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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 -
Annual Review of Biomedical Data Science Aug 2023Autism spectrum disorder (autism) is a neurodevelopmental delay that affects at least 1 in 44 children. Like many neurological disorder phenotypes, the diagnostic... (Review)
Review
Autism spectrum disorder (autism) is a neurodevelopmental delay that affects at least 1 in 44 children. Like many neurological disorder phenotypes, the diagnostic features are observable, can be tracked over time, and can be managed or even eliminated through proper therapy and treatments. However, there are major bottlenecks in the diagnostic, therapeutic, and longitudinal tracking pipelines for autism and related neurodevelopmental delays, creating an opportunity for novel data science solutions to augment and transform existing workflows and provide increased access to services for affected families. Several efforts previously conducted by a multitude of research labs have spawned great progress toward improved digital diagnostics and digital therapies for children with autism. We review the literature on digital health methods for autism behavior quantification and beneficial therapies using data science. We describe both case-control studies and classification systems for digital phenotyping. We then discuss digital diagnostics and therapeutics that integrate machine learning models of autism-related behaviors, including the factors that must be addressed for translational use. Finally, we describe ongoing challenges and potential opportunities for the field of autism data science. Given the heterogeneous nature of autism and the complexities of the relevant behaviors, this review contains insights that are relevant to neurological behavior analysis and digital psychiatry more broadly.
Topics: Humans; Autistic Disorder; Autism Spectrum Disorder; Data Science; Machine Learning; Phenotype
PubMed: 37137169
DOI: 10.1146/annurev-biodatasci-020722-125454 -
Journal of the American Medical... Jan 2023Electronic (e)-phenotype specification by noninformaticist investigators remains a challenge. Although validation of each patient returned by e-phenotype could ensure...
BACKGROUND
Electronic (e)-phenotype specification by noninformaticist investigators remains a challenge. Although validation of each patient returned by e-phenotype could ensure accuracy of cohort representation, this approach is not practical. Understanding the factors leading to successful e-phenotype specification may reveal generalizable strategies leading to better results.
MATERIALS AND METHODS
Noninformaticist experts (n = 21) were recruited to produce expert-mediated e-phenotypes using i2b2 assisted by a honest data-broker and a project coordinator. Patient- and visit-sets were reidentified and a random sample of 20 charts matching each e-phenotype was returned to experts for chart-validation. Attributes of the queries and expert characteristics were captured and related to chart-validation rates using generalized linear regression models.
RESULTS
E-phenotype validation rates varied according to experts' domains and query characteristics (mean = 61%, range 20-100%). Clinical domains that performed better included infectious, rheumatic, neonatal, and cancers, whereas other domains performed worse (psychiatric, GI, skin, and pulmonary). Match-rate was negatively impacted when specification of temporal constraints was required. In general, the increase in e-phenotype specificity contributed positively to match-rate.
DISCUSSIONS AND CONCLUSIONS
Clinical experts and informaticists experience a variety of challenges when building e-phenotypes, including the inability to differentiate clinical events from patient characteristics or appropriately configure temporal constraints; a lack of access to available and quality data; and difficulty in specifying routes of medication administration. Biomedical query mediation by informaticists and honest data-brokers in designing e-phenotypes cannot be overstated. Although tools such as i2b2 may be widely available to noninformaticists, successful utilization depends not on users' confidence, but rather on creating highly specific e-phenotypes.
Topics: Phenotype; Mental Processes; Research Design; Electronic Health Records
PubMed: 36069977
DOI: 10.1093/jamia/ocac157 -
Scientific Reports Mar 2023Macrophages are heterogeneous innate immune cells that are functionally shaped by their surrounding microenvironment. Diverse macrophage populations have multifaceted...
Macrophages are heterogeneous innate immune cells that are functionally shaped by their surrounding microenvironment. Diverse macrophage populations have multifaceted differences related to their morphology, metabolism, expressed markers, and functions, where the identification of the different phenotypes is of an utmost importance in modelling immune response. While expressed markers are the most used signature to classify phenotypes, multiple reports indicate that macrophage morphology and autofluorescence are also valuable clues that can be used in the identification process. In this work, we investigated macrophage autofluorescence as a distinct feature for classifying six different macrophage phenotypes, namely: M0, M1, M2a, M2b, M2c, and M2d. The identification was based on extracted signals from multi-channel/multi-wavelength flow cytometer. To achieve the identification, we constructed a dataset containing 152,438 cell events each having a response vector of 45 optical signals fingerprint. Based on this dataset, we applied different supervised machine learning methods to detect phenotype specific fingerprint from the response vector, where the fully connected neural network architecture provided the highest classification accuracy of 75.8% for the six phenotypes compared simultaneously. Furthermore, by restricting the number of phenotypes in the experiment, the proposed framework produces higher classification accuracies, averaging 92.0%, 91.9%, 84.2%, and 80.4% for a pool of two, three, four, five phenotypes, respectively. These results indicate the potential of the intrinsic autofluorescence for classifying macrophage phenotypes, with the proposed method being quick, simple, and cost-effective way to accelerate the discovery of macrophage phenotypical diversity.
