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Background: Functional parkinsonism is an important differential diagnosis of Parkinson's disease (PD). Based on anecdotal experience, we hypothesized that arm swing while walking and running could differentiate these two conditions, but this assumption has not been previously explored systematically., Objectives: To examine differences in arm swing while walking and running between patients with PD and functional parkinsonism., Methods: We analyzed blinded video assessments of arm swing and other gait parameters in patients with asymmetrical PD (n = 81) and functional parkinsonism (n = 8) while walking and running. The groups were matched for age, sex and disease duration., Results: In contrast to those with PD, patients with functional parkinsonism (i) were more likely to have a marked asymmetry in arm swing while walking (5/8 vs. 25/81; P = 0.06), (ii) were less likely to improve arm swing while running with full effort (3/8 vs. 72/81; P < 0.001) and (iii) demonstrated normal passive arm swing even when asymmetry of arm swing was marked during running/walking (6/6 vs. 9/33; P = 0.002)., Conclusions: Assessment of arm swing while walking and running and passive arm swing could be important differentiating clinical features between functional parkinsonism and PD., (© 2023 International Parkinson and Movement Disorder Society.)

Competing Interests: There are no conflicts of interest.

Social and non-social deficits in autism spectrum disorders (ASD) persist into adulthood and may share common regions of aberrant neural activations. The current meta-analysis investigated activation differences between ASD and neurotypical controls irrespective of task type. Activation likelihood estimation meta-analyses were performed to examine consistent hypo-activated and/or hyper-activated regions for all tasks combined, and for social and non-social tasks separately; meta-analytic connectivity modelling and behavioral/paradigm analyses were performed to examine co-activated regions and associated behaviors. One hundred studies (mean age range = 18-41 years) were included. For all tasks combined, the ASD group showed significant (p < .05) hypo-activation in one cluster around the left amygdala (peak - 26, -2, -20, volume = 1336 mm 3 , maximum ALE = 0.0327), and this cluster co-activated with two other clusters around the right cerebellum (peak 42, -56, -22, volume = 2560mm 3 , maximum ALE = 0.049) Lobule VI/Crus I and the left fusiform gyrus (BA47) (peak - 42, -46, -18, volume = 1616 mm 3 , maximum ALE = 0.046) and left cerebellum (peak - 42, -58, -20, volume = 1616mm 3 , maximum ALE = 0.033) Lobule VI/Crus I. While the left amygdala was associated with negative emotion (fear) (z = 3.047), the left fusiform gyrus/cerebellum Lobule VI/Crus I cluster was associated with language semantics (z = 3.724) and action observation (z = 3.077). These findings highlight the left amygdala as a region consistently hypo-activated in ASD and suggest the potential involvement of fusiform gyrus and cerebellum in social cognition in ASD. Future research should further elucidate if and how amygdala-fusiform/cerebellar connectivity relates to social and non-social cognition in adults with ASD., (© 2022. The Author(s).)

Lewy body disorders (LBD), characterized by the deposition of misfolded α-synuclein (α-Syn), are clinically heterogeneous. Although the distribution of α-Syn correlates with the predominant clinical features, the burden of pathology does not fully explain the observed variability in clinical presentation and rate of disease progression. We hypothesized that this heterogeneity might reflect α-Syn molecular diversity, between both patients and different brain regions. Using an ultra-sensitive assay, we evaluated α-Syn seeding in 8 brain regions from 30 LBD patients with different clinical phenotypes and disease durations. Comparing seeding across the clinical phenotypes revealed that hippocampal α-Syn from patients with a cognitive-predominant phenotype had significantly higher seeding capacity than that derived from patients with a motor-predominant phenotype, whose nigral-derived α-Syn in turn had higher seeding capacity than that from cognitive-predominant patients. Interestingly, α-Syn from patients with rapid disease progression (< 3 years to development of advanced disease) had the highest nigral seeding capacity of all the patients included. To validate these findings and explore factors underlying seeding heterogeneity, we performed in vitro toxicity assays, and detailed neuropathological and biochemical examinations. Furthermore, and for the first time, we performed a proteomic-wide profiling of the substantia nigra from 5 high seeder and 5 low seeder patients. The proteomic data suggests a significant disruption in mitochondrial function and lipid metabolism in high seeder cases compared to the low seeders. These observations suggest that distinct molecular populations of α-Syn may contribute to heterogeneity in phenotypes and progression rates in LBD and imply that effective therapeutic strategies might need to be directed at an ensemble of differently misfolded α-Syn species, with the relative contribution of their differing impacts accounting for heterogeneity in the neurodegenerative process., (© 2022. The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.)

