Identification of key proteins involved in stickleback environmental adaptation with system-level analysis

Using abundance measurements of 1,490 proteins from four separate populations of three-spined sticklebacks, we implemented a system-level approach to correlate proteome dynamics with environmental salinity and temperature and the fish's population and morphotype. We identified robust and accurate fingerprints that classify environmental salinity, temperature, morphotype, and the population sample origin, observing that proteins with specific functions are enriched in these fingerprints. Highly apparent functions represented in all fingerprints include ion transport, proteostasis, growth, and immunity, suggesting that these functions are most diversified in populations inhabiting different environments. Applying a differential network approach, we analyzed the network of protein interactions that differs between populations. Looking at specific population combinations of differential interaction, we identify sets of connected proteins. We find that these sets and their corresponding enriched functions reflect key processes that have diverged between the four populations. Moreover, the extent of divergence, i.e., the number of enriched functions that differ between populations, is highest when all three environmental parameters are different between two populations. Key nodes in the differential interaction network signify functions that are also inherent in the fingerprints, most prominently proteostasis-related functions. However, the differential interaction network also reveals additional functions that have diverged between populations, notably cytoskeletal organization and morphogenesis. The strength of these analyses is that the results are purely data driven. With such an unbiased approach applied on a large proteomic data set, we find the strongest signals given by the data, making it possible to develop more discriminatory and complex biomarkers for specific contexts of interest.

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Identification of key proteins involved in stickleback environmental adaption with system-level analysis

10.1101/2020.02.11.943522 ◽

2020 ◽

Author(s):

Martina Hall ◽

Dietmar Kültz ◽

Eivind Almaas

Keyword(s):

Protein Interactions ◽

Population Sample ◽

Interaction Network ◽

Environmental Parameters ◽

System Level ◽

Environmental Salinity ◽

Differential Interaction ◽

Differential Network ◽

Proteomic Dataset ◽

Two Populations

ABSTRACTUsing abundance measurements of 1,490 proteins from four separate populations of three-spined sticklebacks, we implemented a system-level approach to correlate proteome dynamics with environmental salinity and temperature and the fish’s population and morphotype. We identified sets of robust and accurate fingerprints that predict environmental salinity, temperature, morphotype and the population sample origin, observing that proteins with specific functions are enriched in these fingerprints. Highly apparent functions represented in all fingerprints include ion transport, proteostasis, growth, and immunity, suggesting that these functions are most diversified in populations inhabiting different environments.Applying a differential network approach, we analyzed the network of protein interactions that differs between populations. Looking at specific population combinations of differential interaction, we identify sets of connected proteins. We find that these sets and their corresponding enriched functions reflect key processes that have diverged between the four populations. Moreover, the extent of divergence, i.e. the number of enriched functions that differ between populations, is highest when all three environmental parameters are different between two populations. Key nodes in the differential interaction network signify functions that are also inherent in the fingerprints, most prominently proteostasis-related functions. However, the differential interaction network also reveals additional functions that have diverged between populations, notably cytoskeletal organization and morphogenesis.Having such a large proteomic dataset, the strength of these analyses is that the results are purely data-driven, not based on previous findings and hypotheses about adaptation. With such an unbiased approach applied on a large proteomic dataset, we find the strongest signals given by the data, making it possible to develop more discriminatory and complex biomarkers for specific contexts of interest.

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The Protein Interaction Network of the Epithelial Junctional Complex: A System-Level Analysis

Molecular Biology of the Cell ◽

10.1091/mbc.e08-05-0477 ◽

2008 ◽

Vol 19 (12) ◽

pp. 5409-5421 ◽

Cited By ~ 18

Author(s):

Luca Paris ◽

Gianfranco Bazzoni

Keyword(s):

Protein Interactions ◽

Random Network ◽

Interaction Network ◽

Small World ◽

System Level ◽

Junctional Complex ◽

Protein Protein Interactions ◽

Complex Protein ◽

Organizing Principles

To acquire system-level understanding of the intercellular junctional complex, protein–protein interactions occurring at the junctions of simple epithelial cells have been examined by network analysis. Although proper hubs (i.e., very rare proteins with exceedingly high connectivity) were absent from the junctional network, the most connected (albeit nonhub) proteins displayed a significant association with essential genes and contributed to the “small world” properties of the network (as shown by in vivo and in silico deletion, respectively). In addition, compared with a random network, the junctional network had greater tendency to form modules and subnets of densely interconnected proteins. Module analysis highlighted general organizing principles of the junctional complex. In particular, two major modules (corresponding to the tight junctions and to the adherens junctions/desmosomes) were linked preferentially to two other modules that acted as structural and signaling platforms.

