Mass spectrometry as a tool for studying autism spectrum disorder

A Proteomics Investigation of Salivary Profiles as Potential Biomarkers for Autism Spectrum Disorder (ASD)

Autism Spectrum Disorder (ASD) is a neurodevelopmental disorder that affects approximately 1/68 children, with a more recent study suggesting numbers as high as 1/36. According to Diagnostic and Statistical Manual of Mental Disorders, the etiology of ASD is unknown and diagnosis of this disorder is behavioral. There is currently no biomarker signature for ASD, however, identifying a biomarker signature is crucial as it would aid in diagnosis, identifying treatment targets, monitoring treatments, and identifying the etiology of the disorder. Here we used nanoliquid chromatography-tandem mass spectrometry (nanoLC-MS/MS) to investigate the saliva from individuals with ASD and matched controls in a 14 vs 14 study. We found numerous proteins to have statistically significant dysregulations, including lactotransferrin, transferrin, polymeric immunoglobulin receptor, Ig A L, Ig J chain, mucin 5 AC, and lipocalin 1 isoform X1. These findings are consistent with previous studies by our lab, and others, and point to dysregulations in the immune system, lipid metabolism and/or transport, and gastrointestinal disturbances, which are common and reoccurring topics in ASD research.

Effects of Exercise Training on the Phosphoproteomics of the Medial Prefrontal Cortex in Rats With Autism Spectrum Disorder Induced by Valproic Acid

BACKGROUND

The key neural pathological characteristics of autism spectrum disorder (ASD) include abnormal synaptic plasticity of the medial prefrontal cortex (mPFC). Exercise therapy is widely used to rehabilitate children with ASD, but its neurobiological mechanism is unclear.

METHODS

To clarify whether the structural and molecular plasticity of synapses in the mPFC are related to improvement in ASD behavioral deficits after continuous exercise rehabilitation training, we applied phosphoproteomic, behavioral, morphological, and molecular biological methods to investigate the impact of exercise on the phosphoprotein expression profile and synaptic structure of the mPFC in valproic acid (VPA)-induced ASD rats.

RESULTS

Exercise training differentially regulated the density, morphology, and ultrastructure of synapses in mPFC subregions in the VPA-induced ASD rats. In total, 1031 phosphopeptides were upregulated and 782 phosphopeptides were downregulated in the mPFC in the ASD group. After exercise training, 323 phosphopeptides were upregulated, and 1098 phosphopeptides were downregulated in the ASDE group. Interestingly, 101 upregulated and 33 downregulated phosphoproteins in the ASD group were reversed after exercise training, and these phosphoproteins were mostly involved in synapses. Consistent with the phosphoproteomics data, the total and phosphorylated levels of the proteins MARK1 and MYH10 were upregulated in the ASD group and reversed after exercise training.

CONCLUSIONS

The differential structural plasticity of synapses in mPFC subregions may be the basic neural architecture of ASD behavioral abnormalities. The phosphoproteins involved in mPFC synapses, such as MARK1 and MYH10, may play important roles in the exercise rehabilitation effect on ASD-induced behavioral deficits and synaptic structural plasticity, which requires further investigation.

Mass Spectrometric (MS) Analysis of Proteins and Peptides

The human genome is sequenced and comprised of ~30,000 genes, making humans just a little bit more complicated than worms or flies. However, complexity of humans is given by proteins that these genes code for because one gene can produce many proteins mostly through alternative splicing and tissue-dependent expression of particular proteins. In addition, post-translational modifications (PTMs) in proteins greatly increase the number of gene products or protein isoforms. Furthermore, stable and transient interactions between proteins, protein isoforms/proteoforms and PTM-ed proteins (protein-protein interactions, PPI) add yet another level of complexity in humans and other organisms. In the past, all of these proteins were analyzed one at the time. Currently, they are analyzed by a less tedious method: mass spectrometry (MS) for two reasons: 1) because of the complexity of proteins, protein PTMs and PPIs and 2) because MS is the only method that can keep up with such a complex array of features. Here, we discuss the applications of mass spectrometry in protein analysis.

Emerging proteomic approaches to identify the underlying pathophysiology of neurodevelopmental and neurodegenerative disorders

