Sawsan Khuri

Microbial Relatives of the Seed Storage Proteins of Higher Plants: Conservation of Structure and Diversification of Function during Evolution of the Cupin Superfamily

SUMMARY This review summarizes the recent discovery of the cupin superfamily (from the Latin term “cupa,” a small barrel) of functionally diverse proteins that initially were limited to several higher plant proteins such as seed storage proteins, germin (an oxalate oxidase), germin-like proteins, and auxin-binding protein. Knowledge of the three-dimensional structure of two vicilins, seed proteins with a characteristic β-barrel core, led to the identification of a small number of conserved residues and thence to the discovery of several microbial proteins which share these key amino acids. In particular, there is a highly conserved pattern of two histidine-containing motifs with a varied intermotif spacing. This cupin signature is found as a central component of many microbial proteins including certain types of phosphomannose isomerase, polyketide synthase, epimerase, and dioxygenase. In addition, the signature has been identified within the N-terminal effector domain in a subgroup of bacterial AraC transcription factors. As well as these single-domain cupins, this survey has identified other classes of two-domain bicupins including bacterial gentisate 1,2-dioxygenases and 1-hydroxy-2-naphthoate dioxygenases, fungal oxalate decarboxylases, and legume sucrose-binding proteins. Cupin evolution is discussed from the perspective of the structure-function relationships, using data from the genomes of several prokaryotes, especially Bacillus subtilis. Many of these functions involve aspects of sugar metabolism and cell wall synthesis and are concerned with responses to abiotic stress such as heat, desiccation, or starvation. Particular emphasis is also given to the oxalate-degrading enzymes from microbes, their biological significance, and their value in a range of medical and other applications.

STAT3 Activation in Skeletal Muscle Links Muscle Wasting and the Acute Phase Response in Cancer Cachexia

Background Cachexia, or weight loss despite adequate nutrition, significantly impairs quality of life and response to therapy in cancer patients. In cancer patients, skeletal muscle wasting, weight loss and mortality are all positively associated with increased serum cytokines, particularly Interleukin-6 (IL-6), and the presence of the acute phase response. Acute phase proteins, including fibrinogen and serum amyloid A (SAA) are synthesized by hepatocytes in response to IL-6 as part of the innate immune response. To gain insight into the relationships among these observations, we studied mice with moderate and severe Colon-26 (C26)-carcinoma cachexia. Methodology/Principal Findings Moderate and severe C26 cachexia was associated with high serum IL-6 and IL-6 family cytokines and highly similar patterns of skeletal muscle gene expression. The top canonical pathways up-regulated in both were the complement/coagulation cascade, proteasome, MAPK signaling, and the IL-6 and STAT3 pathways. Cachexia was associated with increased muscle pY705-STAT3 and increased STAT3 localization in myonuclei. STAT3 target genes, including SOCS3 mRNA and acute phase response proteins, were highly induced in cachectic muscle. IL-6 treatment and STAT3 activation both also induced fibrinogen in cultured C2C12 myotubes. Quantitation of muscle versus liver fibrinogen and SAA protein levels indicates that muscle contributes a large fraction of serum acute phase proteins in cancer. Conclusions/Significance These results suggest that the STAT3 transcriptome is a major mechanism for wasting in cancer. Through IL-6/STAT3 activation, skeletal muscle is induced to synthesize acute phase proteins, thus establishing a molecular link between the observations of high IL-6, increased acute phase response proteins and muscle wasting in cancer. These results suggest a mechanism by which STAT3 might causally influence muscle wasting by altering the profile of genes expressed and translated in muscle such that amino acids liberated by increased proteolysis in cachexia are synthesized into acute phase proteins and exported into the blood.

Identifying consensus disease pathways in Parkinson's disease using an integrative systems biology approach.

