13,942 research outputs found
A Path to Implement Precision Child Health Cardiovascular Medicine.
Congenital heart defects (CHDs) affect approximately 1% of live births and are a major source of childhood morbidity and mortality even in countries with advanced healthcare systems. Along with phenotypic heterogeneity, the underlying etiology of CHDs is multifactorial, involving genetic, epigenetic, and/or environmental contributors. Clear dissection of the underlying mechanism is a powerful step to establish individualized therapies. However, the majority of CHDs are yet to be clearly diagnosed for the underlying genetic and environmental factors, and even less with effective therapies. Although the survival rate for CHDs is steadily improving, there is still a significant unmet need for refining diagnostic precision and establishing targeted therapies to optimize life quality and to minimize future complications. In particular, proper identification of disease associated genetic variants in humans has been challenging, and this greatly impedes our ability to delineate gene-environment interactions that contribute to the pathogenesis of CHDs. Implementing a systematic multileveled approach can establish a continuum from phenotypic characterization in the clinic to molecular dissection using combined next-generation sequencing platforms and validation studies in suitable models at the bench. Key elements necessary to advance the field are: first, proper delineation of the phenotypic spectrum of CHDs; second, defining the molecular genotype/phenotype by combining whole-exome sequencing and transcriptome analysis; third, integration of phenotypic, genotypic, and molecular datasets to identify molecular network contributing to CHDs; fourth, generation of relevant disease models and multileveled experimental investigations. In order to achieve all these goals, access to high-quality biological specimens from well-defined patient cohorts is a crucial step. Therefore, establishing a CHD BioCore is an essential infrastructure and a critical step on the path toward precision child health cardiovascular medicine
How to understand the cell by breaking it: network analysis of gene perturbation screens
Modern high-throughput gene perturbation screens are key technologies at the
forefront of genetic research. Combined with rich phenotypic descriptors they
enable researchers to observe detailed cellular reactions to experimental
perturbations on a genome-wide scale. This review surveys the current
state-of-the-art in analyzing perturbation screens from a network point of
view. We describe approaches to make the step from the parts list to the wiring
diagram by using phenotypes for network inference and integrating them with
complementary data sources. The first part of the review describes methods to
analyze one- or low-dimensional phenotypes like viability or reporter activity;
the second part concentrates on high-dimensional phenotypes showing global
changes in cell morphology, transcriptome or proteome.Comment: Review based on ISMB 2009 tutorial; after two rounds of revisio
Protein-protein interactions: network analysis and applications in drug discovery
Physical interactions among proteins constitute the backbone of cellular function, making them an attractive source of therapeutic targets. Although the challenges associated with targeting protein-protein interactions (PPIs) -in particular with small molecules are considerable, a growing number of functional PPI modulators is being reported and clinically evaluated. An essential starting point for PPI inhibitor screening or design projects is the generation of a detailed map of the human interactome and the interactions between human and pathogen proteins. Different routes to produce these biological networks are being combined, including literature curation and computational methods. Experimental approaches to map PPIs mainly rely on the yeast two-hybrid (Y2H) technology, which have recently shown to produce reliable protein networks. However, other genetic and biochemical methods will be essential to increase both coverage and resolution of current protein networks in order to increase their utility towards the identification of novel disease-related proteins and PPIs, and their potential use as therapeutic targets
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Strategies for increasing the applicability of biological network inference
The manipulation of cellular state has many promising applications, including stem cell biology and regenerative medicine, biofuel production, and stress resistant crop development. The construction of interaction maps promises to enhance our ability to engineer cellular behavior. Within the last 15 years, many methods have been developed to infer the structure of the gene regulatory interaction map from gene abundance snapshots provided by high-throughput experimental data. However, relatively little research has focused on using gene regulatory network models for the prediction and manipulation of cellular behavior. This dissertation examines and applies strategies to utilize the predictive power of gene network models to guide experimentation and engineering efforts. First, we developed methods to improve gene network models by integrating interaction evidence sources, in order to utilize the full predictive power of the models. Next, we explored the power of networks models to guide experimental efforts through inference and analysis of a regulatory network in the pathogenic fungus Cryptococcus neoformans. Finally, we develop a novel, network-guided algorithm to select genetic interventions for engineering transcriptional state. We apply this method to select intervention strains for improving biofuel production in a mixed glucose-xylose environment. The contributions in this dissertation provide the first thorough examination, systematic application, and quantitative evaluation of the utilization of network models for guiding cellular engineering
Repurposing drugs to target nonalcoholic steatohepatitis
Nonalcoholic fatty liver disease (NAFLD) is a complex disorder that has evolved in recent years as the leading global cause of chronic liver damage. The main obstacle to better disease management pertains to the lack of approved pharmacological interventions for the treatment of nonalcoholic steatohepatitis (NASH) and NASH-fibrosis-the severe histological forms. Over the past decade, tremendous advances have been made in NAFLD research, resulting in the discovery of disease mechanisms and novel therapeutic targets. Hence, a large number of pharmacological agents are currently being tested for safety and efficacy. These drugs are in the initial pharmacological phases (phase 1 and 2), which involve testing tolerability, therapeutic action, and pharmacological issues. It is thus reasonable to assume that the next generation of NASH drugs will not be available for clinical use for foreseeable future. The expected delay can be mitigated by drug repurposing or repositioning, which essentially relies on identifying and developing new uses for existing drugs. Here, we propose a drug candidate selection method based on the integration of molecular pathways of disease pathogenesis into network analysis tools that use OMICs data as well as multiples sources, including text mining from the medical literature.Fil: Sookoian, Silvia Cristina. Consejo Nacional de Investigaciones CientÃficas y Técnicas. Oficina de Coordinación Administrativa Houssay. Instituto de Investigaciones Médicas. Universidad de Buenos Aires. Facultad de Medicina. Instituto de Investigaciones Médicas; ArgentinaFil: Pirola, Carlos José. Consejo Nacional de Investigaciones CientÃficas y Técnicas. Oficina de Coordinación Administrativa Houssay. Instituto de Investigaciones Médicas. Universidad de Buenos Aires. Facultad de Medicina. Instituto de Investigaciones Médicas; Argentin
