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Philosophical Transactions of the Royal... Jul 2022Identifying the general principles by which genotypes are converted into phenotypes remains a challenge in the post-genomic era. We still lack a predictive understanding... (Review)
Review
Identifying the general principles by which genotypes are converted into phenotypes remains a challenge in the post-genomic era. We still lack a predictive understanding of how genes shape interactions among cells and tissues in response to signalling and environmental cues, and hence how regulatory networks generate the phenotypic variation required for adaptive evolution. Here, we discuss how techniques borrowed from synthetic biology may facilitate a systematic exploration of evolvability across biological scales. Synthetic approaches permit controlled manipulation of both endogenous and fully engineered systems, providing a flexible platform for investigating causal mechanisms . Combining synthetic approaches with multi-level phenotyping (phenomics) will supply a detailed, quantitative characterization of how internal and external stimuli shape the morphology and behaviour of living organisms. We advocate integrating high-throughput experimental data with mathematical and computational techniques from a variety of disciplines in order to pursue a comprehensive theory of evolution. This article is part of the theme issue 'Genetic basis of adaptation and speciation: from loci to causative mutations'.
Topics: Adaptation, Physiological; Animals; Genome; Genomics; Phenotype; Synthetic Biology
PubMed: 35634925
DOI: 10.1098/rstb.2020.0517 -
Current Opinion in Microbiology Aug 2023How microbes interact with their environment and how the complex interplay of their genes enables them to survive and thrive under stress is a fundamental question in... (Review)
Review
How microbes interact with their environment and how the complex interplay of their genes enables them to survive and thrive under stress is a fundamental question in microbial system biology, which is also important from a public health perspective. Large-scale studies of gene-gene, gene-drug, and drug-drug interactions have proven to be powerful tools for elucidating gene function and functional modules in the cell. Approaches that systematically quantify phenotypes in libraries of microbial strains with genome-wide genetic perturbations are crucial for progress in this area. Here, we review recent advances in this field, and point out applications to the study of gene-drug interactions. We highlight newly developed techniques for the rapid generation of genome-wide mutant libraries and the high-throughput measurement of more complex phenotypes and other observables, such as cell morphology or thermal stability of the proteome.
Topics: Phenotype; Genome
PubMed: 37276805
DOI: 10.1016/j.mib.2023.102333 -
Neuropsychopharmacology : Official... Jan 2021Focusing on biomarker identification and using biomarkers individually or in clusters to define biological subgroups in psychiatry requires a re-orientation from... (Review)
Review
Focusing on biomarker identification and using biomarkers individually or in clusters to define biological subgroups in psychiatry requires a re-orientation from behavioral phenomenology to quantifying brain features, requiring big data approaches for data integration. Much still needs to be accomplished, not only to refine but also to build support for the application and customization of such an analytical phenotypic approach. In this review, we present some of what Bipolar-Schizophrenia Network for Intermediate Phenotypes (B-SNIP) has learned so far to guide future applications of multivariate phenotyping and their analyses to understanding psychosis. This paper describes several B-SNIP projects that use phenotype data and big data computations to generate novel outcomes and glimpse what phenotypes contribute to disease understanding and, with aspiration, to treatment. The source of the phenotypes varies from genetic data, structural neuroanatomic localization, immune markers, brain physiology, and cognition. We aim to see guiding principles emerge and areas of commonality revealed. And, we will need to demonstrate not only data stability but also the usefulness of biomarker information for subgroup identification enhancing target identification and treatment development.
Topics: Bipolar Disorder; Brain; Humans; Phenotype; Psychotic Disorders; Schizophrenia
PubMed: 32979849
DOI: 10.1038/s41386-020-00849-8 -
Human Mutation Nov 2022Making a specific diagnosis in neurodevelopmental disorders is traditionally based on recognizing clinical features of a distinct syndrome, which guides testing of its... (Review)
Review
Making a specific diagnosis in neurodevelopmental disorders is traditionally based on recognizing clinical features of a distinct syndrome, which guides testing of its possible genetic etiologies. Scalable frameworks for genomic diagnostics, however, have struggled to integrate meaningful measurements of clinical phenotypic features. While standardization has enabled generation and interpretation of genomic data for clinical diagnostics at unprecedented scale, making the equivalent breakthrough for clinical data has proven challenging. However, increasingly clinical features are being recorded using controlled dictionaries with machine readable formats such as the Human Phenotype Ontology (HPO), which greatly facilitates their use in the diagnostic space. Improving the tractability of large-scale clinical information will present new opportunities to inform genomic research and diagnostics from a clinical perspective. Here, we describe novel approaches for computational phenotyping to harmonize clinical features, improve data translation through revising domain-specific dictionaries, quantify phenotypic features, and determine clinical relatedness. We demonstrate how these concepts can be applied to longitudinal phenotypic information, which represents a critical element of developmental disorders and pediatric conditions. Finally, we expand our discussion to clinical data derived from electronic medical records, a largely untapped resource of deep clinical information with distinct strengths and weaknesses.
