Hsp70–Bag3 Module Regulates Macrophage Motility and Tumor Infiltration via Transcription Factor LITAF and CSF1

The molecular chaperone Hsp70 has been implicated in multiple stages of cancer development. In these processes, a co-chaperone Bag3 links Hsp70 with signaling pathways that control cancer development. Recently, we showed that besides affecting cancer cells, Hsp70 can also regulate the motility of macrophages and their tumor infiltration. However, the mechanisms of these effects have not been explored. Here, we demonstrated that the Hsp70-bound co-chaperone Bag3 associates with a transcription factor LITAF that can regulate the expression of inflammatory cytokines and chemokines in macrophages. Via this interaction, the Hsp70–Bag3 complex regulates expression levels of LITAF by controlling its proteasome-dependent and chaperone-mediated autophagy-dependent degradation. In turn, LITAF regulates the expression of the major chemokine CSF1, and adding this chemokine to the culture medium reversed the effects of Bag3 or LITAF silencing on the macrophage motility. Together, these findings uncover the Hsp70–Bag3–LITAF–CSF1 pathway that controls macrophage motility and tumor infiltration

A Cell Double-Barcoding System for Quantitative Evaluation of Primary Tumors and Metastasis in Animals That Uncovers Clonal-Specific Anti-Cancer Drug Effects

Imaging in monitoring metastasis in mouse models has low sensitivity and is not quantitative. Cell DNA barcoding, demonstrating high sensitivity and resolution, allows monitoring effects of drugs on the number of tumor and metastatic clones. However, this technology is not suitable for comparison of sizes of metastatic clones in different animals, for example, drug treated and untreated, due to high biological and technical variability upon tumor and metastatic growth and isolation of barcodes from tissue DNA. However, both numbers of clones and their sizes are critical parameters for analysis of drug effects. Here we developed a modification of the barcoding approach for monitoring drug effects on tumors and metastasis that is quantitative, highly sensitive and highly reproducible. This novel cell double-barcoding system allows simultaneously following the fate of two or more cell variants or cell lines in xenograft models in vivo, and also following the fates of individual clones within each of these populations. This system allows comparing effects of drugs on different cell populations and thus normalizing drug effects by drug-resistant lines, which corrects for both biological and technical variabilities and significantly increases the reproducibility of results. Using this barcoding system, we uncovered that effects of a novel DYRK1B kinase inhibitor FX9847 on primary tumors and metastasis is clone-dependent, while a distinct drug osimertinib demonstrated clone-independent effects on cancer cell populations. Overall, a cell double-barcoding approach can significantly enrich our understanding of drug effects in basic research and preclinical studies

Integration of the Connectivity Map and Pathway Analysis to Predict Plant Extract’s Medicinal Properties—The Study Case of Sarcopoterium spinosum L.

Medicinal properties of plants are usually identified based on knowledge of traditional medicine or using low-throughput screens for specific pharmacological activities. The former is very biased since it requires prior knowledge of plants’ properties, while the latter depends on a specific screening system and will miss medicinal activities not covered by the screen. We sought to enrich our understanding of the biological activities of Sarcopoterium spinosum L. root extract based on transcriptome changes to uncover a plurality of possible pharmacological effects without the need for prior knowledge or functional screening. We integrated Gene Set Enrichment Analysis of the RNAseq data to identify pathways affected by the treatment of cells with the extract and perturbational signatures in the CMAP database to enhance the validity of the results. Activities of signaling pathways were measured using immunoblotting with phospho-specific antibodies. Mitochondrial membrane potential was assessed using JC-1 staining. SARS-CoV-2-induced cell killing was assessed in Vero E6 and A549 cells using an MTT assay. Here, we identified transcriptome changes following exposure of cultured cells to the medicinal plant Sarcopoterium spinosum L. root extract. By integrating algorithms of GSEA and CMAP, we confirmed known anti-cancer activities of the extract and predicted novel biological effects on oxidative phosphorylation and interferon pathways. Experimental validation of these pathways uncovered strong activation of autophagy, including mitophagy, and excellent protection from SARS-CoV-2 infection. Our study shows that gene expression analysis alone is insufficient for predicting biological effects since some of the changes reflect compensatory effects, and additional biochemical tests provide necessary corrections. This study defines the advantages and limitations of transcriptome analysis in predicting the biological and medicinal effects of the Sarcopoterium spinosum L. extract. Such analysis could be used as a general approach for predicting the medicinal properties of plants

Search for Synergistic Drug Combinations to Treat Chronic Lymphocytic Leukemia

Finding synergistic drug combinations is an important area of cancer research. Here, we sought to rationally design synergistic drug combinations with an inhibitor of BTK kinase, ibrutinib, which is used for the treatment of several types of leukemia. We (a) used a pooled shRNA screen to identify genes that protect cells from the drug, (b) identified protective pathways via bioinformatics analysis of these gene sets, and (c) identified drugs that inhibit these pathways. Based on this analysis, we established that inhibitors of proteasome and mTORC1 could synergize with ibrutinib both in vitro and in vivo. We suggest that FDA-approved inhibitors of these pathways could be effectively combined with ibrutinib for the treatment of chronic lymphocytic leukemia (CLL)