Construction and experimental validation of mouse PDX model by malignant pleural effusion-derived tumor cells from lung cancer

Objective·To establish a patient-derived tumor xenograft (PDX) model using tumor cells sourced from malignant pleural effusion (MPE) in patients with lung cancer, and to conduct experimental validation.Methods·Gene expression data were downloaded from the Gene Expression Omnibus (GEO), including single-cell RNA sequencing data for lung cancer with MPE (GSE131907) and for solid lung cancer (GSE203360). Data were clustered, and differential gene ontology functional enrichment analysis was performed to ascertain the feasibility of modeling by using MPE. MPE samples from patients with lung cancer were collected and processed through centrifugation and red blood cell lysis to enrich cells. The enriched cells were then implanted subcutaneously into non-obese diabetic/severe combined immunodeficient (NOD/SCID) mice. Tumor growth was monitored, and when tumors reached 1 000 mm³, they were passaged and preserved. Histopathological examination was conducted on stable passaged tumors, the cell morphology was observed via hematoxylin-eosin (H-E) staining and the expression of lung cancer biomarkers was detected by using immunohistochemistry (IHC).Results·Single-cell data analysis revealed stronger proliferative functions of tumor cells in MPE, suggesting that PDX modeling using MPE tumor cells may yield better tumor formation. A total of 35 samples of MPE from lung cancer patients were collected, and 13 PDX models were successfully constructed, with a success rate of 37.14%. Histopathological examination showed significant cellular atypia by H-E staining, and IHC result showed positive expression of lung cancer biomarkers such as cytokeratin 7 (CK7), thyroid transcription factor-1 (TTF1), and Napsin A.Conclusion·By enriching tumor cells from MPE of lung cancer patients, a more convenient, efficient, and dynamically modelable PDX model is successfully constructed. This model retains the malignant characteristics and protein expression features of tumor cells from lung cancer patients, providing an important experimental model tool for basic research and clinical drug guidance for lung cancer patients with MPE

YeastWeb: a workset-centric web resource for gene family analysis in yeast

<p>Abstract</p> <p>Background</p> <p>Currently, a number of yeast genomes with different physiological features have been sequenced and annotated, which provides invaluable information to investigate yeast genetics, evolutionary mechanism, structure and function of gene families.</p> <p>Description</p> <p>YeastWeb is a novel database created to provide access to gene families derived from the available yeast genomes by assigning the genes into putative families. It has many useful features that complement existing databases, such as SGD, CYGD and Génolevures: 1) Detailed computational annotation was conducted with each entry with InterProScan, EMBOSS and functional/pathway databases, such as GO, COG and KEGG; 2) A well established user-friendly environment was created to allow users to retrieve the annotated genes and gene families using functional classification browser, keyword search or similarity-based search; 3) Workset offers users many powerful functions to manage the retrieved data efficiently, associate the individual items easily and save the intermediate results conveniently; 4) A series of comparative genomics and molecular evolution analysis tools are neatly implemented to allow users to view multiple sequence alignments and phylogenetic tree of gene families. At present, YeastWeb holds the gene families clustered from various MCL inflation values from a total of 13 available yeast genomes.</p> <p>Conclusions</p> <p>Given the great interest in yeast research, YeastWeb has the potential to become a useful resource for the scientific community of yeast biologists and related researchers investigating the evolutionary relationship of yeast gene families. YeastWeb is available at <url>http://centre.bioinformatics.zj.cn/Yeast/</url>.</p

SUMOylation of Grb2 enhances the ERK activity by increasing its binding with Sos1

BACKGROUND: Grb2 (Growth factor receptor-bound protein 2) is a key adaptor protein in maintaining the ERK activity via linking Sos1 (Son of sevenless homolog 1) or other proteins to activated RTKs, such as EGFR. Currently, little knowledge is available concerning the post-translational modification (PTM) of Grb2 except for its phosphorylation. Since emerging evidences have highlighted the importance of SUMOylation (Small ubiquitin-related modifier), a reversible PTM, in modulating protein functions, we wondered if Grb2 could be SUMOylated and thereby influences its functions especially involved in the Ras/MEK/ERK pathway. METHODS: SUMOylation of Grb2 was analyzed with the in vivo SUMOylation assay using the Ni(2+)-NTA affinity pulldown and the in vitro E.coli-based SUMOylation assay. To test the ERK activity and cell transformation, the murine fibroblast cell line NIH/3T3 and the murine colon cancer cell line CMT-93 were used for the experiments including Grb2 knockdown, ectopic re-expression, cell transformation and migration. Immunoprecipitation (IP) was employed for seeking proteins that interact with SUMO modified Grb2. Xenograft tumor model in mice was conducted to verify that Grb2 SUMOylation regulated tumorigenesis in vivo. RESULTS: Grb2 can be SUMOylated by SUMO1 at lysine 56 (K(56)), which is located in the linker region between the N-terminal SH3 domain and the SH2 domain. Knockdown of Grb2 reduced the ERK activity and suppressed cell motility and tumorigenesis in vitro and in vivo, which were all rescued by stable ectopic re-expression of wild-type Grb2 but not the mutant Grb2(K56R). Furthermore, Grb2 SUMOylation at K(56) increased the formation of Grb2-Sos1 complex, which sequentially leads to the activation of Ras/MEK/MAPK pathway. CONCLUSIONS: Our results provide evidences that Grb2 is SUMOylated in vivo and this modification enhances ERK activities via increasing the formation of Grb2-Sos1 complex, and may consequently promote cell motility, transformation and tumorigenesis

