Isofunctional Clustering and Conformational Analysis of the Arsenate Reductase Superfamily Reveals Nine Distinct Clusters.

Arsenate reductase (ArsC) is a superfamily of enzymes that reduce arsenate. Due to active site similarities, some ArsC can function as low-molecular weight protein tyrosine phosphatases (LMW-PTPs). Broad superfamily classifications align with redox partners (Trx- or Grx-linked). To understand this superfamily's mechanistic diversity, the ArsC superfamily is classified on the basis of active site features utilizing the tools TuLIP (two-level iterative clustering process) and autoMISST (automated multilevel iterative sequence searching technique). This approach identified nine functionally relevant (perhaps isofunctional) protein groups. Five groups exhibit distinct ArsC mechanisms. Three are Grx-linked: group 4AA (classical ArsC), group 3AAA (YffB-like), and group 5BAA. Two are Trx-linked: groups 6AAAAA and 7AAAAAAAA. One is an Spx-like transcriptional regulatory group, group 5AAA. Three are potential LMW-PTP groups: groups 7BAAAA, and 7AAAABAA, which have not been previously identified, and the well-studied LMW-PTP family group 8AAA. Molecular dynamics simulations were utilized to explore functional site details. In several families, we confirm and add detail to literature-based mechanistic information. Mechanistic roles are hypothesized for conserved active site residues in several families. In three families, simulations of the unliganded structure sample specific conformational ensembles, which are proposed to represent either a more ligand-binding-competent conformation or a pathway toward a more binding-competent state; these active sites may be designed to traverse high-energy barriers to the lower-energy conformations necessary to more readily bind ligands. This more detailed biochemical understanding of ArsC and ArsC-like PTP mechanisms opens possibilities for further understanding of arsenate bioremediation and the LMW-PTP mechanism.

Identification of Transcriptional and Receptor Networks That Control Root Responses to Ethylene.

Transcriptomic analyses with high temporal resolution provide substantial new insight into hormonal response networks. This study identified the kinetics of genome-wide transcript abundance changes in response to elevated levels of the plant hormone ethylene in roots from light-grown Arabidopsis (Arabidopsis thaliana) seedlings, which were overlaid on time-matched developmental changes. Functional annotation of clusters of transcripts with similar temporal patterns revealed rapidly induced clusters with known ethylene function and more slowly regulated clusters with novel predicted functions linked to root development. In contrast to studies with dark-grown seedlings, where the canonical ethylene response transcription factor, EIN3, is central to ethylene-mediated development, the roots of ein3 and eil1 single and double mutants still respond to ethylene in light-grown seedlings. Additionally, a subset of these clusters of ethylene-responsive transcripts were enriched in targets of EIN3 and ERFs. These results are consistent with EIN3-independent developmental and transcriptional changes in light-grown roots. Examination of single and multiple gain-of-function and loss-of-function receptor mutants revealed that, of the five ethylene receptors, ETR1 controls lateral root and root hair initiation and elongation and the synthesis of other receptors. These results provide new insight into the transcriptional and developmental responses to ethylene in light-grown seedlings.

An Atlas of Peroxiredoxins Created Using an Active Site Profile-Based Approach to Functionally Relevant Clustering of Proteins.

Peroxiredoxins (Prxs or Prdxs) are a large protein superfamily of antioxidant enzymes that rapidly detoxify damaging peroxides and/or affect signal transduction and, thus, have roles in proliferation, differentiation, and apoptosis. Prx superfamily members are widespread across phylogeny and multiple methods have been developed to classify them. Here we present an updated atlas of the Prx superfamily identified using a novel method called MISST (Multi-level Iterative Sequence Searching Technique). MISST is an iterative search process developed to be both agglomerative, to add sequences containing similar functional site features, and divisive, to split groups when functional site features suggest distinct functionally-relevant clusters. Superfamily members need not be identified initially-MISST begins with a minimal representative set of known structures and searches GenBank iteratively. Further, the method's novelty lies in the manner in which isofunctional groups are selected; rather than use a single or shifting threshold to identify clusters, the groups are deemed isofunctional when they pass a self-identification criterion, such that the group identifies itself and nothing else in a search of GenBank. The method was preliminarily validated on the Prxs, as the Prxs presented challenges of both agglomeration and division. For example, previous sequence analysis clustered the Prx functional families Prx1 and Prx6 into one group. Subsequent expert analysis clearly identified Prx6 as a distinct functionally relevant group. The MISST process distinguishes these two closely related, though functionally distinct, families. Through MISST search iterations, over 38,000 Prx sequences were identified, which the method divided into six isofunctional clusters, consistent with previous expert analysis. The results represent the most complete computational functional analysis of proteins comprising the Prx superfamily. The feasibility of this novel method is demonstrated by the Prx superfamily results, laying the foundation for potential functionally relevant clustering of the universe of protein sequences.

