iDVEIP: A computer-aided approach for the prediction of viral entry inhibitory peptides.

With the notable surge in therapeutic peptide development, various peptides have emerged as potential agents against virus-induced diseases. Viral entry inhibitory peptides (VEIPs), a subset of antiviral peptides (AVPs), offer a promising avenue as entry inhibitors (EIs) with distinct advantages over chemical counterparts. Despite this, a comprehensive analytical platform for characterizing these peptides and their effectiveness in blocking viral entry remains lacking. In this study, we introduce a groundbreaking in silico approach that leverages bioinformatics analysis and machine learning to characterize and identify novel VEIPs. Cross-validation results demonstrate the efficacy of a model combining sequence-based features in predicting VEIPs with high accuracy, validated through independent testing. Additionally, an EI type model has been developed to distinguish peptides specifically acting as Eis from AVPs with alternative activities. Notably, we present iDVEIP, a web-based tool accessible at http://mer.hc.mmh.org.tw/iDVEIP/, designed for automatic analysis and prediction of VEIPs. Emphasizing its capabilities, the tool facilitates comprehensive analyses of peptide characteristics, providing detailed amino acid composition data for each prediction. Furthermore, we showcase the tool's utility in identifying EIs against severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2).

iDVIP: identification and characterization of viral integrase inhibitory peptides.

Antiretroviral peptides are a kind of bioactive peptides that present inhibitory activity against retroviruses through various mechanisms. Among them, viral integrase inhibitory peptides (VINIPs) are a class of antiretroviral peptides that have the ability to block the action of integrase proteins, which is essential for retroviral replication. As the number of experimentally verified bioactive peptides has increased significantly, the lack of in silico machine learning approaches can effectively predict the peptides with the integrase inhibitory activity. Here, we have developed the first prediction model for identifying the novel VINIPs using the sequence characteristics, and the hybrid feature set was considered to improve the predictive ability. The performance was evaluated by 5-fold cross-validation based on the training dataset, and the result indicates the proposed model is capable of predicting the VINIPs, with a sensitivity of 85.82%, a specificity of 88.81%, an accuracy of 88.37%, a balanced accuracy of 87.32% and a Matthews correlation coefficient value of 0.64. Most importantly, the model also consistently provides effective performance in independent testing. To sum up, we propose the first computational approach for identifying and characterizing the VINIPs, which can be considered novel antiretroviral therapy agents. Ultimately, to facilitate further research and development, iDVIP, an automatic computational tool that predicts the VINIPs has been developed, which is now freely available at http://mer.hc.mmh.org.tw/iDVIP/.

Integrating In Silico and In Vitro Approaches to Identify Natural Peptides with Selective Cytotoxicity against Cancer Cells.

Anticancer peptides (ACPs) are bioactive compounds known for their selective cytotoxicity against tumor cells via various mechanisms. Recent studies have demonstrated that in silico machine learning methods are effective in predicting peptides with anticancer activity. In this study, we collected and analyzed over a thousand experimentally verified ACPs, specifically targeting peptides derived from natural sources. We developed a precise prediction model based on their sequence and structural features, and the model's evaluation results suggest its strong predictive ability for anticancer activity. To enhance reliability, we integrated the results of this model with those from other available methods. In total, we identified 176 potential ACPs, some of which were synthesized and further evaluated using the MTT colorimetric assay. All of these putative ACPs exhibited significant anticancer effects and selective cytotoxicity against specific tumor cells. In summary, we present a strategy for identifying and characterizing natural peptides with selective cytotoxicity against cancer cells, which could serve as novel therapeutic agents. Our prediction model can effectively screen new molecules for potential anticancer activity, and the results from in vitro experiments provide compelling evidence of the candidates' anticancer effects and selective cytotoxicity.

Transdermal nanolipoplex simultaneously inhibits subcutaneous melanoma growth and suppresses systemically metastatic melanoma by activating host immunity.

