Screening competition and cross-feeding interactions during utilization of human milk oligosaccharides by gut microbes.

Background: The infant gut microbiome is a complex community that influences short- and long-term health. Its assembly and composition are governed by variables such as the feeding type. Breast milk provides infants an important supply of human milk oligosaccharides (HMO), a broad family of carbohydrates comprising neutral, fucosylated, and sialylated molecules. There is a positive association between HMOs and the overrepresentation of Bifidobacterium species in the infant gut, which is sustained by multiple molecular determinants present in the genomes of these species. Infant-gut-associated Bifidobacterium species usually share a similar niche and display similar HMO inclinations, suggesting they compete for these resources. There is also strong evidence of cross-feeding interactions between HMO-derived molecules and bifidobacteria. Methods: In this study, we screened for unidirectional and bidirectional interactions between Bifidobacterium and other species using individual HMO. Bifidobacterium bifidum and Bacteroides thetaiotaomicron increased the growth of several other species when their supernatants were used, probably mediated by the partial degradation of HMO. In contrast, Bifidobacterium longum subsp. infantis. supernatants did not exhibit positive growth. Results: Bifidobacterium species compete for lacto-N-tetraose, which is associated with reduced bidirectional growth. The outcome of these interactions was HMO-dependent, in which the two species could compete for one substrate but cross-feed on another. 2'-fucosyllactose and lacto-N-neotetraose are associated with several positive interactions that generally originate from the partial degradation of these HMOs. Conclusion: This study presents evidence for complex interactions during HMO utilization, which can be cooperative or competitive, depending on the nature of the HMO. This information could be useful for understanding how breast milk supports the growth of some Bifidobacterium species, shaping the ecology of this important microbial community.

Using metabolic networks to predict cross-feeding and competition interactions between microorganisms.

Understanding the interactions between microorganisms and their impact on bacterial behavior at the community level is a key research topic in microbiology. Different methods, relying on experimental or mathematical approaches based on the diverse properties of bacteria, are currently employed to study these interactions. Recently, the use of metabolic networks to understand the interactions between bacterial pairs has increased, highlighting the relevance of this approach in characterizing bacteria. In this study, we leverage the representation of bacteria through their metabolic networks to build a predictive model aimed at reducing the number of experimental assays required for designing bacterial consortia with specific behaviors. Our novel method for predicting cross-feeding or competition interactions between pairs of microorganisms utilizes metabolic network features. Machine learning classifiers are employed to determine the type of interaction from automatically reconstructed metabolic networks. Several algorithms were assessed and selected based on comprehensive testing and careful separation of manually compiled data sets obtained from literature sources. We used different classification algorithms, including K Nearest Neighbors, XGBoost, Support Vector Machine, and Random Forest, tested different parameter values, and implemented several data curation approaches to reduce the biological bias associated with our data set, ultimately achieving an accuracy of over 0.9. Our method holds substantial potential to advance the understanding of community behavior and contribute to the development of more effective approaches for consortia design.IMPORTANCEUnderstanding bacterial interactions at the community level is critical for microbiology, and leveraging metabolic networks presents an efficient and effective approach. The introduction of this novel method for predicting interactions through machine learning classifiers has the potential to advance the field by reducing the number of experimental assays required and contributing to the development of more effective bacterial consortia.

Genome-scale metabolic modeling of the human milk oligosaccharide utilization by Bifidobacterium longum subsp. infantis.

