Networking Omic Data to Envisage Systems Biological Regulation

2016 ◽  
pp. 121-141
Author(s):  
Saowalak Kalapanulak ◽  
Treenut Saithong ◽  
Chinae Thammarongtham
Keyword(s):  
Biological Regulation ◽  
Omic Data

Artificial Intelligence for epigenetics: towards personalized medicine

2020 ◽  
Vol 27 ◽  
Author(s):  
Giulia De Riso ◽  
Sergio Cocozza
Keyword(s):  
Biological Sciences ◽  
Artificial Intelligence ◽  
Personalized Medicine ◽  
Molecular Mechanisms ◽  
Genomic Sequence ◽  
Health Care Interventions ◽  
Genome Wide ◽  
Genetic And Environmental Factors ◽  
Opening Up ◽  
Omic Data

: Epigenetics is a field of biological sciences focused on the study of reversible, heritable changes in gene function not due to modifications of the genomic sequence. These changes are the result of a complex cross-talk between several molecular mechanisms, that is in turn orchestrated by genetic and environmental factors. The epigenetic profile captures the unique regulatory landscape and the exposure to environmental stimuli of an individual. It thus constitutes a valuable reservoir of information for personalized medicine, which is aimed at customizing health-care interventions based on the unique characteristics of each individual. Nowadays, the complex milieu of epigenomic marks can be studied at the genome-wide level thanks to massive, highthroughput technologies. This new experimental approach is opening up new and interesting knowledge perspectives. However, the analysis of these complex omic data requires to face important analytic issues. Artificial Intelligence, and in particular Machine Learning, are emerging as powerful resources to decipher epigenomic data. In this review, we will first describe the most used ML approaches in epigenomics. We then will recapitulate some of the recent applications of ML to epigenomic analysis. Finally, we will provide some examples of how the ML approach to epigenetic data can be useful for personalized medicine.

scholarly journals Science for the Next Century: Deep Phenotyping

2021 ◽  
pp. 002203452110018
Author(s):  
J.T. Wright ◽  
M.C. Herzberg
Keyword(s):  
Health Care ◽  
Human Health ◽  
Care Delivery ◽  
The Past ◽  
Large Populations ◽  
Health And Disease ◽  
Methodological Approaches ◽  
Omic Data ◽  
Precision Health ◽  
Deep Phenotyping

Our ability to unravel the mysteries of human health and disease have changed dramatically over the past 2 decades. Decoding health and disease has been facilitated by the recent availability of high-throughput genomics and multi-omics analyses and the companion tools of advanced informatics and computational science. Understanding of the human genome and its influence on phenotype continues to advance through genotyping large populations and using “light phenotyping” approaches in combination with smaller subsets of the population being evaluated using “deep phenotyping” approaches. Using our capability to integrate and jointly analyze genomic data with other multi-omic data, the knowledge of genotype-phenotype relationships and associated genetic pathways and functions is being advanced. Understanding genotype-phenotype relationships that discriminate human health from disease is speculated to facilitate predictive, precision health care and change modes of health care delivery. The American Association for Dental Research Fall Focused Symposium assembled experts to discuss how studies of genotype-phenotype relationships are illuminating the pathophysiology of craniofacial diseases and developmental biology. Although the breadth of the topic did not allow all areas of dental, oral, and craniofacial research to be addressed (e.g., cancer), the importance and power of integrating genomic, phenomic, and other -omic data are illustrated using a variety of examples. The 8 Fall Focused talks presented different methodological approaches for ascertaining study populations and evaluating population variance and phenotyping approaches. These advances are reviewed in this summary.

scholarly journals Pharmacological inactivation of the prion protein by targeting a folding intermediate

2021 ◽  
Vol 4 (1) ◽  
Author(s):  
Giovanni Spagnolli ◽  
Tania Massignan ◽  
Andrea Astolfi ◽  
Silvia Biggi ◽  
Marta Rigoli ◽  
...  
Keyword(s):  
Prion Protein ◽  
Prion Diseases ◽  
Cellular Prion Protein ◽  
Surface Glycoprotein ◽  
Folding Intermediate ◽  
Biological Regulation ◽  
Cell Surface Glycoprotein ◽  
Folding Intermediates ◽  
Protein Levels ◽  
Small Molecule Ligands

