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.

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.

Towards a Multivariate Biomarker-Based Diagnosis of Autism Spectrum Disorder: Review and Discussion of Recent Advancements.

An ever-evolving understanding of autism spectrum disorder (ASD) pathophysiology necessitates that diagnostic standards also evolve from being observation-based to include quantifiable clinical measurements. The multisystem nature of ASD motivates the use of multivariate methods of statistical analysis over common univariate approaches for discovering clinical biomarkers relevant to this goal. In addition to characterization of important behavioral patterns for improving current diagnostic instruments, multivariate analyses to date have allowed for thorough investigation of neuroimaging-based, genetic, and metabolic abnormalities in individuals with ASD. This review highlights current research using multivariate statistical analyses to quantify the value of these behavioral and physiological markers for ASD diagnosis. A detailed discussion of a blood-based diagnostic test for ASD using specific metabolite concentrations is also provided. The advancement of ASD biomarker research promises to provide earlier and more accurate diagnoses of the disorder.

Elucidating the unusual reaction kinetics of D-glucuronyl C5-epimerase.

The chemoenzymatic synthesis of heparin, through a multienzyme process, represents a critical challenge in providing a safe and effective substitute for this animal-sourced anticoagulant drug. D-glucuronyl C5-epimerase (C5-epi) is an enzyme acting on a heparin precursor, N-sulfoheparosan, catalyzing the reversible epimerization of D-glucuronic acid (GlcA) to L-iduronic acid (IdoA). The absence of reliable assays for C5-epi has limited elucidation of the enzymatic reaction and kinetic mechanisms. Real time and offline assays are described that rely on 1D 1H NMR to study the activity of C5-epi. Apparent steady-state kinetic parameters for both the forward and the pseudo-reverse reactions of C5-epi are determined for the first time using polysaccharide substrates directly relevant to the chemoenzymatic synthesis and biosynthesis of heparin. The forward reaction shows unusual sigmoidal kinetic behavior, and the pseudo-reverse reaction displays nonsaturating kinetic behavior. The atypical sigmoidal behavior of the forward reaction was probed using a range of buffer additives. Surprisingly, the addition of 25 mM each of CaCl2 and MgCl2 resulted in a forward reaction exhibiting more conventional Michaelis-Menten kinetics. The addition of 2-O-sulfotransferase, the next enzyme involved in heparin synthesis, in the absence of 3'-phosphoadenosine 5'-phosphosulfate, also resulted in C5-epi exhibiting a more conventional Michaelis-Menten kinetic behavior in the forward reaction accompanied by a significant increase in apparent Vmax. This study provides critical information for understanding the reaction kinetics of C5-epi, which may result in improved methods for the chemoenzymatic synthesis of bioengineered heparin.

Clustering of co-occurring conditions in autism spectrum disorder during early childhood: A retrospective analysis of medical claims data.

Individuals with autism spectrum disorder (ASD) are frequently affected by co-occurring medical conditions (COCs), which vary in severity, age of onset, and pathophysiological characteristics. The presence of COCs contributes to significant heterogeneity in the clinical presentation of ASD between individuals and a better understanding of COCs may offer greater insight into the etiology of ASD in specific subgroups while also providing guidance for diagnostic and treatment protocols. This study retrospectively analyzed medical claims data from a private United States health plan between years 2000 and 2015 to investigate patterns of COC diagnoses in a cohort of 3,278 children with ASD throughout their first 5 years of enrollment compared to 279,693 children from the general population without ASD diagnoses (POP cohort). Three subgroups of children with ASD were identified by k-means clustering using these COC patterns. The first cluster was characterized by generally high rates of COC diagnosis and comprised 23.7% (n = 776) of the cohort. Diagnoses of developmental delays were dominant in the second cluster containing 26.5% (n = 870) of the cohort. Children in the third cluster, making up 49.8% (n = 1,632) of the cohort, had the lowest rates of COC diagnosis, which were slightly higher than rates observed in the POP cohort. A secondary analysis using these data found that gastrointestinal and immune disorders showed similar longitudinal patterns of prevalence, as did seizure and sleep disorders. These findings may help to better inform the development of diagnostic workup and treatment protocols for COCs in children with ASD. Autism Res 2019, 12: 1272-1285. © 2019 International Society for Autism Research, Wiley Periodicals, Inc. LAY SUMMARY: Medical conditions that co-occur with autism spectrum disorder (ASD) vary significantly from person to person. This study analyzed patterns in diagnosis of co-occurring conditions from medical claims data and observed three subtypes of children with ASD. These results may aid with screening for co-occurring conditions in children with ASD and with understanding ASD subtypes.

