A dual RNA-seq analyses revealed dynamic arms race during the invasion of walnut by Colletotrichum gloeosporioides.

BACKGROUND: Walnut anthracnose caused by Colletotrichum gloeosporioides seriously endangers the yield and quality of walnut, and has now become a catastrophic disease in the walnut industry. Therefore, understanding both pathogen invasion mechanisms and host response processes is crucial to defense against C. gloeosporioides infection. RESULTS: Here, we investigated the mechanisms of interaction between walnut fruits (anthracnose-resistant F26 fruit bracts and anthracnose-susceptible F423 fruit bracts) and C. gloeosporioides at three infection time points (24hpi, 48hpi, and 72hpi) using a high-resolution time series dual transcriptomic analysis, characterizing the arms race between walnut and C. gloeosporioides. A total of 20,780 and 6670 differentially expressed genes (DEGs) were identified in walnut and C. gloeosporioides against 24hpi, respectively. Generous DEGs in walnut exhibited opposite expression patterns between F26 and F423, which indicated that different resistant materials exhibited different transcriptional responses to C. gloeosporioides during the infection process. KEGG functional enrichment analysis indicated that F26 displayed a broader response to C. gloeosporioides than F423. Meanwhile, the functional analysis of the C. gloeosporioides transcriptome was conducted and found that PHI, SignalP, CAZy, TCDB genes, the Fungal Zn (2)-Cys (6) binuclear cluster domain (PF00172.19) and the Cytochrome P450 (PF00067.23) were largely prominent in F26 fruit. These results suggested that C. gloeosporioides secreted some type of effector proteins in walnut fruit and appeared a different behavior based on the developmental stage of the walnut. CONCLUSIONS: Our present results shed light on the arms race process by which C. gloeosporioides attacked host and walnut against pathogen infection, laying the foundation for the green prevention of walnut anthracnose.

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.

n-Type boron ß-diketone-containing conjugated polymers for high-performance room temperature ammonia sensors.

Organic semiconductor (OSC) gas sensors with good mechanical flexibility have received considerable attention as commercial and wearable devices. However, due to poor resistance to moisture and low conductivity, the improvement in the sensing capability of individual OSCs is limited. Reported here is a promising pathway to construct a series of conjugated organic polymers (COPs) with well-defined pyrimidine (Py-COP) or boron ß-diketone (BF-COP) units. Unlike traditional metal- or carbon-based hybrid materials, the developed COPs can provide abundant absorption sites for gaseous analytes. As a result, the as-prepared BF-COP results in an excellent sensing response of over 1500 (Ra/Rg) toward 40 ppm of NH3 at room temperature, which is the highest value among those of pristine COPs as n-type sensing materials. Notably, they can maintain their initial sensing responses for two months and 90% relative humidity resistance. Combining the results of in situ Fourier transform infrared spectroscopy and theoretical calculations, the ß-diketone skeleton is found to activate the surface electronic environment, verifying that the electron-deficient B ← O groups are adsorption centers. The B/N-heterocyclic decoration effectively modulates the redox properties and electronic interactions, as well as perturbs charge transfer in typical π-conjugated COPs. These results offer insight into developing highly efficient OSC gas sensors, which potentially have broadened sensing applications in the areas of organoboron chemistry.

Scoparone from Artemisia capillaris Thunb. induces apoptosis in HepG2 cells via activation of both intracellular and extracellular pathways.

Six separated compounds were identified from Artemisia capillaris Thunb., and they were 7-methoxycoumarin (1), 6,7-dimethoxycoumarin (2), 7-hydroxy-6-methoxycoumarin (3), quercetin (4), chlorogenic acid (5) and caffeic acid (6). Among them, 6,7-dimethoxycoumarin, as known as scoparone, was the most effective on scavenging ABTS free radicals (IC50 = 0.97 µΜ) and was then tested by cytotoxic activity and pro-apoptotic activity against HepG2 cells. Scoparone dose-dependently and time-dependently inhibited the cell proliferation. Furthermore, scoparone induced the expression of Bax, concurrently suppressing the expression of Bcl-2, resulting in a noteworthy elevation in the Bax/Bcl-2 ratio to up-regulate Caspase-3 activity, thus inducing cell apoptosis via the intracellular pathway. Meanwhile, scoparone promoted the expression of Fas, FasL, FADD, Caspase-8 and Caspase-3, indicating that scoparone also triggered apoptosis via the extracellular pathway. In a word, scoparone demonstrated remarkable antitumor capability to induce apoptosis of HepG2 cells through both intracellular and extracellular pathways.

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.

Repeated methamphetamine exposure decreases plasma brain-derived neurotrophic factor levels in rhesus monkeys.

