Disease Genes Research Articles

Txnip deletions and missense alleles prolong the survival of cones in a retinitis pigmentosa mouse model.

Retinitis pigmentosa (RP) is an inherited retinal disease in which there is a loss of cone-mediated daylight vision. As there are >100 disease genes, our goal is to preserve cone vision in a disease gene-agnostic manner. Previously we showed that overexpressing TXNIP, an α-arrestin protein, prolonged cone vision in RP mouse models, using an AAV to express it only in cones. Here, we expressed different alleles of Txnip in the retinal pigmented epithelium (RPE), a support layer for cones. Our goal was to learn more of TXNIP's structure-function relationships for cone survival, as well as determine the optimal cell type expression pattern for cone survival. The C-terminal half of TXNIP was found to be sufficient to remove GLUT1 from the cell surface, and improved RP cone survival, when expressed in the RPE, but not in cones. Knock-down of HSP90AB1, a TXNIP-interactor which regulates metabolism, improved the survival of cones alone and was additive for cone survival when combined with TXNIP. From these and other results, it is likely that TXNIP interacts with several proteins in the RPE to indirectly support cone survival, with some of these interactions different from those that lead to cone survival when expressed only in cones.

Txnip deletions and missense alleles prolong the survival of cones in a retinitis pigmentosa mouse model

Retinitis pigmentosa (RP) is an inherited retinal disease in which there is a loss of cone-mediated daylight vision. As there are >100 disease genes, our goal is to preserve cone vision in a disease gene-agnostic manner. Previously we showed that overexpressing TXNIP, an α-arrestin protein, prolonged cone vision in RP mouse models, using an AAV to express it only in cones. Here, we expressed different alleles of Txnip in the retinal pigmented epithelium (RPE), a support layer for cones. Our goal was to learn more of TXNIP’s structure-function relationships for cone survival, as well as determine the optimal cell type expression pattern for cone survival. The C-terminal half of TXNIP was found to be sufficient to remove GLUT1 from the cell surface, and improved RP cone survival, when expressed in the RPE, but not in cones. Knock-down of HSP90AB1, a TXNIP-interactor which regulates metabolism, improved the survival of cones alone and was additive for cone survival when combined with TXNIP. From these and other results, it is likely that TXNIP interacts with several proteins in the RPE to indirectly support cone survival, with some of these interactions different from those that lead to cone survival when expressed only in cones.

Diagnostic yield of an inherited retinal disease gene panel in retinopathy of unknown origin

Purpose: Evaluating the presence of class 3, 4 and 5 genetic variants in inherited retinal disease (IRD) genes in patients with retinopathy of unknown origin (RUO). Methods: Multicentric retrospective study of RUO cases diagnosed between January 2012 and February 2022. General and ophthalmological history, complete ophthalmological examination, anti-retinal antibodies and IRD gene panel results were analyzed in every patient. Four RUO categories were defined: non-paraneoplastic autoimmune retinopathy (npAIR), unilateral pigmentary retinopathy (UPR), asymmetrical pigmentary retinopathy (APR) and acute zonal occult outer retinopathy (AZOOR). Results: We included 12 patients (9 females) across these four RUO categories. Mean age at inclusion was 45.6 years (20 – 68 years). Seven patients demonstrated class 3 variants in IRD genes. Of these, 2 also demonstrated class 5 variants in other IRD genes. The remaining 5 patients had negative panel results. IRD gene panel analysis allowed diagnosis refinement in 1 (8.3%) npAIR patient in our RUO cohort. When considering the npAIR subpopulation only, a higher diagnostic yield of 20% (1/5 patients) was achieved. Conclusions: Every suspected npAIR patient should benefit from gene panel testing in order not to overlook undiagnosed IRDs. In contrast, UPR, APR and AZOOR subpopulations did not benefit from genetic analysis in this study.

