Interaction between RNF4 and SART3 is associated with the risk of schizophrenia.

The pathogenesis of schizophrenia (SCZ) is heavily influenced by genetic factors. Ring finger protein 4 (RNF4) and squamous cell carcinoma antigen recognized by T cells 3 (SART3) are thought to be involved in nervous system growth and development via oxidative stress pathways. Moreover, they have previously been linked to SCZ. Yet the role of RNF4 and SART3 in SCZ remains unclear. Here, we investigated how these two genes are involved in SCZ by studying their variants observed in patients. We first observed significantly elevated mRNA levels of RNF4 and SART3 in the peripheral blood in both first-episode (n = 30) and chronic (n = 30) SCZ patients compared to controls (n = 60). Next, we targeted-sequenced three single nucleotide polymorphisms (SNPs) in SART3 and six SNPs in RNF4 for association with SCZ using the genomic DNA extracted from peripheral blood leukocytes from SCZ participants (n = 392) and controls (n = 572). We observed a combination of SNPs that included rs1203860, rs2282765 (both in RNF4), and rs2287550 (in SART3) was associated with increased risk of SCZ, suggesting common pathogenic mechanisms between these two genes. We then conducted experiments in HEK293T cells to better understand the interaction between RNF4 and SART3. We observed that SART3 lowered the expression of RNF4 through ubiquitination and downregulated the expression of nuclear factor E2-related factor 2 (NRF2), a downstream factor of RNF4, implicating the existence of a possible shared regulatory mechanism for RNF4 and SART3. In conclusion, our study provides evidence that the interaction between RNF4 and SART3 contributes to the risk of SCZ. The findings shed light on the underlying molecular mechanisms of SCZ and may lead to the development of new therapies and interventions for this disorder.

Causality Investigation between Gut Microbiome and Sleep-Related Traits: A Bidirectional Two-Sample Mendelian Randomization Study.

Recent research has highlighted associations between sleep and microbial taxa and pathways. However, the causal effect of these associations remains unknown. To investigate this, we performed a bidirectional two-sample Mendelian randomization (MR) analysis using summary statistics of genome-wide association studies (GWAS) from 412 gut microbiome traits (N = 7738) and GWAS studies from seven sleep-associated traits (N = 345,552 to 386,577). We employed multiple MR methods to assess causality, with Inverse Variance Weighted (IVW) as the primary method, alongside a Bonferroni correction ((p < 2.4 × 10-4) to determine significant causal associations. We further applied Cochran's Q statistical analysis, MR-Egger intercept, and Mendelian randomization pleiotropy residual sum and outlier (MR-PRESSO) for heterogeneity and pleiotropy assessment. IVW estimates revealed 79 potential causal effects of microbial taxa and pathways on sleep-related traits and 45 inverse causal relationships, with over half related to pathways, emphasizing their significance. The results revealed two significant causal associations: genetically determined relative abundance of pentose phosphate decreased sleep duration (p = 9.00 × 10-5), and genetically determined increase in fatty acid level increased the ease of getting up in the morning (p = 8.06 × 10-5). Sensitivity analyses, including heterogeneity and pleiotropy tests, as well as a leave-one-out analysis of single nucleotide polymorphisms, confirmed the robustness of these relationships. This study explores the potential causal relationships between sleep and microbial taxa and pathways, offering novel insights into their complex interplay.

Human lineage mutations regulate RNA-protein binding of conserved genes NTRK2 and ITPR1 involved in human evolution.

Background: The role of human lineage mutations (HLMs) in human evolution through post-transcriptional modification is unclear. Aims: To investigate the contribution of HLMs to human evolution through post-transcriptional modification. Methods: We applied a deep learning model Seqweaver to predict how HLMs impact RNA-binding protein affinity. Results: We found that only 0.27% of HLMs had significant impacts on RNA-binding proteins at the threshold of the top 1% of human common variations. These HLMs enriched in a set of conserved genes highly expressed in adult excitatory neurons and prenatal Purkinje neurons, and were involved in synapse organisation and the GTPase pathway. These genes also carried excess damaging coding mutations that caused neurodevelopmental disorders, ataxia and schizophrenia. Among these genes, NTRK2 and ITPR1 had the most aggregated evidence of functional importance, suggesting their essential roles in cognition and bipedalism. Conclusions: Our findings suggest that a small subset of human-specific mutations have contributed to human speciation through impacts on post-transcriptional modification of critical brain-related genes.

