Next Article in Journal
Chemosensory Event-Related Potentials and Power Spectrum Could Be a Possible Biomarker in 3M Syndrome Infants?
Next Article in Special Issue
Movidea: A Software Package for Automatic Video Analysis of Movements in Infants at Risk for Neurodevelopmental Disorders
Previous Article in Journal
EEG Theta Power Activity Reflects Workload among Army Combat Drivers: An Experimental Study
Previous Article in Special Issue
A Two-Stage Screening Approach with I-TC and Q-CHAT to Identify Toddlers at Risk for Autism Spectrum Disorder within the Italian Public Health System
 
 
Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
Journals
Brain Sciences
Volume 10
Issue 4
10.3390/brainsci10040200
brainsci-logo
Submit to this Journal Review for this Journal Propose a Special Issue
► Article Menu

Article Menu

share Share announcement Help format_quote Cite question_answer Discuss in SciProfiles thumb_up
...
Endorse textsms
...
Comment
first_page
settings
Order Article Reprints
Font Type:
Arial Georgia Verdana
Font Size:
Aa Aa Aa
Line Spacing:
Column Width:
Background:
Article

Transcriptomic Analysis Reveals Abnormal Expression of Prion Disease Gene Pathway in Brains from Patients with Autism Spectrum Disorders

by
Salvo Danilo Lombardo
1,
Giuseppe Battaglia
2,3,
Maria Cristina Petralia
4,
Katia Mangano
1,
Maria Sofia Basile
1,
Valeria Bruno
2,3,
Paolo Fagone
1,*,
Rita Bella
5,
Ferdinando Nicoletti
1 and
Eugenio Cavalli
1
1
Department of Biomedical and Biotechnological Sciences, University of Catania, 95123 Catania, Italy
2
Department of Physiology and Pharmacology, University Sapienza, Piazzale A. Moro, 5, 00185 Roma, Italy
3
IRCCS Neuromed, Località Camerelle, 86077 Pozzilli, Italy
4
Department of Educational Sciences, University of Catania, 95124 Catania, Italy
5
Department of Medical Sciences, Surgery and Advanced Technologies, University of Catania, 95123 Catania, Italy
*
Author to whom correspondence should be addressed.
Brain Sci. 2020, 10(4), 200; https://doi.org/10.3390/brainsci10040200
Submission received: 26 February 2020 / Revised: 25 March 2020 / Accepted: 26 March 2020 / Published: 29 March 2020
(This article belongs to the Special Issue Advances in Autism Research)
Browse Figures
Versions Notes

Abstract

:
The role of infections in the pathogenesis of autism spectrum disorder (ASD) is still controversial. In this study, we aimed to evaluate markers of infections and immune activation in ASD by performing a meta-analysis of publicly available whole-genome transcriptomic datasets of brain samples from autistic patients and otherwise normal people. Among the differentially expressed genes, no significant enrichment was observed for infectious diseases previously associated with ASD, including herpes simplex virus-1 (HSV-1), cytomegalovirus and Epstein–Barr virus in brain samples, nor was it found in peripheral blood from ASD patients. Interestingly, a significant number of genes belonging to the “prion diseases” pathway were found to be modulated in our ASD brain meta-analysis. Overall, our data do not support an association between infection and ASD. However, the data do provide support for the involvement of pathways related to other neurodegenerative diseases and give input to uncover novel pathogenetic mechanisms underlying ASD.

1. Introduction

Autism spectrum disorder (ASD) is a heterogeneous group of neurodevelopmental disorders defined by significantly abnormal social interaction, impaired communication and language abilities, and a narrow pattern of interests [1]. It is estimated that the prevalence of ASD is 1%–2% in the general population with an average male-to-female ratio of 5:1 [2]. However, only about 10% of patients with a diagnosis of ASD have a defined etiology (so-called syndromic autism, secondary to Fragile X syndrome, neurofibromatosis and exposure to thalidomide) [3], while 90% of ASD cases are considered idiopathic.
Many authors have hypothesized a connection between genetic and epigenetic factors in ASD etiopathogenesis. In particular, infections have been suggested as a potential trigger of the disease [4,5,6]. In line with this, altered cellular immunity and an altered cytotoxic function of natural killer (NK) cells have been reported in ASD patients [7,8,9]. It has also been shown that fungal mycotoxins, such as deoxynivalenol in urine and Ochratoxin A in serum, are increased in autistic children [10,11]. Finally, expression of immune response genes has been described in cortical tissues from older ASD subjects [12,13].
In the present study, we investigated the expression levels of transcriptional markers of infections and immune activation in brain and blood samples from autistic patients by performing a meta-analysis of publicly available whole-genome expression datasets. The analysis of the data suggests common transcriptional features between ASD and prion-related diseases but does not support the role of infectious disease in the etiopathogenesis of ASD.

2. Materials and Methods

2.1. Data Collection and Metanalysis

The NCBI (National Center for Biotechnology Information) Gene Expression Omnibus (GEO) database (http://www.ncbi.nlm.nih.gov/geo/) was used to identify microarray datasets comparing the transcriptomic profiles of healthy donors and ASD patients. The GEO database was manually searched using the terms “autism” and “autistic disorder”. The collected datasets were further selected if they met the following inclusion criteria: (a) whole-genome transcriptomic profiling; (b) brain or blood samples; (c) consisted of one cohort of ASD patients and another cohort of healthy people; and (d) species of origin was “Homo sapiens”. Finally, five datasets were included in the meta-analysis of brain samples: GSE28521, GSE38322, GSE62098, GSE64018 and GSE102741, while three datasets were used for the meta-analysis of blood samples: GSE6575, GSE42133 and GSE18123. When a dataset included more than one tissue type, data from each tissue type were processed as a separate dataset. The datasets were uploaded to NetworkAnalyst 3.0 software (Ste. Anne de Bellevue, Quebec, Canada). Data were auto-scaled, and an integrity check was performed prior to the meta-analysis stage. Batch effects were corrected using the “ComBat” function. A random effects model of effect size (ES) measure was used to integrate gene expression patterns from the three datasets. The random effects model presumes that different studies present substantial diversity and evaluates between-study variance as well as within-study sampling error. Genes with a False Discovery Rate (FDR) < 0.05 were identified as differentially expressed genes (DEGs) and selected for further analysis. The characteristics of the samples in the datasets used are described in Table 1.

2.2. Pathway Selection and Gene Intersection

Pathway enrichment analysis was performed using the Kyoto Encyclopedia of Genes and Genomes (KEGG) database (https://www.genome.jp/kegg/) implemented in the Enrichr (http://amp.pharm.mssm.edu/Enrichr) web-based utility [24]. Higher-level biological functions are represented by networks of molecular interactions, reactions and relations that are integrated in the pathways from the KEGG database. KEGG integrates the current knowledge on molecular interaction networks and uses a knowledge-based approach for network prediction that aims to predict, given a complete set of genes in the genome, the protein interaction networks that are responsible for various cellular processes [25]. Enrichr computes the p value using the Fisher exact test. The adjusted p value is calculated using the Benjamini–Hochberg method for correction for multiple hypotheses testing. The z-score is computed using a modification to the Fisher exact test and assesses the deviation from the expected rank. Finally, the combined score is calculated using the p value and the z-score (Combined Score = ln(p value) × z-score).

