Association of Metabolic and Endocrine Disorders with Bovine Ovarian Follicular Cysts.

After estrus, when mature follicles fail to ovulate, they may further develop to form follicular cysts, affecting the normal function of ovaries, reducing the reproductive efficiency of dairy cows and causing economic losses to cattle farms. However, the key points of ovarian follicular cysts pathogenesis remain largely unclear. The purpose of the current research was to analyze the formation mechanism of ovarian follicular cysts from hormone and gene expression profiles. The concentrations of progesterone (P4), estradiol (E2), insulin, insulin-like growth factor 1 (IGF1), leptin, adrenocorticotropic hormone (ACTH) and ghrelin in follicle fluid from bovine follicular cysts and normal follicles were examined using enzyme-linked immunosorbent assay (ELISA) or 125I-labeled radioimmunoassay (RIA); the corresponding receptors' expression of theca interna cells was tested via quantitative reverse transcription polymerase chain reaction (RT-qPCR), and the mRNA expression profiling was analyzed via RNA sequencing (RNA-seq). The results showed that the follicular cysts were characterized by significant lower E2, insulin, IGF1 and leptin levels but elevated ACTH and ghrelin levels compared with normal follicles (p < 0.05). The mRNA expressions of corresponding receptors, PGR, ESR1, ESR2, IGF1R, LEPR, IGFBP6 and GHSR, were similarly altered significantly (p < 0.05). RNA-seq identified 2514 differential expressed genes between normal follicles and follicular cysts. The Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis linked the ovarian steroidogenesis pathway, especially the STAR, 3ß-HSD, CYP11A1 and CYP17A1 genes, to the formation of follicular cysts (p < 0.01). These results indicated that hormone metabolic disorders and abnormal expression levels of hormone synthesis pathway genes are associated with the formation of bovine ovarian follicular cysts.

Identification of associations between lncRNA and drug resistance based on deep learning and attention mechanism.

Introduction: Abnormal lncRNA expression can lead to the resistance of tumor cells to anticancer drugs, which is a crucial factor leading to high cancer mortality. Studying the relationship between lncRNA and drug resistance becomes necessary. Recently, deep learning has achieved promising results in predicting biomolecular associations. However, to our knowledge, deep learning-based lncRNA-drug resistance associations prediction has yet to be studied. Methods: Here, we proposed a new computational model, DeepLDA, which used deep neural networks and graph attention mechanisms to learn lncRNA and drug embeddings for predicting potential relationships between lncRNAs and drug resistance. DeepLDA first constructed similarity networks for lncRNAs and drugs using known association information. Subsequently, deep graph neural networks were utilized to automatically extract features from multiple attributes of lncRNAs and drugs. These features were fed into graph attention networks to learn lncRNA and drug embeddings. Finally, the embeddings were used to predict potential associations between lncRNAs and drug resistance. Results: Experimental results on the given datasets show that DeepLDA outperforms other machine learning-related prediction methods, and the deep neural network and attention mechanism can improve model performance. Dicsussion: In summary, this study proposes a powerful deep-learning model that can effectively predict lncRNA-drug resistance associations and facilitate the development of lncRNA-targeted drugs. DeepLDA is available at https://github.com/meihonggao/DeepLDA.

GAE-LGA: integration of multi-omics data with graph autoencoders to identify lncRNA-PCG associations.

Long non-coding RNAs (lncRNAs) can disrupt the biological functions of protein-coding genes (PCGs) to cause cancer. However, the relationship between lncRNAs and PCGs remains unclear and difficult to predict. Machine learning has achieved a satisfactory performance in association prediction, but to our knowledge, it is currently less used in lncRNA-PCG association prediction. Therefore, we introduce GAE-LGA, a powerful deep learning model with graph autoencoders as components, to recognize potential lncRNA-PCG associations. GAE-LGA jointly explored lncRNA-PCG learning and cross-omics correlation learning for effective lncRNA-PCG association identification. The functional similarity and multi-omics similarity of lncRNAs and PCGs were accumulated and encoded by graph autoencoders to extract feature representations of lncRNAs and PCGs, which were subsequently used for decoding to obtain candidate lncRNA-PCG pairs. Comprehensive evaluation demonstrated that GAE-LGA can successfully capture lncRNA-PCG associations with strong robustness and outperformed other machine learning-based identification methods. Furthermore, multi-omics features were shown to improve the performance of lncRNA-PCG association identification. In conclusion, GAE-LGA can act as an efficient application for lncRNA-PCG association prediction with the following advantages: It fuses multi-omics information into the similarity network, making the feature representation more accurate; it can predict lncRNA-PCG associations for new lncRNAs and identify potential lncRNA-PCG associations with high accuracy.

