ABSTRACT Saikosaponin b1 (Ssb1), a natural oleanane‐type triterpenoid saponin, exhibits antifibrosis activity by inhibiting the activation of hepatic stellate cells (HSCs), but the specific underlying molecular mechanisms are unknown. Here, it is found that Ssb1 could directly bind with the signal transducer and activator of transcription 3 (STAT3) and effectively inhibit the activation of HSCs. Proteomic techniques and molecular simulation revealed that Ssb1 is mainly bound to the S319 residues of STAT3 in the coiled‐coil domain. Further studies indicated that Ssb1 binding with STAT3 inhibited its transcriptional activity, and regulated glioma‐associated oncogene‐1 (Gli1) expression in the Hedgehog signaling pathway. Besides, Ssb1 binding blocked interaction between STAT3 and Gli1, which promoted degradation of Gli1 protein by suppressor of fused homolog (SUFU) and the ubiquitin‐proteasome system. The loss function of Gli1 led to decreased expression of Bcl2 and promoted the apoptosis of activated HSCs. Moreover, STAT3 ablation abolished the Ssb1‐mediated antifibrotic effects. These findings show that STAT3 plays a vital role in Ssb1 treatment of liver fibrosis, and Ssb1 as a STAT3 inhibitor might be a promising therapeutic candidate for the treatment of hepatic fibrosis.
Alice Marinangeli, Pinar Cakir Hatir, Mustafa Baris Yagci
et al.
In this work, we report the development of a highly sensitive optical sensor for the detection of cardiac troponin I (cTnI), a key biomarker for early-stage myocardial infarction diagnosis. The sensor combines castor oil-derived biomimetic receptors, called GreenNanoMIPs and prepared via the molecular imprinting technology using as a template an epitope of cTnI (i.e., the NR10 peptide), with a portable multimode plastic optical fiber surface plasmon resonance (POF-SPR) transducer. For sensing, gold SPR chips were functionalized with GreenNanoMIPs as proven by refractive index changes and confirmed by means of XPS. Binding experiments demonstrated the cTnI_nanoMIP-SPR sensor’s ability to detect both the NR10 peptide epitope and the full-length cTnI protein within minutes (t = 10 min), with high sensitivity and selectivity in buffer and serum matrices. The cTnI_nanoMIP-SPR showed an LOD of 3.53 × 10<sup>−15</sup> M, with a linearity range of 1 pM–100 pM, outperforming previously reported sensor platforms and making it a promising tool for early-stage myocardial infarction detection.
Yuri Z. Sinzato, Robert Uittenbogaard, Petra M. Visser
et al.
Fluid flow has a major effect on the aggregation and fragmentation of bacterial colonies. Yet, a generic framework to understand and predict how hydrodynamics affects colony size remains elusive. This study investigates how fluid flow affects the formation and maintenance of large colonial structures in cyanobacteria, using an experimental technique that precisely controls hydrodynamic conditions. We performed experiments on laboratory cultures and lake samples of the cyanobacterium Microcystis, while their colony size distribution was measured simultaneously by direct microscopic imaging. We demonstrate that EPS-embedded cells formed by cell division exhibit significant mechanical resistance to shear forces. However, at elevated hydrodynamic stress levels (exceeding those typically generated by surface wind mixing) these colonies experience fragmentation through an erosion process. We also show that single cells can aggregate into small colonies due to fluid flow. However, the structural integrity of these flow-induced colonies is weaker than that of colonies formed by cell division. We provide a mathematical analysis to support the experiments and demonstrate that a population model with two categories of colonies describes the measured size distributions. Our results shed light on the specific conditions wherein flow-induced fragmentation and aggregation of cyanobacteria are decisive and indicate that colony formation under natural conditions is mainly driven by cell division, although flow-induced aggregation could play a role in dense bloom events. These findings can be used to improve prediction models and mitigation strategies for toxic cyanobacterial blooms and also offer potential applications in other areas such as algal biotechnology or medical settings where the dynamics of biological aggregates play a significant role.
The effects of ligand binding on protein structures and their in vivo functions carry numerous implications for modern biomedical research and biotechnology development efforts such as drug discovery. Although several deep learning (DL) methods and benchmarks designed for protein-ligand docking have recently been introduced, to date no prior works have systematically studied the behavior of the latest docking and structure prediction methods within the broadly applicable context of (1) using predicted (apo) protein structures for docking (e.g., for applicability to new proteins); (2) binding multiple (cofactor) ligands concurrently to a given target protein (e.g., for enzyme design); and (3) having no prior knowledge of binding pockets (e.g., for generalization to unknown pockets). To enable a deeper understanding of docking methods' real-world utility, we introduce PoseBench, the first comprehensive benchmark for broadly applicable protein-ligand docking. PoseBench enables researchers to rigorously and systematically evaluate DL methods for apo-to-holo protein-ligand docking and protein-ligand structure prediction using both primary ligand and multi-ligand benchmark datasets, the latter of which we introduce for the first time to the DL community. Empirically, using PoseBench, we find that (1) DL co-folding methods generally outperform comparable conventional and DL docking baseline algorithms, yet popular methods such as AlphaFold 3 are still challenged by prediction targets with novel binding poses; (2) certain DL co-folding methods are highly sensitive to their input multiple sequence alignments, while others are not; and (3) DL methods struggle to strike a balance between structural accuracy and chemical specificity when predicting novel or multi-ligand protein targets. Code, data, tutorials, and benchmark results are available at https://github.com/BioinfoMachineLearning/PoseBench.
Global food systems confront the urgent challenge of supplying sustainable, nutritious diets in the face of escalating demands. The advent of Artificial Intelligence (AI) is bringing in a personal choice revolution, wherein AI-driven individual decisions transform food systems from dinner tables, to the farms, and back to our plates. In this context, AI algorithms refine personal dietary choices, subsequently shaping agricultural outputs, and promoting an optimized feedback loop from consumption to cultivation. Initially, we delve into AI tools and techniques spanning the food supply chain, and subsequently assess how AI subfields$\unicode{x2013}$encompassing machine learning, computer vision, and speech recognition$\unicode{x2013}$are harnessed within the AI-enabled Food System (AIFS) framework, which increasingly leverages Internet of Things, multimodal sensors and real-time data exchange. We spotlight the AIFS framework, emphasizing its fusion of AI with technologies such as digitalization, big data analytics, biotechnology, and IoT extensively used in modern food systems in every component. This paradigm shifts the conventional "farm to fork" narrative to a cyclical "consumer-driven farm to fork" model for better achieving sustainable, nutritious diets. This paper explores AI's promise and the intrinsic challenges it poses within the food domain. By championing stringent AI governance, uniform data architectures, and cross-disciplinary partnerships, we argue that AI, when synergized with consumer-centric strategies, holds the potential to steer food systems toward a sustainable trajectory. We furnish a comprehensive survey for the state-of-the-art in diverse facets of food systems, subsequently pinpointing gaps and advocating for the judicious and efficacious deployment of emergent AI methodologies.