The MetaCyc database of metabolic pathways and enzymes and the BioCyc collection of Pathway/Genome Databases

AFP: Finding pathways accounting for stoichiometry along with atom group tracking in metabolic network

Automatically finding novel pathways plays an important role in the initial designs of metabolic pathways in synthetic biology and metabolic engineering. Although path-finding methods have been successfully applied in identifying valuable synthetic pathways, few efforts have been made in fusing atom group tracking into building stoichiometry model to search metabolic pathways from arbitrary start compound via Mixed Integer Linear Programming (MILP). We propose a novel method called AFP to find metabolic pathways by incorporating atom group tracking into reaction stoichiometry via MILP. AFP tracks the movements of atom groups in the reaction stoichiometry to construct MILP model to search the pathways containing atom groups exchange in the reactions and adapts the MILP model to provide the options of searching pathways from an arbitrary or given compound to the target compound. Combining atom group tracking with reaction stoichiometry to build MILP model for pathfinding may promote the search of well-designed alternative pathways at the stoichiometric modeling level. The experimental comparisons to the known pathways show that our proposed method AFP is more effective to recover the known pathways than other existing methods and is capable of discovering biochemically feasible pathways producing the metabolites of interest.

Differential effects of pectin-based dietary fibre type and gut microbiota composition on in vitro fermentation outcomes

Interactions between human gut microbiota and dietary fibres (DF) are influenced by the complexity and diversity of both individual microbiota and sources of DF. Based on 480 in vitro fermentations, a full factorial experiment was performed with six faecal inocula representing two enterotypes and three DF sources with nanometer, micrometer, and millimeter length-scales (apple pectin, apple cell walls and apple particles) at two concentrations. Increasing DF size reduced substrate disappearance and fermentation rates but not biomass growth. Concentrated DF enhanced butyrate production and lactate cross-feeding. Enterotype differentiated final microbial compositions but not biomass or fermentation metabolite profiles. Individual donor microbiota differences did not influence DF type or concentration effects but were manifested in the promotion of different functional microbes within each population with the capacity to degrade the DF substrates. Overall, consistent effects (independent of donor microbiota variation) of DF type and concentration on kinetics of substrate degradation, microbial biomass production, gas kinetics and metabolite profiles were found, which can form the basis for informed design of DF for desired rates/sites and consequences of gut fermentation. These results add further evidence to the concept that, despite variations between individuals, the human gut microbiota represents a community with conserved emergent properties.

Machine learning for the advancement of genome-scale metabolic modeling

Constraint-based modeling (CBM) has evolved as the core systems biology tool to map the interrelations between genotype, phenotype, and external environment. The recent advancement of high-throughput experimental approaches and multi-omics strategies has generated a plethora of new and precise information from wide-ranging biological domains. On the other hand, the continuously growing field of machine learning (ML) and its specialized branch of deep learning (DL) provide essential computational architectures for decoding complex and heterogeneous biological data. In recent years, both multi-omics and ML have assisted in the escalation of CBM. Condition-specific omics data, such as transcriptomics and proteomics, helped contextualize the model prediction while analyzing a particular phenotypic signature. At the same time, the advanced ML tools have eased the model reconstruction and analysis to increase the accuracy and prediction power. However, the development of these multi-disciplinary methodological frameworks mainly occurs independently, which limits the concatenation of biological knowledge from different domains. Hence, we have reviewed the potential of integrating multi-disciplinary tools and strategies from various fields, such as synthetic biology, CBM, omics, and ML, to explore the biochemical phenomenon beyond the conventional biological dogma. How the integrative knowledge of these intersected domains has improved bioengineering and biomedical applications has also been highlighted. We categorically explained the conventional genome-scale metabolic model (GEM) reconstruction tools and their improvement strategies through ML paradigms. Further, the crucial role of ML and DL in omics data restructuring for GEM development has also been briefly discussed. Finally, the case-study-based assessment of the state-of-the-art method for improving biomedical and metabolic engineering strategies has been elaborated. Therefore, this review demonstrates how integrating experimental and in silico strategies can help map the ever-expanding knowledge of biological systems driven by condition-specific cellular information. This multiview approach will elevate the application of ML-based CBM in the biomedical and bioengineering fields for the betterment of society and the environment.

Soybean oil-based HFD induces gut dysbiosis that leads to steatosis, hepatic inflammation and insulin resistance in mice

High-fat diets (HFDs) shape the gut microbiome and promote obesity, inflammation, and liver steatosis. Fish and soybean are part of a healthy diet; however, the impact of these fats, in the absence of sucrose, on gut microbial dysbiosis and its association with liver steatosis remains unclear. Here, we investigated the effect of sucrose-free soybean oil-and fish oil-based high fat diets (HFDs) (SF-Soy-HFD and SF-Fish-HFD, respectively) on gut dysbiosis, obesity, steatosis, hepatic inflammation, and insulin resistance. C57BL/6 mice were fed these HFDs for 24 weeks. Both diets had comparable effects on liver and total body weights. But 16S-rRNA sequencing of the gut content revealed induction of gut dysbiosis at different taxonomic levels. The microbial communities were clearly separated, showing differential dysbiosis between the two HFDs. Compared with the SF-Fish-HFD control group, the SF-Soy-HFD group had an increased abundance of Bacteroidetes, Firmicutes, and Deferribacteres, but a lower abundance of Verrucomicrobia. The Clostridia/Bacteroidia (C/B) ratio was higher in the SF-Soy-HFD group (3.11) than in the SF-Fish-HFD group (2.5). Conversely, the Verrucomicrobiacae/S24_7 (also known as Muribaculaceae family) ratio was lower in the SF-Soy-HFD group (0.02) than that in the SF-Fish-HFD group (0.75). The SF-Soy-HFD group had a positive association with S24_7, Clostridiales, Allobaculum, Coriobacteriaceae, Adlercreutzia, Christensenellaceae, Lactococcus, and Oscillospira, but was related to a lower abundance of Akkermansia, which maintains gut barrier integrity. The gut microbiota in the SF-Soy-HFD group had predicted associations with host genes related to fatty liver and inflammatory pathways. Mice fed the SF-Soy-HFD developed liver steatosis and showed increased transcript levels of genes associated with de novo lipogenesis (Acaca, Fasn, Scd1, Elovl6) and cholesterol synthesis (Hmgcr) pathways compared to those in the SF-Fish-HFD-group. No differences were observed in the expression of fat uptake genes (Cd36 and Fabp1). The expression of the fat efflux gene (Mttp) was reduced in the SF-Soy-HFD group. Moreover, hepatic inflammation markers (Tnfa and Il1b) were notably expressed in SF-Soy-HFD-fed mice. In conclusion, SF-Soy-HFD feeding induced gut dysbiosis in mice, leading to steatosis, hepatic inflammation, and impaired glucose homeostasis.

Longitudinal Transcriptomic, Proteomic, and Metabolomic Response of Citrus sinensis to Diaphorina citri Inoculation of Candidatus Liberibacter asiaticus

Huanglongbing (HLB) is a fatal citrus disease that is currently threatening citrus varieties worldwide. One putative causative agent, Candidatus Liberibacter asiaticus (CLas), is vectored by Diaphorina citri, known as the Asian citrus psyllid (ACP). Understanding the details of CLas infection in HLB disease has been hindered by its Candidatus nature and the inability to confidently detect it in diseased trees during the asymptomatic stage. To identify early changes in citrus metabolism in response to inoculation of CLas using its natural psyllid vector, leaves from Madam Vinous sweet orange (Citrus sinensis (L.) Osbeck) trees were exposed to CLas-positive ACP or CLas-negative ACP and longitudinally analyzed using transcriptomics (RNA sequencing), proteomics (liquid chromatography-tandem mass spectrometry; data available in Dryad: 10.25338/B83H1Z), and metabolomics (proton nuclear magnetic resonance). At 4 weeks postexposure (wpe) to psyllids, the initial HLB plant response was primarily to the ACP and, to a lesser extent, the presence or absence of CLas. Additionally, analysis of 4, 8, 12, and 16 wpe identified 17 genes and one protein as consistently differentially expressed between leaves exposed to CLas-positive ACP versus CLas-negative ACP. This study informs identification of early detection molecular targets and contributes to a broader understanding of vector-transmitted plant pathogen interactions.

A quantitative description of light-limited cyanobacterial growth using flux balance analysis

The metabolism of phototrophic cyanobacteria is an integral part of global biogeochemical cycles, and the capability of cyanobacteria to assimilate atmospheric CO2 into organic carbon has manifold potential applications for a sustainable biotechnology. To elucidate the properties of cyanobacterial metabolism and growth, computational reconstructions of genome-scale metabolic networks play an increasingly important role. Here, we present an updated reconstruction of the metabolic network of the cyanobacterium Synechocystis sp. PCC 6803 and its quantitative evaluation using flux balance analysis (FBA). To overcome limitations of conventional FBA, and to allow for the integration of experimental analyses, we develop a novel approach to describe light absorption and light utilization within the framework of FBA. Our approach incorporates photoinhibition and a variable quantum yield into the constraint-based description of light-limited phototrophic growth. We show that the resulting model is capable of predicting quantitative properties of cyanobacterial growth, including photosynthetic oxygen evolution and the ATP/NADPH ratio required for growth and cellular maintenance. Our approach retains the computational and conceptual simplicity of FBA and is readily applicable to other phototrophic microorganisms.

