Integrative Analyses of Biochemical Properties and Transcriptome Reveal the Dynamic Changes in Leaf Senescence of Tobacco (Nicotiana tabacum L.)

Leaf senescence is an important process of growth and development in plant, and it is a programmed decline controlled by a series of genes. In this study, the biochemical properties and transcriptome at five maturity stages (M1∼M5) of tobacco leaves were analyzed to reveal the dynamic changes in leaf senescence of tobacco. A total of 722, 1,534, 3,723, and 6,933 genes were differentially expressed (DEG) between M1 and M2, M1 and M3, M1 and M4, and M1 and M5, respectively. Significant changes of nitrogen, sugars, and the DEGs related to metabolite accumulation were identified, suggesting the importance of energy metabolism during leaf senescence. Gene Ontology (GO) analysis found that DEGs were enriched in biosynthetic, metabolic, photosynthesis, and redox processes, and especially, the nitrogen metabolic pathways were closely related to the whole leaf senescence process (M1∼M5). All the DEGs were grouped into 12 expression profiles according to their distinct expression patterns based on Short Time-series Expression Miner (STEM) software analysis. Furthermore, Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis found that these DEGs were enriched in pathways of carbon metabolism, starch and sucrose metabolism, nitrogen metabolism, and photosynthesis among these expression profiles. A total of 30 core genes were examined by Weight Gene Co-expression Network Analysis (WGCNA), and they appeared to play a crucial role in the regulatory of tobacco senescence. Our results provided valuable information for further functional investigation of leaf senescence in plants.

Identification and expression pattern analysis of miRNAs in pectoral muscle during pigeon (Columba livia) development

MicroRNAs (miRNAs) are a group of crucial regulators in the process of animal growth and development. However, little is known about the expression and function of miRNAs in pigeon muscles. To identify the miRNAs participating in the rapid development of pigeon pectoral muscles and quantitate their expression levels of pectoral muscles in different age stages, we performed miRNA transcriptome analysis in pigeon pectoral muscles by sequencing small RNAs over three different age stages (1-day old, 28 days old, and 2 years old). Dual-luciferase reporter assay was applied to validate the interaction between miRNA and its target gene. We identified 304 known miRNAs, 201 conserved miRNAs, and 86 novel miRNAs in pigeon pectoral muscles. 189 differentially expressed (DE) miRNAs were screened out during pigeon development. A short time-series expression miner (STEM) analysis indicated 89 DE miRNAs were significantly clustered in a progressively decreasing expression profile, and mainly enriched in biosynthesis-related GO categories and signaling pathways for MAPK and TGF-β. Dual-luciferase reporter assay indicated that a progressively down-regulated miRNA (miR-20b-5p) could directly target Krüppel-like factor 3 (KLF3) gene. To sum-up, our data expand the repertoire of pigeon miRNAs and enhance understanding of the mechanisms underlying rapid development in squabs.

Short Time-Series Expression Transcriptome Data Reveal the Gene Expression Patterns of Dairy Cow Mammary Gland as Milk Yield Decreased Process

The existing research on dairy cow mammary gland genes is extensive, but there have been few reports about dynamic changes in dairy cow mammary gland genes as milk yield decrease. For the first time, transcriptome analysis based on short time-series expression miner (STEM) and histological observations were performed using the Holstein dairy cow mammary gland to explore gene expression patterns in this process of decrease (at peak, mid-, and late lactation). Histological observations suggested that the number of mammary acinous cells at peak/mid-lactation was significantly higher than that at mid-/late lactation, and the lipid droplets area secreted by dairy cows was almost unaltered across the three stages of lactation (p > 0.05). Totals of 882 and 1439 genes were differentially expressed at mid- and late lactation, respectively, compared to peak lactation. Function analysis showed that differentially expressed genes (DEGs) were mainly related to apoptosis and energy metabolism (fold change ≥ 2 or fold change ≤ 0.5, p-value ≤ 0.05). Transcriptome analysis based on STEM identified 16 profiles of differential gene expression patterns, including 5 significant profiles (false discovery rate, FDR ≤ 0.05). Function analysis revealed DEGs involved in milk fat synthesis were downregulated in Profile 0 and DEGs in Profile 12 associated with protein synthesis. These findings provide a foundation for future studies on the molecular mechanisms underlying mammary gland development in dairy cows.

