Scientific Program

Conference Series Ltd invites all the participants across the globe to attend 3rd International Conference on Proteomics & Bioinformatics Courtyard by Marriott Philadelphia Downtown, USA.

Day 2 :

  • Track 5: Disease Proteomics
Speaker
Biography:

Hakami completed his Ph.D. in Biochemistry in the laboratory of the Nobel Laureate Professor Har Gobind Khorana at the Massachusetts Institute of Technology. He was then awarded a NRSA fellowship from NIH to complete postdoctoral training at Harvard Medical School and subsequently completed a postdoctoral fellowship at the National Human Genome Research Institute. He is a faculty member at the School of Systems Biology and the National Center for Biodefense and Infectious Diseases at George Mason University. He has published more than 20 peer-reviewed research articles in reputed journals.

Abstract:

We have employed different proteomic approaches to begin an elucidation of host signaling events that occur during infection with pathogenic agents. Using complimentary analytical approaches, we performed a detailed proteomic profiling of purified virions of the hemorrhagic fever virus Rift Valley fever virus (RVFV), followed by functional characterization of select hits. The virion-associated host protein complement was thoroughly examined by coupling the Gel LC/MS/MS approach with an alternative technique that preserves protein complexes and has been traditionally used to investigate mitochondria. Over 300 host proteins and multiple macromolecular complexes were identified. Host chaperones were among the over-represented protein families, and siRNA gene silencing and small molecule inhibitors identified several of them as essential viral host factors. Inhibitor time-of-addition studies and real-time analysis of inhibitor effects on intracellular virus demonstrated a role for specific HSPs during the viral replication phase. As some of the inhibitors tested have already completed phase II clinical trials for cancer treatment, the potential of repurposing them to treat RVF is highly appealing. We have also applied a Reverse Phase Protein Microarray (RPMA) platform to studies of host signaling events during infection with Yersinia pestis. In order to obtain an overall picture of host signaling network connections and changes, multiple MOIs, host cell types, and times post infection were compared using 132 validated antibodies that target a variety of signaling pathways. These studies highlight the utility of different proteomic approaches for identifying critical host factors during infectious diseases in order to devise novel therapeutic strategies.

Speaker
Biography:

Ralf Hoffmann studied chemistry and was awarded a Ph.D. from Saarland University, before he continued his career at the Wistar Institute (Philadelphia, U.S.A.) as research associate and head of the “Analytical Laboratory” at the Biological and Medical Research Center (Düsseldorf, Germany). Since 2002 he is a full professor (C4 level) at Universität Leipzig. His research focuses on protein analytics to study posttranslational modifications in the context of Alzheimer’s disease, cellular aging, oxidative stress, and diabetes as well as the design of peptide drug candidates and compound vaccines. He is the author of more than 130 peer-reviewed scientific publications, co-inventor on eight patent applications, and serves as regional editor for “Protein and Peptide Letters” and three editorial boards

Abstract:

Non-enzymatic glycosylation or glycation is a common posttranslational modification produced by a chemical reaction between amino groups in proteins and sugars (aldoses or ketoses). The resulting Amadori- and Hynes-products can degrade to advanced glycation end-products (AGEs), known as markers of several metabolic diseases including diabetes. Diabetic conditions favour glucose-derived Amadori products, which is already used to measure the long-term blood glucose level by glycated haemoglobin (HbA1c). To establish middle-term diabetes markers, we have developed and optimized chromatographic and mass spectrometry techniques to identify glycation sites in proteins even if present only at low levels. Plasma samples from diabetes type 2 patients and age-matched controls were digested with trypsin and the glycated peptides were enriched by boronic acid affinity chromatography. Thus more than 40 glycation sites in 14 proteins were identified by data-dependent acquisition using nanoRPC-ESI-LTQ-Orbitrap-MS/MS. Several positions in these proteins were found to be differentially glycated in diabetic patients and healthy individuals using label free quantification. Surprisingly, not all glycated peptides demonstrated significantly different abundance in diabetic plasma digests in comparison to controls. Thus, peptides resembling fourteen differentially modified sites in human serum albumin (HSA) were quantified as prospective markers by a MRM-method. Quantification relied on authentic and internal (stable isotope-labelled) synthetic peptide standards. Furthermore, enrichment by affinity chromatography and solid phase extraction were optimized for recovery and precision. Finally, we could show that several sites in HSA are glycated at significantly higher level in diabetes patients (p<0.05) indicating that they might represent promising type 2 diabetes biomarkers.

