Evaluation of soil-vegetation interaction effects on water fluxes revealed by the proxy of model parameter combinations.

The coupled development of soil and vegetation leads to a close interaction between their attributes and impacts the sustainability of eco-hydrology at different scales. In this study, a distributed hydrological model of a watershed was created with the Soil and Water Assessment Tool (SWAT) in a representative tributary watershed for investigating such effects. The results quantify the intensity and interval of the relationship and the impacts on hydrological composition between major model parameters. Among the examined interactions, SCS runoff curve number (CN2) and soil bulk density (BD) show the strongest interaction and effects on surface runoff, lateral flow, percolation, groundwater flow, and soil water content. The interaction between CN2 and BD highlights the importance of the soil surface and topsoil for runoff generation processes. In addition, the soil-vegetation interactions show clear seasonal effects due to impacts from the changes in land use and precipitation patterns, which influence the river discharge and flow variability more significantly at the sub-basin scale than at the watershed scale. The insight into the interactions and hydrological effects of soil and vegetation may help improve the spatial planning for ecological sustainability and hydrological extrema mitigation with a more reliable reflection of the spatial heterogeneity.

Biomarker microRNAs for prostate cancer metastasis: screened with a network vulnerability analysis model.

BACKGROUND: Prostate cancer (PCa) is a fatal malignant tumor among males in the world and the metastasis is a leading cause for PCa death. Biomarkers are therefore urgently needed to detect PCa metastatic signature at the early time. MicroRNAs are small non-coding RNAs with the potential to be biomarkers for disease prediction. In addition, computer-aided biomarker discovery is now becoming an attractive paradigm for precision diagnosis and prognosis of complex diseases. METHODS: In this study, we identified key microRNAs as biomarkers for predicting PCa metastasis based on network vulnerability analysis. We first extracted microRNAs and mRNAs that were differentially expressed between primary PCa and metastatic PCa (MPCa) samples. Then we constructed the MPCa-specific microRNA-mRNA network and screened microRNA biomarkers by a novel bioinformatics model. The model emphasized the characterization of systems stability changes and the network vulnerability with three measurements, i.e. the structurally single-line regulation, the functional importance of microRNA targets and the percentage of transcription factor genes in microRNA unique targets. RESULTS: With this model, we identified five microRNAs as putative biomarkers for PCa metastasis. Among them, miR-101-3p and miR-145-5p have been previously reported as biomarkers for PCa metastasis and the remaining three, i.e. miR-204-5p, miR-198 and miR-152, were screened as novel biomarkers for PCa metastasis. The results were further confirmed by the assessment of their predictive power and biological function analysis. CONCLUSIONS: Five microRNAs were identified as candidate biomarkers for predicting PCa metastasis based on our network vulnerability analysis model. The prediction performance, literature exploration and functional enrichment analysis convinced our findings. This novel bioinformatics model could be applied to biomarker discovery for other complex diseases.

MiRNA-BD: an evidence-based bioinformatics model and software tool for microRNA biomarker discovery.

MicroRNAs (miRNAs) are small non-coding RNAs with the potential as biomarkers for disease diagnosis, prognosis and therapy. In the era of big data and biomedical informatics, computer-aided biomarker discovery has become the current frontier. However, most of the computational models are highly dependent on specific prior knowledge and training-testing procedures, very few are mechanism-guided or evidence-based. To the best of our knowledge, untill now no general rules have been uncovered and applied to miRNA biomarker screening. In this study, we manually collected literature-reported cancer miRNA biomarkers and analyzed their regulatory patterns, including the regulatory modes, biological functions and evolutionary characteristics of their targets in the human miRNA-mRNA network. Two evidences were statistically detected and used to distinguish biomarker miRNAs from others. Based on these observations, we developed a novel bioinformatics model and software tool for miRNA biomarker discovery ( http://sysbio.suda.edu.cn/MiRNA-BD/ ). In contrast to routine methods that focus on miRNA synergic functions, our method searches for vulnerable sites in the miRNA-mRNA network and considers the independent regulatory power of miRNAs, i.e., single-line regulations between miRNAs and mRNAs. The performance comparison demonstrates the generality and precision of our model, which identifies miRNA biomarkers for cancers as well as other complex diseases without training or specific prior knowledge.