AbstractRecent computational advancements in the simulation of biochemical processes allow investigating the mechanisms involved in protein regulation with realistic physics-based models, at an atomistic level of resolution. These techniques allowed us to design a drug discovery approach, named Pharmacological Protein Inactivation by Folding Intermediate Targeting (PPI-FIT), based on the rationale of negatively regulating protein levels by targeting folding intermediates. Here, PPI-FIT was tested for the first time on the cellular prion protein (PrP), a cell surface glycoprotein playing a key role in fatal and transmissible neurodegenerative pathologies known as prion diseases. We predicted the all-atom structure of an intermediate appearing along the folding pathway of PrP and identified four different small molecule ligands for this conformer, all capable of selectively lowering the load of the protein by promoting its degradation. Our data support the notion that the level of target proteins could be modulated by acting on their folding pathways, implying a previously unappreciated role for folding intermediates in the biological regulation of protein expression.

scholarly journals Cerebrospinal fluid metabolomics identifies 19 brain-related phenotype associations

2021 ◽  
Vol 4 (1) ◽  
Author(s):  
Daniel J. Panyard ◽  
Kyeong Mo Kim ◽  
Burcu F. Darst ◽  
Yuetiva K. Deming ◽  
Xiaoyuan Zhong ◽  
...  
Keyword(s):  
Cerebrospinal Fluid ◽  
Drug Targets ◽  
Large Scale ◽  
Prediction Models ◽  
Genome Wide Association ◽  
Large Samples ◽  
Genome Wide ◽  
Metabolomic Data ◽  
Related Phenotype ◽  
Omic Data

AbstractThe study of metabolomics and disease has enabled the discovery of new risk factors, diagnostic markers, and drug targets. For neurological and psychiatric phenotypes, the cerebrospinal fluid (CSF) is of particular importance. However, the CSF metabolome is difficult to study on a large scale due to the relative complexity of the procedure needed to collect the fluid. Here, we present a metabolome-wide association study (MWAS), which uses genetic and metabolomic data to impute metabolites into large samples with genome-wide association summary statistics. We conduct a metabolome-wide, genome-wide association analysis with 338 CSF metabolites, identifying 16 genotype-metabolite associations (metabolite quantitative trait loci, or mQTLs). We then build prediction models for all available CSF metabolites and test for associations with 27 neurological and psychiatric phenotypes, identifying 19 significant CSF metabolite-phenotype associations. Our results demonstrate the feasibility of MWAS to study omic data in scarce sample types.

scholarly journals Overlapping but distinct: a new model for G-quadruplex biochemical specificity

2021 ◽  
Author(s):  
Martin Volek ◽  
Sofia Kolesnikova ◽  
Katerina Svehlova ◽  
Pavel Srb ◽  
Ráchel Sgallová ◽  
...  
Keyword(s):  
Nucleic Acid ◽  
Drug Design ◽  
Solution Structure ◽  
Biochemical Data ◽  
Biological Regulation ◽  
New Model ◽  
G Quadruplex ◽  
Range Of Functions ◽  
Nucleic Acid Structures ◽  
Sequence Requirements

Abstract G-quadruplexes are noncanonical nucleic acid structures formed by stacked guanine tetrads. They are capable of a range of functions and thought to play widespread biological roles. This diversity raises an important question: what determines the biochemical specificity of G-quadruplex structures? The answer is particularly important from the perspective of biological regulation because genomes can contain hundreds of thousands of G-quadruplexes with a range of functions. Here we analyze the specificity of each sequence in a 496-member library of variants of a reference G-quadruplex with respect to five functions. Our analysis shows that the sequence requirements of G-quadruplexes with these functions are different from one another, with some mutations altering biochemical specificity by orders of magnitude. Mutations in tetrads have larger effects than mutations in loops, and changes in specificity are correlated with changes in multimeric state. To complement our biochemical data we determined the solution structure of a monomeric G-quadruplex from the library. The stacked and accessible tetrads rationalize why monomers tend to promote a model peroxidase reaction and generate fluorescence. Our experiments support a model in which the sequence requirements of G-quadruplexes with different functions are overlapping but distinct. This has implications for biological regulation, bioinformatics, and drug design.

scholarly journals Time-resolved observation of protein allosteric communication

2017 ◽  
Vol 114 (33) ◽  
pp. E6804-E6811 ◽  
Author(s):  
Sebastian Buchenberg ◽  
Florian Sittel ◽  
Gerhard Stock
Keyword(s):  
Pdz Domain ◽  
Dynamic Network ◽  
Structural Heterogeneity ◽  
Nonequilibrium Molecular Dynamics ◽  
Elastic Response ◽  
Dynamical Process ◽  
Biological Regulation ◽  
Time Resolved ◽  
Allosteric Communication ◽  
Time Resolved Infrared Spectroscopy

