Gsw-fi: a GLM model incorporating shrinkage and double-weighted strategies for identifying cancer driver genes with functional impact.

BACKGROUND: Cancer, a disease with high morbidity and mortality rates, poses a significant threat to human health. Driver genes, which harbor mutations accountable for the initiation and progression of tumors, play a crucial role in cancer development. Identifying driver genes stands as a paramount objective in cancer research and precision medicine. RESULTS: In the present work, we propose a method for identifying driver genes using a Generalized Linear Regression Model (GLM) with Shrinkage and double-Weighted strategies based on Functional Impact, which is named GSW-FI. Firstly, an estimating model is proposed for assessing the background functional impacts of genes based on GLM, utilizing gene features as predictors. Secondly, the shrinkage and double-weighted strategies as two revising approaches are integrated to ensure the rationality of the identified driver genes. Lastly, a statistical method of hypothesis testing is designed to identify driver genes by leveraging the estimated background function impacts. Experimental results conducted on 31 The Cancer Genome Altas datasets demonstrate that GSW-FI outperforms ten other prediction methods in terms of the overlap fraction with well-known databases and consensus predictions among different methods. CONCLUSIONS: GSW-FI presents a novel approach that efficiently identifies driver genes with functional impact mutations using computational methods, thereby advancing the development of precision medicine for cancer.

Combined-task deep network based on LassoNet feature selection for predicting the comorbidities of acute coronary syndrome.

Acute coronary syndrome (ACS) is a multifaceted cardiovascular condition frequently accompanied by multiple comorbidities, which can have significant implications for patient outcomes and treatment approaches. Precisely predicting these comorbidities is crucial for providing personalized care and making well-informed clinical decisions. However, there is a shortage of research investigating the identification of risk factors associated with ACS comorbidities and accurately predicting their likelihood of occurrence beyond heart failure. In this study, an approach called Combined-task Deep Network based on LassoNet feature selection (CDNL) is presented for predicting ACS comorbidities, including hypertension, diabetes, hyperlipidemia, and heart failure. In order to identify crucial biomarkers associated with ACS comorbidities, the proposed framework first incorporates LassoNet, which extends Lasso regression to the deep network by adding a skip (residual) layer. Additionally, a correlation score calculation method across tasks is introduced based on measuring the overlap of identified biomarkers and their assigned importance. This method enables the development of an optimal combined-task prediction model for each ACS comorbidity, addressing the challenge of limited representations in traditional multi-task learning. Our evaluation, conducted through a meticulous cross-sectional study at a tertiary hospital in China, involved a dataset of 2941 samples with 42 clinical features. The results demonstrate that CDNL facilitates the identification of significant biomarkers and achieves an average improvement in AUC of 4.93% and 8.58% compared to deep learning multi-layer neural network (DNN) and SVM, respectively. Additionally, it shows an average improvement of 2.64% and 1.92% compared to two state-of-the-art multi-task models.

Predicting anticancer drug sensitivity on distributed data sources using federated deep learning.

Drug sensitivity prediction plays a crucial role in precision cancer therapy. Collaboration among medical institutions can lead to better performance in drug sensitivity prediction. However, patient privacy and data protection regulation remain a severe impediment to centralized prediction studies. For the first time, we proposed a federated drug sensitivity prediction model with high generalization, combining distributed data sources while protecting private data. Cell lines are first classified into three categories using the waterfall method. Focal loss for solving class imbalance is then embedded into the horizontal federated deep learning framework, i.e., HFDL-fl is presented. Applying HFDL-fl to homogeneous and heterogeneous data, we obtained HFDL-Cross and HFDL-Within. Our comprehensive experiments demonstrated that (i) collaboration by HFDL-fl outperforms private model on local data, (ii) focal loss function can effectively improve model performance to classify cell lines in sensitive and resistant categories, and (iii) HFDL-fl is not significantly affected by data heterogeneity. To summarize, HFDL-fl provides a valuable solution to break down the barriers between medical institutions for privacy-preserving drug sensitivity prediction and therefore facilitates the development of cancer precision medicine and other privacy-related biomedical research.