Drug discovery and development in the era of artificial intelligence: From machine learning to large language models

Drug Research and Development (R&D) is a complex and difficult process, and current drug R&D faces the challenges of long time span, high investment, and high failure rate. Machine learning, with its powerful learning ability to characterize big data and complex networks, is increasingly effective to improve the efficiency and success rate of drug R&D. Here we review some recent examples of the application of machine learning methods in six areas: disease gene prediction, virtual screening, drug molecule generation, molecular attribute prediction, and prediction of drug combination synergism. We also discuss the advantages of integrative learning in multi-attribute prediction. Integrative models based on base learners constructed from data of different dimensions on the one hand fully utilize the information contained in these data, and on the other hand improve the average prediction performance. Finally, we envision a new paradigm for drug discovery and development: a large language model acts as a central hub to organize public resources into a knowledge base, validating the knowledge with computational software and smaller predictive models, as well as high-throughput automated screening platforms based on organoidal technologies, to speed up development and reduce the differences in efficacy between disease models and humans to improve the success rate of a drug.

Comprehensive analysis of two hotspot codons in the TUBB4B gene and associated phenotypes

Our purpose was to elucidate the genotype and ophthalmological and audiological phenotype in TUBB4B-associated inherited retinal dystrophy (IRD) and sensorineural hearing loss (SNHL), and to model the effects of all possible amino acid substitutions at the hotspot codons Arg390 and Arg391. Six patients from five families with heterozygous missense variants in TUBB4B were included in this observational study. Ophthalmological testing included best-corrected visual acuity, fundus examination, optical coherence tomography, fundus autofluorescence imaging, and full-field electroretinography (ERG). Audiological examination included pure-tone and speech audiometry in adult patients and auditory brainstem response testing in a child. Genetic testing was performed by disease gene panel analysis based on genome sequencing. The molecular consequences of the substitutions of residues 390 and 391 on TUBB4B and its interaction with α-tubulin were predicted in silico on its three-dimensional structure obtained by homology modelling. Two independent patients had amino acid exchanges at position 391 (p.(Arg391His) or p.(Arg391Cys)) of the TUBB4B protein. Both had a distinct IRD phenotype with peripheral round yellowish lesions with pigmented spots and mild or moderate SNHL, respectively. Yet the phenotype was milder with a sectorial pattern of bone spicules in one patient, likely due to a genetically confirmed mosaicism for p.(Arg391His). Three patients were heterozygous for an amino acid exchange at position 390 (p.(Arg390Gln) or p.(Arg390Trp)) and presented with another distinct retinal phenotype with well demarcated pericentral retinitis pigmentosa. All showed SNHL ranging from mild to severe. One additional patient showed a variant distinct from codon 390 or 391 (p.(Tyr310His)), and presented with congenital profound hearing loss and reduced responses in ERG. Variants at codon positions 390 and 391 were predicted to decrease the structural stability of TUBB4B and its complex with α-tubulin, as well as the complex affinity. In conclusion, the twofold larger reduction in heterodimer affinity exhibited by Arg391 substitutions suggested an association with the more severe retinal phenotype, compared to the substitution at Arg390.

Unraveling the genetic architecture of blood unfolded p-53 among non-demented elderlies: novel candidate genes for early Alzheimer's disease

BackgroundAlzheimer's disease (AD) is a heritable neurodegenerative disease whose long asymptomatic phase makes the early diagnosis of it pivotal. Blood U-p53 has recently emerged as a superior predictive biomarker for AD in the early stages. We hypothesized that genetic variants associated with blood U-p53 could reveal novel loci and pathways involved in the early stages of AD.ResultsWe performed a blood U-p53 Genome-wide association study (GWAS) on 484 healthy and mild cognitively impaired subjects from the ADNI cohort using 612,843 Single nucleotide polymorphisms (SNPs). We performed a pathway analysis and prioritized candidate genes using an AD single-cell gene program. We fine-mapped the intergenic SNPs by leveraging a cell-type-specific enhancer-to-gene linking strategy using a brain single-cell multimodal dataset. We validated the candidate genes in an independent brain single-cell RNA-seq and the ADNI blood transcriptome datasets. The rs279686 between AASS and FEZF1 genes was the most significant SNP (p-value = 4.82 × 10–7). Suggestive pathways were related to the immune and nervous systems. Twenty-three candidate genes were prioritized at 27 suggestive loci. Fine-mapping of 5 intergenic loci yielded nine cell-specific candidate genes. Finally, 15 genes were validated in the independent single-cell RNA-seq dataset, and five were validated in the ADNI blood transcriptome dataset.ConclusionsWe underlined the importance of performing a GWAS on an early-stage biomarker of AD and leveraging functional omics datasets for pinpointing causal genes in AD. Our study prioritized nine genes (SORCS1, KIF5C, TMEFF2, TMEM63C, HLA-E, ATAT1, TUBB, ARID1B, and RUNX1) strongly implicated in the early stages of AD.

