Understanding the nutrient sensing branch upstream of mTORC1

Thesis: Ph. D., Massachusetts Institute of Technology, Department of Biology, 2017.Cataloged from PDF version of thesis.Includes bibliographical references.mTORC1 is a master regulator of cell growth that responds to diverse environmental inputs and is deregulated in human diseases, such as cancer and epilepsy. One important input to this system is amino acids, such as leucine, which require the Rag GTPases and its regulators, including GATOR1 and GATOR2, to modulate mTORC1 activity. How amino acids, specifically leucine, are directly sensed, however, was elusive for many years. In this thesis, we first characterize the role of the Rag GTPases in follicular lymphoma. We identify recurrent, mTORC1-activating mutations in RRAGC, the gene that encodes RagC, in a large portion of follicular lymphoma samples (17%). These variants in RagC increase raptor binding and render cells partially insensitive to amino deprivation, implying that mTOR inhibitors may be effective therapies for these patients. In addition, we identify Sestrin2 as the long-sought leucine sensor for the mTORC1 pathway. Sestrin2 acts as a negative regulator of the pathway that binds GATOR2 only under leucine deprivation. We find that Sestrin2 directly binds leucine at concentrations consistent with those sensed by the pathway. Further, we find that the leucine-binding capacity of Sestrin2 is required for leucine to activate mTORC1 in cells, establishing Sestrin2 as a leucine sensor for the pathway. Finally, we identify a four-membered complex, KICSTOR (for KPTN, ITFG2, C12orf66, and SZT2-containing regulator of mTORC1), which is necessary for targeting GATOR1 to the lysosomal surface and for its interaction with its substrates, the Rag GTPases, and its potential regulator, GATOR2. Mutations in three of the components of KICSTOR are found in patients with epilepsy or brain malformation disorders, suggesting that rapalogs or other mTOR inhibitors could have some efficacy in these patients.by Rachel L. Wolfson.Ph. D

Lysosomal amino acid transporter SLC38A9 signals arginine sufficiency to mTORC1

The mechanistic target of rapamycin complex 1 (mTORC1) protein kinase is a master growth regulator that responds to multiple environmental cues. Amino acids stimulate, in a Rag-, Ragulator-, and vacuolar adenosine triphosphatase–dependent fashion, the translocation of mTORC1 to the lysosomal surface, where it interacts with its activator Rheb. Here, we identify SLC38A9, an uncharacterized protein with sequence similarity to amino acid transporters, as a lysosomal transmembrane protein that interacts with the Rag guanosine triphosphatases (GTPases) and Ragulator in an amino acid–sensitive fashion. SLC38A9 transports arginine with a high Michaelis constant, and loss of SLC38A9 represses mTORC1 activation by amino acids, particularly arginine. Overexpression of SLC38A9 or just its Ragulator-binding domain makes mTORC1 signaling insensitive to amino acid starvation but not to Rag activity. Thus, SLC38A9 functions upstream of the Rag GTPases and is an excellent candidate for being an arginine sensor for the mTORC1 pathway.National Institutes of Health (U.S.) (Grant R01 CA103866)National Institutes of Health (U.S.) (Grant AI47389)United States. Dept. of Defense (W81XWH-07-0448)National Institutes of Health (U.S.) (Fellowship F30CA180754)National Institutes of Health (U.S.) (Fellowship T32 GM007753)National Institutes of Health (U.S.) (Fellowship F31 AG044064)National Institutes of Health (U.S.) (Fellowship F31CA180271)United States. Dept. of Defense (National Defense Science and Engineering Graduate Fellowship)National Science Foundation (U.S.). Graduate Research Fellowship ProgramAmerican Cancer Society (Ellison Medical Foundation. Postdoctoral Fellowship PF-13-356-01-TBE)Howard Hughes Medical Institut

