Protein translocation across the bacterial SecYEG channel involves mechanical constraints arising from ATP-driven SecA activity, geometric confinement within the translocon, and folding of the emerging polypeptide on the periplasmic side. Periplasmic chaperones assist substrate maturation during this process, but how they influence protein folding under force remains poorly understood. Using protein L as a model two-state substrate, we applied physiologically relevant force pulses using custom-built single-molecule magnetic tweezers to examine how bacterial periplasmic chaperones modulate folding under tension. To isolate the direct effects of individual chaperones, these experiments were performed in the absence of SecA and the SecYEG translocon. We show that the periplasmic chaperones PpiD and DsbC increase folding probability and accelerate refolding under force, while having minimal effect on unfolding kinetics. In contrast, Spy and Skp reduce folding probability and suppress refolding, consistent with holdase-like behaviour. These observations show that distinct classes of periplasmic chaperones differentially modulate folding probability and refolding kinetics under mechanical force. Increased folding probability correspondingly enhances the expected mechanical work output of substrate folding under force. Together, our findings establish a quantitative framework for investigating how bacterial periplasmic chaperones modulate protein folding under controlled mechanical conditions.
Deep Chaudhuri, Madhubala Bhatt, Shubhasis Haldar· Journal of Molecular Biology· 0 citations
Abstract Cancer stem-like cells (CSCs) play a pivotal role in driving tumor heterogeneity, therapeutic resistance, and disease progression. Despite the power of single-cell RNA sequencing (scRNA-seq) to resolve intratumoral hierarchies, there remains a need for robust, scalable tools to consistently profile CSCs across both single-cell and bulk transcriptomic data. To address this, we developed ACSCeND—a unified, machine learning–based framework that enables high-resolution CSC state classification and tissue-level deconvolution. ACSCeND comprises (i) a supervised classifier trained on curated scRNA-seq datasets to assign cells into pluripotent-like, multipotent-like, or unipotent-like states, and (ii) an attention-guided autoencoder that deconvolves CSC subtype proportions from bulk RNA sequencing data. Compared to existing tissue deconvolution tools, ACSCeND achieves superior performance, with higher accuracy across synthetic and real-world samples. Applied to over 25 000 tumor profiles from The Cancer Genome Atlas (TCGA), PREdiction of Clinical Outcomes from Genomics (PRECOG), tumor-relapse, and checkpoint inhibitor studies, ACSCeND reveals that CSC abundance strongly correlates with poor disease-free survival and reduced immunotherapy efficacy. Moreover, it uncovers distinct CSC-state-specific molecular programs, offering insights into CSC-driven heterogeneity and tumor evolution. The model also recapitulates known developmental hierarchies in noncancerous tissues, supporting its broader biological relevance. By integrating single-cell precision with bulk-level applicability, ACSCeND offers a robust, interpretable approach to profiling CSC dynamics and establishes CSC state as a clinically meaningful, pan-cancer biomarker for guiding stemness-informed therapies. ACSCeND is available as a python package (through pip) at https://pypi.org/project/ACSCeND/.