Topics: Child; Electronic Health Records; Genomics; Humans; Phenotype
PubMed: 35460582
DOI: 10.1002/humu.24389 -
The ISME Journal Sep 2023Experimental evolution in a laboratory helps researchers to understand the genetic and phenotypic background of adaptation under a particular condition. Simultaneously,...
Experimental evolution in a laboratory helps researchers to understand the genetic and phenotypic background of adaptation under a particular condition. Simultaneously, the simplified environment that represents certain aspects of a complex natural niche permits the dissection of relevant parameters behind the selection, including temperature, oxygen availability, nutrients, and biotic factors. The presence of other microorganisms or a host has a major influence on microbial evolution that often differs from the adaptation paths observed in response to abiotic conditions. In the current issue of the ISME Journal, Cosetta and colleagues reveal how cross-kingdom interaction representing the cheese microbiome succession promotes distinct evolution of the food- and animal-associated bacterium, . The authors also identified a global regulator-dependent adaption that leads to evolved derivatives exhibiting reduced pigment production and colony morphologies in addition to altered differentiation phenotypes that potentially contribute to increased fitness.
Topics: Staphylococcus; Biological Evolution; Phenotype
PubMed: 37524911
DOI: 10.1038/s41396-023-01479-w -
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 -
Philosophical Transactions. Series A,... Jul 2022At odds with a traditional view of molecular evolution that seeks a descent-with-modification relationship between functional sequences, new functions can emerge with... (Review)
Review
At odds with a traditional view of molecular evolution that seeks a descent-with-modification relationship between functional sequences, new functions can emerge with relative ease. At early times of molecular evolution, random polymers could have sufficed for the appearance of incipient chemical activity, while the cellular environment harbours a myriad of proto-functional molecules. The emergence of function is facilitated by several mechanisms intrinsic to molecular organization, such as redundant mapping of sequences into structures, phenotypic plasticity, modularity or cooperative associations between genomic sequences. It is the availability of niches in the molecular ecology that filters new potentially functional proposals. New phenotypes and subsequent levels of molecular complexity could be attained through combinatorial explorations of currently available molecular variants. Natural selection does the rest. This article is part of the theme issue 'Emergent phenomena in complex physical and socio-technical systems: from cells to societies'.
Topics: Adaptation, Physiological; Biological Evolution; Evolution, Molecular; Phenotype; Selection, Genetic
PubMed: 35599566
DOI: 10.1098/rsta.2020.0422 -
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 -
Journal of Experimental Botany Sep 2022Global warming has become an issue in recent years in viticulture, as increasing temperatures have a negative impact on grapevine (Vitis vinifera) production and on wine... (Review)
Review
Global warming has become an issue in recent years in viticulture, as increasing temperatures have a negative impact on grapevine (Vitis vinifera) production and on wine quality. Phenotyping for grapevine response to heat stress is, therefore, important to understand thermotolerance mechanisms, with the aim of improving field management strategies or developing more resilient varieties. Nonetheless, the choice of the phenotypic traits to be investigated is not trivial and depends mainly on the objectives of the study, but also on the number of samples and on the availability of instrumentation. Moreover, the grapevine literature reports few studies related to thermotolerance, generally assessing physiological responses, which highlights the need for more holistic approaches. In this context, the present review offers an overview of target traits that are commonly investigated in plant thermotolerance studies, with a special focus on grapevine, and of methods that can be employed to evaluate those traits. With the final goal of providing useful tools and references for future studies on grapevine heat stress resilience, advantages and limitations of each method are highlighted, and the available or possible implementations are described. In this way, the reader is guided in the choice of the best approaches in terms of speed, complexity, range of application, sensitivity, and specificity.
Topics: Heat-Shock Response; Phenotype; Vitis
PubMed: 35532318
DOI: 10.1093/jxb/erac058 -
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