Microbiota and gut-brain axis dysfunction in autism spectrum disorder: Evidence for functional gastrointestinal disorders

Journal Pre-proof Microbiota and gut-brain axis dysfunction in Autism Spectrum Disorder: Evidence for Functional Gastrointestinal disorders I. Lasheras, P. Seral, E. Latorre, E. Barroso, P. Gracia-Garc´ıa, J. ´ Santabarbara

PII:

S1876-2018(19)30747-6

DOI:

https://doi.org/10.1016/j.ajp.2019.101874

Reference:

AJP 101874

To appear in:

Asian Journal of Psychiatry

Received Date:

12 August 2019

Revised Date:

4 November 2019

Accepted Date:

4 November 2019

Please cite this article as: Lasheras I, Seral P, Latorre E, Barroso E, Gracia-Garc´ıa P, ´ Santabarbara J, Microbiota and gut-brain axis dysfunction in Autism Spectrum Disorder: Evidence for Functional Gastrointestinal disorders, Asian Journal of Psychiatry (2019), doi: https://doi.org/10.1016/j.ajp.2019.101874

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Microbiota and gut-brain axis dysfunction in Autism Spectrum Disorder: Evidence for Functional Gastrointestinal disorders Lasheras I a, Seral P a, Latorre E Santabárbara J a,d,g

b,c,d*

, Barroso E e, Gracia-García P

d,f,g

,

a

Department of Preventive Medicine and Public Health, Universidad de Zaragoza, Zaragoza, Spain. b

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Department of Biochemistry and Molecular and Cell Biology, Universidad de Zaragoza, Zaragoza, Spain

Instituto Agroalimentario de Aragón – IA2- (Universidad de Zaragoza – CITA), Zaragoza, Spain c

d

Instituto de Investigación Sanitaria de Aragón (IIS Aragón), Zaragoza, Spain

e

Psychiatry Service. Hospital Clínico Universitario Miguel Servet, Zaragoza, Spain

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f

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Instituto de Investigación en Ciencias de la Alimentación, CIAL (CSIC-UAM), Madrid, Spain

g

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Centro de Investigación Biomédica en Red de Salud Mental (CIBERSAM), Ministry of Science and Innovation, Madrid, Spain.

Dr. Eva Latorre

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*Corresponding and reprint author:

Department of Biochemistry and Molecular and Cell Biology

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University of Zaragoza

Pedro Cerbuna 12, 50009, Zaragoza, Spain

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[email protected]

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Highlights     

Autism spectrum disorders (ASD) are associated with functional gastrointestinal disorders Gut microbiota dysbiosis could be a potential factor for ASD pathogenesis Microbiome may be an interface between genetic and environmental risk factors in ASD We review evidence of gut microbial shifts in ASD and gastrointestinal disorders Gut microbiota-brain axis studies could identify novel targets for ASD

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ABSTRACT INTRODUCTION: The high frequency of functional gastrointestinal disorders (FGIDs)

in autism spectrum disorders (ASD) has drawn attention to the composition of gut

distinctive ASD microbial pattern is still unclear.

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microbiota as a possible factor in ASD pathogenesis. However, characterization of a

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OBJECTIVE: To conduct a narrative review on ASD microbial profile and diversity

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changes relative to NT children and FGID comorbidity and ASD pathogenesis. METHODOLOGY: First, we searched the PubMed database in peer-reviewed journals

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for evidence regarding the current epidemiological evidence on FGID comorbidity. For the identification of a microbial profile in ASD children, only original studies examining

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gut bacterial and fungal abundances and diversity in ASD children and adolescents were included. Lastly, research on the role of microbial dysbiosis as an interface between

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genetic and environmental risk factors in the pathogenesis of neuropsychiatric disorders, and specifically ASD, was examined. RESULTS: Prevalence and risk of FGIDs is significantly higher in ASD children and correlates with the severity of ASD. Bacterial and fungal diversity differ between ASD and NT children, indicating a difference in taxonomic abundance profiles, which have 2

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been reported at all bacterial phylogenetic levels. However, studies analyzing gut microbiota have a heterogeneous methodology and several limitations that could account for the variety of findings for each taxon. Also, covariate analysis reveals influence of demographics, diet, disease severity, GI comorbidity and allergies. Integration of these findings with changes in metabolome and genetic risk factors allowed for a better understanding of microbiota involvement in ASD pathogenesis for future research.

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Keywords: Autism Spectrum Disorder, Microbiota, Gut-brain Axis, Functional

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Gastrointestinal Disorders, Dysbiosis

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1. INTRODUCTION

Autism spectrum disorders (ASD) is an umbrella term that incorporates a set of

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neurodevelopmental disorders previously referred to as autistic disorder (AD), Asperger´s syndrome, pervasive developmental disorder not otherwise specified (PDD-NOS), and

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childhood disintegrative disorders (Gyawali and Patra 2019). The global prevalence of ASD was estimated at 1 in 160 in 2000 (Elsabbagh et al. 2012) and rates have shown an

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increasing trend over time. In 2016, ASD was found to affect 1 in every 37 US children (Xu et al. 2018), with a significantly higher prevalence in boys (Christensen et al. 2018).

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Although this upward trend seems to have leveled off (Tsai 2014), this group of disorders has become a major concern in the scientific community. Along with the core diagnostic traits, a wide range of comorbid conditions varying in severity and combination can be observed in people with ASD. Among the more commonly reported ones are sleep problems (Elrod and Hood 2015), atypical feeding 3

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patterns (Sharp et al. 2013), psychiatric disorders such as obsessive compulsive disorder, bipolar disorder, psychotic spectrum disorders, anxiety and depression (Nahar et al. 2019), epilepsy and gastrointestinal (GI) disorders (McElhanon et al. 2014). Several emerging studies have identified an imbalanced composition of the intestinal microbiota in ASD individuals, often reported under the controversial term “dysbiosis”, providing a plausible explanation for the more frequent occurrence of functional GI disorders (FGIDs) in ASD (Coury et al. 2012; Ding, Taur, and Walkup 2016; Roman, Rueda-

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ruzafa, and Cardona 2018). Three large millionaire research projects on the link between gut microbiota to neurodevelopmental and psychiatric disorders have been launched since 2013. Recent studies support that changes in gut microbiota could affect the brain

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functions and development through the gut-brain axis (H. X. Wang and Wang 2016), which refers to the bidirectional interaction pathways between the central nervous system

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(CNS) and the trillions of microorganisms that inhabit the gut. However, characterization

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of a distinctive ASD microbial pattern and its possible role on ASD remains unclear (Hsiao 2014; Luna, Savidge, and Williams 2016; Yang, Tian, and Yang 2018).

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In view of this emerging evidence in recent years, this narrative review seeks to synthesize what is currently known about: 1) FGIDs in ASD, 2) evidence of a distinctive gut

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microbial signature in ASD patients, and 3) the possible role of gut microbiota dysbiosis

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and gut-brain axis dysfunction in ASD pathogenesis.

1.1 Functional Gastrointestinal Disorders in Autism Spectrum Disorder FGIDs comprise a heterogeneous group of chronic recurrent gastrointestinal symptoms which could not be explained by any underlying anatomical or biochemical abnormalities 4

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(Gorrindo et al. 2012). Several studies found that the prevalence of any GI disorder in patients with ASD ranged from 23% to 70% (Chaidez, Hansen, and Hertz-picciotto 2014; Coury et al. 2012). The wide variation for each symptom across studies has been attributed to methodological limitations, such as retrospective study design and inappropriate control groups, enrollment of clinically heterogeneous ASD children, bias incase selection and reliance on parental reports of symptoms (Holingue et al. 2018). This breadth of estimates was also apparent in a meta-analysis, where it was reported that ASD

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children had a four-fold higher risk for general GI concerns, a three-fold risk for constipation and diarrhea, and a two-fold risk for abdominal pain (McElhanon et al. 2014); in most cases, these complaints received a diagnosis of a FGID.

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In the absence of any identified objective biomarker, the diagnosis of GI disorders is

usually foreshadowed by the emergence or exacerbation of certain behavioral problems

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which could indicate a child’s attempt to communicate the discomfort (Maenner et al.

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2012). In fact, it has been found that ASD patients who showed signs of GI comorbidity displayed increased sleep difficulties, abnormal mood and argumentative, oppositional,

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defiant or destructive behavior, anxiety, sensory responsivity, rigid compulsive behaviors, self-injury, aggression, lack of expressive language and social impairments

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when compared to ASD patients without GI comorbidities (Nikolov et al. 2009). Moreover, in some studies, the presence and intensity of abdominal pain was directly

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associated with the severity of ASD core symptoms (Ding, Taur, and Walkup 2016; Vuong and Hsiao 2016), and the occurrence of constipation correlated with rigidcompulsive behavior (Srikantha and Hasan Mohajeri 2019). Despite the fact that behavioral disturbances are not efficient predictors of GI problems, as they are already frequent in ASD children without GI complaints (Maenner et al. 2012), they could 5

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indicate a more than subtle association between GI dysfunction in ASD and behavioral output through the gut-brain axis (Hsiao 2014; Vuong and Hsiao 2016).

2. EVIDENCE OF AN ALTERED GUT MICROBIOTA IN AUTISM SPECTRUM DISORDERS 2.1 Description of included studies

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A summary of the characteristics of 33 original studies examining gut bacterial and fungal

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profile in children and adolescents with ASD is shown in Table 1.

In most cases (69.7%), unrelated NT children were recruited for the control group (Adams

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et al. 2011; Carissimi et al. 2019; Coretti et al. 2018; Finegold et al. 2002; Inoue et al.

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2016; Iovene et al. 2017; D.-W. Kang et al. 2013; D. W. Kang et al. 2018; Kantarcioglu, Kiraz, and Aydin 2016; Kong et al. 2019; Kushak et al. 2017; Lee et al. 2017; Liu et al. 2019; Luna et al. 2017; Ma et al. 2019; Pärtty et al. 2015; Plaza-Díaz et al. 2019; Sandler

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et al. 2000; Song et al. 2003; Song, Liu, and Finegold 2004; Strati et al. 2017; Williams et al. 2011; Williams, Hornig, and Parekh 2012; Zhang et al. 2018). However, some

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studies contrast their results with a group of NT siblings (9%) (De Angelis et al. 2013a;

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Gondalia et al. 2012; Son et al. 2015), NT family members (6%) (Kong et al. 2019; Pulikkan et al. 2018), or both related and unrelated controls (15.2%) (Finegold et al. 2010; Helena M R T Parracho et al. 2005; Tomova et al. 2015; Lv Wang et al. 2011, 2013). In some cases, ASD sample was stratified by severity of ASD (Finegold et al. 2010; Gondalia et al. 2012; D.-W. Kang et al. 2013; Ma et al. 2019; Strati et al. 2017; Tomova et al. 2015). Likewise, two studies were confined to specific clinical subtypes, such as 6

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mild (Lee et al. 2017), severe (Pulikkan et al. 2018) and late-onset (Finegold et al. 2002; Sandler et al. 2000; Song et al. 2003a; Song, Liu, and Finegold 2004) ASD forms, subjects with Aspergers and attention deficit hyperactivity disorder (ADHD) (Pärtty et al. 2015) or ASD children with developmental delay (Carissimi et al. 2019). Similarly, two studies stratified their sample by PDD-NOS and AD (De Angelis et al. 2013), and ASD with and without mental regression (Plaza-Díaz et al. 2019) respectively. Most studies suffered from methodological issues, since 81.8% had small sample size

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(n<50 subjects on the ASD group), 15.2% had a modest amount of subjects enrolled

(Adams et al. 2011; Gondalia et al. 2012; H M R T Parracho et al. 2010; Son et al. 2015; Strati et al. 2017), and the one study with a larger sample size included subjects with

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suspected ASD diagnosis (Kantarcioglu, Kiraz, and Aydin 2016). Moreover, all but the

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latter suffered from sex bias, as female representation in sample was either low (Adams et al. 2011; Coretti et al. 2018; Finegold et al. 2010b; Gondalia et al. 2012; Iovene et al.

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2017b; D.-W. Kang et al. 2013; D. W. Kang et al. 2018; Kong et al. 2019; Kushak et al. 2017; Lee et al. 2017; Liu et al. 2019; Ma et al. 2019; H M R T Parracho et al. 2010;

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Pulikkan et al. 2018; Son et al. 2015; Strati et al. 2017; Tomova et al. 2015; Lv Wang et al. 2011, 2013; Zhang et al. 2018), none (Carissimi et al. 2019; Luna et al. 2017; Pärtty

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et al. 2015b; Williams et al. 2011; Williams, Hornig, and Parekh 2012) or unspecified (De Angelis et al. 2013; Finegold et al. 2002; Inoue et al. 2016; Plaza-Díaz et al. 2019;

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Sandler et al. 2000; Song et al. 2003; Song, Liu, and Finegold 2004). Regarding study design, only 15.2% of studies were age and gender-matched (Liu et al. 2019; Ma et al. 2019; Plaza-Díaz et al. 2019; Song et al. 2003a; Song, Liu, and Finegold 2004), 12.1% were age-matched only (Kushak et al. 2017; Lee et al. 2017; Pärtty et al. 2015; Pulikkan et al. 2018) and 6% were family-matched (Pulikkan et al. 2018; Son et al. 2015). 7

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However, it should be noted that subjects enrolled in ASD and control groups were often of similar ages. Verification of ASD diagnosis was often carried out in the selection process by clinical observation or using several validated tests. One study included suspected ASD patients (Kantarcioglu, Kiraz, and Aydin 2016), whereas other three performed no validating assessment of a previous ASD diagnosis (Adams et al. 2011; Gondalia et al. 2012; Son et al. 2015), directly studying severity of disease. Four earlier studies claimed to include

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children diagnosed with ASD but did not report any diagnostic method (Finegold et al. 2002; Helena M R T Parracho et al. 2005; Song et al. 2003; Song, Liu, and Finegold

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2004).

