Discovery of Mcl-1 inhibitors through virtual screening, molecular dynamics simulations and in vitro experiments

As a member of the B-cell lymphoma 2 (Bcl-2) protein family, the myeloid leukemia cell differentiation protein (Mcl-1) can inhibit apoptosis and plays an active role in the process of tumor escape from apoptosis. Therefore, inhibition of Mcl-1 protein can effectively promote the apoptosis of tumor cells and may also reduce tumor cell resistance to drugs targeting other anti-apoptotic proteins. This research is dedicated to the development of Mcl-1 inhibitors, aiming to provide more references for lead compounds with different scaffolds for the development of targeted anticancer drugs. We obtained a series of small molecules with a common core skeleton through molecular docking from Specs database and searched the core structure in ZINC database for more similar small molecules. Collecting these small molecules for preliminary experimental screening, we found a batch of active compounds, and selected two small molecules with the strongest inhibitory activity on B16F10 cells: compound 7 and compound 1. Their IC₅₀s are 7.86 ± 1.25 and 24.72 ± 1.94 μM, respectively. These two compounds were also put into cell scratch test for B16F10 cells and cell viability assay of other cell lines. Furthermore, through molecular dynamics (MD) simulation analysis, we found that compound 7 formed strong binding with the key P2, P3 pocket and ARG 263 of Mcl-1. Finally, ADME results showed that compound 7 performs well in terms of drug similarity. In conclusion, this study provides hits with co-scaffolds that may aid in the design of effective clinical drugs targeting Mcl-1 and the future drug development.

ATF3 Modulates the Proliferation, Migration, and Apoptosis of Synovial Fibroblasts after Arthroscopy by Promoting RGS1 Transcription

BACKGROUND

Osteoarthritis (OA) is a degenerative joint disease involving both cartilage and synovium. Activating transcription factor 3 (ATF3) and regulator of G protein signaling 1 (RGS1) have been reported to be up-regulated in OA. However, little is known regarding the relationship between these two genes and the mechanism of this relationship in OA development. Therefore, the present study explores the mechanism of ATF3-mediated RGS1 in the proliferation, migration, and apoptosis of synovial fibroblasts.

METHODS

After the OA cell model was constructed with TGF-β1 induction, human fibroblast-like synoviocytes (HFLSs) were transfected with ATF3 shRNA or RGS1 shRNA alone or co-transfected with ATF3 shRNA and pcDNA3.1-RGS1. Then, proliferation, migration, apoptosis, and the expression of ATF3, RGS1, α-SMA, BCL-2, caspase3, and cleaved-caspase3 were measured. Meanwhile, the potential relationship between ATF3 and RGS1 was predicted and validated.

RESULTS AND DISCUSSION

Analysis of the GSE185059 dataset suggested that RGS1 was up-regulated in OA synovial fluid exosomes. Moreover, ATF3 and RGS1 were both highly expressed in TGF-β1-induced HFLSs. Transfection of ATF3 shRNA or RGS1 shRNA significantly reduced proliferation and migration and promoted apoptosis of TGF- β1-induced HFLSs. Mechanistically, ATF3 bound to the RGS1 promoter and elevated RGS1 expression. Silencing ATF3 repressed proliferation and migration and enhanced apoptosis of TGF-β1-induced HFLSs by down-regulating RGS1.

CONCLUSION

ATF3 binds to the RGS1 promoter and enhances RGS1 expression to accelerate cell proliferation and block cell apoptosis in TGF-β1-induced synovial fibroblasts.

Data analysis guidelines for single-cell RNA-seq in biomedical studies and clinical applications

The application of single-cell RNA sequencing (scRNA-seq) in biomedical research has advanced our understanding of the pathogenesis of disease and provided valuable insights into new diagnostic and therapeutic strategies. With the expansion of capacity for high-throughput scRNA-seq, including clinical samples, the analysis of these huge volumes of data has become a daunting prospect for researchers entering this field. Here, we review the workflow for typical scRNA-seq data analysis, covering raw data processing and quality control, basic data analysis applicable for almost all scRNA-seq data sets, and advanced data analysis that should be tailored to specific scientific questions. While summarizing the current methods for each analysis step, we also provide an online repository of software and wrapped-up scripts to support the implementation. Recommendations and caveats are pointed out for some specific analysis tasks and approaches. We hope this resource will be helpful to researchers engaging with scRNA-seq, in particular for emerging clinical applications.

Cross talk between glucose metabolism and immunosuppression in IFN-γ–primed mesenchymal stem cells

This study reveals a novel relationship between mesenchymal stem cell immunomodulation and metabolism and provides a new strategy to improve their therapeutic efficacy in inflammatory diseases.

The immunosuppressive function “licensed” by IFN-γ is a vital attribute of mesenchymal stem cells (MSCs) widely used in the treatment of inflammatory diseases. However, the mechanism and impact of metabolic reprogramming on MSC immunomodulatory plasticity remain unclear. Here, we explored the mechanism by which glucose metabolism affects the immunomodulatory reprogramming of MSCs “licensed” by IFN-γ. Our data showed that glucose metabolism regulates the immunosuppressive function of human umbilical cord MSCs (hUC-MSCs) challenged by IFN-γ through the Janus kinase–signal transducer and activator of transcription (JAK-STAT) pathway. Furthermore, ATP facilitated the cross talk between glucose metabolism and the JAK-STAT system, which stimulates the phosphorylation of JAK2 and STATs, as well as the expression of indoleamine 2, 3-dioxygenase and programmed cell death-1 ligand. Moreover, ATP synergistically enhanced the therapeutic efficacy of IFN-γ–primed hUC-MSCs against acute pneumonia in mice. These results indicate a novel cross talk between the immunosuppressive function, glucose metabolism, and mitochondrial oxidation and provide a novel targeting strategy to enhance the therapeutic efficacies of hUC-MSCs.