Bifidobacterium longum subsp. infantis is a representative and dominant species in the infant gut and is considered a beneficial microbe. This organism displays multiple adaptations to thrive in the infant gut, regarded as a model for human milk oligosaccharides (HMOs) utilization. These carbohydrates are abundant in breast milk and include different molecules based on lactose. They contain fucose, sialic acid, and N-acetylglucosamine. Bifidobacterium metabolism is complex, and a systems view of relevant metabolic pathways and exchange metabolites during HMO consumption is missing. To address this limitation, a refined genome-scale network reconstruction of this bacterium is presented using a previous reconstruction of B. infantis ATCC 15967 as a template. The latter was expanded based on an extensive revision of genome annotations, current literature, and transcriptomic data integration. The metabolic reconstruction (iLR578) accounted for 578 genes, 1,047 reactions, and 924 metabolites. Starting from this reconstruction, we built context-specific genome-scale metabolic models using RNA-seq data from cultures growing in lactose and three HMOs. The models revealed notable differences in HMO metabolism depending on the functional characteristics of the substrates. Particularly, fucosyl-lactose showed a divergent metabolism due to a fucose moiety. High yields of lactate and acetate were predicted under growth rate maximization in all conditions, whereas formate, ethanol, and 1,2-propanediol were substantially lower. Similar results were also obtained under near-optimal growth on each substrate when varying the empirically observed acetate-to-lactate production ratio. Model predictions displayed reasonable agreement between central carbon metabolism fluxes and expression data across all conditions. Flux coupling analysis revealed additional connections between succinate exchange and arginine and sulfate metabolism and a strong coupling between central carbon reactions and adenine metabolism. More importantly, specific networks of coupled reactions under each carbon source were derived and analyzed. Overall, the presented network reconstruction constitutes a valuable platform for probing the metabolism of this prominent infant gut bifidobacteria.IMPORTANCEThis work presents a detailed reconstruction of the metabolism of Bifidobacterium longum subsp. infantis, a prominent member of the infant gut microbiome, providing a systems view of its metabolism of human milk oligosaccharides.

Microbial interactions and the homeostasis of the gut microbiome: the role of Bifidobacterium.

The human gut is home to trillions of microorganisms that influence several aspects of our health. This dense microbial community targets almost all dietary polysaccharides and releases multiple metabolites, some of which have physiological effects on the host. A healthy equilibrium between members of the gut microbiota, its microbial diversity, and their metabolites is required for intestinal health, promoting regulatory or anti-inflammatory immune responses. In contrast, the loss of this equilibrium due to antibiotics, low fiber intake, or other conditions results in alterations in gut microbiota composition, a term known as gut dysbiosis. This dysbiosis can be characterized by a reduction in health-associated microorganisms, such as butyrate-producing bacteria, enrichment of a small number of opportunistic pathogens, or a reduction in microbial diversity. Bifidobacterium species are key species in the gut microbiome, serving as primary degraders and contributing to a balanced gut environment in various ways. Colonization resistance is a fundamental property of gut microbiota for the prevention and control of infections. This community competes strongly with foreign microorganisms, such as gastrointestinal pathogens, antibiotic-resistant bacteria, or even probiotics. Resistance to colonization is based on microbial interactions such as metabolic cross-feeding, competition for nutrients, or antimicrobial-based inhibition. These interactions are mediated by metabolites and metabolic pathways, representing the inner workings of the gut microbiota, and play a protective role through colonization resistance. This review presents a rationale for how microbial interactions provide resistance to colonization and gut dysbiosis, highlighting the protective role of Bifidobacterium species.

Spanish versions and validation of a series of rating scales and visual analogue scales to assess the subjective effects of cannabis.

Cannabis is being legalized for medical and recreational purposes all around the world. However, the understanding of the psychological effects of cannabis is still limited, and it has been previously linked to mental disorders such as schizophrenia. Lately, new scales have been created and adapted to measure its psychological effects. The aim of this study is to create Spanish versions of some of these scales and test their psychometric characteristics. One hundred sixteen participants were recruited from Cannabis Social Clubs (CSC) in Barcelona, Spain. Participants under the effects of their own cannabis completed the Cannabis Experience Questionnaire-modified version (CEQ-mv), Addiction Research Centre Inventory-18 (ARCI-18), Psychotomimetic States Inventory (PSI) and Visual Analogue Scales (VAS). Questionnaires were completed in the CSC, providing a naturalistic setting for the study. Exploratory factor analysis and internal consistency were analyzed. PSI was reduced from a 6-factor to a 4-factor model with adequate to low reliability, ARCI-18 was reduced from a 3-factor to a 2-factor model with good reliability, and VAS were reduced from a 4-factor to a 3-factor model, also with good reliability. These questionnaires showed adequate reliability and can be used in future studies to test the subjective effects of cannabis in clinical and naturalistic settings.

