Natural Cubic Spline Regression Modeling Followed by Dynamic Network Reconstruction for the Identification of Radiation-Sensitivity Gene Association Networks from Time-Course Transcriptome Data

Dynamics in zebrafish development define transcriptomic specificity after angiogenesis inhibitor exposure

Testing for developmental toxicity is an integral part of chemical regulations. The applied tests are laborious and costly and require a large number of vertebrate test animals. To reduce animal numbers and associated costs, the zebrafish embryo was proposed as an alternative model. In this study, we investigated the potential of transcriptome analysis in the zebrafish embryo model to support the identification of potential biomarkers for key events in developmental toxicity, using the inhibition of angiogenesis as a proof of principle. Therefore, the effects on the zebrafish transcriptome after exposure to the tyrosine kinase inhibitors, sorafenib (1.3 µM and 2.4 µM) and SU4312 (1 µM, 2 µM, and 5 µM), and the putative vascular disruptor compound rotenone (25 nM and 50 nM) were analyzed. An early (2 hpf-hours post fertilization) and a late (24 hpf) exposure start with a time resolved transcriptome analysis was performed to compare the specificity and sensitivity of the responses with respect to anti-angiogenesis. We also showed that toxicodynamic responses were related to the course of the internal concentrations. To identify differentially expressed genes (DEGs) the time series data were compared by applying generalized additive models (GAMs). We observed mainly unspecific developmental toxicity in the early exposure scenario, while a specific repression of vascular related genes was only partially observed. In contrast, differential expression of vascular-related genes could be identified clearly in the late exposure scenario. Rotenone did not show angiogenesis-specific response on a transcriptomic level, indicating that the observed mild phenotype of angiogenesis inhibition may represent a secondary effect.

Single cell lineage tracing reveals clonal dynamics of anti-EGFR therapy resistance in triple negative breast cancer

BACKGROUND

Most primary Triple Negative Breast Cancers (TNBCs) show amplification of the Epidermal Growth Factor Receptor (EGFR) gene, leading to increased protein expression. However, unlike other EGFR-driven cancers, targeting this receptor in TNBC yields inconsistent therapeutic responses.

METHODS

To elucidate the underlying mechanisms of this variability, we employ cellular barcoding and single-cell transcriptomics to reconstruct the subclonal dynamics of EGFR-amplified TNBC cells in response to afatinib, a tyrosine kinase inhibitor (TKI) that irreversibly inhibits EGFR.

RESULTS

Integrated lineage tracing analysis revealed a rare pre-existing subpopulation of cells with distinct biological signature, including elevated expression levels of Insulin-Like Growth Factor Binding Protein 2 (IGFBP2). We show that IGFBP2 overexpression is sufficient to render TNBC cells tolerant to afatinib treatment by activating the compensatory insulin-like growth factor I receptor (IGF1-R) signalling pathway. Finally, based on reconstructed mechanisms of resistance, we employ deep learning techniques to predict the afatinib sensitivity of TNBC cells.

CONCLUSIONS

Our strategy proved effective in reconstructing the complex signalling network driving EGFR-targeted therapy resistance, offering new insights for the development of individualized treatment strategies in TNBC.

Single-embryo RNA sequencing for continuous and sex-specific gene expression analysis on Drosophila

Exploring early embryonic gene expression is challenging due to the rate of development and the limited material available. Here, we present a protocol for ordering Drosophila embryos along a developmental pseudo-time trajectory and determining the sex of the embryos using RNA-seq data. We describe steps for sample collection, RNA isolation, RNA-seq, and RNA-seq data processing. We then detail the establishment of a continuous transcriptome dataset for assessing gene expression throughout early development and in a sex-specific manner. For complete details on the use and execution of this protocol, please refer to Pérez-Mojica et al.1.

