Effects of arsenic exposure on blood trace element levels in rats and sex differences.

Arsenic (As) is a widespread environmental metalloid and human carcinogen, and its exposure is associated with a wide range of toxic effects, leading to serious health hazards. As poisoning is a complex systemic multi-organ and multi-system damage disease. In this study, a rat model of As poisoning was established to investigate the levels of trace elements in the blood of rats and sex differences in the effect of As on every trace elements in rat blood. Twenty 6-week-old SD (Sprague Dawley) rats were randomly divided into the control group and the As-exposed group. After 3 months, the contents of 19 elements including As in the blood were detected in these two groups by inductively coupled plasma mass spectrometry (ICP-MS). As levels in the blood of As-exposed rats were significantly higher than those in the control group, with increased levels of Rb, Sr, Cs and Ce, and decreased levels of Pd. As showed a significant positive correlation with Rb. There were significant sex differences in blood Se, Pd, Eu, Dy, Ho, and Au levels in the As-exposed group. The results showed that As exposure can lead to an increase of As content in blood and an imbalance of some elements. There were sex differences in the concentration and the correlation between elements of some elements. Elemental imbalances may affect the toxic effects of As and play a synergistic or antagonistic role in As toxicity.

Deep transfer learning of structural magnetic resonance imaging fused with blood parameters improves brain age prediction.

Machine learning has been applied to neuroimaging data for estimating brain age and capturing early cognitive impairment in neurodegenerative diseases. Blood parameters like neurofilament light chain are associated with aging. In order to improve brain age predictive accuracy, we constructed a model based on both brain structural magnetic resonance imaging (sMRI) and blood parameters. Healthy subjects (n = 93; 37 males; aged 50-85 years) were recruited. A deep learning network was firstly pretrained on a large set of MRI scans (n = 1,481; 659 males; aged 50-85 years) downloaded from multiple open-source datasets, to provide weights on our recruited dataset. Evaluating the network on the recruited dataset resulted in mean absolute error (MAE) of 4.91 years and a high correlation (r = .67, p <.001) against chronological age. The sMRI data were then combined with five blood biochemical indicators including GLU, TG, TC, ApoA1 and ApoB, and 9 dementia-associated biomarkers including ApoE genotype, HCY, NFL, TREM2, Aß40, Aß42, T-tau, TIMP1, and VLDLR to construct a bilinear fusion model, which achieved a more accurate prediction of brain age (MAE, 3.96 years; r = .76, p <.001). Notably, the fusion model achieved better improvement in the group of older subjects (70-85 years). Extracted attention maps of the network showed that amygdala, pallidum, and olfactory were effective for age estimation. Mediation analysis further showed that brain structural features and blood parameters provided independent and significant impact. The constructed age prediction model may have promising potential in evaluation of brain health based on MRI and blood parameters.

Proteomic Profiling of Cerebrum Mitochondria, Myelin Sheath, and Synaptosome Revealed Mitochondrial Damage and Synaptic Impairments in Association with 3 × Tg-AD Mice Model.

Alzheimer's disease (AD) is the most common age-associated dementia with complex pathological hallmarks. Mitochondrion, synaptosome, and myelin sheath appear to be vulnerable and play a key role in the pathogenesis of AD. To clarify the early mechanism associated with AD, followed by subcellular components separation, we performed iTRAQ (isobaric tags for relative and absolute quantification)-based proteomics analysis to simultaneously investigate the differentially expressed proteins (DEPs) within the mitochondria, synaptosome, and myelin sheath in the cerebrum of the 6-month-old triple transgenic AD (3 × Tg-AD) and 6-month-old wild-type (WT) mice. A large number of DEPs between the AD and WT mice were identified. Most of them are related to mitochondria and synaptic dysfunction and cytoskeletal protein change. Differential expressions of Lrpprc, Nefl, and Sirpa were verified by Western blot analysis. The results suggest that decreased energy metabolism, impaired amino acid metabolism and neurotransmitter synthesis, increase compensatory fatty acid metabolism, up-regulated cytoskeletal protein expression, and oxidative stress are the early events of AD. Among these, mitochondrial damage, synaptic dysfunction, decreased energy metabolism, and abnormal amino acid metabolism are the most significant events. The results indicate that it is feasible to separate and simultaneously perform proteomics analysis on the three subcellular components.

