scholarly journals Personalized Medicine and Molecular Interaction Networks in Amyotrophic Lateral Sclerosis (ALS): Current Knowledge

2018 ◽  
Vol 8 (4) ◽  
pp. 44 ◽  
Author(s):  
Stephen Morgan ◽  
Stephanie Duguez ◽  
William Duddy
Keyword(s):  
Amyotrophic Lateral Sclerosis ◽  
Personalized Medicine ◽  
Molecular Interaction ◽  
Current Knowledge ◽  
Systemic Disease ◽  
Neuromuscular Disorder ◽  
Interaction Networks ◽  
Molecular Interaction Networks ◽  
Analytical Approaches ◽  
Lateral Sclerosis

Multiple genes and mechanisms of pathophysiology have been implicated in amyotrophic lateral sclerosis (ALS), suggesting it is a complex systemic disease. With this in mind, applying personalized medicine (PM) approaches to tailor treatment pipelines for ALS patients may be necessary. The modelling and analysis of molecular interaction networks could represent valuable resources in defining ALS-associated pathways and discovering novel therapeutic targets. Here we review existing omics datasets and analytical approaches, in order to consider how molecular interaction networks could improve our understanding of the molecular pathophysiology of this fatal neuromuscular disorder.

scholarly journals Computational Analysis of Molecular Interaction Networks Underlying Change of HIV-1 Resistance to Selected Reverse Transcriptase Inhibitors

2010 ◽  
Vol 4 ◽  
pp. BBI.S6247 ◽  
Author(s):  
Marcin Kierczak ◽  
Michał Dramiński ◽  
Jacek Koronacki ◽  
Jan Komorowski
Keyword(s):  
Drug Resistance ◽  
Reverse Transcriptase ◽  
Molecular Interaction ◽  
3D Structure ◽  
Resistance Mechanisms ◽  
Interaction Networks ◽  
Resistance Level ◽  
Molecular Interaction Networks ◽  
Monte Carlo Feature Selection ◽  
Hiv 1

Motivation Despite more than two decades of research, HIV resistance to drugs remains a serious obstacle in developing efficient AIDS treatments. Several computational methods have been developed to predict resistance level from the sequence of viral proteins such as reverse transcriptase (RT) or protease. These methods, while powerful and accurate, give very little insight into the molecular interactions that underly acquisition of drug resistance/hypersusceptibility. Here, we attempt at filling this gap by using our Monte Carlo feature selection and interdependency discovery method (MCFS-ID) to elucidate molecular interaction networks that characterize viral strains with altered drug resistance levels. Results We analyzed a number of HIV-1 RT sequences annotated with drug resistance level using the MCFS-ID method. This let us expound interdependency networks that characterize change of drug resistance to six selected RT inhibitors: Abacavir, Lamivudine, Stavudine, Zidovudine, Tenofovir and Nevirapine. The networks consider interdependencies at the level of physicochemical properties of mutating amino acids, eg,: polarity. We mapped each network on the 3D structure of RT in attempt to understand the molecular meaning of interacting pairs. The discovered interactions describe several known drug resistance mechanisms and, importantly, some previously unidentified ones. Our approach can be easily applied to a whole range of problems from the domain of protein engineering. Availability A portable Java implementation of our MCFS-ID method is freely available for academic users and can be obtained at: http://www.ipipan.eu/staff/m.draminski/software.htm .

scholarly journals Neuroinflammation in Amyotrophic Lateral Sclerosis and Frontotemporal Dementia and the Interest of Induced Pluripotent Stem Cells to Study Immune Cells Interactions With Neurons

2021 ◽  
Vol 14 ◽  
Author(s):  
Elise Liu ◽  
Léa Karpf ◽  
Delphine Bohl
Keyword(s):  
Amyotrophic Lateral Sclerosis ◽  
Immune System ◽  
Frontotemporal Dementia ◽  
Immune Cells ◽  
Immune Cell ◽  
Current Knowledge ◽  
Cell Types ◽  
Induced Pluripotent ◽  
Different Cell Types ◽  
Lateral Sclerosis

