PCDHA9 as a candidate gene for amyotrophic lateral sclerosis.

Amyotrophic lateral sclerosis (ALS) is a devastating neurodegenerative disease. To identify additional genetic factors, we analyzed exome sequences in a large cohort of Chinese ALS patients and found a homozygous variant (p.L700P) in PCDHA9 in three unrelated patients. We generated Pcdhα9 mutant mice harboring either orthologous point mutation or deletion mutation. These mice develop progressive spinal motor loss, muscle atrophy, and structural/functional abnormalities of the neuromuscular junction, leading to paralysis and early lethality. TDP-43 pathology is detected in the spinal motor neurons of aged mutant mice. Mechanistically, we demonstrate that Pcdha9 mutation causes aberrant activation of FAK and PYK2 in aging spinal cord, and dramatically reduced NKA-α1 expression in motor neurons. Our single nucleus multi-omics analysis reveals disturbed signaling involved in cell adhesion, ion transport, synapse organization, and neuronal survival in aged mutant mice. Together, our results present PCDHA9 as a potential ALS gene and provide insights into its pathogenesis.

Abnormalities in Clostridioides and related metabolites before ACTH treatment may be associated with its efficacy in patients with infantile epileptic spasm syndrome.

OBJECTIVE: Adrenocorticotropic hormone (ACTH) is the first-line treatment of infantile epileptic spasm syndrome (IESS). Its reported effectiveness varies, and our current understanding regarding the role of gut microbiota composition in IESS treatment response is limited. This study assessed the microbiome-metabolome association to understand the role and mechanism of gut microbiota composition in IESS treatment outcomes. METHODS: Children with IESS undergoing ACTH treatment were enrolled. Pre-treatment stool and serum samples were collected for 16S rRNA gene sequencing and liquid chromatography-tandem mass spectrometry, respectively. The children were divided into "responsive" and "non-responsive" groups, and gut microbiota and serum metabolome differences were analyzed. RESULTS: Of the 30 patients with IESS, 14 responded to ACTH and 16 did not. The "non-responsive" group had larger maleficent Clostridioides and Peptoclostridium_phage_p630P populations (linear discriminant analysis >2; false discovery rate q < 0.05). Ten metabolites were upregulated (e.g., xanthurenic acid) and 15 were downregulated (e.g., vanillylmandelic acid) (p < 0.05). Association analysis of the gut microbiome and serum metabolome revealed that Clostridioides and Peptoclostridium_phage_p630P2 were positively correlated with linoleic and xanthurenic acids, while Clostridioides was negatively correlated with vanillylmandelic acid (p < 0.05). A classifier using differential gut bacteria and metabolites achieved an area under the receiver operating characteristic curve of 0.906 to distinguish responders from non-responders. CONCLUSION: This study found significant differences in pre-treatment gut microbiota and serum metabolome between children with IESS who responded to ACTH and those who did not. Additional exploration may provide valuable information for treatment selection and potential interventions. Our results suggest that varying ACTH responses in patients with IESS may be associated with increased gut Clostridioides bacteria and kynurenine pathway alteration, but additional experiments are needed to verify this association.

Lower serum branched-chain amino acid catabolic intermediates are predictive signatures specific to patients with diabetic foot.

Diabetic foot (DF) is one of the serious chronic complications of diabetes. Accurate prediction of the risk of DF may take timely intervention measures to prevent its occurrence. The understanding of metabolomic changes in the progression of diabetes to DF may reveal new targets for interventions. We hypothesized that changes in metabolic pathways during DF would lead to changes in the metabolic profile, which could be predictive signature specific to it. In the present study, 43 participants with type 2 diabetes mellitus (T2DM), 32 T2DM participants with DF (T2DM-F), and 36 healthy subjects were enrolled and their serum samples were used for targeted and nonpolar metabolic analysis with liquid chromatography-tandem mass spectrometry. Differential metabolites related to T2DM-F were discovered in metabolomic analysis. Lasso machine learning regression model, random forest algorithm, causal mediation analysis, disease risk assessment, and clinical decision model were carried out. T2DM and T2DM-F groups could be distinguished with the healthy control group. The differential metabolites were all enriched in alpha-linolenic acid and linoleic acid metabolic pathways including arachidonic acid, docosapentaenoic-acid 22N-6, and docosahexaenoic-acid, which were significantly lower in the T2DM and T2DM-F groups compared with the healthy control group. The differential metabolites in T2DM-F vs T2DM groups were enriched to branched-chain amino acid (BCAA) catabolic pathways involving in methylmalonic acid, succinic acid, 3-methyl-2-oxovaleric acid, and ketoleucine, which were the BCAA catabolic intermediates and significantly lower in the T2DM-F compared with the T2DM group except for succinic acid. We reveal a new set of predictive signatures and associate the lower BCAA catabolic intermediates with the progression from T2DM to T2DM-F.

