The MetaCyc database of metabolic pathways and enzymes

Proteomes of plasmodium knowlesi early and late ring-stage parasites and infected host erythrocytes

The emerging malaria parasite Plasmodium knowlesi threatens the goal of worldwide malaria elimination due to its zoonotic spread in Southeast Asia. After brief ex-vivo culture we used 2D LC/MS/MS to examine the early and late ring stages of infected Macaca mulatta red blood cells harboring P. knowlesi. The M. mulatta clathrin heavy chain and T-cell and macrophage inhibitor ERMAP were overexpressed in the early ring stage; glutaredoxin 3 was overexpressed in the late ring stage; GO term differential enrichments included response to oxidative stress and the cortical cytoskeleton in the early ring stage. P. knowlesi clathrin heavy chain and 60S acidic ribosomal protein P2 were overexpressed in the late ring stage; GO term differential enrichments included vacuoles in the early ring stage, ribosomes and translation in the late ring stage, and Golgi- and COPI-coated vesicles, proteasomes, nucleosomes, vacuoles, ion-, peptide-, protein-, nucleocytoplasmic- and RNA-transport, antioxidant activity and glycolysis in both stages. SIGNIFICANCE: Due to its zoonotic spread, cases of the emerging human pathogen Plasmodium knowlesi in southeast Asia, and particularly in Malaysia, threaten regional and worldwide goals for malaria elimination. Infection by this parasite can be fatal to humans, and can be associated with significant morbidity. Due to zoonotic transmission from large macaque reservoirs that are untreatable by drugs, and outdoor biting mosquito vectors that negate use of preventive measures such as bed nets, its containment remains a challenge. Its biology remains incompletely understood. Thus we examine the expressed proteome of the early and late ex-vivo cultured ring stages, the first intraerythrocyte developmental stages after infection of host rhesus macaque erythrocytes. We used GO term enrichment strategies and differential protein expression to compare early and late ring stages. The early ring stage is characterized by the enrichment of P. knowlesi vacuoles, and overexpression of the M. mulatta clathrin heavy chain, important for clathrin-coated pits and vesicles, and clathrin-mediated endocytosis. The M. mulatta protein ERMAP was also overexpressed in the early ring stage, suggesting a potential role in early ring stage inhibition of T-cells and macrophages responding to P. knowlesi infection of reticulocytes. This could allow expansion of the host P. knowlesi cellular niche, allowing parasite adaptation to invasion of a wider age range of RBCs than the preferred young RBCs or reticulocytes, resulting in proliferation and increased pathogenesis in infected humans. Other GO terms differentially enriched in the early ring stage include the M. mulatta cortical cytoskeleton and response to oxidative stress. The late ring stage is characterized by overexpression of the P. knowlesi clathrin heavy chain. Combined with late ring stage GO term enrichment of Golgi-associated and coated vesicles, and enrichment of COPI-coated vesicles in both stages, this suggests the importance to P. knowlesi biology of clathrin-mediated endocytosis. P. knowlesi ribosomes and translation were also differentially enriched in the late ring stage. With expression of a variety of heat shock proteins, these results suggest production of folded parasite proteins is increasing by the late ring stage. M. mulatta endocytosis was differentially enriched in the late ring stage, as were clathrin-coated vesicles and endocytic vesicles. This suggests that M. mulatta clathrin-based endocytosis, perhaps in infected reticulocytes rather than mature RBC, may be an important process in the late ring stage. Additional ring stage biology from enriched GO terms includes M. mulatta proteasomes, protein folding and the chaperonin-containing T complex, actin and cortical actin cytoskeletons. P knowlesi biology also includes proteasomes, as well as nucleosomes, antioxidant activity, a variety of transport processes, glycolysis, vacuoles and protein folding. Mature RBCs have lost internal organelles, suggesting infection here may involve immature reticulocytes still retaining organelles. P. knowlesi parasite proteasomes and translational machinery may be ring stage drug targets for known selective inhibitors of these processes in other Plasmodium species. To our knowledge this is the first examination of more than one timepoint within the ring stage. Our results expand knowledge of both host and parasite proteins, pathways and organelles underlying P. knowlesi ring stage biology.

Faecal microbiota of schoolchildren is associated with nutritional status and markers of inflammation: a double-blinded cluster-randomized controlled trial using multi-micronutrient fortified rice

Faecal microbiota plays a critical role in human health, but its relationship with nutritional status among schoolchildren remains under-explored. Here, in a double-blinded cluster-randomized controlled trial on 380 Cambodian schoolchildren, we characterize the impact of six months consumption of two types of rice fortified with different levels of vitamins and minerals on pre-specified outcomes. We investigate the association between the faecal microbiota (16SrRNA sequencing) and age, sex, nutritional status (underweight, stunting), micronutrient status (iron, zinc and vitamin A deficiencies, anaemia, iron deficient anaemia, hemoglobinopathy), inflammation (systemic, gut), and parasitic infection. We show that the faecal microbiota is characterised by a surprisingly high proportion of Lactobacillaceae. We discover that deficiencies in specific micronutrients, such as iron and vitamin A, correlate with particular microbiota profiles, whereas zinc deficiency shows no such association. The nutritional intervention with the two rice treatments impacts both the composition and functions predicted from compositional analysis in different ways. (ClinicalTrials.gov (Identifier: NCT01706419)).

Microbial metagenomic shifts in children with acute lymphoblastic leukaemia during induction therapy and predictive biomarkers for infection

BACKGROUND

Emerging evidence has indicated a link between the gut microbiota and acute lymphoblastic leukaemia (ALL). However, the acute changes in gut microbiota during chemotherapy and the predictive value of baseline gut microbiota in infectious complication remain largely unknown.

METHODS

Faecal samples (n = 126) from children with ALL (n = 49) undergoing induction chemotherapy were collected at three timepoints, i.e., initiation of chemotherapy (baseline, T0), 7 days (T1) and 33 days (T2) after initiation of chemotherapy. Gut microbiome profile was performed via metagenomic shotgun sequencing. The bioBakery3 pipeline (Kneaddata, Metaphlan 3 and HUMAnN) was performed to assign taxonomy and functional annotations. Gut microbiome at T0 were used to predict infection during chemotherapy.

RESULTS

The microbial diversities and composition changed significantly during chemotherapy, with Escherichia coli, Klebsiella pneumoniae and Bifidobacterium longum being the most prominent species. The microbial metabolic pathways were also significantly altered during chemotherapy, including the pathway of pyruvate fermentation to acetate and lactate, and assimilatory sulfate reduction pathway. The receiver operating characteristic (ROC) models based on Bifidobacterium longum at T0 could predict infectious complications during the first month of chemotherapy with the area under the curve (AUC) of 0.720.

CONCLUSIONS

Our study provides new insights into the acute changes in microbial and functional characteristics in children with ALL during chemotherapy. The baseline gut microbiota could be potential biomarkers for infections during chemotherapy.

TRIAL REGISTRATION

The study was approved by the Ethics Committee of Zhujiang Hospital, Southern Medical University (2021-KY-171-01) and registered on http://www.chictr.org.cn (ChiCTR2200065406, Registration Date: November 4, 2022).

Exploring the impact of breast cancer on colonization resistance of mouse microbiota using network node manipulation

Breast cancer, a global health concern affecting women, has been linked to alterations in the gut microbiota, impacting various aspects of human health. This study investigates the interplay between breast cancer and the gut microbiome, particularly focusing on colonization resistance-an essential feature of the microbiota's ability to prevent pathogenic overgrowth. Using a mouse model of breast cancer, we employ diversity analysis, co-occurrence network analysis, and robustness tests to elucidate the impact of breast cancer on microbiome dynamics. Our results reveal that breast cancer exposure affects the bacterial community's composition and structure, with temporal dynamics playing a role. Network analysis demonstrates that breast cancer disrupts microbial interactions and decreases network complexity, potentially compromising colonization resistance. Moreover, network robustness analysis shows the susceptibility of the microbiota to node removal, indicating potential vulnerability to pathogenic colonization. Additionally, predicted metabolic profiling of the microbiome highlights the significance of the enzyme EC 6.2.1.2 - Butyrate--CoA ligase, potentially increasing butyrate, and balancing the reduction of colonization resistance. The identification of Rubrobacter as a key contributor to this enzyme suggests its role in shaping the microbiota's response to breast cancer. This study uncovers the intricate relationship between breast cancer, the gut microbiome, and colonization resistance, providing insights into potential therapeutic strategies and diagnostic approaches for breast cancer patients.