Haplotype function score improves biological interpretation and cross-ancestry polygenic prediction of human complex traits.

We propose a new framework for human genetic association studies: at each locus, a deep learning model (in this study, Sei) is used to calculate the functional genomic activity score for two haplotypes per individual. This score, defined as the Haplotype Function Score (HFS), replaces the original genotype in association studies. Applying the HFS framework to 14 complex traits in the UK Biobank, we identified 3619 independent HFS-trait associations with a significance of p < 5 × 10-8. Fine-mapping revealed 2699 causal associations, corresponding to a median increase of 63 causal findings per trait compared with single-nucleotide polymorphism (SNP)-based analysis. HFS-based enrichment analysis uncovered 727 pathway-trait associations and 153 tissue-trait associations with strong biological interpretability, including 'circadian pathway-chronotype' and 'arachidonic acid-intelligence'. Lastly, we applied least absolute shrinkage and selection operator (LASSO) regression to integrate HFS prediction score with SNP-based polygenic risk scores, which showed an improvement of 16.1-39.8% in cross-ancestry polygenic prediction. We concluded that HFS is a promising strategy for understanding the genetic basis of human complex traits.

Scattered throughout the human genome are variations in the genetic code that make individuals more or less likely to develop certain traits. To identify these variants, scientists carry out Genome-wide association studies (GWAS) which compare the DNA variants of large groups of people with and without the trait of interest. This method has been able to find the underlying genes for many human diseases, but it has limitations. For instance, some variations are linked together due to where they are positioned within DNA, which can result in GWAS falsely reporting associations between genetic variants and traits. This phenomenon, known as linkage equilibrium, can be avoided by analyzing functional genomics which looks at the multiple ways a gene's activity can be influenced by a variation. For instance, how the gene is copied and decoded in to proteins and RNA molecules, and the rate at which these products are generated. Researchers can now use an artificial intelligence technique called deep learning to generate functional genomic data from a particular DNA sequence. Here, Song et al. used one of these deep learning models to calculate the functional genomics of haplotypes, groups of genetic variants inherited from one parent. The approach was applied to DNA samples from over 350 thousand individuals included in the UK BioBank. An activity score, defined as the haplotype function score (or HFS for short), was calculated for at least two haplotypes per individual, and then compared to various complex traits like height or bone density. Song et al. found that the HFS framework was better at finding links between genes and specific traits than existing methods. It also provided more information on the biology that may be underpinning these outcomes. Although more work is needed to reduce the computer processing times required to calculate the HFS, Song et al. believe that their new method has the potential to improve the way researchers identify links between genes and human traits.

Rhesus monkeys exhibiting spontaneous ritualistic behaviors resembling obsessive-compulsive disorder.

Obsessive-compulsive disorder (OCD) is a chronic and debilitating psychiatric disorder that affects ∼2%-3% of the population globally. Studying spontaneous OCD-like behaviors in non-human primates may improve our understanding of the disorder. In large rhesus monkey colonies, we found 10 monkeys spontaneously exhibiting persistent sequential motor behaviors (SMBs) in individual-specific sequences that were repetitive, time-consuming and stable over prolonged periods. Genetic analysis revealed severely damaging mutations in genes associated with OCD risk in humans. Brain imaging showed that monkeys with SMBs had larger gray matter (GM) volumes in the left caudate nucleus and lower fractional anisotropy of the corpus callosum. The GM volume of the left caudate nucleus correlated positively with the daily duration of SMBs. Notably, exposure to a stressor (human presence) significantly increased SMBs. In addition, fluoxetine, a serotonergic medication commonly used for OCD, decreased SMBs in these monkeys. These findings provide a novel foundation for developing better understanding and treatment of OCD.

Emden: A novel method integrating graph and transformer representations for predicting the effect of mutations on clinical drug response.

Precision medicine based on personalized genomics provides promising strategies to enhance the efficacy of molecular-targeted therapies. However, the clinical effectiveness of drugs has been severely limited due to genetic variations that lead to drug resistance. Predicting the impact of missense mutations on clinical drug response is an essential way to reduce the cost of clinical trials and understand genetic diseases. Here, we present Emden, a novel method integrating graph and transformer representations that predicts the effect of missense mutations on drug response through binary classification with interpretability. Emden utilized protein sequences-based features and drug structures as inputs for rapid prediction, employing competitive representation learning and demonstrating strong generalization capabilities and robustness. Our study showed promising potential for clinical drug guidance and deep insight into computer-assisted precision medicine. Emden is freely available as a web server at https://www.psymukb.net/Emden.