Benefit for clinical melanoma treatments, the transdermal neoadjuvant therapy could reduce surgery region and increase immunotherapy efficacy. Using lipoplex (Lipo-PEG-PEI-complex, LPPC) encapsulated doxorubicin (DOX) and carrying CpG oligodeoxynucleotide; the transdermally administered nano-liposomal drug complex (LPPC-DOX-CpG) would have high cytotoxicity and immunostimulatory activity to suppress systemic metastasis of melanoma. LPPC-DOX-CpG dramatically suppressed subcutaneous melanoma growth by inducing tumor cell apoptosis and recruiting immune cells into the tumor area. Animal studies further showed that the colonization and growth of spontaneously metastatic melanoma cells in the liver and lung were suppressed by transdermal LPPC-DOX-CpG. Furthermore, NGS analysis revealed IFN-γ and NF-κB pathways were triggered to recruit and activate the antigen-presenting-cells and effecter cells, which could activate the anti-tumor responses as the major mechanism responsible for the therapeutic effect of LPPC-DOX-CpG. Finally, we have successfully proved transdermal LPPC-DOX-CpG as a promising penetrative carrier to activate systemic anti-tumor immunity against subcutaneous and metastatic tumor.

Associations between Vitamin D Deficiency and Carbohydrate Intake and Dietary Factors in Taiwanese Pregnant Women.

This cross-sectional observation study investigated the vitamin D (VD) status in Taiwanese pregnant women and the effects of VD supplementation and macronutrient intake on serum 25-hydroxy-vitamin D (25[OH]D) level. Data on VD intake, daily sunlight exposure, and carbohydrate intake were obtained from 125 pregnant women at 30−37 weeks' gestation. Serum 25[OH]D level was measured before delivery in all enrolled women; and the mean 25(OH)D level was 43 nmol/L or 17.2 ng/mL. The 25(OH)D level was significantly correlated with total VD intake of pregnant women (r = 0.239; p = 0.007). The severe VD deficiency group (n = 16; mean of 25(OH)D level = 8.5 ng/mL) had significantly lower total VD intake and supplementation than the groups with VD deficiency (n = 69), insufficiency (n = 32), and sufficiency (n = 8). Those with ≥400 IU/day total VD intake (including VD from food and supplementation) had significantly higher 25(OH)D concentration than those with <400 IU/day total VD intake. Those with 400 IU/day VD supplementation could significantly increase serum 25(OH)D concentrations for pregnant women. Among 85 pregnant women with carbohydrate intake of ≥300 g/day, serum 25(OH)D levels were negatively correlated with carbohydrate intake (p = 0.031). In conclusion, VD deficiency was highly prevalent in Taiwanese pregnant women. VD supplementation was the most effective method for increasing 25(OH)D concentration in pregnant women. Higher carbohydrate intake might reduce 25(OH)D levels.

Anticancer peptide Q7 suppresses the growth and migration of human endometrial cancer by inhibiting DHCR24 expression and modulating the AKT-mediated pathway.

Endometrial cancer is one of the most common malignancy affecting women in developed countries. Resection uterus or lesion area is usually the first option for a simple and efficient therapy. Therefore, it is necessary to find a new therapeutic drug to reduce surgery areas to preserve fertility. Anticancer peptides (ACP) are bioactive amino acids with lower toxicity and higher specificity than chemical drugs. This study is to address an ACP, herein named Q7, which could downregulate 24-Dehydrocholesterol Reductase (DHCR24) to disrupt lipid rafts formation, and sequentially affect the AKT signal pathway of HEC-1-A cells to suppress their tumorigenicity such as proliferation and migration. Moreover, lipo-PEI-PEG-complex (LPPC) was used to enhance Q7 anticancer activity in vitro and efficiently show its effects on HEC-1-A cells. Furthermore, LPPC-Q7 exhibited a synergistic effect in combination with doxorubicin or paclitaxel. To summarize, Q7 was firstly proved to exhibit an anticancer effect on endometrial cancer cells and combined with LPPC efficiently improved the cytotoxicity of Q7.

Nanotopography as Artificial Microenvironment for Accurate Visualization of Metastasis Development via Simulation of ECM Dynamics.

Metastatic progression is mediated by complex interactions between deregulated extracellular matrix (ECM) and cancer cells and remains a major challenge in cancer management. To investigate the role of ECM dynamics in promoting metastasis development, we developed an artificial microenvironment (AME) platform comprised of nanodot arrays of increasing diameter. Cells cultured on the platform showed increasing signs of mesenchymal-like cell transition as AME diameter increased, suggesting accurate simulation of ECM-mediated gene regulation. Gene expression was analyzed to determine genes significant to transition, which were then used to select appropriate small molecule drugs for time course treatments. Our results suggest that the platform can identify critical target genes as well as possible drug candidates. Overall, the AME platform allows for the study of intricate ECM-induced gene expression trends across metastasis development that would otherwise be difficult to visualize in vivo and may open new avenues toward successful personalized cancer management.

