Time-course Gene Expression Profiles Research Articles

Comparative single-cell transcriptional atlases of Babesia species reveal conserved and species-specific expression profiles.

Babesia is a genus of apicomplexan parasites that infect red blood cells in vertebrate hosts. Pathology occurs during rapid replication cycles in the asexual blood stage of infection. Current knowledge of Babesia replication cycle progression and regulation is limited and relies mostly on comparative studies with related parasites. Due to limitations in synchronizing Babesia parasites, fine-scale time-course transcriptomic resources are not readily available. Single-cell transcriptomics provides a powerful unbiased alternative for profiling asynchronous cell populations. Here, we applied single-cell RNA sequencing to 3 Babesia species (B. divergens, B. bovis, and B. bigemina). We used analytical approaches and algorithms to map the replication cycle and construct pseudo-synchronized time-course gene expression profiles. We identify clusters of co-expressed genes showing “just-in-time” expression profiles, with gradually cascading peaks throughout asexual development. Moreover, clustering analysis of reconstructed gene curves reveals coordinated timing of peak expression in epigenetic markers and transcription factors. Using a regularized Gaussian graphical model, we reconstructed co-expression networks and identified conserved and species-specific nodes. Motif analysis of a co-expression interactome of AP2 transcription factors identified specific motifs previously reported to play a role in DNA replication in Plasmodium species. Finally, we present an interactive web application to visualize and interactively explore the datasets.

Investigating key cell types and molecules dynamics in PyMT mice model of breast cancer through a mathematical model.

The most common kind of cancer among women is breast cancer. Understanding the tumor microenvironment and the interactions between individual cells and cytokines assists us in arriving at more effective treatments. Here, we develop a data-driven mathematical model to investigate the dynamics of key cell types and cytokines involved in breast cancer development. We use time-course gene expression profiles of a mouse model to estimate the relative abundance of cells and cytokines. We then employ a least-squares optimization method to evaluate the model’s parameters based on the mice data. The resulting dynamics of the cells and cytokines obtained from the optimal set of parameters exhibit a decent agreement between the data and predictions. We perform a sensitivity analysis to identify the crucial parameters of the model and then perform a local bifurcation on them. The results reveal a strong connection between adipocytes, IL6, and the cancer population, suggesting them as potential targets for therapies.

Bayesian approach for predicting responses to therapy from high-dimensional time-course gene expression profiles

BackgroundHistorical and updated information provided by time-course data collected during an entire treatment period proves to be more useful than information provided by single-point data. Accurate predictions made using time-course data on multiple biomarkers that indicate a patient’s response to therapy contribute positively to the decision-making process associated with designing effective treatment programs for various diseases. Therefore, the development of prediction methods incorporating time-course data on multiple markers is necessary.ResultsWe proposed new methods that may be used for prediction and gene selection via time-course gene expression profiles. Our prediction method consolidated multiple probabilities calculated using gene expression profiles collected over a series of time points to predict therapy response. Using two data sets collected from patients with hepatitis C virus (HCV) infection and multiple sclerosis (MS), we performed numerical experiments that predicted response to therapy and evaluated their accuracies. Our methods were more accurate than conventional methods and successfully selected genes, the functions of which were associated with the pathology of HCV infection and MS.ConclusionsThe proposed method accurately predicted response to therapy using data at multiple time points. It showed higher accuracies at early time points compared to those of conventional methods. Furthermore, this method successfully selected genes that were directly associated with diseases.