Microbiota changes and overall bacterial diversity were assessed by quantification of

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targeted bacterial and/or fungal species from a single or several stool samples (84.8%), urine (3%) and/or biopsies (15.2%), which were taken from the stomach and small bowel

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(Finegold et al. 2002) cecum and ileum (Williams et al. 2011; Williams, Hornig, and Parekh 2012), duodenum (Kushak et al. 2017) and rectum (Luna et al. 2017). One study

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analyzed stool fungal profile exclusively (Kantarcioglu, Kiraz, and Aydin 2016), four examined both stool bacteria and fungi (Adams et al. 2011; Finegold et al. 2002; Iovene

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et al. 2017; Strati et al. 2017) and the remaining 28 studied bacterial population only. The most common methods for bacterial identification were sequencing or pyrosequencing of

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the 16s rRNA gene, a very sensitive technique to obtain reliable results (Hang et al. 2014; Rosselli et al. 2016). By contrast, all but one study (Strati et al. 2017) investigating fungi relied on cultural approaches for yeast detection, given that molecular biology methods were not available until very recently (Iovene et al. 2017). It should also be noted that, although statistical analyses were performed on nearly all studies, covariates adjustment 8

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was only carried out in some of the latest ones, reaching more conservative significance levels. Moreover, in some cases, some microbiota-modulating factors were not taken into consideration in the exclusion criteria, such as previous and/or concomitant intake of antibiotics (Iovene et al. 2017; Kushak et al. 2017; Lee et al. 2017; Pärtty et al. 2015; Lv Wang et al. 2011, 2013), probiotics and prebiotics, also known as functional foods (Finegold et al. 2002, 2010; Iovene et al. 2017; D.-W. Kang et al. 2013; Kantarcioglu, Kiraz, and Aydin 2016; Kushak et al. 2017; Luna et al. 2017; Pulikkan et al. 2018; Sandler

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et al. 2000; Tomova et al. 2015; Lv Wang et al. 2011; Williams et al. 2011; Williams, Hornig, and Parekh 2012); presence of concomitant pathologies (Gondalia et al. 2012; Kantarcioglu, Kiraz, and Aydin 2016; Kushak et al. 2017; Pärtty et al. 2015; Sandler et

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al. 2000; Lv Wang et al. 2011, 2013; Williams et al. 2011; Williams, Hornig, and Parekh

2012) and dietary status (Adams et al. 2011; De Angelis et al. 2013; Finegold et al. 2002;

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Gondalia et al. 2012; Inoue et al. 2016; Iovene et al. 2017; D.-W. Kang et al. 2013; D. W.

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Kang et al. 2018; Kantarcioglu, Kiraz, and Aydin 2016; Kushak et al. 2017; Lee et al. 2017; Luna et al. 2017; H M R T Parracho et al. 2010; Pärtty et al. 2015; Sandler et al. 2000; Son et al. 2015; Tomova et al. 2015; Lv Wang et al. 2011, 2013; Williams et al.

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2011; Williams, Hornig, and Parekh 2012). Nevertheless, some of these studies analyzed their association or effect on bacterial outcomes, as will be discussed later. In addition,

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studies that did consider prior antibiotic intake displayed variable discontinuation periods,

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ranging from 2 weeks to 3 months, with the average amount of days being 32 days and 1 month being the most frequent duration (52%), followed by 3 months (30%). In some studies, other medications were assessed, and subjects taking antipsychotics (Lee et al. 2017), dietary supplements (Helena M R T Parracho et al. 2005), steroids (Luna et al. 2017), anti-inflammatories and antioxidants (Pulikkan et al. 2018), sedatives and muscle relaxants (Plaza-Díaz et al. 2019) were ruled out. However, antifungal-taking subjects 9

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were only excluded in two studies (Finegold et al. 2010b; D.-W. Kang et al. 2013; D. W. Kang et al. 2018; Liu et al. 2019), none of which was among those analyzing fungal populations (Adams et al. 2011; Iovene et al. 2017b; Kantarcioglu, Kiraz, and Aydin 2016; Strati et al. 2017). Furthermore, some studies did not provide information on some of these inclusion or exclusion criteria (Finegold et al. 2002, 2010; Inoue et al. 2016; D. W. Kang et al. 2018; Helena M R T Parracho et al. 2005; Song et al. 2003; Song, Liu, and Finegold 2004; Tomova et al. 2015).

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Following microbial sequencing, assessment of bacterial diversity was performed in over 60% of the cases. Species richness and diversity, which make up alpha diversity, were

estimated in 58% and 33% of studies respectively. For the former, bacterial count was

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performed by assemblage into operational taxonomic unit (OTU) clusters, abundance-

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based coverage (ACE) estimator metric or Chao1 index, whereas, for the latter, microbial evenness was measured by Shannon, phylogenic diversity (PD) or Fisher indexes. On the

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other hand, beta diversity, measured by unweighted and weighted UniFrac distances, Jaccard distance and Bray–Curtis dissimilarity was carried out in 45% of studies and

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allowed for identification of differences among microbial communities within groups, which enabled analysis of the effect of covariates in microbial composition. Only two

2017).

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studies analyzed fungal diversity (Kantarcioglu, Kiraz, and Aydin 2016; Strati et al.

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Lastly, additional assessment of GI function was carried out in 26 studies using parental symptom reports and standardized questionnaires in all cases but five (Inoue et al. 2016; Lee et al. 2017; Plaza-Díaz et al. 2019; Pulikkan et al. 2018; Zhang et al. 2018), whose measuring methods were not provided, and one ground-breaking earlier study, which only considered loose stool history (Sandler et al. 2000). Strict inclusion or exclusion of 10

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children with GI disturbances or stratification by this variable prompted classification of the studies in Table 1 for further analysis of the implication of these GI conditions in ASD microbial profile.

2.2 Major findings of included studies The results of studies focusing on bacterial diversity in ASD patients are inconsistent.

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20% of them reported a significant reduction of species richness (Carissimi et al. 2019; D.-W. Kang et al. 2013; D. W. Kang et al. 2018; Ma et al. 2019), one of which attributed this finding to a generally lower number of taxa in ASD, rather than the depth of

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sequencing, since this reduction was not supported by the reads count within groups

(Carissimi et al. 2019). Opposite changes were found in 10% of studies (De Angelis et al.

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2013; Finegold et al. 2010). This, however, is not supported by the remaining 70% of

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studies reporting no significant differences in species richness, a trend that seemed to be consistent regardless of the kinship shared between ASD and controls or GI function,

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since 29% of them included an unrelated group of NT children without GI dysfunction (Coretti et al. 2018; Liu et al. 2019; Plaza-Díaz et al. 2019; Zhang et al. 2018) 21%

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established comparisons with unrelated NT-GI children (Kushak et al. 2017; Strati et al. 2017; Williams et al. 2011), 29% of them had a control group of related individuals

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(Gondalia et al. 2012; Kong et al. 2019; Pulikkan et al. 2018; Son et al. 2015) and 21% analyzed both unrelated and sibling controls (Helena M R T Parracho et al. 2005; Tomova et al. 2015; Lv Wang et al. 2013). Similar tendencies were found for species diversity, with a decreased reported by 33% of studies (Carissimi et al. 2019; D.-W. Kang et al. 2013; D. W. Kang et al. 2018; Liu et al. 2019; Ma et al. 2019), an increase in only one study (7%) (Coretti et al. 2018) and no overall differences in the remaining 60% 11

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(Gondalia et al. 2012; Kong et al. 2019; Kushak et al. 2017; Plaza-Díaz et al. 2019; Pulikkan et al. 2018; Son et al. 2015; Strati et al. 2017; Williams et al. 2011; Zhang et al. 2018). It should be noted that only one study with a control group of relatives found significant changes in species richness (De Angelis et al. 2013), and none regarding species diversity or beta diversity (Gondalia et al. 2012; Kong et al. 2019; Pulikkan et al. 2018; Son et al. 2015). This underpins the hypothesis that changes in siblings to ASD children could be intermediate between NT and ASD ones (Finegold et al. 2010). As

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microbiota is influenced by genetic and environmental factors, certain similarities in microbiome between ASD and their NT siblings could be expected. By contrast, analysis of the beta diversity between ASD and unrelated NT children unanimously revealed a

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separate clustering for healthy and autistic samples (Coretti et al. 2018; D. W. Kang et al. 2018; Liu et al. 2019; Luna et al. 2017; Ma et al. 2019; Strati et al. 2017; Zhang et al.

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2018). In one case, however, this distinction was only significant in the unweighed

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UniFrac analysis (Ma et al. 2019), which exclusively considers the presence or absence

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of bacterial reads and does not account for their abundance.

Overall, all but one study (Kushak et al. 2017) agreed that bacterial diversity did not show

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any significant differences regarding age (D.-W. Kang et al. 2013; Luna et al. 2017; Helena M R T Parracho et al. 2005; Pulikkan et al. 2018) and sex (D.-W. Kang et al.

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2013; Luna et al. 2017; Helena M R T Parracho et al. 2005). This could be ascribed to the fact that samples in the discrepant study are collected from adolescents aged 12.7 to 17.3 years, who could be in pubertal age, and sexual maturation and hormones are considered as major determinants for gender differences. Likewise, no effect was caused by antibiotic history, intake of probiotics and other supplements (Helena M R T Parracho et al. 2005), 12

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body mass index (Pulikkan et al. 2018), diet pattern (D.-W. Kang et al. 2013; Helena M R T Parracho et al. 2005) or the severity of the autistic phenotype (Gondalia et al. 2012; Strati et al. 2017). Moreover, half of the studies analyzing the effect of GI dysfunction concluded that bacterial diversity variations could not be attributed to the latter. However, in some cases, richness was found to negatively correlate with GI severity (D.-W. Kang et al. 2013) and stratification by constipation was found to increase alpha diversity within ASD subjects (Kong et al. 2019), as well as to separate clustering of samples within NT

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subjects (Strati et al. 2017) and between the ASD-C, ASD-NC and NT-NC groups (Liu et al. 2019) in regard to beta diversity.

Changes at the phylum level are shown in Figure 1 and Supplementary Table S1. The

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gut microbiota is mainly defined by two bacterial phylotypes: Bacteroidetes and

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Firmicutes, whose ratio was only reported to vary significantly in five studies (Coretti et al. 2018; Kong et al. 2019; Strati et al. 2017; Tomova et al. 2015; Williams et al. 2011),

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which found opposite tendencies. When both phyla are examined separately, contradictory findings replicate in all cases but when comparing ASD individuals to their

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relatives. Indeed, the only study including family members to ASD in the cohort which found a significant phylum change relative to ASD, specifically an increase in Firmicutes,

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claimed to have a heterogeneous control group regarding kinship (Pulikkan et al. 2018). Likewise, replication of findings was rare within the class, order, family, genera and

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species levels, which are shown in Figure 2, Figure 3 and Table S1. Covariate analysis for correlation between demographics and microbial profile predominantly concluded that there were no associations between age (Coretti et al. 2018; Finegold et al. 2002; D.-W. Kang et al. 2013; Helena M R T Parracho et al. 2005), gender (Adams et al. 2011; Coretti et al. 2018; Finegold et al. 2002; D.-W. Kang et al. 2013; 13

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Luna et al. 2017; Helena M R T Parracho et al. 2005) or ethnicity (D.-W. Kang et al. 2013) and any specific taxa. However, a prospective follow-up study of newborns for 13 years revealed that, while no single constant microbiota composition component or change was detected, some significant differences were found within the ASD and NT groups between the third and eighteenth month of life, namely, decreased levels of Bifidobacterium and decreased Bacteroides and cumulative levels of Enterococcus and Lactobacillus (Pärtty et al. 2015) at 6 and 18 months respectively. Since microbial

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colonization in offspring achieves its stability between the 6th and 36th month of life (Mangiola et al. 2016), these changes could partake in a chain of pathological events

leading to ASD development, which will be further discussed in the following section.

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Although these differences found by Pärtty et al 2015 were not upheld at older ages

(Pärtty et al. 2015), changes in those genera have been reported by several studies in

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young children and adolescents (see Figure 2). Similarly, after stratification by age, Luna et al 2017 found decreased Parabacteroides distasonis and increased Alistipes putrednis

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and Clostridium perfringens in the 13-18th year old group in contrast with higher

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Ruminococcus spp. counts in the <6 years old cohort (Luna et al. 2017). The effect of diet on ASD has been gaining broad attention, since it is one of the key

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modulators of gut microbiota and has been linked to the development of ASD in humans and mice (Buffington et al. 2017; Coury et al. 2012). In fact, elimination diets and intake

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of dietary supplements such as probiotics, prebiotics and symbiotics has gained popularity among ASD children. Early studies included in our analysis conclude that there is no relationship between the absolute levels of any bacterial populations and diet type (Finegold et al. 2002; Helena M R T Parracho et al. 2005). This finding has been contradicted by a recent and thorough study using the mathematical model principal 14

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component analysis (PCA), which found that dissimilarity between ASD and NT children was driven by distinct dietary components and bacterial variables within each cohort. Furthermore, relationships have been established between Bifidobacterium and a high consumption of dairy products, Prevotella and a plant-rich diet and, specifically in ASD children, fish consumption and Lactobacillus (Adams et al. 2011), as well as pastry, processed cold meat and fish, and low vegetable consumption with Hespellia (Plaza-Díaz et al. 2019). This essentially implies that diet variability could be a confounding factor

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for microbiota assessment and might contribute to the heterogeneity of results across the studies analyzed, since they were conducted in different geographical areas. For instance,

the genus Sutterella, which has been put forward as relevant in ASD dysbiosis (D.-W.

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Kang et al. 2013; Lv Wang et al. 2013; Williams et al. 2011), was absent from all subjects

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consuming a Mediterranean diet (Plaza-Díaz et al. 2019).

