NaviCom: A web application to create interactive molecular network portraits using multi-level omics data

TAR DNA-binding protein-43 (TDP-43) is an evolutionarily conserved nuclear protein that regulates gene expression by forming a multimolecular complex with a wide variety of target RNAs and interacting proteins. Abnormally phosphorylated, ubiquitinated, and aggregated TDP-43 proteins constitute a principal component of neuronal and glial cytoplasmic and nuclear inclusions in the brains of patients with amyotrophic lateral sclerosis (ALS) and frontotemporal lobar degeneration (FTLD), establishing a novel clinical entity designated TDP-43 proteinopathy. Although increasing evidence suggests that the neurodegenerative process underlying ALS and FTLD is attributable to a toxic gain of function or a loss of cellular function of TDP-43, the precise molecular mechanisms remain largely unknown. Recent advances in systems biology enable us to characterize the global molecular network extracted from large-scale data of the genome, transcriptome, and proteome with the pathway analysis tools of bioinformatics endowed with a comprehensive knowledge base. The present study was conducted to characterize the comprehensive molecular network of TDP-43 target RNAs and interacting proteins, recently identified by deep sequencing with next-generation sequencers and mass spectrometric analysis. The results propose the systems biological view that TDP-43 serves as a molecular coordinator of the RNA-dependent regulation of gene transcription and translation pivotal for performing diverse neuronal functions and that the disruption of TDP-43-mediated molecular coordination induces neurodegeneration in ALS and FTLD.

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Knowledge Base Commons (KBCommons) v1.1: a universal framework for multi-omics data integration and biological discoveries

BMC Genomics ◽

10.1186/s12864-019-6287-8 ◽

2019 ◽

Vol 20 (S11) ◽

Cited By ~ 3

Author(s):

Shuai Zeng ◽

Zhen Lyu ◽

Siva Ratna Kumari Narisetti ◽

Dong Xu ◽

Trupti Joshi

Keyword(s):

Knowledge Base ◽

Data Storage ◽

High Performance ◽

Data Retrieval ◽

Omics Data ◽

Web Interface ◽

Web Resource ◽

Privilege Management ◽

Multi Level ◽

Comprehensive Framework

Abstract Background Knowledge Base Commons (KBCommons) v1.1 is a universal and all-inclusive web-based framework providing generic functionalities for storing, sharing, analyzing, exploring, integrating and visualizing multiple organisms’ genomics and integrative omics data. KBCommons is designed and developed to integrate diverse multi-level omics data and to support biological discoveries for all species via a common platform. Methods KBCommons has four modules including data storage, data processing, data accessing, and web interface for data management and retrieval. It provides a comprehensive framework for new plant-specific, animal-specific, virus-specific, bacteria-specific or human disease-specific knowledge base (KB) creation, for adding new genome versions and additional multi-omics data to existing KBs, and for exploring existing datasets within current KBs. Results KBCommons has an array of tools for data visualization and data analytics such as multiple gene/metabolite search, gene family/Pfam/Panther function annotation search, miRNA/metabolite/trait/SNP search, differential gene expression analysis, and bulk data download capacity. It contains a highly reliable data privilege management system to make users’ data publicly available easily and to share private or pre-publication data with members in their collaborative groups safely and securely. It allows users to conduct data analysis using our in-house developed workflow functionalities that are linked to XSEDE high performance computing resources. Using KBCommons’ intuitive web interface, users can easily retrieve genomic data, multi-omics data and analysis results from workflow according to their requirements and interests. Conclusions KBCommons addresses the needs of many diverse research communities to have a comprehensive multi-level OMICS web resource for data retrieval, sharing, analysis and visualization. KBCommons can be publicly accessed through a dedicated link for all organisms at http://kbcommons.org/.

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Molecular Network Analysis of Target RNAs and Interacting Proteins of TDP-43, a Causative Gene for the Neurodegenerative Diseases ALS/FTLD

Technological Advancements in Biomedicine for Healthcare Applications - Advances in Bioinformatics and Biomedical Engineering ◽

10.4018/978-1-4666-2196-1.ch032 ◽

2012 ◽

pp. 314-335

Author(s):

Jun-Ichi Satoh

Keyword(s):

