Genome-wide prediction and functional characterization of the genetic basis of autism spectrum disorder

Oral edaravone ameliorates behavioral deficits and pathologies in a valproic acid-induced rat model of autism spectrum disorder

Autism spectrum disorder (ASD) is neurodevelopmental disorder with a high incidence rate, characterized by social deficits and repetitive behaviors. There is currently no effective management available to treat the core symptoms of ASD; however, oxidative stress has been implicated in its pathogenesis. Edaravone (EDA), a free-radical scavenger, is used to treat amyotrophic lateral sclerosis (ALS) and acute ischemic stroke (AIS). Here, we hypothesized that an oral formula of EDA may have therapeutic efficacy in the treatment of core ASD symptoms. A rat model of autism was established by prenatal exposure to valproic acid (VPA), and the offsprings were orally treated with EDA at low (3 mg/kg), medium (10 mg/kg), and high (30 mg/kg) doses once daily for 28 days starting from postnatal day 25 (PND25). Oral EDA administration alleviated the core symptoms in VPA rats in a dose-dependent manner, including repetitive stereotypical behaviors and impaired social interaction. Furthermore, oral administration of EDA significantly reduced oxidative stress in a dose-dependent manner, as evidenced by a reduction in oxidative stress markers and an increase in antioxidants in the blood and brain. In addition, oral EDA significantly attenuated downstream pathologies, including synaptic and mitochondrial damage in the brain. Proteomic analysis further revealed that EDA corrected the imbalance in brain oxidative reduction and mitochondrial proteins induced by prenatal VPA administration. Overall, these findings demonstrate that oral EDA has therapeutic potential for ASD by targeting the oxidative stress pathway of disease pathogenesis and paves the way towards clinical studies.

Transcriptome data are insufficient to control false discoveries in regulatory network inference

Inference of causal transcriptional regulatory networks (TRNs) from transcriptomic data suffers notoriously from false positives. Approaches to control the false discovery rate (FDR), for example, via permutation, bootstrapping, or multivariate Gaussian distributions, suffer from several complications: difficulty in distinguishing direct from indirect regulation, nonlinear effects, and causal structure inference requiring "causal sufficiency," meaning experiments that are free of any unmeasured, confounding variables. Here, we use a recently developed statistical framework, model-X knockoffs, to control the FDR while accounting for indirect effects, nonlinear dose-response, and user-provided covariates. We adjust the procedure to estimate the FDR correctly even when measured against incomplete gold standards. However, benchmarking against chromatin immunoprecipitation (ChIP) and other gold standards reveals higher observed than reported FDR. This indicates that unmeasured confounding is a major driver of FDR in TRN inference. A record of this paper's transparent peer review process is included in the supplemental information.

Computational strategies for cross-species knowledge transfer and translational biomedicine

Research organisms provide invaluable insights into human biology and diseases, serving as essential tools for functional experiments, disease modeling, and drug testing. However, evolutionary divergence between humans and research organisms hinders effective knowledge transfer across species. Here, we review state-of-the-art methods for computationally transferring knowledge across species, primarily focusing on methods that utilize transcriptome data and/or molecular networks. We introduce the term "agnology" to describe the functional equivalence of molecular components regardless of evolutionary origin, as this concept is becoming pervasive in integrative data-driven models where the role of evolutionary origin can become unclear. Our review addresses four key areas of information and knowledge transfer across species: (1) transferring disease and gene annotation knowledge, (2) identifying agnologous molecular components, (3) inferring equivalent perturbed genes or gene sets, and (4) identifying agnologous cell types. We conclude with an outlook on future directions and several key challenges that remain in cross-species knowledge transfer.

Mechanisms of neurodevelopmental toxicity of topiramate

Prescriptions for antiseizure medications (ASMs) have been rapidly growing over the last several decades due, in part, to an expanding list of clinical indications for which they are now prescribed. This trend has raised concern for potential adverse neurodevelopmental outcomes in ASM-exposed pregnancies. Recent large scale population studies have suggested that the use of topiramate (TOPAMAX, Janssen-Cilag), when prescribed for seizure control, migraines, and/or weight management, is associated with an increased risk for autism spectrum disorder (ASD), intellectual disability, and attention-deficit/hyperactivity disorder (ADHD) in exposed offspring. Here, we critically review epidemiologic evidence demonstrating the neurobehavioral teratogenicity of topiramate and speculate on the neuromolecular mechanisms by which prenatal exposure may perturb neurocognitive development. Specifically, we explore the potential role of topiramate's pharmacological interactions with ligand- and voltage-gated ion channels, especially GABAergic signaling, its effects on DNA methylation and histone acetylation, whether topiramate induces oxidative stress, and its association with fetal growth restriction as possible mechanisms contributing to neurodevelopmental toxicity. Resolving this biology will be necessary to reduce the risk of adverse pregnancy outcomes caused by topiramate or other ASMs.

Low-level brain somatic mutations in exonic regions are collectively implicated in autism with germline mutations in autism risk genes

Low-level somatic mutations in the human brain are implicated in various neurological disorders. The contribution of low-level brain somatic mutations to autism spectrum disorder (ASD), however, remains poorly understood. Here, we performed high-depth exome sequencing with an average read depth of 559.3x in 181 cortical, cerebellar, and peripheral tissue samples to identify brain somatic single nucleotide variants (SNVs) in 24 ASD subjects and 31 controls. We detected ~2.4 brain somatic SNVs per exome per single brain region, with a variant allele frequency (VAF) as low as 0.3%. The mutational profiles, including the number, signature, and type, were not significantly different between the ASD patients and controls. Intriguingly, when considering genes with low-level brain somatic SNVs and ASD risk genes with damaging germline SNVs together, the merged set of genes carrying either somatic or germline SNVs in ASD patients was significantly involved in ASD-associated pathophysiology, including dendrite spine morphogenesis (p = 0.025), mental retardation (p = 0.012), and intrauterine growth retardation (p = 0.012). Additionally, the merged gene set showed ASD-associated spatiotemporal expression in the early and mid-fetal cortex, striatum, and thalamus (all p < 0.05). Patients with damaging mutations in the merged gene set had a greater ASD risk than did controls (odds ratio = 3.92, p = 0.025, 95% confidence interval = 1.12-14.79). The findings of this study suggest that brain somatic SNVs and germline SNVs may collectively contribute to ASD-associated pathophysiology.

Imperfect gold standard gene sets yield inaccurate evaluation of causal gene identification methods

Causal gene discovery methods are often evaluated using reference sets of causal genes, which are treated as gold standards (GS) for the purposes of evaluation. However, evaluation methods typically treat genes not in the GS positive set as known negatives rather than unknowns. This leads to inaccurate estimates of sensitivity, specificity, and AUC. Labeling biases in GS gene sets can also lead to inaccurate ordering of alternative causal gene discovery methods. We argue that the evaluation of causal gene discovery methods should rely on statistical techniques like those used for variant discovery rather than on comparison with GS gene sets.

Review: Cancer and neurodevelopmental disorders: multi-scale reasoning and computational guide

The connection and causality between cancer and neurodevelopmental disorders have been puzzling. How can the same cellular pathways, proteins, and mutations lead to pathologies with vastly different clinical presentations? And why do individuals with neurodevelopmental disorders, such as autism and schizophrenia, face higher chances of cancer emerging throughout their lifetime? Our broad review emphasizes the multi-scale aspect of this type of reasoning. As these examples demonstrate, rather than focusing on a specific organ system or disease, we aim at the new understanding that can be gained. Within this framework, our review calls attention to computational strategies which can be powerful in discovering connections, causalities, predicting clinical outcomes, and are vital for drug discovery. Thus, rather than centering on the clinical features, we draw on the rapidly increasing data on the molecular level, including mutations, isoforms, three-dimensional structures, and expression levels of the respective disease-associated genes. Their integrated analysis, together with chromatin states, can delineate how, despite being connected, neurodevelopmental disorders and cancer differ, and how the same mutations can lead to different clinical symptoms. Here, we seek to uncover the emerging connection between cancer, including pediatric tumors, and neurodevelopmental disorders, and the tantalizing questions that this connection raises.

