Structure of the Hsp110:Hsc70 nucleotide exchange machine.

Hsp70s mediate protein folding, translocation, and macromolecular complex remodeling reactions. Their activities are regulated by proteins that exchange ADP for ATP from the nucleotide-binding domain (NBD) of the Hsp70. These nucleotide exchange factors (NEFs) include the Hsp110s, which are themselves members of the Hsp70 family. We report the structure of an Hsp110:Hsc70 nucleotide exchange complex. The complex is characterized by extensive protein:protein interactions and symmetric bridging interactions between the nucleotides bound in each partner protein's NBD. An electropositive pore allows nucleotides to enter and exit the complex. The role of nucleotides in complex formation and dissociation, and the effects of the protein:protein interactions on nucleotide exchange, can be understood in terms of the coupled effects of the nucleotides and protein:protein interactions on the open-closed isomerization of the NBDs. The symmetrical interactions in the complex may model other Hsp70 family heterodimers in which two Hsp70s reciprocally act as NEFs.

Comparison of machine learning approaches and statistical methods for estimation of airborne chemical exposures

Accurate quantitative estimation of exposure using retrospective data has been one of the most challenging tasks in the exposure assessment field. To improve these estimates, some models have been developed using published exposure databases with their corresponding exposure determinants. These models are designed to be applied to reported exposure determinants obtained from study subjects or exposure levels assigned by an industrial hygienist, so quantitative exposure estimates can be obtained. ^ In an effort to improve the prediction accuracy and generalizability of these models, and taking into account that the limitations encountered in previous studies might be due to limitations in the applicability of traditional statistical methods and concepts, the use of computer science- derived data analysis methods, predominantly machine learning approaches, were proposed and explored in this study. ^ The goal of this study was to develop a set of models using decision trees/ensemble and neural networks methods to predict occupational outcomes based on literature-derived databases, and compare, using cross-validation and data splitting techniques, the resulting prediction capacity to that of traditional regression models. Two cases were addressed: the categorical case, where the exposure level was measured as an exposure rating following the American Industrial Hygiene Association guidelines and the continuous case, where the result of the exposure is expressed as a concentration value. Previously developed literature-based exposure databases for 1,1,1 trichloroethane, methylene dichloride and, trichloroethylene were used. ^ When compared to regression estimations, results showed better accuracy of decision trees/ensemble techniques for the categorical case while neural networks were better for estimation of continuous exposure values. Overrepresentation of classes and overfitting were the main causes for poor neural network performance and accuracy. Estimations based on literature-based databases using machine learning techniques might provide an advantage when they are applied to other methodologies that combine `expert inputs' with current exposure measurements, like the Bayesian Decision Analysis tool. The use of machine learning techniques to more accurately estimate exposures from literature-based exposure databases might represent the starting point for the independence from the expert judgment.^

Using citation data to improve retrieval from MEDLINE.

OBJECTIVE: To determine whether algorithms developed for the World Wide Web can be applied to the biomedical literature in order to identify articles that are important as well as relevant. DESIGN AND MEASUREMENTS A direct comparison of eight algorithms: simple PubMed queries, clinical queries (sensitive and specific versions), vector cosine comparison, citation count, journal impact factor, PageRank, and machine learning based on polynomial support vector machines. The objective was to prioritize important articles, defined as being included in a pre-existing bibliography of important literature in surgical oncology. RESULTS Citation-based algorithms were more effective than noncitation-based algorithms at identifying important articles. The most effective strategies were simple citation count and PageRank, which on average identified over six important articles in the first 100 results compared to 0.85 for the best noncitation-based algorithm (p < 0.001). The authors saw similar differences between citation-based and noncitation-based algorithms at 10, 20, 50, 200, 500, and 1,000 results (p < 0.001). Citation lag affects performance of PageRank more than simple citation count. However, in spite of citation lag, citation-based algorithms remain more effective than noncitation-based algorithms. CONCLUSION Algorithms that have proved successful on the World Wide Web can be applied to biomedical information retrieval. Citation-based algorithms can help identify important articles within large sets of relevant results. Further studies are needed to determine whether citation-based algorithms can effectively meet actual user information needs.

