Identification of Topological Network Modules in Perturbed Protein Interaction Networks

Topological Data Analysis with Spherical Fuzzy Soft AHP-TOPSIS for Environmental Mitigation System

The idea of spherical fuzzy soft set (SFSS) is a new hybrid model of a soft set (SS) and spherical fuzzy set (SFS). An SFSS is a new approach for information analysis and information fusion, and fuzzy modeling. We define the concepts of spherical-fuzzy-soft-set topology (SFSS-topology) and spherical-fuzzy-soft-set separation axioms. Several characteristics of SFSS-topology are investigated and related results are derived. We developed an extended choice value method (CVM) and the AHP-TOPSIS (analytical hierarchy process and technique for the order preference by similarity to ideal solution) for SFSSs, and presented their applications in multiple-criteria group decision making (MCGDM). Moreover, an application of the CVM is presented in a stock market investment problem and another application of the AHP-TOPSIS is presented for an environmental mitigation system. The suggested methods are efficiently applied to investigate MCGDM through case studies.

Modularity in Biological Networks

Network modeling, from the ecological to the molecular scale has become an essential tool for studying the structure, dynamics and complex behavior of living systems. Graph representations of the relationships between biological components open up a wide variety of methods for discovering the mechanistic and functional properties of biological systems. Many biological networks are organized into a modular structure, so methods to discover such modules are essential if we are to understand the biological system as a whole. However, most of the methods used in biology to this end, have a limited applicability, as they are very specific to the system they were developed for. Conversely, from the statistical physics and network science perspective, graph modularity has been theoretically studied and several methods of a very general nature have been developed. It is our perspective that in particular for the modularity detection problem, biology and theoretical physics/network science are less connected than they should. The central goal of this review is to provide the necessary background and present the most applicable and pertinent methods for community detection in a way that motivates their further usage in biological research.

Comparison of path-based centrality measures in protein-protein interaction networks revealed proteins with phenotypic relevance during adaptation to changing nitrogen environments

Plants must rapidly adapt to changes in nutrient conditions. Especially adaptations to changing nitrogen environments are very complex involving also major adjustments on the protein level. Here, we used a size-exclusion chromatography-coupled to mass spectrometry approach to study the dynamics of protein-protein interactions induced by transition from full nutrition to nitrogen starvation. Comparison of interaction networks established for each nutrient condition revealed a large overlap of proteins which were part of the protein-protein interaction network, but that same set of proteins underwent different interactions at each treatment. Network topology parameter betweenness centrality (BC) was found to best reflect the relevance of individual proteins in the information flow within each network. Changes in BC for individual proteins may therefore indicate their involvement in the cellular adjustments to the new condition. Based on this analysis, a set of proteins was identified showing high nitrogen-dependent changes in their BC values: The receptor kinase AT5G49770, co-receptor QSK1, and proton-ATPase AHA2. Mutants of those proteins showed a nitrate-dependent root growth phenotype. Individual interactions within the reconstructed network were tested using FRET-FLIM technology. Taken together, we present a systematic strategy comparing dynamic changes in protein-protein interaction networks based on their network parameters to identify regulatory nodes. SIGNIFICANCE: Protein-protein interactions are known to be important in cellular signaling events, but the dynamic changes in interaction networks induced by external stimuli are still rarely studied. We systematically analyzed how changes in the nutrient environment induced a rewiring of protein-protein interactions in roots. We observed small changes in overall protein abundances, but instead a rewiring of pairwise protein-protein interactions. Betweenness centrality was found to be the optimal network topology parameter to identify protein candidates with high relevance to the information flow in the (dynamic) network. Predicted interactions of those relevant nodes were confirmed in FLIM/FRET experiments and in phenotypic analysis. The network approach described here may be a useful application in dynamic network analysis more generally.

Identifying the essential nodes in network pharmacology based on multilayer network combined with random walk algorithm

Compared with the general complex network, the multilayer network is more suitable for the description of reality. It can be used as a tool of network pharmacology to analyze the mechanism of drug action from an overall perspective. Combined with random walk algorithm, it measures the importance of nodes from the entire network rather than a single layer. Here a four-layer network was constructed based on the data about the action process of prescriptions, consisting of ingredients, target proteins, metabolic pathways and diseases. The random walk algorithm was used to calculate the betweenness centrality of the protein layer nodes to get the rank of their importance. According to above method, we screened out the top 10% proteins that play a key role in treatment. Prescriptions Xiaochaihu Decoction was taken as example to prove our method. The selected proteins were measured with the ones that have been validated to be associated with the treated diseases. The results showed that its accuracy was no less than the topology-based method of single-layer network. The applicability of our method was proved by another prescription Yupingfeng Decoction. Our study demonstrated that multilayer network combined with random walk algorithm was an effective method for pre-screening vital target proteins related to prescriptions.

