Analysis of gut microbiota in chinese donkey in different regions using metagenomic sequencing.

BACKGROUND: Gut microbiota plays a significant role in host survival, health, and diseases; however, compared to other livestock, research on the gut microbiome of donkeys is limited. RESULTS: In this study, a total of 30 donkey samples of rectal contents from six regions, including Shigatse, Changdu, Yunnan, Xinjiang, Qinghai, and Dezhou, were collected for metagenomic sequencing. The results of the species annotation revealed that the dominant phyla were Firmicutes and Bacteroidetes, and the dominant genera were Bacteroides, unclassified_o_Clostridiales (short for Clostridiales) and unclassified_f_Lachnospiraceae (short for Lachnospiraceae). The dominant phyla, genera and key discriminators were Bacteroidetes, Clostridiales and Bacteroidetes in Tibet donkeys (Shigatse); Firmicutes, Clostridiales and Clostridiales in Tibet donkeys (Changdu); Firmicutes, Fibrobacter and Tenericutes in Qinghai donkeys; Firmicutes, Clostridiales and Negativicutes in Yunnan donkeys; Firmicutes, Fibrobacter and Fibrobacteres in Xinjiang donkeys; Firmicutes, Clostridiales and Firmicutes in Dezhou donkeys. In the functional annotation, it was mainly enriched in the glycolysis and gluconeogenesis of carbohydrate metabolism, and the abundance was the highest in Dezhou donkeys. These results combined with altitude correlation analysis demonstrated that donkeys in the Dezhou region exhibited strong glucose-conversion ability, those in the Shigatse region exhibited strong glucose metabolism and utilization ability, those in the Changdu region exhibited a strong microbial metabolic function, and those in the Xinjiang region exhibited the strongest ability to decompose cellulose and hemicellulose. CONCLUSION: According to published literature, this is the first study to construct a dataset with multi-regional donkey breeds. Our study revealed the differences in the composition and function of gut microbes in donkeys from different geographic regions and environmental settings and is valuable for donkey gut microbiome research.

Identification of the molecular subgroups in Alzheimer's disease by transcriptomic data.

Background: Alzheimer's disease (AD) is a heterogeneous pathological disease with genetic background accompanied by aging. This inconsistency is present among molecular subtypes, which has led to diagnostic ambiguity and failure in drug development. We precisely distinguished patients of AD at the transcriptome level. Methods: We collected 1,240 AD brain tissue samples collected from the GEO dataset. Consensus clustering was used to identify molecular subtypes, and the clinical characteristics were focused on. To reveal transcriptome differences among subgroups, we certificated specific upregulated genes and annotated the biological function. According to RANK METRIC SCORE in GSEA, TOP10 was defined as the hub gene. In addition, the systematic correlation between the hub gene and "A/T/N" was analyzed. Finally, we used external data sets to verify the diagnostic value of hub genes. Results: We identified three molecular subtypes of AD from 743 AD samples, among which subtypes I and III had high-risk factors, and subtype II had protective factors. All three subgroups had higher neuritis plaque density, and subgroups I and III had higher clinical dementia scores and neurofibrillary tangles than subgroup II. Our results confirmed a positive association between neurofibrillary tangles and dementia, but not neuritis plaques. Subgroup I genes clustered in viral infection, hypoxia injury, and angiogenesis. Subgroup II showed heterogeneity in synaptic pathology, and we found several essential beneficial synaptic proteins. Due to presenilin one amplification, Subgroup III was a risk subgroup suspected of familial AD, involving abnormal neurogenic signals, glial cell differentiation, and proliferation. Among the three subgroups, the highest combined diagnostic value of the hub genes were 0.95, 0.92, and 0.83, respectively, indicating that the hub genes had sound typing and diagnostic ability. Conclusion: The transcriptome classification of AD cases played out the pathological heterogeneity of different subgroups. It throws daylight on the personalized diagnosis and treatment of AD.