The avian gut microbiota plays an important role in shaping the health of its host. However, knowledge of gut bacteria in birds lags behind that of other animals. In this study, we investigated the gut bacterial communities of lesser white‐fronted geese (Anser erythropus) wintering at Shengjin Lake and Caizi Lake, China, using high‐throughput sequencing (Illumina MiSeq). Altogether, 1,053,624 high‐quality sequences and 4,405 operational taxonomic units (OTUs) were acquired from 30 fecal samples (15 per lake). The OTUs represented eight phyla and 17 classes from the Caizi Lake samples and seven phyla and 16 classes from the Shengjin Lake samples. Firmicutes, Proteobacteria, Actinobacteria, and Bacteroidetes were the dominant phyla. The spatial distance and the Chao1, Simpson, and Shannon indices showed that the alpha diversity differed significantly between the samples from both lakes. The phylogenetic tree and heatmap analyses showed that all the Caizi Lake samples were clustered together and all the Shengjin Lake samples were clustered together. These findings suggest that diet may be an important driver of gut microbial community structure in the birds from each lake, and the obvious differentiation in their gut microbial structures may indicate that the bacteria are highly sensitive to food sources at both lakes.

Microbiome and their metabolites are increasingly being recognized for their role in colorectal cancer (CRC) carcinogenesis. Towards revealing new CRC biomarkers, we compared 16S rRNA gene sequencing and liquid chromatography-mass spectrometry (LC–MS) metabolite analyses in 10 CRC (T_CRC) and normal paired tissues (T_HC) along with 10 matched fecal samples (F_CRC) and 10 healthy controls (F_HC). The highest microbial phyla abundance from T_HC and T_CRC were Firmicutes, while the dominant phyla from F_HC and F_CRC were Bacteroidetes, with 72 different microbial genera identified among four groups. No changes in Chao1 indices were detected between tissues or between fecal samples whereas non-metric multidimensional scaling (NMDS) analysis showed distinctive clusters among fecal samples but not tissues. LEfSe analyses indicated Caulobacterales and Brevundimonas were higher in T_HC than in T_CRC, while Burkholderialese, Sutterellaceaed, Tannerellaceaea, and Bacteroidaceae were higher in F_HC than in F_CRC. Microbial association networks indicated some genera had substantially different correlations. Tissue and fecal analyses indicated lipids and lipid-like molecules were the most abundant metabolites detected in fecal samples. Moreover, partial least squares discriminant analysis (PLS-DA) based on metabolic profiles showed distinct clusters for CRC and normal samples with a total of 102 differential metabolites between T_HC and T_CRC groups and 700 metabolites different between F_HC and F_CRC groups. However, only Myristic acid was detected amongst all four groups. Highly significant positive correlations were recorded between genus-level microbiome and metabolomics data in tissue and feces. And several metabolites were associated with paired microbes, suggesting a strong microbiota-metabolome coupling, indicating also that part of the CRC metabolomic signature was attributable to microbes. Suggesting utility as potential biomarkers, most such microbiome and metabolites showed directionally consistent changes in CRC patients. Nevertheless, further studies are needed to increase sample sizes towards verifying these findings.

Colorectal cancer (CRC) accounts for the third highest morbidity burden among malignant tumors worldwide. Previous studies investigated gut microbiome changes that occur during colorectal adenomas (CRA) progression to overt CRC, thus highlighting the importance of the gut microbiome in carcinogenesis. However, few studies have examined gut microbiome characteristics across the entire spectrum, from CRC development to treatment. The study used 16S ribosomal ribonucleic acid and internal transcribed spacer amplicon sequencing to compare the composition of gut bacteria and fungi in a Chinese cohort of healthy controls (HC), CRC patients, CRA patients, and CRC postoperative patients (PP). Our analysis showed that beta diversity was significantly different among the four groups based on the gut bacterial and fungal data. A total of 51 species of bacteria and 8 species of fungi were identified in the HC, CRA, CRC, and PP groups. Correlation networks for both the gut bacteria and fungi in HC vs. CRA, HC vs. CRC, and HC vs. PP indicated some hub bacterial and fungal genera in each model, and the correlation between bacterial and fungal data indicated that a highly significant negative correlation exists among groups. Quantitative polymerase chain reaction (qPCR) analysis in a large cohort of HC, CRC, CRA, and PP patients demonstrated a significantly increasing trend of Fusobacterium nucleatum, Bifidobacterium bifidum, Candida albicans, and Saccharomyces cerevisiae in the feces of CRC patients than that of HC patients (p < 0.01). However, the abundance levels of CRA and PP were significantly lower in HC patients than those in CRC patients. Further studies are required to identify the functional consequences of the altered bacterial/fungal composition on metabolism and CRC tumorigenesis in the host.

Migratory birds are the primary source and reservoir of antibiotic-resistant genes (ARGs) related to their gut microbes. In this study, we performed metagenomics analysis to study the gut microbial communities and ARGs of Anser erythropus wintering at Shengjin (SJ) and Caizi (CZ) Lakes. The results showed that bacteria, fungi, viruses, and archaea were the dominant gut microbes. Principal component analysis (PCA) indicated that the microbiota compositions significantly differed between the two populations. Diet may be the most crucial driver of the gut microbial communities for A. erythropus. This species fed exclusively on Poaceae spp. at Shengjin Lake and primarily on Carex spp. at Caizi Lake. Tetracycline, macrolide, fluoroquinolone, phenicol, and peptide antibiotics were the dominant resistant types. ARGs had a significantly higher abundance of operational taxonomic units (OTUs) in the Shengjin Lake samples than in Caizi Lake samples. PCA indicated that most Shengjin Lake samples significantly differed in gut microbiota composition from those obtained at Caizi Lake. This difference in gut microbiota composition between the two lakes' samples is attributed to more extensive aquaculture operations and poultry farms surrounding Shengjin Lake than Caizi Lake. ARGs–microbes associations indicated that 24 bacterial species, commonly used as indicators of antibiotic resistance in surveillance efforts, were abundant in wintering A. erythropus. The results revealed the composition and structural characteristics of the gut microbiota and ARGs of A. erythropus, pointing to their high sensitivities to diet habits at both lakes. This study also provides primary data for risk prevention and control of potential harmful pathogens that could endanger public health and therefore are of major significance to epidemiological and public health.