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Jornal de Pediatria 2024This study aimed to evaluate the diagnostic utility, disease activity, and phenotypic association of serum anti-Saccharomyces cerevisiae antibody (ASCA), perinuclear...
OBJECTIVE
This study aimed to evaluate the diagnostic utility, disease activity, and phenotypic association of serum anti-Saccharomyces cerevisiae antibody (ASCA), perinuclear anti-neutrophil cytoplasmic antibody (pANCA), PR3-ANCA, and MPO-ANCA in pediatric patients with inflammatory bowel disease (IBD).
METHODS
Pediatric patients diagnosed with IBD were recruited and classified as Crohn's disease (CD), ulcerative colitis (UC), and IBD-unclassified (IBD-U) through full investigation. The Paris classification was used to evaluate disease phenotypes of pediatric CD and UC.
RESULTS
In all, 229 pediatric patients with IBD (CD 147, UC 53, IBD-U 29) were included. The ASCA IgG seropositivity significantly differed among the three groups (CD 75.4%, UC 17.5%, and IBD-U 60.0%; p < 0.001). PR3-ANCA positive rates were the highest in UC (24.0%), followed by IBD-U (17.6%), and none in CD (p = 0.002); pANCA-positive rates were higher in IBD-U (33.6%), followed by UC (28.0%) than in CD (1.4%) (p < 0.001). Regarding disease phenotype, perianal disease revealed higher serum ASCA IgG titers (median 36.7 U/mL in P1 vs. 25.2 U/mL in P0, p = 0.019). Serum ASCA IgG and IgA cutoff values to distinguish CD were 32.7 (U/mL) and 11.9 (U/mL), respectively, with a specificity of 80.0%.
CONCLUSION
Serological biomarkers of ASCA IgG and IgA were effective for differentiating CD in pediatric IBD patients, and serum pANCA and PR3-ANCA, but not MPO-ANCA, were effective in distinguishing UC and IBD-U. Furthermore, measuring serological titers of ASCA IgG and IgA may help differentiate CD and evaluate the disease activity and phenotype of pediatric IBD in practice.
Topics: Humans; Child; Antibodies, Antineutrophil Cytoplasmic; Inflammatory Bowel Diseases; Colitis, Ulcerative; Biomarkers; Phenotype; Immunoglobulin G; Immunoglobulin A
PubMed: 38012956
DOI: 10.1016/j.jped.2023.10.005 -
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 -
Genetics Aug 2023The mutation rate plays an important role in adaptive evolution. It can be modified by mutator and anti-mutator alleles. Recent empirical evidence hints that the...
The mutation rate plays an important role in adaptive evolution. It can be modified by mutator and anti-mutator alleles. Recent empirical evidence hints that the mutation rate may vary among genetically identical individuals: evidence from bacteria suggests that the mutation rate can be affected by expression noise of a DNA repair protein and potentially also by translation errors in various proteins. Importantly, this non-genetic variation may be heritable via a transgenerational epigenetic mode of inheritance, giving rise to a mutator phenotype that is independent from mutator alleles. Here, we investigate mathematically how the rate of adaptive evolution is affected by the rate of mutation rate phenotype switching. We model an asexual population with two mutation rate phenotypes, non-mutator and mutator. An offspring may switch from its parental phenotype to the other phenotype. We find that switching rates that correspond to so-far empirically described non-genetic systems of inheritance of the mutation rate lead to higher rates of adaptation on both artificial and natural fitness landscapes. These switching rates can maintain within the same individuals both a mutator phenotype and intermediary mutations, a combination that facilitates adaptation. Moreover, non-genetic inheritance increases the proportion of mutators in the population, which in turn increases the probability of hitchhiking of the mutator phenotype with adaptive mutations. This in turns facilitates the acquisition of additional adaptive mutations. Our results rationalize recently observed noise in the expression of proteins that affect the mutation rate and suggest that non-genetic inheritance of this phenotype may facilitate evolutionary adaptive processes.
Topics: Mutation Rate; Mutation; Phenotype; Adaptation, Physiological; Bacteria
PubMed: 37293818
DOI: 10.1093/genetics/iyad111 -
Journal of the Royal Society, Interface Aug 2023Selection and variation are both key aspects in the evolutionary process. Previous research on the mapping between molecular sequence (genotype) and molecular fold...