2.3. Machine Learning Prediction and Network Construction

The webtool “ASD Genome-wide predictions of autism-associated genes” was used to evaluate the probability value of association between the selected gene and ASD. This webtool is based on a machine learning approach that, using a Bayesian method, allows the user to predict the role of candidate genes [26]. Briefly, Krishnan et al. developed an evidence-weighted, network-based machine-learning method that uses this brain-specific network to systematically discover new candidate ASD risk genes across the genome. The brain-specific network was constructed using a Bayesian method that extracts and integrates brain-specific functional signals from a gene-interaction network model containing predicted functional relationships for all pairs within 25,825 genes in the human genome. In order to produce a comprehensive, robust, genome-wide ranked list of autism candidate genes, Krishnan et al. first curated 594 genes linked with autism from publicly available databases and based on the strength of evidence of association with ASD. Next, an evidence-weighted support vector machine classifier, using the connectivity of genes to all the genes in the human brain-specific network, was employed to identify novel ASD candidates, defined as those genes whose interaction features in the network most closely resemble those of known ASD-related genes [26].

2.4. Statistical Analysis

For the meta-analysis, a random-effect model of effect size measure was used to integrate gene expression patterns from the selected datasets. Genes with an adjusted p value (FDR, q-value) < 0.05 were identified as DEGs and selected for further analysis. Pathway enrichment analysis was performed using the online server Enrichr (http://amp.pharm.mssm.edu/Enrichr) [24]. For all the analyses, an adjusted p value ≤ 0.05 was considered as the statistical significance threshold.

3. Results

3.1. Identification of an ASD Brain Transcriptomic Profile

Five GEO whole-genome transcriptomic datasets were identified (see Table 1) and used in the following analysis. These datasets included 84 brain samples from ASD patients (n = 55 unique patients) and 109 brain samples from otherwise normal people (n = 81 unique subjects). The meta-analysis identified 516 DEGs: 218 upregulated and 298 downregulated. The most enriched pathways were represented by “Synaptic vesicle cycle”, “Huntington’s disease” and “Sphingolipid signaling pathway” (Table 2).
Figure 1 shows the results from the enrichment analysis for infectious-related pathways enlisted in the KEGG database. No significant enrichment was observed among the DEGs with the exception of the “prion diseases” pathway (q = 0.038) (Figure 1 and Figure 2; Supplementary File 1). In particular, in the “prion diseases” pathway, our analysis identified significantly higher levels of Complement Component 1, q Subcomponent, B Chain (C1QB), Heat Shock Protein Family A Member 5 (HSPA5), Proto-Oncogene Tyrosine-Protein kinase Fyn (FYN), Laminin Subunit Gamma 1 (LAMC1) and ETS Like-1 Protein (ELK1) and significantly lower levels of Mitogen-Activated Protein Kinase 1 (MAP2K1) (Figure 2).
We next wanted to evaluate the enrichment of immune-related processes among the ASD brain DEGs. As shown in Figure 3, only the “Sphingolipid signaling pathway” was significantly enriched, encompassing four downregulated DEGs (MAP2K1, Protein Kinase C Beta (PRKCB), Sphingosine Kinase 2 (SPHK2), Ras-Related C3 Botulinum Toxin Substrate 3 (RAC3)) and six upregulated DEGs (G Protein Subunit Alpha I3 (GNAI3), Sphingosine-1-phosphate receptor 1 (S1PR1), FYN, Rapidly Accelerated Fibrosarcoma 1 (RAF1), TNF Receptor Superfamily Member 1A (TNFRSF1A), G Protein Subunit Alpha I2 (GNAI2)).

3.2. Machine Learning Prediction

The brain autism DEGs belonging to the “prion diseases” pathway from the Kyoto Encyclopedia of Genes and Genomes (KEGG) were investigated for their potential role in ASD using a network machine learning approach implemented in the “ASD Genome-wide predictions of autism-associated genes” web-tool (http://asd.princeton.edu/). The network constructed using the brain ASD DEGs belonging to the “prion diseases” pathway is presented as Figure 4. Among the input genes, the only one significantly associated with ASD is FYN, with an estimated probability of 0.665 and a q-value = 0.0256. Table 3 shows the genes mostly interacting with the input genes, ordered by the edge score. The prioritization and prediction of ranking is based on the network-based approach developed by Krishnan et al. [26]. Among the top-ranking ASD genes associated with the DEGs belonging to the “prion diseases” pathway, Mesencephalic Astrocyte Derived Neurotrophic Factor (MANF), Heat Shock Protein 90 Beta Family Member 1 (HSP90B1) and Mitogen-Activated Protein Kinase 1 (MAPK1) showed edge scores of 0.791, 0.79 and 0.789 with HSPA5, HSPA5 and MAP2K1, respectively (Table 3). The top-ranking ASD gene interacting with the DEGs belonging to the “prion diseases” pathway is Ataxin 1 (ATXN1), with a rank position of 5, a probability value of association with ASD of 0.808 and a q-value = 0.0186. ATXN1 was the most connected gene to FYN (edge score 0.705) (Table 4). None of the predicted top 10 genes are present in the Genome-Wide Association Study (GWAS) Catalog 2019.

3.3. Identification of an ASD Blood Transcriptomic Profile

Three GEO whole-genome transcriptomic datasets, GSE6575, GSE42133 and GSE18123, were identified, as indicated in Table 1, for the following analysis. These datasets included blood samples from 157 ASD patients and blood samples from 101 otherwise normal people. The meta-analysis identified only 24 DEGs: 19 upregulated and 5 downregulated. As shown in Table 5, no significant enrichment for any KEGG pathway was detected (Table 5).