Assessing the long-term effects of land use changes on runoff patterns and food production in a large lake watershed with policy implications.

Effects of land use development on runoff patterns are salient at a hydrological response unit scale. However, quantitative analysis at the watershed scale is still a challenge due to the complex spatial heterogeneity of the upstream and downstream hydrological relationships and the inherent structure of drainage systems. This study aims to use the well-calibrated Soil and Water Assessment Tool (SWAT) to assess the response of hydrological processes under different land use scenarios in a large lake watershed (Lake Dongting) in the middle Yangtze River basin in China. Based on possible land use changes, scale-dependent land use scenarios were developed and parameters embedded in SWAT were calibrated and validated for hydrological systems analysis. This approach leads to the simulation of the land use change impacts on the hydrological cycle. Results indicated that evapotranspiration, surface runoff, groundwater flow, and water yield were affected by the land use change scenarios in different magnitudes. Overall, changes of land use and land cover have significant impacts on runoff patterns at the watershed scale in terms of both the total water yield (i.e., groundwater flow, surface runoff, and interflow, minus transmission losses) and the spatial distribution of runoff. The changes in runoff distribution were resulted in opposite impacts within the two land use scenarios including forest and agriculture. Water yield has a decrease of 1.8 percent in the forest-prone landscape scenario and an increase of 4.2 percent in the agriculture-rich scenario during the simulated period. Surface runoff was the most affected component in the hydrological cycle. Whereas surface runoff as part of water yield has a decrease of 8.2 percent in the forest- prone landscape scenario, there is an increase of 8.6 percent in the agriculture-rich landscape scenario. Different runoff patterns associated with each land use scenario imply the potential effect on flood or drought mitigation policy. Based on the results, key areas were identified to show that hydrological extreme mitigation and flood control can be coordinated by some land use regulations.

Identification of the genes regulated by Wnt-4, a critical signal for commitment of the ovary.

The indifferent mammalian embryonic gonad generates an ovary or testis, but the factors involved are still poorly known. The Wnt-4 signal represents one critical female determinant, since its absence leads to partial female-to-male sex reversal in mouse, but its signalling is as well implicated in the testis development. We used the Wnt-4 deficient mouse as a model to identify candidate gonadogenesis genes, and found that the Notum, Phlda2, Runx-1 and Msx1 genes are typical of the wild-type ovary and the Osr2, Dach2, Pitx2 and Tacr3 genes of the testis. Strikingly, the expression of these latter genes becomes reversed in the Wnt-4 knock-out ovary, suggesting a role in ovarian development. We identified the transcription factor Runx-1 as a Wnt-4 signalling target gene, since it is expressed in the ovary and is reduced upon Wnt-4 knock-out. Consistent with this, introduction of the Wnt-4 signal into early ovary cells ex vivo induces Runx-1 expression, while conversely Wnt-4 expression is down-regulated in the absence of Runx-1. We conclude that the Runx-1 gene can be a Wnt-4 signalling target, and that Runx-1 and Wnt-4 are mutually interdependent in their expression. The changes in gene expression due to the absence of Wnt-4 in gonads reflect the sexually dimorphic role of this signal and its complex gene network in mammalian gonad development.

Identification of candidate miRNA biomarkers from miRNA regulatory network with application to prostate cancer.

