The neuronal ceroid lipofuscinosis family of lysosomal storage diseases, also called CLN1 disease, is characterized by the deficiency of palmitoyl-protein thioesterase 1 (PPT1). In this study, we investigated the impact of PPT1 deficiency on hippocampal GABAergic interneurons (INs) and associated neural network oscillations in a PPT1-KI (CLN1 c.451 C > T (p.R151X)) mouse model. Using a combination of in vivo electrophysiology, immunostaining, and fiber photometry, we observed that PPT1 deficiency led to the activation of caspase 3 in parvalbumin-positive (PV+) INs, an increased activity of pyramidal neurons and theta/gamma oscillation power, and the disruption of theta-gamma cross-frequency coupling (CFC) in the early stage of the CLN1 disease model. In the late stage of the CLN1 disease model, we observed the reduced neuronal activity, extensive neuronal loss including PV+ INs, and the emergence of spontaneous epileptiform discharges and the pathological ripples. Treatment with diazepam partially restored oscillatory coupling and reduced seizure-like activities. Our research indicated that PPT1 deficiency leads to early selective impairment of PV+ INs, triggering overactivation of pyramidal neurons and network dysfunction, which consequently results in seizures and neurodegeneration. This research provides novel insights into the pathogenesis of CLN1 disease and potential therapeutic strategies for the intervention of CLN1 disease by improving the function of inhibitory INs via caspase inhibition.

Morchella is a nutritious and artificially cultivable rare ascomycete, and its growth and development regulation mechanisms are a current research hotspot. High-temperature stress severely limits the annual yield of Morchella, and this challenge is intensifying with global warming. However, previous studies have lacked systematic screening for heat-tolerant Morchella strains, and their molecular response mechanisms to heat stress remain unclear. In this study, we conducted a comprehensive analysis of phenotypic characteristics, physiological metabolism, and transcriptomics on 19 Morchella strains under normal (25 °C) and high-temperature (30 °C) conditions. The heat-tolerant strain HLM exhibited superior performance in mycelial growth, morphology, and field cultivation. It maintained cell homeostasis under heat stress through mild osmotic regulation (elevated levels of proline, soluble sugars, and proteins), a robust antioxidant system (increased activities of CAT, POD, and SOD), and reduced malondialdehyde accumulation. Transcriptomic analysis identified a novel regulatory model of “stress perception—metabolic preparation—terminal detoxification” in the heat-tolerant strain HLM under heat stress. The rapid upregulation of the SMPD1 gene may mediate ceramide signal generation, promoting G6PDH expression to drive carbon flow into the pentose phosphate pathway, thereby increasing NADPH output. As the detoxification terminal, AKR4C uses this reducing power to eliminate toxic carbonyl end products like malondialdehyde, completing the defense loop. These findings offer new insights into the heat-tolerance mechanisms of large ascomycetes, provide a theoretical foundation for stress-resistant Morchella breeding and cultivation in high-temperature areas, and serve as valuable resources for exploring heat-tolerance mechanisms and molecular breeding in other edible fungi.

Attention deficit hyperactivity disorder (ADHD) is a common neurodevelopmental disorder modulated by genetic and environmental factors. Approximately 20-55% of individuals with ADHD experience concurrent sleep disturbances, yet the dynamic relationship between the two and their shared genetic mechanisms remain unclear. This study analyzed 11,288 participants (52.1% male) from the Adolescent Brain Cognitive Development Study using the Sleep Disturbance Scale for Children. We found a robust positive correlation between ADHD and sleep traits, though ADHD medication weakened this relationship. Latent growth curve modeling revealed that ADHD symptoms significantly impacted sleep traits with a lag effect. Polygenic risk score analyses showed significant but very small associations (explained variance ~0.28-0.30%). Pleiotropic analyses identified two loci associated with ADHD and excessive somnolence (implicating 30 genes), and cross-cohort comparisons with UK Biobank insomnia data uncovered 12 additional pleiotropic loci (98 genes) enriched in brain tissue. Functional enrichment analysis highlighted excitatory and inhibitory neuronal subtypes in visual and frontal cortices, suggesting roles in synaptic transmission and neural circuit development. Protein-protein interaction network analysis identified 10 hub genes involved in cell growth regulation and neurodevelopmental processes. This study demonstrates robust ADHD-sleep associations, with medication moderating this link, and provides preliminary insights into their shared genetic architecture.

