-
Pulmonology 2020Chronic Obstructive Pulmonary Disease (COPD) is a heterogeneous and multisystemic disease with progressive increasing morbidity and mortality. COPD is now widely... (Review)
Review
Chronic Obstructive Pulmonary Disease (COPD) is a heterogeneous and multisystemic disease with progressive increasing morbidity and mortality. COPD is now widely accepted as a heterogeneous condition with multiple phenotypes and endotypes. This review will discuss the old and new concepts for the different types of COPD phenotypes, as well as the inclusion of them in current guidelines. Phenotypical approach to COPD is having huge impact on everyday practice and changed nonpharmacological and pharmacological management of COPD in last decade. However, phenotypical approach is small step to precision medicine in COPD management in the absence of big, specific and well-designed COPD trials with exact identification of phenotypes for more personalization of the treatment of COPD.
Topics: Humans; Phenotype; Practice Guidelines as Topic; Precision Medicine; Pulmonary Disease, Chronic Obstructive
PubMed: 31740261
DOI: 10.1016/j.pulmoe.2019.10.006 -
Circulation Research Apr 2018Precision medicine is an integrative approach to cardiovascular disease prevention and treatment that considers an individual's genetics, lifestyle, and exposures as... (Review)
Review
Precision medicine is an integrative approach to cardiovascular disease prevention and treatment that considers an individual's genetics, lifestyle, and exposures as determinants of their cardiovascular health and disease phenotypes. This focus overcomes the limitations of reductionism in medicine, which presumes that all patients with the same signs of disease share a common pathophenotype and, therefore, should be treated similarly. Precision medicine incorporates standard clinical and health record data with advanced panomics (ie, transcriptomics, epigenomics, proteomics, metabolomics, and microbiomics) for deep phenotyping. These phenotypic data can then be analyzed within the framework of molecular interaction (interactome) networks to uncover previously unrecognized disease phenotypes and relationships between diseases, and to select pharmacotherapeutics or identify potential protein-drug or drug-drug interactions. In this review, we discuss the current spectrum of cardiovascular health and disease, population averages and the response of extreme phenotypes to interventions, and population-based versus high-risk treatment strategies as a pretext to understanding a precision medicine approach to cardiovascular disease prevention and therapeutic interventions. We also consider the search for resilience and Mendelian disease genes and argue against the theory of a single causal gene/gene product as a mediator of the cardiovascular disease phenotype, as well as an Erlichian magic bullet to solve cardiovascular disease. Finally, we detail the importance of deep phenotyping and interactome networks and the use of this information for rational polypharmacy. These topics highlight the urgent need for precise phenotyping to advance precision medicine as a strategy to improve cardiovascular health and prevent disease.
Topics: Cardiovascular Agents; Cardiovascular Diseases; Computational Biology; Demography; Drug Discovery; Forecasting; Gene-Environment Interaction; Genetic Association Studies; Genetic Diseases, Inborn; Genetic Variation; Humans; Mutation; Pharmacogenetics; Phenotype; Precision Medicine
PubMed: 29700074
DOI: 10.1161/CIRCRESAHA.117.310782 -
Sleep Medicine Reviews Oct 2017Obstructive sleep apnea (OSA) is a complex and heterogeneous disorder and the apnea hypopnea index alone can not capture the diverse spectrum of the condition. Enhanced... (Review)
Review
Obstructive sleep apnea (OSA) is a complex and heterogeneous disorder and the apnea hypopnea index alone can not capture the diverse spectrum of the condition. Enhanced phenotyping can improve prognostication, patient selection for clinical trials, understanding of mechanisms, and personalized treatments. In OSA, multiple condition characteristics have been termed "phenotypes." To help classify patients into relevant prognostic and therapeutic categories, an OSA phenotype can be operationally defined as: "A category of patients with OSA distinguished from others by a single or combination of disease features, in relation to clinically meaningful attributes (symptoms, response to therapy, health outcomes, quality of life)." We review approaches to clinical phenotyping in OSA, citing examples of increasing analytic complexity. Although clinical feature based OSA phenotypes with significant prognostic and treatment implications have been identified (e.g., excessive daytime sleepiness OSA), many current categorizations lack association with meaningful outcomes. Recent work focused on pathophysiologic risk factors for OSA (e.g., arousal threshold, craniofacial morphology, chemoreflex sensitivity) appears to capture heterogeneity in OSA, but requires clinical validation. Lastly, we discuss the use of machine learning as a promising phenotyping strategy that can integrate multiple types of data (genomic, molecular, cellular, clinical) to identify unique, meaningful OSA phenotypes.