Identification of LPCAT1 as a key biomarker for Crohn's disease based on bioinformatics and machine learnings and experimental verification

Epithelial-mesenchymal transition (EMT) plays a crucial role in regulating inflammatory responses and fibrosis formation. This study aims to explore the molecular mechanisms of EMT-related genes in Crohn's disease (CD) through bioinformatics methods and identify potential key biomarkers. In our research, we identified differentially expressed genes (DEGs) related to EMT based on the GSE52746 dataset and the gene set in the GeneCards database. Key genes were identified through Lasso-cox and Random Forest and validated using the external dataset GSE10616. Immune infiltration analysis showed that Lysophosphatidylcholine acyltransferase 1 (LPCAT1) was positively correlated with Neutrophils and Macrophages M1. The Gene Set Enrichment Analysis (GSEA) results for LPCAT1 showed associations with celladhesionmolecules and ECM receptor interaction. Additionally, a lncRNA-miRNA-mRNA ceRNA network was constructed. Finally, we validated that knocking down LPCAT1 could inhibit the release of inflammatory factors, EMT, and the elevation of fibrosis indices as well as the activation of NF-κB signaling pathway in LPS-induced HT-29 cells. LPCAT1 plays an important role in the occurrence and development of CD and may become a new biomarker.

Three-Dimensional Culture Modelling Reveals Divergent Mycobacterium tuberculosis Virulence and Antimicrobial Treatment Response

Tuberculosis (TB) remains a persistent epidemic, and the emergence of drug-resistant Mycobacterium tuberculosis (Mtb) presents a global healthcare threat. While some new agents have been successfully introduced, innovative technologies to evaluate emerging anti-TB compounds are required to inform transformative approaches. Mtb is an obligate human pathogen, and consequently utilizing models that are consistent with human disease is likely to be critical. We have developed a human 3-dimensional (3-D) cell culture model that reflects human disease gene expression patterns and causes Mtb to become pyrazinamide sensitive in vitro. Here, we identify key differences in virulence between the standard laboratory strain, Mtb H37Rv, and clinical isolates. We demonstrate that Mtb H37Rv is attenuated in the 3-D system, more susceptible to antibiotics and hyperinflammatory compared to clinical isolates. Prolonged in vitro culture of a clinical strain leads to attenuation. We then characterise antibiotic sensitivity of multi-drug-resistant Mtb within the 3-D model and define relative bactericidal activity. Finally, we demonstrate that verapamil increases efficacy of bedaquiline and delamanid antibiotic therapy. Taken together, our findings suggest that studying virulent clinical strains in an advanced cell culture system is a powerful adjunct to established methodologies to evaluate new interventions for TB.

Abstract PO2-01-06: Prognostic value of residual disease (RD) biology and gene expression changes during the neoadjuvant treatment in HER2+ early-breast cancer (EBC)

