Clinical, experimental and pathophysiological effects of Yaq-001: a non-absorbable, gut-restricted adsorbent in models and patients with cirrhosis

OBJECTIVE

Targeting bacterial translocation in cirrhosis is limited to antibiotics with risk of antimicrobial resistance. This study explored the therapeutic potential of a non-absorbable, gut-restricted, engineered carbon bead adsorbent, Yaq-001 in models of cirrhosis and acute-on-chronic liver failure (ACLF) and, its safety and tolerability in a clinical trial in cirrhosis.

DESIGN

Performance of Yaq-001 was evaluated in vitro. Two-rat models of cirrhosis and ACLF, (4 weeks, bile duct ligation with or without lipopolysaccharide), receiving Yaq-001 for 2 weeks; and two-mouse models of cirrhosis (6-week and 12-week carbon tetrachloride (CCl4)) receiving Yaq-001 for 6 weeks were studied. Organ and immune function, gut permeability, transcriptomics, microbiome composition and metabolomics were analysed. The effect of faecal water on gut permeability from animal models was evaluated on intestinal organoids. A multicentre, double-blind, randomised, placebo-controlled clinical trial in 28 patients with cirrhosis, administered 4 gr/day Yaq-001 for 3 months was performed.

RESULTS

Yaq-001 exhibited rapid adsorption kinetics for endotoxin. In vivo, Yaq-001 reduced liver injury, progression of fibrosis, portal hypertension, renal dysfunction and mortality of ACLF animals significantly. Significant impact on severity of endotoxaemia, hyperammonaemia, liver cell death, systemic inflammation and organ transcriptomics with variable modulation of inflammation, cell death and senescence in the liver, kidneys, brain and colon was observed. Yaq-001 reduced gut permeability in the organoids and impacted positively on the microbiome composition and metabolism. Yaq-001 regulated as a device met its primary endpoint of safety and tolerability in the clinical trial.

CONCLUSIONS

This study provides strong preclinical rationale and safety in patients with cirrhosis to allow clinical translation.

TRIAL REGISTRATION NUMBER

NCT03202498.

Combined Metabolic Activators with Different NAD+ Precursors Improve Metabolic Functions in the Animal Models of Neurodegenerative Diseases

BACKGROUND

Mitochondrial dysfunction and metabolic abnormalities are acknowledged as significant factors in the onset of neurodegenerative disorders such as Parkinson's disease (PD) and Alzheimer's disease (AD). Our research has demonstrated that the use of combined metabolic activators (CMA) may alleviate metabolic dysfunctions and stimulate mitochondrial metabolism. Therefore, the use of CMA could potentially be an effective therapeutic strategy to slow down or halt the progression of PD and AD. CMAs include substances such as the glutathione precursors (L-serine and N-acetyl cysteine), the NAD+ precursor (nicotinamide riboside), and L-carnitine tartrate.

METHODS

Here, we tested the effect of two different formulations, including CMA1 (nicotinamide riboside, L-serine, N-acetyl cysteine, L-carnitine tartrate), and CMA2 (nicotinamide, L-serine, N-acetyl cysteine, L-carnitine tartrate), as well as their individual components, on the animal models of AD and PD. We assessed the brain and liver tissues for pathological changes and immunohistochemical markers. Additionally, in the case of PD, we performed behavioral tests and measured responses to apomorphine-induced rotations.

FINDINGS

Histological analysis showed that the administration of both CMA1 and CMA2 formulations led to improvements in hyperemia, degeneration, and necrosis in neurons for both AD and PD models. Moreover, the administration of CMA2 showed a superior effect compared to CMA1. This was further corroborated by immunohistochemical data, which indicated a reduction in immunoreactivity in the neurons. Additionally, notable metabolic enhancements in liver tissues were observed using both formulations. In PD rat models, the administration of both formulations positively influenced the behavioral functions of the animals.

INTERPRETATION

Our findings suggest that the administration of both CMA1 and CMA2 markedly enhanced metabolic and behavioral outcomes, aligning with neuro-histological observations. These findings underscore the promise of CMA2 administration as an effective therapeutic strategy for enhancing metabolic parameters and cognitive function in AD and PD patients.

Terlipressin therapy is associated with increased risk of colonisation with multidrug-resistant bacteria in patients with decompensated cirrhosis

BACKGROUND

Patients with cirrhosis are susceptible to develop bacterial infections that trigger acute decompensation (AD) and acute-on-chronic liver failure (ACLF). Infections with multidrug-resistant organisms (MDRO) are associated with deleterious outcome. MDRO colonisation frequently proceeds MDRO infections and antibiotic therapy has been associated with MDRO colonisation.

AIM

The aim of the study was to assess the influence of non-antibiotic medication contributing to MDRO colonisation.

METHODS

Three hundred twenty-four patients with AD and ACLF admitted to the ICU of Frankfurt University Hospital with MDRO screening were included. Regression models were performed to identify drugs associated with MDRO colonisation. Another cohort (n = 129) from Barcelona was included to validate. A third multi-centre cohort (n = 203) with metagenomic sequencing data of stool was included to detect antibiotic resistance genes.

RESULTS

A total of 97 patients (30%) were identified to have MDRO colonisation and 35 of them (11%) developed MDRO infection. Patients with MDRO colonisation had significantly higher risk of MDRO infection than those without (p = 0.0098). Apart from antibiotic therapy (odds ratio (OR) 2.91, 95%-confidence interval (CI) 1.82-4.93, p < 0.0001), terlipressin therapy in the previous 14 days was the only independent covariate associated with MDRO colonisation in both cohorts, the overall (OR 9.47, 95%-CI 2.96-30.23, p < 0.0001) and after propensity score matching (OR 5.30, 95%-CI 1.22-23.03, p = 0.011). In the second cohort, prior terlipressin therapy was a risk factor for MDRO colonisation (OR 2.49, 95% CI 0.911-6.823, p = 0.075) and associated with risk of MDRO infection during follow-up (p = 0.017). The validation cohort demonstrated that antibiotic inactivation genes were significantly associated with terlipressin administration (p = 0.001).

CONCLUSIONS

Our study reports an increased risk of MDRO colonisation in patients with AD or ACLF, who recently received terlipressin therapy, while other commonly prescribed non-antibiotic co-medications had negligible influence. Future prospective trials are needed to confirm these results.

Leveraging circulating microbial DNA for early cancer detection

Microbial cell-free DNA (microbial cfDNA) offers a minimally invasive approach for profiling the microbiome. Despite technical and biological challenges, the potential of microbial cfDNA for cancer detection is increasingly being evaluated using deep sequencing, novel laboratory approaches, and computational methods. Targeting the microbiome using liquid biopsies could improve early cancer detection.

Drug repositioning targeting glutaminase reveals drug candidates for the treatment of Alzheimer's disease patients

BACKGROUND

Despite numerous clinical trials and decades of endeavour, there is still no effective cure for Alzheimer's disease. Computational drug repositioning approaches may be employed for the development of new treatment strategies for Alzheimer's patients since an extensive amount of omics data has been generated during pre-clinical and clinical studies. However, targeting the most critical pathophysiological mechanisms and determining drugs with proper pharmacodynamics and good efficacy are equally crucial in drug repurposing and often imbalanced in Alzheimer's studies.