Half of the studies assessing the correlation between ASD severity and bacterial taxa did

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not observe any association (Carissimi et al. 2019; D.-W. Kang et al. 2013; Strati et al. 2017). On the contrary, Iovene et al 2017 found a significant positive correlation between

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autism severity measured by Childhood Autism Rating Scale (CARS) with Clostridium spp. and calprotectin value, which is a marker of gut inflammation (Iovene et al. 2017).

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Likewise, Autism Diagnostic Observation Schedules (ADOS) scores positively correlated with Faecalibacterium prasusnitzii and Bacteroides uniformis in another study

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(Coretti et al. 2018). Involvement of these two species in autism is, however, unclear, since evidence on the direction of change in ASD relative to NT is inconsistent (De Angelis et al. 2013; D. W. Kang et al. 2018). Interestingly, Tomova et al 2015 observed fluctuating results depending on the tool used to measure the severity of symptoms. That is, children with CARS>50 had a nearly significant higher Clostridia and Desulfovibrio 15

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amount and lower Bacteroidetes/Firmicutes ratio, while Autism Diagnostic Interview (ADI) scores tended to a positive correlation with the relative amount of Desulfovibrio, emanating from the strong association between this genus and the ADI restricted/repetitive behavior subscale score (Tomova et al. 2015). Conflictingly, other studies revealed that levels of metabolically active Desulfovibrio genus and Desulfovibrionaceae family were 11 and 9.7 times greater in mildly autistic children (Lee et al. 2017), compared to an increase of 2.7 of the relative family levels in severely

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affected ones (Pulikkan et al. 2018). The onset of behavioral signs of autism is usually conceptualized as occurring in either an early onset pattern, in which children show abnormalities in social and communicative

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development in the first years of life, or a regressive pattern (previously referred to as

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“late onset”), in which children develop typically for a certain period of time and then lose previously acquired skills. Information about microbial changes between these two

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phenotypes is scarce. In this review, we found three early studies to exclusively include a group of late-onset ASD (Finegold et al. 2002; Song et al. 2003a; Song, Liu, and

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Finegold 2004), and a recent one which stratified its ASD sample into subgroups by mental regression (AMR) and no regression (ANMR) phenotype (Plaza-Díaz et al. 2019).

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Although no obvious similarities among them were found for the differentially present taxa in ASD relative to NT, the latter study showed a higher relative abundance of

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Proteobacteria in AMR and augmented levels in Actinobacteria phylum and class in ANMR. Likewise, isolated results suggested differences in microbiota between different clinical subtypes included under the term ASD; more specifically, it was found that changes in microbiota and bacterial diversity in PDD-NOS were intermediate between those found in AD and those found in NT siblings (De Angelis et al. 2013). 16

17

For the purposes of this paper, we reviewed studies that stratified their ASD sample by comorbidity with FGIDs. In the ASD-FGIDs group, an increase was observed in the absolute numbers or Ruminococcus torques and the relative abundances of Bacteroides fragilis (Lv Wang et al. 2011). Interestingly, the increase of R. torques did not reach significance before stratification by FGID presence (Lv Wang et al. 2011) or in studies with a mixed ASD cohort in terms of GI function (D. W. Kang et al. 2018; Lv Wang et al. 2011). The rise of B. fragilis, however, was also significant in another study where

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neither ASD nor NT children reported FGIDs (De Angelis et al. 2013). Another study found the mean relative abundance of the Chloroplast genus pertaining the

Cyanobacteria phylum to be increased in ASD-FGIDs and to dominate first-order

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interactions between ASD and FGIDs, a finding that could have been confounded by chia

seed consumption (Son et al. 2015). Additionally, the severity of GI symptoms inversely

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correlated with Clostridia and Desulfovibrio amounts and the Bacteroidetes/Firmicutes

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ratio in one study (Tomova et al. 2015), although this finding did not reach statistical significance and was not supported by a posterior study (Carissimi et al. 2019). Furthermore, cecal and ileal relative levels of Clostridiales and cumulative levels of

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Lachnospiraceae and Ruminococcaceae in the FGIDs subset were significantly higher in children whose first episode of GI symptoms occurred before or at the same time (within

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the same month) as the onset of autism rather than after, and this difference remained

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significant after adjustment by age of onset of GI symptoms (Williams et al. 2011). When FGID symptoms are assessed individually, significant associations are found between irritable bowel syndrome and aerophagia and increased Clostridium aldenense; aerophagia and decreased Blautia luti, Bifidobacterium adolescentis, Eubacterium ventriosum, Anoxystipes fissicatena, Coprococcus comes, Eubacterium ramulus, and 17

18

Phascolarctobacterium faecium; as well as abdominal migraine and decreases in Akkermansia muciniphila, Coprococcus catus, Odoribacter splanchnicus, Clostridium lactatifermentans and Ruminococcus lactaris. Notably, none of these organisms contributed to the separation of the ASD and NT groups (Luna et al. 2017). A greater amount of evidence has focused on functional constipation in ASD children, which has been associated with higher relative abundances of Escherichia/Shigella and Clostridium cluster XVIII (Strati et al. 2017), the genera Fusobacterium, Barnesiella, Coprobacter,

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Olsenella and Allisonella, the family Actinomycetaceae and the order Fusobacteriales (Liu et al. 2019), as well as decreases in Oscillospira plautii, Bacteroides eggerthii, Bacteroides uniformis, Faecalibacterium prausnitzii and Clostridium clariflavum (Luna

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et al. 2017). Remarkably, non-constipated NT children harbored differentiating bacteria

from ASD and NT constipated individuals, with higher abundances of Gemmiger (Strati

Eubacterium

ventriosum

group,

Propionibacterium

and

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Butyricicoccus,

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et al. 2017) and a preponderance of Eubacterium rectale group, Streptococcus,

Lachnospiraceae NC2004 group (Liu et al. 2019). Moreover, children suffering from abdominal pain had higher levels of Turicibacter sanguinis, Clostridium lituseburense,

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Clostridium disporicum, C. aldenense and O. plautii. The latter two, whose link to other GI symptoms has already been reflected in this review, together with Tyzzerella sp. and

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Parasutterella excrementihominis were further enriched in the ASD-FGID pain group

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compared to the ASD-FGID no pain group (Luna et al. 2017). On the contrary, ASD and NT subjects without this symptom had higher levels of Roseburia and Bacteroides, the latter emanating from an increase in ASD patients without abdominal pain, but not in NT (Kong et al. 2019).

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Interestingly, some of the positively correlated bacteria with GI symptoms have been found at very high levels in some ASD children (i.e. Turicibacter sanguinis) (De Angelis et al. 2013; D.-W. Kang et al. 2013), whereas they displayed lower levels of the negatively correlated ones such as Bifidobacterium adolescentis (De Angelis et al. 2013), Phascolarctobacterium faecium (Ma et al. 2019), Blautia luti (Luna et al. 2017), Ruminococcus lactaris (non-significant) (Finegold et al. 2002) and Roseburia (D.-W. Kang et al. 2013; D. W. Kang et al. 2018). For the latter, at a species level, an increase of

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R. inulinovorans (De Angelis et al. 2013) and the decrease of R. faecis, R. hominis and R. intestinalis (De Angelis et al. 2013; Luna et al. 2017) has been observed. In regard to the pathogenesis of GI disorders, some studies have hypothesized a role of disaccharidase

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(Kushak et al. 2017; Williams et al. 2011).

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activity as a connection between microbial abundance, dysbiosis and malabsorption

Finally, given the higher prevalence of allergies in the ASD population, especially food

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relates ones, two studies evaluated the effect of this comorbidity on cecal and ileal (Williams et al. 2011) and stool (Kong et al. 2019) microbial profile. ASD subjects with

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allergies showed increased relative abundance of stool Proteobacteria, a phylum previously associated with autoimmune conditions (Kong et al. 2019). Also, food

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allergies seemed to increase cecal Betaproteobacteria, as well as ileal and cecal Firmicutes and the Firmicutes/Bacteroidetes ratio (Williams et al. 2011). This effect was

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stronger after stratification by milk related allergies, whereas wheat-related ones only exerted a significant effect on ileal proportions. Further evidence in ASD children was provided by the negative correlation of the Firmicutes/Bacteroidetes ratio with allergy/immune function in stool (Kong et al. 2019) and with overall atopic disease

19

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manifestations (asthma, allergic rhinitis and atopic dermatitis) in the cecum (Williams et al. 2011). Fungal diversity was only specifically studied by Strati et al 2017, who, unlike previous culture-based studies by Kantarcioglu et al 2016 or Iovene et al 2017, did not observe significant differences in alpha diversity between ASD and NT children. Nonetheless, he found significant dissimilarity when beta diversity was analyzed (Strati et al. 2017), a distinction that seemed to be mainly driven by a relative two times increase of the genus

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Candida in ASD (uncorrected p value = 0.006, FDR-corrected p value = 0.09), which is consistent with the two previously mentioned studies, where a higher number of isolates

was recovered for every species studied and Candida was isolated from 79.8% of ASD

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versus 19.6% of NT children, and 57.5% versus 0% respectively (Iovene et al. 2017;

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Kantarcioglu, Kiraz, and Aydin 2016). Candida albicans was largely the most represented species in these two studies, whereas Strati et al 2017 did not provide a

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species-level classification of Candida. Correlation analyses among the most abundant fungi and bacteria showed no significant association within ASD individuals, but a

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significant positive correlation between the genera Aspergillus and Bifidobacterium was found within NT subjects (Strati et al. 2017). Candida levels seemed to be increased in

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ASD independent of GI symptoms (Iovene et al. 2017) and, specifically, constipation status (Strati et al. 2017), since no difference was found when ASD and NT groups were

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stratified by this symptom, and difference remained significant when ASD-not constipated (NC) and NT-NC and AD-constipated (C) vs NT-C populations were compared (p=0.007 and p=0.05 respectively) (Strati et al. 2017). Furthermore, correlation analysis with the most abundant and widely distributed fungi genera revealed that, whereas Aspergillus and Malassezia did not differ between NT and ASD children, 20

21

regardless of constipation status, Penicillum levels showed a strong correlation with this symptom in NT children (p<0.01), and a trend to correlate in ASD ones (p=0.07). Further underpinning this association, higher levels of this genus in NT versus ASD children (p=0.08) lost significance when NT-C and ASD-C children were compared (p=0.88) but

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remained in the absence of constipation (p=0.07) (Strati et al. 2017).

3. GUT MICROBIOTA DYSBIOSIS AND GUT-BRAIN-AXIS DYSFUNCTION IN AUTISM SPECTRUM DISORDER

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Currently, there seems to be a consensus about the multifactorial etiology of ASD, and

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that heritability and environmental contributors seem to equally influence its occurrence and interact through epigenetics (Kim and Leventhal 2015; Sandin et al. 2014).

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Microbiota lies at the intersection between genes and environment, as its role and composition are dependent on genetic background and crucially shaped by environmental

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factors (Vuong and Hsiao 2016).

Regarding genetic factors, there are some gene polymorphisms that potentially increase

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the risk of ASD and are distinctively associated with ASD individuals with co-occurring

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GI dysfunction. For instance, a variant of the Chromodomain helicase DNA binding protein 8 gene (CHD8) has been associated with GI complaints, especially constipation, among ASD children (Bernier et al. 2014). CHD8 is a chromatin regulator enzyme which is essential during human fetal development. As evidenced on zebrafish and mice, disruptive mutations of this gene result in a reduced colonization of the GI tract by enteric neurons, and hence, slower intestinal transit and reduced intestine length along with 21

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altered social interaction, increased anxiety and higher brain weight (Bernier et al. 2014; Nithianantharajah et al. 2017), a feature that occurs in 15-35% of ASD children (Bernier et al. 2014). The variant of c-MET is another example of gene that has been related to ASD and comorbid FGID. It encodes the MET tyrosine kinase, leading to MET hypofunction in the GI tract, whose endogenous ligand is called hepatocyte growth factor (HGF), and decreases in MET expression in temporal cortex (Hsiao 2014). Similarly, the autism-associated polymorphisms in SLC6A4, encoding the serotonin transporter

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(SERT), can lead to SERT hyperfunction not only in the brain but also in the gastrointestinal tract, resulting in abnormalities in behavior and gastrointestinal function,

as well as autism-related increase in blood serotonin levels (Hsiao 2014). As shown in

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animal models, transgenic mice expressing a human ASD-associated SERT variant

exhibit the hyperserotonemia phenotype, along with core ASD-related behavioral

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abnormalities and altered serotonergic signaling, causing slower motility and GI transit,

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reduced intestinal permeability and a decrease in the number of neurons in both the myenteric and submucosal plexuses of the enteric nervous system (Adamsen et al. 2014;

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Hsiao 2014).

On the other hand, several environmental risk factors for the development or aggravation

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of ASD have been associated with a dysbiotic microbial community. It has been hypothesized that environmental factors that favor Candida colonization, such as a

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prolonged antibiotic usage or reduced early life encounters with foodborne and environmental bacteria and fungi in urban areas, could be a risk factor for ASD development (Adams et al. 2011; Allen et al. 2017; Ding, Taur, and Walkup 2016; Strati et al. 2017). According to these hypotheses, the increased incidence of ASD cases in the last decades may be partially attributable to “Western” habits (i.e., high-fat diet, 22

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gestational maternal obesity and diabetes, and excessive overall hygiene). Changes in gut microbiota in babies born by Cesarean section delivery, from mothers taking drugs such as Valproate (VPA) and ethanol during pregnancy, and in formula-fed infants have also been proposed as possible factors related to the increased incidence of ASD (Coury et al. 2012; Li et al. 2017; Macfabe 2012; Nithianantharajah et al. 2017; Strati et al. 2017). However, changes in reporting practices are likely to account for most of the increasing ASD rates over the years (Hansen, Schendel, and Parner 2015; Roman, Rueda-ruzafa,

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and Cardona 2018; Sadelhoff et al. 2019). Maternal infections during pregnancy might be a factor linked to ASD (Patterson 2012). Recent findings showed that offspring have an altered fecal microbiota similar to ASD

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individuals in a MIA model (mimic viral infection) (Hsiao et al. 2013). In addition,

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different microbial patterns were found in male and female ASD-like mice, but both presented increased gut permeability, evident inflammatory cells infiltration and ASD-

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like behavioral abnormalities (Coretti et al. 2017). These disturbances were reversed by treatment with Bacteroides fragilis (Hsiao et al. 2013). The relevance of the findings in

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the MIA model is due to the clinically-common etiology and sex-specificity incidence of ASD after maternal infections, which is 4 times higher in males (Ruskin et al. 2017).

