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Journal of Neurotrauma Sep 2023Traumatic spinal cord injury (SCI) causes a sudden onset multi-system disease, permanently altering homeostasis with multiple complications. Consequences include...
Traumatic spinal cord injury (SCI) causes a sudden onset multi-system disease, permanently altering homeostasis with multiple complications. Consequences include aberrant neuronal circuits, multiple organ system dysfunctions, and chronic phenotypes such as neuropathic pain and metabolic syndrome. Reductionist approaches are used to classify SCI patients based on residual neurological function. Still, recovery varies due to interacting variables, including individual biology, comorbidities, complications, therapeutic side effects, and socioeconomic influences for which data integration methods are lacking. Infections, pressure sores, and heterotopic ossification are known recovery modifiers. However, the molecular pathobiology of the disease-modifying factors altering the neurological recovery-chronic syndrome trajectory is mainly unknown, with significant data gaps between intensive early treatment and chronic phases. Changes in organ function such as gut dysbiosis, adrenal dysregulation, fatty liver, muscle loss, and autonomic dysregulation disrupt homeostasis, generating progression-driving allostatic load. Interactions between interdependent systems produce emergent effects, such as resilience, that preclude single mechanism interpretations. Due to many interacting variables in individuals, substantiating the effects of treatments to improve neurological outcomes is difficult. Acute injury outcome predictors, including blood and cerebrospinal fluid biomarkers, neuroimaging signal changes, and autonomic system abnormalities, often do not predict chronic SCI syndrome phenotypes. In systems medicine, network analysis of bioinformatics data is used to derive molecular control modules. To better understand the evolution from acute SCI to chronic SCI multi-system states, we propose a topological phenotype framework integrating bioinformatics, physiological data, and allostatic load tested against accepted established recovery metrics. This form of correlational phenotyping may reveal critical nodal points for intervention to improve recovery trajectories. This study examines the limitations of current classifications of SCI and how these can evolve through systems medicine.
Topics: Humans; Spinal Cord Injuries; Biomarkers; Phenotype; Spinal Cord; Recovery of Function
PubMed: 37335060
DOI: 10.1089/neu.2023.0024 -
International Journal of Molecular... Mar 2024Down syndrome is a well-studied aneuploidy condition in humans, which is associated with various disease phenotypes including cardiovascular, neurological,... (Review)
Review
Down syndrome is a well-studied aneuploidy condition in humans, which is associated with various disease phenotypes including cardiovascular, neurological, haematological and immunological disease processes. This review paper aims to discuss the research conducted on gene expression studies during fetal development. A descriptive review was conducted, encompassing all papers published on the PubMed database between September 1960 and September 2022. We found that in amniotic fluid, certain genes such as and were found to be affected, resulting in phenotypical craniofacial changes. Additionally, other genes such as , , , , and were also identified to be affected in the amniotic fluid. In the placenta, dysregulation of genes like , and was observed, which in turn affected nervous system development. In the brain, dysregulation of genes , , , , and has been shown to contribute to intellectual disability. In the cardiac tissues, dysregulated expression of genes , and was found to cause abnormalities. Furthermore, dysregulation of , , , and was observed, contributing to myeloproliferative disorders. Understanding the differential expression of genes provides insights into the genetic consequences of DS. A better understanding of these processes could potentially pave the way for the development of genetic and pharmacological therapies.
Topics: Pregnancy; Female; Humans; Down Syndrome; Core Binding Factor Alpha 2 Subunit; Phenotype; Intellectual Disability; Gene Expression
PubMed: 38474215
DOI: 10.3390/ijms25052968 -
Evolution & Development Nov 2023For decades, there have been repeated calls for more integration across evolutionary and developmental biology. However, critiques in the literature and recent funding...
