Comparison of Three Sympatric Desert Lizards: Digestive Tract Structure, Digestive Enzyme Activities, Gut Microbiota, and Metabolites.

A comparative study was carried out on the effect of three drugs on the development of goblet cells in the duodenum of broilers. In the first experiment, day-old broilers Smena-8 were divided into 4 groups. The experimental groups received the phytobiotic Butitan with the main diet: E1-0.25%; E2-0.5% and E3-0.75%. In the second experiment, 3 groups were formed from broilers of the Konkurent cross; additives were added for the first 3 days. The Vetom group received a probiotic based on Bacillus subtilis (0.006%); Enterosgel group – Polymethylsiloxane polyhydrate sorbent (0.008%). Histological studies were carried out in the first experiment at 1, 7 and 42 days, in the second at 1, 4, 49 days. Sections were stained to identify acidic and neutral mucus. The number of goblet cells in the field of view was counted and their density over an area of 1000 μm2 was calculated. By the end of the first experiment, the density of neutral goblet cells in the villi E1 and E2 and crypts E2 and E3 increases by 17% and 7.5%, acidic goblet cells increase in the crypts by 32.6% and 33.1%. The probiotic increased the density of acidic goblet cells in the crypts (64%; P≤0.001) without affecting the villi. Enetrosorbent reduces the density of neutral and increases the density of acidic goblet cells in the mucosa (37.3%). Due to the absence of a pathological picture in the structure of the duodenum, as well as the important role of mucus in the formation of chyme and digestion, we consider the increase in the density of goblet cells and the effect of feed additives as a positive reaction. The study of histological parameters to assess the effect of various feed additives on the body parameters of birds is important, since it shows the functional state of the organs.

In order to evaluate the interplay of nutritional status and the digestive capacity, immune function, and gut microbiota in the oriental river prawn Macrobrachium nipponense, a 14-day starvation trial was conducted to detect the effects of starvation on the activities of digestive enzymes and immune enzymes and the structure of prawn gut microbiota. The adult prawns were randomly assigned to a control group (F group) and starvation group (S group) with three replicates. The F group was normally fed with commercial diet, and the S group was starved for 14 days. The result showed that digestive enzyme activities of trypsin and lipase in the hepatopancreas of the S group were significantly lower than those of the F group after 4 d of starvation ( P < 0.05 ), while the activity of amylase significantly increased after 14 d of starvation ( P < 0.05 ). Moreover, the results of the histological analysis of prawn gut showed that both the height of epithelial cells and microvilli of intestine in the S group were obviously decreased than those in the F group ( P < 0.05 ) after 14 d of starvation. The activities of immune-related enzymes including superoxide dismutase, catalase, and acid phosphatase in the hepatopancreas of the S group significantly decreased after 4 d of starvation, and lysozyme activity of the F group was lower than that of the S group after 7 d of starvation ( P < 0.05 ). In addition, the results of Illumina high-throughput sequencing showed that a total of 14,285 OTUs were obtained and classified into 30 phyla, among which Actinobacteria and Proteobacteria were the predominant microflora in the intestinal microbial communities of both groups. However, the relative abundance of opportunistic pathogens was significantly increased in the S group, while the relative abundance of beneficial bacterium was decreased ( P < 0.05 ). The bacterial richness with the Chao estimator was significantly higher in the F group than in the S group ( P < 0.05 ). Both the results of principal coordinate analysis (PCoA) and nonmetric multidimensional scaling (NMDS) demonstrated that the intestinal microbiota of the F group was separate from those of the S group. The result of functional prediction of the metabolic pathways showed that the intestinal microbiota was enriched in the KEGG pathways of amino acid, carbohydrate, fatty acid, and lipid biosynthesis and degradation at level 2. This result implied that the microbiota of shrimp gut decreased nutrition metabolism under the stress of starvation. Meanwhile, comparing to the F group, the immune-related pathway of enterobacterial common antigen biosynthesis was markedly reduced in the S group ( P < 0.05 ). The result of redundancy analysis (RDA) further confirmed that the activities of digestive and immune enzymes were correlated with the microbial community structure. Finally, the structural equation modeling highlighted that changes in the activities of digestive and immune enzymes were directly related to the gut bacterial community and notably affected prawn growth.