Abstract Title Prognostic value of residual disease (RD) biology and gene expression changes during the neoadjuvant treatment in HER2+ early-breast cancer (EBC). Background In HER2+ EBC, neoadjuvant trastuzumab-based therapy is the standard of care, with adjuvant therapy determined by whether residual disease (RD) is present. Patients (pts) with RD have significantly higher relapse rates than pts with pathologic complete response. Differences in tumor and immune biology between pre-treated and post-treated tumors after HER2-blockade have been described, but little is known about how these changes can affect long-term outcomes or can be leveraged to tailor adjuvant treatment. Here we evaluate the biology of RD using gene expression analyses of pre- and post-treated HER2+ tumors, and their prognostic value to predict event-free survival (EFS) in a pooled analysis of 4 neoadjuvant studies in which single-agent trastuzumab or combined with lapatinib was explored, with and without chemotherapy: CALGB 40601 (Alliance), PAMELA, NeoALTTO, and NSABP B-41. Methods Gene expression profiling by RNA sequencing was assessed on 497 pre-treated samples from pts with RD, and on pre-treated and RD in 171 tumor pairs. Intrinsic subtypes and 22 tumor and immune gene expression signatures (GESs) were evaluated. We studied and compared the association of gene expression biomarkers with EFS in different cohorts and time points, including when GESs were calculated at baseline (N=497), in the 171 paired baseline and RD samples, and the nature of biomarker changes during neoadjuvant therapy. Association between GESs and EFS was explored by uni- and multivariable Cox regression models adjusted by clinical parameters (i.e., study, treatment arm, hormone receptor status, clinical tumor size, and clinical node involvement), and the performance of the models was assessed by the c-index. Results In 497 baseline untreated samples from pts with RD after neoadjuvant therapy (N=497), intrinsic subtypes were significantly associated with EFS (log-rank [LR] P 0.007), with Luminal A showing the best prognosis and Basal-like the worst. Among 171 paired tumors, HER2-Enriched tumors were more common in baseline (50.3%) with change to Normal-like (49.7%) and Luminal A (18.7%) in RD (chi-square P < 0.001). This luminal phenotypic change was also confirmed by a significant reduction in the correlation to the HER2-Enriched centroid and a significant increase in the correlation to the Luminal A centroid between pre- and post-treated samples; a significant decrease in ERBB2 gene expression and the HER2 amplicon gene expression signature was also observed (Wilcoxon P < 0.001). At an immune GES level, we found a significant increase in B cell, CD8+ T cell, and NK cell signatures in RD specimens. In contrast, T reg GES was significantly downregulated (Wilcoxon P < 0.05). In uni- and multivariable models, immune GESs provided more prognostic information when assessed in RD samples. In contrast, subtype-related biomarkers provided more prognostic information at baseline. From all the multivariable models, the best (c-index= 0.78) included the IgG signature assessed in RD (adjusted hazard ratio 0.46, 95% CI 0.30-0.70, adjusted P 0.005). IgG signature levels (tertiles) in RD could also stratify pts into different groups based on prognostic information (EFS at 7 years: 69% in IgG low vs. 91% in IgG high, LR P 0.02). Conclusions In pts with HER2+ EBC and RD after neoadjuvant treatment with HER2-blockade, tumor-related biomarkers seem to provide more prognostic information when calculated at baseline, whereas immune biomarkers in the RD perform better for EFS prognostication. This finding could be relevant for implementing RD biomarkers in the clinic to stratify pts into different prognostic groups and tailor the post-operative treatment strategy. Supported in part by U10 CA180821, U10 CA180882, R01-CA229409 https://acknowledgments.alliancefound.org Citation Format: Aranzazu Fernandez-Martinez, Maki Tanioka, Sung Gwe Ahn, Paola Zagami, Tomás Pascual, Mattia Rediti, Gong Tang, Katherine A Hoadley, David Venet, Naim U. Rashid, Patty Spears, Judith Bliss, Jens Huober, Priya Rastogi, Md N. Islam, Javier Cortés, Antonio Llombart-Cussac, Sandra Swain, Christos Sotiriou, Aleix Prat, Charles M Perou, Lisa Carey. Prognostic value of residual disease (RD) biology and gene expression changes during the neoadjuvant treatment in HER2+ early-breast cancer (EBC) [abstract]. In: Proceedings of the 2023 San Antonio Breast Cancer Symposium; 2023 Dec 5-9; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2024;84(9 Suppl):Abstract nr PO2-01-06.

Genetic Analysis of Severe Polycystic Liver Disease in Japan.