Proteomics is the large-scale study of the total protein content and their overall function within a cell through multiple facets of research. Advancements in proteomic methods have moved past the simple quantification of proteins to the identification of post-translational modifications (PTMs) and the ability to probe interactions between these proteins, spatially and temporally. Increased sensitivity and resolution of mass spectrometers and sample preparation protocols have drastically reduced the large amount of cells required and the experimental variability that had previously hindered its use in studying human neurological disorders. Proteomics offers a new perspective to study the altered molecular pathways and networks that are associated with autism spectrum disorders (ASD). The differences between the transcriptome and proteome, combined with the various types of post-translation modifications that regulate protein function and localization, highlight a novel level of research that has not been appropriately investigated. In this review, we will discuss strategies using proteomics to study ASD and other neurological disorders, with a focus on how these approaches can be combined with induced pluripotent stem cell (iPSC) studies. Proteomic analysis of iPSC-derived neurons have already been used to measure changes in the proteome caused by patient mutations, analyze changes in PTMs that resulted in altered biological pathways, and identify potential biomarkers. Further advancements in both proteomic techniques and human iPSC differentiation protocols will continue to push the field towards better understanding ASD disease pathophysiology. Proteomics using iPSC-derived neurons from individuals with ASD offers a window for observing the altered proteome, which is necessary in the future development of therapeutics against specific targets.

Identification of the Key Genes of Autism Spectrum Disorder Through Protein-Protein Interaction Network

Background:

Currently, the prevalence of autism spectrum disorder (ASD) is increasing, which widely spurs the interest in the molecular investigation. Thereby, a better understanding of the given disorder mechanisms is likely to be achieved. Bioinformatics suiting protein-protein interactions analysis via the application of high-throughput studies, such as protein array, is one of these achievements.

Materials and Methods:

The gene expression data from Gene Expression Omnibus (GEO) database were downloaded, and the expression profile of patients with developmental delay and autistic features were analyzed via Cytoscape and its relevant plug-ins.

Results:

Our findings indicated that EGFR, ACTB, RHOA, CALM1, MAPK1, and JUN genes as the hub-bottlenecks and their related terms could be important in ASD risk. In other words, any expression modification in these genes could trigger dysfunctions in the corresponding biological processes.

Conclusion:

We suggest that differentially expressed genes could be used as suitable targets for ASD after being validated.

Effect of disease-associated SLC9A9 mutations on protein-protein interaction networks: implications for molecular mechanisms for ADHD and autism

Na⁺/H⁺ Exchanger 9 (NHE9) is an endosomal membrane protein encoded by the Solute Carrier 9A, member 9 gene (SLC9A9). SLC9A9 has been implicated in attention deficit hyperactivity disorder (ADHD), autism spectrum disorders (ASDs), epilepsy, multiple sclerosis and cancers. To better understand the function of NHE9 and the effects of disease-associated variants on protein-protein interactions, we conducted a quantitative analysis of the NHE9 interactome using co-immunoprecipitation and isobaric labeling-based quantitative mass spectrometry. We identified 100 proteins that interact with NHE9. These proteins were enriched in known functional pathways for NHE9: the endocytosis, protein ubiquitination and phagosome pathways, as well as some novel pathways including oxidative stress, mitochondrial dysfunction, mTOR signaling, cell death and RNA processing pathways. An ADHD-associated mutation (A409P) significantly altered NHE9's interactions with a subset of proteins involved in caveolae-mediated endocytosis and MAP2K2-mediated downstream signaling. An ASD nonsense mutation in SLC9A9, R423X, produced no-detectable amount of NHE9, suggesting the overall loss of NHE9 functional networks. In addition, seven of the NHE9 interactors are products of known autism candidate genes (Simons Foundation Autism Research Initiative, SFARI Gene) and 90% of the NHE9 interactome overlap with SFARI protein interaction network PIN (p < 0.0001), supporting the role of NHE9 interactome in ASDs molecular mechanisms. Our results provide a detailed understanding of the functions of protein NHE9 and its disrupted interactions, possibly underlying ADHD and ASDs. Furthermore, our methodological framework proved useful for functional characterization of disease-associated genetic variants and suggestion of druggable targets.

Maternal SSRI exposure increases the risk of autistic offspring: A meta-analysis and systematic review

BACKGROUND

Selective serotonin reuptake inhibitors (SSRIs) are the most common antidepressants used to preclude maternal pregnancy depression. There is a growing body of literature assessing the association of prenatal exposure to SSRIs with autism spectrum disorder (ASD). The present systematic review and meta-analysis reviewed the medical literature and pooled the results of the association of prenatal exposure to SSRIs with ASD.

METHODS

Published investigations in English by June 2016 with keywords of selective serotonin reuptake inhibitors, SSRI, autism spectrum disorder, ASD, pregnancy, childhood, children, neurodevelopment were identified using databases PubMed and PMC, MEDLINE, EMBASE, SCOPUS, and Google Scholar. Cochran's Q statistic-value (Q), degree of freedom (df), and I² indices (variation in odds ratio [OR] attributable to heterogeneity) were calculated to analyze the risk of heterogeneity of the within- and between-study variability. Pooled odds ratio (OR) and 95% confidence interval (CI) were reported by a Mantel-Haenszel test.