Background: Brain-derived neurotrophic factor (BDNF) is known to prevent methamphetamine (METH)-induced neurotoxicity and plays a role in various stages of METH addiction. However, there is a lack of research with longitudinal design on changes in plasma BDNF levels in active METH-dependent individuals. Aims: The aim of the study was to investigate changes in BDNF levels during METH self-administration in monkeys. Methods: This study measured plasma BDNF levels in three male rhesus monkeys with continuous METH exposure and four male control rhesus monkeys without METH exposure. Changes in plasma BDNF levels were then assessed longitudinally during 40 sessions of METH self-administration in the three monkeys. Results: Repeated METH exposure decreased plasma BDNF levels. Additionally, plasma BDNF decreased with long-term rather than short-term accumulation of METH during METH self-administration. Conclusions: These findings may indicate that the changes in peripheral BDNF may reflect the quantity of accumulative METH intake during a frequent drug use period.

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.

Heterocyclic Modulated Electronic States of Alkynyl-Containing Conjugated Microporous Polymers for Efficient Oxygen Reduction.

The oxygen reduction reaction (ORR) is a key process in green energy conversion technology. Heteroatom doping has been proven to be a prospective strategy to prepare metal-free carbon-based electrocatalysts, but such methods often suffer from uncontrollable catalyst frameworks and imprecise active sites. Herein, an organic heterocyclic strategy is adopted to modulate the charge redistribution of alkynyl-containing conjugated microporous polymers (CMPs) by introducing varied five-membered heterocyclic structures. Among these CMPs, the S, 2N-containing thiadiazole heterocyclic molecule (CMP-Tdz) with carbonized alginate materials (CCA ) displays a remarkable quasi-four-electron-transfer ORR pathway, exhibiting an excellent half-wave potential (E1/2 ) of 0.77 V, coupled with superior methanol tolerance and electrochemical stability, which are among the highest performance in the metal-free organic catalytic material systems. Density functional theory calculations prove that the high catalytic performance of these catalysts originates from the sp-hybridized C atom (site-2) which is activated by their adjacent heterocyclic structures. Importantly, the five-membered heterocyclic structures can also modulate the local charge distribution, and increase dipole moment, with significantly improved catalytic kinetics. This incorporation of chemically designed heterocyclic-containing alkynyl-CMPs provides a new approach to developing efficient metal-free carbon-based electrocatalysts for fuel cells.

Metal-Free Carbon-Based Covalent Organic Frameworks with Heteroatom-Free Units Boost Efficient Oxygen Reduction.

Accurate identification of carbon-based metal-free electrocatalyst (CMFE) activity and enhancing their catalytic efficiency for O2 conversion is an urgent and challenging task. This study reports a promising strategy to simultaneously develop a series of covalent organic frameworks (COFs) with well-defined heterocyclic-free biphenyl or fluorenyl units. Unlike heteroatom doping, the developed method not only supplies methyl-induced molecular configuration to promote activity, but also provides a direct opportunity to identify heteroatom-free carbon active centers. The introduction of methyl groups (MGs) with reversible valence bonds into a pristine biphenyl-based COF results in an excellent performance with a half-wave potential of 0.74 V versus the reversible hydrogen electrode (RHE), which is among the highest values for CMFE-COFs as oxygen reduction reaction (ORR) electrocatalysts. Combined with in situ Raman spectra and theoretical calculations, the MG-bound skeleton (DAF-COF) is found to produce ortho activation, confirming the ortho carbon (site-5) adjacent to MGs as active centers. This may be attributed to the opening and binding of MGs, which effectively regulate the molecular configuration and charge redistribution, as well as improve charge transfer and reduce the energy barrier. This study provides insight into the design of highly efficient metal-free organic electrocatalysts via the regulation of valence bonds.

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.

An Integrative Analysis of Identified Schizophrenia-Associated Brain Cell Types and Gene Expression Changes.