Dynamic assessing silica particle-induced pulmonary fibrosis and associated regulation of long non-coding RNA expression in Wistar rats

Background Exposure to respirable crystalline silica (RCS) can induce accelerated silicosis (AS), a form of silicosis that is more progressive and severe form of silicosis. In this project we aimed to assess processes of silicosis in rats exposed to RCS with focus on the regulation of long noncoding RNAs (lncRNAs). Results The results showed that RCS induced acute inflammatory response as indicated by the appearance of inflammatory cells in the lung from the first day and peaked on day 7 of exposure. The fibroblasts appeared along with the inflammatory cells decreasing gradually on day 14. Extensive fibrosis appeared in the lung tissue, and silicon nodules were getting larger on day 28. Interestingly, the number of altered lncRNAs increased with the exposure time with 193, 424, 455, 421 and 682 lncRNAs on day 1, 7, 14, 21, and 28 after exposure, respectively. We obtained 285 lncRNAs with five significant temporal expression patterns whose expressions might correlate with severity of silicosis. KEGG analysis showed that lncRNAs from short time-series expression miner (STEM)-derived data mainly involved in 17 pathways such as complement and coagulation cascades. Conclusions The differential expression profiles of lncRNAs may be potential biomarkers in silicosis through modulating expressions of their relevant genes in lungs of rat and thus warrant further investigation.

Geometric series expansion of the Neumann–Poincaré operator: Application to composite materials

The Neumann–Poincaré (NP) operator, a singular integral operator on the boundary of a domain, naturally appears when one solves a conductivity transmission problem via the boundary integral formulation. Recently, a series expression of the NP operator was developed in two dimensions based on geometric function theory [34]. In this paper, we investigate geometric properties of composite materials using this series expansion. In particular, we obtain explicit formulas for the polarisation tensor and the effective conductivity for an inclusion or a periodic array of inclusions of arbitrary shape with extremal conductivity, in terms of the associated exterior conformal mapping. Also, we observe by numerical computations that the spectrum of the NP operator has a monotonic behaviour with respect to the shape deformation of the inclusion. Additionally, we derive inequality relations of the coefficients of the Riemann mapping of an arbitrary Lipschitz domain using the properties of the polarisation tensor corresponding to the domain.

ModularBoost: an efficient network inference algorithm based on module decomposition

Background Given expression data, gene regulatory network(GRN) inference approaches try to determine regulatory relations. However, current inference methods ignore the inherent topological characters of GRN to some extent, leading to structures that lack clear biological explanation. To increase the biophysical meanings of inferred networks, this study performed data-driven module detection before network inference. Gene modules were identified by decomposition-based methods. Results ICA-decomposition based module detection methods have been used to detect functional modules directly from transcriptomic data. Experiments about time-series expression, curated and scRNA-seq datasets suggested that the advantages of the proposed ModularBoost method over established methods, especially in the efficiency and accuracy. For scRNA-seq datasets, the ModularBoost method outperformed other candidate inference algorithms. Conclusions As a complicated task, GRN inference can be decomposed into several tasks of reduced complexity. Using identified gene modules as topological constraints, the initial inference problem can be accomplished by inferring intra-modular and inter-modular interactions respectively. Experimental outcomes suggest that the proposed ModularBoost method can improve the accuracy and efficiency of inference algorithms by introducing topological constraints.

Deciphering the miRNA transcriptome of breast muscle from the embryonic to post-hatching periods in chickens

Background miRNAs play critical roles in growth and development. Various studies of chicken muscle development have focused on identifying miRNAs that are important for embryo or adult muscle development. However, little is known about the role of miRNAs in the whole muscle development process from embryonic to post-hatching periods. Here, we present a comprehensive investigation of miRNA transcriptomes at 12-day embryo (E12), E17, and day 1 (D1), D14, D56 and D98 post-hatching stages. Results We identified 337 differentially expressed miRNAs (DE-miRNAs) during muscle development. A Short Time-Series Expression Miner analysis identified two significantly different expression profiles. Profile 4 with downregulated pattern contained 106 DE-miRNAs, while profile 21 with upregulated pattern contained 44 DE-miRNAs. The DE-miRNAs with the upregulated pattern mainly played regulatory roles in cellular turnover, such as pyrimidine metabolism, DNA replication, and cell cycle, whereas DE-miRNAs with the downregulated pattern directly or indirectly contributed to protein turnover metabolism such as glycolysis/gluconeogenesis, pyruvate metabolism and biosynthesis of amino acids. Conclusions The main functional miRNAs during chicken muscle development differ between embryonic and post-hatching stages. miRNAs with an upregulated pattern were mainly involved in cellular turnover, while miRNAs with a downregulated pattern mainly played a regulatory role in protein turnover metabolism. These findings enrich information about the regulatory mechanisms involved in muscle development at the miRNA expression level, and provide several candidates for future studies concerning miRNA-target function in regulation of chicken muscle development.

BENIN: Biologically enhanced network inference

Gene regulatory network inference is one of the central problems in computational biology. We need models that integrate the variety of data available in order to use their complementarity information to overcome the issues of noisy and limited data. BENIN: Biologically Enhanced Network INference is our proposal to integrate data and infer more accurate networks. BENIN is a general framework that jointly considers different types of prior knowledge with expression datasets to improve the network inference. The method states the network inference as a feature selection problem and uses a popular penalized regression method, the Elastic net, combined with bootstrap resampling to solve it. BENIN significantly outperforms the state-of-the-art methods on the simulated data from the DREAM 4 challenge when combining genome-wide location data, knockout gene expression data, and time series expression data.