RESULTS

There was a non-significant heterogeneity for the included studies ([Q=3.61, df=6, P=0.730], I²=0%). The pooled results showed a significant association between prenatal SSRI exposure and ASD (OR=1.82, 95% CI=1.59-2.10, Z=8.49, P=0.00).

CONCLUSION

The evidence from the present study suggests that prenatal exposure to SSRIs is associated with a higher risk of ASD.

Measurement in saliva from neurotypical adults of biomarkers pertinent to autism spectrum disorders

Aim:

Measure biomarkers pertinent to autism in saliva from humans.

Materials & methods:

At 7:30 PM (reading instructions) and 8:30 PM (hearing instructions), neurotypical adults (6 M, 6 F) each spat into tubes containing protease inhibitors. Cells were counted, samples aliquoted, frozen and thawed. Rationale was given for choice of biomarkers. ELISA: CD26, IL-12, carnitine, C4B, GSH, GSSG, MT-2, testosterone, IFN-γ. Mass spectrometry: cystine, glutamine, glutamic acid, GABA, serotonin. Electrochemiluminescentimmunoassay: cortisol. Radioimmunoassay: melatonin.

Results:

Cells averaged 2.16 × 10⁶/ml. M > F: CD-26, C4B, MT-2. Testosterone, cortisol. Glutamine, glutamic acid, IFN-γ, melatonin and GSSG were measurable. Remaining biomarkers were measured in <50% of samples. Concentrations were equal at both times.

Conclusion:

Saliva can be collected by literate individuals without added instruction. Ten biomarkers were measurable.

Salivary proteomics and biomarkers in neurology and psychiatry

Biomarkers are greatly needed in the fields of neurology and psychiatry, to provide objective and earlier diagnoses of CNS conditions. Proteomics and other omics MS-based technologies are tools currently being utilized in much recent CNS research. Saliva is an interesting alternative biomaterial for the proteomic study of CNS disorders, with several advantages. Collection is noninvasive and saliva has many proteins. It is easier to collect than blood and can be collected by professionals without formal medical training. For psychiatric and neurological patients, supplying a saliva sample is less anxiety-provoking than providing a blood sample, and is less embarrassing than producing a urine specimen. The use of saliva as a biomaterial has been researched for the diagnosis of and greater understanding of several CNS conditions, including neurodegenerative diseases, autism, and depression. Salivary biomarkers could be used to rule out nonpsychiatric conditions that are often mistaken for psychiatric/neurological conditions, such as fibromyalgia, and potentially to assess cognitive ability in individuals with compromised brain function. As MS and omics technology advances, the sensitivity and utility of assessing CNS conditions using distal human biomaterials such as saliva is becoming increasingly possible.

Mass spectrometry for proteomics-based investigation

Within the past years, we have witnessed a great improvement in mass spectrometry (MS) and proteomics approaches in terms of instrumentation, protein fractionation, and bioinformatics. With the current technology, protein identification alone is no longer sufficient. Both scientists and clinicians want not only to identify proteins but also to identify the protein's posttranslational modifications (PTMs), protein isoforms, protein truncation, protein-protein interaction (PPI), and protein quantitation. Here, we describe the principle of MS and proteomics and strategies to identify proteins, protein's PTMs, protein isoforms, protein truncation, PPIs, and protein quantitation. We also discuss the strengths and weaknesses within this field. Finally, in our concluding remarks we assess the role of mass spectrometry and proteomics in scientific and clinical settings in the near future. This chapter provides an introduction and overview for subsequent chapters that will discuss specific MS proteomic methodologies and their application to specific medical conditions. Other chapters will also touch upon areas that expand beyond proteomics, such as lipidomics and metabolomics.

Cancer secretomes and their place in supplementing other hallmarks of cancer

The secretome includes all macromolecules secreted by cells, in particular conditions at defined times, allowing cell-cell communication. Cancer cell secretomes that are altered compared to normal cells have shown significant potential for elucidating cancer biology. Proteins of secretomes are secreted by various secretory pathways and can be studied using different methods. Cancer secretomes seem to play an important role in known hallmarks of cancers such as excessive proliferation, reduced apoptosis, immune invasion, angioneogenesis, alteration in energy metabolism, and development of resistance against anti-cancer therapy [1, 2]. If a significant role of an altered secretome can be identified in cancer cells, using advanced mass spectrometry-based techniques, this may allow researchers to screen and characterize the secretome proteins involved in cancer progression and open up new opportunities to develop new therapies. We aim to elaborate upon recent advances in cancer cell secretome analysis using different proteomics techniques. In this review, we highlight the role of the altered secretome in contributing to already recognized and emerging hallmarks of cancer and we discuss new challenges in the field of secretome analysis.

