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Tickotsky-Moskovitz, N.

Publications and source records attributed to Tickotsky-Moskovitz, N..

3 recordsLinked to original sources

The mitochondrial chaperone HSPD1 folds MTHFD2 independently of its co-chaperone HSPE1

Acquiring new cellular states entails metabolic reprogramming driven by changes in the expression of cytosolic and mitochondrial metabolic enzymes. Most mitochondrial proteins are synthesized in the cytosol and imported into the mitochondria in a linear form, after which they are folded by a network of mitochondrial chaperones and co-chaperones. Which mitochondrial protein is dependent upon which chaperone for its folding is largely unknown. HSPD1/HSPE1 (HSP60/HSP10) are evolutionarily conserved mammalian homologues of the bacterial proteins GroEL/GroES, forming a chamber-and-lid chaperonin to facilitate the folding of client proteins. We used gene knockdown and SILAC-based proteomics to identify HSPD1 client proteins. We found that HSPD1 supports the expression of Methylenetetrahydrofolate Dehydrogenase 2 (MTHFD2), a key essential protein in the mitochondrial one-carbon (1C) pathway, in cells and tumors, and directly folds MTHFD2, independently of its co-chaperone HSPE1. HSPD1 interacts with MTHFD2 in mitochondria, and MTHFD2 is degraded by LONP1 in HSPD1 knockdown cells. Consequently, we observed reduced nucleotide and S-adenosylmethionine (SAM) levels in HSPD1 knockdown and found minimal overlap in the transcriptional and metabolic cellular responses to HSPD1 vs. HSPE1 depletion. In C. elegans, knockout of HSP60 triggers the mitochondrial stress response in the gut, while HSP10 knockout triggers the mitochondrial stress response in muscle tissue. Our data support that HSPD1 is an MTHFD2 chaperone and that, in addition to working together, HSPD1 and HSPE1 have distinct biological functions.

cell biology↗

CDR3 and V- genes show distinct reconstitution patterns in T-cell repertoire post allogenic bone marrow transplantation

BackgroundRestoration of T-cell repertoire diversity after allogeneic bone marrow transplantation (allo-BMT) is crucial for immune recovery. T-cell diversity is produced by rearrangements of germline gene segments (V (D) and J) of the T-cell receptor (TCR) and {beta} chains, and selection induced by binding of TCRs to MHC-peptide complexes. This diversity can be measured by many measures. We here focus on the V gene usage and the CDR3 sequences of the beta chain. We compared multiple T-cell repertoires to follow T cell repertoire changes post allo-BMT in HLA-matched related donor and recipient pairs. ResultsOur analyses of the differences between donor and recipient complementarity determining region 3 (CDR3) beta composition and V-gene profile show that the CDR sequence composition does not change during restoration, implying its dependence on the HLA typing. In contrast, V gene usage followed a time-dependent pattern, got following the donor profile post transplant and then shifting back to the recipients profile. The final long-term repertoire was more similar to that of the recipients original one than the donors.; some recipients converged within months while others took multiple years. ConclusionBased on the results of our analyses, we propose that donor-recipient V-gene distribution differences may serve as clinical biomarkers for monitoring immune recovery.

immunology↗

Prediction of specific TCR-peptide binding from large dictionaries of TCR-peptide pairs

Current sequencing methods allow for detailed samples of T cell receptors (TCR) repertoires. To determine from a repertoire whether its host had been exposed to a target, computational tools that predict TCR-epitope binding are required. Currents tools are based on conserved motifs and are applied to peptides with many known binding TCRs. Given any TCR and peptide, we employ new NLP-based methods to predict whether they bind. We combined large-scale TCR-peptide dictionaries with deep learning methods to produce ERGO (pEptide tcR matchinG predictiOn), a highly specific and generic TCR-peptide binding predictor. A set of standard tests are defined for the performance of peptide-TCR binding, including the detection of TCRs binding to a given peptide/antigen, choosing among a set of candidate peptides for a given TCR and determining whether any pair of TCR-peptide bind. ERGO significantly outperforms current methods in these tests even when not trained specifically for each test. The software implementation and data sets are available at https://github.com/louzounlab/ERGO

immunology↗