Background: Despite coronavirus disease-19 (COVID-19) being a major health crisis in the current times, only a few studies have addressed its potential direct effect on mental health, especially among COVID-19 patients., Aims: This study was conducted to assess the mental health of COVID-19 patients., Materials and Methods: In cross sectional study, mental health status of 301 symptomatic and 200 asymptomatic COVID-19 participants was assessed using the General Health Questionnaire-28., Results: Around 8.78% COVID-19 patients were found to be psychologically distressed that was predominantly higher among symptomatic COVID-19 patients. Risk of psychological distress was significantly higher in females, living in nuclear families and having a history of addiction., Conclusions: COVID-19 patients suffer from psychological distress, which needs to be addressed to cope well with this pandemic situation., Competing Interests: There are no conflicts of interest., (Copyright: © 2021 Indian Journal of Psychiatry.)

Glutamatergic, noradrenergic, serotonergic, and cholinergic systems play a critical role in the basal ganglia circuitry. Targeting these non-dopaminergic receptors remains a focus of ongoing research to improve Parkinson's disease (PD) motor symptoms, without the potential side effects of dopamine replacement therapy. This review updates advancements in non-dopaminergic treatments for motor control in PD since 2013. To date, no non-dopaminergic selective drug has shown significant long-term efficacy as monotherapy in PD. The largest area of development in non-dopaminergic targets has been for motor complications of dopamine replacement therapy (motor fluctuations and dyskinesia). For treatment of motor fluctuations, safinamide, zonisamide, and istradefylline are currently approved, and novel glutamatergic and serotonergic drugs are in development. Long-acting formulations of amantadine are approved for treating dyskinesia. Several non-dopaminergic drugs have failed to show anti-dyskinetic efficacy, while some are still in development. Non-dopaminergic targets are also being pursued to treat specific motor symptoms of PD. For example, CX-8998 (a calcium channel modulator) is being evaluated for PD tremor and rivastigmine may improve gait dysfunction in PD. Drug repurposing continues to be a key strategy for non-dopaminergic targets in PD, but the field needs to increase discovery and availability of such drugs.

Movement disorders often emerge from the interplay of complex pathophysiological processes involving the kidneys and the nervous system. Tremor, myoclonus, ataxia, chorea, and parkinsonism can occur in the context of renal dysfunction (azotemia and electrolyte abnormalities) or they can be part of complications of its management (dialysis and renal transplantation). On the other hand, myoglobinuria from rhabdomyolysis in status dystonicus and certain drugs used in the management of movement disorders can cause nephrotoxicity. Distinct from these well-recognized associations, it is important to appreciate that there are several inherited and acquired disorders in which movement abnormalities do not occur as a consequence of renal dysfunction or vice versa but are manifestations of common pathophysiological processes affecting the nervous system and the kidneys. These disorders are the emphasis of this review. Increasing awareness of these conditions among neurologists may help them to identify renal involvement earlier, take timely intervention by anticipating complications and focus on therapies targeting common mechanisms in addition to symptomatic management of movement disorders. Recognition of renal impairment in a patient with complex neurological presentation may narrow down the differentials and aid in reaching a definite diagnosis., (© 2020 International Parkinson and Movement Disorder Society.)