Proteomic analyses of Bacteroides ovatus and Bifidobacterium longum in xylan bidirectional culture shows sugar cross-feeding interactions.

The intestinal microbiome is a community of anaerobic microorganisms whose activities significantly impact human health. Its composition can be modulated by consuming foods rich in dietary fiber, such as xylan, a complex polysaccharide that can be considered an emerging prebiotic. In this work, we evaluated how certain gut bacteria acted as primary degraders, fermenting dietary fibers, and releasing metabolites that other bacteria can further use. Different bacterial strains of Lactobacillus, Bifidobacterium, and Bacteroides were evaluated for their ability to consume xylan and interact with one another. Results from unidirectional assays gave indications of possible cross-feeding between bacteria using xylan as a carbon source. Bidirectional assays showed that Bifidobacterium longum PT4 increased its growth in the presence of Bacteroides ovatus HM222. Proteomic analyses indicated that B. ovatus HM222 synthesizes enzymes facilitating xylan degradation, such as ß-xylanase, arabinosidase, L-arabinose isomerase, and xylosidase. Interestingly, the relative abundance of these proteins remains largely unaffected in the presence of Bifidobacterium longum PT4. In the presence of B. ovatus, B. longum PT4 increased the production of enzymes such as α-L-arabinosidase, L-arabinose isomerase, xylulose kinase, xylose isomerase, and sugar transporters. These results show an example of positive interaction between bacteria mediated by xylan consumption. Bacteroides degraded this substrate to release xylooligosaccharides, or monosaccharides (xylose, arabinose), which might support the growth of secondary degraders such as B. longum.

Screening Microbial Interactions During Inulin Utilization Reveals Strong Competition and Proteomic Changes in Lacticaseibacillus paracasei M38.

Competition for resources is a common microbial interaction in the gut microbiome. Inulin is a well-studied prebiotic dietary fiber that profoundly shapes gut microbiome composition. Several community members and some probiotics, such as Lacticaseibacillus paracasei, deploy multiple molecular strategies to access fructans. In this work, we screened bacterial interactions during inulin utilization in representative gut microbes. Unidirectional and bidirectional assays were used to evaluate the effects of microbial interactions and global proteomic changes on inulin utilization. Unidirectional assays showed the total or partial consumption of inulin by many gut microbes. Partial consumption was associated with cross-feeding of fructose or short oligosaccharides. However, bidirectional assays showed strong competition from L. paracasei M38 against other gut microbes, reducing the growth and quantity of proteins found in the latter. L. paracasei dominated and outcompeted other inulin utilizers, such as Ligilactobacillus ruminis PT16, Bifidobacterium longum PT4, and Bacteroides fragilis HM714. The importance of strain-specific characteristics of L. paracasei, such as its high fitness for inulin consumption, allows it to be favored for bacterial competence. Proteomic studies indicated an increase in inulin-degrading enzymes in co-cultures, such as ß-fructosidase, 6-phosphofructokinase, the PTS D-fructose system, and ABC transporters. These results reveal that intestinal metabolic interactions are strain-dependent and might result in cross-feeding or competition depending on total or partial consumption of inulin. Partial degradation of inulin by certain bacteria favors coexistence. However, when L. paracasei M38 totally degrades the fiber, this does not happen. The synergy of this prebiotic with L. paracasei M38 could determine the predominance in the host as a potential probiotic.

Engineered Bacteria for Short-Chain-Fatty-Acid-Repressed Expression of Biotherapeutic Molecules.