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From random to predictive: a context-specific interaction framework improves selection of drug protein-protein interactions for unknown drug pathways

10.1101/2020.12.15.422844 ◽

2020 ◽

Author(s):

Jennifer Wilson ◽

Alessio Gravina ◽

Kevin Grimes

Keyword(s):

Decision Making ◽

Protein Interactions ◽

False Positive ◽

Specific Interaction ◽

Interaction Network ◽

Protein Protein Interactions ◽

Data Set ◽

Network Methods ◽

Context Specific ◽

Selection Of

With high drug attrition, interaction network methods are increasingly attractive as quick and inexpensive methods for prediction of drug safety and efficacy effects when a drug pathway is unknown. However, these methods suffer from high false positive rates for selecting drug phenotypic effects, their performance is often no better than random (AUROC ~0.5), and this limits the use of network methods in regulatory and industrial decision making. In contrast to many network engineering approaches that apply mathematical thresholds to discover phenotype associations, we hypothesized that interaction networks associated with true positive drug phenotypes are context specific. We tested this hypothesis on 16 designated medical event (DMEs) phenotypes which are a subset of adverse events that are of upmost concern to FDA review using a novel data set extracted from drug labels. We demonstrated that context-specific interactions (CSIs) distinguished true from false positive DMEs with an 50% improvement over non-context-specific approaches (AUROC 0.77 compared to 0.51). By reducing false positives, CSI analysis has the potential to advance network techniques to influence decision making in regulatory and industry settings.

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JDINAC: joint density-based non-parametric differential interaction network analysis and classification using high-dimensional sparse omics data

10.1101/099234 ◽

2017 ◽

Author(s):

Jiadong Ji ◽

Di He ◽

Yang Feng ◽

Yong He ◽

Fuzhong Xue ◽

...

Keyword(s):

Network Analysis ◽

Probability Distribution ◽

Interaction Network ◽

Joint Density ◽

High Dimensional ◽

Omics Data ◽

Interaction Patterns ◽

Differential Interaction ◽

Differential Network ◽

Non Parametric

AbstractMotivationA complex disease is usually driven by a number of genes interwoven into networks, rather than a single gene product. Network comparison or differential network analysis has become an important means of revealing the underlying mechanism of pathogenesis and identifying clinical biomarkers for disease classification. Most studies, however, are limited to network correlations that mainly capture the linear relationship among genes, or rely on the assumption of a parametric probability distribution of gene measurements. They are restrictive in real application.ResultsWe propose a new Joint density based non-parametric Differential Interaction Network Analysis and Classification (JDINAC) method to identify differential interaction patterns of network activation between two groups. At the same time, JDINAC uses the network biomarkers to build a classification model. The novelty of JDINAC lies in its potential to capture non-linear relations between molecular interactions using high-dimensional sparse data as well as to adjust confounding factors, without the need of the assumption of a parametric probability distribution of gene measurements. Simulation studies demonstrate that JDINAC provides more accurate differential network estimation and lower classification error than that achieved by other state-of-the-art methods. We apply JDINAC to a Breast Invasive Carcinoma dataset, which includes 114 patients who have both tumor and matched normal samples. The hub genes and differential interaction patterns identified were consistent with existing experimental studies. Furthermore, JDINAC discriminated the tumor and normal sample with high accuracy by virtue of the identified biomarkers. JDINAC provides a general framework for feature selection and classification using high-dimensional sparse omics data.Availability:R scripts available at https://github.com/jijiadong/JDINACContact:[email protected] information:Supplementary data are available at bioRxiv online.

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Integrated Quantitative Analysis of the Phosphoproteome and Transcriptome in Tamoxifen-resistant Breast Cancer*

10.31234/osf.io/wtxu7 ◽

2020 ◽

Author(s):

Lungwani Muungo

Keyword(s):

Breast Cancer ◽

High Rate ◽

System Level ◽

Clinical Samples ◽

Transcriptome Data ◽

Data Set ◽

Reporter Assays ◽

Treated Breast ◽

Mcf 7 ◽

Resistant Cells

Quantitative phosphoproteome and transcriptome analysisof ligand-stimulated MCF-7 human breast cancer cells wasperformed to understand the mechanisms of tamoxifen resistanceat a system level. Phosphoproteome data revealed thatWT cells were more enriched with phospho-proteins thantamoxifen-resistant cells after stimulation with ligands.Surprisingly, decreased phosphorylation after ligand perturbationwas more common than increased phosphorylation.In particular, 17?-estradiol induced down-regulation inWT cells at a very high rate. 17?-Estradiol and the ErbBligand heregulin induced almost equal numbers of up-regulatedphospho-proteins in WT cells. Pathway and motifactivity analyses using transcriptome data additionallysuggested that deregulated activation of GSK3? (glycogensynthasekinase 3?) and MAPK1/3 signaling might be associatedwith altered activation of cAMP-responsive elementbindingprotein and AP-1 transcription factors intamoxifen-resistant cells, and this hypothesis was validatedby reporter assays. An examination of clinical samples revealedthat inhibitory phosphorylation of GSK3? at serine 9was significantly lower in tamoxifen-treated breast cancerpatients that eventually had relapses, implying that activationof GSK3? may be associated with the tamoxifen-resistantphenotype. Thus, the combined phosphoproteomeand transcriptome data set analyses revealed distinct signal