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Surprisingly, early in life, male infants are more likely to suffer infections than females (Klein and Flanagan 2016). In fact, it has been postulated that this sex-specific immune

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sensitivity could be related to higher male prevalence in early-onset neurodevelopmental disorders (Schwarz and Bilbo 2013). There is strong evidence that early life infections in males can alter microglia, the resident immune cells of the brain, resulting in an exaggerated pro-inflammatory response from microglia and impairment of learning and memory (Bilbo et al. 2005; Williamson et al. 2011). Interestingly, microglial 23

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development and myelination in the prefrontal cortex seems to be regulated by microbiota (Roman, Rueda-ruzafa, and Cardona 2018). Adult mixed-sex groups of mice raised in a germ-free background or depleted gut microbes have perpetually “immature” microglia (Erny et al. 2015). This immature profile, characterized by increased proliferation and decreased immune reactivity, can be reversed by re-colonization with a diverse bacterial population or by treatment with metabolites derived from commensal bacteria (Erny et al. 2015).

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The gut microbiota interacts with the brain through the named gut-brain axis, that refers to the bidirectional interaction pathways comprising the autonomic nervous,

neuroendocrine, immunological and metabolic (via microbial toxin production) systems.

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Herein, we describe the different hypothesized pathways and, when available, specific

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pathways in ASD individuals.

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finding for ASD patients. Figure 4 shows the integration of the impaired gut-brain-axis

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3.1 Increased permeability of barrier paths

In general terms, homeostasis of gut microbiota is achieved when 70% of microorganisms

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are Gram-negative, and the remaining 30% are Gram-positive (Mirielys and Gutiérrez 2018). The microbial signature of ASD individuals, with a higher proportion of Gram-

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negative bacteria (Coretti et al. 2018), increases production of lipopolysaccharides (LPS) and pro-inflammatory cytokines (see section 3.4), which have been suggested as contributing factors for the impairment of both the intestinal and blood-brain barrier (BBB) found in ASD patients (Fiorentino et al. 2016). In fact, by measuring serum levels of adhesion proteins, it has been shown that increased abnormal intestinal permeability 24

25

or “leaky gut” is present in 37% of ASD patients (Hsiao et al. 2013). This could be related to lower number of Lactobacilli in patients with ASD (De Angelis et al. 2013; Iovene et al. 2017; Ma et al. 2019; Pärtty et al. 2015), since they contribute to the maintenance of tight junction in the intestinal epithelial barrier (Srikantha and Hasan Mohajeri 2019) and its depletion has been directly related to chronic constipation in NT children (Kushak et al. 2017). As a result of the impairment of the intestinal barrier, entrance of toxins and bacterial products into the bloodstream is allowed (see below, metabolic pathway) and

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bacterial translocation into the mesenteric lymphoid tissue is favored, where they activate the immune system.

It should be noted that, though mucosal barrier impairment is the most studied mechanism

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connecting GI comorbidity and intestinal dysbiosis in ASD, this relationship appears to be much more complex. In light of this, recent research suggests a relationship between

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ASD microbial profile and an altered metabolism and absorption of disaccharides in their

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gut epithelium (Kushak et al. 2017; Srikantha and Hasan Mohajeri 2019).

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3.2 Anatomical pathway: The gut-brain’s neural network Two neuroanatomical routes are known to deliver the signals from the intestine to the

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brain. The first is the autonomic nervous system and the vagus nerve, and the second is

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the enteric nervous system, including the enteroglial cells and the autonomic nervous system and vagal nerve in the spinal cord. The gut luminal contents and events and the mucosal constituents create the signals to be transmitted by hierarchic integrative levels cephalically (Lerner, Neidhöfer, and Matthias 2017). Regarding this pathway, certain anatomical changes have been found in the brains of autistic individuals, such as impaired GABAergic functioning (Horder et al. 2018) and increased activation of microglial cells 25

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and a lower count of Purkinje cells in the cerebellum, which could be related to the first (Srikantha and Hasan Mohajeri 2019).

3.3 Neuroendocrine pathway: 3.3.1 Hypothalamic-pituitary-adrenal (HPA) axis The primary function of the HPA axis is to regulate the response to environmental factors,

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such as stress. Under stress conditions, the hypothalamus releases Corticotropinreleasing-hormone (CRH) and vasopressin that signal the release of Adrenocorticotropin (ACTH) from the pituitary gland, which in turn influences the secretion of hormones from

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the adrenal glands, such as cortisol, a glucocorticoid that affects many human organs, including the brain, where it can regulate the activity of the intestinal functional effector

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cells, modulating GI motility, permeability, immunity and mucus. These same cells are

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under the influence of the gut microbiota (Carabotti et al. 2015) (Figure 5). Germ free (GF) mice show an increased stress response with augmented levels of ACTH and cortisol, which can be reversed after germ colonization in young mice(Lerner, Neidhöfer,

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and Matthias 2017), by fecal microbial transplant or by Bifidobacterium infantis (Evrensel and Ceylan 2016). GF mice also display reduced brain-derived neurotrophic

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factor (BDNF) and N-methyl-D-aspartate (NMDA) receptor expressions in cortex and

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hippocampus, which affect the release and expression of the CRH in the hypothalamus and thus change the function of the HPA axis (Sudo et al. 2004). Some studies, specifically in ASD patients, have found altered mRNA levels of the glucocorticoid receptor and CRH receptor 1 (Patel et al. 2016), which essentially implies an alteration of this pathway.

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3.3.2 Neurotransmitters and Neural regulators Gut bacteria seem to regulate several key neurotransmitters such as gamma amino butyric acid (GABA), glutamate, serotonin, dopamine (Yano et al. 2015; Yunes et al. 2016), which have shown altered levels in ASD patients (Mohamadkhani 2018; Srikantha and Hasan Mohajeri 2019). In fact, an imbalance in the CNS between excitation (glutamate) and inhibition (GABA) has been postulated to contribute to ASD (El-Ansary et al. 2018; Horder et al. 2018; Srikantha and Hasan Mohajeri 2019). In addition, bacteria can produce

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a wide range of neuroendocrine hormones which can intervene in intestinal homeostasis

and modulate mood and behavior (Lyte 2014; Oleskin, Shenderov, and Rogovsky 2017).

More specifically, it has been theorized that both the SLC6A4 gene polymorphism

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discussed above and the increase of serotonin-producing microbes, such as Candida,

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Streptococcus, Escherichia, Enterococcus and Clostridiales in ASD may rise intestinal production of serotonin at the expense of a lower synthesis in the brain (due to

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consumption of its precursor tryptophan), leading to hyperserotoninemia, intestinal dysmotility and a still inconsistently reported behavioral outcome (Fattorusso et al. 2019;

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Luna et al. 2017; Srikantha and Hasan Mohajeri 2019).

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3.4 Immunological pathway: cytokine release The development of the immune system heavily relies on gut microbiota; hence, GF mice

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have limited immune activity (Lerner, Neidhöfer, and Matthias 2017). Most bacteria and host communication is carried out by TLRs (Toll-Like Receptors), which are found in a variety of cells from the innate immune system, to intestinal epithelial cells or neurons (both from the ENS and CNS). TLRs can recognize the microbiota triggering appropriate intracellular signaling pathways and immune responses. Among other bacterial products, 27

28

increased LPS in ASD subjects could activates TLR4 in the ENS, where stimulates proinflammatory cytokine production, and in the CNS, where it can lead to inflammation through activating microglia once crossed the impaired mucosal and blood-brain-barriers (Li et al. 2017; Srikantha and Hasan Mohajeri 2019), as it has been shown in postmortem ASD brain biopsies (Morgan et al. 2010). Likewise, cytokines can bind TLRs in neurons, inducing changes in the electric layout of neuronal membranes and altering the regulation of emotion and behavior (Evrensel and Ceylan 2016).

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The microbial imbalance in ASD patients could induce an inadequate immune activation leading to cytokine dysregulation, thereby activating the HPA axis and altering the

permeability of the gut and blood-brain barrier through MLCK and MAPCK-mediated

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regulation of tight junction (Ahmad et al. 2017; Barrier et al. 2014; Carabotti et al. 2015;

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Ulluwishewa et al. 2018) (Figure 6). Further evidence is provided by studies carried out in both ASD models and ASD individuals who displayed increased LCR, plasma or serum

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pro-inflammatory and Th2 cell-derived cytokines, such as IL-1, IL-4, IL-5, IL-6, IL- 8, TNF-α, IFN- and IL-17 , as well as low IL-10, TGF- and Treg cells levels, which are

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involved in synapse, survival and differentiation of neurons, and correlate with the severity of ASD symptoms (Eftekharian, Ghafouri-fard, and Noroozi 2018; Sadelhoff et

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al. 2019). Further correlation between faecal TNF-α and the severity of GI symptoms was reported in ASD children (Srikantha and Hasan Mohajeri 2019). Moreover,

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Faecalibacterium abundance in ASD children was strongly correlated with a greater number of differentially expressed genes involved in both the interferon (IFN)-γ and typeI IFN signaling pathways, which were enriched in this cohort (Inoue et al. 2016). T-helper 1 and T-helper 17 cells affect the reactivity of peripheral immune cells in the CNS and the integrity of blood-brain barrier, induce apoptosis in oligodendrocytes and 28

29

increase glutamate excitotoxicity (Eftekharian, Ghafouri-fard, and Noroozi 2018; Kamada et al. 2013; Stolp et al. 2005); their hyper-activation has been linked to the increase of Clostridiales (Luna et al. 2017) and disruptions in the mammalian target of rapamycin (mTOR) pathway in some ASD phenotypes. Enhanced mTOR activity is also responsible for increased autophagy in the intestine and disbalance in allergy-associated Th2 and Treg cells (Sadelhoff et al. 2019). This evidence, together with a ten-fold incidence of mastocytosis found in ASD patients, the similarities in gut biopsies of

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children with ASD and individuals with immunodeficiency and food allergies, and the previously mentioned increased prevalence of food allergies in this population, points to a relationship between intestinal allergic responses and brain circuits involved in social

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(Fattorusso et al. 2019; Sadelhoff et al. 2019).

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functioning, which can be targeted by rapamycin and amino-acid administration

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3.5 Metabolic pathway: microbial metabolome

The gut microbiota produces constantly changing metabolites that impact host physiology

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and susceptibility to disease (Lerner, Neidhöfer, and Matthias 2017). ASD patients show altered levels of potentially toxic phenol compounds (Alabdali, Al-Ayadhi, and El-

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Ansary 2014) produced, among others, by Bifidobacterium, C. difficile, C. histolyticum

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clusters II and I and some Lactobacillus (Ding, Taur, and Walkup 2016; Fattorusso et al. 2019; Mohamadkhani 2018). Among them, the increased abundance of urinary and fecal para-cresol (p-cresol) and its conjugated derivative p-cresylsulfate, which might impair oligodendrocyte differentiation in vitro and can inhibit the enzyme dopamine-betahydroxylase, are considered putative metabolic markers for ASD, especially useful in females and severely autistic males (Persico and Napolioni 2013; Roman, Rueda-ruzafa, 29

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and Cardona 2018). Along with it, increased Clostridia derived metabolite 3-(3hydroxyphenyl)-3-hydroxypropionic acid (HPHPA) could lead to a depletion of catecholamines in the CNS and its reduction has been shown to decrease ASD severity (Roman, Rueda-ruzafa, and Cardona 2018; Xiong et al. 2016). Another significant metabolite increased in stool of children with ASD is isopropanol, which has been related to GI disturbances (Mohamadkhani 2018), whereas relevant findings in tue urinary profile point to the alteration of nicotinamide, tryptophan, vitamine B6 and purine metabolism

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(Srikantha and Hasan Mohajeri 2019). Other metabolites plausibly involved in ASD pathogenesis could be short-chain fatty acids (SCFAs), which are fermentation products of dietary carbohydrates produced by

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intestinal bacteria such as Clostridia, Bifidobacteria, Bacteroidetes, the families

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Ruminococcaceae and Lachnospiraceae, and the genus Desulfovibrio (Fattorusso et al. 2019; Roman, Rueda-ruzafa, and Cardona 2018; Williams et al. 2011). Interestingly, the

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overall structure of ASD children was found to be dominated by carbohydrate metabolism, among other functions (Ma et al. 2019), and the abundance of SCFA-

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producing bacteria has been reported to be altered in ASD individuals (Zhang et al. 2018). Despite data being somewhat discordant, findings suggest decreased butyrate, and

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increased acetate and propionate (PPA) levels in ASD patients (De Angelis et al. 2013b; Grimaldi et al. 2016; L Wang 2010). Although PPA is beneficial at appropriate levels

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(den Besten et al. 2013), excessive amounts are associated with the severity of ASD (Lv Wang et al. 2012). In fact, its acidic properties may make them liposoluble enough to cross the gut and blood-brain barriers and gain access to the brain, where it could induce several ASD-linked behavioral and neurochemical changes, such as neurotransmitter alteration

(dopamine,

serotonin

and

glutamate),

increased

oxidative

stress, 30

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neuroinflammation, and intracellular acidification, which can inhibit mitochondrial function and may contribute to causing gut dysmotility and altering development and behavior (Foley et al. 2014; Macfabe 2012; Roman, Rueda-ruzafa, and Cardona 2018; Srikantha and Hasan Mohajeri 2019).