Methylobacterium nigriterrae sp. nov., isolated from black soil

An aerobic, Gram-stain-negative, motile rod bacterium, designated as SYSU BS000021T, was isolated from a black soil sample in Harbin, Heilongjiang province, China. Phylogenetic analysis based on 16S rRNA gene sequences indicated that the isolate belongs to the genus Methylobacterium, and showed the highest sequence similarity to Methylobacterium segetis KCTC 62267 T (98.51%) and Methylobacterium oxalidis DSM 24028 T (97.79%). Growth occurred at 20-37℃ (optimum, 28 °C), pH 6.0-8.0 (optimum, pH 7.0) and in the presence of 0% (w/v) NaCl. Polar lipids comprised of phosphatidylcholine, phosphatidylethanolamine, phosphatidylglycerol, diphosphatidylglycerol, one unidentified aminolipid and one unidentified polar lipid. The major cellular fatty acids (> 5%) were C18:0 and C18:1 ω7c and/or C18:1 ω6c. The predominant respiratory quinone was Q-10. The genomic G + C content was 68.36% based on the whole genome analysis. The average nucleotide identity (≤ 83.5%) and digital DNA-DNA hybridization (≤ 27.3%) values between strain SYSU BS000021T and other members of the genus Methylobacterium were all lower than the threshold values recommended for distinguishing novel prokaryotic species. Based on the results of phenotypic, chemotaxonomic and phylogenetic analyses, strain SYSU BS000021T represents a novel species of the genus Methylobacterium, for which the name Methylobacterium nigriterrae sp. nov. is proposed. The type strain of the proposed novel species is SYSU BS000021T (= GDMCC 1.3814 T = KCTC 8051 T).

Predicting the Pathway Involvement of Metabolites Based on Combined Metabolite and Pathway Features

A major limitation of most metabolomics datasets is the sparsity of pathway annotations for detected metabolites. It is common for less than half of the identified metabolites in these datasets to have a known metabolic pathway involvement. Trying to address this limitation, machine learning models have been developed to predict the association of a metabolite with a "pathway category", as defined by a metabolic knowledge base like KEGG. Past models were implemented as a single binary classifier specific to a single pathway category, requiring a set of binary classifiers for generating the predictions for multiple pathway categories. This past approach multiplied the computational resources necessary for training while diluting the positive entries in the gold standard datasets needed for training. To address these limitations, we propose a generalization of the metabolic pathway prediction problem using a single binary classifier that accepts the features both representing a metabolite and representing a pathway category and then predicts whether the given metabolite is involved in the corresponding pathway category. We demonstrate that this metabolite-pathway features pair approach not only outperforms the combined performance of training separate binary classifiers but demonstrates an order of magnitude improvement in robustness: a Matthews correlation coefficient of 0.784 ± 0.013 versus 0.768 ± 0.154.

Effect of in ovo feeding of xylobiose and xylotriose on plasma immunoglobulin, cecal metabolites production, microbial ecology, and metabolic pathways in broiler chickens

BACKGROUND

Dietary supplementation of xylooligosaccharides (XOS) has been found to influence gut health by manipulating cecal microbiota and producing microbe-origin metabolites. But no study investigated and compared the effect of in ovo feeding of xylobiose (XOS2) and xylotriose (XOS3) in chickens. This study investigated the effect of in ovo feeding of these XOS compounds on post-hatch gut health parameters in chickens. A total of 144 fertilized chicken eggs were divided into three groups: a) non-injected control (CON), b) XOS2, and c) XOS3. On the 17th embryonic day, the eggs of the XOS2 and XOS3 groups were injected with 3 mg of XOS2 and XOS3 diluted in 0.5 mL of 0.85% normal saline through the amniotic sac. After hatching, the chicks were raised for 21 d. Blood was collected on d 14 to measure plasma immunoglobulin. Cecal digesta were collected for measuring short-chain fatty acids (SCFA) on d 14 and 21, and for microbial ecology and microbial metabolic pathway analyses on d 7 and 21.

RESULTS

The results were considered significantly different at P < 0.05. ELISA quantified plasma IgA and IgG on d 14 chickens, revealing no differences among the treatments. Gas chromatography results showed no significant differences in the concentrations of cecal SCFAs on d 14 but significant differences on d 21. However, the SCFA concentrations were lower in the XOS3 than in the CON group on d 21. The cecal metagenomics data showed that the abundance of the family Clostridiaceae significantly decreased on d 7, and the abundance of the family Oscillospiraceae increased on d 21 in the XOS2 compared to the CON. There was a reduction in the relative abundance of genus Clostridium sensu stricto 1 in the XOS2 compared to the CON on d 7 and the genus Ruminococcus torques in both XOS2 and XOS3 groups compared to the CON on d 21. The XOS2 and XOS3 groups reduced the genes for chondroitin sulfate degradation I and L-histidine degradation I pathways, which contribute to improved gut health, respectively, in the microbiome on d 7. In contrast, on d 21, the XOS2 and XOS3 groups enriched the thiamin salvage II, L-isoleucine biosynthesis IV, and O-antigen building blocks biosynthesis (E. coli) pathways, which are indicative of improved gut health. Unlike the XOS3 and CON, the microbiome enriched the pathways associated with energy enhancement, including flavin biosynthesis I, sucrose degradation III, and Calvin-Benson-Bassham cycle pathways, in the XOS2 group on d 21.

CONCLUSION

In ovo XOS2 and XOS3 feeding promoted beneficial bacterial growth and reduced harmful bacteria at the family and genus levels. The metagenomic-based microbial metabolic pathway profiling predicted a favorable change in the availability of cecal metabolites in the XOS2 and XOS3 groups. The modulation of microbiota and metabolic pathways suggests that in ovo XOS2 and XOS3 feeding improved gut health during the post-hatch period of broilers.

Predicting metabolic modules in incomplete bacterial genomes with MetaPathPredict

The reconstruction of complete microbial metabolic pathways using 'omics data from environmental samples remains challenging. Computational pipelines for pathway reconstruction that utilize machine learning methods to predict the presence or absence of KEGG modules in incomplete genomes are lacking. Here, we present MetaPathPredict, a software tool that incorporates machine learning models to predict the presence of complete KEGG modules within bacterial genomic datasets. Using gene annotation data and information from the KEGG module database, MetaPathPredict employs deep learning models to predict the presence of KEGG modules in a genome. MetaPathPredict can be used as a command line tool or as a Python module, and both options are designed to be run locally or on a compute cluster. Benchmarks show that MetaPathPredict makes robust predictions of KEGG module presence within highly incomplete genomes.

Revealing the dynamics and mechanisms of bacterial interactions in cheese production with metabolic modelling

Cheese taste and flavour properties result from complex metabolic processes occurring in microbial communities. A deeper understanding of such mechanisms makes it possible to improve both industrial production processes and end-product quality through the design of microbial consortia. In this work, we caracterise the metabolism of a three-species community consisting of Lactococcus lactis, Lactobacillus plantarum and Propionibacterium freudenreichii during a seven-week cheese production process. Using genome-scale metabolic models and omics data integration, we modeled and calibrated individual dynamics using monoculture experiments, and coupled these models to capture the metabolism of the community. This model accurately predicts the dynamics of the community, enlightening the contribution of each microbial species to organoleptic compound production. Further metabolic exploration revealed additional possible interactions between the bacterial species. This work provides a methodological framework for the prediction of community-wide metabolism and highlights the added value of dynamic metabolic modeling for the comprehension of fermented food processes.

Links between gut microbiome, metabolome, clinical variables and non-alcoholic fatty liver disease severity in bariatric patients

BACKGROUND AND AIMS

Bacterial species and microbial pathways along with metabolites and clinical parameters may interact to contribute to non-alcoholic fatty liver disease (NAFLD) and disease severity. We used integrated machine learning models and a cross-validation approach to assess this interaction in bariatric patients.

METHODS

113 patients undergoing bariatric surgery had clinical and biochemical parameters, blood and stool metabolite measurements as well as faecal shotgun metagenome sequencing to profile the intestinal microbiome. Liver histology was classified as normal liver obese (NLO; n = 30), simple steatosis (SS; n = 41) or non-alcoholic steatohepatitis (NASH; n = 42); fibrosis was graded F0 to F4.

RESULTS

We found that those with NASH versus NLO had an increase in potentially harmful E. coli, a reduction of potentially beneficial Alistipes putredinis and an increase in ALT and AST. There was higher serum glucose, faecal 3-(3-hydroxyphenyl)-3-hydroxypropionic acid and faecal cholic acid and lower serum glycerophospholipids. In NAFLD, those with severe fibrosis (F3-F4) versus F0 had lower abundance of anti-inflammatory species (Eubacterium ventriosum, Alistipes finegoldii and Bacteroides dorei) and higher AST, serum glucose, faecal acylcarnitines, serum isoleucine and homocysteine as well as lower serum glycerophospholipids. Pathways involved with amino acid biosynthesis and degradation were significantly more represented in those with NASH compared to NLO, with severe fibrosis having an overall stronger significant association with Superpathway of menaquinol-10 biosynthesis and Peptidoglycan biosynthesis IV.

CONCLUSIONS

In bariatric patients, NASH and severe fibrosis were associated with specific bacterial species, metabolic pathways and metabolites that may contribute to NAFLD pathogenesis and disease severity.