Continuous transcriptome analysis reveals novel patterns of early gene expression in Drosophila embryos

The transformative events during early organismal development lay the foundation for body formation and long-term phenotype. The rapid progression of events and the limited material available present major barriers to studying these earliest stages of development. Herein, we report an operationally simple RNA sequencing approach for high-resolution, time-sensitive transcriptome analysis in early (≤3 h) Drosophila embryos. This method does not require embryo staging but relies on single-embryo RNA sequencing and transcriptome ordering along a developmental trajectory (pseudo-time). The resulting high-resolution, time-sensitive mRNA expression profiles reveal the exact onset of transcription and degradation for thousands of transcripts. Further, using sex-specific transcription signatures, embryos can be sexed directly, eliminating the need for Y chromosome genotyping and revealing patterns of sex-biased transcription from the beginning of zygotic transcription. Our data provide an unparalleled resolution of gene expression during early development and enhance the current understanding of early transcriptional processes.

Time-restricted eating alters the 24-hour profile of adipose tissue transcriptome in men with obesity

OBJECTIVE

Time-restricted eating (TRE) restores circadian rhythms in mice, but the evidence to support this in humans is limited. The objective of this study was to investigate the effects of TRE on 24-hour profiles of plasma metabolites, glucoregulatory hormones, and the subcutaneous adipose tissue (SAT) transcriptome in humans.

METHODS

Men (n = 15, age = 63 [4] years, BMI 30.5 [2.4] kg/m2 ) were recruited. A 35-hour metabolic ward stay was conducted at baseline and after 8 weeks of 10-hour TRE. Assessment included 24-hour profiles of plasma glucose, nonesterified fatty acid (NEFA), triglyceride, glucoregulatory hormones, and the SAT transcriptome. Dim light melatonin onset and cortisol area under the curve were calculated.

RESULTS

TRE did not alter dim light melatonin onset but reduced morning cortisol area under the curve. TRE altered 24-hour profiles of insulin, NEFA, triglyceride, and glucose-dependent insulinotropic peptide and increased transcripts of circadian locomotor output cycles protein kaput (CLOCK) and nuclear receptor subfamily 1 group D member 2 (NR1D2) and decreased period circadian regulator 1 (PER1) and nuclear receptor subfamily 1 group D member 1 (NR1D1) at 12:00 am. The rhythmicity of 450 genes was altered by TRE, which enriched in transcripts for transcription corepressor activity, DNA-binding transcription factor binding, regulation of chromatin organization, and small GTPase binding pathways. Weighted gene coexpression network analysis revealed eigengenes that were correlated with BMI, insulin, and NEFA.

CONCLUSIONS

TRE restored 24-hour profiles in hormones, metabolites, and genes controlling transcriptional regulation in SAT, which could underpin its metabolic health benefit.

DNA methylation predicts the outcome of COVID-19 patients with acute respiratory distress syndrome

BACKGROUND

COVID-19 infections could be complicated by acute respiratory distress syndrome (ARDS), increasing mortality risk. We sought to assess the methylome of peripheral blood mononuclear cells in COVID-19 with ARDS.

METHODS

We recruited 100 COVID-19 patients with ARDS under mechanical ventilation and 33 non-COVID-19 controls between April and July 2020. COVID-19 patients were followed at four time points for 60 days. DNA methylation and immune cell populations were measured at each time point. A multivariate cox proportional risk regression analysis was conducted to identify predictive signatures according to survival.

RESULTS

The comparison of COVID-19 to controls at inclusion revealed the presence of a 14.4% difference in promoter-associated CpGs in genes that control immune-related pathways such as interferon-gamma and interferon-alpha responses. On day 60, 24% of patients died. The inter-comparison of baseline DNA methylation to the last recorded time point in both COVID-19 groups or the intra-comparison between inclusion and the end of follow-up in every group showed that most changes occurred as the disease progressed, mainly in the AIM gene, which is associated with an intensified immune response in those who recovered. The multivariate Cox proportional risk regression analysis showed that higher methylation of the "Apoptotic execution Pathway" genes (ROC1, ZNF789, and H1F0) at inclusion increases mortality risk by over twofold.