microRNAs (miRNAs) are short, non-coding, endogenous RNA molecule that regulates messenger RNAs (mRNAs) at the post-transcriptional level. The discovery of this regulatory relationship between miRNAs and mRNAs is an important research direction. In this regard, our method proposed an integrated approach to identify the mRNA targets of dysregulated miRNAs using next-generation sequencing data from six cancer types. For this analysis, a sensible combination of data mining tools is chosen. In particular, Random Forest, log-transformed Fold change, and Pearson correlation coefficient are considered to find the potential miRNA-mRNA pairs. During this study, we have identified six cancer-specific overlapping sets of miRNAs whose classification accuracy is always higher than 91%. Furthermore, a promising correlation signature of significantly dysregulated miRNAs and mRNAs are recognized. A comprehensive analysis found that the cumulative percentage of negative correlation coefficients is higher than its positive counterpart. Moreover, experimentally validated miRNA-target interactions databases called miRTarBase is used to validate significantly correlated mRNAs. According to our study, the smallest set of significantly dysregulated miRNAs is 43 in PRAD data, while for mRNAs the smallest set is 238 in the LUAD cancer type. The obtained miRNA-mRNA pairs are subjected to do pathway enrichment analysis and gene ontology analysis. Regulatory roles of these dysregulated miRNAs with associated diseases are identified by constructing a regulatory network between miRNAs and associated diseases. Moreover, the relation between miRNAs expression level and patient survival is also analyzed. To conclude, the miRNA-mRNA pairs identified in this study may serve as promising candidates for subsequent in-vitro validation., (Copyright © 2019 Elsevier Ltd. All rights reserved.)

Background: Recent advancement in bioinformatics offers the ability to identify informative genes from high dimensional gene expression data. Selection of informative genes from these large datasets has emerged as an issue of major concern among researchers., Objective: Gene functionality and regulatory mechanisms can be understood through the analysis of these gene expression data. Here, we present a computational method to identify informative genes for breast cancer subtypes such as Basal, human epidermal growth factor receptor 2 (Her2), luminal A (LumA), and luminal B (LumB)., Methods: The proposed In Silico Markers method is a wrapper feature selection method based on Least Absolute Shrinkage and Selection Operator (LASSO), Covariance Matrix Adaptation Evolution Strategy (CMA-ES) and Support Vector Machine (SVM) as a classifier. Moreover, the composite measure consisting of relevance, redundancy, and rank score of frequently appeared genes are used to select informative genes., Results: The informative genes are validated by statistical and biologically relevant criteria. For a comparative evaluation of the proposed approach, biological similarity score designed on semantic similarity measure of GO terms are investigated. Further, the proposed technique is evaluated with 7 existing gene selection techniques using two-class annotated breast cancer subtype datasets., Conclusion: The utilization of this method can bring about the discovery of informative genes. Furthermore, under multiple criteria decision-making set-up, informative genes selected by the In Silico Markers are found to be admirable than the compared methods selected genes.

Background: Identification of differentially expressed genes, i.e., genes whose transcript abundance level differs across different biological or physiological conditions, was indeed a challenging task. However, the inception of transcriptome sequencing (RNA-seq) technology revolutionized the simultaneous measurement of the transcript abundance levels for thousands of genes., Objective: In this paper, such next-generation sequencing (NGS) data is used to identify biomarker signatures for several of the most common cancer types (bladder, colon, kidney, brain, liver, lung, prostate, skin, and thyroid) METHODS: Here, the problem is mapped into the comparison of optimization algorithms for selecting a set of genes that lead to the highest classification accuracy of a two-class classification task between healthy and tumor samples. As the optimization algorithms Artificial Bee Colony (ABC), Ant Colony Optimization, Differential Evolution, and Particle Swarm Optimization are chosen for this experiment. A standard statistical method called DESeq2 is used to select differentially expressed genes before being feed to the optimization algorithms. Classification of healthy and tumor samples is done by support vector machine RESULTS: Cancer-specific validation yields remarkably good results in terms of accuracy. Highest classification accuracy is achieved by the ABC algorithm for Brain lower grade glioma data is 99.10%. This validation is well supported by a statistical test, gene ontology enrichment analysis, and KEGG pathway enrichment analysis for each cancer biomarker signature CONCLUSION: The current study identified robust genes as biomarker signatures and these identified biomarkers might be helpful to accurately identify tumors of unknown origin.

