Regulatory Mechanism and Application of lncRNAs in Poultry

Long noncoding RNA (lncRNAs) are transcripts greater than 200 nt in length with decreased coding potential and are widespread in all types of biological organisms. lncRNAs can interact with protein, DNA and RNA, respectively, which may participate in the multilevel regulation of transcriptional, post-transcriptional and epigenetic modifications. It is well known that lncRNA, which length is single-stranded non-coding RNA molecule, plays crucial roles in animal growth, development, cell proliferation and differentiation, and other life activities. In this research, we review the regulation mechanism and current research status of lncRNAs in chicken economic traits and disease, which would contribute to further understanding the regulatory mechanisms and application of lncRNAs in poultry

Myricetin exerts potent anticancer effects on human skin tumor cells

Purpose: To evaluate the anticancer activity of myricetin against skin cancer A431 cell lines.Methods: Cell viability was determined by MTT and colony formation assays. Apoptosis was determined by DAPI and annexin V/PI staining. Cell cycle, ROS and MMP analysis were performed by flow cytometry. Cell migration and invasion were assessed by Boyden Chamber assay, while protein expression was determined using western blotting.Results: Myricetin showed considerable anticancer activity against skin A431 cancer cell lines. However, lower cytotoxic effects were observed in normal FR2 cells. The anticancer activity of myricetin was due to ROS-prompted alterations in mitochondrial membrane potential and initiation of apoptotic cell death. The expressions of Bcl-2 and Bax were altered in response to myricetin treatment. Myricetin also induced cell cycle arrest and suppressed the migration and invasion of A431 cells.Conclusion: These results suggest that myricetin may be an important lead molecule for the development of a suitable treatment of skin cancer.Keywords: Skin carcinoma, ROS, Apoptosis, Myricetin, Cell migratio

A pre-S gene chip to detect pre-S deletions in hepatitis B virus large surface antigen as a predictive marker for hepatoma risk in chronic hepatitis B virus carriers

<p>Abstract</p> <p>Background</p> <p>Chronic hepatitis B virus (HBV) infection is an important cause of hepatocellular carcinoma (HCC) worldwide. The pre-S₁and -S₂mutant large HBV surface antigen (LHBS), in which the pre-S₁and -S₂regions of the LHBS gene are partially deleted, are highly associated with HBV-related HCC.</p> <p>Methods</p> <p>The pre-S region of the LHBS gene in two hundred and one HBV-positive serum samples was PCR-amplified and sequenced. A pre-S oligonucleotide gene chip was developed to efficiently detect pre-S deletions in chronic HBV carriers. Twenty serum samples from chronic HBV carriers were analyzed using the chip.</p> <p>Results</p> <p>The pre-S deletion rates were relatively low (7%) in the sera of patients with acute HBV infection. They gradually increased in periods of persistent HBV infection: pre-S mutation rates were 37% in chronic HBV carriers, and as high as 60% in HCC patients. The Pre-S Gene Chip offers a highly sensitive and specific method for pre-S deletion detection and is less expensive and more efficient (turnaround time 3 days) than DNA sequencing analysis.</p> <p>Conclusion</p> <p>The pre-S_1/2mutants may emerge during the long-term persistence of the HBV genome in carriers and facilitate HCC development. Combined detection of pre-S mutations, other markers of HBV replication, and viral titers, offers a reliable predictive method for HCC risks in chronic HBV carriers.</p

Association Between the Methylation Statuses at CpG Sites in the Promoter Region of the SLCO1B3, RNA Expression and Color Change in Blue Eggshells in Lushi Chickens

The formation mechanism underlying the blue eggshell characteristic has been discovered in birds, and SLCO1B3 is the key gene that regulates the blue eggshell color. Insertion of an endogenous retrovirus, EAV-HP, in the SLCO1B3 5′ flanking region promotes SLCO1B3 expression in the chicken shell gland, and this expression causes bile salts to enter the shell gland, where biliverdin is secreted into the eggshell, forming a blue shell. However, at different laying stages of the same group of chickens, the color of the eggshell can vary widely, and the molecular mechanism underlying the eggshell color change remains unknown. Therefore, to reveal the molecular mechanism of the blue eggshell color variations, we analyzed the change in the eggshell color during the laying period. The results indicated that the eggshell color in Lushi chickens can be divided into three stages: 20–25 weeks for dark blue, 26–45 weeks for medium blue, and 46–60 weeks for light blue. We further investigated the expression and methylation levels of the SLCO1B3 gene at eight different weeks, finding that the relative expression of SLCO1B3 was significantly higher at 25 and 30 weeks than at other laying weeks. Furthermore, the overall methylation rate of the SLCO1B3 gene in Lushi chickens increased gradually with increasing weeks of egg production, as shown by bisulfite sequencing PCR. Pearson correlation analysis showed that methylation of the promoter region of SLCO1B3 was significantly negatively correlated with both SLCO1B3 expression in the shell gland tissue and eggshell color. In addition, we predicted that CpG5 and CpG8 may be key sites for regulating SLCO1B3 gene transcription. Our findings show that as the level of methylation increases, methylation of the CpG5 and CpG8 sites hinders the binding of transcription factors to the promoter, reducing SLCO1B3 expression during the late period and resulting in a lighter eggshell color

Pre-Treatment with Melatonin Enhances Therapeutic Efficacy of Cardiac Progenitor Cells for Myocardial Infarction

