results showed that the addition of G-Y4 live cells could enhance the aggregation ability and hydrophobicity of LAB. Meanwhile, adding live cells and CFS of G-Y4 could promote the release of signaling molecule AI-2 and enhance the expression of the LuxS gene related to biofilm formation. In addition, Fourier-transform infrared spectroscopy and chemical composition analysis were used to investigate the composition of the biofilm, and the results indicated that the biofilm was mainly composed of a small amount of protein but it was rich in polysaccharides including glucose, galactose, and mannose with different ratios. Finally, the formation of biofilm could delay the decline of the number of viable bacteria in storage fermented milk.

Molecular systems biology approaches to investigate mechanisms of gut-brain communication in neurological diseases

BACKGROUND

Whilst the incidence of neurological diseases is increasing worldwide, treatment remains mostly limited to symptom management. The gut-brain axis, which encompasses the communication routes between microbiota, gut and brain, has emerged as a crucial area of investigation for identifying new preventive and therapeutic targets in neurological disease.

METHODS

Due to the inter-organ, systemic nature of the gut-brain axis, together with the multitude of biomolecules and microbial species involved, molecular systems biology approaches are required to accurately investigate the mechanisms of gut-brain communication. High-throughput omics profiling, together with computational methodologies such as dimensionality reduction or clustering, machine learning, network inference and genome-scale metabolic models, allows novel biomarkers to be discovered and elucidates mechanistic insights.

RESULTS

In this review, the general concepts of experimental and computational methodologies for gut-brain axis research are introduced and their applications are discussed, mainly in human cohorts. Important aspects are further highlighted concerning rational study design, sampling procedures and data modalities relevant for gut-brain communication, strengths and limitations of methodological approaches and some future perspectives.

CONCLUSION

Multi-omics analyses, together with advanced data mining, are essential to functionally characterize the gut-brain axis and put forward novel preventive or therapeutic strategies in neurological disease.

Causal inference on microbiome-metabolome relations in observational host-microbiome data via in silico in vivo association pattern analyses

Understanding the effects of the microbiome on the host's metabolism is core to enlightening the role of the microbiome in health and disease. Herein, we develop the paradigm of in silico in vivo association pattern analyses, combining microbiome metabolome association studies with in silico constraint-based community modeling. Via theoretical dissection of confounding and causal paths, we show that in silico in vivo association pattern analyses allow for causal inference on microbiome-metabolome relations in observational data. We justify the corresponding theoretical criterion by structural equation modeling of host-microbiome systems, integrating deterministic microbiome community modeling into population statistics approaches. We show the feasibility of our approach on a published multi-omics dataset (n = 347), demonstrating causal microbiome-metabolite relations for 26 out of 54 fecal metabolites. In summary, we generate a promising approach for causal inference in metabolic host-microbiome interactions by integrating hypothesis-free screening association studies with knowledge-based in silico modeling.

The role of the Mediterranean diet in modulating the gut microbiome: A review of current evidence

The Mediterranean diet (MedDiet) is recognized as one of the United Nations Educational, Scientific and Cultural Organization Intangible Cultural Heritage assets associated with lower rates of cardiometabolic diseases; lower prevalence of cancer, Alzheimer's disease, depression, and onset of inflammatory bowel disease; and more generally low-grade inflammation and mortality risks. Beyond being an input source of beneficial micronutrients, it recently has been discovered that the MedDiet plays a role in a more complex human microbiome-mediated mechanism. An interesting hypothesis suggests a bidirectional relationship between the MedDiet and the gut microbiome, where gut microbiota assembly and biosynthetic capacity are responsive to the diet; in return, the microbiome-reachable nutrients shape and modulate the microbiome toward a characteristic probiotic state. It can be speculated that that primary health benefits of the MedDiet exerted via the gut microbiome are mediated by the bioactive compounds transformed by the microbiome. Furthermore, it is possible that additional probiotic properties of the organisms promoted by diet adherence have secondary benefits. As more detailed omic-based studies take place, more evidence on the MedDiet as a core generic probiotic microbiome modulation strategy surface. However, individual-specific microbiome compositions might impose personal variations on the diet outcome. Therefore, a prospective strategy of a fine-tuned precision nutrition approach might deliver optimized benefits of the MedDiet.

Genome-scale metabolic reconstruction of 7,302 human microorganisms for personalized medicine

The human microbiome influences the efficacy and safety of a wide variety of commonly prescribed drugs. Designing precision medicine approaches that incorporate microbial metabolism would require strain- and molecule-resolved, scalable computational modeling. Here, we extend our previous resource of genome-scale metabolic reconstructions of human gut microorganisms with a greatly expanded version. AGORA2 (assembly of gut organisms through reconstruction and analysis, version 2) accounts for 7,302 strains, includes strain-resolved drug degradation and biotransformation capabilities for 98 drugs, and was extensively curated based on comparative genomics and literature searches. The microbial reconstructions performed very well against three independently assembled experimental datasets with an accuracy of 0.72 to 0.84, surpassing other reconstruction resources and predicted known microbial drug transformations with an accuracy of 0.81. We demonstrate that AGORA2 enables personalized, strain-resolved modeling by predicting the drug conversion potential of the gut microbiomes from 616 patients with colorectal cancer and controls, which greatly varied between individuals and correlated with age, sex, body mass index and disease stages. AGORA2 serves as a knowledge base for the human microbiome and paves the way to personalized, predictive analysis of host-microbiome metabolic interactions.

A Quantitative Systems Pharmacology Model Describing the Cellular Kinetic-Pharmacodynamic Relationship for a Live Biotherapeutic Product to Support Microbiome Drug Development

Live biotherapeutic products (LBPs) are human microbiome therapies showing promise in the clinic for a range of diseases and conditions. Describing the kinetics and behavior of LBPs poses a unique modeling challenge because, unlike traditional therapies, LBPs can expand, contract, and colonize the host digestive tract. Here, we present a novel cellular kinetic-pharmacodynamic quantitative systems pharmacology model of an LBP. The model describes bacterial growth and competition, vancomycin effects, binding and unbinding to the epithelial surface, and production and clearance of butyrate as a therapeutic metabolite. The model is calibrated and validated to published data from healthy volunteers. Using the model, we simulate the impact of treatment dose, frequency, and duration as well as vancomycin pretreatment on butyrate production. This model enables model-informed drug development and can be used for future microbiome therapies to inform decision making around antibiotic pretreatment, dose selection, loading dose, and dosing duration.