Comprehensive systematic review and meta-analysis of the association between common genetic variants and autism spectrum disorder.

BACKGROUND: Autism spectrum disorder (ASD) is neurodevelopmental disorder characterized by stereotyped behavior and deficits in communication and social interactions. To date, numerous studies have investigated the associations between genetic variants and ASD risk. However, the results of these published studies lack a clear consensus. In the present study, we performed a systematic review on the association between genetic variants and ASD risk. Meanwhile, we conducted a meta-analysis on available data to identify the association between the single nucleotide polymorphisms (SNPs) of candidate genes and ASD risk. METHODS: We systematically searched public databases including English and Chinese from their inception to August 1, 2022. Two independent reviewers extracted data and assessed study quality. Odds ratio and 95 % confidence interval were used as effect indexes to evaluate the association between the SNPs of candidate genes and the risk of ASD. Heterogeneity was explored through subgroup, sensitivity, and meta-regression analyses. Publication bias was assessed by using Egger's and Begg's tests for funnel plot asymmetry. In addition, TSA analysis were performed to confirm the study findings. RESULTS: We summarized 84 SNPs of 32 candidate genes from 81 articles included in the study. Subsequently, we analyzed 16 SNPs of eight genes by calculating pooled ORs, and identified eight significant SNPs of contactin associated protein 2 (CNTNAP2), methylentetrahydrofolate reductase (MTHFR), oxytocin receptor (OXTR), and vitamin D receptor (VDR). Results showed that seven SNPs, including the CNTNAP2 rs2710102 (homozygote, heterozygote, dominant and allelic models) and rs7794745 (heterozygote and dominant models), MTHFR C677T (homozygote, heterozygote, dominant, recessive and allelic models) and A1298C (dominant and allelic models), OXTR rs2254298 (homozygote and recessive models), VDR rs731236 (homozygote, dominant, recessive and allelic models) and rs2228570 (homozygote and recessive models), were showed to be correlated with an increased ASD risk. By contrast, the VDR rs7975232 was correlated with a decreased the risk of ASD under the homozygote and allelic models. CONCLUSION: Our study summarized research evidence on the genetic variants of ASD and provides a broad and detailed overview of ASD risk genes. The C677T and A1298C polymorphisms of MTHFR, rs2710102 and rs7794745 polymorphisms of CNTNAP2, rs2254298 polymorphism of OXTR, and rs731236 and rs2228570 polymorphisms of VDR were genetic risk factors. The rs7975232 polymorphism of VDR was a genetic protective factor for ASD. Our study provides novel clues to clinicians and healthcare decision-makers to predict ASD susceptibility.

Multi-level analysis of the gut-brain axis shows autism spectrum disorder-associated molecular and microbial profiles.