Early-life stress and the gut microbiome: A comprehensive population-based investigation

Early-life stress (ELS) has been robustly associated with a range of poor mental and physical health outcomes. Recent studies implicate the gut microbiome in stress-related mental, cardio-metabolic and immune health problems, but research on humans is scarce and thus far often based on small, selected samples, often using retrospective reports of ELS. We examined associations between ELS and the human gut microbiome in a large, population-based study of children. ELS was measured prospectively from birth to 10 years of age in 2,004 children from the Generation R Study. We studied overall ELS, as well as unique effects of five different ELS domains, including life events, contextual risk, parental risk, interpersonal risk, and direct victimization. Stool microbiome was assessed using 16S rRNA sequencing at age 10 years and data were analyzed at multiple levels (i.e. α- and β-diversity indices, individual genera and predicted functional pathways). In addition, we explored potential mediators of ELS-microbiome associations, including diet at age 8 and body mass index at 10 years. While no associations were observed between overall ELS (composite score of five domains) and the microbiome after multiple testing correction, contextual risk - a specific ELS domain related to socio-economic stress, including risk factors such as financial difficulties and low maternal education - was significantly associated with microbiome variability. This ELS domain was associated with lower α-diversity, with β-diversity, and with predicted functional pathways involved, amongst others, in tryptophan biosynthesis. These associations were in part mediated by overall diet quality, a pro-inflammatory diet, fiber intake, and body mass index (BMI). These results suggest that stress related to socio-economic adversity - but not overall early life stress - is associated with a less diverse microbiome in the general population, and that this association may in part be explained by poorer diet and higher BMI. Future research is needed to test causality and to establish whether modifiable factors such as diet could be used to mitigate the negative effects of socio-economic adversity on the microbiome and related health consequences.

Impact of persistently high sea surface temperatures on the rhizobiomes of Zostera marina in a Baltic Sea benthocosms

Persistently high marine temperatures are escalating and threating marine biodiversity. The Baltic Sea, warming faster than other seas, is a good model to study the impact of increasing sea surface temperatures. Zostera marina, a key player in the Baltic ecosystem, faces susceptibility to disturbances, especially under chronic high temperatures. Despite the increasing number of studies on the impact of global warming on seagrasses, little attention has been paid to the role of the holobiont. Using an outdoor benthocosm to replicate near-natural conditions, this study explores the repercussions of persistent warming on the microbiome of Z. marina and its implications for holobiont function. Results show that both seasonal warming and chronic warming, impact Z. marina roots and sediment microbiome. Compared with roots, sediments demonstrate higher diversity and stability throughout the study, but temperature effects manifest earlier in both compartments, possibly linked to premature Z. marina die-offs under chronic warming. Shifts in microbial composition, such as an increase in organic matter-degrading and sulfur-related bacteria, accompany chronic warming. A higher ratio of sulfate-reducing bacteria compared to sulfide oxidizers was found in the warming treatment which may result in the collapse of the seagrasses, due to toxic levels of sulfide. Differentiating predicted pathways for warmest temperatures were related to sulfur and nitrogen cycles, suggest an increase of the microbial metabolism, and possible seagrass protection strategies through the production of isoprene. These structural and compositional variations in the associated microbiome offer early insights into the ecological status of seagrasses. Certain taxa/genes/pathways may serve as markers for specific stresses. Monitoring programs should integrate this aspect to identify early indicators of seagrass health. Understanding microbiome changes under stress is crucial for the use of potential probiotic taxa to mitigate climate change effects. Broader-scale examination of seagrass-microorganism interactions is needed to leverage knowledge on host-microbe interactions in seagrasses.

Deep learning the cis-regulatory code for gene expression in selected model plants

Elucidating the relationship between non-coding regulatory element sequences and gene expression is crucial for understanding gene regulation and genetic variation. We explored this link with the training of interpretable deep learning models predicting gene expression profiles from gene flanking regions of the plant species Arabidopsis thaliana, Solanum lycopersicum, Sorghum bicolor, and Zea mays. With over 80% accuracy, our models enabled predictive feature selection, highlighting e.g. the significant role of UTR regions in determining gene expression levels. The models demonstrated remarkable cross-species performance, effectively identifying both conserved and species-specific regulatory sequence features and their predictive power for gene expression. We illustrated the application of our approach by revealing causal links between genetic variation and gene expression changes across fourteen tomato genomes. Lastly, our models efficiently predicted genotype-specific expression of key functional gene groups, exemplified by underscoring known phenotypic and metabolic differences between Solanum lycopersicum and its wild, drought-resistant relative, Solanum pennellii.

Microbiome signatures associated with clinical stages of gastric Cancer: whole metagenome shotgun sequencing study

BACKGROUND

Gastric cancer is one of the global health concerns. A series of studies on the stomach have confirmed the role of the microbiome in shaping gastrointestinal diseases. Delineation of microbiome signatures to distinguish chronic gastritis from gastric cancer will provide a non-invasive preventative and treatment strategy. In this study, we performed whole metagenome shotgun sequencing of fecal samples to enhance the detection of rare bacterial species and increase genome sequence coverage. Additionally, we employed multiple bioinformatics approaches to investigate the potential targets of the microbiome as an indicator of differentiating gastric cancer from chronic gastritis.

RESULTS

A total of 65 patients were enrolled, comprising 33 individuals with chronic gastritis and 32 with gastric cancer. Within each group, the chronic gastritis group was sub-grouped into intestinal metaplasia (n = 15) and non-intestinal metaplasia (n = 18); the gastric cancer group, early stage (stages 1 and 2, n = 13) and late stage (stages 3 and 4, n = 19) cancer. No significant differences in alpha and beta diversities were detected among the patient groups. However, in a two-group univariate comparison, higher Fusobacteria abundance was identified in phylum; Fusobacteria presented higher abundance in gastric cancer (LDA scored 4.27, q = 0.041 in LEfSe). Age and sex-adjusted MaAsLin and Random Forest variable of importance (VIMP) analysis in species provided meaningful features; Bacteria_caccae was the most contributing species toward gastric cancer and late-stage cancer (beta:2.43, se:0.891, p:0.008, VIMP score:2.543). In contrast, Bifidobacterium_longum significantly contributed to chronic gastritis (beta:-1.8, se:0.699, p:0.009, VIMP score:1.988). Age, sex, and BMI-adjusted MasAsLin on metabolic pathway analysis showed that GLCMANNANAUT-PWY degradation was higher in gastric cancer and one of the contributing species was Fusobacterium_varium.

CONCLUSION

Microbiomes belonging to the pathogenic phylum Fusobacteria and species Bacteroides_caccae and Streptococcus_anginosus can be significant targets for monitoring the progression of gastric cancer. Whereas Bifidobacterium_longum and Lachnospiraceae_bacterium_5_1_63FAA might be protection biomarkers against gastric cancer.

Consistent signatures in the human gut microbiome of old- and young-onset colorectal cancer

The incidence of young-onset colorectal cancer (yCRC) has been increasing in recent decades, but little is known about the gut microbiome of these patients. Most studies have focused on old-onset CRC (oCRC), and it remains unclear whether CRC signatures derived from old patients are valid in young patients. To address this, we assembled the largest yCRC gut metagenomes to date from two independent cohorts and found that the CRC microbiome had limited association with age across adulthood. Differential analysis revealed that well-known CRC-associated taxa, such as Clostridium symbiosum, Peptostreptococcus stomatis, Parvimonas micra and Hungatella hathewayi were significantly enriched (false discovery rate <0.05) in both old- and young-onset patients. Similar strain-level patterns of Fusobacterium nucleatum, Bacteroides fragilis and Escherichia coli were observed for oCRC and yCRC. Almost all oCRC-associated metagenomic pathways had directionally concordant changes in young patients. Importantly, CRC-associated virulence factors (fadA, bft) were enriched in both oCRC and yCRC compared to their respective controls. Moreover, the microbiome-based classification model had similar predication accuracy for CRC status in old- and young-onset patients, underscoring the consistency of microbial signatures across different age groups.

Mapping multi-omics characteristics related to short-term PM2.5 trajectory and their impact on type 2 diabetes in middle-aged and elderly adults in Southern China

The relationship between PM2.5 and metabolic diseases, including type 2 diabetes (T2D), has become increasingly prominent, but the molecular mechanism needs to be further clarified. To help understand the mechanistic association between PM2.5 exposure and human health, we investigated short-term PM2.5 exposure trajectory-related multi-omics characteristics from stool metagenome and metabolome and serum proteome and metabolome in a cohort of 3267 participants (age: 64.4 ± 5.8 years) living in Southern China. And then integrate these features to examine their relationship with T2D. We observed significant differences in overall structure in each omics and 193 individual biomarkers between the high- and low-PM2.5 groups. PM2.5-related features included the disturbance of microbes (carbohydrate metabolism-associated Bacteroides thetaiotaomicron), gut metabolites of amino acids and carbohydrates, serum biomarkers related to lipid metabolism and reducing n-3 fatty acids. The patterns of overall network relationships among the biomarkers differed between T2D and normal participants. The subnetwork membership centered on the hub nodes (fecal rhamnose and glycylproline, serum hippuric acid, and protein TB182) related to high-PM2.5, which well predicted higher T2D prevalence and incidence and a higher level of fasting blood glucose, HbA1C, insulin, and HOMA-IR. Our findings underline crucial PM2.5-related multi-omics biomarkers linking PM2.5 exposure and T2D in humans.

