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Nutritional and environmental stressors can disturb the gut microbiome of horses which may ultimately decrease their health and performance. In this study, we hypothesized that a Saccharomyces cerevisiae fermentation postbiotic (SCFP) improves the robustness of the gut microbiome in stress-challenged horses. Quarter horses (n=20) were randomized based on sex, age (22 ± 3 mo) and body weight (439 ± 3 kg) to receive a basal diet (60% forage:40% concentrate) supplemented without (CON) or with SCFP (21g/d, TruEquine, Diamond V, Cedar Rapids, IA). Treatments were implemented for 60 d. On d 57, horses were tethered with their heads elevated 35 cm above wither height for 12 h to mimic long-distance transport stress. Fecal samples were collected on d 0, 28, and 56 pre-challenge and at 0, 12, 24 and 72 h post-challenge and subjected to DNA extraction and Nanopore shotgun metagenomics. Alpha-diversity and microbial abundances were compared using non-parametric Wilcoxon-Rank-Sum test with Benjamini-Hochberg FDR correction. The SCFP stabilized alpha-diversity across all time points, whereas CON horses had more fluctuation (P < 0.05) at 12, 24 and 72 h post-challenge compared to d 56. This resulted in a significant difference between CON and SCFP at 0 and 12 h. There was no difference in beta-diversity between SCFP and CON on d 56, however, treatments grouped separately (PERMANOVA, P < 0.05) within the 12 h post-challenge showing SCFP horses maintained higher abundances (P < 0.05) of fibrolytic taxa such as Fibrobacter succinogenes, Ruminococcus species and several members of Prevotellaceae and Lachnospiraceae among others. During the post-challenge period compositional clusters in the microbiome of CON horses were characterized by low abundances (P < 0.05) of Butyrivibrio, Pseudobutyrivibrio and Blautia species versus SCFP. Overall, treatments were similar in microbiome diversity and composition pre-challenge, however, post-challenge, SCFP supplementation improved microbiome robustness indicating an ability to resist change due to stress.

Humans often show variable responses to dietary, prebiotic, and probiotic interventions. Emerging evidence indicates that the gut microbiota is a key determinant for this population heterogeneity. Here, we provide an overview of some of the major computational and experimental tools being applied to critical questions of microbiota-mediated personalized nutrition and health. First, we discuss the latest advances in in silico modeling of the microbiota-nutrition-health axis, including the application of statistical, mechanistic, and hybrid artificial intelligence models. Second, we address high-throughput in vitro techniques for assessing interindividual heterogeneity, from ex vivo batch culturing of stool and continuous culturing in anaerobic bioreactors, to more sophisticated organ-on-a-chip models that integrate both host and microbial compartments. Third, we explore in vivo approaches for better understanding of personalized, microbiota-mediated responses to diet, prebiotics, and probiotics, from nonhuman animal models and human observational studies, to human feeding trials and crossover interventions. We highlight examples of existing, consumer-facing precision nutrition platforms that are currently leveraging the gut microbiota. Furthermore, we discuss how the integration of a broader set of the tools and techniques described in this piece can generate the data necessary to support a greater diversity of precision nutrition strategies. Finally, we present a vision of a precision nutrition and healthcare future, which leverages the gut microbiota to design effective, individual-specific interventions.

Polyols are effective against caries-causing streptococci but the effect on oropharynx-derived pyogenic streptococci is not well characterised. We aimed to study the effect of erythritol (ERY) and xylitol (XYL) against Streptococcus pyogenes isolated from peritonsillar abscesses (PTA). We used 31 clinical isolates and 5 throat culture collection strains. Inhibition of bacterial growth by polyols at 2.5%, 5% and 10% concentrations was studied and the results were scored. Amylase levels in PTA pus were compared to polyol effectivity scores (PES). Growth curves of four S. pyogenes isolates were analysed. Our study showed that XYL was more effective than ERY inhibiting 71–97% and 48–84% of isolates, respectively, depending of concentrations. 48% of clinical and all throat strains were inhibited by polyols in all concentrations (PES 3). PES was negative or zero in 26% of the isolates in the presence of ERY and in 19% of XYL. ERY enhanced the growth of S. pyogenes isolated from pus with high amylase levels. Polyols in all concentrations inhibited the growth in exponential phase. In conclusion, ERY and XYL are potent growth inhibitors of S. pyogenes isolated from PTA. Therefore, ERY and XYL may have potential in preventing PTA in the patients with frequent tonsillitis episodes.