Schizophrenia (SCZ) involves immune dysregulation and synaptic deficits, yet the molecular link between peripheral inflammation and central synaptic pathology remains unclear. We integrated blood transcriptomes from four SCZ cohorts (478 samples: 245 patients, 233 controls) and validated findings in single-cell brain data and an ELNI mouse model. After batch correction, 272 genes were differentially expressed, with S100A8 as the top upregulated immune gene. WGCNA identified a disease-associated module (blue, 133 genes, r = 0.16, p = 7 × 10⁻⁴) containing S100A8; its intersection with differentially expressed genes yielded 44 key genes enriched for cytoplasmic translation, mitochondrial electron transport, and innate immunity. Machine learning ranked S100A8 as the top discriminative feature, and its upregulation was robust across four independent analytic pipelines. CIBERSORTx revealed increased neutrophils and decreased regulatory T and resting NK cells in patients, with S100A8 correlating positively with neutrophils. Single-cell analysis showed that S100A8-expressing cells were specifically expanded in microglia (2.98% → 3.94%, OR = 1.34), and S100A8⁺ microglia displayed an activated state with coordinated upregulation of complement (C1QA/B/C) and phagocytic genes and downregulation of homeostatic markers (P2RY12, CX3CR1). In ELNI mice exhibiting SCZ-like behaviors, S100A8/S100A9 were upregulated in hippocampus and frontal cortex, accompanied by CD68 induction and reduced synaptophysin, with S100A8 correlating positively with CD68 and negatively with synaptophysin. Molecular docking identified hydroxyzine as a candidate S100A8 ligand. These convergent findings establish S100A8 as a hub linking peripheral immune dysregulation to microglial activation and synaptic pathology in SCZ, highlighting it as a candidate biomarker and therapeutic target.

Objective The neurodevelopmental hypothesis of schizophrenia posits that early brain developmental abnormalities constitute its core pathological basis. However, the mechanisms by which environmental risk factors regulate specific molecular pathways, leading to long-term behavioral abnormalities, remain incompletely elucidated. This study aims to investigate whether maternal immune activation (MIA) disrupts the ASK1/MAPK signaling pathway in offspring during early development, alters neuronal apoptosis homeostasis, and ultimately mediates the emergence of schizophrenia-like phenotypes. Methods A MIA rat model was established. In offspring, at multiple postnatal developmental time points (P1, P7, P14, P21), the protein expression and phosphorylation levels of ASK1, p-p38, and p-JNK in the hippocampus and prefrontal cortex were detected. Additionally, the expression of apoptosis-related proteins Bax and Bcl-2 was measured. Neuronal structure was assessed using Nissl staining, and behavioral tests were performed. Results MIA offspring exhibited anxiety-like behaviors, cognitive deficits, and sensory gating impairments. The ASK1/MAPK pathway demonstrated spatiotemporal-specific disturbances: hippocampal ASK1 activity showed a triphasic dynamic abnormality, while the prefrontal cortex displayed biphasic suppression. These pathway disruptions were closely associated with brain region-specific imbalances in apoptosis homeostasis. The Bax/Bcl-2 ratio in the prefrontal cortex exhibited biphasic oscillations, whereas the hippocampus showed selective suppression of apoptotic activity at P7 and P21. Nissl staining further confirmed neuronal structural damage in MIA offspring. Conclusion This study first demonstrates that MIA induces spatiotemporal-specific ASK1/MAPK pathway disturbances, thereby altering neuronal apoptosis homeostasis during development and ultimately leading to neuronal structural damage and schizophrenia-like behavioral phenotypes. The differential mechanisms observed in the hippocampus and prefrontal cortex in response to MIA provide new experimental evidence for understanding the neurodevelopmental origins of schizophrenia, suggesting that the ASK1/MAPK pathway may serve as a critical bridge connecting early environmental stress with long-term neuropsychopathological phenotypes.

