Impact of microbial-based biopreparations on soil quality, plant health, and fruit chemistry in raspberry cultivation.

Application of microbial-based biopreparations as a pre-harvest strategy offers a method to obtain sustainable agricultural practices and could be an important approach for advancing food science, promoting sustainability, and meeting global food market demands. The impact of a bacterial-fungal biopreparation mixture on soil-plant-microbe interactions, fruit chemical composition and yield of 7 raspberry clones was investigated by examining the structural and functional profiles of microbial communities within leaves, fruits, and soil. Biopreparation addition caused the enhancement of the microbiological utilization of specific compounds, such as d-mannitol, relevant in plant-pathogen interactions and overall plant health. The biopreparation treatment positively affected the nitrogen availability in soil (9-160%). The analysis of plant stress marker enzymes combined with the evaluation of fruit quality and chemical properties highlight changes inducted by the pre-harvest biopreparation application. Chemical analyses highlight biopreparations' role in soil and fruit quality improvement, promoting sustainable agriculture. This effect was dependent on tested clones, showing increase of soluble solid content in fruits, concentration of polyphenols or the sensory quality of the fruits. The results of the next-generation sequencing indicated increase in the effective number of bacterial species after biopreparation treatment. The network analysis showed stimulating effect of biopreparation on microbial communities by enhancing microbial interactions (increasing the number of network edges up to 260%) of and affecting the proportions of mutual relationships between both bacteria and fungi. These findings show the potential of microbial-based biopreparation in enhancing raspberry production whilst promoting sustainable practices and maintaining environmental homeostasis and giving inshght in holistic understanding of microbial-based approaches for advancing food science monitoring.

Unlocking the potential of fermented beetroot ketchup: Enhancing polyphenol recovery and gut microbiota interactions.

The study aimed to evaluate the effect of digestion and gut microbiota interactions on beetroot ketchup formulations, focusing on the release of polyphenols, bioaccessibility, and microbial interactions on gut microbiota with polyphenols. Tested ketchup samples were evaluated using the TNO Gastro-Intestinal Model 1 (TIM-1) simulated upper part of the gastrointestinal tract and the TNO Gastro-Intestinal Model 2 (TIM-2) simulated colon system. The results showed that fermentation of ketchup with Lactobacillus johnsonii K4, increased the release of bioactive compounds during digestion, with higher polyphenol recoveries observed in fermented samples. In particular, a fermented sample has higher recovery percentages for most of the phenolic acids, flavonoids, and betalains. However, some polyphenolic compounds were degraded during fermentation, suggesting a dynamic process of polyphenol metabolism in the gut environment. The study highlights the potential of fermented foods, such as beetroot ketchup, enriched with polyphenols and beneficial bacteria, to promote gut health and overall well-being.

Apple polyphenols prevent patulin-induced intestinal damage by modulating the gut microbiota and metabolism of the gut-liver axis.

Patulin (PAT), a foodborne toxin, causes severe intestinal damage. To mitigate this health threat, mice were pretreated with apple polyphenols (AP) in their drinking water (0.01 % and 0.05 %) for eight weeks, followed by exposure to PAT during the last two weeks. Subsequently, histopathological and biochemical evaluations of intestinal tissues were conducted, alongside assessments of alterations in gut microbiota, colonic content metabolome, and hepatic metabolome. Consequently, AP alleviated PAT-induced villus and crypt injury, mucus depletion, GSH level decline, GSH-Px and SOD activity reduction, and MPO activity elevation. Notably, AP counteracted PAT-mediated microbiota disruptions and promoted the abundance of beneficial bacteria (Dubosiella, Akkermansia, Lachnospiraceae, and Lactobacillus). Furthermore, AP counteracted PAT-induced metabolic disorders in the colonic contents and liver. Ultimately, AP prevented intestinal injury by regulating the gut microbiota and amino acid, purine, butanoate, and glycerophospholipid metabolism in the gut-liver axis. These results underscore the potential of AP to prevent foodborne toxin-induced intestinal damage.

The effect of medical face masks on inhalation risk of bacterial bioaerosols in hospital waste decontamination station.