Polycystic liver disease (PLD) is present in most patients with autosomal dominant polycystic kidney disease (ADPKD). PLD can alternatively be found with few, if any, kidney cysts as a diagnosis of isolated polycystic liver disease (ADPLD). Several genes are identified as causative for this spectrum of phenotypes, however, the relative incidence of genetic etiologies amongst patients with severe PLD is unknown. Patients with ADPKD or ADPLD having severe PLD defined as height-adjusted total liver volume (hTLV) over 1,800mL/m were recruited. Subsequent clinical care was followed. Genetic analysis was performed using whole exome sequencing. We enrolled and sequenced 49 patients (38 females, 11 males). Pathogenic or suspected pathogenic variants in polycystic disease genes were found in 44 out of 49 patients (90%). The disease gene was PKD1 in 20/44 (45%), PKD2 in 15/44 (34%), PRKCSH in 5/44 (11%), GANAB in 2/44 (5%), SEC63 in 1/44 (2%), and ALG8 in 1/44 (2%). The median hTLV was no different between genetically-defined ADPKD and ADPLD groups (4431 (range 1817-9148) versus 3437 (range 1860-8211) mL, p=0.77), whereas height-adjusted kidney volume (hTKV) was larger as expected in ADPKD than ADPLD (607 (range 190-2842) versus 179 (range 138-234) mL/m, p<0.01). Of the clinically-defined ADPKD cases, 20/38 (53%) were PKD1, 15/38 (39%) were PKD2, and 3 (8%) remain genetically unsolved. Among patients with a pathogenic PKD1 or PKD2 variant, we found three cases with a liver-dominant ADPKD (severe PLD with hTKV <250mL/m). ADPLD-related genes represent 20% of severe PLD patients in our cohort. Of those enrolled with ADPKD, we observed a higher frequency of PKD2 carriers than in any previously reported ADPKD cohorts. While there was no significant difference in the hTLV between PKD1 versus PKD2 patients in this cohort, our data suggests that enrollment on the basis of severe PLD may enrich for PKD2 patients.

The therapeutic effect and targets of herba Sarcandrae on breast cancer and the construction of a prognostic signature consisting of inflammation-related genes

BackgroundThe prevalence of breast cancer (BRCA), which is common among women, is on the rise. This study applied network pharmacology to explore the potential mechanism of action of herba sarcandrae in BRCA and construct a prognostic signature composed of inflammation-related genes. MethodsThe active ingredients of herba sarcandrae were screened using the SymMap, TCMID, and TCMSP platforms, and the molecular targets were determined in the UniProt database. The “drug-active compound-potential target” network was established with Cytoscape 3.7.2. The molecular targets were subjected to disease ontology, gene ontology (GO), and Kyoto Encyclopedia of Genes (KEGG) analyses. AutoDock software was used for molecular docking. Differentially expressed genes (DEGs) related to inflammation were obtained from the BRCA Cancer Genome Atlas (TCGA) database. In the training cohort, the univariate Cox regression model was applied to preliminarily screen prognostic genes. A multigene signature was built by the least absolute shrinkage and selection operator (LASSO) regression model, followed by validation through Kaplan‒Meier, Cox, and receiver operating characteristic (ROC) analyses. ResultsForty-one active compounds were identified, and 265 therapeutic targets for herba sarcandrae were predicted. GO enrichment results revealed significant enrichment of biological processes, such as response to xenobiotic stimuli, response to nutrient levels, and response to lipopolysaccharide. KEGG analysis revealed significant enrichment of pathways such as AGE-RAGE and chemical carcinogenesis receptor activation signaling pathways. In addition, the herbs Marc-Andre and rutin were shown to mediate BRCA cell proliferation and apoptosis via the interferon regulatory factor 1 (IRF1)/signal transducer and activator of transcription 3 (STAT3)/programmed death-ligand 1 (PD-L1) pathway. Sixteen inflammatory signatures, including BST2, GPR132, IL12B, IL18, IL1R1, IL2RB, IRF1, and others, were constructed, and the risk score was found to be a strong independent prognostic factor for overall survival in BRCA patients. The 16-inflammation signature was associated with several clinical features (age, clinical stage, T, and N classifications) and could reflect immune cell infiltration in tumor microenvironments with different immune cells. ConclusionsHerba sarcandrae and rutin were shown to mediate BRCA cell proliferation and apoptosis via the IRF1/STAT3/PD-L1 pathway, and the 16-member inflammatory signature might be a novel biomarker for predicting BRCA patient prognosis, providing more accurate guidance for clinical treatment prognosis evaluation and having important reference value for individualized treatment selection.