MicroRNAs are small non-coding RNAs that influence gene expression by binding to the 3' UTR of target mRNAs in order to repress protein synthesis. Soon after discovery, microRNA dysregulation has been associated to several pathologies. In particular, they have often been reported as differentially expressed in healthy and tumor samples. This fact suggested that microRNAs are likely to be good candidate biomarkers for cancer diagnosis and personalized medicine. With the advent of Next-Generation Sequencing (NGS), measuring the expression level of the whole miRNAome at once is now routine. Yet, the collaborative effort of sharing data opens to the possibility of population analyses. This context motivated us to perform an in-silico study to distill cancer-specific panels of microRNAs that can serve as biomarkers. We observed that the problem of finding biomarkers can be modeled as a two-class classification task where, given the miRNAomes of a population of healthy and cancerous samples, we want to find the subset of microRNAs that leads to the highest classification accuracy. We fulfill this task leveraging on a sensible combination of data mining tools. In particular, we used: differential evolution for candidate selection, component analysis to preserve the relationships among miRNAs, and SVM for sample classification. We identified 10 cancer-specific panels whose classification accuracy is always higher than 92%. These panels have a very little overlap suggesting that miRNAs are not only predictive of the onset of cancer, but can be used for classification purposes as well. We experimentally validated the contribution of each of the employed tools to the selection of discriminating miRNAs. Moreover, we tested the significance of each panel for the corresponding cancer type. In particular, enrichment analysis showed that the selected miRNAs are involved in oncogenesis pathways, while survival analysis proved that miRNAs can be used to evaluate cancer severity. Summarizing: results demonstrated that our method is able to produce cancer-specific panels that are promising candidates for a subsequent in vitro validation., Competing Interests: Department of Science and Technology, India, DST/INT/POL/P-36/2016, Dr. Indrajit Saha Istituto Toscano Tumori, Eligibility of miRNAs modified by docetaxel in prostate cancer cells to plasma biomarkers in patients responsive or no more responsive to docetaxel, Dr Filippo Geraci The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

Summary: Cancer hallmarks, a concept that seeks to explain the complexity of cancer initiation and development, provide a new perspective of studying cancer signaling which could lead to a greater understanding of this complex disease. However, to the best of our knowledge, there is currently a lack of tools that support such hallmark-based study of the cancer signaling network, thereby impeding the gain of knowledge in this area. We present TROVE, an user-friendly software that facilitates hallmark annotation, visualization and analysis in cancer signaling networks. In particular, TROVE facilitates hallmark analysis specific to particular cancer types., Availability and Implementation: Available under the Eclipse Public License from: https://sites.google.com/site/cosbyntu/softwares/trove and https://github.com/trove2017/Trove., (© The Author 2017. Published by Oxford University Press. All rights reserved. For Permissions, please e-mail: journals.permissions@oup.com.)

Given a signaling network, the target combination prediction problem aims to predict efficacious and safe target combinations for combination therapy. State-of-the-art in silico methods use Monte Carlo simulated annealing (mcsa) to modify a candidate solution stochastically, and use the Metropolis criterion to accept or reject the proposed modifications. However, such stochastic modifications ignore the impact of the choice of targets and their activities on the combination's therapeutic effect and off-target effects, which directly affect the solution quality. In this paper, we present mascot, a method that addresses this limitation by leveraging two additional heuristic criteria to minimize off-target effects and achieve synergy for candidate modification. Specifically, off-target effects measure the unintended response of a signaling network to the target combination and is often associated with toxicity. Synergy occurs when a pair of targets exerts effects that are greater than the sum of their individual effects, and is generally a beneficial strategy for maximizing effect while minimizing toxicity. mascot leverages on a machine learning-based target prioritization method which prioritizes potential targets in a given disease-associated network to select more effective targets (better therapeutic effect and/or lower off-target effects); and on Loewe additivity theory from pharmacology which assesses the non-additive effects in a combination drug treatment to select synergistic target activities. Our experimental study on two disease-related signaling networks demonstrates the superiority of mascot in comparison to existing approaches., (Copyright © 2017 Elsevier Inc. All rights reserved.)

