Peptidome Analysis Reveals Novel Serum Biomarkers for Children with Autism Spectrum Disorder in China

Serum proteomic analysis uncovers novel serum biomarkers for depression

Objective

The identification of depression primarily relies on the clinical symptoms and psychiatric evaluation of the patient, in the absence of objective and quantifiable biomarkers within clinical settings. This study aimed to explore potential serum biomarkers associated with depression.

Methods

Serum samples from a training group comprising 48 depression patients and 48 healthy controls underwent proteomic analysis. Magnetic bead-based weak cation exchange (MB-WCX) and MALDI-TOF-MS were used in combination. To screen the differential peaks, ClinProTools software was employed. The proteins were identified using LC-MS/MS. ELISA was employed to confirm the expression of entire protein in the serum of the verification cohort, which encompassed 48 individuals who had been diagnosed with Depression and 48 healthy controls who were collected prospectively. Subsequently, logistic regression analysis was conducted to determine the diagnostic efficacy of the aforementioned predictors.

Results

Five potential biomarker peaks indicating depression were identified in serum samples (peak 1, m/z: 1868.21; peak 2, m/z: 1062.35; peak 3, m/z: 1452.12; peak 4, m/z: 1208.72; peak 5, m/z: 1619.58). All of these peaks had higher expression in the pre-therapy group and were confirmed to be Tubulin beta chain (TUBB), Inter-alpha-trypsin inhibitor heavy chain H4 (ITIH4), Complement component 3 (C3), and Complement C4A precursor (C4A) by ELISA validation. Multivariate logistic regression analysis revealed that serum levels of TUBB, ITIH4, C3, and C4A were significant independent risk factors for the development of depression.

Conclusion

Depression is a prevalent psychiatric condition. Timely detection is challenging, resulting in poor prognoses for patients. Our study on plasma proteomics for depression demonstrated that TUBB, ITIH4, C3, and C4A differentiate between depression patients and healthy controls. The proteins that were identified could potentially function as biomarkers for the diagnosis of depression. Pinpointing these biomarkers could enable early identification of depression, which would advance precise treatment.

A Systematic Investigation of Complement and Coagulation-Related Protein in Autism Spectrum Disorder Using Multiple Reaction Monitoring Technology

Autism spectrum disorder (ASD) is one of the common neurodevelopmental disorders in children. Its etiology and pathogenesis are poorly understood. Previous studies have suggested potential changes in the complement and coagulation pathways in individuals with ASD. In this study, using multiple reactions monitoring proteomic technology, 16 of the 33 proteins involved in this pathway were identified as differentially-expressed proteins in plasma between children with ASD and controls. Among them, CFHR3, C4BPB, C4BPA, CFH, C9, SERPIND1, C8A, F9, and F11 were found to be altered in the plasma of children with ASD for the first time. SERPIND1 expression was positively correlated with the CARS score. Using the machine learning method, we obtained a panel composed of 12 differentially-expressed proteins with diagnostic potential for ASD. We also reviewed the proteins changed in this pathway in the brain and blood of patients with ASD. The complement and coagulation pathways may be activated in the peripheral blood of children with ASD and play a key role in the pathogenesis of ASD.

A study of genetic heterogeneity in autism spectrum disorders based on plasma proteomic and metabolomic analysis: multiomics study of autism heterogeneity

Genetic heterogeneity poses a challenge to research and clinical translation of autism spectrum disorder (ASD). In this study, we conducted a plasma proteomic and metabolomic study of children with ASD with and without risk genes (de novo mutation) and controls to explore the impact of genetic heterogeneity on the search for biomarkers for ASD. In terms of the proteomic and metabolomic profiles, the groups of children with ASD carrying and those not carrying de novo mutation tended to cluster and overlap, and integrating them yielded differentially expressed proteins and differential metabolites that effectively distinguished ASD from controls. The mechanisms associated with them focus on several common and previously reported mechanisms. Proteomics results highlight the role of complement, inflammation and immunity, and cell adhesion. The main pathways of metabolic perturbations include amino acid, vitamin, glycerophospholipid, tryptophan, and glutamates metabolic pathways and solute carriers-related pathways. Integrating the two omics analyses revealed that L-glutamic acid and malate dehydrogenase may play key roles in the pathogenesis of ASD. These results suggest that children with ASD may have important underlying common mechanisms. They are not only potential therapeutic targets for ASD but also important contributors to the study of biomarkers for the disease.

