Comprehensive analysis of novel prognosis-related proteomic signature effectively improve risk stratification and precision treatment for patients with cervical cancer.

OBJECTIVE: Cervical cancer (CC) is one of the most common types of malignant female cancer, and its incidence and mortality are not optimistic. Protein panels can be a powerful prognostic factor for many types of cancer. The purpose of our study was to investigate a proteomic panel to predict the survival of patients with common CC. METHODS AND RESULTS: The protein expression and clinicopathological data of CC were downloaded from The Cancer Proteome Atlas and The Cancer Genome Atlas database, respectively. We selected the prognosis-related proteins (PRPs) by univariate Cox regression analysis and found that the results of functional enrichment analysis were mainly related to apoptosis. We used Kaplan-Meier analysis and multivariable Cox regression analysis further to screen PRPs to establish a prognostic model, including BCL2, SMAD3, and 4EBP1-pT70. The signature was verified to be independent predictors of OS by Cox regression analysis and the area under curves. Nomogram and subgroup classification were established based on the signature to verify its clinical application. Furthermore, we looked for the co-expressed proteins of three-protein panel as potential prognostic proteins. CONCLUSION: A proteomic signature independently predicted OS of CC patients, and the predictive ability was better than the clinicopathological characteristics. This signature can help improve prediction for clinical outcome and provides new targets for CC treatment.

The Weight-Based Feature Selection (WBFS) Algorithm Classifies Lung Cancer Subtypes Using Proteomic Data.

Feature selection plays an important role in improving the performance of classification or reducing the dimensionality of high-dimensional datasets, such as high-throughput genomics/proteomics data in bioinformatics. As a popular approach with computational efficiency and scalability, information theory has been widely incorporated into feature selection. In this study, we propose a unique weight-based feature selection (WBFS) algorithm that assesses selected features and candidate features to identify the key protein biomarkers for classifying lung cancer subtypes from The Cancer Proteome Atlas (TCPA) database and we further explored the survival analysis between selected biomarkers and subtypes of lung cancer. Results show good performance of the combination of our WBFS method and Bayesian network for mining potential biomarkers. These candidate signatures have valuable biological significance in tumor classification and patient survival analysis. Taken together, this study proposes the WBFS method that helps to explore candidate biomarkers from biomedical datasets and provides useful information for tumor diagnosis or therapy strategies.

Fusion-expressed CtxB-TcpA-C-CPE improves both systemic and mucosal humoral and T-cell responses against cholera in mice.

BACKGROUND: Development of an effective oral vaccine against Cholera, a life-threatening dehydrating diarrheal disease, proved to be a challenging task. To improve oral subunit vaccine immunogenicity and to prevent the state of oral tolerance, application of mucosal adjuvants might be a promising approach. In the present study, the CtxB-TcpA-C-CPE fusion was constructed in which CtxB and C-CPE were used as mucosal adjuvants and vaccine delivery system, respectively, to induce mucosal immune responses, and to improve the anti-toxin and anti-colonizing immunity against V. cholerae. MATERIALS & METHODS: The fusion construct was synthesized, sub-cloned in pQE30 and expressed in E. coli. The three antigen, making the fusion protein, were also separately expressed in E. coli. The recombinant proteins were purified by affinity chromatography using Ni-NTA agarose. Western blot analysis using anti-His antibody was applied to confirm identity of the purified proteins. BALB/c mice were subcutaneously immunized with CtxB, TcpA, C-CPE and the fusion protein CtxB-TcpA-C-CPE separately. The mice were orally immunized (in 3 boosts) by the same vaccine. Mucosal immune response stimulation was evaluated by measuring the levels of intestinal IgA. Systemic immune response was evaluated by measuring total serum IgG, IgG1, IgG2a, IgG2b subclasses, and also IL-4, IL-5, IL-10 and IFN-γ cytokines in spleen cell culture. RESULTS: The recombinant proteins CtxB, TcpA, C-CPE and the fusion protein CtxB-TcpA-C-CPE were expressed in E. coli and highly purified in a single step of chromatography. BALB/c mice immunized with the fusion protein had highest levels of intestinal IgA, serum IgG and IgG subclasses, compared to each of the three proteins making the fusion. Moreover, stimulated splenocytes of mice immunized with the fusion protein displayed significantly higher amounts of IL-5 and IFN-ɣ cytokines. Th2 dominance of the immune response was more evident in mice receiving the fusion protein. CONCLUSION: Inclusion of CtxB, as the mucosal adjuvant, and C-CPE, as the vaccine delivery system, in the fusion protein CtxB-TcpA-C-CPE significantly enhanced the elicited mucosal and systemic immune responses, compared to TcpA alone. Of note, significant production of intestinal IgA in mice immunized with the fusion protein is presumably capable of neutralizing TcpA, CtxB and C-CPE antigens, preventing V. cholera colonization, and toxic function of CtxB and C-CPE. Challenge infection of the immunized mice is required to evaluate protective potential of the fusion protein against V. cholera.

