The New York SGX Research Center for Structural Genomics (NYSGXRC) of the NIGMS Protein Structure Initiative (PSI) has applied its high-throughput X-ray crystallographic structure determination platform to systematic studies of all human protein phosphatases and protein phosphatases from biomedically-relevant pathogens. To date, the NYSGXRC has determined structures of 21 distinct protein phosphatases: 14 from human, 2 from mouse, 2 from the pathogen Toxoplasma gondii, 1 from Trypanosoma brucei, the parasite responsible for African sleeping sickness, and 2 from the principal mosquito vector of malaria in Africa, Anopheles gambiae. These structures provide insights into both normal and pathophysiologic processes, including transcriptional regulation, regulation of major signaling pathways, neural development, and type 1 diabetes. In conjunction with the contributions of other international structural genomics consortia, these efforts promise to provide an unprecedented database and materials repository for structureguided experimental and computational discovery of inhibitors for all classes of protein phosphatases.

Thermobifida fusca o-succinylbenzoate synthase (OSBS), a member of the enolase superfamily that catalyzes a step in menaquinone biosynthesis, shares 22% and 28% amino acid sequence identity with two previously characterized OSBS enzymes from Escherichia coli and Amycolatopsis sp. T-1-60, respectively. These values are considerably lower than typical sequence identities among homologous proteins that have the same function. To determine how such divergent enzymes catalyze the same reaction, we solved the structure of T. fusca OSBS and identified amino acids that are important for ligand binding. We discovered significant differences in structure and conformational flexibility between T. fusca OSBS and other members of the enolase superfamily. In particular, the 20s loop, a flexible loop in the active site that permits ligand binding and release in most enolase superfamily proteins, has a four-amino acid deletion and is well ordered in T. fusca OSBS. Instead, flexibility of a different region allows the substrate to enter from the other side of the active site. T. fusca OSBS was more tolerant of mutations at residues that were critical for activity in E. coli OSBS. Also, replacing active site amino acids found in one protein with the amino acids that occur at the same place in the other protein reduces catalytic efficiency. Thus, the extraordinary divergence between these proteins does not appear to reflect a higher tolerance of mutations. Instead, large deletions outside the active site were accompanied by alteration of active site size and electrostatic interactions, resulting in small but significant differences in ligand binding.

There has been intense interest in the cellular response to hypoxia, and a large number of differentially expressed proteins have been identified through various high-throughput experiments. These valuable data are scattered, and there have been no systematic attempts to document the various proteins regulated by hypoxia. Compilation, curation and annotation of these data are important in deciphering their role in hypoxia and hypoxia-related disorders. Therefore, we have compiled HypoxiaDB, a database of hypoxia-regulated proteins. It is a comprehensive, manually-curated, non-redundant catalog of proteins whose expressions are shown experimentally to be altered at different levels and durations of hypoxia. The database currently contains 72 000 manually curated entries taken on 3500 proteins extracted from 73 peer-reviewed publications selected from PubMed. HypoxiaDB is distinctive from other generalized databases: (i) it compiles tissue-specific protein expression changes under different levels and duration of hypoxia. Also, it provides manually curated literature references to support the inclusion of the protein in the database and establish its association with hypoxia. (ii) For each protein, HypoxiaDB integrates data on gene ontology, KEGG (Kyoto Encyclopedia of Genes and Genomes) pathway, protein–protein interactions, protein family (Pfam), OMIM (Online Mendelian Inheritance in Man), PDB (Protein Data Bank) structures and homology to other sequenced genomes. (iii) It also provides pre-compiled information on hypoxia-proteins, which otherwise requires tedious computational analysis. This includes information like chromosomal location, identifiers like Entrez, HGNC, Unigene, Uniprot, Ensembl, Vega, GI numbers and Genbank accession numbers associated with the protein. These are further cross-linked to respective public databases augmenting HypoxiaDB to the external repositories. (iv) In addition, HypoxiaDB provides an online sequence-similarity search tool for users to compare their protein sequences with HypoxiaDB protein database. We hope that HypoxiaDB will enrich our knowledge about hypoxia-related biology and eventually will lead to the development of novel hypothesis and advancements in diagnostic and therapeutic activities. HypoxiaDB is freely accessible for academic and non-profit users via http://www.hypoxiadb.com.Database URL: http://www.hypoxiadb.com

