http://hdl.handle.net/2144/2434 2013-06-19T02:48:45Z 2013-06-19T02:48:45Z Shakhnovich, Boris E http://hdl.handle.net/2144/3211 2012-01-12T07:00:54Z 2005-06-24T00:00:00Z

Improving the Precision of the Structure–Function Relationship by Considering Phylogenetic Context Shakhnovich, Boris E Understanding the relationship between protein structure and function is one of the foremost challenges in post-genomic biology. Higher conservation of structure could, in principle, allow researchers to extend current limitations of annotation. However, despite significant research in the area, a precise and quantitative relationship between biochemical function and protein structure has been elusive. Attempts to draw an unambiguous link have often been complicated by pleiotropy, variable transcriptional control, and adaptations to genomic context, all of which adversely affect simple definitions of function. In this paper, I report that integrating genomic information can be used to clarify the link between protein structure and function. First, I present a novel measure of functional proximity between protein structures (F-score). Then, using F-score and other entirely automatic methods measuring structure and phylogenetic similarity, I present a three-dimensional landscape describing their inter-relationship. The result is a "well-shaped" landscape that demonstrates the added value of considering genomic context in inferring function from structural homology. A generalization of methodology presented in this paper can be used to improve the precision of annotation of genes in current and newly sequenced genomes. Synopsis. The author provides a novel perspective on a key problem of structural biology: the structure–function relationship in proteins. While relatedness in protein structure correlates with general description of function, attempts to use this relationship predictively are often complicated by its ambiguous nature. A structure encoded by a family of sequences may be implicated in a set of diverse functions across a variety of organisms. The author outlines an innovative approach that underlines the importance of considering genomic context when using structure-comparison methods for functional prediction. First, the author defines two distance measures: in genomic space and in function space. Then, the author describes a landscape of functional distance based on both structural and phylogenetic relatedness. It turns out that this landscape forms a "functional well" where proximity occurs when the structures are similar and occur in the same set of genomes. This result may have implications in future research into functional prediction. With the increasing pace of sequence deposition into databanks, this result suggests a simple way to improve functional prediction via structure homology by complementing existing methods with emerging techniques from comparative genomics.

2005-06-24T00:00:00Z Xia, Yu Franzosa, Eric A. Gerstein, Mark B. http://hdl.handle.net/2144/3212 2012-01-12T07:00:54Z 2009-06-12T00:00:00Z

Integrated Assessment of Genomic Correlates of Protein Evolutionary Rate Xia, Yu; Franzosa, Eric A.; Gerstein, Mark B. Rates of evolution differ widely among proteins, but the causes and consequences of such differences remain under debate. With the advent of high-throughput functional genomics, it is now possible to rigorously assess the genomic correlates of protein evolutionary rate. However, dissecting the correlations among evolutionary rate and these genomic features remains a major challenge. Here, we use an integrated probabilistic modeling approach to study genomic correlates of protein evolutionary rate in Saccharomyces cerevisiae. We measure and rank degrees of association between (i) an approximate measure of protein evolutionary rate with high genome coverage, and (ii) a diverse list of protein properties (sequence, structural, functional, network, and phenotypic). We observe, among many statistically significant correlations, that slowly evolving proteins tend to be regulated by more transcription factors, deficient in predicted structural disorder, involved in characteristic biological functions (such as translation), biased in amino acid composition, and are generally more abundant, more essential, and enriched for interaction partners. Many of these results are in agreement with recent studies. In addition, we assess information contribution of different subsets of these protein properties in the task of predicting slowly evolving proteins. We employ a logistic regression model on binned data that is able to account for intercorrelation, non-linearity, and heterogeneity within features. Our model considers features both individually and in natural ensembles ("meta-features") in order to assess joint information contribution and degree of contribution independence. Meta-features based on protein abundance and amino acid composition make strong, partially independent contributions to the task of predicting slowly evolving proteins; other meta-features make additional minor contributions. The combination of all meta-features yields predictions comparable to those based on paired species comparisons, and approaching the predictive limit of optimal lineage-insensitive features. Our integrated assessment framework can be readily extended to other correlational analyses at the genome scale. Author Summary Proteins encoded within a given genome are known to evolve at drastically different rates. Through recent large-scale studies, researchers have measured a wide variety of properties for all proteins in yeast. We are interested to know how these properties relate to one another and to what extent they explain evolutionary rate variation. Protein properties are a heterogeneous mix, a factor which complicates research in this area. For example, some properties (e.g., protein abundance) are numerical, while others (e.g., protein function) are descriptive; protein properties may also suffer from noise and hidden redundancies. We have addressed these issues within a flexible and robust statistical framework. We first ranked a large list of protein properties by the strength of their relationships with evolutionary rate; this confirms many known evolutionary relationships and also highlights several new ones. Similar protein properties were then grouped and applied to predict slowly evolving proteins. Some of these groups were as effective as paired species comparison in making correct predictions, although in both cases a great deal of evolutionary rate variation remained to be explained. Our work has helped to refine the set of protein properties that researchers should consider as they investigate the mechanisms underlying protein evolution.

