LncTar: a tool for predicting the RNA targets of long noncoding RNAs

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.

An analysis of human microbe-disease associations

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.

Analysis of gut microbiota profiles and microbe-disease associations in children with autism spectrum disorders in China

Autism spectrum disorder (ASD) is a set of complex neurodevelopmental disorders. Recent studies reported that children with ASD have altered gut microbiota profiles compared with typical development (TD) children. However, few studies on gut bacteria of children with ASD have been conducted in China. Here, in order to elucidate changes of fecal microbiota in children with ASD, 16S rRNA sequencing was conducted and the 16S rRNA (V3-V4) gene tags were amplified. We investigated differences in fecal microbiota between 35 children with ASD and 6 TD children. At the phylum level, the fecal microbiota of ASD group indicated a significant increase of the Bacteroidetes/Firmicutes ratio. At the genus level, we found that the relative abundance of Sutterella, Odoribacter and Butyricimonas was much more abundant in the ASD group whereas the abundance of Veillonella and Streptococcus was decreased significantly compared to the control group. Functional analysis demonstrated that butyrate and lactate producers were less abundant in the ASD group. In addition, we downloaded the association data set of microbe-disease from human microbe-disease association database and constructed a human disease network including ASD using our gut microbiome results. In this microbe-disease network based on microbe similarity of diseases, we found that ASD is positively correlated with periodontal, negatively related to type 1 diabetes. Therefore, these results suggest that microbe-based disease analysis is able to predict novel connection between ASD and other diseases and may play a role in revealing the pathogenesis of ASD.

Dexamethasone-induced Krüppel-like factor 9 expression promotes hepatic gluconeogenesis and hyperglycemia

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.

LncDisease: a sequence based bioinformatics tool for predicting lncRNA-disease associations

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/.

Shift of Macrophage Phenotype Due to Cartilage Oligomeric Matrix Protein Deficiency Drives Atherosclerotic Calcification

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.

Metformin Alters Gut Microbiota of Healthy Mice: Implication for Its Potential Role in Gut Microbiota Homeostasis

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.

A bioinformatics method for predicting long noncoding RNAs associated with vascular disease

Long noncoding RNAs (lncRNAs) play important roles in human diseases including vascular disease. Given the large number of lncRNAs, however, whether the majority of them are associated with vascular disease remains unknown. For this purpose, here we present a genomic location based bioinformatics method to predict the lncRNAs associated with vascular disease. We applied the presented method to globally screen the human lncRNAs potentially involved in vascular disease. As a result, we predicted 3043 putative vascular disease associated lncRNAs. To test the accuracy of the method, we selected 10 lncRNAs predicted to be implicated in proliferation and migration of vascular smooth muscle cells (VSMCs) for further experimental validation. The results confirmed that eight of the 10 lncRNAs (80%) are validated. This result suggests that the presented method has a reliable prediction performance. Finally, the presented bioinformatics method and the predicted vascular disease associated lncRNAs together may provide helps for not only better understanding of the roles of lncRNAs in vascular disease but also the identification of novel molecules for the diagnosis and therapy of vascular disease.

An integrated transcriptomic analysis of autism spectrum disorder

Autism spectrum disorder (ASD) is not a single disease but a set of disorders. To find clues of ASD pathogenesis in transcriptomic data, we performed an integrated transcriptomic analysis of ASD. After screening based on several standards in Gene Expression Omnibus (GEO) database, we obtained 11 series of transcriptomic data of different human tissues of ASD patients and healthy controls. Multidimensional scaling analysis revealed that datasets from the same tissue had bigger similarity than from different tissues. Functional enrichment analysis demonstrated that differential expressed genes were significantly enriched in inflammation/immune response, mitochondrion-related function and oxidative phosphorylation. Interestingly, genes enriched in inflammation/immune response were up-regulated in the brain tissues and down-regulated in the blood. In addition, drug prediction provided several compounds which might reverse gene expression profiles of ASD patients. And we also replicated the methods and criteria of transcriptomic analysis with datasets of ASD animal models and healthy controls, the results from animal models consolidated the results of transcriptomic analysis of ASD human tissues. In general, the results of our study may provide researchers a new sight of understanding the etiology of ASD and clinicians the possibilities of developing medical therapies.