Ultrasensitive Detection of Exosome Using Biofunctionalized Gold Nanorods on a Silver-Island Film.

Exosomes are often a promising source of biomarkers for cancer diagnosis in the early stages. Therefore, it is important to develop a sensitive and low-cost detection method. Here, we introduce a new substrate using gold nanorods (GNRs) on a silver-island film that produces a 360-fold AF647 molecule fluorescence enhancement compared to glass. The amplified fluorescence was proven theoretically by using finite difference time-domain simulation (FDTD). Utilizing the enhanced fluorescence from the substrate, GNRs attached with the biomolecules and created a sandwich immunoassay that can significantly detect human CD63 antigen on the exosome. By applying the method, the detection limit of mouse IgG goes down to 0.3 ng/mL, which is considerably better than the existing methods. Moreover, the sensitivity and accuracy for clinical plasma from six patients confirm its diagnostic feasibility. The proposed substrate can be uniformly extended to the identification of other biomarkers by modifying the antibodies on the surfaces of the GNRs.

Investigating the Neurotoxic Impacts of Arsenic and the Neuroprotective Effects of Dictyophora Polysaccharide Using SWATH-MS-Based Proteomics.

Arsenic (As) is one of the most important toxic elements in the natural environment. Currently, although the assessment of the potential health risks of chronic arsenic poisoning has received great attention, the research on the effects of arsenic on the brain is still limited. It has been reported that dictyophora polysaccharide (DIP), a common bioactive natural compound found in dietary plants, could reduce arsenic toxicity. Following behavioral research, comparative proteomics was performed to explore the molecular mechanism of arsenic toxicity to the hippocampi of SD (Sprague Dawley) rats and the protective effect of DIP. The results showed that exposure to arsenic impaired the spatial learning and memory ability of SD rats, while DIP treatment improved both the arsenic-exposed rats. Proteomic analysis showed that arsenic exposure dysregulated the expression of energy metabolism, apoptosis, synapse, neuron, and mitochondria related proteins in the hippocampi of arsenic-exposed rats. However, DIP treatment reversed or restored the expression levels of these proteins, thereby improving the spatial learning and memory ability of arsenic-exposed rats. This study is the first to use high-throughput proteomics to reveal the mechanism of arsenic neurotoxicity in rats as well as the protective mechanism of DIP against arsenic neurotoxicity.

Targeted metabolomics study of early pathological features in hippocampus of triple transgenic Alzheimer's disease male mice.

Alzheimer's disease (AD) is a serious neurodegenerative disease in people of age 65 or above. The detailed etiology and pathogenesis of AD have not been elucidated yet. In this study, the hippocampi of 2- and 6-month-old triple transgenic Alzheimer's disease male mice and age-sex-matched wild-type (WT) mice were analyzed by using targeted metabolomics approach. Compared with WT mice, 24 and 60 metabolites were found with significant differences in 2- and 6-month-old AD mice. Among these, 14 metabolites were found common while 10 metabolites showed consistent variable trends in both groups. These differential metabolites are found associated with amino acid, lipid, vitamin, nucleotide-related base, neurotransmitter and energy metabolisms, and oxidative stress. The results suggest that these differential metabolites might play a critical role in AD pathophysiology, and may serve as potential biomarkers for AD. Moreover, the results highlight the involvement of abnormal purine, pyrimidine, arginine, and proline metabolism, along with glycerophospholipid metabolism in early pathology of AD. For the first time, several differential metabolites are found to be associated with AD in this study. Targeted metabolomics can be used for rapid and accurate quantitative analysis of specific target metabolites associated with AD.

Metabolic characteristics of plasma bile acids in patients with intrahepatic cholestasis of pregnancy-mass spectrometric study.

