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Trends in Plant Science Jun 2022Anatomics is a novel phenotyping strategy focused on high-throughput imaging and quantification of plant anatomy from field-grown plants. Here we highlight its potential...
Anatomics is a novel phenotyping strategy focused on high-throughput imaging and quantification of plant anatomy from field-grown plants. Here we highlight its potential applications for genetic and physiological analysis of plant anatomical phenotypes.
Topics: Phenotype; Plants
PubMed: 35307268
DOI: 10.1016/j.tplants.2022.02.009 -
Journal of Morphology Jan 2023The continued use of the idea of homology is questionable on philosophical and scientific grounds. It is based on the widespread idea that a "homologue" in extant taxa...
The continued use of the idea of homology is questionable on philosophical and scientific grounds. It is based on the widespread idea that a "homologue" in extant taxa can be "traced back" to a feature in common ancestor. In contrast, Richard Owen, who first used the term in 1846, saw homology (homologue) differently, as "sameness": "the same organ in different animals under every variety of form and function." At that point in time, he was not influenced by evolutionary thinking, and more focused on the details and approaches to biological comparison and description. His was a perceptive approach to comparison. This paper argues that the concept of homology no longer plays a useful role in comparative biology. It is a conceptual idea with little or no empirical implications for modern comparisons among phenotypes. Comparative biology now uses formal phylogenetic analysis in which similar features in individuals of two or more taxa are treated as characters on a tree and tested for historical "sameness" in terms of the concept of synapomorphy. If we are to understand the complexities of phenotypic evolution, applying this method to detailed comparative data will be essential. At the same time, a deep understanding of the phenotype and its history will emerge only through the use of multidisciplinary approaches that address historical changes at different hierarchical levels.
Topics: Animals; Phylogeny; Biological Evolution; Biology; Phenotype
PubMed: 36314971
DOI: 10.1002/jmor.21530 -
AMIA ... Annual Symposium Proceedings.... 2020Phenotyping algorithms are essential tools for conducting clinical research on observational data. Manually devel- oped phenotyping algorithms, such as those curated...
Phenotyping algorithms are essential tools for conducting clinical research on observational data. Manually devel- oped phenotyping algorithms, such as those curated within the eMERGE (electronic Medical Records and Genomics) Network, represent the gold standard but are time consuming to create. In this work, we propose a framework for learning from the structure of eMERGE phenotype concept sets to assist construction of novel phenotype definitions. We use eMERGE phenotypes as a source of reference concept sets and engineer rich features characterizing the con- cept pairs within each set. We treat these pairwise relationships as edges in a concept graph, train models to perform edge prediction, and identify candidate phenotype concept sets as highly connected subgraphs. Candidate concept sets may then be interrogated and composed to construct novel phenotype definitions.
Topics: Algorithms; Electronic Health Records; Genomics; Humans; Phenotype; Probability
PubMed: 33936484
DOI: No ID Found -
Genetics in Medicine : Official Journal... Sep 2023Congenital hypopituitarism (CH) disorders are phenotypically variable. Variants in multiple genes are associated with these disorders, with variable penetrance and...
PURPOSE
Congenital hypopituitarism (CH) disorders are phenotypically variable. Variants in multiple genes are associated with these disorders, with variable penetrance and inheritance.
METHODS
We screened a large cohort (N = 1765) of patients with or at risk of CH using Sanger sequencing, selected according to phenotype, and conducted next-generation sequencing (NGS) in 51 families within our cohort. We report the clinical, hormonal, and neuroradiological phenotypes of patients with variants in known genes associated with CH.
RESULTS
We identified variants in 178 patients: GH1/GHRHR (51 patients of 414 screened), PROP1 (17 of 253), POU1F1 (15 of 139), SOX2 (13 of 59), GLI2 (7 of 106), LHX3/LHX4 (8 of 110), HESX1 (8 of 724), SOX3 (9 of 354), OTX2 (5 of 59), SHH (2 of 64), and TCF7L1, KAL1, FGFR1, and FGF8 (2 of 585, respectively). NGS identified 26 novel variants in 35 patients (from 24 families). Magnetic resonance imaging showed prevalent hypothalamo-pituitary abnormalities, present in all patients with PROP1, GLI2, SOX3, HESX1, OTX2, LHX3, and LHX4 variants. Normal hypothalamo-pituitary anatomy was reported in 24 of 121, predominantly those with GH1, GHRHR, POU1F1, and SOX2 variants.
CONCLUSION
We identified variants in 10% (178 of 1765) of our CH cohort. NGS has revolutionized variant identification, and careful phenotypic patient characterization has improved our understanding of CH. We have constructed a flow chart to guide genetic analysis in these patients, which will evolve upon novel gene discoveries.