DeltaNeTS+: elucidating the mechanism of drugs and diseases using gene expression and transcriptional regulatory networks

BackgroundKnowledge on the molecular targets of diseases and drugs is crucial for elucidating disease pathogenesis and mechanism of action of drugs, and for driving drug discovery and treatment formulation. In this regard, high-throughput gene transcriptional profiling has become a leading technology, generating whole-genome data on the transcriptional alterations caused by diseases or drug compounds. However, identifying direct gene targets, especially in the background of indirect (downstream) effects, based on differential gene expressions is difficult due to the complexity of gene regulatory network governing the gene transcriptional processes.ResultsIn this work, we developed a network analysis method, called DeltaNeTS+, for inferring direct gene targets of drugs and diseases from gene transcriptional profiles. DeltaNeTS+ uses a gene regulatory network model to identify direct perturbations to the transcription of genes using gene expression data. Importantly, DeltaNeTS+ is able to combine both steady-state and time-course expression profiles, as well as leverage information on the gene network structure. We demonstrated the power of DeltaNeTS+ in predicting gene targets using gene expression data in complex organisms, including Caenorhabditis elegans and human cell lines (T-cell and Calu-3). More specifically, in an application to time-course gene expression profiles of influenza A H1N1 (swine flu) and H5N1 (avian flu) infection, DeltaNeTS+ shed light on the key differences of dynamic cellular perturbations caused by the two influenza strains.ConclusionDeltaNeTS+ is a powerful network analysis tool for inferring gene targets from gene expression profiles. As demonstrated in the case studies, by incorporating available information on gene network structure, DeltaNeTS+ produces accurate predictions of direct gene targets from a small sample size (~ 10 s). Integrating static and dynamic expression data with transcriptional network structure extracted from genomic information, as enabled by DeltaNeTS+, is crucial toward personalized medicine, where treatments can be tailored to individual patients. DeltaNeTS+ can be freely downloaded from http://www.github.com/cabsel/deltanetsplus.

Dynamic transcriptome analysis unveils key proresolving factors of chronic inflammatory arthritis.

Despite recent advances in understanding chronic inflammation remission, global analyses have not been explored to systematically discover genes or pathways underlying the resolution dynamics of chronic inflammatory diseases. Here, we performed time-course gene expression profiling of mouse synovial tissues along progression and resolution of collagen-induced arthritis (CIA) and identified genes associated with inflammation resolution. Through network analysis of these genes, we predicted 3 key secretory factors responsible for the resolution of CIA: Itgb1, Rps3, and Ywhaz. These factors were predominantly expressed by Tregs and antiinflammatory M2 macrophages, suppressing production of proinflammatory cytokines. In particular, Ywhaz was elevated in the sera of mice with arthritis resolution and in the urine of rheumatoid arthritis (RA) patients with good therapeutic responses. Moreover, adenovirus-mediated transfer of the Ywhaz gene to the affected joints substantially inhibited arthritis progression in mice with CIA and suppressed expression of proinflammatory cytokines in joint tissues, lymph nodes, and spleens, suggesting Ywhaz is an excellent target for RA therapy. Therefore, our comprehensive analysis of dynamic synovial transcriptomes provides previously unidentified antiarthritic genes, Itgb1, Rps3, and Ywhaz, which can serve as molecular markers to predict disease remission, as well as therapeutic targets for chronic inflammatory arthritis.

A Multi-Layered Study on Harmonic Oscillations in Mammalian Genomics and Proteomics.

Cellular, organ, and whole animal physiology show temporal variation predominantly featuring 24-h (circadian) periodicity. Time-course mRNA gene expression profiling in mouse liver showed two subsets of genes oscillating at the second (12-h) and third (8-h) harmonic of the prime (24-h) frequency. The aim of our study was to identify specific genomic, proteomic, and functional properties of ultradian and circadian subsets. We found hallmarks of the three oscillating gene subsets, including different (i) functional annotation, (ii) proteomic and electrochemical features, and (iii) transcription factor binding motifs in upstream regions of 8-h and 12-h oscillating genes that seemingly allow the link of the ultradian gene sets to a known circadian network. Our multifaceted bioinformatics analysis of circadian and ultradian genes suggests that the different rhythmicity of gene expression impacts physiological outcomes and may be related to transcriptional, translational and post-translational dynamics, as well as to phylogenetic and evolutionary components.

