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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 -
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 -
PLoS Computational Biology Nov 2023Phenotype prediction is at the center of many questions in biology. Prediction is often achieved by determining statistical associations between genetic and phenotypic...
Phenotype prediction is at the center of many questions in biology. Prediction is often achieved by determining statistical associations between genetic and phenotypic variation, ignoring the exact processes that cause the phenotype. Here, we present a framework based on genome-scale metabolic reconstructions to reveal the mechanisms behind the associations. We calculated a polygenic score (PGS) that identifies a set of enzymes as predictors of growth, the phenotype. This set arises from the synergy of the functional mode of metabolism in a particular setting and its evolutionary history, and is suitable to infer the phenotype across a variety of conditions. We also find that there is optimal genetic variation for predictability and demonstrate how the linear PGS can still explain phenotypes generated by the underlying nonlinear biochemistry. Therefore, the explicit model interprets the black box statistical associations of the genotype-to-phenotype map and helps to discover what limits the prediction in metabolism.
Topics: Genotype; Phenotype; Genome; Biological Evolution; Multifactorial Inheritance
PubMed: 37948461
DOI: 10.1371/journal.pcbi.1011631 -
Comptes Rendus Biologies 2016Elucidating the underlying rules that govern the phenotypic diversity observed in natural populations is an old but still unaccomplished goal in biology. In 1865, Gregor... (Review)
Review
Elucidating the underlying rules that govern the phenotypic diversity observed in natural populations is an old but still unaccomplished goal in biology. In 1865, Gregor Mendel paved the way for the dissection of the underlying genetic basis of traits by setting out to understand the principles of heredity. To date, we still lack a global overview of the spectrum and continuum existing between Mendelian and complex traits within any natural population. In this respect, we recently performed a species-wide survey of Mendelian traits across a large population of isolates using the yeast Saccharomyces cerevisiae. By analyzing the distribution and the inheritance patterns of the trait, we have clearly shown that monogenic mutations can display a significant, variable, and continuous expressivity across different genetic backgrounds. Our study also demonstrated that combining the elegancy of both classical genetics and high-throughput genomics is more than valuable to dissect the genotype-phenotype relationship in natural populations.
Topics: Genetics; Genotype; Humans; Mutation; Penetrance; Phenotype; Saccharomyces cerevisiae
PubMed: 27344551
DOI: 10.1016/j.crvi.2016.04.006 -
The Science of the Total Environment Jan 2019Living organisms are constantly exposed to wide ranges of environmental cues. They react to these cues by undergoing a battery of phenotypic responses, such as by... (Review)
Review
Living organisms are constantly exposed to wide ranges of environmental cues. They react to these cues by undergoing a battery of phenotypic responses, such as by altering their physiological and behavioral traits, in order to adapt and survive in the changed environments. The adaptive response of a species induced by environmental cues is typically thought to be associated with its genetic diversity such that higher genetic diversity provides increased adaptive potential. This originates from the general consensus that phenotypic traits have a genetic basis and are subject to Darwinian natural selection and Mendelian inheritance. There is no doubt about the validity of these principles, supported by the successful introgression of specific traits during (selective) breeding. However, a range of recent studies provided fascinating evidences suggesting that environmental effects experienced by an organism during its lifetime can have marked influences on its phenotype, and additionally the organism can pass on the acquired phenotypes to its subsequent generations through non-genetic mechanisms (also termed as epigenetic mechanism) - a notion that dates back to Lamarck and has been controversial ever since. In this review, we describe how the epigenetics has reshaped our long perception about the inheritance/development of phenotypes within organisms, contrasting with the classical gene-based view of inheritance. We particularly highlighted recent developments in our understanding of inheritance of parental environmental induced phenotypic traits in multicellular organisms under different environmental conditions, and discuss how modifications of the epigenome contribute to the determination of the adult phenotype of future generations.
Topics: Cues; Epigenesis, Genetic; Phenotype
PubMed: 30180336
DOI: 10.1016/j.scitotenv.2018.08.063 -
Sensors (Basel, Switzerland) Jun 2021Plant phenomics has been rapidly advancing over the past few years. This advancement is attributed to the increased innovation and availability of new technologies which... (Review)
Review
Plant phenomics has been rapidly advancing over the past few years. This advancement is attributed to the increased innovation and availability of new technologies which can enable the high-throughput phenotyping of complex plant traits. The application of artificial intelligence in various domains of science has also grown exponentially in recent years. Notably, the computer vision, machine learning, and deep learning aspects of artificial intelligence have been successfully integrated into non-invasive imaging techniques. This integration is gradually improving the efficiency of data collection and analysis through the application of machine and deep learning for robust image analysis. In addition, artificial intelligence has fostered the development of software and tools applied in field phenotyping for data collection and management. These include open-source devices and tools which are enabling community driven research and data-sharing, thereby availing the large amounts of data required for the accurate study of phenotypes. This paper reviews more than one hundred current state-of-the-art papers concerning AI-applied plant phenotyping published between 2010 and 2020. It provides an overview of current phenotyping technologies and the ongoing integration of artificial intelligence into plant phenotyping. Lastly, the limitations of the current approaches/methods and future directions are discussed.
