Zinc finger protein-440 promotes cartilage degenerative mechanisms in human facet and knee osteoarthritis chondrocytes.

OBJECTIVES: To investigate the role of zinc finger protein 440 (ZNF440) in the pathophysiology of cartilage degeneration during facet joint (FJ) and knee osteoarthritis (OA). METHODS: Expression of ZNF440 in FJ and knee cartilage was determined by immunohistochemistry, quantitative (q)PCR, and Western blotting (WB). Human chondrocytes isolated from FJ and knee OA cartilage were cultured and transduced with ZNF440 or control plasmid, or transfected with ZNF440 or control small interfering RNA (siRNA), with/without interleukin (IL)-1ß. Gene and protein levels of catabolic, anabolic and apoptosis markers were determined by qPCR or WB, respectively. In silico analyses were performed to determine compounds with potential to inhibit expression of ZNF440. RESULTS: ZNF440 expression was increased in both FJ and knee OA cartilage compared to control cartilage. In vitro, overexpression of ZNF440 significantly increased expression of MMP13 and PARP p85, and decreased expression of COL2A1. Knockdown of ZNF440 with siRNA partially reversed the catabolic and cell death phenotype of human knee and FJ OA chondrocytes stimulated with IL-1ß. In silico analysis followed by validation assays identified scriptaid as a compound with potential to downregulate the expression of ZNF440. Validation experiments showed that scriptaid reduced the expression of ZNF440 in OA chondrocytes and concomitantly reduced the expression of MMP13 and PARP p85 in human knee OA chondrocytes overexpressing ZNF440. CONCLUSIONS: The expression of ZNF440 is significantly increased in human FJ and knee OA cartilage and may regulate cartilage degenerative mechanisms. Furthermore, scriptaid reduces the expression of ZNF440 and inhibits its destructive effects in OA chondrocytes.

The IMEx coronavirus interactome: an evolving map of Coronaviridae-host molecular interactions.

The current coronavirus disease of 2019 (COVID-19) pandemic, caused by the severe acute respiratory syndrome coronavirus (SARS-CoV)-2, has spurred a wave of research of nearly unprecedented scale. Among the different strategies that are being used to understand the disease and develop effective treatments, the study of physical molecular interactions can provide fine-grained resolution of the mechanisms behind the virus biology and the human organism response. We present a curated dataset of physical molecular interactions focused on proteins from SARS-CoV-2, SARS-CoV-1 and other members of the Coronaviridae family that has been manually extracted by International Molecular Exchange (IMEx) Consortium curators. Currently, the dataset comprises over 4400 binarized interactions extracted from 151 publications. The dataset can be accessed in the standard formats recommended by the Proteomics Standards Initiative (HUPO-PSI) at the IntAct database website (https://www.ebi.ac.uk/intact) and will be continuously updated as research on COVID-19 progresses.

Sequencing identifies a distinct signature of circulating microRNAs in early radiographic knee osteoarthritis.

OBJECTIVE: MicroRNAs act locally and systemically to impact osteoarthritis (OA) pathophysiology, but comprehensive profiling of the circulating miRNome in early vs late stages of OA has yet to be conducted. Sequencing has emerged as the preferred method for microRNA profiling since it offers high sensitivity and specificity. Our objective was to sequence the miRNome in plasma from 91 patients with early [Kellgren-Lawrence (KL) grade 0 or 1 (n = 41)] or late [KL grade 3 or 4 (n = 50)] symptomatic radiographic knee OA to identify unique microRNA signatures in each disease state. DESIGN: MicroRNA libraries were prepared using the QIAseq miRNA Library Kit and sequenced on the Illumina NextSeq 550. Counts were produced for microRNAs captured in miRBase and for novel microRNAs. Statistical, bioinformatics, and computational biology approaches were used to refine and interpret the final list of microRNAs. RESULTS: From 215 differentially expressed microRNAs (FDR < 0.01), 97 microRNAs showed an increase or decrease in expression in ≥85% of samples in the early OA group as compared to the median expression in the late OA group. Increasing this threshold to ≥95%, seven microRNAs were identified: hsa-miR-335-3p, hsa-miR-199a-5p, hsa-miR-671-3p, hsa-miR-1260b, hsa-miR-191-3p, hsa-miR-335-5p, and hsa-miR-543. Four novel microRNAs were present in ≥50% of early OA samples and had 27 predicted gene targets in common with the prioritized set of predicted gene targets from the 97 microRNAs, suggesting common underlying mechanisms. CONCLUSION: Sequencing of well-characterized patient cohorts produced unbiased profiling of the circulating miRNome and identified a unique panel of 11 microRNAs in early radiographic knee OA.