BACKGROUND: MicroRNAs (miRNAs) are a class of non-coding regulatory RNAs approximately 22 nucleotides in length that play a role in a wide range of biological processes. Abnormal miRNA function has been implicated in various human cancers including prostate cancer (PCa). Altered miRNA expression may serve as a biomarker for cancer diagnosis and treatment. However, limited data are available on the role of cancer-specific miRNAs. Integrative computational bioinformatics approaches are effective for the detection of potential outlier miRNAs in cancer. METHODS: The human miRNA-mRNA target network was reconstructed by integrating multiple miRNA-mRNA interaction datasets. Paired miRNA and mRNA expression profiling data in PCa versus benign prostate tissue samples were used as another source of information. These datasets were analyzed with an integrated bioinformatics framework to identify potential PCa miRNA signatures. In vitro q-PCR experiments and further systematic analysis were used to validate these prediction results. RESULTS: Using this bioinformatics framework, we identified 39 miRNAs as potential PCa miRNA signatures. Among these miRNAs, 20 had previously been identified as PCa aberrant miRNAs by low-throughput methods, and 16 were shown to be deregulated in other cancers. In vitro q-PCR experiments verified the accuracy of these predictions. miR-648 was identified as a novel candidate PCa miRNA biomarker. Further functional and pathway enrichment analysis confirmed the association of the identified miRNAs with PCa progression. CONCLUSIONS: Our analysis revealed the scale-free features of the human miRNA-mRNA interaction network and showed the distinctive topological features of existing cancer miRNA biomarkers from previously published studies. A novel cancer miRNA biomarker prediction framework was designed based on these observations and applied to prostate cancer study. This method could be applied for miRNA biomarker prediction in other cancers.

Clinical observation of biomimetic mineralized collagen artificial bone putty for bone reconstruction of calcaneus fracture.

This study investigated clinical outcomes of biomimetic mineralized collagen artificial bone putty for bone reconstruction in the treatment of calcaneus fracture. Sixty cases of calcaneal fractures surgically treated with open reduction and internal fixation in our hospital from June 2014-2015 were chosen and randomly divided into two groups, including 30 cases treated with biomimetic mineralized collagen artificial bone putty as treatment group, and 30 cases treated with autogenous ilia as control group. The average follow-up time was 17.2 ± 3.0 months. The results showed that the surgery duration and postoperative drainage volume of treatment group were significantly lower than control group; there were no statistically significant differences in the fracture healing time, American Orthopaedic Foot and Ankle Society scores at 3 and 12 months after surgery, Böhler's angle, Gissane's angle and height of calcaneus between the two groups. There were no significant differences in wound complication and reject reaction between the two groups, while significant difference in donor site complication. As a conclusion, the implantation of biomimetic mineralized collagen artificial bone putty in the open reduction of calcaneal fracture resulted in reliable effect and less complications, which is suitable for clinical applications in the treatment of bone defect in calcaneal fractures.

Knowledge-Guided Bioinformatics Model for Identifying Autism Spectrum Disorder Diagnostic MicroRNA Biomarkers.

Autism spectrum disorder (ASD) is a severe neurodevelopmental disease with a high incidence and effective biomarkers are urgently needed for its diagnosis. A few previous studies have reported the detection of miRNA biomarkers for autism diagnosis, especially those based on bioinformatics approaches. In this study, we developed a knowledge-guided bioinformatics model for identifying autism miRNA biomarkers. We downloaded gene expression microarray data from the GEO Database and extracted genes with expression levels that differed in ASD and the controls. We then constructed an autism-specific miRNA-mRNA network and inferred candidate autism biomarker miRNAs based on their regulatory modes and functions. We defined a novel parameter called the autism gene percentage as autism-specific knowledge to further facilitate the identification of autism-specific biomarker miRNAs. Finally, 11 miRNAs were screened as putative autism biomarkers, where eight miRNAs (72.7%) were significantly dysregulated in ASD samples according to previous reports. Functional enrichment results indicated that the targets of the identified miRNAs were enriched in autism-associated pathways, such as Wnt signaling (in KEGG and IPA), cell cycle (in KEGG), and glioblastoma multiforme signaling (in IPA), thereby supporting the predictive power of our model.

Novel Biomarker MicroRNAs for Subtyping of Acute Coronary Syndrome: A Bioinformatics Approach.