The gut microbiota derived metabolite trimethylamine N-oxide: Its important role in cancer and other diseases

An expanding body of research indicates a correlation between the gut microbiota and various diseases. Metabolites produced by the gut microbiota act as mediators between the gut microbiota and the host, interacting with multiple systems in the human body to regulate physiological or pathological functions. However, further investigation is still required to elucidate the underlying mechanisms. One such metabolite involved in choline metabolism by gut microbes is trimethylamine (TMA), which can traverse the intestinal epithelial barrier and enter the bloodstream, ultimately reaching the liver where it undergoes oxidation catalyzed by flavin-containing monooxygenase 3 (FMO3) to form trimethylamine N-oxide (TMAO). While some TMAO is eliminated through renal excretion, remaining amounts circulate in the bloodstream, leading to systemic inflammation, endoplasmic reticulum (ER) stress, mitochondrial stress, and disruption of normal physiological functions in humans. As a representative microbial metabolite originating from the gut, TMAO has significant potential both as a biomarker for monitoring disease occurrence and progression and for tailoring personalized treatment strategies for patients. This review provides an extensive overview of TMAO sources and its metabolism in human blood, as well as its impact on several major human diseases. Additionally, we explore the latest research areas related to TMAO along with future directions.

Genome-scale community modelling elucidates the metabolic interaction in Indian type-2 diabetic gut microbiota

Type-2 diabetes (T2D) is a rapidly growing multifactorial metabolic disorder that induces the onset of various diseases in the human body. The compositional and metabolic shift of the gut microbiota is a crucial factor behind T2D. Hence, gaining insight into the metabolic profile of the gut microbiota is essential for revealing their role in regulating the metabolism of T2D patients. Here, we have focused on the genome-scale community metabolic model reconstruction of crucial T2D-associated gut microbes. The model-based analysis of biochemical flux in T2D and healthy gut conditions showed distinct biochemical signatures and diverse metabolic interactions in the microbial community. The metabolic interactions encompass cross-feeding of short-chain fatty acids, amino acids, and vitamins among individual microbes within the community. In T2D conditions, a reduction in the metabolic flux of acetate, butyrate, vitamin B5, and bicarbonate was observed in the microbial community model, which can impact carbohydrate metabolism. The decline in butyrate levels is correlated with both insulin resistance and diminished glucose metabolism in T2D patients. Compared to the healthy gut, an overall reduction in glucose consumption and SCFA production flux was estimated in the T2D gut environment. Moreover, the decreased consumption profiles of branch chain amino acids (BCAAs) and aromatic amino acids (AAAs) in the T2D gut microbiota can be a distinct biomarker for T2D. Hence, the flux-level analysis of the microbial community model can provide insights into the metabolic reprogramming in diabetic gut microbiomes, which may be helpful in personalized therapeutics and diet design against T2D.

Methanomethylovorans are the dominant dimethylsulfide-degrading methanogens in gravel and sandy river sediment microcosms

BACKGROUND

Rivers and streams are important components of the global carbon cycle and methane budget. However, our understanding of the microbial diversity and the metabolic pathways underpinning methylotrophic methane production in river sediments is limited. Dimethylsulfide is an important methylated compound, found in freshwater sediments. Yet, the magnitude of DMS-dependent methanogenesis nor the methanogens carrying out this process in river sediments have been explored before. This study addressed this knowledge gap in DMS-dependent methanogenesis in gravel and sandy river sediments.

RESULTS

Significant methane production via DMS degradation was found in all sediment  microcosms. Sandy, less permeable river sediments had higher methane yields (83 and 92%) than gravel, permeable sediments (40 and 48%). There was no significant difference between the methanogen diversity in DMS-amended gravel and sandy sediment microcosms, which Methanomethylovorans dominated. Metagenomics data analysis also showed the dominance of Methanomethylovorans and Methanosarcina. DMS-specific methyltransferase genes (mts) were found in very low relative abundances whilst the methanol-, trimethylamine- and dimethylamine-specific methyltransferase genes (mtaA, mttB and mtbB) had the highest relative abundances, suggesting their involvement in DMS-dependent methanogenesis.

CONCLUSIONS

This is the first study demonstrating a significant potential for DMS-dependent methanogenesis in river sediments with contrasting geologies. Methanomethylovorans were the dominant methylotrophic methanogen in all river sediment microcosms. Methyltransferases specific to methylotrophic substrates other than DMS are likely key enzymes in DMS-dependent methanogenesis, highlighting their versatility and importance in the methane cycle in freshwater sediments, which would warrant further study.

Trimethylamine N-Oxide in Aquatic Foods

Trimethylamine N-oxide (TMAO), a characteristic nonprotein nitrogen compound, is widely present in seafood, which exhibits osmoregulatory effects for marine organisms in vivo and plays an important role in aquaculture and aquatic product preservation. However, much attention has been focused on the negative effect of TMAO since it has recently emerged as a putative promoter of chronic diseases. To get full knowledge and maximize our ability to balance the positive and negative aspects of TMAO, in this review, we comprehensively discuss the TMAO in aquatic products from the aspects of physiological functions for marine organisms, flavor, quality, the conversion of precursors, the influences on human health, and the seafood ingredients interaction consideration. Though the circulating TMAO level is inevitably enhanced after seafood consumption, dietary seafood still exhibits beneficial health effects and may provide nutraceuticals to balance the possible adverse effects of TMAO.

Emerging tools and best practices for studying gut microbial community metabolism

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

Feeding a Saccharomyces cerevisiae fermentation product during a gut barrier challenge in lactating Holstein cows impacts the ruminal microbiota and metabolome

Through its influence on the gut microbiota, the feeding of Saccharomyces cerevisiae fermentation products (SCFP) has been a successful strategy to enhance the health of dairy cows during periods of physiological stresses. Although production and metabolic outcomes from feeding SCFP are well-known, its combined impacts on the ruminal microbiota and metabolome during gut barrier challenges remain unclear. To address this gap in knowledge, multiparous Holstein cows (97.1 ± 7.6 DIM [SD]; n = 8/group) fed a control diet (CON) or CON plus 19 g/d SCFP for 9 wk were subjected to a feed restriction (FR) challenge for 5 d, during which they were fed 40% of their ad libitum intake from the 7 d before FR. The DNA extracted from ruminal fluid was subjected to PacBio full-length 16S rRNA gene sequencing, real-time PCR of 12 major ruminal bacteria, and metabolomics analysis of up to 189 metabolites via GC/MS. High-quality amplicon sequence analyses were performed with the TADA (Targeted Amplicon Diversity Analysis), MicrobiomeAnalyst, PICRUSt2, and STAMP software packages, and metabolomics data were analyzed via MetaboAnalyst 5.0. Ruminal fluid metabolites from the SCFP group exhibited a greater α-diversity Chao 1 (P = 0.03) and Shannon indices (P = 0.05), and the partial least squares discriminant analysis clearly discriminated metabolite profiles between dietary groups. The abundance of CPla_4_termite_group, Candidatus Saccharimonas, Oribacterium, and Pirellula genus in cows fed SCFP was greater. In the SCFP group, concentrations of ethanolamine, 2-amino-4,6-dihydroxypyrimidine, glyoxylic acid, serine, threonine, cytosine, stearic acid, and pyrrole-2-carboxylic acid were greater in ruminal fluid. Both Fretibacterium and Succinivibrio abundances were positively correlated with metabolites across various biological processes: gamma-aminobutyric acid, galactose, butane-2,3-diol, fructose, 5-amino pentanoic acid, β-aminoisobutyric acid, ornithine, malonic acid, 3-hydroxy-3-methylbutyric acid, hexanoic acid, heptanoic acid, cadaverine, glycolic acid, β-alanine, 2-hydroxybutyric acid, methyl alanine, and alanine. In the SCFP group, compared with CON, the mean proportion of 14 predicted pathways based on metabolomics data was greater, whereas 10 predicted pathways were lower. Integrating metabolites and upregulated predicted enzymes (NADP+-dependent glucose-6-phosphate dehydrogenase, 6-phosphogluconate dehydrogenase, serine: glyoxylate aminotransferase, and d-glycerate 3-kinase) indicated that the pentose phosphate pathway and photorespiration pathway were most upregulated by SCFP. Overall, SCFP during FR led to alterations in ruminal microbiota composition and key metabolic pathways. Among those, we identified a shift from the tricarboxylic acid cycle to the glyoxylate cycle, and nitrogenous base production was enhanced.

