Changes to the Gut Microbiome in Young Children Showing Early Behavioral Signs of Autism

Jones, Jacquelyn; Reinke, Stacey; Mousavi‐Derazmahalleh, Mahsa; Palmer, Debra J.; Christophersen, Claus T.

doi:10.3389/fmicb.2022.905901

Cited by 10 publications

(5 citation statements)

References 77 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Children with autism mainly exhibit psychoneurological symptoms, and gastrointestinal dysfunction and immune imbalance are prevalent in these studies, suggesting that the microbiome is involved in ASD (Adams et al, 2011;Lai et al, 2014;Petra et al, 2015;Madra et al, 2020). Numerous studies have investigated the microbiomes and metabolomes of children with autism compared with TD control groups (Coretti et al, 2018;Kang et al, 2018;Jones et al, 2022). In addition, animal experiments have confirmed that treatment with fecal microbiota from children with autism promotes behavior in mice associated with the core behavioral characteristics of ASD (Sharon et al, 2019).…”

Section: Discussionmentioning

confidence: 99%

The fecal microbiota from children with autism impact gut metabolism and learning and memory abilities of honeybees

Li,

Zhang,

Luo

et al. 2023

Front. Microbiol.

View full text Add to dashboard Cite

Autism spectrum disorder (ASD) is a set of neurodevelopmental disorders, with an increasing incidence. Gastrointestinal symptoms are common comorbidities of ASD. The gut microbiota composition of children with autism is distinct from that of typical developmental (TD) children, suggesting that the gut microbiota probably influences on hosts via the microbiota-gut-brain axis. However, the relationship between intestinal dysbiosis and host brain function remains unclear. In this study, we creatively developed a honeybee model and investigated the potential effects of fecal microbiota on hosts. Fecal microbiota from children with autism and TD children were transplanted into microbiota-free honeybees (Apis mellifera), resulting in induced ASD-fecal microbiota transplantation (FMT) honeybees (A-BEE group) and TD-FMT honeybees (T-BEE group), respectively. We found that cognitive abilities of honeybees in the A-BEE group were significantly impaired in olfactory proboscis extension response conditioning. Metagenomics was used to evaluate fecal microbiota colonization, revealing several differential species responsible for altered tryptophan metabolism and taurine metabolism within the bee gut, including Bacteroides dorei, Bacteroides fragilis, Lactobacillus gasseri, and Lactobacillus paragasseri. Furthermore, fecal microbiota from children with autism downregulated brain genes involved in neural signaling and synaptic transmission within honeybees. Notably, differentially spliced genes observed within brains of honeybees from the A-BEE group largely overlapped with those identified in human diagnosed with autism via SFARI and SPARK gene sets. These differentially spliced genes were also enriched within pathways related to neural synaptic transmission. Our findings provide novel insights into the pivotal role of the human gut microbiota, which may contribute to neurological processes in honeybees. Additionally, we present a few research sources on gut-brain connections in ASD.

show abstract

Section: Discussionmentioning

confidence: 99%

The fecal microbiota from children with autism impact gut metabolism and learning and memory abilities of honeybees

Li,

Zhang,

Luo

et al. 2023

Front. Microbiol.

View full text Add to dashboard Cite

show abstract

“…Alteration in gut microbiome represents a promising strategy for the prevention and treatment of Alzheimer's as it aids in the reduction of inflammation, oxidative stress, amyloid or tau aggregation and repairs neurogenesis, blood brain barrier integrity, and improved behavioural and cognitive functions (Tarawneh & Penhos, 2022). Several articles published that discussed the role of gut microbiomes in Alzheimer's (Escobar et al, 2022; Kowalski & Mulak, 2019; Mulak, 2021; Solanki et al, 2023; Tarawneh & Penhos, 2022; van Olst et al, 2021), Parkinson's (Boertien, Murtomäki, et al, 2022a; Boertien, Murtomäki, et al, 2022b; Boertien, Pereira, et al, 2022; Hashish & Salama, 2023; Heinzel et al, 2021; Weis et al, 2021) and ASD (Iglesias‐Vázquez et al, 2020; Jendraszak et al, 2021; Jones et al, 2022; Wong et al, 2022; Zuffa et al, 2023) have been published over the past 10 years. However, AI based research founds deficit in this direction and still needs exploration.…”

Section: A Comparison Of Biomarkers Available For Non‐ml Based Techni...mentioning

confidence: 99%

Machine learning approaches for neurological disease prediction: A systematic review