There is insufficient research on bioaerosols in hospital waste decontamination stations. This study aimed to investigate the effect of three-layer and N95 masks in reducing the inhalation risk of bacterial bioaerosols in a waste decontamination station at a teaching hospital. Active sampling was conducted on five different days at three locations: the yard, resting room, and autoclave room in three different modes: without a mask, with a three-layer mask, and with an N95 mask. Bacterial bioaerosols passing through the masks were identified using biochemical tests and polymerase chain reaction (PCR). The median concentration and interquartile range (IQR) of bacterial bioaerosols was 217.093 (230.174) colony-forming units per cubic meter (CFU/m3), which is higher than the recommended amount by Pan American Health Organization (PAHO). The resting room had high contamination levels, with a median (IQR) of 321.9 (793.529) CFU/m3 of bacterial bioaerosols. The maximum concentration of bioaerosols was also recorded in the same room (2443.282 CFU/m3). The concentration of bacterial bioaerosols differed significantly between using a three-layer or N95 mask and not using a mask (p-value < 0.001). The non-carcinogenic risk level was acceptable in all cases, except in the resting room without a mask (Hazard Quotient (HQ) = 2.07). The predominant bacteria were Gram-positive cocci (33.98%). Micrococci (three-layer mask = 51.28%, N95 mask = 50%) and Coagulase-negative Staphylococci (three-layer mask = 30.77%, N95 mask = 31.82%) were the most abundant bioaerosols passing through the masks. The results obtained are useful for managerial decisions in hospital waste decontamination stations to reduce exposure to bioaerosols and develop useful guidelines.

The gut microbiome in patients with Cushing's disease affects depression- and anxiety-like behavior in mice.

BACKGROUND: Depression and anxiety significantly impact the quality of life in individuals with Cushing's disease (CD), which originates from pituitary neuroendocrine tumors (PitNETs), yet our understanding of the underlying mechanisms is limited. There is substantial evidence linking gut microbes to depression, anxiety, and endocrinology. RESULTS: The gut bacterial phenotype of patients with Cushing's disease was significantly different from that of the control group, and when the mice were treated with fecal bacteria from these patients, both anxiety- and depression-like behavior were significantly increased. However, this effect can be alleviated by supplementing with 2-(14, 15-epoxyeicosatrienoyl) glycerol (2-14,15-EG) which was found at reduced levels in the peripheral blood of mice treated with coprofecal bacteria from Cushing's disease. In this process, the effects of hormone levels and immune factors were not significant. In addition, in an animal model, corticosterone has been observed to affect behavioral changes in mice through gut microbiota composition, clarifying the cause-and-effect relationship between hormones, microbiota, and behavior. Finally, there was no significant difference in gut microbiome composition and its effects on mouse behavior in patients with Cushing's disease with different levels of depression and anxiety. CONCLUSIONS: In summary, this research enhances our current understanding of how gut microbes in patients with Cushing's disease contribute to depression and anxiety, offering novel insights for clinical treatment approaches. Video Abstract.

Next-generation IgA-SEQ allows for high-throughput, anaerobic, and metagenomic assessment of IgA-coated bacteria.

BACKGROUND: The intestinal microbiota plays a significant role in maintaining systemic and intestinal homeostasis, but can also influence diseases such as inflammatory bowel disease (IBD) and cancer. Certain bacterial species within the intestinal tract can chronically activate the immune system, leading to low-grade intestinal inflammation. As a result, plasma cells produce high levels of secretory antigen-specific immunoglobulin A (IgA), which coats the immunostimulatory bacteria. This IgA immune response against intestinal bacteria may be associated with the maintenance of homeostasis and health, as well as disease. Unraveling this dichotomy and identifying the immunostimulatory bacteria is crucial for understanding the relationship between the intestinal microbiota and the immune system, and their role in health and disease. IgA-SEQ technology has successfully identified immunostimulatory, IgA-coated bacteria from fecal material. However, the original technology is time-consuming and has limited downstream applications. In this study, we aimed to develop a next-generation, high-throughput, magnet-based sorting approach (ng-IgA-SEQ) to overcome the limitations of the original IgA-SEQ protocol. RESULTS: We show, in various settings of complexity ranging from simple bacterial mixtures to human fecal samples, that our magnetic 96-well plate-based ng-IgA-SEQ protocol is highly efficient at sorting and identifying IgA-coated bacteria in a high-throughput and time efficient manner. Furthermore, we performed a comparative analysis between different IgA-SEQ protocols, highlighting that the original FACS-based IgA-SEQ approach overlooks certain nuances of IgA-coated bacteria, due to the low yield of sorted bacteria. Additionally, magnetic-based ng-IgA-SEQ allows for novel downstream applications. Firstly, as a proof-of-concept, we performed metagenomic shotgun sequencing on 10 human fecal samples to identify IgA-coated bacterial strains and associated pathways and CAZymes. Secondly, we successfully isolated and cultured IgA-coated bacteria by performing the isolation protocol under anaerobic conditions. CONCLUSIONS: Our magnetic 96-well plate-based high-throughput next-generation IgA-SEQ technology efficiently identifies a great number of IgA-coated bacteria from fecal samples. This paves the way for analyzing large cohorts as well as novel downstream applications, including shotgun metagenomic sequencing, culturomics, and various functional assays. These downstream applications are essential to unravel the role of immunostimulatory bacteria in health and disease. Video Abstract.

