Network Diffusion-Based Prioritization of Autism Risk Genes Identifies Significantly Connected Gene Modules

CausNet: generational orderings based search for optimal Bayesian networks via dynamic programming with parent set constraints

BACKGROUND

Finding a globally optimal Bayesian Network using exhaustive search is a problem with super-exponential complexity, which severely restricts the number of variables that can feasibly be included. We implement a dynamic programming based algorithm with built-in dimensionality reduction and parent set identification. This reduces the search space substantially and can be applied to large-dimensional data. We use what we call 'generational orderings' based search for optimal networks, which is a novel way to efficiently search the space of possible networks given the possible parent sets. The algorithm supports both continuous and categorical data, as well as continuous, binary and survival outcomes.

RESULTS

We demonstrate the efficacy of our algorithm on both synthetic and real data. In simulations, our algorithm performs better than three state-of-art algorithms that are currently used extensively. We then apply it to an Ovarian Cancer gene expression dataset with 513 genes and a survival outcome. Our algorithm is able to find an optimal network describing the disease pathway consisting of 6 genes leading to the outcome node in just 3.4 min on a personal computer with a 2.3 GHz Intel Core i9 processor with 16 GB RAM.

CONCLUSIONS

Our generational orderings based search for optimal networks is both an efficient and highly scalable approach for finding optimal Bayesian Networks and can be applied to 1000 s of variables. Using specifiable parameters-correlation, FDR cutoffs, and in-degree-one can increase or decrease the number of nodes and density of the networks. Availability of two scoring option-BIC and Bge-and implementation for survival outcomes and mixed data types makes our algorithm very suitable for many types of high dimensional data in a variety of fields.

Integrative Functional Genomic Analysis in Multiplex Autism Families from Kazakhstan

The study of extended pedigrees containing autism spectrum disorder- (ASD-) related broader autism phenotypes (BAP) offers a promising approach to the search for ASD candidate variants. Here, a total of 650,000 genetic markers were tested in four Kazakhstani multiplex families with ASD and BAP to obtain data on de novo mutations (DNMs), common, and rare inherited variants that may contribute to the genetic risk for developing autistic traits. The variants were analyzed in the context of gene networks and pathways. Several previously well-described enriched pathways were identified, including ion channel activity, regulation of synaptic function, and membrane depolarization. Perhaps these pathways are crucial not only for the development of ASD but also for ВАР. The results also point to several additional biological pathways (circadian entrainment, NCAM and BTN family interactions, and interaction between L1 and Ankyrins) and hub genes (CFTR, NOD2, PPP2R2B, and TTR). The obtained results suggest that further exploration of PPI networks combining ASD and BAP risk genes can be used to identify novel or overlooked ASD molecular mechanisms.

Prioritizing de novo autism risk variants with calibrated gene- and variant-scoring models

Whole-exome and whole-genome sequencing studies in autism spectrum disorder (ASD) have identified hundreds of thousands of exonic variants. Only a handful of them, primarily loss-of-function variants, have been shown to increase the risk for ASD, while the contributory roles of other variants, including most missense variants, remain unknown. New approaches that combine tissue-specific molecular profiles with patients' genetic data can thus play an important role in elucidating the functional impact of exonic variation and improve understanding of ASD pathogenesis. Here, we integrate spatio-temporal gene co-expression networks from the developing human brain and protein-protein interaction networks to first reach accurate prioritization of ASD risk genes based on their connectivity patterns with previously known high-confidence ASD risk genes. We subsequently integrate these gene scores with variant pathogenicity predictions to further prioritize individual exonic variants based on the positive-unlabeled learning framework with gene- and variant-score calibration. We demonstrate that this approach discriminates among variants between cases and controls at the high end of the prediction range. Finally, we experimentally validate our top-scoring de novo mutation NP_001243143.1:p.Phe309Ser in the sodium/potassium-transporting ATPase ATP1A3 to disrupt protein binding with different partners.

Strict network analysis of evolutionary conserved and brain-expressed genes reveals new putative candidates implicated in Intellectual Disability and in Global Development Delay

OBJECTIVES

Intellectual Disability (ID) and Global Development Delay (GDD) are frequent reasons for referral to genetic services and although they present overlapping phenotypes concerning cognitive, motor, language, or social skills, they are not exactly synonymous. Aiming to better understand independent or shared mechanisms related to these conditions and to identify new candidate genes, we performed a highly stringent protein-protein interaction network based on genes previously related to ID/GDD in the Human Phenotype Ontology portal.