The IMEx Coronavirus interactome: an evolving map of Coronaviridae-Host molecular interactions.

The current Coronavirus Disease 2019 (COVID-19) pandemic, caused by the Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2), has spurred a wave of research of nearly unprecedented scale. Among the different strategies that are being used to understand the disease and develop effective treatments, the study of physical molecular interactions enables studying fine-grained resolution of the mechanisms behind the virus biology and the human organism response. Here we present a curated dataset of physical molecular interactions, manually extracted by IMEx Consortium curators focused on proteins from SARS-CoV-2, SARS-CoV-1 and other members of the Coronaviridae family. Currently, the dataset comprises over 2,200 binarized interactions extracted from 86 publications. The dataset can be accessed in the standard formats recommended by the Proteomics Standards Initiative (HUPO-PSI) at the IntAct database website ( www.ebi.ac.uk/intact ), and will be continuously updated as research on COVID-19 progresses.

Publisher Correction: Capturing variation impact on molecular interactions in the IMEx Consortium mutations data set.

In the original HTML version of this Article, the order of authors within the author list was incorrect. The IMEx Consortium contributing authors were incorrectly listed as the last author and should have been listed as the first author. This error has been corrected in the HTML version of the Article; the PDF version was correct at the time of publication.

Capturing variation impact on molecular interactions in the IMEx Consortium mutations data set.

The current wealth of genomic variation data identified at nucleotide level presents the challenge of understanding by which mechanisms amino acid variation affects cellular processes. These effects may manifest as distinct phenotypic differences between individuals or result in the development of disease. Physical interactions between molecules are the linking steps underlying most, if not all, cellular processes. Understanding the effects that sequence variation has on a molecule's interactions is a key step towards connecting mechanistic characterization of nonsynonymous variation to phenotype. We present an open access resource created over 14 years by IMEx database curators, featuring 28,000 annotations describing the effect of small sequence changes on physical protein interactions. We describe how this resource was built, the formats in which the data is provided and offer a descriptive analysis of the data set. The data set is publicly available through the IntAct website and is enhanced with every monthly release.

Encompassing new use cases - level 3.0 of the HUPO-PSI format for molecular interactions.

BACKGROUND: Systems biologists study interaction data to understand the behaviour of whole cell systems, and their environment, at a molecular level. In order to effectively achieve this goal, it is critical that researchers have high quality interaction datasets available to them, in a standard data format, and also a suite of tools with which to analyse such data and form experimentally testable hypotheses from them. The PSI-MI XML standard interchange format was initially published in 2004, and expanded in 2007 to enable the download and interchange of molecular interaction data. PSI-XML2.5 was designed to describe experimental data and to date has fulfilled this basic requirement. However, new use cases have arisen that the format cannot properly accommodate. These include data abstracted from more than one publication such as allosteric/cooperative interactions and protein complexes, dynamic interactions and the need to link kinetic and affinity data to specific mutational changes. RESULTS: The Molecular Interaction workgroup of the HUPO-PSI has extended the existing, well-used XML interchange format for molecular interaction data to meet new use cases and enable the capture of new data types, following extensive community consultation. PSI-MI XML3.0 expands the capabilities of the format beyond simple experimental data, with a concomitant update of the tool suite which serves this format. The format has been implemented by key data producers such as the International Molecular Exchange (IMEx) Consortium of protein interaction databases and the Complex Portal. CONCLUSIONS: PSI-MI XML3.0 has been developed by the data producers, data users, tool developers and database providers who constitute the PSI-MI workgroup. This group now actively supports PSI-MI XML2.5 as the main interchange format for experimental data, PSI-MI XML3.0 which additionally handles more complex data types, and the simpler, tab-delimited MITAB2.5, 2.6 and 2.7 for rapid parsing and download.

