A proportion of previously defined benign variants or variants of uncertain significance in humans, which are challenging to identify, may induce an abnormal splicing process. An increasing number of methods have been developed to predict splicing variants, but their performance has not been completely evaluated using independent benchmarks. Here, we manually sourced ∼50 000 positive/negative splicing variants from > 8000 studies and selected the independent splicing variants to evaluate the performance of prediction methods. These methods showed different performances in recognizing splicing variants in donor and acceptor regions, reminiscent of different weight coefficient applications to predict novel splicing variants. Of these methods, 66.67% exhibited higher specificities than sensitivities, suggesting that more moderate cut-off values are necessary to distinguish splicing variants. Moreover, the high correlation and consistent prediction ratio validated the feasibility of integration of the splicing prediction method in identifying splicing variants. We developed a splicing analytics platform called SPCards, which curates splicing variants from publications and predicts splicing scores of variants in genomes. SPCards also offers variant-level and gene-level annotation information, including allele frequency, non-synonymous prediction and comprehensive functional information. SPCards is suitable for high-throughput genetic identification of splicing variants, particularly those located in non-canonical splicing regions.

Substantial evidence shown that the age at onset (AAO) of Parkinson's disease (PD) is a major determinant of clinical heterogeneity. However, the mechanisms underlying heterogeneity in the AAO remain unclear. To investigate the risk factors with the AAO of PD, a total of 3156 patients with PD from the UK Biobank were included in this study. We evaluated the effects of polygenic risk scores (PRS), nongenetic risk factors, and their interaction on the AAO using Mann-Whitney U tests and regression analyses. We further identified the genes interacting with nongenetic risk factors for the AAO using genome-wide environment interaction studies. We newly found physical activity (P < 0.0001) was positively associated with AAO and excessive daytime sleepiness (P < 0.0001) was negatively associated with AAO, and reproduced the positive associations of smoking and non-steroidal anti-inflammatory drug intake and the negative association of family history with AAO. In the dose-dependent analyses, smoking duration (P = 1.95 × 10-6), coffee consumption (P = 0.0150), and tea consumption (P = 0.0008) were positively associated with AAO. Individuals with higher PRS had younger AAO (P = 3.91 × 10-5). In addition, we observed a significant interaction between the PRS and smoking for AAO (P = 0.0316). Specifically, several genes, including ANGPT1 (P = 7.17 × 10-7) and PLEKHA6 (P = 4.87 × 10-6), may influence the positive relationship between smoking and AAO. Our data suggests that genetic and nongenetic risk factors are associated with the AAO of PD and that there is an interaction between the two.

The expression pattern represents a quantitative phenotype that provides an in-depth view of the molecular mechanism in Parkinson's disease (PD); however, the expression patterns of PD-associated genes (PAGs) and their relation to age at onset (AAO) remain unclear.

Folate deficiency is an environmental risk factor for several developmental disorders. De novo mutations (DNMs) also play important etiological roles in various developmental disorders. However, it remains unclear whether DNMs in folate-related genes (FRGs) contribute to developmental disorders. We obtained a list of 1,821 FRGs from folate metabolism pathways and the Comparative Toxicogenomics Database, along with data concerning DNMs in 15,404 cases and 3,391 controls from the Gene4Denovo database. We used a TADA-Denovo model to prioritize candidate disease-associated FRGs, and characterized these genes in terms of genic intolerance, functional networks, and expression patterns. Compared with the controls, FRGs were significantly enriched in likely damaging DNMs (ldDNMs) in patients with developmental disorders (1.54 ≤ odds ratio ≤ 3.39, P adj ≤ 0.0075). Furthermore, FRGs with ldDNMs rather than with likely non-damaging DNMs (lndDNMs) overlapped significantly among the five developmental disorders included in the datasets. The TADA-Denovo model prioritized 96 candidate disease-associated FRGs, which were intolerant to genetic variants. Their functional networks mainly involved pathways associated with chromatin modification, organ development, and signal transduction pathways. DNMT3A, KMT2B, KMT2C, and YY1 emerged as hub FRGs from the protein-protein interaction network. These candidate disease-associated FRGs are preferentially expressed in the excitatory neurones during embryonic development, and in the cortex, cerebellum, striatum, and amygdala during foetal development. Overall, these findings show that DNMs in FRGs are associated with the risk of developmental disorders. Further research on these DNMs may facilitate the discovery of developmental disorder biomarkers and therapeutic targets, enabling detailed, personalized, and precise folate treatment plan.

