A protocol to investigate the effects of lncRNAs on in vivo protein-protein interactions using proximity ligation assay.

A large variety of cellular signals are triggered and transmitted by protein-protein interactions (PPIs). Long noncoding RNAs regulate PPIs by enhancing or destabilizing these interactions. Here, we use the proximity ligation assay technique to determine PPIs between p53 and SET regulated by long intergenic noncoding RNA 324 (LINC00324). We detail procedures for establishing LINC00324 knockdown and overexpression U2OS and HepG2 cells followed by in situ PLA protocol. This approach has many potential applications for the study of cellular factors that regulate PPIs. For complete details on the use and execution of this protocol, please refer to Zhang et al. (2023).1.

A p53/LINC00324 positive feedback loop suppresses tumor growth by counteracting SET-mediated transcriptional repression.

The p53 tumor suppressor exerts antitumor functions through its ability to regulate the transcription of its downstream targets. Long noncoding RNAs (lncRNAs) act as oncogenes or tumor suppressors implicated in tumorigenesis and tumor progression. Here, we identify the lncRNA LINC00324 (long intergenic noncoding RNA 00324) as a direct p53 transcriptional target. Knockdown of LINC00324 expression promotes tumor growth by reducing p53 transcriptional activity, whereas ectopic LINC00324 expression demonstrates a reverse effect. Notably, LINC00324 is present in the endogenous p53 complex in tumor cells and directly binds to the C-terminal domain of p53 in vitro. Mechanistically, LINC00324 enables p53 transactivation by competitively disrupting the p53-SET interaction, resulting in an increase of p300/CBP-mediated H3K18 and H3K27 acetylation on the p53 target promoters. Lower LINC00324 expression is associated with more aggressive disease status and predicts worse overall survival of patients with cancer. Our study identifies a p53/LINC00324 positive feedback loop that suppresses tumor growth by counteracting SET-mediated transcriptional repression.

Ultrasound Image Classification of Thyroid Nodules Based on Deep Learning.

A thyroid nodule, which is defined as abnormal growth of thyroid cells, indicates excessive iodine intake, thyroid degeneration, inflammation, and other diseases. Although thyroid nodules are always non-malignant, the malignancy likelihood of a thyroid nodule grows steadily every year. In order to reduce the burden on doctors and avoid unnecessary fine needle aspiration (FNA) and surgical resection, various studies have been done to diagnose thyroid nodules through deep-learning-based image recognition analysis. In this study, to predict the benign and malignant thyroid nodules accurately, a novel deep learning framework is proposed. Five hundred eight ultrasound images were collected from the Third Hospital of Hebei Medical University in China for model training and validation. First, a ResNet18 model, pretrained on ImageNet, was trained by an ultrasound image dataset, and a random sampling of training dataset was applied 10 times to avoid accidental errors. The results show that our model has a good performance, the average area under curve (AUC) of 10 times is 0.997, the average accuracy is 0.984, the average recall is 0.978, the average precision is 0.939, and the average F1 score is 0.957. Second, Gradient-weighted Class Activation Mapping (Grad-CAM) was proposed to highlight sensitive regions in an ultrasound image during the learning process. Grad-CAM is able to extract the sensitive regions and analyze their shape features. Based on the results, there are obvious differences between benign and malignant thyroid nodules; therefore, shape features of the sensitive regions are helpful in diagnosis to a great extent. Overall, the proposed model demonstrated the feasibility of employing deep learning and ultrasound images to estimate benign and malignant thyroid nodules.

Evaluating the Microsatellite Instability of Colorectal Cancer Based on Multimodal Deep Learning Integrating Histopathological and Molecular Data.

Microsatellite instability (MSI), an important biomarker for immunotherapy and the diagnosis of Lynch syndrome, refers to the change of microsatellite (MS) sequence length caused by insertion or deletion during DNA replication. However, traditional wet-lab experiment-based MSI detection is time-consuming and relies on experimental conditions. In addition, a comprehensive study on the associations between MSI status and various molecules like mRNA and miRNA has not been performed. In this study, we first studied the association between MSI status and several molecules including mRNA, miRNA, lncRNA, DNA methylation, and copy number variation (CNV) using colorectal cancer data from The Cancer Genome Atlas (TCGA). Then, we developed a novel deep learning framework to predict MSI status based solely on hematoxylin and eosin (H&E) staining images, and combined the H&E image with the above-mentioned molecules by multimodal compact bilinear pooling. Our results showed that there were significant differences in mRNA, miRNA, and lncRNA between the high microsatellite instability (MSI-H) patient group and the low microsatellite instability or microsatellite stability (MSI-L/MSS) patient group. By using the H&E image alone, one can predict MSI status with an acceptable prediction area under the curve (AUC) of 0.809 in 5-fold cross-validation. The fusion models integrating H&E image with a single type of molecule have higher prediction accuracies than that using H&E image alone, with the highest AUC of 0.952 achieved when combining H&E image with DNA methylation data. However, prediction accuracy will decrease when combining H&E image with all types of molecular data. In conclusion, combining H&E image with deep learning can predict the MSI status of colorectal cancer, the accuracy of which can further be improved by integrating appropriate molecular data. This study may have clinical significance in practice.