Parkinsons disease (PD) has had six genome-wide association studies (GWAS) conducted as well as several gene expression studies. However, only variants in MAPT and SNCA have been consistently replicated. To improve the utility of these approaches, we applied pathway analyses integrating both GWAS and gene expression. The top 5000 SNPs (p<0.01) from a joint analysis of three existing PD GWAS were identified and each assigned to a gene. For gene expression, rather than the traditional comparison of one anatomical region between sets of patients and controls, we identified differentially expressed genes between adjacent Braak regions in each individual and adjusted using average control expression profiles. Over-represented pathways were calculated using a hyper-geometric statistical comparison. An integrated, systems meta-analysis of the over-represented pathways combined the expression and GWAS results using a Fishers combined probability test. Four of the top seven pathways from each approach were identical. The top three pathways in the meta-analysis, with their corrected p-values, were axonal guidance (p = 2.8E-07), focal adhesion (p = 7.7E-06) and calcium signaling (p = 2.9E-05). These results support that a systems biology (pathway) approach will provide additional insight into the genetic etiology of PD and that these pathways have both biological and statistical support to be important in PD.

MicroRNA signature of the human developing pancreas

BackgroundMicroRNAs are non-coding RNAs that regulate gene expression including differentiation and development by either inhibiting translation or inducing target degradation. The aim of this study is to determine the microRNA expression signature during human pancreatic development and to identify potential microRNA gene targets calculating correlations between the signature microRNAs and their corresponding mRNA targets, predicted by bioinformatics, in genome-wide RNA microarray study.ResultsThe microRNA signature of human fetal pancreatic samples 10-22 weeks of gestational age (wga), was obtained by PCR-based high throughput screening with Taqman Low Density Arrays. This method led to identification of 212 microRNAs. The microRNAs were classified in 3 groups: Group number I contains 4 microRNAs with the increasing profile; II, 35 microRNAs with decreasing profile and III with 173 microRNAs, which remain unchanged. We calculated Pearson correlations between the expression profile of microRNAs and target mRNAs, predicted by TargetScan 5.1 and miRBase altgorithms, using genome-wide mRNA expression data. Group I correlated with the decreasing expression of 142 target mRNAs and Group II with the increasing expression of 876 target mRNAs. Most microRNAs correlate with multiple targets, just as mRNAs are targeted by multiple microRNAs. Among the identified targets are the genes and transcription factors known to play an essential role in pancreatic development.ConclusionsWe have determined specific groups of microRNAs in human fetal pancreas that change the degree of their expression throughout the development. A negative correlative analysis suggests an intertwined network of microRNAs and mRNAs collaborating with each other. This study provides information leading to potential two-way level of combinatorial control regulating gene expression through microRNAs targeting multiple mRNAs and, conversely, target mRNAs regulated in parallel by other microRNAs as well. This study may further the understanding of gene expression regulation in the human developing pancreas.

Conotoxin protein classification using free scores of words and support vector machines

BackgroundConotoxin has been proven to be effective in drug design and could be used to treat various disorders such as schizophrenia, neuromuscular disorders and chronic pain. With the rapidly growing interest in conotoxin, accurate conotoxin superfamily classification tools are desirable to systematize the increasing number of newly discovered sequences and structures. However, despite the significance and extensive experimental investigations on conotoxin, those tools have not been intensively explored.ResultsIn this paper, we propose to consider suboptimal alignments of words with restricted length. We developed a scoring system based on local alignment partition functions, called free score. The scoring system plays the key role in the feature extraction step of support vector machine classification. In the classification of conotoxin proteins, our method, SVM-Freescore, features an improved sensitivity and specificity by approximately 5.864% and 3.76%, respectively, over previously reported methods. For the generalization purpose, SVM-Freescore was also applied to classify superfamilies from curated and high quality database such as ConoServer. The average computed sensitivity and specificity for the superfamily classification were found to be 0.9742 and 0.9917, respectively.ConclusionsThe SVM-Freescore method is shown to be a useful sequence-based analysis tool for functional and structural characterization of conotoxin proteins. The datasets and the software are available at http://faculty.uaeu.ac.ae/nzaki/SVM-Freescore.htm.

Common susceptibility variants examined for association with dilated cardiomyopathy.

Rare mutations in more than 20 genes have been suggested to cause dilated cardiomyopathy (DCM), but explain only a small percentage of cases, mainly in familial forms. We hypothesised that more common variants may also play a role in increasing genetic susceptibility to DCM, similar to that observed in other common complex disorders.