Proteome analysis reveals novel serum biomarkers for Henoch-Schönlein purpura in Chinese children

PURPOSE

Henoch-Schönlein purpura (HSP) is diagnosed based on characteristic skin changes. This study aimed to identify the serum biomarkers of HSP in children.

EXPERIMENTAL DESIGN

We performed proteomic analysis of serum samples from 38 paired pre- and posttherapy HSP patients and 22 healthy controls using a combination of magnetic bead-based weak cation exchange and MALDI-TOF MS. ClinProTools was used to screen the differential peaks. Then, LC-ESI-MS/MS was performed to identify the proteins. ELISA was used to verify the expression of whole protein in the serum of 92 HSP patients, 14 peptic ulcer disease (PUD) patients and 38 healthy controls, which were prospectively collected. Finally, logistic regression analysis was performed to analyze the diagnostic value of the above predictors and existing clinical indicators.

RESULTS

Seven potential HSP serum biomarker peaks (m/z:1228.95, m/z:1781.22, m/z:1468.43, m/z:1619.53, m/z:1868.41, m/z:1694.05, m/z:1743.25) with higher expression in the pretherapy group and one peak (m/z:1947.41) with lower expression in the pretherapy group were all identified as peptide regions of albumin (ALB), complement C4-A precursor (C4A), tubulin beta chain (TUBB), isoform 1 of fibrinogen alpha chain (FGA), and ezrin (EZR). The expression of identified proteins was validated by ELISA. Multivariate logistic regression analysis showed that serum C4A EZR and ALB were independent risk factors for HSP, serum C4A and lgA were independent risk factors for HSPN, and serum D-dimer was an independent risk factor for abdominal HSP.

CONCLUSIONS AND CLINICAL RELEVANCE

These findings revealed the specific etiology of HSP from the perspective of serum proteomics. The identified proteins might serve as potential biomarkers for HSP and HSPN diagnoses.

SIGNIFICANCE

Henoch-Schönlein purpura (HSP) is the most common systemic vasculitis in children, and its diagnosis depends primarily on characteristic skin changes. Early diagnosis of non-rash patients is difficult, especially for abdominal and renal types (Henoch-Schönlein purpura nephritis, HSPN). HSPN has poor outcomes, is diagnosed based on urinary protein and/or haematuria, and cannot be detected early in HSP. Patients with an earlier diagnosis of HSPN appear to have better renal outcomes. Our plasma proteomic analysis of HSP in children revealed that HSP patients could be distinguished from healthy controls and peptic ulcer disease patients using complement C4-A precursor (C4A), ezrin, and albumin. C4A and IgA could distinguish HSPN from HSP in the early stages, and D-dimer was a sensitive index used to distinguish abdominal HSP; identifying these biomarkers could promote the early diagnosis of HSP, especially pediatric HSPN and abdominal HSP, thereby improving precision therapy.

Whole-Transcriptome Analysis of Serum L1CAM-Captured Extracellular Vesicles Reveals Neural and Glycosylation Changes in Autism Spectrum Disorder

The pathophysiology of autistic spectrum disorder (ASD) is not fully understood and there are no diagnostic or predictive biomarkers. Extracellular vesicles (EVs) are cell-derived nano-sized vesicles, carrying nucleic acids, proteins, lipids, and other bioactive substances. As reported, serum neural cell adhesion molecule L1 (L1CAM)-captured EVs (LCEVs) can provide reliable biomarkers for neurological diseases; however, little is known about the LCEVs in children with ASD. The study enrolled 100 children with ASD (2.5-6 years of age; 90 males) and 60 age-matched TD children (54 males) as control. The serum sample was collected and pooled into five ASD subgroups and three TD subgroups (n = 20). LCEVs were isolated and characterized meticulously. Whole-transcriptome of LCEVs was analyzed by lncRNA microarray and RNA-sequencing. All raw data was submitted on GEO Profiles, and GEO accession numbers is GSE186493. RNAs expressed differently in LCEVs from ASD sera vs. TD sera were screened, analyzed, and further validated. A total of 1418 mRNAs, 1745 lncRNAs, and 11 miRNAs were differentially expressed, and most of them were downregulated in ASD. Most RNAs were involved in neuron- and glycan-related networks implicated in ASD. The levels of EDNRA, SLC17A6, HTR3A, OSTC, TMEM165, PC-5p-139289_26, and hsa-miR-193a-5p were validated in at least 15 ASD and 15 TD individual serum samples, which were consistent with the results of transcriptome analysis. In conclusion, whole-transcriptome analysis of serum LCEVs reveals neural and glycosylation changes in ASD, which may help detect predictive biomarkers and molecular mechanisms of ASD, and provide reference for diagnoses and therapeutic management of the disease.