Therapeutic mechanisms of modified Jiawei Juanbi decoction in early knee osteoarthritis: A multimodal analysis

Modified Jiawei Juanbi decoction (MJD) is used for the treatment of early-stage knee osteoarthritis (KOA). Here, modified Jiawei Juanbi decoction (MJD) was employed for the treatment of early-stage knee osteoarthritis (KOA) and its mechanisms were assessed via metabonomics and network pharmacology. A total of 24 male Sprague-Dawley rats were randomly allocated into a normal control group, a model group, and an MJD group (n = 8 rats per group). Each rat group was further equally divided into two subgroups for investigation for either 14 or 28 days. A rat model of early-stage KOA was constructed and rats were treated with MJD. Effects were evaluated based on changes in knee circumference, mechanical withdrawal threshold (MWT) and thermal withdrawal latency (TWL). We also analyzed histopathological changes in articular cartilage. High-resolution mass spectrometry was used to analyze the chemical profile of MJD, identifying 228 components. Using an LC-Q-TOF-MS metabonomics approach, 33 differential metabolites were identified. The relevant pathways significantly associated with MJD include arginine and proline metabolism, vitamin B6 metabolism, as well as the biosynthesis of phenylalanine, tyrosine and tryptophan. The system pharmacology paradigm revealed that MJD contains 1027 components and associates with 1637 genes, of which 862 disease genes are related to osteoarthritis. The construction of the MJD composition-target-KOA network revealed a total of 140 intersection genes. A total of 39 hub genes were identified via integration of betweenness centrality values greater than 100 using CytoHubba. Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis revealed several significantly affected signaling pathways including the HIF-1, AGE-RAGE (in diabetic complications), IL-17, rheumatoid arthritis and TNF pathways. Integrated-omics and network pharmacology approaches revealed a necessity for further detailed investigation focusing on two major targets, namely NOS2 and NOS3, along with their essential metabolite (arginine) and associated pathways (HIF-1 signaling and arginine and proline metabolism). Real-time PCR validated significantly greater downregulation of NOS2 and HIF-1ɑ in the MJD as compared to the model group. Molecular docking analysis further confirmed the binding of active MJD with key active components. Our findings elucidate the impact of MJD on relevant pathophysiological and metabolic networks relevant to KOA and assess the drug efficacy of MJD and its underlying mechanisms of action.

Gene Association Study of the Urokinase Plasminogen Activator and Its Receptor Gene in Alzheimer's Disease.

The role of the innate immune system has long been associated with Alzheimer's disease (AD). There is now accumulating evidence that the soluble Urokinase Plasminogen Activator Receptor pathway, and its genes, PLAU and PLAUR may be important in AD, and yet there have been few genetic association studies to explore this. This study utilizes the DNA bank of the Brains for Dementia Research cohort to investigate the genetic association of common polymorphisms across the PLAU and PLAUR genes with AD. TaqMan genotyping assays were used with standard procedures followed by association analysis in PLINK. No association was observed between the PLAU gene and AD; however, two SNPs located in the PLAUR gene were indicative of a trend towards association but did not surpass multiple testing significance thresholds. Further genotyping studies and exploration of the consequences of these SNPs on gene expression and alternative splicing are warranted to fully uncover the role this system may have in AD.

Biallelic Variants of MRPS36 Cause a New Form of Leigh Syndrome.

The MRPS36 gene encodes a recently identified component of the 2-oxoglutarate dehydrogenase complex (OGDHC), a key enzyme of the Krebs cycle catalyzing the oxidative decarboxylation of 2-oxoglutarate to succinyl-CoA. Defective OGDHC activity causes a clinically variable metabolic disorder characterized by global developmental delay, severe neurological impairment, liver failure, and early-onset lactic acidosis. We investigated the molecular cause underlying Leigh syndrome with bilateral striatal necrosis in two siblings through exome sequencing. Functional studies included measurement of the OGDHC enzymatic activity and MRPS36 mRNA levels in fibroblasts, assessment of protein stability in transfected cells, and structural analysis. A literature review was performed to define the etiological and phenotypic spectrum of OGDHC deficiency. In the two affected brothers, exome sequencing identified a homozygous nonsense variant (c.283G>T, p.Glu95*) of MRPS36. The variant did not affect transcript processing and stability, nor protein levels, but resulted in a shorter protein lacking nine residues that contribute to the structural and functional organization of the OGDHC complex. OGDHC enzymatic activity was significantly reduced. The review of previously reported cases of OGDHC deficiency supports the association of this enzymatic defect with Leigh phenotypic spectrum and early-onset movement disorder. Slightly elevated plasma levels of glutamate and glutamine were observed in our and literature patients with OGDHC defect. Our findings point to MRPS36 as a new disease gene implicated in Leigh syndrome. The slight elevation of plasma levels of glutamate and glutamine observed in patients with OGDHC deficiency represents a candidate metabolic signature of this neurometabolic disorder. © 2024 International Parkinson and Movement Disorder Society.