Rsite: a computational method to identify the functional sites of noncoding RNAs

There is an increasing demand for identifying the functional sites of noncoding RNAs (ncRNAs). Here we introduce a tertiary-structure based computational approach, Rsite, which first calculates the Euclidean distances between each nucleotide and all the other nucleotides in a RNA molecule and then determines the nucleotides that are the extreme points in the distance curve as the functional sites. By analyzing two ncRNAs, tRNA (Lys) and Diels-Alder ribozyme, we demonstrated the efficiency of Rsite. As a result, Rsite recognized all of the known functional sites of the two ncRNAs, suggesting that Rsite could be a potentially useful tool for discovering the functional sites of ncRNAs. The source codes and data sets of Rsite are available at http://www.cuilab.cn/rsite.

MicroPattern: a web-based tool for microbe set enrichment analysis and disease similarity calculation based on a list of microbes

The microbiota colonized on human body is renowned as "a forgotten organ" due to its big impacts on human health and disease. Recently, microbiome studies have identified a large number of microbes differentially regulated in a variety of conditions, such as disease and diet. However, methods for discovering biological patterns in the differentially regulated microbes are still limited. For this purpose, here, we developed a web-based tool named MicroPattern to discover biological patterns for a list of microbes. In addition, MicroPattern implemented and integrated an algorithm we previously presented for the calculation of disease similarity based on disease-microbe association data. MicroPattern first grouped microbes into different sets based on the associated diseases and the colonized positions. Then, for a given list of microbes, MicroPattern performed enrichment analysis of the given microbes on all of the microbe sets. Moreover, using MicroPattern, we can also calculate disease similarity based on the shared microbe associations. Finally, we confirmed the accuracy and usefulness of MicroPattern by applying it to the changed microbes under the animal-based diet condition. MicroPattern is freely available at http://www.cuilab.cn/micropattern.

Enhanced prognostic signature for lung adenocarcinoma through integration of adjacent normal and tumor gene expressions

BACKGROUND

Cancer prognosis-related signatures have traditionally been constructed based on gene expression profiles derived from tumor or normal tissues. However, the potential benefits of incorporating gene expression profiles from both tumor and normal tissues to improve signature performance have not been explored.

METHODS

In this study, we developed three prognostic models for lung adenocarcinoma (LUAD) using gene expression profiles from tumor tissues, normal tissues, and a combination (COM) of both, sourced from The Cancer Genome Atlas (TCGA). To ensure comparability, the same workflow was followed for all three models.

RESULTS

When applied to the TCGA LUAD dataset, the tumor-derived model exhibited the best overall performance, except in calibration analysis, where the normal-derived model performed better. The COM-derived model demonstrated intermediate performance. Validation on three independent test datasets revealed that the COM-derived model showed the best performance, while the normal-derived model showed the worst. In overall survival (OS) analysis, the low-risk group defined by the COM-derived model consistently exhibited longer mean survival times. The tumor-derived model did not consistently show this trend, and the normal-derived model produced opposite results. In discrimination analysis, no significant differences were observed. The COM-derived model demonstrated good discrimination ability for short periods, while the tumor-derived model performed better for longer periods. In calibration analysis, both the COM and tumor-derived models had similar absolute prediction errors, which were better than those of the normal-derived model. However, the tumor-derived model tended to underestimate survival rates. The clinical feature analysis and validation in GSE229705 indicate that the risk score (RS) from the COM model is the most clinically significant. These results demonstrate that the COM model's RS aligns more closely with clinical data, maintaining stable performance and the strongest generalizability.

CONCLUSIONS

Overall, the COM-derived model demonstrated the best generalization ability. The superior performance of the tumor-derived model in the TCGA LUAD dataset might be due to overfitting. Our results suggest that appropriate combinations of gene expression data from tumor and normal tissues can enhance the predictive power of prognostic signatures.

Potential causal association between the oral microbiome and bipolar disorder

BACKGROUND

The oral microbiome can influence the growth, development, and regulation of the nervous system through various pathways; however, its relationship with bipolar disorder (BD) remains ambiguous. This study aims to investigate the causal relationship between the oral microbiome and BD through Mendelian randomization (MR) analysis.

METHODS

Data regarding single nucleotide polymorphisms (SNPs) in GWAS summary statistics for oral microbiota and BD were obtained from two studies: one reported the association between the oral microbiome and the human genome, encompassing both the dorsum of the tongue and saliva microbiomes. The other study investigated the association between BD and the human genome, categorizing BD into BD I and BD II for separate analyses. Thus, three data components were utilized: BD, BD I, and BD II. In this study, GWAS data for saliva and dorsum of the tongue microbiomes were analyzed separately for BD, BD I, and BD II. The inverse variance weighted (IVW) method was used to assess the causal relationship between the oral microbiome and BD. Analyses were conducted only when the number of instrumental variable SNPs was no less than two. The MR-Egger regression and IVW methods were employed to evaluate heterogeneity, whereas the MR-Egger intercept test was utilized to assess pleiotropy. For MR results exhibiting heterogeneity or pleiotropy, sensitivity analyses were performed using the weighted median, simple mode, weighted mode, MR-Egger test, and leave-one-out methods. Furthermore, funnel plots were employed to evaluate publication bias. Reverse MR analysis was also performed to investigate the potential bidirectional interactions between BD and the oral microbiota.