4. Discussion

According to the current DSM-5 criteria, two requirements are needed to obtain an ASD diagnosis: (1) persistent deficits in social communication and social interaction across multiple contexts, and (2) restricted, repetitive patterns of behavior, interests or activities [1]. Although ASD has a complex multifactorial etiology, twin studies have proven a strong genetic contribution, with a concordance rate of autistic disorders in monozygotic twins of 70%–90% and in dizygotic twins of 30% [27,28].
However, the complexity of the disease requires omics approaches to integrate and extrapolate more information. Genome-wide association studies, candidate gene studies and microarray experiments of differential gene expression have been largely used in autism. These studies produce extensive and information-rich data that represent a snapshot of all genetic and/or molecular events occurring in a diseased cell at one particular point in time and can be used to generate hypotheses. The use of whole-genome expression databases has been largely exploited by our group and others [29,30,31,32,33] for the characterization of the etiopathogenesis of a variety of diseases (e.g., autoimmune diseases [34,35,36,37,38,39,40,41,42] and cancer [36,43,44]) and has allowed researchers to characterize pathogenic pathways [45,46,47,48] and potential novel therapeutic targets [49,50,51,52,53,54,55,56,57].
Many authors have suggested that the role of infection during pregnancy or in the first phases of life could trigger the immune system to alter normal neurodevelopment, causing neuronal damage [8,58,59]. In particular, the role of the Herpesviridae family has been largely investigated. For instance, cytomegalovirus (CMV) can directly damage key structures in the developing brain when contracted during pregnancy [60], and indeed, in vitro studies have shown that CMV infection can inhibit neuronal differentiation and induce apoptosis in neural precursor cells [61,62]. Also, other infectious diseases such as influenza A [58], toxoplasmosis [63,64] and measles [6,65] are suspected to be related to ASD.
However, the role of infections in the pathogenesis of autism is still highly debated. The levels of D-arabinitol, a marker of candidiasis fungal infection, as well as of a phenylalanine metabolite of Clostridia species, the 3-(3-hydroxyphenyl)-3-hydroxypropionic acid, are increased in the urine of autistic children [66,67]. Accumulating evidence also suggests that latent chronic toxoplasmosis plays a role in the triggering and development of many psychiatric and neurological disorders, including ASD [68]. On the other hand, other studies have not shown a significant prevalence of infections in ASD [5,69,70,71]. The aim of our analysis was to evaluate, by performing a meta-analysis of available whole-genome transcriptomic datasets, whether infection alone or infection and immune activation processes could be detected in the brains or peripheral blood of autistic patients. To our knowledge, this is the largest meta-analysis of both ASD brain samples and leukocytes to date.
In our study, no significant enrichment for infection-related pathways, including Epstein-Barr virus (EBV), CMV, HSV-1, measles, influenza A and toxoplasmosis, was found among the DEGs identified in the meta-analyses. On the other hand, a significant enrichment of the “prion diseases” pathway was observed. However, it should be pointed out that, with the present data, it is currently not possible to identify ASD as a prion-related disease, but it is possible to describe common biomolecular pathways underlying ASD pathogenesis. Indeed, prion infection is known to affect microglial sensing and homeostasis ability and to reduce microglial phagocytosis of aberrant proteins, including PrPsc (scrapie isoform of the prion protein) and apoptotic debris or cells, despite production of proinflammatory mediators. Furthermore, the effects of PrPsc on microglia appear to be mediated by Toll-like Receptors (TLRs) in a Src-like kinase-dependent manner (reviewed in [72]). So, it may not be surprising to find that prion pathways are modified in the brains of ASD patients, as it may reflect prior inflammatory processes, having modified microglia.
In the present paper, we have combined transcriptomic meta-analysis, pathway enrichment and machine learning prediction in order to prioritize genes of interest with potential pivotal pathogenetic effects in autism. Computational methods have been largely used to investigate the etiopathogenesis of polygenic and idiopathic disorders. Functional interaction networks that integrate gene interaction data can be exploited to identify which genes are most strongly implicated in a disorder. Given a list of genes that are altered in a disease, we can apply methods to identify genes that are near the input genes within a functional interaction network that rely on the connections among genes in a functional interaction network. The major limitation of this kind of approach is that it relies on the methods of selection by which functional terms are included in the network-based prediction. Hence, the better tailored this set of genes is to the disease of interest, the higher reliability we have in the final predictions. The use of the machine learning prediction tool developed by Krishnan and colleagues [26] allows us to evaluate the probability value of association between the selected gene and ASD in the context of the human brain-specific network. With this approach, we likely arrive at a robust set of candidates that are relatively unbiased by previously published works. The final output of this strategy, i.e., a ranked list of candidate genes, is easy to interpret and provides a limited set of hypotheses to test in further investigations. However, while we cannot definitively identify the causal gene or genes, it does provide a much-reduced set of candidates to investigate. In particular, a role for tyrosine kinase Fyn is proposed. Fyn has been described as expressed in the mouse hippocampus, amygdala and cerebellum [73,74]. Mutations of Fyn in mice lead to alteration in the architecture of the hippocampus [75] with consequent impairment in learning and in the amygdala long-term plasticity [73]. Fyn regulates the focal adhesion kinase (FAK), which is required for normal neuronal development [73,76].
In our analysis, Fyn was strongly correlated with ATXN1, a DNA-binding protein that forms a transcriptional repressor complex with capicua (CIC). It has been previously described that the deletion in chromosome 6p22.3-p24.3, which harbors ATXN1, is associated with developmental delay and ASD [77,78]. Moreover, alteration of the ATXN1-CIC complex determines a spectrum of neurobehavioral phenotypes, including intellectual disability, attention deficit/hyperactivity disorder (ADHD) and autism spectrum disorder [79].
Finally, we need to address some important limitations to our study. First, the number of available gene expression datasets of brain samples derived from ASD patients is limited, and the number of samples included in each dataset is often negligible. Second, the meta-analysis here performed encompasses different brain regions (temporal, occipital and frontal cortex, as well as corpus callosum and cerebellum). These facts undermine the statistical power of the differential expression analysis and impede patients’ stratification, in terms of clinical phenotype, which is advisable given the heterogeneity of ASD. It is likely that different subgroups of patients may have peculiar brain transcriptomic patterns. Moreover, gene expression analysis is not enough to determine whether particular biological processes are activated or not, limiting the reliability of the conclusions that can be drawn. Hence, more population- and molecular-based studies are warranted to confirm or negate hypotheses.
Characterizing molecular pathways underlying ASD represents a crucial step for personalized medicine where comprehensive phenotyping of individual patients could be available, providing novel tailored treatment options. The data from this study suggest that infections may not necessarily be responsible for ASD development. However, since some genes involved in the infectious processes can interact with other key genes in autism, infections may likely act as co-factors, possibly causing worse clinical presentations. Future studies are necessary to validate these findings and prove if these genes can be used as biomarkers or even as eventual therapeutic targets. Finally, we have to point out that the present analysis cannot evaluate the potential role of infections in the prenatal period or contracted in the early stage of life.

5. Conclusions

In this paper, we investigated the relationship between infections and autism, proving that they should not be considered as etiological factors but probably as co-factors. We analyzed the gene expression profiles of brain and blood from autistic patients and compared them with the genes involved in the most frequent infectious diseases associated with pregnancy and suspected to be related to ASD. Our analysis does not show any statistically significant associations between ASD and previously studied infectious agents. However, it does show a statistical association between prion disease and autism. Finally, based on a Bayesian machine learning approach, we predicted that new genes may be associated with ASD and possibly, after validation, used as markers or therapeutic targets.

Supplementary Materials

The following are available online at https://www.mdpi.com/2076-3425/10/4/200/s1: File 1: Prion diseases pathway.

Author Contributions

Conceptualization, S.D.L., P.F. and E.C.; Data curation, M.C.P. and M.S.B.; Formal analysis, S.D.L. and P.F.; Funding acquisition, G.B. and V.B.; Supervision, F.N.; Visualization, S.D.L.; Writing—original draft, S.D.L., M.C.P., M.S.B. and E.C.; Writing—review & editing, G.B., K.M., V.B., P.F., R.B. and F.N. All authors have read and agreed to the published version of the manuscript.

Funding

This study was supported by current research funds 2019 of IRCCS “NEUROMED—Mediterranean Neurological Institute”, Pozzilli, Italy.

Acknowledgments

Not applicable.

Conflicts of Interest

The authors declare no conflicts of interest.