Modulatory effect of Vibrio cholerae toxin co-regulated pilus on mucins, toll-like receptors and NOD genes expression in co-culture model of Caco-2 and peripheral blood mononuclear cells (PBMC).

Vibrio cholerae, the causative agent of cholera, tend to colonize the small intestine as a Gram-negative pathogen. The intestinal mucus layer forms mucin physical barrier, consisted of high molecular weight proteins. Regarding the role of toxin-coregulated pilus (TCP) as one of the most important colonization factors of V. cholerae, this experimental study was designed to determine the role of TcpA in induction of mucin production and its regulatory effect on innate immunity molecules including toll like receptors (TLRs) and Nucleotide-binding oligomerization domain-containing proteins (NODs) using Caco2- PBMC co-cultures as an interactive model. The rTcpA protein of V. cholerae was expressed in BL21 Escherichia coli, purified using Ni-column chromatography and confirmed by western blotting. Nontoxic doses of rTcpA was determined on Caco-2 cell lines and different concentrations of rTcpA (1, 5, 10 and 50 µg/mL) showed a statistically significant effect on the expression of muc genes (MUC3 and MUC4) in a dose-dependent manner. This finding is supposed to facilitate physical adhesion and colonization of V. cholerae in intestinal lumen. The rTcpA moderately stimulated the expression of tlr4 and overexpressed tlr1, both of which are supposed to induce a mucosal protective response against bacterial infection. NOD2 was significantly increased which suggests that it may contribute in pro-inflammatory responses observed in cholera disease. No change in NOD1 expression was seen which might be attributed to the non-invasive nature of V. cholerae as an intestinal pathogen. In conclusion, the rTcpA protein of V. cholerae showed a statistically significant modulatory effect on the human gut epithelium gene expression which would help promising protection in prophylaxis applications.

Identification of a protein signature for predicting overall survival of hepatocellular carcinoma: a study based on data mining.

BACKGROUND: Hepatocellular carcinoma (HCC), is the fifth most common cancer in the world and the second most common cause of cancer-related deaths. Over 500,000 new HCC cases are diagnosed each year. Combining advanced genomic analysis with proteomic characterization not only has great potential in the discovery of useful biomarkers but also drives the development of new diagnostic methods. METHODS: This study obtained proteomic data from Clinical Proteomic Tumor Analysis Consortium (CPTAC) and validated in The Cancer Proteome Atlas (TCPA) and TCGA dataset to identify HCC biomarkers and the dysfunctional of proteogenomics. RESULTS: The CPTAC database contained data for 159 patients diagnosed with Hepatitis-B related HCC and 422 differentially expressed proteins (112 upregulated and 310 downregulated proteins). Restricting our analysis to the intersection in survival-related proteins between CPTAC and TCPA database revealed four coverage survival-related proteins including PCNA, MSH6, CDK1, and ASNS. CONCLUSION: This study established a novel protein signature for HCC prognosis prediction using data retrieved from online databases. However, the signatures need to be verified using independent cohorts and functional experiments.

Bayesian data integration and variable selection for pan-cancer survival prediction using protein expression data.

