Stage-specific Modules Research Articles

Dissecting the temporal genetic networks programming soybean embryo development from embryonic morphogenesis to post-germination

Key messageDesiccation-stage transcription factors perform similar functions, with early ones focused on desiccation tolerance and later ones on development. Gene networks governing late embryo development diverge between soybean and Arabidopsis.To understand gene activities programming seed embryo development, we profiled the soybean embryo transcriptome across embryonic morphogenesis through post-germination. Transcriptomic landscapes across embryo development feature highly prevalent transcripts, categorized into early and late groups, with shared and distinct functions. During the mid-storage reserve accumulation stage, the upregulated genes are enriched with regulatory tasks at both the transcriptional and chromatin levels, including DNA methylation and chromatin remodeling. The epigenetic-related functions also dominate in the upregulated genes during germination, involving core histone variants and histone chaperones. Gene network analysis reveals both stage-specific modules and modules active across multiple stages. The desiccation-associated gene module integrates diverse transcription factors (TFs) that are sequentially active during different desiccation stages, transitioning from abiotic stress functions early on to developmental functions later. Two TFs, active during the early and mid-desiccation stages were functionally assessed in Arabidopsis overexpression lines to uncover their potential roles in desiccation processes. Interestingly, nearly half of the Arabidopsis orthologs of soybean TFs active in the desiccation-associated module are inactive during Arabidopsis desiccation. Our results reveal that chromatin and transcriptional regulation coordinate during mid-storage reserve accumulation, while distinct epigenetic mechanisms drive germination. Additionally, gene modules either perform stage-specific functions or are required across multiple stages, and gene networks during late embryogenesis diverge between soybean and Arabidopsis. Our studies provide new information on the biological processes and gene networks underlying development from embryonic morphogenesis to post-germination.

Whole-genome transcriptome and DNA methylation dynamics of pre-implantation embryos reveal progression of embryonic genome activation in buffaloes

BackgroundDuring mammalian pre-implantation embryonic development (PED), the process of maternal-to-zygote transition (MZT) is well orchestrated by epigenetic modification and gene sequential expression, and it is related to the embryonic genome activation (EGA). During MZT, the embryos are sensitive to the environment and easy to arrest at this stage in vitro. However, the timing and regulation mechanism of EGA in buffaloes remain obscure. ResultsBuffalo pre-implantation embryos were subjected to trace cell based RNA-seq and whole-genome bisulfite sequencing (WGBS) to draw landscapes of transcription and DNA-methylation. Four typical developmental steps were classified during buffalo PED. Buffalo major EGA was identified at the 16-cell stage by the comprehensive analysis of gene expression and DNA methylation dynamics. By weighted gene co-expression network analysis, stage-specific modules were identified during buffalo maternal-to-zygotic transition, and key signaling pathways and biological process events were further revealed. Programmed and continuous activation of these pathways was necessary for success of buffalo EGA. In addition, the hub gene, CDK1, was identified to play a critical role in buffalo EGA.ConclusionsOur study provides a landscape of transcription and DNA methylation in buffalo PED and reveals deeply the molecular mechanism of the buffalo EGA and genetic programming during buffalo MZT. It will lay a foundation for improving the in vitro development of buffalo embryos.Graphical

Layer-Specific Modules Detection in Cancer Multi-Layer Networks.

Multi-layer networks provide an effective and efficient tool to model and characterize complex systems with multiple types of interactions, which differ greatly from the traditional single-layer networks. Graph clustering in multi-layer networks is highly non-trivial since it is difficult to balance the connectivity of clusters and the connection of various layers. The current algorithms for the layer-specific clusters are criticized for the low accuracy and sensitivity to the perturbation of networks. To overcome these issues, a novel algorithm for the layer-specific module in multi-layer networks based on nonnegative matrix factorization (LSNMF) is proposed by explicitly exploring the specific features of vertices. LSNMF first extract features of vertices in multi-layer networks by using nonnegative matrix factorization (NMF) and then decompose features of vertices into the common and specific components. The orthogonality constraint is imposed on the specific components to ensure the specificity of features of vertices, which provides a better strategy to characterize and model the structure of layer-specific modules. The extensive experiments demonstrate that the proposed algorithm dramatically outperforms state-of-the-art baselines in terms of various measurements. Furthermore, LSNMF efficiently extracts stage-specific modules, which are more likely to enrich the known functions, and also associate with the survival time of patients.