References

  1. Lai, M.C.; Lombardo, M.V.; Baron-Cohen, S. Autism. Proc. Lancet 2014, 383, 896–910. [Google Scholar] [CrossRef]
  2. Wiśniowiecka-Kowalnik, B.; Nowakowska, B.A. Genetics and epigenetics of autism spectrum disorder—Current evidence in the field. J. Appl. Genet. 2019, 60, 37–47. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  3. Benvenuto, A.; Moavero, R.; Alessandrelli, R.; Manzi, B.; Curatolo, P. Syndromic autism: Causes and pathogenetic pathways. World J. Pediatr. 2009, 5, 169–176. [Google Scholar] [CrossRef] [PubMed]
  4. Gentile, I.; Zappulo, E.; Militerni, R.; Pascotto, A.; Borgia, G.; Bravaccio, C. Etiopathogenesis of autism spectrum disorders: Fitting the pieces of the puzzle together. Med. Hypotheses 2013, 81, 26–35. [Google Scholar] [CrossRef] [PubMed]
  5. Sweeten, T.L.; Croen, L.A.; Windham, G.C.; Odell, J.D.; Stubbs, E.G.; Torres, A.R. Brief Report: Low Rates of Herpesvirus Detection in Blood of Individuals with Autism Spectrum Disorder and Controls. J. Autism Dev. Disord. 2019, 49, 410–414. [Google Scholar] [CrossRef] [PubMed]
  6. Libbey, J.E.; Sweeten, T.L.; McMahon, W.M.; Fujinami, R.S. Autistic disorder and viral infections. J. Neurovirol. 2005, 11, 1–10. [Google Scholar] [CrossRef]
  7. Patterson, P.H. Maternal infection and immune involvement in autism. Trends Mol. Med. 2011, 17, 389–394. [Google Scholar] [CrossRef] [Green Version]
  8. Zerbo, O.; Qian, Y.; Yoshida, C.; Grether, J.K.; Van de Water, J.; Croen, L.A. Maternal Infection during Pregnancy and Autism Spectrum Disorders. J. Autism Dev. Disord. 2015, 45, 4015–4025. [Google Scholar] [CrossRef] [Green Version]
  9. Patterson, P.H. Maternal infection and autism. Brain Behav. Immun. 2012, 17, 389–394. [Google Scholar] [CrossRef]
  10. De Santis, B.; Brera, C.; Mezzelani, A.; Soricelli, S.; Ciceri, F.; Moretti, G.; Debegnach, F.; Bonaglia, M.C.; Villa, L.; Molteni, M.; et al. Role of mycotoxins in the pathobiology of autism: A first evidence. Nutr. Neurosci. 2019, 22, 132–144. [Google Scholar] [CrossRef]
  11. De Santis, B.; Raggi, M.E.; Moretti, G.; Facchiano, F.; Mezzelani, A.; Villa, L.; Bonfanti, A.; Campioni, A.; Rossi, S.; Camposeo, S.; et al. Study on the association among mycotoxins and other variables in children with autism. Toxins 2017, 9, 203. [Google Scholar] [CrossRef] [PubMed]
  12. Gupta, S.; Ellis, S.E.; Ashar, F.N.; Moes, A.; Bader, J.S.; Zhan, J.; West, A.B.; Arking, D.E. Transcriptome analysis reveals dysregulation of innate immune response genes and neuronal activity-dependent genes in autism. Nat. Commun. 2014, 5, 5748. [Google Scholar] [CrossRef] [PubMed]
  13. Ellis, S.E.; Panitch, R.; West, A.B.; Arking, D.E. Transcriptome analysis of cortical tissue reveals shared sets of downregulated genes in autism and schizophrenia. Transl. Psychiatry 2016, 6, e817. [Google Scholar] [CrossRef] [PubMed]
  14. Voineagu, I.; Wang, X.; Johnston, P.; Lowe, J.K.; Tian, Y.; Horvath, S.; Mill, J.; Cantor, R.M.; Blencowe, B.J.; Geschwind, D.H. Transcriptomic analysis of autistic brain reveals convergent molecular pathology. Nature 2011, 474, 380–386. [Google Scholar] [CrossRef] [PubMed]
  15. Ginsberg, M.R.; Rubin, R.A.; Natowicz, M.R. Patterning of regional gene expression in autism: New complexity. Sci. Rep. 2013, 3, 1–3. [Google Scholar] [CrossRef] [Green Version]
  16. Ginsberg, M.R.; Rubin, R.A.; Falcone, T.; Ting, A.H.; Natowicz, M.R. Brain Transcriptional and Epigenetic Associations with Autism. PLoS ONE 2012, 7. [Google Scholar] [CrossRef] [Green Version]
  17. Li, J.; Shi, M.; Ma, Z.; Zhao, S.; Euskirchen, G.; Ziskin, J.; Urban, A.; Hallmayer, J.; Snyder, M. Integrated systems analysis reveals a molecular network underlying autism spectrum disorders. Mol. Syst. Biol. 2014, 10. [Google Scholar] [CrossRef]
  18. Irimia, M.; Weatheritt, R.J.; Ellis, J.D.; Parikshak, N.N.; Gonatopoulos-Pournatzis, T.; Babor, M.; Quesnel-Vallières, M.; Tapial, J.; Raj, B.; O’Hanlon, D.; et al. A highly conserved program of neuronal microexons is misregulated in autistic brains. Cell 2014, 159, 1511–1523. [Google Scholar] [CrossRef] [Green Version]
  19. Wright, C.; Shin, J.H.; Rajpurohit, A.; Deep-Soboslay, A.; Collado-Torres, L.; Brandon, N.J.; Hyde, T.M.; Kleinman, J.E.; Jaffe, A.E.; Cross, A.J.; et al. Altered expression of histamine signaling genes in autism spectrum disorder. Transl. Psychiatry 2017, 7, e1126. [Google Scholar] [CrossRef] [Green Version]
  20. Gregg, J.P.; Lit, L.; Baron, C.A.; Hertz-Picciotto, I.; Walker, W.; Davis, R.A.; Croen, L.A.; Ozonoff, S.; Hansen, R.; Pessah, I.N.; et al. Gene expression changes in children with autism. Genomics 2008, 91, 22–29. [Google Scholar] [CrossRef] [Green Version]
  21. Pramparo, T.; Lombardo, M.V.; Campbell, K.; Barnes, C.C.; Marinero, S.; Solso, S.; Young, J.; Mayo, M.; Dale, A.; Ahrens-Barbeau, C.; et al. Cell cycle networks link gene expression dysregulation, mutation, and brain maldevelopment in autistic toddlers. Mol. Syst. Biol. 2015, 11. [Google Scholar] [CrossRef] [PubMed]
  22. Pramparo, T.; Pierce, K.; Lombardo, M.V.; Barnes, C.C.; Marinero, S.; Ahrens-Barbeau, C.; Murray, S.S.; Lopez, L.; Xu, R.; Courchesne, E. Prediction of autism by translation and immune/inflammation coexpressed genes in toddlers from pediatric community practices. JAMA Psychiatry 2015, 72, 386–394. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  23. Kong, S.W.; Collins, C.D.; Shimizu-Motohashi, Y.; Holm, I.A.; Campbell, M.G.; Lee, I.H.; Brewster, S.J.; Hanson, E.; Harris, H.K.; Lowe, K.R.; et al. Characteristics and Predictive Value of Blood Transcriptome Signature in Males with Autism Spectrum Disorders. PLoS ONE 2012, 7. [Google Scholar] [CrossRef] [PubMed]
  24. Kuleshov, M.V.; Jones, M.R.; Rouillard, A.D.; Fernandez, N.F.; Duan, Q.; Wang, Z.; Koplev, S.; Jenkins, S.L.; Jagodnik, K.M.; Lachmann, A.; et al. Enrichr: A comprehensive gene set enrichment analysis web server 2016 update. Nucleic Acids Res. 2016, 44, W90–W97. [Google Scholar] [CrossRef] [Green Version]
  25. Kanehisa, M.; Furumichi, M.; Tanabe, M.; Sato, Y.; Morishima, K. KEGG: New perspectives on genomes, pathways, diseases and drugs. Nucleic Acids Res. 2017, 45, D353–D361. [Google Scholar] [CrossRef] [Green Version]
  26. Krishnan, A.; Zhang, R.; Yao, V.; Theesfeld, C.L.; Wong, A.K.; Tadych, A.; Volfovsky, N.; Packer, A.; Lash, A.; Troyanskaya, O.G. Genome-wide prediction and functional characterization of the genetic basis of autism spectrum disorder. Nat. Neurosci. 2016, 19, 1454. [Google Scholar] [CrossRef] [Green Version]
  27. Tick, B.; Bolton, P.; Happé, F.; Rutter, M.; Rijsdijk, F. Heritability of autism spectrum disorders: A meta-analysis of twin studies. J. Child Psychol. Psychiatry 2016, 57, 585–595. [Google Scholar] [CrossRef] [Green Version]
  28. Rosenberg, R.E.; Law, J.K.; Yenokyan, G.; McGready, J.; Kaufmann, W.E.; Law, P.A. Characteristics and concordance of autism spectrum disorders among 277 twin pairs. Arch. Pediatr. Adolesc. Med. 2009, 163, 907–914. [Google Scholar] [CrossRef] [Green Version]
