Biology subjects

Chiosis, G.

Publications and source records attributed to Chiosis, G..

5 recordsLinked to original sources

bioRxiv2023

Selective inhibition of hsp90 paralogs: Structure and binding studies uncover the role of helix 1 in Grp94-selective ligand binding

Grp94 is the endoplasmic reticulum paralog of the hsp90 family of chaperones, which have been targeted for therapeutic intervention via their highly conserved ATP binding sites. The design of paralog-selective inhibitors relies on understanding the protein structural elements that drive higher affinity in selective inhibitors. Here, we determined the structures of Grp94 and Hsp90 in complex with the Grp94-selective inhibitor PU-H36, and of Grp94 with the non-selective inhibitor PU-H71. In Grp94, PU-H36 derives its higher affinity by utilizing Site 2, a Grp94-specific side pocket adjoining the ATP binding cavity, but in Hsp90 PU-H36 occupies Site 1, a side pocket that is accessible in all paralogs with which it makes lower affinity interactions. The structure of Grp94 in complex with PU-H71 shows only Site 1 binding. While changes in the conformation of helices 4 and 5 in the N-terminal domain occur when ligands bind to Site 1 of both Hsp90 and Grp94, large conformational shifts that also involve helix 1 are associated with the engagement of the Site 2 pocket in Grp94 only. Site 2 in Hsp90 is blocked and its helix 1 conformation is insensitive to ligand binding. To understand the role of helix 1 in ligand selectivity, we tested the binding of PU-H36 and other Grp94-selective ligands to chimeric Grp94/Hsp90 constructs. These studies show that helix 1 is the major determinant of selectivity for Site 2 targeted ligands, and also influences the rate of ATPase activity in Hsp90 paralogs.

biochemistry↗

bioRxiv2022

How Aberrant N-Glycosylation Can Alter Protein Functionality and Ligand Binding: an Atomistic View

Protein assembly defects due to enrichment of aberrant conformational variants of proteins are emerging as a new frontier in therapeutics design. Understanding, atomistically, structural elements that remodel the energy landscape of proteins, with the consequence of rewiring the conformational dynamics of proteins and pathologically perturbing functionally-oriented ensembles, is key for development of inhibitors. This is particularly relevant for molecular chaperones, hub proteins for the assembly of large multiprotein complexes, where enrichment of aberrant conformers can have a large impact on the cellular proteome, and in turn, on phenotypes. Here, we integrate computational and experimental tools to unveil how N-glycosylation of specific residues in glucose-regulated protein 94 (GRP94) modulates internal dynamics and alters the conformational fitness of regions fundamental for interaction with the nucleotide and synthetic ligands, and impacts substructures dedicated to recognition of interacting proteins. We show how N-glycosylation plays an active role in modulating the energy landscape of the protein, with specific glycosylation patterns determining specific functionally-oriented dynamic signatures. Our results provide support for leveraging the structural-dynamics knowledge on distinct glycosylation variants to design molecules targeting GRP94 disease-associated conformational states and assemblies. Since glycosylation is the most abundant form of post-translational modification, our results and mechanistic models can readily be transferred to other targets and contexts for cancers and other diseases.

biochemistry↗

bioRxiv2020

HSP90 facilitates oncogenic alterations of metabolism in B-cell lymphomas

HSP90 is critical for maintenance of the cellular proteostasis. In cancer cells, HSP90 also becomes a nucleating site for the stabilization of multiprotein complexes including signaling pathways and transcription complexes. Here, we described a novel role of HSP90 in the cytosolic compartmentalization of metabolic pathways in proliferating cancer cells. We found that HSP90 assists in the organization of metabolic enzymes into non-membrane-bound functional compartments termed metabosomes. Under experimental conditions that conserved the cellular proteostasis, we demonstrated that the compartmentalizing activity of HSP90 is critical to sustain the coordinated synthesis of multiple metabolites required for energy production, maintenance of the cellular biomass and secretion of immunometabolites. Conversely, inhibition of the nucleating capacity of HSP90 modified the topology of cytosolic metabosomes before protein degradation was apparent decreasing the efficiency of MYC-driven metabolic pathways. Inhibition of HSP90 decreases cancer metabolism in B-cell lymphoma cells and patients providing a novel mechanism of activity for this class of drugs.

cancer biology↗

bioRxiv2019

Unbiased Assessment of H-STS cells as high-fidelity models for gastro-enteropancreatic neuroendocrine tumor drug mechanism of action analysis

Quantitative metrics to objectively assess the fidelity of cancer models, such as cell lines, organoids, or patient-derived xenografts (PDXs), remain elusive, with histological criteria or the presence of specific mutations often used as driving principles. We show that molecular criteria, based on the regulatory proteins responsible for maintaining transcriptional cell state and its regulatory network, are effective in identifying models that can recapitulate drug mechanism of action and drug sensitivity, independent of histological consideration.

cancer biology↗

bioRxiv2018

Chromatin-informed inference of transcriptional programs in gynecologic and basal breast cancers

Epigenomic data on transcription factor occupancy and chromatin accessibility can elucidate the developmental origin of cancer cells and reveal the enhancer landscape of key oncogenic transcriptional regulators. However, in many cancers, epigenomic analyses have been limited, and computational methods to infer regulatory networks in tumors typically use expression data alone, or rely on transcription factor (TF) motifs in annotated promoter regions. Here, we develop a novel machine learning strategy called PSIONIC (patient-specific inference of networks informed by chromatin) to combine cell line chromatin accessibility data with large tumor expression data sets and model the effect of enhancers on transcriptional programs in multiple cancers. We generated a new ATAC-seq data set profiling chromatin accessibility in gynecologic and basal breast cancer cell lines and applied PSIONIC to 723 RNA-seq experiments from ovarian, uterine, and basal breast tumors as well as 96 cell line RNA-seq profiles. Our computational framework enables us to share information across tumors to learn patient-specific inferred TF activities, revealing regulatory differences between and within tumor types. Many of the identified TF regulators were significantly associated with survival outcome in basal breast, uterine serous and endometrioid carcinomas. Moreover, PSIONIC-predicted activity for MTF1 in cell line models correlated with sensitivity to MTF1 inhibition. Therefore computationally dissecting the role of TFs in gynecologic cancers may ultimately advance personalized therapy.

bioinformatics↗