Dopamine receptors and key elements of the neurotrophins (BDNF, CDNF) expression patterns during critical periods of ontogenesis in the brain structures of mice with autism-like behavior (BTBR) or its absence (С57BL/6 J)

Analysis of the mechanisms underlying autism spectrum disorder (ASD) is an urgent task due to the ever-increasing prevalence of this condition. The study of critical periods of neuroontogenesis is of interest, since the manifestation of ASD is often associated with prenatal disorders of the brain development. One of the currently promising hypotheses postulates a connection between the pathogenesis of ASD and the dysfunction of neurotransmitters and neurotrophins. In this study, we investigated the expression of key dopamine receptors (Drd1, Drd2), brain-derived neurotrophic factor (Bdnf), its receptors (Ntrkb2, Ngfr) and the transcription factor Creb1 that mediates BDNF action, as well as cerebral dopamine neurotrophic factor (Cdnf) during the critical periods of embryogenesis (e14 and e18) and postnatal development (p14, p28, p60) in the hippocampus and frontal cortex of BTBR mice with autism-like behavior compared to the neurotypical C57BL/6 J strain. In BTBR embryos, on the 14th day of prenatal development, an increase in the expression of the Ngfr gene encoding the p75NTR receptor, which may lead to the activation of apoptosis, was found in the hippocampus and frontal cortex. A decrease in the expression of Cdnf, Bdnf and its receptor Ntrkb2, as well as dopamine receptors (Drd1, Drd2) was detected in BTBR mice in the postnatal period of ontogenesis mainly in the frontal cortex, while in the hippocampus of mature mice (p60), only a decrease in the Drd2 mRNA level was revealed. The obtained results suggest that the decrease in the expression levels of CDNF, BDNF-TrkB and dopamine receptors in the frontal cortex in the postnatal period can lead to significant changes in both the morphology of neurons and dopamine neurotransmission in cortical brain structures. At the same time, the increase in p75NTR receptor gene expression observed on the 14th day of embryogenesis, crucial for hippocampus and frontal cortex development, may have direct relevance to the manifestation of early autism.

Generation and transcriptomic characterization of MIR137 knockout miniature pig model for neurodevelopmental disorders

BACKGROUND

Neurodevelopmental disorders (NDD), such as autism spectrum disorders (ASD) and intellectual disorders (ID), are highly debilitating childhood psychiatric conditions. Genetic factors are recognized as playing a major role in NDD, with a multitude of genes and genomic regions implicated. While the functional validation of NDD-associated genes has predominantly been carried out using mouse models, the significant differences in brain structure and gene function between mice and humans have limited the effectiveness of mouse models in exploring the underlying mechanisms of NDD. Therefore, it is important to establish alternative animal models that are more evolutionarily aligned with humans.

RESULTS

In this study, we employed CRISPR/Cas9 and somatic cell nuclear transplantation technologies to successfully generate a knockout miniature pig model of the MIR137 gene, which encodes the neuropsychiatric disorder-associated microRNA miR-137. The homozygous knockout of MIR137 (MIR137-/-) effectively suppressed the expression of mature miR-137 and led to the birth of stillborn or short-lived piglets. Transcriptomic analysis revealed significant changes in genes associated with neurodevelopment and synaptic signaling in the brains of MIR137-/- miniature pig, mirroring findings from human ASD transcriptomic data. In comparison to miR-137-deficient mouse and human induced pluripotent stem cell (hiPSC)-derived neuron models, the miniature pig model exhibited more consistent changes in critical neuronal genes relevant to humans following the loss of miR-137. Furthermore, a comparative analysis identified differentially expressed genes associated with ASD and ID risk genes in both miniature pig and hiPSC-derived neurons. Notably, human-specific miR-137 targets, such as CAMK2A, known to be linked to cognitive impairments and NDD, exhibited dysregulation in MIR137-/- miniature pigs. These findings suggest that the loss of miR-137 in miniature pigs affects genes crucial for neurodevelopment, potentially contributing to the development of NDD.

CONCLUSIONS

Our study highlights the impact of miR-137 loss on critical genes involved in neurodevelopment and related disorders in MIR137-/- miniature pigs. It establishes the miniature pig model as a valuable tool for investigating neurodevelopmental disorders, providing valuable insights for potential applications in human research.

Automated single-cell omics end-to-end framework with data-driven batch inference

To facilitate single-cell multi-omics analysis and improve reproducibility, we present SPEEDI (Single-cell Pipeline for End to End Data Integration), a fully automated end-to-end framework for batch inference, data integration, and cell type labeling. SPEEDI introduces data-driven batch inference and transforms the often heterogeneous data matrices obtained from different samples into a uniformly annotated and integrated dataset. Without requiring user input, it automatically selects parameters and executes pre-processing, sample integration, and cell type mapping. It can also perform downstream analyses of differential signals between treatment conditions and gene functional modules. SPEEDI's data-driven batch inference method works with widely used integration and cell-typing tools. By developing data-driven batch inference, providing full end-to-end automation, and eliminating parameter selection, SPEEDI improves reproducibility and lowers the barrier to obtaining biological insight from these valuable single-cell datasets. The SPEEDI interactive web application can be accessed at https://speedi.princeton.edu/.

Cognitive behavioral phenotyping of DSCAM heterozygosity as a model for autism spectrum disorder

It is estimated that 1 in 36 children are affected by autism spectrum disorder (ASD) in the United States, which is nearly a twofold increase from a decade ago. Recent genetic studies have identified de novo loss-of-function (dnLoF) mutations in the Down Syndrome Cell Adhesion Molecule (DSCAM) as a strong risk factor for ASD. Previous research has shown that DSCAM ablation confers social interaction deficits and perseverative behaviors in mouse models. However, it remains unknown to what extent DSCAM underexpression captures the full range of behaviors, specifically cognitive phenotypes, presented in ASD. Here, we conducted a comprehensive cognitive behavioral phenotyping which revealed that loss of one copy of DSCAM , as in the DSCAM 2J +/- mice, displayed hyperactivity, increased anxiety, and motor coordination impairments. Additionally, hippocampal-dependent learning and memory was affected, including working memory, long-term memory, and contextual fear learning. Interestingly, implicit learning processes remained intact. Therefore, DSCAM LoF produces autistic-like behaviors that are similar to human cases of ASD. These findings further support a role for DSCAM dnLoF mutations in ASD and suggest DSCAM 2J +/- as a suitable model for ASD research.

Summary Statement

Autism spectrum disorder represents a growing patient population. Loss of one copy of the DSCAM gene provides a promising mouse model that reproduces autistic-like behaviors for research and therapeutic testing.

The Importance of Large-Scale Genomic Studies to Unravel Genetic Risk Factors for Autism

Autism spectrum disorder (ASD) is a common and highly heritable neurodevelopmental disorder. During the last 15 years, advances in genomic technologies and the availability of increasingly large patient cohorts have greatly expanded our knowledge of the genetic architecture of ASD and its neurobiological mechanisms. Over two hundred risk regions and genes carrying rare de novo and transmitted high-impact variants have been identified. Additionally, common variants with small individual effect size are also important, and a number of loci are now being uncovered. At the same time, these new insights have highlighted ongoing challenges. In this perspective article, we summarize developments in ASD genetic research and address the enormous impact of large-scale genomic initiatives on ASD gene discovery.

Embryonic origin of two ASD subtypes of social symptom severity: the larger the brain cortical organoid size, the more severe the social symptoms

BACKGROUND

Social affective and communication symptoms are central to autism spectrum disorder (ASD), yet their severity differs across toddlers: Some toddlers with ASD display improving abilities across early ages and develop good social and language skills, while others with "profound" autism have persistently low social, language and cognitive skills and require lifelong care. The biological origins of these opposite ASD social severity subtypes and developmental trajectories are not known.

METHODS

Because ASD involves early brain overgrowth and excess neurons, we measured size and growth in 4910 embryonic-stage brain cortical organoids (BCOs) from a total of 10 toddlers with ASD and 6 controls (averaging 196 individual BCOs measured/subject). In a 2021 batch, we measured BCOs from 10 ASD and 5 controls. In a 2022 batch, we  tested replicability of BCO size and growth effects by generating and measuring an independent batch of BCOs from 6 ASD and 4 control subjects. BCO size was analyzed within the context of our large, one-of-a-kind social symptom, social attention, social brain and social and language psychometric normative datasets ranging from N = 266 to N = 1902 toddlers. BCO growth rates were examined by measuring size changes between 1- and 2-months of organoid development. Neurogenesis markers at 2-months were examined at the cellular level. At the molecular level, we measured activity and expression of Ndel1; Ndel1 is a prime target for cell cycle-activated kinases; known to regulate cell cycle, proliferation, neurogenesis, and growth; and known to be involved in neuropsychiatric conditions.

RESULTS

At the BCO level, analyses showed BCO size was significantly enlarged by 39% and 41% in ASD in the 2021 and 2022 batches. The larger the embryonic BCO size, the more severe the ASD social symptoms. Correlations between BCO size and social symptoms were r = 0.719 in the 2021 batch and r = 0. 873 in the replication 2022 batch. ASD BCOs grew at an accelerated rate nearly 3 times faster than controls. At the cell level, the two largest ASD BCOs had accelerated neurogenesis. At the molecular level, Ndel1 activity was highly correlated with the growth rate and size of BCOs. Two BCO subtypes were found in ASD toddlers: Those in one subtype had very enlarged BCO size with accelerated rate of growth and neurogenesis; a profound autism clinical phenotype displaying severe social symptoms, reduced social attention, reduced cognitive, very low language and social IQ; and substantially altered growth in specific cortical social, language and sensory regions. Those in a second subtype had milder BCO enlargement and milder social, attention, cognitive, language and cortical differences.