4. CONCLUSIONS

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The lack of consistent knowledge inherent to the emerging nature of the field and incomplete understanding of the gut-brain biological signaling pathways, together with the disparity of sample sizes, demographics and methodologies used, translates into

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discordant differences reported for bacterial gut microbiota in ASD individuals. In fact, measuring tools varied widely, not only for autism diagnosis, microbial and GI

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characterization, but also for the statistical analysis of findings, leading to heterogeneous

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p-values in terms of conservatism. Besides, the effect of change for each taxon targeted could not always be given, since not all studies provided calculations or enough data to calculate them. In one case, this data, while existing, could not be accessed (Carissimi et

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al. 2019). Finally, it should be kept in mind that bacterial abundances should be segregated by absolute and relative amounts in order to accurately establish comparisons.

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All in all, inferences regarding changes of individual bacteria at each level should be

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cautiously made.

Despite more research being needed to shed light on the many questions that remain unanswered, the present review concludes that there is general agreement on the existence of a distinctive microbial pattern in ASD individuals that can be affected by demographics, autistic phenotypical profile and symptomatology, diet pattern as well as GI and autoimmune comorbidity. However, for a deeper clarification regarding the 31

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relationship between GI disorders and ASD, studies investigating the correlation between GI symptoms and gut microbiota in NT groups should have been included. Overall, there is a need for further research on the interactions between gut bacterial and fungal microbiota, the role of the bacterial and fungal components in the neuroplastic changes and gastrointestinal physiology in ASDs, and the integration of such data with genetics, immunology, and metabolomics. Not only can this provide novel insights into the intricate gut microbiota-brain axis, whose relevance to ASD is herein evaluated, but

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it can also help us thoroughly identify the distinctive ASD microbiome and ASD specific biomarkers, useful in the determination of at-risk population, early ASD detection and treatment development.

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Acknowledgements

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Declarations of interest: none

The authors are grateful to Dr Manuel Maynar for his decisive role in the genesis and

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development of this research through his critical reading of the first drafts. Also, we thank Dr Jeffery for her critical reading of the manuscript. This research did not receive any

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specific grant from funding agencies in the public, commercial, or not-for-profit sectors.

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REFERENCES Adams, James B et al. 2011. “Gastrointestinal Flora and Gastrointestinal Status in Children with Autism – Comparisons to Typical Children and Correlation with Autism Severity.” BMC Gastroenterology 11(1): 22. http://www.biomedcentral.com/1471-230X/11/22. Adamsen, Dea et al. 2014. “Autism Spectrum Disorder Associated with Low Serotonin in CSF and Mutations in the SLC29A4 Plasma Membrane Monoamine Transporter (PMAT) Gene.” Molecular Autism: 1–11. Ahmad, Sheikh F et al. 2017. “Imbalance between the Anti- and pro-Inflammatory Milieu in Blood Leukocytes of Autistic Children.” Molecular Immunology 82: 57–65.

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Alabdali, Altaf, Laila Al-Ayadhi, and Afaf El-Ansary. 2014. “Association of Social and Cognitive Impairment and Biomarkers in Autism Spectrum Disorders.” Journal of Neuroinflammation 11(1): 4. Allen, Andrew P., Timothy G. Dinan, Gerard Clarke, and John F. Cryan. 2017. “A Psychology of the Human Brain–Gut–Microbiome Axis.” Social and Personality Psychology Compass 11(4): 1–22.

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De Angelis, Maria et al. 2013. “Fecal Microbiota and Metabolome of Children with Autism and Pervasive Developmental Disorder Not Otherwise Specified.” PLoS ONE 8(10): 1–18.

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Barrier, Blood-brain et al. 2014. “The Gut Microbiota Influences Blood-Brain Barrier Permeability in Mice.” 6(263).

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Bernier, Raphael et al. 2014. “Disruptive CHD8 Mutations Define a Subtype of Autism Early in Development.” Cell 158(2): 263–76. http://dx.doi.org/10.1016/j.cell.2014.06.017.

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den Besten, Gijs et al. 2013. “The Role of Short-Chain Fatty Acids in the Interplay between Diet, Gut Microbiota, and Host Energy Metabolism.” Journal of Lipid Research 54(9): 2325–40. http://www.jlr.org/lookup/doi/10.1194/jlr.R036012.

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Bilbo, Staci D et al. 2005. “Neonatal Infection-Induced Memory Impairment after Lipopolysaccharide in Adulthood Is Prevented via Caspase-1 Inhibition.” 25(35): 8000–8009.

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Buffington, Shelly A et al. 2017. “Microbial Reconstitution Reverses Maternal DietInduced Social and Synaptic Deficits in Offspring.” 165(7): 1762–75. Carabotti, Marilia, Annunziata Scirocco, Maria Antonietta Maselli, and Carola Severi. 2015. “The Gut-Brain Axis: Interactions between Enteric Microbiota, Central and Enteric Nervous Systems.” Annals of Gastroenterology 28(2): 203–9. Carissimi, Claudia et al. 2019. “Functional Analysis of Gut Microbiota and Immunoinflammation in Children with Autism Spectrum Disorders.” Digestive and Liver Disease 51: 1366–74. Chaidez, Virginia, Robin L Hansen, and Irva Hertz-picciotto. 2014. “Gastrointestinal 33

34

Problems in Children with Autism , Developmental Delays or Typical Development.” Journal of Autism and Developmental Disorders 44(5): 1117–27. Christensen, Deborah L et al. 2018. “Prevalence and Characteristics of Autism Spectrum Disorder Among Children Aged 8 Years - Autism and Developmental Disabilities Monitoring Network, 11 Sites, United States, 2012.” Morbidity and mortality weekly report. Surveillance summaries (Washington, D.C. : 2002) 65(13): 1–23. Coretti, Lorena et al. 2017. “Sex-Related Alterations of Gut Microbiota Composition in the BTBR Mouse Model of Autism Spectrum Disorder.” Nature Publishing Group (November 2016): 1–10. ———. 2018. “Gut Microbiota Features in Young Children with Autism Spectrum Disorders.” Frontiers in Microbiology 9(December): 1–12.

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Coury, Daniel L. et al. 2012. “Gastrointestinal Conditions in Children With Autism Spectrum Disorder: Developing a Research Agenda.” Pediatrics 130(Supplement 2): S160–68. http://pediatrics.aappublications.org/lookup/doi/10.1542/peds.20120900N.

-p

Ding, Helen T, Ying Taur, and John T Walkup. 2016. “Gut Microbiota and Autism : Key Concepts and Findings.” Journal of Autism and Developmental Disorders 0(0): 0. Eftekharian, Mohammad Mahdi, Soudeh Ghafouri-fard, and Rezvan Noroozi. 2018. “Cytokine Cytokine pro Fi Le in Autistic Patients.” 108(November 2017): 120–26.

re

El-Ansary, Afaf et al. 2018. “Probiotic Treatment Reduces the Autistic-like Excitation/Inhibition Imbalance in Juvenile Hamsters Induced by Orally Administered Propionic Acid and Clindamycin.” Metabolic Brain Disease: 1–10.

lP

Elrod, Marilisa G, and Bradley S Hood. 2015. “Sleep Differences Among Children With Autism Spectrum Disorders and Typically Developing Peers: A Meta-Analysis.” Journal of Developmental & Behavioral Pediatrics 36(3, April): 166–77.

na

Elsabbagh, Mayada et al. 2012. “Global Prevalence of Autism and Other Pervasive Developmental Disorders.” Aurism Research 5(3): 160–79.

ur

Erny, Daniel et al. 2015. “Host Microbiota Constantly Control Maturation and Function of Microglia in the CNS.” Nature Neuroscience 18: 965.

Jo

Evrensel, Alper, and Mehmet Emin Ceylan. 2016. “Fecal Microbiota Transplantation and Its Usage in Neuropsychiatric Disorders.” Clinical psychopharmacology and neuroscience : the official scientific journal of the Korean College of Neuropsychopharmacology 14(3): 231–37. http://www.ncbi.nlm.nih.gov/pubmed/27489376%5Cnhttp://www.pubmedcentral.n ih.gov/articlerender.fcgi?artid=PMC4977816. Fattorusso, Antonella et al. 2019. “Autism Spectrum Disorders and the Gut Microbiota.” Nutrients 11(3). Finegold, Sydney M. et al. 2002. “Gastrointestinal Microflora Studies in Late‐ Onset Autism.” Clinical Infectious Diseases 35(s1): S6–16. https://academic.oup.com/cid/article-lookup/doi/10.1086/341914. 34

35

Finegold, Sydney M. et al. 2010a. “Pyrosequencing Study of Fecal Microflora of Autistic and Control Children.” Anaerobe 16(4): 444–53. http://dx.doi.org/10.1016/j.anaerobe.2010.06.008. Finegold, Sydney M et al. 2010b. “Pyrosequencing Study of Fecal Microflora of Autistic and Control Children.” Anaerobe 16(4): 444–53. Fiorentino, Maria et al. 2016. “Blood–Brain Barrier and Intestinal Epithelial Barrier Alterations in Autism Spectrum Disorders.” Molecular Autism 7(1): 49. Foley, Kelly A., Klaus Peter Ossenkopp, Martin Kavaliers, and Derrick F. MacFabe. 2014. “Pre- and Neonatal Exposure to Lipopolysaccharide or the Enteric Metabolite, Propionic Acid, Alters Development and Behavior in Adolescent Rats in a Sexually Dimorphic Manner.” PLoS ONE 9(1): 1–13.

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Gondalia, Shakuntla V. et al. 2012. “Molecular Characterisation of Gastrointestinal Microbiota of Children With Autism (With and Without Gastrointestinal Dysfunction) and Their Neurotypical Siblings.” Autism Research 5(6): 419–27. Gorrindo, Phillip, Kent C Williams, Evon B Lee, and Lynn S Walker. 2012. “Gastrointestinal Dysfunction in Autism.” Autism Res 5(2): 101–8.

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Grimaldi, Roberta et al. 2016. “In Vitro Fermentation of B-GOS: Impact on Faecal Bacterial Populations and Metabolic Activity in Autistic and Non-Autistic Children.” FEMS Microbiology Ecology 93(2): 1–10.

re

Gyawali, Shreeya, and Bichitra Nanda Patra. 2019. “Trends in Concept and Nosology of Autism Spectrum Disorder: A Review 1.” Asian Journal of Psychiatry. https://doi.org/10.1016/j.ajp.2019.01.021.

lP

Hang, Jun et al. 2014. “16S RRNA Gene Pyrosequencing of Reference and Clinical Samples and Investigation of the Temperature Stability of Microbiome Profiles.” 2(1): 1–15.

na

Hansen, Stefan N, Diana E Schendel, and Erik T Parner. 2015. “Explaining the Increase in the Prevalence of Autism Spectrum Disorders The Proportion Attributable to Changes in Reporting Practices.” JAMA Pediatrics 169(1): 56–62.

ur

Holingue, Calliope et al. 2018. “Gastrointestinal Symptoms in Autism Spectrum Disorder: A Review of the Literature on Ascertainment and Prevalence.” 11(1): 24– 36.

Jo

Horder, Jamie et al. 2018. “Glutamate and GABA in Autism Spectrum Disorder — a Translational Magnetic Resonance Spectroscopy Study in Man and Rodent Models.” Translational Psychiatry: 1–11. Hsiao, Elaine Y. et al. 2013. “Microbiota Modulate Behavioral and Physiological Abnormalities Associated with Neurodevelopmental Disorders.” Cell 155(7): 1451– 63. http://dx.doi.org/10.1016/j.cell.2013.11.024. ———. 2014. “Gastrointestinal Issues in Autism Spectrum Disorder.” Harvard Review of Psychiatry 22(2): 104–11. 35

36

Inoue, Ryo et al. 2016. “A Preliminary Investigation on the Relationship between Gut Microbiota and Gene Expressions in Peripheral Mononuclear Cells of Infants with Autism Spectrum Disorders.” Bioscience, Biotechnology and Biochemistry 80(12): 2450–58. http://dx.doi.org/10.1080/09168451.2016.1222267. Iovene, Maria Rosaria et al. 2017. “Intestinal Dysbiosis and Yeast Isolation in Stool of Subjects with Autism Spectrum Disorders.” Mycopathologia 182(3–4): 349–63. Kamada, Nobuhiko, Sang Uk Seo, Grace Y. Chen, and Gabriel Núñez. 2013. “Role of the Gut Microbiota in Immunity and Inflammatory Disease.” Nature Reviews Immunology 13(5): 321–35. Kang, Dae-Wook et al. 2013. “Reduced Incidence of Prevotella and Other Fermenters in Intestinal Microflora of Autistic Children.” PLOS ONE 8(7): e68322. https://doi.org/10.1371/journal.pone.0068322.

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Kang, Dae Wook et al. 2018. “Differences in Fecal Microbial Metabolites and Microbiota of Children with Autism Spectrum Disorders.” Anaerobe 49: 121–31. https://doi.org/10.1016/j.anaerobe.2017.12.007.

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Kantarcioglu, A. Serda, Nuri Kiraz, and Ahmet Aydin. 2016. “Microbiota–Gut–Brain Axis: Yeast Species Isolated from Stool Samples of Children with Suspected or Diagnosed Autism Spectrum Disorders and In Vitro Susceptibility Against Nystatin and Fluconazole.” Mycopathologia 181(1–2): 1–7.

re

Kim, Young Shin, and Bennett L. Leventhal. 2015. “Genetic Epidemiology and Insights into Interactive Genetic and Environmental Effects in Autism Spectrum Disorders.” Biological Psychiatry 77(1): 66–74.