Serum and CSF metabolomics analysis shows Mediterranean Ketogenic Diet mitigates risk factors of Alzheimer's disease

Alzheimer's disease (AD) is influenced by a variety of modifiable risk factors, including a person's dietary habits. While the ketogenic diet (KD) holds promise in reducing metabolic risks and potentially affecting AD progression, only a few studies have explored KD's metabolic impact, especially on blood and cerebrospinal fluid (CSF). Our study involved participants at risk for AD, either cognitively normal or with mild cognitive impairment. The participants consumed both a modified Mediterranean Ketogenic Diet (MMKD) and the American Heart Association diet (AHAD) for 6 weeks each, separated by a 6-week washout period. We employed nuclear magnetic resonance (NMR)-based metabolomics to profile serum and CSF and metagenomics profiling on fecal samples. While the AHAD induced no notable metabolic changes, MMKD led to significant alterations in both serum and CSF. These changes included improved modifiable risk factors, like increased HDL-C and reduced BMI, reversed serum metabolic disturbances linked to AD such as a microbiome-mediated increase in valine levels, and a reduction in systemic inflammation. Additionally, the MMKD was linked to increased amino acid levels in the CSF, a breakdown of branched-chain amino acids (BCAAs), and decreased valine levels. Importantly, we observed a strong correlation between metabolic changes in the CSF and serum, suggesting a systemic regulation of metabolism. Our findings highlight that MMKD can improve AD-related risk factors, reverse some metabolic disturbances associated with AD, and align metabolic changes across the blood-CSF barrier.

Bile acids and the gut microbiome are involved in the hyperthermia mediated by 3,4-methylenedioxymethamphetamine (MDMA)

Hyperthermia induced by phenethylamines, such as 3,4-methylenedioxymethamphetamine (MDMA), can lead to life-threatening complications and death. Activation of the sympathetic nervous system and subsequent release of norepinephrine and activation of uncoupling proteins have been demonstrated to be the key mediators of phenethylamine-induced hyperthermia (PIH). Recently, the gut microbiome was shown to also play a contributing role in PIH. Here, the hypothesis that bile acids (BAs) produced by the gut microbiome are essential to PIH was tested. Changes in the serum concentrations of unconjugated primary BAs cholic acid (CA) and chenodeoxycholic acid (CDCA) and secondary BA deoxycholic acid (DCA) were measured following MDMA (20 mg/kg, sc) treatment in antibiotic treated and control rats. MDMA-induced a significant hyperthermic response and reduced the serum concentrations of three BAs 60 min post-treatment. Pretreatment with antibiotics (vancomycin, bacitracin and neomycin) in the drinking water for five days resulted in the depletion of BAs and a hypothermic response to MDMA. Gut bacterial communities in the antibiotic-treated group were distinct from the MDMA or saline treatment groups, with decreased microbiome diversity and alteration in taxa. Metagenomic functions inferred using the bioinformatic tool PICRUSt2 on 16S rRNA gene sequences indicated that bacterial genes associated to BA metabolism are less abundant in the antibiotic-MDMA treated group. Overall, these findings suggest that gut bacterial produced BAs might play an important role in MDMA-induced hyperthermia.

Gut Dysbiosis Shaped by Cocoa Butter-Based Sucrose-Free HFD Leads to Steatohepatitis, and Insulin Resistance in Mice

BACKGROUND

High-fat diets cause gut dysbiosis and promote triglyceride accumulation, obesity, gut permeability changes, inflammation, and insulin resistance. Both cocoa butter and fish oil are considered to be a part of healthy diets. However, their differential effects on gut microbiome perturbations in mice fed high concentrations of these fats, in the absence of sucrose, remains to be elucidated. The aim of the study was to test whether the sucrose-free cocoa butter-based high-fat diet (C-HFD) feeding in mice leads to gut dysbiosis that associates with a pathologic phenotype marked by hepatic steatosis, low-grade inflammation, perturbed glucose homeostasis, and insulin resistance, compared with control mice fed the fish oil based high-fat diet (F-HFD).

RESULTS

C57BL/6 mice (5-6 mice/group) were fed two types of high fat diets (C-HFD and F-HFD) for 24 weeks. No significant difference was found in the liver weight or total body weight between the two groups. The 16S rRNA sequencing of gut bacterial samples displayed gut dysbiosis in C-HFD group, with differentially-altered microbial diversity or relative abundances. Bacteroidetes, Firmicutes, and Proteobacteria were highly abundant in C-HFD group, while the Verrucomicrobia, Saccharibacteria (TM7), Actinobacteria, and Tenericutes were more abundant in F-HFD group. Other taxa in C-HFD group included the Bacteroides, Odoribacter, Sutterella, Firmicutes bacterium (AF12), Anaeroplasma, Roseburia, and Parabacteroides distasonis. An increased Firmicutes/Bacteroidetes (F/B) ratio in C-HFD group, compared with F-HFD group, indicated the gut dysbiosis. These gut bacterial changes in C-HFD group had predicted associations with fatty liver disease and with lipogenic, inflammatory, glucose metabolic, and insulin signaling pathways. Consistent with its microbiome shift, the C-HFD group showed hepatic inflammation and steatosis, high fasting blood glucose, insulin resistance, increased hepatic de novo lipogenesis (Acetyl CoA carboxylases 1 (Acaca), Fatty acid synthase (Fasn), Stearoyl-CoA desaturase-1 (Scd1), Elongation of long-chain fatty acids family member 6 (Elovl6), Peroxisome proliferator-activated receptor-gamma (Pparg) and cholesterol synthesis (β-(hydroxy β-methylglutaryl-CoA reductase (Hmgcr). Non-significant differences were observed regarding fatty acid uptake (Cluster of differentiation 36 (CD36), Fatty acid binding protein-1 (Fabp1) and efflux (ATP-binding cassette G1 (Abcg1), Microsomal TG transfer protein (Mttp) in C-HFD group, compared with F-HFD group. The C-HFD group also displayed increased gene expression of inflammatory markers including Tumor necrosis factor alpha (Tnfa), C-C motif chemokine ligand 2 (Ccl2), and Interleukin-12 (Il12), as well as a tendency for liver fibrosis.

CONCLUSION

These findings suggest that the sucrose-free C-HFD feeding in mice induces gut dysbiosis which associates with liver inflammation, steatosis, glucose intolerance and insulin resistance.

ToxoNet: A high confidence map of protein-protein interactions in Toxoplasma gondii

The apicomplexan intracellular parasite Toxoplasma gondii is a major food borne pathogen that is highly prevalent in the global population. The majority of the T. gondii proteome remains uncharacterized and the organization of proteins into complexes is unclear. To overcome this knowledge gap, we used a biochemical fractionation strategy to predict interactions by correlation profiling. To overcome the deficit of high-quality training data in non-model organisms, we complemented a supervised machine learning strategy, with an unsupervised approach, based on similarity network fusion. The resulting combined high confidence network, ToxoNet, comprises 2,063 interactions connecting 652 proteins. Clustering identifies 93 protein complexes. We identified clusters enriched in mitochondrial machinery that include previously uncharacterized proteins that likely represent novel adaptations to oxidative phosphorylation. Furthermore, complexes enriched in proteins localized to secretory organelles and the inner membrane complex, predict additional novel components representing novel targets for detailed functional characterization. We present ToxoNet as a publicly available resource with the expectation that it will help drive future hypotheses within the research community.

Microbiome-driven alterations in metabolic pathways and impaired cognition in aged female TgF344-AD rats

Alzheimer's disease (AD) not only affects cognition and neuropathology, but several other facets capable of negatively impacting quality of life and potentially driving impairments, including altered gut microbiome (GMB) composition and metabolism. Aged (20 + mo) female TgF344-AD and wildtype rats were cognitively characterized on several tasks incorporating several cognitive domains, including task acquisition, object recognition memory, anxiety-like behaviors, and spatial navigation. Additionally, metabolic phenotyping, GMB sequencing throughout the intestinal tract (duodenum, jejunum, ileum, colon, and feces), neuropathological burden assessment and marker gene functional abundance predictions (PICRUSt2) were conducted. TgF344-AD rats demonstrated significant cognitive impairment in multiple domains, as well as regionally specific GMB dysbiosis. Relationships between peripheral factors were investigated using Canonical Correspondence Analysis (CCA), revealing correlations between GMB changes and both cognitive and metabolic factors. Moreover, communities of gut microbes contributing to essential metabolic pathways were significantly altered in TgF344-AD rats. These data indicate dysbiosis may affect cognitive outcomes in AD through alterations in metabolism-related enzymatic pathways that are necessary for proper brain function. Moreover, these changes were mostly observed in intestinal segments required for carbohydrate digestion, not fecal samples. These data support the targeting of intestinal and microbiome health for the treatment of AD.

Meta-analysis of shotgun sequencing of gut microbiota in Parkinson's disease

We aimed to identify gut microbial features in Parkinson's disease (PD) across countries by meta-analyzing our fecal shotgun sequencing dataset of 94 PD patients and 73 controls in Japan with five previously reported datasets from USA, Germany, China1, China2, and Taiwan. GC-MS and LC-MS/MS assays were established to quantify fecal short-chain fatty acids (SCFAs) and fecal polyamines, respectively. α-Diversity was increased in PD across six datasets. Taxonomic analysis showed that species Akkermansia muciniphila was increased in PD, while species Roseburia intestinalis and Faecalibacterium prausnitzii were decreased in PD. Pathway analysis showed that genes in the biosyntheses of riboflavin and biotin were markedly decreased in PD after adjusting for confounding factors. Five out of six categories in carbohydrate-active enzymes (CAZymes) were decreased in PD. Metabolomic analysis of our fecal samples revealed that fecal SCFAs and polyamines were significantly decreased in PD. Genes in the riboflavin and biotin biosyntheses were positively correlated with the fecal concentrations of SCFAs and polyamines. Bacteria that accounted for the decreased riboflavin biosynthesis in Japan, the USA, and Germany were different from those in China1, China2, and Taiwan. Similarly, different bacteria accounted for decreased biotin biosynthesis in the two country groups. We postulate that decreased SCFAs and polyamines reduce the intestinal mucus layer, which subsequently facilitates the formation of abnormal α-synuclein fibrils in the intestinal neural plexus in PD, and also cause neuroinflammation in PD.