Autism spectrum disorder (ASD) is a neurodevelopmental disorder characterized by heterogeneous cognitive, behavioral and communication impairments. Disruption of the gut-brain axis (GBA) has been implicated in ASD although with limited reproducibility across studies. In this study, we developed a Bayesian differential ranking algorithm to identify ASD-associated molecular and taxa profiles across 10 cross-sectional microbiome datasets and 15 other datasets, including dietary patterns, metabolomics, cytokine profiles and human brain gene expression profiles. We found a functional architecture along the GBA that correlates with heterogeneity of ASD phenotypes, and it is characterized by ASD-associated amino acid, carbohydrate and lipid profiles predominantly encoded by microbial species in the genera Prevotella, Bifidobacterium, Desulfovibrio and Bacteroides and correlates with brain gene expression changes, restrictive dietary patterns and pro-inflammatory cytokine profiles. The functional architecture revealed in age-matched and sex-matched cohorts is not present in sibling-matched cohorts. We also show a strong association between temporal changes in microbiome composition and ASD phenotypes. In summary, we propose a framework to leverage multi-omic datasets from well-defined cohorts and investigate how the GBA influences ASD.

Metagenomic next-generation sequencing of bronchoalveolar lavage fluid from children with severe pneumonia in pediatric intensive care unit.

Background: Severe pneumonia due to lower respiratory tract infections (LRTIs) is a significant cause of morbidity and mortality in children. Noninfectious respiratory syndromes resembling LRTIs can complicate the diagnosis and may also make targeted therapy difficult because of the difficulty of identifying LRTI pathogens. In the present study, a highly sensitive metagenomic next-generation sequencing (mNGS) approach was used to characterize the microbiome of bronchoalveolar lavage fluid (BALF) in children with severe lower pneumonia and identify pathogenic microorganisms that may cause severe pneumonia. The purpose of this study was to use mNGS to explore the potential microbiomes of children with severe pneumonia in a PICU. Methods: We enrolled patients meeting diagnostic criteria for severe pneumonia admitted at PICU of the Children's Hospital of Fudan University, China, from February 2018 to February 2020. In total, 126 BALF samples were collected, and mNGS was performed at the DNA and/or RNA level. The pathogenic microorganisms in BALF were identified and correlated with serological inflammatory indicators, lymphocyte subtypes, and clinical symptoms. Results: mNGS of BALF identified potentially pathogenic bacteria in children with severe pneumonia in the PICU. An increased BALF bacterial diversity index was positively correlated with serum inflammatory indicators and lymphocyte subtypes. Children with severe pneumonia in the PICU had the potential for coinfection with viruses including Epstein-Barr virus, Cytomegalovirus, and Human betaherpesvirus 6B, the abundance of which was positively correlated with immunodeficiency and pneumonia severity, suggesting that the virus may be reactivated in children in the PICU. There was also the potential for coinfection with fungal pathogens including Pneumocystis jirovecii and Aspergillus fumigatus in children with severe pneumonia in the PICU, and an increase in potentially pathogenic eukaryotic diversity in BALF was positively associated with the occurrence of death and sepsis. Conclusions: mNGS can be used for clinical microbiological testing of BALF samples from children in the PICU. Bacterial combined with viral or fungal infections may be present in the BALF of patients with severe pneumonia in the PICU. Viral or fungal infections are associated with greater disease severity and death.

Inductively coupled plasma mass spectrometry based urine metallome to construct clinical decision models for autism spectrum disorder.

BACKGROUND: The global prevalence of autism spectrum disorder (ASD) is on the rise, and high levels of exposure to toxic heavy metals may be associated with this increase. Urine analysis is a noninvasive method for investigating the accumulation and excretion of heavy metals. The aim of this study was to identify ASD-associated urinary metal markers. METHODS: Overall, 70 children with ASD and 71 children with typical development (TD) were enrolled in this retrospective case-control study. In this metallomics investigation, inductively coupled plasma mass spectrometry was performed to obtain the urine profile of 27 metals. RESULTS: Children with ASD could be distinguished from children with TD based on the urine metal profile, with ASD children showing an increased urine metal Shannon diversity. A metallome-wide association analysis was used to identify seven ASD-related metals in urine, with cobalt, aluminum, selenium, and lithium significantly higher, and manganese, mercury, and titanium significantly lower in the urine of children with ASD than in children with TD. The least absolute shrinkage and selection operator (LASSO) machine learning method was used to rank the seven urine metals in terms of their effect on ASD. On the basis of these seven urine metals, we constructed a LASSO regression model for ASD classification and found an area under the receiver operating characteristic curve of 0.913. We also constructed a clinical prediction model for ASD based on the seven metals that were different in the urine of children with ASD and found that the model would be useful for the clinical prediction of ASD risk. CONCLUSIONS: The study findings suggest that altered urine metal concentrations may be an important risk factor for ASD, and we recommend further exploration of the mechanisms and clinical treatment measures for such alterations.

