How to obtain an integrated picture of the molecular networks involved in adaptation to microgravity in different biological systems?

Periodically, the European Space Agency (ESA) updates scientific roadmaps in consultation with the scientific community. The ESA SciSpacE Science Community White Paper (SSCWP) 9, "Biology in Space and Analogue Environments", focusses in 5 main topic areas, aiming to address key community-identified knowledge gaps in Space Biology. Here we present one of the identified topic areas, which is also an unanswered question of life science research in Space: "How to Obtain an Integrated Picture of the Molecular Networks Involved in Adaptation to Microgravity in Different Biological Systems?" The manuscript reports the main gaps of knowledge which have been identified by the community in the above topic area as well as the approach the community indicates to address the gaps not yet bridged. Moreover, the relevance that these research activities might have for the space exploration programs and also for application in industrial and technological fields on Earth is briefly discussed.

Identifying Progression-Specific Alzheimer's Subtypes Using Multimodal Transformer.

Alzheimer's disease (AD) is the most prevalent neurodegenerative disease, yet its current treatments are limited to stopping disease progression. Moreover, the effectiveness of these treatments remains uncertain due to the heterogeneity of the disease. Therefore, it is essential to identify disease subtypes at a very early stage. Current data-driven approaches can be used to classify subtypes during later stages of AD or related disorders, but making predictions in the asymptomatic or prodromal stage is challenging. Furthermore, the classifications of most existing models lack explainability, and these models rely solely on a single modality for assessment, limiting the scope of their analysis. Thus, we propose a multimodal framework that utilizes early-stage indicators, including imaging, genetics, and clinical assessments, to classify AD patients into progression-specific subtypes at an early stage. In our framework, we introduce a tri-modal co-attention mechanism (Tri-COAT) to explicitly capture cross-modal feature associations. Data from the Alzheimer's Disease Neuroimaging Initiative (ADNI) (slow progressing = 177, intermediate = 302, and fast = 15) were used to train and evaluate Tri-COAT using a 10-fold stratified cross-testing approach. Our proposed model outperforms baseline models and sheds light on essential associations across multimodal features supported by known biological mechanisms. The multimodal design behind Tri-COAT allows it to achieve the highest classification area under the receiver operating characteristic curve while simultaneously providing interpretability to the model predictions through the co-attention mechanism.

Impact of Suramin on Key Pathological Features of Sporadic Alzheimer's Disease-Derived Forebrain Neurons.

Background: Alzheimer's disease (AD) is characterized by disrupted proteostasis and macroautophagy (hereafter "autophagy"). The pharmacological agent suramin has known autophagy modulation properties with potential efficacy in mitigating AD neuronal pathology. Objective: In the present work, we investigate the impact of forebrain neuron exposure to suramin on the Akt/mTOR signaling pathway, a major regulator of autophagy, in comparison with rapamycin and chloroquine. We further investigate the effect of suramin on several AD-related biomarkers in sporadic AD (sAD)-derived forebrain neurons. Methods: Neurons differentiated from ReNcell neural progenitors were used to assess the impact of suramin on the Akt/mTOR signaling pathway relative to the autophagy inducer rapamycin and autophagy inhibitor chloroquine. Mature forebrain neurons were differentiated from induced pluripotent stem cells (iPSCs) sourced from a late-onset sAD patient and treated with 100µM suramin for 72 h, followed by assessments for amyloid-ß, phosphorylated tau, oxidative/nitrosative stress, and synaptic puncta density. Results: Suramin treatment of sAD-derived neurons partially ameliorated the increased p-Tau(S199)/Tau ratio, and fully remediated the increased glutathione to oxidized nitric oxide ratio, observed in untreated sAD-derived neurons relative to healthy controls. These positive results may be due in part to the distinct increases in Akt/mTOR pathway mediator p-p70S6K noted with suramin treatment of both ReNcell-derived and iPSC-derived neurons. Longer term neuronal markers, such as synaptic puncta density, were unaffected by suramin treatment. Conclusions: These findings provide initial evidence supporting the potential of suramin to reduce the degree of dysregulation in sAD-derived forebrain neurons in part via the modulation of autophagy.

How are cell and tissue structure and function influenced by gravity and what are the gravity perception mechanisms?