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Klein, Sabra L, and Katie L Flanagan. 2016. “Sex Differences in Immune Responses.” Nature Publishing Group 16(10): 626–38.

na

Kong, Xuejun et al. 2019. “New and Preliminary Evidence on Altered Oral and Gut Microbiota in Individuals with Autism Spectrum Disorder (ASD): Implications for ASD Diagnosis and Subtyping Based on Microbial Biomarkers.” Nutrients 11(9): 2128.

ur

Kushak, Rafail I. et al. 2017. “Analysis of the Duodenal Microbiome in Autistic Individuals; Association with Carbohydrate Digestion.” Journal of Pediatric Gastroenterology and Nutrition 64: e110–16.

Jo

Lee, Yunjin et al. 2017. “Rapid Assessment of Microbiota Changes in Individuals with Autism Spectrum Disorder Using Bacteria-Derived Membrane Vesicles in Urine.” Experimental Neurobiology 26(5): 307–17. Lerner, Aaron, Sandra Neidhöfer, and Torsten Matthias. 2017. “The Gut Microbiome Feelings of the Brain: A Perspective for Non-Microbiologists.” Microorganisms 5(4): 66. http://www.mdpi.com/2076-2607/5/4/66. Li, Qinrui, Ying Han, Angel Belle C. Dy, and Randi J. Hagerman. 2017. “The Gut Microbiota and Autism Spectrum Disorders.” Frontiers in Cellular Neuroscience 11(April). http://journal.frontiersin.org/article/10.3389/fncel.2017.00120/full. 36

37

Liu, Simeng et al. 2019. “Altered Gut Microbiota and Short Chain Fatty Acids in Chinese Children with Autism Spectrum Disorder.” Scientific Reports 9(1): 1–9. http://dx.doi.org/10.1038/s41598-018-36430-z. Luna, Ruth Ann et al. 2017. “Distinct Microbiome-Neuroimmune Signatures Correlate With Functional Abdominal Pain in Children With Autism Spectrum Disorder.” Cellular and Molecular Gastroenterology and Hepatology 3(2): 218–30. http://dx.doi.org/10.1016/j.jcmgh.2016.11.008. Luna, Ruth Ann, Tor C Savidge, and Kent C Williams. 2016. “The Brain-GutMicrobiome Axis : What Role Does It Play In.” Current Developmental Disorders Reports 3(1): 75–81. Lyte, Mark. 2014. “Host-Microbiota Neuroendocrine Interactions Influencing Brain and Behavior Microbial Endocrinology.” Gut Microbes 5(3): 381–89.

ro of

Ma, Bingjie et al. 2019. “Altered Gut Microbiota in Chinese Children with Autism Spectrum Disorders.” Frontiers in Cellular and Infection Microbiology 9(MAR): 1– 10.

-p

Macfabe, Derrick F. 2012. “Short-Chain Fatty Acid Fermentation Products of the Gut Microbiome: Implications in Autism Spectrum Disorders.” Microbial Ecology in Health & Disease 1: 1–25.

re

Maenner, Matthew J et al. 2012. “Brief Report : Association Between Behavioral Features and Gastrointestinal Problems Among Children with Autism Spectrum Disorder.” : 1520–25.

lP

Mangiola, Francesca et al. 2016. “Gut Microbiota in Autism and Mood Disorders.” World Journal of Gastroenterology 22(1): 361–68.

na

McElhanon, Barbara O, Courtney McCracken, Saul Karpen, and William G Sharp. 2014. “Gastrointestinal Symptoms in Autism Spectrum Disorder: A Meta-Analysis.” Pediatrics. http://pediatrics.aappublications.org/content/early/2014/04/22/peds.20133995.abstract.

ur

Mirielys, Lic, and Pérez Gutiérrez. 2018. “Nuevos Aportes Desde La Microbiología Para Entender El Autismo New Contributions from the Microbiology in Order to Understand the Autism.” 22(2).

Jo

Mohamadkhani, Ashraf. 2018. “Gut Microbiota and Fecal Metabolome Perturbation in Children with Autism Spectrum Disorder.” Middle East Journal of Digestive Diseases 10(4): 205–12. Morgan, John T et al. 2010. “Microglial Activation and Increased Microglial Density Observed in the Dorsolateral Prefrontal Cortex in Autism.” BPS 68(4): 368–76. Nikolov, Roumen N. et al. 2009. “Gastrointestinal Symptoms in a Sample of Children with Pervasive Developmental Disorders.” 39: 405–13. Nithianantharajah, Jess, Gayathri K. Balasuriya, Ashley E. Franks, and Elisa L. HillYardin. 2017. “Using Animal Models to Study the Role of the Gut–Brain Axis in 37

38

Autism.” Current Developmental Disorders http://link.springer.com/10.1007/s40474-017-0111-4.

Reports

4(2):

28–36.

Oleskin, Alexander V, Boris A Shenderov, and Vladimir S Rogovsky. 2017. “Role of Neurochemicals in the Interaction between the Microbiota and the Immune and the Nervous System of the Host Organism.” Probiotics and Antimicrobial Proteins 9(3): 215–34. Parracho, H M R T et al. 2010. “A Double-Blind, Placebo-Controlled, CrossoverDesigned Probiotic Feeding Study in Children Diagnosed with Autistic Spectrum Disorders.” International Journal of Probiotics and Prebiotics 5(2): 69–74. http://centaur.reading.ac.uk/17353/.

ro of

Parracho, Helena M R T, Max O. Bingham, Glenn R. Gibson, and Anne L. McCartney. 2005. “Differences between the Gut Microflora of Children with Autistic Spectrum Disorders and That of Healthy Children.” Journal of Medical Microbiology 54(10): 987–91. Pärtty, Anna et al. 2015. “A Possible Link between Early Probiotic Intervention and the Risk of Neuropsychiatric Disorders Later in Childhood: A Randomized Trial.” Pediatric Research 77(6): 823–28.

re

-p

Patel, Neil et al. 2016. “Altered MRNA Levels of Glucocorticoid Receptor, Mineralocorticoid Receptor, and Co-Chaperones (FKBP5 and PTGES3) in the Middle Frontal Gyrus of Autism Spectrum Disorder Subjects.” Molecular Neurobiology 53(4): 2090–99. Patterson, Paul H. 2012. “Maternal Infection and Immune Involvement in Autism.” Trends in Molecular Medicine 17(7): 389–94.

lP

Persico, Antonio M, and Valerio Napolioni. 2013. “Neurotoxicology and Teratology Urinary p -Cresol in Autism Spectrum Disorder.” Neurotoxicology and Teratology 36(2012): 82–90.

na

Plaza-Díaz, Julio et al. 2019. “Autism Spectrum Disorder (ASD) with and without Mental Regression Is Associated with Changes in the Fecal Microbiota.” Nutrients 11(2).

ur

Pulikkan, Joby et al. 2018. “Gut Microbial Dysbiosis in Indian Children with Autism Spectrum Disorders.” Microbial Ecology 76(4): 1102–14.

Jo

Roman, Pablo, Lola Rueda-ruzafa, and Diana Cardona. 2018. “Gut – Brain Axis in the Executive Function of Austism Spectrum Disorder.” : 654–63. Rosselli, Riccardo et al. 2016. “Direct 16S RRNA-Seq from Bacterial Communities : A PCR-Independent Approach to Simultaneously Assess Microbial Diversity and Functional Activity Potential of Each Taxon.” Nature Publishing Group (February): 1–12. http://dx.doi.org/10.1038/srep32165. Ruskin, David N., Michelle I. Murphy, Sierra L. Slade, and Susan A. Masino. 2017. “Ketogenic Diet Improves Behaviors in a Maternal Immune Activation Model of Autism Spectrum Disorder.” PLoS ONE 12(2): 1–14. Sadelhoff, Joris H J Van, Paula Perez Pardo, Jiangbo Wu, and Johan Garssen. 2019. “The 38

39

Gut-Immune-Brain Axis in Autism Spectrum Disorders ; A Focus on Amino Acids.” 10(April): 1–15. Sandin, Sven et al. 2014. “The Familial Risk of Autism.” JAMA - Journal of the American Medical Association 311(17): 1770–77. Sandler, Richard H. et al. 2000. “Short-Term Benefit from Oral Vancomycin Treatment of Regressive-Onset Autism.” Journal of Child Neurology 15(7): 429–35. Schwarz, Jaclyn M., and Staci D. Bilbo. 2013. “Sex, Glia, and Development: Interactions in Health and Disease.” Hormones and Behavior 62(3): 243–53. Sharp, William G et al. 2013. “Feeding Problems and Nutrient Intake in Children with Autism Spectrum Disorders : A Meta-Analysis and Comprehensive Review of the Literature.”

ro of

Son, Joshua S. et al. 2015. “Comparison of Fecal Microbiota in Children with Autism Spectrum Disorders and Neurotypical Siblings in the Simons Simplex Collection.” PLoS ONE 10(10): 1–19.

Song, Yuli et al. 2003. “Clostridium Bolteae Sp. Nov., Isolated from Human Sources.” Systematic and Applied Microbiology 26(1): 84–89.

-p

Song, Yuli, Chengxu Liu, and Sydney M Finegold. 2004. “Real-Time PCR Quantitation of Clostridia in Feces of Autistic Children.” 70(11): 6459–65.

re

Srikantha, Piranavie, and M. Hasan Mohajeri. 2019. “The Possible Role of the Microbiota-Gut-Brain-Axis in Autism Spectrum Disorder.” International Journal of Molecular Sciences 20(9): 14–19.

lP

Stolp, H B et al. 2005. “Long-Term Changes in Blood–Brain Barrier Permeability and White Matter Following Prolonged Systemic Inflammation in Early Development in the Rat.” European Journal of Neuroscience 22(11): 2805–16.

na

Strati, Francesco et al. 2017. “New Evidences on the Altered Gut Microbiota in Autism Spectrum Disorders.” Microbiome 5(1): 24. http://microbiomejournal.biomedcentral.com/articles/10.1186/s40168-017-0242-1.

ur

Sudo, Nobuyuki et al. 2004. “Postnatal Microbial Colonization Programs the Hypothalamic – Pituitary – Adrenal System for Stress Response in Mice.” 1: 263– 75.

Jo

Tomova, Aleksandra et al. 2015. “Gastrointestinal Microbiota in Children with Autism in Slovakia.” Physiology and Behavior 138: 179–87. http://dx.doi.org/10.1016/j.physbeh.2014.10.033. Tsai, Luke Y. 2014. “Research in Autism Spectrum Disorders Impact of DSM-5 on Epidemiology of Autism Spectrum Disorder.” Research in Autism Spectrum Disorders 8(11): 1454–70. http://dx.doi.org/10.1016/j.rasd.2014.07.016. Ulluwishewa, Dulantha et al. 2018. “Regulation of Tight Junction Permeability by Intestinal Bacteria and Dietary Components 1 , 2.” (September): 769–76. 39

40

Vuong, Helen E, and Elaine Y Hsiao. 2016. “Author ’ s Accepted Manuscript Emerging Roles for the Gut Microbiome in Autism Spectrum DisorderThe Microbiome in ASD Emerging Roles for the Gut Microbiome in Autism Spectrum Disorder.” Biological Psychiatry. http://dx.doi.org/10.1016/j.biopsych.2016.08.024. Wang, Hong Xing, and Yu Ping Wang. 2016. “Gut Microbiota ‑ Brain Axis.” 129(19): 2373–80. Wang, L. 2010. 25 J Gastroenterol Hepatol Gut Bacterial and Fermentation Profiles Are Altered in Children with Autism. Wang, Lv et al. 2011. “Low Relative Abundances of the Mucolytic Bacterium Akkermansia Muciniphila and Bifidobacterium Spp. in Feces of Children with Autism.” Applied and Environmental Microbiology 77(18): 6718–21.

ro of

———. 2012. “Elevated Fecal Short Chain Fatty Acid and Ammonia Concentrations in Children with Autism Spectrum Disorder.” Digestive Diseases and Sciences 57(8): 2096–2102. ———. 2013. “Increased Abundance of Sutterella Spp . and Ruminococcus Torques in Feces of Children with Autism Spectrum Disorder.” Molecular Autism 4(1): 1. Molecular Autism.

re

-p

Williams, Brent L. et al. 2011. “Impaired Carbohydrate Digestion and Transport and Mucosal Dysbiosis in the Intestines of Children with Autism and Gastrointestinal Disturbances.” PLoS ONE 6(9).

lP

Williams, Brent L, Mady Hornig, and Tanmay Parekh. 2012a. “Application of Novel PCR-Based Methods for Detection, Quantitation, and Phylogenetic Characterization Of.” mBIo 3(1): 1–11. Williamson, Lauren L et al. 2011. “Microglia and Memory : Modulation by Early-Life Infection.” 31(43): 15511–21.

na

Xiong, Xiyue et al. 2016. “Urinary 3-(3-Hydroxyphenyl)-3-Hydroxypropionic Acid, 3Hydroxyphenylacetic Acid, and 3-Hydroxyhippuric Acid Are Elevated in Children with Autism Spectrum Disorders.” BioMed Research International 2016.

ur

Xu, Guifeng, Lane Strathearn, Buyun Liu, and Wei Bao. 2018. “Corrected Prevalence of Autism Spectrum Disorder Among US Children and Adolescents.” Jama 28: 4–5. http://jama.jamanetwork.com/article.aspx?doi=10.1001/jama.2018.0001.

Jo

Yang, Yongshou, Jinhu Tian, and Bo Yang. 2018. “Targeting Gut Microbiome: A Novel and Potential Therapy for Autism.” Life Sciences 194(August 2017): 111–19. https://doi.org/10.1016/j.lfs.2017.12.027. Yano, Jessica M et al. 2015. “Indigenous Bacteria from the Gut Microbiota Regulate Host Serotonin Biosynthesis.” Cell 161: 264–76. Yunes, R A et al. 2016. “Anaerobe GABA Production and Structure of GadB / GadC Genes in Lactobacillus and Bi Fi Dobacterium Strains from Human Microbiota.” Anaerobe 42: 197–204. 40

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Zhang, Mengxiang et al. 2018. “Analysis of Gut Microbiota Profiles and MicrobeDisease Associations in Children with Autism Spectrum Disorders in China.” Scientific Reports 8(1): 1–9.