INTRODUCTION: Intrahepatic cholestasis of pregnancy (ICP) is one of the more common complications in the middle and late stages of pregnancy, which requires early detection and intervention. OBJECTIVE: The aim of the study is to investigate the changes in the metabolic profile of bile acids (BAs) in plasma of pregnant women with ICP and to look biomarkers for the diagnosis and grading of ICP, and to explore the disease mechanism. METHODS: The targeted metabolomics based on high-performance liquid chromatography-tandem mass spectrometry (HPLC-MS/MS) was used to analyze plasma BAs. RESULTS: Twenty-seven BAs can be quantified in all participants. Among them, 22 BAs were identified as differential BAs between ICP and control groups. Five BAs include 3ß-CA, 3ß-DCA, CDCA-3Gln, NCA, and Tß-MCA, were found to be associated with ICP for the first time. Nine BAs include NCA, GCA, GCDCA, GHCA, GUDCA, HCA, TCA, TCDCA and THCA, can be used as possible ICP diagnostic biomarkers. Four BAs, i.e., GLCA, THCA, GHCA and TLCA-3S may be used as potential biomarkers for ICP grading. CONCLUSION: There were significant differences in plasma BA profiles between ICP patients and the control. The BA profiles of mild ICP group and severe ICP group partially overlapped. Potential diagnostic and grading BA markers were identified. A significant characteristic of ICP group was the increase of conjugated BAs. A mechanism to sustain the equilibrium of BA metabolism and adaptive response has been developed in ICP patients to accelerate excretion and detoxification.

Potential biomarkers identified in plasma of patients with gestational diabetes mellitus.

INTRODUCTION: Gestational diabetes mellitus (GDM) is a common complication during pregnancy. Looking for reliable diagnostic markers for early diagnosis can reduce the impact of the disease on the fetus OBJECTIVE: The present study is designed to find plasma metabolites that can be used as potential biomarkers for GDM, and to clarify GDM-related mechanisms METHODS: By non-target metabolomics analysis, compared with their respective controls, the plasma metabolites of GDM pregnant women at 12-16 weeks and 24-28 weeks of pregnancy were analyzed. Multiple reaction monitoring (MRM) analysis was performed to verify the potential marker RESULTS: One hundred and seventy-two (172) and 478 metabolites were identified as differential metabolites in the plasma of GDM pregnant women at 12-16 weeks and 24-28 weeks of pregnancy, respectively. Among these, 40 metabolites were overlapped. Most of them are associated with the mechanism of diabetes, and related to short-term and long-term complications in the perinatal period. Among them, 7 and 10 differential metabolites may serve as potential biomarkers at the 12-16 weeks and 24-28 weeks of pregnancy, respectively. By MRM analysis, compared with controls, increased levels of 17(S)-HDoHE and sebacic acid may serve as early prediction biomarkers of GDM. At 24-28 weeks of pregnancy, elevated levels of 17(S)-HDoHE and L-Serine may be used as auxiliary diagnostic markers for GDM CONCLUSION: Abnormal amino acid metabolism and lipid metabolism in patients with GDM may be related to GDM pathogenesis. Several differential metabolites identified in this study may serve as potential biomarkers for GDM prediction and diagnosis.

Effects of Calcium Oxalate on Expression of Clusterin and Lower Urinary Tract Symptoms in Prostatitis and Benign Prostatic Hyperplasia Patients with Calculi.

BACKGROUND Prostatic calculi are common in urological treatments. Our major purpose in the present study was to explore the occurrence and composition of prostatic calculi, and investigate the effect of calcium oxalate (CaOx) on clusterin expression and lower urinary tract symptom (LUTS) in prostatitis and benign prostatic hyperplasia (BPH) patients with calculi. MATERIAL AND METHODS From December 2016 to January 2017, a total of 79 prostatitis patients aged more than 50 years were enrolled. The patients were divided into 3 groups: group A had small calculi (discrete, small echoes); group B had large calculi (large masses of multiple echoes, much coarser), and group C had no calculi. Immunohistochemical analysis was performed to evaluate the tissue scores. The clusterin expression was detected by quantitative real-time CR (qRT-PCR), Western blot, and immunofluorescence. RESULTS According to multifactor analysis, age was significantly associated with prostatic calculus. The composition of prostatic calculus was an independent factor of LUTS. The clusterin expression was elevated in group B. The mRNA and protein levels of clusterin in prostatitis and BPH patients with stones were obviously higher than those in prostatitis and BPH patients without stones. CaOx enhanced clusterin expression in a dose-dependent manner. CONCLUSIONS Large prostatic calculi were associated with LUST. Furthermore, CaOx enhanced clusterin expression, leading to large prostatic calculi. These results may provide a therapeutic strategy for prostatitis and BPH.

Calcium oxalate crystals induces tight junction disruption in distal renal tubular epithelial cells by activating ROS/Akt/p38 MAPK signaling pathway.