Diurnal rhythmicity of infant fecal microbiota and metabolites: A randomized controlled interventional trial with infant formula

Microbiota assembly in the infant gut is influenced by diet. Breastfeeding and human breastmilk oligosaccharides promote the colonization of beneficial bifidobacteria. Infant formulas are supplemented with bifidobacteria or complex oligosaccharides, notably galacto-oligosaccharides (GOS), to mimic breast milk. To compare microbiota development across feeding modes, this randomized controlled intervention study (German Clinical Trial DRKS00012313) longitudinally sampled infant stool during the first year of life, revealing similar fecal bacterial communities between formula- and breast-fed infants (N = 210) but differences across age. Infant formula containing GOS sustained high levels of bifidobacteria compared with formula containing B. longum and B. breve or placebo. Metabolite and bacterial profiling revealed 24-h oscillations and circadian networks. Rhythmicity in bacterial diversity, specific taxa, and functional pathways increased with age and was strongest following breastfeeding and GOS supplementation. Circadian rhythms in dominant taxa were further maintained ex vivo in a chemostat model. Hence, microbiota rhythmicity develops early in life and is impacted by diet.

Predicting The Pathway Involvement Of Metabolites Based on Combined Metabolite and Pathway Features

A major limitation of most metabolomics datasets is the sparsity of pathway annotations of detected metabolites. It is common for less than half of identified metabolites in these datasets to have known metabolic pathway involvement. Trying to address this limitation, machine learning models have been developed to predict the association of a metabolite with a "pathway category", as defined by one of the metabolic knowledgebases like the Kyoto Encyclopedia of Gene and Genomes. Most of these models are implemented as a single binary classifier specific to a single pathway category, requiring a set of binary classifiers for generating predictions for multiple pathway categories. This single binary classifier per pathway category approach both multiplies the computational resources necessary for training while diluting the positive entries in gold standard datasets needed for training. To address the limitations of training separate classifiers, we propose a generalization of the metabolic pathway prediction problem using a single binary classifier that accepts both features representing a metabolite and features representing a generic pathway category and then predicts whether the given metabolite is involved in the corresponding pathway category. We demonstrate that this metabolite-pathway features-pair approach is not only competitive with the combined performance of training separate binary classifiers, but it outperforms the previous benchmark models.

Intracanal microbiome profiles of two apical periodontitis cases in one patient: A comparison with saliva and plaque profiles

OBJECTIVES

To determine the characteristics of the endodontic microbiome.

MATERIAL AND METHODS

Saliva, plaque, and infected root canal wall dentin of two teeth suffering from apical periodontitis were harvested from a 58-year-old man. Bacterial DNA was extracted from each sample, and 16S rRNA gene analysis targeting the V3-V4 region was conducted on the Illumina MiSeq platform using QIIME2. The functional potential of the microbiomes was inferred using PICRUSt2.

RESULTS

The four microbiomes were different in structure and membership, yet the nine most abundant metabolic pathways were common among them. The two endodontic microbiomes were more anaerobic, rich in Firmicutes, and scarce in Actinobacteriota and Proteobacteria, compared with saliva and plaque microbiomes. Their profiles were dissimilar despite their clinical and radiographic similarities.

CONCLUSIONS

The endodontic microbiomes were anaerobic, rich in Firmicutes, scarce in Actinobacteriota and Proteobacteria, and considerably varied within an individual.

The plant growth promoting rhizobacterium Achromobacter sp. 5B1, rescues Arabidopsis seedlings from alkaline stress by enhancing root organogenesis and hormonal responses

Soil alkalinity is a critical environmental factor for plant growth and distribution in ecosystems. An alkaline condition (pH > 7) is imposed by the rising concentration of hydroxides and cations, and prevails in semiarid and arid environments, which represent more than 25% of the total arable land of the world. Despite the great pressure exerted by alkalinity for root viability and plant survival, scarce information is available to understand how root microbes contribute to alkaline pH adaptation. Here, we assessed the effects of alkalinity on shoot and root biomass production, chlorophyll content, root growth and branching, lateral root primordia formation, and the expression of CYCB1, TOR kinase, and auxin and cytokinin-inducible trangenes in shoots and roots of Arabidopsis seedlings grown in Petri plates with agar-nutrient medium at pH values of 7.0, 7.5, 8.0, 8.5, and 9.0. The results showed an inverse correlation between the rise of pH and most growth, hormonal and genetic traits analyzed. Noteworthy, root inoculation with Achromobacter sp. 5B1, a beneficial rhizospheric bacterium, with plant growth promoting and salt tolerance features, increased biomass production, restored root growth and branching and enhanced auxin responses in WT seedlings and auxin-related mutants aux1-7 and eir1, indicating that stress adaptation operates independently of canonical auxin transporter proteins. Sequencing of the Achromobacter sp. 5B1 genome unveiled 5244 protein-coding genes, including genes possibly involved in auxin biosynthesis, quorum-sensing regulation and stress adaptation, which may account for its plant growth promotion attributes. These data highlight the critical role of rhizobacteria to increase plant resilience under high soil pH conditions potentially through genes for adaptation to an extreme environment and bacteria-plant communication.

Biosynthesis and function of 7-deazaguanine derivatives in bacteria and phages

SUMMARYDeazaguanine modifications play multifaceted roles in the molecular biology of DNA and tRNA, shaping diverse yet essential biological processes, including the nuanced fine-tuning of translation efficiency and the intricate modulation of codon-anticodon interactions. Beyond their roles in translation, deazaguanine modifications contribute to cellular stress resistance, self-nonself discrimination mechanisms, and host evasion defenses, directly modulating the adaptability of living organisms. Deazaguanine moieties extend beyond nucleic acid modifications, manifesting in the structural diversity of biologically active natural products. Their roles in fundamental cellular processes and their presence in biologically active natural products underscore their versatility and pivotal contributions to the intricate web of molecular interactions within living organisms. Here, we discuss the current understanding of the biosynthesis and multifaceted functions of deazaguanines, shedding light on their diverse and dynamic roles in the molecular landscape of life.

Deciphering the metabolism of Lactobacillus delbrueckii subsp. delbrueckii during soy juice fermentation using phenotypic and transcriptional analysis

In the context of sustainable diet, the development of soy-based yogurt fermented with lactic acid bacteria is an attractive alternative to dairy yogurts. To decipher the metabolism of Lactobacillus delbrueckii subsp. delbrueckii during soy juice (SJ) fermentation, the whole genome of the strain CIRM-BIA865 (Ld865) was sequenced and annotated. Then Ld865 was used to ferment SJ. Samples were analyzed throughout fermentation for their cell number, carbohydrate, organic acid, free amino acid, and volatile compound contents. Despite acidification, the number of Ld865 cells did not rise, and microscopic observations revealed the elongation of cells from 3.6 µm (inoculation) to 36.9 µm (end of fermentation). This elongation was observed in SJ but not in laboratory-rich medium MRS. Using transcriptomic analysis, we showed that the biosynthesis genes of peptidoglycan and membrane lipids were stably expressed, in line with the cell elongation observed, whereas no genes implicated in cell division were upregulated. Among the main sugars available in SJ (sucrose, raffinose, and stachyose), Ld865 only used sucrose. The transcriptomic analysis showed that Ld865 implemented the two transport systems that it contains to import sucrose: a PTS system and an ABC transporter. To fulfill its nitrogen needs, Ld865 probably first consumed the free amino acids of the SJ and then implemented different oligopeptide transporters and proteolytic/peptidase enzymes. In conclusion, this study showed that Ld865 enables fast acidification of SJ, despite the absence of cell division, leads to a product rich in free amino acids, and also leads to the production of aromatic compounds of interest.

IMPORTANCE

To reduce the environmental and health concerns related to food, an alternative diet is recommended, containing 50% of plant-based proteins. Soy juice, which is protein rich, is a relevant alternative to animal milk, for the production of yogurt-like products. However, soy "beany" and "green" off-flavors limit the consumption of such products. The lactic acid bacteria (LAB) used for fermentation can help to improve the organoleptic properties of soy products. But metabolic data concerning LAB adapted to soy juice are lacking. The aim of this study was, thus, to decipher the metabolism of Lactobacillus delbrueckii subsp. delbrueckii during fermentation of a soy juice, based on a multidisciplinary approach. This result will contribute to give tracks for a relevant selection of starter. Indeed, the improvement of the organoleptic properties of these types of products could help to promote plant-based proteins in our diet.

The gut microbiome regulates the clinical efficacy of sulfasalazine therapy for IBD-associated spondyloarthritis

Sulfasalazine is a prodrug known to be effective for the treatment of inflammatory bowel disease (IBD)-associated peripheral spondyloarthritis (pSpA), but the mechanistic role for the gut microbiome in regulating its clinical efficacy is not well understood. Here, treatment of 22 IBD-pSpA subjects with sulfasalazine identifies clinical responders with a gut microbiome enriched in Faecalibacterium prausnitzii and the capacity for butyrate production. Sulfapyridine promotes butyrate production and transcription of the butyrate synthesis gene but in F. prausnitzii in vitro, which is suppressed by excess folate. Sulfasalazine therapy enhances fecal butyrate production and limits colitis in wild-type and gnotobiotic mice colonized with responder, but not non-responder, microbiomes. F. prausnitzii is sufficient to restore sulfasalazine protection from colitis in gnotobiotic mice colonized with non-responder microbiomes. These findings reveal a mechanistic link between the efficacy of sulfasalazine therapy and the gut microbiome with the potential to guide diagnostic and therapeutic approaches for IBD-pSpA.