Fibrillin-1 (FBN-1) a new marker of germ cell neoplasia in situ.

BACKGROUND: Germ cell neoplasia in situ (GCNIS), is preinvasive stage of testicular germ cell tumours (TGCTs). Fibrillins, which are integral components of microfibrils are suggested to be involved in cancer pathogenesis and maintenance of embryonic stem cells pluripotency. The aim of this study was to examine fibrillin-1 (FBN-1) expression in TGCTs patients. METHODS: Surgical specimens from 203 patients with TGCTs were included into the translational study. FBN-1 expression was evaluated in the tumour tissue, in GCNIS and in adjacent non-neoplastic testicular tissue in all available cases. Tissue samples were processed by the tissue microarray method. FBN-1 was detected by immunohistochemistry using goat polyclonal antibody and the expression was evaluated by the multiplicative quickscore (QS). RESULTS: The highest FBN-1 positivity was detected in GCNIS (mean QS = 11.30), with overexpression of FBN-1 (QS >9) in the majority (77.1 %) of cases. Expression of FBN-1 in all subtypes of TGCTs was significantly lower in comparison to expression in GCNIS (all p <0.001). Seminoma had significantly higher expression compared to EC, ChC and TER (all p <0.05), but not to YST (p = 0.84). In non-neoplastic testicular tissue the FBN-1 positivity was very low (mean QS = 0.02). Sensitivity, specificity, positive and negative predictive value of FBN-1 expression for diagnosis of GCNIS were 97.1, 98.8, 98.6 and 97.7 %. CONCLUSIONS: FBN-1 is overexpressed in TGCTs and especially in GCNIS when compared to non-neoplastic testicular tissue in patients with germ cell tumors and could be involved in germ cell neoplasia in situ development.

Identification of synovial fluid microRNA signature in knee osteoarthritis: differentiating early- and late-stage knee osteoarthritis.

OBJECTIVES: This study aimed to identify circulating microRNA (miRNA) signatures in knee synovial fluid (SF) from early-stage and late-stage knee osteoarthritis (OA) patients. METHODS: miRNAs were screened by miRNA-PCR-arrays and validated by Real-time quantitative polymerase chain reaction (RT-qPCR) in SF from early-stage (Kellgren-Lawrence (KL): Grade: I/II) and late-stage OA patients (Grade: III/IV). OA cartilage or synovial explants were cultured to study the source/release of identified miRNAs. Computational-approach was utilized to predict gene/pathway targets. RESULTS: Our screening/validation analysis identified a panel of seven (out of 752) circulating miRNAs (23a-3p, 24-3p, 27a-3p, 27b-3p, 29c-3p, 34a-5p and 186-5p) that were significantly differentially expressed in late-stage vs early-stage OA-SF, irrespective of age, gender and Body Mass Index (BMI). miR-378a-5p was mostly detectable in majority of late-stage OA-SF. Cartilage explants stimulated with IL-1ß showed a significant reduction in miR-23a-3p, 27a-3p and 27b-3p expression with no significant changes in other validated miRNAs. However, IL-1ß-stimulated OA synovial explants exhibited significantly increased expression of miR-23a-3p, 24-3p, 27a-3p, 27b-3p, 29c-3p, 186-5p and 378a-5p, and release of only 23a-3p and 27b-3p in supernatants, suggesting that IL-1ß contributes to the release of 23a-3p and 27b-3p into the SF from synovium. Computational-analysis identified 2 genes (ROQUIN-1 [RC3H1] and quaking-gene [QKI]) that are targeted by six out of eight miRNAs; miR-27b-3p exhibited greatest association with RC3H1 and QKI genes. Indeed, synovial explants treated with miR-27b-3p-mimic show significant suppression of both RC3H1 and QKI genes. CONCLUSIONS: We provide first evidence of the differential expression of circulating miRNAs in early-stage vs late-stage knee OA-SF. Further, we provide source, release and genes/pathways regulated by identified miRNAs.