Biomarkers in major depressive disorder: the role of mass spectrometry

Major depressive disorder (MDD) is common. Despite numerous available treatments, many individuals fail to improve clinically. MDD continues to be diagnosed exclusively via behavioral rather than biological methods. Biomarkers-which include measurements of genes, proteins, and patterns of brain activity-may provide an important objective tool for the diagnosis of MDD or in the rational selection of treatments. Proteomic analysis and validation of its results as biomarkers is less explored than other areas of biomarker research in MDD. Mass spectrometry (MS) is a comprehensive, unbiased means of proteomic analysis, which can be complemented by directed protein measurements, such as Western Blotting. Prior studies have focused on MS analysis of several human biomaterials in MDD, including human post-mortem brain, cerebrospinal fluid (CSF), blood components, and urine. Further studies utilizing MS and proteomic analysis in MDD may help solidify and establish biomarkers for use in diagnosis, identification of new treatment targets, and understanding of the disorder. The ultimate goal is the validation of a biomarker or a biomarker signature that facilitates a convenient and inexpensive predictive test for depression treatment response and helps clinicians in the rational selection of next-step treatments.

Mass spectrometry for the study of autism and neurodevelopmental disorders

Mass spectrometry (MS) has been increasingly used to study central nervous system disorders, including autism spectrum disorders (ASDs). The first studies of ASD using MS focused on the identification of external toxins, but current research is more directed at understanding endogenous protein changes that occur in ASD (ASD proteomics). This chapter focuses on how MS has been used to study ASDs, with particular focus on proteomic analysis. Other neurodevelopmental disorders have been investigated using this technique, including genetic syndromes associated with autism such as fragile X syndrome and Smith-Lemli-Opitz syndrome.

Protein-protein interactions: switch from classical methods to proteomics and bioinformatics-based approaches

Following the sequencing of the human genome and many other organisms, research on protein-coding genes and their functions (functional genomics) has intensified. Subsequently, with the observation that proteins are indeed the molecular effectors of most cellular processes, the discipline of proteomics was born. Clearly, proteins do not function as single entities but rather as a dynamic network of team players that have to communicate. Though genetic (yeast two-hybrid Y2H) and biochemical methods (co-immunoprecipitation Co-IP, affinity purification AP) were the methods of choice at the beginning of the study of protein-protein interactions (PPI), in more recent years there has been a shift towards proteomics-based methods and bioinformatics-based approaches. In this review, we first describe in depth PPIs and we make a strong case as to why unraveling the interactome is the next challenge in the field of proteomics. Furthermore, classical methods of investigation of PPIs and structure-based bioinformatics approaches are presented. The greatest emphasis is placed on proteomic methods, especially native techniques that were recently developed and that have been shown to be reliable. Finally, we point out the limitations of these methods and the need to set up a standard for the validation of PPI experiments.

Comparative proteomics reveals novel components at the plasma membrane of differentiated HepaRG cells and different distribution in hepatocyte- and biliary-like cells

Hepatitis B virus (HBV) is a human pathogen causing severe liver disease and eventually death. Despite important progress in deciphering HBV internalization, the early virus-cell interactions leading to infection are not known. HepaRG is a human bipotent liver cell line bearing the unique ability to differentiate towards a mixture of hepatocyte- and biliary-like cells. In addition to expressing metabolic functions normally found in liver, differentiated HepaRG cells support HBV infection in vitro, thus resembling cultured primary hepatocytes more than other hepatoma cells. Therefore, extensive characterization of the plasma membrane proteome from HepaRG cells would allow the identification of new cellular factors potentially involved in infection. Here we analyzed the plasma membranes of non-differentiated and differentiated HepaRG cells using nanoliquid chromatography-tandem mass spectrometry to identify the differences between the proteomes and the changes that lead to differentiation of these cells. We followed up on differentially-regulated proteins in hepatocytes- and biliary-like cells, focusing on Cathepsins D and K, Cyclophilin A, Annexin 1/A1, PDI and PDI A4/ERp72. Major differences between the two proteomes were found, including differentially regulated proteins, protein-protein interactions and intracellular localizations following differentiation. The results advance our current understanding of HepaRG differentiation and the unique properties of these cells.

Identification of Post-Translational Modifications by Mass Spectrometry

Proteins are the effector molecules of many cellular and biological processes and are thus very dynamic and flexible. Regulation of protein activity, structure, stability, and turnover is in part controlled by their post-translational modifications (PTMs). Common PTMs of proteins include phosphorylation, glycosylation, methylation, ubiquitination, acetylation, and oxidation. Understanding the biology of protein PTMs can help elucidate the mechanisms of many pathological conditions and provide opportunities for prevention, diagnostics, and treatment of these disorders. Prior to the era of proteomics, it was standard to use chemistry methods for the identification of protein modifications. With advancements in proteomic technologies, mass spectrometry has become the method of choice for the analysis of protein PTMs. In this brief review, we will highlight the biochemistry of PTMs with an emphasis on mass spectrometry.