Plasma Proteome Profiling of Patients With In-stent Restenosis by Tandem Mass Tag-Based Quantitative Proteomics Approach

Background

Despite the widespread application of new drug-eluting stents, a considerable portion of patients experience in-stent restenosis (ISR). To date, the pathophysiologic mechanisms of ISR remain poorly understood.

Methods

In this study, we collected plasma samples from ISR patients (n = 29) and non-ISR patients (n = 36) after drug-eluting stent implantation, as well as from healthy controls (HCs) (n = 32). Our goal was to investigate differences in plasma protein profiles using tandem mass tag (TMT) labeling coupled with liquid chromatography and tandem mass spectrometry. The proteomic data were validated by enzyme-linked immunosorbent assay (ELISA). Bioinformatic analyses were conducted to analyze potential pathways and protein-protein interaction (PPI) involved in ISR.

Results

A total of 1,696 proteins were identified, of which 278 differed in protein abundance between non-ISR and HCs, 497 between ISR and HCs, and 387 between ISR and non-ISR, respectively. Bioinformatic analyses, including Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG) and PPI, further demonstrated that differentially abundant proteins between ISR and non-ISR are involved in several crucial biological processes and signaling pathways, such as focal adhesion, platelet activation, Rap1 signaling, regulation of actin cytoskeleton, and cholesterol metabolism. Among the identified differentially abundant proteins in ISR, 170 were increased in abundance relative to both non-ISR patients and HCs. Some of these proteins were identified to have critical functions for atherosclerosis development and might be involved in ISR pathology. Among these proteins, 3 proteins with increased abundance including fetuin-B, apolipoprotein C-III (APOC3), and cholesteryl ester transfer protein (CETP) were confirmed by ELISA.

Conclusions

This is the first study provided a comprehensive proteomic profile to understand ISR pathology, which may help identify early diagnostic biomarkers and therapeutic targets.

A metaproteomic-based gut microbiota profiling in children affected by autism spectrum disorders

During the last decade, the evidences on the relationship between neurodevelopmental disorders and the microbial communities of the intestinal tract have considerably grown. Particularly, the role of gut microbiota (GM) ecology and predicted functions in Autism Spectrum Disorders (ASD) has been especially investigated by 16S rRNA targeted and shotgun metagenomics, trying to assess disease signature and their correlation with cognitive impairment or gastrointestinal (GI) manifestations of the disease. Herein we present a metaproteomic approach to point out the microbial gene expression profiles, their functional annotations, and the taxonomic distribution of gut microbial communities in ASD children. We pursued a LC-MS/MS based investigation, to compare the GM profiles of patients with those of their respective relatives and aged-matched controls, providing a quantitative evaluation of bacterial metaproteins by SWATH analysis. All data were managed by a multiple step bioinformatic pipeline, including network analysis. In particular, comparing ASD subjects with CTRLs, up-regulation was found for some metaproteins associated with Clostridia and with carbohydrate metabolism (glyceraldehyde-3-phosphate and glutamate dehydrogenases), while down-regulation was observed for others associated with Bacteroidia (SusC and SusD family together with the TonB dependent receptor). Moreover, network analysis highlighted specific microbial correlations among ASD subgroups characterized by different functioning levels and GI symptoms. SIGNIFICANCE: To the best of our knowledge, this study represents the first metaproteomic investigation on the gut microbiota of ASD children compared with relatives and age-matched CTRLs. Remarkably, the applied SWATH methodology allowed the attribution of differentially regulated functions to specific microbial taxa, offering a novel and complementary point of view with respect to previous studies.