Epidemiological and clinical observations linking schizophrenia (SCZ) to increased dementia risk, together with the occurrence of psychosis in Alzheimer's disease and related dementias (ADRD), suggest that shared genetic liabilities may contribute to their co-occurrence. Leveraging large-scale genome-wide association study summary statistics for SCZ (53,386 cases and 77,258 controls) and ADRD (111,326 cases and 677,663 controls), we systematically investigated their shared genetic architecture and potential biological mechanisms. We identified three significant local genetic correlations (P < 2.0 × 10⁻⁵) and cross-trait polygenic enrichment between SCZ and ADRD, with 39 genomic loci jointly associated at conjunctional false discovery rate (conjFDR) < 0.05. Fifteen high-confidence genes (CNIH4, CD302, PCGF3, TFR2, EPHX2, SNX32, EFEMP2, CTSW, ASPHD1, TAOK2, INO80E, DOC2A, MAPK3, KANSL1, and XPNPEP3) were consistently prioritized across positional, expression quantitative trait locus, and chromatin-interaction mapping. Tissue- and cell-type enrichment analyses highlighted cerebellar tissues and ependymal-cell-related signals, while pathway analyses implicated synaptic signaling, axonal growth, and presynaptic structural organization. At the locus level, colocalization and transcriptome-wide association analyses converged on 16p11.2, prioritizing INO80E, YPEL3, SLX1B, and TMEM219. Developmental trajectory modeling further revealed region- and stage-specific expression divergence of prioritized 16p11.2 genes, with prominent differences spanning childhood and adulthood. Brain-wide association analysis linked the 16p11.2 lead variant rs9932702 to cortical gray-white contrast (β = -0.062, P = 7.5 × 10⁻¹⁵), a neuroimaging phenotype related to gray-white boundary microstructure and myelination. Finally, bidirectional Mendelian randomization supported a modest directional association between genetic liability to SCZ and increased ADRD risk, but not the reverse direction. Collectively, these findings provide convergent genetic, regulatory, transcriptomic, developmental, and imaging evidence for partial shared liability between SCZ and ADRD, highlighting 16p11.2 and biological processes related to neurodevelopment, synaptic and axonal organization, myelination-related microstructure, and later-life brain vulnerability.

Schizophrenia (SCZ) is a common psychiatric disorder with a complex, genetically and environmentally influenced etiology, but the specific pathogenesis remains unclear. In recent years, the SCZ susceptibility gene CNNM2 (encoding cyclin M2) located at the 10q24.32-33 locus has received widespread attention. The well-validated SCZ risk interval 10q24.32-33 harbors two independent risk variants: rs11191580 in NT5C2 (significantly associated with CNNM2 mRNA and protein levels) and rs7914558 in CNNM2. Results from functional genomic analyses indicate that lower CNNM2 expression is significantly associated with SCZ. Imaging genetics studies have demonstrated that carriers of risk alleles of CNNM2 SNPs exhibit alterations in brain structure. Animal model studies have revealed that Cnnm2 downregulation in mice leads to impairments in sensorimotor gating and cognitive function. As an Mg2+ transporter, CNNM2 primarily maintains systemic Mg2+ homeostasis. According to clinical studies, a proportion of patients with SCZ exhibit reduced Mg2+ concentrations in plasma and cerebrospinal fluid. CNNM2 dysfunction may contribute to the pathology of SCZ by disrupting Mg2+ homeostasis, thereby affecting neurodevelopment and synaptic plasticity. A systematic consolidation of current evidence supporting the involvement of CNNM2 in SCZ pathogenesis provides a direction for further investigation of the pathological mechanisms underlying this disease, and for identification of novel targets for clinical intervention..