There is a regional variation in conjunctival goblet cell distribution and density per unit of measurement. By secreting mucin goblet cells are involved in formation of tear film. Tear film instability is the basic sign of dry eye syndrome. Although dry eye syndrome can be a side effect of a disease, the most common cause is normal aging. The objective of the research was to determine goblet cell density in bulbar and lower forniceal conjunctiva and possible changes in goblet cell density related to age and sex. The research was conducted on 30 conjunctival samples of patients with no clinical changes of the conjunctiva. Tissue sections were stained with hematoxylin eosin (HE), periodic acid-schiff (PAS), alcian blue (AB)/PAS, Giemsa methods. Goblet cell density was 1.24 +/- 1.62 in bulbar conjunctiva and 30.21 +/- 14.32 in lower forniceal conjunctiva. Goblet cell distribution in the conjunctiva was unequal. A correlation between goblet cell density and age was not determined. Goblet cell density doesn't decrease with aging. No significant difference in goblet cell density between men and women was established.

ObjectiveTo investigate the relationship between gut microbiota and the fecal metabolites of hypoxic environments in mice.MethodsHigh-fat diet-induced obese mice (n = 20) and normal diet-fed mice (n = 20) were randomly divided into four groups: high altitude obese group (HOB), high altitude normal weight group (HN), low altitude obese group LOB (LOB), and low altitude normal weight group (LN). Fecal samples from each group were 16S rRNA gene sequenced, and five samples from each of the four groups above were selected for non-targeted fecal metabolomics analysis using liquid chromatography-mass spectrometry. The relationship between gut microbiota and fecal metabolites was analyzed using SIMCA 14.1, MetaboAnalyst 5.0 and R 4.1.11.Results(A) Body weight was significantly lower in the hypoxic obesity group than in the normoxic obesity group. (B) Differences in α-diversity and β-diversity were found in the fecal gut microbiota of mice of different body weights and altitude, and the diversity of gut microbiota was higher in the normal group than in the obese group; the results of the comparison between the two groups showed that Faecalibaculum, Romboutsia, Lactobacillus, and A2 were associated with obesity; Romboutsia was associated with hypoxia. (C) The metabolic profiles of fecal metabolites differed between groups: gut microbiota were associated with nucleotide and amino acid metabolism in the same body groups, while gut microbiota were associated with lipid and amino acid metabolism in the same oxygen concentration groups.Conclusion(a) Gut microbiota diversity was reduced in obese groups. Romboutsia was the dominant microbiota in the hypoxia group. (b) Gut microbiota were associated with nucleotide and amino acid metabolism in the same body weight groups, while they were associated with lipid and amino acid metabolism in the same altitude groups.

Dysbiosis of the gut microbiota's composition and function is important in developing insulin resistance and diabetes. Diabetes has also been linked to changes in the circulating and fecal metabolites. Evidence suggests the associations between the gut microbiota and the aberrant diabetes-related metabolome. Metabolites play a crucial role in the host-microbiota interactions. Researchers have used a combination of metagenomic and metabolomic approaches to investigate the relationships between gut microbial dysbiosis and metabolic abnormalities in diabetes. We summarized current discoveries on the associations between the gut microbiota and metabolites in type 1 diabetes, type 2 diabetes, and gestational diabetes mellitus in the scoping review. According to research, the gut microbiota changes might involve in the development of diabetes through modulating the host's metabolic pathways such as immunity, energy metabolism, lipid metabolism, and amino acid metabolism. These results add to our understanding of the interplay between the host and gut microbiota metabolism.