What the Gut Tells the Brain-Is There a Link between Microbiota and Huntington's Disease?

The human intestinal microbiota is a diverse and dynamic microenvironment that forms a complex, bi-directional relationship with the host. The microbiome takes part in the digestion of food and the generation of crucial nutrients such as short chain fatty acids (SCFA), but is also impacts the host's metabolism, immune system, and even brain functions. Due to its indispensable role, microbiota has been implicated in both the maintenance of health and the pathogenesis of many diseases. Dysbiosis in the gut microbiota has already been implicated in many neurodegenerative diseases such as Parkinson's disease (PD) and Alzheimer's disease (AD). However, not much is known about the microbiome composition and its interactions in Huntington's disease (HD). This dominantly heritable, incurable neurodegenerative disease is caused by the expansion of CAG trinucleotide repeats in the huntingtin gene (HTT). As a result, toxic RNA and mutant protein (mHTT), rich in polyglutamine (polyQ), accumulate particularly in the brain, leading to its impaired functions. Interestingly, recent studies indicated that mHTT is also widely expressed in the intestines and could possibly interact with the microbiota, affecting the progression of HD. Several studies have aimed so far to screen the microbiota composition in mouse models of HD and find out whether observed microbiome dysbiosis could affect the functions of the HD brain. This review summarizes ongoing research in the HD field and highlights the essential role of the intestine-brain axis in HD pathogenesis and progression. The review also puts a strong emphasis on indicating microbiome composition as a future target in the urgently needed therapy for this still incurable disease.

Gut-on-a-Chip for the Analysis of Bacteria-Bacteria Interactions in Gut Microbial Community: What Would Be Needed for Bacterial Co-Culture Study to Explore the Diet-Microbiota Relationship?

Bacterial co-culture studies using synthetic gut microbiomes have reported novel research designs to understand the underlying role of bacterial interaction in the metabolism of dietary resources and community assembly of complex microflora. Since lab-on-a-chip mimicking the gut (hereafter "gut-on-a-chip") is one of the most advanced platforms for the simulative research regarding the correlation between host health and microbiota, the co-culture of the synthetic bacterial community in gut-on-a-chip is expected to reveal the diet-microbiota relationship. This critical review analyzed recent research on bacterial co-culture with perspectives on the ecological niche of commensals, probiotics, and pathogens to categorize the experimental approaches for diet-mediated management of gut health as the compositional and/or metabolic modulation of the microbiota and the control of pathogens. Meanwhile, the aim of previous research on bacterial culture in gut-on-a-chip has been mainly limited to the maintenance of the viability of host cells. Thus, the integration of study designs established for the co-culture of synthetic gut consortia with various nutritional resources into gut-on-a-chip is expected to reveal bacterial interspecies interactions related to specific dietary patterns. This critical review suggests novel research topics for co-culturing bacterial communities in gut-on-a-chip to realize an ideal experimental platform mimicking a complex intestinal environment.

Compositionality, sparsity, spurious heterogeneity, and other data-driven challenges for machine learning algorithms within plant microbiome studies

The plant-associated microbiome is a key component of plant systems, contributing to their health, growth, and productivity. The application of machine learning (ML) in this field promises to help untangle the relationships involved. However, measurements of microbial communities by high-throughput sequencing pose challenges for ML. Noise from low sample sizes, soil heterogeneity, and technical factors can impact the performance of ML. Additionally, the compositional and sparse nature of these datasets can impact the predictive accuracy of ML. We review recent literature from plant studies to illustrate that these properties often go unmentioned. We expand our analysis to other fields to quantify the degree to which mitigation approaches improve the performance of ML and describe the mathematical basis for this. With the advent of accessible analytical packages for microbiome data including learning models, researchers must be familiar with the nature of their datasets.

Metabolic Engineering: Methodologies and Applications

Metabolic engineering aims to improve the production of economically valuable molecules through the genetic manipulation of microbial metabolism. While the discipline is a little over 30 years old, advancements in metabolic engineering have given way to industrial-level molecule production benefitting multiple industries such as chemical, agriculture, food, pharmaceutical, and energy industries. This review describes the design, build, test, and learn steps necessary for leading a successful metabolic engineering campaign. Moreover, we highlight major applications of metabolic engineering, including synthesizing chemicals and fuels, broadening substrate utilization, and improving host robustness with a focus on specific case studies. Finally, we conclude with a discussion on perspectives and future challenges related to metabolic engineering.

Neoagaro-Oligosaccharides Ameliorate Chronic Restraint Stress-Induced Depression by Increasing 5-HT and BDNF in the Brain and Remodeling the Gut Microbiota of Mice

Neoagaro-oligosaccharides (NAOs) belong to the algae oligosaccharides. NAOs have been found to have diverse biological activities. However, the effects of NAOs on depression and their underlying mechanism have not been thoroughly studied. A chronic restraint stress (CRS)-induced C57BL/6J mouse model was used to assess the antidepressant effects of NAOs. Anxiety and depression behaviors were assessed by open field tests (OFT) and forced swimming tests (FST), while interleukin 18 (IL-18), 5-hydroxytryptamine (5-HT) and brain-derived neurotrophic factor (BDNF) were the molecular biomarkers of depression. Fecal microbiota transplantation (FMT) was performed. The results showed that NAO treatment significantly improved the body weight of depressed mice and reduced the central area time in the OFT and immobility time in the FST. NAO treatment decreased the levels of IL-18 in the serum and increased the levels of 5-HT in the serum and whole brain and of BDNF in the whole brain. NAO treatment mitigated the gut microbiota dysbiosis in the depressed mice and reversed the decreased levels of short-chain fatty acids (SCFAs) in the cecum of the depressed mice. FMT indicated that the gut microbiota is, indeed, linked to depression, which was reflected in the changes in weight gain and behaviors. In a word, NAOs effectively reversed the CRS-induced mice model of depression, which depended on the changes in the gut microbiota and SCFAs, as well as its modulation of 5-HT and BDNF.