Prediction of Disease Genes Based on Stage-Specific Gene Regulatory Networks in Breast Cancer.

Breast cancer is one of the most common malignant tumors in women, which seriously endangers women’s health. Great advances have been made over the last decades, however, most studies predict driver genes of breast cancer using biological experiments and/or computational methods, regardless of stage information. In this study, we propose a computational framework to predict the disease genes of breast cancer based on stage-specific gene regulatory networks. Firstly, we screen out differentially expressed genes and hypomethylated/hypermethylated genes by comparing tumor samples with corresponding normal samples. Secondly, we construct three stage-specific gene regulatory networks by integrating RNA-seq profiles and TF-target pairs, and apply WGCNA to detect modules from these networks. Subsequently, we perform network topological analysis and gene set enrichment analysis. Finally, the key genes of specific modules for each stage are screened as candidate disease genes. We obtain seven stage-specific modules, and identify 20, 12, and 22 key genes for three stages, respectively. Furthermore, 55%, 83%, and 64% of the genes are associated with breast cancer, for example E2F2, E2F8, TPX2, BUB1, and CKAP2L. So it may be of great importance for further verification by cancer experts.

Deciphering the molecular regulatory mechanism orchestrating ovary development of the Pacific whiteleg shrimp Litopenaeus vannamei through integrated transcriptomic analysis of reproduction-related organs

Ovary development of decapod crustaceans is controlled by a complex neuro-endocrine regulatory network. In crustacean aquaculture, eyestalk ablation (ESA) technique has been widely used to induce ovary development and maturation. However, this technique can cause significant hormonal imbalance leading to inferior egg quality and weak immunity of the animals. Understanding the molecular regulatory mechanism of ovary development will help to develop alternative measures to expedite ovarian maturation. In this study, we performed a comprehensive transcriptomic analysis of five important organs related to ovary development, including the eyestalk ganglion, brain, thoracic ganglion, hepatopancreas, and ovary, at different ovary developmental stages of the Pacific white shrimp Litopenaeus vannamei. A total of 698,118,770 clean reads were obtained from the Illumina HiSeq™ 4000 sequencing platform, resulting in an assembly of 48,722 unique transcripts (or unigenes) with an N50 of 2463 bp and a mean length of 1244 bp. 18,661 unigenes can be matched to homologous sequences in public databases. Pairwise comparisons revealed that a wide variety of genes were differentially expressed and several gene expression trends were significantly enriched in different organs during ovary development. Through weighted gene co-expression network analysis, six tissue- or stage-specific modules of co-expressed genes were identified. GO and KEGG analysis further disclosed that a rich set of genes, including those associated with the CHH family hormones, ecdysone signaling, juvenile hormone signaling, Insulin-PI3K-Akt signaling, EGFR signaling, and TGFβ signaling pathways, were preferentially expressed in specific tissues, implying their crucial roles in the regulation of ovary development process. Taken together, the study reports a comprehensive analysis of transcriptomic modulation in major neuro-endocrine organs related to shrimp ovary development. The data obtained sheds light on the potential coordination between the nervous system and its target organs (i.e. ovary and hepatopancreas), and contributes to a deeper understanding of the regulatory network orchestrating ovary development of shrimp.

Gene co-expression network analysis to identify critical modules and candidate genes of drought-resistance in wheat.

To establish a gene co-expression network for identifying principal modules and hub genes that are associated with drought resistance mechanisms, analyzing their mechanisms, and exploring candidate genes. 42 data sets including PRJNA380841 and PRJNA369686 were used to construct the co-expression network through weighted gene co-expression network analysis (WGCNA). A total of 1,896,897,901 (284.30 Gb) clean reads and 35,021 differentially expressed genes (DEGs) were obtained from 42 samples. Functional enrichment analysis indicated that photosynthesis, DNA replication, glycolysis/gluconeogenesis, starch and sucrose metabolism, arginine and proline metabolism, and cell cycle were significantly influenced by drought stress. Furthermore, the DEGs with similar expression patterns, detected by K-means clustering, were grouped into 29 clusters. Genes involved in the modules, such as dark turquoise, yellow, and brown, were found to be appreciably linked with drought resistance. Twelve central, greatly correlated genes in stage-specific modules were subsequently confirmed and validated at the transcription levels, including TraesCS7D01G417600.1 (PP2C), TraesCS5B01G565300.1 (ERF), TraesCS4A01G068200.1 (HSP), TraesCS2D01G033200.1 (HSP90), TraesCS6B01G425300.1 (RBD), TraesCS7A01G499200.1 (P450), TraesCS4A01G118400.1 (MYB), TraesCS2B01G415500.1 (STK), TraesCS1A01G129300.1 (MYB), TraesCS2D01G326900.1 (ALDH), TraesCS3D01G227400.1 (WRKY), and TraesCS3B01G144800.1 (GT). Analyzing the response of wheat to drought stress during different growth stages, we have detected three modules and 12 hub genes that are associated with drought resistance mechanisms, and five of those genes are newly identified for drought resistance. The references provided by these modules will promote the understanding of the drought-resistance mechanism. In addition, the candidate genes can be used as a basis of transgenic or molecular marker-assisted selection for improving the drought resistance and increasing the yields of wheat.