Critical assessment of variant prioritization methods for rare disease diagnosis within the rare genomes project.

A major obstacle faced by families with rare diseases is obtaining a genetic diagnosis. The average "diagnostic odyssey" lasts over five years and causal variants are identified in under 50%, even when capturing variants genome-wide. To aid in the interpretation and prioritization of the vast number of variants detected, computational methods are proliferating. Knowing which tools are most effective remains unclear. To evaluate the performance of computational methods, and to encourage innovation in method development, we designed a Critical Assessment of Genome Interpretation (CAGI) community challenge to place variant prioritization models head-to-head in a real-life clinical diagnostic setting. We utilized genome sequencing (GS) data from families sequenced in the Rare Genomes Project (RGP), a direct-to-participant research study on the utility of GS for rare disease diagnosis and gene discovery. Challenge predictors were provided with a dataset of variant calls and phenotype terms from 175 RGP individuals (65 families), including 35 solved training set families with causal variants specified, and 30 unlabeled test set families (14 solved, 16 unsolved). We tasked teams to identify causal variants in as many families as possible. Predictors submitted variant predictions with estimated probability of causal relationship (EPCR) values. Model performance was determined by two metrics, a weighted score based on the rank position of causal variants, and the maximum F-measure, based on precision and recall of causal variants across all EPCR values. Sixteen teams submitted predictions from 52 models, some with manual review incorporated. Top performers recalled causal variants in up to 13 of 14 solved families within the top 5 ranked variants. Newly discovered diagnostic variants were returned to two previously unsolved families following confirmatory RNA sequencing, and two novel disease gene candidates were entered into Matchmaker Exchange. In one example, RNA sequencing demonstrated aberrant splicing due to a deep intronic indel in ASNS, identified in trans with a frameshift variant in an unsolved proband with phenotypes consistent with asparagine synthetase deficiency. Model methodology and performance was highly variable. Models weighing call quality, allele frequency, predicted deleteriousness, segregation, and phenotype were effective in identifying causal variants, and models open to phenotype expansion and non-coding variants were able to capture more difficult diagnoses and discover new diagnoses. Overall, computational models can significantly aid variant prioritization. For use in diagnostics, detailed review and conservative assessment of prioritized variants against established criteria is needed.

CAVaLRi: An Algorithm for Rapid Identification of Diagnostic Germline Variation

Clinical exome and genome sequencing (ES/GS) have become indispensable diagnostic tools for rare genetic diseases (RGD). However, the interpretation of ES/GS presents a substantial operational challenge in clinical settings. Test interpretation requires the review of hundreds of genetic variants, a task that has become increasingly challenging given the rising use of ES/GS. In response, we present Clinical Assessment of Variants by Likelihood Ratios (CAVaLRi), which employ a modified likelihood ratio (LR) framework to assign diagnostic probabilities to candidate germline disease genes. CAVaLRi models aspects of the clinical variant assessment process, taking into consideration the predicted impact of the variant, the proband and parental genotypes, and the proband’s clinical characteristics. It also factors in computational phenotype noise and weighs the relative significance of genotype, phenotype, and variant segregation information. We trained and tested CAVaLRi on variant and phenotype data from an internal cohort of 655 clinical ES cases. For validation, CAVaLRi’s performance was benchmarked against four leading gene prioritization algorithms (Exomiser’s hiPHIVE and PhenIX prioritizers, LIRICAL, and XRare) using a distinct cohort of 12,832 ES cases. Our findings reveal that CAVaLRi significantly outperforms its counterparts when clinician-curated phenotype sets are used, as evidenced by its superior precision-recall curve (PR AUC: 0.701) and average diagnostic gene rank (1.59). Notably, even when substituting highly focused clinician-curated phenotype sets with large and potentially nonspecific computationally derived phenotypes, CAVaLRi retains its precision (PR AUC: 0.658; diagnostic gene average rank: 1.68) and markedly outperforms other tools. In a large, heterogeneous validation cohort, CAVaLRi stood out as the most precise prioritization algorithm (PR AUC: 0.335; average diagnostic rank: 1.91). In conclusion, CAVaLRi presents a robust solution for prioritizing diagnostic genes, surpassing current methods. It demonstrates resilience to noisy, computationally-derived phenotypes, providing a scalable strategy to help labs focus on the most diagnostically relevant variants, thus addressing the growing demand for ES/GS interpretation.