Integrin α11ß1 regulates cancer stromal stiffness and promotes tumorigenicity and metastasis in non-small cell lung cancer.

Integrin α11ß1 is a stromal cell-specific receptor for fibrillar collagens and is overexpressed in carcinoma-associated fibroblasts (CAFs). We have investigated its direct role in cancer progression by generating severe combined immune deficient (SCID) mice deficient in integrin α11 (α11) expression. The growth of A549 lung adenocarcinoma cells and two patient-derived non-small cell lung carcinoma (NSCLC) xenografts in these α11 knockout (α11(-/-)) mice was significantly impeded, as compared with wild-type (α11(+/+)) SCID mice. Orthotopic implantation of a spontaneously metastatic NCI-H460SM cell line into the lungs of α11(-/-) and α11(+/+) mice showed significant reduction in the metastatic potential of these cells in the α11(-/-) mice. We identified that collagen cross-linking is associated with stromal α11 expression, and the loss of tumor stromal α11 expression was correlated with decreased collagen reorganization and stiffness. This study shows the role of integrin α11ß1, a receptor for fibrillar collagen in differentiation of fibroblasts into CAFs. Furthermore, our data support an important role for α11 signaling pathway in CAFs, promoting tumor growth and metastatic potential of NSCLC cells and being closely associated with collagen cross-linking and the organization and stiffness of fibrillar collagen matrices.

Kinome-wide screening of HER2+ breast cancer cells for molecules that mediate cell proliferation or sensitize cells to trastuzumab therapy.

Understanding the signaling differences that distinguish human HER2-amplified (HER2-positive (HER2+)) breast cancers from other breast cancer subtypes may help to identify protein drug targets for the specific treatment of HER2+ breast cancers. We performed two kinome-wide small interfering RNA (siRNA) screens on five HER2+ breast cancer cell lines, seven breast cancer cell lines in which HER2 was not amplified and two normal breast cell lines. To pinpoint the main kinases driving HER2 signaling, we performed a comprehensive siRNA screen that identified loss of the HER2/HER3 heterodimer as having the most prominent inhibitory effect on the growth of HER2+ breast cancer cells. In a second siRNA screen focused on identifying genes that could sensitize HER2+ cells to trastuzumab treatment, we found that loss of signaling members downstream of phosphatidylinositol 3 kinase (PI3K) potentiated the growth inhibitory effects of trastuzumab. Loss of HER2 and HER3, as well as proteins involved in mitogenic and environmental stress pathways inhibited the proliferation of HER2+ cells only in the absence of trastuzumab, suggesting that these pathways are inhibited by trastuzumab treatment. Loss of essential G2/M cell cycle mediators or proteins involved in vesicle organization exerted inhibitory effects on HER2+ cell growth that were unaffected by trastuzumab. Furthermore, the use of a sensitization index (SI) identified targeting the PI3K pathway to sensitize to trastuzumab treatment. Antagonism using the SI identified MYO3A, MYO3B and MPZL1 as antagonizers to trastuzumab treatment among HER2+ cell lines. Our results suggest that the dimerization partners of HER2 are important for determining the activation of downstream proliferation pathways. Understanding the complex layers of signaling triggered downstream of HER2 homodimers and heterodimers will facilitate the selection of better targets for combination therapies intended to treat HER2+ breast cancers.

