KICSTOR recruits GATOR1 to the lysosome and is necessary for nutrients to regulate mTORC1
Authors: Xin Gu
Rachel L Wolfson
Kuang Shen
Sabrina Petri
Monther Abu-Remaileh
Lynne Chantranupong
Gregory A Wyant
David M Sabatini
Wayne N Frankel
Jose M Orozco
Jibril Kedir
Kendall J Condon
Sonia M Scaria
Journal: Nature (2017)
Discipline: Multidisciplinary
Keywords: mTORC1; amino acids; Rag GTPases; lysosomal surface; KICSTOR
Affiliations: Whitehead Institute for Biomedical Research and Massachusetts Institute of Technology, Department of Biology, 9 Cambridge Center, Cambridge, Massachusetts 02142, USA., Howard Hughes Medical Institute, Department of Biology, Massachusetts Institute of Technology, Cambridge, Massachusetts 02139, USA., Koch Institute for Integrative Cancer Research, 77 Massachusetts Avenue, Cambridge, Massachusetts 02139, USA., Broad Institute of Harvard and Massachusetts Institute of Technology, 7 Cambridge Center, Cambridge, Massachusetts 02142, USA., Department of Genetics and Development and Institute for Genomic Medicine, Columbia University Medical Center, New York, New York 10032, USA.

Abstract:  The mechanistic target of rapamycin complex 1 (mTORC1) is a central regulator of cell growth that responds to diverse environmental signals and is deregulated in many human diseases, including cancer and epilepsy. Amino acids are a key input to this system, and act through the Rag GTPases to promote the translocation of mTORC1 to the lysosomal surface, its site of activation. 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 protein complex (KICSTOR) that is composed of four proteins, KPTN, ITFG2, C12orf66 and SZT2, and that is required for amino acid or glucose deprivation to inhibit mTORC1 in cultured human cells. In mice that lack SZT2, mTORC1 signalling is increased in several tissues, including in neurons in the brain. KICSTOR localizes to lysosomes; binds and recruits GATOR1, but not GATOR2, to the lysosomal surface; and is necessary for the interaction of GATOR1 with its substrates, the Rag GTPases, and with GATOR2. Notably, several KICSTOR components are mutated in neurological diseases associated with mutations that lead to hyperactive mTORC1 signalling. Thus, KICSTOR is a lysosome-associated negative regulator of mTORC1 signalling, which, like GATOR1, is mutated in human disease. 

