Summary Loss-of-function mutations in TRPML1 (Transient Receptor Potential Mucolipin 1) cause a lysosomal storage disorder called mucolipidosis type IV (MLIV). Previously, we established a Drosophila model for MLIV by knocking out the single TRPML1 homolog expressed in flies [1]. The mutant animals displayed impaired autophagy, and reduced viability during the pupal period—a phase when wild-type animals rely on autophagic sources for nutrients. However, the specific defect in autophagy has remained unclear. Here, we show that TRPML, which was localized to the membranes of late-endosomes and lysosomes in vivo, was required for fusion between late-endosomes with lysosomes. We report that loss of TRPML led to accumulation of vesicles of significantly larger volume, and loss of TRPML from the late-endosomes/lysosomes led to the accumulation of higher luminal Ca2+ within those vesicles. We also found that trpml1 mutant cells showed decreased TORC1 signaling, and a concomitant upregulation of autophagy induction. Both of these defects were reversed by activating TORC1 in the mutants genetically and by feeding the mutant larvae a high-protein diet. Feeding the larvae a high-protein diet also reduced both the pupal lethality, and the increased volume of acidic vesicles. Conversely, further inhibition of TORC1 activity by rapamycin exacerbated the mutant phenotypes. Finally, TORC1 exerted reciprocal control on TRPML function. A high amino acid diet enhanced cortical localization of TRPML, and this effect was blocked by rapamycin. Our findings delineate the interrelationship between TRPML- and TORC1-mediated nutrient sensing pathways, and also raise the intriguing possibility that amino acid supplementation might reduce the severity of the clinical manifestations associated with MLIV.

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Normal termination of signaling is essential to reset signaling cascades, especially those such as phototransduction that are turned on and off with great rapidity. Genetic approaches in Drosophila led to the identification of several proteins required for termination, including protein kinase C (PKC), NINAC (neither inactivation nor afterpotential C) p174, which consists of fused protein kinase and myosin domains, and a PDZ (postsynaptic density-95/Discs Large/zona occludens-1) scaffold protein, INAD (inactivation no afterpotential D). Here, we describe a mutation affecting a poorly characterized but evolutionarily conserved protein, Retinophilin (Retin), which is expressed primarily in the phototransducing compartment of photoreceptor cells, the rhabdomeres. Retin and NINAC formed a complex and were mutually dependent on each other for expression. Loss of retin resulted in an age-dependent impairment in termination of phototransduction. Mutations that affect termination of the photoresponse typically lead to a reduction in levels of the major rhodopsin (Rh1) to attenuate signaling. Consistent with the slower termination in retin 1 , the mutant photoreceptor cells exhibited increased endocytosis of Rh1 and a decline in Rh1 protein. The slower termination in retin 1 was a consequence of a cascade of defects, which began with the reduction in NINAC p174 levels. The diminished p174 concentration caused a decrease in INAD. Because PKC requires interaction with INAD for protein stability, this leads to reduction in PKC levels. The decline in PKC was age dependent and paralleled the onset of the termination phenotype in retin 1 mutant flies. We conclude that the slower termination of the photoresponse in retin 1 resulted from a requirement for the Retin/NINAC complex for stability of INAD and PKC.

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Dissipativity theory is a very important concept in the field of control system. In this paper, we pay attention to the problem of dissipativity analysis of memristive neural networks with time-varying delay and randomly occurring uncertainties(ROUs). Under the framework of Filippov solution, differential inclusion theory, by employing a proper Lyapunov functional, and some inequality techniques, the dissipativity criteria are obtained in terms of LMIs. It should be noteworthy that the uncertainty terms as well as the ROUs are separately taken into consideration, in which the uncertainties are norm-bounded and the ROUs obey certain mutually uncorrelated Bernoulli-distributed white noise sequences. Finally, the effectiveness of the proposed method will be verified via numerical example.

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