Effect of Thermostable Enzymes Produced by Psychrotrophic Bacteria in Raw Milk on the Quality of Ultra-High Temperature Sterilized Milk.

Ultra-high temperature sterilized milk (UHT) is a popular dairy product known for its long shelf life and convenience. However, protein gel aging and fat quality defects like creaming and flavor deterioration may arise during storage. These problems are primarily caused by thermostable enzymes produced by psychrotrophic bacteria. In this study, four representative psychrotrophic bacteria strains which can produce thermostable enzymes were selected to contaminate UHT milk artificially. After 11, 11, 13, and 17 weeks of storage, the milk samples, which were contaminated with Pseudomonas fluorescens, Chryseobacterium carnipullorum, Lactococcus raffinolactis and Acinetobacter guillouiae, respectively, demonstrated notable whey separation. The investigation included analyzing the protein and fat content in the upper and bottom layers of the milk, as well as examining the particle size, Zeta potential, and pH in four sample groups, indicating that the stability of UHT milk decreases over time. Moreover, the spoiled milk samples exhibited a bitter taste, with the dominant odor being attributed to ketones and acids. The metabolomics analysis revealed that three key metabolic pathways, namely ABC transporters, butanoate metabolism, and alanine, aspartate, and glutamate metabolism, were found to be involved in the production of thermostable enzymes by psychrotrophic bacteria. These enzymes greatly impact the taste and nutrient content of UHT milk. This finding provides a theoretical basis for further investigation into the mechanism of spoilage.

Inferring the Effects of Protein Variants on Protein-Protein Interactions with Interpretable Transformer Representations.

Identifying pathogenetic variants and inferring their impact on protein-protein interactions sheds light on their functional consequences on diseases. Limited by the availability of experimental data on the consequences of protein interaction, most existing methods focus on building models to predict changes in protein binding affinity. Here, we introduced MIPPI, an end-to-end, interpretable transformer-based deep learning model that learns features directly from sequences by leveraging the interaction data from IMEx. MIPPI was specifically trained to determine the types of variant impact (increasing, decreasing, disrupting, and no effect) on protein-protein interactions. We demonstrate the accuracy of MIPPI and provide interpretation through the analysis of learned attention weights, which exhibit correlations with the amino acids interacting with the variant. Moreover, we showed the practicality of MIPPI in prioritizing de novo mutations associated with complex neurodevelopmental disorders and the potential to determine the pathogenic and driving mutations. Finally, we experimentally validated the functional impact of several variants identified in patients with such disorders. Overall, MIPPI emerges as a versatile, robust, and interpretable model, capable of effectively predicting mutation impacts on protein-protein interactions and facilitating the discovery of clinically actionable variants.

Inhibition of Cronobacter sakazakii Biofilm Formation and Expression of Virulence Factors by Coenzyme Q0.

In this study, we investigated the inhibitory effects of coenzyme Q0 (CoQ0) on biofilm formation and the expression of virulence genes by Cronobacter sakazakii. We found that the minimum inhibitory concentration of CoQ0 against C. sakazakii strains ATCC29544 and ATCC29004 was 100 µg/mL, while growth curve assays showed that subinhibitory concentrations (SICs) of CoQ0 for both strains were 6.4, 3.2, 1.6 and 0.8 µg/mL. Assays exploring the inhibition of specific biofilm formation showed that SICs of CoQ0 inhibited biofilm formation by C. sakazakii in a dose-dependent manner, which was confirmed by scanning electron microscopy and confocal laser scanning microscopy analyses. CoQ0 inhibited the swimming and swarming motility of C. sakazakii and reduced its ability to adhere to and invade HT-29 cells. In addition, CoQ0 impeded the ability of C. sakazakii to survive and replicate within RAW 264.7 cells. Finally, real-time polymerase chain reaction analysis confirmed that nine C. sakazakii genes associated with biofilm formation and virulence were downregulated in response to CoQ0 treatment. Overall, our findings suggest that CoQ0 is a promising antibiofilm agent and provide new insights for the prevention and control of infections caused by C. sakazakii.

High-throughput characterization of HLA-E-presented CD94/NKG2x ligands reveals peptides which modulate NK cell activation.