TRANSCRIPTOMIC ANALYSIS OF EARLY HEMATOPOIETIC DEVELOPMENT FROM HUMAN PLURIPOTENT STEM CELLS BY SINGLE-CELL RNA-SEQ IDENTIFIES THE PROMOTING EFFECT OF ARTERIAL ENDOTHELIUM

Human pluripotent stem cells (hPSCs) provide a powerful platform for studying the dynamic molecular network towards hematopoiesis. To date, a comprehensive roadmap at single-cell level for hPSC-derived hematopoietic differentiation has not been established. Here, we performed single-cell transcriptomic analyses to map gene expression programs during hematopoietic differentiation of hPSCs, from which new strategies to improve in vitro hematopoietic differentiation may be thus developed. We firstly established a serum-free, stroma-free and chemically defined monolayer system for hematopoietic differentiation of hPSCs, which enabled single-cell transcriptomic analysis. Through identifying sequential molecular events, we generated a time-course gene expression profile revealing the temporally restricted expression dynamics of stage-specific genes associated with hematopoietic cell fate choices. To better understand the lineage trajectories and gene regulatory networks governing diversification of cell fates, we established transcription factor regulatory networks predicting developmental fate choices during hematopoietic differentiation. By pseudotime analysis, we constructed the endothelial-hematopoietic trajectory revealing a novel hematopoietic development model that hemogenic endothelium initiated first and then differentiated into arteries and veins, whereas arteries had the ability to promote endothelial-to-hematopoietic transition. Moreover, we found that the genes related to aerobic metabolism were significantly decreased in hematopoietic cells and hypoxia-primed hematopoietic differentiation showed higher potency for generating more hematopoietic lineages. Further studies showed that hypoxia enhanced hematopoietic differentiation by promoting the development of arterial endothelium, rather than directly acting on hemogenic endothelium.

Identifying pre-disease signals before metabolic syndrome in mice by dynamical network biomarkers

The establishment of new therapeutic strategies for metabolic syndrome is urgently needed because metabolic syndrome, which is characterized by several disorders, such as hypertension, increases the risk of lifestyle-related diseases. One approach is to focus on the pre-disease state, a state with high susceptibility before the disease onset, which is considered as the best period for preventive treatment. In order to detect the pre-disease state, we recently proposed mathematical theory called the dynamical network biomarker (DNB) theory based on the critical transition paradigm. Here, we investigated time-course gene expression profiles of a mouse model of metabolic syndrome using 64 whole-genome microarrays based on the DNB theory, and showed the detection of a pre-disease state before metabolic syndrome defined by characteristic behavior of 147 DNB genes. The results of our study demonstrating the existence of a notable pre-disease state before metabolic syndrome may help to design novel and effective therapeutic strategies for preventing metabolic syndrome, enabling just-in-time preemptive interventions.

Gene expression profile in retinal excitotoxicity induced by L-glutamate in neonatal rats.

In neonatal rats, glutamate could induce retinal thinning depending on the development stage, and the severity peaked at treatment on postnatal day (PND) 8. To elucidate the molecular mechanism of retinal thinning induced by L-glutamate in neonatal rats, we investigated the time-course gene expression profile in the developing retina in addition to initial histopathological changes. Histopathologically, apoptotic cells in the inner retina were observed at 6 hours after treatment on PNDs 4, 6 and 8, and inflammatory cell infiltration was noted at 24 hours. Comprehensive gene expression analysis conducted on PNDs 4 and 8 indicated that cell death/proliferation- and inflammation-related genes were upregulated and that neuron development- and neurotransmitter-related genes were downregulated. Furthermore, quantitative RT-PCR analysis of apoptosis- and inflammation-related genes performed on PNDs 4, 6, 8, 10 and 12 showed that the time-course changes of the gene expression ratios of Gadd45b and Ccl3 seemed to be related to histopathological changes of the retina induced by L-glutamate. These results revealed that the association of initial histopathological changes with the gene expression profile in the retina induced by L-glutamate and that Gadd45b and Ccl3 are considered to participate in retinal thinning induced by L-glutamate in neonatal rats.

