Persistent transcriptional changes in cardiac adaptive immune cells following myocardial infarction: New evidence from the re-analysis of publicly available single cell and nuclei RNA-sequencing data sets.

INTRODUCTION: Chronic immunopathology contributes to the development of heart failure after a myocardial infarction. Both T and B cells of the adaptive immune system are present in the myocardium and have been suggested to be involved in post-MI immunopathology. METHODS: We analyzed the B and T cell populations isolated from previously published single cell RNA-sequencing data sets (PMID: 32130914, PMID: 35948637, PMID: 32971526 and PMID: 35926050), of the mouse and human heart, using differential expression analysis, functional enrichment analysis, gene regulatory inferences, and integration with autoimmune and cardiovascular GWAS. RESULTS: Already at baseline, mature effector B and T cells are present in the human and mouse heart, having increased activity in transcription factors maintaining tolerance (e.g. DEAF1, JDP2, SPI-B). Following MI, T cells upregulate pro-inflammatory transcript levels (e.g. Cd11, Gzmk, Prf1), while B cells upregulate activation markers (e.g. Il6, Il1rn, Ccl6) and collagen (e.g. Col5a2, Col4a1, Col1a2). Importantly, pro-inflammatory and fibrotic transcription factors (e.g. NFKB1, CREM, REL) remain active in T cells, while B cells maintain elevated activity in transcription factors related to immunoglobulin production (e.g. ERG, REL) in both mouse and human post-MI hearts. Notably, genes differentially expressed in post-MI T and B cells are associated with cardiovascular and autoimmune disease. CONCLUSION: These findings highlight the varied and time-dependent dynamic roles of post-MI T and B cells. They appear ready-to-go and are activated immediately after MI, thus participate in the acute wound healing response. However, they subsequently remain in a state of pro-inflammatory activation contributing to persistent immunopathology.

3D Object Detection under Urban Road Traffic Scenarios Based on Dual-Layer Voxel Features Fusion Augmentation.

To enhance the accuracy of detecting objects in front of intelligent vehicles in urban road scenarios, this paper proposes a dual-layer voxel feature fusion augmentation network (DL-VFFA). It aims to address the issue of objects misrecognition caused by local occlusion or limited field of view for targets. The network employs a point cloud voxelization architecture, utilizing the Mahalanobis distance to associate similar point clouds within neighborhood voxel units. It integrates local and global information through weight sharing to extract boundary point information within each voxel unit. The relative position encoding of voxel features is computed using an improved attention Gaussian deviation matrix in point cloud space to focus on the relative positions of different voxel sequences within channels. During the fusion of point cloud and image features, learnable weight parameters are designed to decouple fine-grained regions, enabling two-layer feature fusion from voxel to voxel and from point cloud to image. Extensive experiments on the KITTI dataset demonstrate the significant performance of DL-VFFA. Compared to the baseline network Second, DL-VFFA performs better in medium- and high-difficulty scenarios. Furthermore, compared to the voxel fusion module in MVX-Net, the voxel feature fusion results in this paper are more accurate, effectively capturing fine-grained object features post-voxelization. Through ablative experiments, we conducted in-depth analyses of the three voxel fusion modules in DL-VFFA to enhance the performance of the baseline detector and achieved superior results.

Mitochondrial abnormalities: a hub in metabolic syndrome-related cardiac dysfunction caused by oxidative stress.

Metabolic syndrome (MetS) refers to a group of cardiovascular risk elements comprising insulin resistance, obesity, dyslipidemia, increased glucose intolerance, and increased blood pressure. Individually, all the MetS components can lead to cardiac dysfunction, while their combination generates additional risks of morbidity and mortality. Growing evidence suggests that oxidative stress, a dominant event in cellular damage and impairment, plays an indispensable role in cardiac dysfunction in MetS. Oxidative stress can not only disrupt mitochondrial activity through inducing oxidative damage to mitochondrial DNA, RNA, lipids, and proteins but can also impair cardiomyocyte contractile function via mitochondria-related oxidative modifications of proteins central to excitation-contraction coupling. Furthermore, excessive reactive oxygen species (ROS) generation can lead to the activation of several mitochondria apoptotic signaling pathways, release of cytochrome c, and eventual induction of myocardial apoptosis. This review will focus on such processes of mitochondrial abnormalities in oxidative stress induced cardiac dysfunction in MetS.