Topics: Humans; Phenotype; Sleep Apnea, Obstructive; Sleep, REM
PubMed: 27815038
DOI: 10.1016/j.smrv.2016.10.002 -
Handbook of Clinical Neurology 2018Essential tremor (ET) is one of the most common neurologic disorders, and genetic factors are thought to contribute significantly to disease etiology. There has been a... (Review)
Review
Essential tremor (ET) is one of the most common neurologic disorders, and genetic factors are thought to contribute significantly to disease etiology. There has been a relative lack of progress in understanding the genetic etiology of ET. This could reflect a number of factors, including the presence of substantial phenotypic and genotypic heterogeneity. Thus, a meticulous approach to phenotyping is important for genetic research. A lack of standardized phenotyping across studies and patient centers likely has contributed to the relative lack of success of genomewide association studies in ET. To dissect the genetic architecture of ET, whole-genome sequencing will likely be of value. This will allow specific hypotheses about the mode of inheritance and genetic architecture to be tested. A number of approaches still remain unexplored in ET genetics, including the contribution of copy number variants, uncommon moderate-effect alleles, rare variant large-effect alleles (including Mendelian and complex/polygenic modes of inheritance), de novo and gonadal mosaicism, epigenetic changes, and noncoding variation.
Topics: Essential Tremor; Gene-Environment Interaction; Genetic Predisposition to Disease; Genotype; Humans; Phenotype
PubMed: 29325613
DOI: 10.1016/B978-0-444-63233-3.00015-4 -
Plant Biotechnology Journal Jul 2020Genotyping-by-sequencing has enabled approaches for genomic selection to improve yield, stress resistance and nutritional value. More and more resource studies are... (Review)
Review
Genotyping-by-sequencing has enabled approaches for genomic selection to improve yield, stress resistance and nutritional value. More and more resource studies are emerging providing 1000 and more genotypes and millions of SNPs for one species covering a hitherto inaccessible intraspecific genetic variation. The larger the databases are growing, the better statistical approaches for genomic selection will be available. However, there are clear limitations on the statistical but also on the biological part. Intraspecific genetic variation is able to explain a high proportion of the phenotypes, but a large part of phenotypic plasticity also stems from environmentally driven transcriptional, post-transcriptional, translational, post-translational, epigenetic and metabolic regulation. Moreover, regulation of the same gene can have different phenotypic outputs in different environments. Consequently, to explain and understand environment-dependent phenotypic plasticity based on the available genotype variation we have to integrate the analysis of further molecular levels reflecting the complete information flow from the gene to metabolism to phenotype. Interestingly, metabolomics platforms are already more cost-effective than NGS platforms and are decisive for the prediction of nutritional value or stress resistance. Here, we propose three fundamental pillars for future breeding strategies in the framework of Green Systems Biology: (i) combining genome selection with environment-dependent PANOMICS analysis and deep learning to improve prediction accuracy for marker-dependent trait performance; (ii) PANOMICS resolution at subtissue, cellular and subcellular level provides information about fundamental functions of selected markers; (iii) combining PANOMICS with genome editing and speed breeding tools to accelerate and enhance large-scale functional validation of trait-specific precision breeding.
Topics: Breeding; Genome-Wide Association Study; Genomics; Genotype; Phenotype; Polymorphism, Single Nucleotide
PubMed: 32163658
DOI: 10.1111/pbi.13372 -
Journal of Experimental Botany May 2022Gas exchange techniques revolutionized plant research and advanced understanding, including associated fluxes and efficiencies, of photosynthesis, photorespiration, and... (Review)
Review
Gas exchange techniques revolutionized plant research and advanced understanding, including associated fluxes and efficiencies, of photosynthesis, photorespiration, and respiration of plants from cellular to ecosystem scales. These techniques remain the gold standard for inferring photosynthetic rates and underlying physiology/biochemistry, although their utility for high-throughput phenotyping (HTP) of photosynthesis is limited both by the number of gas exchange systems available and the number of personnel available to operate the equipment. Remote sensing techniques have long been used to assess ecosystem productivity at coarse spatial and temporal resolutions, and advances in sensor technology coupled with advanced statistical techniques are expanding remote sensing tools to finer spatial scales and increasing the number and complexity of phenotypes that can be extracted. In this review, we outline the photosynthetic phenotypes of interest to the plant science community and describe the advances in high-throughput techniques to characterize photosynthesis at spatial scales useful to infer treatment or genotypic variation in field-based experiments or breeding trials. We will accomplish this objective by presenting six lessons learned thus far through the development and application of proximal/remote sensing-based measurements and the accompanying statistical analyses. We will conclude by outlining what we perceive as the current limitations, bottlenecks, and opportunities facing HTP of photosynthesis.