Schizophrenia (SCZ) is a severe mental disorder that may result in hallucinations, delusions, and extremely disordered thinking. How each cell type in the brain contributes to SCZ occurrence is still unclear. Here, we leveraged the human dorsolateral prefrontal cortex bulk RNA-seq data, then used the RNA-seq deconvolution algorithm CIBERSORTx to generate SCZ brain single-cell RNA-seq data for a comprehensive analysis to understand SCZ-associated brain cell types and gene expression changes. Firstly, we observed that the proportions of brain cell types in SCZ differed from normal samples. Among these cell types, astrocyte, pericyte, and PAX6 cells were found to have a higher proportion in SCZ patients (astrocyte: SCZ = 0.163, control = 0.145, P.adj = 4.9 × 10-4, effect size = 0.478; pericyte: SCZ = 0.057, control = 0.066, P.adj = 1.1 × 10-4, effect size = 0.519; PAX6: SCZ = 0.014, control = 0.011, P.adj = 0.014, effect size = 0.377), while the L5/6_IT_CAR3 cells and LAMP5 cells are the exact opposite (L5/6_IT_Car3: SCZ = 0.102, control = 0.108, P.adj = 0.016, effect size = 0.369; LAMP5: SCZ = 0.057, control = 0.066, P.adj = 2.2 × 10-6, effect size = 0.617). Next, we investigated gene expression in cell types and functional pathways in SCZ. We observed chemical synaptic transmission dysregulation in two types of GABAergic neurons (PVALB and LAMP5), and immune reaction involvement in GABAergic neurons (SST) and non-neuronal cell types (endothelial and oligodendrocyte). Furthermore, we observed that some differential expression genes from bulk RNA-seq displayed cell-type-specific abnormalities in the expression of molecules in SCZ. Finally, the cell types with the SCZ-related transcriptomic changes could be considered to belong to the same module since we observed two major similar coordinated transcriptomic changes across these cell types. Together, our results offer novel insights into cellular heterogeneity and the molecular mechanisms underlying SCZ.

Mendelian Randomization and GWAS Meta Analysis Revealed the Risk-Increasing Effect of Schizophrenia on Cancers.

The causal relationship between cancer and Schizophrenia (SCZ) remains controversial. Some researchers have found that SCZ is a cancer-preventive factor in cohort studies or meta-analyses, whereas others have found the opposite. To understand more about this issue, we used two-sample Mendelian randomization (2SMR) on available GWAS summary results to evaluate potential genetic connections between SCZ and 13 cancers. We discovered that the genetic susceptibility to schizophrenia lead to an increasing risk of breast cancer (odds ratio [OR] per log-odds increase in schizophrenia risk: 1.049, 95% confidence interval [CI]:1.023-1.075; p = 0.00012; FDR = 0.0017), ovarian cancer (OR, 1.326; 95% CI, 1.267-1.387; p = 0.0007; FDR = 0.0045), and thyroid cancer (OR, 1.575; 95% CI, 1.048-2.365; p = 0.0285; FDR = 0.123). Secondly, we performed a meta-analysis based on the GWAS summary statistics of SCZ and the three significant cancers. Next, we associated genetic variants to genes using two gene mapping strategies: (a) positional mapping based on genomic proximity and (b) expression quantitative trait loci (eQTL) mapping based on gene expression linkage across multiple tissues. As a result, we identified 114 shared loci and 437 shared genes in three groups, respectively. Functional enrichment analysis shows that the most enriched biological pathways are related to epigenetic modification. In addition, we noticed that SCZ would affect the level of thyroid-stimulating hormone (OR, 1.095; 95% CI, 1.006-1.191; p = 0.0354; FDR = 0.177), which may further affect the level of estrogen and the risk of the above three cancers. In conclusion, our findings under the 2SMR assumption provide crucial insights into the risk-increasing effect of SCZ on three cancers' risk. Furthermore, these results may provide insights into understanding the genetic predisposition and underlying biological pathways of comorbid SCZ and cancers.

Locus-level antagonistic selection shaped the polygenic architecture of human complex diseases.

BACKGROUND: We aimed to evaluate the potential role of antagonistic selection in polygenic diseases: if one variant increases the risk of one disease and decreases the risk of another disease, the signals of genetic risk elimination by natural selection will be distorted, which leads to a higher frequency of risk alleles. METHODS: We applied local genetic correlations and transcriptome-wide association studies to identify genomic loci and genes adversely associated with at least two diseases. Then, we used different population genetic metrics to measure the signals of natural selection for these loci and genes. RESULTS: First, we identified 2120 cases of antagonistic pleiotropy (negative local genetic correlation) among 87 diseases in 716 genomic loci (antagonistic loci). Next, by comparing with non-antagonistic loci, we observed that antagonistic loci explained an excess proportion of disease heritability (median 6%), showed enhanced signals of balancing selection, and reduced signals of directional polygenic adaptation. Then, at the gene expression level, we identified 31,991 cases of antagonistic pleiotropy among 98 diseases at 4368 genes. However, evidence of altered signals of selection pressure and heritability distribution at the gene expression level is limited. CONCLUSION: We conclude that antagonistic pleiotropy is widespread among human polygenic diseases, and it has distorted the evolutionary signal and genetic architecture of diseases at the locus level.

Sensitivity Analysis of a Bench-Scale Pyrolysis Model for Composite Materials: A Case Study of Poly(lactic acid)/Melamine/Ammonium Polyphosphate.