DriverGenePathway: Identifying driver genes and driver pathways in cancer based on MutSigCV and statistical methods.

Although computational methods for driver gene identification have progressed rapidly, it is far from the goal of obtaining widely recognized driver genes for all cancer types. The driver gene lists predicted by these methods often lack consistency and stability across different studies or datasets. In addition to analytical performance, some tools may require further improvement regarding operability and system compatibility. Here, we developed a user-friendly R package (DriverGenePathway) integrating MutSigCV and statistical methods to identify cancer driver genes and pathways. The theoretical basis of the MutSigCV program is elaborated and integrated into DriverGenePathway, such as mutation categories discovery based on information entropy. Five methods of hypothesis testing, including the beta-binomial test, Fisher combined p-value test, likelihood ratio test, convolution test, and projection test, are used to identify the minimal core driver genes. Moreover, de novo methods, which can effectively overcome mutational heterogeneity, are introduced to identify driver pathways. Herein, we describe the computational structure and statistical fundamentals of the DriverGenePathway pipeline and demonstrate its performance using eight types of cancer from TCGA. DriverGenePathway correctly confirms many expected driver genes with high overlap with the Cancer Gene Census list and driver pathways associated with cancer development. The DriverGenePathway R package is freely available on GitHub: https://github.com/bioinformatics-xu/DriverGenePathway.

M1 Microglia Induced Neuronal Injury on Ischemic Stroke via Mitochondrial Crosstalk between Microglia and Neurons.

Among the middle-aged and senile populations, ischemic stroke (IS) is a frequently occurring acute condition of the cerebrovascular system. Traditionally, it is recognized that when stroke occurs, microglia are activated into M1 phenotype and release cytotoxic cytokines, reactive oxygen species, proteases, and other factors, thus exacerbating the injury by further destroying or killing nearby neurons. In the latest research, the crucial role of the intercellular mitochondrial crosstalk on the stroke management has been demonstrated. Therefore, we tried to clarify mitochondrial crosstalk between microglia and neurons, and evaluated M1 microglial mitochondria-mediated neurological performance in transient middle cerebral artery occlusion (tMCAO) rats. We found that when microglia was activated into the proinflammatory M1 type after stroke, mitochondrial fission process was accelerated, and damaged mitochondria were released, further transferred to neurons and fused with neuronal mitochondria. As a result, the function of neuronal mitochondria was damaged by decreasing adenosine triphosphate (ATP), mitochondria membrane potential, and increasing excessive reactive oxygen species (ROS), thus inducing mitochondria-mediated neuronal death and finally aggravating ischemia injury. Taken together, it provides a novel neuroglial crosstalk mechanism at the mitochondrial level.

Pea protein based nanocarriers for lipophilic polyphenols: Spectroscopic analysis, characterization, chemical stability, antioxidant and molecular docking.

The current research aims to construct and assess pea protein isolate (PPI) nanocarriers for lipophilic polyphenols of curcumin (CUR), quercetin (QUE) and resveratrol (RES), respectively. Fluorescence analysis demonstrated that the binding affinity declined in sequence of QUE > CUR > RES and about one polyphenol compound was bound to protein. Thermodynamic parameters revealed that hydrophobic interaction was mainly responsible for complexation between CUR/RES and PPI, while hydrogen bonding for QUE with PPI. All nanoparticles showed particle size of 154-159 nm. Three lipophilic polyphenols were successfully encapsulated into PPI, with loading capacity of RES > QUE > CUR. Complexation of three polyphenols did not change the secondary structure of PPI. Results of FTIR, DSC and XRD confirmed that polyphenols changed from crystalline to amorphous state after combination with PPI. SEM pictures exhibited regular spherical microstructure of nanocomplexes. PPI shielded polyphenols from sensitive environment of ultraviolet light and thermal treatment. ABTS and DPPH radical scavenging activity of polyphenols were considerably improved through complexation with PPI. Molecular docking studies showed binding energy with 11S legumin in sequence of QUE > RES > CUR, and stronger hydrogen bonds were built between QUE and the protein than the other two polyphenols. Data in the present work may provide helpful information for encapsulation of lipophilic polyphenols with pea protein and the potential application in food science, pharmaceutical and cosmetics industries in the future.