Identifying Rare Genetic Variants of Immune Mediators as Risk Factors for Autism Spectrum Disorder.

Autism spectrum disorder (ASD) affects more than 1% of children, and there is no viable pharmacotherapeutic agent to treat the core symptoms of ASD. Studies have shown that children with ASD show changes in their levels of immune response molecules. Our previous studies have shown that ASD is more common in children with folate receptor autoantibodies. We also found that children with ASD have abnormal gut immune function, which was characterized by a significant increase in the content of immunoglobulin A and an increase in gut-microbiota-associated epitope diversity. These studies suggest that the immune mechanism plays an important role in the occurrence of ASD. The present study aims to systematically assess gene mutations in immune mediators in patients with ASD. We collected genetic samples from 72 children with ASD (2−12 years old) and 107 healthy controls without ASD (20−78 years old). We used our previously-designed immune gene panel, which can capture cytokine and receptor genes, the coding regions of MHC genes, and genes of innate immunity. Target region sequencing (500×) and bioinformatics analytical methods were used to identify variants in immune response genes associated with patients with ASD. A total of 4 rare variants were found to be associated with ASD, including HLA-B: p.A93G, HLA-DQB1: p.S229N, LILRB2: p.R322H, and LILRB2: c.956-4C>T. These variants were present in 44.44% (32/72) of the ASD patients and were detected in 3.74% (4/107) of the healthy controls. We expect these genetic variants will serve as new targets for the clinical genetic assessment of ASD, and our findings suggest that immune abnormalities in children with ASD may have a genetic basis.

Clinical Manifestations of Neonatal Hyperbilirubinemia Are Related to Alterations in the Gut Microbiota.

BACKGROUND AND PURPOSE: Neonatal hyperbilirubinemia, also known as neonatal jaundice, is a common and frequent clinical condition with a complex etiology that can lead to brain damage in severe cases. Early recognition of hyperbilirubinemia and timely intervention and treatment can help reduce the occurrence of sequelae. This study was conducted to identify whether the gut microbiota composition can distinguish neonates with hyperbilirubinemia. METHODS: Meconium samples were collected from 69 neonates with neonatal jaundice (NJ) and 69 age- and sex-matched neonates without clinically significant jaundice (healthy controls; HCs) for 16S rRNA gene sequencing and microbiome analysis. RESULTS: Compared with HCs, the Chao 1 richness index of the gut microbiota was significantly decreased in the NJ group. The relative abundance of the probiotic gut bacterium, Lactobacillus, was significantly lower in the NJ group than in the HC group, whereas the abundances of potentially harmful gut bacteria, such as Escherichia coli and Staphylococcus, were significantly higher in the NJ group than in HCs. Correlation of the gut microbiota and clinical indicators revealed a positive correlation between Escherichia coli/Staphylococcus and serum total bilirubin levels. Finally, the results of a random forest machine-learning method to evaluate the possibility of using NJ-associated gut microbiota compositions as potential NJ biomarkers revealed an area under the curve of 96.88%. CONCLUSIONS: The abundances of Escherichia coli and Staphylococcus were positively correlated with serum total bilirubin levels. Hence, the gut microbiota composition is a potential biomarker of NJ.

Machine learning approach identifies meconium metabolites as potential biomarkers of neonatal hyperbilirubinemia.

