The intestinal microbiota is significantly affected by the external environment, but our understanding of the effects of extreme environments such as plateaus is far from adequate. In this study, we systematically analyzed the variation in the intestinal microbiota and 76 blood clinical indexes among 393 healthy adults with different plateau living durations (Han individuals with no plateau living, with plateau living for 4 to 6 days, with plateau living for >3 months, and who returned to the plain for 3 months, as well as plateau-living Tibetans). The results showed that the high-altitude environment rapidly (4 days) and continually (more than 3 months) shaped both the intestinal microbiota and clinical indexes of the Han population. With prolongation of plateau living, the general characteristics of the intestinal microbiota and clinical indexes of the Han population were increasingly similar to those of the Tibetan population. The intestinal microbiota of the Han population that returned to the plain area for 3 months still resembled that of the plateau-living Han population rather than that of the Han population on the plain. Moreover, clinical indexes such as blood glucose were significantly lower in the plateau groups than in the nonplateau groups, while the opposite result was obtained for testosterone. Interestingly, there were Tibetan-specific correlations between glucose levels and Succinivibrio and Sarcina abundance in the intestine. The results of this study suggest that a hypoxic environment could rapidly and lastingly affect both the human intestinal microbiota and blood clinical indexes, providing new insights for the study of plateau adaptability. IMPORTANCE The data presented in the present study demonstrate that the hypoxic plateau environment has a profound impact on the gut microbiota and blood clinical indexes in Han and Tibetan individuals. The plateau-changed signatures of the gut microbiota and blood clinical indexes were not restored to the nonplateau status in the Han cohorts, even when the individuals returned to the plain from the plateau for several months. Our study will improve the understanding of the great impact of hypoxic environments on the gut microbiota and blood clinical indexes as well as the adaptation mechanism and intervention targets for plateau adaptation.

This work investigated the clinical prognostic implications and biological function of plasma soluble programmed cell death ligand 1 in breast cancer patients. Plasma sPD-L1 levels of recurrent/metastatic breast cancer patients were determined, and the association of sPD-L1 levels and metastatic progression-free survival and metastatic overall survival was assessed. The PD-L1 expression on breast cancer cells was analyzed by flow cytometry, and the level of sPD-L1 in the supernatant of breast cancer cells was determined by enzyme-linked immunosorbent assay. Furthermore, the effect of sPD-L1 on the proliferation and apoptosis of T lymphocytes was detected by WST-1 assay and flow cytometry. The plasma sPD-L1 levels in 208 patients with recurrent/metastatic breast cancer before receiving first-line rescue therapy were measured. The optimal cutoff value of plasma sPD-L1 for predicting disease progression was 8.774 ng/ml. Univariate and multivariate analyses identified high sPD-L1 level (≥ 8.774 ng/ml) and visceral metastasis were independent factors associated with poor prognosis. Relevance analysis showed that the plasma sPD-L1 level was weaklyassociated with some systemic inflammation markers, including white cell count (WBC), absolute monocytecount, and absolute neutrophil count. Furthermore, we found sPD-L1 could be found in supernatant of culture with breast cancer cell line expressing PD-L1 on the cell surface and inhibit T lymphocyte function, playing a negative regulatory role in cellular immunity. sPD-L1 was a good tumor predictive maker in breast cancer and it may play a potentially important role in immune tolerance.

Comprehensive analyses of multi-omics data may provide insights into interactions between different biological layers concerning distinct clinical features. We integrated data on the gut microbiota, blood parameters and urine metabolites of treatment-naive individuals presenting a wide range of metabolic disease phenotypes to delineate clinically meaningful associations. Trans-omics correlation networks revealed that candidate gut microbial biomarkers and urine metabolite feature were covaried with distinct clinical phenotypes. Integration of the gut microbiome, the urine metabolome and the phenome revealed that variations in one of these three systems correlated with changes in the other two. In a specific note about clinical parameters of liver function, we identified Eubacteriumeligens, Faecalibacteriumprausnitzii and Ruminococcuslactaris to be associated with a healthy liver function, whereas Clostridium bolteae, Tyzzerellanexills, Ruminococcusgnavus, Blautiahansenii, and Atopobiumparvulum were associated with blood biomarkers for liver diseases. Variations in these microbiota features paralleled changes in specific urine metabolites. Network modeling yielded two core clusters including one large gut microbe-urine metabolite close-knit cluster and one triangular cluster composed of a gut microbe-blood-urine network, demonstrating close inter-system crosstalk especially between the gut microbiome and the urine metabolome. Distinct clinical phenotypes are manifested in both the gut microbiome and the urine metabolome, and inter-domain connectivity takes the form of high-dimensional networks. Such networks may further our understanding of complex biological systems, and may provide a basis for identifying biomarkers for diseases. Deciphering the complexity of human physiology and disease requires a holistic and trans-omics approach integrating multi-layer data sets, including the gut microbiome and profiles of biological fluids. By studying the gut microbiome on carotid atherosclerosis, we identified microbial features associated with clinical parameters, and we observed that groups of urine metabolites correlated with groups of clinical parameters. Combining the three data sets, we revealed correlations of entities across the three systems, suggesting that physiological changes are reflected in each of the omics. Our findings provided insights into the interactive network between the gut microbiome, blood clinical parameters and the urine metabolome concerning physiological variations, and showed the promise of trans-omics study for biomarker discovery.

Background and Aims: Previous work has shown the association between blood-based methylation of coagulation factor II receptor-like 3 gene (F2RL3) and cardiovascular mortality in Caucasians. However, the diagnostic value of F2RL3 methylation for CHD is still unknown. The aim of our study was to evaluate the association between blood-based F2RL3 methylation and the risk of CHD in the Chinese population.Methods: The methylation level of F2RL3 was quantified by mass spectrometry in a case-control study with 180 CHD cases and 184 controls. The association between F2RL3 methylation intensity and CHD was assessed by logistic regression models, controlling confounding factors.Results: The hypomethylation in F2RL3_A amplicon was significantly associated with CHD (odds ratio (ORs) per -10% methylation: 1.22–1.42, p < 0.035 for six out of seven CpG loci). Specifically, this significant association was observed in elderly CHD patients (≥60 years), myocardial infarction (MI) patients, heart failure patients and the patients with minor to medium cardiac function impairment (NYHA Ⅰ&Ⅱ CHD cases) (ORs per -10% methylation: 1.35–1.58, 1.32–2.00, 1.29–1.43, 1.25–1.44; p < 0.024, 0.033, 0.035, 0.025, respectively). However, F2RL3_B CpG sites showed no or very weak association with CHD. The combination of F2RL3_A_CpG_1 and F2RL3_A_CpG_3 methylation levels could efficiently discriminate CHD, MI, heart failure, NYHA I&II CHD, and elderly CHD patients from controls (area under curve (AUC) = 0.75, 0.79, 0.75, 0.76, and 0.82, respectively).Conclusion: We propose blood-based F2RL3 methylation as a potential biomarker for CHD, especially for people with older age or with the status of MI. The combination of F2RL3 methylation and conventional risk factors might be an approach to evaluate CHD at early stage.