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MNBDR: A Module Network Based Method for Drug Repositioning

Genes ◽

10.3390/genes12010025 ◽

2020 ◽

Vol 12 (1) ◽

pp. 25

Author(s):

He-Gang Chen ◽

Xiong-Hui Zhou

Keyword(s):

Gene Expression ◽

Protein Interactions ◽

Drug Response ◽

Drug Repositioning ◽

Expression Profiles ◽

Drug Repurposing ◽

Recent Decade ◽

Data Set ◽

Module Network ◽

The Cross

Drug repurposing/repositioning, which aims to find novel indications for existing drugs, contributes to reducing the time and cost for drug development. For the recent decade, gene expression profiles of drug stimulating samples have been successfully used in drug repurposing. However, most of the existing methods neglect the gene modules and the interactions among the modules, although the cross-talks among pathways are common in drug response. It is essential to develop a method that utilizes the cross-talks information to predict the reliable candidate associations. In this study, we developed MNBDR (Module Network Based Drug Repositioning), a novel method that based on module network to screen drugs. It integrated protein–protein interactions and gene expression profile of human, to predict drug candidates for diseases. Specifically, the MNBDR mined dense modules through protein–protein interaction (PPI) network and constructed a module network to reveal cross-talks among modules. Then, together with the module network, based on existing gene expression data set of drug stimulation samples and disease samples, we used random walk algorithms to capture essential modules in disease development and proposed a new indicator to screen potential drugs for a given disease. Results showed MNBDR could provide better performance than popular methods. Moreover, functional analysis of the essential modules in the network indicated our method could reveal biological mechanism in drug response.

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Short loop functional commonality identified in leukaemia proteome highlights crucial protein sub-networks

NAR Genomics and Bioinformatics ◽

10.1093/nargab/lqab010 ◽

2021 ◽

Vol 3 (1) ◽

Author(s):

Sun Sook Chung ◽

Joseph C F Ng ◽

Anna Laddach ◽

N Shaun B Thomas ◽

Franca Fraternali

Keyword(s):

Protein Interactions ◽

Large Scale ◽

Interaction Network ◽

Protein Protein Interactions ◽

Protein Protein Interaction ◽

Ppi Networks ◽

Short Loop ◽

New Strategy ◽

Loop Network ◽

Protein Protein Interaction Network

Abstract Direct drug targeting of mutated proteins in cancer is not always possible and efficacy can be nullified by compensating protein–protein interactions (PPIs). Here, we establish an in silico pipeline to identify specific PPI sub-networks containing mutated proteins as potential targets, which we apply to mutation data of four different leukaemias. Our method is based on extracting cyclic interactions of a small number of proteins topologically and functionally linked in the Protein–Protein Interaction Network (PPIN), which we call short loop network motifs (SLM). We uncover a new property of PPINs named ‘short loop commonality’ to measure indirect PPIs occurring via common SLM interactions. This detects ‘modules’ of PPI networks enriched with annotated biological functions of proteins containing mutation hotspots, exemplified by FLT3 and other receptor tyrosine kinase proteins. We further identify functional dependency or mutual exclusivity of short loop commonality pairs in large-scale cellular CRISPR–Cas9 knockout screening data. Our pipeline provides a new strategy for identifying new therapeutic targets for drug discovery.

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Frequent assembly of chimeric complexes in the protein interaction network of an interspecies yeast hybrid

Molecular Biology and Evolution ◽

10.1093/molbev/msaa298 ◽

2020 ◽

Author(s):

Rohan Dandage ◽

Caroline M Berger ◽

Isabelle Gagnon-Arsenault ◽

Kyung-Mee Moon ◽

Richard Greg Stacey ◽

...

