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Cell Systems May 2023Cellular and organismal phenotypes are controlled by complex gene regulatory networks. However, reference maps of gene function are still scarce across different...
Cellular and organismal phenotypes are controlled by complex gene regulatory networks. However, reference maps of gene function are still scarce across different organisms. Here, we generated synthetic genetic interaction and cell morphology profiles of more than 6,800 genes in cultured Drosophila cells. The resulting map of genetic interactions was used for machine learning-based gene function discovery, assigning functions to genes in 47 modules. Furthermore, we devised Cytoclass as a method to dissect genetic interactions for discrete cell states at the single-cell resolution. This approach identified an interaction of Cdk2 and the Cop9 signalosome complex, triggering senescence-associated secretory phenotypes and immunogenic conversion in hemocytic cells. Together, our data constitute a genome-scale resource of functional gene profiles to uncover the mechanisms underlying genetic interactions and their plasticity at the single-cell level.
Topics: Animals; Gene Regulatory Networks; Phenotype; Drosophila
PubMed: 37116498
DOI: 10.1016/j.cels.2023.03.003 -
Genes Jun 2022Network and systemic approaches to studying human pathologies are helping us to gain insight into the molecular mechanisms of and potential therapeutic interventions for... (Review)
Review
Network and systemic approaches to studying human pathologies are helping us to gain insight into the molecular mechanisms of and potential therapeutic interventions for human diseases, especially for complex diseases where large numbers of genes are involved. The complex human pathological landscape is traditionally partitioned into discrete "diseases"; however, that partition is sometimes problematic, as diseases are highly heterogeneous and can differ greatly from one patient to another. Moreover, for many pathological states, the set of symptoms (phenotypes) manifested by the patient is not enough to diagnose a particular disease. On the contrary, phenotypes, by definition, are directly observable and can be closer to the molecular basis of the pathology. These clinical phenotypes are also important for personalised medicine, as they can help stratify patients and design personalised interventions. For these reasons, network and systemic approaches to pathologies are gradually incorporating phenotypic information. This review covers the current landscape of phenotype-centred network approaches to study different aspects of human diseases.
Topics: Humans; Phenotype
PubMed: 35741843
DOI: 10.3390/genes13061081 -
Current Opinion in Pulmonary Medicine Sep 2022Pulmonary hypertension (PH) is a common complication of chronic obstructive lung disease (COPD), but clinical presentation is variable and not always 'proportional' to... (Review)
Review
PURPOSE OF REVIEW
Pulmonary hypertension (PH) is a common complication of chronic obstructive lung disease (COPD), but clinical presentation is variable and not always 'proportional' to the severity of the obstructive disease. This review aims to analyze heterogeneity in clinical features of PH-COPD, providing a guide for diagnosis and management according to phenotypes.
RECENT FINDINGS
Recent works have focused on severe PH in COPD, providing insights into the characteristics of patients with predominantly vascular disease. The recently recognized 'pulmonary vascular phenotype', characterized by severe PH and mild airflow obstruction with severe hypoxemia, has markedly worse prognosis and may be a candidate for large trials with pulmonary vasodilators. In severe PH, which might be best described by a pulmonary vascular resistance threshold, there may also be a need to distinguish patients with mild COPD (pulmonary vascular phenotype) from those with severe COPD ('Severe COPD-Severe PH' phenotype).
SUMMARY
Correct phenotyping is key to appropriate management of PH associated with COPD. The lack of evidence regarding the use of pulmonary vasodilators in PH-COPD may be due to the existence of previously unrecognized phenotypes with different responses to therapy. This review offers the clinician caring for patients with COPD and PH a phenotype-focused approach to diagnosis and management, aimed at personalized care.
Topics: Humans; Hypertension, Pulmonary; Lung; Phenotype; Pulmonary Disease, Chronic Obstructive; Vasodilator Agents
PubMed: 35838373
DOI: 10.1097/MCP.0000000000000890 -
Journal of Biomedical Informatics Oct 2022Electronic Health Record (EHR) based phenotyping is a crucial yet challenging problem in the biomedical field. Though clinicians typically determine patient-level...