Plasma SerpinA5 in conjunction with uterine artery pulsatility index and clinical risk factor for the early prediction of preeclampsia

Object

This study aimed to combine plasma protein SerpinA5 with uterine artery doppler ultrasound and clinical risk factor during the first trimester for prediction of preeclampsia.

Methods and materials

This study was a nested cohort study and was divided into the screening set and developing set. The plasma was collected during the first trimester (11⁺⁰–13⁺⁶ weeks), at the same time, UtA-PI was detected and recorded with four-dimensional color Doppler ultrasound. These pregnancies were followed up until after delivery. The plasma proteins were examined using ultra-performance liquid chromatography–mass spectrometry (UPLC-MS) and enzyme linked immunosorbent assay (ELISA). Placental samples preserved after delivery were analysed by immunohistochemistry. Clinical risk factors were obtained from medical records or antenatal questionnaires. Upregulation or downregulation of SerpinA5 expression in TEV-1 cells was performed to investigate the role of SerpinA5 in trophoblasts invasion.

Results

We demonstrated that SerpinA5 levels were greater not only in preeclampsia placental tissue but also in plasma (both p<0.05), and we found that SerpinA5 may interfere with trophoblastic cell invasion by inhibiting MSP. SerpinA5 may be a potential predictor of preeclampsia. What is more, the sensitivity and specificity of predictive power were strengthened when plasma SerpinA5 was combined with UtA-PI and pre-pregnancy BMI & family history of PE for prediction of preeclampsia.

Conclusion

These findings showed that placenta-derived plasma SerpinA5 may be a novel biomarker for preeclampsia, which together with uterine artery Doppler ultrasound and clinical risk factor can more effectively predict preeclampsia.

Convergent Canonical Pathways in Autism Spectrum Disorder from Proteomic, Transcriptomic and DNA Methylation Data

Autism Spectrum Disorder (ASD) is a complex neurodevelopmental disorder with extensive genetic and aetiological heterogeneity. While the underlying molecular mechanisms involved remain unclear, significant progress has been facilitated by recent advances in high-throughput transcriptomic, epigenomic and proteomic technologies. Here, we review recently published ASD proteomic data and compare proteomic functional enrichment signatures with those of transcriptomic and epigenomic data. We identify canonical pathways that are consistently implicated in ASD molecular data and find an enrichment of pathways involved in mitochondrial metabolism and neurogenesis. We identify a subset of differentially expressed proteins that are supported by ASD transcriptomic and DNA methylation data. Furthermore, these differentially expressed proteins are enriched for disease phenotype pathways associated with ASD aetiology. These proteins converge on protein–protein interaction networks that regulate cell proliferation and differentiation, metabolism, and inflammation, which demonstrates a link between canonical pathways, biological processes and the ASD phenotype. This review highlights how proteomics can uncover potential molecular mechanisms to explain a link between mitochondrial dysfunction and neurodevelopmental pathology.

Variability of multi-omics profiles in a population-based child cohort

BACKGROUND

Multiple omics technologies are increasingly applied to detect early, subtle molecular responses to environmental stressors for future disease risk prevention. However, there is an urgent need for further evaluation of stability and variability of omics profiles in healthy individuals, especially during childhood.

METHODS

We aimed to estimate intra-, inter-individual and cohort variability of multi-omics profiles (blood DNA methylation, gene expression, miRNA, proteins and serum and urine metabolites) measured 6 months apart in 156 healthy children from five European countries. We further performed a multi-omics network analysis to establish clusters of co-varying omics features and assessed the contribution of key variables (including biological traits and sample collection parameters) to omics variability.

RESULTS

All omics displayed a large range of intra- and inter-individual variability depending on each omics feature, although all presented a highest median intra-individual variability. DNA methylation was the most stable profile (median 37.6% inter-individual variability) while gene expression was the least stable (6.6%). Among the least stable features, we identified 1% cross-omics co-variation between CpGs and metabolites (e.g. glucose and CpGs related to obesity and type 2 diabetes). Explanatory variables, including age and body mass index (BMI), explained up to 9% of serum metabolite variability.