Weight loss aims to improve glycemic control in obese but strong variability is observed. Using a multi-omics approach, we investigated differences between 174 responders and 201 non-responders, that had lost >8% body weight following a low-caloric diet (LCD, 800 kcal/d for 8 weeks). The two groups were comparable at baseline for body composition, glycemic control, adipose tissue transcriptomics and plasma ketone bodies. But they differed significantly in their response to LCD, including improvements in visceral fat, overall insulin resistance (IR) and tissue-specific IR. Transcriptomics analyses found down-regulation in key lipogenic genes (e.g. SCD, ELOVL5) in responders relative to non-responders; metabolomics showed increase in ketone bodies; while proteomics revealed differences in lipoproteins. Findings were consistent between genders; with women displaying smaller improvements owing to a better baseline metabolic condition. Integrative analyses identified a plasma omics model that was able to predict non-responders with strong performance (on a testing dataset, the Receiving Operating Curve Area Under the Curve (ROC AUC) was 75% with 95% Confidence Intervals (CI) [67%, 83%]). This model was based on baseline parameters without the need for intrusive measurements and outperformed clinical models (p = 0.00075, with a +14% difference on the ROC AUCs). Our approach document differences between responders and non-responders, with strong contributions from liver and adipose tissues. Differences may be due to de novo lipogenesis, keto-metabolism and lipoprotein metabolism. These findings are useful for clinical practice to better characterize non-responders both prior and during weight loss.

The gut microbiome and lipid metabolism are both recognized as essential components in the maintenance of metabolic health. The mechanisms involved are multifactorial and (especially for microbiome) poorly defined. A strategic approach to investigate the complexity of the microbial influence on lipid metabolism would facilitate determination of relevant molecular mechanisms for microbiome-targeted therapeutics. E. coli is associated with obesity and metabolic syndrome and we used this association in conjunction with gnotobiotic models to investigate the impact of E. coli on lipid metabolism. To address the complexities of the integration of the microbiome and lipid metabolism, we developed transcriptomics-driven lipidomics (TDL) to predict the impact of E. coli colonization on lipid metabolism and established mediators of inflammation and insulin resistance including arachidonic acid metabolism, alterations in bile acids and dietary lipid absorption. A microbiome-related therapeutic approach targeting these mechanisms may therefore provide a therapeutic avenue supporting maintenance of metabolic health., Integrative Lipidomics: Microbiome regulation of lipid metabolism Microbes multifactorially impact host lipid metabolism bearing a significant impact in health and disease. A team led by Mojgan Masoodi and Scott Parkinson at Nestlé Institute of Health Sciences (NIHS) developed an integrative data driven approach for predictive lipidomics investigations of host-microbial impacts on lipid metabolism. Results of in-vivo studies with germ-free mice inoculated with E. coli and in-vitro studies demonstrated the multifactorial nature of the impact of E. coli on arachidonic acid metabolism in the ileum and altered host inflammation and lipid absorption. The findings provide insights into understanding the host-microbiome interactions and identifying microbiome-related solutions for maintaining health and tackling disease. The systems approach presented is applicable to investigate broad range of microbiome dependent and independent alterations in host lipid metabolism.

The utilization of alternative energy substrates to glucose could be beneficial in traumatic brain injury (TBI). Recent clinical data obtained in TBI patients reported valine, &beta, hydroxyisobutyrate (ibHB) and 2-ketoisovaleric acid (2-KIV) as three of the main predictors of TBI outcome. In particular, higher levels of ibHB, 2-KIV, and valine in cerebral microdialysis (CMD) were associated with better clinical outcome. In this study, we investigate the correlations between circulating and CMD levels of these metabolites. We hypothesized that the liver can metabolize valine and provide a significant amount of intermediate metabolites, which can be further metabolized in the brain. We aimed to assess the metabolism of valine in human-induced pluripotent stem cell (iPSC)-derived astrocytes and HepG2 cells using 13C-labeled substrate to investigate potential avenues for increasing the levels of downstream metabolites of valine via valine supplementation. We observed that 94 &plusmn, 12% and 84 &plusmn, 16% of ibHB, and 94 &plusmn, 12% and 87 &plusmn, 15% of 2-KIV, in the medium of HepG2 cells and in iPSC-derived astrocytes, respectively, came directly from valine. Overall, these findings suggest that both ibHB and 2-KIV are produced from valine to a large extent in both cell types, which could be of interest in the design of optimal nutritional interventions aiming at stimulating valine metabolism.