Carbonate Minerals and Dissimilatory Iron-Reducing Organisms Trigger Synergistic Abiotic and Biotic Chain Reactions under Elevated CO2 Concentration

Increasing CO₂ emission has resulted in pressing climate and environmental issues. While abiotic and biotic processes mediating the fate of CO₂ have been studied separately, their interactions and combined effects have been poorly understood. To explore this knowledge gap, an iron-reducing organism, Orenia metallireducens, was cultured under 18 conditions that systematically varied in headspace CO₂ concentrations, ferric oxide loading, and dolomite (CaMg(CO₃)₂) availability. The results showed that abiotic and biotic processes interactively mediate CO₂ acidification and sequestration through "chain reactions", with pH being the dominant variable. Specifically, dolomite alleviated CO₂ stress on microbial activity, possibly via pH control that transforms the inhibitory CO₂ to the more benign bicarbonate species. The microbial iron reduction further impacted pH via the competition between proton (H⁺) consumption during iron reduction and H⁺ generation from oxidization of the organic substrate. Under Fe(III)-rich conditions, microbial iron reduction increased pH, driving dissolved CO₂ to form bicarbonate. Spectroscopic and microscopic analyses showed enhanced formation of siderite (FeCO₃) under elevated CO₂, supporting its incorporation into solids. The results of these CO₂-microbe-mineral experiments provide insights into the synergistic abiotic and biotic processes that alleviate CO₂ acidification and favor its sequestration, which can be instructive for practical applications (e.g., acidification remediation, CO₂ sequestration, and modeling of carbon flux).

Sustainable scale resistance on a bioinspired synergistic microspine coating with a collectible liquid barrier

Scale deposition, especially in the petroleum industry, has always been a serious issue because of its potential safety hazards and huge economic cost. However, conventional scale-resistant strategies based on mechanical descaling and chemical detergents can't feed the urgent demand for energy saving and environmental protection. Herein, we report a bioinspired long-term oil collectible mask (BLOCK)-a microspine coating with the synergistic effect of anti-adhesion and oil collection, displaying sustainable scale resistance towards oilfield-produced water. Inspired by pitcher plants, the oil layer as a liquid barrier inhibits scale deposition by changing the underwater scaling micro-environment from liquid/solid/solid to a liquid/solid/liquid triphase system. Oil droplets are collected by cacti-inspired microspines to enhance oil layer stability. Compared with stainless steel, the BLOCK coating shows ca. 98% reduction even after 35 days in artificial produced water. This strategy could be utilized to design integrated functional materials for conquering complex environments such as oil recovery and transportation.

The Effect of Crosslinking Degree of Hydrogels on Hydrogel Adhesion

The development of adhesive hydrogel materials has brought numerous advances to biomedical engineering. Hydrogel adhesion has drawn much attention in research and applications. In this paper, the study of hydrogel adhesion is no longer limited to the surface of hydrogels. Here, the effect of the internal crosslinking degree of hydrogels prepared by different methods on hydrogel adhesion was explored to find the generality. The results show that with the increase in crosslinking degree, the hydrogel adhesion decreased significantly due to the limitation of segment mobility. Moreover, two simple strategies to improve hydrogel adhesion generated by hydrogen bonding were proposed. One was to keep the functional groups used for hydrogel adhesion and the other was to enhance the flexibility of polymer chains that make up hydrogels. We hope this study can provide another approach for improving the hydrogel adhesion generated by hydrogen bonding.

Transient triplet differential-based photoacoustic lifetime imaging with an automatic interleaved data acquisition method for improved scanning speed and stability

Transient triplet differential (TTD) based photoacoustic lifetime (PALT) imaging provides valuable means for background-free molecular imaging and mapping of the oxygen partial pressure (pO₂) in deep tissues. However, the broad application of this method is hindered by its long scanning time, poor accuracy, and low stability. This is mainly because most PALT systems execute the three data acquisition sequences separately without automatic control and neglect the long-time fluctuation of the laser output. In this work, we have proposed a novel automatic interleaved data acquisition method for PALT. This new method not only improved the scanning efficiency but also eliminated the long-time fluctuations of laser pulse energy. Results show that this new method can significantly improve the system's stability and help reduce the scanning time. With this new method, we obtained the 3D background-free TTD images for the first time. We also observed distinct hypoxia inside the tumor due to the high metabolic rate of cancer cells, demonstrating the high reliability of our proposed method. The proposed method in this work can significantly promote the application of PALT imaging in biomedical studies.

Prediction of the Lotus Effect on Solid Surfaces by Machine Learning

Superhydrophobic surfaces with the "lotus effect" have wide applications in daily life and industry, such as self-cleaning, anti-freezing, and anti-corrosion. However, it is difficult to reliably predict whether a designed superhydrophobic surface has the "lotus effect" by traditional theoretical models due to complex surface topographies. Here, a reliable machine learning (ML) model to accurately predict the "lotus effect" of solid surfaces by designing a set of descriptors about nano-scale roughness and micro-scale topographies in addition to the surface hydrophobic modification is demonstrated. Geometrical and mathematical descriptors combined with gray level cooccurrence matrices (GLCM) offer a feasible solution to the puzzle of accurate descriptions of complex topographies. Furthermore, the "black box" is opened by feature importance and Shapley-additive-explanations (SHAP) analysis to extract waterdrop adhesion trends on superhydrophobic surfaces. The accurate prediction on as-fabricated superhydrophobic surfaces strongly affirms the extensionality of the ML model. This approach can be easily generalized to screen solid surfaces with other properties.