For decades, there have been repeated calls for more integration across evolutionary and developmental biology. However, critiques in the literature and recent funding initiatives suggest this integration remains incomplete. We suggest one way forward is to consider how we elaborate the most basic concept of development, the relationship between genotype and phenotype, in traditional models of evolutionary processes. For some questions, when more complex features of development are accounted for, predictions of evolutionary processes shift. We present a primer on concepts of development to clarify confusion in the literature and fuel new questions and approaches. The basic features of development involve expanding a base model of genotype-to-phenotype to include the genome, space, and time. A layer of complexity is added by incorporating developmental systems, including signal-response systems and networks of interactions. The developmental emergence of function, which captures developmental feedbacks and phenotypic performance, offers further model elaborations that explicitly link fitness with developmental systems. Finally, developmental features such as plasticity and developmental niche construction conceptualize the link between a developing phenotype and the external environment, allowing for a fuller inclusion of ecology in evolutionary models. Incorporating aspects of developmental complexity into evolutionary models also accommodates a more pluralistic focus on the causal importance of developmental systems, individual organisms, or agents in generating evolutionary patterns. Thus, by laying out existing concepts of development, and considering how they are used across different fields, we can gain clarity in existing debates around the extended evolutionary synthesis and pursue new directions in evolutionary developmental biology. Finally, we consider how nesting developmental features in traditional models of evolution can highlight areas of evolutionary biology that need more theoretical attention.
Topics: Animals; Biological Evolution; Ecology; Genotype; Phenotype; Genome
PubMed: 37026670
DOI: 10.1111/ede.12434 -
Journal of Sleep Research Dec 2023Insomnia nosology has significantly evolved since the Diagnostic and Statistical Manual (DSM)-III-R first distinguished between 'primary' and 'secondary' insomnia. Prior... (Review)
Review
Insomnia nosology has significantly evolved since the Diagnostic and Statistical Manual (DSM)-III-R first distinguished between 'primary' and 'secondary' insomnia. Prior International Classification of Sleep Disorders (ICSD) nosology 'split' diagnostic phenotypes to address insomnia's heterogeneity and the DSM nosology 'lumped' them into primary insomnia, while both systems assumed causality for insomnia secondary to health conditions. In this systematic review, we discuss the historical phenotypes in prior insomnia nosology, present findings for currently proposed insomnia phenotypes based on more robust approaches, and critically appraise the most relevant ones. Electronic databases PsychINFO, PubMED, Web of Science, and references of eligible articles, were accessed to find diagnostic manuals, literature on insomnia phenotypes, including systematic reviews or meta-analysis, and assessments of the reliability or validity of insomnia diagnoses, identifying 184 articles. The data show that previous insomnia diagnoses lacked reliability and validity, leading current DSM-5-TR and ICSD-3 nosology to 'lump' phenotypes into a single diagnosis comorbid with health conditions. However, at least two new, robust insomnia phenotyping approaches were identified. One approach is multidimensional-multimethod and provides evidence for self-reported insomnia with objective short versus normal sleep duration linked to clinically relevant outcomes, while the other is multidimensional and provides evidence for two to five clusters (phenotypes) based on self-reported trait, state, and/or life-history data. Some approaches still need replication to better support whether their findings identify true phenotypes or simply different patterns of symptomatology. Regardless, these phenotyping efforts aim at improving insomnia nosology both as a classification system and as a mechanism to guide treatment.
Topics: Humans; International Classification of Diseases; Phenotype; Reproducibility of Results; Self Report; Sleep Initiation and Maintenance Disorders
PubMed: 37122153
DOI: 10.1111/jsr.13910 -
Nature Genetics Oct 2023Exploitation of crop heterosis is crucial for increasing global agriculture production. However, the quantitative genomic analysis of heterosis was lacking, and there is...