Enteromorpha prolifera Polysaccharide Alleviates Acute Alcoholic Liver Injury in C57 BL/6 Mice through the Gut-Liver Axis and NF-κB Pathway.

Enteromorpha prolifera polysaccharide (EP2) protection against acute alcoholic liver injury (AALI) in mice was investigated. By integration of physiological indicators, gut microbiota, and short-chain fatty acids (SCFAs), the mechanism of EP2 in alleviating AALI was disclosed. The results showed that EP2 significantly ameliorated alcohol-induced abnormal transaminase activities, liver and intestinal systemic inflammation, and intestinal environmental disorders. EP2 significantly reduces liver and serum LPS contents by 1.69-fold and 1.54-fold. Furthermore, inhibition of the NF-κB signaling pathway by EP2 reduced the production of proinflammatory cytokines such as TNF-α (1.83-fold), IL-6 (11.09-fold), and IL-1ß (1.99-fold). EP2 restored SCFAs to normal levels by upregulating the abundance of beneficial bacteria (Colidextribacter, Ruminococcus, unclassified_Lachnospiraceae, and Akkermansia). The alleviation of AALI by EP2 occurs through protection of the intestinal mucosal barrier and reduction of LPS permeating in serum. The decrease in LPS inactivates the NF-κB signaling pathway and prevents inflammation. In short, EP2 regulates the gut-liver axis and inflammation, alleviating effects in AALI mice.

Intratumoral and fecal microbiota reveals microbial markers associated with gastric carcinogenesis.

Background: The relationship between dysbiosis of the gastrointestinal microbiota and gastric cancer (GC) has been extensively studied. However, microbiota alterations in GC patients vary widely across studies, and reproducible diagnostic biomarkers for early GC are still lacking in multiple populations. Thus, this study aimed to characterize the gastrointestinal microbial communities involved in gastric carcinogenesis through a meta-analysis of multiple published and open datasets. Methods: We analyzed 16S rRNA sequencing data from 1,642 gastric biopsy samples and 394 stool samples across 11 independent studies. VSEARCH, QIIME and R packages such as vegan, phyloseq, cooccur, and random forest were used for data processing and analysis. PICRUSt software was employed to predict functions. Results: The α-diversity results indicated significant differences in the intratumoral microbiota of cancer patients compared to non-cancer patients, while no significant differences were observed in the fecal microbiota. Network analysis showed that the positive correlation with GC-enriched bacteria increased, and the positive correlation with GC-depleted bacteria decreased compared to healthy individuals. Functional analyses indicated that pathways related to carbohydrate metabolism were significantly enriched in GC, while biosynthesis of unsaturated fatty acids was diminished. Additionally, we investigated non-Helicobacter pylori (HP) commensals, which are crucial in both HP-negative and HP-positive GC. Random forest models, constructed using specific taxa associated with GC identified from the LEfSe analysis, revealed that the combination of Lactobacillus and Streptococcus included alone could effectively discriminate between GC patients and healthy individuals in fecal samples (area under the curve (AUC) = 0.7949). This finding was also validated in an independent cohort (AUC = 0.7712). Conclusions: This study examined the intratumoral and fecal microbiota of GC patients from a dual microecological perspective and identified Lactobacillus, Streptococcus, Roseburia, Faecalibacterium and Phascolarctobacterium as intratumoral and intestinal-specific co-differential bacteria. Furthermore, it confirmed the validity of the combination of Lactobacillus and Streptococcus as GC-specific microbial markers across multiple populations, which may aid in the early non-invasive diagnosis of GC.