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Figure 1: overview of phyla whose change in ASD was reported as significant in at least one study. The rooted tree shows the phylogenetic relationships of the different phyla. In the heatmap, colors blue and red denote direction of change (decreased and increased respectively), whereas color intensity refers to the strength of the evidence, light meaning p<0.05 and dark meaning p<0.01. Squares colored in grey correspond to studies finding no significant differences between ASD and controls. Folds change relative to controls are given when data was significant and available and control values differed from 0.

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Letters R and A in the first row indicate if studies provide relative or absolute bacterial levels. Abbreviations: mild=mild ASD, sib=sibling, unr=unrelated, sev=severe ASD,

ANMR=ASD with no mental regression, AMR=ASD with mental regression. Asterisks:

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*= Gene Ontology classification used to assign each gene to a GO term, depending on its functional characteristics, rather than calculating a relative abundance for each gene;

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*1=cumulative level of Firmicutes and Proteobacteria.

Figure 2: overview of genera whose change in ASD was reported as significant in at least one study. The rooted tree shows the phylogenetic relationships of the different genera. Color ranks indicate the pertaining phylum: dark blue = Verrucomicrobia, purple = 42

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Bacteroidetes, green = Proteobacteria; brown = [Thermi]; red = Armatimonadetes, light blue = Actinobacteria, orange = Firmicutes. In the heatmap, colors blue and red denote direction of change (decreased and increased respectively), whereas color intensity refers to the strength of the evidence, light meaning p<0.05 and dark meaning p<0.01. Squares colored in grey correspond to studies finding no significant differences between ASD and controls. Folds change relative to controls are given when data was significant and available and control values differed from 0. Letters R and A in the first row indicate if

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studies provide relative or absolute bacterial levels. Abbreviations: mild=mild ASD, sib=sibling, unr=unrelated, sev=severe ASD, PDD=pervasive developmental disorder, AD=autism disorder, ANMR=ASD with no mental regression, AMR=ASD with mental

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regression. Asterisks: *1=cumulative level of Clostridium and Ruminococcus;

*2=cumulative level of Bacteroides, Porphyromonas and Prevotella; *3=cumulative level

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of Pseudomonas and Aeromonas; *4=cumulative level of Enterococcus and Lactobacillus; *5=cumulative level of Pseudomonas and Aeromonas; *6= retrieved at 18

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months old; *7= retrieved at 6 months old; *8= cumulative levels of Enterococcus and Lactobacillus retrieved at 18 months old; *9= quotient calculated after removal of outlier

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(previous value of 83).

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Figure 3: overview of species whose change in ASD was reported as significant in at least one study. The rooted tree shows the phylogenetic relationships of the different species. Color ranks indicate the pertaining phylum: dark blue = Verrucomicrobia, yellow = Fusobacteria, green = Proteobacteria, purple = Bacteroidetes, light blue = Actinobacteria, orange = Firmicutes. In the heatmap, colors blue and red denote direction of change (decreased and increased respectively), whereas color intensity refers to the strength of the evidence, light meaning p<0.05 and dark meaning p<0.01. Squares colored

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in grey correspond to studies finding no significant differences between ASD and controls. Folds change relative to controls are given when data was significant and

available and control values differed from 0. Letters R and A in the first row indicate if

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studies provide relative or absolute bacterial levels. Abbreviations: mild=mild ASD, sib=sibling, unr=unrelated, sev=severe ASD, PDD=pervasive developmental disorder,

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AD=autism disorder, FGIDs= functional gastrointestinal disorders.

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Figure 4: Integration of the impaired gut-brain-axis pathways in ASD individuals and mouse models. Abbreviations:

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BBB: blood-brain barrier; CHD8: chromodomane helicase DNA binding protein 8 gene; CNS: central nervous system; CRH: corticotropin-releasing hormone; Gbacteria: Gram negative bacteria; G x E: interactions between genes and environmental factors; GABA: gamma amino butyric acid; GI: gastrointestinal; HGF: hepatocyte growth factor, ligand of the MET receptor; LPS: lipopolysaccharides; MET rs1858830: variant of the MET gene which encodes the c-MET tyrosine-protein kinase, also called hepatocyte growth factor receptor; poly I:C: polyinosinic-

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polycytidylic acid, used to mimic maternal viral infection in mice; PPA: propionic acid; SERT: serotonin transporter; SLC6A4: variant of the gene which encodes the

serotonin transporter; 5-HT: serotonin; Th1, 2, 17: T-helper cell 1, 2 and 17; VN: vagus

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nerve; VPA: Valproate.

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Color code:

Navy: increased permeability of barrier pathways: intestinal mucosal barrier and BBB.

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Orange: anatomical pathways: the gut-brain’s neural network. Green: neuroendocrine pathway: HPA axis (colored in grey) and neurotransmitters

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and neural regulators (colored in light green). Light blue: immunological pathway: cytokine release.

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Red: metabolic pathway: microbial metabolome.

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Yellow: microbial and clinical outcomes in ASD.

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Figure 5: Hypothalamic-pituitary-adrenal (HPA) axis. Under stress conditions, the

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hypothalamus releases vasopressin and corticotropin-releasing hormone (CRH) that signal the release of adrenocorticotropic hormone (ACTH) from the pituitary gland,

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which in turn influences the secretion of hormones from the adrenal glands, such as

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cortisol. Cortisol is a glucocorticoid that affects many human organs, including the brain, where it can regulate the activity of the intestinal cells, such as the immune cells, the

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epithelial cells, the enterochromaffin cells, the interstitial cells of Cajal, the enteric neurons and the smooth muscle cells. These same cells are under the influence of the gut

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microbiota, which, when dysbiotic, can induce an early immune activation leading to increased pro-inflammatory cytokines. As will be discussed later, cytokines gain access

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to the hypothalamus throughout the impaired blood-brain barrier, acting on the HPA axis similarly to stress.

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Figure 6: Interaction pathway between bacterial and viral components and the nervous

and immune systems: subepithelial dendritic cells, one of the basic cells of the gut

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immune system, extend their dendrites through epithelial cells into the gut lumen and collect bacteria and their metabolites, which are then processed into proteins, nucleic

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acids, sugars and lipids and carried in exosomes. This content is transferred from dendritic

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cells to T cells in the lymph nodes, allowing exosomes to enter the systemic circulation via the lymphatic system. Once they have reached the brain by passing through the bloodbrain barrier, they bind toll-like receptors (TLRs) in neurons and induce changes in the

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electrical layout of neuronal membranes which are responsible for the regulation of emotion and behavior. On the upper side of the diagram, interactions between bacterial

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and viral components and the immune system are represented, as they are the first step to

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produce cytokine response. When cytokines are spread in the bloodstream and have gained access to the brain, they can activate the HPA axis.

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Not reported.

Song et al 2004

Late-onset AD (n=13); NT unrelated (n=8).

Not reported.

Tomova et al 2015

ASD (m:f = 9:1, aged 2-9y); NT siblings (m:f = 7:2, aged 517y); NT unrelated (m:f = 10:0; 211y). Aspergers and ADHD (m:f = 6:0); NT unrelated (m:f = 34:35).

ICD-10, CARS, ADI

ASD suspected/ diagnosed (m:f = 879:676, aged 9mo17y); NT unrelated (m:f = 234:169; 2-18y). ASD (m:f = 39:6, aged 6.28.34y); NT unrelated-wGI (m:f = 39:6; aged 5.85-8.23y)

No verification of ASD diagnosis

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Kantarcioglu et al 2015

ICD-10

na l

Pärtty et al 2015

Ma et al 2019

GI function not studied Bacterial culture, genotypic (HPLC, PCR) and phenotypic characterization of 15 isolates on stool, blood and an intraabdominal abscess.

DSM-V, CARS

Limitations

NA

1. 2. 3. 4. 5.

Small sample size. Unknown sample age and sex Unknown exclusion criteria No stratification by ASD severity Number of samples per subject not specified (presumably one).

Clostridium quantification by qPCR on stool

1. 2. 3. 4. 5. 1. 2. 3.

Small sample size. Unknown sample age and sex Unknown exclusion criteria No stratification by ASD severity Number of samples per subject not specified. Small sample size. Sex bias. Not considered as exclusion criteria: concomitant intake of other medication besides antibiotics Number of samples per subject not specified. Small sample size. Sex bias. Not considered as exclusion criteria: concomitant antibiotic and intake of other medications, such as probiotics (supplemented in 40 NT subjects), dietary status, concomitant pathologies No stratification by ASD severity Single sample per subject. Unverified diagnosis of ASD subjects Not considered as exclusion criteria: functional foods besides kefir, dietary status, concomitant pathologies No stratification by ASD severity Number of samples per subject not specified. Small sample size. Sex bias Not considered as exclusion criteria: dietary status, but p-value adjusted accordingly Number of samples per subject not specified.

NA

qPCR on stool bacterial population

FISH and qPCR on stool bacterial population

NA

NA

Yeast culture from stool samples and identification by morphological and biochemical tests.

NA

16S rRNA gene pyrosequencing by PCR on stool bacterial population

NA

52

f

Late-onset AD (n=13); NT unrelated (n=8)

Measuring tools for GI function

oo

Song et al 2003

Measuring tools and sample for microbial analysis

pr

ASD diagnostic and categorizing tools

e-

Sample size and demographics

Pr

Author/ yeara

4. 1. 2. 3.

4. 5. 1. 2. 3. 4. 1. 2. 3. 4.

Mixed sample (not stratified by GI function)

52

Bacterial tag-encoded FLX amplicon pyrosequencing on stool samples

Presence of symptoms.

1. Small sample size. 2. Sex bias. 3. Not considered as exclusion criteria: functional food intake, dietary status, unclear if concomitant pathologies 4. Number of samples per subject not specified.

No validating assessment (usage of CARS scores dated up to 12 months prior)

Bacterial tag-encoded FLX amplicon pyrosequencing on stool samples

Presence of symptoms.

1. Modest sample size. 2. Sex bias. 3. Not considered as exclusion criteria: concomitant medication intake (antibiotic intake only of one child on the control group), dietary status, food allergies 5. No unrelated NT control group 6. Number of samples per subject not specified. 1. Small sample size. 2. Sex bias. 3. Not considered as exclusion criteria: concomitant medication intake (including antibiotics), dietary status 4. No stratification by ASD severity 5. Number of samples per subject not specified. 1. Small sample size. 2. Sex bias 3. Not considered as exclusion criteria: concomitant antibiotic in ASD group, other medication besides antipsychotics, dietary status 4. Number of samples per subject not specified. 1. Small sample size. 2. Sex bias 3. Not considered as exclusion criteria: functional foods, dietary status, unclear if concomitant pathologies 4. No stratification by ASD severity 1. Small sample size. 2. Sex bias. 3. Not considered as exclusion criteria: functional foods and antifungal intake 4. No unrelated NT control group 5. Single sample per subjects 1. Small sample size. 2. Sex bias 3. Not considered as exclusion criteria: antifungal intake 4. No stratification by ASD severity 5. Number of samples per subject not specified.

ASD (m:f=40:7; aged 6±2.8y, *33 ASD-GI) NT unrelated-wGI (m:f=23:9; aged 7.3±3.1y)

DSM-V-TR, ADI-R, CARS, ADOS

Lee et al 2017b

ASD (m:f = 18:2, aged 17.527.3y); NT unrelated-wGI (m:f = 24:4; aged 11.6-30.6y

Diagnosis: DSM-V Characterization: K-CARS

Kang et al 2018

ASD (m:f = 22:1, aged 6-14.2y, *21 ASD-GI); NT unrelated (m:f = 15:6; aged 5-11.8y, *10 NT-GI)

Pulikkan et al 2018b

Zhang et al 2018

Bacterial and yeast culture from stool samples with identification of colonies by VITEK2 system.

Pr

Iovene et al 2016

oo

Age range: 2-13y, *some with GI AD or Asperger (m:f = 42:9, *28 ASD-GI); NT siblings (m:f = 19:34, * NTsib-GI). Age range: 2-12y

Clinical observation (no specific test used)

pr

Gondalia et al 2012

ASD (m:f = 24:9 – 3 excluded); NT siblings (m:f= 5:9); NT unrelated (m:f=5:3).

e-

Finegold et al 2010

f

53

QPGS-RIII and LA/MA for intestinal permeability

Not reported

ATEC, PDD-BI

16S rRNA gene pyrosequencing by Genome Sequencer FLX-Titanium System on stool bacterial population

6-GSI

Severe ASD (m:f = 28:2, aged 3-16y); NT mostly related - wGI (m:f = 15:9; 3.5-16y)

CARS, INDT-ASD, ISAA

16S rRNA gene sequencing (NGS) on stool bacterial population

Not reported

ASD (m:f = 29:6, aged 3.46.4y, *21 ASD-GI); NT unrelated-wGI (m:f = 5:1; aged 3.5-5.7y)

DSM-V

16S rRNA gene sequencing (NGS) on stool bacterial population

Not reported

Jo ur

na l

Isolation and 16S rRNA pyrosequencing of bacteriaderived extracellular membrane vesicles from urine samples

53

Diagnosis: DSM-V, ADOS-2 Characterization: ADI-R, GMDS, VABS, CARS

Williams et al 2011

AD-GI (m:f= 15:0, aged 3.55.9y); NT-GI (m:f= 7:0, aged 3.95.5y);

DSM-IV-TR, ADI-R, Shortened CPEA Regression Interview (regression status)

Williams et al 2012

AD-GI (m:f= 15:0, aged 3.55.9y + 8m aged 6-10 for Suterella assessment); NT-GI (m:f= 7:0, aged 3.9-5.5y + 2m aged 6-10 for Suterella assessment).