Background: The discovery of antibodies against aquaporin-4 and evolving concepts of noncompressive myelopathies in the 21 st century have made a major impact on the etiological profile of these diseases, with few cases turning out to be idiopathic., Objective: To find causes of noncompressive myelopathy in a tertiary care hospital of Northeast India., Materials and Methods: An observational study was carried out in the Neurology Department of Gauhati Medical College, Guwahati, from September 2013 to February 2016. Patients of noncompressive myelopathies who underwent magnetic resonance imaging (MRI) of the spine were segregated into two categories: acute-to-subacute myelopathy (ASM) and chronic myelopathy (CM). In addition to routine blood tests, chest X-ray, urinalysis, and visual evoked potentials, investigations included MRI of the brain, cerebrospinal fluid analysis, and immunological, infectious, and metabolic profile based on the pattern of involvement., Results: The study had 151 patients (96 ASM and 55 CM) with a median age of 35 years and male: female ratio 1.4:1. The causes of ASM were neuromyelitis optica spectrum disorder (23), multiple sclerosis (MS) (8), systemic lupus erythematosus (1), Hashimoto's disease (1), postinfectious acute disseminated encephalomyelitis (6), postinfectious myelitis (8), infections (9), spinal cord infarct (5), and electrocution (1). The causes of CM were MS (1), probable or possible sarcoidosis (7), mixed connective tissue disease (1), Hashimoto's disease (2), infections (9), Vitamin B 12 deficiency (4), folate deficiency (2), hepatic myelopathy (2), radiation (11), and paraneoplastic (1). No etiology could be found in 48 (31.8%) patients (34 ASM and 14 CM). In 21/96 (21.9%) patients of ASM, acute transverse myelitis was idiopathic based on current diagnostic criteria., Conclusion: Underlying etiology (demyelinating, autoimmune, infectious, vascular, metabolic disorder, or physical agent) was found in 68% patients of noncompressive myelopathy., Competing Interests: There are no conflicts of interest.

Motivation: Target characterization for a biochemical network is a heuristic evaluation process that produces a characterization model that may aid in predicting the suitability of each molecule for drug targeting. These approaches are typically used in drug research to identify novel potential targets using insights from known targets. Traditional approaches that characterize targets based on their molecular characteristics and biological function require extensive experimental study of each protein and are infeasible for evaluating larger networks with poorly understood proteins. Moreover, they fail to exploit network connectivity information which is now available from systems biology methods. Adopting a network-based approach by characterizing targets using network features provides greater insights that complement these traditional techniques. To this end, we present Tenet (Target charactErization using NEtwork Topology), a network-based approach that characterizes known targets in signalling networks using topological features., Results: Tenet first computes a set of topological features and then leverages a support vector machine-based approach to identify predictive topological features that characterizes known targets. A characterization model is generated and it specifies which topological features are important for discriminating the targets and how these features should be combined to quantify the likelihood of a node being a target. We empirically study the performance of Tenet from a wide variety of aspects, using several signalling networks from BioModels with real-world curated outcomes. Results demonstrate its effectiveness and superiority in comparison to state-of-the-art approaches., Availability and Implementation: Our software is available freely for non-commercial purposes from: https://sites.google.com/site/cosbyntu/softwares/tenet, Contact: hechua@ntu.edu.sg or assourav@ntu.edu.sg, Supplementary Information: Supplementary data are available at Bioinformatics online., (© The Author 2015. Published by Oxford University Press. All rights reserved. For Permissions, please e-mail: journals.permissions@oup.com.)

The Cyclin-Dependent Kinases (CDKs) are the core components coordinating eukaryotic cell division cycle. Generally the crystal structure of CDKs provides information on possible molecular mechanisms of ligand binding. However, reliable and robust estimation of ligand binding activity has been a challenging task in drug design. In this regard, various machine learning techniques, such as Support Vector Machine, Naive Bayesian classifier, Decision Tree, and K-Nearest Neighbor classifier, have been used. The performance of these heterogeneous classification techniques depends on proper selection of features from the data set. This fact motivated us to propose an integrated classification technique using Genetic Algorithm (GA), Rotational Feature Selection (RFS) scheme, and Ensemble of Machine Learning methods, named as the Genetic Algorithm integrated Rotational Ensemble based classification technique, for the prediction of ligand binding activity of CDKs. This technique can automatically find the important features and the ensemble size. For this purpose, GA encodes the features and ensemble size in a chromosome as a binary string. Such encoded features are then used to create diverse sets of training points using RFS in order to train the machine learning method multiple times. The RFS scheme works on Principal Component Analysis (PCA) to preserve the variability information of the rotational nonoverlapping subsets of original data. Thereafter, the testing points are fed to the different instances of trained machine learning method in order to produce the ensemble result. Here accuracy is computed as a final result after 10-fold cross validation, which also used as an objective function for GA to maximize. The effectiveness of the proposed classification technique has been demonstrated quantitatively and visually in comparison with different machine learning methods for 16 ligand binding CDK docking and rescoring data sets. In addition, the best possible features have been reported for CDK docking and rescoring data sets separately. Finally, the Friedman test has been conducted to judge the statistical significance of the results produced by the proposed technique. The results indicate that the integrated classification technique has high relevance in predicting of protein-ligand binding activity.