Accurate prognostic prediction using molecular information is a challenging area of research, which is essential to develop precision medicine. In this paper, we develop translational models to identify major actionable proteins that are associated with clinical outcomes, like the survival time of patients. There are considerable statistical and computational challenges due to the large dimension of the problems. Furthermore, data are available for different tumor types; hence data integration for various tumors is desirable. Having censored survival outcomes escalates one more level of complexity in the inferential procedure. We develop Bayesian hierarchical survival models, which accommodate all the challenges mentioned here. We use the hierarchical Bayesian accelerated failure time model for survival regression. Furthermore, we assume sparse horseshoe prior distribution for the regression coefficients to identify the major proteomic drivers. We borrow strength across tumor groups by introducing a correlation structure among the prior distributions. The proposed methods have been used to analyze data from the recently curated "The Cancer Proteome Atlas" (TCPA), which contains reverse-phase protein arrays-based high-quality protein expression data as well as detailed clinical annotation, including survival times. Our simulation and the TCPA data analysis illustrate the efficacy of the proposed integrative model, which links different tumors with the correlated prior structures.

miRFA: an automated pipeline for microRNA functional analysis with correlation support from TCGA and TCPA expression data in pancreatic cancer.

BACKGROUND: MicroRNAs (miRNAs) are small RNAs that regulate gene expression at a post-transcriptional level and are emerging as potentially important biomarkers for various disease states, including pancreatic cancer. In silico-based functional analysis of miRNAs usually consists of miRNA target prediction and functional enrichment analysis of miRNA targets. Since miRNA target prediction methods generate a large number of false positive target genes, further validation to narrow down interesting candidate miRNA targets is needed. One commonly used method correlates miRNA and mRNA expression to assess the regulatory effect of a particular miRNA. The aim of this study was to build a bioinformatics pipeline in R for miRNA functional analysis including correlation analyses between miRNA expression levels and its targets on mRNA and protein expression levels available from the cancer genome atlas (TCGA) and the cancer proteome atlas (TCPA). TCGA-derived expression data of specific mature miRNA isoforms from pancreatic cancer tissue was used. RESULTS: Fifteen circulating miRNAs with significantly altered expression levels detected in pancreatic cancer patients were queried separately in the pipeline. The pipeline generated predicted miRNA target genes, enriched gene ontology (GO) terms and Kyoto encyclopedia of genes and genomes (KEGG) pathways. Predicted miRNA targets were evaluated by correlation analyses between each miRNA and its predicted targets. MiRNA functional analysis in combination with Kaplan-Meier survival analysis suggest that hsa-miR-885-5p could act as a tumor suppressor and should be validated as a potential prognostic biomarker in pancreatic cancer. CONCLUSIONS: Our miRNA functional analysis (miRFA) pipeline can serve as a valuable tool in biomarker discovery involving mature miRNAs associated with pancreatic cancer and could be developed to cover additional cancer types. Results for all mature miRNAs in TCGA pancreatic adenocarcinoma dataset can be studied and downloaded through a shiny web application at https://emmbor.shinyapps.io/mirfa/ .

Novel Cholera Toxin Variant and ToxT Regulon in Environmental Vibrio mimicus Isolates: Potential Resources for the Evolution of Vibrio cholerae Hybrid Strains.