OBJECTIVE
Electronic Health Record (EHR) based phenotyping is a crucial yet challenging problem in the biomedical field. Though clinicians typically determine patient-level diagnoses via manual chart review, the sheer volume and heterogeneity of EHR data renders such tasks challenging, time-consuming, and prohibitively expensive, thus leading to a scarcity of clinical annotations in EHRs. Weakly supervised learning algorithms have been successfully applied to various EHR phenotyping problems, due to their ability to leverage information from large quantities of unlabeled samples to better inform predictions based on a far smaller number of patients. However, most weakly supervised methods are subject to the challenge to choose the right cutoff value to generate an optimal classifier. Furthermore, since they only utilize the most informative features (i.e., main ICD and NLP counts) they may fail for episodic phenotypes that cannot be consistently detected via ICD and NLP data. In this paper, we propose a label-efficient, weakly semi-supervised deep learning algorithm for EHR phenotyping (WSS-DL), which overcomes the limitations above.
MATERIALS AND METHODS
WSS-DL classifies patient-level disease status through a series of learning stages: 1) generating silver standard labels, 2) deriving enhanced-silver-standard labels by fitting a weakly supervised deep learning model to data with silver standard labels as outcomes and high dimensional EHR features as input, and 3) obtaining the final prediction score and classifier by fitting a supervised learning model to data with a minimal number of gold standard labels as the outcome, and the enhanced-silver-standard labels and a minimal set of most informative EHR features as input. To assess the generalizability of WSS-DL across different phenotypes and medical institutions, we apply WSS-DL to classify a total of 17 diseases, including both acute and chronic conditions, using EHR data from three healthcare systems. Additionally, we determine the minimum quantity of training labels required by WSS-DL to outperform existing supervised and semi-supervised phenotyping methods.
RESULTS
The proposed method, in combining the strengths of deep learning and weakly semi-supervised learning, successfully leverages the crucial phenotyping information contained in EHR features from unlabeled samples. Indeed, the deep learning model's ability to handle high-dimensional EHR features allows it to generate strong phenotype status predictions from silver standard labels. These predictions, in turn, provide highly effective features in the final logistic regression stage, leading to high phenotyping accuracy in notably small subsets of labeled data (e.g. n = 40 labeled samples).
CONCLUSION
Our method's high performance in EHR datasets with very small numbers of labels indicates its potential value in aiding doctors to diagnose rare diseases as well as conditions susceptible to misdiagnosis.
Topics: Algorithms; Electronic Health Records; Logistic Models; Phenotype; Supervised Machine Learning
PubMed: 36064111
DOI: 10.1016/j.jbi.2022.104175 -
Frontiers in Immunology 2023Immune cells are highly heterogeneous and show diverse phenotypes, but the underlying mechanism remains to be elucidated. In this study, we proposed a theoretical...
Immune cells are highly heterogeneous and show diverse phenotypes, but the underlying mechanism remains to be elucidated. In this study, we proposed a theoretical framework for immune cell phenotypic classification based on gene plasticity, which herein refers to expressional change or variability in response to conditions. The system contains two core points. One is that the functional subsets of immune cells can be further divided into subdivisions based on their highly plastic genes, and the other is that loss of phenotype accompanies gain of phenotype during phenotypic conversion. The first point suggests phenotypic stratification or layerability according to gene plasticity, while the second point reveals expressional compatibility and mutual exclusion during the change in gene plasticity states. Abundant transcriptome data analysis in this study from both microarray and RNA sequencing in human CD4 and CD8 single-positive T cells, B cells, natural killer cells and monocytes supports the logical rationality and generality, as well as expansibility, across immune cells. A collection of thousands of known immunophenotypes reported in the literature further supports that highly plastic genes play an important role in maintaining immune cell phenotypes and reveals that the current classification model is compatible with the traditionally defined functional subsets. The system provides a new perspective to understand the characteristics of dynamic, diversified immune cell phenotypes and intrinsic regulation in the immune system. Moreover, the current substantial results based on plasticitomics analysis of bulk and single-cell sequencing data provide a useful resource for big-data-driven experimental studies and knowledge discoveries.