Identification of stem cells from large cell populations with topological scoring

Machine learning and topological analysis methods are becoming increasingly used on various large-scale omics datasets. Modern high dimensional flow cytometry data sets share many features with other omics datasets like genomics and proteomics. For example, genomics or proteomics datasets can be sparse and have high dimensionality, and flow cytometry datasets can also share these features. This makes flow cytometry data potentially a suitable candidate for employing machine learning and topological scoring strategies, for example, to gain novel insights into patterns within the data. We have previously developed a Topological Score (TopS) and implemented it for the analysis of quantitative protein interaction network datasets. Here we show that TopS approach for large scale data analysis is applicable to the analysis of a previously described flow cytometry sorted human hematopoietic stem cell dataset. We demonstrate that TopS is capable of effectively sorting this dataset into cell populations and identify rare cell populations. We demonstrate the utility of TopS when coupled with multiple approaches including topological data analysis, X-shift clustering, and t-Distributed Stochastic Neighbor Embedding (t-SNE). Our results suggest that TopS could be effectively used to analyze large scale flow cytometry datasets to find rare cell populations.

The chromatin bound proteome of the human malaria parasite

Proteins interacting with DNA are fundamental for mediating processes such as gene expression, DNA replication and maintenance of genome integrity. Accumulating evidence suggests that the chromatin of apicomplexan parasites, such as Plasmodium falciparum, is highly organized, and this structure provides an epigenetic mechanism for transcriptional regulation. To investigate how parasite chromatin structure is being regulated, we undertook comparative genomics analysis using 12 distinct eukaryotic genomes. We identified conserved and parasite-specific chromatin-associated domains (CADs) and proteins (CAPs). We then used the chromatin enrichment for proteomics (ChEP) approach to experimentally capture CAPs in P. falciparum. A topological scoring analysis of the proteomics dataset revealed stage-specific enrichments of CADs and CAPs. Finally, we characterized, two candidate CAPs: a conserved homologue of the structural maintenance of chromosome 3 protein and a homologue of the crowded-like nuclei protein, a plant-like protein functionally analogous to animal nuclear lamina proteins. Collectively, our results provide a comprehensive overview of CAPs in apicomplexans, and contribute to our understanding of the complex molecular components regulating chromatin structure and genome architecture in these deadly parasites.

INO80 Chromatin Remodeling Coordinates Metabolic Homeostasis with Cell Division

Adaptive survival requires the coordination of nutrient availability with expenditure of cellular resources. For example, in nutrient-limited environments, 50% of all S. cerevisiae genes synchronize and exhibit periodic bursts of expression in coordination with respiration and cell division in the yeast metabolic cycle (YMC). Despite the importance of metabolic and proliferative synchrony, the majority of YMC regulators are currently unknown. Here, we demonstrate that the INO80 chromatin-remodeling complex is required to coordinate respiration and cell division with periodic gene expression. Specifically, INO80 mutants have severe defects in oxygen consumption and promiscuous cell division that is no longer coupled with metabolic status. In mutant cells, chromatin accessibility of periodic genes, including TORC1-responsive genes, is relatively static, concomitant with severely attenuated gene expression. Collectively, these results reveal that the INO80 complex mediates metabolic signaling to chromatin to restrict proliferation to metabolically optimal states.

Topological scoring of protein interaction networks

It remains a significant challenge to define individual protein associations within networks where an individual protein can directly interact with other proteins and/or be part of large complexes, which contain functional modules. Here we demonstrate the topological scoring (TopS) algorithm for the analysis of quantitative proteomic datasets from affinity purifications. Data is analyzed in a parallel fashion where a prey protein is scored in an individual affinity purification by aggregating information from the entire dataset. Topological scores span a broad range of values indicating the enrichment of an individual protein in every bait protein purification. TopS is applied to interaction networks derived from human DNA repair proteins and yeast chromatin remodeling complexes. TopS highlights potential direct protein interactions and modules within complexes. TopS is a rapid method for the efficient and informative computational analysis of datasets, is complementary to existing analysis pipelines, and provides important insights into protein interaction networks.

Inferring direct protein−protein interactions (PPIs) and modules in PPI networks remains a challenge. Here, the authors introduce an algorithm to infer potential direct PPIs from quantitative proteomic AP-MS data by identifying enriched interactions of each bait relative to the other baits.