CONCLUSION

We observed an epigenetic signature of immune-related genes in COVID-19 patients with ARDS. Further, Hypermethylation of the apoptotic execution pathway genes predicts the outcome.

TRIAL REGISTRATION

IMRPOVIE study, NCT04473131.

Integrated time-series transcriptomic and metabolomic analyses reveal different inflammatory and adaptive immune responses contributing to host resistance to PRRSV

Porcine reproductive and respiratory syndrome virus (PRRSV) is a highly contagious disease that affects the global pig industry. To understand mechanisms of susceptibility/resistance to PRRSV, this study profiled the time-serial white blood cells transcriptomic and serum metabolomic responses to PRRSV in piglets from a crossbred population of PRRSV-resistant Tongcheng pigs and PRRSV-susceptible Large White pigs. Gene set enrichment analysis (GSEA) illustrated that PRRSV infection up-regulated the expression levels of marker genes of dendritic cells, monocytes and neutrophils and inflammatory response, but down-regulated T cells, B cells and NK cells markers. CIBERSORT analysis confirmed the higher T cells proportion in resistant pigs during PRRSV infection. Resistant pigs showed a significantly higher level of T cell activation and lower expression levels of monocyte surface signatures post infection than susceptible pigs, corresponding to more severe suppression of T cell immunity and inflammatory response in susceptible pigs. Differentially expressed genes between resistant/susceptible pigs during the course of infection were significantly enriched in oxidative stress, innate immunity and humoral immunity, cell cycle, biotic stimulated cellular response, wounding response and behavior related pathways. Fourteen of these genes were distributed in 5 different QTL regions associated with PRRSV-related traits. Chemokine CXCL10 levels post PRRSV infection were differentially expressed between resistant pigs and susceptible pigs and can be a promising marker for susceptibility/resistance to PRRSV. Furthermore, the metabolomics dataset indicated differences in amino acid pathways and lipid metabolism between pre-infection/post-infection and resistant/susceptible pigs. The majority of metabolites levels were also down-regulated after PRRSV infection and were significantly positively correlated to the expression levels of marker genes in adaptive immune response. The integration of transcriptome and metabolome revealed concerted molecular events triggered by the infection, notably involving inflammatory response, adaptive immunity and G protein-coupled receptor downstream signaling. This study has increased our knowledge of the immune response differences induced by PRRSV infection and susceptibility differences at the transcriptomic and metabolomic levels, providing the basis for the PRRSV resistance mechanism and effective PRRS control.

Integrative omics approaches for biosynthetic pathway discovery in plants

Covering: up to 2022With the emergence of large amounts of omics data, computational approaches for the identification of plant natural product biosynthetic pathways and their genetic regulation have become increasingly important. While genomes provide clues regarding functional associations between genes based on gene clustering, metabolome mining provides a foundational technology to chart natural product structural diversity in plants, and transcriptomics has been successfully used to identify new members of their biosynthetic pathways based on coexpression. Thus far, most approaches utilizing transcriptomics and metabolomics have been targeted towards specific pathways and use one type of omics data at a time. Recent technological advances now provide new opportunities for integration of multiple omics types and untargeted pathway discovery. Here, we review advances in plant biosynthetic pathway discovery using genomics, transcriptomics, and metabolomics, as well as recent efforts towards omics integration. We highlight how transcriptomics and metabolomics provide complementary information to link genes to metabolites, by associating temporal and spatial gene expression levels with metabolite abundance levels across samples, and by matching mass-spectral features to enzyme families. Furthermore, we suggest that elucidation of gene regulatory networks using time-series data may prove useful for efforts to unwire the complexities of biosynthetic pathway components based on regulatory interactions and events.

Clinical, genomic, and transcriptomic correlates of response to immune checkpoint blockade-based therapy in a cohort of patients with angiosarcoma treated at a single center

Background

Angiosarcoma is a histologically and molecularly heterogeneous vascular neoplasm with aggressive clinical behavior. Emerging data suggests that immune checkpoint blockade (ICB) is efficacious against some angiosarcomas, particularly cutaneous angiosarcoma of the head and neck (CHN).