Eukaryotic elongation factor-2 kinase regulates the cross-talk between autophagy and pyroptosis in doxorubicin-treated human melanoma cells in vitro.

Eukaryotic elongation factor-2 kinase (eEF-2K), a negative regulator of protein synthesis, has been shown to play an important role in modulating autophagy and apoptosis in tumor cells under various stresses. In this study, we investigated the regulatory role of eEF-2K in pyroptosis (a new form of programmed necrosis) in doxorubicin-treated human melanoma cells. We found that doxorubicin (0.5-5 µmol/L) induced pyroptosis in melanoma cell lines SK-MEL-5, SK-MEL-28, and A-375 with high expression of DFNA5, but not in human breast cancer cell line MCF-7 with little expression of DFNA5. On the other hand, doxorubicin treatment activated autophagy in the melanoma cells; inhibition of autophagy by transfecting the cells with siRNA targeting Beclin1 or by pretreatment with chloroquine (20 µmol/L) significantly augmented pyroptosis, thus sensitizing the melanoma cells to doxorubicin. We further demonstrated that doxorubicin treatment activated eEF-2K in the melanoma cells, and silencing of eEF-2K blunted autophagic responses, but promoted doxorubicin-induced pyroptotic cell death. Taken together, the above results demonstrate that eEF-2K dictates the cross-talk between pyroptosis and autophagy in doxorubicin-treated human melanoma cells; suppression of eEF-2K results in inhibiting autophagy and augmenting pyroptosis, thus modulating the sensitivity of melanoma cells to doxorubicin, suggesting that targeting eEF-2K may reinforce the antitumor efficacy of doxorubicin, offering a new insight into tumor chemotherapy.

Targeting RNA-binding motif protein 39 for arginine reduction: unveiling metabolic vulnerability in arginine-dependent liver cancer.

Cancer is increasingly acknowledged as a metabolic disease, characterized by metabolic reprogramming as its hallmark. However, the precise mechanisms behind this phenomenon and the factors contributing to tumorigenicity are still poorly understood. In a recent publication in Cell, Mossmann and colleague reported a study unveiling arginine as a molecule with second messenger-like properties that reshapes metabolism to facilitate the tumor development in hepatocellular carcinoma (HCC). Their research revealed that the RNA-binding motif protein 39 (RBM39)-mediated increase in asparagine synthesis results in increased arginine uptake. This establishes a positive feedback loop that sustains elevated levels of arginine and facilitates oncogenic metabolic reprogramming. Additionally, Mossmann et al. demonstrated that depleting RBM39 with indisulam effectively disrupts the proto-oncogenic metabolic reprogramming in HCC. This discovery presents a novel treatment strategy for arginine-dependent liver cancers.

Predicting Cd accumulation in rice and identifying nonlinear effects of soil nutrient elements based on machine learning methods.

The spatial mismatch of Cd content in soil and rice causes difficulties in environmental management for paddy soil. To investigate the influence of soil environment on the accumulation of Cd in rice grain, we conducted a paired field sampling in the middle of the Xiangjiang River basin, examining the relationships between soil properties, soil nutrient elements, Cd content, plant uptake factor (PUFCd), and translocation factors in different rice organs (root, shoot, and grain). The total soil Cd (CdT) and available Cd (CdA) contents and PUFCd showed large spatial variability with ranges of 0.31-6.19 mg/kg, 0.03-3.07 mg/kg, and 0.02-3.51, respectively. Soil pH, CdT, CdA, and the contents of soil nutrient elements (Mg, Mn, Ca, P, Si, and B) were linearly correlated with grain Cd content (Cdg) and PUFCd. The decision tree analysis identified nonlinear effects of Si, Zn and Fe on rice Cd accumulation, which suggested that low Si and high Zn led to high Cdg, and low Si and Fe caused high PUFCd. Using the soil nutrient elements as predictor variables, random forest models successfully predicted the Cdg and PUFCd and performed better than multiple linear regressions. It suggested the impacts of soil nutrient elements on rice Cd accumulation should receive more attention.

Zero and Few Shot Learning With Semantic Feature Synthesis and Competitive Learning.