BackgroundGrowing evidence suggests the gut microbiota and metabolites in serum or fecal may play a key role in the process of alcohol use disorder (AUD). However, the correlations of gut microbiota and metabolites in both feces and serum in AUD subjects are not well understood.MethodsWe established a rat model of AUD by a chronic intermittent ethanol voluntary drinking procedure, then the AUD syndromes, the gut microbiota, metabolomic profiling in feces and serum of the rats were examined, and correlations between gut microbiota and metabolites were analyzed.ResultsEthanol intake preference increased and maintained at a high level in experimental rats. Anxiety-like behaviors was observed by open field test and elevated plus maze test after ethanol withdraw, indicating that the AUD rat model was successfully developed. The full length 16S rRNA gene sequencing showed AUD significantly changed the β-diversity of gut microbial communities, and significantly decreased the microbial diversity but did not distinctly impact the microbial richness. Microbiota composition significantly changed in AUD rats, such as the abundance of Romboutsia and Turicibacter were significantly increased, whereas uncultured_bacterium_o_Mollicutes_RF39 was decreased. In addition, the untargeted metabolome analysis revealed that many metabolites in both feces and serum were altered in the AUD rats, especially involved in sphingolipid metabolism and glycerophospholipid metabolism pathways. Finally, multiple correlations among AUD behavior, gut microbiota and co-changed metabolites were identified, and the metabolites were directly correlated with the gut microbiota and alcohol preference.ConclusionThe altered metabolites in feces and serum are important links between the gut microbiota dysbiosis and alcohol preference in AUD rats, and the altered gut microbiota and metabolites can be potentially new targets for treating AUD.

BackgroundThe etiology of allergic rhinitis (AR) is complicated. Traditional therapy of AR still has challenges, such as low long-term treatment compliance, unsatisfactory therapeutic outcomes, and a high financial burden. It is urgent to investigate the pathophysiology of allergic rhinitis from different perspectives and explore brand-new possible preventative or treatment initiatives.ObjectiveThe aim is to apply a multi-group technique and correlation analysis to explore more about the pathogenesis of AR from the perspectives of gut microbiota, fecal metabolites, and serum metabolism.MethodsThirty BALB/c mice were randomly divided into the AR and Con(control) groups. A standardized Ovalbumin (OVA)-induced AR mouse model was established by intraperitoneal OVA injection followed by nasal excitation. We detected the serum IL-4, IL-5, and IgE by enzyme-linked immunosorbent assay (ELISA), evaluated the histological characteristics of the nasal tissues by the hematoxylin and eosin (H&E) staining, and observed the nasal symptoms (rubs and sneezes) to evaluate the reliability of the AR mouse model. The colonic NF-κB protein was detected by Western Blot, and the colonic histological characteristics were observed by the H&E staining to evaluate inflammation of colon tissue. We analyzed the V3 and V4 regions of the 16S ribosomal DNA (rDNA) gene from the feces (colon contents) through 16S rDNA sequencing technology. Untargeted metabolomics was used to examine fecal and serum samples to find differential metabolites. Finally, through comparison and correlation analysis of differential gut microbiota, fecal metabolites, and serum metabolites, we further explore the overall impact of AR on gut microbiota, fecal metabolites, and host serum metabolism and its correlation.ResultsIn the AR group, the IL-4, IL-5, IgE, eosinophil infiltration, and the times of rubs and sneezes were significantly higher than those in the Con group, indicating the successful establishment of the AR model. No differences in diversity were detected between the AR and Con groups. However, there were modifications in the microbiota’s structure. At the phylum level, the proportion of Firmicutes and Proteobacteria in the AR group increased significantly, while the proportion of Bacteroides decreased significantly, and the ratio of Firmicutes/Bacteroides was higher. The key differential genera, such as Ruminococcus, were increased significantly in the AR group, while the other key differential genera, such as Lactobacillus, Bacteroides, and Prevotella, were significantly decreased in the Con group. Untargeted metabolomics analysis identified 28 upregulated and 4 downregulated differential metabolites in feces and 11 upregulated and 16 downregulated differential metabolites in serum under AR conditions. Interestingly, one of the significant difference metabolites, α-Linoleic acid (ALA), decreased consistently in feces and serum of AR. KEGG functional enrichment analysis and correlation analysis showed a close relationship between differential serum metabolites and fecal metabolites, and changes in fecal and serum metabolic patterns are associated with altered gut microbiota in AR. The NF-κB protein and inflammatory infiltration of the colon increased considerably in the AR group.ConclusionOur study reveals that AR alters fecal and serum metabolomic signatures and gut microbiota characteristics, and there is a striking correlation between the three. The correlation analysis of the microbiome and metabolome provides a deeper understanding of AR’s pathogenesis, which may provide a theoretical basis for AR’s potential prevention and treatment strategies.

