The mechanistic target of rapamycin (mTOR) signaling pathway senses and integrates a variety of environmental cues to regulate organismal growth and homeostasis. The pathway regulates many major cellular processes and is implicated in an increasing number of pathological conditions, including cancer, obesity, type 2 diabetes, and neurodegeneration. Here, we review recent advances in our understanding of the mTOR pathway and its role in health, disease, and aging. We further discuss pharmacological approaches to treat human pathologies linked to mTOR deregulation.

mTOR Signaling in Growth Control and Disease

mTOR signaling in growth control and disease.

Autophagy is a process by which components of the cell are degraded to maintain essential activity and viability in response to nutrient limitation. Extensive genetic studies have shown that the yeast ATG1 kinase has an essential role in autophagy induction. Furthermore, autophagy is promoted by AMP activated protein kinase (AMPK), which is a key energy sensor and regulates cellular metabolism to maintain energy homeostasis. Conversely, autophagy is inhibited by the mammalian target of rapamycin (mTOR), a central cell-growth regulator that integrates growth factor and nutrient signals. Here we demonstrate a molecular mechanism for regulation of the mammalian autophagy-initiating kinase Ulk1, a homologue of yeast ATG1. Under glucose starvation, AMPK promotes autophagy by directly activating Ulk1 through phosphorylation of Ser 317 and Ser 777. Under nutrient sufficiency, high mTOR activity prevents Ulk1 activation by phosphorylating Ulk1 Ser 757 and disrupting the interaction between Ulk1 and AMPK. This coordinated phosphorylation is important for Ulk1 in autophagy induction. Our study has revealed a signalling mechanism for Ulk1 regulation and autophagy induction in response to nutrient signalling.

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AMPK and mTOR regulate autophagy through direct phosphorylation of Ulk1

In 2008 we published the first set of guidelines for standardizing research in autophagy. Since then, research on this topic has continued to accelerate, and many new scientists have entered the field. Our knowledge base and relevant new technologies have also been expanding. Accordingly, it is important to update these guidelines for monitoring autophagy in different organisms. Various reviews have described the range of assays that have been used for this purpose. Nevertheless, there continues to be confusion regarding acceptable methods to measure autophagy, especially in multicellular eukaryotes.

For example, a key point that needs to be emphasized is that there is a difference between measurements that monitor the numbers or volume of autophagic elements (e.g., autophagosomes or autolysosomes) at any stage of the autophagic process versus those that measure flux through the autophagy pathway (i.e., the complete process including the amount and rate of cargo sequestered and degraded). In particular, a block in macroautophagy that results in autophagosome accumulation must be differentiated from stimuli that increase autophagic activity, defined as increased autophagy induction coupled with increased delivery to, and degradation within, lysosomes (in most higher eukaryotes and some protists such as Dictyostelium) or the vacuole (in plants and fungi). In other words, it is especially important that investigators new to the field understand that the appearance of more autophagosomes does not necessarily equate with more autophagy. In fact, in many cases, autophagosomes accumulate because of a block in trafficking to lysosomes without a concomitant change in autophagosome biogenesis, whereas an increase in autolysosomes may reflect a reduction in degradative activity. It is worth emphasizing here that lysosomal digestion is a stage of autophagy and evaluating its competence is a crucial part of the evaluation of autophagic flux, or complete autophagy.

Here, we present a set of guidelines for the selection and interpretation of methods for use by investigators who aim to examine macroautophagy and related processes, as well as for reviewers who need to provide realistic and reasonable critiques of papers that are focused on these processes. These guidelines are not meant to be a formulaic set of rules, because the appropriate assays depend in part on the question being asked and the system being used. In addition, we emphasize that no individual assay is guaranteed to be the most appropriate one in every situation, and we strongly recommend the use of multiple assays to monitor autophagy. Along these lines, because of the potential for pleiotropic effects due to blocking autophagy through genetic manipulation, it is imperative to target by gene knockout or RNA interference more than one autophagy-related protein. In addition, some individual Atg proteins, or groups of proteins, are involved in other cellular pathways implying that not all Atg proteins can be used as a specific marker for an autophagic process. In these guidelines, we consider these various methods of assessing autophagy and what information can, or cannot, be obtained from them. Finally, by discussing the merits and limits of particular assays, we hope to encourage technical innovation in the field.