Atypical El Tor strains of Vibrio cholerae O1 harboring variant ctxB genes of cholera toxin (CT) have gradually become a major cause of recent cholera epidemics. Vibrio mimicus occasionally produces CT, encoded by ctxAB on CTXФ genome; toxin-coregulated pilus (TCP), a major intestinal colonization factor; and also the CTXФ-specific receptor. This study carried out extensive molecular characterization of CTXФ and ToxT regulon in V. mimicusctx-positive (ctx+) strains (i.e., V. mimicus strains containing ctx) isolated from the Bengal coast. Southern hybridization, PCR, and DNA sequencing of virulence-related genes revealed the presence of an El Tor type CTX prophage (CTXET) carrying a novel ctxAB, tandem copies of environmental type pre-CTX prophage (pre-CTXEnv), and RS1 elements, which were organized as an RS1-CTXET-RS1-pre-CTXEnv-pre-CTXEnv array. Additionally, novel variants of tcpA and toxT, respectively, showing phylogenetic lineage to a clade of V. cholerae non-O1 and to a clade of V. cholerae non-O139, were identified. The V. mimicus strains lacked the RTX (repeat in toxin) and TLC (toxin-linked cryptic) elements and lacked Vibrio seventh-pandemic islands of the El Tor strains but contained five heptamer (TTTTGAT) repeats in ctxAB promoter region similar to those seen with some classical strains of V. cholerae O1. Pulsed-field gel electrophoresis (PFGE) analysis showed that all the ctx+V. mimicus strains were clonally related. However, their in vitro CT production and in vivo toxigenicity characteristics were variable, which could be explainable by differential transcription of virulence genes along with the ToxR regulon. Taken together, our findings strongly suggest that environmental V. mimicus strains act as a potential reservoir of atypical virulence factors, including variant CT and ToxT regulons, and may contribute to the evolution of V. cholerae hybrid strains.IMPORTANCE Natural diversification of CTXФ and ctxAB genes certainly influences disease severity and shifting patterns in major etiological agents of cholera, e.g., the overwhelming emergence of hybrid El Tor variants, replacing the prototype El Tor strains of V. cholerae This report, showing the occurrence of CTXET comprising a novel variant of ctxAB in V. mimicus, points out a previously unnoticed evolutionary event that is independent of the evolutionary event associated with the El Tor strains of V. cholerae Identification and cluster analysis of the newly discovered alleles of tcpA and toxT suggest their horizontal transfer from an uncommon clone of V. cholerae The genomic contents of ToxT regulon and of tandemly arranged multiple pre-CTXФEnv and of a CTXФET in V. mimicus probably act as salient raw materials that induce natural recombination among the hallmark virulence genes of hybrid V. cholerae strains. This report provides valuable information to enrich our knowledge on the evolution of new variant CT and ToxT regulons.

TcpA, a novel Yersinia ruckeri TIR-containing virulent protein mediates immune evasion by targeting MyD88 adaptors.

TIR domain-containing protein is an important member for some bacterial pathogens to subvert host defenses. Here we described a fish virulent Yersinia ruckeri SC09 strain that interfered directly with Toll-like receptor (TLR) function by a TIR-containing protein. Firstly, the novel TIR-containing protein was identified by bioinformatics analysis and named as TcpA. Secondly, the toxic effects of TcpA in fish was demonstrated in vivo challenge experiments through knockout mutant and complement mutant of tcpA gene. Thirdly, The study in vitro revealed that TcpA could down-regulate the expression and secretion of IL-6, IL-1ß and TNF-α. Finally, we demonstrated that TcpA could inhibit the TLR signaling pathway through interaction with myeloid differentiation factor 88 (MyD88) in experiments such as NF-κB dependent luciferase reporter system, co-immunoprecipitation, GST pull-down and yeast two-hybrid. The study revealed that TcpA was essential for virulence and was able to interact with the TIR adaptor protein MyD88 and inhibit the pre-inflammatory signal of immune cells and promote the intracellular survival of pathogenic Yersinia ruckeri SC09 strain. In conclusion, our results showed that TcpA acted as a new virulence factor in Y. ruckeri could suppress innate immune response and increase virulence by inhibiting TLR and MyD88-mediated specific signaling, highlighting a novel strategy for innate immune evasion in bacteria.

Analytical Platforms 3: Processing Samples via the RPPA Pipeline to Generate Large-Scale Data for Clinical Studies.