METHODS

Here, we investigated central co-expressed genes upregulated in Alzheimer's disease to determine a proper therapeutic target. We backed our reasoning by checking the target gene's estimated non-essentiality for survival in multiple human tissues. We screened transcriptome profiles of various human cell lines perturbed by drug induction (for 6798 compounds) and gene knockout using data available in the Connectivity Map database. Then, we applied a profile-based drug repositioning approach to discover drugs targeting the target gene based on the correlations between these transcriptome profiles. We evaluated the bioavailability, functional enrichment profiles and drug-protein interactions of these repurposed agents and evidenced their cellular viability and efficacy in glial cell culture by experimental assays and Western blotting. Finally, we evaluated their pharmacokinetics to anticipate to which degree their efficacy can be improved.

RESULTS

We identified glutaminase as a promising drug target. Glutaminase overexpression may fuel the glutamate excitotoxicity in neurons, leading to mitochondrial dysfunction and other neurodegeneration hallmark processes. The computational drug repurposing revealed eight drugs: mitoxantrone, bortezomib, parbendazole, crizotinib, withaferin-a, SA-25547 and two unstudied compounds. We demonstrated that the proposed drugs could effectively suppress glutaminase and reduce glutamate production in the diseased brain through multiple neurodegeneration-associated mechanisms, including cytoskeleton and proteostasis. We also estimated the human blood-brain barrier permeability of parbendazole and SA-25547 using the SwissADME tool.

CONCLUSIONS

This study method effectively identified an Alzheimer's disease marker and compounds targeting the marker and interconnected biological processes by use of multiple computational approaches. Our results highlight the importance of synaptic glutamate signalling in Alzheimer's disease progression. We suggest repurposable drugs (like parbendazole) with well-evidenced activities that we linked to glutamate synthesis hereby and novel molecules (SA-25547) with estimated mechanisms for the treatment of Alzheimer's patients.

Palidis: fast discovery of novel insertion sequences

The diversity of microbial insertion sequences, crucial mobile genetic elements in generating diversity in microbial genomes, needs to be better represented in current microbial databases. Identification of these sequences in microbiome communities presents some significant problems that have led to their underrepresentation. Here, we present a bioinformatics pipeline called Palidis that recognizes insertion sequences in metagenomic sequence data rapidly by identifying inverted terminal repeat regions from mixed microbial community genomes. Applying Palidis to 264 human metagenomes identifies 879 unique insertion sequences, with 519 being novel and not previously characterized. Querying this catalogue against a large database of isolate genomes reveals evidence of horizontal gene transfer events across bacterial classes. We will continue to apply this tool more widely, building the Insertion Sequence Catalogue, a valuable resource for researchers wishing to query their microbial genomes for insertion sequences.

Systematic diet composition swap in a mouse genome-scale metabolic model reveals determinants of obesogenic diet metabolism in liver cancer

Dietary nutrient availability and gene expression, together, influence tissue metabolic activity. Here, we explore whether altering dietary nutrient composition in the context of mouse liver cancer suffices to overcome chronic gene expression changes that arise from tumorigenesis and western-style diet (WD). We construct a mouse genome-scale metabolic model and estimate metabolic fluxes in liver tumors and non-tumoral tissue after computationally varying the composition of input diet. This approach, called Systematic Diet Composition Swap (SyDiCoS), revealed that, compared to a control diet, WD increases production of glycerol and succinate irrespective of specific tissue gene expression patterns. Conversely, differences in fatty acid utilization pathways between tumor and non-tumor liver are amplified with WD by both dietary carbohydrates and lipids together. Our data suggest that combined dietary component modifications may be required to normalize the distinctive metabolic patterns that underlie selective targeting of tumor metabolism.

Combined metabolic activators improve metabolic functions in the animal models of neurodegenerative diseases

BACKGROUND

Neurodegenerative diseases (NDDs), including Alzheimer's disease (AD) and Parkinson's disease (PD), are associated with metabolic abnormalities. Integrative analysis of human clinical data and animal studies have contributed to a better understanding of the molecular and cellular pathways involved in the progression of NDDs. Previously, we have reported that the combined metabolic activators (CMA), which include the precursors of nicotinamide adenine dinucleotide and glutathione can be utilized to alleviate metabolic disorders by activating mitochondrial metabolism.

METHODS

We first analysed the brain transcriptomics data from AD patients and controls using a brain-specific genome-scale metabolic model (GEM). Then, we investigated the effect of CMA administration in animal models of AD and PD. We evaluated pathological and immunohistochemical findings of brain and liver tissues. Moreover, PD rats were tested for locomotor activity and apomorphine-induced rotation.

FINDINGS

Analysis of transcriptomics data with GEM revealed that mitochondrial dysfunction is involved in the underlying molecular pathways of AD. In animal models of AD and PD, we showed significant damage in the high-fat diet groups' brain and liver tissues compared to the chow diet. The histological analyses revealed that hyperemia, degeneration and necrosis in neurons were improved by CMA administration in both AD and PD animal models. These findings were supported by immunohistochemical evidence of decreased immunoreactivity in neurons. In parallel to the improvement in the brain, we also observed dramatic metabolic improvement in the liver tissue. CMA administration also showed a beneficial effect on behavioural functions in PD rats.

INTERPRETATION

Overall, we showed that CMA administration significantly improved behavioural scores in parallel with the neurohistological outcomes in the AD and PD animal models and is a promising treatment for improving the metabolic parameters and brain functions in NDDs.

Combined metabolic activators improve cognitive functions in Alzheimer's disease patients: a randomised, double-blinded, placebo-controlled phase-II trial

BACKGROUND

Alzheimer's disease (AD) is associated with metabolic abnormalities linked to critical elements of neurodegeneration. We recently administered combined metabolic activators (CMA) to the AD rat model and observed that CMA improves the AD-associated histological parameters in the animals. CMA promotes mitochondrial fatty acid uptake from the cytosol, facilitates fatty acid oxidation in the mitochondria, and alleviates oxidative stress.

METHODS

Here, we designed a randomised, double-blinded, placebo-controlled phase-II clinical trial and studied the effect of CMA administration on the global metabolism of AD patients. One-dose CMA included 12.35 g L-serine (61.75%), 1 g nicotinamide riboside (5%), 2.55 g N-acetyl-L-cysteine (12.75%), and 3.73 g L-carnitine tartrate (18.65%). AD patients received one dose of CMA or placebo daily during the first 28 days and twice daily between day 28 and day 84. The primary endpoint was the difference in the cognitive function and daily living activity scores between the placebo and the treatment arms. The secondary aim of this study was to evaluate the safety and tolerability of CMA. A comprehensive plasma metabolome and proteome analysis was also performed to evaluate the efficacy of the CMA in AD patients.

RESULTS

We showed a significant decrease of AD Assessment Scale-cognitive subscale (ADAS-Cog) score on day 84 vs day 0 (P = 0.00001, 29% improvement) in the CMA group. Moreover, there was a significant decline (P = 0.0073) in ADAS-Cog scores (improvement of cognitive functions) in the CMA compared to the placebo group in patients with higher ADAS-Cog scores. Improved cognitive functions in AD patients were supported by the relevant alterations in the hippocampal volumes and cortical thickness based on imaging analysis. Moreover, the plasma levels of proteins and metabolites associated with NAD + and glutathione metabolism were significantly improved after CMA treatment.