Schizophrenia is characterized by striking symptom heterogeneity, yet the mapping between specific clinical phenotypes and their underlying biological substrates remains elusive. To bridge this gap, we applied an integrative, multi-scale framework combining symptom network analysis, connectome-based predictive modeling (CPM), and transcriptomic mapping in a multi-center cohort of schizophrenia patients. This approach revealed a hierarchical dissociation between phenotypic topology and underlying biological mechanisms among the identified high-centrality core symptoms: Conceptual Disorganization, Unusual Thought Content, and Blunted Affect. While Conceptual Disorganization and Unusual Thought Content exhibited clinical coherence as psychosis-related features, CPM uncovered a divergence in their neural substrates. Conceptual Disorganization shared greater neurofunctional isomorphism with Blunted Affect—characterized by converging dysconnectivity within the Somatomotor Network (SMN) and subcortical circuits—whereas Unusual Thought Content displayed a distinct architecture driven by prominent Default Mode Network regulation beyond the shared sensorimotor substrate. Transcriptomic annotation further stratified these dimensions: psychosis-related networks were underpinned by synaptic regulatory genes, whereas Blunted Affect was enriched for intracellular MAPK signaling and metabolic processes. These findings delineate a hierarchical model in which distinct molecular etiologies—synaptic versus metabolic—cascade into shared systems-level failures at the “somato-cognitive interface”. We conclude that while symptoms may group clinically, their treatment requires targeting separable molecular pathways that converge on common circuit bottlenecks. This framework reconciles symptom heterogeneity with overlapping biological substrates, advocating for a mechanism-based stratification of schizophrenia.

Moderate leaf rolling in rice is crucial for plant architecture and stress adaptation, but its molecular regulation remains unclear. We investigated the role of RLC3/OsBLH4, a BELL-type homeobox transcription factor, in controlling leaf rolling and drought tolerance, addressing gaps in lignin biosynthesis and cell wall development mechanisms. We used gene map-based cloning (rlc3-1, rlc3-2), CRISPR/Cas9 knockout lines (rlc3-ko#11, rlc3-ko#12), and allelic complementation to validate RLC3's function. Additionally, we employed biochemical assays, gene expression analysis, and protein interaction studies to explore its regulatory network. RLC3 mutations impaired lignin biosynthesis and secondary cell wall formation, reducing bulliform cells area and causing midrib defects. These structural abnormalities accelerated water loss, leading to excessive leaf rolling and compromised drought tolerance. Mechanistically, RLC3 directly activates lignin synthesis genes (OsPAL5, OsCOMT5, OsCCR4, OsCAld5H1) and interacts with KNOX transcription factors (OSH1, OSH45, OSH71) to form a KNOX-BELL complex, further regulating lignin content and cell wall development. RLC3 orchestrates lignin deposition and secondary cell wall development to control leaf rolling, water transport, and drought tolerance. This study reveals a novel KNOX-BELL-lignin regulatory module governing leaf morphology and stress adaptation, offering targets for crop improvement under drought conditions. ### Competing Interest Statement The authors have declared no competing interest. National Natural Science Foundation of China, 32070197

