Identification and evolutionary analysis of long non-coding RNAs in zebra finch.

BACKGROUND: Long non-coding RNAs (lncRNAs) are important in various biological processes, but very few studies on lncRNA have been conducted in birds. To identify IncRNAs expressed during feather development, we analyzed single-stranded RNA-seq (ssRNA-seq) data from the anterior and posterior dorsal regions during zebra finch (Taeniopygia guttata) embryonic development. Using published transcriptomic data, we further analyzed the evolutionary conservation of IncRNAs in birds and amniotes. RESULTS: A total of 1,081 lncRNAs, including 965 intergenic lncRNAs (lincRNAs), 59 intronic lncRNAs, and 57 antisense lncRNAs (lncNATs), were identified using our newly developed pipeline. These avian IncRNAs share similar characteristics with lncRNAs in mammals, such as shorter transcript length, lower exon number, lower average expression level and less sequence conservation than mRNAs. However, the proportion of lncRNAs overlapping with transposable elements in birds is much lower than that in mammals. We predicted the functions of IncRNAs based on the enriched functions of co-expressed protein-coding genes. Clusters of lncRNAs associated with natal down development were identified. The sequences and expression levels of candidate lncRNAs that shared conserved sequences among birds were validated by qPCR in both zebra finch and chicken. Finally, we identified three highly conserved lncRNAs that may be associated with natal down development. CONCLUSIONS: Our study provides the first systematical identification of avian lncRNAs using ssRNA-seq analysis and offers a resource of embryonically expressed lncRNAs in zebra finch. We also predicted the biological function of identified lncRNAs.

Regulatory Differences in Natal Down Development between Altricial Zebra Finch and Precocial Chicken.

Birds can be classified into altricial and precocial. The hatchlings of altricial birds are almost naked, whereas those of precocial birds are covered with natal down. This regulatory divergence is thought to reflect environmental adaptation, but the molecular basis of the divergence is unclear. To address this issue, we chose the altricial zebra finch and the precocial chicken as the model animals. We noted that zebra finch hatchlings show natal down growth suppressed anterior dorsal (AD) skin but partially down-covered posterior dorsal (PD) skin. Comparing the transcriptomes of AD and PD skins, we found that the feather growth promoter SHH (sonic hedgehog) was expressed higher in PD skin than in AD skin. Moreover, the data suggested that the FGF (fibroblast growth factor)/Mitogen-activated protein kinase (MAPK) signaling pathway is involved in natal down growth suppression and that FGF16 is a candidate upstream signaling suppressor. Ectopic expression of FGF16 on chicken leg skin showed downregulation of SHH, upregulation of the feather growth suppressor FGF10, and suppression of feather bud elongation, similar to the phenotype found in zebra finch embryonic AD skin. Therefore, we propose that FGF16-related signals suppress natal down elongation and cause the naked AD skin in zebra finch. Our study provides insights into the regulatory divergence in natal down formation between precocial and altricial birds.

Anatomical and transcriptional dynamics of maize embryonic leaves during seed germination.

Our anatomical analysis revealed that a dry maize seed contains four to five embryonic leaves at different developmental stages. Rudimentary kranz structure (KS) is apparent in the first leaf with a substantial density, but its density decreases toward younger leaves. Upon imbibition, leaf expansion occurs rapidly with new KSs initiated from the palisade-like ground meristem cells in the middle of the leaf. In parallel to the anatomical analysis, we obtained the time course transcriptomes for the embryonic leaves in dry and imbibed seeds every 6 h up to hour 72. Over this time course, the embryonic leaves exhibit transcripts of 30,255 genes at a level that can be regarded as "expressed." In dry seeds, ∼25,500 genes are expressed, showing functional enrichment in transcription, RNA processing, protein synthesis, primary metabolic pathways, and calcium transport. During the 72-h time course, ∼13,900 genes, including 590 transcription factor genes, are differentially expressed. Indeed, by 30 h postimbibition, ∼2,200 genes expressed in dry seeds are already down-regulated, and ∼2,000 are up-regulated. Moreover, the top 1% expressed genes at 54 h or later are very different from those before 30 h, reflecting important developmental and physiological transitions. Interestingly, clusters of genes involved in hormone metabolism, signaling, and responses are differentially expressed at various time points and TF gene expression is also modular and stage specific. Our dataset provides an opportunity for hypothesizing the timing of regulatory actions, particularly in the context of KS development.

Transcriptomic analyses of regenerating adult feathers in chicken.

BACKGROUND: Feathers have diverse forms with hierarchical branching patterns and are an excellent model for studying the development and evolution of morphological traits. The complex structure of feathers allows for various types of morphological changes to occur. The genetic basis of the structural differences between different parts of a feather and between different types of feather is a fundamental question in the study of feather diversity, yet there is only limited relevant information for gene expression during feather development. RESULTS: We conducted transcriptomic analysis of five zones of feather morphologies from two feather types at different times during their regeneration after plucking. The expression profiles of genes associated with the development of feather structure were examined. We compared the gene expression patterns in different types of feathers and different portions of a feather and identified morphotype-specific gene expression patterns. Many candidate genes were identified for growth control, morphogenesis, or the differentiation of specific structures of different feather types. CONCLUSION: This study laid the ground work for studying the evolutionary origin and diversification of feathers as abundant data were produced for the study of feather morphogenesis. It significantly increased our understanding of the complex molecular and cellular events in feather development processes and provided a foundation for future studies on the development of other skin appendages.