HLA-E is a non-classical class I MHC protein involved in innate and adaptive immune recognition. While recent studies have shown HLA-E can present diverse peptides to NK cells and T cells, the HLA-E repertoire recognized by CD94/NKG2x has remained poorly defined, with only a limited number of peptide ligands identified. Here we screen a yeast-displayed peptide library in the context of HLA-E to identify 500 high-confidence unique peptides that bind both HLA-E and CD94/NKG2A or CD94/NKG2C. Utilizing the sequences identified via yeast display selections, we train prediction algorithms and identify human and cytomegalovirus (CMV) proteome-derived, HLA-E-presented peptides capable of binding and signaling through both CD94/NKG2A and CD94/NKG2C. In addition, we identify peptides which selectively activate NKG2C+ NK cells. Taken together, characterization of the HLA-E-binding peptide repertoire and identification of NK activity-modulating peptides present opportunities for studies of NK cell regulation in health and disease, in addition to vaccine and therapeutic design.

Real-time jellyfish classification and detection algorithm based on improved YOLOv4-tiny and improved underwater image enhancement algorithm.

The outbreak of jellyfish blooms poses a serious threat to human life and marine ecology. Therefore, jellyfish detection techniques have earned great interest. This paper investigates the jellyfish detection and classification algorithm based on optical images and deep learning theory. Firstly, we create a dataset comprising 11,926 images. A MSRCR underwater image enhancement algorithm with fusion is proposed. Finally, an improved YOLOv4-tiny algorithm is proposed by incorporating a CBMA module and optimizing the training method. The results demonstrate that the detection accuracy of the improved algorithm can reach 95.01%, the detection speed is 223FPS, both of which are better than the compared algorithms such as YOLOV4. In summary, our method can accurately and quickly detect jellyfish. The research in this paper lays the foundation for the development of an underwater jellyfish real-time monitoring system.

Machine learning-based screening for biomarkers of psoriasis and immune cell infiltration.

BACKGROUND: Psoriasis is a chronic immune-mediated skin disease. However, the pathogenesis is not yet well established. OBJECTIVES: This study aimed to screen psoriasis biomarker genes and analyse their significance in immune cell infiltration. MATERIALS & METHODS: GSE13355 and GSE14905 datasets were downloaded from Gene Expression Omnibus (GEO) as training groups to establish the model. GSE30999 obtained from GEO was used to validate the model. Differential expression and multiple enrichment analyses were performed on 91 psoriasis samples and 171 control samples from the training group. The "LASSO" regression model and support vector machine model were used to screen and verify genes implicated in psoriasis. Genes with an area under the ROC curve >0.9 were selected as candidate biomarkers and verified in the validation group. Differential analysis of immune cell infiltration was performed on psoriasis and control samples using the "CIBERSORT" algorithm. Correlation analyses between the screened psoriasis biomarkers and 22 types of immune cell infiltration were performed. RESULTS: In total, 101 differentially expressed genes were identified, which were mainly shown to be involved in regulating cell proliferation and immune functions. Three psoriasis biomarkers, BTC, IGFL1, and SERPINB3, were identified using two machine learning algorithms. These genes showed high diagnostic value in training and validation groups. The proportion of immune cells during immune infiltration differed between psoriasis and control samples, which was associated with the three biomarkers. CONCLUSION: BTC, IGFL1, and SERPINB3 are associated with the infiltration of multiple immune cells, and may therefore be used as biomarkers for psoriasis.

Bioinformatic Analysis of the Potential Common Pathogenic Mechanisms for Psoriasis and Metabolic Syndrome.

The pathogeneses of psoriasis and metabolic syndrome are closely related; however, the underlying biological mechanisms are yet to be clarified. A psoriasis training set was downloaded from the Gene Expression Omnibus database and analyzed to identify the differentially expressed genes (|logFC|> 1 and adjust P < 0.05). Differentially expressed genes for metabolic syndrome were obtained from the GeneCards, Online Mendelian Inheritance in Man, and DisGeNET databases, and crosstalk genes were obtained for multiple enrichment analysis after identifying the disease intersection. Characteristic crosstalk genes were screened using the least absolute shrinkage and selection operator regression model and random forest tree model, and the genes with area under the receiver operating characteristic curve > 0.7 were selected for validation by the two validation sets. Differential analyses of immune cell infiltration were performed on psoriasis lesion and control samples using the CIBERSORT and ImmuCellAI methods, and correlation analyses were performed between the screened signature crosstalk genes and immune cell infiltration. Significant crosstalk genes were analyzed based on the psoriasis area and severity index and on the responses to biological agents. We found five signature genes (NLRX1, KYNU, ABCC1, BTC, and SERPINB4) were screened based on two machine learning algorithms, and NLRX1 was validated. The infiltration of multiple immune cells in psoriatic lesions and non-lesions was associated with NLRX1 expression. NLRX1 was found to be associated with psoriasis severity and response rate after the use of biologics. NLRX1 could be a significant crosstalk gene for psoriasis and metabolic syndrome.