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Guidelines for the use and interpretation of assays for monitoring autophagy (3rd edition)

Autophagy: Renovation of Cells and Tissues

Liquid-liquid phase separation (LLPS) underlies the formation mechanism of membraneless biomolecular condensates locally to perform important physiological functions such as selective autophagy, but little is known about the relationship between their dynamic structural organization and biophysical properties. Here, a dark-field microscopy based single plasmonic nanoparticle tracking (DFSPT) technique was introduced to simultaneously monitor the diffusion dynamics of multiple gold nanorod (AuNR) probes in a protein LLPS system and to quantitatively characterize the spatiotemporal heterogeneity of the LLPS condensates during their phase transformation. Based on spatially and temporally resolved analysis of the diffusional behavior of the AuNRs, structure and material properties of p62 condensates, such as the viscoelasticity, the compartmentalization, and the recruitment of protein-covered nanoparticles into the large droplet, have been observed. Moreover, the nonsmooth droplet interface, its solidification after further phase transition or maturation, and the size effect of the inner vacuoles have also been revealed. Our method can be potentially applied to in vitro investigation of different reconstituted membrane-free biomolecular condensates and in vivo study of their dynamic evolution.

Real-Time Study of Protein Phase Separation with Spatiotemporal Analysis of Single-Nanoparticle Trajectories.

This paper deals with the visual regulation problem of wheeled mobile robots (WMRs) in the presence of uncalibrated camera-to-robot parameters and unknown image depth. A two-stage controller is designed by using a switching approach. Specifically, in the first stage, an invariant-manifold-based adaptive controller is presented to bring the lateral error and angular error within an arbitrarily small neighborhood of zero; in the second stage, the longitudinal error is regulated by employing a proportional controller. Utilizing the Lyapunov stability analysis, the exponentially bounded stability of the closed-loop system is proved. Both simulation and experimental results are presented to validate the effectiveness of the proposed approach.

Adaptive Visual Regulation of Wheeled Mobile Robots: a Switching Approach

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A special review collection on autophagy

As a novel virtual reality (VR) format, panorama maps are attracting increasing attention, while the compression of panorama images is still a concern. In this paper, a densely connected convolutional network block (dense block) based autoencoder is proposed to compress panorama maps. In the proposed autoencoder, dense blocks are specially designed to reuse feature maps and reduce redundancy of features. Meanwhile, a loss function, which imports a position-dependent weight item for each pixel, is proposed to train and adjust network parameters, in order to make the autoencoder fit to properties of panorama maps. Based on the proposed autoencoder and the weighted loss function, a greedy block-wise training scheme is also designed to avoid gradient vanishing problem and speed up training. During training process, the autoencoder is divided into several sub-nets. After each sub-net is trained separately, the whole network is fine-tuned to achieve the best performance. Experimental results demonstrate that the proposed autoencoder, compared with JPEG, saves up to 79.69 % bit rates, and obtains 7.27dB gain in BD-WS-PSNR or 0.0789 gain in BD-WS-SSIM. The proposed autoencoder also outperforms JPEG 2000, HEVC and VVC in both BD-WS-PSNR and BD-WS-SSIM. Meanwhile, subjective results show that the proposed autoencoder can recover details of panorama images, and reconstruct maps with high visual quality.

Densely connected convolutional network block based autoencoder for panorama map compression

Mounting requirements of a rotating accelerometer gravity gradiometer (RAGG) beyond the art of manufacture, the RAGG is susceptible to the motion of itself. Motion error is a main source of error in airborne gravity gradiometry data. This study presented a posterror compensation method, which consists of postmotion error compensation and post-self-gradient compensation. The motion error and self-gradients are compensated before that the combined output of the four accelerometers is quadrature amplitude demodulated. The motion error compensation is based on the RAGG analytical model; motion response of the RAGG (namely, motion error) is related well to the motion of the RAGG, independent of the gravitational gradient response; recording the motion status of the RAGG, with least squares regression method and the RAGG analytical model, the motion response of the RAGG are separated and removed. RAGG numerical simulation experiment results show that the postmotion error compensation method is feasible, and its limit accuracy could reach 0.5 E/ $\surd $ Hz.

Li Yu

Papers

Real-Time Study of Protein Phase Separation with Spatiotemporal Analysis of Single-Nanoparticle Trajectories.

Adaptive Visual Regulation of Wheeled Mobile Robots: a Switching Approach

A special review collection on autophagy

Densely connected convolutional network block based autoencoder for panorama map compression

Posterror Compensation of Moving-Base Rotating Accelerometer Gravity Gradiometer