CONCLUSION

Our results indicate that treatment of AD patients with CMA can lead to enhanced cognitive functions and improved clinical parameters associated with phenomics, metabolomics, proteomics and imaging analysis. Trial registration ClinicalTrials.gov NCT04044131 Registered 17 July 2019, https://clinicaltrials.gov/ct2/show/NCT04044131.

Metabolic modelling of the human gut microbiome in type 2 diabetes patients in response to metformin treatment

The human gut microbiome has been associated with several metabolic disorders including type 2 diabetes mellitus. Understanding metabolic changes in the gut microbiome is important to elucidate the role of gut bacteria in regulating host metabolism. Here, we used available metagenomics data from a metformin study, together with genome-scale metabolic modelling of the key bacteria in individual and community-level to investigate the mechanistic role of the gut microbiome in response to metformin. Individual modelling predicted that species that are increased after metformin treatment have higher growth rates in comparison to species that are decreased after metformin treatment. Gut microbial enrichment analysis showed prior to metformin treatment pathways related to the hypoglycemic effect were enriched. Our observations highlight how the key bacterial species after metformin treatment have commensal and competing behavior, and how their cellular metabolism changes due to different nutritional environment. Integrating different diets showed there were specific microbial alterations between different diets. These results show the importance of the nutritional environment and how dietary guidelines may improve drug efficiency through the gut microbiota.

Integrative functional analysis uncovers metabolic differences between Candida species

Candida species are a dominant constituent of the human mycobiome and associated with the development of several diseases. Understanding the Candida species metabolism could provide key insights into their ability to cause pathogenesis. Here, we have developed the BioFung database, providing an efficient annotation of protein-encoding genes. Along, with BioFung, using carbohydrate-active enzyme (CAZymes) analysis, we have uncovered core and accessory features across Candida species demonstrating plasticity, adaption to the environment and acquired features. We show a greater importance of amino acid metabolism, as functional analysis revealed that all Candida species can employ amino acid metabolism. However, metabolomics revealed that only a specific cluster of species (AGAu species—C. albicans, C. glabrata and C. auris) utilised amino acid metabolism including arginine, cysteine, and methionine metabolism potentially improving their competitive fitness in pathogenesis. We further identified critical metabolic pathways in the AGAu cluster with biomarkers and anti-fungal target potential in the CAZyme profile, polyamine, choline and fatty acid biosynthesis pathways. This study, combining genomic analysis, and validation with gene expression and metabolomics, highlights the metabolic diversity with AGAu species that underlies their remarkable ability to dominate they mycobiome and cause disease.

Metabolic differences between Candida species are uncovered using the BioFung database alongside genomic and metabolic analysis.

Genome-scale metabolic modelling of the human gut microbiome reveals changes of the glyoxylate and dicarboxylate metabolism in metabolic disorders

The human gut microbiome has been associated with metabolic disorders including obesity, type 2 diabetes, and atherosclerosis. Understanding the contribution of microbiome metabolic changes is important for elucidating the role of gut bacteria in regulating metabolism. We used available metagenomics data from these metabolic disorders, together with genome-scale metabolic modeling of key bacteria in the individual and community-level to investigate the mechanistic role of the gut microbiome in metabolic diseases. Modeling predicted increased levels of glutamate consumption along with the production of ammonia, arginine, and proline in gut bacteria common across the disorders. Abundance profiles and network-dependent analysis identified the enrichment of tartrate dehydrogenase in the disorders. Moreover, independent plasma metabolite levels showed associations between metabolites including proline and tyrosine and an increased tartrate metabolism in healthy obese individuals. We, therefore, propose that an increased tartrate metabolism could be a significant mediator of the microbiome metabolic changes in metabolic disorders.

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Metagenomic analysis highlights key common bacterial species across metabolic diseases

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Metabolic models showed higher levels of acetate produced by disease enriched bacteria

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Reaction analysis revealed increases in the glyoxylate and dicarboxylate pathway

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Metabolomics and modeling analysis showed the potential role of tartrate metabolism

Irritable bowel syndrome and microbiome; Switching from conventional diagnosis and therapies to personalized interventions

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.

Prediction of drug candidates for clear cell renal cell carcinoma using a systems biology-based drug repositioning approach

BACKGROUND

The response rates of the clinical chemotherapies are still low in clear cell renal cell carcinoma (ccRCC). Computational drug repositioning is a promising strategy to discover new uses for existing drugs to treat patients who cannot get benefits from clinical drugs.

METHODS

We proposed a systematic approach which included the target prediction based on the co-expression network analysis of transcriptomics profiles of ccRCC patients and drug repositioning for cancer treatment based on the analysis of shRNA- and drug-perturbed signature profiles of human kidney cell line.

FINDINGS

First, based on the gene co-expression network analysis, we identified two types of gene modules in ccRCC, which significantly enriched with unfavorable and favorable signatures indicating poor and good survival outcomes of patients, respectively. Then, we selected four genes, BUB1B, RRM2, ASF1B and CCNB2, as the potential drug targets based on the topology analysis of modules. Further, we repurposed three most effective drugs for each target by applying the proposed drug repositioning approach. Finally, we evaluated the effects of repurposed drugs using an in vitro model and observed that these drugs inhibited the protein levels of their corresponding target genes and cell viability.

INTERPRETATION

These findings proved the usefulness and efficiency of our approach to improve the drug repositioning researches for cancer treatment and precision medicine.

FUNDING

This study was funded by Knut and Alice Wallenberg Foundation and Bash Biotech Inc., San Diego, CA, USA.

Multiomics Analysis Reveals the Impact of Microbiota on Host Metabolism in Hepatic Steatosis

Metabolic dysfunction-associated fatty liver disease (MAFLD) is a complex disease involving alterations in multiple biological processes regulated by the interactions between obesity, genetic background, and environmental factors including the microbiome. To decipher hepatic steatosis (HS) pathogenesis by excluding critical confounding factors including genetic variants and diabetes, 56 heterogenous MAFLD patients are characterized by generating multiomics data including oral and gut metagenomics as well as plasma metabolomics and inflammatory proteomics data. The dysbiosis in the oral and gut microbiome is explored and the host-microbiome interactions based on global metabolic and inflammatory processes are revealed. These multiomics data are integrated using the biological network and HS's key features are identified using multiomics data. HS is finally predicted using these key features and findings are validated in a follow-up cohort, where 22 subjects with varying degree of HS are characterized.

A Gene Co-Expression Network-Based Drug Repositioning Approach Identifies Candidates for Treatment of Hepatocellular Carcinoma

Simple Summary

Hepatocellular carcinoma (HCC) is the most common malignancy of liver cancer. However, treatment of HCC is still severely limited due to limitation of drug therapy. We aimed to screen more possible target genes and candidate drugs for HCC, exploring the possibility of drug treatments from systems biological perspective. We identified ten candidate target genes, which are hub genes in HCC co-expression networks, which also possess significant prognostic value in two independent HCC cohorts. The rationality of these target genes was well demonstrated through variety analyses of patient expression profiles. We then screened candidate drugs for target genes and finally identified withaferin-a and mitoxantrone as the candidate drug for HCC treatment. The drug effectiveness was validated in in vitro model and computational analysis, providing more evidence for our drug repositioning method and results.