Resistance to chemotherapy is the main reason for treatment failure and poor prognosis in patients with triple-negative breast cancer (TNBC). Although the association of RNA N6-methyladenosine (m6A) modifications with therapy resistance is noticed, its role in the development of therapeutic resistance in TNBC is not well documented. This study aimed to investigate the potential mechanisms underlying reactive oxygen species (ROS) regulation in doxorubicin (DOX)-resistant TNBC. Here, we found that DOX-resistant TNBC cells displayed low ROS levels because of increased expression of superoxide dismutase (SOD2), thus maintaining cancer stem cells (CSCs) characteristics and DOX resistance. FOXO1 is a master regulator that reduces cellular ROS in DOX-resistant TNBC cells, and knockdown of FOXO1 significantly increased ROS levels by inhibiting SOD2 expression. Moreover, the m6A demethylase ALKBH5 promoted m6A demethylation of FOXO1 mRNA and increased FOXO1 mRNA stability in DOX-resistant TNBC cells. The analysis of clinical samples revealed that the increased expression levels of ALKBH5, FOXO1, and SOD2 were significantly positively correlated with chemoresistance and poor prognosis in patients with TNBC. To our knowledge, this is the first study to highlight that ALKBH5-mediated FOXO1 mRNA demethylation contributes to CSCs characteristics and DOX resistance in TNBC cells. Furthermore, pharmacological targeting of FOXO1 profoundly restored the response of DOX-resistant TNBC cells, both in vitro and in vivo. In conclusion, we demonstrated a critical function of ALKBH5-mediated m6A demethylation of FOXO1 mRNA in restoring redox balance, which in turn promoting CSCs characteristics and DOX resistance in TNBC, and suggested that targeting the ALKBH5/FOXO1 axis has therapeutic potential for patients with TNBC refractory to chemotherapy.

Activating mutations of epidermal growth factor receptor (EGFR) contributes to the progression of non-small cell lung cancer (NSCLC). EGFR tyrosine kinase inhibitor (TKI)-targeted therapy has become the standard treatment for NSCLC patients with EGFR-mutations. However, acquired resistance to these agents remains a major obstacle for managing NSCLC. Here, we investigated a novel strategy to overcome EGFR TKI resistance by targeting the nicotinamide N-methyltransferase (NNMT). Using iTRAQ-based quantitative proteomics analysis, we identified that NNMT was significantly increased in EGFR-TKI-resistant NSCLC cells. Moreover, we found that NNMT expression was increased in EGFR-TKI-resistant NSCLC tissue samples, and higher levels were correlated with shorter progression-free survival in EGFR-TKI-treated NSCLC patients. Knockdown of NNMT rendered EGFR-TKI-resistant cells more sensitive to EGFR-TKI, whereas overexpression of NNMT in EGFR-TKI-sensitive cells resulted in EGFR-TKI resistance. Mechanically, upregulation of NNMT increased c-myc expression via SIRT1-mediated c-myc deacetylation, which in turn promoted glycolysis and EGFR-TKI resistance. Furthermore, we demonstrated that the combination of NNMT inhibitor and EGFR-TKI strikingly suppressed the growth of EGFR-TKI-resistant NSCLC cells both in vitro and in vivo. In conclusion, our research indicated that NNMT overexpression is important for acquired resistance to EGFR-TKI and that targeting NNMT might be a potential therapeutic strategy to overcome resistance to EGFR TKI.

Long single-molecular sequencing technologies, such as PacBio circular consensus sequencing (CCS) and nanopore sequencing, are advantageous in detecting DNA 5-methylcytosine in CpGs (5mCpGs), especially in repetitive genomic regions. However, existing methods for detecting 5mCpGs using PacBio CCS are less accurate and robust. Here, we present ccsmeth, a deep-learning method to detect DNA 5mCpGs using CCS reads. We sequence polymerase-chain-reaction treated and M.SssI-methyltransferase treated DNA of one human sample using PacBio CCS for training ccsmeth. Using long (≥10 Kb) CCS reads, ccsmeth achieves 0.90 accuracy and 0.97 Area Under the Curve on 5mCpG detection at single-molecule resolution. At the genome-wide site level, ccsmeth achieves >0.90 correlations with bisulfite sequencing and nanopore sequencing using only 10× reads. Furthermore, we develop a Nextflow pipeline, ccsmethphase, to detect haplotype-aware methylation using CCS reads, and then sequence a Chinese family trio to validate it. ccsmeth and ccsmethphase can be robust and accurate tools for detecting DNA 5-methylcytosines.