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Network analysis reveals stage-specific changes in zebrafish embryo development using time course whole transcriptome profiling and prior biological knowledge
© Zhang. 2015
- Received: 24 December 2014
- Accepted: 30 July 2015
- Published: 28 August 2015
Molecular networks act as the backbone of molecular activities within cells, offering a unique opportunity to better understand the mechanism of diseases. While network data usually constitute only static network maps, integrating them with time course gene expression information can provide clues to the dynamic features of these networks and unravel the mechanistic driver genes characterizing cellular responses. Time course gene expression data allow us to broadly “watch” the dynamics of the system. However, one challenge in the analysis of such data is to establish and characterize the interplay among genes that are altered at different time points in the context of a biological process or functional category. Integrative analysis of these data sources will lead us a more complete understanding of how biological entities (e.g., genes and proteins) coordinately perform their biological functions in biological systems.
In this paper, we introduced a novel network-based approach to extract functional knowledge from time-dependent biological processes at a system level using time course mRNA sequencing data in zebrafish embryo development. The proposed method was applied to investigate 1α, 25(OH)2D3-altered mechanisms in zebrafish embryo development. We applied the proposed method to a public zebrafish time course mRNA-Seq dataset, containing two different treatments along four time points. We constructed networks between gene ontology biological process categories, which were enriched in differential expressed genes between consecutive time points and different conditions. The temporal propagation of 1α, 25-Dihydroxyvitamin D3-altered transcriptional changes started from a few genes that were altered initially at earlier stage, to large groups of biological coherent genes at later stages. The most notable biological processes included neuronal and retinal development and generalized stress response. In addition, we also investigated the relationship among biological processes enriched in co-expressed genes under different conditions. The enriched biological processes include translation elongation, nucleosome assembly, and retina development. These network dynamics provide new insights into the impact of 1α, 25-Dihydroxyvitamin D3 treatment in bone and cartilage development.
We developed a network-based approach to analyzing the DEGs at different time points by integrating molecular interactions and gene ontology information. These results demonstrate that the proposed approach can provide insight on the molecular mechanisms taking place in vertebrate embryo development upon treatment with 1α, 25(OH)2D3. Our approach enables the monitoring of biological processes that can serve as a basis for generating new testable hypotheses. Such network-based integration approach can be easily extended to any temporal- or condition-dependent genomic data analyses.
- Gene Ontology
- Coexpression Network
- Biological Process Term
- Consecutive Time Point
- Notable Biological Process
The active form of Vitamin D3 - 1α,25-dihydroxyvitamin D3 [1α,25(OH)2D3] – have demonstrated playing a critical role in calcium and phosphorus homeostasis by increasing intestinal calcium and phosphorus transport, thereby maintaining normal serum calcium and phosphorus concentrations and allowing bone mineralization to proceed [1, 2]. In previous works, we have shown that 1α,25(OH)2D3 alters expression of hundreds to thousands of genes at different developmental stages in early zebrafish embryos in vivo . However, it is challenging to digest and interpret the regulatory relationships among these differentially expressed genes at adjacent developmental stages. Novel informatics approaches are needed to fill in the gap how to interpret these thousands of differentially expressed genes at different time points in a systematic manner.
Biological systems are highly dynamic and responsive to the external environment. The gene expression in these systems is a temporal process. Different genes are required to play different functional roles under different conditions. This is highly regulated by a complex regulatory system of diverse molecular interactions, such as protein-protein interactions (PPIs), protein-DNA interactions (PDIs), and metabolic signaling pathways . Taking a snapshot of the gene expression profile in a biological system (e.g., cell cycle system and development) under a certain condition can reveal some of the genes that are specially expressed under this condition. However, to investigate how all the genes are regulated in the context of a biological system, and to determine the interaction relationships between these genes, it is necessary to measure the gene expression profile in a time series manner . This can also provide the distinct possibility of unraveling the mechanistic drivers characterizing cellular responses . Time series gene expression data have been widely applied to study a wide range of biological systems, including cell cycle , genetic interaction and knockouts [8, 9], and development . Despite their unique features, many computational challenges still remain in analyzing such gene expression profiles. For instance, it is difficult to study the relationships among differentially expressed genes (DEGs) at each time point in a case–control time series experiment, due to large number of DEGs and limited time points available. To address such challenges, algorithms are required that are specifically designed to improve the interpretability of these data by integrating multi-source prior biological evidence.