METHODS

ID/GDD genes were searched for reliable interactions through STRING and clustering analysis was applied to detect biological complexes through the MCL algorithm. Six coding hub genes (TP53, CDC42, RAC1, GNB1, APP, and EP300) were recognised by the Cytoscape NetworkAnalyzer plugin, interacting with 1625 proteins not yet associated with ID or GDD. Genes encoding these proteins were explored by gene ontology, associated diseases, evolutionary conservation, and brain expression.

RESULTS

One hundred and seventy-two new putative genes playing a role in enriched processes/pathways previously related to ID and GDD were revealed, some of which were already postulated to be linked to ID/GDD in additional databases.

CONCLUSIONS

Our findings expanded the aetiological genetic landscape of ID/GDD and showed evidence that both conditions are closely related at the molecular and functional levels.

Network diffusion with centrality measures to identify disease-related genes

Disease-related gene prioritization is one of the most well-established pharmaceutical techniques used to identify genes that are important to a biological process relevant to a disease. In identifying these essential genes, the network diffusion (ND) approach is a widely used technique applied in gene prioritization. However, there is still a large number of candidate genes that need to be evaluated experimentally. Therefore, it would be of great value to develop a new strategy to improve the precision of the prioritization. Given the efficiency and simplicity of centrality measures in capturing a gene that might be important to the network structure, herein, we propose a technique that extends the scope of ND through a centrality measure to identify new disease-related genes. Five common centrality measures with different aspects were examined for integration in the traditional ND model. A total of 40 diseases were used to test our developed approach and to find new genes that might be related to a disease. Results indicated that the best measure to combine with the diffusion is closeness centrality. The novel candidate genes identified by the model for all 40 diseases were provided along with supporting evidence. In conclusion, the integration of network centrality in ND is a simple but effective technique to discover more precise disease-related genes, which is extremely useful for biomedical science.

Clustering Analysis Supports the Detection of Biological Processes Related to Autism Spectrum Disorder

Genome sequencing has identified a large number of putative autism spectrum disorder (ASD) risk genes, revealing possible disrupted biological pathways; however, the genetic and environmental underpinnings of ASD remain mostly unanswered. The presented methodology aimed to identify genetically related clusters of ASD individuals. By using the VariCarta dataset, which contains data retrieved from 13,069 people with ASD, we compared patients pairwise to build “patient similarity matrices”. Hierarchical-agglomerative-clustering and heatmapping were performed, followed by enrichment analysis (EA). We analyzed whole-genome sequencing retrieved from 2062 individuals, and isolated 11,609 genetic variants shared by at least two people. The analysis yielded three clusters, composed, respectively, by 574 (27.8%), 507 (24.6%), and 650 (31.5%) individuals. Overall, 4187 variants (36.1%) were common to the three clusters. The EA revealed that the biological processes related to the shared genetic variants were mainly involved in neuron projection guidance and morphogenesis, cell junctions, synapse assembly, and in observational, imitative, and vocal learning. The study highlighted genetic networks, which were more frequent in a sample of people with ASD, compared to the overall population. We suggest that itemizing not only single variants, but also gene networks, might support ASD etiopathology research. Future work on larger databases will have to ascertain the reproducibility of this methodology.

3-Aminobenzamide alleviates elevated DNA damage and DNA methylation in a BTBR T+Itpr3tf/J mouse model of autism by enhancing repair gene expression

Little is known about genetic and epigenetic alterations in autism spectrum disorder. Moreover, the efficiency of DNA repair in autism must be improved to correct these alterations. We examined whether 3-aminobenzamide (3-AB) could reverse these alterations. We conducted experiments to clarify the molecular mechanism underlying these ameliorations. An assessment of genetic and epigenetic alterations by a modified comet assay showed elevated levels of oxidative DNA strand breaks and DNA hypermethylation in BTBR T⁺Itpr3^tf/J (BTBR) mice used as a model of autism. Oxidative DNA strand breaks and DNA methylation were further quantified fluorometrically, and the results showed similar changes. Conversely, 3-AB treated BTBR mice showed a significant reduction in these alterations compared with untreated mice. The expressions of 43 genes involved in DNA repair were altered in BTBR mice. RT² Profiler PCR Array revealed significantly altered expression of seven genes, which was confirmed by RT-PCR analyses. 3-AB treatment relieved these disturbances and significantly improved Ogg1 and Rad1 up-regulation. Moreover, autism-like behaviors were also mitigated in BTBR animals by 3-AB treatment without alterations in locomotor activities. The simultaneous effects of reduced DNA damage and DNA methylation levels as well as the regulation of repair gene expression indicate the potential of 3-AB as a therapeutic agent to decrease the levels of DNA damage and DNA methylation in autistic patients. The current data may help in the development of therapies that ultimately provide a better quality of life for individuals suffering from autism.