Progress in mechanobiology allowed us to better understand the important role of mechanical forces in the regulation of biological processes. Space research in the field of life sciences clearly showed that gravity plays a crucial role in biological processes. The space environment offers the unique opportunity to carry out experiments without gravity, helping us not only to understand the effects of gravitational alterations on biological systems but also the mechanisms underlying mechanoperception and cell/tissue response to mechanical and gravitational stresses. Despite the progress made so far, for future space exploration programs it is necessary to increase our knowledge on the mechanotransduction processes as well as on the molecular mechanisms underlying microgravity-induced cell and tissue alterations. This white paper reports the suggestions and recommendations of the SciSpacE Science Community for the elaboration of the section of the European Space Agency roadmap "Biology in Space and Analogue Environments" focusing on "How are cells and tissues influenced by gravity and what are the gravity perception mechanisms?" The knowledge gaps that prevent the Science Community from fully answering this question and the activities proposed to fill them are discussed.

Predicting Problematic Behavior in Autism Spectrum Disorder Using Medical History and Environmental Data.

Autism spectrum disorder (ASD), characterized by social, communication, and behavioral abnormalities, affects 1 in 36 children according to the CDC. Several co-occurring conditions are often associated with ASD, including sleep and immune disorders and gastrointestinal (GI) problems. ASD is also associated with sensory sensitivities. Some individuals with ASD exhibit episodes of challenging behaviors that can endanger themselves or others, including aggression and self-injurious behavior (SIB). In this work, we explored the use of artificial intelligence models to predict behavior episodes based on past data of co-occurring conditions and environmental factors for 80 individuals in a residential setting. We found that our models predict occurrences of behavior and non-behavior with accuracies as high as 90% for some individuals, and that environmental, as well as gastrointestinal, factors are notable predictors across the population examined. While more work is needed to examine the underlying connections between the factors and the behaviors, having reasonably accurate predictions for behaviors has the potential to improve the quality of life of some individuals with ASD.

How do gravity alterations affect animal and human systems at a cellular/tissue level?

The present white paper concerns the indications and recommendations of the SciSpacE Science Community to make progress in filling the gaps of knowledge that prevent us from answering the question: "How Do Gravity Alterations Affect Animal and Human Systems at a Cellular/Tissue Level?" This is one of the five major scientific issues of the ESA roadmap "Biology in Space and Analogue Environments". Despite the many studies conducted so far on spaceflight adaptation mechanisms and related pathophysiological alterations observed in astronauts, we are not yet able to elaborate a synthetic integrated model of the many changes occurring at different system and functional levels. Consequently, it is difficult to develop credible models for predicting long-term consequences of human adaptation to the space environment, as well as to implement medical support plans for long-term missions and a strategy for preventing the possible health risks due to prolonged exposure to spaceflight beyond the low Earth orbit (LEO). The research activities suggested by the scientific community have the aim to overcome these problems by striving to connect biological and physiological aspects in a more holistic view of space adaptation effects.

Towards the Development of a Diagnostic Test for Autism Spectrum Disorder: Big Data Meets Metabolomics.

Autism spectrum disorder (ASD) is defined as a neurodevelopmental disorder which results in impairments in social communications and interactions as well as repetitive behaviors. Despite current estimates showing that approximately 2.2% of children are affected in the United States, relatively little about ASD pathophysiology is known in part due to the highly heterogenous presentation of the disorder. Given the limited knowledge into the biological mechanisms governing its etiology, the diagnosis of ASD is performed exclusively based on an individual's behavior assessed by a clinician through psychometric tools. Although there is no readily available biochemical test for ASD diagnosis, multivariate statistical methods show considerable potential for effectively leveraging multiple biochemical measurements for classification and characterization purposes. In this work, markers associated with the folate dependent one-carbon metabolism and transulfuration (FOCM/TS) pathways analyzed via both Fisher Discriminant Analysis and Support Vector Machine showed strong capability to distinguish between ASD and TD cohorts. Furthermore, using Kernel Partial Least Squares regression it was possible to assess some degree of behavioral severity from metabolomic data. While the results presented need to be replicated in independent future studies, they represent a promising avenue for uncovering clinically relevant ASD biomarkers.

