Characterization of the Small Bowel Microbiome Reveals Different Profiles in Human Subjects Who Are Overweight or Have Obesity.

INTRODUCTION: Gut microbiome changes are linked to obesity, but findings are based on stool data. In this article, we analyzed the duodenal microbiome and serum biomarkers in subjects with normal weight, overweight, and obesity. METHODS: Duodenal aspirates and serum samples were obtained from subjects undergoing standard-of-care esophagogastroduodenoscopy without colon preparation. Aspirate DNAs were analyzed by 16S rRNA and shotgun sequencing. Predicted microbial metabolic functions and serum levels of metabolic and inflammatory biomarkers were also assessed. RESULTS: Subjects with normal weight (N = 105), overweight (N = 67), and obesity (N = 42) were identified. Overweight-specific duodenal microbial features include lower relative abundance (RA) of Bifidobacterium species and Escherichia coli strain K-12 and higher Lactobacillus intestinalis , L. johnsonii , and Prevotella loescheii RA. Obesity-specific features include higher Lactobacillus gasseri RA and lower L. reuteri (subspecies rodentium ), Alloprevotella rava , and Leptotrichia spp RA. Escalation features (progressive changes from normal weight through obesity) include decreasing Bacteroides pyogenes , Staphylococcus hominis , and unknown Faecalibacterium species RA, increasing RA of unknown Lactobacillus and Mycobacterium species, and decreasing microbial potential for biogenic amines metabolism. De-escalation features (direction of change altered in normal to overweight and overweight to obesity) include Lactobacillus acidophilus , L. hominis , L. iners , and Bifidobacterium dentium . An unknown Lactobacillus species is associated with type IIa dyslipidemia and overweight, whereas Alloprevotella rava is associated with type IIb and IV dyslipidemias. DISCUSSION: Direct analysis of the duodenal microbiome has identified key genera associated with overweight and obesity, including some previously identified in stool, e.g., Bifidobacterium and Lactobacillus . Specific species and strains exhibit differing associations with overweight and obesity, including escalation and de-escalation features that may represent targets for future study and therapeutics.

Hydrogen Sulfide Producers Drive a Diarrhea-Like Phenotype and a Methane Producer Drives a Constipation-Like Phenotype in Animal Models.

BACKGROUND: We recently demonstrated that diarrhea-predominant irritable bowel syndrome (IBS-D) subjects have higher relative abundance (RA) of hydrogen sulfide (H2S)-producing Fusobacterium and Desulfovibrio species, and constipation-predominant IBS (IBS-C) subjects have higher RA of methanogen Methanobrevibacter smithii. AIMS: In this study, we investigate the effects of increased methanogens or H2S producers on stool phenotypes in rat models. METHODS: Adult Sprague-Dawley rats were fed high-fat diet (HFD) for 60 days to increase M. smithii levels, then gavaged for 10 days with water (controls) or methanogenesis inhibitors. To increase H2S producers, rats were gavaged with F. varium or D. piger. Stool consistency (stool wet weight (SWW)) and gas production were measured. 16S rRNA gene sequencing was performed on stool samples. RESULTS: In HFD diet-fed rats (N = 30), stool M. smithii levels were increased (P < 0.001) after 52 days, correlating with significantly decreased SWW (P < 0.0001) at 59 days (R = - 0.38, P = 0.037). Small bowel M. smithii levels decreased significantly in lovastatin lactone-treated rats (P < 0.0006), and SWW increased (normalized) in lovastatin hydroxyacid-treated rats (P = 0.0246), vs. controls (N = 10/group). SWW increased significantly in D. piger-gavaged rats (N = 16) on day 10 (P < 0.0001), and in F. varium-gavaged rats (N = 16) at all timepoints, vs. controls, with increased stool H2S production. 16S sequencing revealed stool microbiota alterations in rats gavaged with H2S producers, with higher relative abundance (RA) of other H2S producers, particularly Lachnospiraceae and Bilophila in F. varium-gavaged rats, and Sutterella in D. piger-gavaged rats. CONCLUSIONS: These findings suggest that increased M. smithii levels result in a constipation-like phenotype in a rat model that is partly reversible with methanogenesis inhibitors, whereas gavage with H2S producers D. piger or F. varium results in increased colonization with other H2S producers and diarrhea-like phenotypes. This supports roles for the increased RA of methanogens and H2S producers identified in IBS-C and IBS-D subjects, respectively, in contributing to stool phenotypes.

