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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 -
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 -
Nature Communications Oct 2023Biological sciences, drug discovery and medicine rely heavily on cell phenotype perturbation and microscope observation. However, most cellular phenotypic changes are...
Biological sciences, drug discovery and medicine rely heavily on cell phenotype perturbation and microscope observation. However, most cellular phenotypic changes are subtle and thus hidden from us by natural cell variability: two cells in the same condition already look different. In this study, we show that conditional generative models can be used to transform an image of cells from any one condition to another, thus canceling cell variability. We visually and quantitatively validate that the principle of synthetic cell perturbation works on discernible cases. We then illustrate its effectiveness in displaying otherwise invisible cell phenotypes triggered by blood cells under parasite infection, or by the presence of a disease-causing pathological mutation in differentiated neurons derived from iPSCs, or by low concentration drug treatments. The proposed approach, easy to use and robust, opens the door to more accessible discovery of biological and disease biomarkers.
Topics: Cell Differentiation; Induced Pluripotent Stem Cells; Drug Discovery; Phenotype
PubMed: 37821450
DOI: 10.1038/s41467-023-42124-6 -
Yi Chuan = Hereditas Oct 2023This study aimed to assess and compare the performance of different machine learning models in predicting selected pig growth traits and genomic estimated breeding...
This study aimed to assess and compare the performance of different machine learning models in predicting selected pig growth traits and genomic estimated breeding values (GEBV) using automated machine learning, with the goal of optimizing whole-genome evaluation methods in pig breeding. The research employed genomic information, pedigree matrices, fixed effects, and phenotype data from 9968 pigs across multiple companies to derive four optimal machine learning models: deep learning (DL), random forest (RF), gradient boosting machine (GBM), and extreme gradient boosting (XGB). Through 10-fold cross-validation, predictions were made for GEBV and phenotypes of pigs reaching weight milestones (100 kg and 115 kg) with adjustments for backfat and days to weight. The findings indicated that machine learning models exhibited higher accuracy in predicting GEBV compared to phenotypic traits. Notably, GBM demonstrated superior GEBV prediction accuracy, with values of 0.683, 0.710, 0.866, and 0.871 for B100, B115, D100, and D115, respectively, slightly outperforming other methods. In phenotype prediction, GBM emerged as the best-performing model for pigs with B100, B115, D100, and D115 traits, achieving prediction accuracies of 0.547, followed by DL at 0.547, and then XGB with accuracies of 0.672 and 0.670. In terms of model training time, RF required the most time, while GBM and DL fell in between, and XGB demonstrated the shortest training time. In summary, machine learning models obtained through automated techniques exhibited higher GEBV prediction accuracy compared to phenotypic traits. GBM emerged as the overall top performer in terms of prediction accuracy and training time efficiency, while XGB demonstrated the ability to train accurate prediction models within a short timeframe. RF, on the other hand, had longer training times and insufficient accuracy, rendering it unsuitable for predicting pig growth traits and GEBV.
Topics: Swine; Animals; Models, Genetic; Genome; Phenotype; Genomics; Genotype; Polymorphism, Single Nucleotide
PubMed: 37872114
DOI: 10.16288/j.yczz.23-120 -
Plant Biotechnology Journal Apr 2024The microphenotype plays a key role in bridging the gap between the genotype and the complex macro phenotype. In this article, we review the advances in data acquisition... (Review)
Review
The microphenotype plays a key role in bridging the gap between the genotype and the complex macro phenotype. In this article, we review the advances in data acquisition and the intelligent analysis of plant microphenotyping and present applications of microphenotyping in plant science over the past two decades. We then point out several challenges in this field and suggest that cross-scale image acquisition strategies, powerful artificial intelligence algorithms, advanced genetic analysis, and computational phenotyping need to be established and performed to better understand interactions among genotype, environment, and management. Microphenotyping has entered the era of Microphenotyping 3.0 and will largely advance functional genomics and plant science.