A Multiscale Spatiotemporal Model Including a Switch from Aerobic to Anaerobic Metabolism Reproduces Succession in the Early Infant Gut Microbiota

The human intestinal microbiota starts to form immediately after birth and is important for the health of the host. During the first days, facultatively anaerobic bacterial species generally dominate, such as Enterobacteriaceae. These are succeeded by strictly anaerobic species, particularly Bifidobacterium species. An early transition to Bifidobacterium species is associated with health benefits; for example, Bifidobacterium species repress growth of pathogenic competitors and modulate the immune response. Succession to Bifidobacterium is thought to be due to consumption of intracolonic oxygen present in newborns by facultative anaerobes, including Enterobacteriaceae. To study if oxygen depletion suffices for the transition to Bifidobacterium species, here we introduced a multiscale mathematical model that considers metabolism, spatial bacterial population dynamics, and cross-feeding. Using publicly available metabolic network data from the AGORA collection, the model simulates ab initio the competition of strictly and facultatively anaerobic species in a gut-like environment under the influence of lactose and oxygen. The model predicts that individual differences in intracolonic oxygen in newborn infants can explain the observed individual variation in succession to anaerobic species, in particular Bifidobacterium species. Bifidobacterium species became dominant in the model by their use of the bifid shunt, which allows Bifidobacterium to switch to suboptimal yield metabolism with fast growth at high lactose concentrations, as predicted here using flux balance analysis. The computational model thus allows us to test the internal plausibility of hypotheses for bacterial colonization and succession in the infant colon. IMPORTANCE The composition of the infant microbiota has a great impact on infant health, but its controlling factors are still incompletely understood. The frequently dominant anaerobic Bifidobacterium species benefit health, e.g., they can keep harmful competitors under control and modulate the intestinal immune response. Controlling factors could include nutritional composition and intestinal mucus composition, as well as environmental factors, such as antibiotics. We introduce a modeling framework of a metabolically realistic intestinal microbial ecology in which hypothetical scenarios can be tested and compared. We present simulations that suggest that greater levels of intraintestinal oxygenation more strongly delay the dominance of Bifidobacterium species, explaining the observed variety of microbial composition and demonstrating the use of the model for hypothesis generation. The framework allowed us to test a variety of controlling factors, including intestinal mixing and transit time. Future versions will also include detailed modeling of oligosaccharide and mucin metabolism.

Hyperbaric oxygen improves depression-like behaviors in chronic stress model mice by remodeling gut microbiota and regulating host metabolism

AIMS

There is growing evidence that the gut microbiota plays a significant part in the pathophysiology of chronic stress. The dysbiosis of the gut microbiota closely relates to dysregulation of microbiota-host cometabolism. Composition changes in the gut microbiota related to perturbations in metabolic profiles are vital risk factors for disease development. Hyperbaric oxygen therapy is commonly applied as an alternative or primary therapy for various diseases. Therefore, a metabolic and gut bacteria perspective is essential to uncover possible mechanisms of chronic stress and the therapeutic effect of hyperbaric oxygenation. We determined that there were significantly disturbed metabolites and disordered gut microbiota between control and chronic stress group. The study aims to offer further information on the interactions between host metabolism, gut microbiota, and chronic stress.

METHODS

At present, chronic unpredictable mild stress is considered the most widespread method of modeling chronic stress in animals, so we used a chronic unpredictable mild stress mouse model to characterize changes in the metabolome and microbiome of depressed mice by combining 16S rRNA gene sequencing and UHPLC-MS/MS-based metabolomics. Pearson's correlation-based clustering analysis was performed with above metabolomics and fecal microbiome data to determine gut microbiota-associated metabolites.

RESULTS

We found that 18 metabolites showed a significant correlation with campylobacterota. Campylobacterota associated metabolites were significantly enriched mainly in the d-glutamate and d-glutamine metabolism. Hyperoxia treatment may improve depression-like behaviors in chronic stress model mice through regulating the disrupted metabolites.

CONCLUSIONS

Hyperbaric oxygen improves depression-like behaviors in chronic stress model mice by remodeling Campylobacterota associated metabolites.

Material Engineering in Gut Microbiome and Human Health

Tremendous progress has been made in the past decade regarding our understanding of the gut microbiome's role in human health. Currently, however, a comprehensive and focused review marrying the two distinct fields of gut microbiome and material research is lacking. To bridge the gap, the current paper discusses critical aspects of the rapidly emerging research topic of "material engineering in the gut microbiome and human health." By engaging scientists with diverse backgrounds in biomaterials, gut-microbiome axis, neuroscience, synthetic biology, tissue engineering, and biosensing in a dialogue, our goal is to accelerate the development of research tools for gut microbiome research and the development of therapeutics that target the gut microbiome. For this purpose, state-of-the-art knowledge is presented here on biomaterial technologies that facilitate the study, analysis, and manipulation of the gut microbiome, including intestinal organoids, gut-on-chip models, hydrogels for spatial mapping of gut microbiome compositions, microbiome biosensors, and oral bacteria delivery systems. In addition, a discussion is provided regarding the microbiome-gut-brain axis and the critical roles that biomaterials can play to investigate and regulate the axis. Lastly, perspectives are provided regarding future directions on how to develop and use novel biomaterials in gut microbiome research, as well as essential regulatory rules in clinical translation. In this way, we hope to inspire research into future biomaterial technologies to advance gut microbiome research and gut microbiome-based theragnostics.

Targeting the gut to prevent and counteract metabolic disorders and pathologies during aging

Impairment of gut function is one of the explanatory mechanisms of health status decline in elderly population. These impairments involve a decline in gut digestive physiology, metabolism and immune status, and associated to that, changes in composition and function of the microbiota it harbors. Continuous deteriorations are generally associated with the development of systemic dysregulations and ultimately pathologies that can worsen the initial health status of individuals. All these alterations observed at the gut level can then constitute a wide range of potential targets for development of nutritional strategies that can impact gut tissue or associated microbiota pattern. This can be key, in a preventive manner, to limit gut functionality decline, or in a curative way to help maintaining optimum nutrients bioavailability in a context on increased requirements, as frequently observed in pathological situations. The aim of this review is to give an overview on the alterations that can occur in the gut during aging and lead to the development of altered function in other tissues and organs, ultimately leading to the development of pathologies. Subsequently is discussed how nutritional strategies that target gut tissue and gut microbiota can help to avoid or delay the occurrence of aging-related pathologies.