Methods

Patients with histologically confirmed angiosarcoma treated with ICB-based therapy at a comprehensive cancer center were retrospectively identified. Clinical characteristics and the results of targeted exome sequencing, transcriptome sequencing, and immunohistochemistry analyses were examined for correlation with clinical benefit. Durable clinical benefit was defined as a progression-free survival (PFS) of ≥16 weeks.

Results

For the 35 patients included in the analyses, median PFS and median overall survival (OS) from the time of first ICB-based treatment were 11.9 (95% CI 7.4 to 31.9) and 42.5 (95% CI 19.6 to 114.2) weeks, respectively. Thirteen patients (37%) had PFS ≥16 weeks. Clinical factors associated with longer PFS and longer OS in multivariate analyses were ICB plus other therapy regimens, CHN disease, and white race. Three of 10 patients with CHN angiosarcoma evaluable for tumor mutational burden (TMB) had a TMB ≥10. Five of six patients with CHN angiosarcoma evaluable for mutational signature analysis had a dominant mutational signature associated with ultraviolet (UV) light. No individual gene or genomic pathway was significantly associated with PFS or OS; neither were TMB or UV signature status. Analyses of whole transcriptomes from nine patient tumor samples found upregulation of angiogenesis, inflammatory response, and KRAS signaling pathways, among others, in patients with PFS ≥16 weeks, as well as higher levels of cytotoxic T cells, dendritic cells, and natural killer cells. Patients with PFS <16 weeks had higher numbers of cancer-associated fibroblasts. Immunohistochemistry findings for 12 patients with baseline samples available suggest that neither PD-L1 expression nor presence of tumor-infiltrating lymphocytes at baseline appears necessary for a response to ICB-based therapy.

Conclusions

ICB-based therapy benefits only a subset of angiosarcoma patients. Patients with CHN angiosarcoma are more likely to have PFS ≥16 weeks, a dominant UV mutational signature, and higher TMB than angiosarcomas arising from other primary sites. However, clinical benefit was seen in other angiosarcomas also and was not restricted to tumors with a high TMB, a dominant UV signature, PD-L1 expression, or presence of tumor infiltrating lymphocytes at baseline.

Rewiring of the Liver Transcriptome across Multiple Time-Scales Is Associated with the Weight Loss-Independent Resolution of NAFLD Following RYGB

Roux-en-Y gastric bypass (RYGB) surgery potently improves obesity and a myriad of obesity-associated co-morbidities including type 2 diabetes and non-alcoholic fatty liver disease (NAFLD). Time-series omics data are increasingly being utilized to provide insight into the mechanistic underpinnings that correspond to metabolic adaptations in RYGB. However, the conventional computational biology methods used to interpret these temporal multi-dimensional datasets have been generally limited to pathway enrichment analysis (PEA) of isolated pair-wise comparisons based on either experimental condition or time point, neither of which adequately capture responses to perturbations that span multiple time scales. To address this, we have developed a novel graph network-based analysis workflow designed to identify modules enriched with biomolecules that share common dynamic profiles, where the network is constructed from all known biological interactions available through the Kyoto Encyclopedia of Genes and Genomes (KEGG) resource. This methodology was applied to time-series RNAseq transcriptomics data collected on rodent liver samples following RYGB, and those of sham-operated and weight-matched control groups, to elucidate the molecular pathways involved in the improvement of as NAFLD. We report several network modules exhibiting a statistically significant enrichment of genes whose expression trends capture acute-phase as well as long term physiological responses to RYGB in a single analysis. Of note, we found the HIF1 and P53 signaling cascades to be associated with the immediate and the long-term response to RYGB, respectively. The discovery of less intuitive network modules that may have gone overlooked with conventional PEA techniques provides a framework for identifying novel drug targets for NAFLD and other metabolic syndrome co-morbidities.