High throughput transcriptome analysis of coffee reveals prehaustorial resistance in response to Hemileia vastatrix infection

We provide a transcriptional profile of coffee rust interaction and identified putative up regulated resistant genes Coffee rust disease, caused by the fungus Hemileia vastatrix, is one of the major diseases in coffee throughout the world. The use of resistant cultivars is considered to be the most effective control strategy for this disease. To identify candidate genes related to different mechanism defense in coffee, we present a time-course comparative gene expression profile of Caturra (susceptible) and Híbrido de Timor (HdT, resistant) in response to H. vastatrix race XXXIII infection. The main objectives were to obtain a global overview of transcriptome in both interaction, compatible and incompatible, and, specially, analyze up-regulated HdT specific genes with inducible resistant and defense signaling pathways. Using both Coffea canephora as a reference genome and de novo assembly, we obtained 43,159 transcripts. At early infection events (12 and 24h after infection), HdT responded to the attack of H. vastatrix with a larger number of up-regulated genes than Caturra, which was related to prehaustorial resistance. The genes found in HdT at early hours were involved in receptor-like kinases, response ion fluxes, production of reactive oxygen species, protein phosphorylation, ethylene biosynthesis and callose deposition. We selected 13 up-regulated HdT-exclusive genes to validate by real-time qPCR, which most of them confirmed their higher expression in HdT than in Caturra at early stage of infection. These genes have the potential to assist the development of new coffee rust control strategies. Collectively, our results provide understanding of expression profiles in coffee-H. vastatrix interaction over a time course in susceptible and resistant coffee plants.

Time Course Expression Analysis of 1[2-cyano-3,12-dioxooleana-1,9(11)-dien-28-oyl]imidazole Induction of Cytoprotection in Human Endothelial Cells.

1[2-cyano-3,12-dioxooleana-1,9(11)-dien-28-oyl]imidazole (CDDO-Im), a synthetic derivative of oleanolic acid that exhibits antioxidant and anti-inflammatory activity in several animal and in vitro models, has been shown to be beneficial if given after injury. Although induction of heme oxygenase 1 appears to be a major effector of cytoprotection, the mechanism by which the overall effect is mediated is largely unknown. This study evaluated temporal gene expression profiles to better characterize the early transcriptional events and their relationship to the dynamics of the cytoprotective response in human umbilical vein endothelial cells (HUVEC) to CDDO-Im. Time-course gene expression profiling was performed on HUVEC treated with CDDO-Im for 0.5, 1, 3, 6, and 24 hours. More than 10 000 genes were statistically altered in their expression in at least 1 time point across the time course. Large alterations in immediate-early gene expression were readily detectable within 0.5 hour after administration of CDDO-Im.

Emerging Putative Associations between Non-Coding RNAs and Protein-Coding Genes in Neuropathic Pain: Added Value from Reusing Microarray Data.

Regeneration of injured nerves is likely occurring in the peripheral nervous system, but not in the central nervous system. Although protein-coding gene expression has been assessed during nerve regeneration, little is currently known about the role of non-coding RNAs (ncRNAs). This leaves open questions about the potential effects of ncRNAs at transcriptome level. Due to the limited availability of human neuropathic pain (NP) data, we have identified the most comprehensive time-course gene expression profile referred to sciatic nerve (SN) injury and studied in a rat model using two neuronal tissues, namely dorsal root ganglion (DRG) and SN. We have developed a methodology to identify differentially expressed bioentities starting from microarray probes and repurposing them to annotate ncRNAs, while analyzing the expression profiles of protein-coding genes. The approach is designed to reuse microarray data and perform first profiling and then meta-analysis through three main steps. First, we used contextual analysis to identify what we considered putative or potential protein-coding targets for selected ncRNAs. Relevance was therefore assigned to differential expression of neighbor protein-coding genes, with neighborhood defined by a fixed genomic distance from long or antisense ncRNA loci, and of parental genes associated with pseudogenes. Second, connectivity among putative targets was used to build networks, in turn useful to conduct inference at interactomic scale. Last, network paths were annotated to assess relevance to NP. We found significant differential expression in long-intergenic ncRNAs (32 lincRNAs in SN and 8 in DRG), antisense RNA (31 asRNA in SN and 12 in DRG), and pseudogenes (456 in SN and 56 in DRG). In particular, contextual analysis centered on pseudogenes revealed some targets with known association to neurodegeneration and/or neurogenesis processes. While modules of the olfactory receptors were clearly identified in protein–protein interaction networks, other connectivity paths were identified between proteins already investigated in studies on disorders, such as Parkinson, Down syndrome, Huntington disease, and Alzheimer. Our findings suggest the importance of reusing gene expression data by meta-analysis approaches.