AGROBACTERIUM VIRB10 CONTRIBUTIONS TO TYPE IV SUBSTRATE SECRETION, T-PILUS BIOGENESIS, AND OUTER MEMBRANE PORE FORMATION

The VirB/D4 type IV secretion system (T4SS) of Agrobacterium tumefaciens functions to transfer substrates to infected plant cells through assembly of a translocation channel and a surface structure termed a T-pilus. This thesis is focused on identifying contributions of VirB10 to substrate transfer and T-pilus formation through a mutational analysis. VirB10 is a bitopic protein with several domains, including a: (i) cytoplasmic N-terminus, (ii) single transmembrane (TM) α-helix, (iii) proline-rich region (PRR), and (iv) large C-terminal modified β-barrel. I introduced cysteine insertion and substitution mutations throughout the length of VirB10 in order to: (i) test a predicted transmembrane topology, (ii) identify residues/domains contributing to VirB10 stability, oligomerization, and function, and (iii) monitor structural changes accompanying energy activation or substrate translocation. These studies were aided by recent structural resolution of a periplasmic domain of a VirB10 homolog and a ‘core’ complex composed of homologs of VirB10 and two outer membrane associated subunits, VirB7 and VirB9. By use of the substituted cysteine accessibility method (SCAM), I confirmed the bitopic topology of VirB10. Through phenotypic studies of Ala-Cys insertion mutations, I identified “uncoupling” mutations in the TM and β-barrel domains that blocked T-pilus assembly but permitted substrate transfer. I showed that cysteine replacements in the C-terminal periplasmic domain yielded a variety of phenotypes in relation to protein accumulation, oligomerization, substrate transfer, and T-pilus formation. By SCAM, I also gained further evidence that VirB10 adopts different structural states during machine biogenesis. Finally, I showed that VirB10 supports substrate transfer even when its TM domain is extensively mutagenized or substituted with heterologous TM domains. By contrast, specific residues most probably involved in oligomerization of the TM domain are required for biogenesis of the T-pilus.

Exploration of genetic susceptibility to pancreatic cancer: A GWAS approach

Pancreatic cancer is the 4th most common cause for cancer death in the United States, accompanied by less than 5% five-year survival rate based on current treatments, particularly because it is usually detected at a late stage. Identifying a high-risk population to launch an effective preventive strategy and intervention to control this highly lethal disease is desperately needed. The genetic etiology of pancreatic cancer has not been well profiled. We hypothesized that unidentified genetic variants by previous genome-wide association study (GWAS) for pancreatic cancer, due to stringent statistical threshold or missing interaction analysis, may be unveiled using alternative approaches. To achieve this aim, we explored genetic susceptibility to pancreatic cancer in terms of marginal associations of pathway and genes, as well as their interactions with risk factors. We conducted pathway- and gene-based analysis using GWAS data from 3141 pancreatic cancer patients and 3367 controls with European ancestry. Using the gene set ridge regression in association studies (GRASS) method, we analyzed 197 pathways from the Kyoto Encyclopedia of Genes and Genomes (KEGG) database. Using the logistic kernel machine (LKM) test, we analyzed 17906 genes defined by University of California Santa Cruz (UCSC) database. Using the likelihood ratio test (LRT) in a logistic regression model, we analyzed 177 pathways and 17906 genes for interactions with risk factors in 2028 pancreatic cancer patients and 2109 controls with European ancestry. After adjusting for multiple comparisons, six pathways were marginally associated with risk of pancreatic cancer ( P < 0.00025): Fc epsilon RI signaling, maturity onset diabetes of the young, neuroactive ligand-receptor interaction, long-term depression (Ps < 0.0002), and the olfactory transduction and vascular smooth muscle contraction pathways (P = 0.0002; Nine genes were marginally associated with pancreatic cancer risk (P < 2.62 × 10−5), including five reported genes (ABO, HNF1A, CLPTM1L, SHH and MYC), as well as four novel genes (OR13C4, OR 13C3, KCNA6 and HNF4 G); three pathways significantly interacted with risk factors on modifying the risk of pancreatic cancer (P < 2.82 × 10−4): chemokine signaling pathway with obesity ( P < 1.43 × 10−4), calcium signaling pathway (P < 2.27 × 10−4) and MAPK signaling pathway with diabetes (P < 2.77 × 10−4). However, none of the 17906 genes tested for interactions survived the multiple comparisons corrections. In summary, our current GWAS study unveiled unidentified genetic susceptibility to pancreatic cancer using alternative methods. These novel findings provide new perspectives on genetic susceptibility to and molecular mechanisms of pancreatic cancer, once confirmed, will shed promising light on the prevention and treatment of this disease. ^