Topics: Artificial Intelligence; Phenotype; Genomics; Genotype; Plants
PubMed: 38217351
DOI: 10.1111/pbi.14244 -
Forensic Science International. Genetics Nov 2023Over a decade after the publication of the first forensic DNA phenotyping (FDP) studies, DNA-based appearance predictions are now becoming a reality in routine crime...
Over a decade after the publication of the first forensic DNA phenotyping (FDP) studies, DNA-based appearance predictions are now becoming a reality in routine crime scene investigations. The significant number of publications dedicated to the subject of FDP clearly demonstrates a sustained interest and a strong need for further method development. However, the implementation of FDP in routine work still encounters obstacles, and one of these challenges is making phenotype predictions from DNA mixtures. In this study, we examined single-cell sequencing as a potential tool to enable reliable phenotyping of contributors within mixtures. Two mock mixtures, each containing two contributors with similar and different physical appearances, were analyzed using two different workflows. In the first workflow, the mixtures were sequenced using the Ion AmpliSeq™ PhenoTrivium Panel, which includes 41 HIrisPlex-S (HPS) markers. Subsequently, the genotypes were analyzed using the HPS Deconvolution Tool to predict the phenotypes of both contributors. The second workflow involved the introduction of single-cell separation and collection using the DEPArray™ PLUS System. Two different PhenoTrivium amplification protocols were tested, and the phenotype predictions from single cells were compared with the results obtained using the HPS Tool. Our results suggest that the approach presented here allows for the obtainment of nearly complete HIrisPlex-S profiles with accurate genotypes and reliable phenotype predictions from single cells. This method proves successful in deconvoluting mixtures submitted to forensic DNA phenotyping.
Topics: Humans; Polymorphism, Single Nucleotide; Phenotype; DNA Fingerprinting; DNA; High-Throughput Nucleotide Sequencing; Sequence Analysis, DNA; Single-Cell Analysis
PubMed: 37832204
DOI: 10.1016/j.fsigen.2023.102938 -
Pharmacological Research Jan 2024Macrophages, as highly phenotypic plastic immune cells, play diverse roles in different pathological conditions. Changing and controlling the phenotypes of macrophages... (Review)
Review
Macrophages, as highly phenotypic plastic immune cells, play diverse roles in different pathological conditions. Changing and controlling the phenotypes of macrophages is considered a novel potential therapeutic intervention. Meanwhile, specific transmembrane proteins anchoring on the surface of the macrophage membrane are relatively conserved, supporting its functional properties, such as inflammatory chemotaxis and tumor targeting. Thus, a series of drug delivery systems related to specific macrophage membrane proteins are commonly used to treat chronic inflammatory diseases. This review summarizes macrophages-based strategies for chronic diseases, discusses the regulation of macrophage phenotypes and their polarization processes, and presents how to design and apply the site-specific targeted drug delivery systems in vivo based on the macrophages and their derived membrane receptors. It aims to provide a better understanding of macrophages in immunoregulation and proposes macrophages-based targeted therapeutic approaches for chronic diseases.
Topics: Humans; Drug Delivery Systems; Phenotype; Macrophages; Neoplasms; Chronic Disease
PubMed: 38043691
DOI: 10.1016/j.phrs.2023.107022 -
Genetics in Medicine : Official Journal... Sep 2023Congenital hypopituitarism (CH) disorders are phenotypically variable. Variants in multiple genes are associated with these disorders, with variable penetrance and...
PURPOSE
Congenital hypopituitarism (CH) disorders are phenotypically variable. Variants in multiple genes are associated with these disorders, with variable penetrance and inheritance.
METHODS
We screened a large cohort (N = 1765) of patients with or at risk of CH using Sanger sequencing, selected according to phenotype, and conducted next-generation sequencing (NGS) in 51 families within our cohort. We report the clinical, hormonal, and neuroradiological phenotypes of patients with variants in known genes associated with CH.