Medium-chain triglyceride (MCT) ketogenic diets increase ketone bodies, which are believed to act as alternative energy substrates in the injured brain. Octanoic (C8:0) and decanoic (C10:0) acids, which produce ketone bodies through β-oxidation, are used as part of MCT ketogenic diets. Although the ketogenic role of MCT is well-established, it remains unclear how the network metabolism underlying β-oxidation of these medium-chain fatty acids (MCFA) differ. We aim to elucidate basal β-oxidation of these commonly used MCFA at the cellular level. Human-induced pluripotent stem cell-derived (iPSC) astrocytes were incubated with [U-13C]-C8:0 or [U-13C]-C10:0, and the fractional enrichments (FE) of the derivatives were used for metabolic flux analysis. Data indicate higher extracellular concentrations and faster secretion rates of β-hydroxybutyrate (βHB) and acetoacetate (AcAc) with C8:0 than C10:0, and an important contribution from unlabeled substrates. Flux analysis indicates opposite direction of metabolic flux between the MCFA intermediates C6:0 and C8:0, with an important contribution of unlabeled sources to the elongation in the C10:0 condition, suggesting different β-oxidation pathways. Finally, larger intracellular glutathione concentrations and secretions of 3-OH-C10:0 and C6:0 were measured in C10:0-treated astrocytes. These findings reveal MCFA-specific ketogenic properties. Our results provide insights into designing different MCT-based ketogenic diets to target specific health benefits.

Background: Traumatic brain injury (TBI) is recognized as a metabolic disease, characterized by acute cerebral glucose hypo-metabolism. Adaptive metabolic responses to TBI involve the utilization of alternative energy substrates, such as ketone bodies. Cerebral microdialysis (CMD) has evolved as an accurate technique allowing continuous sampling of brain extracellular fluid and assessment of regional cerebral metabolism. We present the successful application of a combined hypothesis- and data-driven metabolomics approach using repeated CMD sampling obtained routinely at patient bedside. Investigating two patient cohorts (n = 26 and n = 12), we identified clinically relevant metabolic patterns at the acute post-TBI critical care phase. Methods: Clinical and CMD metabolomics data were integrated and analysed using in silico and data modelling approaches. We used both unsupervised and supervised multivariate analysis techniques to investigate structures within the time series and associations with patient outcome. Findings: The multivariate metabolite time series exhibited two characteristic brain metabolic states that were attributed to changes in key metabolites: valine, 4-methyl-2-oxovaleric acid (4-MOV), isobeta-hydroxybutyrate (iso-bHB), tyrosyine, and 2-ketoisovaleric add (2-KIV). These identified cerebral metabolic states differed significantly with respect to standard clinical values. We validated our findings in a second cohort using a classification model trained on the cerebral metabolic states. We demonstrated that short-term (therapeutic intensity level (TIL)) and mid-term patient outcome (6-month Glasgow Outcome Score (GOS)) can be predicted from the time series characteristics. Interpretation: We identified two specific cerebral metabolic patterns that are closely linked to ketometabolism and were associated with both TIL and GOS. Our findings support the view that advanced metabolomics approaches combined with CMD may be applied in real-time to predict short-term treatment intensity and long-term patient outcome. (C) 2019 The Authors. Published by Elsevier B.V.

In Figure 4, the panels showing the variability by cycle phases for Glycine, Serine, Methionine, Asparagine, Proline, Glutamine, Tyrosine, Gamma-glutamyl-alanine, Citrulline, O-Acetyl-serine, Alpha-aminobutyric acid and Gamma-glutamylglutamine were omitted. The correct Figure 4 appears below as Figure 1.

The menstrual cycle is an essential life rhythm governed by interacting levels of progesterone, estradiol, follicular stimulating, and luteinizing hormones. To study metabolic changes, biofluids were collected at four timepoints in the menstrual cycle from 34 healthy, premenopausal women. Serum hormones, urinary luteinizing hormone and self-reported menstrual cycle timing were used for a 5-phase cycle classification. Plasma and urine were analyzed using LC-MS and GC-MS for metabolomics and lipidomics; serum for clinical chemistries; and plasma for B vitamins using HPLC-FLD. Of 397 metabolites and micronutrients tested, 208 were significantly (p

Escherichia coli is one of the common inhabitants of the mammalian gastrointestinal track. We isolated a strain from an ob/ob mouse and performed whole-genome sequencing, which yielded a chromosome of ~5.1 Mb and three plasmids of ~160 kb, ~6 kb, and ~4 kb.