Shotgun Metagenomics Study Suggests Alteration in Sulfur Metabolism and Oxidative Stress in Children with Autism and Improvement after Microbiota Transfer Therapy.

Links between gut microbiota and autism spectrum disorder (ASD) have been explored in many studies using 16S rRNA gene amplicon and shotgun sequencing. Based on these links, microbiome therapies have been proposed to improve gastrointestinal (GI) and ASD symptoms in ASD individuals. Previously, our open-label microbiota transfer therapy (MTT) study provided insight into the changes in the gut microbial community of children with ASD after MTT and showed significant and long-term improvement in ASD and GI symptoms. Using samples from the same study, the objective of this work was to perform a deeper taxonomic and functional analysis applying shotgun metagenomic sequencing. Taxonomic analyses revealed that ASD Baseline had many bacteria at lower relative abundances, and their abundance increased after MTT. The relative abundance of fiber consuming and beneficial microbes including Prevotella (P. dentalis, P. enoeca, P. oris, P. meloninogenica), Bifidobacterium bifidum, and a sulfur reducer Desulfovibrio piger increased after MTT-10wks in children with ASD compared to Baseline (consistent at genus level with the previous 16S rRNA gene study). Metabolic pathway analysis at Baseline compared to typically developing (TD) children found an altered abundance of many functional genes but, after MTT, they became similar to TD or donors. Important functional genes that changed included: genes encoding enzymes involved in folate biosynthesis, sulfur metabolism and oxidative stress. These results show that MTT treatment not only changed the relative abundance of important genes involved in metabolic pathways, but also seemed to bring them to a similar level to the TD controls. However, at a two-year follow-up, the microbiota and microbial genes shifted into a new state, distinct from their levels at Baseline and distinct from the TD group. Our current findings suggest that microbes from MTT lead to initial improvement in the metabolic profile of children with ASD, and major additional changes at two years post-treatment. In the future, larger cohort studies, mechanistic in vitro experiments and metatranscriptomics studies are recommended to better understand the role of these specific microbes, functional gene expression, and metabolites relevant to ASD.

Effects of Stromal Cell Conditioned Medium and Antipurinergic Treatment on Macrophage Phenotype.

The immunomodulatory capacity of the human mesenchymal stromal cell (MSC) secretome has been a critical driver for the development of cell-free MSC products, such as conditioned medium (CM), for regenerative medicine applications. This is particularly true as cell-free MSC products present several advantages over direct autologous or allogeneic MSC delivery with respect to safety, manufacturability, and defined potency. Recently, significant effort has been placed into creating novel MSC CM formulations with an immunomodulatory capacity tailored for specific regenerative contexts. For instance, the immunoregulatory nature of MSC CM has previously been tuned through a number of cytokine-priming strategies. Herein, we propose an alternate method to tailor the immunomodulatory "phenotype" of cytokine-primed MSC CM through coupling with the pharmacological agent, suramin. Suramin interferes with the signaling of purines including extracellular adenosine triphosphate (ATP), which plays a critical role in the activation of the innate immune system after injury. Toward this end, human THP-1-derived macrophages were activated to a proinflammatory phenotype and treated with (1) unprimed/native MSC CM, (2) interferon-γ/tumor necrosis factor α-primed MSC CM (primed CM), (3) suramin alone, or (4) primed MSC CM and suramin (primed CM/suramin). Markers of key macrophage functions-cytokine secretion, autophagy, oxidative stress modulation, and activation/migration-were assessed. Consistent with previous literature, primed CM elevated macrophage secretion of several proinflammatory and pleiotropic cytokines relative to native CM; whereas addition of suramin imparted consistent shifts in terms of TNFα (↓), interleukin-10 (↓), and hepatocyte growth factor (↑) irrespective of CM. In addition, both primed CM and suramin, individually and combined, increased reactive oxygen species production relative to native CM, and addition of suramin to primed CM shifted levels of CX3CL1, a factor involved in ATP-associated macrophage regulation. Varimax rotation assessment of the secreted cytokine profiles confirmed that primed CM/suramin resulted in a THP-1 phenotypic shift away from the lipopolysaccharide-activated proinflammatory state that was distinct from that of primed CM or native CM alone. This altered primed CM/suramin-associated phenotype may prove beneficial for healing in certain regenerative contexts. These results may inform future work coupling antipurinergic treatments with MSC-derived therapies in regenerative medicine applications.