Precise atom-to-atom mapping for organic reactions via human-in-the-loop machine learning

Atom-to-atom mapping (AAM) is a task of identifying the position of each atom in the molecules before and after a chemical reaction, which is important for understanding the reaction mechanism. As more machine learning (ML) models were developed for retrosynthesis and reaction outcome prediction recently, the quality of these models is highly dependent on the quality of the AAM in reaction datasets. Although there are algorithms using graph theory or unsupervised learning to label the AAM for reaction datasets, existing methods map the atoms based on substructure alignments instead of chemistry knowledge. Here, we present LocalMapper, an ML model that learns correct AAM from chemist-labeled reactions via human-in-the-loop machine learning. We show that LocalMapper can predict the AAM for 50 K reactions with 98.5% calibrated accuracy by learning from only 2% of the human-labeled reactions from the entire dataset. More importantly, the confident predictions given by LocalMapper, which cover 97% of 50 K reactions, show 100% accuracy for 3,000 randomly sampled reactions. In an out-of-distribution experiment, LocalMapper shows favorable performance over other existing methods. We expect LocalMapper can be used to generate more precise reaction AAM and improve the quality of future ML-based reaction prediction models.

Alistipes indistinctus-derived hippuric acid promotes intestinal urate excretion to alleviate hyperuricemia

Hyperuricemia induces inflammatory arthritis and accelerates the progression of renal and cardiovascular diseases. Gut microbiota has been linked to the development of hyperuricemia through unclear mechanisms. Here, we show that the abundance and centrality of Alistipes indistinctus are depleted in subjects with hyperuricemia. Integrative metagenomic and metabolomic analysis identified hippuric acid as the key microbial effector that mediates the uric-acid-lowering effect of A. indistinctus. Mechanistically, A. indistinctus-derived hippuric acid enhances the binding of peroxisome-proliferator-activated receptor γ (PPARγ) to the promoter of ATP-binding cassette subfamily G member 2 (ABCG2), which in turn boosts intestinal urate excretion. To facilitate this enhanced excretion, hippuric acid also promotes ABCG2 localization to the brush border membranes in a PDZ-domain-containing 1 (PDZK1)-dependent manner. These findings indicate that A. indistinctus and hippuric acid promote intestinal urate excretion and offer insights into microbiota-host crosstalk in the maintenance of uric acid homeostasis.

Computational analysis into the potential of azo dyes as a feedstock for actinorhodin biosynthesis in Pseudomonas putida

Fermentation-based biosynthesis in synthetic biology relies heavily on sugar-derived feedstocks, a limited and carbon-intensive commodity. Unconventional feedstocks from less-noble sources such as waste are being utilized to produce high-value chemical products. Azo dyes, a major pollutant commonly discharged by food, textile, and pharmaceutical industries, present significant health and environmental risks. We explore the potential of engineering Pseudomonas putida KT2440 to utilize azo dyes as a substrate to produce a polyketide, actinorhodin (ACT). Using the constrained minimal cut sets (cMCS) approach, we identified metabolic interventions that optimize ACT biosynthesis and compare the growth-coupling solutions attainable on an azo dye compared to glucose. Our results predicted that azo dyes could perform better as a feedstock for ACT biosynthesis than glucose as it allowed growth-coupling regimes that are unfeasible with glucose and generated an 18.28% higher maximum ACT flux. By examining the flux distributions enabled in different carbon sources, we observed that carbon fluxes from aromatic compounds like azo dyes have a unique capability to leverage gluconeogenesis to support both growth and production of secondary metabolites that produce excess NADH. Carbon sources are commonly chosen based on the host organism, availability, cost, and environmental implications. We demonstrated that careful selection of carbon sources is also crucial to ensure that the resulting flux distribution is suitable for further metabolic engineering of microbial cell factories.

Biomass attachment and microbiota shifts during porcine faecal in vitro fermentation of almond and macadamia nuts differing in particle sizes

Nuts are highly nutritious and good sources of dietary fibre, when consumed as part of a healthy human diet. Upon consumption, nut particles of various sizes containing lipids entrapped by the plant cell walls enter the large intestine where they are fermented by the resident microbiota. This study investigated the microbial community shifts during in vitro fermentation of almond and macadamia substrates, of two particle sizes including fine particles (F = 250-500 μm) and cell clusters (CC = 710-1000 μm). The aim was to determine how particle size and biomass attachment altered the microbiota. Over the 48 h fermentation duration, short chain fatty acid concentrations increased due to particle size rather than nut type (almond or macadamia). However, nut type did change microbial population dynamics by stimulating specific genera. Tyzzerella, p253418B5 gut group, Lachnospiraceae UCG001, Geotrichum, Enterococcus, Amnipila and Acetitomaculum genera were unique for almonds. For macadamia, three unique genera including Prevotellaceae UCG004, Candidatus Methanomethylophilus and Alistipes were noted. Distinct shifts in the attached microbial biomass were noted due to nut particle size. Bacterial attachment to nut particles was visualised in situ during fermentation, revealing a decrease in lipids and an increase in attached bacteria over time. This interaction may be a pre-requisite for lipid breakdown during nut particle disappearance. Overall, this study provides insights into how nut fermentation alters the gut microbiota and the possible role that gut microbes have in lipid degradation.

Bifidobacterium longum modifies a nutritional intervention for stunting in Zimbabwean infants

Child stunting is an indicator of chronic undernutrition and reduced human capital. However, it remains a poorly understood public health problem. Small-quantity lipid-based nutrient supplements (SQ-LNS) have been widely tested to reduce stunting, but have modest effects. The infant intestinal microbiome may contribute to stunting, and is partly shaped by mother and infant histo-blood group antigens (HBGA). We investigated whether mother-infant fucosyltransferase status, which governs HBGA, and the infant gut microbiome modified the impact of SQ-LNS on stunting at age 18 months among Zimbabwean infants in the SHINE Trial ( NCT01824940 ). We found that mother-infant fucosyltransferase discordance and Bifidobacterium longum reduced SQ-LNS efficacy. Infant age-related microbiome shifts in B. longum subspecies dominance from infantis , a proficient human milk oligosaccharide utilizer, to suis or longum , proficient plant-polysaccharide utilizers, were partly influenced by discordance in mother-infant FUT2+/FUT3- phenotype, suggesting that a "younger" microbiome at initiation of SQ-LNS reduces its benefits on stunting.

Different microbial communities in paddy soils under organic and nonorganic farming

Organic agriculture is a farming method that provides healthy food and is friendly to the environment, and it is developing rapidly worldwide. This study compared microbial communities in organic farming (Or) paddy fields to those in nonorganic farming (Nr) paddy fields based on 16S rDNA sequencing and analysis. The predominant microorganisms in both soils were Proteobacteria, Chloroflexi, Acidobacteria, Actinobacteria, and Nitrospirota. The alpha diversity of the paddy soil microbial communities was not different between the nonorganic and organic farming systems. The beta diversity of nonmetric multidimensional scaling (NMDS) revealed that the two groups were significantly separated. Distance-based redundancy analysis (db-RDA) suggested that soil pH and electrical conductivity (EC) had a positive relationship with the microbes in organic paddy soils. There were 23 amplicon sequence variants (ASVs) that showed differential abundance. Among them, g_B1-7BS (Proteobacteria), s_Sulfuricaulis limicola (Proteobacteria), g_GAL15 (p_GAL15), c_Thermodesulfovibrionia (Nitrospirota), two of f_Anaerolineaceae (Chloroflexi), and two of g_S085 (Chloroflexi) showed that they were more abundant in organic soils, whereas g_11-24 (Acidobacteriota), g__Subgroup_7 (Acidobacteriota), and g_Bacillus (Firmicutes) showed differential abundance in nonorganic paddy soils. Functional prediction of microbial communities in paddy soils showed that functions related to carbohydrate metabolism could be the major metabolic activities. Our work indicates that organic farming differs from nonorganic farming in terms of microbial composition in paddy soils and provides specific microbes that might be helpful for understanding soil fertility.

Cancer invasion and anaerobic bacteria: new insights into mechanisms

There is growing evidence that altered microbiota abundance of a range of specific anaerobic bacteria are associated with cancer, including Peptoniphilus spp., Porphyromonas spp., Fusobacterium spp., Fenollaria spp., Prevotella spp., Sneathia spp., Veillonella spp. and Anaerococcus spp. linked to multiple cancer types. In this review we explore these pathogenic associations. The mechanisms by which bacteria are known or predicted to interact with human cells are reviewed and we present an overview of the interlinked mechanisms and hypotheses of how multiple intracellular anaerobic bacterial pathogens may act together to cause host cell and tissue microenvironment changes associated with carcinogenesis and cancer cell invasion. These include combined effects on changes in cell signalling, DNA damage, cellular metabolism and immune evasion. Strategies for early detection and eradication of anaerobic cancer-associated bacterial pathogens that may prevent cancer progression are proposed.