The gut microbiota is the largest and most complex microecosystem in animals. It is influenced by the host’s dietary habits and living environment, and its composition and diversity play irreplaceable roles in animal nutrient metabolism, immunity, and adaptation to the environment. Although the gut microbiota of red deer has been studied, the composition and function of the gut microbiota in Gansu red deer (Cervus elaphus kansuensis), an endemic subspecies of red deer in China, has not been reported. In this study, the composition and diversity of the gut microbiome and fecal metabolomics of C. elaphus kansuensis were identified and compared for the first time by using 16S rDNA sequencing, metagenomic sequencing, and LC-MS/MS. There were significant differences in gut microbiota structure and diversity between wild and farmed C. elaphus kansuensis. The 16S rDNA sequencing results showed that the genus UCRD-005 was dominant in both captive red deer (CRD) and wild red deer (WRD). Metagenomic sequencing showed similar results to those of 16S rDNA sequencing for gut microbiota in CRD and WRD at the phylum and genus levels. 16S rDNA and metagenomics sequencing data suggested that Bacteroides and Bacillus might serve as marker genera for CRD and WRD, respectively. Fecal metabolomics results showed that 520 metabolites with significant differences were detected between CRD and WRD and most differential metabolites were involved in lipid metabolism. The results suggested that large differences in gut microbiota composition and fecal metabolites between CRD and WRD, indicating that different dietary habits and living environments over time have led to the development of stable gut microbiome characteristics for CRD and WRD to meet their respective survival and reproduction needs.Key points• Environment and food affected the gut microbiota and fecal metabolites in red deer• Genera Bacteroides and Bacillus may play important roles in CRD and WRD, respectively• Flavonoids and ascorbic acid in fecal metabolites may influence health of red deer

Over the past decade several studies have reported that the gut microbiomes of mammals with similar dietary niches exhibit similar compositional and functional traits. However, these studies rely heavily on samples from captive individuals and often confound host phylogeny, gut morphology, and diet. To more explicitly test the influence of host dietary niche on the mammalian gut microbiome we use 16S rRNA gene amplicon sequencing and shotgun metagenomics to compare the gut microbiota of 18 species of wild non-human primates classified as either folivores or closely related non-folivores, evenly distributed throughout the primate order and representing a range of gut morphological specializations. While folivory results in some convergent microbial traits, collectively we show that the influence of host phylogeny on both gut microbial composition and function is much stronger than that of host dietary niche. This pattern does not result from differences in host geographic location or actual dietary intake at the time of sampling, but instead appears to result from differences in host physiology. These findings indicate that mammalian gut microbiome plasticity in response to dietary shifts over both the lifespan of an individual host and the evolutionary history of a given host species is constrained by host physiological evolution. Therefore, the gut microbiome cannot be considered separately from host physiology when describing host nutritional strategies and the emergence of host dietary niches.