TIMEOR: a web-based tool to uncover temporal regulatory mechanisms from multi-omics data

Uncovering how transcription factors regulate their targets at DNA, RNA and protein levels over time is critical to define gene regulatory networks (GRNs) and assign mechanisms in normal and diseased states. RNA-seq is a standard method measuring gene regulation using an established set of analysis stages. However, none of the currently available pipeline methods for interpreting ordered genomic data (in time or space) use time-series models to assign cause and effect relationships within GRNs, are adaptive to diverse experimental designs, or enable user interpretation through a web-based platform. Furthermore, methods integrating ordered RNA-seq data with protein–DNA binding data to distinguish direct from indirect interactions are urgently needed. We present TIMEOR (Trajectory Inference and Mechanism Exploration with Omics data in R), the first web-based and adaptive time-series multi-omics pipeline method which infers the relationship between gene regulatory events across time. TIMEOR addresses the critical need for methods to determine causal regulatory mechanism networks by leveraging time-series RNA-seq, motif analysis, protein–DNA binding data, and protein-protein interaction networks. TIMEOR’s user-catered approach helps non-coders generate new hypotheses and validate known mechanisms. We used TIMEOR to identify a novel link between insulin stimulation and the circadian rhythm cycle. TIMEOR is available at https://github.com/ashleymaeconard/TIMEOR.git and http://timeor.brown.edu.

Discovering key transcriptomic regulators in pancreatic ductal adenocarcinoma using Dirichlet process Gaussian mixture model

Pancreatic Ductal Adenocarcinoma (PDAC) is the most lethal type of pancreatic cancer, late detection leading to its therapeutic failure. This study aims to determine the key regulatory genes and their impacts on the disease’s progression, helping the disease’s etiology, which is still mostly unknown. We leverage the landmark advantages of time-series gene expression data of this disease and thereby identified the key regulators that capture the characteristics of gene activity patterns in the cancer progression. We have identified the key gene modules and predicted the functions of top genes from a reconstructed gene association network (GAN). A variation of the partial correlation method is utilized to analyze the GAN, followed by a gene function prediction task. Moreover, we have identified regulators for each target gene by gene regulatory network inference using the dynamical GENIE3 (dynGENIE3) algorithm. The Dirichlet process Gaussian process mixture model and cubic spline regression model (splineTimeR) are employed to identify the key gene modules and differentially expressed genes, respectively. Our analysis demonstrates a panel of key regulators and gene modules that are crucial for PDAC disease progression.

Jonckheere-Terpstra-Kendall-based non-parametric analysis of temporal differential gene expression

Time-course experiments using parallel sequencers have the potential to uncover gradual changes in cells over time that cannot be observed in a two-point comparison. An essential step in time-series data analysis is the identification of temporal differentially expressed genes (TEGs) under two conditions (e.g. control versus case). Model-based approaches, which are typical TEG detection methods, often set one parameter (e.g. degree or degree of freedom) for one dataset. This approach risks modeling of linearly increasing genes with higher-order functions, or fitting of cyclic gene expression with linear functions, thereby leading to false positives/negatives. Here, we present a Jonckheere-Terpstra-Kendall (JTK)-based non-parametric algorithm for TEG detection. Benchmarks, using simulation data, show that the JTK-based approach outperforms existing methods, especially in long time-series experiments. Additionally, application of JTK in the analysis of time-series RNA-seq data from seven tissue types, across developmental stages in mouse and rat, suggested that the wave pattern contributes to the TEG identification of JTK, not the difference in expression levels. This result suggests that JTK is a suitable algorithm when focusing on expression patterns over time rather than expression levels, such as comparisons between different species. These results show that JTK is an excellent candidate for TEG detection.