Topics: Macrophages; Phenotype; Machine Learning
PubMed: 36997576
DOI: 10.1038/s41598-023-32158-7 -
Theory in Biosciences = Theorie in Den... Feb 2023In animal species with separate sexes, abnormal individuals with a mix of phenotypically male and phenotypically female body parts are generally indicated as...
In animal species with separate sexes, abnormal individuals with a mix of phenotypically male and phenotypically female body parts are generally indicated as gynandromorphs, whereas individuals with intermediate sexual phenotypic traits are generally indicated as intersexes. However, this distinction, clear as it may seem, is neither universally agreed upon, nor free of critical issues. In consideration of the role of sex anomalies in understanding normal development, we reassess these phenomena of abnormal sexual development, taking into consideration the more recent advances in the study of sex determination and sexual differentiation. We argue that a distinction between gynandromorphism and intersexuality, although useful for descriptive purposes, is not always possible or sensible. We discuss the conceptual and terminological intricacies of the literature on this subject and provide reasons for largely, although not strictly, preferring a terminology based on descriptive rather than causal morphology, that is, on the observed phenotypic patterns rather on the causal process behind them.
Topics: Animals; Male; Female; Disorders of Sex Development; Phenotype
PubMed: 36633802
DOI: 10.1007/s12064-023-00385-1 -
Clinical and Experimental Rheumatology 2019In the most recent years, an extraordinary research effort has emerged to disentangle osteoarthritis heterogeneity, opening new avenues for progressing with therapeutic... (Review)
Review
In the most recent years, an extraordinary research effort has emerged to disentangle osteoarthritis heterogeneity, opening new avenues for progressing with therapeutic development and unravelling the pathogenesis of this complex condition. Several phenotypes and endotypes have been proposed albeit none has been sufficiently validated for clinical or research use as yet. This review discusses the latest advances in OA phenotyping including how new modern statistical strategies based on machine learning and big data can help advance this field of research.
Topics: Big Data; Forecasting; Humans; Osteoarthritis; Phenotype; Precision Medicine
PubMed: 31621574
DOI: No ID Found -
Physics of Life Reviews Sep 2021Understanding how genotypes map onto phenotypes, fitness, and eventually organisms is arguably the next major missing piece in a fully predictive theory of evolution. We... (Review)
Review
Understanding how genotypes map onto phenotypes, fitness, and eventually organisms is arguably the next major missing piece in a fully predictive theory of evolution. We refer to this generally as the problem of the genotype-phenotype map. Though we are still far from achieving a complete picture of these relationships, our current understanding of simpler questions, such as the structure induced in the space of genotypes by sequences mapped to molecular structures, has revealed important facts that deeply affect the dynamical description of evolutionary processes. Empirical evidence supporting the fundamental relevance of features such as phenotypic bias is mounting as well, while the synthesis of conceptual and experimental progress leads to questioning current assumptions on the nature of evolutionary dynamics-cancer progression models or synthetic biology approaches being notable examples. This work delves with a critical and constructive attitude into our current knowledge of how genotypes map onto molecular phenotypes and organismal functions, and discusses theoretical and empirical avenues to broaden and improve this comprehension. As a final goal, this community should aim at deriving an updated picture of evolutionary processes soundly relying on the structural properties of genotype spaces, as revealed by modern techniques of molecular and functional analysis.
Topics: Genotype; Phenotype
PubMed: 34088608
DOI: 10.1016/j.plrev.2021.03.004 -
The Journal of General Virology Oct 2022Herpes simplex virus 1 (HSV1) is best known for causing oral lesions and mild clinical symptoms, but it can produce a significant range of disease severities and rates...