An approach to functionally relevant clustering of the protein universe: Active site profile-based clustering of protein structures and sequences.

Protein function identification remains a significant problem. Solving this problem at the molecular functional level would allow mechanistic determinant identification-amino acids that distinguish details between functional families within a superfamily. Active site profiling was developed to identify mechanistic determinants. DASP and DASP2 were developed as tools to search sequence databases using active site profiling. Here, TuLIP (Two-Level Iterative clustering Process) is introduced as an iterative, divisive clustering process that utilizes active site profiling to separate structurally characterized superfamily members into functionally relevant clusters. Underlying TuLIP is the observation that functionally relevant families (curated by Structure-Function Linkage Database, SFLD) self-identify in DASP2 searches; clusters containing multiple functional families do not. Each TuLIP iteration produces candidate clusters, each evaluated to determine if it self-identifies using DASP2. If so, it is deemed a functionally relevant group. Divisive clustering continues until each structure is either a functionally relevant group member or a singlet. TuLIP is validated on enolase and glutathione transferase structures, superfamilies well-curated by SFLD. Correlation is strong; small numbers of structures prevent statistically significant analysis. TuLIP-identified enolase clusters are used in DASP2 GenBank searches to identify sequences sharing functional site features. Analysis shows a true positive rate of 96%, false negative rate of 4%, and maximum false positive rate of 4%. F-measure and performance analysis on the enolase search results and comparison to GEMMA and SCI-PHY demonstrate that TuLIP avoids the over-division problem of these methods. Mechanistic determinants for enolase families are evaluated and shown to correlate well with literature results.

DASP3: identification of protein sequences belonging to functionally relevant groups.

BACKGROUND: Development of automatable processes for clustering proteins into functionally relevant groups is a critical hurdle as an increasing number of sequences are deposited into databases. Experimental function determination is exceptionally time-consuming and can't keep pace with the identification of protein sequences. A tool, DASP (Deacon Active Site Profiler), was previously developed to identify protein sequences with active site similarity to a query set. Development of two iterative, automatable methods for clustering proteins into functionally relevant groups exposed algorithmic limitations to DASP. RESULTS: The accuracy and efficiency of DASP was significantly improved through six algorithmic enhancements implemented in two stages: DASP2 and DASP3. Validation demonstrated DASP3 provides greater score separation between true positives and false positives than earlier versions. In addition, DASP3 shows similar performance to previous versions in clustering protein structures into isofunctional groups (validated against manual curation), but DASP3 gathers and clusters protein sequences into isofunctional groups more efficiently than DASP and DASP2. CONCLUSIONS: DASP algorithmic enhancements resulted in improved efficiency and accuracy of identifying proteins that contain active site features similar to those of the query set. These enhancements provide incremental improvement in structure database searches and initial sequence database searches; however, the enhancements show significant improvement in iterative sequence searches, suggesting DASP3 is an appropriate tool for the iterative processes required for clustering proteins into isofunctional groups.

Impact of the Type I Interferon Receptor on the Global Gene Expression Program During the Course of Dendritic Cell Maturation Induced by Polyinosinic Polycytidylic Acid.