Acute coronary syndrome (ACS) is a life-threatening disease that affects more than half a million people in United States. We currently lack molecular biomarkers to distinguish the unstable angina (UA) and acute myocardial infarction (AMI), which are the two subtypes of ACS. MicroRNAs play significant roles in biological processes and serve as good candidates for biomarkers. In this work, we collected microRNA datasets from the Gene Expression Omnibus database and identified specific microRNAs in different subtypes and universal microRNAs in all subtypes based on our novel network-based bioinformatics approach. These microRNAs were studied for ACS association by pathway enrichment analysis of their target genes. AMI and UA were associated with 27 and 26 microRNAs, respectively, nine of them were detected for both AMI and UA, and five from each subtype had been reported previously. The remaining 22 and 21 microRNAs are novel microRNA biomarkers for AMI and UA, respectively. The findings are then supported by pathway enrichment analysis of the targets of these microRNAs. These novel microRNAs deserve further validation and will be helpful for personalized ACS diagnosis.

MicroRNA biomarker identification for pediatric acute myeloid leukemia based on a novel bioinformatics model.

Acute myeloid leukemia (AML) in children is a complex and heterogeneous disease. The identification of reliable and stable molecular biomarkers for diagnosis, especially early diagnosis, remains a significant therapeutic challenge. Aberrant microRNA expression could be used for cancer diagnosis and treatment selection. Here, we describe a novel bioinformatics model for the prediction of microRNA biomarkers for the diagnosis of paediatric AML based on computational functional analysis of the microRNA regulatory network substructure. microRNA-196b, microRNA-155 and microRNA-25 were identified as putative diagnostic biomarkers for pediatric AML. Further systematic analysis confirmed the association of the predicted microRNAs with the leukemogenesis of AML. In vitro q-PCR experiments showed that microRNA-155 is significantly overexpressed in children with AML and microRNA-196b is significantly overexpressed in subgroups M4-M5 of the French-American-British classification system. These results suggest that microRNA-155 is a potential diagnostic biomarker for all subgroups of paediatric AML, whereas microRNA-196b is specific for subgroups M4-M5.

Identification of microRNAs as potential biomarker for gastric cancer by system biological analysis.

Gastric cancers (GC) have the high morbidity and mortality rates worldwide and there is a need to identify sufficiently sensitive biomarkers for GC. MicroRNAs (miRNAs) could be promising potential biomarkers for GC diagnosis. We employed a systematic and integrative bioinformatics framework to identify GC-related microRNAs from the public microRNA and mRNA expression dataset generated by RNA-seq technology. The performance of the 17 candidate miRNAs was evaluated by hierarchal clustering, ROC analysis, and literature mining. Fourteen have been found to be associated with GC and three microRNAs (miR-211, let-7b, and miR-708) were for the first time reported to associate with GC and may be used for diagnostic biomarkers for GC.

Identification of microRNA as sepsis biomarker based on miRNAs regulatory network analysis.

Sepsis is regarded as arising from an unusual systemic response to infection but the physiopathology of sepsis remains elusive. At present, sepsis is still a fatal condition with delayed diagnosis and a poor outcome. Many biomarkers have been reported in clinical application for patients with sepsis, and claimed to improve the diagnosis and treatment. Because of the difficulty in the interpreting of clinical features of sepsis, some biomarkers do not show high sensitivity and specificity. MicroRNAs (miRNAs) are small noncoding RNAs which pair the sites in mRNAs to regulate gene expression in eukaryotes. They play a key role in inflammatory response, and have been validated to be potential sepsis biomarker recently. In the present work, we apply a miRNA regulatory network based method to identify novel microRNA biomarkers associated with the early diagnosis of sepsis. By analyzing the miRNA expression profiles and the miRNA regulatory network, we obtained novel miRNAs associated with sepsis. Pathways analysis, disease ontology analysis, and protein-protein interaction network (PIN) analysis, as well as ROC curve, were exploited to testify the reliability of the predicted miRNAs. We finally identified 8 novel miRNAs which have the potential to be sepsis biomarkers.