Large Scale ◽

Molecular Mechanisms ◽

Principal Component ◽

Molecular Network ◽

Spectrometric Analysis ◽

Interacting Proteins ◽

Causative Gene ◽

Neurodegenerative Process ◽

Large Scale Data ◽

Neuronal Functions

TAR DNA-binding protein-43 (TDP-43) is an evolutionarily conserved nuclear protein that regulates gene expression by forming a multimolecular complex with a wide variety of target RNAs and interacting proteins. Abnormally phosphorylated, ubiquitinated, and aggregated TDP-43 proteins constitute a principal component of neuronal and glial cytoplasmic and nuclear inclusions in the brains of patients with amyotrophic lateral sclerosis (ALS) and frontotemporal lobar degeneration (FTLD), establishing a novel clinical entity designated TDP-43 proteinopathy. Although increasing evidence suggests that the neurodegenerative process underlying ALS and FTLD is attributable to a toxic gain of function or a loss of cellular function of TDP-43, the precise molecular mechanisms remain largely unknown. Recent advances in systems biology enable us to characterize the global molecular network extracted from large-scale data of the genome, transcriptome, and proteome with the pathway analysis tools of bioinformatics endowed with a comprehensive knowledge base. The present study was conducted to characterize the comprehensive molecular network of TDP-43 target RNAs and interacting proteins, recently identified by deep sequencing with next-generation sequencers and mass spectrometric analysis. The results propose the systems biological view that TDP-43 serves as a molecular coordinator of the RNA-dependent regulation of gene transcription and translation pivotal for performing diverse neuronal functions and that the disruption of TDP-43-mediated molecular coordination induces neurodegeneration in ALS and FTLD.

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BioNetComp: a Python package for biological network development and comparison

10.1101/2021.04.14.439897 ◽

2021 ◽

Author(s):

Lucas Miguel Carvalho

Keyword(s):

Biological Networks ◽

Large Scale ◽

Biological Network ◽

Biological Data ◽

Command Line ◽

Omics Data ◽

Network Development ◽

Network Metrics ◽

Web Platform ◽

Python Package

Due to the large generation of omics data on a large scale in the last few years, the extraction of information from biological data has become more complex and its integration or comparison as well. One of the ways to represent interactions of biological data is through networks, which summarize information on interactions between their nodes through edges. The comparison of two biological networks using network metrics, biological enrichment, and visualization consists of data that allows us to understand differences in the interactomes of contrasting conditions. We describe BioNetComp, a python package to compare two different interactomes through different metrics and data visualization without the need for a web platform or software, just by command-line. As a result, we present a comparison made between the interactomes generated from the differentially expressed genes at two different points during a typical bioethanol fermentation. BioNetComp is available at github.com/lmigueel/BioNetComp.

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QuoVidi: a open-source web application for the organisation of large scale biological treasure hunts

10.1101/2020.06.30.177006 ◽

2020 ◽

Author(s):

Guillaume Lobet ◽

Charlotte Descamps ◽

Lola Leveau ◽

Alain Guillet ◽

Jean-François Rees

Keyword(s):

Open Source ◽

Web Application ◽

Large Scale ◽

Traditional Teaching ◽

Research Perspective ◽

Large Numbers ◽

Hands On ◽

Teaching Setting ◽

Web Platform ◽

Large Image Databases

AbstractLearning biology, and in particular systematics, requires learning a substantial amount of specific vocabulary, both for botanical and zoological studies. While crucial, the precise identification of structures serving as evolutionary traits and systematic criteria is not per se a highly motivating task for students. Teaching this in a traditional teaching setting is quite challenging especially with a large crowd of students to be kept engaged. This is even more difficult if, as during the COVID-19 crisis, students are not allowed to access laboratories for hands-on observation on fresh specimens and sometimes restricted to short-range movements outside their home.Here we present QuoVidi, a new open-source web platform for the organisation of large scale treasure hunts. The platform works as follows: students, organised in teams, receive a list of quests that contain morphologic, ecologic or systematic terms. They have to first understand the meaning of the quests, then go and find them in the environment. Once they find the organism corresponding to a quest, they upload a geotagged picture of their finding and submit this on the platform. The correctness of each submission is evaluated by the staff. During the COVID-19 lockdown, previously validated pictures were also submitted for evaluation to students that were locked in low-biodiversity areas. From a research perspective, the system enables the creation of large image databases by the students, similar to citizen-science projects.Beside the enhanced motivation of students to learn the vocabulary and perform observations on self-found specimens, this system allows faculties to remotely follow and assess the work performed by large numbers of students. The interface is freely available, open-source and customizable. It can be used in other disciplines with adapted quests and we expect it to be of interest in many classroom settings.

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EGCG binds intrinsically disordered N-terminal domain of p53 and disrupts p53-MDM2 interaction

Nature Communications ◽

10.1038/s41467-021-21258-5 ◽

2021 ◽

Vol 12 (1) ◽

Author(s):

Jing Zhao ◽

Alan Blayney ◽

Xiaorong Liu ◽

Lauren Gandy ◽

Weihua Jin ◽

...