  29. Candido, S.; Lupo, G.; Pennisi, M.; Basile, M.; Anfuso, C.; Petralia, M.; Gattuso, G.; Vivarelli, S.; Spandidos, D.; Libra, M.; et al. The analysis of miRNA expression profiling datasets reveals inverse microRNA patterns in glioblastoma and Alzheimer’s disease. Oncol. Rep. 2019, 42, 911–922. [Google Scholar] [CrossRef] [Green Version]
  30. Lombardo, S.D.; Mazzon, E.; Basile, M.S.; Cavalli, E.; Bramanti, P.; Nania, R.; Fagone, P.; Nicoletti, F.; Petralia, M.C. Upregulation of IL-1 Receptor Antagonist in a Mouse Model of Migraine. Brain Sci. 2019, 9, 172. [Google Scholar] [CrossRef] [Green Version]
  31. Lombardo, S.D.; Presti, M.; Mangano, K.; Petralia, M.C.; Basile, M.S.; Libra, M.; Candido, S.; Fagone, P.; Mazzon, E.; Nicoletti, F.; et al. Prediction of PD-L1 Expression in Neuroblastoma via Computational Modeling. Brain Sci. 2019, 9, 221. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  32. Petralia, M.C.; Mazzon, E.; Fagone, P.; Falzone, L.; Bramanti, P.; Nicoletti, F.; Basile, M.S. Retrospective follow-up analysis of the transcriptomic patterns of cytokines, cytokine receptors and chemokines at preconception and during pregnancy, in women with post-partum depression. Exp. Ther. Med. 2019, 18, 2055–2062. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  33. Lombardo, S.D.; Mazzon, E.; Mangano, K.; Basile, M.S.; Cavalli, E.; Mammana, S.; Fagone, P.; Nicoletti, F.; Petralia, M.C. Transcriptomic Analysis Reveals Involvement of the Macrophage Migration Inhibitory Factor Gene Network in Duchenne Muscular Dystrophy. Genes 2019, 10, 939. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  34. Lombardo, S.D.; Mazzon, E.; Basile, M.S.; Campo, G.; Corsico, F.; Presti, M.; Bramanti, P.; Mangano, K.; Petralia, M.C.; Nicoletti, F.; et al. Modulation of Tetraspanin 32 (TSPAN32) Expression in T Cell-Mediated Immune Responses and in Multiple Sclerosis. Int. J. Mol. Sci. 2019, 20, 4323. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  35. Petralia, M.C.; Mazzon, E.; Basile, M.S.; Cutuli, M.; Di Marco, R.; Scandurra, F.; Saraceno, A.; Fagone, P.; Nicoletti, F.; Mangano, K. Mangano Effects of Treatment with the Hypomethylating Agent 5-aza-2’-deoxycytidine in Murine Type II Collagen-Induced Arthritis. Pharmaceuticals 2019, 12, 174. [Google Scholar] [CrossRef] [Green Version]
  36. Fagone, P.; Mazzon, E.; Mammana, S.; Di Marco, R.; Spinasanta, F.; Basile, M.S.; Petralia, M.C.; Bramanti, P.; Nicoletti, F.; Mangano, K. Identification of CD4+ T cell biomarkers for predicting the response of patients with relapsing-remitting multiple sclerosis to natalizumab treatment. Mol. Med. Rep. 2019, 20, 678–684. [Google Scholar] [CrossRef] [Green Version]
  37. Nicoletti, F.; Mazzon, E.; Fagone, P.; Mangano, K.; Mammana, S.; Cavalli, E.; Basile, M.S.; Bramanti, P.; Scalabrino, G.; Lange, A.; et al. Prevention of clinical and histological signs of MOG-induced experimental allergic encephalomyelitis by prolonged treatment with recombinant human EGF. J. Neuroimmunol. 2019, 332, 224–232. [Google Scholar] [CrossRef]
  38. Cavalli, E.; Mazzon, E.; Basile, M.S.; Mangano, K.; Di Marco, R.; Bramanti, P.; Nicoletti, F.; Fagone, P.; Petralia, M.C. Upregulated Expression of Macrophage Migration Inhibitory Factor, Its Analogue D-Dopachrome Tautomerase, and the CD44 Receptor in Peripheral CD4 T Cells from Clinically Isolated Syndrome Patients with Rapid Conversion to Clinical Defined Multiple Sclerosis. Medicina 2019, 55, 667. [Google Scholar] [CrossRef] [Green Version]
  39. Cavalli, E.; Mazzon, E.; Basile, M.S.; Mammana, S.; Pennisi, M.; Fagone, P.; Kalfin, R.; Martinovic, V.; Ivanovic, J.; Andabaka, M.; et al. In Silico and in vivo Analysis of IL37 in Multiple Sclerosis Reveals Its Probable Homeostatic Role on the Clinical Activity, Disability, and Treatment with Fingolimod. Molecules 2019, 25, 20. [Google Scholar] [CrossRef] [Green Version]
  40. Günther, S.; Fagone, P.; Jalce, G.; Atanasov, A.G.; Guignabert, C.; Nicoletti, F. Role of MIF and D-DT in immune-inflammatory, autoimmune, and chronic respiratory diseases: From pathogenic factors to therapeutic targets. Drug Discov. Today 2019, 24, 428–439. [Google Scholar] [CrossRef]
  41. Basile, M.S.; Mazzon, E.; Mangano, K.; Pennisi, M.; Petralia, M.C.; Lombardo, S.D.; Nicoletti, F.; Fagone, P.; Cavalli, E. Impaired Expression of Tetraspanin 32 (TSPAN32) in Memory T Cells of Patients with Multiple Sclerosis. Brain Sci. 2020, 10, 52. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  42. Fagone, P.; Mangano, K.; Mammana, S.; Cavalli, E.; Di Marco, R.; Barcellona, M.L.; Salvatorelli, L.; Magro, G.; Nicoletti, F. Carbon monoxide-releasing molecule-A1 (CORM-A1) improves clinical signs of experimental autoimmune uveoretinitis (EAU) in rats. Clin. Immunol. 2015, 157, 198–204. [Google Scholar] [CrossRef] [PubMed]
  43. Cavalli, E.; Mazzon, E.; Mammana, S.; Basile, M.S.; Lombardo, S.D.; Mangano, K.; Bramanti, P.; Nicoletti, F.; Fagone, P.; Petralia, M.C. Overexpression of Macrophage Migration Inhibitory Factor and Its Homologue D-Dopachrome Tautomerase as Negative Prognostic Factor in Neuroblastoma. Brain Sci. 2019, 9, 284. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  44. Petralia, M.C.; Mazzon, E.; Fagone, P.; Russo, A.; Longo, A.; Avitabile, T.; Nicoletti, F.; Reibaldi, M.; Basile, M.S. Characterization of the Pathophysiological Role of CD47 in Uveal Melanoma. Molecules 2019, 24, 2450. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  45. Mangano, K.; Cavalli, E.; Mammana, S.; Basile, M.S.; Caltabiano, R.; Pesce, A.; Puleo, S.; Atanasov, A.G.; Magro, G.; Nicoletti, F.; et al. Involvement of the Nrf2/HO-1/CO axis and therapeutic intervention with the CO-releasing molecule CORM-A1, in a murine model of autoimmune hepatitis. J. Cell. Physiol. 2018, 233, 4156–4165. [Google Scholar] [CrossRef]
  46. Mammana, S.; Bramanti, P.; Mazzon, E.; Cavalli, E.; Basile, M.S.; Fagone, P.; Petralia, M.C.; McCubrey, J.A.; Nicoletti, F.; Mangano, K. Preclinical evaluation of the PI3K/Akt/mTOR pathway in animal models of multiple sclerosis. Oncotarget 2018, 9, 8263–8277. [Google Scholar] [CrossRef] [Green Version]
  47. Mammana, S.; Fagone, P.; Cavalli, E.; Basile, M.S.; Petralia, M.C.; Nicoletti, F.; Bramanti, P.; Mazzon, E. The role of macrophages in neuroinflammatory and neurodegenerative pathways of alzheimer’s disease, amyotrophic lateral sclerosis, and multiple sclerosis: Pathogenetic cellular effectors and potential therapeutic targets. Int. J. Mol. Sci. 2018, 19, 831. [Google Scholar] [CrossRef] [Green Version]
  48. Petralia, M.C.; Mazzon, E.; Fagone, P.; Basile, M.S.; Lenzo, V.; Quattropani, M.C.; Bendtzen, K.; Nicoletti, F. Pathogenic contribution of the Macrophage migration inhibitory factor family to major depressive disorder and emerging tailored therapeutic approaches. J. Affect. Disord. 2020, 263, 15–24. [Google Scholar] [CrossRef]
  49. Fagone, P.; Mangano, K.; Mammana, S.; Pesce, A.; Pesce, A.; Caltabiano, R.; Giorlandino, A.; Rosanna Portale, T.; Cavalli, E.; Lombardo, G.A.G.; et al. Identification of novel targets for the diagnosis and treatment of liver fibrosis. Int. J. Mol. Med. 2015, 36, 747–752. [Google Scholar] [CrossRef] [Green Version]