RESULTS

A causal relationship exists between the oral microbiome and BD. The effects of the microbiome from different regions of the oral cavity on BD are variable, with a more pronounced impact noted on BD I. This study identified two overlapping causal relationships shared between BD I and BD II, both exhibiting the same directional influence: ①Salivary s Prevotella aurantiaca SGB 2854 (Taxonomy ID: 596085, species); ② Tongue s Prevotella sp000467895 SGB 1817 (Taxonomy ID: 838, genus). Additionally, there are two overlapping bacteria with opposing directional effects: ① Salivary g Eggerthia (Taxonomy ID: 1279384, genus); ② Salivary s unclassified SGB 2636. Three differential bacteria that exclusively regulate one subtype were identified: ① Salivary s Lachnoanaerobaculum sp000296385 SGB 3537 (Taxonomy ID: 1164882, genus); ② Tongue s unclassified SGB 689; ③ Tongue s unclassified SGB 572. Among these, the genus g Eggerthia in saliva inhibits BD I while promoting BD II; conversely, salivary s unclassified SGB 2636 inhibits BD II while promoting BD I. The analysis of tongue s unclassified SGB 489 and s unclassified SGB 1215 demonstrated pleiotropy without causal significance. The reverse MR analysis identified that BD I may influence four microbial species, including f Leptotrichiaceae (Taxonomy ID: 1129771, family), f Streptococcaceae (Taxonomy ID: 1300, family), s unclassified SGB 1210, and s unclassified SGB 1950. There may be a bidirectional causal relationship between s unclassified SGB 1950 and BD I. Additionally, Reverse Mendel suggested that there was no significant causal relationship between BD and salivary and dorsal tongue microbes.

CONCLUSION

Our Mendelian randomization results indicate a causal relationship between the oral microbiome and the development of BD. However, the microbial profiles associated with the different subtypes, BD I and BD II, differ significantly; even within the same genus, the direction of influence on BD I and BD II varies, suggesting that the underlying mechanisms for the development of BD I and BD II may differ substantially.

Clinical significance, molecular characterization, and immune microenvironment analysis of coagulation-related genes in clear cell renal cell carcinoma

BACKGROUND

Numerous studies have revealed a tight connection between tumor development and the coagulation system. However, the effects of coagulation on the prognosis and tumor microenvironment (TME) of clear cell renal cell carcinoma (ccRCC) remain poorly understood.

METHODS

We employed the consensus clustering method to characterize distinct molecular subtypes associated with coagulation patterns. Subsequently, we examined variations in the overall survival (OS), genomic profiles, and TME characteristics between these subtypes. To develop a prognostic coagulation-related risk score (CRRS) model, we utilized the least absolute shrinkage and selection operator Cox regression and stepwise multivariate Cox regression analyses. We also created a nomogram to aid in the clinical application of the risk score, evaluating the relationships between the CRRS and the immune microenvironment, responsiveness to immunotherapy, and targeted treatment. The clinical significance of PLAUR and its biological function in ccRCC were also further analyzed.

RESULTS

There were significant differences in clinical features, prognostic stratification, genomic variation, and TME characteristics between the two coagulation-related subtypes. We established and validated a CRRS using six coagulation-related genes that can be employed as an effective indicator of risk stratification and prognosis estimation for ccRCC patients. Significant variations in survival outcomes were observed between the high- and low-risk groups. The nomogram was proficient in predicting the 1-, 3-, and 5-year OS. Additionally, the CRRS emerged as a novel tool for evaluating the clinical effectiveness of immunotherapy and targeted treatments in ccRCC. Moreover, we confirmed upregulated PLAUR expression in ccRCC samples that was significantly correlated with poor patient prognosis. PLAUR knockdown notably inhibited ccRCC cell proliferation and migration.

CONCLUSION

Our data suggested that CRRS may be employed as a reliable predictive biomarker that can provide therapeutic benefits for immunotherapy and targeted therapy in ccRCC.