Moderate precipitation reduction enhances nitrogen cycling and soil nitrous oxide emissions in a semi-arid grassland.

The ongoing climate change is predicted to induce more weather extremes such as frequent drought and high-intensity precipitation events, causing more severe drying-rewetting cycles in soil. However, it remains largely unknown how these changes will affect soil nitrogen (N)-cycling microbes and the emissions of potent greenhouse gas nitrous oxide (N2 O). Utilizing a field precipitation manipulation in a semi-arid grassland on the Loess Plateau, we examined how precipitation reduction (ca. -30%) influenced soil N2 O and carbon dioxide (CO2 ) emissions in field, and in a complementary lab-incubation with simulated drying-rewetting cycles. Results obtained showed that precipitation reduction stimulated plant root turnover and N-cycling processes, enhancing soil N2 O and CO2 emissions in field, particularly after each rainfall event. Also, high-resolution isotopic analyses revealed that field soil N2 O emissions primarily originated from nitrification process. The incubation experiment further showed that in field soils under precipitation reduction, drying-rewetting stimulated N mineralization and ammonia-oxidizing bacteria in favor of genera Nitrosospira and Nitrosovibrio, increasing nitrification and N2 O emissions. These findings suggest that moderate precipitation reduction, accompanied with changes in drying-rewetting cycles under future precipitation scenarios, may enhance N cycling processes and soil N2 O emissions in semi-arid ecosystems, feeding positively back to the ongoing climate change.

Kangxianruangan granule­containing serum mediated inhibition of hepatic oval cell differentiation into hepatocellular carcinoma cells via the Wnt­1/ß­catenin signaling pathway.

Hepatocellular carcinoma is a malignancy with poor clinical prognosis. Hepatic oval cells (HOCs) tend to differentiate into cancerous hepatocellular carcinoma cells (HCCs) in the tumor microenvironment. The purpose of the present study was to explore the role of kangxianruangan granule (KXRG)­containing serum in inhibiting the differentiation of HOCs into HCCs via the Wnt­1/ß­catenin signaling pathway. N­methyl­N'­nitro­N­nitrosoguanidine (MNNG) was applied to induce the transformation of the rat HOC cell line WB­F344 into HCCs. The overexpression plasmid, Wnt­1­up, was utilized to increase Wnt­1 expression. Subsequently, high, medium and low concentrations of KXRG were applied to MNNG­treated WB­F344 cells to assess the inhibitory effect of KXRG on cell differentiation. Flow cytometry was conducted to detect the cell cycle distribution, apoptotic rate and expression of cytokeratin­19 (CK­19) protein in cells. An immunofluorescence double staining protocol was used to detect the expression of Wnt­1 and ß­catenin. ELISAs were performed to detect α fetoprotein in the cell supernatants. Reverse transcription­quantitative PCR and western blotting were conducted to detect the mRNA and protein expression levels of Wnt­1, ß­catenin, Cyclin D1, C­myc, matrix metalloproteinase­7 (MMP­7), Axin2 and epithelial cell adhesion molecule (EpCAM) in cells. Compared with the normal group, the apoptotic rate, proportion of S phase cells, concentration of AFP in the cell supernatant, level of CK­19 protein, and mRNA and protein expression levels of Wnt­1, ß­catenin, Cyclin D1, C­myc, MMP­7, Axin2 and EpCAM were all significantly increased in the model group. Addition of KXRG significantly reduced the aforementioned indicators compared with the model group. Moreover, Wnt­1 overexpression further increased the aforementioned indicators compared with the model group, whereas KXRG significantly inhibited these effects. The results indicated that KXRG inhibited the differentiation of HOCs into HCCs via the Wnt­1/ß­catenin signaling pathway, which suggested the potential clinical application of KXRG for the prevention of hepatocellular carcinoma.

Symptom-based network classification identifies distinct clinical subgroups of liver diseases with common molecular pathways.

BACKGROUND AND OBJECTIVE: Liver disease is a multifactorial complex disease with high global prevalence and poor long-term clinical efficacy and liver disease patients with different comorbidities often incorporate multiple phenotypes in the clinic. Thus, there is a pressing need to improve understanding of the complexity of clinical liver population to help gain more accurate disease subtypes for personalized treatment. METHODS: Individualized treatment of the traditional Chinese medicine (TCM) provides a theoretical basis to the study of personalized classification of complex diseases. Utilizing the TCM clinical electronic medical records (EMRs) of 6475 liver inpatient cases, we built a liver disease comorbidity network (LDCN) to show the complicated associations between liver diseases and their comorbidities, and then constructed a patient similarity network with shared symptoms (PSN). Finally, we identified liver patient subgroups using community detection methods and performed enrichment analyses to find both distinct clinical and molecular characteristics (with the phenotype-genotype associations and interactome networks) of these patient subgroups. RESULTS: From the comorbidity network, we found that clinical liver patients have a wide range of disease comorbidities, in which the basic liver diseases (e.g. hepatitis b, decompensated liver cirrhosis), and the common chronic diseases (e.g. hypertension, type 2 diabetes), have high degree of disease comorbidities. In addition, we identified 303 patient modules (representing the liver patient subgroups) from the PSN, in which the top 6 modules with large number of cases include 51.68% of the whole cases and 251 modules contain only 10 or fewer cases, which indicates the manifestation diversity of liver diseases. Finally, we found that the patient subgroups actually have distinct symptom phenotypes, disease comorbidity characteristics and their underlying molecular pathways, which could be used for understanding the novel disease subtypes of liver conditions. For example, three patient subgroups, namely Module 6 (M6, n = 638), M2 (n = 623) and M1 (n = 488) were associated to common chronic liver disease conditions (hepatitis, cirrhosis, hepatocellular carcinoma). Meanwhile, patient subgroups of M30 (n = 36) and M36 (n = 37) were mostly related to acute gastroenteritis and upper respiratory infection, respectively, which reflected the individual comorbidity characteristics of liver subgroups. Furthermore, we identified the distinct genes and pathways of patient subgroups and the basic liver diseases (hepatitis b and cirrhosis), respectively. The high degree of overlapping pathways between them (e.g. M36 with 93.33% shared enriched pathways) indicates the underlying molecular network mechanisms of each patient subgroup. CONCLUSIONS: Our results demonstrate the utility and comprehensiveness of disease classification study based on community detection of patient network using shared TCM symptom phenotypes and it can be used to other more complex diseases.