Flotillin-2 deficiency leads to reduced lung metastases in a mouse breast cancer model.

Flotillin microdomains, specialized lipid raft domains in cell membranes, serve as physical platforms for many different molecules important in crucial intracellular signaling pathways. Flotillin-2 (Flot2), together with flotillin-1, is a marker for lipid raft microdomains distinct from caveolar lipid rafts, and has been implicated in the progression of cancer and metastasis formation. Based largely on studies in xenograft models, flotillin-2 has been implicated in the progression of multiple types of human tumors, including breast cancer. In our studies, we identified flotillin-2 as highly amplified in a high-throughput comparative genomic hybridization screen of human breast cancer cell lines and breast tumor samples. Short hairpin RNA-mediated reduction of flotillin-2 protein levels significantly reduced the tumorigenicity and metastatic capability of a human breast cancer cell line in vivo. We generated mice deficient for flotillin-2 and also found a reduction of flotillin-1 protein levels and complete absence of flotillin-specific membrane microdomains in these mice. To examine the role of Flot2 in mammary tumorigenesis and lung metastasis, we used an in vivo molecular genetics approach, crossing a well-characterized transgenic mouse model of breast cancer, the MMTV-PyMT (mouse mammary tumor virus-polyoma middle T antigen) mouse, with gene-targeted Flot2(-/-) mice. Flotillin-2 deficiency lead to a striking reduction in the number of lung metastasis observed, but had no influence on primary tumor formation in this model. Our results indicate, using a novel in vivo animal model approach, that Flot2 is an important regulator of mammary tumor-derived lung metastasis.

Sex-specific differences in placental global gene expression in pregnancies complicated by asthma.

BACKGROUND: Chronic maternal asthma is associated with reduced growth of the female fetus and normal growth of the male fetus. The mechanisms that control the differential effects of maternal asthma on the fetus have not been fully elucidated but alterations in placental function may play a role. In the current study we have used microarray platform to examine fetal sex-specific global changes in placental gene expression in pregnancies complicated by asthma as compared to non-asthmatic subjects. METHODS: Placental RNA was extracted from 11 control subjects and 38 asthmatic subjects. Labeled cDNA was hybridized to an oligonucleotide chip with 1700 double spotted well-characterized human genes. Global gene expression data analysis and visualization were performed using the Binary Tree-Structured Vector Quantization (BTSVQ) software. Functional relationships of differentially expressed genes were assessed using protein-protein interaction database I2D, network analysis and visualization software NAViGaTOR and Ingenuity Pathway Analysis software. RESULTS: Overall, 65 genes were found to be altered in placentae of pregnancies complicated by asthma. Of these, only 6 genes were altered in male placentae. There were 59 gene changes in female placentae of asthmatic mothers relative to control placentae. Some of the sex-specific genes were associated with growth, inflammation and immune pathways. CONCLUSION: There are sex-specific alterations in placental gene expression in the presence of maternal asthma. Given that many of the identified genes in the female placentae were associated with or involved in cellular growth and tissue development, these may contribute to the sexually dimorphic difference in fetal growth in response to maternal asthma.

Differentially androgen-modulated genes in ovarian epithelial cells from BRCA mutation carriers and control patients predict ovarian cancer survival and disease progression.