Short-chain fatty acids (SCFA) such as propionate and butyrate are critical metabolites produced by the gut microbiota. Microbiome dysbiosis resulting in altered SCFA profiles is associated with certain diseases, including inflammatory bowel diseases (IBD), characterized by a reduction in butyrate concentration and active intestinal inflammation. There is an increasing interest in the use of engineered bacteria as diagnostic and therapeutic tools for gut diseases. In this study, we developed genetic circuits capable of sensing SCFA concentrations to build biosensors that express a response protein (superfolder green fluorescent protein [sfGFP]) in amounts inversely proportional to the SCFA concentration. We also built biotherapeutics expressing the cytokine granulocyte-macrophage colony-stimulating factor (GM-CSF) using the same logic. The propionate biotherapeutic expressed larger amounts of mouse GM-CSF in the absence of propionate. The butyrate biotherapeutics presented the expected behavior only at the beginning of the kinetics and an accelerated response in the absence of butyrate. Overall, these genetic systems may function as complementary diagnostic tools for measuring SCFAs and as delivery vehicles for biotherapeutic molecules. IMPORTANCE Short-chain fatty acids are key molecules produced by the gut microbiome. Their concentrations are altered in certain diseases. Here, we created molecular biosensors that quantify the absence of propionate and butyrate, using logic "NOT" gates and bacterial promoters. Finally, we show that these genetic systems could be useful for the delivery of therapeutic molecules in the gut, in the absence of these acids.

Alcoholic vs. Nonalcoholic Steatohepatitis: Vascular Branching Heterogeneity on Magnetic Resonance Imaging as a Diagnostic Marker.

Background and Aims: Distinguishing alcoholic steatohepatitis (ASH) and nonalcoholic steatohepatitis (NASH) with biopsy alone is often difficult without a reliable clinical context. A novel finding on liver imaging, perivascular branching heterogeneity, has shown promise in distinguishing between these chronic liver diseases. Our study investigated the role of this finding on imaging to differentiate between ASH and NASH. The aim of this study was to determine the utility and reproducibility of this novel radiographic marker to help distinguish ASH from NASH. Methods: This was a retrospective cohort study conducted between 2016 and 2020 in patients with both liver biopsy-confirmed steatohepatitis/chronic hepatitis and abdominal magnetic resonance imaging within 13 months of each other. Two radiologists, blinded to patient clinical history and diagnosis, categorized the appearance of the liver as: 1- homogeneity, 2- mild heterogeneity, 3- moderate heterogeneity, 4- possible perivascular branching, 5- definite perivascular branching. Results: Of the 90 patients in the study, 60 were identified as NASH and 30 as ASH. The area under the curve (AUC) for both reader 1 and 2 when using the 5-point scale was 0.69 (CI: 0.56-0.82, p=0.006) and 0.72 (CI: 0.60-0.85, p=0.001), respectively. The positive predictive value (PPV) for identification of ASH when scoring 5 was 64.7% and 66.7% for reader 1 and 2, respectively. Interclass correlation coefficient was 0.74 in patients with ASH, indicating moderate reliability among both readers. Conclusions: Identification of this perivascular branching pattern on imaging is a promising novel diagnostic marker that can be used with other methods to help distinguish between ASH and NASH.

Developing a Fluorescent Inducible System for Free Fucose Quantification in Escherichia coli.