Background: The gut microbiota plays an important role in the early stages of human life. Our previous study showed that the abundance of intestinal flora involved in galactose metabolism was altered and correlated with increased serum bilirubin levels in children with jaundice. We conducted the present study to systematically evaluate alterations in the meconium metabolome of neonates with jaundice and search for metabolic markers associated with neonatal jaundice. Methods: We included 68 neonates with neonatal hyperbilirubinemia, also known as neonatal jaundice (NJ) and 68 matched healthy controls (HC), collected meconium samples from them at birth, and performed metabolomic analysis via liquid chromatography-mass spectrometry. Results: Gut metabolites enabled clearly distinguishing the neonatal jaundice (NJ) and healthy control (HC) groups. We also identified the compositions of the gut metabolites that differed significantly between the NJ and HC groups; these differentially significant metabolites were enriched in aminyl tRNA biosynthesis; pantothenic acid and coenzyme biosynthesis; and the valine, leucine and isoleucine biosynthesis pathways. Gut branched-chain amino acid (BCAA) levels were positively correlated with serum bilirubin levels, and the area under the receiver operating characteristic curve of the random forest classifier model based on BCAAs, proline, methionine, phenylalanine and total bilirubin reached 96.9%, showing good potential for diagnostic applications. Machine learning-based causal inference analysis revealed the causal effect of BCAAs on serum total bilirubin and NJ. Conclusions: Altered gut metabolites in neonates with jaundice showed that increased BCAAs and total serum bilirubin were positively correlated. BCAAs proline, methionine, phenylalanine are potential biomarkers of NJ.

Dendritic Spine in Autism Genetics: Whole-Exome Sequencing Identifying De Novo Variant of CTTNBP2 in a Quad Family Affected by Autism Spectrum Disorder.

Autism spectrum disorder (ASD) affects around 1% of children with no effective blood test or cure. Recent studies have suggested that these are neurological disorders with a strong genetic basis and that they are associated with the abnormal formation of dendritic spines. Chromosome microarray (CMA) together with high-throughput sequencing technology has been used as a powerful tool to identify new candidate genes for ASD. In the present study, CMA was first used to scan for genome-wide copy number variants in a proband, and no clinically significant copy number variants were found. Whole-exome sequencing (WES) was used further for genetic testing of the whole quad family affected by ASD, including the proband, his non-autistic sister, and his parents. Sanger sequencing and MassARRAY-based validation were used to identify and confirm variants associated with ASD. WES yielded a 151-fold coverage depth for each sample. A total of 98.65% of the targeted whole-exome region was covered at >20-fold depth. A de novo variant in CTTNBP2, p.M115T, was identified. The CTTNBP2 gene belongs to a family of ankyrin repeat domain-containing proteins associated with dendritic spine formation. Although CTTNBP2 has been associated with ASD, limited studies have been developed to identify clinically relevant de novo mutations of CTTNBP2 in children with ASD; family-based WES successfully identified a clinically relevant mutation in the CTTNBP2 gene in a quad family affected by ASD. Considering the neuron-specific expression of CTTNBP2 and its role in dendritic spine formation, our results suggest a correlation between the CTTNBP2 mutation and ASD, providing genetic evidence for ASD spine pathology. Although the present study is currently insufficient to support the assertion that the de novo mutation M115T in CTTNBP2 directly causes the autism phenotype, our study provides support for the assertion that this mutation is a candidate clinically relevant variant in autism.

Oral, Nasal, and Gut Microbiota in Parkinson's Disease.

Parkinson's disease (PD) is the second most frequently diagnosed neurodegenerative disease. The purpose of this study was to investigate the link between microbiota composition in important mucosal interfaces (oral, nasal, and intestinal) and PD. Sequencing was undertaken of the V4-V5 region of the 16S ribosomal RNA (rRNA) gene of the microbiome from the oral cavity, nasal cavity, and gut of 91 PD patients and 91 healthy controls. Significant differences were found in microbiota composition in the oral cavity and gut, but not the nasal cavity, between PD patients and healthy controls after adjusting for age, gender, and body mass index (BMI). More genera in the oral cavity were significantly positively correlated with clinical characteristics, such as the HAMA and HAMD rating scales. The taxa c_Clostridia, o_Clostridiales, and f_Ruminococcaceae in the gut microbiota were associated with weight and MMSE score. Furthermore, as a result of dysbiosis, there was an enrichment of ion channel-, oxidative phosphorylation-, and carbohydrate metabolism-related pathways in the oral cavity and glycolysis/gluconeogenesis- and propanoate metabolism-related pathways in the intestine. Changes in these pathways can influence metabolism and inflammation, thereby contributing to PD pathogenesis. In addition, several subnetworks containing differentially abundant microbiota in the oral cavity and gut samples from PD patients may regulate microbial composition and function in PD. Overall, our results indicate that oral and gut dysbiosis may affect PD progression and provide a basis for understanding the pathogenesis of PD and identifying potential therapeutic targets for the treatment of this disease.

