Sestrin2 is a leucine sensor for the mTORC1 pathway

Leucine is a proteogenic amino acid that also regulates many aspects of mammalian physiology, in large part by activating the mTOR complex 1 (mTORC1) protein kinase, a master growth controller. Amino acids signal to mTORC1 through the Rag guanosine triphosphatases (GTPases). Several factors regulate the Rags, including GATOR1, aGTPase-activating protein; GATOR2, a positive regulator of unknown function; and Sestrin2, a GATOR2-interacting protein that inhibits mTORC1 signaling. We find that leucine, but not arginine, disrupts the Sestrin2-GATOR2 interaction by binding to Sestrin2 with a dissociation constant of 20 micromolar, which is the leucine concentration that half-maximally activates mTORC1. The leucine-binding capacity of Sestrin2 is required for leucine to activate mTORC1 in cells. These results indicate that Sestrin2 is a leucine sensor for the mTORC1 pathway.United States. National Institutes of Health (R01CA103866)United States. National Institutes of Health (AI47389)United States. Department of Defense (W81XWH-07-0448)United States. National Institutes of Health (T32 GM007753)United States. National Institutes of Health (F30 CA189333)United States. National Institutes of Health (F31 CA180271

The CASTOR Proteins Are Arginine Sensors for the mTORC1 Pathway

Amino acids signal to the mTOR complex I (mTORC1) growth pathway through the Rag GTPases. Multiple distinct complexes regulate the Rags, including GATOR1, a GTPase activating protein (GAP), and GATOR2, a positive regulator of unknown molecular function. Arginine stimulation of cells activates mTORC1, but how it is sensed is not well understood. Recently, SLC38A9 was identified as a putative lysosomal arginine sensor required for arginine to activate mTORC1 but how arginine deprivation represses mTORC1 is unknown. Here, we show that CASTOR1, a previously uncharacterized protein, interacts with GATOR2 and is required for arginine deprivation to inhibit mTORC1. CASTOR1 homodimerizes and can also heterodimerize with the related protein, CASTOR2. Arginine disrupts the CASTOR1-GATOR2 complex by binding to CASTOR1 with a dissociation constant of ∼30 μM, and its arginine-binding capacity is required for arginine to activate mTORC1 in cells. Collectively, these results establish CASTOR1 as an arginine sensor for the mTORC1 pathway.United States. National Institutes of Health (R01CA103866)United States. National Institutes of Health (AI47389)United States. Department of Energy (W81XWH-07-0448)United States. National Institutes of Health (F31 CA180271)United States. National Institutes of Health (F31 CA189437

KICSTOR recruits GATOR1 to the lysosome and is necessary for nutrients to regulate mTORC1

The mechanistic target of rapamycin complex 1 kinase (mTORC1) is a central regulator of cell growth that responds to diverse environmental signals and is deregulated in many human diseases, including cancer and epilepsy1–3. Amino acids are a key input, and act through the Rag GTPases to promote the translocation of mTORC1 to the lysosomal surface, its site of activation4. Multiple protein complexes regulate the Rag GTPases in response to amino acids, including GATOR1, a GTPase activating protein for RagA, and GATOR2, a positive regulator of unknown molecular function. Here, we identify a four-membered protein complex (KICSTOR) composed of the KPTN, ITFG2, C12orf66, and SZT2 gene products as required for amino acid or glucose deprivation to inhibit mTORC1 in cultured cells. In mice lacking SZT2, mTORC1 signaling is increased in several tissues, including in neurons in the brain. KICSTOR localizes to lysosomes; binds to GATOR1 and recruits it, but not GATOR2, to the lysosomal surface; and is necessary for the interaction of GATOR1 with its substrates, the Rag GTPases, and with GATOR2. Interestingly, several KICSTOR components are mutated in neurological diseases associated with mutations that lead to hyperactive mTORC1 signaling5–10. Thus, KICSTOR is a lysosome-associated negative regulator of mTORC1 signaling that, like GATOR1, is mutated in human disease11,12

Global, regional, and national incidence, prevalence, and years lived with disability for 354 diseases and injuries for 195 countries and territories, 1990–2017: A systematic analysis for the Global Burden of Disease Study 2017