Tight junction plays important roles in regulating paracellular transports and maintaining cell polarity. Calcium oxalate monohydrate (COM) crystals, the major crystalline composition of kidney stones, have been demonstrated to be able to cause tight junction disruption to accelerate renal cell injury. However, the cellular signaling involved in COM crystal-induced tight junction disruption remains largely to be investigated. In the present study, we proved that COM crystals induced tight junction disruption by activating ROS/Akt/p38 MAPK pathway. Treating Madin-Darby canine kidney (MDCK) cells with COM crystals induced a substantial increasing of ROS generation and activation of Akt that triggered subsequential activation of ASK1 and p38 mitogen-activated protein kinase (MAPK). Western blot revealed a significantly decreased expression of ZO-1 and occludin, two important structural proteins of tight junction. Besides, redistribution and dissociation of ZO-1 were observed by COM crystals treatment. Inhibition of ROS by N-acetyl-l-cysteine (NAC) attenuated the activation of Akt, ASK1, p38 MAPK, and down-regulation of ZO-1 and occludin. The redistribution and dissociation of ZO-1 were also alleviated by NAC treatment. These results indicated that ROS were involved in the regulation of tight junction disruption induced by COM crystals. In addition, the down-regulation of ZO-1 and occludin, the phosphorylation of ASK1 and p38 MAPK were also attenuated by MK-2206, an inhibitor of Akt kinase, implying Akt was involved in the disruption of tight junction upstream of p38 MAPK. Thus, these results suggested that ROS-Akt-p38 MAPK signaling pathway was activated in COM crystal-induced disruption of tight junction in MDCK cells.

Autism spectrum disorder: pathogenesis, biomarker, and intervention therapy.

Autism spectrum disorder (ASD) has become a common neurodevelopmental disorder. The heterogeneity of ASD poses great challenges for its research and clinical translation. On the basis of reviewing the heterogeneity of ASD, this review systematically summarized the current status and progress of pathogenesis, diagnostic markers, and interventions for ASD. We provided an overview of the ASD molecular mechanisms identified by multi-omics studies and convergent mechanism in different genetic backgrounds. The comorbidities, mechanisms associated with important physiological and metabolic abnormalities (i.e., inflammation, immunity, oxidative stress, and mitochondrial dysfunction), and gut microbial disorder in ASD were reviewed. The non-targeted omics and targeting studies of diagnostic markers for ASD were also reviewed. Moreover, we summarized the progress and methods of behavioral and educational interventions, intervention methods related to technological devices, and research on medical interventions and potential drug targets. This review highlighted the application of high-throughput omics methods in ASD research and emphasized the importance of seeking homogeneity from heterogeneity and exploring the convergence of disease mechanisms, biomarkers, and intervention approaches, and proposes that taking into account individuality and commonality may be the key to achieve accurate diagnosis and treatment of ASD.

Targeted Metabolomic Analysis of the Eye Tissue of Triple Transgenic Alzheimer's Disease Mice at an Early Pathological Stage.

Alzheimer's disease (AD) is a severe neurodegenerative disease in older people. Despite some consensus on pathogenesis of AD established by previous researches, further elucidation is still required for better understanding. This study analyzed the eye tissues of 2- and 6-month-old triple transgenic AD (3 × Tg-AD) male mice and age-sex-matched wild-type (WT) mice using a targeted metabolomics approach. Compared with WT mice, 20 and 44 differential metabolites were identified in 2- and 6-month-old AD mice, respectively. They were associated with purine metabolism, pantothenate and CoA biosynthesis, pyruvate metabolism, lysine degradation, glycolysis/gluconeogenesis, and pyrimidine metabolism pathways. Among them, 8 metabolites presented differences in both the two groups, and 5 of them showed constant trend of change. The results indicated that the eye tissues of 3 × Tg-AD mice underwent changes in the early stages of the disease, with changes in metabolites observed at 2 months of age and more pronounced at 6 months of age, which is consistent with our previous studies on hippocampal targeted metabolomics in 3 × Tg-AD mice. Therefore, a joint analysis of data from this study and previous hippocampal study was performed, and the differential metabolites and their associated mechanisms were similar in eye and hippocampal tissues, but with tissue specificity.

The use of data independent acquisition based proteomic analysis and machine learning to reveal potential biomarkers for autism spectrum disorder.