MetSim: Integrated Programmatic Access and Pathway Management for Xenobiotic Metabolism Simulators

Xenobiotic metabolism is a key consideration in evaluating the hazards and risks posed by environmental chemicals. A number of software tools exist that are capable of simulating metabolites, but each reports its predictions in a different format and with varying levels of detail. This makes comparing the performance and coverage of the tools a practical challenge. To address this shortcoming, we developed a metabolic simulation framework called MetSim, which comprises three main components. A graph-based schema was developed to allow metabolism information to be harmonized. The schema was implemented in MongoDB to store and retrieve metabolic graphs for subsequent analysis. MetSim currently includes an application programming interface for four metabolic simulators: BioTransformer, the OECD Toolbox, EPA's chemical transformation simulator (CTS), and tissue metabolism simulator (TIMES). Lastly, MetSim provides functions to help evaluate simulator performance for specific data sets. In this study, a set of 112 drugs with 432 reported metabolites were compiled, and predictions were made using the 4 simulators. Fifty-nine of the 112 drugs were taken from the Small Molecule Pathway Database, with the remainder sourced from the literature. The human models within BioTransformer and CTS (Phase I only) and the rat models within TIMES and the OECD Toolbox (Phase I only) were used to make predictions for the chemicals in the data set. The recall and precision (recall, precision) ranked in order of highest recall for each individual tool were CTS (0.54, 0.017), BioTransformer (0.50, 0.008), Toolbox in vitro (0.40, 0.144), TIMES in vivo (0.40, 0.133), Toolbox in vivo (0.40, 0.118), and TIMES in vitro (0.39, 0.128). Combining all of the model predictions together increased the overall recall (0.73, 0.008). MetSim enabled insights into the performance and coverage of in silico metabolic simulators to be more efficiently derived, which in turn should aid future efforts to evaluate other data sets.

Divergence within the Taxon 'Candidatus Phytoplasma asteris' Confirmed by Comparative Genome Analysis of Carrot Strains

Phytoplasmas are linked to diseases in hundreds of economically important crops, including carrots. In carrots, phytoplasmosis is associated with leaf chlorosis and necrosis, coupled with inhibited root system development, ultimately leading to significant economic losses. During a field study conducted in Baden-Württemberg (Germany), two strains of the provisional taxon 'Candidatus Phytoplasma asteris' were identified within a carrot plot. For further analysis, strains M8 and M33 underwent shotgun sequencing, utilising single-molecule-real-time (SMRT) long-read sequencing and sequencing-by-synthesis (SBS) paired-end short-read sequencing techniques. Hybrid assemblies resulted in complete de novo assemblies of two genomes harboring circular chromosomes and two plasmids. Analyses, including average nucleotide identity and sequence comparisons of established marker genes, confirmed the phylogenetic divergence of 'Ca. P. asteris' and a different assignment of strains to the 16S rRNA subgroup I-A for M33 and I-B for M8. These groups exhibited unique features, encompassing virulence factors and genes, associated with the mobilome. In contrast, pan-genome analysis revealed a highly conserved gene set related to metabolism across these strains. This analysis of the Aster Yellows (AY) group reaffirms the perception of phytoplasmas as bacteria that have undergone extensive genome reduction during their co-evolution with the host and an increase of genome size by mobilome.

Microbiome changes under enteral deprivation are dynamic and dependent on intestinal location

BACKGROUND

The microbiome has a pivotal role in intestinal health, and nutrition has a major role shaping its structure. Enteral deprivation, in which no oral/enteral nutrition is administered, is common in hospitalized/gastrointestinal patients. The dynamics that enteral deprivation exerts on the microbial community, specifically in the small intestine, are not well understood.

METHODS

Enteral deprivation was modeled with exclusive parenteral nutrition (EPN) mice. Mice were allocated to receive either EPN or saline and chow (control) and euthanized after 0, 2, 4, or 6 days. DNA was extracted from jejunum, ileum, and colon content. 16S sequencing was used to compare changes in microbial communities between groups. Functional pathways were predicted using Phylogenetic Investigation of Communities by Reconstruction of Unobserved States.

RESULTS

EPN-treated mice showed community changes throughout the intestine. Beta diversity in colon showed clear separation between the groups (Bray-Curtis, P < 0.001). Time-dependent dynamics were seen in ileal but not jejunal samples. Alpha diversity was lower in the colon of EPN mice compared with control/baseline mice (Chao1, P < 0.01) but not in ileum/jejunum. Progressive loss of single-taxon domination was seen, most notably in the small intestine. This was accompanied by increases/decreases in specific taxa. A clear separation was seen in the functional capacity of the community between fed and enterally deprived mice at the ileum and colon, which was observed early on.

CONCLUSIONS

Enteral deprivation disturbs the microbial community in a spatial and dynamic manner. There should be further focus on studying the effect of these changes on the host.

A Two-Faced Gut Microbiome: Butyrogenic and Proinflammatory Bacteria Predominate in the Intestinal Milieu of People Living with HIV from Western Mexico

HIV infection results in marked alterations in the gut microbiota (GM), such as the loss of microbial diversity and different taxonomic and metabolic profiles. Despite antiretroviral therapy (ART) partially ablating gastrointestinal alterations, the taxonomic profile after successful new ART has shown wide variations. Our objective was to determine the GM composition and functions in people living with HIV (PLWHIV) under ART in comparison to seronegative controls (SC). Fecal samples from 21 subjects (treated with integrase strand-transfer inhibitors, INSTIs) and 18 SC were included. We employed 16S rRNA amplicon sequencing, coupled with PICRUSt2 and fecal short-chain fatty acid (SCFA) quantification by gas chromatography. The INSTI group showed a decreased α-diversity (p < 0.001) compared to the SC group, at the expense of increased amounts of Pseudomonadota (Proteobacteria), Segatella copri, Lactobacillus, and Gram-negative bacteria. Concurrently, we observed an enrichment in Megasphaera and Butyricicoccus, both SCFA-producing bacteria, and significant elevations in fecal butyrate in this group (p < 0.001). Interestingly, gut dysbiosis in PLWHIV was characterized by a proinflammatory environment orchestrated by Pseudomonadota and elevated levels of butyrate associated with bacterial metabolic pathways, as well as the evident presence of butyrogenic bacteria. The role of this unique GM in PLWHIV should be evaluated, as well as the use of butyrate-based supplements and ART regimens that contain succinate, such as tenofovir disoproxil succinate. This mixed profile is described for the first time in PLWHIV from Mexico.

Bad company? The pericardium microbiome in people investigated for tuberculosis pericarditis in an HIV-prevalent setting

Background

The microbiome likely plays a role in tuberculosis (TB) pathogenesis. We evaluated the site-of-disease microbiome and predicted metagenome in people with presumptive tuberculous pericarditis, a major cause of mortality, and explored for the first time, the interaction between its association with C-reactive protein (CRP), a potential diagnostic biomarker and the site-of-disease microbiome in extrapulmonary TB.

Methods

People with effusions requiring diagnostic pericardiocentesis (n=139) provided background sampling controls and pericardial fluid (PF) for 16S rRNA gene sequencing analysed using QIIME2 and PICRUSt2. Blood was collected to measure CRP.

Results

PF from people with definite (dTB, n=91), probable (pTB, n=25), and non- (nTB, n=23) tuberculous pericarditis differed in β-diversity. dTBs were, vs. nTBs, Mycobacterium-, Lacticigenium-, and Kocuria- enriched. Within dTBs, HIV-positives were Mycobacterium-, Bifidobacterium- , Methylobacterium- , and Leptothrix -enriched vs. HIV-negatives and HIV-positive dTBs on ART were Mycobacterium - and Bifidobacterium -depleted vs. those not on ART. Compared to nTBs, dTBs exhibited short-chain fatty acid (SCFA) and mycobacterial metabolism microbial pathway enrichment. People with additional non-pericardial involvement had differentially PF taxa (e.g., Mycobacterium -enrichment and Streptococcus -depletion associated with pulmonary infiltrates). Mycobacterium reads were in 34% (31/91), 8% (2/25) and 17% (4/23) of dTBs, pTBs, and nTBs, respectively. β-diversity differed between patients with CRP above vs. below the median value ( Pseudomonas -depleted). There was no correlation between enriched taxa in dTBs and CRP.

Conclusions

PF is compositionally distinct based on TB status, HIV (and ART) status and dTBs are enriched in SCFA-associated taxa. The clinical significance of these findings, including mycobacterial reads in nTBs and pTBs, requires evaluation.

A randomized clinical trial investigating the impact of magnesium supplementation on clinical and biochemical measures in COVID-19 patients

BACKGROUND

This study sought to examine the impact of magnesium supplementation on clinical outcomes and biochemical factors among hospitalized patients with COVID-19.