Abstract

Hepatocellular carcinoma (HCC) is a malignant liver cancer that continues to increase deaths worldwide owing to limited therapies and treatments. Computational drug repurposing is a promising strategy to discover potential indications of existing drugs. In this study, we present a systematic drug repositioning method based on comprehensive integration of molecular signatures in liver cancer tissue and cell lines. First, we identify robust prognostic genes and two gene co-expression modules enriched in unfavorable prognostic genes based on two independent HCC cohorts, which showed great consistency in functional and network topology. Then, we screen 10 genes as potential target genes for HCC on the bias of network topology analysis in these two modules. Further, we perform a drug repositioning method by integrating the shRNA and drug perturbation of liver cancer cell lines and identifying potential drugs for every target gene. Finally, we evaluate the effects of the candidate drugs through an in vitro model and observe that two identified drugs inhibited the protein levels of their corresponding target genes and cell migration, also showing great binding affinity in protein docking analysis. Our study demonstrates the usefulness and efficiency of network-based drug repositioning approach to discover potential drugs for cancer treatment and precision medicine approach.

Rifaximin-α reduces gut-derived inflammation and mucin degradation in cirrhosis and encephalopathy: RIFSYS randomised controlled trial

BACKGROUND & AIMS

Rifaximin-α is efficacious for the prevention of recurrent hepatic encephalopathy (HE), but its mechanism of action remains unclear. We postulated that rifaximin-α reduces gut microbiota-derived endotoxemia and systemic inflammation, a known driver of HE.

METHODS

In a placebo-controlled, double-blind, mechanistic study, 38 patients with cirrhosis and HE were randomised 1:1 to receive either rifaximin-α (550 mg BID) or placebo for 90 days.

PRIMARY OUTCOME

50% reduction in neutrophil oxidative burst (OB) at 30 days.

SECONDARY OUTCOMES

changes in psychometric hepatic encephalopathy score (PHES) and neurocognitive functioning, shotgun metagenomic sequencing of saliva and faeces, plasma and faecal metabolic profiling, whole blood bacterial DNA quantification, neutrophil toll-like receptor (TLR)-2/4/9 expression and plasma/faecal cytokine analysis.

RESULTS

Patients were well-matched: median MELD (11 rifaximin-α vs. 10 placebo). Rifaximin-α did not lead to a 50% reduction in spontaneous neutrophil OB at 30 days compared to baseline (p = 0.48). However, HE grade normalised (p = 0.014) and PHES improved (p = 0.009) after 30 days on rifaximin-α. Rifaximin-α reduced circulating neutrophil TLR-4 expression on day 30 (p = 0.021) and plasma tumour necrosis factor-α (TNF-α) (p <0.001). Rifaximin-α suppressed oralisation of the gut, reducing levels of mucin-degrading sialidase-rich species, Streptococcus spp, Veillonella atypica and parvula, Akkermansia and Hungatella. Rifaximin-α promoted a TNF-α- and interleukin-17E-enriched intestinal microenvironment, augmenting antibacterial responses to invading pathobionts and promoting gut barrier repair. Those on rifaximin-α were less likely to develop infection (odds ratio 0.21; 95% CI 0.05-0.96).

CONCLUSION

Rifaximin-α led to resolution of overt and covert HE, reduced the likelihood of infection, reduced oralisation of the gut and attenuated systemic inflammation. Rifaximin-α plays a role in gut barrier repair, which could be the mechanism by which it ameliorates bacterial translocation and systemic endotoxemia in cirrhosis.

CLINICAL TRIAL NUMBER

ClinicalTrials.gov NCT02019784.

LAY SUMMARY

In this clinical trial, we examined the underlying mechanism of action of an antibiotic called rifaximin-α which has been shown to be an effective treatment for a complication of chronic liver disease which effects the brain (termed encephalopathy). We show that rifaximin-α suppresses gut bacteria that translocate from the mouth to the intestine and cause the intestinal wall to become leaky by breaking down the protective mucus barrier. This suppression resolves encephalopathy and reduces inflammation in the blood, preventing the development of infection.

Host-mycobiome metabolic interactions in health and disease

Fungal communities (mycobiome) have an important role in sustaining the resilience of complex microbial communities and maintenance of homeostasis. The mycobiome remains relatively unexplored compared to the bacteriome despite increasing evidence highlighting their contribution to host-microbiome interactions in health and disease. Despite being a small proportion of the total species, fungi constitute a large proportion of the biomass within the human microbiome and thus serve as a potential target for metabolic reprogramming in pathogenesis and disease mechanism. Metabolites produced by fungi shape host niches, induce immune tolerance and changes in their levels prelude changes associated with metabolic diseases and cancer. Given the complexity of microbial interactions, studying the metabolic interplay of the mycobiome with both host and microbiome is a demanding but crucial task. However, genome-scale modelling and synthetic biology can provide an integrative platform that allows elucidation of the multifaceted interactions between mycobiome, microbiome and host. The inferences gained from understanding mycobiome interplay with other organisms can delineate the key role of the mycobiome in pathophysiology and reveal its role in human disease.

Revealing the Molecular Mechanisms of Alzheimer's Disease Based on Network Analysis

The complex pathology of Alzheimer's disease (AD) emphasises the need for comprehensive modelling of the disease, which may lead to the development of efficient treatment strategies. To address this challenge, we analysed transcriptome data of post-mortem human brain samples of healthy elders and individuals with late-onset AD from the Religious Orders Study and Rush Memory and Aging Project (ROSMAP) and Mayo Clinic (MayoRNAseq) studies in the AMP-AD consortium. In this context, we conducted several bioinformatics and systems medicine analyses including the construction of AD-specific co-expression networks and genome-scale metabolic modelling of the brain in AD patients to identify key genes, metabolites and pathways involved in the progression of AD. We identified AMIGO1 and GRPRASP2 as examples of commonly altered marker genes in AD patients. Moreover, we found alterations in energy metabolism, represented by reduced oxidative phosphorylation and ATPase activity, as well as the depletion of hexanoyl-CoA, pentanoyl-CoA, (2E)-hexenoyl-CoA and numerous other unsaturated fatty acids in the brain. We also observed that neuroprotective metabolites (e.g., vitamins, retinoids and unsaturated fatty acids) tend to be depleted in the AD brain, while neurotoxic metabolites (e.g., β-alanine, bilirubin) were more abundant. In summary, we systematically revealed the key genes and pathways related to the progression of AD, gained insight into the crucial mechanisms of AD and identified some possible targets that could be used in the treatment of AD.

Combined metabolic activators therapy ameliorates liver fat in nonalcoholic fatty liver disease patients

Nonalcoholic fatty liver disease (NAFLD) refers to excess fat accumulation in the liver. In animal experiments and human kinetic study, we found that administration of combined metabolic activators (CMAs) promotes the oxidation of fat, attenuates the resulting oxidative stress, activates mitochondria, and eventually removes excess fat from the liver. Here, we tested the safety and efficacy of CMA in NAFLD patients in a placebo-controlled 10-week study. We found that CMA significantly decreased hepatic steatosis and levels of aspartate aminotransferase, alanine aminotransferase, uric acid, and creatinine, whereas found no differences on these variables in the placebo group after adjustment for weight loss. By integrating clinical data with plasma metabolomics and inflammatory proteomics as well as oral and gut metagenomic data, we revealed the underlying molecular mechanisms associated with the reduced hepatic fat and inflammation in NAFLD patients and identified the key players involved in the host-microbiome interactions. In conclusion, we showed that CMA can be used to develop a pharmacological treatment strategy in NAFLD patients.