Molecular interactions such as PPIs and PDIs are essential for a wide range of cellular processes and form a network of astonishing complexity. Until recently, our knowledge of such complex networks was rather limited. The emergence of high-throughput technologies has given us possibilities to systematically survey and study the underlying biological system. The molecular interaction maps have been built in model organisms (e.g., S.cerevisiae , D.melanogaster  and C.elegans ), as well as in higher vertebrate organisms (e.g., zebrafish , mouse  and human ). Evidently, the generated interaction maps offer us a rich resource for systematic studies of molecular networks and complement other types of biological data. However, current interaction databases include a large amount of false positive and false negative interactions due to the unreliability of interaction mapping technologies available. In addition, these molecular interactions are static. There is little direct information available on the temporal dynamics of these molecular interactions. To understand time-dependent biological processes at network level, molecular networks need to be considered as temporal and spatial rather than static information flow between molecules . Recently, attempts have been made in integrating different types of biological data with molecular network interactions to reveal the dynamics of molecular networks . However, only a few studies have investigated the dynamics of the molecular network interactions in time course gene expression data with limited success. For instance, Tang et al.  proposed to reconstruct time course protein interaction networks (TC-PINs) by incorporating time series gene expression into PPI networks. The functional modules from TC-PINs were enriched in related gene ontology (GO) biological processes than those from static PPI networks. However, the causal relationship between TC-PINs across time points could not be inferred. Such causal relationships are crucial to understand the underlying regulated biological processes in a time-dependent and context-specific manner. A propagation of such interactions from gene level to biological process/pathway level (e.g., gene ontology information) will help us identify the altered biological processes during the time in which these gene expressions are examined.
The gene ontology (GO) Consortium  has developed three separate ontologies-molecular function (MF), biological process (BP) and cellular component (CC) - to describe the attributes of gene products. Several studies have demonstrated that the molecular interactions and GO provide substantially congruent yet subtle different view of biological systems . The hypothesis is that the interaction between any two proteins/genes indicates a general likelihood that these two proteins are functionally coupled or involved in the same biological process. Identifying enriched interactions between any two GO terms based on molecular interactions between genes assigned to these two GO terms are more statistically reliable: interactions reflect statistically enriched temporal connections between multiple genes of one GO term and multiple genes of another. However, this could not tell the temporal directionality in these connections. By incorporating time series gene expression data, the causal relations can be inferred in this GO network by highlighting information flow between GO biological processes enriched in DEGs at consecutive time points.
In this section, we present: (1) a description of generation and initial characterization of the mRNA-seq dataset obtained from zebrafish embryos altered by 1α,25(OH)2D3 treatment; (2) an overview the interactome-based analysis that we proposed; (3) a chronologically organized analysis of the transcriptome changes and interactome dynamics altered by 1α,25(OH)2D3 treatment during early zebrafish development. Figure 1 illustrates the overview of the proposed analysis workflow.
Characterization of mRNA-seq dataset during zebrafish embryo development
Genome-wide transcriptional profiling were performed using Illumina HiSeq sequencing technique for four replicate cDNA libraries of 1α,25(OH)2D3- or vehicle-treated zebrafish, 48, 96, 144, and 168 hours post fertilization (hpf) as described in our previous publication . Overall, the RNA-seq data obtained from 32 independent zebrafish RNA libraries had comparable number of total reads . These reads were mapped to the latest zebrafish genome assembly (zv9) from the UCSC website (http://genome.ucsc.edu/). The refFlat annotation file from the University of California Santa Clara (UCSC) Table Browser was used to generate raw reads mapped to each annotated gene in the annotation file. The genes altered by 1α,25(OH)2D3 treatment at each time point were identified using the negative binomial model as describe in . A list of altered genes identified along with the days on which they were differentially expressed is presented in Additional file 1: Table S1. We also carried out the gene ontology (GO) enrichment analysis using the GOMiner tool . However, due to the limited number of DEGs identified at each time point and the limitation associated with the Fisher’s Exact Test, the results of these analyses could not provide much indication of the biological processes being modulated in response to 1α, 25(OH)2D3 treatment. To more efficiently derive biological insights from the genome-wide transcriptomic response to the treatment, we proposed a network-based analysis in the following sections.
Interactome-based analysis of differentially expressed genes during zebrafish development
We overlaid the DEGs onto the zebrafish functional interactome from the FunCoup database . The DEGs were overlaid on their corresponding nodes in the interactome, and related functional interactions between genes were extracted and reconstructed the 1α,25(OH)2D3 specific interactome. Many network interactions connect the few genes altered on day 2 and many altered on later days. We found that there was a statistically significant enrichment in links between genes that were 1α, 25(OH)2D3-altered earlier and genes regulated later in the course of experiment. This suggested that treatment affected signals were propagated along network routes from the initially affected genes (on day 2) towards network regions that were perturbed later.