CONCLUSIONS

Methylation and targeted serum metabolomics are the most reliable omics to implement in single time-point measurements in large cross-sectional studies. In the case of metabolomics, sample collection and individual traits (e.g. BMI) are important parameters to control for improved comparability, at the study design or analysis stage. This study will be valuable for the design and interpretation of epidemiological studies that aim to link omics signatures to disease, environmental exposures, or both.

A proteomic analysis of urine biomarkers in autism spectrum disorder

Autism spectrum disorder (ASD) is a neurodevelopmental condition characterized by early-onset social-communication challenges, restricted and repetitive behaviors, or unusual sensory-motor behaviors. A lack of specific biomarkers hinders the early diagnosis and treatment of this disease in many children. This study analyzes and validates potential urinary biomarkers using mass spectrometry proteomics. Global proteomics profiles of urine from 19 ASD patients and 19 healthy control subjects were compared to identify significantly changed proteins. These proteins were validated with targeted proteomics using parallel reaction monitoring (PRM) in an independent validation set consisting of samples from 40 ASD patients and 38 healthy controls. A total of 34 significantly changed proteins were found in the discovery set, among which seven proteins were identified as potential biomarkers for ASD through PRM assays in the validation set. Of these seven proteins, immunoglobulin kappa variable 4-1, immunoglobulin kappa variable 3-20, and immunoglobulin lambda variable 1-51 were up-regulated, while ATP synthase F1 subunit alpha, 10 kDa heat shock protein, apolipoprotein C-III, and arylsulfatase F were down-regulated. Six of these seven proteins support previous findings that ASD is accompanied by altered immune response and lipid metabolism, as well as mitochondrial dysfunction. This study lays the groundwork for additional research using biomarkers to clinically diagnose ASD. The proteomics and PRM raw data of this study have been deposited under the accession number IPX0002592000 at iProX. SIGNIFICANCE: This study identified 34 proteins in urine of ASD patients that were significantly changed from the urinary proteins of healthy subjects using LC-MS/MS-based proteomics in a discovery set. Seven of these proteins were validated by PRM analysis in an independent validation set. This report represents the first description of combined label-free quantitative proteomics and PRM analysis of targeted proteins for discovery of ASD urinary biomarkers. The results will be helpful for early diagnosis and can provide additional insight into the molecular mechanisms of ASD.

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.

Sex-Specific Social Behavior and Amygdala Proteomic Deficits in Foxp2 +/- Mutant Mice

In humans, mutations in the transcription factor encoding gene, FOXP2, are associated with language and Autism Spectrum Disorders (ASD), the latter characterized by deficits in social interactions. However, little is known regarding the function of Foxp2 in male or female social behavior. Our previous studies in mice revealed high expression of Foxp2 within the medial subnucleus of the amygdala (MeA), a limbic brain region highly implicated in innate social behaviors such as mating, aggression, and parental care. Here, using a comprehensive panel of behavioral tests in male and female Foxp2 ^+/- heterozygous mice, we investigated the role Foxp2 plays in MeA-linked innate social behaviors. We reveal significant deficits in olfactory processing, social interaction, mating, aggressive, and parental behaviors. Interestingly, some of these deficits are displayed in a sex-specific manner. To examine the consequences of Foxp2 loss of function specifically in the MeA, we conducted a proteomic analysis of microdissected MeA tissue. This analyses revealed putative sex differences expression of a host of proteins implicated in neuronal communication, connectivity, and dopamine signaling. Consistent with this, we discovered that MeA Foxp2-lineage cells were responsive to dopamine with differences between males and females. Thus, our findings reveal a central and sex-specific role for Foxp2 in social behavior and MeA function.