Background: The Global Burden of Diseases, Injuries, and Risk Factors Study 2017 (GBD 2017) includes a comprehensive assessment of incidence, prevalence, and years lived with disability (YLDs) for 354 causes in 195 countries and territories from 1990 to 2017. Previous GBD studies have shown how the decline of mortality rates from 1990 to 2016 has led to an increase in life expectancy, an ageing global population, and an expansion of the non-fatal burden of disease and injury. These studies have also shown how a substantial portion of the world's population experiences non-fatal health loss with considerable heterogeneity among different causes, locations, ages, and sexes. Ongoing objectives of the GBD study include increasing the level of estimation detail, improving analytical strategies, and increasing the amount of high-quality data. Methods: We estimated incidence and prevalence for 354 diseases and injuries and 3484 sequelae. We used an updated and extensive body of literature studies, survey data, surveillance data, inpatient admission records, outpatient visit records, and health insurance claims, and additionally used results from cause of death models to inform estimates using a total of 68 781 data sources. Newly available clinical data from India, Iran, Japan, Jordan, Nepal, China, Brazil, Norway, and Italy were incorporated, as well as updated claims data from the USA and new claims data from Taiwan (province of China) and Singapore. We used DisMod-MR 2.1, a Bayesian meta-regression tool, as the main method of estimation, ensuring consistency between rates of incidence, prevalence, remission, and cause of death for each condition. YLDs were estimated as the product of a prevalence estimate and a disability weight for health states of each mutually exclusive sequela, adjusted for comorbidity. We updated the Socio-demographic Index (SDI), a summary development indicator of income per capita, years of schooling, and total fertility rate. Additionally, we calculated differences between male and female YLDs to identify divergent trends across sexes. GBD 2017 complies with the Guidelines for Accurate and Transparent Health Estimates Reporting. Findings: Globally, for females, the causes with the greatest age-standardised prevalence were oral disorders, headache disorders, and haemoglobinopathies and haemolytic anaemias in both 1990 and 2017. For males, the causes with the greatest age-standardised prevalence were oral disorders, headache disorders, and tuberculosis including latent tuberculosis infection in both 1990 and 2017. In terms of YLDs, low back pain, headache disorders, and dietary iron deficiency were the leading Level 3 causes of YLD counts in 1990, whereas low back pain, headache disorders, and depressive disorders were the leading causes in 2017 for both sexes combined. All-cause age-standardised YLD rates decreased by 3·9% (95% uncertainty interval [UI] 3·1-4·6) from 1990 to 2017; however, the all-age YLD rate increased by 7·2% (6·0-8·4) while the total sum of global YLDs increased from 562 million (421-723) to 853 million (642-1100). The increases for males and females were similar, with increases in all-age YLD rates of 7·9% (6·6-9·2) for males and 6·5% (5·4-7·7) for females. We found significant differences between males and females in terms of age-standardised prevalence estimates for multiple causes. The causes with the greatest relative differences between sexes in 2017 included substance use disorders (3018 cases [95% UI 2782-3252] per 100 000 in males vs 1400 [1279-1524] per 100 000 in females), transport injuries (3322 [3082-3583] vs 2336 [2154-2535]), and self-harm and interpersonal violence (3265 [2943-3630] vs 5643 [5057-6302]). Interpretation: Global all-cause age-standardised YLD rates have improved only slightly over a period spanning nearly three decades. However, the magnitude of the non-fatal disease burden has expanded globally, with increasing numbers of people who have a wide spectrum of conditions. A subset of conditions has remained globally pervasive since 1990, whereas other conditions have displayed more dynamic trends, with different ages, sexes, and geographies across the globe experiencing varying burdens and trends of health loss. This study emphasises how global improvements in premature mortality for select conditions have led to older populations with complex and potentially expensive diseases, yet also highlights global achievements in certain domains of disease and injury

Global, regional, and national age-sex-specific mortality and life expectancy, 1950-2017: a systematic analysis for the Global Burden of Disease Study 2017