Discovery of Tetrahydro Tanshinone I as a Naturally Occurring Covalent Pan-Inhibitor Against Gut Microbial Bile Salt Hydrolases.

Gut microbial bile salt hydrolases (gmBSHs), an important class of bacteria-produced cysteine hydrolases, play a crucial role in bile acid metabolism. Modulating the total gmBSH activity is a feasible way for ameliorating some metabolic diseases including colorectal cancer, type 2 diabetes, and obesity. This study reported the discovery and characterization of a botanical compound as a covalent pan-inhibitor of gmBSHs. Following the screening of more than 100 botanical compounds, tanshinones were found with strong time-dependent anti-EfBSH effects. After that, a total of 17 naturally occurring tanshinones were collected, and their anti-EfBSH potentials were tested. Among all tested tanshinones, tetrahydro tanshinone I (THTI) exhibited the most potent inhibitory effects against five gmBSHs (EfBSH, LsBSH, BtBSH, CpBSH, and BlBSH), showing the IC50 values ranging from 0.28 ± 0.05 µM to 1.62 ± 0.07 µM. Further investigations showed that THTI could covalently modify the conserved catalytic cysteine (Cys2) of all tested gmBSHs, while this agent could strongly inhibit the total gmBSHs activity in live microorganisms and murine gut luminal content. Collectively, THTI is identified as a naturally occurring covalent pan-inhibitor of gmBSHs, which offers a promising lead compound to develop more efficacious gmBSHs inhibitors for the management of bile acid metabolism and related metabolic disorders.

Nicotine promotes pathogenic bacterial growth and biofilm formation in peri-implant.

Introduction. Peri-implantitis is a plaque-associated disease that leads to implant loss and arises from bacterial biofilms on the surface of the implant. Smoking is a risk factor for peri-implantitis and impedes treatment effectiveness. Additionally, aryl hydrocarbon receptor (AHR), IL-6, and IL-22 levels are related to peri-implantitis.Aim. We aimed to investigate the effects of nicotine on inflammatory response, bacterial growth and biofilm formation.Hypothesis/Gap Statement. We hypothesized that nicotine promoted pathogenic bacterial growth and biofilm formation, thereby aggravating inflammation.Methodology. The expression of AHR, IL-6 and IL-22 was measured in peri-implant sulci fluid using quantitative PCR and Western blot analyses. The cementum was incubated with bacterial suspension including Porphyromonas gingivalis, Streptococcus sanguinis and Fusobacterium nucleatum and treated with 100, 200, 250 and 300 µg ml-1 nicotine, and then, the absorbance and number of colony-forming units were detected. Biofilm formation was evaluated using the tissue culture plate method and safranin O staining. Carbohydrates and proteins were measured by the phenol-sulfuric acid method and the bicinchoninic acid method, respectively.Results. The results indicated that smoking increased the levels of AHR, IL-6 and IL-22. Functionally, nicotine promoted the growth of P. gingivalis, S. sanguinis and F. nucleatum. Additionally, it promoted the biofilm formation of these bacteria and increased the contents of carbohydrates and proteins.Conclusion. Nicotine promoted bacterial growth and biofilm build-up, suggesting that smoking may aggravate the progression of peri-implantitis.

Mucosal microbiota characterization in gastric cancer identifies immune-activated-related transcripts relevant gastric microbiome signatures.