Human gut microbiota from hepatitis B virus-infected individuals is associated with reduced triglyceride level in mice: faecal transplantation study

BACKGROUND AND AIMS

Chronic hepatitis B virus (HBV) infection is associated with a reduced risk of dyslipidaemia. Using a human faecal microbiota transplantation (FMT), we compared changes in gut microbiota and lipid profiles in mice transplanted with human faeces from HBV-infected and non-infected individuals.

APPROACH AND RESULTS

A total of 19 mice received human FMT from four HBV-infected individuals and were categorised into the HBV-positive mice group, while 20 mice received FMT from four HBV-non-infected individuals into the HBV-negative one. In the analysis of gut microbiota in FMT mice, we observed a robust increase in alpha diversity and abundance of Akkermansia muciniphila in HBV-positive mice, compared to that in HBV-negative. Functional inference analysis revealed that the pathways involved in glycerolipid metabolism were more enriched in HBV-positive mice. At 5 weeks of FMT, the reduced triglyceride (TG) level was predominantly observed in HBV-positive mice.

CONCLUSIONS

Altered gut microbiota accompanied by HBV infection was associated with a robust increase in alpha diversity and butyrate producers, which resulted in a reduced level of TG at 5 weeks post-FMT. This indicates that the reduced risk of dyslipidaemia in chronic HBV infection may be due to the altered gut microbiota accompanied by HBV infection.

The Predictive Value of Gut Microbiota Composition for Sustained Immunogenicity following Two Doses of CoronaVac

CoronaVac immunogenicity decreases with time, and we aimed to investigate whether gut microbiota associate with longer-term immunogenicity of CoronaVac. This was a prospective cohort study recruiting two-dose CoronaVac recipients from three centres in Hong Kong. We collected blood samples at baseline and day 180 after the first dose and used chemiluminescence immunoassay to test for neutralizing antibodies (NAbs) against the receptor-binding domain (RBD) of wild-type SARS-CoV-2 virus. We performed shotgun metagenomic sequencing performed on baseline stool samples. The primary outcome was the NAb seroconversion rate (seropositivity defined as NAb ≥ 15AU/mL) at day 180. Linear discriminant analysis [LDA] effect size analysis was used to identify putative bacterial species and metabolic pathways. A univariate logistic regression model was used to derive the odds ratio (OR) of seropositivity with bacterial species. Of 119 CoronaVac recipients (median age: 53.4 years [IQR: 47.8-61.3]; male: 39 [32.8%]), only 8 (6.7%) remained seropositive at 6 months after vaccination. Bacteroides uniformis (log10LDA score = 4.39) and Bacteroides eggerthii (log10LDA score = 3.89) were significantly enriched in seropositive than seronegative participants. Seropositivity was associated with B. eggerthii (OR: 5.73; 95% CI: 1.32-29.55; p = 0.022) and B. uniformis with borderline significance (OR: 3.27; 95% CI: 0.73-14.72; p = 0.110). Additionally, B. uniformis was positively correlated with most enriched metabolic pathways in seropositive vaccinees, including the superpathway of adenosine nucleotide de novo biosynthesis I (log10LDA score = 2.88) and II (log10LDA score = 2.91), as well as pathways related to vitamin B biosynthesis, all of which are known to promote immune functions. In conclusion, certain gut bacterial species (B. eggerthii and B. uniformis) and metabolic pathways were associated with longer-term CoronaVac immunogenicity.

Light-Exposed Metabolic Responses of Cordyceps militaris through Transcriptome-Integrated Genome-Scale Modeling

The genome-scale metabolic model (GSMM) of Cordyceps militaris provides a comprehensive basis of carbon assimilation for cell growth and metabolite production. However, the model with a simple mass balance concept shows limited capability to probe the metabolic responses of C. militaris under light exposure. This study, therefore, employed the transcriptome-integrated GSMM approach to extend the investigation of C. militaris's metabolism under light conditions. Through the gene inactivity moderated by metabolism and expression (GIMME) framework, the iPS1474-tiGSMM model was furnished with the transcriptome data, thus providing a simulation that described reasonably well the metabolic responses underlying the phenotypic observation of C. militaris under the particular light conditions. The iPS1474-tiGSMM obviously showed an improved prediction of metabolic fluxes in correlation with the expressed genes involved in the cordycepin and carotenoid biosynthetic pathways under the sucrose culturing conditions. Further analysis of reporter metabolites suggested that the central carbon, purine, and fatty acid metabolisms towards carotenoid biosynthesis were the predominant metabolic processes responsible in light conditions. This finding highlights the key responsive processes enabling the acclimatization of C. militaris metabolism in varying light conditions. This study provides a valuable perspective on manipulating metabolic genes and fluxes towards the target metabolite production of C. militaris.

Comparative analysis of microbial succession and proteolysis focusing on amino acid pathways in Asiago-PDO cheese from two dairies

In this study, a comprehensive and comparative analysis was conducted on Italian Asiago-PDO cheese obtained from two different dairies named Dairy I and Dairy II using industrial and natural fermented milk, respectively. The analysis encompassed the evaluation of chemical composition, the succession of the microbiota during manufacture and ripening, and proteolysis mainly focusing on free individual amino acid (FAA) profiles. A metagenomic approach was used to investigate the cheese microbiome functionality. Differences in gross chemical composition were more evident during ripening, with Dairy II showing higher variability within batches. The microbiota varied significantly between the two dairies and ripening stages. The choice of starter culture shaped the microbiota during production and affected the microbial diversity of non-starter lactic acid bacteria (NSLAB) originated from the raw milk during ripening. Peptide chromatographic profiles and FAA concentrations increased as ripening progressed, with Dairy I showing higher production of FAA. Functional analysis of the metagenomes linked species to specific amino acid metabolism/catabolism pathways. The amino acid metabolism pathways, particularly those related to aromatic amino acids, lysine, and branched-chain amino acids, were affected by the presence of specific NSLAB species, which differed between the two dairies. The results obtained in this study reveal the impact of starter culture on peculiar cheese microbiota assemblies, which selectively targets amino acid pathways, providing insights into the potential flavor and aroma characteristics of Asiago-PDO cheese.

Associations between vaginal microbiota and endometrial polypoid lesions in women of reproductive age: a cross-sectional study

RESEARCH QUESTION

What are the different characteristics of vaginal microbial composition between patients with endometrial polypoid lesions and controls?

DESIGN

This cohort study compared the pre-operative microbial compositions of vaginal samples in a cohort of 703 women with endometrial polypoid lesions [293 and 410 women diagnosed and not diagnosed with polyps pathologically (polyps group and not-polyps group, respectively] and 703 women in the control group. Bacterial abundance, diversity, differential taxa and microbial network structure were assessed using 16S rRNA gene sequencing. Predictive algorithms were used to determine the functional pathways of vaginal microbiota within the cohort.

RESULTS

The control group exhibited higher relative abundance of Lactobacillus crispatus in comparison with the polypoid lesions group (P = 0.0427). Beta diversity of vaginal microbiota differed significantly between the groups (P < 0.05). Comparing the polyps group with the not-polyps group, Leptotrichia spp. and Cutibacterium spp. were more abundant in the polyps group, and Fannyhessea spp., Acinetobacter spp. and Achromobacter spp. were more abundant in the not-polyps group. The control group exhibited higher abundance of Bifidobacterium spp., Achromobacter spp. and Escherichia/Shigella spp. (false discovery rate < 0.05). Furthermore, the polyps group and not-polyps group displayed more complex co-occurrence networks compared with the control group.

CONCLUSIONS

The results of this study provide compelling evidence supporting associations between vaginal microbiota and endometrial polypoid lesions, highlighting the potential relationship between a well-balanced vaginal microbial ecosystem and a healthy intrauterine environment.

Metabolomic profiling of human bladder tissue extracts

INTRODUCTION

Bladder cancer is a common malignancy affecting the urinary tract and effective biomarkers and for which monitoring therapeutic interventions have yet to be identified.

OBJECTIVES

Major aim of this work was to perform metabolomic profiling of human bladder cancer and adjacent normal tissue and to evaluate cancer biomarkers.

METHODS

This study utilized nuclear magnetic resonance (NMR) and high-resolution nanoparticle-based laser desorption/ionization mass spectrometry (LDI-MS) methods to investigate polar metabolite profiles in tissue samples from 99 bladder cancer patients.

RESULTS

Through NMR spectroscopy, six tissue metabolites were identified and quantified as potential indicators of bladder cancer, while LDI-MS allowed detection of 34 compounds which distinguished cancer tissue samples from adjacent normal tissue. Thirteen characteristic tissue metabolites were also found to differentiate bladder cancer tumor grades and thirteen metabolites were correlated with tumor stages. Receiver-operating characteristics analysis showed high predictive power for all three types of metabolomics data, with area under the curve (AUC) values greater than 0.853.

CONCLUSION

To date, this is the first study in which bladder human normal tissues adjacent to cancerous tissues are analyzed using both NMR and MS method. These findings suggest that the metabolite markers identified in this study may be useful for the detection and monitoring of bladder cancer stages and grades.