Fatima,

Masood

2024

Expert Systems

View full text Add to dashboard Cite

In this article, we present a systematic and exhaustive review regarding the trends, datasets employed, as well as findings achieved in the last 11 years in neurological disorder prediction using machine learning models. In this work we present a comparison between the biomarkers used in ML field with the biomarkers that are obtained through other non‐ml‐based research fields. This will help in identifying the potential research gaps for ML domain. As the study of neurological disorders is a far‐reaching task due to the wide variety of diseases, hence the scope of this study is restricted to the three most prevalent neurological diseases, that is, Alzheimer's, Parkinson's, and Autism Spectrum Disorder (ASD). From our analysis, it has been found that over time deep learning techniques especially Convolutional Neural Networks have proved to be beneficial for the disease prediction task. For this reason, Magnetic Resonance Imaging have been a popular modality across all three considered diseases. It is also notable that the employment of a transfer learning approach and maintenance of a global data centre helps in dealing with data scarcity problems for model training. The manuscript also discusses the potential challenges and future scope in this field. To the best of our knowledge, unlike other studies, this work attempts to put forth a conclusion of every article discussed highlighting the salient aspects of the major studies for a particular problem.

show abstract

“…Studies have reported that about 9%–91% of individuals with ASD have gastrointestinal symptoms ( Leader et al, 2022 ) and that prominent dysbiosis is observed in the gut microbiome of children with ASD ( Pulikkan et al, 2018 ; Jones et al, 2022 ). Meanwhile, the symptoms of individuals with ASD can be improved by regulating gut microbiota via fecal microbiota transplantation ( Kang et al, 2017 ) or probiotics supplements ( Shaaban et al, 2018 ).…”

Section: Introductionmentioning

confidence: 99%

Associations between dysbiosis gut microbiota and changes of neurotransmitters and short-chain fatty acids in valproic acid model rats

et al. 2023

View full text Add to dashboard Cite

Introduction: The microbiota–gut–brain axis plays an important role in the pathophysiology of autism spectrum disorder, but its specific mechanisms remain unclear. This study aimed to explore the associations of changes in neurotransmitters and short-chain fatty acids with alterations in gut microbiota in valproic acid model rats.Methods: The autism model rats were established by prenatal exposure to valproic acid (VPA). The Morris water maze test, open field test, and three-chamber test were conducted to assess the behaviors of rats. 16S rRNA gene sequences extracted from fecal samples were used to assess the gut microbial composition. Gas and liquid chromatography–mass spectroscopy was used to identify short-chain fatty acids in fecal samples and neurotransmitters in the prefrontal cortex (PFC).Results: The results showed that 28 bacterial taxa between valproic acid model rats and control rats were identified, and the most differential bacterial taxa in valproic acid model rats and control rats belonged to metagenomic species and Lactobacillus intestinalis. Acetic acid, butyric acid, valeric acid, isobutyric acid, and isovaleric acid were significantly decreased in the valproic acid model rats compared to those in control rats. Five neurotransmitters (threonine, kynurenine, tryptophan, 5-hydroxyindoleacetic acid, denoted as 5-HIAA, and betaine aldehyde chloride, denoted as BAC) were significantly decreased, whereas betaine was increased in the prefrontal cortex of valproic acid model rats compared to control rats. A variety of neurotransmitters (≥4) were correlated with Pseudomonas, Collisella, and Streptococcus at the genus level, and they were also related to the decrease of short-chain fatty acids.Discussion: According to this study, we can preliminarily infer that gut microbiota or their metabolic productions (such as SCFAs) may influence central neurotransmitter metabolism through related pathways of the gut-brain axis. These results provide microbial and short-chain fatty acid (SCFA) frameworks for understanding the role of the microbiota–gut–brain axis in autism spectrum disorder and shed new light on autism spectrum disorder treatment.

show abstract

Changes to the Gut Microbiome in Young Children Showing Early Behavioral Signs of Autism

Cited by 10 publications

References 77 publications

The fecal microbiota from children with autism impact gut metabolism and learning and memory abilities of honeybees

The fecal microbiota from children with autism impact gut metabolism and learning and memory abilities of honeybees

Machine learning approaches for neurological disease prediction: A systematic review

Associations between dysbiosis gut microbiota and changes of neurotransmitters and short-chain fatty acids in valproic acid model rats

Contact Info

Product

Resources

About