CXC chemokine ligand-13 promotes metastasis via CXCR5-dependent signaling pathway in non-small cell lung cancer.

The CXC chemokine ligand-13 (CXCL13) is a chemoattractant of B cells and has been implicated in the progression of many cancers. So far, CXCL13 and its related receptor CXCR5 have been proved to regulate cancer cell migration as well as tumour metastasis. However, the role of CXCL13-CXCR5 axis in metastasis of lung cancer is still poorly understood. In this study, we found that CXCL13 and CXCR5 were commonly up-regulated in lung cancer specimens compared with normal tissues among different cohorts. Our evidence showed that CXCL13 obviously promoted migration of lung cancer cells, and this effect was mediated by vascular cell adhesion molecule-1 (VCAM-1) expression. We also confirmed that CXCR5, the major receptor responsible for CXCL13 function, was required for CXCL13-promoted cell migration. We also test the candidate components which are activated after CXCL13 treatment and found that phospholipase C-ß (PLCß), protein kinase C-α (PKCα) and c-Src signalling pathways were involved in CXCL13-promoted cell migration and VCAM-1 expression in lung cancer cells. Finally, CXCL13 stimulated NF-κB transcription factor in lung cancer cells, contributing to VCAM-1 expression in translational level. These evidences propose a novel insight into lung cancer metastasis which is regulated by CXCL13.

iDPGK: characterization and identification of lysine phosphoglycerylation sites based on sequence-based features.

BACKGROUND: Protein phosphoglycerylation, the addition of a 1,3-bisphosphoglyceric acid (1,3-BPG) to a lysine residue of a protein and thus to form a 3-phosphoglyceryl-lysine, is a reversible and non-enzymatic post-translational modification (PTM) and plays a regulatory role in glucose metabolism and glycolytic process. As the number of experimentally verified phosphoglycerylated sites has increased significantly, statistical or machine learning methods are imperative for investigating the characteristics of phosphoglycerylation sites. Currently, research into phosphoglycerylation is very limited, and only a few resources are available for the computational identification of phosphoglycerylation sites. RESULT: We present a bioinformatics investigation of phosphoglycerylation sites based on sequence-based features. The TwoSampleLogo analysis reveals that the regions surrounding the phosphoglycerylation sites contain a high relatively of positively charged amino acids, especially in the upstream flanking region. Additionally, the non-polar and aliphatic amino acids are more abundant surrounding phosphoglycerylated lysine following the results of PTM-Logo, which may play a functional role in discriminating between phosphoglycerylation and non-phosphoglycerylation sites. Many types of features were adopted to build the prediction model on the training dataset, including amino acid composition, amino acid pair composition, positional weighted matrix and position-specific scoring matrix. Further, to improve the predictive power, numerous top features ranked by F-score were considered as the final combination for classification, and thus the predictive models were trained using DT, RF and SVM classifiers. Evaluation by five-fold cross-validation showed that the selected features was most effective in discriminating between phosphoglycerylated and non-phosphoglycerylated sites. CONCLUSION: The SVM model trained with the selected sequence-based features performed well, with a sensitivity of 77.5%, a specificity of 73.6%, an accuracy of 74.9%, and a Matthews Correlation Coefficient value of 0.49. Furthermore, the model also consistently provides the effective performance in independent testing set, yielding sensitivity of 75.7% and specificity of 64.9%. Finally, the model has been implemented as a web-based system, namely iDPGK, which is now freely available at http://mer.hc.mmh.org.tw/iDPGK/ .

Multi-omics profiling reveals microRNA-mediated insulin signaling networks.