Temporal Dynamic Methods for Bulk RNA-Seq Time Series Data

Dynamic studies in time course experimental designs and clinical approaches have been widely used by the biomedical community. These applications are particularly relevant in stimuli-response models under environmental conditions, characterization of gradient biological processes in developmental biology, identification of therapeutic effects in clinical trials, disease progressive models, cell-cycle, and circadian periodicity. Despite their feasibility and popularity, sophisticated dynamic methods that are well validated in large-scale comparative studies, in terms of statistical and computational rigor, are less benchmarked, comparing to their static counterparts. To date, a number of novel methods in bulk RNA-Seq data have been developed for the various time-dependent stimuli, circadian rhythms, cell-lineage in differentiation, and disease progression. Here, we comprehensively review a key set of representative dynamic strategies and discuss current issues associated with the detection of dynamically changing genes. We also provide recommendations for future directions for studying non-periodical, periodical time course data, and meta-dynamic datasets.

rmRNAseq: differential expression analysis for repeated-measures RNA-seq data

MOTIVATION

With the reduction in price of next-generation sequencing technologies, gene expression profiling using RNA-seq has increased the scope of sequencing experiments to include more complex designs, such as designs involving repeated measures. In such designs, RNA samples are extracted from each experimental unit at multiple time points. The read counts that result from RNA sequencing of the samples extracted from the same experimental unit tend to be temporally correlated. Although there are many methods for RNA-seq differential expression analysis, existing methods do not properly account for within-unit correlations that arise in repeated-measures designs.

RESULTS

We address this shortcoming by using normalized log-transformed counts and associated precision weights in a general linear model pipeline with continuous autoregressive structure to account for the correlation among observations within each experimental unit. We then utilize parametric bootstrap to conduct differential expression inference. Simulation studies show the advantages of our method over alternatives that do not account for the correlation among observations within experimental units.

AVAILABILITY AND IMPLEMENTATION

We provide an R package rmRNAseq implementing our proposed method (function TC_CAR1) at https://cran.r-project.org/web/packages/rmRNAseq/index.html. Reproducible R codes for data analysis and simulation are available at https://github.com/ntyet/rmRNAseq/tree/master/simulation.

Inhibition of FGF and TGF-β Pathways in hESCs Identify STOX2 as a Novel SMAD2/4 Cofactor

Simple Summary

Signaling pathways are the means by which cells and tissue communicate, orchestrating key events during mammalian development, homeostasis, and disease. During development, signaling determines the identity of cells, and thereby controls morphogenesis and organ specification. Depending on the cellular context, these pathways can exert a broad range of even opposing functions. This is achieved, among other mechanisms, by crosstalk between pathways. Here, we examined how two pathways (the transforming growth factor-β (TGF-β) and the fibroblast growth factor (FGF)) cooperate in the maintenance and cell fate specification of human embryonic stem cells. We used inhibitory molecules for individual pathways on a short time series and analyzed the resulting variation in gene expression. In contrast to our expectations, we did not observe an extended crosstalk between the pathway at the gene regulatory level. However, we discovered STOX2 as a new primary target of the TGF-β signaling pathway. Our results show that STOX2 might act as a novel TGF-β signaling co-factor. Our work will contribute to understand how signaling by the TGF-β is mediated. In the future, these results might help to deepen our understanding of how signaling is propagated.

Abstract

The fibroblast growth factor (FGF) and the transforming growth factor-β (TGF-β) pathways are both involved in the maintenance of human embryonic stem cells (hESCs) and regulate the onset of their differentiation. Their converging functions have suggested that these pathways might share a wide range of overlapping targets. Published studies have focused on the long-term effects (24–48 h) of FGF and TGF-β inhibition in hESCs, identifying direct and indirect target genes. In this study, we focused on the earliest transcriptome changes occurring between 3 and 9 h after FGF and TGF-β inhibition to identify direct target genes only. Our analysis clearly shows that only a handful of target transcripts are common to both pathways. This is surprising in light of the previous literature, and has implications for models of cell signaling in human pluripotent cells. In addition, we identified STOX2 as a novel primary target of the TGF-β signaling pathway. We show that STOX2 might act as a novel SMAD2/4 cofactor. Taken together, our results provide insights into the effect of cell signaling on the transcription profile of human pluripotent cells