The ongoing cancer research has shown that malignant tumour cells have highly disrupted signalling transduction pathways. In cancer cells, signalling pathways are altered to satisfy the demands of continuous proliferation and survival. The changes in signalling pathways supporting uncontrolled cell growth, termed as rewiring, can lead to dysregulation of cell fates e.g. apoptosis. Hence comparative analysis of normal and oncogenic signal transduction pathways may provide insights into mechanisms of cancer drug-resistance and facilitate the discovery of novel and effective anti-cancer therapies. Here we propose a hybrid modelling approach based on ordinary differential equation (ODE) and machine learning to map network rewiring in the apoptotic pathways that may be responsible for the increase of drug sensitivity of tumour cells in triple-negative breast cancer. Our method employs Genetic Algorithm to search for the most likely network topologies by iteratively generating simulated protein phosphorylation data using ODEs and the rewired network and then fitting the simulated data with real data of cancer signalling and cell fate. Most of our predictions are consistent with experimental evidence from literature. Combining the strengths of knowledge-driven and data-driven approaches, our hybrid model can help uncover molecular mechanisms of cancer cell fate at systems level.

The study of genetic interaction networks that respond to changing conditions is an emerging research problem. Recently, Bandyopadhyay et al. (2010) proposed a technique to construct a differential network (dE-MAPnetwork) from two static gene interaction networks in order to map the interaction differences between them under environment or condition change (e.g., DNA-damaging agent). This differential network is then manually analyzed to conclude that DNA repair is differentially effected by the condition change. Unfortunately, manual construction of differential functional summary from a dE-MAP network that summarizes all pertinent functional responses is time-consuming, laborious and error-prone, impeding large-scale analysis on it. To this end, we propose DiffNet, a novel data-driven algorithm that leverages Gene Ontology (go) annotations to automatically summarize a dE-MAP network to obtain a high-level map of functional responses due to condition change. We tested DiffNet on the dynamic interaction networks following MMS treatment and demonstrated the superiority of our approach in generating differential functional summaries compared to state-of-the-art graph clustering methods. We studied the effects of parameters in DiffNet in controlling the quality of the summary. We also performed a case study that illustrates its utility., (Copyright © 2014 Elsevier Inc. All rights reserved.)

Motivation: Given the growth of large-scale protein-protein interaction (PPI) networks obtained across multiple species and conditions, network alignment is now an important research problem. Network alignment performs comparative analysis across multiple PPI networks to understand their connections and relationships. However, PPI data in high-throughput experiments still suffer from significant false-positive and false-negatives rates. Consequently, high-confidence network alignment across entire PPI networks is not possible. At best, local network alignment attempts to alleviate this problem by completely ignoring low-confidence mappings; global network alignment, on the other hand, pairs all proteins regardless. To this end, we propose an alternative strategy: instead of full alignment across the entire network or completely ignoring low-confidence regions, we aim to perform highly specific protein-to-protein alignments where data confidence is high, and fall back on broader functional region-to-region alignment where detailed protein-protein alignment cannot be ascertained. The basic idea is to provide an alignment of multiple granularities to allow biological predictions at varying specificity., Results: DualAligner performs dual network alignment, in which both region-to-region alignment, where whole subgraph of one network is aligned to subgraph of another, and protein-to-protein alignment, where individual proteins in networks are aligned to one another, are performed to achieve higher accuracy network alignments. Dual network alignment is achieved in DualAligner via background information provided by a combination of Gene Ontology annotation information and protein interaction network data. We tested DualAligner on the global networks from IntAct and demonstrated the superiority of our approach compared with state-of-the-art network alignment methods. We studied the effects of parameters in DualAligner in controlling the quality of the alignment. We also performed a case study that illustrates the utility of our approach., Availability and Implementation: http://www.cais.ntu.edu.sg/∼assourav/DualAligner/., (© The Author 2014. Published by Oxford University Press. All rights reserved. For Permissions, please e-mail: journals.permissions@oup.com.)