Methanogens and Hydrogen Sulfide Producing Bacteria Guide Distinct Gut Microbe Profiles and Irritable Bowel Syndrome Subtypes.

INTRODUCTION: Irritable bowel syndrome (IBS) includes diarrhea-predominant (IBS-D) and constipation-predominant (IBS-C) subtypes. We combined breath testing and stool microbiome sequencing to identify potential microbial drivers of IBS subtypes. METHODS: IBS-C and IBS-D subjects from 2 randomized controlled trials (NCT03763175 and NCT04557215) were included. Baseline breath carbon dioxide, hydrogen (H 2 ), methane (CH 4 ), and hydrogen sulfide (H 2 S) levels were measured by gas chromatography, and baseline stool microbiome composition was analyzed by 16S rRNA sequencing. Microbial metabolic pathways were analyzed using Kyoto Encyclopedia of Genes and Genomes collection databases. RESULTS: IBS-C subjects had higher breath CH 4 that correlated with higher gut microbial diversity and higher relative abundance (RA) of stool methanogens, predominantly Methanobrevibacter , as well as higher absolute abundance of Methanobrevibacter smithii in stool. IBS-D subjects had higher breath H 2 that correlated with lower microbial diversity and higher breath H 2 S that correlated with higher RA of H 2 S-producing bacteria, including Fusobacterium and Desulfovibrio spp. The predominant H 2 producers were different in these distinct microtypes, with higher RA of Ruminococcaceae and Christensenellaceae in IBS-C/CH 4 + (which correlated with Methanobacteriaceae RA) and higher Enterobacteriaceae RA in IBS-D. Finally, microbial metabolic pathway analysis revealed enrichment of Kyoto Encyclopedia of Genes and Genomes modules associated with methanogenesis and biosynthesis of methanogenesis cofactor F420 in IBS-C/CH 4 + subjects, whereas modules associated with H 2 S production, including sulfate reduction pathways, were enriched in IBS-D. DISCUSSION: Our findings identify distinct gut microtypes linked to breath gas patterns in IBS-C and IBS-D subjects, driven by methanogens such as M. smithii and H 2 S producers such as Fusobacterium and Desulfovibrio spp, respectively.

Spatio-temporal biodistribution of 89Zr-oxine labeled huLym-1-A-BB3z-CAR T-cells by PET imaging in a preclinical tumor model.

Quantitative in vivo monitoring of cell biodistribution offers assessment of treatment efficacy in real-time and can provide guidance for further optimization of chimeric antigen receptor (CAR) modified cell therapy. We evaluated the utility of a non-invasive, serial 89Zr-oxine PET imaging to assess optimal dosing for huLym-1-A-BB3z-CAR T-cell directed to Lym-1-positive Raji lymphoma xenograft in NOD Scid-IL2Rgammanull (NSG) mice. In vitro experiments showed no detrimental effects in cell health and function following 89Zr-oxine labeling. In vivo experiments employed simultaneous PET/MRI of Raji-bearing NSG mice on day 0 (3 h), 1, 2, and 5 after intravenous administration of low (1.87 ± 0.04 × 106 cells), middle (7.14 ± 0.45 × 106 cells), or high (16.83 ± 0.41 × 106 cells) cell dose. Biodistribution (%ID/g) in regions of interests defined over T1-weighted MRI, such as blood, bone, brain, liver, lungs, spleen, and tumor, were analyzed from PET images. Escalating doses of CAR T-cells resulted in dose-dependent %ID/g biodistributions in all regions. Middle and High dose groups showed significantly higher tumor %ID/g compared to Low dose group on day 2. Tumor-to-blood ratios showed the enhanced extravascular tumor uptake by day 2 in the Low dose group, while the Middle dose showed significant tumor accumulation starting on day 1 up to day 5. From these data obtained over time, it is apparent that intravenously administered CAR T-cells become trapped in the lung for 3-5 h and then migrate to the liver and spleen for up to 2-3 days. This surprising biodistribution data may be responsible for the inactivation of these cells before targeting solid tumors. Ex vivo biodistributions confirmed in vivo PET-derived biodistributions. According to these studies, we conclude that in vivo serial PET imaging with 89Zr-oxine labeled CAR T-cells provides real-time monitoring of biodistributions crucial for interpreting efficacy and guiding treatment in patient care.