Gastrointestinal Symptoms and Oral Antibiotic Use in Children with Autism Spectrum Disorder: Retrospective Analysis of a Privately Insured U.S. Population.

A retrospective analysis of administrative claims data from a large U.S. health insurer was performed to study a potential association between oral antibiotic use during early childhood and occurrence of later gastrointestinal (GI) symptoms in children with autism spectrum disorder (ASD). Among 3253 children with ASD, 37.0% had a GI-related diagnosis during the last 2 years of their 5-year health coverage enrollment period, compared to 20.0% of 278,370 children from the general population without an ASD diagnosis. Greater numbers of oral antibiotic fills during the first 3 years of enrollment were found to significantly increase the hazard rate of having a later GI-related diagnosis (adjusted hazard ratio 1.48; 95% confidence interval 1.34, 1.63) in children both with and without ASD.

Multivariate techniques enable a biochemical classification of children with autism spectrum disorder versus typically-developing peers: A comparison and validation study.

Autism spectrum disorder (ASD) is a developmental disorder which is currently only diagnosed through behavioral testing. Impaired folate-dependent one carbon metabolism (FOCM) and transsulfuration (TS) pathways have been implicated in ASD, and recently a study involving multivariate analysis based upon Fisher Discriminant Analysis returned very promising results for predicting an ASD diagnosis. This article takes another step toward the goal of developing a biochemical diagnostic for ASD by comparing five classification algorithms on existing data of FOCM/TS metabolites, and also validating the classification results with new data from an ASD cohort. The comparison results indicate a high sensitivity and specificity for the original data set and up to a 88% correct classification of the ASD cohort at an expected 5% misclassification rate for typically-developing controls. These results form the foundation for the development of a biochemical test for ASD which promises to aid diagnosis of ASD and provide biochemical understanding of the disease, applicable to at least a subset of the ASD population.

Investigating Plasma Amino Acids for Differentiating Individuals with Autism Spectrum Disorder and Typically Developing Peers.

BACKGROUND: Plasma amino acid measurements have been extensively investigated in individuals with autism spectrum disorder (ASD). Results thus far have been inconclusive as studies generally disagree on which amino acids are different in individuals with ASD versus their typically developing (TD) peers, due in part to methodological limitations of several studies. METHOD: This paper investigates plasma amino acids in children and adults with ASD using data from Arizona State University's Comprehensive Nutritional and Dietary Intervention Study. Measurements from 64 individuals with ASD and 49 TD controls were analyzed using univariate and multivariate statistical techniques. RESULTS: Univariate analysis indicated increased median levels of glutamate (+21%, p=0.014) and serine (+8%, p=0.043), and increased mean levels of hydroxyproline (+17%, p=0.018) for the ASD cohort, although these differences were insignificant after correcting for multiple comparisons. A multivariate approach was used to classify study participants into ASD/TD cohorts using Fisher discriminant analysis (FDA) and its nonlinear extension, kernel Fisher discriminant analysis (KFDA). Model fitting with FDA using all available measurements produced Type I and Type II errors of 27.0% and 27.8%, respectively. KFDA was most effective when using hydroxyproline, leucine, and threonine as inputs; however, leave-one-out cross-validation with this nonlinear model only resulted in 70.3% sensitivity and 77.6% specificity. CONCLUSIONS: The finding of elevated glutamate in ASD is in agreement with several other studies. Overall, however, these results suggest that plasma amino acid measurements are of limited use for purposes of ASD classification, which may explain some of the inconsistencies in results presented in the literature.