Inflammation is a shared hallmark between amyotrophic lateral sclerosis (ALS) and frontotemporal dementia (FTD). For long, studies were conducted on tissues of post-mortem patients and neuroinflammation was thought to be only bystander result of the disease with the immune system reacting to dying neurons. In the last two decades, thanks to improving technologies, the identification of causal genes and the development of new tools and models, the involvement of inflammation has emerged as a potential driver of the diseases and evolved as a new area of intense research. In this review, we present the current knowledge about neuroinflammation in ALS, ALS-FTD, and FTD patients and animal models and we discuss reasons of failures linked to therapeutic trials with immunomodulator drugs. Then we present the induced pluripotent stem cell (iPSC) technology and its interest as a new tool to have a better immunopathological comprehension of both diseases in a human context. The iPSC technology giving the unique opportunity to study cells across differentiation and maturation times, brings the hope to shed light on the different mechanisms linking neurodegeneration and activation of the immune system. Protocols available to differentiate iPSC into different immune cell types are presented. Finally, we discuss the interest in studying monocultures of iPS-derived immune cells, co-cultures with neurons and 3D cultures with different cell types, as more integrated cellular approaches. The hope is that the future work with human iPS-derived cells helps not only to identify disease-specific defects in the different cell types but also to decipher the synergistic effects between neurons and immune cells. These new cellular tools could help to find new therapeutic approaches for all patients with ALS, ALS-FTD, and FTD.

scholarly journals A Graph Feature Auto-Encoder for the prediction of unobserved node features on biological networks

2021 ◽  
Vol 22 (1) ◽  
Author(s):  
Ramin Hasibi ◽  
Tom Michoel
Keyword(s):  
Gene Expression ◽  
Neural Networks ◽  
Gene Expression Data ◽  
Biological Networks ◽  
Molecular Interaction ◽  
Interaction Networks ◽  
Omics Data ◽  
Expression Data ◽  
Molecular Interaction Networks ◽  
Graph Neural Networks

Abstract Background Molecular interaction networks summarize complex biological processes as graphs, whose structure is informative of biological function at multiple scales. Simultaneously, omics technologies measure the variation or activity of genes, proteins, or metabolites across individuals or experimental conditions. Integrating the complementary viewpoints of biological networks and omics data is an important task in bioinformatics, but existing methods treat networks as discrete structures, which are intrinsically difficult to integrate with continuous node features or activity measures. Graph neural networks map graph nodes into a low-dimensional vector space representation, and can be trained to preserve both the local graph structure and the similarity between node features. Results We studied the representation of transcriptional, protein–protein and genetic interaction networks in E. coli and mouse using graph neural networks. We found that such representations explain a large proportion of variation in gene expression data, and that using gene expression data as node features improves the reconstruction of the graph from the embedding. We further proposed a new end-to-end Graph Feature Auto-Encoder framework for the prediction of node features utilizing the structure of the gene networks, which is trained on the feature prediction task, and showed that it performs better at predicting unobserved node features than regular MultiLayer Perceptrons. When applied to the problem of imputing missing data in single-cell RNAseq data, the Graph Feature Auto-Encoder utilizing our new graph convolution layer called FeatGraphConv outperformed a state-of-the-art imputation method that does not use protein interaction information, showing the benefit of integrating biological networks and omics data with our proposed approach. Conclusion Our proposed Graph Feature Auto-Encoder framework is a powerful approach for integrating and exploiting the close relation between molecular interaction networks and functional genomics data.

scholarly journals UniHI 7: an enhanced database for retrieval and interactive analysis of human molecular interaction networks

2013 ◽  
Vol 42 (D1) ◽  
pp. D408-D414 ◽  
Author(s):  
Ravi Kiran Reddy Kalathur ◽  
José Pedro Pinto ◽  
Miguel A. Hernández-Prieto ◽  
Rui S.R. Machado ◽  
Dulce Almeida ◽  
...  
Keyword(s):  
Molecular Interaction ◽  
Interaction Networks ◽  
Interactive Analysis ◽  
Molecular Interaction Networks