LIMITATIONS

Larger samples of ASD toddler-derived BCO and clinical phenotypes may reveal additional ASD embryonic subtypes.

CONCLUSIONS

By embryogenesis, the biological bases of two subtypes of ASD social and brain development-profound autism and mild autism-are already present and measurable and involve dysregulated cell proliferation and accelerated neurogenesis and growth. The larger the embryonic BCO size in ASD, the more severe the toddler's social symptoms and the more reduced the social attention, language ability, and IQ, and the more atypical the growth of social and language brain regions.

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.

Deconvolution of polygenic risk score in single cells unravels cellular and molecular heterogeneity of complex human diseases

Polygenic risk scores (PRSs) are commonly used for predicting an individual's genetic risk of complex diseases. Yet, their implication for disease pathogenesis remains largely limited. Here, we introduce scPRS, a geometric deep learning model that constructs single-cell-resolved PRS leveraging reference single-cell chromatin accessibility profiling data to enhance biological discovery as well as disease prediction. Real-world applications across multiple complex diseases, including type 2 diabetes (T2D), hypertrophic cardiomyopathy (HCM), and Alzheimer's disease (AD), showcase the superior prediction power of scPRS compared to traditional PRS methods. Importantly, scPRS not only predicts disease risk but also uncovers disease-relevant cells, such as hormone-high alpha and beta cells for T2D, cardiomyocytes and pericytes for HCM, and astrocytes, microglia and oligodendrocyte progenitor cells for AD. Facilitated by a layered multi-omic analysis, scPRS further identifies cell-type-specific genetic underpinnings, linking disease-associated genetic variants to gene regulation within corresponding cell types. We substantiate the disease relevance of scPRS-prioritized HCM genes and demonstrate that the suppression of these genes in HCM cardiomyocytes is rescued by Mavacamten treatment. Additionally, we establish a novel microglia-specific regulatory relationship between the AD risk variant rs7922621 and its target genes ANXA11 and TSPAN14. We further illustrate the detrimental effects of suppressing these two genes on microglia phagocytosis. Our work provides a multi-tasking, interpretable framework for precise disease prediction and systematic investigation of the genetic, cellular, and molecular basis of complex diseases, laying the methodological foundation for single-cell genetics.

Deep learning modeling of rare noncoding genetic variants in human motor neurons defines CCDC146 as a therapeutic target for ALS

Amyotrophic lateral sclerosis (ALS) is a fatal and incurable neurodegenerative disease caused by the selective and progressive death of motor neurons (MNs). Understanding the genetic and molecular factors influencing ALS survival is crucial for disease management and therapeutics. In this study, we introduce a deep learning-powered genetic analysis framework to link rare noncoding genetic variants to ALS survival. Using data from human induced pluripotent stem cell (iPSC)-derived MNs, this method prioritizes functional noncoding variants using deep learning, links cis-regulatory elements (CREs) to target genes using epigenomics data, and integrates these data through gene-level burden tests to identify survival-modifying variants, CREs, and genes. We apply this approach to analyze 6,715 ALS genomes, and pinpoint four novel rare noncoding variants associated with survival, including chr7:76,009,472:C>T linked to CCDC146. CRISPR-Cas9 editing of this variant increases CCDC146 expression in iPSC-derived MNs and exacerbates ALS-specific phenotypes, including TDP-43 mislocalization. Suppressing CCDC146 with an antisense oligonucleotide (ASO), showing no toxicity, completely rescues ALS-associated survival defects in neurons derived from sporadic ALS patients and from carriers of the ALS-associated G4C2-repeat expansion within C9ORF72. ASO targeting of CCDC146 may be a broadly effective therapeutic approach for ALS. Our framework provides a generic and powerful approach for studying noncoding genetics of complex human diseases.

VCD-induced menopause mouse model reveals reprogramming of hepatic metabolism

OBJECTIVE

Menopause adversely impacts systemic energy metabolism and increases the risk of metabolic disease(s) including hepatic steatosis, but the mechanisms are largely unknown. Dosing female mice with vinyl cyclohexene dioxide (VCD) selectively causes follicular atresia in ovaries, leading to a murine menopause-like phenotype.

METHODS

In this study, we treated female C57BL6/J mice with VCD (160 mg/kg i.p. for 20 consecutive days followed by verification of the lack of estrous cycling) to investigate changes in body composition, energy expenditure (EE), hepatic mitochondrial function, and hepatic steatosis across different dietary conditions.

RESULTS

VCD treatment induced ovarian follicular loss and increased follicle-stimulating hormone (FSH) levels in female mice, mimicking a menopause-like phenotype. VCD treatment did not affect body composition, or EE in mice on a low-fat diet (LFD) or in response to a short-term (1-week) high-fat, high sucrose diet (HFHS). However, the transition to a HFHS lowered cage activity in VCD mice. A chronic HFHS diet (16 weeks) significantly increased weight gain, fat mass, and hepatic steatosis in VCD-treated mice compared to HFHS-fed controls. In the liver, VCD mice showed suppressed hepatic mitochondrial respiration on LFD, while chronic HFHS resulted in compensatory increases in hepatic mitochondrial respiration. Also, liver RNA sequencing revealed that VCD promoted global upregulation of hepatic lipid/cholesterol synthesis pathways.

CONCLUSION

Our findings suggest that the VCD-induced menopause model compromises hepatic mitochondrial function and lipid/cholesterol homeostasis that sets the stage for HFHS diet-induced steatosis while also increasing susceptibility to obesity.

Graph Node Classification to Predict Autism Risk in Genes

This study explores the genetic risk associations with autism spectrum disorder (ASD) using graph neural networks (GNNs), leveraging the Sfari dataset and protein interaction network (PIN) data. We built a gene network with genes as nodes, chromosome band location as node features, and gene interactions as edges. Graph models were employed to classify the autism risk associated with newly introduced genes (test set). Three classification tasks were undertaken to test the ability of our models: binary risk association, multi-class risk association, and syndromic gene association. We tested graph convolutional networks, Graph Sage, graph transformer, and Multi-Layer Perceptron (Baseline) architectures on this problem. The Graph Sage model consistently outperformed the other models, showcasing its utility in classifying ASD-related genes. Our ablation studies show that the chromosome band location and protein interactions contain useful information for this problem. The models achieved 85.80% accuracy on the binary risk classification, 81.68% accuracy on the multi-class risk classification, and 90.22% on the syndromic classification.

Network-based analysis predicts interacting genetic modifiers from a meta-mapping study of spike-wave discharge in mice

Absence seizures are characterized by brief lapses in awareness accompanied by a hallmark spike-and-wave discharge (SWD) electroencephalographic pattern and are common to genetic generalized epilepsies (GGEs). While numerous genes have been associated with increased risk, including some Mendelian forms with a single causal allele, most cases of GGE are idiopathic and there are many unknown genetic modifiers of GGE influencing risk and severity. In a previous meta-mapping study, crosses between transgenic C57BL/6 and C3HeB/FeJ strains, each carrying one of three SWD-causing mutations (Gabrg2tm1Spet(R43Q) , Scn8a8j or Gria4spkw1 ), demonstrated an antagonistic epistatic interaction between loci on mouse chromosomes 2 and 7 influencing SWD. These results implicate universal modifiers in the B6 background that mitigate SWD severity through a common pathway, independent of the causal mutation. In this study, we prioritized candidate modifiers in these interacting loci. Our approach integrated human genome-wide association results with gene interaction networks and mouse brain gene expression to prioritize candidate genes and pathways driving variation in SWD outcomes. We considered candidate genes that are functionally associated with human GGE risk genes and genes with evidence for coding or non-coding allele effects between the B6 and C3H backgrounds. Our analyses output a summary ranking of gene pairs, one gene from each locus, as candidates for explaining the epistatic interaction. Our top-ranking gene pairs implicate microtubule function, cytoskeletal stability and cell cycle regulation as novel hypotheses about the source of SWD variation across strain backgrounds, which could clarify underlying mechanisms driving differences in GGE severity in humans.