Dendritic cell (DC) maturation involves widespread changes in cellular function and gene expression. The regulatory role of IFNAR in the program of DC maturation remains incompletely defined. Thus, the time evolution impact of IFNAR on this process was evaluated. Changes in DC phenotype, function, and gene expression induced by poly I:C were measured in wild-type and IFNAR(-/-) DC at 9 time points over 24 h. Temporal gene expression profiles were filtered on consistency and response magnitude across replicates. The number of genes whose expression was altered by poly I:C treatment was greatly reduced in IFNAR(-/-) DC, including the majority of the downregulated gene expression program previously observed in wild-type (WT) DC. Furthermore, the number of genes upregulated was almost equal between WT and IFNAR(-/-) DC, yet the identities of those genes were distinct. Integrating these data with protein-protein interaction data revealed several novel subnetworks active during maturation, including nucleotide synthesis, metabolism, and repair. A subnetwork associated with redox activity was uniquely identified in IFNAR(-/-) DC. Overall, temporal gene expression and network analyses identified many genes regulated by the type I interferon response and revealed previously unidentified aspects of the DC maturation process.

Mutating a Highly Conserved Residue in Diverse Cytochrome P450s Facilitates Diastereoselective Olefin Cyclopropanation.

Cytochrome P450s and other heme-containing proteins have recently been shown to have promiscuous activity for the cyclopropanation of olefins using diazoacetate reagents. Despite the progress made thus far, engineering selective catalysts for all possible stereoisomers for the cyclopropanation reaction remains a considerable challenge. Previous investigations of a model P450 (P450BM3 ) revealed that mutation of a conserved active site threonine (Thr268) to alanine transformed the enzyme into a highly active and selective cyclopropanation catalyst. By incorporating this mutation into a diverse panel of P450 scaffolds, we were able to quickly identify enantioselective catalysts for all possible diastereomers in the model reaction of styrene with ethyl diazoacetate. Some alanine variants exhibited selectivities that were markedly different from the wild-type enzyme, with a few possessing moderate to high diastereoselectivity and enantioselectivities up to 97 % for synthetically challenging cis-cyclopropane diastereomers.

Comparison of topological clustering within protein networks using edge metrics that evaluate full sequence, full structure, and active site microenvironment similarity.

The development of accurate protein function annotation methods has emerged as a major unsolved biological problem. Protein similarity networks, one approach to function annotation via annotation transfer, group proteins into similarity-based clusters. An underlying assumption is that the edge metric used to identify such clusters correlates with functional information. In this contribution, this assumption is evaluated by observing topologies in similarity networks using three different edge metrics: sequence (BLAST), structure (TM-Align), and active site similarity (active site profiling, implemented in DASP). Network topologies for four well-studied protein superfamilies (enolase, peroxiredoxin (Prx), glutathione transferase (GST), and crotonase) were compared with curated functional hierarchies and structure. As expected, network topology differs, depending on edge metric; comparison of topologies provides valuable information on structure/function relationships. Subnetworks based on active site similarity correlate with known functional hierarchies at a single edge threshold more often than sequence- or structure-based networks. Sequence- and structure-based networks are useful for identifying sequence and domain similarities and differences; therefore, it is important to consider the clustering goal before deciding appropriate edge metric. Further, conserved active site residues identified in enolase and GST active site subnetworks correspond with published functionally important residues. Extension of this analysis yields predictions of functionally determinant residues for GST subgroups. These results support the hypothesis that active site similarity-based networks reveal clusters that share functional details and lay the foundation for capturing functionally relevant hierarchies using an approach that is both automatable and can deliver greater precision in function annotation than current similarity-based methods.

Broad phenotypic changes associated with gain of radiation resistance in head and neck squamous cell cancer.

AIMS: The central issue of resistance to radiation remains a significant challenge in the treatment of cancer despite improvements in treatment modality and emergence of new therapies. To facilitate the identification of molecular factors that elicit protection against ionizing radiation, we developed a matched model of radiation resistance for head and neck squamous cell cancer (HNSCC) and characterized its properties using quantitative mass spectrometry and complementary assays. RESULTS: Functional network analysis of proteomics data identified DNA replication and base excision repair, extracellular matrix-receptor interaction, cell cycle, focal adhesion, and regulation of actin cytoskeleton as significantly up- or downregulated networks in resistant (rSCC-61) HNSCC cells. Upregulated proteins in rSCC-61 included a number of cytokeratins, fatty acid synthase, and antioxidant proteins. In addition, the rSCC-61 cells displayed two unexpected features compared with parental radiation-sensitive SCC-61 cells: (i) rSCC-61 had increased sensitivity to Erlotinib, a small-molecule inhibitor of epidermal growth factor receptor; and (ii) there was evidence of mesenchymal-to-epithelial transition in rSCC-61, confirmed by the expression of protein markers and functional assays (e.g., Vimentin, migration). INNOVATION: The matched model of radiation resistance presented here shows that multiple signaling and metabolic pathways converge to produce the rSCC-61 phenotype, and this points to the function of the antioxidant system as a major regulator of resistance to ionizing radiation in rSCC-61, a phenomenon further confirmed by analysis of HNSCC tumor samples. CONCLUSION: The rSCC-61/SCC-61 model provides the opportunity for future investigations of the redox-regulated mechanisms of response to combined radiation and Erlotinib in a preclinical setting.