The microbiome has been demonstrated to play an integral role in the maintenance of many aspects of health that are also associated with aging. In order to identify areas of potential exploration and intervention, we simultaneously characterized age-related alterations in gut microbiome, muscle physiology and serum proteomic and lipidomic profiles in aged rats to define an integrated signature of the aging phenotype. We demonstrate that aging skews the composition of the gut microbiome, in particular by altering the Sutterella to Barneseilla ratio, and alters the metabolic potential of intestinal bacteria. Age-related changes of the gut microbiome were associated with the physiological decline of musculoskeletal function, and with molecular markers of nutrient processing/availability, and inflammatory/immune status in aged versus adult rats. Altogether, our study highlights that aging leads to a complex interplay between the microbiome and host physiology, and provides candidate microbial species to target physical and metabolic decline during aging by modulating gut microbial ecology.

Both physiological and pathological tissue remodeling (e.g., during wound healing and cancer, respectively) require new blood vessel formation via angiogenesis, but the underlying microenvironmental mechanisms remain poorly defined due in part to the lack of biologically relevant in vitro models. Here, we present a biomaterials-based microfluidic 3D platform for analysis of endothelial sprouting in response to morphogen gradients. This system consists of three lithographically defined channels embedded in type I collagen hydrogels. A central channel is coated with endothelial cells, and two parallel side channels serve as a source and a sink for the steady-state generation of biochemical gradients. Gradients of vascular endothelial growth factor (VEGF) promoted sprouting, whereby endothelial cell responsiveness was markedly dependent on cell density and vessel geometry regardless of treatment conditions. These results point toward mechanical and/or autocrine mechanisms that may overwhelm pro-angiogenic paracrine signaling under certain conditions. To date, neither geometrical effects nor cell density have been considered critical determinants of angiogenesis in health and disease. This biomimetic vessel platform demonstrated utility for delineating hitherto underappreciated contributors of angiogenesis, and future studies may enable important new mechanistic insights that will inform anti-angiogenic cancer therapy.

Mathematical modeling is an essential tool for the comprehensive understanding of cell metabolism and its interactions with the environmental and process conditions. Recent developments in the construction and analysis of stoichiometric models made it possible to define limits on steady-state metabolic behavior using flux balance analysis. However, detailed information on enzyme kinetics and enzyme regulation is needed to formulate kinetic models that can accurately capture the dynamic metabolic responses. The use of mechanistic enzyme kinetics is a difficult task due to uncertainty in the kinetic properties of enzymes. Therefore, the majority of recent works considered only mass action kinetics for reactions in metabolic networks. Herein, we applied the optimization and risk analysis of complex living entities (ORACLE) framework and constructed a large-scale mechanistic kinetic model of optimally grown Escherichia coli. We investigated the complex interplay between stoichiometry, thermodynamics, and kinetics in determining the flexibility and capabilities of metabolism. Our results indicate that enzyme saturation is a necessary consideration in modeling metabolic networks and it extends the feasible ranges of metabolic fluxes and metabolite concentrations. Our results further suggest that enzymes in metabolic networks have evolved to function at different saturation states to ensure greater flexibility and robustness of cellular metabolism.

The importance of gut microbes to metabolic health is becoming more evident and nutrition-based therapies to alter the composition of bacterial communities to manage metabolic disease are an attractive avenue to ameliorate some effects of Western diets. While the composition of gut microbial communities can vary significantly across disease states, it is not well known if these communities have common responses to nutritional interventions. To better understand fiber-bacterial community interactions, we collected biological parameters and fecal samples of overweight non-diabetic (OND) and diabetic (OD) individuals before and after daily supplementation of 2.8 g β-glucan on their habitual diet for 30 days. Fecal bacterial communities in an age-matched cohort were measured by sequencing partial 16S rRNA genes and imputed metagenomic content. Unexpectedly, we observed disconnected responses of biological measurements and the bacterial community. Based on average effect size, biological measurements were greater in the OND group while effects on the bacterial community were greatest on the OD cohort, and we suspect these observations are due to the significantly lower alpha diversity in the OD cohort. Our data indicate that responses to fiber supplementation are cohort specific and this should be considered when manipulating the microbiome via fiber supplementation.