Improving nitrogen use efficiency (NUE) is essential for sustainable agriculture, yet conventionally measured plant characteristics have limited value as NUE proxies. Here we show that artificial intelligence (AI) can uncover previously unrecognized phenotypic variation associated with NUE, revealing genetic variation that is largely missed by conventional phenotypes. We trained a convolutional neural network (CNN) on 25,080 maize images to learn features that distinguish how plants respond to low- and high-N conditions, achieving 96.7% accuracy. The learned features were defined as deep phenotypes. Compared with conventional phenotypes, deep phenotypes showed greater phenotypic variation and higher heritability, enabling the identification of 523 significant loci compared with 21 for conventional phenotypes. We next investigated candidate genes underlying these loci and used these findings to interpret the learned features. Lower CNN layers primarily reflected visual patterns overlapping with conventional phenotypes, whereas deeper layers encoded additional features associated with N-responsive genetic variation. To validate candidate genes identified by the AI framework, we functionally characterized Liguleless2 (LG2), a basic-leucine zipper (bZIP) transcription factor, and demonstrated that lg2 mutants exhibit enhanced root architecture and increased N uptake efficiency. Field trials of 200 hybrids across diverse N environments further supported the AI findings, with each beneficial allele increasing ear weight by an average of 18 g per plot under low-N conditions. These results show how integrating AI and biology can uncover biologically relevant variation underlying complex traits such as NUE and enhance the interpretability of AI models.

Although copy number variants (CNVs) represent well-established genetic contributors to schizophrenia (SCZ), their role in bipolar disorder (BD), especially within non-European ancestries, has been inadequately explored. We evaluated the genome-wide load of rare CNVs, encompassing deletions and duplications, in a Han Chinese sample of 3915 BD cases and 7820 ethnically matched controls. We observed a marked overrepresentation of rare deletions in BD patients relative to controls, with affected genes showing enrichment in neural signaling and dosage-dependent networks, indicating that haploinsufficiency in neurodevelopmental loci could underlie a central etiological pathway in BD. Among the 12 previously reported CNV loci from European cohorts, only deletions at 3q29 and 15q11.2 exhibited robust associations with BD susceptibility in Han Chinese individuals. Through genome-wide, gene-centric CNV association testing, we uncovered novel BD-linked loci, including deletions spanning GLIS2 and PAM16 at 16p13.3, GRID2IP at 7p22.1, and CFLAR at 2q33.1, alongside a duplication affecting ZNF878 and ZNF844 at 19p13.2. These disrupted genes are chiefly implicated in neuronal maturation, synaptic modulation, and mitochondrial dynamics. This work delivers the most thorough delineation of BD-associated CNVs in Han Chinese to date, underscoring the imperative for ancestry-inclusive research to comprehensively unravel psychiatric genomics and unveiling fresh mechanistic perspectives on BD etiology.

Objective Predicting early symptom severity and treatment response in schizophrenia is crucial for selecting optimal therapeutic strategies. This study aimed to develop machine learning (ML) models utilizing functional near-infrared spectroscopy (fNIRS) to predict clinical symptoms, cognitive function, and treatment responses. Methods We enrolled 139 acutely ill schizophrenia patients and collected fNIRS data alongside clinical measures before and after a 4-week course of antipsychotic treatment. Based on the connectome-based predictive modeling (CPM) framework, regression models were constructed to predict symptom severity and cognitive function, while classification models were built to distinguish treatment response. All models were evaluated using a leave-one-out cross-validation. Results The models demonstrated significant predictive power for clinical symptoms, cognitive function, and improvement (p < 0.001). However, the predictive efficacy for negative symptoms was relatively limited across all models (optimal model: r = 0.242, p < 0.05). All the classification models exhibited high sensitivity (>84%). The Support Vector Machine achieved an accuracy of 77.5%, and the Random Forest model achieved an area under the curve of 0.914. Conclusion This study indicates that integrating fNIRS with ML not only deepens our understanding of the heterogeneity of schizophrenia but also enhances the accuracy of predicting disease severity and treatment outcomes. This provides a potential objective and reliable clinical tool for precision medicine in schizophrenia.

