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.

/pdf/guidelines-for-the-use-and-interpretation-of-assays-for-ijf2t30pbq.pdf

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

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. 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 vs. those that measure flux through the autophagy pathway (i.e., the complete process); thus, a block in macroautophagy that results in autophagosome accumulation needs to be differentiated from stimuli that result in increased 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. 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. 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 autophagy assays, we hope to encourage technical innovation in the field.

/pdf/guidelines-for-the-use-and-interpretation-of-assays-for-28d1pi1jvo.pdf

Guidelines for the use and interpretation of assays for monitoring autophagy

Over the past decade, the Nomenclature Committee on Cell Death (NCCD) has formulated guidelines for the definition and interpretation of cell death from morphological, biochemical, and functional perspectives. Since the field continues to expand and novel mechanisms that orchestrate multiple cell death pathways are unveiled, we propose an updated classification of cell death subroutines focusing on mechanistic and essential (as opposed to correlative and dispensable) aspects of the process. As we provide molecularly oriented definitions of terms including intrinsic apoptosis, extrinsic apoptosis, mitochondrial permeability transition (MPT)-driven necrosis, necroptosis, ferroptosis, pyroptosis, parthanatos, entotic cell death, NETotic cell death, lysosome-dependent cell death, autophagy-dependent cell death, immunogenic cell death, cellular senescence, and mitotic catastrophe, we discuss the utility of neologisms that refer to highly specialized instances of these processes. The mission of the NCCD is to provide a widely accepted nomenclature on cell death in support of the continued development of the field.

https://biblio.ugent.be/publication/8555786/file/8555788.pdf

Molecular mechanisms of cell death: recommendations of the Nomenclature Committee on Cell Death 2018.

In 2009, the Nomenclature Committee on Cell Death (NCCD) proposed a set of recommendations for the definition of distinct cell death morphologies and for the appropriate use of cell death-related terminology, including 'apoptosis', 'necrosis' and 'mitotic catastrophe'. In view of the substantial progress in the biochemical and genetic exploration of cell death, time has come to switch from morphological to molecular definitions of cell death modalities. Here we propose a functional classification of cell death subroutines that applies to both in vitro and in vivo settings and includes extrinsic apoptosis, caspase-dependent or -independent intrinsic apoptosis, regulated necrosis, autophagic cell death and mitotic catastrophe. Moreover, we discuss the utility of expressions indicating additional cell death modalities. On the basis of the new, revised NCCD classification, cell death subroutines are defined by a series of precise, measurable biochemical features.

/pdf/molecular-definitions-of-cell-death-subroutines-q5g428rlyi.pdf

Molecular definitions of cell death subroutines: recommendations of the Nomenclature Committee on Cell Death 2012

The mechanistic target of rapamycin (mTOR) coordinates eukaryotic cell growth and metabolism with environmental inputs, including nutrients and growth factors. Extensive research over the past two decades has established a central role for mTOR in regulating many fundamental cell processes, from protein synthesis to autophagy, and deregulated mTOR signaling is implicated in the progression of cancer and diabetes, as well as the aging process. Here, we review recent advances in our understanding of mTOR function, regulation, and importance in mammalian physiology. We also highlight how the mTOR signaling network contributes to human disease and discuss the current and future prospects for therapeutically targeting mTOR in the clinic.

mTOR Signaling in Growth, Metabolism, and Disease

The ability of organisms to live long depends largely on the maintenance of proteome stability via proteostatic mechanisms including translational regulation, protein chaperoning and degradation machineries. In several long-lived Caenorhabditis elegans strains, such as insulin/IGF-1 receptor daf-2 mutants, enhanced proteostatic mechanisms are accompanied by elevated intestinal lipid stores, but the role of lipid droplets in longevity has remained obscure. Here, while determining the regulatory network of the selective autophagy receptor SQST-1/SQSTM1, we unexpectedly uncovered a novel role for lipid droplets in proteostasis and longevity. Using an unbiased genome-wide RNAi screening approach, we identified several SQST-1 modulators, including proteins found on lipid droplets and those prone to aggregate with age. SQST-1 accumulated on lipid droplets when autophagy was inhibited, suggesting that lipid droplets may serve a role in facilitating selective autophagy. Expansion of intestinal lipid droplets by silencing the conserved cytosolic triacylglycerol lipase gene atgl-1/ATGL enhanced autophagy, and extended lifespan in an HSF-1/HSF1-dependent and CDC-48/VCP-dependent manner. Silencing atgl-1 mitigated the age-related accumulation of SQST-1 and reduced overall ubiquitination of proteins. Reducing atgl-1 also improved proteostasis in a nematode model of Alzheimer’s disease. Subcellular analyses revealed that lipid droplets unexpectedly harbor more ubiquitinated proteins than the cytosol. Accordingly, low lipid droplet levels exacerbated the proteostatic collapse when autophagy or proteasome function was compromised. Altogether, our study uncovers a key role for lipid droplets in C. elegans as a proteostatic mediator that reduces protein ubiquitination, facilitates autophagy, and promotes longevity.