Early pregnancy affects expression of Toll-like receptor signaling members in ovine spleen.

Toll-like receptors (TLRs) are involved to the maternal immune tolerance. The spleen is essential for adaptive immune reactions. However, it is unclear that early pregnancy regulates TLR-mediated signalings in the maternal spleen. The purpose of this study was to investigate the effects of early pregnancy on expression of TLR signaling members in the ovine spleen. Ovine spleens were collected at day 16 of the estrous cycle, and at days 13, 16 and 25 of pregnancy (n = 6 for each group). Real-time quantitative PCR, western blot and immunohistochemistry analysis were used to detect TLR signaling members, including TLR2, TLR3, TLR4, TLR5, TLR7, TLR9, myeloid differentiation primary-response protein 88 (MyD88), tumor necrosis factor receptor associated factor 6 (TRAF6) and interleukin-1-receptor-associated kinase 1 (IRAK1). The results showed that expression levels of TLR2, TLR4 and IRAK1 were downregulated, but expression levels of TLR3, TLR5, TLR7, TLR9, TRAF6 and MyD88 were increased during early pregnancy. In addition, MyD88 protein was located in the capsule, trabeculae and splenic cords of the maternal spleen. This paper reports for the first time that early pregnancy has effects on TLR signaling pathways in the ovine spleen, which is beneficial for understanding the maternal immune tolerance during early pregnancy.

Comprehensive analyses of correlation and survival reveal informative lncRNA prognostic signatures in colon cancer.

BACKGROUND: Colon cancer is a commonly worldwide cancer with high morbidity and mortality. Long non-coding RNAs (lncRNAs) are involved in many biological processes and are closely related to the occurrence of colon cancer. Identification of the prognostic signatures of lncRNAs in colon cancer has great significance for its treatment. METHODS: We first identified the colon cancer-related mRNAs and lncRNAs according to the differential analysis methods using the expression data in TCGA. Then, we performed correlation analysis between the identified mRNAs and lncRNAs by integrating their expression values and secondary structure information to estimate the co-regulatory relationships between the cancer-related mRNAs and lncRNAs. Besides, the competing endogenous RNA regulation network based on co-regulatory relationships was constructed to reveal cancer-related regulatory patterns. Meanwhile, we used traditional regression analysis (univariate Cox analysis, random survival forest analysis, and lasso regression analysis) to screen the cancer-related lncRNAs. Finally, by combining the identified colon cancer-related lncRNAs according to the above analyses, we constructed a risk prognosis model for colon cancer through multivariate Cox analysis and also validated the model in the colon cancer dataset in TCGA cohorts. RESULTS: Six lncRNAs were found highly correlated with the overall survival of colon cancer patients, and a risk prognosis model based on them was constructed to predict the overall survival of colon cancer patients. In particular, EVX1-AS, ZNF667-AS1, CTC-428G20.6, and CTC-297N7.9 were first reported to be related to colon cancer by using our model, among which EVX1-AS and ZNF667-AS1 have been predicted to be related to colon cancer in LncRNADisease database. CONCLUSIONS: This study identified the potential regulatory relationships between lncRNAs and mRNAs by integrating their expression values and secondary structure information and presented a significant 6-lncRNA risk prognosis model to predict the overall survival of colon cancer patients.

Toll-like receptor signaling is changed in ovine lymph node during early pregnancy.

Toll-like receptors (TLRs) participate in regulation of adaptive immune responses, and lymph nodes play key roles in the initiation of immune responses. There is a tolerance to the allogenic fetus during pregnancy, but it is unclear that expression of TLR signaling is in ovine lymph node during early pregnancy. In this study, lymph nodes were sampled from day 16 of nonpregnant ewes and days 13, 16, and 25 of pregnant ewes, and the expressions of TLR family (TLR2, TLR3, TLR4, TLR5 and TLR9), adaptor proteins, including myeloid differentiation primary-response protein 88 (MyD88), tumor necrosis factor receptor associated factor 6 (TRAF6), and interleukin-1-receptor-associated kinase 1 (IRAK1), were analyzed through real-time quantitative polymerase chain reaction, Western blot, and immunohistochemistry analysis. The results showed that mRNA and protein levels of TLR2, TLR3, TLR4, TRAF6, and MyD88 were upregulated in the maternal lymph node, but TLR5, TLR9, and IRAK1 were downregulated during early pregnancy. In addition, MyD88 protein was located in the subcapsular sinus and lymph sinuses. Therefore, it is suggested that early pregnancy induces changes in TLR signaling in maternal lymph node, which may be involved in regulation of maternal immune responses in sheep.