Maize primary metabolism drives complex agronomic traits, yet its genetic regulation remains difficult to resolve. Here we integrated genomic, transcriptomic and metabolomic data from 1,404 maize progenies derived from 24 diverse founders to dissect the genetic architecture of primary metabolism. We constructed a high-confidence regulatory network that resolved causal genes underlying metabolic quantitative trait loci and successfully identified targets for improving maize nutritional quality. This systems-level framework further prioritized ZmAVT1A-1, encoding a putative amino acid transporter, as a key regulator of amino acid accumulation. Natural variation and transgenic analyses showed that ZmAVT1A-1 modulates nitrogen partitioning between vegetative tissues and kernels, revealing pleiotropic effects on agronomic traits. These findings illustrated the intricate trade-offs inherent in metabolic regulation. Together, our study provides a comprehensive multiomics resource for decoding metabolic networks and underscores the necessity of a systems approach to navigate the pleiotropic nature of crop improvement targets.

Global climate change has amplified both the frequency and severity of abiotic stresses including drought, salinity and extreme temperatures, while concurrently modifying pathogen population dynamics, thereby imposing unparalleled threats to agricultural crop productivity and the health of forest ecosystems [...]

In maize hybrid breeding, synergic multi-trait selection of elite hybrids in specific target environments remains a major challenge. Enviromic data and functional gene knowledge are rapidly increasing; however, they have not been effectively integrated into crop breeding decisions. Here, we present TOPlus, a multi-trait hybrid prioritization framework comprising predictive and selective modules. The predictive module uses environmental information to improve phenotype prediction for untested genotypes and environments, whereas the selective module incorporates functional gene priors and multiple predicted traits to prioritize hybrids for target environments. Across 17 agronomic traits, TOPlus improved average cross-environment prediction accuracy by 12% over JGRA and 3% over EADW+GW. Incorporating functional gene priors further improved hybrid prioritization: hybrids selected by TOPlus showed 5.90-19.64% higher yield than those selected by the original TOP method while maintaining comparable performance for other traits. The TOPlus algorithm internally clustered the functional genes into environmentally stable and plastic gene groups, with stable genes associated with core plant developmental processes and plastic genes enriched in stress-response and environmental-adaptation pathways. Independent validation in commercial hybrid panels demonstrated that TOPlus supports region-level suitability assessment and extrapolative deployment across diverse agroecological zones for specific candidate maize hybrid varieties. Overall, by integrating enviromic data and functional gene priors within an interpretable framework, TOPlus provides a biologically grounded and data-driven approach for cross-environment prediction and multi-trait synergic selection of hybrids for specific regions in maize breeding.

BACKGROUND AND HYPOTHESIS:Protein disulfide isomerase A3 (PDIA3), a critical regulator of endoplasmic reticulum stress, was identified as a risk gene in schizophrenia (SZ). This implies the role of PDIA3 in SZ-associated immune dysregulation. STUDY DESIGN:We integrated Psychiatric Genomics Consortium and PsychENCODE expression quantitative trait loci data, employing summary data-based Mendelian randomization analyses to investigate PDIA3-SZ associations. Clinical characterization and C-reactive protein (CRP)-based inflammatory profiling were analyzed in people with SZ categorized by PDIA3 genotypes. Plasma PDIA3 levels were quantified and correlated with clinical profiles, CRP concentrations, and white matter density. Hierarchical linear regression with mediation modeling characterized PDIA3-CRP-cognitive interactions. Finally, LOC14 (a PDIA3 inhibitor) was administered in mice to elucidate its role in SZ-related pathobiology. STUDY RESULTS:PDIA3 was identified as a robust SZ risk factor, and people with SZ carrying the risk A allele of rs7174732 in PDIA3 gene exhibit more severe cognitive impairment and a trend toward higher plasma CRP levels. Plasma PDIA3 in people with SZ was positively correlated with cognition and white matter density of the hippocampus, and negatively correlated with CRP levels. Furthermore, decreased plasma PDIA3 level in people with SZ was a significant predictor of worse working memory, and CRP acted as a mediator with an 16.2% effect. Inhibition of PDIA3 in mice was associated with increased anxiety, impaired cognition, and increased power in low-frequency signals in vivo, alongside activated inflammatory responses and regulated SZ-related biological pathways. CONCLUSIONS:We established a PDIA3-CRP-cognitive interaction network in SZ.