The gut microbiota is critical for host function. Among mammals, host phylogenetic relatedness and diet are strong drivers of gut microbiota structure, but one factor may be more influential than the other. Here, we used 16S rRNA gene sequencing to determine the relative contributions of host phylogeny and host diet in structuring the gut microbiotas of 11 herbivore species from 5 families living sympatrically in southwest Kenya. Herbivore species were classified as grazers, browsers, or mixed-feeders and dietary data (% C4 grasses in diet) were compiled from previously published sources. We found that herbivore gut microbiotas were highly species-specific, and that host taxonomy accounted for more variation in the gut microbiota (30%) than did host dietary guild (10%) or sample month (8%). Overall, similarity in the gut microbiota increased with host phylogenetic relatedness (r = 0.74) across the 11 species of herbivores, but among 7 closely related Bovid species, dietary %C4 grass values more strongly predicted gut microbiota structure (r = 0.64). Additionally, within bovids, host dietary guild explained more of the variation in the gut microbiota (17%) than did host species (12%). Lastly, while we found that the gut microbiotas of herbivores residing in southwest Kenya converge with those of distinct populations of conspecifics from central Kenya, fine-scale differences in the abundances of bacterial amplicon sequence variants (ASVs) between individuals from the two regions were also observed. Overall, our findings suggest that host phylogeny and taxonomy strongly structure the gut microbiota across broad host taxonomic scales, but these gut microbiotas can be further modified by host ecology (i.e., diet, geography), especially among closely related host species.

Acute high-altitude hypoxia can lead to intestinal damage and changes in gut microbiota. Sustained and reliable oxygen enrichment can resist hypoxic damage at high altitude to a certain extent. However, it remains unclear whether oxygen enrichment can protect against gut damage and changes in intestinal flora caused by acute altitude hypoxia. For this study, eighteen male Sprague–Dawley rats were divided into three groups, control (NN), hypobaric hypoxic (HH), and oxygen-enriched (HO). The NN group was raised under normobaric normoxia, whereas the HH group was placed in a hypobaric hypoxic chamber simulating 7,000 m for 3 days. The HO group was exposed to oxygen-enriched air in the same hypobaric hypoxic chamber as the HH group for 12 h daily. Our findings indicate that an acute HH environment caused a fracture of the crypt structure, loss of epithelial cells, and reduction in goblet cells. Additionally, the structure and diversity of bacteria decreased in richness and evenness. The species composition at Phylum and Genus level was characterized by a higher ratio of Firmicutes and Bacteroides and an increased abundance of Lactobacillus with the abundance of Prevotellaceae_NK3B31_group decreased in the HH group. Interestingly, after oxygen enrichment intervention, the intestinal injury was significantly restrained. This was confirmed by an increase in the crypt depth, intact epithelial cell morphology, increased relative density of goblet cells, and higher evenness and richness of the gut microbiota, Bacteroidetes and Prevotellaceae as the main microbiota in the HO group. Finally, functional analysis showed significant differences between the different groups with respect to different metabolic pathways, including Amino acid metabolism, energy metabolism, and metabolism. In conclusion, this study verifies, for the first time, the positive effects of oxygen enrichment on gut structure and microbiota in animals experiencing acute hypobaric hypoxia.