Tumor microenvironment (TME) immune cells and gastric mucosal microbiome constitute two vital elements of tumor tissue. Increasing evidence has elucidated their clinicopathological significance in predicting outcomes and therapeutic efficacy. However, comprehensive characterization of immune cell-associated microbiome signatures in the TME is still in the early stages of development. Here, we characterized the gastric mucosa microbiome and its associations with immune-activated related transcripts (IATs) in 170 GC tumor tissues and matched non-tumor tissues using 16s rRNA gene sequencing and quantitative reverse transcription-PCR. Microbial diversity and richness were significantly higher in GC tumor tissues than in non-tumor tissues. Differences in microbial composition between the groups were evident, with Firmicutes, Proteobacteria, Bacteroidota, Campilobacterota, Actinobacteria, Fusobacteriota, Verrucomicrobiota, Acidobacteriota, and Cyanobacteria being the dominant phyla in the gastric mucosal microbiota. Microbial interaction network analysis revealed distinctive centralities of oral bacteria (such as Fusobacterium, Porphyromonas, Prevotella, etc.) in both tumor and normal mucosae networks, suggesting their significant influence on GC microbial ecology. Furthermore, we analyzed the expression of IATs (CXCL9, CXCL10, GZMA, GZMB, PRF1, CD8A, IFNG, TBX2, and TNF) and characterized IAT-relevant gastric microbiome signatures in GC patients. Our results showed that the expression of CXCL9, CXCL10, GZMA, GZMB, PRF1 and IFNG was significantly higher in tumor tissues than in adjacent normal tissues in GC patients. Notably, high expression of IATs in tumor tissues was associated with improved survival in GC patients and could serve as a powerful predictor for disease-free survival. Additionally, analysis of IAT levels and mucosal microbiota diversity revealed a correlation between higher IAT expression and increased microbiota richness and evenness in the IATs high group, suggesting potential interactions between mucosal microbiota and tumor immunopathology. Spearman correlation analysis showed positive associations between IAT expression and specific mucosal bacterial species. Notably, Akkermansia muciniphila demonstrated potential involvement in modulating GZMB expression in the GC mucosal microenvironment. These findings underscore the importance of mucosal microbiota alterations in GC and suggest potential therapeutic targets focusing on modulating the tumor microbiota for improved clinical outcomes. The detailed characterization of these elements has profound implications for both treatment and survival prediction in GC. We observed that microbial diversity and richness were significantly higher in GC tumor tissues compared to non-tumor tissues. These differences highlight the unique microbial landscape of GC tumors and suggest that the microbiome could influence tumor development and progression. Importantly, our study demonstrated that high expression levels of IATs in GC tumor tissues were associated with improved patient survival. This suggests that IATs not only reflect immune activation but also serve as valuable biomarkers for predicting disease-free survival. The potential of IATs as predictive markers underscores their utility in guiding therapeutic strategies and personalizing treatment approaches. Moreover, the correlation between higher IAT expression and increased microbiota richness and evenness suggests that a diverse and balanced microbiome may enhance immune responses and contribute to better clinical outcomes. These findings highlight the critical need to consider mucosal microbiota alterations in GC management. Targeting the tumor microbiota could emerge as a promising therapeutic strategy, potentially leading to more effective treatments and improved patient outcomes. Understanding and modulating the microbiome's role in GC opens new avenues for innovative therapeutic interventions and personalized medicine.

Robust prediction of colorectal cancer via gut microbiome 16S rRNA sequencing data.

Introduction. The study addresses the challenge of utilizing human gut microbiome data for the early detection of colorectal cancer (CRC). The research emphasizes the potential of using machine learning techniques to analyze complex microbiome datasets, providing a non-invasive approach to identifying CRC-related microbial markers.Hypothesis/Gap Statement. The primary hypothesis is that a robust machine learning-based analysis of 16S rRNA microbiome data can identify specific microbial features that serve as effective biomarkers for CRC detection, overcoming the limitations of classical statistical models in high-dimensional settings.Aim. The primary objective of this study is to explore and validate the potential of the human microbiome, specifically in the colon, as a valuable source of biomarkers for colorectal cancer (CRC) detection and progression. The focus is on developing a classifier that effectively predicts the presence of CRC and normal samples based on the analysis of three previously published faecal 16S rRNA sequencing datasets.Methodology. To achieve the aim, various machine learning techniques are employed, including random forest (RF), recursive feature elimination (RFE) and a robust correlation-based technique known as the fuzzy forest (FF). The study utilizes these methods to analyse the three datasets, comparing their performance in predicting CRC and normal samples. The emphasis is on identifying the most relevant microbial features (taxa) associated with CRC development via partial dependence plots, i.e. a machine learning tool focused on explainability, visualizing how a feature influences the predicted outcome.Results. The analysis of the three faecal 16S rRNA sequencing datasets reveals the consistent and superior predictive performance of the FF compared to the RF and RFE. Notably, FF proves effective in addressing the correlation problem when assessing the importance of microbial taxa in explaining the development of CRC. The results highlight the potential of the human microbiome as a non-invasive means to detect CRC and underscore the significance of employing FF for improved predictive accuracy.Conclusion. In conclusion, this study underscores the limitations of classical statistical techniques in handling high-dimensional information such as human microbiome data. The research demonstrates the potential of the human microbiome, specifically in the colon, as a valuable source of biomarkers for CRC detection. Applying machine learning techniques, particularly the FF, is a promising approach for building a classifier to predict CRC and normal samples. The findings advocate for integrating FF to overcome the challenges associated with correlation when identifying crucial microbial features linked to CRC development.

