Alternative splicing events as peripheral biomarkers for motor learning deficit caused by adverse prenatal environments

Dipankar J. Dutta, Center for Neuroscience Research, Children's National Hospital, Washington, DC 20010.
Junko Sasaki, Center for Neuroscience Research, Children's National Hospital, Washington, DC 20010.
Ankush Bansal, Center for Neuroscience Research, Children's National Hospital, Washington, DC 20010.
Keiji Sugai, Center for Neuroscience Research, Children's National Hospital, Washington, DC 20010.
Satoshi Yamashita, Center for Neuroscience Research, Children's National Hospital, Washington, DC 20010.
Guojiao Li, Department of Diabetes, Endocrinology and Metabolism, Tokyo Medical University, Tokyo 160-8402, Japan.
Christopher Lazarski, Center for Cancer and Immunology Research, Children's National Hospital, Washington, DC 20010.
Li Wang, Center for Neuroscience Research, Children's National Hospital, Washington, DC 20010.
Toru Sasaki, Department of Obstetrics and Gynecology, Tokyo Medical University, Tokyo 160-8402, Japan.
Chiho Yamashita, Center for Neuroscience Research, Children's National Hospital, Washington, DC 20010.
Heather Carryl, Center for Neuroscience Research, Children's National Hospital, Washington, DC 20010.
Ryo Suzuki, Department of Diabetes, Endocrinology and Metabolism, Tokyo Medical University, Tokyo 160-8402, Japan.
Masato Odawara, Department of Diabetes, Endocrinology and Metabolism, Tokyo Medical University, Tokyo 160-8402, Japan.
Yuka Imamura Kawasawa, Department of Biochemistry and Molecular Biology, Pennsylvania State University College of Medicine, Hershey, PA 17033.
Pasko Rakic, Kavli Institute for Neuroscience, Yale School of Medicine, New Haven, CT 06520.
Masaaki Torii, Center for Neuroscience Research, Children's National Hospital, Washington, DC 20010.
Kazue Hashimoto-Torii, Center for Neuroscience Research, Children's National Hospital, Washington, DC 20010.

Document Type

Journal Article

Publication Date

12-12-2023

Journal

Proceedings of the National Academy of Sciences of the United States of America

Volume

120

Issue

50

DOI

10.1073/pnas.2304074120

Keywords

RNA splicing; machine learning; offspring of mother with diabetes; peripheral biomarker; prenatal alcohol exposure

Abstract

Severity of neurobehavioral deficits in children born from adverse pregnancies, such as maternal alcohol consumption and diabetes, does not always correlate with the adversity's duration and intensity. Therefore, biological signatures for accurate prediction of the severity of neurobehavioral deficits, and robust tools for reliable identification of such biomarkers, have an urgent clinical need. Here, we demonstrate that significant changes in the alternative splicing (AS) pattern of offspring lymphocyte RNA can function as accurate peripheral biomarkers for motor learning deficits in mouse models of prenatal alcohol exposure (PAE) and offspring of mother with diabetes (OMD). An aptly trained deep-learning model identified 29 AS events common to PAE and OMD as superior predictors of motor learning deficits than AS events specific to PAE or OMD. Shapley-value analysis, a game-theory algorithm, deciphered the trained deep-learning model's learnt associations between its input, AS events, and output, motor learning performance. Shapley values of the deep-learning model's input identified the relative contribution of the 29 common AS events to the motor learning deficit. Gene ontology and predictive structure-function analyses, using Alphafold2 algorithm, supported existing evidence on the critical roles of these molecules in early brain development and function. The direction of most AS events was opposite in PAE and OMD, potentially from differential expression of RNA binding proteins in PAE and OMD. Altogether, this study posits that AS of lymphocyte RNA is a rich resource, and deep-learning is an effective tool, for discovery of peripheral biomarkers of neurobehavioral deficits in children of diverse adverse pregnancies.

Department

Pediatrics

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