METHODS

This double-blind, randomized clinical trial was conducted at Razi Hospital, Ahvaz, Iran, between September 2021 and March 2022. Participants aged 18-70 years with moderate disease severity were enrolled. Magnesium supplementation (300 mg daily) was administered to the intervention group, while the control group received a placebo. Clinical outcomes, including the need for oxygen therapy, oxygen saturation, respiratory rate, fever, hs-CRP and TNF-α levels, as well as quality of life and mental health, were assessed. Blood samples were collected to measure biochemical variables.

RESULTS

The main result was the count of individuals requiring oxygen therapy. Additional outcomes comprised of oxygen saturation, respiratory rate, fever, hs-CRP and TNF-α levels, as well as quality of life and mental health. Out of 64 participants, 60 completed the study. The results showed that magnesium supplementation significantly reduced the number of patients requiring oxygen therapy (9 vs. 14; P < 0.001). Moreover, the magnesium group demonstrated improved oxygen saturation compared to the control group (4.55 ± 2.35 vs. 1.8 ± 1.67; P < 0.001). Furthermore, we observed a noteworthy enhancement in the quality of life and depression score in the magnesium group. No significant differences were observed in respiratory rate, fever, hs-CRP, and TNF-α levels (P > 0.05).

CONCLUSION

The findings suggest that magnesium supplementation may have beneficial effects on clinical outcomes and arterial oxygen saturation in COVID-19 patients. More investigation is necessary to delve into its potential mechanisms and long-term effects on patient outcomes.

TRIAL REGISTRATION

This study is registered on Iranian Registry of Clinical Trials (IRCT) under identifier IRCT20210413050957N1. (The registration date: May 1, 2021).

Soil exposure modulates the immune response to an influenza challenge in a mouse model

There is increasing evidence that early life microbial exposure aids in immune system maturation, more recently known as the "old friends" hypothesis. To test this hypothesis, 4-week-old mice were exposed to soils of increasing microbial diversity for four weeks followed by an intranasal challenge with either live or heat inactivated influenza A virus and monitored for 7 additional days. Perturbations of the gut and lung microbiomes were explored through 16S rRNA amplicon sequencing. RNA-sequencing was used to examine the host response in the lung tissue through differential gene expression. We determined that compared to the gut microbiome, the lung microbiome is more susceptible to changes in beta diversity following soil exposure with Lachnospiraceae ASVs accounting for most of the differences between groups. While several immune system genes were found to be significantly differentially expressed in lung tissue due to soil exposures, there were no differences in viral load or weight loss. This study shows that exposure to diverse microbial communities through soil exposure alters the gut and lung microbiomes resulting in differential expression of specific immune system related genes within the lung following an influenza challenge.

Exploring the potential effects of forest urbanization on the interplay between small mammal communities and their gut microbiota

Urbanization significantly impacts wild populations, favoring urban dweller species over those that are unable to adapt to rapid changes. These differential adaptative abilities could be mediated by the microbiome, which may modulate the host phenotype rapidly through a high degree of flexibility. Conversely, under anthropic perturbations, the microbiota of some species could be disrupted, resulting in dysbiosis and negative impacts on host fitness. The links between the impact of urbanization on host communities and their gut microbiota (GM) have only been scarcely explored. In this study, we tested the hypothesis that the bacterial composition of the GM could play a role in host adaptation to urban environments. We described the GM of several species of small terrestrial mammals sampled in forested areas along a gradient of urbanization, using a 16S metabarcoding approach. We tested whether urbanization led to changes in small mammal communities and in their GM, considering the presence and abundance of bacterial taxa and their putative functions. This enabled to decipher the processes underlying these changes. We found potential impacts of urbanization on small mammal communities and their GM. The urban dweller species had a lower bacterial taxonomic diversity but a higher functional diversity and a different composition compared to urban adapter species. Their GM assembly was mostly governed by stochastic effects, potentially indicating dysbiosis. Selection processes and an overabundance of functions were detected that could be associated with adaptation to urban environments despite dysbiosis. In urban adapter species, the GM functional diversity and composition remained relatively stable along the urbanization gradient. This observation can be explained by functional redundancy, where certain taxa express the same function. This could favor the adaptation of urban adapter species in various environments, including urban settings. We can therefore assume that there are feedbacks between the gut microbiota and host species within communities, enabling rapid adaptation.

Microbiome single cell atlases generated with a commercial instrument

Single cell sequencing is useful for resolving complex systems into their composite cell types and computationally mining them for unique features that are masked in pooled sequencing. However, while commercial instruments have made single cell analysis widespread for mammalian cells, analogous tools for microbes are limited. Here, we present EASi-seq (Easily Accessible Single microbe sequencing). By adapting the single cell workflow of the commercial Mission Bio Tapestri instrument, this method allows for efficient sequencing of individual microbes' genomes. EASi-seq allows thousands of microbes to be sequenced per run and, as we show, can generate detailed atlases of human and environmental microbiomes. The ability to capture large shotgun genome datasets from thousands of single microbes provides new opportunities in discovering and analyzing species subpopulations. To facilitate this, we develop a companion bioinformatic pipeline that clusters microbes by similarity, improving whole genome assembly, strain identification, taxonomic classification, and gene annotation. In addition, we demonstrate integration of metagenomic contigs with the EASi-seq datasets to reduce capture bias and increase coverage. Overall, EASi-seq enables high quality single cell genomic data for microbiome samples using an accessible workflow that can be run on a commercially available platform.

Longitudinal gut microbiome changes in immune checkpoint blockade-treated advanced melanoma

Multiple clinical trials targeting the gut microbiome are being conducted to optimize treatment outcomes for immune checkpoint blockade (ICB). To improve the success of these interventions, understanding gut microbiome changes during ICB is urgently needed. Here through longitudinal microbiome profiling of 175 patients treated with ICB for advanced melanoma, we show that several microbial species-level genome bins (SGBs) and pathways exhibit distinct patterns from baseline in patients achieving progression-free survival (PFS) of 12 months or longer (PFS ≥12) versus patients with PFS shorter than 12 months (PFS <12). Out of 99 SGBs that could discriminate between these two groups, 20 were differentially abundant only at baseline, while 42 were differentially abundant only after treatment initiation. We identify five and four SGBs that had consistently higher abundances in patients with PFS ≥12 and <12 months, respectively. Constructing a log ratio of these SGBs, we find an association with overall survival. Finally, we find different microbial dynamics in different clinical contexts including the type of ICB regimen, development of immune-related adverse events and concomitant medication use. Insights into the longitudinal dynamics of the gut microbiome in association with host factors and treatment regimens will be critical for guiding rational microbiome-targeted therapies aimed at enhancing ICB efficacy.

BioISO: An Objective-Oriented Application for Assisting the Curation of Genome-Scale Metabolic Models

As the reconstruction of Genome-Scale Metabolic Models (GEMs) becomes standard practice in systems biology, the number of organisms having at least one metabolic model is peaking at an unprecedented scale. The automation of laborious tasks, such as gap-finding and gap-filling, allowed the development of GEMs for poorly described organisms. However, the quality of these models can be compromised by the automation of several steps, which may lead to erroneous phenotype simulations. Biological networks constraint-based In Silico Optimisation (BioISO) is a computational tool aimed at accelerating the reconstruction of GEMs. This tool facilitates manual curation steps by reducing the large search spaces often met when debugging in silico biological models. BioISO uses a recursive relation-like algorithm and Flux Balance Analysis (FBA) to evaluate and guide debugging of in silico phenotype simulations. The potential of BioISO to guide the debugging of model reconstructions was showcased and compared with the results of two other state-of-the-art gap-filling tools (Meneco and fastGapFill). In this assessment, BioISO is better suited to reducing the search space for errors and gaps in metabolic networks by identifying smaller ratios of dead-end metabolites. Furthermore, BioISO was used as Meneco's gap-finding algorithm to reduce the number of proposed solutions for filling the gaps.

Characterisation of Indian gut microbiome for B-vitamin production and its comparison with Chinese cohort

The human gut microbiota can biosynthesize essential micronutrients such as B-vitamins and is also known for its metabolic cooperative behaviour. The present study characterises such B-vitamin biosynthesizers, their biosynthetic pathways, explores their prevalence and abundance, examines how lifestyle or diet affects them in multiple Indian cohorts and compares it with the Chinese cohort. To achieve this, publicly available faecal metagenome data of healthy individuals from multiple Indian (two urban and three tribal populations) and a Chinese cohort were analysed. The distribution of prevalence and abundance of B-vitamin biosynthesizers showed similar profiles to that of the entire gut community of the Indian cohort, and there were 28 B-vitamin biosynthesizers that had modest or higher prevalence and abundance. The omnivorous diet affected only the prevalence of a few B-vitamin biosynthesizers; however, lifestyle and/or location affected both prevalence and abundance. A comparison with the Chinese cohort showed that fourteen B-vitamin biosynthesizers were significantly more prevalent and abundant in Chinese as compared with Indian samples (False Discovery Rate (FDR) <= 0·05). The metabolic potential of the entire gut community for B-vitamin production showed that within India, the tribal cohort has a higher abundance of B-vitamin biosynthesis pathways as compared with two urban cohorts namely, Bhopal and Kasargod, and comparison with the Chinese cohort revealed a higher abundance in the latter group. Potential metabolic cooperative behaviour of the Indian gut microbiome for biosynthesis of the B-vitamins showed multiple pairs of species showed theoretical complementarity for complete biosynthetic pathways genes of thiamine, riboflavin, niacin and pantothenate.