Autism spectrum disorder (ASD) is a complex neurological developmental disorder in children, and is associated with social isolation and restricted interests. The etiology of this disorder is still unknown. There is neither any confirmed laboratory test nor any effective therapeutic strategy to diagnose or cure it. We performed data independent acquisition (DIA) and multiple reaction monitoring (MRM) analysis of plasma from children with ASD and controls. The result showed that 45 differentially expressed proteins (DEPs) were identified between autistic subjects and controls. Among these, only one DEP was down-regulated in ASD; other DEPs were up-regulated in ASD children's plasma. These proteins are found associated with complement and coagulation cascades, vitamin digestion and absorption, cholesterol metabolism, platelet degranulation, selenium micronutrient network, extracellular matrix organization and inflammatory pathway, which have been reported to be related to ASD. After MRM verification, five key proteins in complement pathway (PLG, SERPINC1, and A2M) and inflammatory pathway (CD5L, ATRN, SERPINC1, and A2M) were confirmed to be significantly up-regulated in ASD group. Through the screening of machine learning model and MRM verification, we found that two proteins (biotinidase and carbonic anhydrase 1) can be used as early diagnostic markers of ASD (AUC = 0.8, p = 0.0001). SIGNIFICANCE: ASD is the fastest growing neurodevelopmental disorder in the world and has become a major public health problem worldwide. Its prevalence has been steadily increasing, with a global prevalence rate of 1%. Early diagnosis and intervention can achieve better prognosis. In this study, data independent acquisition (DIA) and multiple reaction monitoring (MRM) analysis was applied to analyze the plasma proteome of ASD patients (31 (±5) months old), and 378 proteins were quantified. 45 differentially expressed proteins (DEPs) were identified between the ASD group and the control group. They mainly were associated with platelet degranulation, ECM proteoglycar, complement and coagulation cascades, selenium micronutrient network, regulation of insulin-like growth factor (IGF) transport and uptake by insulin-like growth factor binding proteins (IGFBPs), cholesterol metabolism, vitamin metabolism, and inflammatory pathway. Through the integrated machine learning methods and the MRM verification of independent samples, it is considered that biotinidase and carbon anhydrase 1 have the potential to become biomarkers for the early diagnosis of ASD. These results complement proteomics database of the ASD patients, broaden our understanding of ASD, and provide a panel of biomarkers for the early diagnosis of ASD.

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.

iTRAQ-Based Proteomics Analysis of Rat Cerebral Cortex Exposed to Valproic Acid before Delivery.

Autism spectrum disorder (ASD) is a neurological and developmental disorder characterized by social and communication difficulties. Valproic acid (VPA) injection during pregnancy elicits autism-like behavior in the offspring, making it a classic animal model of ASD. However, the mechanisms involved have not yet been determined. In this study, we used iTRAQ (isobaric tags for relative and absolute quantification) proteomics analysis of the cerebral cortex of a VPA rat model (VPA group) and controls (CON group). The results showed that 79 differentially expressed proteins (DEPs) were identified between the VPA group and the CON group. Based on bioinformatics analysis, the DEPs were mainly enriched at synapses, especially glutamatergic synapses and GABAergic synapses. Some DEPs were involved in energy metabolism, thyroid hormone synthesis pathway, and Na+-K+-ATPase. Cytoskeleton and endoplasmic reticulum (ER) stress-related proteins were also involved. Some DEPs matched either the ASD gene database or previous reports on cerebral cortical transcriptome studies in VPA rat models. Dysregulation of these DEPs in the cerebral cortex of VPA rats may be responsible for autism-like behavior in rats. We also found that some DEPs were associated with neuropsychiatric disorders, implying that these diseases share common signaling pathways and mechanisms. Moreover, increased expression of DEPs was associated with energy metabolism in the cerebral cortex of VPA rats, implying that ASD may be a distinct type of mitochondrial dysfunction that requires further investigation.

Oxidative Stress in Autism Spectrum Disorder-Current Progress of Mechanisms and Biomarkers.

Autism spectrum disorder (ASD) is a type of neurodevelopmental disorder that has been diagnosed in an increasing number of children around the world. Existing data suggest that early diagnosis and intervention can improve ASD outcomes. However, the causes of ASD remain complex and unclear, and there are currently no clinical biomarkers for autism spectrum disorder. More mechanisms and biomarkers of autism have been found with the development of advanced technology such as mass spectrometry. Many recent studies have found a link between ASD and elevated oxidative stress, which may play a role in its development. ASD is caused by oxidative stress in several ways, including protein post-translational changes (e.g., carbonylation), abnormal metabolism (e.g., lipid peroxidation), and toxic buildup [e.g., reactive oxygen species (ROS)]. To detect elevated oxidative stress in ASD, various biomarkers have been developed and employed. This article summarizes recent studies about the mechanisms and biomarkers of oxidative stress. Potential biomarkers identified in this study could be used for early diagnosis and evaluation of ASD intervention, as well as to inform and target ASD pharmacological or nutritional treatment interventions.