Cells monitor protein folding by an inbuilt quality-control system in which incorrectly or misfolded folded proteins are tagged for degradation or sent back through a refolding cycle. However, continued accumulation of incorrectly folded proteins triggers the Unfolded Protein Response (UPR), which attempts to re-establish folding homeostasis or commits the cell to apoptosis. In this study, we developed a family of mechanistic models of the mammalian UPR system. An ensemble of models parameters was estimated by minimizing the difference between simulations and experimental measurements using multiobjective optimization. The ensemble of model parameters was validated using cross-validation. Analysis of the model ensemble suggested the three branches of UPR fired simultaneously. However, the importance of each brach was ranked ordered in time; PERK and IRE1 were more important early, while ATF6 was important later in the response. The activity of all three branches as coordinated by the molecular chaperone BiP. Model analysis suggested that BiP feedback was critical to the overall robustness of the system. Removal of any one branch of BiP feedback, destabilized the other branches. On the other hand, removal of all nodes of BiP feedback increased the overall robustness of the system. Thus, while BiP feedback is crucial to allowing the cell to adapt to small perturbations, it also makes the system fragile and susceptible to manipulation.

Breast cancer initiation, invasion and metastasis span multiple length and time scales. Molecular events at short length scales lead to an initial tumorigenic population, which left unchecked by immune action, acts at increasingly longer length scales until eventually the cancer cells escape from the primary tumor site. This series of events is highly complex, involving multiple cell types interacting with (and shaping) the microenvironment. Multiscale mathematical models have emerged as a powerful tool to quantitatively integrate the convective-diffusion-reaction processes occurring on the systemic scale, with the molecular signaling processes occurring on the cellular and subcellular scales. In this study, we reviewed the current state of the art in cancer modeling across multiple length scales, with an emphasis on the integration of intracellular signal transduction models with pro-tumorigenic chemical and mechanical microenvironmental cues. First, we reviewed the underlying biomolecular origin of breast cancer, with a special emphasis on angiogenesis. Then, we summarized the development of tissue engineering platforms which could provide high-fidelity ex vivo experimental models to identify and validate multiscale simulations. Lastly, we reviewed top-down and bottom-up multiscale strategies that integrate subcellular networks with the microenvironment. We present models of a variety of cancers, in addition to breast cancer specific models. Taken together, we expect as the sophistication of the simulations increase, that multiscale modeling and bottom-up agent-based models in particular will become an increasingly important platform technology for basic scientific discovery, as well as the identification and validation of potentially novel therapeutic targets.

Development of NGS has revolutionized the analysis in microbial ecology contributing to our deeper understanding of microbiota in health and disease. However, the quality, quantity and confidence of summarized taxonomic abundances are in need of further scrutiny due to sample dependent and independent effects. In this article we introduce ‘AVIT (Abundance and Variability In Taxonomy), an unbiased method to enrich for assigned members of microbial communities. As opposed to using a priori thresholds, ‘AVIT uses inherent abundance and variability of taxa in a dataset to determine the inclusion or rejection of each taxa for further downstream analysis. Using in-vitro and in-vivo studies, we benchmarked performance and parameterized ‘AVIT to establish a framework for investigating the dynamic range of microbial community membership in clinically relevant scenarios.