A Computational Framework for Studying Gut-Brain Axis in Autism Spectrum Disorder

Introduction

The integrity of the intestinal epithelium is crucial for human health and is harmed in autism spectrum disorder (ASD). An aberrant gut microbial composition resulting in gut-derived metabolic toxins was found to damage the intestinal epithelium, jeopardizing tissue integrity. These toxins further reach the brain via the gut-brain axis, disrupting the normal function of the brain. A mechanistic understanding of metabolic disturbances in the brain and gut is essential to design effective therapeutics and early intervention to block disease progression. Herein, we present a novel computational framework integrating constraint based tissue specific metabolic (CBM) model and whole-body physiological pharmacokinetics (PBPK) modeling for ASD. Furthermore, the role of gut microbiota, diet, and oxidative stress is analyzed in ASD.

Methods

A representative gut model capturing host-bacteria and bacteria-bacteria interaction was developed using CBM techniques and patient data. Simultaneously, a PBPK model of toxin metabolism was assembled, incorporating multi-scale metabolic information. Furthermore, dynamic flux balance analysis was performed to integrate CBM and PBPK. The effectiveness of a probiotic and dietary intervention to improve autism symptoms was tested on the integrated model.

Results

The model accurately highlighted critical metabolic pathways of the gut and brain that are associated with ASD. These include central carbon, nucleotide, and vitamin metabolism in the host gut, and mitochondrial energy and amino acid metabolisms in the brain. The proposed dietary intervention revealed that a high-fiber diet is more effective than a western diet in reducing toxins produced inside the gut. The addition of probiotic bacteria Lactobacillus acidophilus, Bifidobacterium longum longum, Akkermansia muciniphila, and Prevotella ruminicola to the diet restores gut microbiota balance, thereby lowering oxidative stress in the gut and brain.

Conclusion

The proposed computational framework is novel in its applicability, as demonstrated by the determination of the whole-body distribution of ROS toxins and metabolic association in ASD. In addition, it emphasized the potential for developing novel therapeutic strategies to alleviate autism symptoms. Notably, the presented integrated model validates the importance of combining PBPK modeling with COBRA -specific tissue details for understanding disease pathogenesis.

Advances in Microbiome-Derived Solutions and Methodologies Are Founding a New Era in Skin Health and Care

The microbiome, as a community of microorganisms and their structural elements, genomes, metabolites/signal molecules, has been shown to play an important role in human health, with significant beneficial applications for gut health. Skin microbiome has emerged as a new field with high potential to develop disruptive solutions to manage skin health and disease. Despite an incomplete toolbox for skin microbiome analyses, much progress has been made towards functional dissection of microbiomes and host-microbiome interactions. A standardized and robust investigation of the skin microbiome is necessary to provide accurate microbial information and set the base for a successful translation of innovations in the dermo-cosmetic field. This review provides an overview of how the landscape of skin microbiome research has evolved from method development (multi-omics/data-based analytical approaches) to the discovery and development of novel microbiome-derived ingredients. Moreover, it provides a summary of the latest findings on interactions between the microbiomes (gut and skin) and skin health/disease. Solutions derived from these two paths are used to develop novel microbiome-based ingredients or solutions acting on skin homeostasis are proposed. The most promising skin and gut-derived microbiome interventional strategies are presented, along with regulatory, safety, industrial, and technical challenges related to a successful translation of these microbiome-based concepts/technologies in the dermo-cosmetic industry.

Gut microbiota and gut tissue metabolites involved in development and prevention of depression

Depression is a prevalent, life-threatening, and highly recurrent psychiatric illness. Several studies have shown that depression is associated with endogenous metabolites and the gut microbiota. However, it is unclear whether metabolites in different gut tissues play a role in the pathogenesis of depression and whether the gut microbiota has an impact on depression. Here, we investigated the metabolic signatures in the jejunum, ileum, and colorectum using metabolomics and explored the influence of the gut microbiota on both the development of chronic variable stress (CVS)-induced depression rat model and variations in gut tissue metabolites using a gnotobiotic rat model. The results showed that CVS induced disturbances in gut metabolites (29 differential metabolites) and had different effects on the different segments. When CVS rats were treated with antibiotics, depression-like ethological disorders disappeared, and the decreased catecholamine levels almost normalized. The depression recovery was attributed to the influence of antibiotics on the gut microbiota, especially inhibiting Clostridiaceae (F1), Candidatus arthromitus (G2), Lactobacillus (G6), and elevating Pseudomonadaceae (F6). Moreover, 16 of 29 varied metabolites in CVS rats were reversed with antibiotic treatment. Among them, 12 increased metabolites were decreased, suggesting a trigger for depression. However, four decreased metabolites were increased, indicating a potential therapeutic effect on depression. Based on the Pearson's correlation analysis, hypoxanthine, 3-hydroxypristanic acid, threonic acid, and L-carnitine were strongly associated with F6, F1, G2, and G6, which are involved in the development and prevention of depression. These findings provide a possibility for further exploration of the pathogenesis and prevention of depression.

A compendium of predicted growths and derived symbiotic relationships between 803 gut microbes in 13 different diets

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Simulated growth of 803 gut microbes in mono- and co-cultures in 13 distinct diets.

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Inferred symbiotic relationships and metabolic co-operation among gut microbes.

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Diet-based variations in metabolic co-operation among gut microbes.

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Validation of in silico findings against existing literature evidence.

Gut health is intimately linked to dietary habits and the microbial community (microbiota) that flourishes within. The delicate dependency of the latter on nutritional availability is also strongly influenced by interactions (such as, parasitic or mutualistic) between the resident microbes, often affecting their growth rate and ability to produce key metabolites. Since, cultivating the entire repertoire of gut microbes is a challenging task, metabolic models (genome-based metabolic reconstructions) could be employed to predict their growth patterns and interactions. Here, we have used 803 gut microbial metabolic models from the Virtual Metabolic Human repository, and subsequently optimized and simulated them to grow on 13 dietary compositions. The presented pairwise interaction data (https://osf.io/ay8bq/) and the associated bacterial growth rates are expected to be useful for (a) deducing microbial association patterns, (b) diet-based inference of personalised gut profiles, and (c) as a steppingstone for studying multi-species metabolic interactions.