A low abundance of genus Bacteroides in gut microbiota is negatively correlated with blood phenylalanine levels in Uygur patients with phenylketonuria.

BACKGROUND: A low-phenylalanine (Phe) diet affects the metabolism and diversity of gut microbial communities in children with phenylketonuria (PKU). Our study examined gut microbiota characteristics and metabolic pathways, and their correlations with clinical phenotypes in a high-incidence population. METHODS: We assessed clinical phenotypes and gut microbiota by 16S ribosomal RNA (rRNA) sequencing, and performed a correlation analysis between phenotype and gut microbiota in a PKU group (n=11) and a healthy group (n=11). RESULTS: The PKU group had significantly lower microbiota diversity than the healthy group (Pshannon=0.014). Phylum-level composition differed significantly between the PKU and healthy groups (Firmicutes: 44.3% vs. 43.1%; Actinobacteria: 25.9% vs. 3.3%; Bacteroidetes: 16.6% vs. 53.2%; and Proteobacteria: 10.9% vs. 0.12%, respectively). Further, a significantly decreased level of genus Bacteroidetes (P<0.0001) in the PKU group was negatively correlated with blood Phe level (P=0.014). The microbial function prediction of the Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways exhibited a decreased ability of glycan degradation and glutamate metabolism in the PKU group. CONCLUSIONS: Our findings revealed that genus Bacteroide was not only in extremely low abundance in the PKU group, but was also negatively correlated with blood Phe level. The remarkable capability of genus Bacteroides to use complex recalcitrant glycans may be the main reason for the decreased ability of glycan degradation in the PKU group.

Metagenomic Next-Generation Sequencing of Bloodstream Microbial Cell-Free Nucleic Acid in Children With Suspected Sepsis in Pediatric Intensive Care Unit.

Bloodstream infection is a life-threatening complication in critically ill patients. Multi-drug resistant bacteria or fungi may increase the risk of invasive infections in hospitalized children and are difficult to treat in intensive care units. The purpose of this study was to use metagenomic next-generation sequencing (mNGS) to understand the bloodstream microbiomes of children with suspected sepsis in a pediatric intensive care unit (PICU). mNGS were performed on microbial cell-free nucleic acid from 34 children admitted to PICU, and potentially pathogenic microbes were identified. The associations of serological inflammation indicators, lymphocyte subpopulations, and other clinical phenotypes were also examined. mNGS of blood samples from children in PICU revealed potential eukaryotic microbial pathogens. The abundance of Pneumocystis jirovecii was positively correlated with a decrease in total white blood cell count and immunodeficiency. Hospital-acquired pneumonia patients showed a significant increase in blood bacterial species richness compared with community-acquired pneumonia children. The abundance of bloodstream bacteria was positively correlated with serum procalcitonin level. Microbial genome sequences from potential pathogens were detected in the bloodstream of children with suspected sepsis in PICU, suggesting the presence of bloodstream infections in these children.

Altered Actinobacteria and Firmicutes Phylum Associated Epitopes in Patients With Parkinson's Disease.