Integration of gene expression data with network-based analysis to identify signaling and metabolic pathways regulated during the development of osteoarthritis.

Osteoarthritis (OA) is characterized by remodeling and degradation of joint tissues. Microarray studies have led to a better understanding of the molecular changes that occur in tissues affected by conditions such as OA; however, such analyses are limited to the identification of a list of genes with altered transcript expression, usually at a single time point during disease progression. While these lists have identified many novel genes that are altered during the disease process, they are unable to identify perturbed relationships between genes and gene products. In this work, we have integrated a time course gene expression dataset with network analysis to gain a better systems level understanding of the early events that occur during the development of OA in a mouse model. The subnetworks that were enriched at one or more of the time points examined (2, 4, 8, and 16 weeks after induction of OA) contained genes from several pathways proposed to be important to the OA process, including the extracellular matrix-receptor interaction and the focal adhesion pathways and the Wnt, Hedgehog and TGF-ß signaling pathways. The genes within the subnetworks were most active at the 2 and 4 week time points and included genes not previously studied in the OA process. A unique pathway, riboflavin metabolism, was active at the 4 week time point. These results suggest that the incorporation of network-type analyses along with time series microarray data will lead to advancements in our understanding of complex diseases such as OA at a systems level, and may provide novel insights into the pathways and processes involved in disease pathogenesis.

A kinetic analysis of the auxin transcriptome reveals cell wall remodeling proteins that modulate lateral root development in Arabidopsis.

To identify gene products that participate in auxin-dependent lateral root formation, a high temporal resolution, genome-wide transcript abundance analysis was performed with auxin-treated Arabidopsis thaliana roots. Data analysis identified 1246 transcripts that were consistently regulated by indole-3-acetic acid (IAA), partitioning into 60 clusters with distinct response kinetics. We identified rapidly induced clusters containing auxin-response functional annotations and clusters exhibiting delayed induction linked to cell division temporally correlated with lateral root induction. Several clusters were enriched with genes encoding proteins involved in cell wall modification, opening the possibility for understanding mechanistic details of cell structural changes that result in root formation following auxin treatment. Mutants with insertions in 72 genes annotated with a cell wall remodeling function were examined for alterations in IAA-regulated root growth and development. This reverse-genetic screen yielded eight mutants with root phenotypes. Detailed characterization of seedlings with mutations in cellulase3/glycosylhydrolase9b3 and leucine rich extensin2, genes not normally linked to auxin response, revealed defects in the early and late stages of lateral root development, respectively. The genes identified here using kinetic insight into expression changes lay the foundation for mechanistic understanding of auxin-mediated cell wall remodeling as an essential feature of lateral root development.

Disease progression and phasic changes in gene expression in a mouse model of osteoarthritis.