On the basis of a well-developed bench-scale pyrolysis model that relates material composition to flammability, this paper applied mathematical simulations to explore the model sensitivity for the prediction of fire behavior of composite materials. A pyrolysis model for poly(lactic acid) blended with melamine and ammonium polyphosphate as the reference material was selected as the case for analysis. The model input parameters for simulations include the heat of reaction, apparent activation energy, and pre-exponential factor of 15 reactions, as well as the thermal conductivity, emissivity coefficient, absorption coefficient, and density of 17 condensed-phase components. Each reaction-related or component-related parameter was adjusted from 80% of the model value to 120% with a 5% or 10% gradient. Finally, 826 simulation cases in total were calculated for analysis. Both the mass loss rate and the heat release rate of each case were calculated to characterize the sensitivity, which showed the same pattern. Finally, seven primary reactions and five key condensed-phase components with high sensitivity were identified. The predicted fire behaviors are highly related to the kinetics of the reactions between virgin components or reactions where virgin components play an important role in, including the pyrolysis of melted poly(lactic acid), the first step in the pyrolysis of melamine, the first step in the pyrolysis of ammonium polyphosphate, the reaction between melted poly(lactic acid) and melamine, the reaction between ammonium polyphosphate and melamine, and further decomposition of the generated new condensed-phase component. Particularly, the activation energy of these reactions is of sensitivity larger than 5% or 15%. The heat of decomposition of pyrolysis of melted poly(lactic acid) also showed a sensitivity of 2%-5%. The pre-exponential factor of all reactions showed a sensitivity of less than 2%, which can be ignored. Inputting the proper density is important for the prediction of fire behavior as the sensitivity is larger than 2%. The sensitivity of the milligram-scale model was also processed and compared. These simulations provided a fundamental understanding of the sensitivity of thermophysical and chemical properties and thus provide advanced insights into fire behavior modeling and new composite material design.

An Integrative Segmentation Framework for Cell Nucleus of Fluorescence Microscopy.

Nucleus segmentation of fluorescence microscopy is a critical step in quantifying measurements in cell biology. Automatic and accurate nucleus segmentation has powerful applications in analyzing intrinsic characterization in nucleus morphology. However, existing methods have limited capacity to perform accurate segmentation in challenging samples, such as noisy images and clumped nuclei. In this paper, inspired by the idea of cascaded U-Net (or W-Net) and its remarkable performance improvement in medical image segmentation, we proposed a novel framework called Attention-enhanced Simplified W-Net (ASW-Net), in which a cascade-like structure with between-net connections was used. Results showed that this lightweight model could reach remarkable segmentation performance in the BBBC039 testing set (aggregated Jaccard index, 0.90). In addition, our proposed framework performed better than the state-of-the-art methods in terms of segmentation performance. Moreover, we further explored the effectiveness of our designed network by visualizing the deep features from the network. Notably, our proposed framework is open source.

Genome-wide identification of the shared genetic basis of cannabis and cigarette smoking and schizophrenia implicates NCAM1 and neuronal abnormality.

OBJECTIVES: Confirming the existence and composition of the shared genetic basis of Schizophrenia and cannabis and cigarette smoking has critical values for the clinical prevention and intervention of psychosis. METHODS: To achieve this goal, we leveraged Genome-Wide summary statistics of Schizophrenia (n = 99,934), cigarette smoking (n = 518,633) and cannabis usage (n = 162,082). We applied Causal Analysis Using Summary Effect Estimates (CAUSE) and genomic structural equation modeling (GenomicSEM) to quantify the contribution of a common genetic factor of cannabis and cigarette smoking and schizophrenia (referred to as SCZ_SMO), then identified genome-wide loci that made up SCZ_SMO. RESULTS: We estimated that SCZ_SMO explained 8.6% of Schizophrenia heritability (Z score <-2.5 in CAUSE, p<10-20 in Genomic SEM). There were 20 independent loci showing association with SCZ_SMO at the genome-wide threshold of p<5 × 10-8. At the top locus on chromosome 11, fine-mapping identified rs7945073 (posterior inclusion probability =0.12, p = 2.24 × 10-32) as the top risk variants. Gene-level association and fine-mapping highlighted NCAM1, PHC2, and SEMA6D as risk genes of SCZ_SMO. Other risk genes were enriched in cortex, neuron, and dendritic spines (adjusted p<0.05). SCZ_SMO showed significant positive correlation (p<10-6) with the genetic risk of attention deficit hyperactivity disorder (r = 0.50), lifestyle problems (r = 0.83), social deprivation (r = 0.58) and all-cause pregnant loss (r = 0.60). CONCLUSION: Our result provided new evidence on the shared genetic basis model for the association between Schizophrenia and smoking and provided genetic and biological insights into their shared mechanism.