DSM-IV-TR, ADI-R, Shortened CPEA Regression Interview (regression status)

16S rRNA gene pyrosequencing by qPCR on bacterial population from ileal and cecal biopsies.

Standardized questionnarie

Kushak et al 2017

ASD-GI (m:f = 19:2, aged 12.7—16.1y); NT-GI (m:f = 10:9, aged 14.817.3y).

DSM-IV

16S rRNA gene pyrosequencing on duodenal bacterial population

Presence of symptoms.

1. Small sample size. 2. Sex bias. 3. Not considered as exclusion criteria: concomitant medication intake (including antibiotics), dietary status, concomitant pathologies 4. No stratification by ASD severity 5. Number of samples per subject not specified.

PDD-NOS-wGI (n=10); AD-wGI (n=10); NT siblings-wGI (n=10); Age range: 4-10y, m:f = 14:16

DSM-IV-TR, ADI-R, ADOS, CARS

AD-wGI versus NT-wGI Bacterial tag-encoded FLX amplicon (16S rDNA and 16S rRNA) pyrosequencing on stool samples

Presence of symptoms.

1. Small sample size. 2. No information about age and sex distribution across groups 3. Not considered as exclusion criteria: antifungal intake, dietary status 4. No stratification by ASD severity 5. No unrelated NT control group

De Angelis et al 2013

QPGS-RIII

Not reported

e-

pr

DSM-V, ADI-R, ICD-10, 16S rRNA gene pyrosequencing by ADOS, PDDBI, CARS, PCR on stool bacterial population Battelle developmental test (developmental delay), 5item questionnaire (developmental regression)

AD-GI versus NT-GI 16S rRNA gene pyrosequencing by qPCR on bacterial population from ileal and cecal biopsies.

Pr

Jo ur

Plaza-Díaz 2019

16S rRNA and rDNA gene sequencing by droplet digital PCR on stool bacterial population

oo

ASD (m:f = 9:2, aged 2.4-3.4y, *2 ASD-GI); NT unrelated-wGI (m:f = 8:6; aged 2.2-3.6y) ASD-MR (n=18); ASD-wMR (n=30); NT unrelated (n=48; ged 3.53.9y; *2 ASD-abdominal pain)

na l

Coretti et al 2018

f

54

Standardized questionnarie

1. Small sample size. 2. Sex bias 3. Not considered as exclusion criteria: antifungal intake 1. Small sample size. 2. Unknown sample sex 3. Not considered as exclusion criteria: concomitant medication intake (including antibiotics), dietary status (but analysis of its effect on microbial pattern). 4. Number of samples per subject not specified.

1. Small sample size. 2. Sex bias. 3. Not considered as exclusion criteria: concomitant medication intake (antibiotic intake only of one child on the control group), dietary status, concomitant pathologies (but analysis of their effect on microbial pattern) 4. No stratification by ASD severity. 1. Small sample size. 2. Sex bias. 3. Not considered as exclusion criteria: concomitant medication intake (antibiotic intake only of one child on the control group), dietary status, concomitant pathologies 4. No stratification by ASD severity.

54

ASD-wGI (n=6); NT unrelated - wGI (n=6) Age range: 3-5y

DSM-V, PARS, M-CHAT

Sandler et al 2000

Late-onset AD + antecedents of antibiotic exposure followed by chronic diarrhea (m:f = 10:1 – 7 excluded, aged 2-8y); NT adults n=104.

DSM-IV, behavioral questionnaire

Late-onset AD-GI n = 13 (stool data) and n = 7 (gastric, small bowel data); NT unrelated n = 8 and n= 4 respectively.

Not reported (possibly not validated)

AD (55), Aspergers (3)-GI (m:f = 50:8, aged 3.5-10.3) NT unrelated-wGI (m:f = 18:21, aged 3.3-12.1)

Carissimi et al 2019

Wang et al. 2011

Not reported

pr

Loose stool history

e-

AD-GI versus NT-wGI Quantification of aerobic and anaerobic species on stool (unreported exact method).

Pr

No validating assessment for diagnosis (previous diagnosis by physician), ATEC (severity)

Jo ur

Adams et al 2011

na l

Finegold et al 2002

16S rRNA gene pyrosequencing by PCR on stool bacterial population

oo

Inoue et al 2016

f

55

ASD-severe develop-mental delay (m:f = 16:0, aged 2-6y, *12 ASD-GI, *3mild, 9 moderate, 4 severe); NT unrelated-wGI (m:f = 2:5; aged 5-16y)

Clinical observation GMDS, ADOS-2

AD (17), Aspergers (6) (m:f = 21:2, aged 3-17y, *9 ASD-GI); NT siblings (m:f = 11:11, aged 4.5-18.5y, *6 NTsib-GI);

DSM-V, CARS

Bacterial culture, analysis of bacterial metabolites and cellular fatty acids, PCR of the 16S and 16S rRNA gene sequencing on stool, gastric and small-bowel samples.

Bacterial culture, quantification and identification of bacteria and yeast from stool samples.

16S rRNA gene sequencing (NGS) on stool bacterial population

1. 2. 3. 4.

Small sample size. Unknown sample sex No information about age and sex distribution across groups Not considered as exclusion criteria: dietary status, unclear if concomitant pathologies 5. No stratification by ASD severity 6. Number of samples per subject not specified. 1. 2. 3. 4. 5. 6.

Presence of symptoms.

7. 1. 2. 3.

6-GSI

4. 5. 1. 2. 3.

GIH

4. 5. 1. 2. 3. 4.

AD-GI versus AD-wGI qPCR on stool bacterial population

FGID questionnaire

Small sample size. Unknown age from control group Unknown sample sex Not considered as exclusion criteria: dietary status, concomitant pathologies No stratification by ASD severity Number of samples per subject not specified (presumably one). No statistical analysis between groups Small sample size. Unknown sample age and sex Not considered as exclusion criteria: concomitant intake of other medication besides antibiotics, dietary status, unclear If concomitant pathologies No stratification by ASD severity Number of samples per subject not specified. Modest sample size. Sex bias. Not considered as exclusion criteria: antifungal intake, dietary status No stratification by ASD severity. Single sample per subject. Small sample size. Sex bias Not considered as exclusion criteria: antifungal intake, dietary status, unclear if concomitant pathologies Number of samples per subject not specified.

1. Small sample size. 2. Sex bias. 3. Not considered as exclusion criteria: concomitant medication intake (including antibiotics), dietary status, concomitant

55

f

56

AD (17), Asperger (6) (m:f = 21:2, aged 3-17y, *9 ASD-GI); NT siblings (m:f = 11:11, aged 4.5-18.5y, *6 NTsib-GI); NT unrelated (m:f = 4:5, aged 3.5-15y, *1 NT-GI).

DSM-V, CARS

Parracho et al. 2005

ASD (m:f = 48:10, aged 3-16y, *53 ASD-GI); NT siblings (m:f = 7:5, aged 210y, *3 NTsib-GI); NT unrelated - wGI (m:f = 6:4; aged 3-12y)

Not reported

Kang et al. 2013

AD-GI (m:f = 18:12, aged 311y); NT unrelated (m:f = 17:3, aged 3-16y, *7 NT-GI).

ADI-R, ADOS, PDD-BI

Liu et al 2019

ASD (m:f = 25:5, aged 3-5.9y, *15 ASG-GI); NT unrelated-wGI (m:f = 16:4; aged 3.3-5.3y)

DSM-V, ICD-10

ASD (m:f = 15:5, aged 1318y); NT related (m:f = 8:11, aged 11-50y)

DSM-V

FGID questionnaire

ePr

AD-GI versus NT-GI versus NT-wGI FISH analysis and microscopic Questionnaire examination of stool bacterial population

na l

Jo ur

Kong et al 2019

qPCR on stool bacterial population

pr

Wang et al. 2013

oo

NT unrelated (m:f = 4:5, aged 3.5-15y, *1 NT-GI).

qPCR analysis of 16S rDNA stool bacterial population.

Modified version of GSI

16S rRNA gene pyrosequencing by PCR on stool bacterial population

Modified versión of 6-GSI

16S rRNA gene pyrosequencing by PCR on stool bacterial population

GSI

pathologies (epilepsy and intellectual disability included in ASD group) 1. No stratification by ASD severity. 2. Small sample size. 3. Sex bias. 4. Not considered as exclusion criteria: concomitant medication intake (including antibiotics), dietary status, concomitant pathologies (epilepsy and intellectual disability included in ASD group Epilepsy and intellectual disability included in ASD group) 5. No stratification by ASD severity. 1. Modest sample size. 2. Sex bias. 3. Not considered as exclusion criteria: concomitant medication intake (including antibiotics), dietary status, but analysis of their association with bacterial profiles. Unclear if concomitant pathologies were considered. 4. No stratification by ASD severity 5. Number of samples per subject not specified. 1. Small sample size. 2. Sex bias. 3. Not considered as exclusion criteria: functional foods, dietary status and dietary supplements 4. Single sample per subject. 1. Small sample size. 2. Sex bias 3. No stratification by ASD severity 4. Number of samples per subject not specified. 1. Small sample size. 2. Sex bias 3. Not considered as exclusion criteria: antifungal intake, dietary status and allergies, but analysis of the effect of the last two on bacterial profiles 4. No stratification by ASD severity 5. Number of samples per subject not specified.

AD-wGI versus NT-GI versus NT-wGI

56

ASD-GI (m:f = 14:0, aged 413y); NT unrelated-GI (m:f = 12:3, aged 3-18y); NT unrelated-wGI (m:f = 6:0; 3-14y)

ADOS; SRS for NT children

Son et al 2015

ASD (m:f = 52:7, *25 ASD-GI); NT siblings (m:f = 21:23, *13 NT-GI). Age range: 7-14y, only 37 family-matched pairs.

Unclear verification of diagnosis, behavior problems assessed by CBCL

16S rDNA characterization from rectal bacterial population

QPGS-RIII

oo

Luna et al 2017c

f

57

1. Small sample size. 2. Sex bias. 3. Not considered as exclusion criteria: concomitant intake of other medication besides antibiotics and steroids, dietary status 4. No stratification by ASD severity

pr

AD-GI versus AD-wGI versus NT-GI versus NT-wGI qPCR and 16S rRNA gene QPGS-RIII pyrosequencing of stool bacterial population.

Jo ur

na l

Pr

e-

1. Modest sample size. 2. Sex bias. 3. Not considered as exclusion criteria: antifungal intake, dietary status, concomitant pathologies 4. No stratification by ASD severity 5. No unrelated NT control group 6. Number of samples per subject not specified. Strati et al 2017 AD (m:f = 31:9, aged 5-17y, *5 DSM-V, ADOS, ABC, CARS 16S rRNA gene pyrosequencing on Constipation 1. Modest sample size. constipated, *29 non-cons., bacterial and fungal stool sample defined according 2. Sex bias. *36 severe AD); populations. to Rome III 3. Not considered as exclusion criteria: antifungal intake, dietary NT unrelated (m:f = 28:12, status, celiac disease (present in 2 ASD subjects aged 3.6-12y, *11 cons.). 4. Number of samples per subject not specified. 6-GSI: 6-item gastrointestinal severity index; ABC: Autism Behavior Checklist; AD: autism disorder; ADI/ ADI-R: Autism Diagnostic Interview/ Revised Version; ADOS-2: Autism Diagnostic Observation Schedules – 2; ASD: autism spectrum disorder; ATEC: Autism Treatment Evaluation Checklist; CARS: Childhood Autism Rating Scale; CBCL: Child Behavior Checklist; DSM-IV/V/TR: Diagnostic and Statistical Manual of Mental Disorders 4th Edition/ 5th Edition/ Text revision; FGID: functional gastrointestinal disorders; FGIDs: functional gastrointestinal disorders; FISH: fluorescence in situ hybridization; GI: gastrointestinal disorders; GIH: CHARGE Gastrointestinal History Questionnaire; GMDS: Griffiths Mental Development Scales; HPLC: high performance liquid chromatography; ICD-10: International Statistics Classification of Diseases and Related Health Problems, 10th Revision; INDT-ASD: INCLEN Diagnostic Tool for Autism Spectrum Disorder (DSM-V approved); ISAA: Indian Scale for Assessment of Autism; KCARS: Korean Childhood Autism Rating Scale; LA/MA: lactulose/mannitol test; M-CHAT: Modified Check-list for Autism in Toddlers; m:f: male/female ratio; MR: mental regression; NA: not applicable; NGS: next-generation sequencing; NT: neurotypical; PARS: Pervasive Developmental Disorders Autism Society Japan Rating Scale; PCoA: principal coordinate analysis; PCR: Polymerase Chain Reaction; PDD-NOS: pervasive developmental disorder not otherwise specified; PDDBI: Pervasive Developmental Disorders Behavior Inventory; qPCR quantitative polymerase chain reaction, also known as real-time PCR (RT-PCR), QPGS-RIII: Questionnaire on Pediatric Gastrointestinal Symptoms-Rome III version; SRS: Social Responsiveness Scale; VABS: Vineland Adaptive Behavior Scales; wGI: without gastrointestinal disorders; wMR: without mental regression

Table 1: gut bacterial and fungal profile in children and adolescents with ASD. a Studies classified by assessment of GI function on microbial analysis and displayed in ascending order of publication date; b Unclear whether ASD children suffer from GI conditions, c For specific analysis of correlation between microbiota and abdominal pain, stratification on four groups was conducted: ASD-GI with no pain reported (n=5), ASD-GI with abdominal pain (n=9), NT with no pain reported (n=10), and NT with abdominal pain (n=11).

57

na l

Jo ur

oo

pr

e-

Pr

f

58

58