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Grafting is a widely used technique for pecan propagation; however, the background molecular events underlying grafting are still poorly understood. In our study, the graft partners during pecan [ Carya illinoinensis (Wangenh.) K. Koch] graft union formation were separately sampled for RNA-seq, and the transcriptional dynamics were described via weighted gene co-expression network analysis. To reveal the main events underlying grafting, the correlations between modules and grafting traits were analyzed. Functional annotation showed that during the entire graft process, signal transduction was activated in the scion, while messenger RNA splicing was induced in the rootstock. At 2 days after grafting, the main processes occurring in the scion were associated with protein synthesis and processing, while the primary processes occurring in the rootstock were energy release-related. During the period of 7–14 days after grafting, defense response was a critical process taking place in the scion; however, the main process functioning in the rootstock was photosynthesis. From 22 to 32 days after grafting, the principal processes taking place in the scion were jasmonic acid biosynthesis and defense response, whereas the highly activated processes associated with the rootstock were auxin biosynthesis and plant-type secondary cell wall biogenesis. To further prove that the graft partners responded asymmetrically to stress, hydrogen peroxide contents as well as peroxidase and β-1,3-glucanase activities were detected, and the results showed that their levels were increased in the scion not the rootstock at certain time points after grafting. Our study reveals that the scion and rootstock might respond asymmetrically to grafting in pecan, and the scion was likely associated with stress response, while the rootstock was probably involved in energy supply and xylem bridge differentiation during graft union formation. [ABSTRACT FROM AUTHOR]

Discovering a genetic regulatory network (GRN) from time series gene expression data plays an essential role in the field of biomedical research. In its development, many methods have been proposed for inferring GRNs. Although most of them are effective, they have limitations in terms of network size and the number of regulatory genes due to high computational cost. Thus, it is necessary to develop an efficient method that can operate with large networks and provide reliable results within an acceptable run time. In this study, we propose a new method using mutual information based on multi-level discretization network inference (MIDNI) from time series gene expression profiles. The proposed method discretizes time series gene expression data to minimize information loss and computational consumption through K-means clustering. We do not fix the number of clusters, instead varying it depending on the distribution of gene expression values. We compared MIDNI with three well-known inference methods through extensive simulations on both artificial and real gene expression datasets. Our results illustrate that MIDNI significantly outperforms the alternatives in terms of dynamic accuracy. The proposed method represents an efficient and scalable tool for inferring GRNs from time series gene expression data. [ABSTRACT FROM AUTHOR]

Discovering a genetic regulatory network (GRN) from time series gene expression data plays an essential role in the field of biomedical research. In its development, many methods have been proposed for inferring GRNs. Although most of them are effective, they have limitations in terms of network size and the number of regulatory genes due to high computational cost. Thus, it is necessary to develop an efficient method that can operate with large networks and provide reliable results within an acceptable run time. In this study, we propose a new method using mutual information based on multi-level discretization network inference (MIDNI) from time series gene expression profiles. The proposed method discretizes time series gene expression data to minimize information loss and computational consumption through K-means clustering. We do not fix the number of clusters, instead varying it depending on the distribution of gene expression values. We compared MIDNI with three well-known inference methods through extensive simulations on both artificial and real gene expression datasets. Our results illustrate that MIDNI significantly outperforms the alternatives in terms of dynamic accuracy. The proposed method represents an efficient and scalable tool for inferring GRNs from time series gene expression data.

Cells, in order to regulate their activities, process transcripts by controlling which genes to transcribe and by what amount. The transcription level of genes often change over time. Rate of change of gene transcription varies between genes. It can even change for the same gene across different members of a population. Thus, for a given gene, it is important to study the transcription level not only at a single time point, but across multiple time points to capture changes in patterns of gene expression which underlies several phenotypic or external factors. In such a dataset perturbation can happen due to which it may have missing transcription values for different samples at different time points. In this paper, we define three data perturbation models that are significant with respect to random deletion. We also define a recovery method that recovers data loss in the perturbed dataset such that the error is minimized. Our experimental results show that the recovery method compensates for the loss made by perturbation models. We show by means of two measures, namely, normalized distance and Pearson’s correlation coefficient that the distance between the original and perturbed dataset is more than the distance between original and recovered dataset. [ABSTRACT FROM AUTHOR]