/pdf/lipid-droplets-modulate-proteostasis-sqst-1-sqstm1-dynamics-2c9twqmpht.pdf

Lipid droplets modulate proteostasis, SQST-1/SQSTM1 dynamics, and lifespan in C. elegans

Provided herein, are compositions and methods of treatment for neurodegenerative diseases, such as neurodegenerative diseases associated with aging and methods for increasing longevity by inhibiting the expression of the protein exportin-1 (XPO1, CRM-1 or karyopherin) or a fragment thereof.

Nucleocytoplasmic regulator of autophagy-associated transcription factors

Several age predictors based on DNA methylation, dubbed epigenetic clocks, have been created in recent years. Their accuracy and potential for generalization vary widely based on the training data. Here, we gathered 143 publicly available data sets from several human tissues to develop AltumAge, a highly accurate and precise age predictor based on deep learning. Compared to Horvath9s 2013 model, AltumAge performs better across both normal and malignant tissues and is more generalizable to new data sets. Interestingly, it can predict gestational week from placental tissue with low error. Lastly, we used deep learning interpretation methods to learn which methylation sites contributed to the final model predictions. We observed that while most important CpG sites are linearly related to age, some highly-interacting CpG sites can influence the relevance of such relationships. We studied the associated genes of these CpG sites and found literary evidence of their involvement in age-related gene regulation. Using chromatin annotations, we observed that the CpG sites with the highest contribution to the model predictions were related to heterochromatin and gene regulatory regions in the genome. We also found age-related KEGG pathways for genes containing these CpG sites. In general, neural networks are better predictors due to their ability to capture complex feature interactions compared to the typically used regularized linear regression. Altogether, our neural network approach provides significant improvement and flexibility to current epigenetic clocks without sacrificing model interpretability.

/pdf/altumage-a-pan-tissue-dna-methylation-epigenetic-clock-based-35wwbwd2ud.pdf

AltumAge: A Pan-Tissue DNA-Methylation Epigenetic Clock Based on Deep Learning

Transcription factor EB (TFEB) is a conserved master transcriptional activator of autophagy and lysosomal genes that modulates organismal lifespan regulation and stress resistance. As neurons can coordinate organism-wide mechanisms, we investigated the role of neuronal TFEB in stress resistance and longevity. To this end, the C. elegans TFEB orthologue, hlh-30, was rescued panneuronally in hlh-30 loss of function mutants. While important in the long lifespan of daf-2 animals, neuronal hlh-30 was not sufficient to restore normal lifespan in short-lived hlh-30 mutants. However, neuronal HLH-30/TFEB rescue mediated robust improvements in the heat stress resistance of wild-type but not daf-2 animals. Notably, these mechanisms can be uncoupled, as neuronal HLH-30/TFEB regulates longevity and thermoresistance dependently and independently of DAF-16/FOXO respectively. Through transcriptomics profiling and functional analysis, we identified the uncharacterized gene W06A11.1 as a bona fide mediator of heat stress resistance via the induction of mitochondrial fragmentation in distal muscles. Neuron-to-muscle communication occurred through a modulation of neurotransmission. Taken together, this study uncovers a novel mechanism of heat stress protection mediated by neuronal HLH-30/TFEB.

Neuronal HLH-30/TFEB modulates muscle mitochondrial fragmentation to improve thermoresistance in C. elegans

Several age predictors based on DNA methylation, dubbed epigenetic clocks, have been created in recent years, with the vast majority based on regularized linear regression. This study explores the improvement in the performance and interpretation of epigenetic clocks using deep learning. First, we gathered 143 publicly available data sets from several human tissues to develop AltumAge, a neural network framework that is a highly accurate and precise age predictor. Compared to ElasticNet, AltumAge performs better for within-data set and cross-data set age prediction, being particularly more generalizable in older ages and new tissue types. We then used deep learning interpretation methods to learn which methylation sites contributed to the final model predictions. We observe that while most important CpG sites are linearly related to age, some highly-interacting CpG sites can influence the relevance of such relationships. Using chromatin annotations, we show that the CpG sites with the highest contribution to the model predictions were related to gene regulatory regions in the genome, including proximity to CTCF binding sites. We also found age-related KEGG pathways for genes containing these CpG sites. Lastly, we compared the age acceleration of AltumAge with Horvath’s model, showing that our neural network approach predicts higher age acceleration for tumors and for cells that exhibit age-related changes in vitro, such as replicative senescence and mitochondrial dysfunction. Altogether, our neural network approach provides significant improvement and flexibility to current epigenetic clocks for both performance and model interpretability.

/pdf/improving-epigenetic-clock-performance-and-interpretation-2t2qm3dlh5.pdf

Louis R. Lapierre

Papers

Lipid droplets modulate proteostasis, SQST-1/SQSTM1 dynamics, and lifespan in C. elegans

Nucleocytoplasmic regulator of autophagy-associated transcription factors

AltumAge: A Pan-Tissue DNA-Methylation Epigenetic Clock Based on Deep Learning

Neuronal HLH-30/TFEB modulates muscle mitochondrial fragmentation to improve thermoresistance in C. elegans

Improving epigenetic clock performance and interpretation with deep learning