Causal Modeling Using Network Ensemble Simulations of Genetic and Gene Expression Data Predicts Genes Involved in Rheumatoid Arthritis
Tumor necrosis factor α (TNF-α) is a key regulator of inflammation and rheumatoid arthritis (RA). TNF-α blocker therapies can be very effective for a substantial number of patients, but fail to work in one third of patients who show no or minimal response. It is therefore necessary to discover new molecular intervention points involved in TNF-α blocker treatment of rheumatoid arthritis patients. We describe a data analysis strategy for predicting gene expression measures that are critical for rheumatoid arthritis using a combination of comprehensive genotyping, whole blood gene expression profiles and the component clinical measures of the arthritis Disease Activity Score 28 (DAS28) score. Two separate network ensembles, each comprised of 1024 networks, were built from molecular measures from subjects before and 14 weeks after treatment with TNF-α blocker. The network ensemble built from pre-treated data captures TNF-α dependent mechanistic information, while the ensemble built from data collected under TNF-α blocker treatment captures TNF-α independent mechanisms. In silico simulations of targeted, personalized perturbations of gene expression measures from both network ensembles identify transcripts in three broad categories. Firstly, 22 transcripts are identified to have new roles in modulating the DAS28 score; secondly, there are 6 transcripts that could be alternative targets to TNF-α blocker therapies, including CD86 - a component of the signaling axis targeted by Abatacept (CTLA4-Ig), and finally, 59 transcripts that are predicted to modulate the count of tender or swollen joints but not sufficiently enough to have a significant impact on DAS28
Principles for the post-GWAS functional characterisation of risk loci
Several challenges lie ahead in assigning functionality to susceptibility SNPs. For example, most effect sizes are small relative to effects seen in monogenic diseases, with per allele odds ratios usually ranging from 1.15 to 1.3. It is unclear whether current molecular biology methods have enough resolution to differentiate such small effects. Our objective here is therefore to provide a set of recommendations to optimize the allocation of effort and resources in order maximize the chances of elucidating the functional contribution of specific loci to the disease phenotype. It has been estimated that 88% of currently identified disease-associated SNP are intronic or intergenic. Thus, in this paper we will focus our attention on the analysis of non-coding variants and outline a hierarchical approach for post-GWAS functional studies
Genetic implications of individual intervention and neuronal dysfunction in neurodevelopmental disorders
Neurodevelopmental disorders (NDDs) are a group of conditions appearing in childhood,
with developmental deficits that produce impairments of functioning. Autism spectrum
disorder (ASD) is a common NDD with a high heritability affected by complex genetic
factors, including both common and rare variants. Behavior interventions such as social skills
group training (SSGT) have been widely used in school-aged autistic individuals to relieve
social communication difficulties in a group setting. Studies have confirmed that intervention
outcomes can be influenced by sex and age, but how the genetic risk contributes to the
outcome variability remains elusive. Furthermore, although large population cohorts have
been well studied and have found numerous genes associated with ASD and NDDs, the
molecular and neuronal outcomes of risk variants and genes are unclear. Therefore, this thesis
included four studies in which the effects of genetic factors on intervention outcomes and
cellular level neuronal functions were investigated. Results from this thesis may provide a
genetic perspective for further studies to explore potential individualized treatments for ASD
and other NDDs.
Specifically, In STUDY 1-3, exome sequencing and microarray were performed on
individuals from a randomized controlled trial of SSGT (KONTAKT®). Common and rare
variants, including copy number variations (CNVs) and exome variants, were tested for
association effects with SSGT and standard care intervention outcomes. Polygenic risk scores
(PRSs) were calculated from common variants, and clinically significant rare CNVs and rare
exome variants were prioritized. Molecular diagnoses were identified in 12.6% of the autistic
participants. PRSs and carrier status of clinically significant rare variants were associated
with intervention outcomes, although with varied effects on both SSGT and standard care. In
addition, genetic scores representing variant loads in specific gene sets were obtained from
rare and common variants in ASD-related pathways. Outcomes of interventions were
differentially associated with genetic scores for ASD-related gene sets including synaptic
transmission and transcription regulation from RNA polymerase II. After combining genetic
information and behavior measures, a machine learning model was able to select important
features and confirm that the intervention outcomes were predictable.
In STUDY 4, genetic variants affecting Calcium/Calmodulin Dependent Serine Protein
Kinase (CASK) gene, a risk gene for NDDs, were examined using human induced pluripotent
stem cell-derived neuronal models to identify the cellular effects of these mutation
consequences. CASK protein was reduced in maturing neurons from mutation carriers. Bulk
RNA sequencing results revealed that the global expression of genes from presynaptic
development and CASK network were downregulated in CASK-deficient neurons compared
to controls. Neuronal cells influenced by CASK mutations showed a decrease of inhibitory
presynapse size and changed excitatory-inhibitory (E/I) balance in developing neural
circuitries.
In summary, this is the first study to investigate the association of genome-wide rare and
common variants with ASD intervention outcomes. Differential variant effects were found
for individuals receiving SSGT or standard care. Future studies should include genetic
information at different levels to improve molecular genetic testing for diagnoses and
intervention plans. Presynapses and E/I imbalance could be an option to be developed for the
treatment of CASK-related disorders
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