Single-cell histone chaperones patterns guide intercellular communication of tumor microenvironment that contribute to breast cancer metastases.

BACKGROUND: Histone chaperones (HCs) are crucial for governing genome stability and gene expression in multiple cancers. However, the functioning of HCs in the tumor microenvironment (TME) is still not clearly understood. METHODS: Self-tested single-cell RNA-seq data derived from 6 breast cancer (BC) patients with brain and liver metastases were reanalyzed by nonnegative matrix factorization (NMF) algorithm for 36 HCs. TME subclusters were observed with BC and immunotherapy public cohorts to assess their prognosis and immune response. The biological effect of HSPA8, one of the HCs, was verified by transwell assay and wound-healing assays. RESULTS: Cells including fibroblasts, macrophages, B cells, and T cells, were classified into various subclusters based on marker genes. Additionally, it showed that HCs might be strongly associated with biological and clinical features of BC metastases, along with the pseudotime trajectory of each TME cell type. Besides, the results of bulk-seq analysis revealed that TME cell subclusters mediated by HCs distinguished significant prognostic value for BC patients and were relevant to patients' immunotherapy responses, especially for B cells and macrophages. In particular, CellChat analysis exhibited that HCs-related TME cell subclusters revealed extensive and diverse interactions with malignant cells. Finally, transwell and wound-healing assays exhibited that HSPA8 deficiency inhibited BC cell migration and invasion. CONCLUSIONS: Collectively, our study first dissected HCs-guided intercellular communication of TME that contribute to BC metastases.

Blocking salt-inducible kinases with YKL-06-061 prevents PTZ-induced seizures in mice.

INTRODUCTION: Epilepsy is one of the most common neurological diseases, while over one third of adults with epilepsy still have inadequate seizure control. Although mutations in salt-inducible kinases (SIKs) have been identified in epileptic encephalopathy, it is not known whether blocking SIKs can prevent pentylenetetrazole (PTZ)-induced seizures. METHODS: We first determined the time course of SIKs (including SIK 1, 2, and 3) in the hippocampus of PTZ treated mice. And then, we evaluated the effects of anti-epilepsy drug valproate acid (VPA) on the expression of SIK 1, 2, and 3 in the hippocampus of PTZ treated mice. Next, we investigated the effect of different dose of SIKs inhibitor YKL-06-061 on the epileptic seizures and neuronal activation by determining the expression of immediate early genes (IEGs) in the PTZ treated mice. RESULTS: We found that PTZ selectively induced enhanced expression of SIK1 in the hippocampus, which was blocked by VPA treatment. Notably, YKL-06-061 decreased seizure activity and prevented neuronal overactivity, as indicated by the reduced expression of IEGs in the hippocampus and prefrontal cortex. CONCLUSION: Our findings provide the first evidence that SIK1 affects gene regulation in neuronal hyperactivity, which is involved in seizure behavior. Targeting SIK1 through the development of selective inhibitors may lead to disease-modifying therapies that reduce epilepsy progression.

Multi-omics analyses unravel DNA damage repair-related clusters in breast cancer with experimental validation.

Background: As one of the most common malignancies worldwide, breast cancer (BC) exhibits high heterogeneity of molecular phenotypes. The evolving view regarding DNA damage repair (DDR) is that it is context-specific and heterogeneous, but its role in BC remains unclear. Methods: Multi-dimensional data of transcriptomics, genomics, and single-cell transcriptome profiling were obtained to characterize the DDR-related features of BC. We collected 276 DDR-related genes based on the Molecular Signature Database (MSigDB) database and previous studies. We acquired public datasets included the SCAN-B dataset (GEO: GSE96058), METABRIC database, and TCGA-BRCA database. Corresponding repositories such as transcriptomics, genomics, and clinical information were also downloaded. We selected scRNA-seq data from GEO: GSE176078, GSE114727, GSE161529, and GSE158724. Bulk RNA-seq data from GEO: GSE176078, GSE18728, GSE5462, GSE20181, and GSE130788 were extracted for independent analyses. Results: The DDR classification was constructed in the SCAN-B dataset (GEO: GSE96058) and METABRIC database, Among BC patients, there were two clusters with distinct clinical and molecular characteristics: the DDR-suppressed cluster and the DDR-active cluster. A superior survival rate is found for tumors in the DDR-suppressed cluster, while those with the DDR-activated cluster tend to have inferior prognoses and clinically aggressive behavior. The DDR classification was validated in the TCGA-BRCA cohort and shown similar results. We also found that two clusters have different pathway activities at the genomic level. Based on the intersection of the different expressed genes among these cohorts, we found that PRAME might play a vital role in DDR. The DDR classification was then enabled by establishing a DDR score, which was verified through multilayer cohort analysis. Furthermore, our results revealed that malignant cells contributed more to the DDR score at the single-cell level than nonmalignant cells. Particularly, immune cells with immunosuppressive properties (such as FOXP3+ CD4+ T cells) displayed higher DDR scores among those with distinguishable characteristics. Conclusion: Collectively, this study performs general analyses of DDR heterogeneity in BC and provides insight into the understanding of individualized molecular and clinicopathological mechanisms underlying unique DDR profiles.