Co-expression clustering across flower development identifies modules for diverse floral forms in Achimenes (Gesneriaceae).

BackgroundGenetic pathways involved with flower color and shape are thought to play an important role in the development of flowers associated with different pollination syndromes, such as those associated with bee, butterfly, or hummingbird pollination. Because pollination syndromes are complex traits that are orchestrated by multiple genes and pathways, the gene regulatory networks have not been explored. Gene co-expression networks provide a systems level approach to identify important contributors to floral diversification.MethodsRNA-sequencing was used to assay gene expression across two stages of flower development (an early bud and an intermediate stage) in 10 species of Achimenes (Gesneriaceae). Two stage-specific co-expression networks were created from 9,503 orthologs and analyzed to identify module hubs and the network periphery. Module association with bee, butterfly, and hummingbird pollination syndromes was tested using phylogenetic mixed models. The relationship between network connectivity and evolutionary rates (dN/dS) was tested using linear models.ResultsNetworks contained 65 and 62 modules that were largely preserved between developmental stages and contained few stage-specific modules. Over a third of the modules in both networks were associated with flower color, shape, and pollination syndrome. Within these modules, several hub nodes were identified that related to the production of anthocyanin and carotenoid pigments and the development of flower shape. Evolutionary rates were decreased in highly connected genes and elevated in peripheral genes.DiscussionThis study aids in the understanding of the genetic architecture and network properties underlying the development of floral form and provides valuable candidate modules and genes for future studies.

Maternal transcription profiles at different stages for the development of early embryo in buffalo.

Maternal mRNAs deposited in the egg during oogenesis are critical during the development of early embryo, before the activation of the embryonic genome. However, there is little known about the dynamic expression of maternally expressed genes in mammals. In this study, we made buffalo parthenogenesis as our research model to analyse maternal transcription profiles of pre-implantation embryo in buffalo using RNA sequencing. In total, 3,567 unique genes were detected to be differentially expressed among all constant stages during early embryo development (FPKM>0). Interestingly, a total of 10,442 new genes were discovered in this study, and gene ontology analysis of the new differentially expressed genes indicated that the new genes have a wide cellular localization and are involved in many molecular functions and biological processes. Moreover, we identified eight clusters that were only highly expressed in a particular developmental stage and enriched a number of GO terms and KEGG pathways that were related to specific stage. Furthermore, we identified 1,530 hub genes (or key members) from the maternally expressed gene networks, and these hub genes were involved in 11 stage-specific modules. After visualization using Cytoscape 3.2.1 software, we obtained complex interaction network of hub genes, indicating the highly efficient cooperation between genes during the development in buffalo embryos. Further research of these genes will greatly deepen our understanding of embryo development in buffalo. Collectively, this research lays the foundation for future studies on the maternal genome function, buffalo nuclear transfer and parthenogenetic embryonic stem cells.

Transcription profiles of oocytes during maturation and embryos during preimplantation development in vivo in the goat.

RNA sequencing performed on goat matured oocytes and preimplantation embryos generated invivo enabled us to define the transcriptome for goat preimplantation embryo development. The largest proportion of changes in gene expression in goat was found at the 16-cell stage, not as previously defined at the 8-cell stage, and is later than in other mammalian species. In all, 6482 genes were identified to be significantly differentially expressed across all consecutive developmental stage comparisons, and the important signalling pathways involved in each development transition were determined. In addition, we identified genes that appear to be transcribed only at a specific stage of development. Using weighted gene coexpression network analysis, we found nine stage-specific modules of coexpressed genes that represent the corresponding stage of development. Furthermore, we identified conserved key members (or hub genes) of the goat transcriptional networks. Their association with other embryo genes suggests that they may have important regulatory roles in embryo development. Our cross-mammalian species transcriptomic comparisons demonstrate both conserved and goat-specific features of preimplantation development.