L-Fucose is a monosaccharide abundant in mammalian glycoconjugates. In humans, fucose can be found in human milk oligosaccharides (HMOs), mucins, and glycoproteins in the intestinal epithelium. The bacterial consumption of fucose and fucosylated HMOs is critical in the gut microbiome assembly of infants, dominated by Bifidobacterium. Fucose metabolism is important for the production of short-chain fatty acids and is involved in cross-feeding microbial interactions. Methods for assessing fucose concentrations in complex media are lacking. Here we designed and developed a molecular quantification method of free fucose using fluorescent Escherichia coli. For this, low- and high-copy plasmids were evaluated with and without the transcription factor fucR and its respective fucose-inducible promoter controlling the reporter gene sfGFP. E. coli BL21 transformed with a high copy plasmid containing pFuc and fucR displayed a high resolution across increasing fucose concentrations and high fluorescence/OD values after 18 h. The molecular circuit was specific against other monosaccharides and showed a linear response in the 0-45 mM range. Adjusting data to the Hill equation suggested non-cooperative, simple regulation of FucR to its promoter. Finally, the biosensor was tested on different concentrations of free fucose and the supernatant of Bifidobacterium bifidum JCM 1254 supplemented with 2-fucosyl lactose, indicating the applicability of the method in detecting free fucose. In conclusion, a bacterial biosensor of fucose was validated with good sensitivity and precision. A biological method for quantifying fucose could be useful for nutraceutical and microbiological applications, as well as molecular diagnostics.

Structure of co-expression networks of Bifidobacterium species in response to human milk oligosaccharides.

Biological systems respond to environmental perturbations and a large diversity of compounds through gene interactions, and these genetic factors comprise complex networks. Experimental information from transcriptomic studies has allowed the identification of gene networks that contribute to our understanding of microbial adaptations. In this study, we analyzed the gene co-expression networks of three Bifidobacterium species in response to different types of human milk oligosaccharides (HMO) using weighted gene co-expression analysis (WGCNA). RNA-seq data obtained from Geo Datasets were obtained for Bifidobacterium longum subsp. Infantis, Bifidobacterium bifidum and Bifidobacterium longum subsp. Longum. Between 10 and 20 co-expressing modules were obtained for each dataset. HMO-associated genes appeared in the modules with more genes for B. infantis and B. bifidum, in contrast with B. longum. Hub genes were identified in each module, and in general they participated in conserved essential processes. Certain modules were differentially enriched with LacI-like transcription factors, and others with certain metabolic pathways such as the biosynthesis of secondary metabolites. The three Bifidobacterium transcriptomes showed distinct regulation patterns for HMO utilization. HMO-associated genes in B. infantis co-expressed in two modules according to their participation in galactose or N-Acetylglucosamine utilization. Instead, B. bifidum showed a less structured co-expression of genes participating in HMO utilization. Finally, this category of genes in B. longum clustered in a small module, indicating a lack of co-expression with main cell processes and suggesting a recent acquisition. This study highlights distinct co-expression architectures in these bifidobacterial genomes during HMO consumption, and contributes to understanding gene regulation and co-expression in these species of the gut microbiome.

Characterization and Identification of Probiotic Features in Lacticaseibacillus Paracasei Using a Comparative Genomic Analysis Approach.

Lacticaseibacillus paracasei species are widely used for their health-promoting properties in food and agricultural applications. These bacteria have been isolated from various habitats such as the oral cavity, cereals, vegetables, meats, and dairy products conferring them the ability to consume different carbohydrates. Two subspecies are recognized, Lacticaseibacillus paracasei subsp. paracasei and Lacticaseibacillus paracasei subsp. tolerans according to their acid production from carbohydrates. Some strains are currently used as probiotics. In this study, we performed a comparative genomic analysis of 181 genomes of the Lacticaseibacillus paracasei species to reveal genomic differences at the subspecies level and to reveal adaptive and probiotic features, and special emphasis is given to inulin consumption. No clear distinction at the subspecies level for L. paracasei was shown using a phylogenetic tree with orthologous genes from the core-genome set. In general, a good correlation was observed between genomic distance and isolation origin, suggesting that L. paracasei strains are adapted to their natural habitat, giving rise to genetic differences at the genomic level. A low frequency of undesirable characteristics such as plasmids, prophages, antibiotic resistance genes, absence of virulence factors, and frequent bacteriocin production supports these species being good candidates for use as probiotics. Lastly, we found that the inulin gene cluster in L. paracasei strains seems to differ slightly in the presence or absence of some genes but maintains a core defined by at least three fructose-PTS proteins, one hypothetical protein, and extracellular ß-fructosidase. Finally, we conclude that further work has to be done for L. paracasei subspecies classification. Improving outgroup selection criteria is a key factor for their correct subspecies assignation.

