Efficient discovery of binding motif pairs from protein-protein interactions

Ph.DDOCTOR OF PHILOSOPH

Precision drug repurposing via convergent eQTL-based molecules and pathway targeting independent disease-associated polymorphisms

Repurposing existing drugs for new therapeutic indications can improve success rates and streamline development. Use of large-scale biomedical data repositories, including eQTL regulatory relationships and genome-wide disease risk associations, offers opportunities to propose novel indications for drugs targeting common or convergent molecular candidates associated to two or more diseases. This proposed novel computational approach scales across 262 complex diseases, building a multi-partite hierarchical network integrating (i) GWAS-derived SNP-to-disease associations, (ii) eQTL-derived SNP-to-eGene associations incorporating both cis-and trans-relationships from 19 tissues, (iii) protein target-to-drug, and (iv) drug-to-disease indications with (iv) Gene Ontology-based information theoretic semantic (ITS) similarity calculated between protein target functions. Our hypothesis is that if two diseases are associated to a common or functionally similar eGene -and a drug targeting that eGene/protein in one disease exists - the second disease becomes a potential repurposing indication. To explore this, all possible pairs of independently segregating GWAS-derived SNPs were generated, and a statistical network of similarity within each SNP-SNP pair was calculated according to scale-free overrepresentation of convergent biological processes activity in regulated eGenes (ITSeGENE-eGENE) and scale-free overrepresentation of common eGene targets between the two SNPs (ITSSNP-SNP). Significance of ITSSNP-SNP was conservatively estimated using empirical scale-free permutation resampling keeping the node-degree constant for each molecule in each permutation. We identified 26 new drug repurposing indication candidates spanning 89 GWAS diseases, including a potential repurposing of the calcium-channel blocker Verapamil from coronary disease to gout. Predictions from our approach are compared to known drug indications using DrugBank as a gold standard (odds ratio=13.1, p-value=2.49x10(-8)). Because of specific disease-SNPs associations to candidate drug targets, the proposed method provides evidence for future precision drug repositioning to a patient's specific polymorphisms.University of Arizona Health Sciences CB2; BIO5 Institute; UA Cancer Center; NIH [U01AI122275]Open access journalThis item from the UA Faculty Publications collection is made available by the University of Arizona with support from the University of Arizona Libraries. If you have questions, please contact us at [email protected]

Heterologous expression, biochemical characterization, and overproduction of alkaline α-amylase from Bacillus alcalophilus in Bacillus subtilis

<p>Abstract</p> <p>Background</p> <p>Alkaline α-amylases have potential applications for hydrolyzing starch under high pH conditions in the starch and textile industries and as ingredients in detergents for automatic dishwashers and laundries. While the alkaline α-amylase gains increased industrial interest, the yield of alkaline α-amylases from wild-type microbes is low, and the combination of genetic engineering and process optimization is necessary to achieve the overproduction of alkaline α-amylase.</p> <p>Results</p> <p>The alkaline α-amylase gene from <it>Bacillus alcalophilus </it>JN21 (CCTCC NO. M 2011229) was cloned and expressed in <it>Bacillus subtilis </it>strain WB600 with vector pMA5. The recombinant alkaline α-amylase was stable at pH from 7.0 to 11.0 and temperature below 40°C. The optimum pH and temperature of alkaline α-amylase was 9.0 and 50°C, respectively. Using soluble starch as the substrate, the <it>K</it>_mand <it>V</it>_maxof alkaline α-amylase were 9.64 g/L and 0.80 g/(L·min), respectively. The effects of medium compositions (starch, peptone, and soybean meal) and temperature on the recombinant production of alkaline α-amylase in <it>B. subtilis </it>were investigated. Under the optimal conditions (starch concentration 0.6% (w/v), peptone concentration 1.45% (w/v), soybean meal concentration 1.3% (w/v), and temperature 37°C), the highest yield of alkaline α-amylase reached 415 U/mL. The yield of alkaline α-amylase in a 3-L fermentor reached 441 U/mL, which was 79 times that of native alkaline α-amylase from <it>B. alcalophilus </it>JN21.</p> <p>Conclusions</p> <p>This is the first report concerning the heterologous expression of alkaline α-amylase in <it>B. subtilis</it>, and the obtained results make it feasible to achieve the industrial production of alkaline α-amylase with the recombinant <it>B. subtilis</it>.</p