Mammals rely on the metabolic functions of their gut microbiota to meet their energetic needs and digest potentially toxic components in their diet. The gut microbiome plastically responds to shifts in host diet and may buffer variation in energy and nutrient availability. However, it is unclear how seasonal differences in the gut microbiome influence microbial metabolism and nutrients available to hosts. In this study, we examine seasonal variation in the gut metabolome of black howler monkeys (Alouatta pigra) to determine whether those variations are associated with differences in gut microbiome composition and nutrient intake, and if plasticity in the gut microbiome buffers shortfalls in energy or nutrient intake. We integrated data on the metabolome of 81 faecal samples from 16 individuals collected across three distinct seasons with gut microbiome, nutrient intake and plant metabolite consumption data from the same period. Faecal metabolite profiles differed significantly between seasons and were strongly associated with changes in plant metabolite consumption. However, microbial community composition and faecal metabolite composition were not strongly associated. Additionally, the connectivity and stability of faecal metabolome networks varied seasonally, with network connectivity being highest during the dry, fruit‐dominated season when black howler monkey diets were calorically and nutritionally constrained. Network stability was highest during the dry, leaf‐dominated season when most nutrients were being consumed at intermediate rates. Our results suggest that the gut microbiome buffers seasonal variation in dietary intake, and that the buffering effect is most limited when host diet becomes calorically or nutritionally restricted.

Assess the relationships of gut microbiota (GMB)-related metabolites in feces and blood with GMB and type 2 diabetes (T2D) in the context of HIV infection, the presence of which could disrupt host metabolism. We conducted a cross-sectional study among 111 women with HIV (WWH) and 56 women without HIV (WWOH) in the MACS/WIHS Combined Cohort Study. We measured 62 targeted metabolites in both feces and plasma and examined their associations with GMB composition (243 species) and prevalent T2D. We observed 44 metabolites with detection rates ≥25% in both feces and plasma. Correlations between fecal and plasma metabolites were stronger in WWOH than in WWH (median r : 0.13 vs. 0.04). Fecal metabolites showed stronger correlations with GMB than plasma metabolites among all participants (median r [IQR] of measured vs. GMB-predicted metabolites: 0.24 [0.11, 0.33] vs. 0.08 [-0.03, 0.24]; P = 0.002), and the difference in this comparison was more pronounced in WWOH compared to WWH. We found a moderate consistency for the associations of fecal and plasma metabolites with T2D in WWH ( r for effect sizes of fecal and plasma metabolites on T2D = 0.36; P = 0.03), but not in WWOH ( r = 0.13; P = 0.45). Fecal and plasma kynurenate, a tryptophan catabolism metabolite, showed opposite associations with T2D, with a positive association for plasma (odds ratio (OR): 2.54, 95% confidence interval (CI): [1.28-5.76]; P = 0.01) and an inverse association for feces (0.59 [0.27-1.23]; P = 0.18) in WWH. Fecal metabolites are more strongly associated with GMB than plasma metabolites, especially among WWOH. HIV infection might also influence associations of fecal and plasma metabolites with T2D.

Untangling the 2-Way Relationship Between Red Wine Polyphenols and Gut Microbiota

To assess goblet cell size and numbers in relation to the extent of multilayering of conjunctival impression cytology (CIC) samples as a basis for reducing variability in image selection for goblet cell density (GCD) estimates. CIC was undertaken immediately postmortem off the superior bulbar conjunctiva of healthy young adult rabbits onto Millicell-CM Biopore filter units. After fixation with buffered glutaraldehyde and Giemsa staining, two × 200 images were selected from each sample representative of either slight multilayering or substantial multilayering, projected at × 1000, an overlay of the outlines of the goblet cells was made, and their dimensions and areas were measured. From measures of 4918 goblet cells, the average value (+/- SD) for the longest dimension was 17.7 ± 6.4μm and 14.6 ± 5.3μm for the shortest dimension. The GCD values ranged from 210 to 2069/mm2, with a mean of 1074 ± 601/mm2, but was lower for slightly multilayered images (at 537 ± 239 cells/mm) compared with multilayered regions (at 1612 ± 601 cells/mm2; p < 0.001). The measured areas ranged from 72 to 491μm2, with average values from any particular image ranging from 110 to 370μm2, which were inversely correlated with the estimated GCD (Spearman's rho = - 0.722, p < 0.05). Larger goblet cells but in fewer numbers were predictably found across the filter surface where there were fewer layers of cells and vice versa. This difference could be considered in selection of images for counts of goblet cells from CIC specimens.