Molecular Psychiatry
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Published By Springer Nature

1359-4184, 1359-4184

Swatabdi R. Kamal ◽  
Shreya Potukutchi ◽  
David J. Gelovani ◽  
Robin E. Bonomi ◽  
Srinivasu Kallakuri ◽  

Stamatina Tzanoulinou ◽  
Stefano Musardo ◽  
Alessandro Contestabile ◽  
Sebastiano Bariselli ◽  
Giulia Casarotto ◽  

AbstractMutations in the SHANK3 gene have been recognized as a genetic risk factor for Autism Spectrum Disorder (ASD), a neurodevelopmental disease characterized by social deficits and repetitive behaviors. While heterozygous SHANK3 mutations are usually the types of mutations associated with idiopathic autism in patients, heterozygous deletion of Shank3 gene in mice does not commonly induce ASD-related behavioral deficit. Here, we used in-vivo and ex-vivo approaches to demonstrate that region-specific neonatal downregulation of Shank3 in the Nucleus Accumbens promotes D1R-medium spiny neurons (D1R-MSNs) hyperexcitability and upregulates Transient Receptor Potential Vanilloid 4 (Trpv4) to impair social behavior. Interestingly, genetically vulnerable Shank3+/− mice, when challenged with Lipopolysaccharide to induce an acute inflammatory response, showed similar circuit and behavioral alterations that were rescued by acute Trpv4 inhibition. Altogether our data demonstrate shared molecular and circuit mechanisms between ASD-relevant genetic alterations and environmental insults, which ultimately lead to sociability dysfunctions.

Michael Maes ◽  
Walton Luiz Del Tedesco Junior ◽  
Marcell Alysson Batisti Lozovoy ◽  
Mayara Tiemi Enokida Mori ◽  
Tiago Danelli ◽  

Steven D. Targum ◽  
Jeffrey Schappi ◽  
Athanasia Koutsouris ◽  
Runa Bhaumik ◽  
Mark H. Rapaport ◽  

Yichuan Liu ◽  
Hui-Qi Qu ◽  
Frank D. Mentch ◽  
Jingchun Qu ◽  
Xiao Chang ◽  

AbstractMental disorders present a global health concern, while the diagnosis of mental disorders can be challenging. The diagnosis is even harder for patients who have more than one type of mental disorder, especially for young toddlers who are not able to complete questionnaires or standardized rating scales for diagnosis. In the past decade, multiple genomic association signals have been reported for mental disorders, some of which present attractive drug targets. Concurrently, machine learning algorithms, especially deep learning algorithms, have been successful in the diagnosis and/or labeling of complex diseases, such as attention deficit hyperactivity disorder (ADHD) or cancer. In this study, we focused on eight common mental disorders, including ADHD, depression, anxiety, autism, intellectual disabilities, speech/language disorder, delays in developments, and oppositional defiant disorder in the ethnic minority of African Americans. Blood-derived whole genome sequencing data from 4179 individuals were generated, including 1384 patients with the diagnosis of at least one mental disorder. The burden of genomic variants in coding/non-coding regions was applied as feature vectors in the deep learning algorithm. Our model showed ~65% accuracy in differentiating patients from controls. Ability to label patients with multiple disorders was similarly successful, with a hamming loss score less than 0.3, while exact diagnostic matches are around 10%. Genes in genomic regions with the highest weights showed enrichment of biological pathways involved in immune responses, antigen/nucleic acid binding, chemokine signaling pathway, and G-protein receptor activities. A noticeable fact is that variants in non-coding regions (e.g., ncRNA, intronic, and intergenic) performed equally well as variants in coding regions; however, unlike coding region variants, variants in non-coding regions do not express genomic hotspots whereas they carry much more narrow standard deviations, indicating they probably serve as alternative markers.

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