Speaker
Biography:

Sanela Kjellqvist has completed her Ph.D 2008 at the age of 29 years from Uppsala University and postdoctoral studies 2012 from Karolinska Institutet. She has published 16 papers in reputed journals in the area of protein biochemistry and bioinformatics. Her main research interest is transcriptomics, proteomics and bioinformatics in cardiovascular diseases.

Abstract:

Aortic aneurysm is one of the major diseases that affect the aorta. The disease involves degradation of the extracellular matrix eventually leading to a dilatation and rupture of the vessel wall, a potentially lethal condition if not treated on time. The focus of present study is thoracic aortic aneurysm (TAA). There are several different etiologies of TAA involving aneurysm associated with bicuspid aortic valve (BAV) disease. TAA is a widespread complication in individuals having BAV disease that is a common congenital disorder present in 1-2% of the population. Studies have indicated that increased susceptibility of aneurysm formation associated with BAV is regardless of the presence or absence of hemodynamically significant valve dysfunction. In order to better understand the underlying molecular mechanisms of TAA, 2D DIGE gel electrophoresis, LC-MS/MS, and GeneChip Human Exon 1.0 ST Affymetrix arrays followed by multivariate data analysis (MVA) of human biopsies were used in order to investigate proteomic and transcriptomic differences in dilated and non-dilated TAA with BAV and TAV. Our results show that TAV and BAV patients have diverging protein expression level patterns in dilated and non-dilated aorta tissues. Furthermore, our results suggest that dilatation in TAV and BAV patients has different gene expression and alternative splicing fingerprints. Diverging protein expression and alternative splicing patterns observed between the two valve types indicate that dilatation in patients with TAV has different underlying molecular mechanisms compared with BAV patients.

Biography:

Manuela Piazzi received her Ph.D. in 2009 from the University of Bologna. Following the completion of her Ph.D., she initiated her post-doctoral studies in Mass Spectrometry analysis in the laboratory of Anthony Whetton at the University of Manchester, School of Enabling Science (UK), before returning to the University of Bologna, cellular Signaling laboratory, under the direction of Lucio Cocco. Currently, she is a Researcher Assistant Professor at the University of Bologna, where she is responsible for the Mass Spectrometry-Proteomic Unit of the Cell Biology Laboratory, Rizzoli Orthopedic Institute, Bologna. She has published more than 10 papers in peer-reviewed journals.

Abstract:

In the last years, the study of protein interactomes, meaning the complex map of molecular interactions among proteins, genes, and RNA, has gained increasing popularity. The ability of mass spectrometry to identify thousands of proteins as well as to quantify protein expression and post-translational modifications using labelling technologies has proven the invaluable in deciphering the whole proteome of an organism. In order to study the protein interactome, researcher biologists need comprehensive tools that allow analysis of bias, reproducibility, statistical significance and biologically significant pattern in the data set. Thus, genomic catalogues of proteinprotein interactions are a rich source of information to explore the relationships between proteins. A variety of bioinformatic software has been released to allow biologists to compile novel molecular pathways and networks. Here, two examples of protein interactome analysis are compared; the analysis of protein interacting with the interferon-induced, double-stranded RNA-activated protein kinase (PKR) and those interacting with the 1-phosphatidylinositol 4,5-bisphosphate phosphodiesterase beta-1, isoform b (PI-PLCβ1b), within the nuclear compartment. Advantages and limits of affinity purification mass spectrometry are discussed, along with data analysis redundancy and statistical reproducibility. Moreover, analysis of protein interaction networks, the integration of data and the comparison of results from different bioinformatic programs, are reported.

Speaker
Biography:

Yashoda Mittal did Ph.D. from University of Roorkee (now IIT-Roorkee), India. They have authored several research papers, published a book and developed 4 biological/lectins databases jointly.