Evaluation of DNA repair efficiency in autistic children by molecular cytogenetic analysis and transcriptome profiling

Data regarding DNA repair perturbations in autism, which might increase the risk of malignancy, are scarce. To evaluate whether DNA repair may be disrupted in autistic children, we assessed the incidence of endogenous basal DNA strand breaks as well as the efficiency of repairing DNA damage caused by γ-ray in lymphocytes isolated from autistic and healthy children. The incidence of DNA damage and the kinetics of DNA repair were determined by comet assay, while the incidence of residual DNA damage was evaluated by structural chromosomal aberration analysis. Transcriptome profiling of 84 genes associated with DNA damage and repair-signaling pathways was performed by RT² Profiler PCR Array. The array data were confirmed by RT-PCR and western blot studies. Our data indicate that the incidence of basal oxidative DNA strand breaks in autistic children was greater than that in nonautistic controls. Lymphocytes from autistic children displayed higher susceptibility to damage by γ-irradiation and slower repair rate than those from nonautistic children. Although the total unstable chromosomal aberrations were unaffected, lymphocytes from autistic children were more susceptible to chromosomal damage caused by γ-ray than those from nonautistic children. Transcriptomic analysis revealed that several genes associated with repair were downregulated in lymphocytes from autistic individuals and in those exposed to γ-irradiation. This may explain the increased oxidative DNA damage and reduced repair rate in lymphocytes from autistic individuals. These features may be related to the possible correlation between autism and the elevated risk of cancer and may explain the role of the disruption of the DNA repair process in the pathogenesis of autism.

A Survey of Gene Prioritization Tools for Mendelian and Complex Human Diseases

Modern high-throughput experiments provide us with numerous potential associations between genes and diseases. Experimental validation of all the discovered associations, let alone all the possible interactions between them, is time-consuming and expensive. To facilitate the discovery of causative genes, various approaches for prioritization of genes according to their relevance for a given disease have been developed. In this article, we explain the gene prioritization problem and provide an overview of computational tools for gene prioritization. Among about a hundred of published gene prioritization tools, we select and briefly describe 14 most up-to-date and user-friendly. Also, we discuss the advantages and disadvantages of existing tools, challenges of their validation, and the directions for future research.

Gene relevance based on multiple evidences in complex networks

MOTIVATION

Multi-omics approaches offer the opportunity to reconstruct a more complete picture of the molecular events associated with human diseases, but pose challenges in data analysis. Network-based methods for the analysis of multi-omics leverage the complex web of macromolecular interactions occurring within cells to extract significant patterns of molecular alterations. Existing network-based approaches typically address specific combinations of omics and are limited in terms of the number of layers that can be jointly analysed. In this study, we investigate the application of network diffusion to quantify gene relevance on the basis of multiple evidences (layers).

RESULTS

We introduce a gene score (mND) that quantifies the relevance of a gene in a biological process taking into account the network proximity of the gene and its first neighbours to other altered genes. We show that mND has a better performance over existing methods in finding altered genes in network proximity in one or more layers. We also report good performances in recovering known cancer genes. The pipeline described in this article is broadly applicable, because it can handle different types of inputs: in addition to multi-omics datasets, datasets that are stratified in many classes (e.g., cell clusters emerging from single cell analyses) or a combination of the two scenarios.

AVAILABILITY AND IMPLEMENTATION

The R package 'mND' is available at URL: https://www.itb.cnr.it/mnd.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Assessment of DNA repair efficiency in the inbred BTBR T+tf/J autism spectrum disorder mouse model exposed to gamma rays and treated with JNJ7777120

Information regarding DNA repair in autism is limited to a few studies, which have reported inconsistent results. Therefore, we designed a study to determine whether DNA repair efficiency is altered in autism and to investigate whether the H₄ ligand JNJ7777120 can enhance DNA repair efficiency in BTBR T⁺tf/J (BTBR) mice; we also attempted to elucidate the mechanism(s) underlying this amelioration. Evaluation of DNA damage using the comet assay on bone marrow cells showed increased levels of DNA damage in BTBR mice compared with age-matched control C57BL/6J mice. Conversely, BTBR animals pretreated with 20 mg/kg JNJ7777120 for five days exhibited significant decreases in DNA damage compared with that of control BTBR mice. Our results also indicated higher sensitivity of BTBR mice exposed to gamma rays to DNA damage generation. A marked difference was observed between BTBR and C57BL/6J mice at different sampling times after irradiation, with BTBR mice showing a higher percentage of DNA damage and slower repair rate than that of C57BL/6J mice. JNJ7777120 led to enhanced repair of the DNA damage induced by radiation when administered to BTBR mice five days prior to radiation. Additionally, oxidative stress in BTBR mice was significantly elevated with a reduced GSH/GSSG ratio; significant amelioration was subsequently observed in JNJ7777120-pretreated BTBR mice. Furthermore, repetitive behaviors were also attenuated in BTBR mice by JNJ7777120 treatment without altering locomotor activity. Our results suggest that JNJ7777120 can be developed for use as a therapeutic agent to enhance DNA repair efficiency in autism spectrum disorder.