Analyzing gene expression time-courses based on multi-resolution shape mixture model

Objective:Biological processes actually are a dynamic molecular process over time. Time course gene expression experiments provide opportunities to explore patterns of gene expression change over a time and understand the dynamic behavior of gene expression, which is crucial for study on development and progression of biology and disease. Analysis of the gene expression time-course profiles has not been fully exploited so far. It is still a challenge problem. We propose a novel shape-based mixture model clustering method for gene expression time-course profiles to explore the significant gene groups.Results: Based on multi-resolution fractal features and mixture clustering model, we proposed a multi-resolution shape mixture model algorithm. Multi-resolution fractal features is computed by wavelet decomposition, which explore patterns of change over time of gene expression at different resolution. Our proposed multi-resolution shape mixture model algorithm is a probabilistic framework which offers a more natural and robust way of clustering time-course gene expression. We assessed the performance of our proposed algorithm using yeast time-course gene expression profiles compared with several popular clustering methods for gene expression profiles. The grouped genes identified by different methods are evaluated by enrichment analysis of biological pathways and known protein–protein interactions from experiment evidence. The grouped genes identified by our proposed algorithm have more strong biological significance.Conclusion: A novel multi-resolution shape mixture model algorithm based on multi-resolution fractal features is proposed. Our proposed model provides a novel horizons and an alternative tool for visualization and analysis of time-course gene expression profiles.Availability: The R and Matlab program is available upon the request.

Time-Course Gene Expression Profiling Reveals a Novel Role of Non-Canonical WNT Signaling During Neural Induction.

The process of neuroepithelial differentiation from human pluripotent stem cells (PSCs) resembles in vivo neuroectoderm induction in the temporal course, morphogenesis, and biochemical changes. This in vitro model is therefore well-suited to reveal previously unknown molecular mechanisms underlying neural induction in humans. By transcriptome analysis of cells along PSC differentiation to early neuroepithelia at day 6 and definitive neuroepithelia at day 10, we found downregulation of genes that are associated with TGF-β and canonical WNT/β-CATENIN signaling, confirming the roles of classical signaling in human neural induction. Interestingly, WNT/Ca2+ signaling was upregulated. Pharmacological inhibition of the downstream effector of WNT/Ca2+ pathway, Ca2+/calmodulin-dependent protein kinase II (CaMKII), led to an inhibition of the neural marker PAX6 and upregulation of epidermal marker K18, suggesting that Ca2+/CaMKII signaling promotes neural induction by preventing the alternative epidermal fate. In addition, our analyses revealed known and novel expression patterns of genes that are involved in DNA methylation, histone modification, as well as epithelial-mesenchymal transition, highlighting potential roles of those genes and signaling pathways during neural differentiation.

Transcriptomic analyses of the radiation response in head and neck squamous cell carcinoma subclones with different radiation sensitivity: time-course gene expression profiles and gene association networks.