Abstract:

Common pathogenic bacteria like E. coli, V. cholera and S. typhimurium display surface lectins that could be potentially involved in pathogenesis. Due to the importance of these strains in causing the infectious disease, especially in developing countries we performed an extensive analysis of the lectins displayed on pathogenic bacterial surfaces using “BacterialLectinDb”. Lectins from E. coli, V. cholera and S. typhimurium were compared to gain insights into the possibilities of common motifs that could be responsible for contributing to host pathogen interaction. The various information about these lectins such as amino acid sequence, gene name, PDB code etc. were obtained using Swiss-prot, SOPMA, Prodom, ClustalW etc. from “BacterialLectinDb”. Multiple sequence alignment using ClustalW was used to compare the protein sequences of the E. coli lectins F17a-G fimbrial adhesion, Gafd (F17C-Type) fimbrial adhesion and and S. typhimurium lectin sialidase. Multiple sequence alignment of F17a-G fimbrial adhesin and Gafd (F17C-Type) fimbrial adhesin both from E. coli show positive similarity with high score of 95. Comparison of E.coli, F17a-G fimbrial adhesion and Gafd (F17C-Type) fimbrial adhesion lectin sequences to that of sialidase from S. typhimurium generated a very low similarity score of only 1. These results thus point out to the role of these lectins in host pathogen interactions.

Speaker
Biography:

Nikhil Sharma is currently pursuing his Ph.D. in the Department of Biotechnology and his research entails the study of microbial enzyme both in vitro and in silico. He has contributed chapters in books and research articles in peer review journals.

Abstract:

Nitrilase is one of the nitrile metabolizing enzymes that catalyzes the conversion of nitriles to corresponding acids. Based on substrate specificities, nitrilases are classified as aliphatic and aromatic nitrilases. Number of nitrilases from microbial/ plant sources have been purified, characterized and their genes have been cloned and sequenced. In order to identify as to which group the sequences of nitrilases belong to, two types of motifs i.e. aliphatic nitrilase motifs (MDMAl) and aromatic nitrilase motifs (MDMAr) each with four motifs based on conserved catalytic triad (Glu-48, Lys-131, Cys-165) were designed. Conserved regions were identified by performing multiple sequence alignment (MSA) using multiple EM for motif elicitation (MEME). The manually designed motifs (MDM’s) were validated by ScanProsite and their presence was also confirmed by PRATT, Gblocks and MEME. The ScanProsite search against the MDMAr exhibited some new sources of aromatic nitrilase from plant, animals and microbes whereas MDMAl only exhibited nitrilase from microbes. Besides identifying unique motifs in nitrilase sequences and to confirm their specificity towards nitriles, randomly selected sequences were validated or motifs were validated by analyzing some important physiochemical parameters of corresponding nitrilases.

Speaker
Biography:

Soumita Podder is from Bose Institute, India and her research interest is in Immunological genes.

Abstract:

One of the main issues of molecular evolution is to divulge the principles in dictating the evolutionary rate differences among various gene classes. Immunological genes have received considerable attention in evolutionary biology as candidates for local adaptation and for studying functionally important polymorphisms. The normal structure and function of immunological genes will be distorted when they experience mutations leading to immunological dysfunctions. Here, we examined the fundamental differences between the genes which on mutation give rise to autoimmune or other immune system related diseases and the immunological genes that do not cause any disease phenotypes. Although the disease genes examined are analogous to non-disease genes in product, expression, function, and pathway affiliation, a statistically significant decrease in evolutionary rate has been found in autoimmune disease genes relative to all other immune related diseases and non–disease genes. Possible ways of accumulation of mutation in the three steps of the central dogma (DNA-mRNA-Protein) have been studied to trace the mutational effects predisposed to disease consequence and acquiring higher selection pressure. Principal Component Analysis and Multivariate Regression Analysis have established the predominant role of phosphorylation residues in guiding the evolutionary rate of immunological disease and non-disease genes followed by the m-RNA abundance, paralogs number, SNPs, alternatively spliced exon, protein disorder and protein residue burial. Our study provides an empirical insight into the etiology of autoimmune disease genes and other immunological diseases. The immediate utility of our study is to help in disease gene identification and may also help in medicinal improvement of immune related disease.