Learning with convex loss and indefinite kernels.

We consider a kind of kernel-based regression with general convex loss functions in a regularization scheme. The kernels used in the scheme are not necessarily symmetric and thus are not positive semidefinite; l(1)-norm of the coefficients in the kernel ensembles is taken as the regularizer. Our setting in this letter is quite different from the classical regularized regression algorithms such as regularized networks and support vector machines regression. Under an established error decomposition that consists of approximation error, hypothesis error, and sample error, we present a detailed mathematical analysis for this scheme and, in particular, its learning rate. A reweighted empirical process theory is applied to the analysis of produced learning algorithms, which plays a key role in deriving the explicit learning rate under some assumptions.

A new sequence convergent to Euler-Mascheroni constant.

In this paper, we provide a new sequence converging to the Euler-Mascheroni constant. Finally, we establish some inequalities for the Euler-Mascheroni constant by the new sequence.

Learning Rates of Regularized Regression With Multiple Gaussian Kernels for Multi-Task Learning.

This paper considers a least square regularized regression algorithm for multi-task learning in a union of reproducing kernel Hilbert spaces (RKHSs) with Gaussian kernels. It is assumed that the optimal prediction function of the target task and those of related tasks are in an RKHS with the same but with unknown Gaussian kernel width. The samples for related tasks are used to select the Gaussian kernel width, and the sample for the target task is used to obtain the prediction function in the RKHS with this selected width. With an error decomposition result, a fast learning rate is obtained for the target task. The key step is to estimate the sample errors of related tasks in the union of RKHSs with Gaussian kernels. The utility of this algorithm is illustrated with one simulated data set and four real data sets. The experiment results illustrate that the underlying algorithm can result in significant improvements in prediction error when few samples of the target task and more samples of related tasks are available.

Detection of human papillomavirus in squamous cell carcinoma arising from dermoid cysts.

OBJECTIVE: Primary squamous cell carcinoma (SCC) of the ovary in humans is rare. Most cases represent a malignant transformation of ovarian teratoma, Brenner tumor, or endometriosis. The etiology of this cancer remains largely unknown. Human papillomavirus (HPV) infection is a critical factor that induces tumor formation, particularly cervical cancer. Therefore, this study aimed to evaluate the association of HPV with malignant transformation of mature cystic teratoma (MCT) into SCC of the ovary. MATERIALS AND METHODS: The samples included four formalin-fixed paraffin-embedded SCC-MCT tissues and their adjacent tissues from the cervix to the ovaries, 12 cases of benign teratoma ovarian tissues (dermoid tissues), and 11 cases of benign nonteratoma ovarian tissues (nondermoid tissues). The two squamous carcinoma tissues of the cervix were used as control samples. HPV was detected by immunohistochemistry (IHC) with anti-HPV capsid or E6 (HPV type 16/18) antibodies and in situ hybridization (ISH) with three sets of genotyping probes, HPV types 6/11, 16/18, and 31/33. RESULTS: IHC revealed HPV infection associated with the four cases of SCC-MCT and the two cases of control cervical cancer samples. Importantly, HPV was also detected in adjacent reproductive tissues of the SCC-MCT cases, which suggested that the viral particles might spread in an ascending route through the fallopian tubes, endometrium, endocervix, and cervix to the ovaries. ISH revealed HPV type 16/18 in all SCC-MCT cases and HPV type 31/33 in two, with no HPV type 6/11 in any SCC-MCT cases. However, compared with the SCC-MCT cases, the lower detection rates of HPV in dermoid cysts and nondermoid tissues suggested that HPV might not be associated with normal ovarian tissues or benign ovarian teratomas. CONCLUSION: Our data suggest that high-risk HPV infection might be a causal factor that induces malignant transformation of MCT into SCC of the ovary, although further investigation is still required.

Least square regularized regression in sum space.

This paper proposes a least square regularized regression algorithm in sum space of reproducing kernel Hilbert spaces (RKHSs) for nonflat function approximation, and obtains the solution of the algorithm by solving a system of linear equations. This algorithm can approximate the low- and high-frequency component of the target function with large and small scale kernels, respectively. The convergence and learning rate are analyzed. We measure the complexity of the sum space by its covering number and demonstrate that the covering number can be bounded by the product of the covering numbers of basic RKHSs. For sum space of RKHSs with Gaussian kernels, by choosing appropriate parameters, we tradeoff the sample error and regularization error, and obtain a polynomial learning rate, which is better than that in any single RKHS. The utility of this method is illustrated with two simulated data sets and five real-life databases.