BackgroundAcquired and inherent radioresistance of tumor cells is related to tumor relapse and poor prognosis – not only in head and neck squamous cell carcinoma (HNSCC). The underlying molecular mechanisms are largely unknown. Therefore, systemic in-depth analyses are needed to identify key regulators of radioresistance. In the present study, subclones of the CAL-33 HNSCC cell line with different radiosensitivity were analyzed to identify signaling pathways related to the different phenotypes.MethodsSubclones with altered radiosensitivity were generated by fractionated irradiation of the parental CAL-33 cells. Differences in radiosensitivity were confirmed in colony formation assays. Selected subclones were characterized at the genomic and transcriptomic level by SKY, array CGH, and mRNA-microarray analyses. Time-course gene expression analyses upon irradiation using a natural cubic spline regression model identified temporally differentially expressed genes. Moreover, early and late responding genes were identified. Gene association networks were reconstructed using partial correlation. The Reactome pathway database was employed to conduct pathway enrichment analyses.ResultsThe characterization of two subclones with enhanced radiation resistance (RP) and enhanced radiosensitivity (SP) revealed distinct genomic and transcriptomic changes compared to the parental cells. Differentially expressed genes after irradiation shared by both subclones pointed to important pathways of the early and late radiation response, including senescence, apoptosis, DNA repair, Wnt, PI3K/AKT, and Rho GTPase signaling. The analysis of the most important nodes of the gene association networks revealed pathways specific to the radiation response in different phenotypes of radiosensitivity. Exemplarily, for the RP subclone the senescence-associated secretory phenotype (SASP) together with GPCR ligand binding were considered as crucial. Also, the expression of endogenous retrovirus ERV3-1in response to irradiation has been observed, and the related gene association networks have been identified.ConclusionsOur study presents comprehensive gene expression data of CAL-33 subclones with different radiation sensitivity. The resulting networks and pathways associated with the resistant phenotype are of special interest and include the SASP. The radiation-associated expression of ERV3-1 also appears highly attractive for further studies of the molecular mechanisms underlying acquired radioresistance. The identified pathways may represent key players of radioresistance, which could serve as potential targets for molecularly designed, therapeutical intervention.Electronic supplementary materialThe online version of this article (doi:10.1186/s13014-016-0672-0) contains supplementary material, which is available to authorized users.

Detecting shifts in gene regulatory networks during time-course experiments at single-time-point temporal resolution

Comprehensively understanding the dynamics of biological systems is one of the greatest challenges in biology. Vastly improved biological technologies have provided vast amounts of information that must be understood by bioinformatics and systems biology researchers. Gene regulations have been frequently modeled by ordinary differential equations or graphical models based on time-course gene expression profiles. The state-of-the-art computational approaches for analyzing gene regulations assume that their models are same throughout time-course experiments. However, these approaches cannot easily analyze transient changes at a time point, such as diauxic shift. We propose a score that analyzes the gene regulations at each time point. The score is based on the information gains of information criterion values. The method detects the shifts in gene regulatory networks (GRNs) during time-course experiments with single-time-point resolution. The effectiveness of the method is evaluated on the diauxic shift from glucose to lactose in Escherichia coli. Gene regulation shifts were detected at two time points: the first corresponding to the time at which the growth of E. coli ceased and the second corresponding to the end of the experiment, when the nutrient sources (glucose and lactose) had become exhausted. According to these results, the proposed score and method can appropriately detect the time of gene regulation shifts. The method based on the proposed score provides a new tool for analyzing dynamic biological systems. Because the score value indicates the strength of gene regulation at each time point in a gene expression profile, it can potentially infer hidden GRNs from time-course experiments.

Functional Analysis and Characterization of Differential Coexpression Networks.

Differential coexpression analysis is emerging as a complement to conventional differential gene expression analysis. The identified differential coexpression links can be assembled into a differential coexpression network (DCEN) in response to environmental stresses or genetic changes. Differential coexpression analyses have been successfully used to identify condition-specific modules; however, the structural properties and biological significance of general DCENs have not been well investigated. Here, we analyzed two independent Saccharomyces cerevisiae DCENs constructed from large-scale time-course gene expression profiles in response to different situations. Topological analyses show that DCENs are tree-like networks possessing scale-free characteristics, but not small-world. Functional analyses indicate that differentially coexpressed gene pairs in DCEN tend to link different biological processes, achieving complementary or synergistic effects. Furthermore, the gene pairs lacking common transcription factors are sensitive to perturbation and hence lead to differential coexpression. Based on these observations, we integrated transcriptional regulatory information into DCEN and identified transcription factors that might cause differential coexpression by gain or loss of activation in response to different situations. Collectively, our results not only uncover the unique structural characteristics of DCEN but also provide new insights into interpretation of DCEN to reveal its biological significance and infer the underlying gene regulatory dynamics.