  • Track 6: Genomics
Speaker
Biography:

Harold Drabkin received his PhD from Wesleyan University and postdoctoral training at Roche Institute of Molecular Biology (Nutley NJ) and Massachusetts Institute of Technology (Cambridge, MA). He was a research scientist and lecturer at MIT, and is currently a Senior Scientific Curator for the Mouse Genome Informatics system at the Jackson Laboratory (Bar Harbor, ME), working on the Gene Ontology and Protein Ontology projects.

Abstract:

The Gene Ontology (GO) is a structured controlled vocabulary used by numerous model organism databases for the functional annotation of gene products. GO terms define molecular functions, biological processes, and cellular components that cover all life forms. Until recently, the GO annotation framework was limited in its ability to adequately capture the biological context of each annotation such as when and where a protein was active. Now the GO Consortium has created a methodology that supports the addition of contextual detail to GO annotations extracted from biomedical literature. These extensions include several “effector-target” relationships, such as cellular and anatomical localization dependencies, enzyme substrates, and regulation targets of signally pathways and transcription factors, in addition to the spatial and temporal aspects of processes. These details are captured using relationships to a variety of external ontologies such as the Cell Type Ontology. The Mouse Genome Informatics (MGI) database system supported capturing such details privately for many years. Here we describe the migration of this information into the structured method now available. The enhanced annotations will support more specific queries and allow computational reasoning to augment discovery by inference.

Gromov P

Danish Cancer Society Research Center, Denmark

Title: Tumor interstitial fluid - A treasure trove of cancer biomarkers
Biography:

romov P is an Assistant professor at Danish Cancer Society Research Gromov P is an Assistant professor at Danish Cancer Society Research Center. He completed his Ph.D. in 2005. He has published more than 25 papers in reputed journals and serving as an editorial board member of repute.

Abstract:

Tumor interstitial fluid (TIF) is a proximal fluid that, in addition to the set of blood soluble phase-borne proteins, holds a subset of aberrantly externalized components, mainly proteins, released by tumor cells and tumor microenvironment through various mechanisms, which include classical secretion, non-classical secretion, secretion via exosomes and membrane protein shedding. Consequently, the interstitial aqueous phase of solid tumors is a highly promising resource for discovery of molecules associated with pathological changes in tissues. Firstly, it allows one to delve deeper into the regulatory mechanisms and functions of secretion-related processes in tumor development. Secondly, the anomalous secretion of molecules that is innate to tumors and the tumor microenvironment, being associated with cancer progression, offers a valuable source for biomarker discovery and possible targets for therapeutic intervention. Here we provide an overview of the features of tumor-associated interstitial fluids, based on recent and updated information obtained mainly from our studies of breast cancer. Data from the study of interstitial fluids recovered from several other types of cancer are also discussed.

Speaker
Biography:

Brett Lidbury is a Science Honours graduate from the University of Newcastle and completed his Ph.D. (Immunobiology) at The Australian National University (ANU) in 1993. Since graduation he has conducted research at other Australian universities and in the United States, primarily on virushost interaction, and remains active in university education. Brett is currently the Associate Professor of Alternatives to Animal Research (and MAWA Fellow) at the ANU, based at the John Curtin School of Medical Research. In pursuing medical progress without animal models, machine-learning applications to massive human datasets are being investigated as a key component to an alternative system for fundamental research.