Epidemiological studies have implicated androgens in the etiology and progression of epithelial ovarian cancer. We previously reported that some androgen responses were dysregulated in malignant ovarian epithelial cells relative to control, non-malignant ovarian surface epithelial (OSE) cells. Moreover, dysregulated androgen responses were observed in OSE cells derived from patients with germline BRCA-1 or -2 mutations (OSEb), which account for the majority of familial ovarian cancer predisposition, and such altered responses may be involved in ovarian carcinogenesis or progression. In the present study, gene expression profiling using cDNA microarrays identified 17 genes differentially expressed in response to continuous androgen exposure in OSEb cells and ovarian cancer cells as compared to OSE cells derived from control patients. A subset of these differentially affected genes was selected and verified by quantitative real-time reverse transcription-polymerase chain reaction. Six of the gene products mapped to the OPHID protein-protein interaction database, and five were networked within two interacting partners. Basic leucine zipper transcription factor 2 (BACH2) and acetylcholinesterase (ACHE), which were upregulated by androgen in OSEb cells relative to OSE cells, were further investigated using an ovarian cancer tissue microarray from a separate set of 149 clinical samples. Both cytoplasmic ACHE and BACH2 immunostaining were significantly increased in ovarian cancer relative to benign cases. High levels of cytoplasmic ACHE staining correlated with decreased survival, whereas nuclear BACH2 staining correlated with decreased time to disease recurrence. The finding that products of genes differentially responsive to androgen in OSEb cells may predict survival and disease progression supports a role for altered androgen effects in ovarian cancer. In addition to BACH2 and ACHE, this study highlights a set of potentially functionally related genes for further investigation in ovarian cancer.

Efficient estimation of graphlet frequency distributions in protein-protein interaction networks.

MOTIVATION: Algorithmic and modeling advances in the area of protein-protein interaction (PPI) network analysis could contribute to the understanding of biological processes. Local structure of networks can be measured by the frequency distribution of graphlets, small connected non-isomorphic induced subgraphs. This measure of local structure has been used to show that high-confidence PPI networks have local structure of geometric random graphs. Finding graphlets exhaustively in a large network is computationally intensive. More complete PPI networks, as well as PPI networks of higher organisms, will thus require efficient heuristic approaches. RESULTS: We propose two efficient and scalable heuristics for finding graphlets in high-confidence PPI networks. We show that both PPI and their model geometric random networks, have defined boundaries that are sparser than the 'inner parts' of the networks. In addition, these networks exhibit 'uniformity' of local structure inside the networks. Our first heuristic exploits these two structural properties of PPI and geometric random networks to find good estimates of graphlet frequency distributions in these networks up to 690 times faster than the exhaustive searches. Our second heuristic is a variant of a more standard sampling technique and it produces accurate approximate results up to 377 times faster than the exhaustive searches. We indicate how the combination of these approaches may result in an even better heuristic. AVAILABILITY: Supplementary information is available at http://www.cs.toronto.edu/~natasha/BIOINF-2005-0946/Supplementary.pdf. Software implementing the algorithms is available at http://www.cs.toronto.edu/~natasha/BIOINF-2005-0946/estimate_grap-hlets.html. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Transcriptional targets of hepatocyte growth factor signaling and Ki-ras oncogene activation in colorectal cancer.

Both Ki-ras mutation and hepatocyte growth factor (HGF) receptor Met overexpression occur at high frequency in colon cancer. This study investigates the transcriptional changes induced by Ki-ras oncogene and HGF/Met signaling activation in colon cancer cell lines in vitro and in vivo. The model system used in these studies included the DLD-1 colon cancer cell line with a mutated Ki-ras allele, and the DKO-4 cell line generated from DLD-1, with its mutant Ki-ras allele inactivated by targeted disruption. These cell lines were transduced with cDNAs of full-length Met receptor. Microarray transcriptional profiling was conducted on cell lines stimulated with HGF, as well as on tumor xenograft tissues. Overlapping genes between in vitro and in vivo microarray data sets were selected as a subset of HGF/Met and Ki-ras oncogene-regulated targets. Using the Online Predicted Human Interaction Database, novel HGF/Met and Ki-ras regulated proteins with putative functional linkage were identified. Novel proteins identified included histone acetyltransferase 1, phosphoribosyl pyrophosphate synthetase 2, chaperonin containing TCP1, subunit 8, CSE1 chromosome segregation 1-like (yeast)/cellular apoptosis susceptibility (mammals), CCR4-NOT transcription complex, subunit 8, and cyclin H. Transcript levels for these Met-signaling targets were correlated with Met expression levels, and were significantly elevated in both primary and metastatic human colorectal cancer samples compared to normal colorectal mucosa. These genes represent novel Met and/or Ki-ras transcriptionally coregulated genes with a high degree of validation in human colorectal cancers.