Amyotrophic lateral sclerosis is a systemic disease

2021 ◽  
Vol Publish Ahead of Print ◽  
Author(s):  
Stanley H. Appel ◽  
David R. Beers ◽  
Weihua Zhao
Keyword(s):  
Amyotrophic Lateral Sclerosis ◽  
Systemic Disease ◽  
Lateral Sclerosis

scholarly journals Glial Cells—The Strategic Targets in Amyotrophic Lateral Sclerosis Treatment

2020 ◽  
Vol 9 (1) ◽  
pp. 261 ◽  
Author(s):  
Tereza Filipi ◽  
Zuzana Hermanova ◽  
Jana Tureckova ◽  
Ondrej Vanatko ◽  
Miroslava Anderova
Keyword(s):  
Amyotrophic Lateral Sclerosis ◽  
Motor Neurons ◽  
Current Knowledge ◽  
Gene Mutations ◽  
Cell Types ◽  
In Vivo Models ◽  
Cell Therapies ◽  
Lateral Sclerosis

Amyotrophic lateral sclerosis (ALS) is a fatal neurological disease, which is characterized by the degeneration of motor neurons in the motor cortex and the spinal cord and subsequently by muscle atrophy. To date, numerous gene mutations have been linked to both sporadic and familial ALS, but the effort of many experimental groups to develop a suitable therapy has not, as of yet, proven successful. The original focus was on the degenerating motor neurons, when researchers tried to understand the pathological mechanisms that cause their slow death. However, it was soon discovered that ALS is a complicated and diverse pathology, where not only neurons, but also other cell types, play a crucial role via the so-called non-cell autonomous effect, which strongly deteriorates neuronal conditions. Subsequently, variable glia-based in vitro and in vivo models of ALS were established and used for brand-new experimental and clinical approaches. Such a shift towards glia soon bore its fruit in the form of several clinical studies, which more or less successfully tried to ward the unfavourable prognosis of ALS progression off. In this review, we aimed to summarize current knowledge regarding the involvement of each glial cell type in the progression of ALS, currently available treatments, and to provide an overview of diverse clinical trials covering pharmacological approaches, gene, and cell therapies.

scholarly journals What Can Machine Learning Approaches in Genomics Tell Us about the Molecular Basis of Amyotrophic Lateral Sclerosis?

2020 ◽  
Vol 10 (4) ◽  
pp. 247
Author(s):  
Christina Vasilopoulou ◽  
Andrew P. Morris ◽  
George Giannakopoulos ◽  
Stephanie Duguez ◽  
William Duddy
Keyword(s):  
Machine Learning ◽  
Amyotrophic Lateral Sclerosis ◽  
Genetic Architecture ◽  
Molecular Mechanisms ◽  
Current Knowledge ◽  
Regulatory Elements ◽  
Specific Information ◽  
Learning Models ◽  
Lateral Sclerosis ◽  
Machine Learning Models

Amyotrophic Lateral Sclerosis (ALS) is the most common late-onset motor neuron disorder, but our current knowledge of the molecular mechanisms and pathways underlying this disease remain elusive. This review (1) systematically identifies machine learning studies aimed at the understanding of the genetic architecture of ALS, (2) outlines the main challenges faced and compares the different approaches that have been used to confront them, and (3) compares the experimental designs and results produced by those approaches and describes their reproducibility in terms of biological results and the performances of the machine learning models. The majority of the collected studies incorporated prior knowledge of ALS into their feature selection approaches, and trained their machine learning models using genomic data combined with other types of mined knowledge including functional associations, protein-protein interactions, disease/tissue-specific information, epigenetic data, and known ALS phenotype-genotype associations. The importance of incorporating gene-gene interactions and cis-regulatory elements into the experimental design of future ALS machine learning studies is highlighted. Lastly, it is suggested that future advances in the genomic and machine learning fields will bring about a better understanding of ALS genetic architecture, and enable improved personalized approaches to this and other devastating and complex diseases.

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