Maternal risk factors vary between subpopulations of children with autism spectrum disorder.

Previous work identified three subgroups of children with ASD based upon co-occurring conditions (COCs) diagnosed during the first 5 years of life. This work examines prenatal risk factors, given by maternal medical claims, for each of the three subgroups: children with a High-Prevalence of COCs, children with mainly developmental delay and seizures (DD/Seizure COCs), and children with a Low-Prevalence of COCs. While some risk factors are shared by all three subgroups, the majority of the factors identified for each subgroup were unique; infections, anti-inflammatory and other complex medications were associated with the High-Prevalence COCs group; immune deregulatory conditions such as asthma and joint disorders were associated with the DD/Seizure COCs group; and overall pregnancy complications were associated with the Low-Prevalence COCs group. Thus, we have found that the previously identified subgroups of children with ASD have distinct associated prenatal risk factors. As such, this work supports subgrouping children with ASD based upon COCs, which may provide a framework for elucidating some of the heterogeneity associated with ASD.

Framework for Testing Robustness of Machine Learning-Based Classifiers.

There has been a rapid increase in the number of artificial intelligence (AI)/machine learning (ML)-based biomarker diagnostic classifiers in recent years. However, relatively little work has focused on assessing the robustness of these biomarkers, i.e., investigating the uncertainty of the AI/ML models that these biomarkers are based upon. This paper addresses this issue by proposing a framework to evaluate the already-developed classifiers with regard to their robustness by focusing on the variability of the classifiers' performance and changes in the classifiers' parameter values using factor analysis and Monte Carlo simulations. Specifically, this work evaluates (1) the importance of a classifier's input features and (2) the variability of a classifier's output and model parameter values in response to data perturbations. Additionally, it was found that one can estimate a priori how much replacement noise a classifier can tolerate while still meeting accuracy goals. To illustrate the evaluation framework, six different AI/ML-based biomarkers are developed using commonly used techniques (linear discriminant analysis, support vector machines, random forest, partial-least squares discriminant analysis, logistic regression, and multilayer perceptron) for a metabolomics dataset involving 24 measured metabolites taken from 159 study participants. The framework was able to correctly predict which of the classifiers should be less robust than others without recomputing the classifiers itself, and this prediction was then validated in a detailed analysis.

Multivariate Analysis of Metabolomic and Nutritional Profiles among Children with Autism Spectrum Disorder.

There have been promising results regarding the capability of statistical and machine-learning techniques to offer insight into unique metabolomic patterns observed in ASD. This work re-examines a comparative study contrasting metabolomic and nutrient measurements of children with ASD (n = 55) against their typically developing (TD) peers (n = 44) through a multivariate statistical lens. Hypothesis testing, receiver characteristic curve assessment, and correlation analysis were consistent with prior work and served to underscore prominent areas where metabolomic and nutritional profiles between the groups diverged. Improved univariate analysis revealed 46 nutritional/metabolic differences that were significantly different between ASD and TD groups, with individual areas under the receiver operator curve (AUROC) scores of 0.6-0.9. Many of the significant measurements had correlations with many others, forming two integrated networks of interrelated metabolic differences in ASD. The TD group had 189 significant correlation pairs between metabolites, vs. only 106 for the ASD group, calling attention to underlying differences in metabolic processes. Furthermore, multivariate techniques identified potential biomarker panels with up to six metabolites that were able to attain a predictive accuracy of up to 98% for discriminating between ASD and TD, following cross-validation. Assessing all optimized multivariate models demonstrated concordance with prior physiological pathways identified in the literature, with some of the most important metabolites for discriminating ASD and TD being sulfate, the transsulfuration pathway, uridine (methylation biomarker), and beta-amino isobutyrate (regulator of carbohydrate and lipid metabolism).

Quantitative Assessment of Balance for Accurate Prediction of Return to Sport From Sport-Related Concussion.