Herpes simplex virus 1 (HSV1) is best known for causing oral lesions and mild clinical symptoms, but it can produce a significant range of disease severities and rates of reactivation. To better understand this phenotypic variation, we characterized 11 HSV1 strains that were isolated from individuals with diverse infection outcomes. We provide new data on genomic and plaque phenotype analysis for these isolates and compare these data to previously reported quantitation of the disease phenotype of each strain in a murine animal model. We show that integration of these three types of data permitted clustering of these HSV1 strains into four groups that were not distinguishable by any single dataset alone, highlighting the benefits of combinatorial multi-parameter phenotyping. Two strains (group 1) produced a partially or largely syncytial plaque phenotype and attenuated disease phenotypes in mice. Three strains of intermediate plaque size, causing severe disease in mice, were genetically clustered to a second group (group 2). Six strains with the smallest average plaque sizes were separated into two subgroups (groups 3 and 4) based on their different genetic clustering and disease severity in mice. Comparative genomics and network graph analysis suggested a separation of HSV1 isolates with attenuated vs. virulent phenotypes. These observations imply that virulence phenotypes of these strains may be traceable to genetic variation within the HSV1 population.
Topics: Mice; Animals; Herpesvirus 1, Human; Herpes Simplex; Phenotype; Disease Models, Animal; Genomics
PubMed: 36264606
DOI: 10.1099/jgv.0.001780 -
Acta Biomaterialia Mar 2021Cord blood (CB) mononuclear cell populations have demonstrated significant promise in biomaterials-based regenerative therapies; however, the contributions of monocyte...
Cord blood (CB) mononuclear cell populations have demonstrated significant promise in biomaterials-based regenerative therapies; however, the contributions of monocyte and macrophage subpopulations towards proper tissue healing and regeneration are not well understood, and the phenotypic responses of macrophage to microenvironmental cues have not been well-studied. In this work, we evaluated the effects of cytokine stimulation and altered substrate stiffness. Macrophage derived from CB CD14 monocytes adopted distinct inflammatory (M1) and anti-inflammatory (M2a and M2c) phenotypes in response to cytokine stimulation (M1: lipopolysaccharide (LPS) and interferon (IFN-γ); M2a: interleukin (IL)-4 and IL-13; M2c: IL-10) as determined through expression of relevant cell surface markers and growth factors. Cytokine-induced macrophage readily altered their phenotypes upon sequential administration of different cytokine cocktails. The impact of substrate stiffness on macrophage phenotype was evaluated by seeding CB-derived macrophage on 3wt%, 6wt%, and 14wt% poly(ethylene glycol)-based hydrogels, which exhibited swollen shear moduli of 0.1, 3.4, and 10.3 kPa, respectively. Surface marker expression and cytokine production varied depending on modulus, with anti-inflammatory phenotypes increasing with elevated substrate stiffness. Integration of specific hydrogel moduli and cytokine cocktail treatments resulted in the differential regulation of macrophage phenotypic biomarkers. These data suggest that CB-derived macrophages exhibit predictable behaviors that can be directed and finely tuned by combinatorial modulation of substrate physical properties and cytokine profiles.
Topics: Cell Differentiation; Cytokines; Fetal Blood; Macrophages; Phenotype
PubMed: 33359292
DOI: 10.1016/j.actbio.2020.12.040 -
Nature Communications Nov 2023Most of life's vast diversity of species and phenotypes is often attributed to adaptive radiation. Yet its contribution to species and phenotypic diversity of a major...
Most of life's vast diversity of species and phenotypes is often attributed to adaptive radiation. Yet its contribution to species and phenotypic diversity of a major group has not been examined. Two key questions remain unresolved. First, what proportion of clades show macroevolutionary dynamics similar to adaptive radiations? Second, what proportion of overall species richness and phenotypic diversity do these adaptive-radiation-like clades contain? We address these questions with phylogenetic and morphological data for 1226 frog species across 43 families (which represent >99% of all species). Less than half of frog families resembled adaptive radiations (with rapid diversification and morphological evolution). Yet, these adaptive-radiation-like clades encompassed ~75% of both morphological and species diversity, despite rapid rates in other clades (e.g., non-adaptive radiations). Overall, we support the importance of adaptive-radiation-like evolution for explaining diversity patterns and provide a framework for characterizing macroevolutionary dynamics and diversity patterns in other groups.
Topics: Phylogeny; Biological Evolution; Phenotype; Genetic Speciation
PubMed: 37925440
DOI: 10.1038/s41467-023-42745-x