ImReLnc: Identifying Immune-Related LncRNA Characteristics in Human Cancers Based on Heuristic Correlation Optimization.

Long non-coding RNAs (lncRNAs) play critical roles in cancer through gene expression and immune regulation. Identifying immune-related lncRNA (irlncRNA) characteristics would contribute to dissecting the mechanism of cancer pathogenesis. Some computational methods have been proposed to identify irlncRNA characteristics in human cancers, but most of them are aimed at identifying irlncRNA characteristics in specific cancer. Here, we proposed a new method, ImReLnc, to recognize irlncRNA characteristics for 33 human cancers and predict the pathogenicity levels of these irlncRNAs across cancer types. We first calculated the heuristic correlation coefficient between lncRNAs and mRNAs for immune-related enrichment analysis. Especially, we analyzed the relationship between lncRNAs and 17 immune-related pathways in 33 cancers to recognize the irlncRNA characteristics of each cancer. Then, we calculated the Pscore of the irlncRNA characteristics to evaluate their pathogenicity levels. The results showed that highly pathogenic irlncRNAs appeared in a higher proportion of known disease databases and had a significant prognostic effect on cancer. In addition, it was found that the expression of irlncRNAs in immune cells was higher than that of non-irlncRNAs, and the proportion of irlncRNAs related to the levels of immune infiltration was much higher than that of non-irlncRNAs. Overall, ImReLnc accurately identified the irlncRNA characteristics in multiple cancers based on the heuristic correlation coefficient. More importantly, ImReLnc effectively evaluated the pathogenicity levels of irlncRNAs across cancer types. ImReLnc is freely available at https://github.com/meihonggao/ImReLnc.

CONDEL: Detecting Copy Number Variation and Genotyping Deletion Zygosity from Single Tumor Samples Using Sequence Data.

Characterizing copy number variations (CNVs) from sequenced genomes is a both feasible and cost-effective way to search for driver genes in cancer diagnosis. A number of existing algorithms for CNV detection only explored part of the features underlying sequence data and copy number structures, resulting in limited performance. Here, we describe CONDEL, a method for detecting CNVs from single tumor samples using high-throughput sequence data. CONDEL utilizes a novel statistic in combination with a peel-off scheme to assess the statistical significance of genome bins, and adopts a Bayesian approach to infer copy number gains, losses, and deletion zygosity based on statistical mixture models. We compare CONDEL to six peer methods on a large number of simulation datasets, showing improved performance in terms of true positive and false positive rates, and further validate CONDEL on three real datasets derived from the 1000 Genomes Project and the EGA archive. CONDEL obtained higher consistent results in comparison with other three single sample-based methods, and exclusively identified a number of CNVs that were previously associated with cancers. We conclude that CONDEL is a powerful tool for detecting copy number variations on single tumor samples even if these are sequenced at low-coverage.

SVSR: A Program to Simulate Structural Variations and Generate Sequencing Reads for Multiple Platforms.

Structural variation accounts for a major fraction of mutations in the human genome and confers susceptibility to complex diseases. Next generation sequencing along with the rapid development of computational methods provides a cost-effective procedure to detect such variations. Simulation of structural variations and sequencing reads with real characteristics is essential for benchmarking the computational methods. Here, we develop a new program, SVSR, to simulate five types of structural variations (indels, tandem duplication, CNVs, inversions, and translocations) and SNPs for the human genome and to generate sequencing reads with features from popular platforms (Illumina, SOLiD, 454, and Ion Torrent). We adopt a selection model trained from real data to predict copy number states, starting from the first site of a particular genome to the end. Furthermore, we utilize references of microbial genomes to produce insertion fragments and design probabilistic models to imitate inversions and translocations. Moreover, we create platform-specific errors and base quality profiles to generate normal, tumor, or normal-tumor mixture reads. Experimental results show that SVSR could capture more features that are realistic and generate datasets with satisfactory quality scores. SVSR is able to evaluate the performance of structural variation detection methods and guide the development of new computational methods.