Pathogen invasion changes the intestinal microbiota composition and induces innate immune responses in the zebrafish intestine.

Numerous bacteria are harbored in the animal digestive tract and are impacted by several factors. Intestinal microbiota homeostasis is critical for maintaining the health of an organism. However, how pathogen invasion affects the microbiota composition has not been fully clarified. The mechanisms for preventing invasion by pathogenic microorganisms are yet to be elucidated. Zebrafish is a useful model for developmental biology, and studies in this organism have gradually become focused on intestinal immunity. In this study, we analyzed the microbiota of normal cultivated and infected zebrafish intestines, the aquarium water and feed samples. We found that the predominant bacteria in the zebrafish intestine belonged to Gammaproteobacteria (67%) and that feed and environment merely influenced intestinal microbiota composition only partially. Intestinal microbiota changed after a pathogenic bacterial challenge. At the genus level, the abundance of some pathogenic intestinal bacteria increased, and these genera included Halomonas (50%), Pelagibacterium (3.6%), Aeromonas (2.6%), Nesterenkonia (1%), Chryseobacterium (3.4‰), Mesorhizobium (1.4‰), Vibrio (1‰), Mycoplasma (0.7‰) and Methylobacterium (0.6‰) in IAh group. However, the abundance of some beneficial intestinal bacteria decreased, and these genera included Nitratireductor (0.8‰), Enterococcus (0.8‰), Brevundimonas (0.7‰), Lactococcus (0.7‰) and Lactobacillus (0.4‰). Additionally, we investigated the innate immune responses after infection. ROS levels in intestine increased in the early stages after a challenge and recovered subsequently. The mRNA levels of antimicrobial peptide genes lectin, hepcidin and defensin1, were upregulated in the intestine after pathogen infection. These results suggested that the invasion of pathogen could change the intestinal microbiota composition and induce intestinal innate immune responses in zebrafish.

Cost-effective low-coverage whole-genome sequencing assay for the risk stratification of gastric cancer.

BACKGROUND: Gastric cancer (GC), a multifactorial disease, is caused by pathogens, such as Helicobacter pylori (H. pylori) and Epstein-Barr virus (EBV), and genetic components. AIM: To investigate microbiomes and host genome instability by cost-effective, low-coverage whole-genome sequencing, as biomarkers for GC subtyping. METHODS: Samples from 40 GC patients were collected from Taizhou Hospital, Zhejiang Province, affiliated with Wenzhou Medical University. DNA from the samples was subjected to low-coverage whole-genome sequencing with a median genome coverage of 1.86 × (range: 1.03 × to 3.17 ×) by Illumina × 10, followed by copy number analyses using a customized bioinformatics workflow ultrasensitive chromosomal aneuploidy detector. RESULTS: Of the 40 GC samples, 20 (50%) were found to be enriched with microbiomes. EBV DNA was detected in 5 GC patients (12.5%). H. pylori DNA was found in 15 (37.5%) patients. The other 20 (50%) patients were found to have relatively higher genomic instability. Copy number amplifications of the oncogenes, ERBB2 and KRAS, were found in 9 (22.5%) and 7 (17.5%) of the GC samples, respectively. EBV enrichment was found to be associated with tumors in the gastric cardia and fundus. H. pylori enrichment was found to be associated with tumors in the pylorus and antrum. Tumors with elevated genomic instability showed no localization and could be observed in any location. Additionally, H. pylori-enriched GC was found to be associated with the Borrmann type II/III and gastritis history. EBV-enriched GC was not associated with gastritis. No statistically significant correlation was observed between genomic instability and gastritis. Furthermore, these three different molecular subtypes showed distinct survival outcomes (P = 0.019). EBV-positive tumors had the best prognosis, whereas patients with high genomic instability (CIN+) showed the worst survival. Patients with H. pylori infection showed intermediate prognosis compared with the other two subtypes. CONCLUSION: Thus, using low-coverage whole-genome sequencing, GC can be classified into three categories based on disease etiology; this classification may prove useful for GC diagnosis and precision medicine.