Blood Proteomics Analysis Reveals Potential Biomarkers and Convergent Dysregulated Pathways in Autism Spectrum Disorder: A Pilot Study.

Autism spectrum disorder (ASD) is an umbrella term that encompasses several disabling neurodevelopmental conditions. These conditions are characterized by impaired manifestation in social and communication skills with repetitive and restrictive behaviors or interests. Thus far, there are no approved biomarkers for ASD screening and diagnosis; also, the current diagnosis depends heavily on a physician's assessment and family's awareness of ASD symptoms. Identifying blood proteomic biomarkers and performing deep blood proteome profiling could highlight common underlying dysfunctions between cases of ASD, given its heterogeneous nature, thus laying the foundation for large-scale blood-based biomarker discovery studies. This study measured the expression of 1196 serum proteins using proximity extension assay (PEA) technology. The screened serum samples included ASD cases (n = 91) and healthy controls (n = 30) between 6 and 15 years of age. Our findings revealed 251 differentially expressed proteins between ASD and healthy controls, of which 237 proteins were significantly upregulated and 14 proteins were significantly downregulated. Machine learning analysis identified 15 proteins that could be biomarkers for ASD with an area under the curve (AUC) = 0.876 using support vector machine (SVM). Gene Ontology (GO) analysis of the top differentially expressed proteins (TopDE) and weighted gene co-expression analysis (WGCNA) revealed dysregulation of SNARE vesicular transport and ErbB pathways in ASD cases. Furthermore, correlation analysis showed that proteins from those pathways correlate with ASD severity. Further validation and verification of the identified biomarkers and pathways are warranted.

Blood-Based Proteomic Profiling Identifies Potential Biomarker Candidates and Pathogenic Pathways in Dementia.

Dementia is a progressive and debilitating neurological disease that affects millions of people worldwide. Identifying the minimally invasive biomarkers associated with dementia that could provide insights into the disease pathogenesis, improve early diagnosis, and facilitate the development of effective treatments is pressing. Proteomic studies have emerged as a promising approach for identifying the protein biomarkers associated with dementia. This pilot study aimed to investigate the plasma proteome profile and identify a panel of various protein biomarkers for dementia. We used a high-throughput proximity extension immunoassay to quantify 1090 proteins in 122 participants (22 with dementia, 64 with mild cognitive impairment (MCI), and 36 controls with normal cognitive function). Limma-based differential expression analysis reported the dysregulation of 61 proteins in the plasma of those with dementia compared with controls, and machine learning algorithms identified 17 stable diagnostic biomarkers that differentiated individuals with AUC = 0.98 ± 0.02. There was also the dysregulation of 153 plasma proteins in individuals with dementia compared with those with MCI, and machine learning algorithms identified 8 biomarkers that classified dementia from MCI with an AUC of 0.87 ± 0.07. Moreover, multiple proteins selected in both diagnostic panels such as NEFL, IL17D, WNT9A, and PGF were negatively correlated with cognitive performance, with a correlation coefficient (r2) ≤ -0.47. Gene Ontology (GO) and pathway analysis of dementia-associated proteins implicated immune response, vascular injury, and extracellular matrix organization pathways in dementia pathogenesis. In conclusion, the combination of high-throughput proteomics and machine learning enabled us to identify a blood-based protein signature capable of potentially differentiating dementia from MCI and cognitively normal controls. Further research is required to validate these biomarkers and investigate the potential underlying mechanisms for the development of dementia.

High-throughput autoantibody screening identifies differentially abundant autoantibodies in autism spectrum disorder.

Introduction: Autism spectrum disorder (ASD) is a neurodevelopmental condition characterized by defects in two core domains, social/communication skills and restricted/repetitive behaviors or interests. There is no approved biomarker for ASD diagnosis, and the current diagnostic method is based on clinical manifestation, which tends to vary vastly between the affected individuals due to the heterogeneous nature of ASD. There is emerging evidence that supports the implication of the immune system in ASD, specifically autoimmunity; however, the role of autoantibodies in ASD children is not yet fully understood. Materials and methods: In this study, we screened serum samples from 93 cases with ASD and 28 healthy controls utilizing high-throughput KoRectly Expressed (KREX) i-Ome protein-array technology. Our goal was to identify autoantibodies with differential expressions in ASD and to gain insights into the biological significance of these autoantibodies in the context of ASD pathogenesis. Result: Our autoantibody expression analysis identified 29 differential autoantibodies in ASD, 4 of which were upregulated and 25 downregulated. Subsequently, gene ontology (GO) and network analysis showed that the proteins of these autoantibodies are expressed in the brain and involved in axonal guidance, chromatin binding, and multiple metabolic pathways. Correlation analysis revealed that these autoantibodies negatively correlate with the age of ASD subjects. Conclusion: This study explored autoantibody reactivity against self-antigens in ASD individuals' serum using a high-throughput assay. The identified autoantibodies were reactive against proteins involved in axonal guidance, synaptic function, amino acid metabolism, fatty acid metabolism, and chromatin binding.

Profiling the autoantibody repertoire reveals autoantibodies associated with mild cognitive impairment and dementia.