Reverse phase protein array (RPPA) is a functional proteomics technology amenable to moderately high throughputs of samples and antibodies. The University of Texas MD Anderson Cancer Center RPPA Core Facility has implemented various processes and techniques to maximize RPPA throughput; key among them are maximizing array configuration and relying on database management and automation. One major tool used by the RPPA Core is a semi-automated RPPA process management system referred to as the RPPA Pipeline. The RPPA Pipeline, developed with the aid of MD Avnderson's Department of Bioinformatics and Computational Biology and InSilico Solutions, has streamlined sample and antibody tracking as well as advanced quality control measures of various RPPA processes. This chapter covers RPPA Core processes associated with the RPPA Pipeline workflow from sample receipt to sample printing to slide staining and RPPA report generation that enables the RPPA Core to process at least 13,000 samples per year with approximately 450 individual RPPA-quality antibodies. Additionally, this chapter will cover results of large-scale clinical sample processing, including The Cancer Genome Atlas Project and The Cancer Proteome Atlas.

Changes in TcpA gene frequency explain 2,4,6-trichlorophenol degradation in mesocosms.

Soils are often polluted by chlorophenols in timber production areas in the northern hemisphere. The tcpA gene encodes the first step of 2,4,6-trichlorophenol (246-TCP) degradation. We tested tcpA gene frequency in three natural pristine soils with different 246-TCP degradation capacity. Gene tcpA frequency increased more in spiked than non-spiked 10-L pails containing coniferous humus soil with high degradation capacity, in contrast to soils where degradation was slower. As the soil in each mesocosm originated from a spatially separate field plot, changes in tcpA gene frequency affected 246-TCP degradation over a range of soil origins. This indicates that the abundance of and changes in tcpA gene frequency could be utilized in estimating the efficacy of natural attenuation and biostimulation treatments in controlled conditions.

Functional Proteomic Profiling Analysis in Four Major Types of Gastrointestinal Cancers.

Gastrointestinal (GI) cancer accounts for one in four cancer cases and one in three cancer-related deaths globally. A deeper understanding of cancer development mechanisms can be applied to cancer medicine. Comprehensive sequencing applications have revealed the genomic landscapes of the common types of human cancer, and proteomics technology has identified protein targets and signalling pathways related to cancer growth and progression. This study aimed to explore the functional proteomic profiles of four major types of GI tract cancer based on The Cancer Proteome Atlas (TCPA). We provided an overview of functional proteomic heterogeneity by performing several approaches, including principal component analysis (PCA), partial least squares discriminant analysis (PLS-DA), t-stochastic neighbour embedding (t-SNE) analysis, and hierarchical clustering analysis in oesophageal carcinoma (ESCA), stomach adenocarcinoma (STAD), colon adenocarcinoma (COAD), and rectum adenocarcinoma (READ) tumours, to gain a system-wide understanding of the four types of GI cancer. The feature selection approach, mutual information feature selection (MIFS) method, was conducted to screen candidate protein signature subsets to better distinguish different cancer types. The potential clinical implications of candidate proteins in terms of tumour progression and prognosis were also evaluated based on TCPA and The Cancer Genome Atlas (TCGA) databases. The results suggested that functional proteomic profiling can identify different patterns among the four types of GI cancers and provide candidate proteins for clinical diagnosis and prognosis evaluation. We also highlighted the application of feature selection approaches in high-dimensional biological data analysis. Overall, this study could improve the understanding of the complexity of cancer phenotypes and genotypes and thus be applied to cancer medicine.

Identification of a Novel Protein-Based Prognostic Model in Gastric Cancers.

Gastric cancer (GC) is the third leading cause of cancer-related deaths worldwide. However, there are still no reliable biomarkers for the prognosis of this disease. This study aims to construct a robust protein-based prognostic prediction model for GC patients. The protein expression data and clinical information of GC patients were downloaded from the TCPA and TCGA databases, and the expressions of 218 proteins in 352 GC patients were analyzed using bioinformatics methods. Additionally, Kaplan-Meier (KM) survival analysis and univariate and multivariate Cox regression analysis were applied to screen the prognosis-related proteins for establishing the prognostic prediction risk model. Finally, five proteins, including NDRG1_pT346, SYK, P90RSK, TIGAR, and XBP1, were related to the risk prognosis of gastric cancer and were selected for model construction. Furthermore, a significant trend toward worse survival was found in the high-risk group (p = 1.495 × 10-7). The time-dependent ROC analysis indicated that the model had better specificity and sensitivity compared to the clinical features at 1, 2, and 3 years (AUC = 0.685, 0.673, and 0.665, respectively). Notably, the independent prognostic analysis results revealed that the model was an independent prognostic factor for GC patients. In conclusion, the robust protein-based model based on five proteins was established, and its potential benefits in the prognostic prediction of GC patients were demonstrated.

