scholarly journals Studying Autism Using Untargeted Metabolomics in Newborn Screening Samples

2021 ◽  
Author(s):  
Julie Courraud ◽  
Madeleine Ernst ◽  
Susan Svane Laursen ◽  
David M. Hougaard ◽  
Arieh S. Cohen
Keyword(s):  
Newborn Screening ◽  
Biochemical Markers ◽  
Chemical Structure ◽  
Autism Spectrum ◽  
Untargeted Metabolomics ◽  
Future Studies ◽  
Structure Information ◽  
Mass Spectral ◽  
Feature Based ◽  
Broad Level

AbstractMain risk factors of autism spectrum disorder (ASD) include both genetic and non-genetic factors, especially prenatal and perinatal events. Newborn screening dried blood spot (DBS) samples have great potential for the study of early biochemical markers of disease. To study DBS strengths and limitations in the context of ASD research, we analyzed the metabolomic profiles of newborns later diagnosed with ASD. We performed LC-MS/MS-based untargeted metabolomics on DBS from 37 case-control pairs randomly selected from the iPSYCH sample. After preprocessing using MZmine 2.41, metabolites were putatively annotated using mzCloud, GNPS feature-based molecular networking, and MolNetEnhancer. A total of 4360 mass spectral features were detected, of which 150 (113 unique) could be putatively annotated at a high confidence level. Chemical structure information at a broad level could be retrieved for 1009 metabolites, covering 31 chemical classes. Although no clear distinction between cases and controls was revealed, our method covered many metabolites previously associated with ASD, suggesting that biochemical markers of ASD are present at birth and may be monitored during newborn screening. Additionally, we observed that gestational age, age at sampling, and month of birth influence the metabolomic profiles of newborn DBS, which informs us on the important confounders to address in future studies.

scholarly journals Untargeted metabolomic study of autism in newborn screening samples: a pilot study

2020 ◽  
Author(s):  
Julie Courraud ◽  
Madeleine Ernst ◽  
Susan Svane Laursen ◽  
David M. Hougaard ◽  
Arieh S. Cohen
Keyword(s):  
Newborn Screening ◽  
Autism Spectrum ◽  
Spectral Features ◽  
Pilot Studies ◽  
Metabolic Markers ◽  
Structure Information ◽  
Mass Spectral ◽  
Principal Coordinates ◽  
Chemical Profiles ◽  
Metabolomic Study

AbstractBackgroundThe etiopathology of autism spectrum disorder (ASD) is unclear. Main risk factors include both genetic and non-genetic factors, especially prenatal and perinatal events. The Danish Neonatal Screening Biobank in connection with registry data provides unique opportunities to study early signs of disease. Therefore, we aimed to study the metabolomic profiles of dried blood spot (DBS) of newborns later diagnosed with ASD.MethodsFrom the iPsych cohort, we randomly selected 37 subjects born in 2005 and diagnosed with ASD in 2012 (cases) together with 37 matched controls and submitted their biobanked DBS to an LC-MS/MS-based untargeted metabolomics protocol. Raw data were preprocessed using MZmine 2.41.2 and metabolites were subsequently putatively annotated using mzCloud, GNPS feature-based molecular networking and other metabolome mining tools (MolNetEnhancer). Statistical analyses and data visualization included principal coordinates analyses, PERMANOVAs, t-tests, and fold-change analyses.Results4360 mass spectral features were detected, of which 150 could be putatively annotated at a high confidence level. Chemical structure information at a broad level could be retrieved for a total of 1009 metabolites, covering 31 chemical classes including bile acids, various lipids, nucleotides, amino acids, acylcarnitines and steroids. Although the untargeted analysis revealed no clear distinction between cases and controls, 18 compounds repeatedly reported in the ASD literature could be detected in our study and three mass spectral features were found differentially abundant in cases and controls before FDR correction. In addition, our results pinpointed important other factors influencing chemical profiles of newborn DBS samples such as gestational age, age at sampling and month of birth.LimitationsInherent to pilot studies, our sample size was insufficient to reveal metabolic markers of ASD. Nevertheless, we were able to establish an efficient metabolomic data acquisition and analysis pipeline and flag main confounders to be considered in future studies.ConclusionsIn this first untargeted DBS metabolomic study, newborns later diagnosed with ASD did not show a significantly different metabolic profile when compared to controls. Nevertheless, our method covered many metabolites associated with ASD in previous studies, suggesting that biochemical markers of ASD are present at birth and may be monitored during newborn screening.

scholarly journals Automated extraction of chemical structure information from digital raster images

2009 ◽  
Vol 3 (1) ◽  
Author(s):  
Jungkap Park ◽  
Gus R Rosania ◽  
Kerby A Shedden ◽  
Mandee Nguyen ◽  
Naesung Lyu ◽  
...  
Keyword(s):  
Chemical Structure ◽  
Automated Extraction ◽  
Structure Information ◽  
Digital Raster

scholarly journals Brief Report: Preferred Processing of Social Stimuli in Autism: A Perception Task

2021 ◽  
Author(s):  
A. Meermeier ◽  
M. Jording ◽  
Y. Alayoubi ◽  
David H. V. Vogel ◽  
K. Vogeley ◽  
...  
Keyword(s):  
Autism Spectrum Disorder ◽  
Autism Spectrum ◽  
Exploratory Analysis ◽  
Spectrum Disorder ◽  
Social Stimuli ◽  
Future Studies ◽  
Perception Task ◽  
Social Images