Endocytic recycling as cellular trafficking fate of simvastatin-loaded discoidal reconstituted high-density lipoprotein to coordinate cholesterol efflux and drug influx.

Reconstituted high-density lipoproteins (rHDLs) hold promise as nanocarriers for atherosclerosis-targeted delivery, with biofunctions typified by mediating cholesterol efflux. The paradox is how rHDL offloads the delivered drugs into atherosclerotic foam cells, while simultaneously transferring cholesterol out of cells. Herein, simvastatin-loaded discoidal rHDL (ST-d-rHDL), constructed based on established paradigms, was employed to investigate its basic trafficking mechanism in foam cells. As proved, ST-d-rHDL was resecreted via lysosomal and Golgi apparatus-recycling endosome-mediated pathways following clathrin-mediated endocytosis. And the resecretion ratio reached 60% within 6-h chase with excessive ST-d-rHDLs. During the rHDL resecretion, 39% of cellular cholesterol efflux was detected, accompanied by 85% of the encapsulated cargo released intracellularly. Furthermore, the recycling rate was demonstrated to be promoted by smaller rHDL size and higher cellular lipid contents. Collectively, endocytic recycling confers the synergism in ST-d-rHDL to coordinate cholesterol efflux and intracellular drug release, providing new insights into design of biofunctional rHDL.

Dynamically enhancing plaque targeting via a positive feedback loop using multifunctional biomimetic nanoparticles for plaque regression.

A paradigm shift from preventive therapy to aggressive plaque regression and eventual eradication is much needed to address increasing atherosclerotic burden and risks. Herein, we report a biologically inspired dual-targeting multifunctional recombinant high-density lipoprotein (rHDL)-mimicking core-shell nanoplatform. It is composed of an ATP-responsive ternary polyplexes core for SR-A siRNA and catalase complexation, and a phosphatidylserine-modified rHDL-based outer shell for SR-BI and CD36 targeting, in which pitavastatin is packaged. We demonstrated that dual-targeting biomimetic core-shell nanoparticles dynamically enhanced macrophage CD36 targeting in the plaques by establishing a positive feedback loop via the reciprocal regulation of SR-A and CD36. Positive feedback-enabled accumulation of the nanoparticles in the atherosclerotic plaques increased by 3.3-fold following 4-week repeated administration. A 3-month dosage regimen of the dual-targeting rHDL-mimicking nanoparticles reduced plaque areas by 65.8%, and decreased macrophages by 57.3%. Collectively, this work shows that dynamically enhancing plaque targeting via a positive feedback loop and dual action of cholesterol deposition inhibition and efflux enhancement accomplished with our novel multifunctional biomimetic nanoparticles provides a new way to regress plaques and alleviate the atherosclerotic burden.

Rational Design of Lovastatin-Loaded Spherical Reconstituted High Density Lipoprotein for Efficient and Safe Anti-Atherosclerotic Therapy.