BACKGROUND: Determining when athletes are able to return to sport after sports-related concussion (SRC) can be difficult. HYPOTHESIS: A multimodal algorithm using cognitive testing, postural stability, and clinical assessment can predict return to sports after SRC. STUDY DESIGN: Prospective cohort. LEVEL OF EVIDENCE: Level 2b. METHODS: Athletes were evaluated within 2 to 3 weeks of SRC. Clinical assessment, Immediate Post Concussion and Cognitive Testing (ImPACT), and postural stability (Equilibrate) were conducted. Resulting data and machine learning techniques were used to optimize an algorithm discriminating between patients ready to return to sports versus those who are not yet recovered. A Fisher discriminant analysis with leave-one-out cross-validation assessed every combination of 2 to 5 factors to optimize the algorithm with lowest combination of type I and type II errors. RESULTS: A total of 193 athletes returned to contact sports after SRC at a mean 84.6 days (±88.8). Twelve subjects (6.2%) sustained repeat SRC within 12 months after return to sport. The combination of (1) days since injury, (2) total symptom score, and (3) nondominant foot tandem eyes closed postural stability score created the best algorithm for discriminating those ready to return to sports after SRC with lowest type I error (13.85%) and type II error (11.25%). The model was able to discriminate between patients who were ready to successfully return to sports versus those who were not with area under the receiver operating characteristic (ROC) curve of 0.82. CONCLUSION: The algorithm predicts successful return to sports with an acceptable sensitivity and specificity. Tandem balance with eyes closed measured with a video-force plate discriminated athletes ready to return to sports from SRC when combined in multivariate analysis with symptom score and time since injury. The combination of these factors may pose advantages over computerized neuropsychological testing when evaluating young athletes with SRC for return to contact sports. CLINICAL RELEVANCE: When assessing young athletes sustaining an SRC in a concussion clinic, measuring postural stability in tandem stance with eyes closed combined with clinical assessment and cognitive recovery is effective to determine who is ready to successfully return to sports.

Genomics transformer for diagnosing Parkinson's disease.

Parkinson's disease (PD) is the second most common neurodegenerative disease and presents a complex etiology with genomic and environmental factors and no recognized cures. Genotype data, such as single nucleotide polymorphisms (SNPs), could be used as a prodromal factor for early detection of PD. However, the polygenic nature of PD presents a challenge as the complex relationships between SNPs towards disease development are difficult to model. Traditional assessment methods such as polygenic risk scores and machine learning approaches struggle to capture the complex interactions present in the genotype data, thus limiting their discriminative capabilities in diagnosis. On the other hand, deep learning models are better suited for this task. Nevertheless, they encounter difficulties of their own such as a lack of interpretability. To overcome these limitations, in this work, a novel transformer encoder-based model is introduced to classify PD patients from healthy controls based on their genotype. This method is designed to effectively model complex global feature interactions and enable increased interpretability through the learned attention scores. The proposed framework outperformed traditional machine learning models and multilayer perceptron (MLP) baseline models. Moreover, visualization of the learned SNP-SNP associations provides not only interpretability to the model but also valuable insights into the biochemical pathways underlying PD development, which are corroborated by pathway enrichment analysis. Our results suggest novel SNP interactions to be further studied in wet lab and clinical settings.

Pregnant Mothers' Medical Claims and Associated Risk of Their Children being Diagnosed with Autism Spectrum Disorder.

A retrospective analysis of administrative claims containing a diverse mixture of ages, ethnicities, and geographical regions across the United States was conducted in order to identify medical events that occur during pregnancy and are associated with autism spectrum disorder (ASD). The dataset used in this study is comprised of 123,824 pregnancies of which 1265 resulted in the child being diagnosed with ASD during the first five years of life. Logistic regression analysis revealed significant relationships between several maternal medical claims, made during her pregnancy and segmented by trimester, and the child's diagnosis of ASD. Having a biological sibling with ASD, maternal use of antidepressant medication and psychiatry services as well as non-pregnancy related claims such hospital visits, surgical procedures, and radiology exposure were related to an increased risk of ASD regardless of trimester. Urinary tract infections during the first trimester and preterm delivery during the second trimester were also related to an increased risk of ASD. Preventative and obstetrical care were associated with a decreased risk for ASD. A better understanding of the medical factors that increase the risk of having a child with ASD can lead to strategies to decrease risk or identify those children who require increased surveillance for the development of ASD to promote early diagnosis and intervention.