T7 peptide-decorated exosome-based nanocarrier system for delivery of Galectin-9 siRNA to stimulate macrophage repolarization in glioblastoma.

PURPOSE: Exosomes are nano-vesicular carriers capable of delivering cargoes for intercellular communication, which holds potential as biocompatible and high efficiency systems for drug delivery. In this study, we evaluated the potential effect of T7 peptide-decorated exosome-loaded Galectin-9 siRNA (T7-Exo/siGalectin-9) in the M1 polarization of macrophages and immunosuppression of glioblastoma (GBM). METHODS: Differentially expressed genes in GBM were in silico predicted and then experimentally verified. Galectin-9 was knocked down by siRNA to assess its role in tumor-bearing mice. T7 peptide-decorated exosomes (derived from human embryonic kidney [HEK]-293T cells) targeting GBM were prepared, and loaded with Galectin-9 siRNA by electroporation to prepare nanoformulations (T7-Exo/siGalectin-9). The role of T7-Exo/siGalectin-9 in CD8+ T cell cytotoxicity to target GBM cells and polarization of macrophages was evaluated after artificial modulation of Galectin-9 expression. Anti-tumor effects of T7-Exo/siGalectin-9 were elucidated in vitro and in vivo. RESULTS: Galectin-9 was highly expressed in GBM tissues and cell lines. The siRNA-mediated knockdown of Galectin-9 repressed the growth of xenografts of GBM cells in C57BL/6 mice and activated immune response in the tumor microenvironment. T7-Exo/siGalectin-9 effectively delivered siGalectin-9 to GBM cells. T7-Exo/siGalectin-9 contributed to activation of the TLR7-IRF5 pathway, which polarized macrophages to M1 phenotype. By this mechanism, phagocytosis of GBM cells by macrophages was increased, the anti-tumor effect of CD8+ T cells was enhanced and the inflammatory responses were suppressed. CONCLUSION: Overall, T7-Exo/siGalectin-9 promotes macrophage repolarization and restricts the immunosuppression of GBM, thus providing novel insights into and drug delivery system of immunotherapy for GBM.

Characteristics and influencing factors of soil preferential flow in typical stands of Karst area in southwest China.

The study of preferential flow phenomena in Karst areas and the identification of the main factors influencing preferential flow are of great importance for the recovery of local vegetation. The distribution of the dyeing solution in the vertical and horizontal directions was examined by field staining tracer test and image processing technique. We analyzed the total dyeing area ratio, matrix flow depth, preferential flow ratio, and length index as pre-ferential flow characteristic parameters, and 14 factors affecting preferential flow using grey correlation analysis. The results showed that there were two main types of preferential flow, funnel-shaped and dendritic, with lateral water movement occurring in the soil of typical Karst stands. The mean value of the dyeing area ratio of the understory in Karst areas was 19.4%, and that of the matrix flow depth, preferential flow ratio, and length index was 4.96 cm, 62.9%, and 385.5%, respectively. Among the 14 environmental factors influencing preferential flow, the initial soil moisture content had the strongest influence on the dyeing area ratio, the available potassium content had the most significant influence on the matrix flow depth, and available phosphorous content had the most significant influence on both the preferential flow ratio and the length index. The high degree of development and spatial variability of preferential flow under typical forest stands in Karst areas was strongly influenced by physical properties such as initial soil water content, while soil nutrient also exerts important influence on preferential flow.

All-Trans Retinoic Acid Promotes M2 Macrophage Polarization in Vitro by Activating the p38MAPK/STAT6 Signaling Pathway.