Combined Metabolic Activators Accelerates Recovery in Mild-to-Moderate COVID-19

COVID-19 is associated with mitochondrial dysfunction and metabolic abnormalities, including the deficiencies in nicotinamide adenine dinucleotide (NAD+ ) and glutathione metabolism. Here it is investigated if administration of a mixture of combined metabolic activators (CMAs) consisting of glutathione and NAD+ precursors can restore metabolic function and thus aid the recovery of COVID-19 patients. CMAs include l-serine, N-acetyl-l-cysteine, nicotinamide riboside, and l-carnitine tartrate, salt form of l-carnitine. Placebo-controlled, open-label phase 2 study and double-blinded phase 3 clinical trials are conducted to investigate the time of symptom-free recovery on ambulatory patients using CMAs. The results of both studies show that the time to complete recovery is significantly shorter in the CMA group (6.6 vs 9.3 d) in phase 2 and (5.7 vs 9.2 d) in phase 3 trials compared to placebo group. A comprehensive analysis of the plasma metabolome and proteome reveals major metabolic changes. Plasma levels of proteins and metabolites associated with inflammation and antioxidant metabolism are significantly improved in patients treated with CMAs as compared to placebo. The results show that treating patients infected with COVID-19 with CMAs lead to a more rapid symptom-free recovery, suggesting a role for such a therapeutic regime in the treatment of infections leading to respiratory problems.

Synergies of Systems Biology and Synthetic Biology in Human Microbiome Studies

A number of studies have shown that the microbial communities of the human body are integral for the maintenance of human health. Advances in next-generation sequencing have enabled rapid and large-scale quantification of the composition of microbial communities in health and disease. Microorganisms mediate diverse host responses including metabolic pathways and immune responses. Using a system biology approach to further understand the underlying alterations of the microbiota in physiological and pathological states can help reveal potential novel therapeutic and diagnostic interventions within the field of synthetic biology. Tools such as biosensors, memory arrays, and engineered bacteria can rewire the microbiome environment. In this article, we review the computational tools used to study microbiome communities and the current limitations of these methods. We evaluate how genome-scale metabolic models (GEMs) can advance our understanding of the microbe-microbe and microbe-host interactions. Moreover, we present how synergies between these system biology approaches and synthetic biology can be harnessed in human microbiome studies to improve future therapeutics and diagnostics and highlight important knowledge gaps for future research in these rapidly evolving fields.

Advances in the Relationships Between Cow's Milk Protein Allergy and Gut Microbiota in Infants

Cow's milk protein allergy (CMPA) is an immune response to cow's milk proteins, which is one of the most common food allergies in infants and young children. It is estimated that 2-3% of infants and young children have CMPA. The diet, gut microbiota, and their interactions are believed to be involved in the alterations of mucosal immune tolerance, which might lead to the development of CMPA and other food allergies. In this review, the potential molecular mechanisms of CMPA, including omics technologies used for analyzing microbiota, impacts of early microbial exposures on CMPA development, and microbiota-host interactions, are summarized. The probiotics, prebiotics, synbiotics, fecal microbiota transplantation, and other modulation strategies for gut microbiota and the potential application of microbiota-based design of diets for the CMPA treatment are also discussed. This review not only summarizes the current studies about the interactions of CMPA with gut microbiota but also gives insights into the possible CMPA treatment strategies by modulating gut microbiota, which might help in improving the life quality of CMPA patients in the future.

Comparative Genomics Analysis of Keratin-Degrading Chryseobacterium Species Reveals Their Keratinolytic Potential for Secondary Metabolite Production

A promising keratin-degrading strain from the genus Chryseobacterium (Chryseobacterium sp. KMC2) was investigated using comparative genomic tools against three publicly available reference genomes to reveal the keratinolytic potential for biosynthesis of valuable secondary metabolites. Genomic features and metabolic potential of four species were compared, showing genomic differences but similar functional categories. Eleven different secondary metabolite gene clusters of interest were mined from the four genomes successfully, including five common ones shared across all genomes. Among the common metabolites, we identified gene clusters involved in biosynthesis of flexirubin-type pigment, microviridin, and siderophore, showing remarkable conservation across the four genomes. Unique secondary metabolite gene clusters were also discovered, for example, ladderane from Chryseobacterium sp. KMC2. Additionally, this study provides a more comprehensive understanding of the potential metabolic pathways of keratin utilization in Chryseobacterium sp. KMC2, with the involvement of amino acid metabolism, TCA cycle, glycolysis/gluconeogenesis, propanoate metabolism, and sulfate reduction. This work uncovers the biosynthesis of secondary metabolite gene clusters from four keratinolytic Chryseobacterium species and shades lights on the keratinolytic potential of Chryseobacterium sp. KMC2 from a genome-mining perspective, can provide alternatives to valorize keratinous materials into high-value bioactive natural products.

Systematic analysis of gut microbiome reveals the role of bacterial folate and homocysteine metabolism in Parkinson's disease

Parkinson's disease (PD) is the most common progressive neurological disorder compromising motor functions. However, nonmotor symptoms, such as gastrointestinal (GI) dysfunction, precede those affecting movement. Evidence of an early involvement of the GI tract and enteric nervous system highlights the need for better understanding of the role of gut microbiota in GI complications in PD. Here, we investigate the gut microbiome of patients with PD using metagenomics and serum metabolomics. We integrate these data using metabolic modeling and construct an integrative correlation network giving insight into key microbial species linked with disease severity, GI dysfunction, and age of patients with PD. Functional analysis reveals an increased microbial capability to degrade mucin and host glycans in PD. Personalized community-level metabolic modeling reveals the microbial contribution to folate deficiency and hyperhomocysteinemia observed in patients with PD. The metabolic modeling approach could be applied to uncover gut microbial metabolic contributions to PD pathophysiology.

Acute kidney injury leading to CKD is associated with a persistence of metabolic dysfunction and hypertriglyceridemia

Fibrosis is the pathophysiological hallmark of progressive chronic kidney disease (CKD). The kidney is a highly metabolically active organ, and it has been suggested that disruption in its metabolism leads to renal fibrosis. We developed a longitudinal mouse model of acute kidney injury leading to CKD and an in vitro model of epithelial to mesenchymal transition to study changes in metabolism, inflammation, and fibrosis. Using transcriptomics, metabolic modeling, and serum metabolomics, we observed sustained fatty acid metabolic dysfunction in the mouse model from early to late stages of CKD. Increased fatty acid biosynthesis and downregulation of catabolic pathways for triglycerides and diacylglycerides were associated with a marked increase in these lipids in the serum. We therefore suggest that the kidney may be the source of the abnormal lipid profile seen in patients with CKD, which may provide insights into the association between CKD and cardiovascular disease.