Distinct microbial communities supported by iron oxidation.

Microbial biostalactites and streamers commonly grow at iron seepages in abandoned mines worldwide. This study addresses the diversity and composition of these simple prokaryotic communities, which thrive in pH ranges from 2.4 to 6.6 across six different mines. Our analysis of 85 communities reveals that a pH of approximately 3.2 is a critical threshold where alpha and beta diversity change discretely. Below this pH, the average number of ASVs per sample is 2.91 times lower than above this boundary. Autotrophs, heterotrophs, and symbionts of eukaryotes originate from nearly non-overlapping species pools in the two habitat types that differ only in pH. Communities below pH 3.2 further divide into two distinct groups, differing in diversity, taxonomic, and functional composition. Both types of communities coexist within the same stalactites, likely corresponding to zones where the capillary structure of the stalactite is either perfused or clogged. These findings indicate that microbial community structure can be significantly influenced by the intricate spatial organization of the ecosystem, rather than solely by measurable environmental parameters.

Microbiota-associated mechanisms in colorectal cancer.

Colorectal cancer (CRC) is one of the most common cancers worldwide, ranking third in terms of incidence and second as a cause of cancer-related death. There is growing scientific evidence that the gut microbiota plays a key role in the initiation and development of CRC. Specific bacterial species and complex microbial communities contribute directly to CRC pathogenesis by promoting the neoplastic transformation of intestinal epithelial cells or indirectly through their interaction with the host immune system. As a result, a protumoural and immunosuppressive environment is created conducive to CRC development. On the other hand, certain bacteria in the gut microbiota contribute to protection against CRC. In this chapter, we analysed the relationship of the gut microbiota to CRC and the associations identified with specific bacteria. Microbiota plays a key role in CRC through various mechanisms, such as increased intestinal permeability, inflammation and immune system dysregulation, biofilm formation, genotoxin production, virulence factors and oxidative stress. Exploring the interaction between gut microbiota and tumourigenesis is essential for developing innovative therapeutic approaches in the fight against CRC.

Microbiome and metabolome patterns after lung transplantation reflect underlying disease and chronic lung allograft dysfunction.

BACKGROUND: Progression of chronic lung disease may lead to the requirement for lung transplant (LTx). Despite improvements in short-term survival after LTx, chronic lung allograft dysfunction (CLAD) remains a critical challenge for long-term survival. This study investigates the molecular and microbial relationships between underlying lung disease and the development of CLAD in bronchoalveolar lavage fluid (BALF) from subjects post-LTx, which is crucial for tailoring treatment strategies specific to allograft dysfunctions. METHODS: Paired 16S rRNA gene amplicon sequencing and untargeted LC-MS/MS metabolomics were performed on 856 BALF samples collected over 10 years from LTx recipients (n = 195) with alpha-1-antitrypsin disease (AATD, n = 23), cystic fibrosis (CF, n = 47), chronic obstructive pulmonary disease (COPD, n = 78), or pulmonary fibrosis (PF, n = 47). Data were analyzed using random forest (RF) machine learning and multivariate statistics for associations with underlying disease and CLAD development. RESULTS: The BALF microbiome and metabolome after LTx differed significantly according to the underlying disease state (PERMANOVA, p = 0.001), with CF and AATD demonstrating distinct microbiome and metabolome profiles, respectively. Uniqueness in CF was mainly driven by Pseudomonas abundance and its metabolites, whereas AATD had elevated levels of phenylalanine and a lack of shared metabolites with the other underlying diseases. BALF microbiome and metabolome composition were also distinct between those who did or did not develop CLAD during the sample collection period (PERMANOVA, p = 0.001). An increase in the average abundance of Veillonella (AATD, COPD) and Streptococcus (CF, PF) was associated with CLAD development, and decreases in the abundance of phenylalanine-derivative alkaloids (CF, COPD) and glycerophosphorylcholines (CF, COPD, PF) were signatures of the CLAD metabolome. Although the relative abundance of Pseudomonas was not associated with CLAD, the abundance of its virulence metabolites, including siderophores, quorum-sensing quinolones, and phenazines, were elevated in those with CF who developed CLAD. There was a positive correlation between the abundance of these molecules and the abundance of Pseudomonas in the microbiome, but there was no correlation between their abundance and the time in which BALF samples were collected post-LTx. CONCLUSIONS: The BALF microbiome and metabolome after LTx are particularly distinct in those with underlying CF and AATD. These data reflect those who developed CLAD, with increased virulence metabolite production from Pseudomonas, an aspect of CF CLAD cases. These findings shed light on disease-specific microbial and metabolic signatures in LTx recipients, offering valuable insights into the underlying causes of allograft rejection. Video Abstract.