KIF3A accelerates KIF3C within the kinesin-2 heterodimer to generate symmetrical phosphate release rates for each processive step.

Heterodimeric KIF3AC is a mammalian kinesin-2 that is highly expressed in the central nervous system and associated with vesicles in neurons. KIF3AC is an intriguing member of the kinesin-2 family because the intrinsic kinetics of KIF3A and KIF3C when expressed as homodimers and analyzed in vitro are distinctively different from each other. For example, the single-molecule velocities of the engineered homodimers KIF3AA and KIF3CC are 293 and 7.5 nm/s, respectively, whereas KIF3AC has a velocity of 186 nm/s. These results led us to hypothesize that heterodimerization alters the intrinsic catalytic properties of the two heads, and an earlier computational analysis predicted that processive steps would alternate between a fast step for KIF3A followed by a slow step for KIF3C resulting in asymmetric stepping. To test this hypothesis directly, we measured the presteady-state kinetics of phosphate release for KIF3AC, KIF3AA, and KIF3CC followed by computational modeling of the KIF3AC phosphate release transients. The results reveal that KIF3A and KIF3C retain their intrinsic ATP-binding and hydrolysis kinetics. Yet within KIF3AC, KIF3A activates the rate of phosphate release for KIF3C such that the coupled steps of phosphate release and dissociation from the microtubule become more similar for KIF3A and KIF3C. These coupled steps are the rate-limiting transition for the ATPase cycle suggesting that within KIF3AC, the stepping kinetics are similar for each head during the processive run. Future work will be directed to define how these properties enable KIF3AC to achieve its physiological functions.

Altered metabolism of mothers of young children with Autism Spectrum Disorder: a case control study.

BACKGROUND: Previous research studies have demonstrated abnormalities in the metabolism of mothers of young children with autism. METHODS: Metabolic analysis was performed on blood samples from 30 mothers of young children with Autism Spectrum Disorder (ASD-M) and from 29 mothers of young typically-developing children (TD-M). Targeted metabolic analysis focusing on the folate one-carbon metabolism (FOCM) and the transsulfuration pathway (TS) as well as broad metabolic analysis were performed. Statistical analysis of the data involved both univariate and multivariate statistical methods. RESULTS: Univariate analysis revealed significant differences in 5 metabolites from the folate one-carbon metabolism and the transsulfuration pathway and differences in an additional 48 metabolites identified by broad metabolic analysis, including lower levels of many carnitine-conjugated molecules. Multivariate analysis with leave-one-out cross-validation allowed classification of samples as belonging to one of the two groups of mothers with 93% sensitivity and 97% specificity with five metabolites. Furthermore, each of these five metabolites correlated with 8-15 other metabolites indicating that there are five clusters of correlated metabolites. In fact, all but 5 of the 50 metabolites with the highest area under the receiver operating characteristic curve were associated with the five identified groups. Many of the abnormalities appear linked to low levels of folate, vitamin B12, and carnitine-conjugated molecules. CONCLUSIONS: Mothers of children with ASD have many significantly different metabolite levels compared to mothers of typically developing children at 2-5 years after birth.

Multivariate Analysis of Fecal Metabolites from Children with Autism Spectrum Disorder and Gastrointestinal Symptoms before and after Microbiota Transfer Therapy.

Fecal microbiota transplant (FMT) holds significant promise for patients with Autism Spectrum Disorder (ASD) and gastrointestinal (GI) symptoms. Prior work has demonstrated that plasma metabolite profiles of children with ASD become more similar to those of their typically developing (TD) peers following this treatment. This work measures the concentration of 669 biochemical compounds in feces of a cohort of 18 ASD and 20 TD children using ultrahigh performance liquid chromatography-tandem mass spectroscopy. Subsequent measurements were taken from the ASD cohort over the course of 10-week Microbiota Transfer Therapy (MTT) and 8 weeks after completion of this treatment. Univariate and multivariate statistical analysis techniques were used to characterize differences in metabolites before, during, and after treatment. Using Fisher Discriminant Analysis (FDA), it was possible to attain multivariate metabolite models capable of achieving a sensitivity of 94% and a specificity of 95% after cross-validation. Observations made following MTT indicate that the fecal metabolite profiles become more like those of the TD cohort. There was an 82-88% decrease in the median difference of the ASD and TD group for the panel metabolites, and among the top fifty most discriminating individual metabolites, 96% report more comparable values following treatment. Thus, these findings are similar, although less pronounced, as those determined using plasma metabolites.