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Following AKI, markers of fibrosis and inflammation go up simultaneously

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AKI is associated with reduced fatty acid oxidation and oxidative phosphorylation

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Changes in metabolism persist as chronic kidney disease develops

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Changes in metabolism are associated with increased serum levels of triglycerides

Compositional and functional differences of the mucosal microbiota along the intestine of healthy individuals

Gut mucosal microbes evolved closest to the host, developing specialized local communities. There is, however, insufficient knowledge of these communities as most studies have employed sequencing technologies to investigate faecal microbiota only. This work used shotgun metagenomics of mucosal biopsies to explore the microbial communities' compositions of terminal ileum and large intestine in 5 healthy individuals. Functional annotations and genome-scale metabolic modelling of selected species were then employed to identify local functional enrichments. While faecal metagenomics provided a good approximation of the average gut mucosal microbiome composition, mucosal biopsies allowed detecting the subtle variations of local microbial communities. Given their significant enrichment in the mucosal microbiota, we highlight the roles of Bacteroides species and describe the antimicrobial resistance biogeography along the intestine. We also detail which species, at which locations, are involved with the tryptophan/indole pathway, whose malfunctioning has been linked to pathologies including inflammatory bowel disease. Our study thus provides invaluable resources for investigating mechanisms connecting gut microbiota and host pathophysiology.

Disease, Drugs and Dysbiosis: Understanding Microbial Signatures in Metabolic Disease and Medical Interventions

Since the discovery of the potential role for the gut microbiota in health and disease, many studies have gone on to report its impact in various pathologies. These studies have fuelled interest in the microbiome as a potential new target for treating disease Here, we reviewed the key metabolic diseases, obesity, type 2 diabetes and atherosclerosis and the role of the microbiome in their pathogenesis. In particular, we will discuss disease associated microbial dysbiosis; the shift in the microbiome caused by medical interventions and the altered metabolite levels between diseases and interventions. The microbial dysbiosis seen was compared between diseases including Crohn’s disease and ulcerative colitis, non-alcoholic fatty liver disease, liver cirrhosis and neurodegenerative diseases, Alzheimer’s and Parkinson’s. This review highlights the commonalities and differences in dysbiosis of the gut between diseases, along with metabolite levels in metabolic disease vs. the levels reported after an intervention. We identify the need for further analysis using systems biology approaches and discuss the potential need for treatments to consider their impact on the microbiome.

Systems Biology: A Multi-Omics Integration Approach to Metabolism and the Microbiome

The complex and dynamic nature of human physiology, as exemplified by metabolism, has often been overlooked due to the lack of quantitative and systems approaches. Recently, systems biology approaches have pushed the boundaries of our current understanding of complex biochemical, physiological, and environmental interactions, enabling proactive medicine in the near future. From this perspective, we review how state-of-the-art computational modelling of human metabolism, i.e., genome-scale metabolic modelling, could be used to identify the metabolic footprints of diseases, to guide the design of personalized treatments, and to estimate the microbiome contributions to host metabolism. These state-of-the-art models can serve as a scaffold for integrating multi-omics data, thereby enabling the identification of signatures of dysregulated metabolism by systems approaches. For example, increased plasma mannose levels due to decreased uptake in the liver have been identified as a potential biomarker of early insulin resistance by multi-omics approaches. In addition, we also review the emerging axis of human physiology and the human microbiome, discussing its contribution to host metabolism and quantitative approaches to study its variations in individuals.

Systems Biology Approaches to Understand the Host-Microbiome Interactions in Neurodegenerative Diseases

Neurodegenerative diseases (NDDs) comprise a broad range of progressive neurological disorders with multifactorial etiology contributing to disease pathophysiology. Evidence of the microbiome involvement in the gut-brain axis urges the interest in understanding metabolic interactions between the microbiota and host physiology in NDDs. Systems Biology offers a holistic integrative approach to study the interplay between the different biologic systems as part of a whole, and may elucidate the host–microbiome interactions in NDDs. We reviewed direct and indirect pathways through which the microbiota can modulate the bidirectional communication of the gut-brain axis, and explored the evidence of microbial dysbiosis in Alzheimer’s and Parkinson’s diseases. As the gut microbiota being strongly affected by diet, the potential approaches to targeting the human microbiota through diet for the stimulation of neuroprotective microbial-metabolites secretion were described. We explored the potential of Genome-scale metabolic models (GEMs) to infer microbe-microbe and host-microbe interactions and to identify the microbiome contribution to disease development or prevention. Finally, a systemic approach based on GEMs and ‘omics integration, that would allow the design of sustainable personalized anti-inflammatory diets in NDDs prevention, through the modulation of gut microbiota was described.

A systems biology approach for studying neurodegenerative diseases

Neurodegenerative diseases (NDDs), such as Alzheimer's (AD) and Parkinson's (PD), are among the leading causes of lost years of healthy life and exert a great strain on public healthcare systems. Despite being first described more than a century ago, no effective cure exists for AD or PD. Although extensively characterised at the molecular level, traditional neurodegeneration research remains marred by narrow-sense approaches surrounding amyloid β (Aβ), tau, and α-synuclein (α-syn). A systems biology approach enables the integration of multi-omics data and informs discovery of biomarkers, drug targets, and treatment strategies. Here, we present a comprehensive timeline of high-throughput data collection, and associated biotechnological advancements and computational analysis related to AD and PD. We hereby propose that a philosophical change in the definitions of AD and PD is now needed.

Integrating the "Immunome" in the Stratification of Myelodysplastic Syndromes and Future Clinical Trial Design

Myelodysplastic syndromes (MDS) are characterized by ineffective hematopoiesis and often include a dysregulation and dysfunction of the immune system. In the context of population aging, MDS incidence is set to increase substantially, with exponential increases in health care costs, given the limited and expensive treatment options for these patients. Treatment selection is mainly based on calculated risk categories according to a Revised International Prognostic Scoring System (IPSS-R). However, although IPSS-R is an excellent predictor of disease progression, it is an ineffective predictor of response to disease-modifying therapies. Redressing these unmet needs, the "immunome" is a key, multifaceted component in the initiation and overall response against malignant cells in MDS, and the current omission of immune status monitoring may in part explain the insufficiencies of current prognostic stratification methods. Nevertheless, integrating these and other recent molecular advances into clinical practice proves difficult. This review highlights the complexity of immune dysregulation in MDS pathophysiology and the fine balance between smoldering inflammation, adaptive immunity, and somatic mutations in promoting or suppressing malignant clones. We review the existing knowledge and discuss how state-of-the-art immune monitoring strategies could potentially permit novel patient substratification, thereby empowering practical predictions of response to treatment in MDS. We propose novel multicenter studies, which are needed to achieve this goal.

Postmortem Cortical Transcriptomics of Lewy Body Dementia Reveal Mitochondrial Dysfunction and Lack of Neuroinflammation

OBJECTIVE

Prevalence of Lewy body dementias (LBD) is second only to Alzheimer's disease (AD) among people with neurodegenerative dementia. LBD cause earlier mortality, more intense neuropsychiatric symptoms, more caregivers' burden, and higher costs than AD. The molecular mechanisms underlying LBD are largely unknown. As advancing molecular level mechanistic understanding is essential for identifying reliable peripheral biomarkers and novel therapeutic targets for LBD, the authors aimed to identify differentially expressed genes (DEG), and dysfunctional molecular networks in postmortem LBD brains.

METHODS

The authors investigated the transcriptomics of postmortem anterior cingulate and dorsolateral prefrontal cortices of people with pathology-verified LBD using next-generation RNA-sequencing. The authors verified the identified DEG using high-throughput quantitative polymerase chain reactions. Functional implications of identified DEG and the consequent metabolic reprogramming were evaluated by Ingenuity pathway analyses, genome-scale metabolic modeling, reporter metabolite analyses, and in silico gene silencing.