Identification and Functional Analysis of Long Intergenic Non-coding RNAs Underlying Intramuscular Fat Content in Pigs.

Intramuscular fat (IMF) content is an important trait that can affect pork quality. Previous studies have identified many genes that can regulate IMF. Long intergenic non-coding RNAs (lincRNAs) are emerging as key regulators in various biological processes. However, lincRNAs related to IMF in pig are largely unknown, and the mechanisms by which they regulate IMF are yet to be elucidated. Here we reconstructed 105,687 transcripts and identified 1,032 lincRNAs in pig longissimus dorsi muscle (LDM) of four stages with different IMF contents based on published RNA-seq. These lincRNAs show typical characteristics such as shorter length and lower expression compared with protein-coding genes. Combined with methylation data, we found that both the promoter and genebody methylation of lincRNAs can negatively regulate lincRNA expression. We found that lincRNAs exhibit high correlation with their protein-coding neighbors in expression. Co-expression network analysis resulted in eight stage-specific modules, gene ontology and pathway analysis of them suggested that some lincRNAs were involved in IMF-related processes, such as fatty acid metabolism and peroxisome proliferator-activated receptor signaling pathway. Furthermore, we identified hub lincRNAs and found six of them may play important roles in IMF development. This work detailed some lincRNAs which may affect of IMF development in pig, and facilitated future research on these lincRNAs and molecular assisted breeding for pig.

Multi-Objective Optimization Algorithm to Discover Condition-Specific Modules in Multiple Networks.

The advances in biological technologies make it possible to generate data for multiple conditions simultaneously. Discovering the condition-specific modules in multiple networks has great merit in understanding the underlying molecular mechanisms of cells. The available algorithms transform the multiple networks into a single objective optimization problem, which is criticized for its low accuracy. To address this issue, a multi-objective genetic algorithm for condition-specific modules in multiple networks (MOGA-CSM) is developed to discover the condition-specific modules. By using the artificial networks, we demonstrate that the MOGA-CSM outperforms state-of-the-art methods in terms of accuracy. Furthermore, MOGA-CSM discovers stage-specific modules in breast cancer networks based on The Cancer Genome Atlas (TCGA) data, and these modules serve as biomarkers to predict stages of breast cancer. The proposed model and algorithm provide an effective way to analyze multiple networks.

Identifying condition-specific modules by clustering multiple networks.

Condition-specific modules in multiple networks must be determined to reveal the underlying molecular mechanisms of diseases. Current algorithms exhibit limitations such as low accuracy and high sensitivity to the number of networks because these algorithms discover condition-specific modules in multiple networks by separating specificity and modularity of modules. To overcome these limitations, we characterize condition-specific module as a group of genes whose connectivity is strong in the corresponding network and weak in other networks; this strategy can accurately depict the topological structure of condition-specific modules. We then transform the condition-specific module discovery problem into a clustering problem in multiple networks. We develop an efficient heuristic algorithm for the Specific Modules in Multiple Networks (SMMN), which discovers the condition-specific modules by considering multiple networks. By using the artificial networks, we demonstrate that SMMN outperforms state-of-the-art methods. In breast cancer networks, stage-specific modules discovered by SMMN are more discriminative in predicting cancer stages than those obtained by other techniques. In pan-cancer networks, cancer-specific modules are more likely to associate with survival time of patients, which is critical for cancer therapy.