Network-Based Integrative Analysis of Genomics, Epigenomics and Transcriptomics in Autism Spectrum Disorders

Current studies suggest that autism spectrum disorders (ASDs) may be caused by many genetic factors. In fact, collectively considering multiple studies aimed at characterizing the basic pathophysiology of ASDs, a large number of genes has been proposed. Addressing the problem of molecular data interpretation using gene networks helps to explain genetic heterogeneity in terms of shared pathways. Besides, the integrative analysis of multiple omics has emerged as an approach to provide a more comprehensive view of a disease. In this work, we carry out a network-based meta-analysis of the genes reported as associated with ASDs by studies that involved genomics, epigenomics, and transcriptomics. Collectively, our analysis provides a prioritization of the large number of genes proposed to be associated with ASDs, based on genes' relevance within the intracellular circuits, the strength of the supporting evidence of association with ASDs, and the number of different molecular alterations affecting genes. We discuss the presence of the prioritized genes in the SFARI (Simons Foundation Autism Research Initiative) database and in gene networks associated with ASDs by other investigations. Lastly, we provide the full results of our analyses to encourage further studies on common targets amenable to therapy.

Latrophilins: A Neuro-Centric View of an Evolutionary Conserved Adhesion G Protein-Coupled Receptor Subfamily

The adhesion G protein-coupled receptors latrophilins have been in the limelight for more than 20 years since their discovery as calcium-independent receptors for α-latrotoxin, a spider venom toxin with potent activity directed at neurotransmitter release from a variety of synapse types. Latrophilins are highly expressed in the nervous system. Although a substantial amount of studies has been conducted to describe the role of latrophilins in the toxin-mediated action, the recent identification of endogenous ligands for these receptors helped confirm their function as mediators of adhesion events. Here we hypothesize a role for latrophilins in inter-neuronal contacts and the formation of neuronal networks and we review the most recent information on their role in neurons. We explore molecular, cellular and behavioral aspects related to latrophilin adhesion function in mice, zebrafish, Drosophila melanogaster and Caenorhabditis elegans, in physiological and pathophysiological conditions, including autism spectrum, bipolar, attention deficit and hyperactivity and substance use disorders.

Integrating Biological Networks for Drug Target Prediction and Prioritization

Computational prediction of the clinical success or failure of a potential drug target for therapeutic use is a challenging problem. Novel network propagation algorithms that integrate heterogeneous biological networks are proving useful for drug target identification and prioritization. These approaches typically utilize a network describing relationships between targets, a method to disseminate the relevant information through the network, and a method to elucidate new associations between targets and diseases. Here, we utilize one such network propagation-based approach, DTINet, which starts with diffusion component analysis of networks of both potential drug targets and diseases. Then an inductive matrix completion algorithm is applied to identify novel disease targets based on their network topological similarities with known disease targets with successfully launched drugs. DTINet performed well as assessed with area under the precision-recall curve (AUPR = 0.88 ± 0.007) and area under the receiver operating characteristic curve (AUROC = 0.86 ± 0.008). These metrics improved when we combined data from multiple networks in the target space but reduced significantly when we used a more conservative method to define negative controls (AUPR = 0.56 ± 0.007, AUROC = 0.57 ± 0.007). We are optimistic that integration of more relevant and cleaner datasets and networks, careful calibration of model parameters, as well as algorithmic improvements will improve prediction accuracy. However, we also recognize that predicting drug targets that are likely to be successful is an extremely challenging problem due to its complex nature and sparsity of known disease targets.

Brain Connectivity and Neuroimaging of Social Networks in Autism

Impairments in social communication (SC) predominate among the core diagnostic features of autism spectrum disorders (ASDs). Neuroimaging has revealed numerous findings of atypical activity and connectivity of 'social brain' networks, yet no consensus view on crucial developmental causes of SC deficits has emerged. Aside from methodological challenges, the deeper problem concerns the clinical label of ASD. While genetic studies have not comprehensively explained the causes of nonsyndromic ASDs, they highlight that the clinical label encompasses many etiologically different disorders. The question of how potential causes and etiologies converge onto a comparatively narrow set of SC deficits remains. Only neuroimaging designs searching for subtypes within ASD cohorts (rather than conventional group level designs) can provide translationally informative answers.