Construction and Validation of a Protein-associated Prognostic Model for Gastrointestinal Cancer.

Background Gastrointestinal cancer (GIC) is a prevalent and lethal malignant tumor. It is obligatory to investigate innovative biomarkers for the diagnosis and prognosis. Proteins play a crucial role in regulating the occurrence and progression of GIC. However, the prognostic value of proteins is unclear in GIC. OBJECTIVE: This paper aims to identify the hub prognosis-related proteins (PAPs) and construct a prognosis model for GIC patients for clinical application. METHODS: Protein expression data of GIC was obtained from The Cancer Proteome Atlas (TCPA) and downloaded the clinicopathological data from The Cancer Genome Atlas database (TCGA). Besides, hub proteins were filtrated via univariate and multivariate Cox regression analysis. Moreover, survival analysis and nomogram were used to predict overall survival (OS). We used the calibration curves to assess the consistency of predictive and actual survival rates. The consistency index (C-index) was used to evaluate the prognostic ability of the predictive model. Furthermore, functional enrichment analysis and protein co-expression of PAPs were used to explore their roles in GIC. RESULTS: Finally, a prognosis model was conducted based on ten PAPs (CYCLIND1, DVL3, NCADHERIN, SYK, ANNEXIN VII, CD20, CMET, RB, TFRC, and PREX1). The risk score calculated by the model was an independent prognostic predictor. Compared with the high-risk subgroup, the low-risk subgroup had better OS. In the TCGA cohort, the area under the curve value of the receiver operating characteristic curve of the prognostic model was 0.692. The expression of proteins and risk score had a significant association with the clinicopathological characteristics of GIC. Besides, a nomogram based on GIC clinicopathological features and risk scores could properly predict the OS of individual GIC patients. The C-index is 0.71 in the TCGA cohort and 0.73 in the GEO cohort. CONCLUSION: The results indicate that the risk score is an independent prognostic biomarker and is related to the malignant clinical features of GIC patients. Besides, several PAPs associated with the survival and clinicopathological characteristics of GIC might be potential biomarkers for GIC diagnosis and treatment.

TcpA from the Clostridium perfringens plasmid pCW3 is more closely related to the DNA translocase FtsK than to coupling proteins.

Conjugative DNA transfer is a major factor in the dissemination of antibiotic resistance and virulence genes. In the Gram-positive pathogen Clostridium perfringens, the majority of conjugative plasmids share the conserved tcp locus that governs the assembly of the transfer system. Here, we describe multiple structures of the coupling protein TcpA, an essential ATPase that is suggested to provide the mechanical force to propel the DNA through the transfer apparatus. The structures of TcpA in the presence and absence of nucleotides revealed conformational rearrangements and highlight a crucial role for the unstructured C terminus. Our findings reveal that TcpA shares most structural similarity with the FtsK DNA translocase, a central component of the bacterial cell division machinery. Our structural data suggest that conjugation in C. perfringens may have evolved from the bacterial chromosome segregation system and, accordingly, suggest the possibility that double-stranded DNA is transferred through the Tcp conjugation apparatus.

Using feature selection and Bayesian network identify cancer subtypes based on proteomic data.

