Integrated multi-omics approach reveals the role of striated muscle preferentially expressed protein kinase in skeletal muscle including its relationship with myospryn complex.

BACKGROUND: Autosomal-recessive mutations in SPEG (striated muscle preferentially expressed protein kinase) have been linked to centronuclear myopathy with or without dilated cardiomyopathy (CNM5). Loss of SPEG is associated with defective triad formation, abnormal excitation-contraction coupling, calcium mishandling and disruption of the focal adhesion complex in skeletal muscles. To elucidate the underlying molecular pathways, we have utilized multi-omics tools and analysis to obtain a comprehensive view of the complex biological processes and molecular functions. METHODS: Skeletal muscles from 2-month-old SPEG-deficient (Speg-CKO) and wild-type (WT) mice were used for RNA sequencing (n = 4 per genotype) to profile transcriptomics and mass spectrometry (n = 4 for WT; n = 3 for Speg-CKO mice) to profile proteomics and phosphoproteomics. In addition, interactomics was performed using the SPEG antibody on pooled muscle lysates (quadriceps, gastrocnemius and triceps) from WT and Speg-CKO mice. Based on the multi-omics results, we performed quantitative real-time PCR, co-immunoprecipitation and immunoblot to verify the findings. RESULTS: We identified that SPEG interacts with myospryn complex proteins CMYA5, FSD2 and RyR1, which are critical for triad formation, and that SPEG deficiency results in myospryn complex abnormalities (protein levels decreased to 22 ± 3% for CMYA5 [P < 0.05] and 18 ± 3% for FSD2 [P < 0.01]). Furthermore, SPEG phosphorylates RyR1 at S2902 (phosphorylation level decreased to 55 ± 15% at S2902 in Speg-CKO mice; P < 0.05), and its loss affects JPH2 phosphorylation at multiple sites (increased phosphorylation at T161 [1.90 ± 0.24-fold], S162 [1.61 ± 0.37-fold] and S165 [1.66 ± 0.13-fold]; decreased phosphorylation at S228 and S231 [39 ± 6%], S234 [50 ± 12%], S593 [48 ± 3%] and S613 [66 ± 10%]; P < 0.05 for S162 and P < 0.01 for other sites). On analysing the transcriptome, the most dysregulated pathways affected by SPEG deficiency included extracellular matrix-receptor interaction (P < 1e-15) and peroxisome proliferator-activated receptor signalling (P < 9e-14). CONCLUSIONS: We have elucidated the critical role of SPEG in the triad as it works closely with myospryn complex proteins (CMYA5, FSD2 and RyR1), it regulates phosphorylation levels of various residues in JPH2 and S2902 in RyR1, and its deficiency is associated with dysregulation of several pathways. The study identifies unique SPEG-interacting proteins and their phosphorylation functions and emphasizes the importance of using a multi-omics approach to comprehensively evaluate the molecular function of proteins involved in various genetic disorders.

An opponent striatal circuit for distributional reinforcement learning.

Machine learning research has achieved large performance gains on a wide range of tasks by expanding the learning target from mean rewards to entire probability distributions of rewards - an approach known as distributional reinforcement learning (RL)1. The mesolimbic dopamine system is thought to underlie RL in the mammalian brain by updating a representation of mean value in the striatum2,3, but little is known about whether, where, and how neurons in this circuit encode information about higher-order moments of reward distributions4. To fill this gap, we used high-density probes (Neuropixels) to acutely record striatal activity from well-trained, water-restricted mice performing a classical conditioning task in which reward mean, reward variance, and stimulus identity were independently manipulated. In contrast to traditional RL accounts, we found robust evidence for abstract encoding of variance in the striatum. Remarkably, chronic ablation of dopamine inputs disorganized these distributional representations in the striatum without interfering with mean value coding. Two-photon calcium imaging and optogenetics revealed that the two major classes of striatal medium spiny neurons - D1 and D2 MSNs - contributed to this code by preferentially encoding the right and left tails of the reward distribution, respectively. We synthesize these findings into a new model of the striatum and mesolimbic dopamine that harnesses the opponency between D1 and D2 MSNs5-15 to reap the computational benefits of distributional RL.