2009-06-12T00:00:00Z Steiling, Katrina Kadar, Aran Y. Bergerat, Agnes Flanigon, James Sridhar, Sriram Shah, Vishal Ahmad, Q. Rushdy Brody, Jerome S. Lenburg, Marc E. Steffen, Martin Spira, Avrum http://hdl.handle.net/2144/3213 2012-01-12T07:00:54Z 2009-04-09T00:00:00Z

Comparison of Proteomic and Transcriptomic Profiles in the Bronchial Airway Epithelium of Current and Never Smokers Steiling, Katrina; Kadar, Aran Y.; Bergerat, Agnes; Flanigon, James; Sridhar, Sriram; Shah, Vishal; Ahmad, Q. Rushdy; Brody, Jerome S.; Lenburg, Marc E.; Steffen, Martin; Spira, Avrum BACKGROUND. Although prior studies have demonstrated a smoking-induced field of molecular injury throughout the lung and airway, the impact of smoking on the airway epithelial proteome and its relationship to smoking-related changes in the airway transcriptome are unclear. METHODOLOGY/PRINCIPAL FINDINGS. Airway epithelial cells were obtained from never (n=5) and current (n=5) smokers by brushing the mainstem bronchus. Proteins were separated by one dimensional polyacrylamide gel electrophoresis (1D-PAGE). After in-gel digestion, tryptic peptides were processed via liquid chromatography/ tandem mass spectrometry (LC-MS/MS) and proteins identified. RNA from the same samples was hybridized to HG-U133A microarrays. Protein detection was compared to RNA expression in the current study and a previously published airway dataset. The functional properties of many of the 197 proteins detected in a majority of never smokers were similar to those observed in the never smoker airway transcriptome. LC-MS/MS identified 23 proteins that differed between never and current smokers. Western blotting confirmed the smoking-related changes of PLUNC, P4HB1, and uteroglobin protein levels. Many of the proteins differentially detected between never and current smokers were also altered at the level of gene expression in this cohort and the prior airway transcriptome study. There was a strong association between protein detection and expression of its corresponding transcript within the same sample, with 86% of the proteins detected by LC-MS/MS having a detectable corresponding probeset by microarray in the same sample. Forty-one proteins identified by LC-MS/MS lacked detectable expression of a corresponding transcript and were detected in =5% of airway samples from a previously published dataset. CONCLUSIONS/SIGNIFICANCE. 1D-PAGE coupled with LC-MS/MS effectively profiled the airway epithelium proteome and identified proteins expressed at different levels as a result of cigarette smoke exposure. While there was a strong correlation between protein and transcript detection within the same sample, we also identified proteins whose corresponding transcripts were not detected by microarray. This noninvasive approach to proteomic profiling of airway epithelium may provide additional insights into the field of injury induced by tobacco exposure.

2009-04-09T00:00:00Z Yu, Wen-Han Hu, Han Zhou, Qingde Xia, Yu Amar, Salomon http://hdl.handle.net/2144/3214 2012-01-12T07:00:55Z 2010-12-23T00:00:00Z

Bioinformatics Analysis of Macrophages Exposed to Porphyromonas gingivalis: Implications in Acute vs. Chronic Infections Yu, Wen-Han; Hu, Han; Zhou, Qingde; Xia, Yu; Amar, Salomon BACKGROUND. Periodontitis is the most common human infection affecting tooth-supporting structures. It was shown to play a role in aggravating atherosclerosis. To deepen our understanding of the pathogenesis of this disease, we exposed human macrophages to an oral bacteria, Porphyromonas gingivalis (P. gingivalis), either as live bacteria or its LPS or fimbria. Microarray data from treated macrophages or control cells were analyzed to define molecular signatures. Changes in genes identified in relevant pathways were validated by RT-PCR. METHODOLOGY/PRINCIPAL FINDINGS. We focused our analysis on three important groups of genes. Group PG (genes differentially expressed by live bacteria only); Group LFG (genes differentially expressed in response to exposure to LPS and/or FimA); Group CG (core gene set jointly activated by all 3 stimulants). A total of 842 macrophage genes were differentially expressed in at least one of the three conditions compared to naïve cells. Using pathway analysis, we found that group CG activates the initial phagocytosis process and induces genes relevant to immune response, whereas group PG can de-activate the phagocytosis process associated with phagosome-lysosome fusion. LFG mostly affected RIG-I-like receptor signaling pathway. CONCLUSION/SIGNIFICANCE. In light of the fact that acute infections involve live bacteria while chronic infections involve a combination of live bacteria and their byproducts, group PG could represent acute P. gingivalis infection while group LFG could represent chronic P. gingivalis infection. Group CG may be associated with core immune pathways, triggered irrespective of the specific stimulants and indispensable to mount an appropriate immune response. Implications in acute vs. chronic infection are discussed.

2010-12-23T00:00:00Z