Keyword(s):

Large Scale ◽

Molecular Mechanisms ◽

Epigallocatechin Gallate ◽

Cancer Drug ◽

Dynamic Interactions ◽

Cancer Drug Discovery ◽

Intrinsically Disordered ◽

Terminal Domain ◽

Cancerous Cells

AbstractEpigallocatechin gallate (EGCG) from green tea can induce apoptosis in cancerous cells, but the underlying molecular mechanisms remain poorly understood. Using SPR and NMR, here we report a direct, μM interaction between EGCG and the tumor suppressor p53 (KD = 1.6 ± 1.4 μM), with the disordered N-terminal domain (NTD) identified as the major binding site (KD = 4 ± 2 μM). Large scale atomistic simulations (>100 μs), SAXS and AUC demonstrate that EGCG-NTD interaction is dynamic and EGCG causes the emergence of a subpopulation of compact bound conformations. The EGCG-p53 interaction disrupts p53 interaction with its regulatory E3 ligase MDM2 and inhibits ubiquitination of p53 by MDM2 in an in vitro ubiquitination assay, likely stabilizing p53 for anti-tumor activity. Our work provides insights into the mechanisms for EGCG’s anticancer activity and identifies p53 NTD as a target for cancer drug discovery through dynamic interactions with small molecules.

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The three paradoxes of the energy transition - Assessing sustainability of large-scale solar photovoltaic through multi-level and multi-scalar perspective in Rwanda

Journal of Cleaner Production ◽

10.1016/j.jclepro.2020.125519 ◽

2021 ◽

Vol 288 ◽

pp. 125519

Author(s):

Carole Brunet ◽

Oumarou Savadogo ◽

Pierre Baptiste ◽

Michel A. Bouchard ◽

Céline Cholez ◽

...

Keyword(s):

Large Scale ◽

Energy Transition ◽

Solar Photovoltaic ◽

Multi Level

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PEDRERA. Positive Energy District Renovation Model for Large Scale Actions

Energies ◽

10.3390/en14102833 ◽

2021 ◽

Vol 14 (10) ◽

pp. 2833

Author(s):

Paolo Civiero ◽

Jordi Pascual ◽

Joaquim Arcas Abella ◽

Ander Bilbao Figuero ◽

Jaume Salom

Keyword(s):

Simulation Model ◽

Performance Indicators ◽

Large Scale ◽

Key Performance Indicators ◽

Positive Energy ◽

Design Phase ◽

Multiple Sources ◽

Reliable Prediction ◽

Sensitive Analysis ◽

Web Platform

In this paper, we provide a view of the ongoing PEDRERA project, whose main scope is to design a district simulation model able to set and analyze a reliable prediction of potential business scenarios on large scale retrofitting actions, and to evaluate the overall co-benefits resulting from the renovation process of a cluster of buildings. According to this purpose and to a Positive Energy Districts (PEDs) approach, the model combines systemized data—at both building and district scale—from multiple sources and domains. A sensitive analysis of 200 scenarios provided a quick perception on how results will change once inputs are defined, and how attended results will answer to stakeholders’ requirements. In order to enable a clever input analysis and to appraise wide-ranging ranks of Key Performance Indicators (KPIs) suited to each stakeholder and design phase targets, the model is currently under the implementation in the urbanZEB tool’s web platform.

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Integration of enzyme constraints in a genome-scale metabolic model of Aspergillus niger improves phenotype predictions

Microbial Cell Factories ◽

10.1186/s12934-021-01614-2 ◽

2021 ◽

Vol 20 (1) ◽

Author(s):

Jingru Zhou ◽

Yingping Zhuang ◽

Jianye Xia

Keyword(s):

Aspergillus Niger ◽

Large Scale ◽

Measurement Techniques ◽

Metabolic Model ◽

System Level ◽

Metabolic Phenotype ◽

Omics Data ◽

Prediction Ability ◽

Phenotype Prediction ◽

Genome Scale

Abstract Background Genome-scale metabolic model (GSMM) is a powerful tool for the study of cellular metabolic characteristics. With the development of multi-omics measurement techniques in recent years, new methods that integrating multi-omics data into the GSMM show promising effects on the predicted results. It does not only improve the accuracy of phenotype prediction but also enhances the reliability of the model for simulating complex biochemical phenomena, which can promote theoretical breakthroughs for specific gene target identification or better understanding the cell metabolism on the system level. Results Based on the basic GSMM model iHL1210 of Aspergillus niger, we integrated large-scale enzyme kinetics and proteomics data to establish a GSMM based on enzyme constraints, termed a GEM with Enzymatic Constraints using Kinetic and Omics data (GECKO). The results show that enzyme constraints effectively improve the model’s phenotype prediction ability, and extended the model’s potential to guide target gene identification through predicting metabolic phenotype changes of A. niger by simulating gene knockout. In addition, enzyme constraints significantly reduced the solution space of the model, i.e., flux variability over 40.10% metabolic reactions were significantly reduced. The new model showed also versatility in other aspects, like estimating large-scale $$k_{{cat}}$$ k cat values, predicting the differential expression of enzymes under different growth conditions. Conclusions This study shows that incorporating enzymes’ abundance information into GSMM is very effective for improving model performance with A. niger. Enzyme-constrained model can be used as a powerful tool for predicting the metabolic phenotype of A. niger by incorporating proteome data. In the foreseeable future, with the fast development of measurement techniques, and more precise and rich proteomics quantitative data being obtained for A. niger, the enzyme-constrained GSMM model will show greater application space on the system level.

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