Stratifying ASD and characterizing the functional connectivity of subtypes in resting-state fMRI.

BACKGROUND: Although stratifying autism spectrum disorder (ASD) into different subtypes is a common effort in the research field, few papers have characterized the functional connectivity alterations of ASD subgroups classified by their clinical presentations. METHODS: This is a case-control rs-fMRI study, based on large samples of open database (Autism Brain Imaging Data Exchange, ABIDE). The rs-MRI data from n = 415 ASD patients (males n = 357), and n = 574 typical development (TD) controls (males n = 410) were included. Clinical features of ASD were extracted and classified using data from each patient's Autism Diagnostic Interview-Revised (ADI-R) evaluation. Each subtype of ASD was characterized by local functional connectivity using regional homogeneity (ReHo) for assessment, remote functional connectivity using voxel-mirrored homotopic connectivity (VMHC) for assessment, the whole-brain functional connectivity, and graph theoretical features. These identified imaging properties from each subtype were integrated to create a machine learning model for classifying ASD patients into the subtypes based on their rs-fMRI data, and an independent dataset was used to validate the model. RESULTS: All ASD participants were classified into Cluster-1 (patients with more severe impairment) and Cluster-2 (patients with moderate impairment) according to the dimensional scores of ADI-R. When compared to the TD group, Cluster-1 demonstrated increased local connection and decreased remote connectivity, and widespread hyper- and hypo-connectivity variations in the whole-brain functional connectivity. Cluster-2 was quite similar to the TD group in both local and remote connectivity. But at the level of whole-brain functional connectivity, the MCC-related connections were specifically impaired in Cluster-2. These properties of functional connectivity were fused to build a machine learning model, which achieved ∼75% for identifying ASD subtypes (Cluster-1 accuracy = 81.75%; Cluster-2 accuracy = 76.48%). CONCLUSIONS: The stratification of ASD by clinical presentations can help to minimize disease heterogeneity and highlight the distinguished properties of brain connectivity in ASD subtypes.

Activity-Dependent Differential Regulation of Auts2 Isoforms In Vitro and In Vivo.

Autism spectrum disorder (ASD) is a neurodevelopmental disorder of unknown cause, although one hypothesis suggests a potential imbalance between excitation and inhibition that leads to changes in neuronal activity and a disturbance in the brain network. However, the mechanisms through which neuronal activity contributes to the development of ASD remain largely unexplained. In this study, we described that neuronal activity at the transcriptional and translational levels regulated the expression of Auts2 isoforms. The prolonged stimulation of cultured cortical neurons significantly reduced the auts2 transcripts, accompanied by the decrease of FL-Auts2 protein, as well as one of the short isoforms (S-Auts2 var.1). Blocking neuronal activity increased the number of auts2 transcripts but not protein levels. Furthermore, blocking the NMDA receptors during stimulation could partially restore the FL-Auts2 and S-Auts2 var.1 at protein level, but not at mRNA level. Finally, Auts2 expression in the hippocampus was reduced in mice exposed to an enriched environment, a behavior paradigm designed to increase the brain activity through abundant sensory and social stimulations. Thus, our study revealed a novel regulatory effect of neuronal activity on the transcription and translation of ASD-risk gene auts2.

R2-8018 reduces the proliferation and migration of non-small cell lung cancer cells by disturbing transactivation between M3R and EGFR.

AIMS: The M3 muscarinic acetylcholine receptor (M3R) is a G protein-coupled receptor that is expressed in cases of non-small cell lung cancer (NSCLC). Previous studies demonstrated that M3R antagonists reduce the proliferation of NSCLC. However, how antagonists inhibit the NSCLC proliferation and migration is still little known. This study aims to investigate the mechanism of M3R involved in the growth of NSCLC. MAIN METHODS: The CRISPR/Cas9 was used to knock out (KO) the M3R gene. A real-time cell analyzer (RTCA) was used to record the proliferation of NSCLC cells. The migration and cell cycle of NSCLC cells were evaluated with scratch test and flow cytometry (FCM), respectively. Antibody microarray analysis was performed to detect the expression of proteins after antagonizing M3R and knocking out of M3R, subsequently some of these important proteins were verified by western blot. KEY FINDINGS: The proliferation and migration of NSCLC cells were inhibited by M3R antagonist R2-8018 and knocking out of M3R. Antagonism or knocking out of M3R reduced the phosphorylation of EGFR. Moreover, c-Src and ß-arrestin-1 are involved in the mechanism of how the inhibition of M3R affects EGFR in NSCLC. Further study demonstrated that PI3K/AKT and MEK/ERK signal pathways are involved in M3R-induced EGFR transactivation in NSCLC, and the molecules involved in the cell cycle progression and migration of NSCLC cells were identified. SIGNIFICANCE: This further understanding of the relationship between M3R and NSCLC facilitates the design of therapeutic strategy with M3R antagonist as an adjuvant drug for NSCLC treatment.