RESULTS

The authors identified and verified 12 novel DEGs (MPO, SELE, CTSG, ALPI, ABCA13, GALNT6, SST, RBM3, CSF3, SLC4A1, OXTR, and RAB44) in LBD brains with genome-wide statistical significance. The authors documented statistically significant down-regulation of several cytokine genes. Identified dysfunctional molecular networks highlighted the contributions of mitochondrial dysfunction, oxidative stress, and immunosenescence toward neurodegeneration in LBD.

CONCLUSION

Our findings support that chronic microglial activation and neuroinflammation, well-documented in AD, are notably absent in LBD. The lack of neuroinflammation in LBD brains was corroborated by statistically significant down-regulation of several inflammatory markers. Identified DEGs, especially down-regulated inflammatory markers, may aid distinguishing LBD from AD, and their biomarker potential warrant further investigation.

The Intestine Harbors Functionally Distinct Homeostatic Tissue-Resident and Inflammatory Th17 Cells

T helper 17 (Th17) cells are pathogenic in many inflammatory diseases, but also support the integrity of the intestinal barrier in a non-inflammatory manner. It is unclear what distinguishes inflammatory Th17 cells elicited by pathogens and tissue-resident homeostatic Th17 cells elicited by commensals. Here, we compared the characteristics of Th17 cells differentiating in response to commensal bacteria (SFB) to those differentiating in response to a pathogen (Citrobacter rodentium). Homeostatic Th17 cells exhibited little plasticity towards expression of inflammatory cytokines, were characterized by a metabolism typical of quiescent or memory T cells, and did not participate in inflammatory processes. In contrast, infection-induced Th17 cells showed extensive plasticity towards pro-inflammatory cytokines, disseminated widely into the periphery, and engaged aerobic glycolysis in addition to oxidative phosphorylation typical for inflammatory effector cells. These findings will help ensure that future therapies directed against inflammatory Th17 cells do not inadvertently damage the resident gut population.

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Tissue-resident, SFB-elicited Th17 cells are non-inflammatory

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Citrobacter-elicited Th17 cells show high plasticity towards inflammatory cytokines

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SFB Th17 cells are metabolically similar to resting memory cells

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Citrobacter Th17 cells are highly glycolytic effector cells

Understanding the Representative Gut Microbiota Dysbiosis in Metformin-Treated Type 2 Diabetes Patients Using Genome-Scale Metabolic Modeling

Dysbiosis in the gut microbiome composition may be promoted by therapeutic drugs such as metformin, the world’s most prescribed antidiabetic drug. Under metformin treatment, disturbances of the intestinal microbes lead to increased abundance of Escherichia spp., Akkermansia muciniphila, Subdoligranulum variabile and decreased abundance of Intestinibacter bartlettii. This alteration may potentially lead to adverse effects on the host metabolism, with the depletion of butyrate producer genus. However, an increased production of butyrate and propionate was verified in metformin-treated Type 2 diabetes (T2D) patients. The mechanisms underlying these nutritional alterations and their relation with gut microbiota dysbiosis remain unclear. Here, we used Genome-scale Metabolic Models of the representative gut bacteria Escherichia spp., I. bartlettii, A. muciniphila, and S. variabile to elucidate their bacterial metabolism and its effect on intestinal nutrient pool, including macronutrients (e.g., amino acids and short chain fatty acids), minerals and chemical elements (e.g., iron and oxygen). We applied flux balance analysis (FBA) coupled with synthetic lethality analysis interactions to identify combinations of reactions and extracellular nutrients whose absence prevents growth. Our analyses suggest that Escherichia sp. is the bacteria least vulnerable to nutrient availability. We have also examined bacterial contribution to extracellular nutrients including short chain fatty acids, amino acids, and gasses. For instance, Escherichia sp. and S. variabile may contribute to the production of important short chain fatty acids (e.g., acetate and butyrate, respectively) involved in the host physiology under aerobic and anaerobic conditions. We have also identified pathway susceptibility to nutrient availability and reaction changes among the four bacteria using both FBA and flux variability analysis. For instance, lipopolysaccharide synthesis, nucleotide sugar metabolism, and amino acid metabolism are pathways susceptible to changes in Escherichia sp. and A. muciniphila. Our observations highlight important commensal and competing behavior, and their association with cellular metabolism for prevalent gut microbes. The results of our analysis have potential important implications for development of new therapeutic approaches in T2D patients through the development of prebiotics, probiotics, or postbiotics.

The Human Mucosal Mycobiome and Fungal Community Interactions

With the advent of high-throughput sequencing techniques, the astonishing extent and complexity of the microbial communities that reside within and upon us has begun to become clear. Moreover, with advances in computing and modelling methods, we are now beginning to grasp just how dynamic our interactions with these communities are. The diversity of both these communities and their interactions—both within the community and with us—are dependent on a multitude of factors, both microbial- and host-mediated. Importantly, it is becoming clear that shifts in the makeup of these communities, or their responses, are linked to different disease states. Although much of the work to define these interactions and links has been investigating bacterial communities, recently there has been significant growth in the body of knowledge, indicating that shifts in the host fungal communities (mycobiome) are also intimately linked to disease status. In this review, we will explore these associations, along with the interactions between fungal communities and their human and microbial habitat, and discuss the future applications of systems biology in determining their role in disease status.

Systems biology analysis of hepatitis C virus infection reveals the role of copy number increases in regions of chromosome 1q in hepatocellular carcinoma metabolism

Hepatitis C virus (HCV) infection is a worldwide healthcare problem; however, traditional treatment methods have failed to cure all patients, and HCV has developed resistance to new drugs. Systems biology-based analyses could play an important role in the holistic analysis of the impact of HCV on hepatocellular metabolism. Here, we integrated HCV assembly reactions with a genome-scale hepatocyte metabolic model to identify metabolic targets for HCV assembly and metabolic alterations that occur between different HCV progression states (cirrhosis, dysplastic nodule, and early and advanced hepatocellular carcinoma (HCC)) and healthy liver tissue. We found that diacylglycerolipids were essential for HCV assembly. In addition, the metabolism of keratan sulfate and chondroitin sulfate was significantly changed in the cirrhosis stage, whereas the metabolism of acyl-carnitine was significantly changed in the dysplastic nodule and early HCC stages. Our results explained the role of the upregulated expression of BCAT1, PLOD3 and six other methyltransferase genes involved in carnitine biosynthesis and S-adenosylmethionine metabolism in the early and advanced HCC stages. Moreover, GNPAT and BCAP31 expression was upregulated in the early and advanced HCC stages and could lead to increased acyl-CoA consumption. By integrating our results with copy number variation analyses, we observed that GNPAT, PPOX and five of the methyltransferase genes (ASH1L, METTL13, SMYD2, TARBP1 and SMYD3), which are all located on chromosome 1q, had increased copy numbers in the cancer samples relative to the normal samples. Finally, we confirmed our predictions with the results of metabolomics studies and proposed that inhibiting the identified targets has the potential to provide an effective treatment strategy for HCV-associated liver disorders.