Bacterial acetate metabolism and its influence on human epithelia

Short-chain fatty acids are known modulators of host-microbe interactions and can affect human health, inflammation, and outcomes of microbial infections. Acetate is the most abundant but least well-studied of these modulators, with most studies focusing on propionate and butyrate, which are considered to be more potent. In this mini-review, we summarize current knowledge of acetate as an important anti-inflammatory modulator of interactions between hosts and microorganisms. This includes a summary of the pathways by which acetate is metabolized by bacteria and human cells, the functions of acetate in bacterial cells, and the impact that microbially derived acetate has on human immune function.

Intestinal Dysbiosis in Subjects with Obesity from Western Mexico and Its Association with a Proinflammatory Profile and Disturbances of Folate (B9) and Carbohydrate Metabolism

Obesity is a public health problem with a growing prevalence worldwide. In Mexico, it is estimated that one out of three adults suffer from obesity. In these patients, the intestinal microbiota (IM) undergoes pathological changes that are associated with a dysbiotic state; however, the microbiota profile of adult subjects with obesity from western Mexico has not been described. To assess this, fecal samples were obtained from 65 participants (Obese = 38; Control = 27). The microbial composition was characterized by 16S rRNA amplicon sequencing. The IM of the group with obesity revealed a clear decrease in richness and diversity (p < 0.001), as well as a significant increase in proinflammatory bacterial groups, mainly genera belonging to the Negativicutes class, Escherichia/Shigella, and Prevotella. Likewise, an increase in short-chain fatty acid-producing bacteria was found, especially the genus Lachnoclostridium. Additionally, PICRUSt2 analysis showed a depletion of vitamin B9 metabolism and an increase in saccharolytic pathways. The IM of patients with obesity possesses a dysbiotic, proinflammatory environment, possibly contributing to lipogenesis and adiposity. Thus, assessing the IM will allow for a better understanding of the pathophysiology of metabolic diseases of high prevalence, such as obesity. These findings are described for the first time in the adult population of western Mexico.

Time-Course Responses of Apple Leaf Endophytes to the Infection of Gymnosporangium yamadae

Apple rust, caused by Gymnosporangium yamadae, poses a significant challenge to apple production. Prior studies have underscored the pivotal role played by endophytic microbial communities, intimately linked with the host, in influencing plant diseases and their pathogenic outcomes. The objective of this study is to scrutinize alternations in endophytic microbial communities within apple leaves at different stages of apple rust using high-throughput sequencing technology. The findings revealed a discernible pattern characterized by an initial increase and subsequent decrease in the alpha diversity of microbial communities in diseased leaves. A microbial co-occurrence network analysis revealed that the complexity of the bacterial community in diseased leaves diminished initially and then rebounded during the progression of the disease. Additionally, employing the PICRUSt2 platform, this study provided preliminary insights into the functions of microbial communities at specific disease timepoints. During the spermogonial stage, endophytic bacteria particularly exhibited heightened activity in genetic information processing, metabolism, and environmental information processing pathways. Endophytic fungi also significantly enriched a large number of metabolic pathways during the spermogonial stage and aecial stage, exhibiting abnormally active life activities. These findings establish a foundation for comprehending the role of host endophytes in the interaction between pathogens and hosts. Furthermore, they offer valuable insights for the development and exploitation of plant endophytic resources, thereby contributing to enhanced strategies for managing apple rust.

Differential peripheral immune signatures elicited by vegan versus ketogenic diets in humans

Nutrition has broad impacts on all physiological processes. However, how nutrition affects human immunity remains largely unknown. Here we explored the impact of a dietary intervention on both immunity and the microbiota by performing a post hoc analysis of a clinical trial in which each of the 20 participants sequentially consumed vegan or ketogenic diets for 2 weeks ( NCT03878108 ). Using a multiomics approach including multidimensional flow cytometry, transcriptomic, proteomic, metabolomic and metagenomic datasets, we assessed the impact of each diet, and dietary switch, on host immunity and the microbiota. Our data revealed that overall, a ketogenic diet was associated with a significant upregulation of pathways and enrichment in cells associated with the adaptive immune system. In contrast, a vegan diet had a significant impact on the innate immune system, including upregulation of pathways associated with antiviral immunity. Both diets significantly and differentially impacted the microbiome and host-associated amino acid metabolism, with a strong downregulation of most microbial pathways following ketogenic diet compared with baseline and vegan diet. Despite the diversity of participants, we also observed a tightly connected network between datasets driven by compounds associated with amino acids, lipids and the immune system. Collectively, this work demonstrates that in diverse participants 2 weeks of controlled dietary intervention is sufficient to significantly and divergently impact host immunity, which could have implications for precision nutritional interventions. ClinicalTrials.gov registration: NCT03878108 .

Geomicrobiological characterization of the evaporitic ecosystem in the hypersaline lake Laguna Verde (Andean Puna, Northwestern Argentina)

Laguna Verde's dome-shaped structures are distinctive formations within the Central Andes, displaying unique geomicrobiological features. This study represents a pioneering investigation into these structures, assessing their formation, associated taxa, and ecological significance. Through a multifaceted approach that includes chemical analysis of the water body, multiscale characterization of the domes, and analysis of the associated microorganisms, we reveal the complex interplay between geology and biology in this extreme environment. The lake's alkaline waters that are rich in dissolved cations and anions such as chloride, sodium sulfate, and potassium, coupled with its location at the margin of the Antofalla salt flat, fed by alluvial fans and hydrothermal input, provide favorable conditions for mineral precipitation and support for the microorganism's activity. Laguna Verde's dome-shaped structures are mainly composed of gypsum and halite, displaying an internal heterogeneous mesostructure consisting of three zones: microcrystalline, organic (orange and green layers), and crystalline. The green layer of the organic zone is predominantly composed of Proteobacteria, Bacteroidetes, and Cyanobacteria, while the orange layer is mostly inhabited by Cyanobacteria. The results of the study suggest that oxygenic photosynthesis performed by Cyanobacteria is the main carbon fixation pathway in the microbial community, supported by carbon isotopic ratios of specific biomarkers. This finding highlights the important role played by Cyanobacteria in this ecosystem.

Falcon gut microbiota is shaped by diet and enriched in Salmonella

The gut microbiome is increasingly being appreciated as a master regulator of animal health. However, avian gut microbiome studies commonly focus on birds of economic importance and the gut microbiomes of raptors remain underexplored. Here we examine the gut microbiota of 29 captive falcons-raptors of historic importance-in the context of avian evolution by sequencing the V4 region of the 16S rRNA gene. Our results reveal that evolutionary histories and diet are significantly associated with avian gut microbiota in general, whereas diet plays a major role in shaping the falcon gut microbiota. Multiple analyses revealed that gut microbial diversity, composition, and relative abundance of key diet-discriminating bacterial genera in the falcon gut closely resemble those of carnivorous raptors rather than those of their closest phylogenetic relatives. Furthermore, the falcon microbiota is dominated by Firmicutes and contains Salmonella at appreciable levels. Salmonella presence was associated with altered functional capacity of the falcon gut microbiota as its abundance is associated with depletion of multiple predicted metabolic pathways involved in protein mass buildup, muscle maintenance, and enrichment of antimicrobial compound degradation, thus increasing the pathogenic potential of the falcon gut. Our results point to the necessity of screening for Salmonella and other human pathogens in captive birds to safeguard both the health of falcons and individuals who come in contact with these birds.

Untargeted metabolomics profiling of oat (Avena sativa L.) and wheat (Triticum aestivum L.) infested with wheat stem sawfly (Cephus cinctus Norton) reveals differences associated with plant defense and insect nutrition

Introduction

Wheat stem sawfly (WSS), Cephus cinctus Norton, is a major pest of common bread wheat (Triticum aestivum L.) and other cultivated cereals in North America. Planting of cultivars with solid stems has been the primary management strategy to prevent yield loss due to WSS infestation, however expression of this phenotype can vary depending on environmental conditions and solid stems hinder biological control of WSS via braconid parasitoids Bracon cephi (Gahan) and Bracon lissogaster Muesebeck. In the hollow stems of oat (Avena sativa L.), WSS larvae experience 100% mortality before they reach late instars, but the mechanisms for this observed resistance have not been characterized.

Objective

The objective of this study was to explore additional sources of resistance outside of the historic solid stem phenotype.

Methods

Here, we use an untargeted metabolomics approach to examine the response of the metabolome of two cultivars of oat and four cultivars of spring wheat to infestation by WSS. Using liquid chromatography-mass spectrometry (LC-MS), differentially expressed metabolites were identified between oat and wheat which were associated with the phenylpropanoid pathway, phospholipid biosynthesis and signaling, the salicylic acid signaling pathway, indole-3-acetic acid (IAA) degradation, and biosynthesis of 1,4-benzoxazin-3-ones (Bxs). Several phospho- and galacto- lipids were found in higher abundance in oat, and with the exception of early stem solidness cultivar Conan, both species experienced a decrease in abundance once infested. In all wheat cultivars except Conan, an increase in abundance was observed for Bxs HMDBOA-glc and DIBOA-β-D-glucoside after infestation, indicating that this pathway is involved in wheat response to infestation in both solid and hollow stemmed cultivars. Differences between species in compounds involved in IAA biosynthesis, degradation and inactivation suggest that wheat may respond to infestation by inactivating IAA or altering the IAA pool in stem tissue.