Osteoarthritis (OA) is the most common form of arthritis and has multiple risk factors including joint injury. The purpose of this study was to characterize the histologic development of OA in a mouse model where OA is induced by destabilization of the medial meniscus (DMM model) and to identify genes regulated during different stages of the disease, using RNA isolated from the joint "organ" and analyzed using microarrays. Histologic changes seen in OA, including articular cartilage lesions and osteophytes, were present in the medial tibial plateaus of the DMM knees beginning at the earliest (2 week) time point and became progressively more severe by 16 weeks. 427 probe sets (371 genes) from the microarrays passed consistency and significance filters. There was an initial up-regulation at 2 and 4 weeks of genes involved in morphogenesis, differentiation, and development, including growth factor and matrix genes, as well as transcription factors including Atf2, Creb3l1, and Erg. Most genes were off or down-regulated at 8 weeks with the most highly down-regulated genes involved in cell division and the cytoskeleton. Gene expression increased at 16 weeks, in particular extracellular matrix genes including Prelp, Col3a1 and fibromodulin. Immunostaining revealed the presence of these three proteins in cartilage and soft tissues including ligaments as well as in the fibrocartilage covering osteophytes. The results support a phasic development of OA with early matrix remodeling and transcriptional activity followed by a more quiescent period that is not maintained. This implies that the response to an OA intervention will depend on the timing of the intervention. The quiescent period at 8 weeks may be due to the maturation of the osteophytes which are thought to temporarily stabilize the joint.

Electrostatics of cysteine residues in proteins: parameterization and validation of a simple model.

One of the most popular and simple models for the calculation of pK(a) s from a protein structure is the semi-macroscopic electrostatic model MEAD. This model requires empirical parameters for each residue to calculate pK(a) s. Analysis of current, widely used empirical parameters for cysteine residues showed that they did not reproduce expected cysteine pK(a) s; thus, we set out to identify parameters consistent with the CHARMM27 force field that capture both the behavior of typical cysteines in proteins and the behavior of cysteines which have perturbed pK(a) s. The new parameters were validated in three ways: (1) calculation across a large set of typical cysteines in proteins (where the calculations are expected to reproduce expected ensemble behavior); (2) calculation across a set of perturbed cysteines in proteins (where the calculations are expected to reproduce the shifted ensemble behavior); and (3) comparison to experimentally determined pK(a) values (where the calculation should reproduce the pK(a) within experimental error). Both the general behavior of cysteines in proteins and the perturbed pK(a) in some proteins can be predicted reasonably well using the newly determined empirical parameters within the MEAD model for protein electrostatics. This study provides the first general analysis of the electrostatics of cysteines in proteins, with specific attention paid to capturing both the behavior of typical cysteines in a protein and the behavior of cysteines whose pK(a) should be shifted, and validation of force field parameters for cysteine residues.

Structural and electrostatic asymmetry at the active site in typical and atypical peroxiredoxin dimers.

The peroxiredoxins (Prx) are ubiquitous peroxidases involved in important biological processes; however, details of their enzymatic mechanism remain elusive. To probe potential dynamics-function relationships, molecular dynamics simulations and electrostatic calculations were performed on the atypical 2-cysteine thiol peroxidase (Tpx) from Streptococcus pneumoniae and results compared to a previous study of a typical 2-cysteine Prx from Trypanosoma cruzi. The analyses indicate a commonality between both typical and atypical Prx: dynamic asymmetry. Asymmetry is observed in structure, fluctuations, and active site electrostatics. Key residues, including Glu150 and Phe153, play roles in the developing asymmetry; furthermore, in the atypical 2-Cys Tpx, Glu150 exhibits conformation fluctuations suggesting involvement in a proton shuttle. The existence of a pathway of connected residues appears to propagate the asymmetry. The commonality of asymmetry and coupling pathways in both typical and atypical Prxs suggests a driving force toward dimer asymmetry as a common feature that plays a functional role in creating one active site with a lower cysteine pK(a).

Microarray analysis reveals age-related differences in gene expression during the development of osteoarthritis in mice.

OBJECTIVE: To better understand the contribution of age to the development of osteoarthritis (OA). METHODS: Surgical destabilization of the medial meniscus (DMM) was used to model OA in 12-week-old and 12-month-old male C57BL/6 mice. OA severity was evaluated histologically. RNA used for microarray and real-time polymerase chain reaction analysis was isolated from joint tissue collected from the medial side of the joint, including cartilage, meniscus, subchondral bone, and the joint capsule with synovium. Computational analysis was used to identify patterns of gene expression, and immunohistochemistry was used to evaluate tissue distribution of selected proteins. RESULTS: OA was more severe in older mice than in young mice. Only 55 genes showed a similar expression with DMM-induced OA in the 2 age groups, while 493 genes showed differential expression, the majority having increased expression in older mice. Functional categories for similarly expressed genes included extracellular matrix- and cell adhesion-related genes; differentially expressed genes included those related to muscle structure and development and immune response genes. Comparison of expression in sham-operated control joints revealed an age-related decrease in matrix gene expression and an increase in immune and defense response gene expression. Interleukin-33 was present in multiple joint tissue cells, while CCL21 was more localized to chondrocytes and meniscal cells. Periostin was found in the extracellular matrix of cartilage and meniscus. CONCLUSION: Age affects both the basal pattern of gene expression in joint tissues and the response to surgically induced OA. Examining tissue from the joint beyond only cartilage revealed novel genes and proteins that would be important to consider in OA.