BACKGROUND: M2-type macrophages are inflammation-suppressing cells that are differentiated after induction by cytokines such as IL-4 or IL-13, which play an important regulatory role in inflammation and influence the regression of inflammation-related diseases. All-trans retinoic acid (ATRA) has an important role in suppressing immune-mediated inflammatory responses but the effect and underlying mechanism of ATRA on the polarization of M2 macrophages remains unclear. METHODS: Macrophages were isolated from peritoneal wash fluid, and IL-4 (20 ng/mL) was used to construct a m2-type macrophage polarization model. The model was incubated with different concentrations of ATRA (15 µg/ml, 30 µg/ml, 45 µg/ml) for 24 h, and pretreated macrophages with p38MAPKα inhibitor SB202190 (20 µM). MTT, Trypan blue staining, Annexin V-PE/7-AAD staining, flow cytometry, real-time PCR and western blotting were used to investigate the effect and mechanism of ATRA on the polarization of M2 macrophages. RESULTS: Compared with the IL-4 group, the proportion of F4/80+CD206+ M2-type macrophages was significantly higher in the ATRA group (P < 0.01). mRNA and protein expression levels of Arg-1, IL-10 and TGF-ß1 were as significantly higher (P < 0.01) in the ATRA group as phosphorylation levels of STAT6 and p38MAPK (P < 0.01). After pretreatment with the addition of the inhibitor SB202190, M2-type macrophages proportion and their associated factors expression were significantly (P < 0.01) reduced, as compared with those in the ATRA group, but they were comparable (P > 0.05) with the IL-4 group. CONCLUSION: The combination of ATRA and IL-4 activated the p38MAPK/STAT6-signaling pathway to promote polarization of M2 macrophages.

Study on the imbalance of M1/M2 macrophage polarization in severe chronic periodontitis.

BACKGROUND: Macrophages commonly exist in two distinct subsets in different microenvironments: classically activated macrophages (M1) and alternatively activated macrophages (M2). The imbalance of M1-M2 macrophage polarization is often related to various diseases or inflammatory states. OBJECTIVE: The purpose of this study was to determine whether there is an imbalance in the expression of M1 and M2 macrophage-related cytokines in severe chronic periodontitis. METHODS: A total of 30 clinical specimens, including severe chronic periodontitis tissues (n= 15) and healthy control tissues (n= 15), were used in this study. Reverse transcription polymerase chain reaction (RT-PCR) and Western blot methods were used to detect the mRNA and protein expression levels of M1 macrophage-related cytokines (inducible nitric oxide synthase (iNOS) and signal transducer and activator of transcription 1 (STAT1)) and M2 macrophage-related cytokines (arginase-1 (Arg-1) and STAT6), respectively. RESULTS: The mRNA and protein expression levels of M1 macrophage-related cytokines (iNOS and STAT1) and M2 macrophage-related cytokines (Arg-1 and STAT6) were significantly increased in severe chronic periodontitis patients. In addition, the ratios of iNOS/Arg-1 and STAT1/STAT6 in the severe chronic periodontitis group were also significantly increased (P< 0.01). CONCLUSION: The imbalance of M1/M2 macrophages exists in the pathogenesis of severe chronic periodontitis, and has a tendency towards M1 polarization. Therefore, maintaining the immune balance of M1/M2 macrophages may be a novel therapeutic alternative for the management of severe chronic periodontitis.

A Comprehensive Study of De Novo Mutations on the Protein-Protein Interaction Interfaces Provides New Insights into Developmental Delay.

Mutations, especially those at the protein-protein interaction (PPI) interface, have been associated with various diseases. Meanwhile, though de novo mutations (DNMs) have been proven important in neuropsychiatric disorders, such as developmental delay (DD), the relationship between PPI interface DNMs and DD has not been well studied. Here we curated developmental delay DNM datasets from the PsyMuKB database and showed that DD patients showed a higher rate and deleteriousness in DNM missense on the PPI interface than sibling control. Next, we identified 302 DD-related PsychiPPIs, defined as PPIs harboring a statistically significant number of DNM missenses at their interface, and 42 DD candidate genes from PsychiPPI. We observed that PsychiPPIs preferentially affected the human protein interactome network hub proteins. When analyzing DD candidate genes using gene ontology and gene spatio-expression, we found that PsychiPPI genes carrying PPI interface mutations, such as FGFR3 and ALOX5, were enriched in development-related pathways and the development of the neocortex, and cerebellar cortex, suggesting their potential involvement in the etiology of DD. Our results demonstrated that DD patients carried an excess burden of PPI-truncating DNM, which could be used to efficiently search for disease-related genes and mutations in large-scale sequencing studies. In conclusion, our comprehensive study indicated the significant role of PPI interface DNMs in developmental delay pathogenicity.