BACKGROUND: MicroRNAs (miRNAs) play a key role in mediating the action of insulin on cell growth and the development of diabetes. However, few studies have been conducted to provide a comprehensive overview of the miRNA-mediated signaling network in response to glucose in pancreatic beta cells. In our study, we established a computational framework integrating multi-omics profiles analyses, including RNA sequencing (RNA-seq) and small RNA sequencing (sRNA-seq) data analysis, inverse expression pattern analysis, public data integration, and miRNA targets prediction to illustrate the miRNA-mediated regulatory network at different glucose concentrations in INS-1 pancreatic beta cells (INS-1), which display important characteristics of the pancreatic beta cells. RESULTS: We applied our computational framework to the expression profiles of miRNA/mRNA of INS-1, at different glucose concentrations. A total of 1437 differentially expressed genes (DEGs) and 153 differentially expressed miRNAs (DEmiRs) were identified from multi-omics profiles. In particular, 121 DEmiRs putatively regulated a total of 237 DEGs involved in glucose metabolism, fatty acid oxidation, ion channels, exocytosis, homeostasis, and insulin gene regulation. Moreover, Argonaute 2 immunoprecipitation sequencing, qRT-PCR, and luciferase assay identified Crem, Fn1, and Stc1 are direct targets of miR-146b and elucidated that miR-146b acted as a potential regulator and promising target to understand the insulin signaling network. CONCLUSIONS: In this study, the integration of experimentally verified data with system biology framework extracts the miRNA network for exploring potential insulin-associated miRNA and their target genes. The findings offer a potentially significant effect on the understanding of miRNA-mediated insulin signaling network in the development and progression of pancreatic diabetes.

Genome-wide discovery of viral microRNAs based on phylogenetic analysis and structural evolution of various human papillomavirus subtypes.

In mammals, microRNAs (miRNAs) play key roles in controlling posttranscriptional regulation through binding to the mRNAs of target genes. Recently, it was discovered that viral miRNAs may be involved in human cancers and diseases. It is likely that viral miRNAs help viruses enter the latent phase of their life cycle and become undetected by the host's immune system, while increasing the host's risk for cancer development. Cervical cancer is typically related to the infection of human papillomavirus (HPV) through sexual transmission. To further understand the molecular mechanisms underlying the associations of HPV infection with genital diseases, we developed a systematic method for viral miRNA identification and viral miRNA-mediated regulatory network construction based on genome-wide sequence analysis. The complete genomes of certain high-risk HPV subtypes were used to predict putative viral pre-miRNAs by bioinformatics approaches. In addition, small RNA libraries in human cervical lesions from existing publications were collected to validate the predicted HPV pre-miRNAs. For the construction of virally encoded miRNA-mediated regulatory network of HPV infection, cervical squamous epithelial carcinoma gene expression data were extracted from the RNA sequencing platform in The Cancer Genome Atlas; the differentially expressed genes were used to identify the putative targets of viral miRNAs. Predicted cellular target genes of HPV-encoded miRNAs provide an overview of these viral miRNA's putative functions. Finally, a large-scale genome analysis was carried out to examine the phylogenetic relationship and structural evolution among genital HPV types that have the potential to cause genital cancer. In this study, we discovered putative HPV-encoded miRNAs, which were validated against the small RNA libraries in human cervical lesions. Furthermore, as indicated by their biological functions, host genes targeted by HPV-encoded miRNAs may play significant roles in virus infection and carcinogenesis. These viral miRNAs pose as promising candidates for the development of antiviral drugs. More importantly, the identified subtype-specific miRNAs have the potential to be used as biomarkers for HPV subtype determination.

The relationships of genetic polymorphisms of the long noncoding RNA growth arrest-specific transcript 5 with uterine cervical cancer.

The purposes of the investigation were to examine the implications of long noncoding RNA growth arrest-specific transcript 5 (GAS5) in progression and clinicopathological factors of uterine cervical cancer, and patient survival in Taiwan. Genotypic distributions of two GAS5 genetic variants rs145204276 and rs55829688 were detected in 208 patients including 111 patients with invasive cancer, 97 with precancerous lesions as well as 307 control women using real-time polymerase chain reaction. It explored that patients with cervical precancerous lesion had lower rate of AGGCA deletion (Del) in both alleles (Del/Del) of GAS5 rs145204276 as compared with control women. Patients with invasive cancer did not exhibit higher rate of Del/Del. Meanwhile, there were no different genotypic distributions in rs55829688 among patients with cervical invasive cancer and those with precancerous lesions as well as control women. Moreover, cervical cancer patients with Ins (insertion, AGGCA)/Del and Del/Del (-/-) in GAS5 rs55829688 tended to have poorer hazard ratio (HR) of 5 years survival. In addition, lymph node metastasis status exerted the most significantly predictive of 5 years survival rate. Conclusively, GAS5 polymorphism rs145204276 is probably applicable to predict 5 years survival HR of cervical cancer patients. However, the mechanism elucidating the methylation status and transcription function of rs145204276 in uterine cervical cancer needs to be delineated for its unique implication in uterine cervical cancer.