RESULTS
We identified variants in 178 patients: GH1/GHRHR (51 patients of 414 screened), PROP1 (17 of 253), POU1F1 (15 of 139), SOX2 (13 of 59), GLI2 (7 of 106), LHX3/LHX4 (8 of 110), HESX1 (8 of 724), SOX3 (9 of 354), OTX2 (5 of 59), SHH (2 of 64), and TCF7L1, KAL1, FGFR1, and FGF8 (2 of 585, respectively). NGS identified 26 novel variants in 35 patients (from 24 families). Magnetic resonance imaging showed prevalent hypothalamo-pituitary abnormalities, present in all patients with PROP1, GLI2, SOX3, HESX1, OTX2, LHX3, and LHX4 variants. Normal hypothalamo-pituitary anatomy was reported in 24 of 121, predominantly those with GH1, GHRHR, POU1F1, and SOX2 variants.
CONCLUSION
We identified variants in 10% (178 of 1765) of our CH cohort. NGS has revolutionized variant identification, and careful phenotypic patient characterization has improved our understanding of CH. We have constructed a flow chart to guide genetic analysis in these patients, which will evolve upon novel gene discoveries.
Topics: Humans; Mutation; Hypopituitarism; Transcription Factors; Phenotype; Genes, Homeobox
PubMed: 37165954
DOI: 10.1016/j.gim.2023.100881 -
European Journal of Human Genetics :... Nov 2023
Topics: Humans; Phenotype; Genomics
PubMed: 37914779
DOI: 10.1038/s41431-023-01483-w -
Arthritis Research & Therapy Sep 2023We investigated sensitivity of the 2020 Revised Comprehensive Diagnostic Criteria (RCD) and the 2019 ACR/EULAR classification criteria across the four identified...
Differential sensitivity of the 2020 revised comprehensive diagnostic criteria and the 2019 ACR/EULAR classification criteria across IgG4-related disease phenotypes: results from a Norwegian cohort.
BACKGROUND
We investigated sensitivity of the 2020 Revised Comprehensive Diagnostic Criteria (RCD) and the 2019 ACR/EULAR classification criteria across the four identified IgG4-related disease (IgG4-RD) phenotypes: "Pancreato-Hepato-Biliary", "Retroperitoneum and Aorta", "Head and Neck-limited" and "Mikulicz' and Systemic" in a well-characterized patient cohort.
METHODS
We included adult patients diagnosed with IgG4-RD after comprehensive clinical assessment at Oslo University Hospital in Norway. We assigned patients to IgG4-RD phenotypes based on pattern of organ involvement and assessed fulfillment of RCD and 2019 ACR/EULAR classification criteria. Differences between phenotype groups were analyzed using one-way ANOVA for continuous variables, and contingency tables for categorical variables.
RESULTS
The study cohort included 79 IgG4-RD patients assigned to the "Pancreato-Hepato-Biliary" (22.8%), Retroperitoneum and Aorta" (22.8%) "Head and Neck-limited" (29.1%), and "Mikulicz' and Systemic" (25.3%) phenotype groups, respectively. While 72/79 (91.1%) patients in total fulfilled the RCD, proportion differed across phenotype groups and was lowest in the "Retroperitoneum and Aorta" group (66.7%, p < 0.001). Among the 57 (72.2%) patients meeting the 2019 ACR/EULAR classification criteria, proportion was again lowest in the "Retroperitoneum and Aorta" group (27.8%, p < 0.001).
CONCLUSION
The results from this study indicate that IgG4-RD patients having the "Retroperitoneum and Aorta" phenotype less often fulfill diagnostic criteria and classification criteria than patients with other IgG4-RD phenotypes. Accordingly, this phenotype is at risk of being systematically selected against in observational studies and randomized clinical trials, with potential implications for patients, caregivers and future definitions of IgG4-RD.
Topics: Humans; Immunoglobulin G4-Related Disease; Norway; Phenotype
PubMed: 37670401
DOI: 10.1186/s13075-023-03155-y