Zero-shot learning (ZSL) is made possible by learning a projection function between a feature space and a semantic space (e.g., an attribute space). Key to ZSL is thus to learn a projection that is robust against the often large domain gap between the seen and unseen class domains. In this work, this is achieved by unseen class data synthesis and robust projection function learning. Specifically, a novel semantic data synthesis strategy is proposed, by which semantic class prototypes (e.g., attribute vectors) are used to simply perturb seen class data for generating unseen class ones. As in any data synthesis/hallucination approach, there are ambiguities and uncertainties on how well the synthesised data can capture the targeted unseen class data distribution. To cope with this, the second contribution of this work is a novel projection learning model termed competitive bidirectional projection learning (BPL) designed to best utilise the ambiguous synthesised data. Specifically, we assume that each synthesised data point can belong to any unseen class; and the most likely two class candidates are exploited to learn a robust projection function in a competitive fashion. As a third contribution, we show that the proposed ZSL model can be easily extended to few-shot learning (FSL) by again exploiting semantic (class prototype guided) feature synthesis and competitive BPL. Extensive experiments show that our model achieves the state-of-the-art results on both problems.

PKM2-c-Myc-Survivin Cascade Regulates the Cell Proliferation, Migration, and Tamoxifen Resistance in Breast Cancer.

The M2 isoform of pyruvate kinase (PKM2), as a key glycolytic enzyme, plays important roles in tumorigenesis and chemotherapeutic drug resistance. However, the intricate mechanism of PKM2 as a protein kinase regulating breast cancer progression and tamoxifen resistance needs to be further clarified. Here, we reported that PKM2 controls the expression of survivin by phosphorylating c-Myc at Ser-62. Functionally, PKM2 knockdown suppressed breast cancer cell proliferation and migration, which could be rescued by overexpression of survivin. Interestingly, we found that the level of PKM2 expression was upregulated in the tamoxifen resistant breast cancer cells MCF-7/TAMR, and knockdown of PKM2 sensitized the cells to 4-hydroxytamoxifen (4OH-T). In addition, the elevated level of PKM2 correlates with poor relapse-free survival in breast cancer patients treated with tamoxifen. Overall, our findings demonstrated that PKM2-c-Myc-survivin cascade regulated the proliferation, migration and tamoxifen resistance of breast cancer cells, suggesting that PKM2 represents a novel prognostic marker and an attractive target for breast cancer therapeutics, and that PKM2 inhibitor combined with tamoxifen may be a promising strategy to reverse tamoxifen resistance in breast cancer patients.

RSK2 protects human breast cancer cells under endoplasmic reticulum stress through activating AMPKα2-mediated autophagy.

Autophagy can protect stressed cancer cell by degradation of damaged proteins and organelles. However, the regulatory mechanisms behind this cellular process remain incompletely understood. Here, we demonstrate that RSK2 (p90 ribosomal S6 kinase 2) plays a critical role in ER stress-induced autophagy in breast cancer cells. We demonstrated that the promotive effect of RSK2 on autophagy resulted from directly binding of AMPKα2 in nucleus and phosphorylating it at Thr172 residue. IRE1α, an ER membrane-associated protein mediating unfolded protein response (UPR), is required for transducing the signal for activation of ERK1/2-RSK2 under ER stress. Suppression of autophagy by knockdown of RSK2 enhanced the sensitivity of breast cancer cells to ER stress both in vitro and in vivo. Furthermore, we demonstrated that inhibition of RSK2-mediated autophagy rendered breast cancer cells more sensitive to paclitaxel, a chemotherapeutic agent that induces ER stress-mediated cell death. This study identifies RSK2 as a novel controller of autophagy in tumor cells and suggests that targeting RSK2 can be exploited as an approach to reinforce the efficacy of ER stress-inducing agents against cancer.

Large-Scale Sparse Learning From Noisy Tags for Semantic Segmentation.

In this paper, we present a large-scale sparse learning (LSSL) approach to solve the challenging task of semantic segmentation of images with noisy tags. Different from the traditional strongly supervised methods that exploit pixel-level labels for semantic segmentation, we make use of much weaker supervision (i.e., noisy tags of images) and then formulate the task of semantic segmentation as a weakly supervised learning (WSL) problem from the view point of noise reduction of superpixel labels. By learning the data manifolds, we transform the WSL problem into an LSSL problem. Based on nonlinear approximation and dimension reduction techniques, a linear-time-complexity algorithm is developed to solve the LSSL problem efficiently. We further extend the LSSL approach to visual feature refinement for semantic segmentation. The experiments demonstrate that the proposed LSSL approach can achieve promising results in semantic segmentation of images with noisy tags.