Lactation Defect in a Widely Used MMTV-Cre Transgenic Line of Mice

MMTV-Cre mouse lines have played important roles in our understanding about the functions of numerous genes in mouse mammary epithelial cells during mammary gland development and tumorigenesis. However, numerous studies have not included MMTV-Cre mice as controls, and many investigators have not indicated which of the different MMTV-Cre founder lines were used in their studies. Here, we describe a lactation defect that severely limits the use of one of the most commonly used MMTV-Cre founder lines.To explore the role of protein tyrosine phosphatase Shp1 in mammary gland development, mice bearing the floxed Shp1 gene were crossed with MMTV-Cre mice and mammary gland development was examined by histological and biochemical techniques, while lactation competency was assessed by monitoring pup growth. Surprisingly, both the Shp1fl/+;MMTV-Cre and MMTV-Cre female mice displayed a severe lactation defect when compared to the Shp1 fl/+ control mice. Histological and biochemical analyses reveal that female mice expressing the MMTV-Cre transgene, either alone or in combination with floxed genes, exhibit defects in lobuloalveolar expansion, presence of large cytoplasmic lipid droplets in luminal alveolar epithelial cells postpartum, and precocious induction of involution. Using a PCR-based genotyping method, the three different founder lines can be distinguished, and we determined that the MMTV-Cre line A, the most widely used MMTV-Cre founder line, exhibits a profound lactation defect that limits its use in studies on mammary gland development.The identification of a lactation defect in the MMTV-Cre line A mice indicates that investigators must use MMTV-Cre alone mice as control in studies that utilize Cre recombinase to excise genes of interest from mammary epithelial cells. Our results also suggest that previous results obtained in studies using the MMTV-Cre line A line should be re-evaluated if the controls did not include mice expressing only Cre recombinase

Effect of Cinnamaldehyde and Citral Combination on Transcriptional Profile, Growth, Oxidative Damage and Patulin Biosynthesis of Penicillium expansum

Penicillium expansum, as a main postharvest pathogen of fruits, can secrete patulin (PAT), causing fruit decay and health problems. In this study, the antifungal test, SEM (scanning electron microscope) observation, transcriptional profile, PAT biosynthesis, and physiological characters of P. expansum exposed to cinnamaldehyde and citral combination (Cin/Cit) were evaluated. Cin/Cit could inhibit the mycelial growth and spore germination of P. expansum in a dose-dependent manner. Besides, Cin/Cit caused spores and mycelia wrinkled and depressed by SEM observation. Gene expression profiles of P. expansum were conducted by RNA sequencing (RNA-seq) in the presence or absence of Cin/Cit treatment. A total of 1713 differentially expressed genes (DEGs) were obtained, including 793 down-regulated and 920 up-regulated genes. Most of the DEGs participated in the biosynthesis of secondary metabolites, amino acid metabolism, and oxidation-reduction process, etc. Cin/Cit induced the dysfunction of the mitochondrial membrane, causing the potential influence on energy metabolism and reactive oxidative species production. The changes of superoxide dismutase (SOD) and catalase (CAT) activities combing with the increase of hydrogen peroxide content indicated the oxidative stress on P. expansum induced by Cin/Cit, which corresponded well with the transcriptional results. Moreover, both the RNA-seq data and the qRT-PCR showed the remarkable down-regulation of genes included in the PAT biosynthetic pathway under the Cin/Cit treatment. These findings provided more useful information about the antifungal mechanism of Cin/Cit against P. expansum at molecular and gene levels and suggested that Cin/Cit is a potential candidate to control P. expansum

Radar Complex Intermediate Frequency Signal Denosing Based on Convolutional Auto-Encoder Network

In radar systems, target state features are commonly extracted from intermediate frequency signals. However, these signals often have a low signal-to-noise ratio due to noisy environments and limitations of the radar hardware. This can lead to a significant loss in performance during target state feature extraction. Therefore, improving the signal-to-noise ratio of intermediate frequency signals is crucial for the effective operation of radar systems. To solve this problem, we developed a deep learning-based method for denoising intermediate frequency signals in this paper. Our approach involves using an auto-encoder network to remove unstructured noise and recover the original signal. During the signal preprocessing stage, it is important to ensure that the phase of the complex signal remains undistorted and that differences in signal amplitudes do not negatively affect the denoising performance. To achieve this, the real and imaginary parts of the complex signal are separated and subjected to 0&#x2013;1 normalization. The loss function of the denoising network is then established based on signal correlation. The numerical results demonstrate that the proposed method outperforms other denoising techniques in terms of mean square error and denoising performance