A metabolic modeling platform for the computation of microbial ecosystems in time and space (COMETS)

Genome-scale stoichiometric modeling of metabolism has become a standard systems biology tool for modeling cellular physiology and growth. Extensions of this approach are emerging as a valuable avenue for predicting, understanding and designing microbial communities. Computation of microbial ecosystems in time and space (COMETS) extends dynamic flux balance analysis to generate simulations of multiple microbial species in molecularly complex and spatially structured environments. Here we describe how to best use and apply the most recent version of COMETS, which incorporates a more accurate biophysical model of microbial biomass expansion upon growth, evolutionary dynamics and extracellular enzyme activity modules. In addition to a command-line option, COMETS includes user-friendly Python and MATLAB interfaces compatible with the well-established COBRA models and methods, as well as comprehensive documentation and tutorials. This protocol provides a detailed guideline for installing, testing and applying COMETS to different scenarios, generating simulations that take from a few minutes to several days to run, with broad applicability to microbial communities across biomes and scales.

A meta-analysis study of the robustness and universality of gut microbiome-metabolome associations

BACKGROUND

Microbiome-metabolome studies of the human gut have been gaining popularity in recent years, mostly due to accumulating evidence of the interplay between gut microbes, metabolites, and host health. Statistical and machine learning-based methods have been widely applied to analyze such paired microbiome-metabolome data, in the hope of identifying metabolites that are governed by the composition of the microbiome. Such metabolites can be likely modulated by microbiome-based interventions, offering a route for promoting gut metabolic health. Yet, to date, it remains unclear whether findings of microbially associated metabolites in any single study carry over to other studies or cohorts, and how robust and universal are microbiome-metabolites links.

RESULTS

In this study, we addressed this challenge by performing a comprehensive meta-analysis to identify human gut metabolites that can be predicted based on the composition of the gut microbiome across multiple studies. We term such metabolites "robustly well-predicted". To this end, we processed data from 1733 samples from 10 independent human gut microbiome-metabolome studies, focusing initially on healthy subjects, and implemented a machine learning pipeline to predict metabolite levels in each dataset based on the composition of the microbiome. Comparing the predictability of each metabolite across datasets, we found 97 robustly well-predicted metabolites. These include metabolites involved in important microbial pathways such as bile acid transformations and polyamines metabolism. Importantly, however, other metabolites exhibited large variation in predictability across datasets, suggesting a cohort- or study-specific relationship between the microbiome and the metabolite. Comparing taxonomic contributors to different models, we found that some robustly well-predicted metabolites were predicted by markedly different sets of taxa across datasets, suggesting that some microbially associated metabolites may be governed by different members of the microbiome in different cohorts. We finally examined whether models trained on a control group of a given study successfully predicted the metabolite's level in the disease group of the same study, identifying several metabolites where the model was not transferable, indicating a shift in microbial metabolism in disease-associated dysbiosis.

CONCLUSIONS

Combined, our findings provide a better understanding of the link between the microbiome and metabolites and allow researchers to put identified microbially associated metabolites within the context of other studies. Video abstract.

CRISPR-Cas9: A method for establishing rat models of drug metabolism and pharmacokinetics

The 2020 Nobel Prize in Chemistry recognized CRISPR-Cas9, a super-selective and precise gene editing tool. CRISPR-Cas9 has an obvious advantage in editing multiple genes in the same cell, and presents great potential in disease treatment and animal model construction. In recent years, CRISPR-Cas9 has been used to establish a series of rat models of drug metabolism and pharmacokinetics (DMPK), such as Cyp, Abcb1, Oatp1b2 gene knockout rats. These new rat models are not only widely used in the study of drug metabolism, chemical toxicity, and carcinogenicity, but also promote the study of DMPK related mechanism, and further strengthen the relationship between drug metabolism and pharmacology/toxicology. This review systematically introduces the advantages and disadvantages of CRISPR-Cas9, summarizes the methods of establishing DMPK rat models, discusses the main challenges in this field, and proposes strategies to overcome these problems.

An extended reconstruction of human gut microbiota metabolism of dietary compounds

Understanding how diet and gut microbiota interact in the context of human health is a key question in personalized nutrition. Genome-scale metabolic networks and constraint-based modeling approaches are promising to systematically address this complex problem. However, when applied to nutritional questions, a major issue in existing reconstructions is the limited information about compounds in the diet that are metabolized by the gut microbiota. Here, we present AGREDA, an extended reconstruction of diet metabolism in the human gut microbiota. AGREDA adds the degradation pathways of 209 compounds present in the human diet, mainly phenolic compounds, a family of metabolites highly relevant for human health and nutrition. We show that AGREDA outperforms existing reconstructions in predicting diet-specific output metabolites from the gut microbiota. Using 16S rRNA gene sequencing data of faecal samples from Spanish children representing different clinical conditions, we illustrate the potential of AGREDA to establish relevant metabolic interactions between diet and gut microbiota.

Key Technologies for Progressing Discovery of Microbiome-Based Medicines

A growing number of experimental and computational approaches are illuminating the “microbial dark matter” and uncovering the integral role of commensal microbes in human health. Through this work, it is now clear that the human microbiome presents great potential as a therapeutic target for a plethora of diseases, including inflammatory bowel disease, diabetes and obesity. The development of more efficacious and targeted treatments relies on identification of causal links between the microbiome and disease; with future progress dependent on effective links between state-of-the-art sequencing approaches, computational analyses and experimental assays. We argue determining causation is essential, which can be attained by generating hypotheses using multi-omic functional analyses and validating these hypotheses in complex, biologically relevant experimental models. In this review we discuss existing analysis and validation methods, and propose best-practice approaches required to enable the next phase of microbiome research.

Gut-inhabiting Clostridia build human GPCR ligands by conjugating neurotransmitters with diet- and human-derived fatty acids

Human physiology is regulated by endogenous signalling compounds, including fatty acid amides (FAAs), chemical mimics of which are made by bacteria. The molecules produced by human-associated microbes are difficult to identify because they may only be made in a local niche or they require a substrate sourced from the host, diet or other microbes. We identified a set of uncharacterized gene clusters in metagenomics data from the human gut microbiome. These clusters were discovered to make FAAs by fusing exogenous fatty acids with amines. Using an in vitro assay, we tested their ability to incorporate 25 fatty acids and 53 amines known to be present in the human gut, from which the production of six FAAs was deduced (oleoyl dopamine, oleoyl tyramine, lauroyl tryptamine, oleoyl aminovaleric acid, α-linolenoyl phenylethylamine and caproyl tryptamine). These molecules were screened against panels of human G-protein-coupled receptors to deduce their putative human targets. Lauroyl tryptamine is found to be an antagonist to the immunomodulatory receptor EBI2 against its native oxysterol ligand (0.98 μM half-maximal inhibitory concentration), is produced in culture by Eubacterium rectale and is present in human faecal samples. FAAs produced by Clostridia may serve as a mechanism to modulate their host by mimicking human signalling molecules.