To gain a better perspective on what this temporal pattern in enriched connections between 1α,25(OH)2D3-altered genes might mean, we analyzed the GO categories associated with the connected nodes in the context of interactome.
Network propagation analysis of differentially expressed genes during zebrafish development
The FunCoup network links among these genes can indicate a general likelihood how they are functionally related, but don’t highlight the temporal directionality in these connections. Causal relations can be suggested by examining temporal changes, i.e., if information associated with gene A at time point t helps to predict the state of gene B at time point (t + 1), then a causal relation A- > B might be inferred [24, 25]. However, traditional network inference approaches could not identify such temporal regulatory relationship due to limited time points available. The statistical power from only four time points would be too low for robust analysis. To gain a better perspective on the temporal pattern among 1α, 25(OH)2D3-altered genes, we generalized a network of GO terms connected by the links between these DEGs on consecutive time points. At this broader scale, relations between nodes (GO biological processes) are statistical reliable: links reflect statistically enriched temporal connections between multiple genes of one node with multiple genes of another. Thus, this GO-GO network highlights flow between GO biological processes altered by 1α, 25(OH)2D3 on different days.
1α, 25(OH)2D3-altered genes in individual gene-gene interactions in FunCoup interactome were labeled with days when these genes were detected as differentially expressed. We were particularly interested in identifying the links in which one gene was altered earlier than the other. Thus, if there were a significant number of genes in GO category X altered on day d interacting with gene in GO category Y altered on day (d + 1), we hypothesize that a causative relation X - > Y. Limiting the output to only enriched GO-GO connections allowed us to focus on the major changes of propagation of 1α, 25(OH)2D3 and organismal response to it. Compared to the individual category enrichment approach such as GOMiner, our approach yielded a much richer analysis for interpretation of time series changes unique to time series gene expression data. The Figs. 2–4 presented day-to-day enriched interactions at GO biological process level. We provided a chronological interpretation on these findings below.
Chronological analysis of the interaction network altered by 1α, 25(OH)2D3 at gene ontology level
The approach described above enabled flexible and deep monitoring of 1α, 25(OH)2D3 altered changes in the transcriptome at GO level in the context of functional interactome. To show time-dependent information flow in embryonic development altered by 1α,25(OH)2D3 treatment, GO networks of enriched GO-GO interactions were reconstructed.
Day 2 to day 4 transition
Day 4 to day 6 transition
Day 4 was marked by the most significant increase of linkage from transcription factors altered on day 4 towards others altered later. The most central node on day 4 is organ development, which became connected to multiple biological processes, such as cardiovascular system development, blood vessel development, immune system process, heart development, brain development, tube development, and others. This observation suggests that vitamin D treatment can alter biological processes involved in the development of many organs. One network of GO terms between day 4 and 6 is presented in Fig. 3(b).
Day 6 to day 7 transition
The organ morphogenesis was identified as a central node in the GO network of day 6 - > 7, connecting to multiple biological processes, such as nervous system development, circulatory system development, vasculature development, epithelium development, retina development in camera type eye, and many embryonic development terms including cartilage development and neuron generation. One network of GO terms between day 6 and 7 is presented in Fig. 3(c).
Condition-specific GO network analysis
Statistics of co-expressed links
In this work, we have developed a network-based computational approach that analyzes time series mRNA-seq gene expression profiles in the context of molecular interactome and GO information to reveal temporal transcriptional changes altered by 1α,25(OH)2D3 in zebrafish embryo development. This enabled us to review the progression of 1α,25(OH)2D3-induced changes in gene expression and the network structure itself in zebrafish embryo development. The efficiency of our analysis of 1α,25(OH)2D3-alered global gene expression was enhanced by the interactome approach, as the network-based analysis approach were superior to their single-gene approach in terms of both statistical power and biological interpretability, A variety of interesting biological hypotheses were derived from our analysis. The significant biological processes include iron metabolism, neuronal and retinal development, and many organ development related pathways. Our approach is useful for discovering candidate biological processes that can serve as a basis for generating new testable hypotheses. Such network-based integration approach can be extended to any temporal- or condition-dependent genomic data analyses. Other types of interaction or ontology data can also be incorporated into this approach.