SC²ATmd: a tool for integration of the figure of merit with cluster analysis for gene expression data.

UNLABELLED: Standard and Consensus Clustering Analysis Tool for Microarray Data (SC²ATmd) is a MATLAB-implemented application specifically designed for the exploration of microarray gene expression data via clustering. Implementation of two versions of the clustering validation method figure of merit allows for performance comparisons between different clustering algorithms, and tailors the cluster analysis process to the varying characteristics of each dataset. Along with standard clustering algorithms this application also offers a consensus clustering method that can generate reproducible clusters across replicate experiments or different clustering algorithms. This application was designed specifically for the analysis of gene expression data, but may be used with any numerical data as long as it is in the right format. AVAILABILITY: SC²ATmd may be freely downloaded from http://www.compbiosci.wfu.edu/tools.htm.

Analysis of the peroxiredoxin family: using active-site structure and sequence information for global classification and residue analysis.

Peroxiredoxins (Prxs) are a widespread and highly expressed family of cysteine-based peroxidases that react very rapidly with H2O2, organic peroxides, and peroxynitrite. Correct subfamily classification has been problematic because Prx subfamilies are frequently not correlated with phylogenetic distribution and diverge in their preferred reductant, oligomerization state, and tendency toward overoxidation. We have developed a method that uses the Deacon Active Site Profiler (DASP) tool to extract functional-site profiles from structurally characterized proteins to computationally define subfamilies and to identify new Prx subfamily members from GenBank(nr). For the 58 literature-defined Prx test proteins, 57 were correctly assigned, and none were assigned to the incorrect subfamily. The >3500 putative Prx sequences identified were then used to analyze residue conservation in the active site of each Prx subfamily. Our results indicate that the existence and location of the resolving cysteine vary in some subfamilies (e.g., Prx5) to a greater degree than previously appreciated and that interactions at the A interface (common to Prx5, Tpx, and higher order AhpC/Prx1 structures) are important for stabilization of the correct active-site geometry. Interestingly, this method also allows us to further divide the AhpC/Prx1 into four groups that are correlated with functional characteristics. The DASP method provides more accurate subfamily classification than PSI-BLAST for members of the Prx family and can now readily be applied to other large protein families.

Continuous cotemporal probabilistic modeling of systems biology networks from sparse data.

Modeling of biological networks is a difficult endeavor, but exploration of this problem is essential for understanding the systems behavior of biological processes. In this contribution, developed for sparse data, we present a new continuous Bayesian graphical learning algorithm to cotemporally model proteins in signaling networks and genes in transcriptional regulatory networks. In this continuous Bayesian algorithm, the correlation matrix is singular because the number of time points is less than the number of biological entities (genes or proteins). A suitable restriction on the degree of the graph's vertices is applied and a Metropolis-Hastings algorithm is guided by a BIC-based posterior probability score. Ten independent and diverse runs of the algorithm are conducted, so that the probability space is properly well-explored. Diagnostics to test the applicability of the algorithm to the specific data sets are developed; this is a major benefit of the methodology. This novel algorithm is applied to two time course experimental data sets: 1) protein modification data identifying a potential signaling network in chondrocytes, and 2) gene expression data identifying the transcriptional regulatory network underlying dendritic cell maturation. This method gives high estimated posterior probabilities to many of the proteins' directed edges that are predicted by the literature; for the gene study, the method gives high posterior probabilities to many of the literature-predicted sibling edges. In simulations, the method gives substantially higher estimated posterior probabilities for true edges and true subnetworks than for their false counterparts.