DIP2C polymorphisms are implicated in susceptibility and clinical phenotypes of autism spectrum disorder

De novo variants predicting haploinsufficiency for DIP2C are associated with expressive speech delay

The disconnected (disco)-interacting protein 2 (DIP2) gene was first identified in D. melanogaster and contains a DNA methyltransferase-associated protein 1 (DMAP1) binding domain, Acyl-CoA synthetase domain and AMP-binding sites. DIP2 regulates axonal bifurcation of the mushroom body neurons in D. melanogaster and is required for axonal regeneration in the neurons of C. elegans. The DIP2 homologues in vertebrates, Disco-interacting protein 2 homolog A (DIP2A), Disco-interacting protein 2 homolog B (DIP2B), and Disco-interacting protein 2 homolog C (DIP2C), are highly conserved and expressed widely in the central nervous system. Although there is evidence that DIP2C plays a role in cognition, reports of pathogenic variants in these genes are rare and their significance is uncertain. We present 23 individuals with heterozygous DIP2C variants, all manifesting developmental delays that primarily affect expressive language and speech articulation. Eight patients had de novo variants predicting loss-of-function in the DIP2C gene, two patients had de novo missense variants, three had paternally inherited loss of function variants and six had maternally inherited loss-of-function variants, while inheritance was unknown for four variants. Four patients had cardiac defects (hypertrophic cardiomyopathy, atrial septal defects, and bicuspid aortic valve). Minor facial anomalies were inconsistent but included a high anterior hairline with a long forehead, broad nasal tip, and ear anomalies. Brainspan analysis showed elevated DIP2C expression in the human neocortex at 10-24 weeks after conception. With the cases presented herein, we provide phenotypic and genotypic data supporting the association between loss-of-function variants in DIP2C with a neurocognitive phenotype.

Six-gene prognostic signature for non-alcoholic fatty liver disease susceptibility using machine learning

BACKGROUND

nonalcoholic fatty liver disease (NAFLD) is a common liver disease affecting the global population and its impact on human health will continue to increase. Genetic susceptibility is an important factor influencing its onset and progression, and there is a lack of reliable methods to predict the susceptibility of normal populations to NAFLD using appropriate genes.

METHODS

RNA sequencing data relating to nonalcoholic fatty liver disease was analyzed using the "limma" package within the R software. Differentially expressed genes were obtained through preliminary intersection screening. Core genes were analyzed and obtained by establishing and comparing 4 machine learning models, then a prediction model for NAFLD was constructed. The effectiveness of the model was then evaluated, and its applicability and reliability verified. Finally, we conducted further gene correlation analysis, analysis of biological function and analysis of immune infiltration.

RESULTS

By comparing 4 machine learning algorithms, we identified SVM as the optimal model, with the first 6 genes (CD247, S100A9, CSF3R, DIP2C, OXCT 2 and PRAMEF16) as predictive genes. The nomogram was found to have good reliability and effectiveness. Six genes' receiver operating characteristic curves (ROC) suggest an essential role in NAFLD pathogenesis, and they exhibit a high predictive value. Further analysis of immunology demonstrated that these 6 genes were closely connected to various immune cells and pathways.

CONCLUSION

This study has successfully constructed an advanced and reliable prediction model based on 6 diagnostic gene markers to predict the susceptibility of normal populations to NAFLD, while also providing insights for potential targeted therapies.

Novel and known minor alleles of CNTNAP2 gene variants are associated with comorbidity of intellectual disability and epilepsy phenotypes: a case-control association study reveals potential biomarkers

BACKGROUND

Neurodevelopmental disorders are heterogeneous due to underlying multiple shared genetic pathways and risk factors. Intellectual disability, epilepsy and autism spectrum disorder phenotypes overlap which indicates the diverse effects of common genes. Recent studies suggested the probable contribution of CNTNAP2 gene polymorphisms to the comorbidity of these neurological conditions.

METHODS AND RESULTS

This study was conducted to investigate the role of CNTNAP2 polymorphisms rs147815978 (G>T) and rs2710102 (A>G) as a risk factor for comorbidity of intellectual disability and epilepsy in a group of 345 individuals including 170 patients and 175 healthy controls recruited from various ethnic groups of Pakistani population. Our case-control study group was genotyped by tetra primer ARMS-PCR technique and results were analysed to know the effects of CNTNAP2 rs147815978 (G>T) and rs2710102 (A>G) polymorphisms in the group. The frequency of risk allele T (rs147815978) and risk allele G (rs2710102) for homozygous recessive genotypes (TT/GG) in our study group was 36.47% while odds ratios for risk allele T (rs147815978) was 5.45 (3.90-7.61: 95% CI, P = 0.000) and that for risk allele G (rs2710102) was 2.39 (1.76-3.24: 95% CI, P = 0.0001). Homozygous recessive genotypes (TT/GG) appeared only in cases and not in control group which indicated these as suspected risk genotypes and the significant association (p < 0.05%) of CNTNAP2 gene polymorphisms rs147815978 (G>T) and rs2710102 (A>G) with co-occurrence of intellectual disability and epilepsy phenotypes in our study group which is in HWE (χ2 = 174, P < 0.0001). Logistic regression analysis shows additive (p < 0.0001) and multiplicative (p < 0.001) models which confirms significant association of both the polymorphisms in our data, which are closely located on same haplotype (D' = - 0.168).

CONCLUSIONS

We propose that CNTNAP2 rs147815978 (G>T) and rs2710102 (A>G) polymorphisms are possible risk loci for overlapping neurodevelopmental disorders in Pakistani population. We propose the role of a previously reported common SNP rs2710102 (A>G) with a rarely reported novel SNP rs147815978 (G>T) for CNTNAP2 gene association with neurodevelopmental disorders in our data. Our study has expanded the knowledge of CNTNAP2 gene polymorphisms as probable biomarkers for susceptibility of co-occurrence of intellectual disability and epilepsy phenotypes in Pakistani population. We hope that our study will open new horizons of CNTNAP2 gene variants research to cure the neurological conditions in Pakistani population where consanguinity is a tradition and prevalence of neurodevelopmental disorders has increased from 1 to 2% during last 5 years.

Structural Variations Contribute to the Genetic Etiology of Autism Spectrum Disorder and Language Impairments

Autism spectrum disorder (ASD) is a neurodevelopmental disorder characterized by restrictive interests and/or repetitive behaviors and deficits in social interaction and communication. ASD is a multifactorial disease with a complex polygenic genetic architecture. Its genetic contributing factors are not yet fully understood, especially large structural variations (SVs). In this study, we aimed to assess the contribution of SVs, including copy number variants (CNVs), insertions, deletions, duplications, and mobile element insertions, to ASD and related language impairments in the New Jersey Language and Autism Genetics Study (NJLAGS) cohort. Within the cohort, ~77% of the families contain SVs that followed expected segregation or de novo patterns and passed our filtering criteria. These SVs affected 344 brain-expressed genes and can potentially contribute to the genetic etiology of the disorders. Gene Ontology and protein-protein interaction network analysis suggested several clusters of genes in different functional categories, such as neuronal development and histone modification machinery. Genes and biological processes identified in this study contribute to the understanding of ASD and related neurodevelopment disorders.