Topics: Humans; Mutation; Hypopituitarism; Transcription Factors; Phenotype; Genes, Homeobox
PubMed: 37165954
DOI: 10.1016/j.gim.2023.100881 -
Plant Communications Nov 2022Plant phenomics (PP) has been recognized as a bottleneck in studying the interactions of genomics and environment on plants, limiting the progress of smart breeding and... (Review)
Review
Plant phenomics (PP) has been recognized as a bottleneck in studying the interactions of genomics and environment on plants, limiting the progress of smart breeding and precise cultivation. High-throughput plant phenotyping is challenging owing to the spatio-temporal dynamics of traits. Proximal and remote sensing (PRS) techniques are increasingly used for plant phenotyping because of their advantages in multi-dimensional data acquisition and analysis. Substantial progress of PRS applications in PP has been observed over the last two decades and is analyzed here from an interdisciplinary perspective based on 2972 publications. This progress covers most aspects of PRS application in PP, including patterns of global spatial distribution and temporal dynamics, specific PRS technologies, phenotypic research fields, working environments, species, and traits. Subsequently, we demonstrate how to link PRS to multi-omics studies, including how to achieve multi-dimensional PRS data acquisition and processing, how to systematically integrate all kinds of phenotypic information and derive phenotypic knowledge with biological significance, and how to link PP to multi-omics association analysis. Finally, we identify three future perspectives for PRS-based PP: (1) strengthening the spatial and temporal consistency of PRS data, (2) exploring novel phenotypic traits, and (3) facilitating multi-omics communication.
Topics: Phenomics; Plant Breeding; Crops, Agricultural; Remote Sensing Technology; Phenotype
PubMed: 35655429
DOI: 10.1016/j.xplc.2022.100344 -
Using phenotypic plasticity to understand the structure and evolution of the genotype-phenotype map.Genetica Aug 2022Deciphering the genotype-phenotype map necessitates relating variation at the genetic level to variation at the phenotypic level. This endeavour is inherently limited by... (Review)
Review
Deciphering the genotype-phenotype map necessitates relating variation at the genetic level to variation at the phenotypic level. This endeavour is inherently limited by the availability of standing genetic variation, the rate of spontaneous mutation to novo genetic variants, and possible biases associated with induced mutagenesis. An interesting alternative is to instead rely on the environment as a source of variation. Many phenotypic traits change plastically in response to the environment, and these changes are generally underlain by changes in gene expression. Relating gene expression plasticity to the phenotypic plasticity of more integrated organismal traits thus provides useful information about which genes influence the development and expression of which traits, even in the absence of genetic variation. We here appraise the prospects and limits of such an environment-for-gene substitution for investigating the genotype-phenotype map. We review models of gene regulatory networks, and discuss the different ways in which they can incorporate the environment to mechanistically model phenotypic plasticity and its evolution. We suggest that substantial progress can be made in deciphering this genotype-environment-phenotype map, by connecting theory on gene regulatory network to empirical patterns of gene co-expression, and by more explicitly relating gene expression to the expression and development of phenotypes, both theoretically and empirically.
Topics: Adaptation, Physiological; Biological Evolution; Gene Regulatory Networks; Genetic Variation; Genotype; Phenotype
PubMed: 34617196
DOI: 10.1007/s10709-021-00135-5 -
Cell Systems Jun 2021Single-cell image analysis provides a powerful approach for studying cell-to-cell heterogeneity, which is an important attribute of isogenic cell populations, from... (Review)
Review
Single-cell image analysis provides a powerful approach for studying cell-to-cell heterogeneity, which is an important attribute of isogenic cell populations, from microbial cultures to individual cells in multicellular organisms. This phenotypic variability must be explained at a mechanistic level if biologists are to fully understand cellular function and address the genotype-to-phenotype relationship. Variability in single-cell phenotypes is obscured by bulk readouts or averaging of phenotypes from individual cells in a sample; thus, single-cell image analysis enables a higher resolution view of cellular function. Here, we consider examples of both small- and large-scale studies carried out with isogenic cell populations assessed by fluorescence microscopy, and we illustrate the advantages, challenges, and the promise of quantitative single-cell image analysis.
Topics: Biological Variation, Population; Microscopy, Fluorescence; Phenotype; Single-Cell Analysis
PubMed: 34139168
DOI: 10.1016/j.cels.2021.05.010 -
Proceedings of the National Academy of... Feb 2023Microbes in the wild face highly variable and unpredictable environments and are naturally selected for their average growth rate across environments. Apart from using...