Metabolic Modeling and Bidirectional Culturing of Two Gut Microbes Reveal Cross-Feeding Interactions and Protective Effects on Intestinal Cells.

The gut microbiota is constituted by thousands of microbial interactions, some of which correspond to the exchange of metabolic by-products or cross-feeding. Inulin and xylan are two major dietary polysaccharides that are fermented by members of the human gut microbiota, resulting in different metabolic profiles. Here, we integrated community modeling and bidirectional culturing assays to study the metabolic interactions between two gut microbes, Phocaeicola dorei and Lachnoclostridium symbiosum, growing in inulin or xylan, and how they provide a protective effect in cultured cells. P. dorei (previously belonging to the Bacteroides genus) was able to consume inulin and xylan, while L. symposium only used certain inulin fractions to produce butyrate as a major end product. Constrained-based flux simulations of refined genome-scale metabolic models of both microbes predicted high lactate and succinate cross-feeding fluxes between P. dorei and L. symbiosum when growing in each fiber. Bidirectional culture assays in both substrates revealed that L. symbiosum growth increased in the presence of P. dorei. Carbohydrate consumption analyses showed a faster carbohydrate consumption in cocultures compared to monocultures. Lactate and succinate concentrations in bidirectional cocultures were lower than in monocultures, pointing to cross-feeding as initially suggested by the model. Butyrate concentrations were similar across all conditions. Finally, supernatants from both bacteria cultured in xylan in bioreactors significantly reduced tumor necrosis factor-α-induced inflammation in HT-29 cells and exerted a protective effect against the TcdB toxin in Caco-2 epithelial cells. Surprisingly, this effect was not observed in inulin cocultures. Overall, these results highlight the predictive value of metabolic models integrated with microbial culture assays for probing microbial interactions in the gut microbiota. They also provide an example of how metabolic exchange could lead to potential beneficial effects in the host. IMPORTANCE Microbial interactions represent the inner connections in the gut microbiome. By integrating mathematical modeling tools and microbial bidirectional culturing, we determined how two gut commensals engage in the exchange of cross-feeding metabolites, lactate and succinate, for increased growth in two fibers. These interactions underpinned butyrate production in cocultures, resulting in a significant reduction in cellular inflammation and protection against microbial toxins when applied to cellular models.

The Effects of Cannabidiol and δ-9-Tetrahydrocannabinol in Social Cognition: A Naturalistic Controlled Study.

Background: Social cognition abilities such as empathy and the Theory of Mind (ToM) have been shown to be impaired in neuropsychiatric conditions such as psychotic, autistic, and bipolar disorders. The endocannabinoid system (ECS) seems to play a role in social behavior and emotional processing while it also seems to play a role in those neuropsychiatric conditions showing social cognition impairments. Main plant cannabinoids delta-9-tetrahydrocannabinol (THC) and cannabidiol (CBD) modulate the ECS and, due to their opposite effects, have been proposed as both cause and treatment for neuropsychiatric-related disorders such as schizophrenia, anxiety, or post-traumatic stress disorder (PTSD). The aim of this study was to test the effects of THC and CBD on social cognition abilities in chronic cannabis users. Method: Eighteen members from a cannabis social club were tested for social cognition effects under the effects of different full spectrum cannabis extracts containing either THC, CBD, THC+CBD, or placebo in a naturalistic randomized double-blind crossover placebo-controlled study. Results: Results showed that participants under the effects of THC showed lower cognitive empathy when compared with the effects of CBD but not when those were compared with THC+CBD or placebo. Also, participants showed higher cognitive ToM under the effects of CBD when compared with the effects of placebo, but not when those were compared with THC or THC+CBD. However, we did not find differences on the emotional scales for empathy or ToM. Conclusions: This study provides evidence for the interaction between the effects of THC and CBD and social cognition abilities in a naturalistic environment, which can be of special interest for the clinical practice of medical cannabis on neuropsychiatric disorders. We show for the first time that CBD can improve ToM abilities in chronic cannabis users. Our results might help to understand the role of the ECS in social cognition, and their association with psychiatric and neurodevelopmental disorders such as schizophrenia or autism. Finally, we demonstrate how reliable methodologies can be implemented in naturalistic environments to collect valid ecological evidence outside classic laboratory settings.