Plant Genetics as a Tool for Manipulating Crop Microbiomes: Opportunities and Challenges

Growing human population size and the ongoing climate crisis create an urgent need for new tools for sustainable agriculture. Because microbiomes have profound effects on host health, interest in methods of manipulating agricultural microbiomes is growing rapidly. Currently, the most common method of microbiome manipulation is inoculation of beneficial organisms or engineered communities; however, these methods have been met with limited success due to the difficulty of establishment in complex farm environments. Here we propose genetic manipulation of the host plant as another avenue through which microbiomes could be manipulated. We discuss how domestication and modern breeding have shaped crop microbiomes, as well as the potential for improving plant-microbiome interactions through conventional breeding or genetic engineering. We summarize the current state of knowledge on host genetic control of plant microbiomes, as well as the key challenges that remain.

Measuring barrier function in organ-on-chips with cleanroom-free integration of multiplexable electrodes

Transepithelial/transendothelial electrical resistance (TEER) measurements can be applied in organ-on-chips (OoCs) to estimate the barrier properties of a tissue or cell layer in a continuous, non-invasive, and label-free manner. Assessing the barrier integrity in in vitro models is valuable for studying and developing barrier targeting drugs. Several systems for measuring the TEER have been shown, but each of them having their own drawbacks. This article presents a cleanroom-free fabrication method for the integration of platinum electrodes in a polydimethylsiloxane OoC, allowing the real-time assessment of the barrier function by employing impedance spectroscopy. The proposed method and electrode arrangement allow visual inspection of the cells cultured in the device at the site of the electrodes, and multiplexing of both the electrodes in one OoC and the number of OoCs in one device. The effectiveness of our system is demonstrated by lining the OoC with intestinal epithelial cells, creating a gut-on-chip, where we monitored the formation, as well as the disruption and recovery of the cell barrier during a 21 day culture period. The application is further expanded by creating a blood-brain-barrier, to show that the proposed fabrication method can be applied to monitor the barrier formation in the OoC for different types of biological barriers.

Factors Affecting Gut Microbiome in Daily Diet

There is a growing recognition that a good diet can help people maintain mental and physical health, while a bad one will cause the disorder of body function, and even lead to several diseases. A lot of attentions have been devoted to analyze every possible health-related factor in the daily diet, including food ingredients, additives, and cooking process. With the support of high-throughput sequencing technology, there is accumulating evidence gradually clarifying that most of these factors are mainly through the interactions with gut microbiome to trigger downstream effects. The gut microbiome may be able to act as a very sensitive mirror in response to human daily diet. A complex network of interactions among diet, gut microbiome, and health has been gradually depicted, but it is rarely discussed from a more comprehensive perspective. To this end, this review summarized the latest updates in diet-gut microbiome interactions, analyzed most identified factors involved in this process, showed the possibility of maintaining health or alleviating diseases by diet intervention, aiming to help people choose a suitable recipe more accurately.

Metabolic modelling reveals broad changes in gut microbial metabolism in inflammatory bowel disease patients with dysbiosis

Inflammatory bowel diseases, such as Crohn's Disease, are characterised by an altered blood and faecal metabolome, and changes in gut microbiome composition. Here, we present an efficient, scalable, tractable systems biology framework to mechanistically link microbial strains and faecal metabolites. We retrieve strain-level relative abundances from metagenomics data from a cohort of paediatric Crohn's Disease patients with and without dysbiosis and healthy control children and construct and interrogate a personalised microbiome model for each sample. Predicted faecal secretion profiles and strain-level contributions to each metabolite vary broadly between healthy, dysbiotic, and non-dysbiotic microbiomes. The reduced microbial diversity in IBD results in reduced numbers of secreted metabolites, especially in sulfur metabolism. We demonstrate that increased potential to synthesise amino acids is linked to Proteobacteria contributions, in agreement with experimental observations. The established modelling framework yields testable hypotheses that may result in novel therapeutic and dietary interventions targeting the host-gut microbiome-diet axis.

Gardenia Jasminoides Ameliorates Antibiotic-Associated Aggravation of DNCB-Induced Atopic Dermatitis by Restoring the Intestinal Microbiome Profile

The intestinal microbiome is considered one of the key regulators of health. Accordingly, the severity of atopic dermatitis (AD) is mediated by the skin and intestinal microbiome environment. In this study, while evaluating the aggravation in AD symptoms by the antibiotics cocktail (ABX)-induced depletion of the intestinal microbiome, we sought to verify the effect of Gardenia jasminoides (GJ), a medicinal herb used for inflammatory diseases, on AD regarding its role on the intestinal microbiome. To verify the aggravation in AD symptoms induced by the depletion of the intestinal microbiome, we established a novel mouse model by administrating an ABX to create a microbiome-free environment in the intestine, and then applied 2,4-dinitrochlorobenzene (DNCB) to induce an AD-like skin inflammatory response. While ABX treatment aggravated AD-like symptoms, the 2-week administration of GJ improved these pathological changes. DNCB application upregulated immune cell count and serum cytokine expression, which were alleviated by GJ. Moreover, pathological alterations by antibiotics and DNCB, including histological damage of the intestine and the intestinal expression of IL-17, were recovered in GJ-treated mice. The beneficial effect of GJ was due to the restoration of the intestinal microbiome composition. Overall, we suggest GJ as a potential therapeutic agent for AD due to its regulation of the intestinal microbiome.