Analysis of time-course gene expression profiles to study regulation of cell growth in fed-batch bioreactors

Compared to other systems biology tools, genomic microarrays represent a mature platform that allows for facile access to the internal biological mechanisms of cell culture. While the large datasets generated by microarrays are a potential goldmine of information, ironically, it is their large size that also makes data-mining a cumbersome task. This can get further complicated by unavoidable batch effects generated when different datasets are combined. Furthermore, gene expression profiles are dependent on combinations of various complex intracellular events and as such identifying the signals primarily related to the phenotype of interest poses a substantial challenge. In this study we addressed these issues by utilizing a workflow that allows adjustment of time-course gene expression datasets for batch effects and incorporates the use of sparse partial least squares analysis to identify specific genes of interest. We were able to identify a set of relevant genes that displayed a strong correlation with cell growth in fed-batch bioreactors under different nutrient compositions. By conducting further biological network analysis, we identified four transcriptional regulators, namely ATP7B, SREBP1, SCAP and INSIG2 that are responsible for regulation of these genes and are likely important drivers for cell growth differences in response to change in nutrient composition.

The regulation of mechanosensory motile cilium formation

In contrast to the progress in understanding ciliogenesis and cilium function, we know less about the pathways for generating ciliary diversity. Drosophila has a variety of sensory neurons with ciliary dendrites that are structurally and functionally specialised for receiving different sensory modalities. For instance, chordotonal (Ch) neurons have mechanosensory ciliary dendrites and are required for proprioception and hearing. Time-course gene expression profiling of differentiating Ch neurons allowed us to characterise the roles of two transcription factors for ciliogenic gene regulation: the well-known cilia gene regulator, Rfx, and a factor of the Forkhead family (Fd3F), which appears to be a diverged homologue of FOXJ1. Fd3F and Rfx cooperate to regulate a cohort of genes required for ciliary motility - in Drosophila this is a specialisation unique to Ch neuron cilia and is essential for the hearing mechanism. Analysing the target genes of Fd3F has led to the implication of new factors in the assembly of axonemal dynein complexes. Two of these are also mutated in human primary ciliary dyskinesia. Further analysis of these genes in Drosophila and mouse suggests that ZMYND10 may be linked to an emerging chaperone pathway, while HEATR2 appears to have a distinct function related to transport.

PI3K/Akt pathway regulates retinoic acid-induced Hox gene expression in F9 cells.

Retinoic acid (RA), the most potent natural form of vitamin A, is a key morphogen in vertebrate development and a potent regulator of both adult and embryonic cell differentiation. Specifically, RA regulates clustered Hox gene expression during embryogenesis and is required to establish the anteroposterior body plan. The PI3K/Akt pathway was also reported to play an essential role in the process of RA-induced cell differentiation. Therefore, we tested whether the PI3K/Akt pathway is involved in RA-induced Hox gene expression in a F9 murine embryonic teratocarcinoma cells. To examine the effect of PI3K/Akt signaling on RA-induced initiation of collinear expression of Hox genes, F9 cells were treated with RA in the presence or absence of PI3K inhibitor LY294002, and time-course gene expression profiles for all 39 Hox genes located in four different clusters-Hoxa, Hoxb, Hoxc, and Hoxd-were analyzed. Collinear expression of Hoxa and -b cluster genes was initiated earlier than that of the -c and -d clusters upon RA treatment. When LY294002 was applied along with RA, collinear expression induced by RA was delayed, suggesting that the PI3K/Akt signaling pathway somehow regulates RA-induced collinear expression of Hox genes in F9 cells. The initiation of Hox collinear expression by RA and the delayed expression following LY294002 in F9 cells would provide a good model system to decipher the yet to be answered de novo collinear expression of Hox genes during gastrulation, which make the gastrulating cells to remember their positional address along the AP body axis in the developing embryo.