Lipoplex-based targeted gene therapy for the suppression of tumours with VEGFR expression by producing anti-angiogenic molecules.

BACKGROUND: The anti-angiogenic fusion protein RBDV-IgG1 Fc (RBDV), which comprises the receptor-binding domain of vascular endothelial growth factor-A (VEGF-A), has shown antitumour effects by reducing angiogenesis in vivo. This study used the cationic lipoplex lipo-PEG-PEI-complex (LPPC) to simultaneously encapsulate both the RBDV targeting protein and the RBDV plasmid (pRBDV) without covalent bonds to assess VEGFR targeting gene therapy in mice with melanoma in vivo. RESULTS: LPPC protected the therapeutic transgene from degradation by DNase, and the LPPC/RBDV complexes could specifically target VEGFR-positive B16-F10 cells both in vitro and in vivo. With or without RBDV protein-targeting direction, the pRBDV-expressing RBDV proteins were expressed and reached a maximal concentration on the 7th day in the sera after transfection in vivo and significantly elicited growth suppression against B16-F10 melanoma but not IgG1 control proteins. In particular, LPPC/pRBDV/RBDV treatment with the targeting molecules dramatically inhibited B16-F10 tumour growth in vivo to provide better therapeutic efficacy than the treatments with gene therapy with IgG1 protein targeting or administration of a protein drug with RBDV. CONCLUSIONS: The simultaneous combination of the LPPC complex with pRBDV gene therapy and RBDV protein targeting might be a potential tool to conveniently administer targeted gene therapy for cancer therapy.

miRTarBase update 2018: a resource for experimentally validated microRNA-target interactions.

MicroRNAs (miRNAs) are small non-coding RNAs of ∼ 22 nucleotides that are involved in negative regulation of mRNA at the post-transcriptional level. Previously, we developed miRTarBase which provides information about experimentally validated miRNA-target interactions (MTIs). Here, we describe an updated database containing 422 517 curated MTIs from 4076 miRNAs and 23 054 target genes collected from over 8500 articles. The number of MTIs curated by strong evidence has increased ∼1.4-fold since the last update in 2016. In this updated version, target sites validated by reporter assay that are available in the literature can be downloaded. The target site sequence can extract new features for analysis via a machine learning approach which can help to evaluate the performance of miRNA-target prediction tools. Furthermore, different ways of browsing enhance user browsing specific MTIs. With these improvements, miRTarBase serves as more comprehensively annotated, experimentally validated miRNA-target interactions databases in the field of miRNA related research. miRTarBase is available at http://miRTarBase.mbc.nctu.edu.tw/.

Characterization and identification of lysine glutarylation based on intrinsic interdependence between positions in the substrate sites.

BACKGROUND: Glutarylation, the addition of a glutaryl group (five carbons) to a lysine residue of a protein molecule, is an important post-translational modification and plays a regulatory role in a variety of physiological and biological processes. As the number of experimentally identified glutarylated peptides increases, it becomes imperative to investigate substrate motifs to enhance the study of protein glutarylation. We carried out a bioinformatics investigation of glutarylation sites based on amino acid composition using a public database containing information on 430 non-homologous glutarylation sites. RESULTS: The TwoSampleLogo analysis indicates that positively charged and polar amino acids surrounding glutarylated sites may be associated with the specificity in substrate site of protein glutarylation. Additionally, the chi-squared test was utilized to explore the intrinsic interdependence between two positions around glutarylation sites. Further, maximal dependence decomposition (MDD), which consists of partitioning a large-scale dataset into subgroups with statistically significant amino acid conservation, was used to capture motif signatures of glutarylation sites. We considered single features, such as amino acid composition (AAC), amino acid pair composition (AAPC), and composition of k-spaced amino acid pairs (CKSAAP), as well as the effectiveness of incorporating MDD-identified substrate motifs into an integrated prediction model. Evaluation by five-fold cross-validation showed that AAC was most effective in discriminating between glutarylation and non-glutarylation sites, according to support vector machine (SVM). CONCLUSIONS: The SVM model integrating MDD-identified substrate motifs performed well, with a sensitivity of 0.677, a specificity of 0.619, an accuracy of 0.638, and a Matthews Correlation Coefficient (MCC) value of 0.28. Using an independent testing dataset (46 glutarylated and 92 non-glutarylated sites) obtained from the literature, we demonstrated that the integrated SVM model could improve the predictive performance effectively, yielding a balanced sensitivity and specificity of 0.652 and 0.739, respectively. This integrated SVM model has been implemented as a web-based system (MDDGlutar), which is now freely available at http://csb.cse.yzu.edu.tw/MDDGlutar/ .