Keyword(s):

Protein Interactions ◽

Molecular Level ◽

Yeast Species ◽

Protein Complexes ◽

Mitochondrial Protein ◽

Chimeric Protein ◽

Interaction Network ◽

Protein Protein Interactions ◽

Extreme Phenotypes ◽

Yeast Hybrid

Abstract Hybrids between species often show extreme phenotypes, including some that take place at the molecular level. In this study, we investigated the phenotypes of an interspecies diploid hybrid in terms of protein-protein interactions inferred from protein correlation profiling. We used two yeast species, Saccharomyces cerevisiae and Saccharomyces uvarum, which are interfertile, but yet have proteins diverged enough to be differentiated using mass spectrometry. Most of the protein-protein interactions are similar between hybrid and parents, and are consistent with the assembly of chimeric complexes, which we validated using an orthogonal approach for the prefoldin complex. We also identified instances of altered protein-protein interactions in the hybrid, for instance in complexes related to proteostasis and in mitochondrial protein complexes. Overall, this study uncovers the likely frequent occurrence of chimeric protein complexes with few exceptions, which may result from incompatibilities or imbalances between the parental proteins.

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Prioritizing and Analyzing the Role of Climate and Urban Parameters in the Confirmed Cases of COVID-19 Based on Artificial Intelligence Applications

International Journal of Environmental Research and Public Health ◽

10.3390/ijerph17103730 ◽

2020 ◽

Vol 17 (10) ◽

pp. 3730 ◽

Cited By ~ 9

Author(s):

Sina Shaffiee Haghshenas ◽

Behrouz Pirouz ◽

Sami Shaffiee Haghshenas ◽

Behzad Pirouz ◽

Patrizia Piro ◽

...

Keyword(s):

Artificial Intelligence ◽

Infectious Disease ◽

Relative Humidity ◽

Cost Function ◽

Selection Process ◽

Environmental Parameters ◽

Pso Algorithm ◽

Data Set ◽

De Algorithm

Nowadays, an infectious disease outbreak is considered one of the most destructive effects in the sustainable development process. The outbreak of new coronavirus (COVID-19) as an infectious disease showed that it has undesirable social, environmental, and economic impacts, and leads to serious challenges and threats. Additionally, investigating the prioritization parameters is of vital importance to reducing the negative impacts of this global crisis. Hence, the main aim of this study is to prioritize and analyze the role of certain environmental parameters. For this purpose, four cities in Italy were selected as a case study and some notable climate parameters—such as daily average temperature, relative humidity, wind speed—and an urban parameter, population density, were considered as input data set, with confirmed cases of COVID-19 being the output dataset. In this paper, two artificial intelligence techniques, including an artificial neural network (ANN) based on particle swarm optimization (PSO) algorithm and differential evolution (DE) algorithm, were used for prioritizing climate and urban parameters. The analysis is based on the feature selection process and then the obtained results from the proposed models compared to select the best one. Finally, the difference in cost function was about 0.0001 between the performances of the two models, hence, the two methods were not different in cost function, however, ANN-PSO was found to be better, because it reached to the desired precision level in lesser iterations than ANN-DE. In addition, the priority of two variables, urban parameter, and relative humidity, were the highest to predict the confirmed cases of COVID-19.

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RISC-V Virtual Platform-Based Convolutional Neural Network Accelerator Implemented in SystemC

Electronics ◽

10.3390/electronics10131514 ◽

2021 ◽

Vol 10 (13) ◽

pp. 1514

Author(s):

Seung-Ho Lim ◽

WoonSik William Suh ◽

Jin-Young Kim ◽

Sang-Young Cho

Keyword(s):

Neural Network ◽

Deep Learning ◽

Network Model ◽

Neural Network Model ◽

Deep Neural Network ◽

System Level ◽

Neural Network Models ◽

Data Set ◽

Embedded Device ◽

Virtual Platform

The optimization for hardware processor and system for performing deep learning operations such as Convolutional Neural Networks (CNN) in resource limited embedded devices are recent active research area. In order to perform an optimized deep neural network model using the limited computational unit and memory of an embedded device, it is necessary to quickly apply various configurations of hardware modules to various deep neural network models and find the optimal combination. The Electronic System Level (ESL) Simulator based on SystemC is very useful for rapid hardware modeling and verification. In this paper, we designed and implemented a Deep Learning Accelerator (DLA) that performs Deep Neural Network (DNN) operation based on the RISC-V Virtual Platform implemented in SystemC in order to enable rapid and diverse analysis of deep learning operations in an embedded device based on the RISC-V processor, which is a recently emerging embedded processor. The developed RISC-V based DLA prototype can analyze the hardware requirements according to the CNN data set through the configuration of the CNN DLA architecture, and it is possible to run RISC-V compiled software on the platform, can perform a real neural network model like Darknet. We performed the Darknet CNN model on the developed DLA prototype, and confirmed that computational overhead and inference errors can be analyzed with the DLA prototype developed by analyzing the DLA architecture for various data sets.

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