Recent evidence suggests that inflammation was participated in the pathogenesis of PD, thus, to understand the potential mechanism of gut microbiota in the pathogenesis of Parkinson's disease (PD), we performed a metagenomic analysis of fecal samples from PD patient and controls. Using a two-stage metagenome-wide association strategy, fecal DNA samples from 69 PD patients and 244 controls in three groups (comprising 66 spouses, 97 age-matched, and 81 normal samples, respectively) were analyzed, and differences between candidate gut microbiota and microbiota-associated epitopes (MEs) were compared. In the study, 27 candidate bacterial biomarkers and twenty-eight candidate epitope peptides were significantly different between the PD patients and control groups. Further, enriched 4 and 13 MEs in PD were positively associated with abnormal inflammatory indicators [neutrophil percentage (NEUT.1), monocyte count/percentage (MONO/MONO.1), white blood cell count (WBC)] and five candidate bacterial biomarkers (c_Actinobacteria, f_Bifidobacteriaceae, g_Bifidobacterium, o_Bifidobacteriales, p_Actinobacteria) from Actinobacteria phylum, and they were also positively associated with histidine degradation and proline biosynthesis pathways, respectively. Additionally, enriched 2 MEs and 1 ME in PD were positively associated with above inflammatory indicators and two bacteria (f_Lactobacillaceae, g_Lactobacillus) from Firmicutes phylum, and they were also positively associated with pyruvate fermentation to propanoate I and negatively associated with isopropanol biosynthesis, respectively. Of these MEs, two MEs from GROEL2, RPSC were derived from Mycobacterium tuberculosis, triggered the T cell immune response, as previously reported. Additionally, other candidate epitope peptides derived from Mycobacterium tuberculosis and Mycobacterium leprae may also have potential immune effects in PD. In all, the altered MEs in PD may relate to abnormalities in immunity and glutamate and propionate metabolism, which furthers our understanding of the pathogenesis of PD.

The Alteration of Salivary Immunoglobulin A in Autism Spectrum Disorders.

Objectives: Autism spectrum disorders (ASD) are neurodevelopmental disorders with changes in the gut and oral microbiota. Based on the intimate relationship between the oral microbiota and oral mucosal immunity, this study aimed to investigate changes in salivary immunoglobulin A (IgA) level in ASD and the underlying mechanism for any such changes. Methods: We recruited 36 children diagnosed with ASD and 35 normally developing children and measured their salivary IgA content using enzyme-linked immunosorbent assay (ELISA). The valproate (VPA) -treated ASD mouse model was established by prenatal exposure to valproate and mouse salivary IgA content was also quantified by ELISA. The submandibular glands of VPA and control mice were isolated and analyzed using qRT-PCR, immunofluorescence staining, and flow cytometry. ASD-related Streptococci were co-incubated with the human salivary gland (HSG) cell line, and western blotting was used to detect the levels of relevant proteins. Results: We found that salivary IgA content was significantly decreased in patients with ASD and had a significant ASD diagnostic value. The salivary IgA content also decreased in VPA mice and was significantly correlated with autistic-like behaviors among them. The mRNA and protein levels of the polymeric immunoglobulin receptor (Pigr) were downregulated in the submandibular glands of VPA mice and the Pigr mRNA level was positively correlated with mouse salivary IgA content. HSG cells treated with ASD-related Streptococci had reduced PIGR protein level. Conclusion: Therefore, protective IgA levels were reduced in the saliva of individuals with ASD, which correlated with the bacteria-induced downregulation of Pigr in salivary glands. This study suggests a new direction for ASD diagnosis and prevention of oral diseases in ASD cohorts and provides evidence for the ASD mucosal immunophenotype in the oral cavity.

Machine learning applied to serum and cerebrospinal fluid metabolomes revealed altered arginine metabolism in neonatal sepsis with meningoencephalitis.

BACKGROUND: Neonatal sepsis with meningoencephalitis is a common complication of sepsis, which is a leading cause of neonatal death and neurological dysfunction. Early identification of neonatal sepsis with meningoencephalitis is particularly important for reducing brain damage. We recruited 70 patients with neonatal sepsis, 42 of which were diagnosed as meningoencephalitis, and collected cerebrospinal fluid (CSF) and serum samples. The purpose of this study was to find neonatal sepsis with meningoencephalitis-related markers using unbiased metabolomics technology and artificial intelligence analysis based on machine learning methods. RESULTS: We found that the characteristics of neonatal sepsis with meningoencephalitis were manifested mainly as significant decreases in the concentrations of homo-l-arginine, creatinine, and other arginine metabolites in serum and CSF, suggesting possible changes in nitric oxide synthesis. The antioxidants taurine and proline in the serum of the neonatal sepsis with meningoencephalitis increased significantly, suggesting abnormal oxidative stress. Potentially harmful bile salts and aromatic compounds were significantly increased in the serum of the group with meningoencephalitis. We compared different machine learning methods and found that the lasso algorithm performed best. Combining the lasso and XGBoost algorithms was successful in predicting the concentration of homo-l-arginine in CSF per the concentrations of metabolite markers in the serum. CONCLUSIONS: On the basis of machine learning combined with analysis of the serum and CSF metabolomes, we found metabolite markers related to neonatal sepsis with meningoencephalitis. The characteristics of neonatal sepsis with meningoencephalitis were manifested mainly by changes in arginine metabolism and related changes in creatinine metabolism.