Resistin Enhances Monocyte Chemoattractant Protein-1 Production in Human Synovial Fibroblasts and Facilitates Monocyte Migration.

BACKGROUND/AIMS: The adipocyte-secreting adipokine, resistin, may play a critical role in the modulation of inflammatory diseases. Migration and infiltration of mononuclear cells into inflammatory sites are critical events during the development of osteoarthritis (OA). Monocyte chemoattractant protein-1 (MCP-1), also known as chemokine ligand 2 (CCL2), plays a critical role in the regulation of monocyte migration and infiltration. In this study, we show how resistin promotes MCP-1 expression in OA synovial fibroblasts and monocyte migration. METHODS: We used qPCR to detect MCP-1 and miRNA expression. THP-1 migration was investigated by Transwell assay. The Western blotting was used to examine the resistinmediated signaling pathways. RESULTS: Resistin activated the phosphatidylinositol-3-kinase (PI3K), Akt and mammalian target of rapamycin (mTOR) signaling pathways, while PI3K, Akt and mTOR inhibitors or small interfering RNAs diminished resistin-induced MCP-1 expression and monocyte migration. We also demonstrate that resistin stimulates MCP-1mediated monocyte migration by suppressing microRNA (miR)-33a and miR-33b via the PI3K, Akt and mTOR signaling pathways. CONCLUSION: These results provide new insights into the mechanisms of resistin action that may have therapeutic implications for patients with OA.

Vaginal microbiome variances in sample groups categorized by clinical criteria of bacterial vaginosis.

BACKGROUND: One of the most common and recurrent vaginal infections is bacterial vaginosis (BV). The diagnosis is based on changes to the "normal" vaginal microbiome; however, the normal microbiome appears to differ according to reproductive status and ethnicity, and even among individuals within these groups. The Amsel criteria and Nugent score test are widely used for diagnosing BV; however, these tests are based on different criteria, and so may indicate distinct changes in the vaginal microbial community. Nevertheless, few studies have compared the results of these test against metagenomics analysis. METHODS: Vaginal flora samples from 77 participants were classified according to the Amsel criteria and Nugent score test. The microbiota composition was analyzed using 16S ribosome RNA gene amplicon sequencing. Bioinformatics analysis and multivariate statistical analysis were used to evaluate the microbial diversity and function. RESULTS: Only 3 % of the participants diagnosed BV negative using the Amsel criteria (A-) were BV-positive according to the Nugent score test (N+), while over half of the BV-positive patients using the Amsel criteria (A+) were BV-negative according to the Nugent score test (N-). Thirteen genera showed significant differences in distribution among BV status defined by BV tests (e.g., A - N-, A + N- and A + N+). Variations in the four most abundant taxa, Lactobacillus, Gardnerella, Prevotella, and Escherichia, were responsible for most of this dissimilarity. Furthermore, vaginal microbial diversity differed significantly among the three groups classified by the Nugent score test (N-, N+, and intermediate flora), but not between the Amsel criteria groups. Numerous predictive microbial functions, such as bacterial chemotaxis and bacterial invasion of epithelial cells, differed significantly among multiple BV test, but not between the A- and A+ groups. CONCLUSIONS: Metagenomics analysis can greatly expand our current understanding of vaginal microbial diversity in health and disease. Metagenomics profiling may also provide more reliable diagnostic criteria for BV testing.

Gut microbiome changes in overweight male adults following bowel preparation.