Topics: Humans; Monocytes; Gene Expression Profiling; Transcriptome; Phenotype; B-Lymphocytes
PubMed: 36936975
DOI: 10.3389/fimmu.2023.1128423 -
Progress in Biophysics and Molecular... Mar 2023In the predominately gene-centered view of 20th century biology, the relationship between genotype and phenotype was essentially a relationship between cause and effect,... (Review)
Review
In the predominately gene-centered view of 20th century biology, the relationship between genotype and phenotype was essentially a relationship between cause and effect, between a plan and a product. Abandoning the idea of genes as inherited instructions or blueprints for phenotypes raises the question of how to best account for observed phenotypic stability and variability within and across generations of a population. We argue that the processes responsible for phenotypic stability and the processes responsible for phenotypic variability are one and the same, namely, the dynamics of development. This argument proposes that stability of phenotypic form is found not because of the transmission of genotypes, genetic programs, or the transfer of internal blueprints, but because similar internal and external conditions-collectively conceptualized as resources of development-can be reliably reconstituted in each generation. Variability of phenotypic form, which is an indispensable feature of any evolving system, relies on these same resources, but because the internal and external conditions of development are not reconstituted identically in succeeding generations, these conditions-and the phenotypes to which they give rise-will always be characterized by at least some variability.
Topics: Biological Evolution; Genetic Determinism; Phenotype; Genotype
PubMed: 36682588
DOI: 10.1016/j.pbiomolbio.2023.01.003 -
Seminars in Cell & Developmental Biology Sep 2019Processes in collective migration span many length and time scales. In this review, we focus on length scales ranging from tens of microns (single cells) to a few... (Review)
Review
Processes in collective migration span many length and time scales. In this review, we focus on length scales ranging from tens of microns (single cells) to a few millimeters (cell clusters) and the motion of these cells and cell groups on time scales of minutes to hours. We focus on epithelial cell sheets and metrics of motion developed to measure migration phenotypes in this system. Comparisons between cell motion and fluid flows, facilitated by the popular image analysis technique particle image velocimetry, yield metrics that can be used to study migration across a range of length and time scales. Measuring collective cell migration across these scales provides a complex, quantitative phenotype useful for migration models, in particular those that compare and contrast collective cell migration to movement of particles near a transition to jamming. Contrasting the motion of epithelial cells and the jamming transition illustrates aspects of collective motion that can be attributed to the jammed character of cell clusters, and highlights aspects of collective behavior that likely involve active motility and cell-cell guidance. The application of multiple migration metrics, which span multiple scales of the system, thus allows us to link cell-scale signals and mechanics to collective behavior.
Topics: Cell Movement; Humans; Phenotype; Time Factors
PubMed: 31429407
DOI: 10.1016/j.semcdb.2018.10.010 -
Advanced high-throughput plant phenotyping techniques for genome-wide association studies: A review.Journal of Advanced Research Jan 2022Linking phenotypes and genotypes to identify genetic architectures that regulate important traits is crucial for plant breeding and the development of plant genomics. In... (Review)
Review
Linking phenotypes and genotypes to identify genetic architectures that regulate important traits is crucial for plant breeding and the development of plant genomics. In recent years, genome-wide association studies (GWASs) have been applied extensively to interpret relationships between genes and traits. Successful GWAS application requires comprehensive genomic and phenotypic data from large populations. Although multiple high-throughput DNA sequencing approaches are available for the generation of genomics data, the capacity to generate high-quality phenotypic data is lagging far behind. Traditional methods for plant phenotyping mostly rely on manual measurements, which are laborious, inaccurate, and time-consuming, greatly impairing the acquisition of phenotypic data from large populations. In contrast, high-throughput phenotyping has unique advantages, facilitating rapid, non-destructive, and high-throughput detection, and, in turn, addressing the shortcomings of traditional methods. This review summarizes the current status with regard to the integration of high-throughput phenotyping and GWAS in plants, in addition to discussing the inherent challenges and future prospects. High-throughput phenotyping, which facilitates non-contact and dynamic measurements, has the potential to offer high-quality trait data for GWAS and, in turn, to enhance the unraveling of genetic structures of complex plant traits. In conclusion, high-throughput phenotyping integration with GWAS could facilitate the revealing of coding information in plant genomes.