A network-based method for associating genes with autism spectrum disorder

Autism spectrum disorder (ASD) is a highly heritable complex disease that affects 1% of the population, yet its underlying molecular mechanisms are largely unknown. Here we study the problem of predicting causal genes for ASD by combining genome-scale data with a network propagation approach. We construct a predictor that integrates multiple omic data sets that assess genomic, transcriptomic, proteomic, and phosphoproteomic associations with ASD. In cross validation our predictor yields mean area under the ROC curve of 0.87 and area under the precision-recall curve of 0.89. We further show that it outperforms previous gene-level predictors of autism association. Finally, we show that we can use the model to predict genes associated with Schizophrenia which is known to share genetic components with ASD.

Tapioca: a platform for predicting de novo protein-protein interactions in dynamic contexts

Protein-protein interactions (PPIs) drive cellular processes and responses to environmental cues, reflecting the cellular state. Here we develop Tapioca, an ensemble machine learning framework for studying global PPIs in dynamic contexts. Tapioca predicts de novo interactions by integrating mass spectrometry interactome data from thermal/ion denaturation or cofractionation workflows with protein properties and tissue-specific functional networks. Focusing on the thermal proximity coaggregation method, we improved the experimental workflow. Finely tuned thermal denaturation afforded increased throughput, while cell lysis optimization enhanced protein detection from different subcellular compartments. The Tapioca workflow was next leveraged to investigate viral infection dynamics. Temporal PPIs were characterized during the reactivation from latency of the oncogenic Kaposi's sarcoma-associated herpesvirus. Together with functional assays, NUCKS was identified as a proviral hub protein, and a broader role was uncovered by integrating PPI networks from alpha- and betaherpesvirus infections. Altogether, Tapioca provides a web-accessible platform for predicting PPIs in dynamic contexts.

Peptide-based capture-and-release purification of extracellular vesicles and statistical algorithm enabled quality assessment

Circulating extracellular vesicles (EVs) have gained significant attention for discovering tumor biomarkers. However, isolating EVs with well-defined homogeneous populations from complex biological samples is challenging. Different isolation methods have been found to derive different EV populations carrying different molecular contents, which confounds current investigations and hinders subsequent clinical translation. Therefore, standardizing and building a rigorous assessment of isolated EV quality associated with downstream molecular analysis is essential. To address this need, we introduce a statistical algorithm (ExoQuality Index, EQI) by integrating multiple EV characterizations (size, particle concentration, zeta potential, total protein, and RNA), enabling direct EV quality assessment and comparisons between different isolation methods. We also introduced a novel capture-release isolation approach using a pH-responsive peptide conjugated with NanoPom magnetic beads (ExCy) for simple, fast, and homogeneous EV isolation from various biological fluids. Bioinformatic analysis of next-generation sequencing (NGS) data of EV total RNAs from pancreatic cancer patient plasma samples using our novel EV isolation approach and quality index strategy illuminates how this approach improves the identification of tumor associated molecular markers. Results showed higher human mRNA coverage compared to existing isolation approaches in terms of both pancreatic cancer pathways and EV cellular component pathways using gProfiler pathway analysis. This study provides a valuable resource for researchers, establishing a workflow to prepare and analyze EV samples carefully and contributing to the advancement of reliable and rigorous EV quality assessment and clinical translation.

Improving CNV Detection Performance in Microarray Data Using a Machine Learning-Based Approach

Copy number variation (CNV) is a primary source of structural variation in the human genome, leading to several disorders. Therefore, analyzing neonatal CNVs is crucial for managing CNV-related chromosomal disabilities. However, genomic waves can hinder accurate CNV analysis. To mitigate the influences of the waves, we adopted a machine learning approach and developed a new method that uses a modified log R ratio instead of the commonly used log R ratio. Validation results using samples with known CNVs demonstrated the superior performance of our method. We analyzed a total of 16,046 Korean newborn samples using the new method and identified CNVs related to 39 genetic disorders were identified in 342 cases. The most frequently detected CNV-related disorder was Joubert syndrome 4. The accuracy of our method was further confirmed by analyzing a subset of the detected results using NGS and comparing them with our results. The utilization of a genome-wide single nucleotide polymorphism array with wave offset was shown to be a powerful method for identifying CNVs in neonatal cases. The accurate screening and the ability to identify various disease susceptibilities offered by our new method could facilitate the identification of CNV-associated chromosomal disease etiologies.

VCD-induced menopause mouse model reveals reprogramming of hepatic metabolism

Menopause adversely impacts systemic energy metabolism and increases the risk of metabolic disease(s) including hepatic steatosis, but the mechanisms are largely unknown. Dosing female mice with vinyl cyclohexene dioxide (VCD) selectively causes follicular atresia in ovaries, leading to a murine menopause-like phenotype. In this study, we treated female C57BL6/J mice with VCD (160mg/kg i.p. for 20 consecutive days followed by verification of the lack of estrous cycling) to investigate changes in body composition, energy expenditure (EE), hepatic mitochondrial function, and hepatic steatosis across different dietary conditions. VCD treatment induced ovarian follicular loss and increased follicle-stimulating hormone (FSH) levels in female mice, mimicking a menopause-like phenotype. VCD treatment did not affect body composition, or EE in mice on a low-fat diet or in response to a short-term (1-week) high-fat, high sucrose diet (HFHS). However, the transition to a HFHS lowered cage activity in VCD mice. A chronic HFHS diet (16 weeks) significantly increased weight gain, fat mass, and hepatic steatosis in VCD-treated mice compared to HFHS-fed controls. In the liver, VCD mice showed suppressed hepatic mitochondrial respiration on LFD, while chronic HFHS diet resulted in compensatory increases in hepatic mitochondrial respiration. Also, liver RNA sequencing revealed that VCD promoted global upregulation of hepatic lipid/cholesterol synthesis pathways. Our findings suggest that the VCD- induced menopause model compromises hepatic mitochondrial function and lipid/cholesterol homeostasis that sets the stage for HFHS diet-induced steatosis while also increasing susceptibility to obesity.

Spatiotemporal expression patterns of anxiety disorder-associated genes

Anxiety disorders (ADs) are the most common form of mental disorder that affects millions of individuals worldwide. Although physiological studies have revealed the neural circuits related to AD symptoms, how AD-associated genes are spatiotemporally expressed in the human brain still remains unclear. In this study, we integrated genome-wide association studies of four human AD subtypes-generalized anxiety disorder, social anxiety disorder, panic disorder, and obsessive-compulsive disorder-with spatial gene expression patterns. Our investigation uncovered a novel division among AD-associated genes, marked by significant and distinct expression enrichments in the cerebral nuclei, limbic, and midbrain regions. Each gene cluster was associated with specific anxiety-related behaviors, signaling pathways, region-specific gene networks, and cell types. Notably, we observed a significant negative correlation in the temporal expression patterns of these gene clusters during various developmental stages. Moreover, the specific brain regions enriched in each gene group aligned with neural circuits previously associated with negative decision-making and anxious temperament. These results suggest that the two distinct gene clusters may underlie separate neural systems involved in anxiety. As a result, our findings bridge the gap between genes and neural circuitry, shedding light on the mechanisms underlying AD-associated behaviors.

Systematic bibliometric and visualized analysis of research hotspots and trends in artificial intelligence in autism spectrum disorder

Background

Artificial intelligence (AI) has been the subject of studies in autism spectrum disorder (ASD) and may affect its identification, diagnosis, intervention, and other medical practices in the future. Although previous studies have used bibliometric techniques to analyze and investigate AI, there has been little research on the adoption of AI in ASD. This study aimed to explore the broad applications and research frontiers of AI used in ASD.

Methods

Citation data were retrieved from the Web of Science Core Collection (WoSCC) database to assess the extent to which AI is used in ASD. CiteSpace.5.8. R3 and VOSviewer, two online tools for literature metrology analysis, were used to analyze the data.

Results

A total of 776 publications from 291 countries and regions were analyzed; of these, 256 publications were from the United States and 173 publications were from China, and England had the largest centrality of 0.33; Stanford University had the highest H-index of 17; and the largest cluster label of co-cited references was machine learning. In addition, keywords with a high number of occurrences in this field were autism spectrum disorder (295), children (255), classification (156) and diagnosis (77). The burst keywords from 2021 to 2023 were infants and feature selection, and from 2022 to 2023, the burst keyword was corpus callosum.

Conclusion

This research provides a systematic analysis of the literature concerning AI used in ASD, presenting an overall demonstration in this field. In this area, the United States and China have the largest number of publications, England has the greatest influence, and Stanford University is the most influential. In addition, the research on AI used in ASD mostly focuses on classification and diagnosis, and "infants, feature selection, and corpus callosum are at the forefront, providing directions for future research. However, the use of AI technologies to identify ASD will require further research.

