ProDomAs, protein domain assignment algorithm using center-based clustering and independent dominating set.

Decomposition of structural domains is an essential task in classifying protein structures, predicting protein function, and many other proteomics problems. As the number of known protein structures in PDB grows exponentially, the need for accurate automatic domain decomposition methods becomes more essential. In this article, we introduce a bottom-up algorithm for assigning protein domains using a graph theoretical approach. This algorithm is based on a center-based clustering approach. For constructing initial clusters, members of an independent dominating set for the graph representation of a protein are considered as the centers. A distance matrix is then defined for these clusters. To obtain final domains, these clusters are merged using the compactness principle of domains and a method similar to the neighbor-joining algorithm considering some thresholds. The thresholds are computed using a training set consisting of 50 protein chains. The algorithm is implemented using C++ language and is named ProDomAs. To assess the performance of ProDomAs, its results are compared with seven automatic methods, against five publicly available benchmarks. The results show that ProDomAs outperforms other methods applied on the mentioned benchmarks. The performance of ProDomAs is also evaluated against 6342 chains obtained from ASTRAL SCOP 1.71. ProDomAs is freely available at http://www.bioinf.cs.ipm.ir/software/prodomas.

Multi-omics comparison of malignant and normal uveal melanocytes reveals molecular features of uveal melanoma.

Uveal melanoma (UM) is a rare cancer resulting from the transformation of melanocytes in the uveal tract. Integrative analysis has identified four molecular and clinical subsets of UM. To improve our molecular understanding of UM, we performed extensive multi-omics characterization comparing two aggressive UM patient-derived xenograft models with normal choroidal melanocytes, including DNA optical mapping, specific histone modifications, and DNA topology analysis using Hi-C. Our gene expression and cytogenetic analyses suggest that genomic instability is a hallmark of UM. We also identified a recurrent deletion in the BAP1 promoter resulting in loss of expression and associated with high risk of metastases in UM patients. Hi-C revealed chromatin topology changes associated with the upregulation of PRAME, an independent prognostic biomarker in UM, and a potential therapeutic target. Our findings illustrate how multi-omics approaches can improve our understanding of tumorigenesis and reveal two distinct mechanisms of gene expression dysregulation in UM.

Combination Therapies Targeting ALK-aberrant Neuroblastoma in Preclinical Models.

PURPOSE: ALK-activating mutations are identified in approximately 10% of newly diagnosed neuroblastomas and ALK amplifications in a further 1%-2% of cases. Lorlatinib, a third-generation anaplastic lymphoma kinase (ALK) inhibitor, will soon be given alongside induction chemotherapy for children with ALK-aberrant neuroblastoma. However, resistance to single-agent treatment has been reported and therapies that improve the response duration are urgently required. We studied the preclinical combination of lorlatinib with chemotherapy, or with the MDM2 inhibitor, idasanutlin, as recent data have suggested that ALK inhibitor resistance can be overcome through activation of the p53-MDM2 pathway. EXPERIMENTAL DESIGN: We compared different ALK inhibitors in preclinical models prior to evaluating lorlatinib in combination with chemotherapy or idasanutlin. We developed a triple chemotherapy (CAV: cyclophosphamide, doxorubicin, and vincristine) in vivo dosing schedule and applied this to both neuroblastoma genetically engineered mouse models (GEMM) and patient-derived xenografts (PDX). RESULTS: Lorlatinib in combination with chemotherapy was synergistic in immunocompetent neuroblastoma GEMM. Significant growth inhibition in response to lorlatinib was only observed in the ALK-amplified PDX model with high ALK expression. In this PDX, lorlatinib combined with idasanutlin resulted in complete tumor regression and significantly delayed tumor regrowth. CONCLUSIONS: In our preclinical neuroblastoma models, high ALK expression was associated with lorlatinib response alone or in combination with either chemotherapy or idasanutlin. The synergy between MDM2 and ALK inhibition warrants further evaluation of this combination as a potential clinical approach for children with neuroblastoma.

Diagnostic peritoneal lavage in hemodynamically stable patients with lower chest or anterior abdominal stab wounds.

BACKGROUND: Managing hemodynamically stable patients with thoracoabdominal stab wounds is still under dispute. This study aimed at discussing cut-off points of red blood cell (RBC) count in diagnostic peritoneal lavage (DPL) effluent in these patients. METHODS: Three hundred and eighty-eight patients with thoracoabdominal stab wounds and hemodynamically stable status were enrolled. In cases without a clear indication of laparotomy, the peritoneal cavity was washed out with 1000 ml of normal saline and the effluent fluid was analyzed for RBC count. RBC counts of >100,000/mm3 in abdominal wounds and of >10,000/mm3 in lower chest wounds were considered as indications for exploratory laparotomy (conventional approach). New cut-off points for RBC count were calculated in backward analysis. RESULTS: Sensitivity and specificity of the conventional approach were 90% and 84%, respectively. RBC counts >15,000/mm3 in abdominal wounds and >25,000/mm3 in lower chest wounds were the best cut-off points in distinguishing patients with and without need of operation, with a sensitivity and specificity of 94% and 96%, respectively. CONCLUSION: New cut-off points of RBC count in DPL effluent may promote management of patients with thoracoabdominal stab wounds and no obvious indication for operation.