Riboflavin instability is a key factor underlying the requirement of a gut microbiota for mosquito development

We previously determined that several diets used to rear Aedes aegypti and other mosquito species support the development of larvae with a gut microbiota but do not support the development of axenic larvae. In contrast, axenic larvae have been shown to develop when fed other diets. To understand the mechanisms underlying this dichotomy, we developed a defined diet that could be manipulated in concert with microbiota composition and environmental conditions. Initial studies showed that axenic larvae could not grow under standard rearing conditions (27 °C, 16-h light: 8-h dark photoperiod) when fed a defined diet but could develop when maintained in darkness. Downstream assays identified riboflavin decay to lumichrome as the key factor that prevented axenic larvae from growing under standard conditions, while gut community members like Escherichia coli rescued development by being able to synthesize riboflavin. Earlier results showed that conventional and gnotobiotic but not axenic larvae exhibit midgut hypoxia under standard rearing conditions, which correlated with activation of several pathways with essential growth functions. In this study, axenic larvae in darkness also exhibited midgut hypoxia and activation of growth signaling but rapidly shifted to midgut normoxia and arrested growth in light, which indicated that gut hypoxia was not due to aerobic respiration by the gut microbiota but did depend on riboflavin that only resident microbes could provide under standard conditions. Overall, our results identify riboflavin provisioning as an essential function for the gut microbiota under most conditions A. aegypti larvae experience in the laboratory and field.

The role of the microbiome in ovarian cancer: mechanistic insights into oncobiosis and to bacterial metabolite signaling

Ovarian cancer is characterized by dysbiosis, referred to as oncobiosis in neoplastic diseases. In ovarian cancer, oncobiosis was identified in numerous compartments, including the tumor tissue itself, the upper and lower female genital tract, serum, peritoneum, and the intestines. Colonization was linked to Gram-negative bacteria with high inflammatory potential. Local inflammation probably participates in the initiation and continuation of carcinogenesis. Furthermore, local bacterial colonies in the peritoneum may facilitate metastasis formation in ovarian cancer. Vaginal infections (e.g. Neisseria gonorrhoeae or Chlamydia trachomatis) increase the risk of developing ovarian cancer. Bacterial metabolites, produced by the healthy eubiome or the oncobiome, may exert autocrine, paracrine, and hormone-like effects, as was evidenced in breast cancer or pancreas adenocarcinoma. We discuss the possible involvement of lipopolysaccharides, lysophosphatides and tryptophan metabolites, as well as, short-chain fatty acids, secondary bile acids and polyamines in the carcinogenesis of ovarian cancer. We discuss the applicability of nutrients, antibiotics, and probiotics to harness the microbiome and support ovarian cancer therapy. The oncobiome and the most likely bacterial metabolites play vital roles in mediating the effectiveness of chemotherapy. Finally, we discuss the potential of oncobiotic changes as biomarkers for the diagnosis of ovarian cancer and microbial metabolites as possible adjuvant agents in therapy.

Systematic evaluation of parameters for genome-scale metabolic models of cultured mammalian cells

Genome-scale metabolic models describe cellular metabolism with mechanistic detail. Given their high complexity, such models need to be parameterized correctly to yield accurate predictions and avoid overfitting. Effective parameterization has been well-studied for microbial models, but it remains unclear for higher eukaryotes, including mammalian cells. To address this, we enumerated model parameters that describe key features of cultured mammalian cells - including cellular composition, bioprocess performance metrics, mammalian-specific pathways, and biological assumptions behind model formulation approaches. We tested these parameters by building thousands of metabolic models and evaluating their ability to predict the growth rates of a panel of phenotypically diverse Chinese Hamster Ovary cell clones. We found the following considerations to be most critical for accurate parameterization: (1) cells limit metabolic activity to maintain homeostasis, (2) cell morphology and viability change dynamically during a growth curve, and (3) cellular biomass has a particular macromolecular composition. Depending on parameterization, models predicted different metabolic phenotypes, including contrasting mechanisms of nutrient utilization and energy generation, leading to varying accuracies of growth rate predictions. Notably, accurate parameter values broadly agreed with experimental measurements. These insights will guide future investigations of mammalian metabolism.

Expanding the drug discovery space with predicted metabolite-target interactions

Metabolites produced in the human gut are known modulators of host immunity. However, large-scale identification of metabolite-host receptor interactions remains a daunting challenge. Here, we employed computational approaches to identify 983 potential metabolite-target interactions using the Inflammatory Bowel Disease (IBD) cohort dataset of the Human Microbiome Project 2 (HMP2). Using a consensus of multiple machine learning methods, we ranked metabolites based on importance to IBD, followed by virtual ligand-based screening to identify possible human targets and adding evidence from compound assay, differential gene expression, pathway enrichment, and genome-wide association studies. We confirmed known metabolite-target pairs such as nicotinic acid-GPR109a or linoleoyl ethanolamide-GPR119 and inferred interactions of interest including oleanolic acid-GABRG2 and alpha-CEHC-THRB. Eleven metabolites were tested for bioactivity in vitro using human primary cell-types. By expanding the universe of possible microbial metabolite-host protein interactions, we provide multiple drug targets for potential immune-therapies.

Synthetic biology in the clinic: engineering vaccines, diagnostics, and therapeutics

Synthetic biology is a design-driven discipline centered on engineering novel biological functions through the discovery, characterization, and repurposing of molecular parts. Several synthetic biological solutions to critical biomedical problems are on the verge of widespread adoption and demonstrate the burgeoning maturation of the field. Here, we highlight applications of synthetic biology in vaccine development, molecular diagnostics, and cell-based therapeutics, emphasizing technologies approved for clinical use or in active clinical trials. We conclude by drawing attention to recent innovations in synthetic biology that are likely to have a significant impact on future applications in biomedicine.

Gut microbiota And Human Body Interactions; Its Impact on Health: a review

Gut microbiota (GM) as an organ of the human body has a particular and autonomous function that related to it. This review aimed to investigate human intestinal and gut microbiota interaction and its impact on health. As a creation referable database about this dynamic and complex organ, several comprehensive projects are implemented by using culture-dependent (culturomics), culture independent methods (e.g metagenomics, mathematics model), and Gnotobiological together. This study was done by searching PubMed, Scopus and Google scholar database in the gut, health microbiota and interaction keywords. The first acquired microbiota during pregnancy or childbirth is colonized in the gut by using specific and non-specific mechanisms. That`s structure and shape reach relative stability with selection pressure along with host development until adulthood and keep its resilience against external or internal variables depending on the host genetics and negative feedback. Due to several research individuals have 2 functional group microbiota including the core (common between vast majorities human) and flexible (transient population) microbiome. The most important role of the GM in the human body can be summarized in three basic landscapes: metabolic, immune system, and gut-brain axis interaction. So that loss of microbial population balance will lead to disorder and disease.

