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Zanotelli, V. R. T.

Publications and source records attributed to Zanotelli, V. R. T..

5 recordsLinked to original sources

Evidence for Interaction of 5,10-Methylenetetrahydrofolate Reductase (MTHFR) with Methylenetetrahydrofolate Dehydrogenase (MTHFD1) and General Control Nonderepressible 1 (GCN1)

5,10-methylenetetrahydrofolate reductase (MTHFR) is a folate cycle enzyme required for the intracellular synthesis of methionine. MTHFR was previously shown to be partially phosphorylated at 16 residues, which was abrogated by conversion of threonine 34 to alanine (T34A) or truncation of the first 37 amino acids (i.e. expression of amino acids 38-656), and promoted by methionine supplementation. Here, we over-expressed wild-type MTHFR (MTFHRWT), as well as the variants MTHFRT34A and MTHFR38-656 in 293T cells to provide further insights into these mechanisms. We demonstrate that following incubation in high methionine conditions (100-1000 M) MTHFRWT is almost completely phosphorylated, but in methionine restricted conditions (0-10 M) phosphorylation is reduced, while MTHFRT34A always remains unphosphorylated. Following affinity purification coupled mass spectrometry of an empty vector, MTHFRWT, MTHFRT34A and MTHFR38-656 in three separate experiments, we identified 134 proteins consistently pulled-down by all three MTHFR protein variants, of which 5 were indicated to be likely true interactors (SAINT prediction threshold of 0.95 and 2 fold-change). Amongst these were the folate cycle enzyme methylenetetrahydrofolate dehydrogenase (MTHFD1) and the amino acid starvation sensor General Control Nonderepressible 1 (GCN1). Immunoprecipitation-immunoblotting of MTHFRWT replicated interaction with both proteins. An AlphaFold 3 generated model of the MTHFR-MTHFD1 interaction places the MTHFD1 dehydrogenase/cyclohydrolase domain in direct contact with the MTHFR catalytic domain, suggesting their interaction may facilitate direct delivery of methylenetetrahydrofolate. Overall, we confirm methionine availability increases MTHFR phosphorylation, and identified potential interaction of MTHFR with MTHFD1 and GCN1.

biochemistry↗

A quantitative analysis of the interplay of environment, neighborhood and cell state in 3D spheroids

1Cells react to their microenvironment by integrating external stimuli into phenotypic decisions via an intracellular signaling network. Even cells with deregulated signaling can adapt to their environment. To analyze the interplay of environment, neighborhood, and cell state on phenotypic variability, we developed an experimental approach that enables multiplexed mass cytometric imaging to analyze up to 240 pooled spheroid microtissues. This system allowed us to quantify the contributions of environment, neighborhood, and intracellular state to phenotypic variability in spheroid cells. A linear model explained on average more than half of the variability of 34 markers across four cell lines and six growth conditions. We found that the contributions of cell-intrinsic and environmental factors are hierarchically interdependent. By overexpression of 51 signaling protein constructs in subsets of cells, we identified proteins that have cell-intrinsic and extrinsic effects, exemplifying how cell states depend on the cellular neighborhood in spheroid culture. Our study deconvolves factors influencing cellular phenotype in a 3D tissue and provides a scalable experimental system, analytical principles, and rich multiplexed imaging datasets for future studies.

systems biology↗

Analysis of the human kinome and phosphatome reveals diseased signaling networks induced by overexpression

Kinase and phosphatase overexpression drives tumorigenesis and drug resistance in many cancer types. Signaling networks reprogrammed by protein overexpression remain largely uncharacterized, hindering discovery of paths to therapeutic intervention. We previously developed a single cell proteomics approach based on mass cytometry that enables quantitative assessment of overexpression effects on the signaling network. Here we applied this approach in a human kinome- and phosphatome-wide study to assess how 649 individually overexpressed proteins modulate the cancer-related signaling network in HEK293T cells. Based on these data we expanded the functional classification of human kinases and phosphatases and detected 208 novel signaling relationships. In the signaling dynamics analysis, we showed that increased ERK-specific phosphatases sustained proliferative signaling, and using a novel combinatorial overexpression approach, we confirmed this phosphatase-driven mechanism of cancer progression. Finally, we identified 54 proteins that caused ligand-independent ERK activation with potential as biomarkers for drug resistance in cells carrying BRAF mutations.

systems biology↗

Channel crosstalk correction in suspension and imaging mass cytometry

Mass cytometry enables simultaneous analysis of over 40 proteins and their modifications in single cells through use of metal-tagged antibodies. Compared to fluorescent dyes, the use of pure metal isotopes strongly reduces spectral overlap among measurement channels. Crosstalk still exists, however, caused by isotopic impurity, oxide formation, and mass cytometer properties. Spillover effects can be minimized, but not avoided, by following a set of constraining rules when designing an antibody panel. Generation of such low crosstalk panels requires considerable expert knowledge, knowledge of the abundance of each marker and substantial experimental effort. Here we describe a novel bead-based compensation workflow that includes R-based software and a web tool, which enables correction for interference between channels. We demonstrate utility in suspension mass cytometry and show how this approach can be applied to imaging mass cytometry. Our approach greatly simplifies the development of new antibody panels, increases flexibility for antibody-metal pairing, improves overall data quality, thereby reducing the risk of reporting cell phenotype and function artifacts, and greatly facilitates analysis of complex samples for which antigen abundances are unknown.

bioinformatics↗

Systematic analysis of cell phenotypes and cellular social networks in tissues using the multiplexed image cytometry analysis toolbox (miCAT)

Single-cell, spatially resolved omics analysis of tissues is poised to transform biomedical research and clinical practice. We have developed an open-source computational histology topography analysis toolbox (histoCAT) to enable the interactive, quantitative, and comprehensive exploration of phenotypes of individual cells, cell-to-cell interactions, microenvironments, and morphological structures within intact tissues. histoCAT will be useful in all areas of tissue-based research. We highlight the unique abilities of histoCAT by analysis of highly multiplexed mass cytometry images of human breast cancer tissues.\n\nTechnological advances in the multi-parametric analysis of single cells have revealed an unprecedented heterogeneity of cellular phenotypes and functional states that are concealed in population-based studies1-3. Each cellular phenotype is defined by the interplay of its internal state and the environment in which ...

systems biology↗