Explore the vast range of titles available.

The human microbiome has been linked to several diseases. Gastrointestinal diseases are still one of the most prominent area of study in host-microbiome interactions however the underlying microbial mechanisms in these disorders are not fully established. Irritable bowel syndrome (IBS) remains as one of the prominent disorders with significant changes in the gut microbiome composition and without definitive treatment. IBS has a severe impact on socio-economic and patient’s lifestyle. The association studies between the IBS and microbiome have shed a light on relevance of microbial composition, and hence microbiome-based trials were designed. However, there are no clear evidence of potential treatment for IBS. This review summarizes the epidemiology and socioeconomic impact of IBS and then focus on microbiome observational and clinical trials. At the end, we propose a new perspective on using data-driven approach and applying computational modelling and machine learning to design microbiome-aware personalized treatment for IBS.

The mammalian heart undergoes maturation during postnatal life to meet the increased functional requirements of an adult. However, the key drivers of this process remain poorly defined. We are currently unable to recapitulate postnatal maturation in human pluripotent stem cell-derived cardiomyocytes (hPSC-CMs), limiting their potential as a model system to discover regenerative therapeutics. Here, we provide a summary of our studies, where we developed a 96-well device for functional screening in human pluripotent stem cell-derived cardiac organoids (hCOs). Through interrogation of >10,000 organoids, we systematically optimize parameters, including extracellular matrix (ECM), metabolic substrate, and growth factor conditions, that enhance cardiac tissue viability, function, and maturation. Under optimized maturation conditions, functional and molecular characterization revealed that a switch to fatty acid metabolism was a central driver of cardiac maturation. Under these conditions, hPSC-CMs were refractory to mitogenic stimuli, and we found that key proliferation pathways including β-catenin and Yes-associated protein 1 (YAP1) were repressed. This proliferative barrier imposed by fatty acid metabolism in hCOs could be rescued by simultaneous activation of both β-catenin and YAP1 using genetic approaches or a small molecule activating both pathways. These studies highlight that human organoids coupled with higher-throughput screening platforms have the potential to rapidly expand our knowledge of human biology and potentially unlock therapeutic strategies.

Kinetic models are essential to quantitatively understand and predict the behaviour of metabolic networks. Detailed and thermodynamically feasible kinetic models of metabolism are inherently difficult to formulate and fit. They have a large number of heterogeneous parameters, are non-linear and have complex interactions. Many powerful fitting strategies are ruled out by the intractability of the likelihood function. Here, we have developed a computational framework capable of fitting feasible and accurate kinetic models using Approximate Bayesian Computation. This framework readily supports advanced modelling features such as model selection and model-based experimental design. We illustrate this approach on the tightly-regulated mammalian methionine cycle. Sampling from the posterior distribution, the proposed framework generated thermodynamically feasible parameter samples that converged on the true values and displayed remarkable prediction accuracy in several validation tests. Furthermore, a posteriori analysis of the parameter distributions enabled appraisal of the systems properties of the network (e.g., control structure) and key metabolic regulations. Finally, the framework was used to predict missing allosteric interactions.

Background Predictable control of gene expression is necessary for the rational design and optimization of cell factories. In the yeast Saccharomyces cerevisiae , the promoter is one of the most important tools available for controlling gene expression. However, the complex expression patterns of yeast promoters have not been fully characterised and compared on different carbon sources (glucose, sucrose, galactose and ethanol) and across the diauxic shift in glucose batch cultivation. These conditions are of importance to yeast cell factory design because they are commonly used and encountered in industrial processes. Here, the activities of a series of “constitutive” and inducible promoters were characterised in single cells throughout the fermentation using green fluorescent protein (GFP) as a reporter. Results The “constitutive” promoters, including glycolytic promoters, transcription elongation factor promoters and ribosomal promoters, differed in their response patterns to different carbon sources; however, in glucose batch cultivation, expression driven by these promoters decreased sharply as glucose was depleted and cells moved towards the diauxic shift. Promoters induced at low-glucose levels ( P HXT7 , P SSA1 and P ADH2 ) varied in induction strength on non-glucose carbon sources (sucrose, galactose and ethanol); in contrast to the “constitutive” promoters, GFP expression increased as glucose decreased and cells moved towards the diauxic shift. While lower than several “constitutive” promoters during the exponential phase, expression from the SSA1 promoter was higher in the post-diauxic phase than the commonly-used TEF1 promoter. The galactose-inducible GAL1 promoter provided the highest GFP expression on galactose, and the copper-inducible CUP1 promoter provided the highest induced GFP expression following the diauxic shift. Conclusions The data provides a foundation for predictable and optimised control of gene expression levels on different carbon sources and throughout batch fermentation, including during and after the diauxic shift. This information can be applied for designing expression approaches to improve yields, rates and titres in yeast cell factories.