Reconstituted high density lipoprotein (rHDL) is a biomimetic nanoparticle with plaque targeting and anti-atherosclerotic efficacy. In this work, we report on a strategy to rational design of lovastatin (LOV)-loaded spherical rHDL (LOV-s-rHDL) for efficient and safe anti-atherosclerotic therapy. Briefly, three LOV-s-rHDLs were formulated with LOV/s-rHDL at ratios of 8:1, 10:1, and 15:1 upon their respective median-effect values ( Dm). The combined inhibitory effect between LOV and s-rHDL of different LOV-s-rHDL formulations on DiI-labeled oxLDL internalization was systemically investigated in RAW 264.7 cells based on the median-effect principle. Median-effect analysis demonstrated that the optimized LOV-s-rHDL was formulated with a ratio of 10:1 ( Dm LOV: Dm s-rHDL), in which LOV and s-rHDL carrier showed the best synergistic effect, presumably ascribed to their inhibitory effect on CD36 and SR-A expression according to the Western blot analysis. In vivo pharmacodynamics studies showed that the optimized LOV-s-rHDL displayed the most pronounced anti-atherosclerotic effect on decreasing plaque area and reducing the MMP level following an 8-week dosing regimen. In vivo atherosclerotic plaque targeting analysis revealed that s-rHDL had potent plaque targeting efficacy, probably owing to the interaction between apoA-I and scavenger receptor B-I. Furthermore, we observed that the optimized LOV-s-rHDL exhibited a favorable safety profile as evidenced by the results of a hemolysis assay, cell cytotoxicity study, and in vivo safety test. Collectively, the rational design of the biomimetic LOV-s-rHDL based on the median-effect analysis provides an efficient strategy to achieve a synergistic and safe anti-atherosclerotic therapy.

ATP-Responsive Low-Molecular-Weight Polyethylenimine-Based Supramolecular Assembly via Host-Guest Interaction for Gene Delivery.

In this work, we report on an ATP-responsive low-molecular-weight polyethylenimine (LMW-PEI)-based supramolecular assembly. It formed via host-guest interaction between PEI (MW = 1.8 kDa)-α-cyclodextrin (α-CD) conjugates and PEI1.8k-phenylboronic acid (PBA) conjugates. The host-guest interaction between PEI1.8k-α-CD and PEI1.8k-PBA was confirmed by the 2D-NOESY chromatogram experiment and competition test. The ATP-responsive property of the supramolecular assembly was evaluated by a series of ATP-triggered degradation and siRNA release studies in terms of fluorescence resonance energy transfer, agarose gel electrophoresis assay, and the time course monitoring of the particle size and morphology. Confocal laser scanning microscopy confirmed the intracellular disassembly of the supramolecular polymer and the release of siRNA. The supramolecular assembly showed high buffering capability and was capable of protecting siRNA from RNase degradation. It had high cytocompatibility according to in vitro cytotoxicity and hemolysis assays. LMW-PEI-based supramolecular assembly facilitated cellular entry of siRNA via energy-dependent endocytosis. Moreover, the assembly/SR-A siRNA polyplexes at N/P ratio of 30 was most effective in knocking down SR-A mRNA and inhibiting uptake of modified LDL. Taken together, this work shows that ATP-responsive LMW-PEI-based supramolecular assembly is a promising gene vector and has potential application in treating atherosclerosis.

Biofunctional Polymer-Lipid Hybrid High-Density Lipoprotein-Mimicking Nanoparticles Loading Anti-miR155 for Combined Antiatherogenic Effects on Macrophages.

A biofunctional polymer-lipid hybrid high-density lipoprotein-mimicking nanoparticle (HNP) loading anti-miR155 was constructed for combined antiatherogenic effects on macrophages. The HNP consisted of an anti-miR155 core condensed by acid-labile polyethylenimine (acid-labile PEI) polymers and a lipid bilayer coat that was decorated with apolipoprotein A-1, termed acid-labile PEI/HNP. The acid-labile PEI was synthesized with low-molecular-weight PEI and glutaraldehyde to reduce the cytotoxicity and facilitate nucleic acids escaping from acidic endolysosomes. The increased silencing efficiency of acid-labile PEI/HNP was ascribed to the clathrin-mediated endocytosis and successful endolysosomal escape. Decreased intracellular reactive oxygen species production and DiI-oxLDL uptake revealed the antioxidant activities of both anti-miR155 and HNP. Cholesterol efflux assay indicated the potential of HNP in reverse cholesterol transport. Collectively, the acid-labile PEI/HNP not only realized the efficacy of anti-miR155 in macrophages but also exerted the antiatherosclerotic biofunction of HNP.