Expansion of the circadian transcriptome in Brassica rapa and genome-wide diversification of paralog expression patterns

An important challenge of crop improvement strategies is assigning function to paralogs in polyploid crops. Here we describe the circadian transcriptome in the polyploid crop Brassica rapa. Strikingly, almost three-quarters of the expressed genes exhibited circadian rhythmicity. Genetic redundancy resulting from whole genome duplication is thought to facilitate evolutionary change through sub- and neo-functionalization among paralogous gene pairs. We observed genome-wide expansion of the circadian expression phase among retained paralogous pairs. Using gene regulatory network models, we compared transcription factor targets between B. rapa and Arabidopsis circadian networks to reveal evidence for divergence between B. rapa paralogs that may be driven in part by variation in conserved non-coding sequences (CNS). Additionally, differential drought response among retained paralogous pairs suggests further functional diversification. These findings support the rapid expansion and divergence of the transcriptional network in a polyploid crop and offer a new approach for assessing paralog activity at the transcript level.

HTRgene: a computational method to perform the integrated analysis of multiple heterogeneous time-series data: case analysis of cold and heat stress response signaling genes in Arabidopsis

Background

Integrated analysis that uses multiple sample gene expression data measured under the same stress can detect stress response genes more accurately than analysis of individual sample data. However, the integrated analysis is challenging since experimental conditions (strength of stress and the number of time points) are heterogeneous across multiple samples.

Results

HTRgene is a computational method to perform the integrated analysis of multiple heterogeneous time-series data measured under the same stress condition. The goal of HTRgene is to identify “response order preserving DEGs” that are defined as genes not only which are differentially expressed but also whose response order is preserved across multiple samples. The utility of HTRgene was demonstrated using 28 and 24 time-series sample gene expression data measured under cold and heat stress in Arabidopsis. HTRgene analysis successfully reproduced known biological mechanisms of cold and heat stress in Arabidopsis. Also, HTRgene showed higher accuracy in detecting the documented stress response genes than existing tools.

Conclusions

HTRgene, a method to find the ordering of response time of genes that are commonly observed among multiple time-series samples, successfully integrated multiple heterogeneous time-series gene expression datasets. It can be applied to many research problems related to the integration of time series data analysis.

Implementation of the Chick Chorioallantoic Membrane (CAM) Model in Radiation Biology and Experimental Radiation Oncology Research

Radiotherapy (RT) is part of standard cancer treatment. Innovations in treatment planning and increased precision in dose delivery have significantly improved the therapeutic gain of radiotherapy but are reaching their limits due to biologic constraints. Thus, a better understanding of the complex local and systemic responses to RT and of the biological mechanisms causing treatment success or failure is required if we aim to define novel targets for biological therapy optimization. Moreover, optimal treatment schedules and prognostic biomarkers have to be defined for assigning patients to the best treatment option. The complexity of the tumor environment and of the radiation response requires extensive in vivo experiments for the validation of such treatments. So far in vivo investigations have mostly been performed in time- and cost-intensive murine models. Here we propose the implementation of the chick chorioallantoic membrane (CAM) model as a fast, cost-efficient model for semi high-throughput preclinical in vivo screening of the modulation of the radiation effects by molecularly targeted drugs. This review provides a comprehensive overview on the application spectrum, advantages and limitations of the CAM assay and summarizes current knowledge of its applicability for cancer research with special focus on research in radiation biology and experimental radiation oncology.