ImageNomer: Description of a functional connectivity and omics analysis tool and case study identifying a race confound

Most packages for the analysis of fMRI-based functional connectivity (FC) and genomic data are used with a programming language interface, lacking an easy-to-navigate GUI frontend. This exacerbates two problems found in these types of data: demographic confounds and quality control in the face of high dimensionality of features. The reason is that it is too slow and cumbersome to use a programming interface to create all the necessary visualizations required to identify all correlations, confounding effects, or quality control problems in a dataset. FC in particular usually contains tens of thousands of features per subject, and can only be summarized and efficiently explored using visualizations. To remedy this situation, we have developed ImageNomer, a data visualization and analysis tool that allows inspection of both subject-level and cohort-level demographic, genomic, and imaging features. The software is Python-based, runs in a self-contained Docker image, and contains a browser-based GUI frontend. We demonstrate the usefulness of ImageNomer by identifying an unexpected race confound when predicting achievement scores in the Philadelphia Neurodevelopmental Cohort (PNC) dataset, which contains multitask fMRI and single nucleotide polymorphism (SNP) data of healthy adolescents. In the past, many studies have attempted to use FC to identify achievement-related features in fMRI. Using ImageNomer to visualize trends in achievement scores between races, we find a clear potential for confounding effects if race can be predicted using FC. Using correlation analysis in the ImageNomer software, we show that FCs correlated with Wide Range Achievement Test (WRAT) score are in fact more highly correlated with race. Investigating further, we find that whereas both FC and SNP (genomic) features can account for 10-15% of WRAT score variation, this predictive ability disappears when controlling for race. We also use ImageNomer to investigate race-FC correlation in the Bipolar and Schizophrenia Network for Intermediate Phenotypes (BSNIP) dataset. In this work, we demonstrate the advantage of our ImageNomer GUI tool in data exploration and confound detection. Additionally, this work identifies race as a strong confound in FC data and casts doubt on the possibility of finding unbiased achievement-related features in fMRI and SNP data of healthy adolescents.

Effects of prefrontal transcranial direct current stimulation on social functioning in autism spectrum disorder: A randomized clinical trial

LAY ABSTRACT

Currently available pharmacological and behavioral interventions for adolescents and young adults with autism spectrum disorder (ASD) yield only modest effect in alleviating their core behavioral and cognitive symptoms, and some of these treatment options are associated with undesirable side effects. Hence, developing effective treatment protocols is urgently needed. Given emerging evidence shows that the abnormal connections of the frontal brain regions contribute to the manifestations of ASD behavioral and cognitive impairments, noninvasive treatment modalities that are capable in modulating brain connections, such as transcranial direct current stimulation (tDCS), have been postulated to be potentially promising for alleviating core symptoms in ASD. However, whether tDCS can reduce behavioral symptoms and enhance cognitive performance in ASD remains unclear. This randomized controlled trial involving 105 adolescents and young adults with ASD showed that multiple sessions of a tDCS protocol, which was paired up with computerized cognitive training, was effective in improving social functioning in adolescents and young adults with ASD. No prolonged and serious side effects were observed. With more future studies conducted in different clinical settings that recruit participants from a wider age range, this tDCS protocol may be potentially beneficial to a broad spectrum of individuals with autism.

Screening for Fragile X Syndrome Among Filipino Children with Autism Spectrum Disorder

Individuals with autism spectrum disorder present with difficulties in social communication, restricted interests or behaviors and other co-morbidities. About 2 to 10% of cases of autism have a genetic cause, and Fragile X Syndrome (FXS) is reported in 0 to 6.5% of individuals with autism. However, the FXS and premutation prevalence among Filipino children has never been reported. The aim of the study was to establish the presence of FXS or premutation carriers among Filipino children with autism and to describe the phenotypic characteristic of cases identified. Blood was collected from 235 children aged 2-6 years old and diagnosed with autism. Samples were analyzed using PCR methods to amplify CGG repeats in the FMRI gene. The diagnosis of autism was confirmed through the Autism Diagnostic Observation Schedule-2. Additional characteristics were documented from a physical examination, Griffiths Scales of Child Development assessment and a parent-answered questionnaire using the Vineland Adaptive Behavior Scale. Fragile X testing through PCR methods in 235 children with diagnosed autism showed 220 (93.6%) were negative, no full mutations, 1 (0.436%) premutation carrier and 14 (5.95%) cases contained intermediate alleles. The FXS testing was limited to confirmed cases of autism, which is considered a high-risk group and does not provide prevalence for the general Filipino population. Subjects were self-referred or referred by clinicians, which may not represent the Filipino autism population with a bias towards those with means for clinical consultations and ability to travel to the place of testing. Samples were not measured for mosaicism, DNA methylation or AGG interspersion patterns. These may have effects on the CGG repeat expansion and overall presentation of FXS. Findings from a single premutation carrier cannot characterize features distinctly present in Filipinos with the mutation. Nevertheless, these results support the data that the prevalence of FXS in Asian populations may be lower than non-Asian populations. This can contribute to a better understanding of FXS and genetic causes of autism in the Philippines and other Asian populations.

A hybrid Stacking-SMOTE model for optimizing the prediction of autistic genes

PURPOSE

Autism spectrum disorder(ASD) is a disease associated with the neurodevelopment of the brain. The autism spectrum can be observed in early childhood, where the symptoms of the disease usually appear in children within the first year of their life. Currently, ASD can only be diagnosed based on the apparent symptoms due to the lack of information on genes related to the disease. Therefore, in this paper, we need to predict the largest number of disease-causing genes for a better diagnosis.

METHODS

A hybrid stacking ensemble model with Synthetic Minority Oversampling TEchnique (Stack-SMOTE) is proposed to predict the genes associated with ASD. The proposed model uses the gene ontology database to measure the similarities between the genes using a hybrid gene similarity function(HGS). HGS is effective in measuring the similarity as it combines the features of information gain-based methods and graph-based methods. The proposed model solves the imbalanced ASD dataset problem using the Synthetic Minority Oversampling Technique (SMOTE), which generates synthetic data rather than duplicates the data to reduce the overfitting. Sequentially, a gradient boosting-based random forest classifier (GBBRF) is introduced as a new combination technique to enhance the prediction of ASD genes. Moreover, the GBBRF classifier combined with random forest(RF), k-nearest neighbor, support vector machine(SVM), and logistic regression(LR) to form the proposed Stacking-SMOTE model to optimize the prediction of ASD genes.

RESULTS

The proposed Stacking-SMOTE model is evaluated using the Simons Foundation Autism Research Initiative (SFARI) gene database and a set of candidates ASD genes.The results of the proposed model-based SMOTE outperform other reported undersampling and oversampling techniques. Sequentially, the results of GBBRF achieve higher accuracy than using the basic classifiers. Moreover, the experimental results show that the proposed Stacking-SMOTE model outperforms the existing ASD prediction models with approximately 95.5% accuracy.

CONCLUSION

The proposed Stacking-SMOTE model demonstrates that SMOTE is effective in handling the autism imbalanced data. Sequentially, the integration between the gradient boosting and random forest classifier (GBBRF) support to build a robust stacking ensemble model(Stacking-SMOTE).

Semaphorin 3A influences neuronal processes that are altered in patients with autism spectrum disorder: Potential diagnostic and therapeutic implications

Autism spectrum disorder (ASD) is a pervasive disorder that most frequently manifests in early childhood and lasts for their entire lifespan. Several behavioural traits characterise the phenotype of patients with ASD, including difficulties in reciprocal social communication as well as compulsive/repetitive stereotyped verbal and non-verbal behaviours. Although multiple hypotheses have been proposed to explain the aetiology of ASD and many resources have been used to improve our understanding of ASD, several aspects remain largely unexplored. Class 3 semaphorins (SEMA3) are secreted proteins involved in the organisation of structural and functional connectivity in the brain that regulate synaptic and dendritic development. Alterations in brain connectivity and aberrant neuronal development have been described in some patients with ASD. Mutations and polymorphisms in SEMA3A and alterations in its receptors and signalling have been associated with some neurological disorders such as schizophrenia and epilepsy, which are comorbidities in ASD, but also with ASD itself. In addition, SEMA3A is a key regulator of the immune response and neuroinflammatory processes, which have been found to be dysregulated in mothers of children who develop ASD and in affected patients. In this review, we highlight neurodevelopmental-related processes in which SEMA3A is involved, which are altered in ASD, and provide a viewpoint emphasising the development of strategies targeting changes in the SEMA3A signal to identify patterns of anomalies distinctive of ASD or to predict the prognosis of affected patients.