Newly differentiated pancreatic β cells lack proper insulin secretion profiles of mature functional β cells. The global gene expression differences between paired immature and mature β cells have been studied, but the dynamics of transcriptional events, correlating with temporal development of glucose-stimulated insulin secretion (GSIS), remain to be fully defined. This aspect is important to identify which genes and pathways are necessary for β-cell development or for maturation, as defective insulin secretion is linked with diseases such as diabetes. In this study, we assayed through RNA sequencing the global gene expression across six β-cell developmental stages in mice, spanning from β-cell progenitor to mature β cells. A computational pipeline then selected genes differentially expressed with respect to progenitors and clustered them into groups with distinct temporal patterns associated with biological functions and pathways. These patterns were finally correlated with experimental GSIS, calcium influx, and insulin granule formation data. Gene expression temporal profiling revealed the timing of important biological processes across β-cell maturation, such as the deregulation of β-cell developmental pathways and the activation of molecular machineries for vesicle biosynthesis and transport, signal transduction of transmembrane receptors, and glucose-induced Ca2+ influx, which were established over a week before β-cell maturation completes. In particular, β cells developed robust insulin secretion at high glucose several days after birth, coincident with the establishment of glucose-induced calcium influx. Yet the neonatal β cells displayed high basal insulin secretion, which decreased to the low levels found in mature β cells only a week later. Different genes associated with calcium-mediated processes, whose alterations are linked with insulin resistance and deregulation of glucose homeostasis, showed increased expression across β-cell stages, in accordance with the temporal acquisition of proper GSIS. Our temporal gene expression pattern analysis provided a comprehensive database of the underlying molecular components and biological mechanisms driving β-cell maturation at different temporal stages, which are fundamental for better control of the in vitro production of functional β cells from human embryonic stem/induced pluripotent cell for transplantation-based type 1 diabetes therapy.

Aspergillus oryzae is a filamentous fungus that has historically been utilized in the fermentation of food products. In recent times, it has also been introduced as a component in the industrial biosynthesis of consumable compounds, including free fatty acids (FFAs), which are valuable and versatile products that can be utilized as feedstocks in the production of other commodities, such as pharmaceuticals and dietary supplements. To improve the FFA secretory productivity of A. oryzae in the presence of Triton X-100, we analyzed the gene expression of a wild-type control strain and a disruptant strain of an acyl-CoA synthetase gene, faaA , in a time-series experiment. We employed a comprehensive analysis strategy using the baySeq, DESeq2, and edgeR algorithms to clarify the vital pathways for FFA secretory productivity and select genes for gene modification. We found that the transport and metabolism of inorganic ions are crucial in the initial stages of FFA production and revealed 16 candidate genes to be modified in conjunction with the faaA disruption. These genes were verified through the construction of overexpression strains, and showed that the manipulation of reactions closer to the FFA biosynthesis step led to a higher increase in FFA secretory productivity. This resulted in the most successful overexpression strains to have an FFA secretory productivity more than two folds higher than that of the original faaA disruptant. Our study provides guidance for further gene modification for FFA biosynthesis in A. oryzae and for enhancing the productivity of other metabolites in other microorganisms through metabolic engineering. [ABSTRACT FROM AUTHOR]

Identification of differentially expressed genes (DEGs) in time course studies is very useful for understanding gene function, and can help determine key genes during specific stages of plant development. A few existing methods focus on the detection of DEGs within a single biological group, enabling to study temporal changes in gene expression. To utilize a rapidly increasing amount of single-group time-series expression data, we propose a two-step method that integrates the temporal characteristics of time-series data to obtain a B-spline curve fit. Firstly, a flat gene filter based on the Ljung-Box test is used to filter out flat genes. Then, a B-spline model is used to identify DEGs. For use in biological experiments, these DEGs should be screened, to determine their biological importance. To identify high-confidence promising DEGs for specific biological processes, we propose a novel gene prioritization approach based on the partner evaluation principle. This novel gene prioritization approach utilizes existing co-expression information to rank DEGs that are likely to be involved in a specific biological process/condition. The proposed method is validated on the Arabidopsis thaliana seed germination dataset and on the rice anther development expression dataset. [ABSTRACT FROM AUTHOR]

The reconstruction of gene regulatory networks from gene expression data has been the subject of intense research activity. A variety of models and methods have been developed to address different aspects of this important problem. However, these techniques are narrowly focused on particular biological and experimental platforms, and require experimental data that are typically unavailable and difficult to ascertain. The more recent availability of higher-throughput sequencing platforms, combined with more precise modes of genetic perturbation, presents an opportunity to formulate more robust and comprehensive approaches to gene network inference. Here, we propose a step-wise framework for identifying gene-gene regulatory interactions that expand from a known point of genetic or chemical perturbation using time series gene expression data. This novel approach sequentially identifies non-steady state genes post-perturbation and incorporates them into a growing series of low-complexity optimization problems. The governing ordinary differential equations of this model are rooted in the biophysics of stochastic molecular events that underlie gene regulation, delineating roles for both protein and RNA-mediated gene regulation. We show the successful application of our core algorithms for network inference using simulated and real datasets. [ABSTRACT FROM AUTHOR]

