Autism spectrum disorders (ASD) affect 1% of children. Although there is no cure, early diagnosis and behavioral intervention can relieve the symptoms. The clinical heterogeneity of ASD has created a need for improved sensitive and specific laboratory diagnostic methods. Liquid chromatography-tandem mass spectrometry (LC-MS/MS)-based analysis of the metabolome has shown great potential to uncover biomarkers for complex diseases such as ASD. Here, we used a two-step discovery–validation approach to identify potential novel metabolic biomarkers for ASD. Urine samples from 57 children with ASD and 81 matched children with typical development (TD) were analyzed by LS-MS/MS to assess differences in urinary amino acids and their metabolites (referred to as UAA indicators). A total of 63 UAA indicators were identified, of which 21 were present at significantly different levels in the urine of ASD children compared with TD children. Of these 21, the concentrations of 19 and 10 were higher and lower, respectively, in the urine of ASD children compared with TD children. Using support vector machine modeling and receiver operating characteristic curve analysis, we identified a panel of 7 UAA indicators that discriminated between the samples from ASD and TD children (lysine, 2-aminoisobutyric acid, 5-hydroxytryptamine, proline, aspartate, arginine/ornithine, and 4-hydroxyproline). Among the significantly changed pathways in ASD children were the ornithine/urea cycle (decreased levels of the excitatory amino acid aspartate [p = 2.15 × 10-10] and increased arginine/ornithine [p = 5.21 × 10-9]), tryptophan metabolism (increased levels of inhibitory 5-hydroxytryptamine p = 3.62 × 10-9), the methionine cycle (increased methionine sulfoxide [p = 1.46 × 10-10] and decreased homocysteine [p = 2.73 × 10-7]), and lysine metabolism (reduced lysine [p = 7.8 × 10-9], α-aminoadipic acid [p = 1.16 × 10-9], and 5-aminovaleric acid [p = 1.05 × 10-5]). Collectively, the data presented here identify a possible imbalance between excitatory and inhibitory amino acid metabolism in ASD children. The significantly altered UAA indicators could therefore be potential diagnostic biomarkers for ASD.

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Background The characterization of colonization and dynamic changes related to gut microorganisms might be vital, as it presents an opportunity to quantify the co-variation between stocking densities and gut microbiome of dynamic distribution. The objective of this study was to determine the stocking density on physiological performance and dynamic distribution of gut microbiome (including bacterial and fungal communities) of Langya laying hens in the two development stages. Methods A randomized design with 2 × 3 factorial controls consisting of two development stages (24, 43 weeks-old) with three different stocking densities was performed. Three different stocking densities were allocated to a total of 300 11-week-old Langya laying hens (450 cm2/bird, 675 cm2/bird, 900 cm2/bird). Three housing densities were accomplished by raising different chickens per cage with the same floor size. The dependent variables of stocking densities at each sampling point were; growth performance, organs index, egg quality and the changes of dynamic gut bacterial and fungal communities in the cecum. Results Results showed that the stocking density didn’t affect liver index, eggshell thickness, breaking shell strength and egg shape index. Hens from the highest stocking density had the lowest body weight, fallopian tube index, egg weight and yolk colour score. Except for the yolk colour score, the measurement changes caused by age followed the opposite pattern as stocking density. We observed a substantial rise in taxa linked with health threats when stocking density was increased, including Talaromyces, Oscillospiraceae_UCG-002, Oscillospira, and Dielma. The opposite was observed with Bacteroides, Bifidobacterium, Lachnoclostridium, Eisenbergiella, and Kurtzmaniella. Also, most taxa were linked to polymicrobial infection in clinical cases, especially species whose percentage declined as the hens aged, such as Terrisporobacter, Faecalicoccus, Dialister, Cylindrocarpon etc. Whereas Sellimonas, Mitsuokella, Eurotium, Wardomyces and Cephalotheca had the opposite trend. Conclusion We speculated that excessive high density drove the abundance of bacteria and fungi connected with health problems. Where the gut microecology gradually reach a mature and balance status with age. Overall, this study demonstrates gut microbiome ecological processes in Langya layers at various stocking densities and finds possible connections between stocking density, microbiome and production performance. Our study will contribute to new insights associating suitable density patterns and production performance in laying hens by harnessing such a relative microbiome.