The histone deacetylase inhibitor valproic acid alters sensitivity towards all trans retinoic acid in acute myeloblastic leukemia cells.

Acute myeloblastic leukemia (AML) may be classified in a number of ways. Using the French American British classification, the M3 form of the disease or acute promyelocytic leukemia (APL) has been found to be sensitive in vitro and in vivo to the retinoid all trans retinoic acid (ATRA). The mechanism for this is by restoration of normal gene expression through the release of histone deacetylase complexes (HDACs). In contrast to APL, other forms of AML are either nonresponsive or show blunted responses to ATRA. We evaluated if the inhibitor of HDAC activity, valproic acid (VPA), could mimic or enhance retinoid sensitivity in the AML cell line, OCI/AML-2, and clinical samples derived from patients with AML. An Affymetrix GeneChip experiment demonstrated that VPA modulated the expression of numerous genes in OCI/AML-2 cells that were not affected by ATRA including p21, a retinoid responsive gene in APL. VPA induced p21 expression in OCI/AML-2 cells and the majority of the AML samples tested; this was associated with cell cycle arrest and apoptosis not seen with ATRA alone. The addition of ATRA to VPA accentuated many of these responses, supporting the potential beneficial combination of these drugs in the treatment of AML.

Modeling interactome: scale-free or geometric?

MOTIVATION: Networks have been used to model many real-world phenomena to better understand the phenomena and to guide experiments in order to predict their behavior. Since incorrect models lead to incorrect predictions, it is vital to have as accurate a model as possible. As a result, new techniques and models for analyzing and modeling real-world networks have recently been introduced. RESULTS: One example of large and complex networks involves protein-protein interaction (PPI) networks. We analyze PPI networks of yeast Saccharomyces cerevisiae and fruitfly Drosophila melanogaster using a newly introduced measure of local network structure as well as the standardly used measures of global network structure. We examine the fit of four different network models, including Erdos-Renyi, scale-free and geometric random network models, to these PPI networks with respect to the measures of local and global network structure. We demonstrate that the currently accepted scale-free model of PPI networks fails to fit the data in several respects and show that a random geometric model provides a much more accurate model of the PPI data. We hypothesize that only the noise in these networks is scale-free. CONCLUSIONS: We systematically evaluate how well-different network models fit the PPI networks. We show that the structure of PPI networks is better modeled by a geometric random graph than by a scale-free model. SUPPLEMENTARY INFORMATION: Supplementary information is available at http://www.cs.utoronto.ca/~juris/data/data/ppiGRG04/

Protein complex prediction via cost-based clustering.

MOTIVATION: Understanding principles of cellular organization and function can be enhanced if we detect known and predict still undiscovered protein complexes within the cell's protein-protein interaction (PPI) network. Such predictions may be used as an inexpensive tool to direct biological experiments. The increasing amount of available PPI data necessitates an accurate and scalable approach to protein complex identification. RESULTS: We have developed the Restricted Neighborhood Search Clustering Algorithm (RNSC) to efficiently partition networks into clusters using a cost function. We applied this cost-based clustering algorithm to PPI networks of Saccharomyces cerevisiae, Drosophila melanogaster and Caenorhabditis elegans to identify and predict protein complexes. We have determined functional and graph-theoretic properties of true protein complexes from the MIPS database. Based on these properties, we defined filters to distinguish between identified network clusters and true protein complexes. CONCLUSIONS: Our application of the cost-based clustering algorithm provides an accurate and scalable method of detecting and predicting protein complexes within a PPI network.