Background: Cellular senescence irreversibly arrests growth of human diploid cells. In addition, recent studies have indicated that senescence is a multi-step evolving process related to important complex biological processes. Most studies analyzed only the genes and their functions representing each senescence phase without considering gene-level interactions and continuously perturbed genes. It is necessary to reveal the genotypic mechanism inferred by affected genes and their interaction underlying the senescence process. Results: We suggested a novel computational approach to identify an integrative network which profiles an underlying genotypic signature from time-series gene expression data. The relatively perturbed genes were selected for each time point based on the proposed scoring measure denominated as perturbation scores. Then, the selected genes were integrated with protein-protein interactions to construct time point specific network. From these constructed networks, the conserved edges across time point were extracted for the common network and statistical test was performed to demonstrate that the network could explain the phenotypic alteration. As a result, it was confirmed that the difference of average perturbation scores of common networks at both two time points could explain the phenotypic alteration. We also performed functional enrichment on the common network and identified high association with phenotypic alteration. Remarkably, we observed that the identified cell cycle specific common network played an important role in replicative senescence as a key regulator. Conclusions: Heretofore, the network analysis from time series gene expression data has been focused on what topological structure was changed over time point. Conversely, we focused on the conserved structure but its context was changed in course of time and showed it was available to explain the phenotypic changes. We expect that the proposed method will help to elucidate the biological mechanism unrevealed by the existing approaches. [ABSTRACT FROM AUTHOR]

The ever-increasing number of chemicals and complex mixtures demands a time-saving and cost-effective platform for environmental risk assessment. However, there is limit promising tool for evaluating the contribution of each component to the total toxicity effects of the mixture. Here, four widely distributed environmental pollutants with different mode-of-actions, i.e., cadmium chloride (Cd), nitrofurazone (NFZ), triclosan (TCS), and tris(2-chloroethyl) phosphate (TCEP), were selected as components of artificial mixture. Integration of leave-one-out method and high-dimensional live cell array system was used to explore relative contribution of each component from the mixture. A quaternary mixture (All_4_chems) and four ternary mixtures (Leave_Cd, Leave_NFZ, Leave_TCS and Leave_TCEP) were investigated by Escherichia coli (E. coli) live cell array system with 90 environmental stress genes modified by green fluorescent protein (GFP) expressing reporter vectors. E. coli cytotoxicity tests demonstrated that TCS has antagonism effect with other three chemicals (Cd, NFZ and TCEP), while it was additive effect in other three binary combinations. A total of 26, 23, 13, 31 and 23 genes were significantly altered with fold-change greater than 2 over the 4 h exposure by All_4_chems, Leave_Cd, Leave_NFZ, Leave_TCS and Leave_TCEP, respectively. Clustering analysis based on time-series gene expression patterns and transcriptional effect level index (TELI) showed that Leave_TCEP has similar profiles with All_4_chems, demonstrating TCEP has the least contribution among four components to the quaternary mixture. Leave_NFZ has the least number of significantly altered genes, implying NFZ has the largest toxicity effect contribution to the quaternary mixture. The relative contribution in different pathways indicated that Cd has the most contribution to the mixture in redox stress, while TCS has the least contribution in DNA stress pathway. Collectively, our results demonstrated the utility of high-dimensional toxicogenomics data and leave-one-out method in prioritizing the relative contribution of each component in mixture. • TCS has antagonism effect with other three chemicals (Cd, NFZ and TCEP). • Leave-one-out combined live cell array is powerful for exploring effect contribution. • NFZ and TCEP have the greatest and least contribution to the mixture, respectively. • Cd has the most contribution in redox stress, while TCS has the least in DNA stress. [ABSTRACT FROM AUTHOR]

