Long noncoding RNAs (lncRNAs) represent a big category of noncoding RNA molecules, and increasing studies have shown that they play important roles in various critical biological processes. They show a diversity of functions through diverse mechanisms, among which regulating RNA molecules is one of the most popular ones. Given the big number of lncRNAs, it becomes urgent and important to predict the RNA targets of lncRNAs in a large scale for the comprehensive understanding of lncRNA functions and action mechanisms. Although several methods have been developed to predict RNA–RNA interactions, none of them can be used to predict the RNA targets of lncRNAs in a large scale. Here we presented a tool, LncTar, which shows the ability to efficiently predict the RNA targets of lncRNAs in a large scale. To test the accuracy of LncTar, we applied it to 10 experimentally supported lncRNA–mRNA interactions. As a result, LncTar successfully predicted 8 (80%) of the 10 lncRNA–mRNA pairs, suggesting that LncTar has a reliable accuracy. Finally, we believe that LncTar could be an efficient tool for the fast identification of the RNA targets of lncRNAs. LncTar is freely available at http://www.cuilab.cn/lnctar.

The microbiota living in the human body has critical impacts on our health and disease, but a systems understanding of its relationships with disease remains limited. Here, we use a large-scale text mining-based manually curated microbe–disease association data set to construct a microbe-based human disease network and investigate the relationships between microbes and disease genes, symptoms, chemical fragments and drugs. We reveal that microbe-based disease loops are significantly coherent. Microbe-based disease connections have strong overlaps with those constructed by disease genes, symptoms, chemical fragments and drugs. Moreover, we confirm that the microbe-based disease analysis is able to predict novel connections and mechanisms for disease, microbes, genes and drugs. The presented network, methods and findings can be a resource helpful for addressing some issues in medicine, for example, the discovery of bench knowledge and bedside clinical solutions for disease mechanism understanding, diagnosis and therapy.

Chronic glucocorticoid therapy has serious side effects, including diabetes and fatty liver. However, the molecular mechanisms responsible for steroid-induced diabetes remain largely enigmatic. Here, we show that hepatic Krüppel-like factor 9 (Klf9) gene expression is induced by dexamethasone and fasting. The overexpression of Klf9 in primary hepatocytes strongly stimulated Pgc1a gene expression through direct binding to its promoter, thereby activating the gluconeogenic program. However, Klf9 mutation abolished the stimulatory effect of dexamethasone on cellular glucose output. Adenovirus-mediated overexpression of KLF9 in the mouse liver markedly increased blood glucose levels and impaired glucose tolerance. Conversely, both global Klf9-mutant mice and liver-specific Klf9-deleted mice displayed fasting hypoglycemia. Moreover, the knockdown of Klf9 in the liver in diabetic mouse models, including ob/ob and db/db mice, markedly lowered fasting blood glucose levels. Notably, hepatic Klf9 deficiency in mice alleviated hyperglycemia induced by chronic dexamethasone treatment. These results suggest a critical role for KLF9 in the regulation of hepatic glucose metabolism and identify hepatic induction of KLF9 as a mechanism underlying glucocorticoid therapy-induced diabetes.

LncRNAs represent a large class of noncoding RNA molecules that have important functions and play key roles in a variety of human diseases. There is an urgent need to develop bioinformatics tools as to gain insight into lncRNAs. This study developed a sequence-based bioinformatics method, LncDisease, to predict the lncRNA-disease associations based on the crosstalk between lncRNAs and miRNAs. Using LncDisease, we predicted the lncRNAs associated with breast cancer and hypertension. The breast-cancer-associated lncRNAs were studied in two breast tumor cell lines, MCF-7 and MDA-MB-231. The qRT-PCR results showed that 11 (91.7%) of the 12 predicted lncRNAs could be validated in both breast cancer cell lines. The hypertension-associated lncRNAs were further evaluated in human vascular smooth muscle cells (VSMCs) stimulated with angiotensin II (Ang II). The qRT-PCR results showed that 3 (75.0%) of the 4 predicted lncRNAs could be validated in Ang II-treated human VSMCs. In addition, we predicted 6 diseases associated with the lncRNA GAS5 and validated 4 (66.7%) of them by literature mining. These results greatly support the specificity and efficacy of LncDisease in the study of lncRNAs in human diseases. The LncDisease software is freely available on the Software Page: http://www.cuilab.cn/.

In recent years, the first-line anti-diabetic drug metformin has been shown to be also useful for the treatment of other diseases like cancer. To date, few reports were about the impact of metformin on gut microbiota. To fully understand the mechanism of action of metformin in treating diseases other than diabetes, it is especially important to investigate the impact of long-term metformin treatment on the gut microbiome in non-diabetic status. In this study, we treated healthy mice with metformin for 30 days, and observed 46 significantly changed gut microbes by using the 16S rRNA-based microbiome profiling technique. We found that microbes from the Verrucomicrobiaceae and Prevotellaceae classes were enriched, while those from Lachnospiraceae and Rhodobacteraceae were depleted. We further compared the altered microbiome profile with the profiles under various disease conditions using our recently developed comparative microbiome tool known as MicroPattern. Interestingly, the treatment of diabetes patients with metformin positively correlates with colon cancer and type 1 diabetes, indicating a confounding effect on the gut microbiome in patients with diabetes. However, the treatment of healthy mice with metformin exhibits a negative correlation with multiple inflammatory diseases, indicating a protective anti-inflammatory role of metformin in non-diabetes status. This result underscores the potential effect of metformin on gut microbiome homeostasis, which may contribute to the treatment of non-diabetic diseases.

Rationale:: Intimal calcification is highly correlated with atherosclerotic plaque burden, but the underlying mechanism is poorly understood. We recently reported that cartilage oligomeric matrix protein (COMP), a component of vascular extracellular matrix, is an endogenous inhibitor of vascular smooth muscle cell calcification. Objective:: To investigate whether COMP affects atherosclerotic calcification. Methods and Results:: ApoE ^−/− COMP ^−/− mice fed with chow diet for 12 months manifested more extensive atherosclerotic calcification in the innominate arteries than did ApoE ^−/− mice. To investigate which origins of COMP contributed to atherosclerotic calcification, bone marrow transplantation was performed between ApoE ^−/− and ApoE ^−/− COMP ^−/− mice. Enhanced calcification was observed in mice transplanted with ApoE ^−/− COMP ^−/− bone marrow compared with mice transplanted with ApoE ^−/− bone marrow, indicating that bone marrow–derived COMP may play a critical role in atherosclerotic calcification. Furthermore, microarray profiling of wild-type and COMP ^−/− macrophages revealed that COMP-deficient macrophages exerted atherogenic and osteogenic characters. Integrin β3 protein was attenuated in COMP ^−/− macrophages, and overexpression of integrin β3 inhibited the shift of macrophage phenotypes by COMP deficiency. Furthermore, adeno-associated virus 2–integrin β3 infection attenuated atherosclerotic calcification in ApoE ^−/− COMP ^−/− mice. Mechanistically, COMP bound directly to β-tail domain of integrin β3 via its C-terminus, and blocking of the COMP–integrin β3 association by β-tail domain mimicked the COMP deficiency–induced shift in macrophage phenotypes. Similar to COMP deficiency in mice, transduction of adeno-associated virus 2–β-tail domain enhanced atherosclerotic calcification in ApoE ^−/− mice. Conclusions:: These results reveal that COMP deficiency acted via integrin β3 to drive macrophages toward the atherogenic and osteogenic phenotype and thereby aggravate atherosclerotic calcification.