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Abstract Background Clinical classification of autistic patients based on current WHO criteria provides a valuable but simplified depiction of the true nature of the disorder. Our goal is to determine the biology of the disorder and the ASD-associated genes that lead to differences in the severity and variability of clinical features, which can enhance the ability to predict clinical outcomes. Method Novel Whole Exome Sequencing data from children (n = 33) with ASD were collected along with extended cognitive and linguistic assessments. A machine learning methodology and a literature-based approach took into consideration known effects of genetic variation on the translated proteins, linking them with specific ASD clinical manifestations, namely non-verbal IQ, memory, attention and oral language deficits. Results Linear regression polygenic risk score results included the classification of severe and mild ASD samples with a 81.81% prediction accuracy. The literature-based approach revealed 14 genes present in all sub-phenotypes (independent of severity) and others which seem to impair individual ones, highlighting genetic profiles specific to mild and severe ASD, which concern non-verbal IQ, memory, attention and oral language skills. Conclusions These genes can potentially contribute toward a diagnostic gene-set for determining ASD severity. However, due to the limited number of patients in this study, our classification approach is mostly centered on the prediction and verification of these genes and does not hold a diagnostic nature per se. Substantial further experimentation is required to validate their role as diagnostic markers. The use of these genes as input for functional analysis highlights important biological processes and bridges the gap between genotype and phenotype in ASD.

Abstract Background Spastic ataxias (SAs) encompass a group of rare and severe neurodegenerative diseases, characterized by an overlap between ataxia and spastic paraplegia clinical features. They have been associated with pathogenic variants in a number of genes, including GBA2. This gene codes for the non-lysososomal β-glucosylceramidase, which is involved in sphingolipid metabolism through its catalytic role in the degradation of glucosylceramide. However, the mechanism by which GBA2 variants lead to the development of SA is still unclear. Methods In this work, we perform next-generation RNA-sequencing (RNA-seq), in an attempt to discover differentially expressed genes (DEGs) in lymphoblastoid, fibroblast cell lines and induced pluripotent stem cell-derived neurons derived from patients with SA, homozygous for the GBA2 c.1780G > C missense variant. We further exploit DEGs in pathway analyses in order to elucidate candidate molecular mechanisms that are implicated in the development of the GBA2 gene-associated SA. Results Our data reveal a total of 5217 genes with significantly altered expression between patient and control tested tissues. Furthermore, the most significant extracted pathways are presented and discussed for their possible role in the pathogenesis of the disease. Among them are the oxidative stress, neuroinflammation, sphingolipid signaling and metabolism, PI3K-Akt and MAPK signaling pathways. Conclusions Overall, our work examines for the first time the transcriptome profiles of GBA2-associated SA patients and suggests pathways and pathway synergies that could possibly have a role in SA pathogenesis. Lastly, it provides a list of DEGs and pathways that could be further validated towards the discovery of disease biomarkers.

Abstract Background Approximately 50% of systemic lupus erythematosus (SLE) patients develop nephritis, which is among the most severe and frequent complications of the disease and a leading cause of morbidity and mortality. Despite intensive research, there are still no reliable lupus nephritis (LN) markers in clinical use that can assess renal damage and activity with a high sensitivity and specificity. To this end, the aim of this study was to identify new clinically relevant tissue-specific protein biomarkers and possible underlying molecular mechanisms associated with renal involvement in SLE, using mass spectrometry (MS)-based proteomics. Methods Kidneys were harvested from female triple congenic B6.NZMsle1/sle2/sle3 lupus mice model, and the respective sex- and age-matched C57BL/6 control mice at 12, 24 and 36 weeks of age, representing pre-symptomatic, established and end-stage LN, respectively. Proteins were extracted from kidneys, purified, reduced, alkylated and digested by trypsin. Purified peptides were separated by liquid chromatography and analysed by high-resolution MS. Data were processed by the Progenesis QIp software, and functional annotation analysis was performed using DAVID bioinformatics resources. Immunofluorescence and multiple reaction monitoring (MRM) MS methods were used to confirm prospective biomarkers in SLE mouse strains as well as human serum samples. Results Proteomic profiling of kidney tissues from SLE and control mice resulted in the identification of more than 3800 unique proteins. Pathway analysis revealed a number of dysregulated molecular pathways that may be mechanistically involved in renal pathology, including phagosome and proximal tubule bicarbonate reclamation pathways. Proteomic analysis supported by human transcriptomic data and pathway analysis revealed Coronin-1A, Ubiquitin-like protein ISG15, and Rho GDP-dissociation inhibitor 2, as potential LN biomarkers. These results were further validated in other SLE mouse strains using MRM-MS. Most importantly, experiments in humans showed that measurement of Coronin-1A in human sera using MRM-MS can segregate LN patients from SLE patients without nephritis with a high sensitivity (100%) and specificity (100%). Conclusions These preliminary findings suggest that serum Coronin-1A may serve as a promising non-invasive biomarker for LN and, upon validation in larger cohorts, may be employed in the future as a screening test for renal disease in SLE patients.

Abstract Background Pathogenesis and aetiology of systemic sclerosis (SSc) are currently unclear, thus rendering disease prognosis, diagnosis and treatment challenging. The aim of this study was to use paired skin biopsy samples from affected and unaffected areas of the same patient, in order to compare the proteomes and identify biomarkers and pathways which are associated with SSc pathogenesis. Methods Biopsies were obtained from affected and unaffected skin areas of SSc patients. Samples were cryo-pulverised and proteins were extracted and analysed using mass spectrometry (MS) discovery analysis. Differentially expressed proteins were revealed after analysis with the Progenesis QIp software. Pathway analysis was performed using the Enrichr Web server. Using specific criteria, fifteen proteins were selected for further validation with targeted-MS analysis. Results Proteomic analysis led to the identification and quantification of approximately 2000 non-redundant proteins. Statistical analysis showed that 169 of these proteins were significantly differentially expressed in affected versus unaffected tissues. Pathway analyses showed that these proteins are involved in multiple pathways that are associated with autoimmune diseases (AIDs) and fibrosis. Fifteen of these proteins were further investigated using targeted-MS approaches, and five of them were confirmed to be significantly differentially expressed in SSc affected versus unaffected skin biopsies. Conclusion Using MS-based proteomics analysis of human skin biopsies from patients with SSc, we identified a number of proteins and pathways that might be involved in SSc progression and pathogenesis. Fifteen of these proteins were further validated, and results suggest that five of them may serve as potential biomarkers for SSc.