Abstract: Measuring expression levels of genes at the whole genome level can be useful for many purposes, especially for revealing biological pathways underlying specific phenotype conditions. When gene expression is measured over a time period, we have opportunities to understand how organisms react to stress conditions over time. Thus many biologists routinely measure whole genome level gene expressions at multiple time points. However, there are several technical difficulties for analyzing such whole genome expression data. In addition, these days gene expression data is often measured by using RNA-sequencing rather than microarray technologies and then analysis of expression data is much more complicated since the analysis process should start with mapping short reads and produce differentially activated pathways and also possibly interactions among pathways. In addition, many useful tools for analyzing microarray gene expression data are not applicable for the RNA-seq data. Thus a comprehensive package for analyzing time series transcriptome data is much needed. In this article, we present a comprehensive package, Time-series RNA-seq Analysis Package (TRAP), integrating all necessary tasks such as mapping short reads, measuring gene expression levels, finding differentially expressed genes (DEGs), clustering and pathway analysis for time-series data in a single environment. In addition to implementing useful algorithms that are not available for RNA-seq data, we extended existing pathway analysis methods, ORA and SPIA, for time series analysis and estimates statistical values for combined dataset by an advanced metric. TRAP also produces visual summary of pathway interactions. Gene expression change labeling, a practical clustering method used in TRAP, enables more accurate interpretation of the data when combined with pathway analysis. We applied our methods on a real dataset for the analysis of rice (Oryza sativa L. Japonica nipponbare) upon drought stress. The result showed that TRAP was able to detect pathways more accurately than several existing methods. TRAP is available at http://biohealth.snu.ac.kr/software/TRAP/. [Copyright &y& Elsevier]

Newly differentiated pancreatic β cells lack proper insulin secretion profiles of mature functional β cells. The global gene expression differences between paired immature and mature β cells have been studied, but the dynamics of transcriptional events, correlating with temporal development of glucose-stimulated insulin secretion (GSIS), remain to be fully defined. This aspect is important to identify which genes and pathways are necessary for β-cell development or for maturation, as defective insulin secretion is linked with diseases such as diabetes. In this study, we assayed through RNA sequencing the global gene expression across six β-cell developmental stages in mice, spanning from β-cell progenitor to mature β cells. A computational pipeline then selected genes differentially expressed with respect to progenitors and clustered them into groups with distinct temporal patterns associated with biological functions and pathways. These patterns were finally correlated with experimental GSIS, calcium influx, and insulin granule formation data. Gene expression temporal profiling revealed the timing of important biological processes across β-cell maturation, such as the deregulation of β-cell developmental pathways and the activation of molecular machineries for vesicle biosynthesis and transport, signal transduction of transmembrane receptors, and glucose-induced Ca 2+ influx, which were established over a week before β-cell maturation completes. In particular, β cells developed robust insulin secretion at high glucose several days after birth, coincident with the establishment of glucose-induced calcium influx. Yet the neonatal β cells displayed high basal insulin secretion, which decreased to the low levels found in mature β cells only a week later. Different genes associated with calcium-mediated processes, whose alterations are linked with insulin resistance and deregulation of glucose homeostasis, showed increased expression across β-cell stages, in accordance with the temporal acquisition of proper GSIS. Our temporal gene expression pattern analysis provided a comprehensive database of the underlying molecular components and biological mechanisms driving β-cell maturation at different temporal stages, which are fundamental for better control of the in vitro production of functional β cells from human embryonic stem/induced pluripotent cell for transplantation-based type 1 diabetes therapy., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2021 Sanavia, Huang, Manduchi, Xu, Dadi, Potter, Jacobson, Di Camillo, Magnuson, Stoeckert and Gu.)

Background: Cellular senescence irreversibly arrests growth of human diploid cells. In addition, recent studies have indicated that senescence is a multi-step evolving process related to important complex biological processes. Most studies analyzed only the genes and their functions representing each senescence phase without considering gene-level interactions and continuously perturbed genes. It is necessary to reveal the genotypic mechanism inferred by affected genes and their interaction underlying the senescence process., Results: We suggested a novel computational approach to identify an integrative network which profiles an underlying genotypic signature from time-series gene expression data. The relatively perturbed genes were selected for each time point based on the proposed scoring measure denominated as perturbation scores. Then, the selected genes were integrated with protein-protein interactions to construct time point specific network. From these constructed networks, the conserved edges across time point were extracted for the common network and statistical test was performed to demonstrate that the network could explain the phenotypic alteration. As a result, it was confirmed that the difference of average perturbation scores of common networks at both two time points could explain the phenotypic alteration. We also performed functional enrichment on the common network and identified high association with phenotypic alteration. Remarkably, we observed that the identified cell cycle specific common network played an important role in replicative senescence as a key regulator., Conclusions: Heretofore, the network analysis from time series gene expression data has been focused on what topological structure was changed over time point. Conversely, we focused on the conserved structure but its context was changed in course of time and showed it was available to explain the phenotypic changes. We expect that the proposed method will help to elucidate the biological mechanism unrevealed by the existing approaches.