Abstract:

Diagnostic pathology laboratories are essential for modern health systems, providing high quality blood test results that reflect health/disease status. These laboratories sample the surrounding population continuously, and in doing so accumulate enormous volumes of human physiological/biochemical data, for example on liver and kidney function, lipid metabolism, and blood cell biology. Pathology data also reflect naturally acquired disease processes in human subjects that while diverse compared to research laboratory conditions, represents true human disease biology. With patient health currently evaluated via individual pathology results in relation to laboratory reference ranges, the availability of massive data sets and machinelearning methods provide opportunities to advance laboratory diagnostics, and fundamental research, through “knowledge discovery” bioinformatics, particularly pattern recognition methods. Pattern recognition in aggregated pathology data is being explored via combinations of tree-based machine-learning and support vector machines (SVM) executed through R statistical computing packages. Tree methods (recursive partitioning) bring the advantage of multiple decision boundaries, while SVMs provide powerful categorisation and regression modelling in high dimensional feature space. Tree methods and SVMs have been used on large data sets comprising immunoassay data for hepatitis B virus (HBV) or Chlamydia pneumoniae (response variable) and associated routine pathology test results (predictor variables). Challenges involving unbalanced data and missing values have been met, with data patterns of high predictive value identified for future biological validation. As well as benefits for laboratory medicine, this strategy is also included in a novel system designed to provide an alternative to mouse models in fundamental research.

Speaker
Biography:

The researches of Zhang (Associate Professor) are mainly on applications of environmental bioinformatics, including metagenomics, metatranscriptomics, proteomics, etc., in studies of biological wastewater treatment (N removal and P recovery), bioenergy from wastewater/ wastes (cellulosic biomass, sludge, kitchen waste), biodegradation of emerging pollutants (antibiotics, PPCP and EDCs), antibiotic and heavy metal resistance genes, and environmental toxicology of nanoparticles and metals to microorganisms. He has an H Index of 23 with >110 SCI journal publications and 1700 citations. He serves as Advisor for BGI (Beijing Genomics Institute) on Environmental Microbiology and Biotechnology, and ASM (American Society of Microbiology) Country Liaison to China.

Abstract:

High-throughput sequencing is being widely applied in various biological studies, including wastewater treatment. This presentation mainly summarizes its applications in multiple aspects of biological wastewater treatment using the case studies conducted at Environmental Biotechnology Laboratory in Department of Civil Engineering, The University of Hong Kong. Section I introduces bacterial diversity of activated sludge taken from 14 wastewater treatment plants (WWTPs), profile of major functional groups, and primer evaluation, based on 454 pyrosequencing platform. Section II presents the major nitrification groups and processes revealed using Illumina metagenomics approaches. Section III discusses the antibiotics resistance genes identified using high-throughput sequencing. Section IV reveals seasonal variation of microbial communities for 4 years in a WWTP and the correlations of bacterial populations with operational and environmental factors. Section V shows expression of nitrification/denitrification genes using the combined metagenomic and metatranscriptomic approaches. Section VI demonstrates mining of cellulose-conversion genes from the assembled ORFs from metagenomic data. Section VII displays the draft genome of an isolated Bacillus species assembled from metagenomic data.

  • Track 7: Evolutionary Phylogenetic Networks

Session Introduction

Alan J Tackett

University of Arkansas for Medical Sciences, USA

Title: A comprehensive proteomic and epigenomic analysis of FFPE patient melanoma
Speaker
Biography:

Dr. Alan J. Tackett has obtained his PhD from University of Arkansas for Medical Sciences in 2002. Currently, he is working as Professor in University of Arkansas. He is serving as an editorial member of several reputed journals like Conference Papers in Biology. He has authored 41 research articles/books. In 2011 he received UAMS Founders Society Research Award.

Abstract:

Molecular pathways regulating melanoma initiation and progression are potential targets of therapeutic development for this aggressive cancer. We present the most comprehensive analysis of formalin-fixed paraffin-embedded human melanoma tissues using quantitative proteomics. From 61 patient samples, we identified 171 proteins varying in abundance among benign nevi, primary melanoma, and metastatic melanoma. Seventy-three percent of these proteins were validated by immunohistochemistry staining of malignant melanoma tissues from the Human Protein Atlas database. Our results reveal that molecular pathways involved with tumor cell proliferation, motility, and apoptosis are mis-regulated in melanoma. These data provide the most comprehensive proteome resource on patient melanoma and reveal insight into the molecular mechanisms driving melanoma progression. Furthermore, we have used this quantitative mass spectrometry approach to uncover a panel of histone post-translational modifications that are differentially regulated in metastatic melanoma. This has allowed us to explore epigenomic mechanisms that regulate melanoma progression.