Distinct Fecal and Plasma Metabolites in Children with Autism Spectrum Disorders and Their Modulation after Microbiota Transfer Therapy.

Accumulating evidence has strengthened a link between dysbiotic gut microbiota and autism. Fecal microbiota transplant (FMT) is a promising therapy to repair dysbiotic gut microbiota. We previously performed intensive FMT called microbiota transfer therapy (MTT) for children with autism spectrum disorders (ASD) and observed a substantial improvement of gastrointestinal and behavioral symptoms. We also reported modulation of the gut microbiome toward a healthy one. In this study, we report comprehensive metabolite profiles from plasma and fecal samples of the children who participated in the MTT trial. With 619 plasma metabolites detected, we found that the autism group had distinctive metabolic profiles at baseline. Eight metabolites (nicotinamide riboside, IMP, iminodiacetate, methylsuccinate, galactonate, valylglycine, sarcosine, and leucylglycine) were significantly lower in the ASD group at baseline, while caprylate and heptanoate were significantly higher in the ASD group. MTT drove global shifts in plasma profiles across various metabolic features, including nicotinate/nicotinamide and purine metabolism. In contrast, for 669 fecal metabolites detected, when correcting for multiple hypotheses, no metabolite was significantly different at baseline. Although not statistically significant, p-cresol sulfate was relatively higher in the ASD group at baseline, and after MTT, the levels decreased and were similar to levels in typically developing (TD) controls. p-Cresol sulfate levels were inversely correlated with Desulfovibrio, suggesting a potential role of Desulfovibrio on p-cresol sulfate modulation. Further studies of metabolites in a larger ASD cohort, before and after MTT, are warranted, as well as clinical trials of other therapies to address the metabolic changes which MTT was not able to correct.IMPORTANCE Despite the prevalence of autism and its extensive impact on our society, no U.S. Food and Drug Administration-approved treatment is available for this complex neurobiological disorder. Based on mounting evidences that support a link between autism and the gut microbiome, we previously performed a pioneering open-label clinical trial using intensive fecal microbiota transplant. The therapy significantly improved gastrointestinal and behavioral symptoms. Comprehensive metabolomic measurements in this study showed that children with autism spectrum disorder (ASD) had different levels of many plasma metabolites at baseline compared to those in typically developing children. Microbiota transfer therapy (MTT) had a systemic effect, resulting in substantial changes in plasma metabolites, driving a number of metabolites to be more similar to those from typically developing children. Our results provide evidence that changes in metabolites are one mechanism of the gut-brain connection mediated by the gut microbiota and offer plausible clinical evidence for a promising autism treatment and biomarkers.

MODELING INTER-KINGDOM REGULATION OF INFLAMMATORY SIGNALING IN HUMAN INTESTINAL EPITHELIAL CELLS.

The human gastrointestinal (GI) tract is colonized by a highly diverse and complex microbial community (i.e., microbiota). The microbiota plays an important role in the development of the immune system, specifically mediating inflammatory responses, however the exact mechanisms are poorly understood. We have developed a mathematical model describing the effect of indole on host inflammatory signaling in HCT-8 human intestinal epithelial cells. In this model, indole modulates transcription factor nuclear factor κ B (NF-κB) and produces the chemokine interleukin-8 (IL-8) through the activation of the aryl hydrocarbon receptor (AhR). Phosphorylated NF-κB exhibits dose and time-dependent responses to indole concentrations and IL-8 production shows a significant down-regulation for 0.1 ng/mL TNF-α stimulation. The model shows agreeable simulation results with the experimental data for IL-8 secretion and normalized NF-κB values. Our results suggest that microbial metabolites such as indole can modulate inflammatory signaling in HTC-8 cells through receptor-mediated processes.