Unveiling nature's treasures: actinobacteria from Meghalaya's mining sites as sources of bioactive compounds

Coal and sillimanite mining sites present unique ecological niches favoring the growth of actinobacteria, a group of Gram-positive bacteria known for producing a wide array of bioactive compounds. Isolating these bacteria from such environments could unveil novel compounds with potential biotechnological applications. This study involved the isolation of actinobacteria from two mining sites in Meghalaya, India. The dominant genera from both sites were Streptomyces, Amycolatopsis, Nocardia, and Streptosporangium. Metabolic pathway prediction from 16S rRNA gene revealed several pathways beneficial for plant growth. Exploration of biosynthetic genes indicated a prevalence of the type-II polyketide synthase gene. Sequencing the ketosynthase-alpha domain of the gene led to predictions of various bioactive secondary metabolites. Around 44% of the isolates demonstrated antimicrobial properties, with some also displaying plant growth-promoting traits. Amycolatopsis SD-15 exhibited promising results in planta when tested on tomato plants. These findings highlight the potential of actinobacteria from Meghalaya's mining sites across medical, agricultural, and industrial domains.

Genomic and morphological characterization of a new Thiothrix species from a sulfide hot spring of the Zmeinaya bay (Northern Baikal, Russia)

A survey for bacteria of the genus Thiothrix indicated that they inhabited the area where the water of the Zmeiny geothermal spring (northern basin of Lake Baikal, Russia) mixed with the lake water. In the coastal zone of the lake oxygen (8.25 g/L) and hydrogen sulfide (up to 1 mg/L) were simultaneously present at sites of massive growth of these particular Thiothrix bacteria. Based on the analysis of the morphological characteristics and sequence of individual genes (16S rRNA, rpoB and tilS), we could not attribute the Thiothrix from Lake Baikal to any of the known species of this genus. To determine metabolic capabilities and phylogenetic position of the Thiothrix sp. from Lake Baikal, we analyzed their whole genome. Like all members of this genus, the bacteria from Lake Baikal were capable of organo-heterotrophic, chemolithoheterotrophic, and chemolithoautotrophic growth and differed from its closest relatives in the spectrum of nitrogen and sulfur cycle genes as well as in the indices of average nucleotide identity (ANI < 75-94%), amino acid identity (AAI < 94%) and in silico DNA-DNA hybridization (dDDH < 17-57%), which were below the boundary of interspecies differences, allowing us to identify them as novel candidate species.

PathBank 2.0-the pathway database for model organism metabolomics

PathBank (https://pathbank.org) and its predecessor database, the Small Molecule Pathway Database (SMPDB), have been providing comprehensive metabolite pathway information for the metabolomics community since 2010. Over the past 14 years, these pathway databases have grown and evolved significantly to meet the needs of the metabolomics community and respond to continuing changes in computing technology. This year's update, PathBank 2.0, brings a number of important improvements and upgrades that should make the database more useful and more appealing to a larger cross-section of users. In particular, these improvements include: (i) a significant increase in the number of primary or canonical pathways (from 1720 to 6951); (ii) a massive increase in the total number of pathways (from 110 234 to 605 359); (iii) significant improvements to the quality of pathway diagrams and pathway descriptions; (iv) a strong emphasis on drug metabolism and drug mechanism pathways; (v) making most pathway images more slide-compatible and manuscript-compatible; (vi) adding tools to support better pathway filtering and selecting through a more complete pathway taxonomy; (vii) adding pathway analysis tools for visualizing and calculating pathway enrichment. Many other minor improvements and updates to the content, the interface and general performance of the PathBank website have also been made. Overall, we believe these upgrades and updates should greatly enhance PathBank's ease of use and its potential applications for interpreting metabolomics data.

Early microbial intervention reshapes phenotypes of newborn Bos taurus through metabolic regulations

BACKGROUND

The rumen of neonatal calves has limited functionality, and establishing intestinal microbiota may play a crucial role in their health and performance. Thus, we aim to explore the temporal colonization of the gut microbiome and the benefits of early microbial transplantation (MT) in newborn calves.

RESULTS

We followed 36 newborn calves for 2 months and found that the composition and ecological interactions of their gut microbiomes likely reached maturity 1 month after birth. Temporal changes in the gut microbiome of newborn calves are widely associated with changes in their physiological statuses, such as growth and fiber digestion. Importantly, we observed that MT reshapes the gut microbiome of newborns by altering the abundance and interaction of Bacteroides species, as well as amino acid pathways, such as arginine biosynthesis. Two-year follow-up of those calves further showed that MT improves their later milk production. Notably, MT improves fiber digestion and antioxidant capacity of newborns while reducing diarrhea. MT also contributes to significant changes in the metabolomic landscape, and with putative causal mediation analysis, we suggest that altered gut microbial composition in newborns may influence physiological status through microbial-derived metabolites.

CONCLUSIONS

Our study provides a metagenomic and metabolomic atlas of the temporal development of the gut microbiome in newborn calves. MT can alter the gut microbiome of newborns, leading to improved physiological status and later milk production. The data may help develop strategies to manipulate the gut microbiota during early life, which may be relevant to the health and production of newborn calves.

Prescription opioids induced microbial dysbiosis worsens severity of chronic pancreatitis and drives pain hypersensitivity

Opioids, such as morphine and oxycodone, are widely used for pain management associated with chronic pancreatitis (CP); however, their impact on the progression and pain sensitivity of CP has never been evaluated. This report investigates the impact of opioid use on the severity of CP, pain sensitivity, and the gut microbiome. C57BL/6 mice were divided into control, CP, CP with morphine/oxycodone, and either morphine or oxycodone alone groups. CP was induced by administration of caerulein (50ug/kg/h, i.p. hourly x7, twice a week for 10 weeks). The mouse-to-pancreas weight ratio, histology, and Sirius red staining were performed to measure CP severity. Tail flick and paw pressure assays were used to measure thermal and mechanical pain. DNA was extracted from the fecal samples and subjected to whole-genome shotgun sequencing. Germ-free mice were used to validate the role of gut microbiome in sensitizing acute pancreatic inflammation. Opioid treatment exacerbates CP by increasing pancreatic necrosis, fibrosis, and immune-cell infiltration. Opioid-treated CP mice exhibited enhanced pain hypersensitivity and showed distinct clustering of the gut microbiome compared to untreated CP mice, with severely compromised gut barrier integrity. Fecal microbiota transplantation (FMT) from opioid-treated CP mice into germ-free mice resulted in pancreatic inflammation in response to a suboptimal caerulein dose. Together, these analyses revealed that opioids worsen the severity of CP and induce significant alterations in pain sensitivity and the gut microbiome in a caerulein CP mouse model. Microbial dysbiosis plays an important role in sensitizing the host to pancreatic inflammation.

Dissecting the impact of Anaplasma phagocytophilum infection on functional networks and community stability of the tick microbiome

CONTEXT

Pathogens can manipulate microbial interactions to ensure survival, potentially altering the functional patterns and microbiome assembly. The present study investigates how Anaplasma phagocytophilum infection affects the functional diversity, composition, and assembly of the Ixodes scapularis microbiome, with a focus on high central pathways-those characterized by elevated values in centrality metrics such as eigenvector, betweenness, and degree measures, in the microbial community.

METHODS

Using previously published data from nymphs' gut V4 region's amplicons of bacterial 16S rRNA, we predicted the functional diversity and composition in control and A. phagocytophilum-infected ticks and inferred co-occurrence networks of taxa and ubiquitous pathways in each condition to associate the high central pathways to the microbial community assembly.

RESULTS

Although no differences were observed concerning pathways richness and diversity, there was a significant impact on taxa and functional assembly when ubiquitous pathways in each condition were filtered. Moreover, a notable shift was observed in the microbiome's high central functions. Specifically, pathways related to the degradation of nucleosides and nucleotides emerged as the most central functions in response to A. phagocytophilum infection. This finding suggests a reconfiguration of functional relationships within the microbial community, potentially influenced by the pathogen's limited metabolic capacity. This limitation implies that the tick microbiome may provide additional metabolic resources to support the pathogen's functional needs.

CONCLUSIONS

Understanding the metabolic interactions within the tick microbiome can enhance our knowledge of pathogen colonization mechanisms and uncover new disease control and prevention strategies. For example, certain pathways that were more abundant or highly central during infection may represent potential targets for microbiota-based vaccines.

Transcription factor-based biosensors for detection of naturally occurring phenolic acids

Phenolic acids including hydroxybenzoic and hydroxycinnamic acids are secondary plant and fungal metabolites involved in many physiological processes offering health and dietary benefits. They are often utilised as precursors for production of value-added compounds. The limited availability of synthetic biology tools, such as whole-cell biosensors suitable for monitoring the dynamics of phenolic acids intracellularly and extracellularly, hinders the capabilities to develop high-throughput screens to study their metabolism and forward engineering. Here, by applying a multi-genome approach, we have identified phenolic acid-inducible gene expression systems composed of transcription factor-inducible promoter pairs responding to eleven different phenolic acids. Subsequently, they were used for the development of whole-cell biosensors based on model bacterial hosts, such as Escherichia coli, Cupriavidus necator and Pseudomonas putida. The dynamics and range of the biosensors were evaluated by establishing their response and sensitivity landscapes. The specificity and previously uncharacterised interactions between transcription factor and its effector(s) were identified by a screen of twenty major phenolic acids. To exemplify applicability, we utilise a protocatechuic acid-biosensor to identify enzymes with enhanced activity for conversion of p-hydroxybenzoate to protocatechuate. Transcription factor-based biosensors developed in this study will advance the analytics of phenolic acids and expedite research into their metabolism.