Conclusion

We propose that the species differences found here likely affect the survival of WSS larvae and may also be associated with differences in stem architecture at the molecular level. Our findings suggest pathways to focus on for future studies in elucidating plant response to WSS infestation.

Saccharomyces cerevisiae biofactory to produce naringenin using a systems biology approach and a bicistronic vector expression strategy in flavonoid production

IMPORTANCE

Flavonoids are a group of compounds generally produced by plants with proven biological activity, which have recently beeen recommended for the treatment and prevention of diseases and ailments with diverse causes. In this study, naringenin was produced in adequate amounts in yeast after in silico design. The four genes of the involved enzymes from several organisms (bacteria and plants) were multi-expressed in two vectors carrying each two genes linked by a short viral peptide sequence. The batch kinetic behavior of the product, substrate, and biomass was described at lab scale. The engineered strain might be used in a more affordable and viable bioprocess for industrial naringenin procurement.

Associations of central obesity and habitual food consumption with saliva microbiota and its enzymatic profiles - a pilot study in Finnish children

Background

Variation in diversity and composition of saliva microbiota has been linked to weight status, but findings have been inconsistent. Focusing on clinically relevant conditions such as central obesity and using advanced sequencing techniques might fill in the gaps of knowledge.

Aims

We investigated saliva microbiota with shallow metagenome sequencing in children with (n = 14) and without (n = 36) central obesity. Additionally, we examined the role of habitual food consumption on microbial enzymatic repertoire.

Methods

Data comprised 50 children (50% male) with a mean age of 14.2 (SD 0.3) years, selected from the Finnish Health in Teens (Fin-HIT) cohort. Dietary scores for consumption frequency of sweet treats (STI), dairy products (DCI) and plants (PCI) were derived based on a self-administered food frequency questionnaire. Central obesity was defined based on waist-height ratio using the cut-off 0.5. Saliva samples were subjected to whole-metagenome shotgun sequencing, and taxonomic and functional profiling was achieved with METAnnotatorX2 bioinformatics platform.

Results

Groups had an average 20 (95% CI 14-27) cm difference in waist circumference. We identified the lack of Pseudomonas guguagenesis and Prevotella scopos, oulorum and oris as putative biomarkers associated with central obesity and observed a total of 16 enzymatic reactions differing between the groups. DCI was associated with the highest number of enzyme profiles (122), followed by STI (60) and DCI (25) (Pearson correlation p < 0.05). Intriguingly, STI showed a high positive/negative correlation ratio (5.09), while DCI and PCI showed low ratios (0.54 and 0.33, respectively). Thus, the main driver of enzymatic reactions was STI, and the related pathways involved nitrate metabolism induced by Haemophilus parainfluenzae and Veilonella dispar among others.

Conclusion

Clinically relevant differences in central obesity were only modestly reflected in the composition of saliva microbiota. Habitual consumption of sweet treats was a strong determinant of enzymatic reactions of saliva microbiota in children with and without central obesity. The clinical relevance of these findings warrants further studies.

Comprehensive assessment of machine learning methods for diagnosing gastrointestinal diseases through whole metagenome sequencing data

The gut microbiome, linked significantly to host diseases, offers potential for disease diagnosis through machine learning (ML) pipelines. These pipelines, crucial in modeling diseases using high-dimensional microbiome data, involve selecting profile modalities, data preprocessing techniques, and classification algorithms, each impacting the model accuracy and generalizability. Despite whole metagenome shotgun sequencing (WMS) gaining popularity for human gut microbiome profiling, a consensus on the optimal methods for ML pipelines in disease diagnosis using WMS data remains elusive. Addressing this gap, we comprehensively evaluated ML methods for diagnosing Crohn's disease and colorectal cancer, using 2,553 fecal WMS samples from 21 case-control studies. Our study uncovered crucial insights: gut-specific, species-level taxonomic features proved to be the most effective for profiling; batch correction was not consistently beneficial for model performance; compositional data transformations markedly improved the models; and while nonlinear ensemble classification algorithms typically offered superior performance, linear models with proper regularization were found to be more effective for diseases that are linearly separable based on microbiome data. An optimal ML pipeline, integrating the most effective methods, was validated for generalizability using holdout data. This research offers practical guidelines for constructing reliable disease diagnostic ML models with fecal WMS data.

Simulating compatible solute biosynthesis using a metabolic flux model of the biomining acidophile, Acidithiobacillus ferrooxidans ATCC 23270

Halotolerant, acidophilic, bioleaching microorganisms are crucial to biomining operations that utilize saline water. Compatible solutes play an important role in the adaptation of these microorganisms to saline environments. Acidithiobacillus ferrooxidans ATCC 23270, an iron- and sulfur-oxidizing acidophilic bacterium, synthesizes trehalose as its native compatible solute but is still sensitive to salinity. Recently, halotolerant bioleaching bacteria were found to use ectoine as their key compatible solute. Previously, bioleaching bacteria were recalcitrant to genetic manipulation; however, recent advancements in genetic tools and techniques allow successful genetic modification of A. ferrooxidans ATCC 23270. Therefore, this study aimed to test, in silico, the effect of native and synthetic compatible solute biosynthesis by A. ferrooxidans ATCC 23270 on its growth and metabolism. Metabolic network flux modelling was used to provide a computational framework for the prediction of metabolic fluxes during production of native and synthetic compatible solutes by A. ferrooxidans ATCC 23270, in silico. Complete pathways for trehalose biosynthesis by the bacterium are proposed and captured in the updated metabolic model including a newly discovered UDP-dependent trehalose synthesis pathway. Finally, the effect of nitrogen sources on compatible solute production was simulated and showed that using nitrogen gas as the sole nitrogen source enables the ectoine-producing 'engineered' microbe to oxidize up to 20% more ferrous iron in comparison to the native microbe that only produces trehalose. Therefore, the predictive outcomes of the model have the potential to guide the design and optimization of a halotolerant strain of A. ferrooxidans ATCC 23270 for saline bioleaching operations.

Genome-Scale Model of Rhizopus microsporus: Metabolic integration of a fungal holobiont with its bacterial and viral endosymbionts

Rhizopus microsporus often lives in association with bacterial and viral symbionts that alter its biology. This fungal model represents an example of the complex interactions established among diverse organisms in functional holobionts. We constructed a Genome-Scale Model (GSM) of the fungal-bacterial-viral holobiont (iHol). We employed a constraint-based method to calculate the metabolic fluxes to decipher the metabolic interactions of the symbionts with their host. Our computational analyses of iHol simulate the holobiont's growth and the production of the toxin rhizoxin. Analyses of the calculated fluxes between R. microsporus in symbiotic (iHol) versus asymbiotic conditions suggest that changes in the lipid and nucleotide metabolism of the host are necessary for the functionality of the holobiont. Glycerol plays a pivotal role in the fungal-bacterial metabolic interaction, as its production does not compromise fungal growth, and Mycetohabitans bacteria can efficiently consume it. Narnavirus RmNV-20S and RmNV-23S affected the nucleotide metabolism without impacting the fungal-bacterial symbiosis. Our analyses highlighted the metabolic stability of Mycetohabitans throughout its co-evolution with the fungal host. We also predicted changes in reactions of the bacterial metabolism required for the active production of rhizoxin. This iHol is the first GSM of a fungal holobiont.

Fecal microbiota transplantation significantly improved respiratory failure of amyotrophic lateral sclerosis

Amyotrophic lateral sclerosis (ALS) is a progressive neurodegenerative disease that leads to respiratory failure, and eventually death. However, there is a lack of effective treatments for ALS. Here we report the results of fecal microbiota transplantation (FMT) in two patients with late-onset classic ALS with a Japan ALS severity classification of grade 5 who required tracheostomy and mechanical ventilation. In both patients, significant improvements in respiratory function were observed following two rounds of FMT, leading to weaning off mechanical ventilation. Their muscle strength improved, allowing for assisted standing and mobility. Other notable treatment responses included improved swallowing function and reduced muscle fasciculations. Metagenomic and metabolomic analysis revealed an increase in beneficial Bacteroides species (Bacteroides stercoris, Bacteroides uniformis, Bacteroides vulgatus), and Faecalibacterium prausnitzii after FMT, as well as elevated levels of metabolites involved in arginine biosynthesis and decreased levels of metabolites involved in branched-chain amino acid biosynthesis. These findings offer a potential rescue therapy for ALS with respiratory failure and provide new insights into ALS in general.