  50. Basile, M.S.; Mazzon, E.; Krajnovic, T.; Draca, D.; Cavalli, E.; Al-Abed, Y.; Bramanti, P.; Nicoletti, F.; Mijatovic, S.; Maksimovic-Ivanic, D. Anticancer and Differentiation Properties of the Nitric Oxide Derivative of Lopinavir in Human Glioblastoma Cells. Molecules 2018, 23, 2463. [Google Scholar] [CrossRef] [Green Version]
  51. Petralia, M.C.; Battaglia, G.; Bruno, V.; Pennisi, M.; Mangano, K.; Lombardo, S.D.; Fagone, P.; Cavalli, E.; Saraceno, A.; Nicoletti, F.; et al. The Role of Macrophage Migration Inhibitory Factor in Alzheimer’s Disease: Conventionally Pathogenetic or Unconventionally Protective? Molecules 2020, 25, 291. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  52. Mammana, S.; Cavalli, E.; Gugliandolo, A.; Silvestro, S.; Pollastro, F.; Bramanti, P.; Mazzon, E. Could the Combination of Two Non-Psychotropic Cannabinoids Counteract Neuroinflammation? Effectiveness of Cannabidiol Associated with Cannabigerol. Medicina 2019, 55, 747. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  53. Schepici, G.; Cavalli, E.; Bramanti, P.; Mazzon, E. Mazzon Autism Spectrum Disorder and miRNA: An Overview of Experimental Models. Brain Sci. 2019, 9, 265. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  54. Paskaš, S.; Krajnović, T.; Basile, M.S.; Dunđerović, D.; Cavalli, E.; Mangano, K.; Mammana, S.; Al-Abed, Y.; Nicoletti, F.; Mijatović, S.; et al. Senescence as a main mechanism of Ritonavir and Ritonavir-NO action against melanoma. Mol. Carcinog. 2019, 58, 1362–1375. [Google Scholar] [CrossRef] [PubMed]
  55. Paskas, S.; Mazzon, E.; Basile, M.S.; Cavalli, E.; Al-Abed, Y.; He, M.; Rakocevic, S.; Nicoletti, F.; Mijatovic, S.; Maksimovic-Ivanic, D. Lopinavir-NO, a nitric oxide-releasing HIV protease inhibitor, suppresses the growth of melanoma cells in vitro and in vivo. Investig. New Drugs 2019, 37, 1014–1028. [Google Scholar] [CrossRef]
  56. Fagone, P.; Mangano, K.; Quattrocchi, C.; Cavalli, E.; Mammana, S.; Lombardo, G.A.G.; Pennisi, V.; Zocca, M.-B.; He, M.; Al-Abed, Y.; et al. Effects of NO-Hybridization on the Immunomodulatory Properties of the HIV Protease Inhibitors Lopinavir and Ritonavir. Basic Clin. Pharmacol. Toxicol. 2015, 117, 306–315. [Google Scholar] [CrossRef]
  57. Maksimovic-Ivanic, D.; Mojic, M.; Bulatovic, M.; Radojkovic, M.; Kuzmanovic, M.; Ristic, S.; Stosic-Grujicic, S.; Miljkovic, D.; Cavalli, E.; Libra, M.; et al. The NO-modified HIV protease inhibitor as a valuable drug for hematological malignancies: Role of p70S6K. Leuk. Res. 2015, 39, 1088–1095. [Google Scholar] [CrossRef] [Green Version]
  58. Atladóttir, H.Ó.; Henriksen, T.B.; Schendel, D.E.; Parner, E.T. Autism after infection, febrile episodes, and antibiotic use during pregnancy: An exploratory study. Pediatrics 2012, 130, e1447–e1454. [Google Scholar] [CrossRef] [Green Version]
  59. Lee, B.K.; Magnusson, C.; Gardner, R.M.; Blomström, Å.; Newschaffer, C.J.; Burstyn, I.; Karlsson, H.; Dalman, C. Maternal hospitalization with infection during pregnancy and risk of autism spectrum disorders. Brain Behav. Immun. 2015, 44, 100–105. [Google Scholar] [CrossRef] [Green Version]
  60. Engman, M.L.; Sundin, M.; Miniscalco, C.; Westerlund, J.; Lewensohn-Fuchs, I.; Gillberg, C.; Fernell, E. Prenatal acquired cytomegalovirus infection should be considered in children with autism. Acta Paediatr. 2015, 104, 792–795. [Google Scholar] [CrossRef]
  61. Odeberg, J.; Wolmer, N.; Falci, S.; Westgren, M.; Sundtröm, E.; Seiger, Å.; Söderberg-Nauclér, C. Late human cytomegalovirus (HCMV) proteins inhibit differentiation of human neural precursor cells into astrocytes. J. Neurosci. Res. 2007, 85, 583–893. [Google Scholar] [CrossRef] [PubMed]
  62. Odeberg, J.; Wolmer, N.; Falci, S.; Westgren, M.; Seiger, A.; Soderberg-Naucler, C. Human Cytomegalovirus Inhibits Neuronal Differentiation and Induces Apoptosis in Human Neural Precursor Cells. J. Virol. 2006, 80, 8929–8939. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  63. Spann, M.N.; Sourander, A.; Surcel, H.M.; Hinkka-Yli-Salomäki, S.; Brown, A.S. Prenatal toxoplasmosis antibody and childhood autism. Autism Res. 2017, 10, 769–777. [Google Scholar] [CrossRef]
  64. Prandota, J. Autism spectrum disorders may be due to cerebral toxoplasmosis associated with chronic neuroinflammation causing persistent hypercytokinemia that resulted in an increased lipid peroxidation, oxidative stress, and depressed metabolism of endogenous and exo. Res. Autism Spectr. Disord. 2010, 4, 119–155. [Google Scholar] [CrossRef]
  65. Taylor, B.; Miller, E.; Farrington, C.P.; Petropoulos, M.C.; Favot-Mayaud, I.; Li, J.; Waight, P.A. Autism and measles, mumps, and rubella vaccine: No epidemiological evidence for a causal association. Lancet 1999, 353, 2026–2029. [Google Scholar] [CrossRef] [Green Version]
  66. Kałuzna-Czaplińska, J.; Błaszczyk, S. The level of arabinitol in autistic children after probiotic therapy. Nutrition 2012, 28, 124–126. [Google Scholar] [CrossRef] [PubMed]
  67. Shaw, W. Increased urinary excretion of a 3-(3-hydroxyphenyl)-3-hydroxypropionic acid (HPHPA), an abnormal phenylalanine metabolite of Clostridia spp. in the gastrointestinal tract, in urine samples from patients with autism and schizophrenia. Nutr. Neurosci. 2010, 13, 135–143. [Google Scholar] [CrossRef]
  68. Flegr, J.; Horáček, J. Negative Effects of Latent Toxoplasmosis on Mental Health. Front. Psychiatry 2020, 10, 1012. [Google Scholar] [CrossRef] [Green Version]
  69. Valayi, S.; Eftekharian, M.M.; Taheri, M.; Alikhani, M.Y. Evaluation of antibodies to cytomegalovirus and Epstein-Barr virus in patients with autism spectrum disorder. Hum. Antibodies 2017, 26, 165–169. [Google Scholar] [CrossRef]
  70. Gentile, I.; Zappulo, E.; Bonavolta, R.; Maresca, R.; Riccio, M.P.; Buonomo, A.R.; Portella, G.; Vallefuoco, L.; Settimi, A.; Pascotto, A.; et al. Prevalence of herpes simplex virus 1 and 2 antibodies in patients with autism spectrum disorders. In Vivo 2014, 28, 667–671. [Google Scholar]
  71. Gentile, I.; Zappulo, E.; Coppola, N.; Bonavolta, R.; Portella, G.; Cernia, D.S.; Riccio, M.P.; Settimi, A.; Pascotto, A.; Borgia, G.; et al. Prevalence of HHV-6 and HHV-8 antibodies in patients with autism spectrum disorders. In Vivo 2013, 27, 843–849. [Google Scholar]
  72. Hickman, S.; Izzy, S.; Sen, P.; Morsett, L.; El Khoury, J. Microglia in neurodegeneration. Nat. Neurosci. 2018, 21, 1359–1369. [Google Scholar] [CrossRef] [PubMed]
  73. Grant, S.G.N.; Karl, K.A.; Kiebler, M.A.; Kandel, E.R. Focal adhesion kinase in the brain: Novel subcellular localization and specific regulation by Fyn tyrosine kinase in mutant mice. Genes Dev. 1995, 9, 1909–1921. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  74. Olive, S.; Dubois, C.; Schachner, M.; Rougon, G. The F3 Neuronal Glycosylphosphatidylinositol-Linked Molecule Is Localized to Glycolipid-Enriched Membrane Subdomains and Interacts with L1 and Fyn Kinase in Cerebellum. J. Neurochem. 1995, 65, 2307–2317. [Google Scholar] [CrossRef]
  75. Grant, S.G.N.; O’Dell, T.J.; Karl, K.A.; Stein, P.L.; Soriano, P.; Kandel, E.R. Impaired long-term potentiation, spatial learning, and hippocampal development in fyn mutant mice. Science 1992, 258, 1903–1910. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  76. Waterhouse, L. Genes tPA, Fyn, and FAK in autism? J. Autism Dev. Disord. 1997, 27, 220–223. [Google Scholar]