A multi-institutional study of outcomes in stage I–III uterine carcinosarcoma

OBJECTIVE: To evaluate the use of adjuvant therapy after primary surgery for stage I–III uterine carcinosarcoma (CS). METHODS: A multi-institutional retrospective study of women with stage I–III CS was conducted. Analyses were stratified by stage (I/II and III). Patients were categorized according to adjuvant therapy: observation (OBS), radiation (RT), chemotherapy (CT) or multimodal therapy (CT + RT). Overall survival (OS) and progression-free survival (PFS) were analyzed using log-rank tests and Cox proportional hazards models. RESULTS: 303 patients were identified across four institutions: 195 with stage I/II and 108 with stage III disease. In stage I/II disease, 75 (39.9%) received OBS, 33 (17.6%) CT, 37 (19.7%) RT, and 43 (22.9%) CT + RT. OBS was associated with a fourfold increased risk of death compared to CT (adjusted hazard ratio (aHR) = 4.48, p = 0.003). Patients receiving CT + RT had significantly improved PFS compared to those receiving CT alone (aHR = 0.43, p = 0.04), but no difference in OS. In the stage III cohort, 16 (15.0%) received OBS, 34 (31.8%) CT, 20 (18.7%) RT, and 37 (34.6%) CT + RT. OBS was associated with worse OS and PFS compared to CT (OS: aHR = 2.46, p = 0.04; PFS: aHR = 2.39, p = 0.03, respectively). A potential improvement in PFS was seen for those treated with CT + RT compared to CT alone, however it was not statistically significant (aHR = 0.53, p = 0.09). CONCLUSIONS: Observation after surgery was associated with poor outcomes in uterine CS compared to CT and RT alone. Multimodality therapy for women with stage I/II disease was associated with improved PFS compared to chemotherapy alone. Novel treatment options are needed to improve outcomes in this aggressive disease

Same data, different analysts: variation in effect sizes due to analytical decisions in ecology and evolutionary biology

Gould E, Fraser H, Parker T, et al. Same data, different analysts: variation in effect sizes due to analytical decisions in ecology and evolutionary biology. 2023.Although variation in effect sizes and predicted values among studies of similar phenomena is inevitable, such variation far exceeds what might be produced by sampling error alone. One possible explanation for variation among results is differences among researchers in the decisions they make regarding statistical analyses. A growing array of studies has explored this analytical variability in different (mostly social science) fields, and has found substantial variability among results, despite analysts having the same data and research question. We implemented an analogous study in ecology and evolutionary biology, fields in which there have been no empirical exploration of the variation in effect sizes or model predictions generated by the analytical decisions of different researchers. We used two unpublished datasets, one from evolutionary ecology (blue tit, Cyanistes caeruleus, to compare sibling number and nestling growth) and one from conservation ecology (Eucalyptus, to compare grass cover and tree seedling recruitment), and the project leaders recruited 174 analyst teams, comprising 246 analysts, to investigate the answers to prespecified research questions. Analyses conducted by these teams yielded 141 usable effects for the blue tit dataset, and 85 usable effects for the Eucalyptus dataset. We found substantial heterogeneity among results for both datasets, although the patterns of variation differed between them. For the blue tit analyses, the average effect was convincingly negative, with less growth for nestlings living with more siblings, but there was near continuous variation in effect size from large negative effects to effects near zero, and even effects crossing the traditional threshold of statistical significance in the opposite direction. In contrast, the average relationship between grass cover and Eucalyptus seedling number was only slightly negative and not convincingly different from zero, and most effects ranged from weakly negative to weakly positive, with about a third of effects crossing the traditional threshold of significance in one direction or the other. However, there were also several striking outliers in the Eucalyptus dataset, with effects far from zero. For both datasets, we found substantial variation in the variable selection and random effects structures among analyses, as well as in the ratings of the analytical methods by peer reviewers, but we found no strong relationship between any of these and deviation from the meta-analytic mean. In other words, analyses with results that were far from the mean were no more or less likely to have dissimilar variable sets, use random effects in their models, or receive poor peer reviews than those analyses that found results that were close to the mean. The existence of substantial variability among analysis outcomes raises important questions about how ecologists and evolutionary biologists should interpret published results, and how they should conduct analyses in the future