Epithelial to mesenchymal transition (EMT) is an essential differentiation program during tissue morphogenesis and remodeling. EMT is induced by soluble transforming growth factor β (TGF-β) family members, and restricted by vascular endothelial growth factor family members. While many downstream molecular regulators of EMT have been identified, these have been largely evaluated individually without considering potential crosstalk. In this study, we created an ensemble of dynamic mathematical models describing TGF-β induced EMT to better understand the operational hierarchy of this complex molecular program. We used ordinary differential equations (ODEs) to describe the transcriptional and post-translational regulatory events driving EMT. Model parameters were estimated from multiple data sets using multiobjective optimization, in combination with cross-validation. TGF-β exposure drove the model population toward a mesenchymal phenotype, while an epithelial phenotype was enhanced following vascular endothelial growth factor A (VEGF-A) exposure. Simulations predicted that the transcription factors phosphorylated SP1 and NFAT were master regulators promoting or inhibiting EMT, respectively. Surprisingly, simulations also predicted that a cellular population could exhibit phenotypic heterogeneity (characterized by a significant fraction of the population with both high epithelial and mesenchymal marker expression) if treated simultaneously with TGF-β and VEGF-A. We tested this prediction experimentally in both MCF10A and DLD1 cells and found that upwards of 45% of the cellular population acquired this hybrid state in the presence of both TGF-β and VEGF-A. We experimentally validated the predicted NFAT/Sp1 signaling axis for each phenotype response. Lastly, we found that cells in the hybrid state had significantly different functional behavior when compared to VEGF-A or TGF-β treatment alone. Together, these results establish a predictive mechanistic model of EMT susceptibility, and potentially reveal a novel signaling axis which regulates carcinoma progression through an EMT versus tubulogenesis response., Author Summary Tissue formation and remodeling requires a complex and dynamic balance of interactions between epithelial cells, which reside on the surface, and mesenchymal cells that reside in the tissue interior. During embryonic development, wound healing, and cancer, epithelial cells transform into a mesenchymal cell to form new types of tissues. It is important to understand this process so that it can be controlled to generate beneficial effects and limit pathological differentiation. Much research over the past 20 years has identified many different molecular species that are relevant, but these have mainly been studied one at a time. In this study, we developed and implemented a novel computational strategy to interrogate the key players in this transformation process to identify which are the major bottlenecks. We determined that NFATc1 and pSP1 are essential for promoting epithelial or mesenchymal differentiation, respectively. We then predicted the existence of a partially transformed cell that exhibits both epithelial and mesenchymal characteristics. We found this partial cell type develops a network of invasive but stunted vascular structures that may be a unique cell target for understanding cancer progression and angiogenesis.

Both physiological and pathological tissue remodeling (e.g., during wound healing and cancer, respectively) require new blood vessel formation via angiogenesis, but the underlying microenvironmental mechanisms remain poorly defined due in part to the lack of biologically relevant in vitro models. Here, we present a biomaterials-based microfluidic 3-D platform for analysis of endothelial sprouting in response to morphogen gradients. This system consists of three lithographically defined channels embedded in type I collagen hydrogels. A central channel is coated with endothelial cells, and two parallel side channels serve as a source and a sink for the steady-state generation of biochemical gradients. Gradients of vascular endothelial growth factor (VEGF) promoted sprouting, whereby endothelial cell responsiveness was markedly dependent on cell density and vessel geometry regardless of treatment conditions. These results point toward mechanical and/or autocrine mechanisms that may overwhelm pro-angiogenic paracrine signaling under certain conditions. To date, neither geometrical effects nor cell density have been considered critical determinants of angiogenesis in health and disease. This biomimetic vessel platform demonstrated utility for delineating hitherto underappreciated contributors of angiogenesis, and future studies may enable important new mechanistic insights that will inform anti-angiogenic cancer therapy.

Insulin, the primary hormone regulating the level of glucose in the bloodstream, modulates a variety of cellular and enzymatic processes in normal and diseased cells. Insulin signals are processed by a complex network of biochemical interactions which ultimately induce gene expression programs or other processes such as translation initiation. Surprisingly, despite the wealth of literature on insulin signaling, the relative importance of the components linking insulin with translation initiation remains unclear. We addressed this question by developing and interrogating a family of mathematical models of insulin induced translation initiation. The insulin network was modeled using mass-action kinetics within an ordinary differential equation (ODE) framework. A family of model parameters was estimated, starting from an initial best fit parameter set, using 24 experimental data sets taken from literature. The residual between model simulations and each of the experimental constraints were simultaneously minimized using multiobjective optimization. Interrogation of the model population, using sensitivity and robustness analysis, identified an insulin-dependent switch that controlled translation initiation. Our analysis suggested that without insulin, a balance between the pro-initiation activity of the GTP-binding protein Rheb and anti-initiation activity of PTEN controlled basal initiation. On the other hand, in the presence of insulin a combination of PI3K and Rheb activity controlled inducible initiation, where PI3K was only critical in the presence of insulin. Other well known regulatory mechanisms governing insulin action, for example IRS-1 negative feedback, modulated the relative importance of PI3K and Rheb but did not fundamentally change the signal flow., Author Summary Insulin is a hormone produced by the body that regulates uptake of glucose from the bloodstream. The cellular response to insulin is governed by a complex network of intracellular interactions that ultimately influence cell growth and metabolism. Because of its central role in physiology, insulin signaling has been extensively studied. Yet despite this wealth of research, the relative importance of components in insulin signaling remains unclear. Mechanistic computer simulations have been shown to provide insight into the function of complex systems, such as insulin signaling. In this work we constructed and interrogated a mathematical computer simulation of insulin signaling to better understand the important components of the insulin signaling network. We determined the most important network components and identified network perturbations that can induce dramatic shifts in cellular phenotype. Our results offer an in-depth analysis of the insulin signaling pathway and provide a unique paradigm towards understanding how malfunctions in insulin signaling can result in numerous disease states.