Selection and variation are both key aspects in the evolutionary process. Previous research on the mapping between molecular sequence (genotype) and molecular fold (phenotype) has shown the presence of several structural properties in different biological contexts, implying that these might be universal in evolutionary spaces. The deterministic genotype-phenotype (GP) map that links short RNA sequences to minimum free energy secondary structures has been studied extensively because of its computational tractability and biologically realistic nature. However, this mapping ignores the phenotypic plasticity of RNA. We define a GP map that incorporates non-deterministic (ND) phenotypes, and take RNA as a case study; we use the Boltzmann probability distribution of folded structures and examine the structural properties of ND GP maps for RNA sequences of length 12 and coarse-grained RNA structures of length 30 (RNAshapes30). A framework is presented to study robustness, evolvability and neutral spaces in the ND map. This framework is validated by demonstrating close correspondence between the ND quantities and sample averages of their deterministic counterparts. When using the ND framework we observe the same structural properties as in the deterministic GP map, such as bias, negative correlation between genotypic robustness and evolvability, and positive correlation between phenotypic robustness and evolvability.
Topics: Adaptation, Physiological; Biological Evolution; Genotype; Phenotype; RNA
PubMed: 37608711
DOI: 10.1098/rsif.2023.0132 -
Scientific Reports Jul 2023Diabetes is a heterogenous, multimorbid disorder with a large variation in manifestations, trajectories, and outcomes. The aim of this study is to validate a novel...
Diabetes is a heterogenous, multimorbid disorder with a large variation in manifestations, trajectories, and outcomes. The aim of this study is to validate a novel machine learning method for the phenotyping of diabetes in the context of comorbidities. Data from 9967 multimorbid patients with a new diagnosis of diabetes were extracted from Clinical Practice Research Datalink. First, using BEHRT (a transformer-based deep learning architecture), the embeddings corresponding to diabetes were learned. Next, topological data analysis (TDA) was carried out to test how different areas in high-dimensional manifold correspond to different risk profiles. The following endpoints were considered when profiling risk trajectories: major adverse cardiovascular events (MACE), coronary artery disease (CAD), stroke (CVA), heart failure (HF), renal failure (RF), diabetic neuropathy, peripheral arterial disease, reduced visual acuity and all-cause mortality. Kaplan Meier curves were plotted for each derived phenotype. Finally, we tested the performance of an established risk prediction model (QRISK) by adding TDA-derived features. We identified four subgroups of patients with diabetes and divergent comorbidity patterns differing in their risk of future cardiovascular, renal, and other microvascular outcomes. Phenotype 1 (young with chronic inflammatory conditions) and phenotype 2 (young with CAD) included relatively younger patients with diabetes compared to phenotypes 3 (older with hypertension and renal disease) and 4 (older with previous CVA), and those subgroups had a higher frequency of pre-existing cardio-renal diseases. Within ten years of follow-up, 2592 patients (26%) experienced MACE, 2515 patients (25%) died, and 2020 patients (20%) suffered RF. QRISK3 model's AUC was augmented from 67.26% (CI 67.25-67.28%) to 67.67% (CI 67.66-67.69%) by adding specific TDA-derived phenotype and the distances to both extremities of the TDA graph improving its performance in the prediction of CV outcomes. We confirmed the importance of accounting for multimorbidity when risk stratifying heterogenous cohort of patients with new diagnosis of diabetes. Our unsupervised machine learning method improved the prediction of clinical outcomes.
Topics: Humans; Diabetes Mellitus; Comorbidity; Data Analysis; Cardiovascular Diseases; Risk Assessment; Kidney Diseases; Unsupervised Machine Learning; Male; Female; Middle Aged; Aged; Phenotype
PubMed: 37455284
DOI: 10.1038/s41598-023-38251-1 -
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 -
Scientific Reports Aug 2023Alzheimer's disease (AD) is a progressive neurodegenerative disorder characterized by the accumulation of Aβ plaques and neurofibrillary tangles, resulting in synaptic...
Alzheimer's disease (AD) is a progressive neurodegenerative disorder characterized by the accumulation of Aβ plaques and neurofibrillary tangles, resulting in synaptic loss and neurodegeneration. The retina is an extension of the central nervous system within the eye, sharing many structural similarities with the brain, and previous studies have observed AD-related phenotypes within the retina. Three-dimensional retinal organoids differentiated from human pluripotent stem cells (hPSCs) can effectively model some of the earliest manifestations of disease states, yet early AD-associated phenotypes have not yet been examined. Thus, the current study focused upon the differentiation of hPSCs into retinal organoids for the analysis of early AD-associated alterations. Results demonstrated the robust differentiation of retinal organoids from both familial AD and unaffected control cell lines, with familial AD retinal organoids exhibiting a significant increase in the Aβ42:Aβ40 ratio as well as phosphorylated Tau protein, characteristic of AD pathology. Further, transcriptional analyses demonstrated the differential expression of many genes and cellular pathways, including those associated with synaptic dysfunction. Taken together, the current study demonstrates the ability of retinal organoids to serve as a powerful model for the identification of some of the earliest retinal alterations associated with AD.
Topics: Humans; Alzheimer Disease; Organoids; Central Nervous System; Phenotype; Retina
PubMed: 37620502
DOI: 10.1038/s41598-023-40382-4