Background/Aims: Melatonin possesses many biological activities such as antioxidant and anti-aging. Cardiac progenitor cells (CPCs) have emerged as a promising therapeutic strategy for myocardial infarction (MI). However, the low survival of transplanted CPCs in infarcted myocardium limits the successful use in treating MI. In the present study, we aimed to investigate if melatonin protects against oxidative stress-induced CPCs damage and enhances its therapeutic efficacy for MI. Methods: TUNEL assay and EdU assay were used to detect the effects of melatonin and miR-98 on H2O2-induced apoptosis and proliferation. MI model was used to evaluate the potential cardioprotective effects of melatonin and miR-98. Results: Melatonin attenuated H2O2-induced the proliferation reduction and apoptosis of c-kit+ CPCs in vitro, and CPCs which pretreated with melatonin significantly improved the functions of post-infarct hearts compared with CPCs alone in vivo. Melatonin was capable to inhibit the increase of miR-98 level by H2O2 in CPCs. The proliferation reduction and apoptosis of CPCs induced by H2O2 was aggravated by miR-98. In vivo, transplantation of CPCs with miR-98 silencing caused the more significant improvement of cardiac functions in MI than CPCs. MiR-98 targets at the signal transducer and activator of the transcription 3 (STAT3), and thus aggravated H2O2-induced the reduction of Bcl-2 protein. Conclusions: Pre-treatment with melatonin protects c-kit+ CPCs against oxidative stress-induced damage via downregulation of miR-98 and thereby increasing STAT3, representing a potentially new strategy to improve CPC-based therapy for MI

Induction of Bcl-2 Expression by Hepatitis B Virus Pre-S2 Mutant Large Surface Protein Resistance to 5-Fluorouracil Treatment in Huh-7 Cells

BACKGROUND: Hepatocellular carcinoma (HCC) is one of the most common malignancies worldwide with poor prognosis due to resistance to conventional chemotherapy and limited efficacy of radiotherapy. Our previous studies have indicated that expression of Hepatitis B virus pre-S2 large mutant surface antigen (HBV pre-S2Δ) is associated with a significant risk of developing HCC. However, the relationship between HBV pre-S2Δ protein and the resistance of chemotherapeutic drug treatment is still unclear. METHODOLOGY/PRINCIPAL FINDINGS: Here, we show that the expression of HBV pre-S2Δ mutant surface protein in Huh-7 cell significantly promoted cell growth and colony formation. Furthermore, HBV pre-S2Δ protein increased both mRNA (2.7±0.5-fold vs. vehicle, p=0.05) and protein (3.2±0.3-fold vs. vehicle, p=0.01) levels of Bcl-2 in Huh-7 cells. HBV pre-S2Δ protein also enhances Bcl-2 family, Bcl-xL and Mcl-1, expression in Huh-7 cells. Meanwhile, induction of NF-κB p65, ERK, and Akt phosphorylation, and GRP78 expression, an unfolded protein response chaperone, were observed in HBV pre-S2Δ and HBV pre-S-expressing cells. Induction of Bcl-2 expression by HBV pre-S2Δ protein resulted in resistance to 5-fluorouracil treatment in colony formation, caspase-3 assay, and cell apoptosis, and can enhance cell death by co-incubation with Bcl-2 inhibitor. Similarly, transgenic mice showed higher expression of Bcl-2 in liver tissue expressing HBV pre-S2Δ large surface protein in vivo. CONCLUSION/SIGNIFICANCE: Our result demonstrates that HBV pre-S2Δ increased Bcl-2 expression which plays an important role in resistance to 5-fluorouracil-caused cell death. Therefore, these data provide an important chemotherapeutic strategy in HBV pre-S2Δ-associated tumor

Atmospheric PM2.5 Prediction Using DeepAR Optimized by Sparrow Search Algorithm with Opposition-Based and Fitness-Based Learning

There is an important significance for human health in predicting atmospheric concentration precisely. However, due to the complexity and influence of contingency, atmospheric concentration prediction is a challenging topic. In this paper, we propose a novel hybrid learning method to make point and interval predictions of PM2.5 concentration simultaneously. Firstly, we optimize Sparrow Search Algorithm (SSA) by opposition-based learning, fitness-based learning, and Lévy flight. The experiments show that the improved Sparrow Search Algorithm (FOSSA) outperforms SSA-based algorithms. In addition, the improved Sparrow Search Algorithm (FOSSA) is employed to optimize the initial weights of probabilistic forecasting model with autoregressive recurrent network (DeepAR). Then, the FOSSA–DeepAR learning method is utilized to achieve the point prediction and interval prediction of PM2.5 concentration in Beijing, China. The performance of FOSSA–DeepAR is compared with other hybrid models and a single DeepAR model. Furthermore, hourly data of PM2.5 and O3 concentration in Taian of China, O3 concentration in Beijing, China are used to verify the effectiveness and robustness of the proposed FOSSA–DeepAR learning method. Finally, the empirical results illustrate that the proposed FOSSA–DeepAR learning model can achieve more efficient and accurate predictions in both interval and point prediction

A Contrastive Learning Pre-Training Method for Motif Occupancy Identification

Motif occupancy identification is a binary classification task predicting the binding of DNA motif instances to transcription factors, for which several sequence-based methods have been proposed. However, through direct training, these end-to-end methods are lack of biological interpretability within their sequence representations. In this work, we propose a contrastive learning method to pre-train interpretable and robust DNA encoding for motif occupancy identification. We construct two alternative models to pre-train DNA sequential encoder, respectively: a self-supervised model and a supervised model. We augment the original sequences for contrastive learning with edit operations defined in edit distance. Specifically, we propose a sequence similarity criterion based on the Needleman–Wunsch algorithm to discriminate positive and negative sample pairs in self-supervised learning. Finally, a DNN classifier is fine-tuned along with the pre-trained encoder to predict the results of motif occupancy identification. Both proposed contrastive learning models outperform the baseline end-to-end CNN model and SimCLR method, reaching AUC of 0.811 and 0.823, respectively. Compared with the baseline method, our models show better robustness for small samples. Specifically, the self-supervised model is proved to be practicable in transfer learning