The dynamic landscape of gene regulation during Bombyx mori oogenesis

BackgroundOogenesis in the domestic silkworm (Bombyx mori) is a complex process involving previtellogenesis, vitellogenesis and choriogenesis. During this process, follicles show drastic morphological and physiological changes. However, the genome-wide regulatory profiles of gene expression during oogenesis remain to be determined.ResultsIn this study, we obtained time-series transcriptome data and used these data to reveal the dynamic landscape of gene regulation during oogenesis. A total of 1932 genes were identified to be differentially expressed among different stages, most of which occurred during the transition from late vitellogenesis to early choriogenesis. Using weighted gene co-expression network analysis, we identified six stage-specific gene modules that correspond to multiple regulatory pathways. Strikingly, the biosynthesis pathway of the molting hormone 20-hydroxyecdysone (20E) was enriched in one of the modules. Further analysis showed that the ecdysteroid 20-hydroxylase gene (CYP314A1) of steroidgenesis genes was mainly expressed in previtellogenesis and early vitellogenesis. However, the 20E–inactivated genes, particularly the ecdysteroid 26-hydroxylase encoding gene (Cyp18a1), were highly expressed in late vitellogenesis. These distinct expression patterns between 20E synthesis and catabolism-related genes might ensure the rapid decline of the hormone titer at the transition point from vitellogenesis to choriogenesis. In addition, we compared landscapes of gene regulation between silkworm (Lepidoptera) and fruit fly (Diptera) oogeneses. Our results show that there is some consensus in the modules of gene co-expression during oogenesis in these insects.ConclusionsThe data presented in this study provide new insights into the regulatory mechanisms underlying oogenesis in insects with polytrophic meroistic ovaries. The results also provide clues for further investigating the roles of epigenetic reconfiguration and circadian rhythm in insect oogenesis.

Genome-Wide Analysis of lncRNA and mRNA Expression During Differentiation of Abdominal Preadipocytes in the Chicken.

Long noncoding RNAs (lncRNAs) regulate adipogenesis and other processes associated with metabolic tissue development and function. However, little is known about the function and profile of lncRNAs during preadipocyte differentiation in the chicken (Gallus gallus). Herein, lncRNA and mRNA expression in preadipocytes at different stages of differentiation were analyzed using RNA sequencing. A total of 1,300,074,528 clean reads and 27,023 novel lncRNAs were obtained from 12 samples. The number of genes (1336 lncRNAs and 1759 mRNAs; 3095 in total) differentially expressed across various stages declined as differentiation progressed. Differentially expressed genes were found to be involved in several pathways related to preadipocyte differentiation that have been extensively studied, including glycerolipid metabolism, and the mammalian target of rapamycin, peroxisome proliferator-activated receptor, and mitogen-activated protein kinase signaling pathways. To our knowledge, some pathways are being reported for the first time, including the propanoate metabolism, fatty acid metabolism, and oxidative phosphorylation pathways. Furthermore, 3095 differentially expressed genes were clustered into eight clusters, and their expression patterns were determined through K-means clustering. Genes involved in the K2 cluster likely play important roles in preadipocyte differentiation. Six stage-specific modules related to A0 (day 0), A2 (day 2), and A6 (day 6) stages were identified, using weighted coexpression network analysis. Nine central, highly connected .genes in stage-specific modules were subsequently identified, including XLOC_068731, XLOC_022661, XLOC_045161, XLOC_070302, CHD6, LLGL1, NEURL1B, KLHL38, and ACTR6. This study provides a valuable resource for further study of chicken lncRNA and facilitates a better understanding of preadipocyte differentiation in the chicken

Analysis of long noncoding RNA and mRNA using RNA sequencing during the differentiation of intramuscular preadipocytes in chicken.

Long noncoding RNAs (lncRNAs) regulate metabolic tissue development and function, including adipogenesis. However, little is known about the function and profile of lncRNAs in intramuscular preadipocyte differentiation in chicken. Here, we identified lncRNAs in chicken intramuscular preadipocytes at different differentiation stages using RNA sequencing. A total of 1,311,382,604 clean reads and 25,435 lncRNAs were obtained from 12 samples. In total, 7,433 differentially expressed genes (4,698 lncRNAs and 2,735 mRNAs) were identified by pairwise comparison. These 7,433 differentially expressed genes were grouped into 11 clusters based on their expression patterns by K-means clustering. Using Weighted Gene Coexpression Network Analysis, we identified four stage-specific modules positively related to I0, I2, I4, and I6 stages and two stage-specific modules negatively related to I0 and I2 stages, respectively. Many well-known and novel pathways associated with intramuscular preadipocyte differentiation were identified. We also identified hub genes in each stage-specific module and visualized them in Cytoscape. Our analysis revealed many highly-connected genes, including XLOC_058593, BMP3, MYOD1, and LAMP3. This study provides a valuable resource for chicken lncRNA study and improves our understanding of the biology of preadipocyte differentiation in chicken.

Extracting Stage-Specific and Dynamic Modules Through Analyzing Multiple Networks Associated with Cancer Progression.