Propionic acid (propionate) is a commercially valuable carboxylic acid produced through microbial fermentation. Propionic acid is mainly used in the food industry but has recently found applications in the cosmetic, plastics and pharmaceutical industries. Propionate can be produced via various metabolic pathways, which can be classified into three major groups: fermentative pathways, biosynthetic pathways, and amino acid catabolic pathways. The current review provides an in-depth description of the major metabolic routes for propionate production from an energy optimization perspective. Biological propionate production is limited by high downstream purification costs which can be addressed if the target yield, productivity and titre can be achieved. Genome shuffling combined with high throughput omics and metabolic engineering is providing new opportunities, and biological propionate production is likely to enter the market in the not so distant future. In order to realise the full potential of metabolic engineering and heterologous expression, however, a greater understanding of metabolic capabilities of the native producers, the fittest producers, is required.

Kinetic models provide the means to understand and predict the dynamic behaviour of enzymes upon different perturbations. Despite their obvious advantages, classical parameterizations require large amounts of data to fit their parameters. Particularly, enzymes displaying complex reaction and regulatory (allosteric) mechanisms require a great number of parameters and are therefore often represented by approximate formulae, thereby facilitating the fitting but ignoring many real kinetic behaviours. Here, we show that full exploration of the plausible kinetic space for any enzyme can be achieved using sampling strategies provided a thermodynamically feasible parameterization is used. To this end, we developed a General Reaction Assembly and Sampling Platform (GRASP) capable of consistently parameterizing and sampling accurate kinetic models using minimal reference data. The former integrates the generalized MWC model and the elementary reaction formalism. By formulating the appropriate thermodynamic constraints, our framework enables parameterization of any oligomeric enzyme kinetics without sacrificing complexity or using simplifying assumptions. This thermodynamically safe parameterization relies on the definition of a reference state upon which feasible parameter sets can be efficiently sampled. Uniform sampling of the kinetics space enabled dissecting enzyme catalysis and revealing the impact of thermodynamics on reaction kinetics. Our analysis distinguished three reaction elasticity regions for common biochemical reactions: a steep linear region (0> ΔGr >-2 kJ/mol), a transition region (-2> ΔGr >-20 kJ/mol) and a constant elasticity region (ΔGr <-20 kJ/mol). We also applied this framework to model more complex kinetic behaviours such as the monomeric cooperativity of the mammalian glucokinase and the ultrasensitive response of the phosphoenolpyruvate carboxylase of Escherichia coli. In both cases, our approach described appropriately not only the kinetic behaviour of these enzymes, but it also provided insights about the particular features underpinning the observed kinetics. Overall, this framework will enable systematic parameterization and sampling of enzymatic reactions.

Hek293 cells are the predominant hosts for transient expression of recombinant proteins and are used for stable expression of proteins where post-translational modifications performed by CHO cells are inadequate. Nevertheless, there is little information available on the key cellular features underpinning recombinant protein production in Hek293 cells. To improve our understanding of recombinant protein production in Hek293 cells and identify targets for the engineering of an improved host cell line, we have compared a stable, recombinant protein producing Hek293 cell line and its parental cell line using a combination of transcriptomics, metabolomics and fluxomics. Producer cultures consumed less glucose than non-producer cultures while achieving the same growth rate, despite the additional burden of recombinant protein production. Surprisingly, there was no indication that producer cultures compensated for the reduction in glycolytic energy by increasing the efficiency of glucose utilization or increasing glutamine consumption. In contrast, glutamine consumption was lower and the majority of genes involved in oxidative phosphorylation were downregulated in producer cultures. We observed an overall downregulation of a large number of genes associated with broad cellular functions (e.g., cell growth and proliferation) in producer cultures, and therefore speculate that a broad adaptation of the cellular network freed up resources for recombinant protein production while maintaining the same growth rate. Increased abundance of genes associated with endoplasmic reticulum stress indicated a possible bottleneck at the point of protein folding and assembly.