Sodium glucose co-transporter 2 inhibition increases epidermal growth factor expression and improves outcomes in patients with type 2 diabetes

Underlying molecular mechanisms of the kidney protective effects of sodium glucose co-transporter 2 (SGLT2) inhibitors are not fully elucidated. Therefore, we studied the association between urinary epidermal growth factor (uEGF), a mitogenic factor involved in kidney repair, and kidney outcomes in patients with type 2 diabetes (T2D). The underlying molecular mechanisms of the SGLT2 inhibitor canagliflozin on EGF using single-cell RNA sequencing from kidney tissue were examined. Urinary EGF-to-creatinine ratio (uEGF/Cr) was measured in 3521 CANagliflozin cardioVascular Assessment Study (CANVAS) participants at baseline and week 52. Associations of uEGF/Cr with kidney outcome were assessed using multivariable-adjusted Cox regression models. Single-cell RNA sequencing was performed using protocol kidney biopsy tissue from ten young patients with T2D on SGLT2i, six patients with T2D on standard care only, and six healthy controls (HCs). In CANVAS, each doubling in baseline uEGF/Cr was associated with a 12% (95% confidence interval 1-22) decreased risk of kidney outcome. uEGF/Cr decreased after 52 weeks with placebo and remained stable with canagliflozin (between-group difference +7.3% (2.0-12.8). In young persons with T2D, EGF mRNA was primarily expressed in the thick ascending loop of Henle. Expression in biopsies from T2D without SGLT2i was significantly lower compared to HCs, whereas treatment with SGLT2i increased EGF levels closer to the healthy state. In young persons with T2D without SGLT2i, endothelin-1 emerged as a key regulator of the EGF co-expression network. SGLT2i treatment was associated with a shift towards normal EGF expression. Thus, decreased uEGF represents increased risk of kidney disease progression in patients with T2D. Canagliflozin increased kidney tissue expression of EGF and was associated with a downstream signaling cascade linked to tubular repair and reversal of tubular injury.

Uncovering the link between air pollution and neurodevelopmental alterations during pregnancy and early life exposure: A systematic review

Air pollution plays, nowadays, a huge role in human's health and in the personal economy. Moreover, there has been a rise in the prevalence of neurodevelopmental disorders like the Autism Spectrum Disorder (ASD) in recent years. Current scientific studies have established a link between prenatal or perinatal exposure to environmental pollutants and ASD. This systematic review summarizes the current literature available about the relationship between exposure to air pollutants (particulate matter [PM], Second Organic Aerosols [SOA], Diesel Exhaust [DE], and Traffic Related Air Pollution [TRAP]) and neurodevelopmental disorders in preclinical models using rats and mice. The articles were selected and filtered using the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) methodology, and bias-evaluated using the SYstematic Review Centre for Laboratory animal Experimentation (SYRCLE) tool. Overall, our findings suggest that air pollutants are associated with negative developmental outcomes characterized by ASD-like behaviors, abnormal biochemical patterns, and impaired achievement of developmental milestones in rodents. However, there is not sufficient information in certain domains to establish a clear relationship. Short phrases for indexing terms: Air pollution affects neurodevelopment; PM exposure modifies glutamate system; Prenatal exposure combined with postnatal affect more to behavioral / cognitive domain; Air pollution modifies social behavior in rodents; Cognitive deficits can be detected after gestational exposure to air pollution.

Enhancing early autism prediction based on electronic records using clinical narratives

Recent work has shown that predictive models can be applied to structured electronic health record (EHR) data to stratify autism likelihood from an early age (<1 year). Integrating clinical narratives (or notes) with structured data has been shown to improve prediction performance in other clinical applications, but the added predictive value of this information in early autism prediction has not yet been explored. In this study, we aimed to enhance the performance of early autism prediction by using both structured EHR data and clinical narratives. We built models based on structured data and clinical narratives separately, and then an ensemble model that integrated both sources of data. We assessed the predictive value of these models from Duke University Health System over a 14-year span to evaluate ensemble models predicting later autism diagnosis (by age 4 years) from data collected from ages 30 to 360 days. Our sample included 11,750 children above by age 3 years (385 meeting autism diagnostic criteria). The ensemble model for autism prediction showed superior performance and at age 30 days achieved 46.8% sensitivity (95% confidence interval, CI: 22.0%, 52.9%), 28.0% positive predictive value (PPV) at high (90%) specificity (CI: 2.0%, 33.1%), and AUC4 (with at least 4-year follow-up for controls) reaching 0.769 (CI: 0.715, 0.811). Prediction by 360 days achieved 44.5% sensitivity (CI: 23.6%, 62.9%), and 13.7% PPV at high (90%) specificity (CI: 9.6%, 18.9%), and AUC4 reaching 0.797 (CI: 0.746, 0.840). Results show that incorporating clinical narratives in early autism prediction achieved promising accuracy by age 30 days, outperforming models based on structured data only. Furthermore, findings suggest that additional features learned from clinician narratives might be hypothesis generating for understanding early development in autism.

7p22.2 Microduplication: A Pathogenic CNV?

Partial duplication of the short arm of chromosome 7 is a rare chromosome rearrangement. The phenotype spectrum associated with this rearrangement is extremely variable even if in the last decade the use of high-resolution microarray technology for the investigation of patients carrying this rearrangement allowed for the identification of the 7p22.1 sub-band causative of this phenotype and to recognize the corresponding 7p22.1 microduplication syndrome. We report two unrelated patients that carry a microduplication involving the 7.22.2 sub-band. Unlike 7p22.1 microduplication carriers, both patients only show a neurodevelopmental disorder without malformations. We better characterized the clinical pictures of these two patients providing insight into the clinical phenotype associated with the microduplication of the 7p22.2 sub-band and support for a possible role of this sub-band in the 7p22 microduplication syndrome.

Pre- and Post-Sexual Maturity Liver-specific ERα Knockout Does Not Impact Hepatic Mitochondrial Function

Compared with males, premenopausal women and female rodents are protected against hepatic steatosis and present with higher functioning mitochondria (greater hepatic mitochondrial respiration and reduced H2O2 emission). Despite evidence that estrogen action mediates female protection against steatosis, mechanisms remain unknown. Here we validated a mouse model with inducible reduction of liver estrogen receptor alpha (ERα) (LERKO) via adeno-associated virus (AAV) Cre. We phenotyped the liver health and mitochondrial function of LERKO mice (n = 10-12 per group) on a short-term high-fat diet (HFD), and then tested whether timing of LERKO induction at 2 timepoints (sexually immature: 4 weeks old [n = 11 per group] vs sexually mature: 8-10 weeks old [n = 8 per group]) would impact HFD-induced outcomes. We opted for an inducible LERKO model due to known estrogen-mediated developmental programming, and we reported both receptor and tissue specificity with our model. Control mice were ERαfl/fl receiving AAV with green fluorescent protein (GFP) only. Results show that there were no differences in body weight/composition or hepatic steatosis in LERKO mice with either short-term (4-week) or chronic (8-week) high-fat feeding. Similarly, LERKO genotype nor timing of LERKO induction (pre vs post sexual maturity) did not alter hepatic mitochondrial O2 and H2O2 flux, coupling, or OXPHOS protein. Transcriptomic analysis showed that hepatic gene expression in LERKO was significantly influenced by developmental stage. Together, these studies suggest that hepatic ERα is not required in female protection against HFD-induced hepatic steatosis nor does it mediate sexual dimorphism in liver mitochondria function.

DGH-GO: dissecting the genetic heterogeneity of complex diseases using gene ontology

BACKGROUND

Complex diseases such as neurodevelopmental disorders (NDDs) exhibit multiple etiologies. The multi-etiological nature of complex-diseases emerges from distinct but functionally similar group of genes. Different diseases sharing genes of such groups show related clinical outcomes that further restrict our understanding of disease mechanisms, thus, limiting the applications of personalized medicine approaches to complex genetic disorders.

RESULTS

Here, we present an interactive and user-friendly application, called DGH-GO. DGH-GO allows biologists to dissect the genetic heterogeneity of complex diseases by stratifying the putative disease-causing genes into clusters that may contribute to distinct disease outcome development. It can also be used to study the shared etiology of complex-diseases. DGH-GO creates a semantic similarity matrix for the input genes by using Gene Ontology (GO). The resultant matrix can be visualized in 2D plots using different dimension reduction methods (T-SNE, Principal component analysis, umap and Principal coordinate analysis). In the next step, clusters of functionally similar genes are identified from genes functional similarities assessed through GO. This is achieved by employing four different clustering methods (K-means, Hierarchical, Fuzzy and PAM). The user may change the clustering parameters and explore their effect on stratification immediately. DGH-GO was applied to genes disrupted by rare genetic variants in Autism Spectrum Disorder (ASD) patients. The analysis confirmed the multi-etiological nature of ASD by identifying four clusters of genes that were enriched for distinct biological mechanisms and clinical outcome. In the second case study, the analysis of genes shared by different NDDs showed that genes causing multiple disorders tend to aggregate in similar clusters, indicating a possible shared etiology.