Allostery represents a fundamental mechanism of biological regulation that is mediated via long-range communication between distant protein sites. Although little is known about the underlying dynamical process, recent time-resolved infrared spectroscopy experiments on a photoswitchable PDZ domain (PDZ2S) have indicated that the allosteric transition occurs on multiple timescales. Here, using extensive nonequilibrium molecular dynamics simulations, a time-dependent picture of the allosteric communication in PDZ2S is developed. The simulations reveal that allostery amounts to the propagation of structural and dynamical changes that are genuinely nonlinear and can occur in a nonlocal fashion. A dynamic network model is constructed that illustrates the hierarchy and exceeding structural heterogeneity of the process. In compelling agreement with experiment, three physically distinct phases of the time evolution are identified, describing elastic response (≲0.1 ns), inelastic reorganization (∼100 ns), and structural relaxation (≳1μs). Issues such as the similarity to downhill folding as well as the interpretation of allosteric pathways are discussed.

scholarly journals Receptor compaction and GTPase rearrangement drive SRP-mediated cotranslational protein translocation into the ER

2021 ◽  
Vol 7 (21) ◽  
pp. eabg0942
Author(s):  
Jae Ho Lee ◽  
Ahmad Jomaa ◽  
SangYoon Chung ◽  
Yu-Hsien Hwang Fu ◽  
Ruilin Qian ◽  
...  
Keyword(s):  
Single Molecule ◽  
Protein Translocation ◽  
Molecular Model ◽  
Genetic Diseases ◽  
Signal Recognition Particle ◽  
Biological Regulation ◽  
Single Molecule Studies ◽  
Guanosine Triphosphatase ◽  
Gtpase Cycle

The conserved signal recognition particle (SRP) cotranslationally delivers ~30% of the proteome to the eukaryotic endoplasmic reticulum (ER). The molecular mechanism by which eukaryotic SRP transitions from cargo recognition in the cytosol to protein translocation at the ER is not understood. Here, structural, biochemical, and single-molecule studies show that this transition requires multiple sequential conformational rearrangements in the targeting complex initiated by guanosine triphosphatase (GTPase)–driven compaction of the SRP receptor (SR). Disruption of these rearrangements, particularly in mutant SRP54G226E linked to severe congenital neutropenia, uncouples the SRP/SR GTPase cycle from protein translocation. Structures of targeting intermediates reveal the molecular basis of early SRP-SR recognition and emphasize the role of eukaryote-specific elements in regulating targeting. Our results provide a molecular model for the structural and functional transitions of SRP throughout the targeting cycle and show that these transitions provide important points for biological regulation that can be perturbed in genetic diseases.

scholarly journals The Epigenome in Atherosclerosis

2020 ◽  
Author(s):  
Sarah Costantino ◽  
Francesco Paneni
Keyword(s):  
Vascular Disease ◽  
Posttranslational Modifications ◽  
Epigenetic Modifications ◽  
Atherosclerotic Vascular Disease ◽  
Biological Regulation ◽  
Genetic Changes ◽  
Epigenetic Biomarkers ◽  
The Individual ◽  
Novel Therapeutic Strategies ◽  
Vascular Oxidative Stress

AbstractEmerging evidence suggests the growing importance of “nongenetic factors” in the pathogenesis of atherosclerotic vascular disease. Indeed, the inherited genome determines only part of the risk profile as genomic approaches do not take into account additional layers of biological regulation by “epi”-genetic changes. Epigenetic modifications are defined as plastic chemical changes of DNA/histone complexes which critically affect gene activity without altering the DNA sequence. These modifications include DNA methylation, histone posttranslational modifications, and non-coding RNAs and have the ability to modulate gene expression at both transcriptional and posttranscriptional level. Notably, epigenetic signals are mainly induced by environmental factors (i.e., pollution, smoking, noise) and, once acquired, may be transmitted to the offspring. The inheritance of adverse epigenetic changes may lead to premature deregulation of pathways involved in vascular damage and endothelial dysfunction. Here, we describe the emerging role of epigenetic modifications as fine-tuners of gene transcription in atherosclerosis. Specifically, the following aspects are described in detail: (1) discovery and impact of the epigenome in cardiovascular disease, (2) the epigenetic landscape in atherosclerosis; (3) inheritance of epigenetic signals and premature vascular disease; (4) epigenetic control of lipid metabolism, vascular oxidative stress, inflammation, autophagy, and apoptosis; (5) epigenetic biomarkers in patients with atherosclerosis; (6) novel therapeutic strategies to modulate epigenetic marks. Understanding the individual epigenetic profile may pave the way for new approaches to determine cardiovascular risk and to develop personalized therapies to treat atherosclerosis and its complications.

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