Cadmium (Cd), a pervasive and highly phytotoxic metal pollutant, poses severe threats to agricultural productivity, ecosystem stability, and human health through its entry into the food chain. Plants have evolved intricate defense mechanisms, among which the strategic manipulation of nutrient elements emerges as a critical physiological and biochemical strategy for mitigating Cd stress. This comprehensive review delves deeply into the multifaceted roles of essential macronutrient elements (nitrogen, phosphorus, potassium, calcium, magnesium, sulfur), essential micronutrient elements (zinc, iron, manganese, copper) and non-essential beneficial elements (silicon, selenium) in modulating plant responses to Cd toxicity. We meticulously dissect the physiological, biochemical, and molecular underpinnings of how these nutrients influence Cd bioavailability in the rhizosphere, Cd uptake and translocation pathways, sequestration and compartmentalization within plant tissues, and the activation of antioxidant defense systems. Nutrient elements exert their influence through diverse mechanisms: competing with Cd for root uptake transporters, promoting the synthesis of complexes that reduce Cd mobility, stabilizing cell walls and plasma membranes to restrict apoplastic flow and symplastic influx, modulating redox homeostasis by enhancing antioxidant enzyme activities and non-enzymatic antioxidant pools, regulating signal transduction pathways, and influencing gene expression profiles related to metal transport, chelation, and detoxification. The complex interactions between nutrients themselves further shape the plant’s capacity to withstand Cd stress. Recent advances elucidating nutrient-mediated epigenetic regulation, microRNA involvement, and the role of nutrient-sensing signaling hubs in Cd responses are critically evaluated. Furthermore, we synthesize the practical implications of nutrient management strategies, including optimized fertilization regimes, selection of nutrient-efficient genotypes, and utilization of nutrient-enriched amendments, for enhancing phytoremediation efficiency and developing low-Cd-accumulating crops, thereby contributing to safer food production and environmental restoration in Cd-contaminated soils. The intricate interplay between plant nutritional status and Cd stress resilience underscores the necessity for a holistic, nutrient-centric approach in managing Cd toxicity in agroecosystems.

OBJECTIVE:Emerging evidence suggests that dysregulated neuroimmune pathways are implicated in schizophrenia (SCZ); however, the mechanisms connecting early-life inflammation to adult psychiatric outcomes remain inadequately understood. This study examines the role of the pro-inflammatory alarmin S100A8, a calcium- and zinc-binding protein that significantly influences the regulation of inflammatory processes and immune responses, as a potential convergent hub in pediatric infections and SCZ. Furthermore, the study characterizes the behavioral effects of S100A8 using a novel ELNI model. METHODS:We analyzed gene expression datasets from blood and brain of SCZ patients and pediatric infections (GEO accessions: GSE53987, GSE73464, GSE38484) using limma. Genetic regulation of S100A8 was examined by integrating GWAS (PGC-SCZ3) and TWAS (PsychENCODE) data. An ELNI mouse model was established via LPS injections on postnatal days 24-30. Behavioral tests, qRT-PCR, and immunofluorescence were used to assess neuroinflammation and behavioral phenotypes. RESULTS:S100A8 was upregulated in the blood and postmortem brain tissues of SCZ patients, as well as in the blood of children with bacterial infections. The rs10908557 risk allele was associated with increased S100A8 expression. LPS-induced early-life inflammation in mice led to transient growth impairment, prepulse inhibition deficits, and depressive-like behaviors. S100A8 overexpression in the hippocampus correlated with microglial activation and impaired sensorimotor gating. CONCLUSION:S100A8 serves as a shared genetic and transcriptional biomarker between pediatric infections and SCZ. Early-life inflammation induces persistent SCZ-relevant behaviors through S100A8-mediated neuroinflammation. The ELNI model offers a translational platform for studying neurodevelopmental origins of psychiatric disorders, highlighting S100A8 as a potential biomarker and therapeutic target.