Biology subjects

Tiwary, K.

Publications and source records attributed to Tiwary, K..

3 recordsLinked to original sources

bioRxiv2025

Novel peptide targeting CXCR4 disrupt tumor-stroma crosstalk to eliminate migrating cancer stem cells

Pancreatic ductal adenocarcinoma (PDAC) is one of the most aggressive and metastatic malignancies worldwide. Migrating cancer stem cells (miCSCs) marked by CD133+CXCR4+ expression drives metastasis but lacks effective drug targets. Here, we show that activated pancreatic stellate cells secrete the CXCR4 ligand CXCL12 to foster stemness, epithelial-to-mesenchymal transition (EMT), and chemoresistance. Protein interaction network analyses links CXCL12/CXCR4 signaling axis and the downstream transcription factor BMI1. Knockdown experiments confirmed the BMI1s role in (mi)CSCs maintenance and survival. Novel CXCR4 inhibitors, i.e., the endogenous human peptide EPI-X4 and its derivatives (e.g., JM#21) strongly inhibited the in vitro migration of miCSCs. In particular, the most potent EPI-X4 derivate JM#21 sufficiently suppressed EMT, stemness, and self-renewal of human PDAC cell lines. In addition, JM#21 sensitized cell lines towards gemcitabine and paclitaxel. Overall, our study reveals that (mi)CSCs are enhanced and maintained via a tumor-stroma crosstalk through BMI1, ultimately promoting metastases and therapeutic resistance in PDAC. Peptide targeting of the CXCL12/CXCR4/BMI1 signaling axis via JM#21 could enhance PDAC combination therapies, offering a promising strategy against this deadly cancer. SynopsisThe study identifies a tumor-stroma interaction mediated by pancreatic stellate cells (PSCs) secreting CXCL12, which binds to CXCR4 on (mi)CSCs, fostering stemness, epithelial-to-mesenchymal transition (EMT), and chemoresistance. The CXCL12/CXCR4 axis activates the downstream BMI1 transcription factor, crucial for migration and stemness maintenance. O_FIG O_LINKSMALLFIG WIDTH=173 HEIGHT=200 SRC="FIGDIR/small/641126v1_ufig1.gif" ALT="Figure 1"> View larger version (50K): org.highwire.dtl.DTLVardef@14e117org.highwire.dtl.DTLVardef@c5db35org.highwire.dtl.DTLVardef@1ba7d2dorg.highwire.dtl.DTLVardef@702ccf_HPS_FORMAT_FIGEXP M_FIG C_FIG O_LICXCL12 enhances (mi)CSC populations and metastatic potential through CXCR4 signaling. C_LIO_LIBMI1 is identified as a pivotal downstream effector linking CXCR4 to EMT and stemness. C_LIO_LIJM#21 effectively blocks CXCL12-induced migration, EMT, and stemness in vitro, demonstrating superior efficacy compared to other CXCR4 inhibitors. C_LIO_LIEncapsulation of JM#21 in silica nanoparticles enhances its stability and delivery, reducing chemoresistance and miCSC populations in co-culture systems. C_LIO_LICombining JM#21 with chemotherapy significantly impairs colony formation and CSC-mediated drug resistance. C_LI

cancer biology↗

bioRxiv2020

Pancreatic cancer stem cells (CSCs) are regulated via a feedback-loop of telomerase activity and stemness factors

To date, it is still unclear how cancer stem cells (CSCs) regulate their stemness properties, and to what extent they share common features with normal stem cells. Telomerase regulation is a key factor in stem cell maintenance. In this study, we investigate how telomerase regulation affects cancer stem cell biology in pancreatic ductal adenocarcinoma (PDAC), and delineate the mechanisms by which telomerase activity and CSC properties are linked. Using primary patient-derived pancreatic cancer cells, we show that CSCs have higher telomerase activity and longer telomeres than bulk tumor cells. Inhibition of telomerase activity, using genetic TERT-knockdown or pharmacological inhibitor (BIBR1532) resulted in CSC marker depletion in vitro, and reduced tumorigenicity in vivo. Furthermore, we identify a positive feedback loop between stemness factors (KLF4, SOX2, OCT3/4, NANOG) and telomerase, which is essential for the self-renewal of pancreatic CSCs. Disruption the balance between telomerase activity and stemness factors, eliminates CSCs via induction of DNA damage and apoptosis, opening future perspectives to avoid CSC driven therapy resistance and tumor relapse in PDAC patients.

cancer biology↗

bioRxiv2019

How helpful are the protein-protein interaction databases and which ones?

Protein-protein interactions (PPIs) are critical, and so are the databases and tools (resources) concerning PPIs. But in absence of systematic comparisons, biologists/bioinformaticians may be forced to make a subjective selection among such protein interaction databases and tools. In fact, a comprehensive list of such bioinformatics resources has not been reported so far. For the first time, we compiled 375 PPI resources, short-listed and performed preliminary comparison of 125 important ones (both lists available publicly at startbioinfo.com), and then systematically compared human PPIs from 16 carefully-selected databases. General features have been first compared in detail. The coverage of experimentally verified vs. all PPIs, as well as those significant in case of disease-associated and other types of genes among the chosen databases has been compared quantitatively. This has been done in two ways: outputs manually obtained using web-interfaces, and all interactions downloaded from the databases. For the first approach, PPIs obtained in response to gene queries using the web interfaces were compared. As a query set, 108 genes associated with different tissues (specific to kidney, testis, and uterus, and ubiquitous) or diseases (breast cancer, lung cancer, Alzheimers, cystic fibrosis, diabetes, and cardiomyopathy) were chosen. PPI-coverage for well-studied genes was also compared with that of less-studied ones. For the second approach, the back-end-data from the databases was downloaded and compared. Based on the results, we recommend the use of STRING and UniHI for retrieving the majority of experimentally verified protein interactions, and hPRINT and STRING for obtaining maximum number of total (experimentally verified as well as predicted) PPIs. The analysis of experimentally verified PPIs found exclusively in each database revealed that STRING contributed about 71% of exclusive hits. Overall, hPRINT, STRING and IID together retrieved ~94% of total protein interactions available in the databases. The coverage of certain databases was skewed for some gene-types. The results also indicate that the database usage frequency may not correlate with their advantages, thereby justifying the need for more frequent studies of this nature.

bioinformatics↗