Exploitation of crop heterosis is crucial for increasing global agriculture production. However, the quantitative genomic analysis of heterosis was lacking, and there is currently no effective prediction tool to optimize cross-combinations. Here 2,839 rice hybrid cultivars and 9,839 segregation individuals were resequenced and phenotyped. Our findings demonstrated that indica-indica hybrid-improving breeding was a process that broadened genetic resources, pyramided breeding-favorable alleles through combinatorial selection and collaboratively improved both parents by eliminating the inferior alleles at negative dominant loci. Furthermore, we revealed that widespread genetic complementarity contributed to indica-japonica intersubspecific heterosis in yield traits, with dominance effect loci making a greater contribution to phenotypic variance than overdominance effect loci. On the basis of the comprehensive dataset, a genomic model applicable to diverse rice varieties was developed and optimized to predict the performance of hybrid combinations. Our data offer a valuable resource for advancing the understanding and facilitating the utilization of heterosis in rice.
Topics: Humans; Hybrid Vigor; Oryza; Plant Breeding; Phenotype; Alleles
PubMed: 37679493
DOI: 10.1038/s41588-023-01495-8 -
HGG Advances Oct 2023Inherited metabolic disorders (IMDs) are variably expressive, complicating identification of affected individuals. A genotype-first approach can identify individuals at...
Inherited metabolic disorders (IMDs) are variably expressive, complicating identification of affected individuals. A genotype-first approach can identify individuals at risk for morbidity and mortality from undiagnosed IMDs and can lead to protocols that improve clinical detection, counseling, and management. Using data from 57,340 participants in two hospital biobanks, we assessed the frequency and phenotypes of individuals with pathogenic/likely pathogenic variants (PLPVs) in two IMD genes: , associated with Fabry disease, and , associated with ornithine transcarbamylase deficiency. Approximately 1 in 19,100 participants harbored an undiagnosed PLPV in or . We identified three individuals (2 male, 1 female) with PLPVs in , all of whom were undiagnosed, and three individuals (3 female) with PLPVs in , two of whom were undiagnosed. All three individuals with PLPVs in (100%) had symptoms suggestive of mild Fabry disease, and one individual (14.2%) had an ischemic stroke at age 33, likely indicating the presence of classic disease. No individuals with PLPVs in had documented hyperammonemia despite exposure to catabolic states, but all (100%) had chronic symptoms suggestive of attenuated disease, including mood disorders and migraines. Our findings suggest that and variants identified via a genotype-first approach are of high penetrance and that population screening of these genes can be used to facilitate stepwise phenotyping and appropriate care.
Topics: Female; Male; Humans; Fabry Disease; Phenotype; Genotype; Penetrance; Hospitals
PubMed: 37593415
DOI: 10.1016/j.xhgg.2023.100226 -
Bioinformatics (Oxford, England) May 2024Whole-exome and genome sequencing have become common tools in diagnosing patients with rare diseases. Despite their success, this approach leaves many patients...
MOTIVATION
Whole-exome and genome sequencing have become common tools in diagnosing patients with rare diseases. Despite their success, this approach leaves many patients undiagnosed. A common argument is that more disease variants still await discovery, or the novelty of disease phenotypes results from a combination of variants in multiple disease-related genes. Interpreting the phenotypic consequences of genomic variants relies on information about gene functions, gene expression, physiology, and other genomic features. Phenotype-based methods to identify variants involved in genetic diseases combine molecular features with prior knowledge about the phenotypic consequences of altering gene functions. While phenotype-based methods have been successfully applied to prioritizing variants, such methods are based on known gene-disease or gene-phenotype associations as training data and are applicable to genes that have phenotypes associated, thereby limiting their scope. In addition, phenotypes are not assigned uniformly by different clinicians, and phenotype-based methods need to account for this variability.
RESULTS
We developed an Embedding-based Phenotype Variant Predictor (EmbedPVP), a computational method to prioritize variants involved in genetic diseases by combining genomic information and clinical phenotypes. EmbedPVP leverages a large amount of background knowledge from human and model organisms about molecular mechanisms through which abnormal phenotypes may arise. Specifically, EmbedPVP incorporates phenotypes linked to genes, functions of gene products, and the anatomical site of gene expression, and systematically relates them to their phenotypic effects through neuro-symbolic, knowledge-enhanced machine learning. We demonstrate EmbedPVP's efficacy on a large set of synthetic genomes and genomes matched with clinical information.