BACKGROUND: Human gut microbiome has an essential role in human health and disease. Although the major dominant microbiota within individuals have been reported, the change of gut microbiome caused by external factors, such as antibiotic use and bowel cleansing, remains unclear. We conducted this study to investigate the change of gut microbiome in overweight male adults after bowel preparation, where none of the participants had been diagnosed with any systemic diseases. METHODS: A total of 20 overweight, male Taiwanese adults were recruited, and all participants were omnivorous. The participants provided fecal samples and blood samples at three time points: prior to bowel preparation, 7 days after colonoscopy, and 28 days after colonoscopy. The microbiota composition in fecal samples was analyzed using 16S ribosome RNA gene amplicon sequencing. RESULTS: Our results demonstrated that the relative abundance of the most dominant bacteria hardly changed from prior to bowel preparation to 28 days after colonoscopy. Using the ratio of Prevotella to the sum of Prevotella and Bacteroides in the fecal samples at baseline, the participants were separated into two groups. The fecal samples of the Type 1 group was Bacteroides-dominant, and that of the Type 2 group was Prevotella-dominant with a noticeable presence Bacteroides. Bulleidia appears more in the Type 1 fecal samples, while Akkermensia appears more in the Type 2 fecal samples. Of each type, the gut microbial diversity differed slightly among the three collection times. Additionally, the Type 2 fecal microbiota was temporarily susceptible to bowel cleansing. Predictive functional analysis of microbial community reveals that their activities for the mineral absorption metabolism and arachidonic acid metabolism differed significantly between the two types. Depending on their fecal type, the variance of triglycerides and C-reactive protein also differed between the two types of participants. CONCLUSIONS: Depending upon the fecal type, the microbial diversity and the predictive functional modules of microbial community differed significantly after bowel preparation. In addition, blood biochemical markers presented somewhat associated with fecal type. Therefore, our results might provide some insights as to how knowledge of the microbial community could be used to promote health through personalized clinical treatment.

Significant association of long non-coding RNAs HOTAIR genetic polymorphisms with cancer recurrence and patient survival in patients with uterine cervical cancer.

Up to date, no study explores the relationship of single nucleotide polymorphisms (SNPs) of long non-coding RNAs HOTAIR (lncRNAs HOTAIR) with cancer recurrence and patient survival in uterine cervical cancer for Taiwanese women. We therefore designed this study to investigate the clinical roles of lncRNAs HOTAIR SNPs in cervical cancer. One hundred and sixteen patients with cervical invasive cancer and 96 patients with preinvasive lesions as well as 318 control women were consecutively recruited. LncRNAs HOTAIR SNPs rs920778, rs12427129, rs4759314 and rs1899663 were analyzed and their genotypic frequencies were examined by real-time polymerase chain reaction. The results indicated that there were no genotypic differences between patients with cervical neoplasia and normal controls as well as among patients with invasive and invasive cancer, and normal controls. However, genotype GG in lncRNAs HOTAIR SNP rs920778 was demonstrated to be a predictor for poorer cancer recurrence probability [p=0.001, hazard ratio (HR): 7.25, 95% CI: 2.19-23.96]. Furthermore, cervical cancer patients with genotype GG in lncRNAs HOTAIR rs920778 had worse overall survival (p =0.002, HR: 7.22, 95% CI: 2.09-24.92). No significant associations exhibited between lncRNAs HOTAIR SNP rs920778 and clinicopathological parameters. In conclusion, this studied lncRNAs HOTAIR SNPs are not associated with cervical carcinongensis. However, lncRNAs HOTAIR SNP rs920778 may be regarded as an independent predictor of cancer recurrence probability and overall survival in cervical cancer patients.

CircNet: a database of circular RNAs derived from transcriptome sequencing data.

Circular RNAs (circRNAs) represent a new type of regulatory noncoding RNA that only recently has been identified and cataloged. Emerging evidence indicates that circRNAs exert a new layer of post-transcriptional regulation of gene expression. In this study, we utilized transcriptome sequencing datasets to systematically identify the expression of circRNAs (including known and newly identified ones by our pipeline) in 464 RNA-seq samples, and then constructed the CircNet database (http://circnet.mbc.nctu.edu.tw/) that provides the following resources: (i) novel circRNAs, (ii) integrated miRNA-target networks, (iii) expression profiles of circRNA isoforms, (iv) genomic annotations of circRNA isoforms (e.g. 282 948 exon positions), and (v) sequences of circRNA isoforms. The CircNet database is to our knowledge the first public database that provides tissue-specific circRNA expression profiles and circRNA-miRNA-gene regulatory networks. It not only extends the most up to date catalog of circRNAs but also provides a thorough expression analysis of both previously reported and novel circRNAs. Furthermore, it generates an integrated regulatory network that illustrates the regulation between circRNAs, miRNAs and genes.