The gut microbiota modulates host amino acid and glutathione metabolism in mice

The gut microbiota has been proposed as an environmental factor that promotes the progression of metabolic diseases. Here, we investigated how the gut microbiota modulates the global metabolic differences in duodenum, jejunum, ileum, colon, liver, and two white adipose tissue depots obtained from conventionally raised (CONV-R) and germ-free (GF) mice using gene expression data and tissue-specific genome-scale metabolic models (GEMs). We created a generic mouse metabolic reaction (MMR) GEM, reconstructed 28 tissue-specific GEMs based on proteomics data, and manually curated GEMs for small intestine, colon, liver, and adipose tissues. We used these functional models to determine the global metabolic differences between CONV-R and GF mice. Based on gene expression data, we found that the gut microbiota affects the host amino acid (AA) metabolism, which leads to modifications in glutathione metabolism. To validate our predictions, we measured the level of AAs and N-acetylated AAs in the hepatic portal vein of CONV-R and GF mice. Finally, we simulated the metabolic differences between the small intestine of the CONV-R and GF mice accounting for the content of the diet and relative gene expression differences. Our analyses revealed that the gut microbiota influences host amino acid and glutathione metabolism in mice.

Quantifying Diet-Induced Metabolic Changes of the Human Gut Microbiome

The human gut microbiome is known to be associated with various human disorders, but a major challenge is to go beyond association studies and elucidate causalities. Mathematical modeling of the human gut microbiome at a genome scale is a useful tool to decipher microbe-microbe, diet-microbe and microbe-host interactions. Here, we describe the CASINO (Community And Systems-level INteractive Optimization) toolbox, a comprehensive computational platform for analysis of microbial communities through metabolic modeling. We first validated the toolbox by simulating and testing the performance of single bacteria and whole communities in vitro. Focusing on metabolic interactions between the diet, gut microbiota, and host metabolism, we demonstrated the predictive power of the toolbox in a diet-intervention study of 45 obese and overweight individuals and validated our predictions by fecal and blood metabolomics data. Thus, modeling could quantitatively describe altered fecal and serum amino acid levels in response to diet intervention.

Identifying anti-growth factors for human cancer cell lines through genome-scale metabolic modeling

Human cancer cell lines are used as important model systems to study molecular mechanisms associated with tumor growth, hereunder how genomic and biological heterogeneity found in primary tumors affect cellular phenotypes. We reconstructed Genome scale metabolic models (GEMs) for eleven cell lines based on RNA-Seq data and validated the functionality of these models with data from metabolite profiling. We used cell line-specific GEMs to analyze the differences in the metabolism of cancer cell lines, and to explore the heterogeneous expression of the metabolic subsystems. Furthermore, we predicted 85 antimetabolites that can inhibit growth of, or even kill, any of the cell lines, while at the same time not being toxic for 83 different healthy human cell types. 60 of these antimetabolites were found to inhibit growth in all cell lines. Finally, we experimentally validated one of the predicted antimetabolites using two cell lines with different phenotypic origins, and found that it is effective in inhibiting the growth of these cell lines. Using immunohistochemistry, we also showed high or moderate expression levels of proteins targeted by the validated antimetabolite. Identified anti-growth factors for inhibition of cell growth may provide leads for the development of efficient cancer treatment strategies.

Elucidating the interactions between the human gut microbiota and its host through metabolic modeling

Increased understanding of the interactions between the gut microbiota, diet and environmental effects may allow us to design efficient treatment strategies for addressing global health problems. Existence of symbiotic microorganisms in the human gut provides different functions for the host such as conversion of nutrients, training of the immune system, and resistance to pathogens. The gut microbiome also plays an influential role in maintaining human health, and it is a potential target for prevention and treatment of common disorders including obesity, type 2 diabetes, and atherosclerosis. Due to the extreme complexity of such disorders, it is necessary to develop mathematical models for deciphering the role of its individual elements as well as the entire system and such models may assist in better understanding of the interactions between the bacteria in the human gut and the host by use of genome-scale metabolic models (GEMs). Recently, GEMs have been employed to explore the interactions between predominant bacteria in the gut ecosystems. Additionally, these models enabled analysis of the contribution of each species to the overall metabolism of the microbiota through the integration of omics data. The outcome of these studies can be used for proposing optimal conditions for desired microbiome phenotypes. Here, we review the recent progress and challenges for elucidating the interactions between the human gut microbiota and host through metabolic modeling. We discuss how these models may provide scaffolds for analyzing high-throughput data, developing probiotics and prebiotics, evaluating the effects of probiotics and prebiotics and eventually designing clinical interventions.

Dynamic 13C-labeling experiments prove important differences in protein turnover rate between two Saccharomyces cerevisiae strains

We developed a method for the quantification of protein turnover rates using (13)C-labeled substrates combined with the analysis of the labeling patterns of proteinogenic amino acids. Using this method, the specific amino acid turnover rates between proteins and the pool of free amino acids were determined for eight different amino acids (alanine, valine, proline, aspartic acid, glycine, leucine, isoleucine, and threonine) in two Saccharomyces cerevisiae strains (CEN.PK 113-7D and YSBN2). Furthermore, proteasome activities were compared for both strains. Both results confirmed the hypothesis of a higher protein turnover rates in CEN.PK 113-7D, which was generated in a previous comparative systems biology study of these two yeast strains. The ATP costs associated with the observed differences in protein turnover were quantified and could explain accurately the differences in biomass yield between both strains that are observed in chemostat cultures. The percentage of maintenance ATP associated with protein polymerization (polymerization for growth and re-polymerization because of turnover) and degradation was estimated to be 72% for YSBN2 and 79% for CEN.PK 113-7D, which makes these processes the dominant nonbiosynthetic drain of ATP in living cells, and hence, it represents an energetic parameter of great relevance.

Genome-scale metabolic reconstructions of Pichia stipitis and Pichia pastoris and in silico evaluation of their potentials

Background

Pichia stipitis and Pichia pastoris have long been investigated due to their native abilities to metabolize every sugar from lignocellulose and to modulate methanol consumption, respectively. The latter has been driving the production of several recombinant proteins. As a result, significant advances in their biochemical knowledge, as well as in genetic engineering and fermentation methods have been generated. The release of their genome sequences has allowed systems level research.

Results

In this work, genome-scale metabolic models (GEMs) of P. stipitis (iSS884) and P. pastoris (iLC915) were reconstructed. iSS884 includes 1332 reactions, 922 metabolites, and 4 compartments. iLC915 contains 1423 reactions, 899 metabolites, and 7 compartments. Compared with the previous GEMs of P. pastoris, PpaMBEL1254 and iPP668, iLC915 contains more genes and metabolic functions, as well as improved predictive capabilities. Simulations of physiological responses for the growth of both yeasts on selected carbon sources using iSS884 and iLC915 closely reproduced the experimental data. Additionally, the iSS884 model was used to predict ethanol production from xylose at different oxygen uptake rates. Simulations with iLC915 closely reproduced the effect of oxygen uptake rate on physiological states of P. pastoris expressing a recombinant protein. The potential of P. stipitis for the conversion of xylose and glucose into ethanol using reactors in series, and of P. pastoris to produce recombinant proteins using mixtures of methanol and glycerol or sorbitol are also discussed.

Conclusions

In conclusion the first GEM of P. stipitis (iSS884) was reconstructed and validated. The expanded version of the P. pastoris GEM, iLC915, is more complete and has improved capabilities over the existing models. Both GEMs are useful frameworks to explore the versatility of these yeasts and to capitalize on their biotechnological potentials.