Untargeted metabolomics to expand the chemical space of the marine diatom Skeletonema marinoi

Diatoms (Bacillariophyceae) are aquatic photosynthetic microalgae with an ecological role as primary producers in the aquatic food web. They account substantially for global carbon, nitrogen, and silicon cycling. Elucidating the chemical space of diatoms is crucial to understanding their physiology and ecology. To expand the known chemical space of a cosmopolitan marine diatom, Skeletonema marinoi, we performed High-Resolution Liquid Chromatography-Tandem Mass Spectrometry (LC-MS2) for untargeted metabolomics data acquisition. The spectral data from LC-MS2 was used as input for the Metabolome Annotation Workflow (MAW) to obtain putative annotations for all measured features. A suspect list of metabolites previously identified in the Skeletonema spp. was generated to verify the results. These known metabolites were then added to the putative candidate list from LC-MS2 data to represent an expanded catalog of 1970 metabolites estimated to be produced by S. marinoi. The most prevalent chemical superclasses, based on the ChemONT ontology in this expanded dataset, were organic acids and derivatives, organoheterocyclic compounds, lipids and lipid-like molecules, and organic oxygen compounds. The metabolic profile from this study can aid the bioprospecting of marine microalgae for medicine, biofuel production, agriculture, and environmental conservation. The proposed analysis can be applicable for assessing the chemical space of other microalgae, which can also provide molecular insights into the interaction between marine organisms and their role in the functioning of ecosystems.

Coupling chemistry and biology for the synthesis of advanced bioproducts

Chemical and biological syntheses can both lead to a myriad of compounds. Biology enables us to harness the metabolism of microbial cell factories to produce key target molecules from renewable biomass-derived substrates. Although bio-based feedstocks are sustainably sourced and more benign than the rapidly depleting fossil fuels that chemical processes have historically relied on, limiting pathways solely to biological reactions may not equate to a greener process overall. In fact, bioreactors rely on substantial quantities of water and can be inefficient since organisms typically operate around ambient conditions and are sensitive to perturbations in their environment. Hybridizing biosynthetic pathways with green chemistry can instead be a more potent strategy to reduce our net manufacturing footprint. Emerging chemistries have demonstrated considerable success in performing complex transformations on biological feedstocks without significant solvent use. Many of these transformations would be too slow to perform enzymatically or infeasible altogether. Here, we put forth the concept that by carefully considering the merits and drawbacks of synthetic biology and chemistry as well as one's own use case, there exist many opportunities for coupling the two. Merging these syntheses can unlock a wider suite of functional group transformations, thereby enabling future manufacturing processes to sustainably access a larger space of valuable, platform chemicals.

Gut microbiomes of agropastoral children from the Adadle region of Ethiopia reflect their unique dietary habits

The composition and function of the intestinal microbiota are major determinants of human health and are strongly influenced by diet, antibiotic treatment, lifestyle and geography. Nevertheless, we currently have only little data on microbiomes of non-westernized communities. We assess the stool microbiota composition in 59 children aged 2-5 years from the Adadle district of Ethiopia, Somali Regional State. Here, milk and starch-rich food are predominant components of the local diet, where the inhabitants live a remote, traditional agropastoral lifestyle. Microbiota composition, function and the resistome were characterized by both 16S rRNA gene amplicon and shotgun metagenomic sequencing and compared to 1471 publicly available datasets from children living in traditional, transitional, and industrial communities with different subsistence strategies. Samples from the Adadle district are low in Bacteroidaceae, and Prevotellaceae, the main bacterial representatives in the feces of children living in industrialized and non-industrialized communities, respectively. In contrast, they had a higher relative abundance in Streptococcaceae, Bifidobacteriaceae and Erysipelatoclostridiaceae. Further, genes involved in degradation pathways of lactose, D-galactose and simple carbohydrates were enriched. Overall, our study revealed a unique composition of the fecal microbiota of these agropastoral children, highlighting the need to further characterize the fecal bacterial composition of human populations living different lifestyles.

A Modified Mediterranean Ketogenic Diet mitigates modifiable risk factors of Alzheimer's Disease: a serum and CSF-based metabolic analysis

Alzheimer's disease (AD) is influenced by a variety of modifiable risk factors, including a person's dietary habits. While the ketogenic diet (KD) holds promise in reducing metabolic risks and potentially affecting AD progression, only a few studies have explored KD's metabolic impact, especially on blood and cerebrospinal fluid (CSF). Our study involved participants at risk for AD, either cognitively normal or with mild cognitive impairment. The participants consumed both a modified Mediterranean-ketogenic diet (MMKD) and the American Heart Association diet (AHAD) for 6 weeks each, separated by a 6-week washout period. We employed nuclear magnetic resonance (NMR)-based metabolomics to profile serum and CSF and metagenomics profiling on fecal samples. While the AHAD induced no notable metabolic changes, MMKD led to significant alterations in both serum and CSF. These changes included improved modifiable risk factors, like increased HDL-C and reduced BMI, reversed serum metabolic disturbances linked to AD such as a microbiome-mediated increase in valine levels, and a reduction in systemic inflammation. Additionally, the MMKD was linked to increased amino acid levels in the CSF, a breakdown of branched-chain amino acids (BCAAs), and decreased valine levels. Importantly, we observed a strong correlation between metabolic changes in the CSF and serum, suggesting a systemic regulation of metabolism. Our findings highlight that MMKD can improve AD-related risk factors, reverse some metabolic disturbances associated with AD, and align metabolic changes across the blood-CSF barrier.

Reconstruction of a Genome-Scale Metabolic Model of Scenedesmus obliquus and Its Application for Lipid Production under Three Trophic Modes

Green microalgae have emerged as beneficial feedstocks for biofuel production. A systems-level understanding of the biochemical network is needed to harness the microalgal metabolic capacity for bioproduction. Genome-scale metabolic modeling (GEM) showed immense potential in rational metabolic engineering, utilizing biochemical flux distribution analysis. Here, we report the first GEM for the green microalga, Scenedesmus obliquus (iAR632), a promising biodiesel feedstock with high lipid-storing capability. iAR632 comprises 1467 reactions, 734 metabolites, and 632 genes distributed among 7 compartments. The model was optimized under three different trophic modes of microalgal cultivation, i.e., autotrophy, mixotrophy, and heterotrophy. The robustness of the reconstructed network was confirmed by analyzing its sensitivity to the biomass components. Pathway-level flux profiles were analyzed, and significant flux space expansion was noticed majorly in reactions associated with lipid biosynthesis. In agreement with the experimental observation, iAR632 predicted about 3.8-fold increased biomass and almost 4-fold higher lipid under mixotrophy than the other trophic modes. Thus, the assessment of the condition-specific metabolic flux distribution of iAR632 suggested that mixotrophy is the preferred cultivation condition for improved microalgal growth and lipid production. Overall, the reconstructed GEM and subsequent analyses will provide a systematic framework for developing model-driven strategies to improve microalgal bioproduction.

KIPEs3: Automatic annotation of biosynthesis pathways

Flavonoids and carotenoids are pigments involved in stress mitigation and numerous other processes. Both pigment classes can contribute to flower and fruit coloration. Flavonoid aglycones and carotenoids are produced by a pathway that is largely conserved across land plants. Glycosylations, acylations, and methylations of the flavonoid aglycones can be species-specific and lead to a plethora of biochemically diverse flavonoids. We previously developed KIPEs for the automatic annotation of biosynthesis pathways and presented an application on the flavonoid aglycone biosynthesis. KIPEs3 is an improved version with additional features and the potential to identify not just the core biosynthesis players, but also candidates involved in the decoration steps and in the transport of flavonoids. Functionality of KIPEs3 is demonstrated through the analysis of the flavonoid biosynthesis in Arabidopsis thaliana Nd-1, Capsella grandiflora, and Dioscorea dumetorum. We demonstrate the applicability of KIPEs to other pathways by adding the carotenoid biosynthesis to the repertoire. As a technical proof of concept, the carotenoid biosynthesis was analyzed in the same species and Daucus carota. KIPEs3 is available as an online service to enable access without prior bioinformatics experience. KIPEs3 facilitates the automatic annotation and analysis of biosynthesis pathways with a consistent and high quality in a large number of plant species. Numerous genome sequencing projects are generating a huge amount of data sets that can be analyzed to identify evolutionary patterns and promising candidate genes for biotechnological and breeding applications.

Inflammatory bowel disease biomarkers revealed by the human gut microbiome network

Inflammatory bowel diseases (IBDs) are complex medical conditions in which the gut microbiota is attacked by the immune system of genetically predisposed subjects when exposed to yet unclear environmental factors. The complexity of this class of diseases makes them suitable to be represented and studied with network science. In this paper, the metagenomic data of control, Crohn's disease, and ulcerative colitis subjects' gut microbiota were investigated by representing this data as correlation networks and co-expression networks. We obtained correlation networks by calculating Pearson's correlation between gene expression across subjects. A percolation-based procedure was used to threshold and binarize the adjacency matrices. In contrast, co-expression networks involved the construction of the bipartite subjects-genes networks and the monopartite genes-genes projection after binarization of the biadjacency matrix. Centrality measures and community detection were used on the so-built networks to mine data complexity and highlight possible biomarkers of the diseases. The main results were about the modules of Bacteroides, which were connected in the control subjects' correlation network, Faecalibacterium prausnitzii, where co-enzyme A became central in IBD correlation networks and Escherichia coli, whose module has different patterns of integration within the whole network in the different diagnoses.