Motivation: The availability of large-scale curated protein interaction datasets has given rise to the opportunity to investigate higher level organization and modularity within the protein-protein interaction (PPI) network using graph theoretic analysis. Despite the recent progress, systems level analysis of high-throughput PPIs remains a daunting task because of the amount of data they present. In this article, we propose a novel PPI network decomposition algorithm called FACETS in order to make sense of the deluge of interaction data using Gene Ontology (GO) annotations. FACETS finds not just a single functional decomposition of the PPI network, but a multi-faceted atlas of functional decompositions that portray alternative perspectives of the functional landscape of the underlying PPI network. Each facet in the atlas represents a distinct interpretation of how the network can be functionally decomposed and organized. Our algorithm maximizes interpretative value of the atlas by optimizing inter-facet orthogonality and intra-facet cluster modularity., Results: We tested our algorithm on the global networks from IntAct, and compared it with gold standard datasets from MIPS and KEGG. We demonstrated the performance of FACETS. We also performed a case study that illustrates the utility of our approach., Supplementary Information: Supplementary data are available at the Bioinformatics online., Availability: Our software is available freely for non-commercial purposes from: http://www.cais.ntu.edu.sg/~assourav/Facets/

Transforming growth factor-β1 (TGF-β1) is a potent regulator of extracellular matrix production, wound healing, differentiation, and immune response, and is implicated in the progression of fibrotic diseases and cancer. Extracellular activation of TGF-β1 from its latent form provides spatiotemporal control over TGF-β1 signaling, but the current understanding of TGF-β1 activation does not emphasize cross talk between activators. Plasmin (PLS) and thrombospondin-1 (TSP1) have been studied individually as activators of TGF-β1, and in this work we used a systems-level approach with mathematical modeling and in vitro experiments to study the interplay between PLS and TSP1 in TGF-β1 activation. Simulations and steady-state analysis predicted a switch-like bistable transition between two levels of active TGF-β1, with an inverse correlation between PLS and TSP1. In particular, the model predicted that increasing PLS breaks a TSP1-TGF-β1 positive feedback loop and causes an unexpected net decrease in TGF-β1 activation. To test these predictions in vitro, we treated rat hepatocytes and hepatic stellate cells with PLS, which caused proteolytic cleavage of TSP1 and decreased activation of TGF-β1. The TGF-β1 activation levels showed a cooperative dose response, and a test of hysteresis in the cocultured cells validated that TGF-β1 activation is bistable. We conclude that switch-like behavior arises from natural competition between two distinct modes of TGF-β1 activation: a TSP1-mediated mode of high activation and a PLS-mediated mode of low activation. This switch suggests an explanation for the unexpected effects of the plasminogen activation system on TGF-β1 in fibrotic diseases in vivo, as well as novel prognostic and therapeutic approaches for diseases with TGF-β dysregulation., (Copyright © 2012 Biophysical Society. Published by Elsevier Inc. All rights reserved.)