  77. Celestino-Soper, P.B.; Skinner, C.; Schroer, R.; Eng, P.; Shenai, J.; Nowaczyk, M.M.J.; Terespolsky, D.; Cushing, D.; Patel, G.S.; Immken, L.; et al. Deletions in chromosome 6p22.3–p24.3, including ATXN1, are associated with developmental delay and autism spectrum disorders. Mol. Cytogenet. 2012, 5, 17. [Google Scholar] [CrossRef] [Green Version]
  78. Di Benedetto, D.; Di Vita, G.; Romano, C.; Lo Giudice, M.; Vitello, G.A.; Zingale, M.; Grillo, L.; Castiglia, L.; Musumeci, S.A.; Fichera, M. 6p22.3 deletion: Report of a patient with autism, severe intellectual disability and electroencephalographic anomalies. Mol. Cytogenet. 2013, 6, 4. [Google Scholar] [CrossRef] [Green Version]
  79. Lu, H.C.; Tan, Q.; Rousseaux, M.W.C.; Wang, W.; Kim, J.Y.; Richman, R.; Wan, Y.W.; Yeh, S.Y.; Patel, J.M.; Liu, X.; et al. Disruption of the ATXN1-CIC complex causes a spectrum of neurobehavioral phenotypes in mice and humans. Nat. Genet. 2017, 49, 527–536. [Google Scholar] [CrossRef]
Brainsci 10 00200 g001 550
Figure 1. Infection-related pathways enriched in brain samples from ASD patients. Dotted line indicates the threshold of significance.
Figure 1. Infection-related pathways enriched in brain samples from ASD patients. Dotted line indicates the threshold of significance.
Brainsci 10 00200 g001
Brainsci 10 00200 g002 550
Figure 2. “Prion diseases pathway” from the Kyoto Encyclopedia of Genes and Genomes (KEGG) database with genes significantly modulated in brain samples from ASD patients that have been color-coded from blue (downregulated) to red (upregulated).
Figure 2. “Prion diseases pathway” from the Kyoto Encyclopedia of Genes and Genomes (KEGG) database with genes significantly modulated in brain samples from ASD patients that have been color-coded from blue (downregulated) to red (upregulated).
Brainsci 10 00200 g002
Brainsci 10 00200 g003 550
Figure 3. Immune-related pathways enriched in brain samples from ASD patients. Dotted line indicates the threshold of significance.
Figure 3. Immune-related pathways enriched in brain samples from ASD patients. Dotted line indicates the threshold of significance.
Brainsci 10 00200 g003
Brainsci 10 00200 g004 550
Figure 4. Network constructed using the differentially expressed genes in the ASD brain belonging to the “prion diseases” pathway using a minimum confidence score of 0.04 and a maximum of 20 interacting genes.
Figure 4. Network constructed using the differentially expressed genes in the ASD brain belonging to the “prion diseases” pathway using a minimum confidence score of 0.04 and a maximum of 20 interacting genes.
Brainsci 10 00200 g004
Table
Table 1. Characteristics of the datasets used in the meta-analyses.
Table 1. Characteristics of the datasets used in the meta-analyses.
Dataset IDTissueSamplesPlatformReference
GSE28521Temporal cortexn = 13 ASD
n = 13 HD *		Illumina HumanRef-8 v3.0 Expression BeadChip[14]
Frontal cortexn = 16 ASD
n = 16 HD *
Cerebellumn = 10 ASD
n = 11 HD *
GSE38322Occipital cortex (BA19)n = 6 ASD
n = 4 HD *		Illumina HumanHT-12 V4.0 Expression BeadChip[15]
[16]
Cerebellumn = 8 ASD
n = 8 HD *
GSE62098Corpus callosumn = 6 ASD
n = 6 HD *		Illumina HiSeq 2000 (Homo sapiens)[17]
GSE64018Superior temporal gyrusn = 12 ASD
n = 12 HD *		Illumina HiSeq 2000 (Homo sapiens)[18]
GSE102741Dorsolateral prefrontal cortexn = 13 ASD
n = 39 HD *		Illumina HiSeq 2000 (Homo sapiens)[19]
GSE6575Whole bloodn = 35 ASD
n = 12 HD *		Affymetrix Human Genome U133 Plus 2.0 Array[20]
GSE42133Leukocytesn = 91 ASD
n = 56 HD *		Illumina HumanHT-12 V4.0 Expression BeadChip[21]
[22]
GSE18123Whole bloodn = 31 ASD
n = 33 HD *		Affymetrix Human Genome U133 Plus 2.0 Array[23]
* HD: Healthy donors.
Table
Table 2. Top 10 enriched KEGG pathways in brain samples from ASD patients.
Table 2. Top 10 enriched KEGG pathways in brain samples from ASD patients.
Termp ValueAdj. p-ValueOdds RatioCombined Score
Synaptic vesicle cycle8.95E-040.0306423.97535327.90006
Huntington’s disease5.06E-040.0311922.81158421.33503
Sphingolipid signaling pathway0.0010340.0318553.25711722.38962
Thyroid hormone signaling pathway8.49E-040.032693.34135323.62788
Parkinson’s disease3.20E-040.0328423.27546726.35943
Gap junction4.43E-040.0341033.96405930.61119
VEGF signaling pathway7.81E-040.0343694.59860732.90204
Prion diseases2.46E-040.0378936.64451855.21555
Valine, leucine and isoleucine degradation0.0014020.039264.84496131.83014
Lysine degradation7.81E-040.0400974.59860732.90204
Table
Table 3. Top 10 genes interacting with ASD brain DEGs belonging to the “prion diseases” pathway.
Table 3. Top 10 genes interacting with ASD brain DEGs belonging to the “prion diseases” pathway.
Query GeneGeneGene DescriptionEdge Score
HSPA5MANFmesencephalic astrocyte-derived neurotrophic factor0.791
HSPA5HSP90B1heat shock protein 90kDa beta (Grp94), member 10.79
MAP2K1MAPK1mitogen-activated protein kinase 10.789
HSPA5RAB1ARAB1A, member RAS oncogene family0.761
MAP2K1PGK1phosphoglycerate kinase 10.728
MAP2K1YWHAZtyrosine 3-monooxygenase/tryptophan 5-monooxygenase activation protein, zeta polypeptide0.706
FYNATXN1ataxin 10.705
HSPA5ARF4ADP-ribosylation factor 40.702
HSPA5HERPUD1homocysteine-inducible, endoplasmic reticulum stress-inducible, ubiquitin-like domain member 10.69
LAMC1AKT3v-akt murine thymoma viral oncogene homolog 30.669
Table
Table 4. Top 10 ranking ASD genes interacting with brain DEGs belonging to the “prion diseases” pathway.
Table 4. Top 10 ranking ASD genes interacting with brain DEGs belonging to the “prion diseases” pathway.
GeneDescriptionAvg. Edge Score to QueryRankProbability of ASD Associationp-Valueq-Value
ATXN1ataxin 10.21650.8280.0020.0186
GNB1guanine nucleotide binding protein (G protein), beta polypeptide 10.231240.8110.0010.0113
AKT3v-akt murine thymoma viral oncogene homolog 30.205750.7220.0010.0113
YWHABtyrosine 3-monooxygenase/tryptophan 5-monooxygenase activation protein, beta polypeptide0.1761070.710.0060.0438
YWHAZtyrosine 3-monooxygenase/tryptophan 5-monooxygenase activation protein, zeta polypeptide0.2122690.6970.080.3199
RAB1ARAB1A, member RAS oncogene family0.2574530.6960.0660.2786
MACF1microtubule-actin crosslinking factor 10.2057150.6670.0050.0381
PPIBpeptidylprolyl isomerase B (cyclophilin B)0.1527530.6660.2210.6426
BHLHE40basic helix-loop-helix family, member e400.18710540.6630.1490.4976
MAPK1mitogen-activated protein kinase 10.23411280.6610.1330.4605
Table
Table 5. Top 10 enriched KEGG pathways in blood from ASD patients.
Table 5. Top 10 enriched KEGG pathways in blood from ASD patients.
Termp-ValueAdjusted p-ValueOdds RatioCombined Score
Autophagy0.010230.45011513.0208359.6672
Osteoclast differentiation0.0100770.51730613.1233660.33413
cGMP-PKG signaling pathway0.0167660.57378510.0401641.04796
Tuberculosis0.0193210.5950739.31098736.74661
Oocyte meiosis0.0097750.6021713.3333361.70503
Cellular senescence0.0156410.60217210.4166743.31114
AMPK signaling pathway0.0090390.69599813.8888965.36409
Thermogenesis0.0310250.735057.21500725.05748
Regulation of actin cytoskeleton0.0269470.7545187.78816228.14549
Insulin resistance0.0073790.75757415.432175.75804