Proteins requiring post-translational modifications such as N-linked glycosylation are processed in the endoplasmic reticulum (ER). A diverse array of cellular stresses can lead to dysfunction of the ER and ultimately to an imbalance between protein-folding capacity and protein-folding load. Cells monitor protein folding by an inbuilt quality control system involving both the ER and the Golgi apparatus. Unfolded or misfolded proteins are tagged for degradation via ER-associated degradation (ERAD) or sent back through the folding cycle. Continued accumulation of incorrectly folded proteins can also trigger the unfolded protein response (UPR). In mammalian cells, UPR is a complex signaling program mediated by three ER transmembrane receptors: activating transcription factor 6 (ATF6), inositol requiring kinase 1 (IRE1) and double-stranded RNA-activated protein kinase (PKR)-like endoplasmic reticulum kinase (PERK). UPR performs three functions, adaptation, alarm, and apoptosis. During adaptation, the UPR tries to reestablish folding homeostasis by inducing the expression of chaperones that enhance protein folding. Simultaneously, global translation is attenuated to reduce the ER folding load while the degradation rate of unfolded proteins is increased. If these steps fail, the UPR induces a cellular alarm and mitochondrial mediated apoptosis program. UPR malfunctions have been associated with a wide range of disease states including tumor progression, diabetes, as well as immune and inflammatory disorders. This review describes recent advances in understanding the molecular structure of UPR in mammalian cells, its functional role in cellular stress, and its pathophysiology.

Mathematical modeling of complex gene expression programs is an emerging tool for understanding disease mechanisms. However, identification of large models sometimes requires training using qualitative, conflicting or even contradictory data sets. One strategy to address this challenge is to estimate experimentally constrained model ensembles using multiobjective optimization. In this study, we used Pareto Optimal Ensemble Techniques (POETs) to identify a family of proof-of-concept signal transduction models. POETs integrate Simulated Annealing (SA) with Pareto optimality to identify models near the optimal tradeoff surface between competing training objectives. We modeled a prototypical-signaling network using mass-action kinetics within an ordinary differential equation (ODE) framework (64 ODEs in total). The true model was used to generate synthetic immunoblots from which the POET algorithm identified the 117 unknown model parameters. POET generated an ensemble of signaling models, which collectively exhibited population-like behavior. For example, scaled gene expression levels were approximately normally distributed over the ensemble following the addition of extracellular ligand. Also, the ensemble recovered robust and fragile features of the true model, despite significant parameter uncertainty. Taken together, these results suggest that experimentally constrained model ensembles could capture qualitatively important network features without exact parameter information.

Background: Micronutrient supplementation has been extensively explored as a strategy to improve health and reduce risk of chronic diseases. Fat-soluble vitamins like A and E with their antioxidant properties and mechanistic interactions with lipoproteins, have potentially a key impact on lipid metabolism and lipidemia., Objective: The impact of micronutrients on lipid metabolism requires further investigation including characterization of plasma lipidome following supplementation and any cause-effect on circulating lipids., Design: In this study, we elucidate the effect and associations of a multi-micronutrient intervention in Brazilian children and teens with lipoprotein alterations and lipid metabolism., Results: Our analysis suggests a combination of short and long-term impact of supplementation on lipid metabolism, potentially mediated primarily by alpha-tocopherol (vitamin E) and retinol (vitamin A). Among the lipid classes, levels of phospholipids, lysophospholipids, and cholesterol esters were impacted the most along with differential incorporation of stearic, palmitic, oleic and arachidonic acids. Integrated analysis with proteomic data suggested potential links to supplementation-mediated alterations in protein levels of phospholipases and pyruvate dehydrogenase kinase 1 (PDK1)., Conclusions: Associations between the observed differences in lipidemia, total triglyceride, and VLDL-cholesterol levels suggest that micronutrients may play a role in reducing these risk factors for cardiovascular disease in children. This would require further investigation. (C) 2019 The Author(s). Published by Elsevier Ltd.