Speaker
Biography:

Suphioglu graduated with PhD from the University of Melbourne in 1994 and has over 18 years of research experience (h-index: 22) and an international recognition on the (i) molecular and environmental analysis of pollen, latex and nut allergens, (ii) molecular analysis of phosphoinositide 3-kinase (PI3-K) isoforms in cardiovascular disease and (iii) importance of omega-3 fatty acids and zinc in human neuronal cell survival and epigenetics. Prof Suphioglu has more than 76 publications and several patents. He is currently the Associate Head of School (Development), Course Leader of Bachelor of Biomedical Science and Head of the NeuroAllergy Research Laboratory (NARL).

Abstract:

Dietary intake of zinc and omega-3 fatty acids (DHA) have health benefits for a number of human diseases. However, the molecular basis of these health benefits remains unclear. In this study, we aimed to identify novel protein candidates that are differentially expressed in human neuronal cell line M17 in response to zinc and DHA that would explain the molecular basis of this interaction. Two-dimensional gel electrophoresis and mass spectrometry were applied to identify major protein expression changes in the protein lysates of human Ml7 neuronal cells that had been grown in the presence and absence of zinc and DHA. Four protein spots, which had significant differential expression, were identified as human histones H3 and H4. Both H3 and H4 were down-regulated by zinc and up-regulated by DHA. These proteomic findings were further supported by Western immunoblot and real-time PCR analyses. In addition, zinc and DHA influenced post-translational modifications (e.g. acetylation, methylation, phosphorylation) of histones H3 and H4. Specifically, we identified that while zinc reduced acetylation, DHA increased acetylation of histones, suggesting importance of zinc and DHA in the epigenetic regulation of neuronal cell gene expression. In summary, we show that dietary zinc and DHA cause a global effect on gene expression, which is mediated by the expression and post-translational modification of histones. Such novel information provides possible clues to the molecular mechanism of action of DHA and zinc in the brain that may contribute to the future treatment, prevention and management of neurodegenerative diseases such as Alzheimer’s disease.

Speaker
Biography:

David O. Ojo is from University of Ibadan Post, Nigeria.

Abstract:

  • Track 8: Bioinformatics in Biopharmaceuticals and Therapeutics

Session Introduction

Agnieszka A. Kaczor

University of Eastern Finland, Finland

Title: Molecular modeling of GPCR dimers
Speaker
Biography:

Alexander Kister graduated from Moscow State University with a degree in Chemistry and received Ph.D in Biophysics from the Institute of Biophysics (Pushino, Russia). He worked in the National Institute of Genetics, in Moscow, Russia; Dana-Farber Cancer Institute in Boston; School of Health-Related Professions, University of Medicine and Dentistry of New Jersey; and now in the Department of Mathematics, Rutgers University, Piscataway, NJ. He has published about 90 papers in peer-reviewed journals, authored chapters in several books and an entry on “Immunoglobulin Fold” in Encyclopedia of Life Sciences, and is an editor of a volume on ‘Supersecondary Structure of Proteins’ in Methods in Molecular Biology (Springer).

Abstract:

The goal of this research is to find rules that describe the relationship between distribution of residues in a sequence and structural characteristics of a protein. The residue distribution rules identify 'key residues’ that have to occupy certain positions in a sequence in order for polypeptide chain to form into a particular protein fold. The rules also specify which residues are 'unfavorable', i.e. incompatible with a given fold. These rules are based on statistical analysis of residue variability at different positions in polypeptide chains and investigation of residue-residue contact maps of proteins from different protein families but with similar structures. Our analysis focuses on beta proteins that are characterized by a specific arrangement of beta-strands in two beta sheets. These so-called ‘sandwich structures’ are typical of immunoglobulins, different types of cell receptors and many other proteins. We describe residue distribution rules for sandwich proteins and show that they allow one to correctly identify ~75% of sandwich proteins. The advantage of our approach is that it makes possible prediction of protein fold even in polypeptide chains that have very low global sequence similarities. Another potential benefit is that better understanding of which residues play essential roles for a given protein fold may facilitate rational protein engineering design.