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Zirak, B.

Publications and source records attributed to Zirak, B..

5 recordsLinked to original sources

The adaptive architecture of tRNA dependencies across physiological tumor environments

The genetic code is decoded by a highly redundant transfer RNA (tRNA) repertoire, yet whether this apparent redundancy serves a functional role beyond ensuring robust translation remains unclear. Here, we establish an atlas of tRNA dependencies through large-scale CRISPRi perturbation mapping across matched in vivo tumor growth and in vitro cell culture for 16 cancer cell lines spanning seven tissue types. During in vivo tumor growth, tRNA dependencies exhibited tissue organization at isodecoder resolution and were associated with codon demand at the isoacceptor level. Strikingly, these relationships were reorganized when cells were cultured in vitro, establishing environmental sensitivity of tRNA dependency--in contrast to protein components of the decoding machinery such as aminoacyl-tRNA synthetases (aaRSs), whose dependencies were largely preserved across environments. Despite pronounced context specificity, environmental alterations in tRNA dependency were dominated by a shared response across cancer cell lines, organized across isoacceptor and isodecoder levels. By disentangling stable cellular identity from environment-associated dependency states, we resolved a shared axis of isoacceptor dependency remodeling and identified specific tRNA isoacceptors associated with promoting or restraining adaptation to the tumor environment. We further connect this functional architecture to differential translation of codon-usage-biased programs associated with proliferation and tumor-microenvironmental stress. Together, our study reveals that tRNA redundancy does not imply functional equivalence, but instead forms a structured and environmentally plastic control layer linking coding information to translational output.

genomics↗

Targeting Osteosarcoma heterogeneity to improve therapeutic response

Intra tumor heterogeneity complicates cancer therapy by providing tumors with the ability to alter their phenotypes and become more therapy resistant. Here, we tested the hypothesis that identifying and modulating expression of key state-specific transcription factors could be used as a strategy for driving cells to a more therapy-sensitive state. Recent single-cell studies have explored the inter and intra tumoral heterogeneity of osteosarcoma and identified gene pathways enriched in specific cell states. For example, metastatic tumors are characterized by an expression of genes in the TNF-, PI3K, TGF{beta} and mTOR pathways. We identified similar profiles in osteosarcoma patient-derived xenograft-derived cell lines and potential transcription factor drivers of these states. We then used perturb-seq to downregulate expression of key transcription factors and evaluated the effect of these modulations on single cell RNA profiles and drug responses. Knockdown of NFE2L3 or NR0B1 increased the proportion of cells sensitive to targeted therapy. This approach, which could potentially be applied to other cancers, could be used as a strategy to increase the response to targeted therapies by increasing the proportion of cells in a drug-sensitive state. HighlightsO_LIDistinct transcriptomic states were identified in osteosarcoma cell lines using single-cell RNA sequencing. C_LIO_LILineage tracing identified states with differential sensitivity to therapy. C_LIO_LIUsing perturb-seq, we identified transcription factors that drive cells towards a more sensitive state. C_LIO_LIThe transcription factor NFE2L3 was identified as targets capable of reprogramming cells to a sensitive state. C_LI

genomics↗

Macrophages redeploy functional cancer cell surface proteins following phagocytosis

Macrophage-mediated phagocytosis is a vital innate immune process altered in cancer. We show here that tumor-associated macrophages (TAMs) redeploy intact cell surface proteins from cancer cells to their own cell surface. We initially observed the canonical epithelial cancer surface marker EpCAM on the surface of TAMs in primary human solid tumors but not paired peripheral blood macrophages. In a murine model of metastatic breast cancer, we also observed EpCAM on the surface of primary TAMs that have phagocytosed breast cancer cells. In a model of a myeloproliferative neoplasm, we again found engulfed cell-derived surface proteins on the surface of macrophages following phagocytosis. A co-culture system and proteomics assay that tags proteins based on their cell-of-origin revealed hundreds of cell surface proteins synthesized in cancer cells are present and fully intact on the surface of macrophages following phagocytosis. Using a biotin transfer assay, we determined that these proteins were on the surface of the cancer cell prior to redeployment by the macrophage following phagocytosis. Furthermore, macrophages that redeploy a neutral amino acid transporter correspondingly show increased transport of an unnatural amino acid substrate. Widespread acquisition of proteins from engulfed cells may contribute to two critical TAM phenotypes: the inability to phagocytose and reprogrammed metabolism.

immunology↗

Revealing the Grammar of Small RNA Secretion Using Interpretable Machine Learning

Small non-coding RNAs can be secreted through a variety of mechanisms, including exosomal sorting, in small extracellular vesicles, and within lipoprotein complexes 1,2. However, the mechanisms that govern their sorting and secretion are still not well understood. In this study, we present ExoGRU, a machine learning model that predicts small RNA secretion probabilities from primary RNA sequence. We experimentally validated the performance of this model through ExoGRU-guided mutagenesis and synthetic RNA sequence analysis, and confirmed that primary RNA sequence is a major determinant in small RNA secretion. Additionally, we used ExoGRU to reveal cis and trans factors that underlie small RNA secretion, including known and novel RNA-binding proteins, e.g., YBX1, HNRNPA2B1, and RBM24. We also developed a novel technique called exoCLIP, which reveals the RNA interactome of RBPs within the cell-free space. We used exoCLIP to reveal the RNA interactome of HNRNPA2B1 and RBM24 in extracellular vesicles. Together, our results demonstrate the power of machine learning in revealing novel biological mechanisms. In addition to providing deeper insight into complex processes such as small RNA secretion, this knowledge can be leveraged in therapeutic and synthetic biology applications.

systems biology↗

Integrin αvβ8 on T cells is responsible for suppression of anti-tumor immunity in multiple syngeneic models and is a promising target for tumor immunotherapy

The v{beta}8 integrin is a key activator of transforming growth factor {beta} (TGF {beta}), which has been shown to inhibit anti-tumor immunity. Previous work has suggested that v{beta}8 on tumor cells could modulate tumor growth and responses to immune checkpoint blockade. We now show that a potent blocking monoclonal antibody against v{beta}8 (ADWA-11) causes growth suppression or complete regression in syngeneic models of squamous cell carcinoma (CCK168), mammary cancer (EMT-6), colon cancer (CT26), and prostate cancer (TRAMPC2), especially when it is combined with other immunomodulators (anti-PD-1, anti-CTLA-4 or 4-1BB) or radiotherapy. v{beta}8 is expressed on tumor cells in some of these models, but tumor cell expression of v{beta}8 is not essential for the beneficial effects of ADWA-11 therapy. v{beta}8 is consistently expressed at highest levels on CD4+CD25+ T cells within tumors, and specific deletion of Itgb8 from T cells is as effective as ADWA-11 in suppressing tumor growth. Treatment with ADWA-11 increases expression of a suite of genes in tumor infiltrating CD8+ T cells that are normally inhibited by TGF{beta} and are involved in tumor cell killing, including Granzyme B and Interferon-{gamma}. These findings solidify v{beta}8 integrin as a promising target for cancer immunotherapy, even for tumors that do not express this integrin.

cancer biology↗