AbstractIn this study we investigate whether persons with autism spectrum disorder (ASD) perceive social images differently than control participants (CON) in a graded perception task in which stimuli emerged from noise before dissipating into noise again. We presented either social stimuli (humans) or non-social stimuli (objects or animals). ASD were slower to recognize images during their emergence, but as fast as CON when indicating the dissipation of the image irrespective of its content. Social stimuli were recognized faster and remained discernable longer in both diagnostic groups. Thus, ASD participants show a largely intact preference for the processing of social images. An exploratory analysis of response subsets reveals subtle differences between groups that could be investigated in future studies.

scholarly journals Vocal markers of Autism Spectrum Disorder: assessing the generalizability of machine learning models

2021 ◽  
Author(s):  
Astrid Rybner ◽  
Emil Trenckner Jessen ◽  
Marie Damsgaard Mortensen ◽  
Stine Nyhus Larsen ◽  
Ruth Grossman ◽  
...  
Keyword(s):  
Machine Learning ◽  
Autism Spectrum Disorder ◽  
Model Performance ◽  
Autism Spectrum ◽  
Spectrum Disorder ◽  
Future Studies ◽  
Out Of Sample ◽  
Biobehavioral Markers ◽  
Similar Task ◽  
Machine Learning Models

Background: Machine learning (ML) approaches show increasing promise to identify vocal markers of Autism Spectrum Disorder (ASD). Nonetheless, it is unclear to what extent such markers generalize to new speech samples collected in diverse settings such as using a different speech task or a different language. Aim: In this paper, we systematically assess the generalizability of ML findings across a variety of contexts. Methods: We re-train a promising published ML model of vocal markers of ASD on novel cross-linguistic datasets following a rigorous pipeline to minimize overfitting, including cross-validated training and ensemble models. We test the generalizability of the models by testing them on i) different participants from the same study, performing the same task; ii) the same participants, performing a different (but similar) task; iii) a different study with participants speaking a different language, performing the same type of task. Results: While model performance is similar to previously published findings when trained and tested on data from the same study (out-of-sample performance), there is considerable variance between studies. Crucially, the models do not generalize well to new similar tasks and not at all to new languages. The ML pipeline is openly shared. Conclusion: Generalizability of ML models of vocal markers - and more generally biobehavioral markers - of ASD is an issue. We outline three recommendations researchers could take in order to be more explicit about generalizability and improve it in future studies.

scholarly journals Development of an in silico prediction system of human renal excretion and clearance from chemical structure information incorporating fraction unbound in plasma as a descriptor

2019 ◽  
Vol 9 (1) ◽  
Author(s):  
Reiko Watanabe ◽  
Rikiya Ohashi ◽  
Tsuyoshi Esaki ◽  
Hitoshi Kawashima ◽  
Yayoi Natsume-Kitatani ◽  
...  
Keyword(s):  
Renal Clearance ◽  
In Silico ◽  
Regression Models ◽  
Chemical Structure ◽  
Prediction Models ◽  
Renal Excretion ◽  
Early Stage ◽  
Classification Model ◽  
Prediction System ◽  
Structure Information

AbstractPrediction of pharmacokinetic profiles of new chemical entities is essential in drug development to minimize the risks of potential withdrawals. The excretion of unchanged compounds by the kidney constitutes a major route in drug elimination and plays an important role in pharmacokinetics. Herein, we created in silico prediction models of the fraction of drug excreted unchanged in the urine (fe) and renal clearance (CLr), with datasets of 411 and 401 compounds using freely available software; notably, all models require chemical structure information alone. The binary classification model for fe demonstrated a balanced accuracy of 0.74. The two-step prediction system for CLr was generated using a combination of the classification model to predict excretion-type compounds and regression models to predict the CLr value for each excretion type. The accuracies of the regression models increased upon adding a descriptor, which was the observed and predicted fraction unbound in plasma (fu,p); 78.6% of the samples in the higher range of renal clearance fell within 2-fold error with predicted fu,p value. Our prediction system for renal excretion is freely available to the public and can be used as a practical tool for prioritization and optimization of compound synthesis in the early stage of drug discovery.

The Chemical Structure Association Trust

2016 ◽  
Vol 38 (2) ◽  
Author(s):  
Bonnie Lawlor
Keyword(s):  
Chemical Structure ◽  
Scientific Discovery ◽  
Research Work ◽  
50Th Anniversary ◽  
The United States ◽  
Chemical Information ◽  
Structure Information ◽  
Chemical Structures ◽  
Grant Program ◽  
Structure Association

AbstractThe Chemical Structure Association Trust (CSA Trust) is an internationally-recognized, registered charity that promotes and supports the advancement of scientific discovery through the application of computer technologies in the management and analysis of chemical structure information. In support of its Charter, the Trust provides grants specifically to nurture young scientists, ages thirty-five or younger, who have demonstrated excellence in research related to the storage, retrieval, and analysis of chemical structures, reactions, and compounds. Since its inception in 1988, almost one hundred students and researchers worldwide have benefited from travel bursaries and the CSA Trust Grant Program to further their education and research work, but the organization has a rich history that predates the formalization of its charity status. Its roots were planted half a century ago in 1965, when the Chemical Notation Association (CNA) was formed in the United States. It has been an interesting journey from the CNA to the CSA Trust and I have been blessed to have been a part of it almost from the beginning, along with other members of the American Chemical Society’s Division of Chemical Information. In honor of the organization’s 50th Anniversary, I’d like to give a brief overview of its past and its present activities.

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