Background Uniform random sampling of mass-balanced flux solutions offers an unbiased appraisal of the capabilities of metabolic networks. Unfortunately, it is impossible to avoid thermodynamically infeasible loops in flux samples when using convex samplers on large metabolic models. Current strategies for randomly sampling the non-convex loopless flux space display limited efficiency and lack theoretical guarantees. Results Here, we present LooplessFluxSampler, an efficient algorithm for exploring the loopless mass-balanced flux solution space of metabolic models, based on an Adaptive Directions Sampling on a Box (ADSB) algorithm. ADSB is rooted in the general Adaptive Direction Sampling (ADS) framework, specifically the Parallel ADS, for which theoretical convergence and irreducibility results are available for sampling from arbitrary distributions. By sampling directions that adapt to the target distribution, ADSB traverses more efficiently the sample space achieving faster mixing than other methods. Importantly, the presented algorithm is guaranteed to target the uniform distribution over convex regions, and it provably converges on the latter distribution over more general (non-convex) regions provided the sample can have full support. Conclusions LooplessFluxSampler enables scalable statistical inference of the loopless mass-balanced solution space of large metabolic models. Grounded in a theoretically sound framework, this toolbox provides not only efficient but also reliable results for exploring the properties of the almost surely non-convex loopless flux space. Finally, LooplessFluxSampler includes a Markov Chain diagnostics suite for assessing the quality of the final sample and the performance of the algorithm.

Background Our view of host-associated microbiota remains incomplete due to the presence of as yet uncultured constituents. The Bacteroidales family S24-7 is a prominent example of one of these groups. Marker gene surveys indicate that members of this family are highly localized to the gastrointestinal tracts of homeothermic animals and are increasingly being recognized as a numerically predominant member of the gut microbiota; however, little is known about the nature of their interactions with the host. Results Here, we provide the first whole genome exploration of this family, for which we propose the name “ Candidatus Homeothermaceae,” using 30 population genomes extracted from fecal samples of four different animal hosts: human, mouse, koala, and guinea pig. We infer the core metabolism of “ Ca. Homeothermaceae” to be that of fermentative or nanaerobic bacteria, resembling that of related Bacteroidales families. In addition, we describe three trophic guilds within the family, plant glycan (hemicellulose and pectin), host glycan, and α-glucan, each broadly defined by increased abundance of enzymes involved in the degradation of particular carbohydrates. Conclusions “ Ca. Homeothermaceae” representatives constitute a substantial component of the murine gut microbiota, as well as being present within the human gut, and this study provides important first insights into the nature of their residency. The presence of trophic guilds within the family indicates the potential for niche partitioning and specific roles for each guild in gut health and dysbiosis.

The complexity of microbial communities, combined with technical biases in next-generation sequencing, pose a challenge to metagenomic analysis. Here, we develop a set of internal DNA standards, termed “sequins” (sequencing spike-ins), that together constitute a synthetic community of artificial microbial genomes. Sequins are added to environmental DNA samples prior to library preparation, and undergo concurrent sequencing with the accompanying sample. We validate the performance of sequins by comparison to mock microbial communities, and demonstrate their use in the analysis of real metagenome samples. We show how sequins can be used to measure fold change differences in the size and structure of accompanying microbial communities, and perform quantitative normalization between samples. We further illustrate how sequins can be used to benchmark and optimize new methods, including nanopore long-read sequencing technology. We provide metagenome sequins, along with associated data sets, protocols, and an accompanying software toolkit, as reference standards to aid in metagenomic studies. Complex microbial communities pose a challenge to metagenomic analysis. Here the authors develop ‘sequins’, internal DNA standards that represent a synthetic community of artificial genomes.