Protein Biomarkers of Autism Spectrum Disorder Identified by Computational and Experimental Methods.

Background: Autism spectrum disorder (ASD) is a complex neurodevelopmental disorder that affects millions of people worldwide. However, there are currently no reliable biomarkers for ASD diagnosis. Materials and Methods: The strategy of computational prediction combined with experimental verification was used to identify blood protein biomarkers for ASD. First, brain tissue-based transcriptome data of ASD were collected from Gene Expression Omnibus database and analyzed to find ASD-related genes by bioinformatics method of significance analysis of microarrays. Then, a prediction program of blood-secretory proteins was applied on these genes to predict ASD-related proteins in blood. Furthermore, ELISA was used to verify these proteins in plasma samples of ASD patients. Results: A total of 364 genes were identified differentially expressed in brain tissue of ASD, among which 59 genes were predicted to encode ASD-related blood-secretory proteins. After functional analysis and literature survey, six proteins were chosen for experimental verification and five were successfully validated. Receiver operating characteristic curve analyses showed that the area under the curve of SLC25A12, LIMK1, and RARS was larger than 0.85, indicating that they are more powerful in discriminating ASD cases from controls. Conclusion: SLC25A12, LIMK1, and RARS might serve as new potential blood protein biomarkers for ASD. Our findings provide new insights into the pathogenesis and diagnosis of ASD.

Se-Methylselenocysteine (SMC) Improves Cognitive Deficits by Attenuating Synaptic and Metabolic Abnormalities in Alzheimer's Mice Model: A Proteomic Study.

Se-methylselenocysteine (SMC) is a major selenocompound in selenium (Se) enriched plants and has been found to ameliorate neuropathology and cognitive deficits in triple-transgenic mice model of Alzheimer's disease (3 × Tg-AD mice). To explore the underlying molecular mechanisms, the present study is designed to elucidate the protein changes in the cortex of SMC-treated 3 × Tg-AD mice. After SMC supplementation, proteomic analysis revealed that 181, 271, and 41 proteins were identified as differentially expressed proteins (DEPs) between 3 × Tg-AD mice vs wild type (AD/WT group), SMC-treated AD mice vs AD (AD + SMC/AD), and AD + SMC/WT group, respectively. Among these, 138 proteins in the diseased group were reversed by SMC treatment. The DEPs in AD/WT group and AD + SMC/AD group were mainly related to metabolism, synapses, and antioxidant proteins, while their levels were decreased in AD mice but up-regulated after treating with SMC. In addition, we found reduced ATP levels and destroyed synaptic structures in the AD mice brains, which were significantly ameliorated upon SMC treatment. Our study suggests that energy metabolism disorders, abnormal amino acid metabolism, synaptic dysfunction, and oxidative stress may be the key pathogenic phenomena of AD. SMC reversed the expression of proteins associated with them, which might be the main mechanism of its intervention in AD.

Biomarkers in autism spectrum disorders: Current progress.

Autism spectrum disorder (ASD) refers to a group of complex neurodevelopmental disorders characterized by social interaction and communication deficits and repetitive and stereotyped behaviors. As the etiology and pathogenesis of the disorder have not yet been elucidated, specific treatment and reliable diagnostic biomarkers are not available. Early behavioral interventions have been shown to substantially improve symptoms in children with ASD. Given the rapidly increasing prevalence of ASD, there is an urgent need to identify related diagnostic biomarkers. Although specific diagnostic markers for ASD have not been identified, the related research has made progress in different aspects. This review summarizes recent findings of the use of genes, proteins, peptides, and metabolites as diagnostic markers for ASD. The associated techniques include genetic testing and proteomic and metabolomic analyses. In addition, some studies have focused on single or several proteins and metabolites. Moreover, transcriptomic analysis, immune disturbances and cytokine may also be used for this purpose. The pathogenesis involving genes, proteins, and metabolites is also discussed here.