Etiology and clinical characteristics of pediatric non-neoplastic posterior reversible encephalopathy: systematic review.

Although more commonly seen in adult population, posterior reversible encephalopathy syndrome (PRES) can also be observed in pediatric patients. The etiopathogenesis of pediatric PRES is poorly understood, and the available evidence comes mostly from childhood cancer. Analysis of the sociodemographic and clinical characteristics of the different noncancer-related types can improve the understanding of pediatric PRES. Methods: Systematic review of characteristics and outcome of noncancer pediatric PRES. Primary sources of investigation were identified and selected through Pubmed and Scopus databases. The research was complemented by reference search in relevant publications. Study protocol was registered (Prospero CRD42020165798). Results: We identified 449 cases of noncancer pediatric PRES from 272 studies, median age 10 (newborn to 17 years), 49.9% girls. The 4 most common groups of conditions were renal 165 (36.7%), hematologic 84 (18.7%), autoimmune 64 (14.3%), and cardiovascular 28 (6.2%) disorders. The 4 most prevalent precipitants identified were hypertensive crisis 119 (26.5%), corticosteroids 56 (12.5%), immunosuppression drugs 44 (9.8%), and biologic drugs 14 (3.1%). Clinical presentations included seizures 100 (22.3%), headaches 22 (4.9%), encephalopathy 17 (3.8%), visual disturbances 6 (1.3%), and focal deficit 3 (0.7%). The distribution of lesions was (n = 380): combined anterior/posterior circulation (40.8%), isolated posterior circulation (24.1%), anterior circulation (6.2%), and deep structures (1.6%). Residual neurological deficits occurred in about 1 out 10 cases. Conclusion: Pediatric PRES differs from the adult in etiology, precipitants, and clinical manifestations. Renal diseases predominate, acute raised blood pressure is less frequent, and cortical deficits are rarer. In addition, the proportion of patients with combined anterior/posterior circulation was higher. Permanent neurological sequels can occur.

Comparative Genomics of Clostridium baratii Reveals Strain-Level Diversity in Toxin Abundance.

Clostridium baratii strains are rare opportunistic pathogens associated with botulism intoxication. They have been isolated from foods, soil and be carried asymptomatically or cause botulism outbreaks. Is not taxonomically related to Clostridium botulinum, but some strains are equipped with BoNT/F7 cluster. Despite their relationship with diseases, our knowledge regarding the genomic features and phylogenetic characteristics is limited. We analyzed the pangenome of C. baratii to understand the diversity and genomic features of this species. We compared existing genomes in public databases, metagenomes, and one newly sequenced strain isolated from an asymptomatic subject. The pangenome was open, indicating it comprises genetically diverse organisms. The core genome contained 28.49% of the total genes of the pangenome. Profiling virulence factors confirmed the presence of phospholipase C in some strains, a toxin capable of disrupting eukaryotic cell membranes. Furthermore, the genomic analysis indicated significant horizontal gene transfer (HGT) events as defined by the presence of prophage genomes. Seven strains were equipped with BoNT/F7 cluster. The active site was conserved in all strains, identifying a missing 7-aa region upstream of the active site in C. baratii genomes. This analysis could be important to advance our knowledge regarding opportunistic clostridia and better understand their contribution to disease.