Modeling and interpreting mesoscale network dynamics

Recent advances in brain imaging techniques, measurement approaches, and storage capacities have provided an unprecedented supply of high temporal resolution neural data. These data present a remarkable opportunity to gain a mechanistic understanding not just of circuit structure, but also of circuit dynamics, and its role in cognition and disease. Such understanding necessitates a description of the raw observations, and a delineation of computational models and mathematical theories that accurately capture fundamental principles behind the observations. Here we review recent advances in a range of modeling approaches that embrace the temporally-evolving interconnected structure of the brain and summarize that structure in a dynamic graph. We describe recent efforts to model dynamic patterns of connectivity, dynamic patterns of activity, and patterns of activity atop connectivity. In the context of these models, we review important considerations in statistical testing, including parametric and non-parametric approaches. Finally, we offer thoughts on careful and accurate interpretation of dynamic graph architecture, and outline important future directions for method development.

Malaria intensity in Colombia by regions and populations

Determining the distribution of disease prevalence among heterogeneous populations at the national scale is fundamental for epidemiology and public health. Here, we use a combination of methods (spatial scan statistic, topological data analysis and epidemic profile) to study measurable differences in malaria intensity by regions and populations of Colombia. This study explores three main questions: What are the regions of Colombia where malaria is epidemic? What are the regions and populations in Colombia where malaria is endemic? What associations exist between epidemic outbreaks between regions in Colombia? Plasmodium falciparum is most prevalent in the Pacific Coast, some regions of the Amazon Basin, and some regions of the Magdalena Basin. Plasmodium vivax is the most prevalent parasite in Colombia, particularly in the Northern Amazon Basin, the Caribbean, and municipalities of Sucre, Antioquia and Cordoba. We find an acute peak of malarial infection at 25 years of age. Indigenous and Afrocolombian populations experience endemic malaria (with household transmission). We find that Plasmodium vivax decreased in the most important hotspots, often with moderate urbanization rate, and was re-introduced to locations with moderate but sustained deforestation. Infection by Plasmodium falciparum, on the other hand, steadily increased in incidence in locations where it was introduced in the 2009-2010 generalized epidemic. Our findings suggest that Colombia is entering an unstable transmission state, where rapid decreases in one location of the country are interconnected with rapid increases in other parts of the country.

Genome maintenance functions of the INO80 chromatin remodeller

Chromatin modification is conserved in all eukaryotes and is required to facilitate and regulate DNA-templated processes. For example, chromatin manipulation, such as histone post-translational modification and nucleosome positioning, play critical roles in genome stability pathways. The INO80 chromatin-remodelling complex, which regulates the abundance and positioning of nucleosomes, is particularly important for proper execution of inducible responses to DNA damage. This review discusses the participation and activity of the INO80 complex in DNA repair and cell cycle checkpoint pathways, with emphasis on the Saccharomyces cerevisiae model system. Furthermore, the role of ATM/ATR kinases, central regulators of DNA damage signalling, in the regulation of INO80 function will be reviewed. In addition, emerging themes of chromatin remodelling in mitotic stability pathways and chromosome segregation will be introduced. These studies are critical to understanding the dynamic chromatin landscape that is rapidly and reversibly modified to maintain the integrity of the genome.This article is part of the themed issue 'Chromatin modifiers and remodellers in DNA repair and signalling'.

Unravelling the biology of chromatin in health and cancer using proteomic approaches

INTRODUCTION

Chromatin remodeling complexes play important roles in the control of genome regulation in both normal and diseased states, and are therefore critical components for the regulation of epigenetic states in cells. Given the role epigenetics plays in cancer, for example, chromatin remodeling complexes are routinely targeted for therapeutic intervention. Areas covered: Protein mass spectrometry and proteomics are powerful technologies used to study and understand chromatin remodeling. While impressive progress has been made in this area, there remain significant challenges in the application of proteomic technologies to the study of chromatin remodeling. As parts of large multi-subunit complexes that can be heavily modified with dynamic post-translational modifications, challenges in the study of chromatin remodeling complexes include defining the content, determining the regulation, and studying the dynamics of the complexes under different cellular states. Expert commentary: Impwortant considerations in the study of chromatin remodeling complexes include the complexity of sample preparation, the choice of proteomic methods for the analysis of samples, and data analysis challenges. Continued research in these three areas promise to yield even greater insights into the biology of chromatin remodeling and epigenetics and the dynamics of these systems in human health and cancer.