Pleurodesis by erythromycin, tetracycline, Aerosil™ 200, and erythromycin plus Aerosil™ 200 in a rat model: a preliminary study.

BACKGROUND: None of the current pleurodesing agents fulfil all the criteria for best pleural sclerosant. Therefore, the search for the ideal agent for chemical pleurodesis still continues. The aim of the present study was to compare the effectiveness of erythromycin, tetracycline, Aerosil™ 200 (hydrophilic fumed amorphous silica), and erythromycin plus Aerosil™ 200 in producing pleurodesis in rats. In the present study, talc was not used as a pleurodesing agent due to an unavailability of its sterile and pure form in Iran. METHODS: Overall, 75 adult male Spraque-Dawley rats were randomized to 5 treatment groups. Each group received an intrapleural injection via 5 Fr Silastic tubes of one of the following sterile agents: 35mg/kg erythromycin in 2 ml of saline, 35mg/kg tetracycline in 2 ml of saline, 35mg/kg Aerosil™ 200 in 2ml of saline, erythromycin (35mg/kg in 2 ml of saline) plus Aerosil™ 200 (35mg/kg in 2 ml of saline), or 2 ml of saline as a control. The animals were euthanized and necropsied 30 days after injection. The pleurae were assessed for macroscopic and microscopic evidence of surrounding inflammation and fibrosis. RESULTS: The median macroscopic score in the Aerosil™ 200 group was significantly higher than that in the erythromycin group (P < 0.005). The median microscopic score in the erythromycin group was significantly lower than that in the Aerosil™ 200 and erythromycin plus Aerosil™ 200 groups (P < 0.005). Furthermore, maximum and minimum pleural fibrosis was observed in the erythromycin plus Aerosil™ 200 and erythromycin groups, respectively (P < 0.05). CONCLUSION: This study suggests that Aerosil™ 200 with or without erythromycin may be more potent pleurodesis agent than erythromycin and tetracycline.

Kavosh: a new algorithm for finding network motifs.

BACKGROUND: Complex networks are studied across many fields of science and are particularly important to understand biological processes. Motifs in networks are small connected sub-graphs that occur significantly in higher frequencies than in random networks. They have recently gathered much attention as a useful concept to uncover structural design principles of complex networks. Existing algorithms for finding network motifs are extremely costly in CPU time and memory consumption and have practically restrictions on the size of motifs. RESULTS: We present a new algorithm (Kavosh), for finding k-size network motifs with less memory and CPU time in comparison to other existing algorithms. Our algorithm is based on counting all k-size sub-graphs of a given graph (directed or undirected). We evaluated our algorithm on biological networks of E. coli and S. cereviciae, and also on non-biological networks: a social and an electronic network. CONCLUSION: The efficiency of our algorithm is demonstrated by comparing the obtained results with three well-known motif finding tools. For comparison, the CPU time, memory usage and the similarities of obtained motifs are considered. Besides, Kavosh can be employed for finding motifs of size greater than eight, while most of the other algorithms have restriction on motifs with size greater than eight. The Kavosh source code and help files are freely available at: http://Lbb.ut.ac.ir/Download/LBBsoft/Kavosh/.

The Ability of Different Imputation Methods to Preserve the Significant Genes and Pathways in Cancer.

Deciphering important genes and pathways from incomplete gene expression data could facilitate a better understanding of cancer. Different imputation methods can be applied to estimate the missing values. In our study, we evaluated various imputation methods for their performance in preserving significant genes and pathways. In the first step, 5% genes are considered in random for two types of ignorable and non-ignorable missingness mechanisms with various missing rates. Next, 10 well-known imputation methods were applied to the complete datasets. The significance analysis of microarrays (SAM) method was applied to detect the significant genes in rectal and lung cancers to showcase the utility of imputation approaches in preserving significant genes. To determine the impact of different imputation methods on the identification of important genes, the chi-squared test was used to compare the proportions of overlaps between significant genes detected from original data and those detected from the imputed datasets. Additionally, the significant genes are tested for their enrichment in important pathways, using the ConsensusPathDB. Our results showed that almost all the significant genes and pathways of the original dataset can be detected in all imputed datasets, indicating that there is no significant difference in the performance of various imputation methods tested. The source code and selected datasets are available on http://profiles.bs.ipm.ir/softwares/imputation_methods/.