Determining the dynamics of pathways associated with cancer progression is critical for understanding the etiology of diseases. Advances in biological technology have facilitated the simultaneous genomic profiling of multiple patients at different clinical stages, thus generating the dynamic genomic data for cancers. Such data provide enable investigation of the dynamics of related pathways. However, methods for integrative analysis of dynamic genomic data are inadequate. In this study, we develop a novel nonnegative matrix factorization algorithm for dynamic modules ( NMF-DM), which simultaneously analyzes multiple networks for the identification of stage-specific and dynamic modules. NMF-DM applies the temporal smoothness framework by balancing the networks at the current stage and the previous stage. Experimental results indicate that the NMF-DM algorithm is more accurate than the state-of-the-art methods in artificial dynamic networks. In breast cancer networks, NMF-DM reveals the dynamic modules that are important for cancer stage transitions. Furthermore, the stage-specific and dynamic modules have distinct topological and biochemical properties. Finally, we demonstrate that the stage-specific modules significantly improve the accuracy of cancer stage prediction. The proposed algorithm provides an effective way to explore the time-dependent cancer genomic data.

Transcriptional profiles of bovine in vivo pre-implantation development

BackgroundDuring mammalian pre-implantation embryonic development dramatic and orchestrated changes occur in gene transcription. The identification of the complete changes has not been possible until the development of the Next Generation Sequencing Technology.ResultsHere we report comprehensive transcriptome dynamics of single matured bovine oocytes and pre-implantation embryos developed in vivo. Surprisingly, more than half of the estimated 22,000 bovine genes, 11,488 to 12,729 involved in more than 100 pathways, is expressed in oocytes and early embryos. Despite the similarity in the total numbers of genes expressed across stages, the nature of the expressed genes is dramatically different. A total of 2,845 genes were differentially expressed among different stages, of which the largest change was observed between the 4- and 8-cell stages, demonstrating that the bovine embryonic genome is activated at this transition. Additionally, 774 genes were identified as only expressed/highly enriched in particular stages of development, suggesting their stage-specific roles in embryogenesis. Using weighted gene co-expression network analysis, we found 12 stage-specific modules of co-expressed genes that can be used to represent the corresponding stage of development. Furthermore, we identified conserved key members (or hub genes) of the bovine expressed gene networks. Their vast association with other embryonic genes suggests that they may have important regulatory roles in embryo development; yet, the majority of the hub genes are relatively unknown/under-studied in embryos. We also conducted the first comparison of embryonic expression profiles across three mammalian species, human, mouse and bovine, for which RNA-seq data are available. We found that the three species share more maternally deposited genes than embryonic genome activated genes. More importantly, there are more similarities in embryonic transcriptomes between bovine and humans than between humans and mice, demonstrating that bovine embryos are better models for human embryonic development.ConclusionsThis study provides a comprehensive examination of gene activities in bovine embryos and identified little-known potential master regulators of pre-implantation development.Electronic supplementary materialThe online version of this article (doi:10.1186/1471-2164-15-756) contains supplementary material, which is available to authorized users.

Genetic programs in human and mouse early embryos revealed by single-cell RNA sequencing

Mammalian pre-implantation development is a complex process involving dramatic changes in the transcriptional architecture. We report here a comprehensive analysis of transcriptome dynamics from oocyte to morula in both human and mouse embryos, using single-cell RNA sequencing. Based on single-nucleotide variants in human blastomere messenger RNAs and paternal-specific single-nucleotide polymorphisms, we identify novel stage-specific monoallelic expression patterns for a significant portion of polymorphic gene transcripts (25 to 53%). By weighted gene co-expression network analysis, we find that each developmental stage can be delineated concisely by a small number of functional modules of co-expressed genes. This result indicates a sequential order of transcriptional changes in pathways of cell cycle, gene regulation, translation and metabolism, acting in a step-wise fashion from cleavage to morula. Cross-species comparisons with mouse pre-implantation embryos reveal that the majority of human stage-specific modules (7 out of 9) are notably preserved, but developmental specificity and timing differ between human and mouse. Furthermore, we identify conserved key members (or hub genes) of the human and mouse networks. These genes represent novel candidates that are likely to be key in driving mammalian pre-implantation development. Together, the results provide a valuable resource to dissect gene regulatory mechanisms underlying progressive development of early mammalian embryos.