Microbes in the wild face highly variable and unpredictable environments and are naturally selected for their average growth rate across environments. Apart from using sensory regulatory systems to adapt in a targeted manner to changing environments, microbes employ bet-hedging strategies where cells in an isogenic population switch stochastically between alternative phenotypes. Yet, bet-hedging suffers from a fundamental trade-off: Increasing the phenotype-switching rate increases the rate at which maladapted cells explore alternative phenotypes but also increases the rate at which cells switch out of a well-adapted state. Consequently, it is currently believed that bet-hedging strategies are effective only when the number of possible phenotypes is limited and when environments last for sufficiently many generations. However, recent experimental results show that gene expression noise generally decreases with growth rate, suggesting that phenotype-switching rates may systematically decrease with growth rate. Such growth rate dependent stability (GRDS) causes cells to be more explorative when maladapted and more phenotypically stable when well-adapted, and we show that GRDS can almost completely overcome the trade-off that limits bet-hedging, allowing for effective adaptation even when environments are diverse and change rapidly. We further show that even a small decrease in switching rates of faster-growing phenotypes can substantially increase long-term fitness of bet-hedging strategies. Together, our results suggest that stochastic strategies may play an even bigger role for microbial adaptation than hitherto appreciated.
Topics: Biological Evolution; Phenotype; Acclimatization; Adaptation, Physiological
PubMed: 36780518
DOI: 10.1073/pnas.2211091120 -
IEEE/ACM Transactions on Computational... 2023Identifying drug phenotypic effects, including therapeutic effects and adverse drug reactions (ADRs), is an inseparable part for evaluating the potentiality of new drug...
Identifying drug phenotypic effects, including therapeutic effects and adverse drug reactions (ADRs), is an inseparable part for evaluating the potentiality of new drug candidates (NDCs). However, current computational methods for predicting phenotypic effects of NDCs are mainly based on the overall structure of an NDC or a related target. These approaches often lead to inconsistencies between the structures and functions and limit the prediction space of NDCs. In this study, first, we constructed quantitative associations of substructure-domain, domain-ADR, and domain-ATC (Anatomical Therapeutic Chemical Classification System code) through L1LOG and L1SVM machine learning models. These associations represent relationships between phenotypes (ADRs and ATCs) and local structures of drugs and proteins. Then, based on these established associations, substructure-phenotype relationships were constructed which were utilized to quantify drug-phenotype relationships. Thus, this approach could achieve high-throughput and effective evaluations of the druggability of NDCs by referring to the established substructure-phenotype relationships and structural information of NDCs without additional prior knowledge. Using this computational pipeline, 83,205 drug-ATC relationships (including 1,479 drugs and 178 ATCs) and 306,421 drug-ADR relationships (including 1,752 drugs and 454 ADRs) were predicted in total. The prediction results were validated at four levels: five-fold cross validation, public databases, literature, and molecular docking. Furthermore, three case studies demonstrated the feasibility of our method. 79 ATCs and 269 ADRs were predicted to be related to Maraviroc, an approved drug, including the existing antiviral effect in clinical use. Additionally, we also found risk substructures of severe ADRs, for example, SUB215 (>= 1, saturated or only aromatic carbon ring size 7) can result in shock. And we analyzed the mechanism of action (MOA) of interested drugs based on the established drug-substructure-domain-protein associations. In a word, this approach through establishing drug-substructure-phenotype relationships can achieve quantitative prediction of phenotypes for a given NDC or drug without any prior knowledge except its structure information. Using that way, we can directly obtain the relationships between substructure and phenotype of a compound, which is more convenient to analyze the phenotypic mechanism of drugs and accelerate the process of rational drug design.
Topics: Humans; Molecular Docking Simulation; Drug-Related Side Effects and Adverse Reactions; Databases, Factual; Machine Learning; Phenotype
PubMed: 35239490
DOI: 10.1109/TCBB.2022.3155453 -
Clinical Laboratory Aug 2022This study was performed to provide information on the frequencies of Rh antigens, alleles, and phenotypes from our region in Tianjin, China. (Observational Study)
Observational Study
BACKGROUND
This study was performed to provide information on the frequencies of Rh antigens, alleles, and phenotypes from our region in Tianjin, China.
METHODS
This observational study was conducted on patients from January 2018 to March 2021 using a fully automated system for ABO and Rh typing of blood cells. The phenotypes of C, c, E, and e were detected by the slide method. The data were collected and calculations done to determine the antigen, phenotypes and allele frequencies.
RESULTS
Four hundred thirty-three cases of Rh (D) negative phenotype were confirmed in 88,856 patients. Of the four Rh antigens (C, c, E, e) that were phenotyped by serological methods, the "e" antigen was found to have the highest frequency (99.74%). The most common Rh negative phenotype observed was ccdee, followed by Ccdee. The prevalence of Rh phenotypes ccdEe, CCdee, CcdEe, CCdEe, ccdEE were found to be rare in our population with percentages of 0.0473%, 0.018%, 0.018%, 0.0034%, and 0.0011%, respectively.
CONCLUSIONS
Knowledge of red cell antigen phenotype frequencies in a population is helpful in terms of their ethnic distribution. We have determined the prevalence of Rh antigens and Rh phenotypes in China. The Rh blood group distribution in this population was different from that in other populations.
Topics: Alleles; Blood Group Antigens; China; Gene Frequency; Phenotype; Rh-Hr Blood-Group System
PubMed: 35975497
DOI: 10.7754/Clin.Lab.2021.211037