Monitoring and analysis of pathogenic microorganisms and resistance genes.

The objectives are to improve the rapid identification method of microbial risk and cut off the route of transmission of resistance genes. When new pathogenic microorganisms are found, intervention can be carried out as early as possible to identify the risk of potential pathogen transmission, and timely cut off the transmission route. Hospital air samples were collected to analyse the distribution of environmental pathogenic microorganisms and the characteristics of ARGs resistance genes. The air samples were collected from 12 sampling sites in the Affiliated Hospital of Yangzhou University. In the infusion room, general ward and intensive care unit, no significant difference was found in microorganisms, and no significant difference was found in microbial resistance genes. There were some differences in resistance genes between east and west districts. Combined with the detection of pathogenic microorganisms and resistance genes in our hospital, it is necessary to improve the daily disinfection measures such as air conditioning and fresh air equipment, cut off the infection route, block the transmission of resistance genes, and monitor pathogens and resistance genes in airborne diseases.

Medicaid Spending and Utilization of Gene and RNA Therapies for Rare Inherited Conditions

Abstract Gene and RNA therapies are promising treatments for many rare diseases. Pediatric populations that could benefit from these drugs are overrepresented among state Medicaid programs. Using Medicaid State Drug Utilization Data, we examined Medicaid spending and utilization of rare disease gene and RNA therapies. Between 2017 and 2022, the number of available gene and RNA therapies increased from 3 to 13, yearly Medicaid spending increased from $148.3 million to $879.7 million, and the number of yearly treatments (a proxy for number of patients) increased from 327 to 1,638. Nearly all spending was attributed to spinal muscular atrophy and Duchenne muscular dystrophy drugs. States participating in Medicaid pooled purchasing initiatives had 39% higher treatments per 100,000 enrollees with no differences in spending. States that carved SMA drugs out of managed Medicaid contracts had higher utilization (54%) and higher spending than those without the carve-out, but spending for carve-out states was higher for those with &amp;lt;80% of enrollees in managed care as compared to those with 80% and above. This suggests multi-state purchasing initiatives and managed care carve-outs can help increase access to gene and RNA therapies among Medicaid beneficiaries, but it is unclear if these strategies are effective at managing spending.

Exome sequencing of 1190 non-syndromic clubfoot cases reveals HOXD12 as a novel disease gene

BackgroundClubfoot, presenting as a rigid inward and downward turning of the foot, is one of the most common congenital musculoskeletal anomalies. The aetiology of clubfoot is poorly understood and variants...

Generating Clinical-Grade Gene-Disease Validity Classifications Through the ClinGen Data Platforms.

Clinical genetic laboratories must have access to clinically validated biomedical data for precision medicine. A lack of accessibility, normalized structure, and consistency in evaluation complicates interpretation of disease causality, resulting in confusion in assessing the clinical validity of genes and genetic variants for diagnosis. A key goal of the Clinical Genome Resource (ClinGen) is to fill the knowledge gap concerning the strength of evidence supporting the role of a gene in a monogenic disease, which is achieved through a process known as Gene-Disease Validity curation. Here we review the work of ClinGen in developing a curation infrastructure that supports the standardization, harmonization, and dissemination of Gene-Disease Validity data through the creation of frameworks and the utilization of common data standards. This infrastructure is based on several applications, including the ClinGen GeneTracker, Gene Curation Interface, Data Exchange, GeneGraph, and website.