Background: Assessments of age-specific mortality and life expectancy have been done by the UN Population Division, Department of Economics and Social Affairs (UNPOP), the United States Census Bureau, WHO, and as part of previous iterations of the Global Burden of Diseases, Injuries, and Risk Factors Study (GBD). Previous iterations of the GBD used population estimates from UNPOP, which were not derived in a way that was internally consistent with the estimates of the numbers of deaths in the GBD. The present iteration of the GBD, GBD 2017, improves on previous assessments and provides timely estimates of the mortality experience of populations globally. Methods: The GBD uses all available data to produce estimates of mortality rates between 1950 and 2017 for 23 age groups, both sexes, and 918 locations, including 195 countries and territories and subnational locations for 16 countries. Data used include vital registration systems, sample registration systems, household surveys (complete birth histories, summary birth histories, sibling histories), censuses (summary birth histories, household deaths), and Demographic Surveillance Sites. In total, this analysis used 8259 data sources. Estimates of the probability of death between birth and the age of 5 years and between ages 15 and 60 years are generated and then input into a model life table system to produce complete life tables for all locations and years. Fatal discontinuities and mortality due to HIV/AIDS are analysed separately and then incorporated into the estimation. We analyse the relationship between age-specific mortality and development status using the Socio-demographic Index, a composite measure based on fertility under the age of 25 years, education, and income. There are four main methodological improvements in GBD 2017 compared with GBD 2016: 622 additional data sources have been incorporated; new estimates of population, generated by the GBD study, are used; statistical methods used in different components of the analysis have been further standardised and improved; and the analysis has been extended backwards in time by two decades to start in 1950. Findings: Globally, 18·7% (95% uncertainty interval 18·4–19·0) of deaths were registered in 1950 and that proportion has been steadily increasing since, with 58·8% (58·2–59·3) of all deaths being registered in 2015. At the global level, between 1950 and 2017, life expectancy increased from 48·1 years (46·5–49·6) to 70·5 years (70·1–70·8) for men and from 52·9 years (51·7–54·0) to 75·6 years (75·3–75·9) for women. Despite this overall progress, there remains substantial variation in life expectancy at birth in 2017, which ranges from 49·1 years (46·5–51·7) for men in the Central African Republic to 87·6 years (86·9–88·1) among women in Singapore. The greatest progress across age groups was for children younger than 5 years; under-5 mortality dropped from 216·0 deaths (196·3–238·1) per 1000 livebirths in 1950 to 38·9 deaths (35·6–42·83) per 1000 livebirths in 2017, with huge reductions across countries. Nevertheless, there were still 5·4 million (5·2–5·6) deaths among children younger than 5 years in the world in 2017. Progress has been less pronounced and more variable for adults, especially for adult males, who had stagnant or increasing mortality rates in several countries. The gap between male and female life expectancy between 1950 and 2017, while relatively stable at the global level, shows distinctive patterns across super-regions and has consistently been the largest in central Europe, eastern Europe, and central Asia, and smallest in south Asia. Performance was also variable across countries and time in observed mortality rates compared with those expected on the basis of development. Interpretation: This analysis of age-sex-specific mortality shows that there are remarkably complex patterns in population mortality across countries. The findings of this study highlight global successes, such as the large decline in under-5 mortality, which reflects significant local, national, and global commitment and investment over several decades. However, they also bring attention to mortality patterns that are a cause for concern, particularly among adult men and, to a lesser extent, women, whose mortality rates have stagnated in many countries over the time period of this study, and in some cases are increasing

The Sestrins Interact with GATOR2 to Negatively Regulate the Amino-Acid-Sensing Pathway Upstream of mTORC1

Summary: The mechanistic target of rapamycin complex 1 (mTORC1) kinase is a major regulator of cell growth that responds to numerous environmental cues. A key input is amino acids, which act through the heterodimeric Rag GTPases (RagA or RagB bound to RagC or RagD) in order to promote the translocation of mTORC1 to the lysosomal surface, its site of activation. GATOR2 is a complex of unknown function that positively regulates mTORC1 signaling by acting upstream of or in parallel to GATOR1, which is a GTPase-activating protein (GAP) for RagA or RagB and an inhibitor of the amino-acid-sensing pathway. Here, we find that the Sestrins, a family of poorly understood growth regulators (Sestrin1–Sestrin3), interact with GATOR2 in an amino-acid-sensitive fashion. Sestrin2-mediated inhibition of mTORC1 signaling requires GATOR1 and the Rag GTPases, and the Sestrins regulate the localization of mTORC1 in response to amino acids. Thus, we identify the Sestrins as GATOR2-interacting proteins that regulate the amino-acid-sensing branch of the mTORC1 pathway. : The mTORC1 kinase is a major growth regulator that responds to numerous environmental inputs, including amino acids. Chantranupong et al. now identify the Sestrins as components of the mTORC1 amino-acid-sensing pathway. The Sestrins interact with GATOR2 in an amino-acid-sensitive manner and function as negative regulators of the pathway by preventing proper mTORC1 localization to the lysosome in response to amino acids