We have developed a network-based analysis approach that integrated mRNA-seq gene expression profiles with molecular network and GO annotation to reveal dynamic propagation of 1α,25(OH)2D3-altered transcriptional changes from a few genes that altered initially, to large groups of biologically coherent genes at later times. The most notable biological processes included calcium and iron metabolism, neuronal and retinal development, and generalized stress response. Such network-based integration approach can be extended to other condition-dependent studies. Also graph theory can be incorporated to compare condition-specific coexpression networks and meta-flow networks of GO terms can be inferred based on such information.
mRNA-Seq gene expression data
The mRNA-Seq profiling in four biological replicate samples of 1α,25(OH)2D3- or ethanol-treated zebrafish, 2, 4, 6 and 7 days post-fertilization (hpf) was obtained by the Illumina HiSEQ 2000 platform. The generated 50-bp FASTQ sequence reads were aligned to both the latest Zebrafish genome assembly (zv9) and our in-house exon junction database using BWA . The aligned sequence tags were counted for each annotated genes/exons using custom scripts based on the UCSC genome binning approach . A total of 14267 genes were annotated using RefSeq database and the raw read counts for genes were generated for further downstream analyses.
Zebrafish molecular interaction network
The zebrafish molecular interaction network was downloaded from FunCoup database (http://FunCoup.sbc.su.se/). In total, there are 1,999,529 interactions between 13033 proteins in the zebrafish interactome downloaded on January 3rd, 2012.
Gene ontology annotation in zebrafish
The gene ontology annotation was downloaded from the original website (http://www.geneontolgy.org/) on Januray 20th, 2012. In this paper, we used the biological process terms only since our goal is to identify the 1α,25(OH)2D3-altered mechanisms.
Differential gene expression analysis
For differential gene expression analysis between conditions, we eliminated genes without any reads across all samples. We used DESeq package in R to test for differential expression for all the remaining genes . We conservatively accounted for multiple testing, employing a Bonferroni correction, and yielding an adjusted p-value for differential expression for each gene. A strict adjusted P value cut-off of 0.01 was used to select significant DEGs.
Construction of time-dependent GO-GO networks
For any two GO terms, a link was counted if any two DEGs in these two GO terms were connected in the original FunCoup network;
- 2)The GO-GO links were classified into time-dependent patterns according to the days when the gene were differentially expressed for the first time:
Day 2 - > Day 4: one gene was differentially expressed on Day 2, while the other on Day 4;
Day 4 - > Day 6: similar definition as in (a);
Day 6 - > Day 7: similar definition as in (a).
The GO For each candidate GO-GO network link, its statistical significance was evaluated by the permutation test, i.e. gene names were randomized in the FunCoup network for 10,000 times. The links between GO terms with P value less than 0.01 were considered statistically significant.
Enriched GO-GO links were kept in the GO-GO network, i.e. ones with P value less than 0.01. The network was visualized in the Cytoscape tool .
Construction of condition-specific co-expressed interaction networks
1α, 25(OH)2D3 – related coexpression network with strong correlation between observed gene expression profiles only after 1α, 25(OH)2D3 treatment;
Ethanol – related coexpression network with strong correlation between observed gene expression profiles only in ethanol treatment;
Developmental - related coexpression network with strong correlation between observed gene expression profiles under both conditions and with a significant developmental pattern and synchronous between two genes.
where PCC VD3 refers to the PCC value for the 1 α, 25(OH)2D3-treated samples, and PCC ethanol refers to the PCC value for the ethanol-treated samples. Eq. (2) insures that at the least one of the PCC values exceed the threshold min PCC , while Eq. (3) requires that the difference between two PCC values in different conditions is big enough, i.e., larger than diff PCC . Eq. (4) states that at least one of the three effects from ANOVA analysis must be significant (i.e. P <0.05).
where PCC all refers to the PCC value for all samples across all conditions, and full PCC is the minimum PCC value for a link to be considered coexpressed. In this paper, we set the cutoff values 0.9, 0.6, 0.9 for min PCC , diff PCC and full PCC .
Construction of condition-specific GO-GO networks
For any two GO “biological process” categories, a link was counted if any two genes in these two GO categories were connected in the condition-specific coexpression network;
For each potential GO-GO network link, its statistical significance was evaluated by the permutation test, i.e. gene names were randomized in the co-expression network for 10,000 times. The links between GO biological process terms with P value less than 0.01 were considered statistically significant.
Enriched GO-GO links were kept in the GO-GO network, i.e. ones with P value less than 0.01. The network was visualized in the Cytoscape tool.
The gene level Gene ontology enrichment analysis was performed using GoMiner  on the DEGs that were identified at each time point.
This project was supported by the National Cancer Institute grant P30 CA 134274–04 to the University of Maryland Baltimore Greenbaum Cancer Center.
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