The Cancer Proteome Atlas (TCPA) project collects reverse-phase protein arrays (RPPA)-based proteome datasets from nearly 8000 samples across 32 cancer types. This study aims to investigate the pan-cancer proteome signature and identify cancer subtypes of glioma, kidney cancer, and lung cancer based on TCPA data. We first visualized the tumor clustering models using t-distributed stochastic neighbour embedding (t-SNE) and bi-clustering heatmap. Then, three feature selection methods (pyHSICLasso, XGBoost, and Random Forest) were performed to select protein features for classifying cancer subtypes in training dataset, and the LibSVM algorithm was empolyed to test classification accuracy in the validation dataset. Clustering analysis revealed that different kinds of tumors have relatively distinct proteomic profiling based on tissue or origin. We identified 20, 10, and 20 protein features with the highest accuracies in classifying subtypes of glioma, kidney cancer, and lung cancer, respectively. The predictive abilities of the selected proteins were confirmed by receiving operating characteristic (ROC) analysis. Finally, the Bayesian network was utilized to explore the protein biomarkers that have direct causal relationships with cancer subtypes. Overall, we highlight the theoretical and technical applications of machine learning based feature selection approaches in the analysis of high-throughput biological data, particularly for cancer biomarker research. SIGNIFICANCE: Functional proteomics is a powerful approach for characterizing cell signaling pathways and understanding their phenotypic effects on cancer development. The TCPA database provides a platform to explore and analyze TCGA pan-cancer RPPA-based protein expression. With the advent of the RPPA technology, the availability of high-throughput data in TCPA platform has made it possible to use machine learning methods to identify protein biomarkers and further differentiate subtypes of cancer based on proteomic data. In this study, we highlight the role of feature selection and Bayesian network in discovery protein biomarker for classifying cancer subtypes based on functional proteomic data. The application of machine learning methods in the analysis of high-throughput biological data, particularly for cancer biomarker researches, which have potential clinical values in developing individualized treatment strategies.

Structural and catalytic differences between two FADH(2)-dependent monooxygenases: 2,4,5-TCP 4-monooxygenase (TftD) from Burkholderia cepacia AC1100 and 2,4,6-TCP 4-monooxygenase (TcpA) from Cupriavidus necator JMP134.

2,4,5-TCP 4-monooxygenase (TftD) and 2,4,6-TCP 4-monooxygenase (TcpA) have been discovered in the biodegradation of 2,4,5-trichlorophenol (2,4,5-TCP) and 2,4,6-trichlorophenol (2,4,6-TCP). TcpA and TftD belong to the reduced flavin adenine dinucleotide (FADH(2))-dependent monooxygenases and both use 2,4,6-TCP as a substrate; however, the two enzymes produce different end products. TftD catalyzes a typical monooxygenase reaction, while TcpA catalyzes a typical monooxygenase reaction followed by a hydrolytic dechlorination. We have previously reported the 3D structure of TftD and confirmed the catalytic residue, His289. Here we have determined the crystal structure of TcpA and investigated the apparent differences in specificity and catalysis between these two closely related monooxygenases through structural comparison. Our computational docking results suggest that Ala293 in TcpA (Ile292 in TftD) is possibly responsible for the differences in substrate specificity between the two monooxygenases. We have also identified that Arg101 in TcpA could provide inductive effects/charge stabilization during hydrolytic dechlorination. The collective information provides a fundamental understanding of the catalytic reaction mechanism and the parameters for substrate specificity. The information may provide guidance for designing bioremediation strategies for polychlorophenols, a major group of environmental pollutants.

Screening Protein Prognostic Biomarkers for Stomach Adenocarcinoma Based on The Cancer Proteome Atlas.

The objective was to construct a prognostic risk model of stomach adenocarcinoma (STAD) based on The Cancer Proteome Atlas (TCPA) to search for prognostic biomarkers. Protein data and clinical data on STAD were downloaded from the TCGA database, and differential expressions of proteins between carcinoma and para-carcinoma tissues were screened using the R package. The STAD data were randomly divided into a training set and a testing set in a 1:1 ratio. Subsequently, a linear prognostic risk model of proteins was constructed using Cox regression analysis based on training set data. Based on the scores of the prognostic model, sampled patients were categorized into two groups: a high-risk group and a low-risk group. Using the testing set and the full sample, ROC curves and K-M survival analysis were conducted to measure the predictive power of the prognostic model. The target genes of proteins in the prognostic model were predicted and their biological functions were analyzed. A total of 34 differentially expressed proteins were screened (19 up-regulated, 15 down-regulated). Based on 176 cases in the training set, a prognostic model consisting of three proteins (COLLAGEN VI, CD20, TIGAR) was constructed, with moderate prediction accuracy (AUC=0.719). As shown by the Kaplan-Meier and survival status charts, the overall survival rate of the low-risk group was better than that of the high-risk group. Moreover, a total of 48 target proteins were identified to have predictive power, and the level of proteins in hsa05200 (Pathways in cancer) was the highest. According to the results of the Univariate and multivariate COX analysis, tri-protein was identified as an independent prognostic factor. Therefore, the tri-protein prognostic risk model can be used to predict the likelihood of STAD and guide clinical treatment.

