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Abstract Angiogenesis plays the critical roles in promoting tumor progression, aggressiveness, and metastasis. Although few studies have revealed some angiogenesis-related genes (ARGs) could serve as prognosis-related biomarkers for the prostate cancer (PCa), the integrated role of ARGs has not been systematically studied. The RNA-sequencing data and clinical information of prostate adenocarcinoma (PRAD) were downloaded from The Cancer Genome Atlas (TCGA) as discovery dataset. Twenty-three ARGs in total were identified to be correlated with prognosis of PRAD by the univariate Cox regression analysis, and a 19-ARG signature was further developed with significant correlation with the disease-free survival (DFS) of PRAD by the least absolute shrinkage and selection operator (LASSO) Cox regression with tenfold cross-validation. The signature stratified PRAD patients into high- and low-ARGs signature score groups, and those with high ARGs signature score were associated with significantly poorer outcomes (median DFS: 62.71 months vs unreached, p

Optical filtering is an indispensable part of fluorescence microscopy for selectively highlighting molecules labeled with a specific fluorophore and suppressing background noise. However, the utilization of optical filtering sets increases the complexity, size, and cost of microscopic systems, making them less suitable for multifluorescence channel, high-speed imaging. Here, we present filter-free fluorescence microscopic imaging enabled with deep learning-based digital spectral filtering. This approach allows for automatic fluorescence channel selection after image acquisition and accurate prediction of fluorescence by computing color changes due to spectral shifts with the presence of excitation scattering. Fluorescence prediction for cells and tissues labeled with various fluorophores was demonstrated under different magnification powers. The technique offers accurate identification of labeling with robust sensitivity and specificity, achieving consistent results with the reference standard. Beyond fluorescence microscopy, the deep learning-enabled spectral filtering strategy has the potential to drive the development of other biomedical applications, including cytometry and endoscopy. [ABSTRACT FROM AUTHOR]

Abstract Background RAD51 proteins, which are conserved in all eukaryotes, repair DNA double-strand breaks. This is critical to homologous chromosome pairing and recombination enabling successful reproduction. Work in Arabidopsis suggests that RAD51 also plays a role in plant defense; the Arabidopsis rad51 mutant is more susceptible to Pseudomonas syringae. However, the defense functions of RAD51 and the proteins interacting with RAD51 have not been thoroughly investigated in maize. Uncovering ligands of RAD51 would help to understand meiotic recombination and possibly the role of RAD51 in defense. This study used phage display, a tool for discovery of protein-protein interactions, to search for proteins interacting with maize RAD51A1. Results Maize RAD51A1 was screened against a random phage library. Eleven short peptide sequences were recovered from 15 phages which bound ZmRAD51A1 in vitro; three sequences were found in multiple successfully binding phages. Nine of these phage interactions were verified in vitro through ELISA and/or dot blotting. BLAST searches did not reveal any maize proteins which contained the exact sequence of any of the selected phage peptides, although one of the selected phages had a strong alignment (E-value = 0.079) to a binding domain of maize BRCA2. Therefore, we designed 32 additional short peptides using amino acid sequences found in the predicted maize proteome. These peptides were not contained within phages. Of these synthesized peptides, 14 bound to ZmRAD51A1 in a dot blot experiment. These 14 sequences are found in known maize proteins including transcription factors putatively involved in defense. Conclusions These results reveal several peptides which bind ZmRAD51A1 and support a potential role for ZmRAD51A1 in transcriptional regulation and plant defense. This study also demonstrates the applicability of phage display to basic science questions, such as the search for binding partners of a known protein, and raises the possibility of an iterated approach to test peptide sequences that closely but imperfectly align with the selected phages.

ObjectiveFor respiratory failure patients, mechanical ventilation (MV) is a life-saving therapy to maintain respiratory function. However, MV could also cause damage to pulmonary structures, result in ventilator-induced lung injury (VILI) and eventually progress to mechanical ventilation-induced pulmonary fibrosis (MVPF). Mechanically ventilated patients with MVPF are closely related to increased mortality and poor quality of life in long-term survival. Thus, a thorough understanding of the involved mechanism is necessary.MethodsWe used next-generation sequencing to identify differentially expressed non-coding RNAs (ncRNAs) in BALF EVs which were isolated from Sham and MV mice. Bioinformatics analysis was conducted to identify the engaged ncRNAs and related signaling pathways in the process of MVPF.ResultsWe found 1801 messenger RNAs (mRNA), 53 micro RNAs (miRNA), 273 circular RNAs (circRNA) and 552 long non-coding RNAs (lncRNA) in mice BALF EVs of two groups, which showed significant differential expression. TargetScan predicted that 53 differentially expressed miRNAs targeted 3105 mRNAs. MiRanda revealed that 273 differentially expressed circRNAs were associated with 241 mRNAs while 552 differentially expressed lncRNAs were predicated to target 20528 mRNAs. GO, KEGG pathway analysis and KOG classification showed that these differentially expressed ncRNA-targeted mRNAs were enriched in fibrosis related signaling pathways and biological processes. By taking the intersection of miRNAs target genes, circRNAs target genes and lncRNAs target genes, we found 24 common key genes and 6 downregulated genes were confirmed by qRT-PCR.ConclusionsChanges in BALF-EV ncRNAs may contribute to MVPF. Identification of key target genes involved in the pathogenesis of MVPF could lead to interventions that slow or reverse fibrosis progression.