Enabling rational gut microbiome manipulations by understanding gut ecology through experimentally-evidenced in silico models

The gut microbiome has emerged as a contributing factor in non-communicable disease, rendering it a target of health-promoting interventions. Yet current understanding of the host-microbiome dynamic is insufficient to predict the variation in intervention outcomes across individuals. We explore the mechanisms that underpin the gut bacterial ecosystem and highlight how a more complete understanding of this ecology will enable improved intervention outcomes. This ecology varies within the gut over space and time. Interventions disrupt these processes, with cascading consequences throughout the ecosystem. In vivo studies cannot isolate and probe these processes at the required spatiotemporal resolutions, and in vitro studies lack the representative complexity required. However, we highlight that, together, both approaches can inform in silico models that integrate cellular-level dynamics, can extrapolate to explain bacterial community outcomes, permit experimentation and observation over ecological processes at high spatiotemporal resolution, and can serve as predictive platforms on which to prototype interventions. Thus, it is a concerted integration of these techniques that will enable rational targeted manipulations of the gut ecosystem.

Synthetic gut microbiome: Advances and challenges

An exponential rise in studies regarding the association among human gut microbial communities, human health, and diseases is currently attracting the attention of researchers to focus on human gut microbiome research. However, even with the ever-growing number of studies on the human gut microbiome, translation into improved health is progressing slowly. This hampering is due to the complexities of the human gut microbiome, which is composed of >1,000 species of microorganisms, such as bacteria, archaea, viruses, and fungi. To overcome this complexity, it is necessary to reduce the gut microbiome, which can help simplify experimental variables to an extent, such that they can be deliberately manipulated and controlled. Reconstruction of synthetic or established gut microbial communities would make it easier to understand the structure, stability, and functional activities of the complex microbial community of the human gut. Here, we provide an overview of the developments and challenges of the synthetic human gut microbiome, and propose the incorporation of multi-omics and mathematical methods in a better synthetic gut ecosystem design, for easy translation of microbiome information to therapies.

Reconstructing organisms in silico: genome-scale models and their emerging applications

Escherichia coli is considered to be the best-known microorganism given the large number of published studies detailing its genes, its genome and the biochemical functions of its molecular components. This vast literature has been systematically assembled into a reconstruction of the biochemical reaction networks that underlie E. coli's functions, a process which is now being applied to an increasing number of microorganisms. Genome-scale reconstructed networks are organized and systematized knowledge bases that have multiple uses, including conversion into computational models that interpret and predict phenotypic states and the consequences of environmental and genetic perturbations. These genome-scale models (GEMs) now enable us to develop pan-genome analyses that provide mechanistic insights, detail the selection pressures on proteome allocation and address stress phenotypes. In this Review, we first discuss the overall development of GEMs and their applications. Next, we review the evolution of the most complete GEM that has been developed to date: the E. coli GEM. Finally, we explore three emerging areas in genome-scale modelling of microbial phenotypes: collections of strain-specific models, metabolic and macromolecular expression models, and simulation of stress responses.

Defining the nutritional input for genome-scale metabolic models: A roadmap

The reconstruction and application of genome-scale metabolic network models is a central topic in the field of systems biology with numerous applications in biotechnology, ecology, and medicine. However, there is no agreed upon standard for the definition of the nutritional environment for these models. The objective of this article is to provide a guideline and a clear paradigm on how to translate nutritional information into an in-silico representation of the chemical environment. Step-by-step procedures explain how to characterise and categorise the nutritional input and to successfully apply it to constraint-based metabolic models. In parallel, we illustrate the proposed procedure with a case study of the growth of Escherichia coli in a complex nutritional medium and show that an accurate representation of the medium is crucial for physiological predictions. The proposed framework will assist researchers to expand their existing metabolic models of their microbial systems of interest with detailed representations of the nutritional environment, which allows more accurate and reproducible predictions of microbial metabolic processes.

Syntrophic splitting of central carbon metabolism in host cells bearing functionally different symbiotic bacteria

Insects feeding on the nutrient-poor diet of xylem plant sap generally bear two microbial symbionts that are localized to different organs (bacteriomes) and provide complementary sets of essential amino acids (EAAs). Here, we investigate the metabolic basis for the apparent paradox that xylem-feeding insects are under intense selection for metabolic efficiency but incur the cost of maintaining two symbionts for functions mediated by one symbiont in other associations. Using stable isotope analysis of central carbon metabolism and metabolic modeling, we provide evidence that the bacteriomes of the spittlebug Clastoptera proteus display high rates of aerobic glycolysis, with syntrophic splitting of glucose oxidation. Specifically, our data suggest that one bacteriome (containing the bacterium Sulcia, which synthesizes seven EAAs) predominantly processes glucose glycolytically, producing pyruvate and lactate, and the exported pyruvate and lactate is assimilated by the second bacteriome (containing the bacterium Zinderia, which synthesizes three energetically costly EAAs) and channeled through the TCA cycle for energy generation by oxidative phosphorylation. We, furthermore, calculate that this metabolic arrangement supports the high ATP demand in Zinderia bacteriomes for Zinderia-mediated synthesis of energy-intensive EAAs. We predict that metabolite cross-feeding among host cells may be widespread in animal-microbe symbioses utilizing low-nutrient diets.

Towards Predicting Gut Microbial Metabolism: Integration of Flux Balance Analysis and Untargeted Metabolomics

Genomics-based metabolic models of microorganisms currently have no easy way of corroborating predicted biomass with the actual metabolites being produced. This study uses untargeted mass spectrometry-based metabolomics data to generate a list of accurate metabolite masses produced from the human commensal bacteria Citrobacter sedlakii grown in the presence of a simple glucose carbon source. A genomics-based flux balance metabolic model of this bacterium was previously generated using the bioinformatics tool PyFBA and phenotypic growth curve data. The high-resolution mass spectrometry data obtained through timed metabolic extractions were integrated with the predicted metabolic model through a program called MS_FBA. This program correlated untargeted metabolomics features from C. sedlakii with 218 of the 699 metabolites in the model using an exact mass match, with 51 metabolites further confirmed using predicted isotope ratios. Over 1400 metabolites were matched with additional metabolites in the ModelSEED database, indicating the need to incorporate more specific gene annotations into the predictive model through metabolomics-guided gap filling.