Gut microbiota-associated taurine metabolism dysregulation in a mouse model of Parkinson's disease

IMPORTANCE

PD is recognized as a multisystem disease concerning GI dysfunction and microbiota dysbiosis but still lacks ideal therapies. Recently, aberrant microbiota-derived metabolites are emerging as important participants in PD etiology. However, the alterations of gut microbiota community and serum untargeted metabolite profile have not been fully investigated in a PD mice model. Here, we discover sharply reduced levels of Lactobacillus and taurine in MPTP-treated mice. Moreover, Lactobacillus, Adlercreutzia, and taurine-related metabolites showed the most significant correlation with pathological and GI performance of PD mice. The abundances of microbial transporter and enzymes participating in the degeneration of taurine were disturbed in PD mice. Most importantly, taurine supplement ameliorates MPTP-induced motor deficits, DA neuron loss, and microglial activation. Our data highlight the impaired taurine-based microbiome-metabolism axis during the progression of PD and reveal a novel and previously unrecognized role of genera in modulating taurine metabolism.

Amino acid auxotrophies in human gut bacteria are linked to higher microbiome diversity and long-term stability

Amino acid auxotrophies are prevalent among bacteria. They can govern ecological dynamics in microbial communities and indicate metabolic cross-feeding interactions among coexisting genotypes. Despite the ecological importance of auxotrophies, their distribution and impact on the diversity and function of the human gut microbiome remain poorly understood. This study performed the first systematic analysis of the distribution of amino acid auxotrophies in the human gut microbiome using a combined metabolomic, metagenomic, and metabolic modeling approach. Results showed that amino acid auxotrophies are ubiquitous in the colon microbiome, with tryptophan auxotrophy being the most common. Auxotrophy frequencies were higher for those amino acids that are also essential to the human host. Moreover, a higher overall abundance of auxotrophies was associated with greater microbiome diversity and stability, and the distribution of auxotrophs was found to be related to the human host's metabolome, including trimethylamine oxide, small aromatic acids, and secondary bile acids. Thus, our results suggest that amino acid auxotrophies are important factors contributing to microbiome ecology and host-microbiome metabolic interactions.

Integrative approaches based on genomic techniques in the functional studies on enhancers

With the development of sequencing technology and the dramatic drop in sequencing cost, the functions of noncoding genes are being characterized in a wide variety of fields (e.g. biomedicine). Enhancers are noncoding DNA elements with vital transcription regulation functions. Tens of thousands of enhancers have been identified in the human genome; however, the location, function, target genes and regulatory mechanisms of most enhancers have not been elucidated thus far. As high-throughput sequencing techniques have leapt forwards, omics approaches have been extensively employed in enhancer research. Multidimensional genomic data integration enables the full exploration of the data and provides novel perspectives for screening, identification and characterization of the function and regulatory mechanisms of unknown enhancers. However, multidimensional genomic data are still difficult to integrate genome wide due to complex varieties, massive amounts, high rarity, etc. To facilitate the appropriate methods for studying enhancers with high efficacy, we delineate the principles, data processing modes and progress of various omics approaches to study enhancers and summarize the applications of traditional machine learning and deep learning in multi-omics integration in the enhancer field. In addition, the challenges encountered during the integration of multiple omics data are addressed. Overall, this review provides a comprehensive foundation for enhancer analysis.

Sequential host-bacteria and bacteria-bacteria interactions determine the microbiome establishment of Nematostella vectensis

BACKGROUND

The microbiota of multicellular organisms undergoes considerable changes during host ontogeny but the general mechanisms that control community assembly and succession are poorly understood. Here, we use bacterial recolonization experiments in Nematostella vectensis as a model to understand general mechanisms determining bacterial establishment and succession. We compared the dynamic establishment of the microbiome on the germfree host and on inert silicone tubes.

RESULTS

Following the dynamic reconstruction of microbial communities on both substrates, we show that the initial colonization events are strongly influenced by the host but not by the silicone tube, while the subsequent bacteria-bacteria interactions are the main driver of bacterial succession. Interestingly, the recolonization pattern on adult hosts resembles the ontogenetic colonization succession. This process occurs independently of the bacterial composition of the inoculum and can be followed at the level of individual bacteria. To identify potential metabolic traits associated with initial colonization success and potential metabolic interactions among bacteria associated with bacterial succession, we reconstructed the metabolic networks of bacterial colonizers based on their genomes. These analyses revealed that bacterial metabolic capabilities reflect the recolonization pattern, and the degradation of chitin might be a selection factor during early recolonization of the animal. Concurrently, transcriptomic analyses revealed that Nematostella possesses two chitin synthase genes, one of which is upregulated during early recolonization.

CONCLUSIONS

Our results show that early recolonization events are strongly controlled by the host while subsequent colonization depends on metabolic bacteria-bacteria interactions largely independent of host ontogeny. Video Abstract.

Tumor-resident Lactobacillus iners confer chemoradiation resistance through lactate-induced metabolic rewiring

Tumor microbiota can produce active metabolites that affect cancer and immune cell signaling, metabolism, and proliferation. Here, we explore tumor and gut microbiome features that affect chemoradiation response in patients with cervical cancer using a combined approach of deep microbiome sequencing, targeted bacterial culture, and in vitro assays. We identify that an obligate L-lactate-producing lactic acid bacterium found in tumors, Lactobacillus iners, is associated with decreased survival in patients, induces chemotherapy and radiation resistance in cervical cancer cells, and leads to metabolic rewiring, or alterations in multiple metabolic pathways, in tumors. Genomically similar L-lactate-producing lactic acid bacteria commensal to other body sites are also significantly associated with survival in colorectal, lung, head and neck, and skin cancers. Our findings demonstrate that lactic acid bacteria in the tumor microenvironment can alter tumor metabolism and lactate signaling pathways, causing therapeutic resistance. Lactic acid bacteria could be promising therapeutic targets across cancer types.

Brain signatures of chronic gut inflammation

Gut inflammation is thought to modify brain activity and behaviour via modulation of the gut-brain axis. However, how relapsing and remitting exposure to peripheral inflammation over the natural history of inflammatory bowel disease (IBD) contributes to altered brain dynamics is poorly understood. Here, we used electroencephalography (EEG) to characterise changes in spontaneous spatiotemporal brain states in Crohn's Disease (CD) (n = 40) and Ulcerative Colitis (UC) (n = 30), compared to healthy individuals (n = 28). We first provide evidence of a significantly perturbed and heterogeneous microbial profile in CD, consistent with previous work showing enduring and long-standing dysbiosis in clinical remission. Results from our brain state assessment show that CD and UC exhibit alterations in the temporal properties of states implicating default-mode network, parietal, and visual regions, reflecting a shift in the predominance from externally to internally-oriented attentional modes. We investigated these dynamics at a finer sub-network resolution, showing a CD-specific and highly selective enhancement of connectivity between the insula and medial prefrontal cortex (mPFC), regions implicated in cognitive-interoceptive appraisal mechanisms. Alongside overall higher anxiety scores in CD, we also provide preliminary support to suggest that the strength of chronic interoceptive hyper-signalling in the brain co-occurs with disease duration. Together, our results demonstrate that a long-standing diagnosis of CD is, in itself, a key factor in determining the risk of developing altered brain network signatures.

What can be lost? Genomic perspective on the lipid metabolism of Mucoromycota

Mucoromycota is a phylum of early diverging fungal (EDF) lineages, of mostly plant-associated terrestrial fungi. Some strains have been selected as promising biotechnological organisms due to their ability to produce polyunsaturated fatty acids and efficient conversion of nutrients into lipids. Others get their lipids from the host plant and are unable to produce even the essential ones on their own. Following the advancement in EDF genome sequencing, we carried out a systematic survey of lipid metabolism protein families across different EDF lineages. This enabled us to explore the genomic basis of the previously documented ability to produce several types of lipids within the fungal tree of life. The core lipid metabolism genes showed no significant diversity in distribution, however specialized lipid metabolic pathways differed in this regard among different fungal lineages. In total 165 out of 202 genes involved in lipid metabolism were present in all tested fungal lineages, while remaining 37 genes were found to be absent in some of fungal lineages. Duplications were observed for 69 genes. For the first time we demonstrate that ergosterol is not being produced by several independent groups of plant-associated fungi due to the losses of different ERG genes. Instead, they possess an ancestral pathway leading to the synthesis of cholesterol, which is absent in other fungal lineages. The lack of diacylglycerol kinase in both Mortierellomycotina and Blastocladiomycota opens the question on sterol equilibrium regulation in these organisms. Early diverging fungi retained most of beta oxidation components common with animals including Nudt7, Nudt12 and Nudt19 pointing at peroxisome divergence in Dikarya. Finally, Glomeromycotina and Mortierellomycotina representatives have a similar set of desaturases and elongases related to the synthesis of complex, polyunsaturated fatty acids pointing at an ancient expansion of fatty acid metabolism currently being explored by biotechnological studies.