Topics: Artificial Intelligence; Machine Learning; Phenomics; Phenotype; Software
PubMed: 34202291
DOI: 10.3390/s21134363 -
Schizophrenia Research May 2018Several studies of complex psychotic disorders with large numbers of neurobiological phenotypes are currently under way, in living patients and controls, and on... (Review)
Review
Several studies of complex psychotic disorders with large numbers of neurobiological phenotypes are currently under way, in living patients and controls, and on assemblies of brain specimens. Genetic analyses of such data typically present challenges, because of the choice of underlying hypotheses on genetic architecture of the studied disorders and phenotypes, large numbers of phenotypes, the appropriate multiple testing corrections, limited numbers of subjects, imputations required on missing phenotypes and genotypes, and the cross-disciplinary nature of the phenotype measures. Advances in genotype and phenotype imputation, and in genome-wide association (GWAS) methods, are useful in dealing with these challenges. As compared with the more traditional single-trait analyses, deep phenotyping with simultaneous genome-wide analyses serves as a discovery tool for previously unsuspected relationships of phenotypic traits with each other, and with specific molecular involvements.
Topics: Genetic Predisposition to Disease; Genetic Variation; Genome-Wide Association Study; Genotype; Humans; Mental Disorders; Phenotype
PubMed: 29056493
DOI: 10.1016/j.schres.2017.09.031 -
Evolution; International Journal of... Feb 2023Natural selection acts on developmentally constructed phenotypes, but how does development affect evolution? This question prompts a simultaneous consideration of...
Natural selection acts on developmentally constructed phenotypes, but how does development affect evolution? This question prompts a simultaneous consideration of development and evolution. However, there has been a lack of general mathematical frameworks mechanistically integrating the two, which may have inhibited progress on the question. Here, we use a new mathematical framework that mechanistically integrates development into evolution to analyse how development affects evolution. We show that, while selection pushes genotypic and phenotypic evolution up the fitness landscape, development determines the admissible evolutionary pathway, such that evolutionary outcomes occur at path peaks rather than landscape peaks. Changes in development can generate path peaks, triggering genotypic or phenotypic diversification, even on constant, single-peak landscapes. Phenotypic plasticity, niche construction, extra-genetic inheritance, and developmental bias alter the evolutionary path and hence the outcome. Thus, extra-genetic inheritance can have permanent evolutionary effects by changing the developmental constraints, even if extra-genetically acquired elements are not transmitted to future generations. Selective development, whereby phenotype construction points in the adaptive direction, may induce adaptive or maladaptive evolution depending on the developmental constraints. Moreover, developmental propagation of phenotypic effects over age enables the evolution of negative senescence. Overall, we find that development plays a major evolutionary role.
Topics: Biological Evolution; Phenotype; Genotype; Selection, Genetic; Adaptation, Physiological
PubMed: 36691368
DOI: 10.1093/evolut/qpac003 -
Current Opinion in Biotechnology Dec 2020Raman spectroscopy and chemometric analyses are used to characterize phenotypes of biological samples. The approach is relevant in biotechnology to identify and monitor... (Review)
Review
Raman spectroscopy and chemometric analyses are used to characterize phenotypes of biological samples. The approach is relevant in biotechnology to identify and monitor productive cell cultures. It can also detect the presence of pathogens in food products and screen for disease in clinical applications. Raman-based phenotyping is of interest because it is inexpensive, rapid, label-free, and is not obscured by water molecules. Here, recent applications in microbial species and tissue identification, isogenic cell/tissue phenotype changes, and characterizing biological fluids were surveyed along with the myriad spectral processing and chemometric analysis approaches. Suggestions are also given to help standardize and solidify Raman-based phenotyping as an -omics analysis method. These include offering repositories for raw spectral data and molecular assignment libraries.
Topics: Biotechnology; Cell Culture Techniques; Phenotype; Spectrum Analysis, Raman
PubMed: 33142112
DOI: 10.1016/j.copbio.2020.09.007 -
Cell Systems Mar 2019Research on aging requires the ability to measure aging, and therein lies a challenge: it is impossible to measure every molecular, cellular, and physiological change... (Review)
Review
Research on aging requires the ability to measure aging, and therein lies a challenge: it is impossible to measure every molecular, cellular, and physiological change that develops over time, but it is difficult to prioritize phenotypes for measurement because it is unclear which biological changes should be considered aspects of aging and, further, which species and environments exhibit "real aging." Here, I propose a strategy to address this challenge: rather than classify phenotypes as "real aging" or not, conceptualize aging as the set of all age-dependent phenotypes and appreciate that this set and its underlying mechanisms may vary by population. Use automated phenotyping technologies to measure as many age-dependent phenotypes as possible within individuals over time, prioritizing organism-level (i.e., physiological) phenotypes in order to enrich for health relevance. Use those high-dimensional phenotypic data to construct dynamic networks that allow aging to be studied with unprecedented sophistication and rigor.
Topics: Aging; Animals; Humans; Models, Biological; Phenotype
PubMed: 30878357
DOI: 10.1016/j.cels.2019.02.005