Topics: Ecosystem; Genotype; Phenotype; Photosynthesis
PubMed: 35218184
DOI: 10.1093/jxb/erac077 -
Trends in Ecology & Evolution Jan 2020Genetically identical individuals can be phenotypically variable, even in constant environmental conditions. The ubiquity of this phenomenon, known as 'intra-genotypic... (Review)
Review
Genetically identical individuals can be phenotypically variable, even in constant environmental conditions. The ubiquity of this phenomenon, known as 'intra-genotypic variability', is increasingly evident and the relevant mechanistic underpinnings are beginning to be understood. In parallel, theory has delineated a number of formal expectations for contexts in which such a feature would be adaptive. Here, we review empirical evidence across biological systems and theoretical expectations, including nonlinear averaging and bet hedging. We synthesize existing results to illustrate the dependence of selection outcomes both on trait characteristics, features of environmental variability, and species' demographic context. We conclude by discussing ways to bridge the gap between empirical evidence of intra-genotypic variability, studies demonstrating its genetic component, and evidence that it is adaptive.
Topics: Biological Evolution; Genotype; Humans; Phenotype; Selection, Genetic
PubMed: 31519463
DOI: 10.1016/j.tree.2019.08.005 -
Journal of Neurology, Neurosurgery, and... Apr 2022Neurometabolic diseases are a group of individually rare but numerous and heterogeneous genetic diseases best known to paediatricians. The more recently reported adult... (Review)
Review
Neurometabolic diseases are a group of individually rare but numerous and heterogeneous genetic diseases best known to paediatricians. The more recently reported adult forms may present with phenotypes strikingly different from paediatric ones and may mimic other more common neurological disorders in adults. Furthermore, unlike most neurogenetic diseases, many neurometabolic diseases are treatable, with both conservative and more recent innovative therapeutics. However, the phenotypical complexity of this group of diseases and the growing number of specialised biochemical tools account for a significant diagnostic delay and underdiagnosis. We reviewed all series and case reports of patients with a confirmed neurometabolic disease and a neurological onset after the age of 10 years, with a focus on the 36 treatable ones, and classified these diseases according to their most relevant clinical manifestations. The biochemical diagnostic approach of neurometabolic diseases lays on the use of numerous tests studying a set of metabolites, an enzymatic activity or the function of a given pathway; and therapeutic options aim to restore the enzyme activity or metabolic function, limit the accumulation of toxic substrates or substitute the deficient products. A quick diagnosis of a treatable neurometabolic disease can have a major impact on patients, leading to the stabilisation of the disease and cease of repeated diagnostic investigations, and allowing for familial screening. For the aforementioned, in addition to an exhaustive and clinically meaningful review of these diseases, we propose a simplified diagnostic approach for the neurologist with the aim to help determine when to suspect a neurometabolic disease and how to proceed in a rational manner. We also discuss the place of next-generation sequencing technologies in the diagnostic process, for which deep phenotyping of patients (both clinical and biochemical) is necessary for improving their diagnostic yield.
Topics: Child; Delayed Diagnosis; High-Throughput Nucleotide Sequencing; Humans; Nervous System Diseases; Phenotype
PubMed: 35140137
DOI: 10.1136/jnnp-2021-328045 -
ELife Sep 2023Experiments on worms suggest that a statistical measure called the G matrix can accurately predict how phenotypes will adapt to a novel environment over multiple...
Experiments on worms suggest that a statistical measure called the G matrix can accurately predict how phenotypes will adapt to a novel environment over multiple generations.
Topics: Phenotype; Biological Evolution; Adaptation, Biological; Animals
PubMed: 37671937
DOI: 10.7554/eLife.91450 -
Proceedings. Biological Sciences Sep 2013In biology, noise implies error and disorder and is therefore something which organisms may seek to minimize and mitigate against. We argue that such noise can be... (Review)
Review
In biology, noise implies error and disorder and is therefore something which organisms may seek to minimize and mitigate against. We argue that such noise can be adaptive. Recent studies have shown that gene expression can be noisy, noise can be genetically controlled, genes and gene networks vary in how noisy they are and noise generates phenotypic differences among genetically identical cells. Such phenotypic differences can have fitness benefits, suggesting that evolution can shape noise and that noise may be adaptive. For example, gene networks can generate bistable states resulting in phenotypic diversity and switching among individual cells of a genotype, which may be a bet hedging strategy. Here, we review the sources of noise in gene expression, the extent to which noise in biological systems may be adaptive and suggest that applying evolutionary rigour to the study of noise is necessary to fully understand organismal phenotypes.
Topics: Biological Evolution; Gene Expression; Gene Regulatory Networks; Genetic Fitness; Models, Genetic; Phenotype
PubMed: 23902900
DOI: 10.1098/rspb.2013.1104