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BackgroundThe tissue-specific Unigene Sets derived from more than one million expressed sequence tags (ESTs) in the NCBI, GenBank database offers a platform for identifying significantly and differentially expressed tissue-specific genes by in-silico methods. Digital differential display (DDD) rapidly creates transcription profiles based on EST comparisons and numerically calculates, as a fraction of the pool of ESTs, the relative sequence abundance of known and novel genes. However, the process of identifying the most likely tissue for a specific disease in which to search for candidate genes from the pool of differentially expressed genes remains difficult. Therefore, we have used ‘Gene Ontology semantic similarity score’ to measure the GO similarity between gene products of lung tissue-specific candidate genes from control (normal) and disease (cancer) sets. This semantic similarity score matrix based on hierarchical clustering represents in the form of a dendrogram. The dendrogram cluster stability was assessed by multiple bootstrapping. Multiple bootstrapping also computes a p-value for each cluster and corrects the bias of the bootstrap probability.ResultsSubsequent hierarchical clustering by the multiple bootstrapping method (α = 0.95) identified seven clusters. The comparative, as well as subtractive, approach revealed a set of 38 biomarkers comprising four distinct lung cancer signature biomarker clusters (panel 1–4). Further gene enrichment analysis of the four panels revealed that each panel represents a set of lung cancer linked metastasis diagnostic biomarkers (panel 1), chemotherapy/drug resistance biomarkers (panel 2), hypoxia regulated biomarkers (panel 3) and lung extra cellular matrix biomarkers (panel 4).ConclusionsExpression analysis reveals that hypoxia induced lung cancer related biomarkers (panel 3), HIF and its modulating proteins (TGM2, CSNK1A1, CTNNA1, NAMPT/Visfatin, TNFRSF1A, ETS1, SRC-1, FN1, APLP2, DMBT1/SAG, AIB1 and AZIN1) are significantly down regulated. All down regulated genes in this panel were highly up regulated in most other types of cancers. These panels of proteins may represent signature biomarkers for lung cancer and will aid in lung cancer diagnosis and disease monitoring as well as in the prediction of responses to therapeutics.

The substrate specificities of two incorrectly annotated enzymes belonging to cog3964 from the amidohydrolase superfamily (AHS) were determined. This group of enzymes is currently misannotated as either dihydroorotase or adenine deaminase. Atu3266 from Agrobacterium tumefaciens C58 and Oant2987 from Ochrobactrum anthropi ATCC 49188 were determined to catalyze the hydrolysis of acetyl-R-mandelate and similar esters with values of kcat/Km that exceed 105 M−1 s−1. These enzymes do not catalyze the deamination of adenine or the hydrolysis of dihydroorotate. Atu3266 was crystallized and the structure determined to a resolution of 2.62 Å. The protein folds as a distorted (β/α)8-barrel and binds two zincs in the active site. The substrate profile was determined via a combination of computational docking to the three-dimensional structure of Atu3266 and screening of a highly focused library of potential substrates. The initial weak hit was the hydrolysis of N-acetyl-D-serine (kcat/Km = 4 M−1s−1). This was followed by the progressive identification of acetyl-R-glycerate (4 × 102 M−1s−1), acetyl glycolate (kcat/Km = 1.3 × 104 M−1 s−1) and ultimately acetyl-R-mandelate (kcat/Km =2.8 × 105 M−1 s−1).

Hypoxic respiratory diseases or hypoxia exposures are frequently accompanied by glucose intolerance and impaired nitric oxide (NO) availability. However, the molecular mechanism responsible for impaired NO production and insulin resistance (IR) during hypoxia remains obscure. In this study, we investigated the possible mechanism of impaired NO production and IR during hypoxia in a mouse model. Mice were exposed to hypoxia for different periods of time (0-24 h), and parameters of IR and endothelial dysfunctions were analyzed. Exposure to hypoxia resulted in a time-dependent increase in IR as well as multimeric forms of von Willebrand factor (vWF) and subsequently a decrease in eNOS activity. Preincubation with plasma of hypoxia-exposed animals (different time points) or human vWF inhibited insulin-induced NO production in a dose-dependent manner; larger doses of insulin reversed the effect. In contrast, preincubation of vWF-immunodepleted plasma failed to inhibit insulin-induced NO production, whereas vWF immunoneutralization abolished the effect of hypoxia-induced IR and D-[U-(14)C]glucose uptake. Furthermore, the interactions between vWF and eNOS were studied by far-Western blotting, co-immunoprecipitation, and surface plasma resonance spectroscopy. Kinetic analyses showed that the dissociation constant (KD), inhibitory constant (Ki), and half-maximal inhibitory concentration (IC50) were 1.79 × 10(-8) M, 250 pM, and 18.31 pM, respectively, suggesting that vWF binds to eNOS with a high affinity and greater efficacy for activator (insulin) inhibition. These results indicated that vWF, an antagonist of eNOS, inhibits insulin-induced NO production and causes IR.

Hypoxia is a complex pathophysiological condition. The physiological and molecular responses to this stress have been extensively studied. However, the management of its ill effects still poses a challenge to clinicians. MicroRNAs (miRNAs) are short non-coding RNA molecules that control post-transcriptional gene expression. The regulatory role of miRNAs in hypoxic environments has been studied in many hypoxia-related disorders, however a comprehensive compilation and analysis of all data and the significance of miRNAs in hypoxia adaption is still lacking. This review summarizes the miRNAs related to various hypoxia-related disorders and highlights the computational approaches to study them. This would help in designing novel strategies toward efficient management of hypoxia-related disorders.