A search for bacteria identified from cerebrospinal fluid shunt infections in previous surgical events.

Shunt infections are a common complication when treating hydrocephalus by cerebrospinal fluid (CSF) shunt placement. The source of infecting pathogens is not well understood. One hypothesis, which we explored here, is that microorganisms persist chronically in the host long before a symptomatic infection occurs and may be detectable in surgical events preceding infection. A cohort of 13 patients was selected, for which CSF samples were available from an infection episode and from a previous surgery event, which was either an initial shunt placement or a revision. Microbiota were analyzed both directly from CSF and from isolates cultured from CSF on aerobic and anaerobic media. The detection and identification of bacteria was done with high throughput DNA sequencing methods and mass spectrometry. The presence of bacteria was confirmed in 4 infection samples, of which 2 were after initial placement and 2 after revision surgery. Taxonomic identification was consistent with clinical microbiology laboratory results. Bacteria were not detected in any of the CSF samples collected at the time of the previous surgical events. While our findings do not provide direct evidence for long-term persistence of pathogens, they suggest the need for consideration of additional source material, such as biofilm and environmental swabs, and/or the use of more sensitive and specific analytical methods.

Revealing the association between East Asian oral microbiome and colorectal cancer through Mendelian randomization and multi-omics analysis.

Background: Colorectal cancer (CRC) poses a global health threat, with the oral microbiome increasingly implicated in its pathogenesis. This study leverages Mendelian Randomization (MR) to explore causal links between oral microbiota and CRC using data from the China National GeneBank and Biobank Japan. By integrating multi-omics approaches, we aim to uncover mechanisms by which the microbiome influences cellular metabolism and cancer development. Methods: We analyzed microbiome profiles from 2017 tongue and 1915 saliva samples, and GWAS data for 6692 CRC cases and 27178 controls. Significant bacterial taxa were identified via MR analysis. Single-cell RNA sequencing and enrichment analyses elucidated underlying pathways, and drug predictions identified potential therapeutics. Results: MR identified 19 bacterial taxa significantly associated with CRC. Protective effects were observed in taxa like RUG343 and Streptococcus_umgs_2425, while HOT-345_umgs_976 and W5053_sp000467935_mgs_712 increased CRC risk. Single-cell RNA sequencing revealed key pathways, including JAK-STAT signaling and tyrosine metabolism. Drug prediction highlighted potential therapeutics like Menadione Sodium Bisulfite and Raloxifene. Conclusion: This study establishes the critical role of the oral microbiome in colorectal cancer development, identifying specific microbial taxa linked to CRC risk. Single-cell RNA sequencing and drug prediction analyses further elucidate key pathways and potential therapeutics, providing novel insights and personalized treatment strategies for CRC.

Colorectal cancer-associated bacteria are broadly distributed in global microbiomes and drivers of precancerous change.