Comparison of Three Clinical Trial Treatments for Autism Spectrum Disorder Through Multivariate Analysis of Changes in Metabolic Profiles and Adaptive Behavior.

Several studies associate autism spectrum disorder (ASD) pathophysiology with metabolic abnormalities related to DNA methylation and intracellular redox homeostasis. In this regard, three completed clinical trials are reexamined in this work: treatment with (i) methylcobalamin (MeCbl) in combination with low-dose folinic acid (LDFA), (ii) tetrahydrobiopterin, and (iii) high-dose folinic acid (HDFA) for counteracting abnormalities in the folate-dependent one-carbon metabolism (FOCM) and transsulfuration (TS) pathways and also for improving ASD-related symptoms and behaviors. Although effects of treatment on individual metabolites and behavioral measures have previously been investigated, this study is the first to consider the effect of interventions on a set of metabolites of the FOCM/TS pathways and to correlate FOCM/TS metabolic changes with behavioral improvements across several studies. To do so, this work uses data from one case-control study and the three clinical trials to develop multivariate models for considering these aspects of treatment. Fisher discriminant analysis (FDA) is first used to establish a model for distinguishing individuals with ASD from typically developing (TD) controls, which is subsequently evaluated on the three treatment data sets, along with one data set for a placebo, to characterize the shift of FOCM/TS metabolism toward that of the TD population. Treatment with MeCbl plus LDFA and, separately, treatment with tetrahydrobiopterin significantly shifted the metabolites toward the values of the control group. Contrary to this, treatment with HDFA had a lesser, though still noticeable, effect whilst the placebo group showed marginal, but not insignificant, variations in metabolites. A second analysis is then performed with non-linear kernel partial least squares (KPLS) regression to predict changes in adaptive behavior, quantified by the Vineland Adaptive Behavior Composite, from changes in FOCM/TS biochemical measurements provided by treatment. Incorporating the 74 samples receiving any treatment, including placebo, into the regression analysis yields an R 2 of 0.471 after cross-validation when using changes in six metabolic measurements as predictors. These results are suggestive of an ability to effectively improve pathway-wide FOCM/TS metabolic and behavioral abnormalities in ASD with clinical treatment.

Mathematical modeling of the methionine cycle and transsulfuration pathway in individuals with autism spectrum disorder.

Previous research has shown a connection between metabolic abnormalities in the methionine cycle and transsulfuration pathway and autism spectrum disorder. Using clinical data from a case-control study investigating measurements of transmethylation and transsulfuration metabolites, a steady-state model of these metabolites in liver cells was developed and participant-specific parameters were identified. Comparison of mean parameter values and parameter distributions between neurotypical study participants and those on the autism spectrum revealed significant differences for four model parameters. Sensitivity analysis identified the parameter describing the rate of glutamylcysteine synthesis, the rate-limiting step in glutathione production, to be particularly important in determining steady-state metabolite concentrations. These results may provide insight into key reactions to target for potential intervention strategies relating to autism spectrum disorder.

On the Use of Multivariate Methods for Analysis of Data from Biological Networks.

Data analysis used for biomedical research, particularly analysis involving metabolic or signaling pathways, is often based upon univariate statistical analysis. One common approach is to compute means and standard deviations individually for each variable or to determine where each variable falls between upper and lower bounds. Additionally, p-values are often computed to determine if there are differences between data taken from two groups. However, these approaches ignore that the collected data are often correlated in some form, which may be due to these measurements describing quantities that are connected by biological networks. Multivariate analysis approaches are more appropriate in these scenarios, as they can detect differences in datasets that the traditional univariate approaches may miss. This work presents three case studies that involve data from clinical studies of autism spectrum disorder that illustrate the need for and demonstrate the potential impact of multivariate analysis.