Background: Dementia is a debilitating neurological disease affecting millions of people worldwide. The exact mechanisms underlying the initiation and progression of the disease remain to be fully defined. There is an increasing body of evidence for the role of immune dysregulation in the pathogenesis of dementia, where blood-borne autoimmune antibodies have been studied as potential markers associated with pathological mechanisms of dementia. Methods: This study included plasma from 50 cognitively normal individuals, 55 subjects with MCI (mild cognitive impairment), and 22 subjects with dementia. Autoantibody profiling for more than 1,600 antigens was performed using a high throughput microarray platform to identify differentially expressed autoantibodies in MCI and dementia. Results: The differential expression analysis identified 33 significantly altered autoantibodies in the plasma of patients with dementia compared to cognitively normal subjects, and 38 significantly altered autoantibodies in the plasma of patients with dementia compared to subjects with MCI. And 20 proteins had significantly altered autoantibody responses in MCI compared to cognitively normal individuals. Five autoantibodies were commonly dysregulated in both dementia and MCI, including anti-CAMK2A, CKS1B, ETS2, MAP4, and NUDT2. Plasma levels of anti-ODF3, E6, S100P, and ARHGDIG correlated negatively with the cognitive performance scores (MoCA) (r2 -0.56 to -0.42, value of p < 0.001). Additionally, several proteins targeted by autoantibodies dysregulated in dementia were significantly enriched in the neurotrophin signaling pathway, axon guidance, cholinergic synapse, long-term potentiation, apoptosis, glycolysis and gluconeogenesis. Conclusion: We have shown multiple dysregulated autoantibodies in the plasma of subjects with MCI and dementia. The corresponding proteins for these autoantibodies are involved in neurodegenerative pathways, suggesting a potential impact of autoimmunity on the etiology of dementia and the possible benefit for future therapeutic approaches. Further investigations are warranted to validate our findings.

Paving the Way toward Personalized Medicine: Current Advances and Challenges in Multi-OMICS Approach in Autism Spectrum Disorder for Biomarkers Discovery and Patient Stratification.

Autism spectrum disorder (ASD) is a multifactorial neurodevelopmental disorder characterized by impairments in two main areas: social/communication skills and repetitive behavioral patterns. The prevalence of ASD has increased in the past two decades, however, it is not known whether the evident rise in ASD prevalence is due to changes in diagnostic criteria or an actual increase in ASD cases. Due to the complexity and heterogeneity of ASD, symptoms vary in severity and may be accompanied by comorbidities such as epilepsy, attention deficit hyperactivity disorder (ADHD), and gastrointestinal (GI) disorders. Identifying biomarkers of ASD is not only crucial to understanding the biological characteristics of the disorder, but also as a detection tool for its early screening. Hence, this review gives an insight into the main areas of ASD biomarker research that show promising findings. Finally, it covers success stories that highlight the importance of precision medicine and the current challenges in ASD biomarker discovery studies.

Laboratory challenges in the diagnosis of hepatitis E virus.

Hepatitis E virus (HEV) is an RNA virus that is an important cause of both acute and chronic hepatitis worldwide. To date, there are eight HEV genotypes that can infect mammals. HEV-1 and HEV-2 infect exclusively humans, while HEV-3 and HEV-4 infect humans and various animals, mainly pigs and deer. Additionally, two new genotypes (HEV-5 and HEV-6) infect mainly wild boar. Recently, newly discovered genotypes HEV-7 and HEV-8 were found to infect camels and possibly humans. Nevertheless, the epidemiological distribution of HEV-7 is not well established. HEV-8 is another newly discovered genotype that was identified in 2016 in Chinese Bactrian camels. Although faecal-oral transmission is the most common route of HEV transmission, HEV can be vertically transmitted from infected mothers to their fetuses. HEV may also spread by zoonotic transmission from infected animals to humans and through person-to-person contact. Nowadays, since the number of reported cases linked to blood donations is increasing annually, HEV is recognized as a transfusion-transmitted virus. Laboratory diagnostic techniques vary in their specificity and sensitivity for HEV detection. Direct techniques allow for detection of the viral proteins, antigens and viral nucleic acid, while HEV-specific IgG and IgM antibodies can help establish a diagnosis in acute and chronic infections. In this review, we will discuss recent technologies in the laboratory diagnosis of HEV, including serological and molecular methods to assess the specificity and sensitivity of currently available HEV commercial assays.

Performance evaluation of five commercial assays in assessing seroprevalence of HEV antibodies among blood donors.

INTRODUCTION: Although hepatitis E virus (HEV) is mainly transmitted via the faecal-oral route, the rate of HEV transmission via blood donation is on the rise. However, the seroprevalence of HEV among blood donors is not well established and is thought to be affected by the type of diagnostic assay used. We aimed to evaluate performance and correlation among widely used commercial diagnostic assays for the seroprevalence assessment of HEV-IgM/IgG among blood donors. METHODOLOGY: A total of 1049 blood donor samples were tested for HEV IgG and IgM using different enzyme immunoassays (Wantai, Eruoimmune, MP diagnostics, Mikrogen immunoblot, HEV-IgM rapid test). The performance of each assay was evaluated according to our established silver standard value based on three or more IgG concordant assay results. RESULTS: HEV seroprevalence varied considerably using these assays, ranging from 10.1 % (Euroimmune-ELISA) to 18.0 % (Wanti-ELISA) for HEV-IgG, and from 0.2 % (Wanti-ELISA) to 2.6 % (MP Rapid test) for HEV-IgM. A total of 155 of 216 (71.6%) samples tested positive for HEV-IgG by three or more concordant assays. On the other hand, IgM assays showed poor agreement as only 7.6 % (4/52) of the specimens were positive according to three or more concordant assay test results. All HEV-IgG assays revealed high sensitivity and specificity (ranging 96.5-100 %),and excellent Kappa concordance (0.88-0.95), except for Euroimmun ELISA (sensitivity=61.5 %, kappa=0.63). MP ELISA showed the highest levels of sensitivity (100 %) and specificity (98.5 %). CONCLUSIONS: Due to discrepancies in the performance of various IgG and IgM assays, seroprevalence studies should be based on furher confirmatory testing for decisive conclusions to be reached.