Online identification and classification of Gannan navel oranges with Cu contamination by LIBS with IGA-optimized SVM.

Elements such as minerals and heavy metals play important roles in the nutrition and safety of agricultural products. It is necessary to develop rapid, online, real-time and in situ methods for monitoring the substances in farm products. Gannan navel oranges are a unique variety of fruit, which may be affected by Cu pollution due to abundant copper mines and other factors. An online identification and classification system based on laser-induced breakdown spectroscopy (LIBS) was developed to detect possible Cu residue in Gannan navel oranges. First, transmission and classification equipment for Gannan navel oranges was built. Second, an LIBS detection module was designed. Finally, a software system for the whole online detection platform was developed based on the C# programming language. The series of operations for the online detection system, which includes the loading, transmission, detection and classification of orange samples, can be controlled. Since the navel orange has an elliptical shape, the LIBS detection module was designed with a long focal length to reduce the influence of fruit plane size fluctuation. The long focal length was optimized to 698 mm, and the depth of field was ±6 mm. Furthermore, a parameter optimization model using a support vector machine (SVM) based on an improved genetic algorithm (IGA) is proposed to improve the classification effect of Gannan navel oranges. This model avoids the over-learning or under-learning caused by improper parameter selection in the regression prediction of SVM. The IGA is used to optimize the penalty parameter c and the kernel parameter g of SVM. LIBS spectral data from two types of navel orange samples with and without Cu contamination were selected as test datasets, and the classification results were compared with those of the standard genetic algorithm-support vector machine (GA-SVM). The investigation showed that the IGA-SVM can provide better classification of navel oranges based on analysis of the LIBS spectral data, and the classification accuracy can reach 98%, which provides significant guidance for the use of LIBS to quickly realize online screening of heavy metals in agriculture products.

circFBXL5 promotes breast cancer progression by sponging miR-660.

Increasing studies have revealed that circular RNAs (circRNAs) play important roles in cancer progression. However, the potential involvement of circRNAs in breast cancer metastasis to lung is not clear so far. In this study, we conducted circular RNA microarrays of primary breast cancer tissues and lung metastatic tissues. The results revealed that circFBXL5 (hsa_circ_0125597) up-regulated the most in lung metastatic tissues. Survival analysis revealed that high levels of circFBXL5 correlated with worse outcome of breast cancer. Further experiments showed that knockdown of circFBXL5 inhibited breast cancer cell proliferation and migration to lung. Mechanism study showed that circFBXL5 acted as a sponge for miR-660 and compete binding to miR-660 with SRSF6, leading to increased expression of SRSF6. Collectively, our study highlighted the regulatory function of the circFBXL5/miR-660/SRSF6 pathway in breast cancer progression, which could be potential therapeutic targets for breast cancer.

Rapid identification of rice species by laser-induced breakdown spectroscopy combined with pattern recognition.

Laser-induced breakdown spectroscopy (LIBS) combined with pattern recognition was proposed to discriminate rice species. LIBS spectra in the range of 210-480 nm wavelength from 11 different rice species were collected and preprocessed. Principal component analysis was applied to extract the characteristic variables from LIBS spectral data. Three pattern recognition methods, discriminant analysis, radial basis function neural network, and multi-layer perceptron neural network (MLP) were performed to compare the precision in identifying rice species. The results showed that the performance of the MLP model was better. The average identification rate of rice species reached 100% and 97.9% in the training and test sets, respectively, with MLP. The highest and lowest percentages for correct identification were 100% for early indica rice, Huai rice 5, Yan japonica 6, Lian japonica 8, Xuhan 1, Lvhan 1, Sheng rice 16, Yang japonica 687, and Fenghan 30, and 77.8% for Wuyu japonica rice in test sets. The overall results demonstrate that LIBS combined with MLP could be utilized to rapidly discriminate rice species.