Essentiality and centrality in protein interaction networks revisited

BackgroundMinimum dominating sets (MDSet) of protein interaction networks allow the control of underlying protein interaction networks through their topological placement. While essential proteins are enriched in MDSets, we hypothesize that the statistical properties of biological functions of essential genes are enhanced when we focus on essential MDSet proteins (e-MDSet).ResultsHere, we determined minimum dominating sets of proteins (MDSet) in interaction networks of E. coli, S. cerevisiae and H. sapiens, defined as subsets of proteins whereby each remaining protein can be reached by a single interaction. We compared several topological and functional parameters of essential, MDSet, and essential MDSet (e-MDSet) proteins. In particular, we observed that their topological placement allowed e-MDSet proteins to provide a positive correlation between degree and lethality, connect more protein complexes, and have a stronger impact on network resilience than essential proteins alone. In comparison to essential proteins we further found that interactions between e-MDSet proteins appeared more frequently within complexes, while interactions of e-MDSet proteins between complexes were depleted. Finally, these e-MDSet proteins classified into functional groupings that play a central role in survival and adaptability.ConclusionsThe determination of e-MDSet of an organism highlights a set of proteins that enhances the enrichment signals of biological functions of essential proteins. As a consequence, we surmise that e-MDSets may provide a new method of evaluating the core proteins of an organism.

Prediction of protein-glucose binding sites using support vector machines.

Glucose is a simple sugar that plays an essential role in many basic metabolic and signaling pathways. Many proteins have binding sites that are highly specific to glucose. The exponential increase of genomic data has revealed the identity of many proteins that seem to be central to biological processes, but whose exact functions are unknown. Many of these proteins seem to be associated with disease processes. Being able to predict glucose‐specific binding sites in these proteins will greatly enhance our ability to annotate protein function and may significantly contribute to drug design. We hereby present the first glucose‐binding site classifier algorithm. We consider the sugar‐binding pocket as a spherical spatio‐chemical environment and represent it as a vector of geometric and chemical features. We then perform Random Forests feature selection to identify key features and analyze them using support vector machines classification. Our work shows that glucose binding sites can be modeled effectively using a limited number of basic chemical and residue features. Using a leave‐one‐out cross‐validation method, our classifier achieves a 8.11% error, a 89.66% sensitivity and a 93.33% specificity over our dataset. From a biochemical perspective, our results support the relevance of ordered water molecules and ions in determining glucose specificity. They also reveal the importance of carboxylate residues in glucose binding and the high concentration of negatively charged atoms in direct contact with the bound glucose molecule. Proteins 2009.

NXSensor web tool for evaluating DNA for nucleosome exclusion sequences and accessibility to binding factors

Nucleosomes, a basic structural unit of eukaryotic chromatin, play a significant role in regulating gene expression. We have developed a web tool based on DNA sequences known from empirical and theoretical studies to influence DNA bending and flexibility, and to exclude nucleosomes. NXSensor (available at ) finds nucleosome exclusion sequences, evaluates their length and spacing, and computes an ‘accessibility score’ giving the proportion of base pairs likely to be nucleosome-free. Application of NXSensor to the promoter regions of housekeeping (HK) genes and those of tissue-specific (TS) genes revealed a significant difference between the two classes of gene, the former being significantly more open, on average, particularly near transcription start sites (TSSs). NXSensor should be a useful tool in assessing the likelihood of nucleosome formation in regions involved in gene regulation and other aspects of chromatin function.

GenSensor Suite: A Web-Based Tool for the Analysis of Gene and Protein Interactions, Pathways, and Regulation

The GenSensor Suite consists of four web tools for elucidating relationships among genes and proteins. GenPath results show which biochemical, regulatory, or other gene set categories are over- or under-represented in an input list compared to a background list. All common gene sets are available for searching in GenPath, plus some specialized sets. Users can add custom background lists. GenInteract builds an interaction gene list from a single gene input and then analyzes this in GenPath. GenPubMed uses a PubMed query to identify a list of PubMed IDs, from which a gene list is extracted and queried in GenPath. GenViewer allows the user to query one gene set against another in GenPath. GenPath results are presented with relevant P- and q-values in an uncluttered, fully linked, and integrated table. Users can easily copy this table and paste it directly into a spreadsheet or document.