AVAILABILITY AND IMPLEMENTATION
EmbedPVP and all evaluation experiments are freely available at https://github.com/bio-ontology-research-group/EmbedPVP.
Topics: Humans; Genomics; Phenotype; Genetic Variation; Computational Biology; Machine Learning
PubMed: 38696757
DOI: 10.1093/bioinformatics/btae301 -
Nature Genetics Aug 2023
Topics: Genetic Variation; Phenotype
PubMed: 37558887
DOI: 10.1038/s41588-023-01484-x -
F1000Research 2023Plant architecture develops post-embryonically and emerges from a dialogue between the developmental signals and environmental cues. Length and branching of the... (Review)
Review
Plant architecture develops post-embryonically and emerges from a dialogue between the developmental signals and environmental cues. Length and branching of the vegetative and reproductive tissues were the focus of improvement of plant performance from the early days of plant breeding. Current breeding priorities are changing, as we need to prioritize plant productivity under increasingly challenging environmental conditions. While it has been widely recognized that plant architecture changes in response to the environment, its contribution to plant productivity in the changing climate remains to be fully explored. This review will summarize prior discoveries of genetic control of plant architecture traits and their effect on plant performance under environmental stress. We review new tools in phenotyping that will guide future discoveries of genes contributing to plant architecture, its plasticity, and its contributions to stress resilience. Subsequently, we provide a perspective into how integrating the study of new species, modern phenotyping techniques, and modeling can lead to discovering new genetic targets underlying the plasticity of plant architecture and stress resilience. Altogether, this review provides a new perspective on the plasticity of plant architecture and how it can be harnessed for increased performance under environmental stress.
Topics: Resilience, Psychological; Climate; Cues; Phenotype
PubMed: 38434638
DOI: 10.12688/f1000research.140649.1 -
Neurological Sciences : Official... Jun 2024Oculodentodigital dysplasia (ODDD) is a rare autosomal dominant congenital malformation syndrome characterized by high penetrance and great phenotypic heterogeneity....
OBJECTIVES
Oculodentodigital dysplasia (ODDD) is a rare autosomal dominant congenital malformation syndrome characterized by high penetrance and great phenotypic heterogeneity. Neurological manifestations are thought to occur in about one third of cases, but systematic studies are not available. We performed deep neurological phenotyping of 10 patients in one ODDD pedigree.
METHODS
Retrospective case series. We analyzed in depth the neurological phenotype of a three-generation family segregating the heterozygous c.416 T > C, p.(Ile139Thr) in GJA1. Clinical and neuroradiological features were retrospectively evaluated. Brain MRI and visual evoked potentials were performed in 8 and 6 cases, respectively.
RESULTS
Central nervous system manifestations occurred in 5 patients, the most common being isolated ataxia either in isolation or combined with spasticity. Furthermore, sphincteric disturbances (neurogenic bladder and fecal incontinence) were recognized as the first manifestation in most of the patients. Subclinical electrophysiological alteration of the optic pathway occurred in all the examined patients. Neuroimaging was significant for supratentorial hypomyelination pattern and hyperintense superior cerebellar peduncle in all examined patients.
CONCLUSION
The neurological involvement in ODDD carriers is often missed but peculiar clinical and radiological patterns can be recognized. Deep neurological phenotyping is needed to help untangle ODDD syndrome complexity and find genotype-phenotype correlations.
Topics: Humans; Female; Male; Phenotype; Retrospective Studies; Adult; Adolescent; Evoked Potentials, Visual; Pedigree; Young Adult; Child; Magnetic Resonance Imaging; Eye Abnormalities; Middle Aged; Brain
PubMed: 38253744
DOI: 10.1007/s10072-024-07331-z