Retracted: No

PubPeer comments:
 On behalf of commenter *Orchestes quercus*, I report here a few considerations on the RRM model used in this article. The Resonant Recognition Model (RRM) is lacking physical basis. The idea behind the model goes to go back to a 1980 book publication by V. Veljkovic (1). I unfortunately cannot read this 40+ year old seminal work, but from a [short review] it is clear that prominent features like the u2018electron-ion interaction potentialu2019 are contained in this publication. The same holds true for the somewhat younger but still 35+ year old PhD thesis of I. Cosic on the same topic (2). The roads of Veljkovic and Cosic seem to have diverged. In [this 1985 paper] Veljkovic and Cosic (somewhat) describe their then-unnamed model. In a next [1986 paper] 80248-0) Cosic introduces the model as the u2018Informational Spectrum Methodu2019 (ISM). Veljkovic keeps using this ISM label for the next 35 (!) years, see e.g. a recent [2020 paper] applying the model to the COVID pandemic. Or this [2015 paper] tackling the then-hot H5N1 influenza, a [2015 paper] on an Ebola outbreak, or maybe this [2008 paper] battling anthrax and HIV using the ISM model. Cosic continued using the ISM until 1988, when she rebranded it u2018[Resonant Recognition Model](http: dx.doi.org 10.1109 IEMBS.1988.94709)u2019. Also the RRM is still going strong and is more then 30 years later tackling the COVID pandemic in multiple papers, the [latest from 2022](http: dx.doi.org 10.18483 ijSci.2558). In the intervening years RMM was used to find a cure for many diseases, including [Cystis Fibrosis (2019)](http: dx.doi.org 10.18483 ijSci.2148), [autism (2018)] , [cancer (2017)](http: dx.doi.org 10.22606 jamb.2017.12003), [Crigler-Najjar syndrome (2016)](http: dx.doi.org 10.1140 epjnbp s40366-016-0036-6), [malaria (2015)](http: dx.doi.org 10.1140 epjnbp s40366-015-0025-1), [HIV (2014)] , and [aging (2014)] . The basis of the model is, well, thin and ignores everything that has been learned the past 40 years on protein folding and molecular interactions. The premise is that DNA and proteins are linear molecules that can be described by a linear sequence. For DNA it is the sequence of nuclear acids, for proteins the amino acids. Each acid is assigned a number, the so-called u2018electron-ion interaction potentialu2019 (EIIP). The numerical values for the acids are published in e.g. [this 1989 RMM paper] . One can then take a DNA or protein sequence, convert it into an array of numbers and take the Fourier transform to obtain a frequency spectrum (1 m). The spectrum contains peaks, and the idea is that peaks at a similar frequencies have similar biological functions. Or that molecules with peaks at similar frequencies interact with each other. In [this 1994 paper ] (presenting the then 10 year old model as a u2018new physicomathematical approachu2019) an attempt is made to provide a physical basis. On page 1105 Cosic states: u201c*The process can be observed as the interaction between transmitting and receiving antennae of a radio system. The RRM model assumes that characteristic frequencies are responsible for resonant recognition between macromolecules at a distance. Thus, these frequencies have to represent oscillations of some physical field which can propagate through water dipoles. One possibility is that this field is electromagnetic in nature. There is evidence that proteins and DNA have certain conducting properties. If so, charges would be moving through the backbone of the macromolecule and pass through different energy stages caused by different side groups of various amino acids or nucleotides. This process provides sufficient conditions for the emission of electromagnetic waves. Their frequency range depends on charge velocity, which then depends on the nature of charge movement (superconductive, conductive, soliton transfer, etc.) and on the energy of the field that causes charge transfer. The nature of this physical process is still unknown.*u201d. So it is assumed that electrons move with a constant velocity from one end of the molecule to the other end, on their way bumping over the AGTC acids and emitting (possibly) electromagnetic radiation at frequencies related to the EIIP-encoded sequence. Which then are propagating through water dipoles to interact with neighbouring molecules. In 1994 the author stated that u201c*The nature of this physical process is still unknown*u201d, a statement that still holds true in 2022. There are many things wrong with the model. In the 1994 paper the author stresses the role of the phase of emitted u2018frequenciesu2019 (page 1113): u201c*we can conclude that RRM frequencies, together with their corresponding phases, are important physicochemical parameters that characterize specific biorecognition processes.*u201c. Specifically, opposite phases attract (page 1104): u201c*It has been shown that proteins and their DNA or protein targets share the same characteristic frequency, but of opposite phase for each in a pair of interacting macromolecules.*u201d.When taking a Fourier transform the u2018opposite phaseu2019 can be obtained by reversing the sequence. In other words, the nuclear acid sequences AGCT and TCGA have the same RMM spectral peaks but of opposite phase. Which means that there are not only electrons flying through the DNA molecule, but they only fly from left to right. One could also have a look at the specific EIIP values u2018derivedu2019 in the 1989 RMM paper. With the information provided in the paper it is impossible to reproduce the given numbers. The paper refers to a [1972 article] of (incidentally) Veljkoviu0107 as the basis for their EIIP values. That paper is about an approximate method to derive pseudo-potentials for **metal ions** in a regular lattice. Apparently this method is general enough to reduce the interactions of the presumed fixed-velocity energy electron flying through a nuclear or amino acid *molecule* (non-metal, complex structure) into a single EIPP number. And through a physical process of which the nature is unknown the Fourier transform of the EIPP number sequence correlates with biological function... I want to end this review with a question: [Is there a link between acupuncture meridians, Earth-ionosphere resonances and cerebral activity] ? (1) A Theoretical Approach to the Preselection of Carcinogens and Chemical Carcinogenesis V. Veljkovic Gordon and Breach, Science Publishers, Inc., New York, London and Paris, 1980, 114 pp). (2) Digital Signal Analysis Applied to Protein and Nucleotide Sequences. Cosic I. (1985), Ph. D. Thesis, University of Belgrade ,