Differences in the composition and predicted functions of the intestinal microbiome of obese and normal weight adult dogs.

Obesity is a multifactorial nutritional disorder highly prevalent in dogs, observed in developed and developing countries. It is estimated that over 40% of the canine population suffers from obesity, which manifests in an increased risk of chronic osteoarticular, metabolic, and cardiovascular diseases. The intestinal microbiome of obese animals shows increases in the abundance of certain members capable of extracting energy from complex polysaccharides. The objective of this study was to compare the composition and predicted function of the intestinal microbiome of Chilean obese and normal weight adult dogs. Twenty clinically healthy dogs were classified according to their body condition score (BCS) as obese (n = 10) or normal weight (n = 10). DNA was extracted from stool samples, followed by next-generation sequencing of the 16S rRNA V3-V4 region and bioinformatics analysis targeting microbiome composition and function. Significant differences were observed between these groups at the phylum level, with anincrease in Firmicutes and a decrease in Bacteroidetes in obese dogs. Microbiome compositions of these animals correlated with their BCS, and obese dogs showed enrichment in pathways related to transport, chemotaxis, and flagellar assembly. These results highlight the differences in the gut microbiome between normal weight and obese dogs and prompt further research to improve animal health by modulating the gut microbiome.

Modeling approaches for probing cross-feeding interactions in the human gut microbiome.

Microbial communities perform emergent activities that are essentially different from those carried by their individual members. The gut microbiome and its metabolites have a significant impact on the host, contributing to homeostasis or disease. Food molecules shape this community, being fermented through cross-feeding interactions of metabolites such as lactate, acetate, and amino acids, or products derived from macromolecule degradation. Mathematical and experimental approaches have been applied to understand and predict the interactions between microorganisms in complex communities such as the gut microbiota. Rational and mechanistic understanding of microbial interactions is essential to exploit their metabolic activities and identify keystone taxa and metabolites. The latter could be used in turn to modulate or replicate the metabolic behavior of the community in different contexts. This review aims to highlight recent experimental and modeling approaches for studying cross-feeding interactions within the gut microbiome. We focus on short-chain fatty acid production and fiber fermentation, which are fundamental processes in human health and disease. Special attention is paid to modeling approaches, particularly kinetic and genome-scale stoichiometric models of metabolism, to integrate experimental data under different diet and health conditions. Finally, we discuss limitations and challenges for the broad application of these modeling approaches and their experimental verification for improving our understanding of the mechanisms of microbial interactions.

Assessment of Changes in the Oral Microbiome That Occur in Dogs with Periodontal Disease.

The oral microbiome in dogs is a complex community. Under some circumstances, it contributes to periodontal disease, a prevalent inflammatory disease characterized by a complex interaction between oral microbes and the immune system. Porphyromonas and Tannerella spp. are usually dominant in this disease. How the oral microbiome community is altered in periodontal disease, especially sub-dominant microbial populations is unclear. Moreover, how microbiome functions are altered in this disease has not been studied. In this study, we compared the composition and the predicted functions of the microbiome of the cavity of healthy dogs to those with from periodontal disease. The microbiome of both groups clustered separately, indicating important differences. Periodontal disease resulted in a significant increase in Bacteroidetes and reductions in Actinobacteria and Proteobacteria. Porphyromonas abundance increased 2.7 times in periodontal disease, accompanied by increases in Bacteroides and Fusobacterium. It was predicted that aerobic respiratory processes are decreased in periodontal disease. Enrichment in fermentative processes and anaerobic glycolysis were suggestive of an anaerobic environment, also characterized by higher lipopolysaccharide biosynthesis. This study contributes to a better understanding of how periodontal disease modifies the oral microbiome and makes a prediction of the metabolic pathways that contribute to the inflammatory process observed in periodontal disease.