CONCLUSION

DGH-GO is a user-friendly application that allows biologists to study the multi-etiological nature of complex diseases by dissecting their genetic heterogeneity. In summary, functional similarities, dimension reduction and clustering methods, coupled with interactive visualization and control over analysis allows biologists to explore and analyze their datasets without requiring expert knowledge on these methods. The source code of proposed application is available at https://github.com/Muh-Asif/DGH-GO.

The role of RIM in neurotransmitter release: promotion of synaptic vesicle docking, priming, and fusion

There are many special sites at the end of a synapse called active zones (AZs). Synaptic vesicles (SVs) fuse with presynaptic membranes at these sites, and this fusion is an important step in neurotransmitter release. The cytomatrix in the active zone (CAZ) is made up of proteins such as the regulating synaptic membrane exocytosis protein (RIM), RIM-binding proteins (RIM-BPs), ELKS/CAST, Bassoon/Piccolo, Liprin-α, and Munc13-1. RIM is a scaffold protein that interacts with CAZ proteins and presynaptic functional components to affect the docking, priming, and fusion of SVs. RIM is believed to play an important role in regulating the release of neurotransmitters (NTs). In addition, abnormal expression of RIM has been detected in many diseases, such as retinal diseases, Asperger's syndrome (AS), and degenerative scoliosis. Therefore, we believe that studying the molecular structure of RIM and its role in neurotransmitter release will help to clarify the molecular mechanism of neurotransmitter release and identify targets for the diagnosis and treatment of the aforementioned diseases.

T cell egress via lymphatic vessels is tuned by antigen encounter and limits tumor control

Antigen-specific CD8+ T cell accumulation in tumors is a prerequisite for effective immunotherapy, and yet the mechanisms of lymphocyte transit are not well defined. Here we show that tumor-associated lymphatic vessels control T cell exit from tumors via the chemokine CXCL12, and intratumoral antigen encounter tunes CXCR4 expression by effector CD8+ T cells. Only high-affinity antigen downregulates CXCR4 and upregulates the CXCL12 decoy receptor, ACKR3, thereby reducing CXCL12 sensitivity and promoting T cell retention. A diverse repertoire of functional tumor-specific CD8+ T cells, therefore, exit the tumor, which limits the pool of CD8+ T cells available to exert tumor control. CXCR4 inhibition or loss of lymphatic-specific CXCL12 boosts T cell retention and enhances tumor control. These data indicate that strategies to limit T cell egress might be an approach to boost the quantity and quality of intratumoral T cells and thereby response to immunotherapy.

Integrative analysis of multi-omics and imaging data with incorporation of biological information via structural Bayesian factor analysis

MOTIVATION

With the rapid development of modern technologies, massive data are available for the systematic study of Alzheimer's disease (AD). Though many existing AD studies mainly focus on single-modality omics data, multi-omics datasets can provide a more comprehensive understanding of AD. To bridge this gap, we proposed a novel structural Bayesian factor analysis framework (SBFA) to extract the information shared by multi-omics data through the aggregation of genotyping data, gene expression data, neuroimaging phenotypes and prior biological network knowledge. Our approach can extract common information shared by different modalities and encourage biologically related features to be selected, guiding future AD research in a biologically meaningful way.

METHOD

Our SBFA model decomposes the mean parameters of the data into a sparse factor loading matrix and a factor matrix, where the factor matrix represents the common information extracted from multi-omics and imaging data. Our framework is designed to incorporate prior biological network information. Our simulation study demonstrated that our proposed SBFA framework could achieve the best performance compared with the other state-of-the-art factor-analysis-based integrative analysis methods.

RESULTS

We apply our proposed SBFA model together with several state-of-the-art factor analysis models to extract the latent common information from genotyping, gene expression and brain imaging data simultaneously from the ADNI biobank database. The latent information is then used to predict the functional activities questionnaire score, an important measurement for diagnosis of AD quantifying subjects' abilities in daily life. Our SBFA model shows the best prediction performance compared with the other factor analysis models.

AVAILABILITY

Code are publicly available at https://github.com/JingxuanBao/SBFA.

CONTACT

qlong@upenn.edu.

Convergent coexpression of autism-associated genes suggests some novel risk genes may not be detectable in large-scale genetic studies

Autism spectrum disorder (ASD) is a heritable neurodevelopmental disorder characterized by deficits in social interactions and communication. Protein-altering variants in many genes have been shown to contribute to ASD; however, understanding the convergence across many genes remains a challenge. We demonstrate that coexpression patterns from 993 human postmortem brains are significantly correlated with the transcriptional consequences of CRISPR perturbations in human neurons. Across 71 ASD risk genes, there was significant tissue-specific convergence implicating synaptic pathways. Tissue-specific convergence was further demonstrated across schizophrenia and atrial fibrillation risk genes. The degree of ASD convergence was significantly correlated with ASD association from rare variation and differential expression in ASD brains. Positively convergent genes showed intolerance to functional mutations and had shorter coding lengths than known risk genes even after removing association with ASD. These results indicate that convergent coexpression can identify potentially novel genes that are unlikely to be discovered by sequencing studies.

Monocyte, neutrophil, and whole blood transcriptome dynamics following ischemic stroke

BACKGROUND

After ischemic stroke (IS), peripheral leukocytes infiltrate the damaged region and modulate the response to injury. Peripheral blood cells display distinctive gene expression signatures post-IS and these transcriptional programs reflect changes in immune responses to IS. Dissecting the temporal dynamics of gene expression after IS improves our understanding of immune and clotting responses at the molecular and cellular level that are involved in acute brain injury and may assist with time-targeted, cell-specific therapy.

METHODS

The transcriptomic profiles from peripheral monocytes, neutrophils, and whole blood from 38 ischemic stroke patients and 18 controls were analyzed with RNA-seq as a function of time and etiology after stroke. Differential expression analyses were performed at 0-24 h, 24-48 h, and >48 h following stroke.

RESULTS

Unique patterns of temporal gene expression and pathways were distinguished for monocytes, neutrophils, and whole blood with enrichment of interleukin signaling pathways for different time points and stroke etiologies. Compared to control subjects, gene expression was generally upregulated in neutrophils and generally downregulated in monocytes over all times for cardioembolic, large vessel, and small vessel strokes. Self-organizing maps identified gene clusters with similar trajectories of gene expression over time for different stroke causes and sample types. Weighted Gene Co-expression Network Analyses identified modules of co-expressed genes that significantly varied with time after stroke and included hub genes of immunoglobulin genes in whole blood.

CONCLUSIONS

Altogether, the identified genes and pathways are critical for understanding how the immune and clotting systems change over time after stroke. This study identifies potential time- and cell-specific biomarkers and treatment targets.

PyGenePlexus: a Python package for gene discovery using network-based machine learning

SUMMARY

PyGenePlexus is a Python package that enables a user to gain insight into any gene set of interest through a molecular interaction network informed supervised machine learning model. PyGenePlexus provides predictions of how associated every gene in the network is to the input gene set, offers interpretability by comparing the model trained on the input gene set to models trained on thousands of known gene sets, and returns the network connectivity of the top predicted genes.

AVAILABILITY AND IMPLEMENTATION

https://pypi.org/project/geneplexus/ and https://github.com/krishnanlab/PyGenePlexus.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

A predictive ensemble classifier for the gene expression diagnosis of ASD at ages 1 to 4 years

Autism Spectrum Disorder (ASD) diagnosis remains behavior-based and the median age of diagnosis is ~52 months, nearly 5 years after its first-trimester origin. Accurate and clinically-translatable early-age diagnostics do not exist due to ASD genetic and clinical heterogeneity. Here we collected clinical, diagnostic, and leukocyte RNA data from 240 ASD and typically developing (TD) toddlers (175 toddlers for training and 65 for test). To identify gene expression ASD diagnostic classifiers, we developed 42,840 models composed of 3570 gene expression feature selection sets and 12 classification methods. We found that 742 models had AUC-ROC ≥ 0.8 on both Training and Test sets. Weighted Bayesian model averaging of these 742 models yielded an ensemble classifier model with accurate performance in Training and Test gene expression datasets with ASD diagnostic classification AUC-ROC scores of 85-89% and AUC-PR scores of 84-92%. ASD toddlers with ensemble scores above and below the overall ASD ensemble mean of 0.723 (on a scale of 0 to 1) had similar diagnostic and psychometric scores, but those below this ASD ensemble mean had more prenatal risk events than TD toddlers. Ensemble model feature genes were involved in cell cycle, inflammation/immune response, transcriptional gene regulation, cytokine response, and PI3K-AKT, RAS and Wnt signaling pathways. We additionally collected targeted DNA sequencing smMIPs data on a subset of ASD risk genes from 217 of the 240 ASD and TD toddlers. This DNA sequencing found about the same percentage of SFARI Level 1 and 2 ASD risk gene mutations in TD (12 of 105) as in ASD (13 of 112) toddlers, and classification based only on the presence of mutation in these risk genes performed at a chance level of 49%. By contrast, the leukocyte ensemble gene expression classifier correctly diagnostically classified 88% of TD and ASD toddlers with ASD risk gene mutations. Our ensemble ASD gene expression classifier is diagnostically predictive and replicable across different toddler ages, races, and ethnicities; out-performs a risk gene mutation classifier; and has potential for clinical translation.