Share and Cite

MDPI and ACS Style

Lombardo, S.D.; Battaglia, G.; Petralia, M.C.; Mangano, K.; Basile, M.S.; Bruno, V.; Fagone, P.; Bella, R.; Nicoletti, F.; Cavalli, E. Transcriptomic Analysis Reveals Abnormal Expression of Prion Disease Gene Pathway in Brains from Patients with Autism Spectrum Disorders. Brain Sci. 2020, 10, 200. https://doi.org/10.3390/brainsci10040200

AMA Style

Lombardo SD, Battaglia G, Petralia MC, Mangano K, Basile MS, Bruno V, Fagone P, Bella R, Nicoletti F, Cavalli E. Transcriptomic Analysis Reveals Abnormal Expression of Prion Disease Gene Pathway in Brains from Patients with Autism Spectrum Disorders. Brain Sciences. 2020; 10(4):200. https://doi.org/10.3390/brainsci10040200

Chicago/Turabian Style

Lombardo, Salvo Danilo, Giuseppe Battaglia, Maria Cristina Petralia, Katia Mangano, Maria Sofia Basile, Valeria Bruno, Paolo Fagone, Rita Bella, Ferdinando Nicoletti, and Eugenio Cavalli. 2020. "Transcriptomic Analysis Reveals Abnormal Expression of Prion Disease Gene Pathway in Brains from Patients with Autism Spectrum Disorders" Brain Sciences 10, no. 4: 200. https://doi.org/10.3390/brainsci10040200

Note that from the first issue of 2016, this journal uses article numbers instead of page numbers. See further details here.

Article Metrics

Back to TopTop