Chance, M. R. | bioRxiv Science

bioRxiv2021

GABARAP membrane conjugation sequesters the FLCN-FNIP tumor suppressor complex to activate TFEB and lysosomal biogenesis

Adaptive changes in lysosomal capacity are driven by the transcription factors TFEB and TFE3 in response to increased autophagic flux and endolysosomal stress, yet the molecular details of their activation are unclear. LC3 and GABARAP members of the ATG8 protein family are required for selective autophagy and sensing perturbation within the endolysosomal system. Here we show that during single membrane ATG8 conjugation (SMAC), Parkin-dependent mitophagy, and Salmonella-induced xenophagy, the membrane conjugation of GABARAP, but not LC3, is required for activation of TFEB/TFE3 to control lysosomal homeostasis and capacity. GABARAP directly binds to a novel LC3-interacting motif (LIR) in the FLCN/FNIP tumor suppressor complex with picomolar affinity and regulates its relocalization to these GABARAP-conjugated membrane compartments. This disrupts the regulation of RagC/D by the FLCN/FNIP GAP complex, resulting in impaired mTOR-dependent phosphorylation of TFEB without changing mTOR activity towards other substrates. Thus, the GABARAP-FLCN/FNIP-TFEB axis serves as a universal molecular sensor that coordinates lysosomal homeostasis with perturbations and cargo flux within the autophagy-lysosomal network.

cell biology↗

bioRxiv2020

Co-Phosphorylation Networks Reveal Subtype-Specific Signaling Modules in Breast Cancer

MotivationProtein phosphorylation is a ubiquitous mechanism of post-translational modification that plays a central role in cellular signaling. Phosphorylation is particularly important in the context of cancer, as down-regulation of tumor suppressors and up-regulation of oncogenes by the dysregulation of associated kinase and phosphatase networks are shown to have key roles in tumor growth and progression. Despite recent advances that enable large-scale monitoring of protein phosphorylation, these data are not fully incorporated into such computational tasks as phenotyping and subtyping of cancers. ResultsWe develop a network-based algorithm, CoPPNO_SCPLOWETC_SCPLOW, to enable unsupervised subtyping of cancers using phosphorylation data. For this purpose, we integrate prior knowledge on evolutionary, structural, and functional association of phosphosites, kinase-substrate associations, and protein-protein interactions with the correlation of phosphorylation of phosphosites across different tumor samples (a.k.a co-phosphorylation) to construct a context-specific weighted network of phosphosites. We then mine these networks to identify subnetworks with correlated phosphorylation patterns. We apply the proposed framework to two mass-spectrometry based phosphorylation datasets for breast cancer, and observe that (i) the phosphorylation pattern of the identified subnetworks are highly correlated with clinically identified subtypes, and (ii) the identified subnetworks are highly reproducible across datasets that are derived from different studies. Our results show that integration of quantitative phosphorylation data with network frameworks can provide mechanistic insights into the differences between the signaling mechanisms that drive breast cancer subtypes. Furthermore, the reproducibility of the identified subnetworks suggests that phosphorylation can provide robust classification of disease response and markers. Availability and implementationCoPPNO_SCPLOWETC_SCPLOW is available at http://compbio.case.edu/coppnet/

bioinformatics↗