Construction and validation of a prognostic risk model for breast cancer based on protein expression.

Breast cancer (BRCA) is the primary cause of mortality among females globally. The combination of advanced genomic analysis with proteomics characterization to construct a protein prognostic model will help to screen effective biomarkers and find new therapeutic directions. This study obtained proteomics data from The Cancer Proteome Atlas (TCPA) dataset and clinical data from The Cancer Genome Atlas (TCGA) dataset. Kaplan-Meier and Cox regression analyses were used to construct a prognostic risk model, which was consisted of 6 proteins (CASPASE7CLEAVEDD198, NFKBP65-pS536, PCADHERIN, P27, X4EBP1-pT70, and EIF4G). Based on risk curves, survival curves, receiver operating characteristic curves, and independent prognostic analysis, the protein prognostic model could be viewed as an independent factor to accurately predict the survival time of BRCA patients. We further validated that this prognostic model had good predictive performance in the GSE88770 dataset. The expression of 6 proteins was significantly associated with the overall survival of BRCA patients. The 6 proteins and encoding genes were differentially expressed in normal and primary tumor tissues and in different BRCA stages. In addition, we verified the expression of 3 differential proteins by immunohistochemistry and found that CDH3 and EIF4G1 were significantly higher in breast cancer tissues. Functional enrichment analysis indicated that the 6 genes were mainly related to the HIF-1 signaling pathway and the PI3K-AKT signaling pathway. This study suggested that the prognosis-related proteins might serve as new biomarkers for BRCA diagnosis, and that the risk model could be used to predict the prognosis of BRCA patients.

Immunogenic efficacy of DNA and protein-based vaccine from a chimeric gene consisting OmpW, TcpA and CtxB, of Vibrio cholerae.

Vibrio cholerae is one of the major causes of morbidity and mortality in developing countries. CtxB, responsible for toxin binding to eukaryotic cells, TcpA, involved in bacterial colonization, and OmpW, the highly conserved extracellular protein, are the three of the significant essential virulence factors in V. cholerae with enhanced immunogenic properties. Increasing emergence of antimicrobial-resistant strains (AMR) highlights the urgent need for new therapeutic agents. Uncomplicated high yield production, simple design, inducing either or both humoral and cellular immunity, and long-term immune responsiveness are some of the advantages of IgG antibodies over other immunotherapy agents. Chimeric proteins have the potential of presenting multiple antigens to immune system, simultaneously. Thus, the current study was aimed to evaluate the stability and protective efficacy of DNA and protein-based vaccine candidates of a chimeric gene harboring OTC (OmpW, TcpA and CtxB) against V. cholerae. The immunogenicity and specificity of induced IgGs were confirmed through indirect ELISA and western blot analysis, respectively. The DNA and protein immunized mice sera were able to neutralize the cytotoxicity effects of the cholera toxin (CT) at 5% and 10% dilutions in Y1 cell line, and inhibited 60% and 68% of the bacterial adhesion to HT-29 cells, respectively. The DNA and protein immunized sera provided 99% and 95% viability percent in spleen cell viability assays, and inhibited the bacteria-induced fluid accumulation in ileal loop assay at 1/80 and 1/160 dilutions, respectively. Different groups of passively immunized infant mice and actively immunized adult mice were challenged with V. cholerae. The OTC construct provided high survival rates against lethal infection, and significantly reduced the bacterial loads. Our results highlight the potential therapeutic effect of the recombinant OTC chimeric construct, either as a DNA or protein vaccine, due to its remarkable immunogenicity and protectivity against V. cholerae.