Background: The availability of large-scale curated protein interaction datasets has given rise to the opportunity to investigate higher level organization and modularity within the protein interaction network (PPI) using graph theoretic analysis. Despite the recent progress, systems level analysis of PPIS remains a daunting task as it is challenging to make sense out of the deluge of high-dimensional interaction data. Specifically, techniques that automatically abstract and summarize PPIS at multiple resolutions to provide high level views of its functional landscape are still lacking. We present a novel data-driven and generic algorithm called FUSE (Functional Summary Generator) that generates functional maps of a PPI at different levels of organization, from broad process-process level interactions to in-depth complex-complex level interactions, through a pro t maximization approach that exploits Minimum Description Length (MDL) principle to maximize information gain of the summary graph while satisfying the level of detail constraint., Results: We evaluate the performance of FUSE on several real-world PPIS. We also compare FUSE to state-of-the-art graph clustering methods with GO term enrichment by constructing the biological process landscape of the PPIS. Using AD network as our case study, we further demonstrate the ability of FUSE to quickly summarize the network and identify many different processes and complexes that regulate it. Finally, we study the higher-order connectivity of the human PPI., Conclusion: By simultaneously evaluating interaction and annotation data, FUSE abstracts higher-order interaction maps by reducing the details of the underlying PPI to form a functional summary graph of interconnected functional clusters. Our results demonstrate its effectiveness and superiority over state-of-the-art graph clustering methods with GO term enrichment.

Plasmin (PLS) and urokinase-type plasminogen activator (UPA) are ubiquitous proteases that regulate the extracellular environment. Although they are secreted in inactive forms, they can activate each other through proteolytic cleavage. This mutual interplay creates the potential for complex dynamics, which we investigated using mathematical modeling and in vitro experiments. We constructed ordinary differential equations to model the conversion of precursor plasminogen into active PLS, and precursor urokinase (scUPA) into active urokinase (tcUPA). Although neither PLS nor UPA exhibits allosteric cooperativity, modeling showed that cooperativity occurred at the system level because of substrate competition. Computational simulations and bifurcation analysis predicted that the system would be bistable over a range of parameters for cooperativity and positive feedback. Cell-free experiments with recombinant proteins tested key predictions of the model. PLS activation in response to scUPA stimulus was found to be cooperative in vitro. Finally, bistability was demonstrated in vitro by the presence of two significantly different steady-state levels of PLS activation for the same levels of stimulus. We conclude that ultrasensitive, bistable activation of UPA-PLS is possible in the presence of substrate competition. An ultrasensitive threshold for activation of PLS and UPA would have ramifications for normal and disease processes, including angiogenesis, metastasis, wound healing, and fibrosis., (Copyright © 2011 Biophysical Society. Published by Elsevier Inc. All rights reserved.)

For acute, chronic, or hereditary diseases of the liver, cell transplantation therapies can stimulate liver regeneration or serve as a bridge until liver transplantation can be performed. Recently, fetal hepatocytes, stem cells, liver progenitor cells, or other primitive and proliferative cell types have been employed for cell transplantation therapies, in an effort to improve the survival, proliferation, and engraftment of the transplanted cells. Reviewing earlier studies, which achieved success by transplanting mature hepatocytes, we propose that there is a switch-like regulation of liver regeneration that changes state according to a stimulus threshold of extracellular influences such as cytokines, matrices and neighboring cells. Important determinants of a successful clinical outcome include sufficient quantities and functional levels of the transplanted cells (even for short periods to alter the environment), rather than just engraftment levels or survival durations of the exogenously transplanted cells. The relative importance of these determining factors will impact future choices of cell sources, delivery vehicles, and sites of cell transplantation to stimulate liver regeneration for patients with severe liver diseases., (2010 Elsevier B.V. All rights reserved.)

Plasmin and urokinase-type plasminogen activator (uPA) are ubiquitous proteases regulating the extracellular environment. They can activate each other via proteolytic cleavage, suggesting the potential for complex dynamic behaviors that could be elucidated by computational modeling. Ordinary differential equations are constructed to model the activation dynamics of plasminogen into plasmin, and single-chain uPA (scUPA) into two-chain uPA (tcUPA). Computational simulations and phase plane analysis reveal two stable steady states for the activation of each protein. Bifurcation analysis shows the in silico system to be bistable. Cell-free experiments verify the system to have ultrasensitive activation behavior, where scUPA is the stimulus and plasmin the output. Furthermore, two significantly different steady states could be seen in vitro for the same stimulus levels, depending on the initial activation level of the plasmin. The switch-like dynamics of the uPA-plasmin system could have potential relevance to many normal and disease processes including angiogenesis, migration and metastasis, wound healing and fibrosis.