Association between de novo variants of nuclear-encoded mitochondrial-related genes and undiagnosed developmental disorder and autism

BACKGROUND

Evidence suggests that mitochondrial abnormalities increase the risk of two neurodevelopmental disorders: undiagnosed developmental disorder (UDD) and autism spectrum disorder (ASD). However, which nuclear-encoded mitochondrial-related genes (NEMGs) were associated with UDD-ASD is unclear.

AIM

To explore the association between de novo variants (DNVs) of NEMGs and UDD-ASD.

DESIGN

Comprehensive analysis based on DNVs of NEMGs identified in patients (31 058 UDD probands and 10 318 ASD probands) and 4262 controls.

METHODS

By curating NEMGs and cataloging publicly published DNVs in NEMGs, we compared the frequency of DNVs in cases and controls. We also applied a TADA-denovo model to highlight disease-associated NEMGs and characterized them based on gene intolerance, functional networks and expression patterns.

RESULTS

Compared with levels in 4262 controls, an excess of protein-truncating variants and deleterious missense variants in 1421 cataloged NEMGs from 41 376 patients (31 058 UDD and 10 318 ASD probands) was observed. Overall, 3.23% of de novo deleterious missense variants and 3.20% of de novo protein-truncating variants contributed to 1.1% and 0.39% of UDD-ASD cases, respectively. We prioritized 130 disease-associated NEMGs and showed distinct expression patterns in the developing human brain. Disease-associated NEMGs expression was enriched in both excitatory and inhibitory neuronal lineages from the developing human cortex.

CONCLUSIONS

Rare genetic alterations of disease-associated NEMGs may play a role in UDD-ASD development and lay the groundwork for a better understanding of the biology of UDD-ASD.

ToxMPNN: A deep learning model for small molecule toxicity prediction

Machine learning (ML) has shown a great promise in predicting toxicity of small molecules. However, the availability of data for such predictions is often limited. Because of the unsatisfactory performance of models trained on a single toxicity endpoint, we collected toxic small molecules with multiple toxicity endpoints from previous study. The dataset comprises 27 toxic endpoints categorized into seven toxicity classes, namely, carcinogenicity and mutagenicity, acute oral toxicity, respiratory toxicity, irritation and corrosion, cardiotoxicity, CYP450, and endocrine disruption. In addition, a binary classification Common-Toxicity task was added based on the aforementioned dataset. To improve the performance of the models, we added marketed drugs as negative samples. This study presents a toxicity predictive model, ToxMPNN, based on the message passing neural network (MPNN) architecture, aiming to predict the toxicity of small molecules. The results demonstrate that ToxMPNN outperforms other models in capturing toxic features within the molecular structure, resulting in more precise predictions with the ROC_AUC testing score of 0.886 for the Toxicity_drug dataset. Furthermore, it was observed that adding marketed drugs as negative samples not only improves the predictive performance of the binary classification Common-Toxicity task but also enhances the stability of the model prediction. It shows that the graph-based deep learning (DL) algorithms in this study can be used as a trustworthy and effective tool to assess small molecule toxicity in the development of new drugs.

HBV-miR-3 is closely related to HBV replication and strongly predictive of HBeAg seroconversion in PegIFN-α treated patients

HBV-miR-3 is encoded by HBV and takes part in pathogenesis of HBV-related liver disease. Whether HBV-miR-3 has a relationship with HBV replication and is predictive of PegIFN-α treatment response is still unknown. HBV-miR-3 quantification is based on qRT-PCR. The relationship of HBV-miR-3 and HBV replication, and predictive value of HBV-miR-3 were evaluated in a cohort of 650 HBeAg positive patients from a multi-center, randomized phase III clinical trial for the study of PegIFN-a2b. HBV-miR-3 is significantly positively related to HBVDNA, HBVpgRNA, HBeAg and HBsAg at baseline and at all the different time points during PegIFN-α treatment. Both univariate regression analyses and multivariate logistic regression analyses showed HBV-miR-3 is a predictor of HBeAg seroconversion in the patients treated with PegIFN-α at weeks of 0, 12, and 24. 70.0% of patients with HBV-miR-3 < 3log at week 12 achieved HBeAg seroconversion, otherwise, with HBV-miR-3 > 6log at week 12 no patient obtained HBeAg seroconversion. Conbination of HBV-miR-3 and HBeAg is more strongly predictive of HBeAg seroconversion (83.64%) at week 12. HBV-miR-3 is new biomarker for HBV replication and positively correlated to HBV replication. HBV-miR-3 is also an early predictor of HBeAg seroconversion in the patients treated with PegIFN-α.

VarCards2: an integrated genetic and clinical database for ACMG-AMP variant-interpretation guidelines in the human whole genome

VarCards, an online database, combines comprehensive variant- and gene-level annotation data to streamline genetic counselling for coding variants. Recognising the increasing clinical relevance of non-coding variations, there has been an accelerated development of bioinformatics tools dedicated to interpreting non-coding variations, including single-nucleotide variants and copy number variations. Regrettably, most tools remain as either locally installed databases or command-line tools dispersed across diverse online platforms. Such a landscape poses inconveniences and challenges for genetic counsellors seeking to utilise these resources without advanced bioinformatics expertise. Consequently, we developed VarCards2, which incorporates nearly nine billion artificially generated single-nucleotide variants (including those from mitochondrial DNA) and compiles vital annotation information for genetic counselling based on ACMG-AMP variant-interpretation guidelines. These annotations include (I) functional effects; (II) minor allele frequencies; (III) comprehensive function and pathogenicity predictions covering all potential variants, such as non-synonymous substitutions, non-canonical splicing variants, and non-coding variations and (IV) gene-level information. Furthermore, VarCards2 incorporates 368 820 266 documented short insertions and deletions and 2 773 555 documented copy number variations, complemented by their corresponding annotation and prediction tools. In conclusion, VarCards2, by integrating over 150 variant- and gene-level annotation sources, significantly enhances the efficiency of genetic counselling and can be freely accessed at http://www.genemed.tech/varcards2/.