Altered Gut Microbiota Taxonomic Compositions of Patients With Sepsis in a Pediatric Intensive Care Unit.

Background: The gut is thought to play an important role in the pathogenesis of sepsis. Changes in the gut microbiota are closely related to the occurrence and development of human diseases, but few studies have focused on taxonomic composition of gut microbiota in septic patients. Knowledge of changes in the gut microbiota is a key issue in intensive care. Clinicians must understand how an altered gut microbiota affects the susceptibility and prognosis of septic patients. Measurements and Main Results: In the single-center case control study, 20 septic patients and 20 healthy children were recruited. The taxonomic composition of gut microbiota was determined via 16S rRNA gene sequencing. Gut microbiota diversity in children with sepsis was significantly reduced compared with that in healthy children. The taxonomic composition of gut microbiota can effectively distinguish children with sepsis from healthy children. Thirteen taxa of gut microbiota were significantly increased in the guts of children with sepsis compared with those of healthy children. The increased abundances of Enterococcaceae, Enterococcus, and Enterococcus durans in gut of septic patients were significantly positively correlated with blood inflammation indicators CRP and WBC. The abundances of seven bacteria were significantly decreased in the guts of septic children compared with those of healthy children. The decreased abundance of Bifidobacteriales in gut of septic patients is significantly negatively correlated with blood inflammation index WBC. A machine-learning classifier was built for distinguishing sepsis and achieved the AUC value of 81.25%. It shows that the composition of gut microbiota has certain potential for diagnosis of sepsis. Conclusions: Gut microbiota alterations in septic patients exhibit proliferation of opportunistic pathogenic bacteria, the massive reduction of the commensal flora, and the significant decrease in the diversity of the gut microbiota. Dysbiosis may also account for some changes in the inflammation indexes.

Virulence factor-related gut microbiota genes and immunoglobulin A levels as novel markers for machine learning-based classification of autism spectrum disorder.

Autism spectrum disorder (ASD) is a neurodevelopmental condition for which early identification and intervention is crucial for optimum prognosis. Our previous work showed gut Immunoglobulin A (IgA) to be significantly elevated in the gut lumen of children with ASD compared to typically developing (TD) children. Gut microbiota variations have been reported in ASD, yet not much is known about virulence factor-related gut microbiota (VFGM) genes. Upon determining the VFGM genes distinguishing ASD from TD, this study is the first to utilize VFGM genes and IgA levels for a machine learning-based classification of ASD. Sequence comparisons were performed of metagenome datasets from children with ASD (n = 43) and TD children (n = 31) against genes in the virulence factor database. VFGM gene composition was associated with ASD phenotype. VFGM gene diversity was higher in children with ASD and positively correlated with IgA content. As Group B streptococcus (GBS) genes account for the highest proportion of 24 different VFGMs between ASD and TD and positively correlate with gut IgA, GBS genes were used in combination with IgA and VFGMs diversity to distinguish ASD from TD. Given that VFGM diversity, increases in IgA, and ASD-enriched VFGM genes were independent of sex and gastrointestinal symptoms, a classification method utilizing them will not pertain only to a specific subgroup of ASD. By introducing the classification value of VFGM genes and considering that VFs can be isolated in pregnant women and newborns, these findings provide a novel machine learning-based early risk identification method for ASD.