Dihydroxyacetone phosphate signals glucose availability to mTORC1

© 2020, The Author(s), under exclusive licence to Springer Nature Limited. The mechanistic target of rapamycin complex 1 (mTORC1) kinase regulates cell growth by setting the balance between anabolic and catabolic processes. To be active, mTORC1 requires the environmental presence of amino acids and glucose. While a mechanistic understanding of amino acid sensing by mTORC1 is emerging, how glucose activates mTORC1 remains mysterious. Here, we used metabolically engineered human cells lacking the canonical energy sensor AMP-activated protein kinase to identify glucose-derived metabolites required to activate mTORC1 independent of energetic stress. We show that mTORC1 senses a metabolite downstream of the aldolase and upstream of the GAPDH-catalysed steps of glycolysis and pinpoint dihydroxyacetone phosphate (DHAP) as the key molecule. In cells expressing a triose kinase, the synthesis of DHAP from DHA is sufficient to activate mTORC1 even in the absence of glucose. DHAP is a precursor for lipid synthesis, a process under the control of mTORC1, which provides a potential rationale for the sensing of DHAP by mTORC1

A Hierarchy of Proliferative and Migratory Keratinocytes Maintains the Tympanic Membrane

The tympanic membrane (TM) is critical for hearing and requires continuous clearing of cellular debris, but little is known about homeostatic mechanisms in the TM epidermis. Using single-cell RNA sequencing, lineage tracing, whole-organ explant, and live-cell imaging, we show that homeostatic TM epidermis is distinct from other epidermal sites and has discrete proliferative zones with a three-dimensional hierarchy of multiple keratinocyte populations. TM stem cells reside in a discrete location of the superior TM and generate long-lived clones and committed progenitors (CPs). CP clones exhibit lateral migration, and their proliferative capacity is supported by Pdgfra+ fibroblasts, generating migratory but non-proliferative progeny. Single-cell sequencing of the human TM revealed similar cell types and transcriptional programming. Thus, during homeostasis, TM keratinocytes transit through a proliferative CP state and exhibit directional lateral migration. This work forms a foundation for understanding TM disorders and modeling keratinocyte biology

SAMTOR is an S-adenosylmethionine sensor for the mTORC1 pathway

mTOR complex 1 (mTORC1) regulates cell growth and metabolism in response to multiple environmental cues. Nutrients signal via the Rag guanosine triphosphatases (GTPases) to promote the localization of mTORC1 to the lysosomal surface, its site of activation. We identified SAMTOR, a previously uncharacterized protein, which inhibits mTORC1 signaling by interacting with GATOR1, the GTPase activating protein (GAP) for RagA/B. We found that the methyl donor S-adenosylmethionine (SAM) disrupts the SAMTOR-GATOR1 complex by binding directly to SAMTOR with a dissociation constant of approximately 7 μM. In cells, methionine starvation reduces SAM levels below this dissociation constant and promotes the association of SAMTOR with GATOR1, thereby inhibiting mTORC1 signaling in a SAMTOR-dependent fashion. Methionine-induced activation of mTORC1 requires the SAM binding capacity of SAMTOR. Thus, SAMTOR is a SAM sensor that links methionine and one-carbon metabolism to mTORC1 signaling.National Institutes of Health (U.S.) (Grant R01 CA103866)National Institutes of Health (U.S.) (Grant R37 AI47389)United States. Department of Defense (Grant W81XWH-07-0448)National Institutes of Health (U.S.) (Fellowship T32 GM007753)National Institutes of Health (U.S.) (Grant T32 GM007753)National Institutes of Health (U.S.) (Grant F30 CA210373)National Institutes of Health (U.S.) (Grant F31 GM121093-01A1)National Science Foundation (U.S.) (Grant 2016197106)Massachusetts Institute of Technology. Paul Gray UROP Fund (Grant 3143900

Titrating gene expression using libraries of systematically attenuated CRISPR guide RNAs.

A lack of tools to precisely control gene expression has limited our ability to evaluate relationships between expression levels and phenotypes. Here, we describe an approach to titrate expression of human genes using CRISPR interference and series of single-guide RNAs (sgRNAs) with systematically modulated activities. We used large-scale measurements across multiple cell models to characterize activities of sgRNAs containing mismatches to their target sites and derived rules governing mismatched sgRNA activity using deep learning. These rules enabled us to synthesize a compact sgRNA library to titrate expression of ~2,400 genes essential for robust cell growth and to construct an in silico sgRNA library spanning the human genome. Staging cells along a continuum of gene expression levels combined with single-cell RNA-seq readout revealed sharp transitions in cellular behaviors at gene-specific expression thresholds. Our work provides a general tool to control gene expression, with applications ranging from tuning biochemical pathways to identifying suppressors for diseases of dysregulated gene expression