TransportTP: A two-phase classification approach for membrane transporter prediction and characterization

<p>Abstract</p> <p>Background</p> <p>Membrane transporters play crucial roles in living cells. Experimental characterization of transporters is costly and time-consuming. Current computational methods for transporter characterization still require extensive curation efforts, especially for eukaryotic organisms. We developed a novel genome-scale transporter prediction and characterization system called TransportTP that combined homology-based and machine learning methods in a two-phase classification approach. First, traditional homology methods were employed to predict novel transporters based on sequence similarity to known classified proteins in the Transporter Classification Database (TCDB). Second, machine learning methods were used to integrate a variety of features to refine the initial predictions. A set of rules based on transporter features was developed by machine learning using well-curated proteomes as guides.</p> <p>Results</p> <p>In a cross-validation using the yeast proteome for training and the proteomes of ten other organisms for testing, TransportTP achieved an equivalent recall and precision of 81.8%, based on TransportDB, a manually annotated transporter database. In an independent test using the Arabidopsis proteome for training and four recently sequenced plant proteomes for testing, it achieved a recall of 74.6% and a precision of 73.4%, according to our manual curation.</p> <p>Conclusions</p> <p>TransportTP is the most effective tool for eukaryotic transporter characterization up to date.</p

Prevalence of lymph node metastases in superficial esophageal squamous cell carcinoma

ObjectiveEndoscopic treatment of superficial esophageal carcinoma has been increasingly conducted around the world. Because no lymph nodes are removed in such a procedure, the risk of lymph node metastases (LNMs) should be clearly understood. The aim of the present study was to accurately clarify the pattern of lymphatic spread in patients with superficial esophageal squamous cell carcinoma and analyze the factors potentially related to LNMs.MethodsThe pattern of lymphatic spread was studied in 189 patients who had undergone radical lymphadenectomy from 2006 to 2011. The risk factors associated with LNMs were determined by multivariate logistic regression analysis. According to the depth of tumor invasion, mucosal tumors were classified as M1, M2, and M3 and submucosal tumors as SM1, SM2, and SM3.ResultsA total of 4252 lymph nodes were resected (average, 23 ± 9; range, 12-68). LNMs occurred in 49 patients (25.9%). The frequency of LNMs was 4.3% in those with mucosal and 33.1% in those with submucosal cancer. LNMs were found in 0%, 0%, 11.8%, 24.0%, 20.5%, and 43.8% of the M1, M2, M3, SM1, SM2, and SM3 cancer, respectively. For submucosal cancer, SM3 cancer (P = .006) and lymphovascular invasion (P = .001) were significant independent risk factors for LNMs. Paratracheal nodes were the most frequently involved. “Skip” metastases occurred in 20 of 49 patients (40.8%).ConclusionsEndoscopic treatment can be attempted when the tumor is limited to the lamina propria mucosa. However, 2-field radical lymphadenectomy with careful upper mediastinal lymph node resection should be conducted for submucosal squamous cell carcinoma

Translational informatics of population health: How large biomolecular and clinical datasets unite

This paper summarizes the workshop content on how the integration of large biomolecular and clinical datasets can enhance the field of population health via translational informatics. Large volumes of data present diverse challenges for existing informatics technology, in terms of computational efficiency, modeling effectiveness, statistical computing, discovery algorithms, and heterogeneous data integration. While accumulating large 'omics measurements on subjects linked with their electronic record remains a challenge, this workshop focuses on non-trivial linkages between large clinical and biomolecular datasets. For example, exposures and clinical datasets can relate through zip codes, while comorbidities and shared molecular mechanisms can relate diseases. Workshop presenters will discuss various methods developed in their respective labs/organizations to overcome the difficulties of combining together such large complex datasets and knowledge to enable the translation to clinical practice for improving health outcomes.Open access journalThis item from the UA Faculty Publications collection is made available by the University of Arizona with support from the University of Arizona Libraries. If you have questions, please contact us at [email protected]