The gut microbiome is implicated in the pathogenesis of colorectal cancer (CRC), but the full scope of this dialogue is unknown. Here we aimed to define the scale and membership of the body of CRC- and health-associated gut bacteria in global populations. We performed a microbiome-CRC correlation analysis of published ultra-deep shotgun metagenomic sequencing data from global microbiome surveys, utilizing a de novo (reference-agnostic) gene-level clustering approach to identify protein-coding co-abundant gene (CAGs) clusters. We link an unprecedented ~ 23-40% of gut bacteria to CRC or health, split nearly evenly as CRC- or health-associated. These microbes encode 2319 CAGs encompassing 427,261 bacterial genes significantly enriched or depleted in CRC. We identified many microbes that had not previously been linked to CRC, thus expanding the scope of "known unknowns" of CRC-associated microbes. We performed an agnostic CAG-based screen of bacterial isolates and validated predicted effects of previously unimplicated bacteria in preclinical models, in which we observed differential induction of precancerous adenomas and field effects. Single-cell RNA sequencing disclosed microbiome-induced senescence-associated gene expression signatures in discrete colonic populations including fibroblasts. In organoid co-cultures, primary colon fibroblasts from mice with microbiomes promoted significantly greater growth than fibroblasts from microbiome-depleted mice. These results offer proof-of-principle for gene-level metagenomic analysis enabling discovery of microbiome links to health and demonstrate that the microbiome can drive precancer states, thereby potentially revealing novel cancer prevention opportunities.

Differential gut microbiota composition in ß-Thalassemia patients and its correlation with iron overload.

Recent research highlights the significant impact of the gut microbiota on health and disease. Thalassemia, a hereditary blood disorder, requires regular blood transfusions, leading to an accumulation of iron in the body. Such changes could potentially alter the intestinal microbiota, thereby increasing the susceptibility of thalassemic patients to infection. In this study, we analyzed the fecal microbiota of 70 non-transfusion-dependent (NTDT) ß-thalassemia/HbE patients and 30 healthy controls. Our findings indicate that iron chelation intervention had no detectable effect on the microbiome profile of thalassemic patients. However, the cross-sectional analysis revealed that the bacterial diversity and community structure in patients were significantly less diverse and distinct compared to those of healthy subjects. Using reference frames, we were also able to demonstrate that bacterial taxa that are known to produce short chain fatty acids, from the genera Alistipes, Coprococcus, and Oscillospira, and those from the family Ruminococcaceae, were less prevalent in the patients. In contrast, bacterial taxa associated with an unhealthy gut, including the genus Clostridium and those from the families Fusobacteriaceae, Enterobacteriaceae, and Peptostrptococcaceae, were more prevalent in patients and found to be correlated with higher levels of ferritin. Collectively, these changes in the microbiota could be regarded as markers of raised ferritin levels, and therefore, awareness should be exercised as they could interfere, albeit indirectly, with the treatment of the co-morbidities of thalassemia.

Gut microbiota and interstitial cystitis: exploring the gut-bladder axis through mendelian randomization, biological annotation and bulk RNA sequencing.

Background: Several observational studies have indicated an association between interstitial cystitis and the composition of the gut microbiota; however, the causality and underlying mechanisms remain unclear. Understanding the link between gut microbiota and interstitial cystitis could inform strategies for prevention and treatment. Methods: A two-sample Mendelian randomization analysis was conducted using published genome-wide association study summary statistics. We employed inverse variance weighted, weighted mode, MR-Egger, weighted median, simple mode, and cML-MA methods to investigate the causal relationship between gut microbiota and interstitial cystitis. Sensitivity analysis was performed to validate the results. Relevant gut microbiota was examined through reverse MR. Single nucleotide polymorphisms were annotated using FUMA to identify genes associated with these genetic variants, thereby revealing potential host gene-microbiota associations in interstitial cystitis patients. Results: Eight bacterial taxa were identified in our analysis as associated with interstitial cystitis. Among these, Butyricimonas, Coprococcus, Lactobacillales, Lentisphaerae, and Bilophila wadsworthia were positively correlated with interstitial cystitis risk, while taxa such as Desulfovibrio piger, Oscillibacter unclassified and Ruminococcus lactaris exhibited protective effects against interstitial cystitis. The robustness of these associations was confirmed through sensitivity analyses. Reverse MR analysis did not reveal evidence of reverse causality. Single nucleotide polymorphisms were annotated using FUMA and subjected to biological analysis. Seven hub genes (SPTBN1, PSME4, CHAC2, ERLEC1, ASB3, STAT5A, and STAT3) were identified as differentially expressed between interstitial cystitis patients and healthy individuals, representing potential therapeutic targets. Conclusion: Our two-sample Mendelian randomization study established a causal relationship between gut microbiota and interstitial cystitis. Furthermore, our identification of a host gene-microbiota association offers a new avenue for investigating the potential pathogenesis of interstitial cystitis and suggests avenues for the development of personalized treatment strategies.