Accurately modeling biased random walks on weighted networks using node2vec

MOTIVATION

Accurately representing biological networks in a low-dimensional space, also known as network embedding, is a critical step in network-based machine learning and is carried out widely using node2vec, an unsupervised method based on biased random walks. However, while many networks, including functional gene interaction networks, are dense, weighted graphs, node2vec is fundamentally limited in its ability to use edge weights during the biased random walk generation process, thus under-using all the information in the network.

RESULTS

Here, we present node2vec+, a natural extension of node2vec that accounts for edge weights when calculating walk biases and reduces to node2vec in the cases of unweighted graphs or unbiased walks. Using two synthetic datasets, we empirically show that node2vec+ is more robust to additive noise than node2vec in weighted graphs. Then, using genome-scale functional gene networks to solve a wide range of gene function and disease prediction tasks, we demonstrate the superior performance of node2vec+ over node2vec in the case of weighted graphs. Notably, due to the limited amount of training data in the gene classification tasks, graph neural networks such as GCN and GraphSAGE are outperformed by both node2vec and node2vec+.

AVAILABILITY AND IMPLEMENTATION

The data and code are available on GitHub at https://github.com/krishnanlab/node2vecplus_benchmarks. All additional data underlying this article are available on Zenodo at https://doi.org/10.5281/zenodo.7007164.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Integrative genetic and single cell RNA sequencing analysis provides new clues to the amyotrophic lateral sclerosis neurodegeneration

Introduction

The gradual loss of motor neurons (MNs) in the brain and spinal cord is a hallmark of amyotrophic lateral sclerosis (ALS), but the mechanisms underlying neurodegeneration in ALS are still not fully understood.

Methods

Based on 75 ALS-pathogenicity/susceptibility genes and large-scale single-cell transcriptomes of human/mouse brain/spinal cord/muscle tissues, we performed an expression enrichment analysis to identify cells involved in ALS pathogenesis. Subsequently, we created a strictness measure to estimate the dosage requirement of ALS-related genes in linked cell types.

Results

Remarkably, expression enrichment analysis showed that α- and γ-MNs, respectively, are associated with ALS-susceptibility genes and ALS-pathogenicity genes, revealing differences in biological processes between sporadic and familial ALS. In MNs, ALS-susceptibility genes exhibited high strictness, as well as the ALS-pathogenicity genes with known loss of function mechanism, indicating the main characteristic of ALS-susceptibility genes is dosage-sensitive and the loss of function mechanism of these genes may involve in sporadic ALS. In contrast, ALS-pathogenicity genes with gain of function mechanism exhibited low strictness. The significant difference of strictness between loss of function genes and gain of function genes provided a priori understanding for the pathogenesis of novel genes without an animal model. Besides MNs, we observed no statistical evidence for an association between muscle cells and ALS-related genes. This result may provide insight into the etiology that ALS is not within the domain of neuromuscular diseases. Moreover, we showed several cell types linked to other neurological diseases [i.e., spinocerebellar ataxia (SA), hereditary motor neuropathies (HMN)] and neuromuscular diseases [i.e. hereditary spastic paraplegia (SPG), spinal muscular atrophy (SMA)], including an association between Purkinje cells in brain and SA, an association between α-MNs in spinal cord and SA, an association between smooth muscle cells and SA, an association between oligodendrocyte and HMN, a suggestive association between γ-MNs and HMN, a suggestive association between mature skeletal muscle and HMN, an association between oligodendrocyte in brain and SPG, and no statistical evidence for an association between cell type and SMA.

Discussion

These cellular similarities and differences deepened our understanding of the heterogeneous cellular basis of ALS, SA, HMN, SPG, and SMA.

HEC-ASD: a hybrid ensemble-based classification model for predicting autism spectrum disorder disease genes

PURPOSE

Autism spectrum disorder (ASD) is the most prevalent disease today. The causes of its infection may be attributed to genetic causes by 80% and environmental causes by 20%. In spite of this, the majority of the current research is concerned with environmental causes, and the least proportion with the genetic causes of the disease. Autism is a complex disease, which makes it difficult to identify the genes that cause the disease.

METHODS

Hybrid ensemble-based classification (HEC-ASD) model for predicting ASD genes using gradient boosting machines is proposed. The proposed model utilizes gene ontology (GO) to construct a gene functional similarity matrix using hybrid gene similarity (HGS) method. HGS measures the semantic similarity between genes effectively. It combines the graph-based method, such as Wang method with the number of directed children's nodes of gene term from GO. Moreover, an ensemble gradient boosting classifier is adapted to enhance the prediction of genes forming a robust classification model.

RESULTS

The proposed model is evaluated using the Simons Foundation Autism Research Initiative (SFARI) gene database. The experimental results are promising as they improve the classification performance for predicting ASD genes. The results are compared with other approaches that used gene regulatory network (GRN), protein to protein interaction network (PPI), or GO. The HEC-ASD model reaches the highest prediction accuracy of 0.88% using ensemble learning classifiers.

CONCLUSION

The proposed model demonstrates that ensemble learning technique using gradient boosting is effective in predicting autism spectrum disorder genes. Moreover, the HEC-ASD model utilized GO rather than using PPI network and GRN.

The Role of Zinc and NMDA Receptors in Autism Spectrum Disorders

NMDA-type glutamate receptors are critical for synaptic plasticity in the central nervous system. Their unique properties and age-dependent arrangement of subunit types underpin their role as a coincidence detector of pre- and postsynaptic activity during brain development and maturation. NMDAR function is highly modulated by zinc, which is co-released with glutamate and concentrates in postsynaptic spines. Both NMDARs and zinc have been strongly linked to autism spectrum disorders (ASDs), suggesting that NMDARs are an important player in the beneficial effects observed with zinc in both animal models and children with ASDs. Significant evidence is emerging that these beneficial effects occur via zinc-dependent regulation of SHANK proteins, which form the backbone of the postsynaptic density. For example, dietary zinc supplementation enhances SHANK2 or SHANK3 synaptic recruitment and rescues NMDAR deficits and hypofunction in Shank3^ex13-16-/- and Tbr1^+/- ASD mice. Across multiple studies, synaptic changes occur in parallel with a reversal of ASD-associated behaviours, highlighting the zinc-dependent regulation of NMDARs and glutamatergic synapses as therapeutic targets for severe forms of ASDs, either pre- or postnatally. The data from rodent models set a strong foundation for future translational studies in human cells and people affected by ASDs.

The impact of paternal age on new mutations and disease in the next generation

Advanced paternal age is associated with an increased risk of fathering children with genetic disorders and other adverse reproductive consequences. However, the mechanisms underlying this phenomenon remain largely unexplored. In this review, we focus on the impact of paternal age on de novo mutations that are an important contributor to genetic disease and can be studied both indirectly through large-scale sequencing studies and directly in the tissue in which they predominantly arise-the aging testis. We discuss the recent data that have helped establish the origins and frequency of de novo mutations, and highlight experimental evidence about the close link between new mutations, parental age, and genetic disease. We then focus on a small group of rare genetic conditions, the so-called "paternal age effect" disorders that show a strong association between paternal age and disease prevalence, and discuss the underlying mechanism ("selfish selection") and implications of this process in more detail. More broadly, understanding the causes and consequences of paternal age on genetic risk has important implications both for individual couples and for public health advice given that the average age of fatherhood is steadily increasing in many developed nations.

Network analysis of genome-wide association studies for drug target prioritisation

Over the past decades, genome-wide association studies (GWAS) have led to a dramatic expansion of genetic variants implicated with human traits and diseases. These advances are expected to result in new drug targets but the identification of causal genes and the cell biology underlying human diseases from GWAS remains challenging. Here, we review protein interaction network-based methods to analyse GWAS data. These approaches can rank candidate drug targets at GWAS-associated loci or among interactors of disease genes without direct genetic support. These methods identify the cell biology affected in common across diseases, offering opportunities for drug repurposing, as well as be combined with expression data to identify focal tissues and cell types. Going forward, we expect that these methods will further improve from advances in the characterisation of context specific interaction networks and the joint analysis of rare and common genetic signals.