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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.

Background: while most gut microbiota research has focused on term infants, the health outcomes of preterm infants are equally important. Very-low-birth-weight (VLBW) or extremely-low-birth-weight (ELBW) preterm infants have a unique gut microbiota structure, and probiotics have been reported to somewhat accelerate the maturation of the gut microbiota and reduce intestinal inflammation in very-low preterm infants, thereby improving their long-term outcomes. The aim of this study was to investigate the structure of gut microbiota in ELBW neonates to facilitate the early identification of different types of low-birth-weight (LBW) preterm infants. Methods: a total of 98 fecal samples from 39 low-birth-weight preterm infants were included in this study. Three groups were categorized according to different birth weights: ELBW (n = 39), VLBW (n = 39), and LBW (n = 20). The gut microbiota structure of neonates was obtained by 16S rRNA gene sequencing, and microbiome analysis was conducted. The community state type (CST) of the microbiota was predicted, and correlation analysis was conducted with clinical indicators. Differences in the gut microbiota composition among ELBW, VLBW, and LBW were compared. The value of gut microbiota composition in the diagnosis of extremely low birth weight was assessed via a random forest-machine learning approach. Results: we briefly analyzed the structure of the gut microbiota of preterm infants with low birth weight and found that the ELBW, VLBW, and LBW groups exhibited gut microbiota with heterogeneous compositions. Low-birth-weight preterm infants showed five CSTs dominated by Enterococcus, Staphylococcus, Klebsiella, Streptococcus, Pseudescherichia, and Acinetobacter. The birth weight and clinical indicators related to prematurity were associated with the CST. We found the composition of the gut microbiota was specific to the different types of low-birth-weight premature infants, namely, ELBW, VLBW, and LBW. The ELBW group exhibited significantly more of the potentially harmful intestinal bacteria Acinetobacter relative to the VLBW and LBW groups, as well as a significantly lower abundance of the intestinal probiotic Bifidobacterium. Based on the gut microbiota's composition and its correlation with low weight, we constructed random forest model classifiers to distinguish ELBW and VLBW/LBW infants. The area under the curve of the classifiers constructed with Enterococcus, Klebsiella, and Acinetobacter was found to reach 0.836 by machine learning evaluation, suggesting that gut microbiota composition may be a potential biomarker for ELBW preterm infants. Conclusions: the gut bacteria of preterm infants showed a CST with Enterococcus, Klebsiella, and Acinetobacter as the dominant genera. ELBW preterm infants exhibit an increase in the abundance of potentially harmful bacteria in the gut and a decrease in beneficial bacteria. These potentially harmful bacteria may be potential biomarkers for ELBW preterm infants. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]