Dopaminergic neurons project to the nucleus accumbens regulates anxiety-like behaviors through dopamine D1 signaling.

Dopamine (DA) transmission is important in the regulation of mood and anxiety behaviors. However, how specific dopaminergic signaling pathways respond to anxiogenic stimuli as well as regulate behaviors remains unknown. To understand how DA regulates the animal behaviors under anxiety we performed retrograde labeling and c-Fos staining of midbrain DA neurons. Our c-Fos labeling results showed that DA neurons projected to nucleus accumbens (NAc) are activated in animals treated with the elevated plus-maze (EPM). Real-time measurement of DA release using fast scanning cyclic voltammetry (FSCV) in NAc of freely behaving mice showed that increased DA release and more DA transients in the close arms than the open arms in the EPM. Meanwhile, we also observed a reduction of DA level from the close arms to the open arms. Local infusion of DA D1 receptor antagonist, SCH23390 in the core of NAc, leads to an anxiolytic-like effect in the open-field and EPM. These anxiolytic effects were not observed in animals received D2 receptor antagonist sulpiride infusion in the core of NAc. Taken together, our results reveal a novel function of the mesolimbic DA pathway through the D1 receptor in the regulation of anxiety-like behaviors.

A bispecific enediyne-energized fusion protein targeting both epidermal growth factor receptor and insulin-like growth factor 1 receptor showing enhanced antitumor efficacy against non-small cell lung cancer.

Epidermal growth factor receptor (EGFR) and insulin-like growth factor 1 receptor (IGF-1R) both overexpressed on non-small cell lung cancer (NSCLC) and are known cooperatively to promote tumor progression and drug resistance. This study was to construct a novel bispecific fusion protein EGF-IGF-LDP-AE consisting of EGFR and IGF-IR specific ligands (EGF and IGF-1) and lidamycin, an enediyne antibiotic with potent antitumor activity, and investigate its antitumor efficacy against NSCLC. Binding and internalization assays showed that EGF-IGF-LDP protein could bind to NSCLC cells with high affinity and then internalized into cells with higher efficiency than that of monospecific proteins. In vitro, the enediyne-energized analogue of bispecific fusion protein (EGF-IGF-LDP-AE) displayed extremely potent cytotoxicity to NSCLC cell lines with IC50<10-11 mol/L. Moreover, the bispecific protein EGF-IGF-LDP-AE was more cytotoxic than monospecific proteins (EGF-LDP-AE and LDP-IGF-AE) and lidamycin. In vivo, EGF-IGF-LDP-AE markedly inhibited the growth of A549 xenografts, and the efficacy was more potent than that of lidamycin and monospecific counterparts. EGF-IGF-LDP-AE caused significant cell cycle arrest and it also induced cell apoptosis in a dosage-dependent manner. Pretreatment with EGF-IGF-LDP-AE inhibited EGF-, IGF-stimulated EGFR and IGF-1R phosphorylation, and blocked two main downstream signaling molecules AKT and ERK activation. These data suggested that EGF-LDP-IGF-AE protein would be a promising targeted agent for NSCLC patients with EGFR and/or IGF-1R overexpression.

[Antibacterial activity of recombinant thanatin expressed by E.coli ER2566].

OBJECTIVE: To express antibacterial peptide thanatin in the prokaryotic expression system and test its antibacterial activity. METHODS: The DNA sequence coding for the 21 peptides of thanatin was synthesized using the preferred genetic codes of E. coli, cloned into pTYB11 plasmid, and transformed into E.coli ER2566. The expression of thanatin fused with intein was induced by IPTG in E.coli, and intein-thanatin specifically bound to the column through intein tag was cleaved overnight at 4 degrees celsius; in DTT/cysteine buffer. RESULTS: The cleaved thanatin was eluted with a protein concentration of 245 microg/ml in the first 4 ml. The purified thanatin had showed strong antibacterial activities against G- bacteria such as Shigella flexneri, Klebsiella pneumoniae, Shigella snnei, Escherichia coli O157, toxin producing Escherichia coli, Pseudomonas aeruginosa, and fungi such as Candida albicans, with especial potency in killing drug-resistant Klebsiella pneumoniae, Pseudomonas aeruginosa, and extended-spectrum beta-lactamases (ESBL)-producing E.coli. Eighty strains of drug-resistant (ESBL-producing) and 30 strains of sensitive E. coli were used for anti-bacterial assay, and no significant differences in the antibacterial activity of thanatin were found between the sensitive and drug-resistant E. coli (P>0.05). CONCLUSION: The recombinant thanatin obtained shows strong antibacterial activity against drug-resistant and sensitive bacteria, and can be a potential substitute for routine antibiotics in the treatment of G- bacterial infections.