Topics: Genome, Plant; Genome-Wide Association Study; Genotype; Phenotype; Plant Breeding
PubMed: 35003802
DOI: 10.1016/j.jare.2021.05.002 -
BMC Bioinformatics Dec 2022Gene set enrichment analysis (detecting phenotypic terms that emerge as significant in a set of genes) plays an important role in bioinformatics focused on diseases of...
BACKGROUND
Gene set enrichment analysis (detecting phenotypic terms that emerge as significant in a set of genes) plays an important role in bioinformatics focused on diseases of genetic basis. To facilitate phenotype-oriented gene set analysis, we developed PhenoExam, a freely available R package for tool developers and a web interface for users, which performs: (1) phenotype and disease enrichment analysis on a gene set; (2) measures statistically significant phenotype similarities between gene sets and (3) detects significant differential phenotypes or disease terms across different databases.
RESULTS
PhenoExam generates sensitive and accurate phenotype enrichment analyses. It is also effective in segregating gene sets or Mendelian diseases with very similar phenotypes. We tested the tool with two similar diseases (Parkinson and dystonia), to show phenotype-level similarities but also potentially interesting differences. Moreover, we used PhenoExam to validate computationally predicted new genes potentially associated with epilepsy.
CONCLUSIONS
We developed PhenoExam, a freely available R package and Web application, which performs phenotype enrichment and disease enrichment analysis on gene set G, measures statistically significant phenotype similarities between pairs of gene sets G and G' and detects statistically significant exclusive phenotypes or disease terms, across different databases. We proved with simulations and real cases that it is useful to distinguish between gene sets or diseases with very similar phenotypes. Github R package URL is https://github.com/alexcis95/PhenoExam . Shiny App URL is https://alejandrocisterna.shinyapps.io/phenoexamweb/ .
Topics: Databases, Factual; Software; Computational Biology; Phenotype; Databases, Genetic
PubMed: 36587217
DOI: 10.1186/s12859-022-05122-x -
Bioinformatics (Oxford, England) Mar 2022Structural genomic variants account for much of human variability and are involved in several diseases. Structural variants are complex and may affect coding regions of...
MOTIVATION
Structural genomic variants account for much of human variability and are involved in several diseases. Structural variants are complex and may affect coding regions of multiple genes, or affect the functions of genomic regions in different ways from single nucleotide variants. Interpreting the phenotypic consequences of structural variants relies on information about gene functions, haploinsufficiency or triplosensitivity and other genomic features. Phenotype-based methods to identifying variants that are involved in genetic diseases combine molecular features with prior knowledge about the phenotypic consequences of altering gene functions. While phenotype-based methods have been applied successfully to single nucleotide variants as well as short insertions and deletions, the complexity of structural variants makes it more challenging to link them to phenotypes. Furthermore, structural variants can affect a large number of coding regions, and phenotype information may not be available for all of them.
RESULTS
We developed DeepSVP, a computational method to prioritize structural variants involved in genetic diseases by combining genomic and gene functions information. We incorporate phenotypes linked to genes, functions of gene products, gene expression in individual cell types and anatomical sites of expression, and systematically relate them to their phenotypic consequences through ontologies and machine learning. DeepSVP significantly improves the success rate of finding causative variants in several benchmarks and can identify novel pathogenic structural variants in consanguineous families.
AVAILABILITY AND IMPLEMENTATION
https://github.com/bio-ontology-research-group/DeepSVP.
SUPPLEMENTARY INFORMATION
Supplementary data are available at Bioinformatics online.
Topics: Humans; Deep Learning; Genotype; Phenotype; Genomics; Nucleotides
PubMed: 34951628
DOI: 10.1093/bioinformatics/btab859