Proteomics and Metabolomics Approaches towards a Functional Insight onto AUTISM Spectrum Disorders: Phenotype Stratification and Biomarker Discovery

Autism spectrum disorders (ASDs) are neurodevelopmental disorders characterized by behavioral alterations and currently affect about 1% of children. Significant genetic factors and mechanisms underline the causation of ASD. Indeed, many affected individuals are diagnosed with chromosomal abnormalities, submicroscopic deletions or duplications, single-gene disorders or variants. However, a range of metabolic abnormalities has been highlighted in many patients, by identifying biofluid metabolome and proteome profiles potentially usable as ASD biomarkers. Indeed, next-generation sequencing and other omics platforms, including proteomics and metabolomics, have uncovered early age disease biomarkers which may lead to novel diagnostic tools and treatment targets that may vary from patient to patient depending on the specific genomic and other omics findings. The progressive identification of new proteins and metabolites acting as biomarker candidates, combined with patient genetic and clinical data and environmental factors, including microbiota, would bring us towards advanced clinical decision support systems (CDSSs) assisted by machine learning models for advanced ASD-personalized medicine. Herein, we will discuss novel computational solutions to evaluate new proteome and metabolome ASD biomarker candidates, in terms of their recurrence in the reviewed literature and laboratory medicine feasibility. Moreover, the way to exploit CDSS, performed by artificial intelligence, is presented as an effective tool to integrate omics data to electronic health/medical records (EHR/EMR), hopefully acting as added value in the near future for the clinical management of ASD.

Peptidome characterization of ovarian cancer serum and the identification of tumor suppressive peptide ZYX36-58

Background

Several serum biomarkers, including miRNA, mRNA, protein and peptides in cancer patients are also important mediators of cancer progression.

Methods

The differentially expressed peptides between the serum of ovarian cancer patients and healthy controls were analyzed by liquid chromatography-tandem mass spectrometry (LC-MS/MS). The function of the peptides was analyzed by CCK8, transwell, wound healing, and flow cytometry analysis. And the mechanism of the peptides was analyzed by peptide pull down, and high-throughput RNA-sequencing.

Results

A total of 7 and 46 peptides were significantly up-regulated and down-regulated in the serum of ovarian cancer patients, respectively. The precursor proteins of the differentially expressed peptides mainly involved in the complement and coagulation cascades, platelet activation, phagosome and focal adhesion pathways. Interestingly, focal adhesion, platelet activation, platelet-cancer cell interaction, complement activation, coagulation cascades and phagosome formation are all critical factors for cancer initiation or progression, which indicated that the peptides may play a crucial role in cancer development. And we identified one peptide, ZYX_36-58, which was down-regulated in the serum of ovarian cancer patients, significantly inhibited invasion and migration and promoted the apoptosis of ovarian cancer cells. Mechanistic study indicated that ZYX_36-58 interacted with and increased the protein level of the antiangiogenic protein thrombospondin-1 (TSP1), which has a tumor suppressive effect on ovarian cancer.

Conclusions

ZYX_36-58, which was significantly down-regulated in the serum of ovarian cancer patients can significantly inhibit cell invasion, migration and promote apoptosis of ovarian cancer cells by binding and up-regulating TSP1 protein expression.

Genomics, transcriptomics, proteomics and big data analysis in the discovery of new diagnostic markers and targets for therapy development

Highly complex endophenotypes and underlying molecular mechanisms have prevented effective diagnosis and treatment of autism spectrum disorder. Despite extensive studies to identify relevant biosignatures, no biomarker and therapeutic targets are available in the current clinical practice. While our current knowledge is still largely incomplete, -omics technology and machine learning-based big data analysis have provided novel insights on the etiology of autism spectrum disorders, elucidating systemic impairments that can be translated into biomarker and therapy target candidates. However, more integrated and sophisticated approaches are vital to realize molecular stratification and individualized treatment strategy. Ultimately, systemic approaches based on -omics and big data analysis will significantly contribute to more effective biomarker and therapy development for autism spectrum disorder.

Molecular Variants in Human Trace Amine-Associated Receptors and Their Implications in Mental and Metabolic Disorders

We provide a comprehensive review of the available evidence on the pathophysiological implications of genetic variants in the human trace amine-associated receptor (TAAR) superfamily. Genes coding for trace amine-associated receptors (taars) represent a multigene family of G-protein-coupled receptors, clustered to a small genomic region of 108 kb located in chromosome 6q23, which has been consistently identified by linkage analyses as a susceptibility locus for schizophrenia and affective disorders. Most TAARs are expressed in brain areas involved in emotions, reward and cognition. TAARs are activated by endogenous trace amines and thyronamines, and evidence for a modulatory action on other monaminergic systems has been reported. Therefore, linkage analyses were followed by fine mapping association studies in schizophrenia and affective disorders. However, none of these reports has received sufficient universal replication, so their status remains uncertain. Single nucleotide polymorphisms in taars have emerged as susceptibility loci from genome-wide association studies investigating migraine and brain development, but none of the detected variants reached the threshold for genome-wide significance. In the last decade, technological advances enabled single-gene or whole-exome sequencing, thus allowing the detection of rare genetic variants, which may have a greater impact on the risk of complex disorders. Using these approaches, several taars (especially taar1) variants have been detected in patients with mental and metabolic disorders, and in some cases, defective receptor function has been demonstrated in vitro. Finally, with the use of transcriptomic and peptidomic techniques, dysregulations of TAARs (especially TAAR6) have been identified in brain disorders characterized by cognitive impairment.

Hypertensive disorders of pregnancy: Strategy to develop clinical peptide biomarkers for more accurate evaluation of the pathophysiological status of this syndrome

Hypertensive disorders of pregnancy (HDP) is the most common and widely known as serious complication of pregnancy. As this syndrome is a major leading cause of maternal, fetal, and neonatal morbidity/mortality worldwide, many studies have sought to identify candidate molecules as potential disease biomarkers (DBMs) for use in clinical examinations. Accumulating evidence over the past 2 decades that the many proteolytic peptides appear in human humoral fluids, including peripheral blood, in association with an individual's health condition. This review provides the potential utility of peptidomic analysis for monitoring for pathophysiological status in HDP, and presents an overview of current status of peptide quantification technology. Especially, the technical limitations of the methods used for DBM discovery in the blood are discussed.

ANKRD22 is involved in the progression of prostate cancer

Prostate cancer is a common malignant tumor in elderly men. As a novel metabolic-reprogramming molecule, the role of ankyrin repeat domain 22 (ANKRD22) in the tumorigenesis and progression of prostate cancer remains unknown. In the present study, mouse monoclonal antibodies against human ANKRD22 were prepared using recombinant ANKRD22 from prokaryotic expression and validated. Subsequently, these antibodies were used to evaluate ANKRD22 levels via immunohistochemical staining in prostate cancer tissues. Finally, the association between ANKRD22 levels and prostate cancer progression was analyzed in 636 samples of prostate cancer using The Cancer Genome Atlas (TCGA) database. A total of four anti-ANKRD22 monoclonal antibodies were generated and validated, which could be effectively blocked by recombinant ANKRD22 protein. Using these antibodies for immunohistochemical staining, ANKRD22 was detected in prostate cancer cells in both the cytoplasm and nucleus. Bioinformatics analysis demonstrated that the mRNA level of ANKRD22 was inversely associated with prostate cancer stage (P<0.05) and Gleason score (P<0.01) in TCGA database. Patients with higher ANKRD22 mRNA levels exhibited longer disease-free survival following radical prostatectomy. These findings suggest that ANKRD22 may negatively regulate the progression of prostate cancer. The prepared ANKRD22 antibodies with high specificity provide a powerful tool in ANKRD22 research.

Advances in Biomarker Studies in Autism Spectrum Disorders

Autism spectrum disorder (ASD) is a neurological and developmental condition that begins early in childhood and lasts throughout life. The epidemiology of ASD is continuously increasing all over the world with huge social and economical burdens. As the etiology of autism is not completely understood, there is still no medication available for the treatment of this disorder. However, some behavioral interventions are available to improve the core and associated symptoms of autism, particularly when initiated at an early stage. Thus, there is an increasing demand for finding biomarkers for ASD. Although diagnostic biomarkers have not yet been established, research efforts have been carried out in neuroimaging and biological analyses including genomics and gene testing, proteomics, metabolomics, transcriptomics, and studies of the immune system, inflammation, and microRNAs. Here, we will review the current progress in these fields and focus on new methods, developments, research strategies, and studies of blood-based biomarkers.