Comparative analysis of differential gene expression tools for RNA sequencing time course data

RNA sequencing (RNA-seq) has become a standard procedure to investigate transcriptional changes between conditions and is routinely used in research and clinics. While standard differential expression (DE) analysis between two conditions has been extensively studied, and improved over the past decades, RNA-seq time course (TC) DE analysis algorithms are still in their early stages. In this study, we compare, for the first time, existing TC RNA-seq tools on an extensive simulation data set and validated the best performing tools on published data. Surprisingly, TC tools were outperformed by the classical pairwise comparison approach on short time series (<8 time points) in terms of overall performance and robustness to noise, mostly because of high number of false positives, with the exception of ImpulseDE2. Overlapping of candidate lists between tools improved this shortcoming, as the majority of false-positive, but not true-positive, candidates were unique for each method. On longer time series, pairwise approach was less efficient on the overall performance compared with splineTC and maSigPro, which did not identify any false-positive candidate.

Temporal dynamics in meta longitudinal RNA-Seq data

Identification of differentially expressed genes has been a high priority task of downstream analyses to further advances in biomedical research. Investigators have been faced with an array of issues in dealing with more complicated experiments and metadata, including batch effects, normalization, temporal dynamics (temporally differential expression), and isoform diversity (isoform-level quantification and differential splicing events). To date, there are currently no standard approaches to precisely and efficiently analyze these moderate or large-scale experimental designs, especially with combined metadata. In this report, we propose comprehensive analytical pipelines to precisely characterize temporal dynamics in differential expression of genes and other genomic features, i.e., the variability of transcripts, isoforms and exons, by controlling batch effects and other nuisance factors that could have significant confounding effects on the main effects of interest in comparative models and may result in misleading interpretations.

GPrank: an R package for detecting dynamic elements from genome-wide time series

Background

Genome-wide high-throughput sequencing (HTS) time series experiments are a powerful tool for monitoring various genomic elements over time. They can be used to monitor, for example, gene or transcript expression with RNA sequencing (RNA-seq), DNA methylation levels with bisulfite sequencing (BS-seq), or abundances of genetic variants in populations with pooled sequencing (Pool-seq). However, because of high experimental costs, the time series data sets often consist of a very limited number of time points with very few or no biological replicates, posing challenges in the data analysis.

Results

Here we present the GPrank R package for modelling genome-wide time series by incorporating variance information obtained during pre-processing of the HTS data using probabilistic quantification methods or from a beta-binomial model using sequencing depth. GPrank is well-suited for analysing both short and irregularly sampled time series. It is based on modelling each time series by two Gaussian process (GP) models, namely, time-dependent and time-independent GP models, and comparing the evidence provided by data under two models by computing their Bayes factor (BF). Genomic elements are then ranked by their BFs, and temporally most dynamic elements can be identified.

Conclusions

Incorporating the variance information helps GPrank avoid false positives without compromising computational efficiency. Fitted models can be easily further explored in a browser. Detection and visualisation of temporally most active dynamic elements in the genome can provide a good starting point for further downstream analyses for increasing our understanding of the studied processes.

Decoupling Yeast Cell Division and Stress Defense Implicates mRNA Repression in Translational Reallocation during Stress

Stress tolerance and rapid growth are often competing interests in cells. Upon severe environmental stress, many organisms activate defense systems concurrent with growth arrest. There has been debate as to whether aspects of the stress-activated transcriptome are regulated by stress or an indirect byproduct of reduced proliferation. For example, stressed Saccharomyces cerevisiae cells mount a common gene expression program called the environmental stress response (ESR) [1] comprised of ∼300 induced (iESR) transcripts involved in stress defense and ∼600 reduced (rESR) mRNAs encoding ribosomal proteins (RPs) and ribosome biogenesis factors (RiBi) important for division. Because ESR activation also correlates with reduced growth rate in nutrient-restricted chemostats and prolonged G1 in slow-growing mutants, an alternate proposal is that the ESR is simply a consequence of reduced division [2-5]. A major challenge is that past studies did not separate effects of division arrest and stress defense; thus, the true responsiveness of the ESR-and the purpose of stress-dependent rESR repression in particular-remains unclear. Here, we decoupled cell division from the stress response by following transcriptome, proteome, and polysome changes in arrested cells responding to acute stress. We show that the ESR cannot be explained by changes in growth rate or cell-cycle phase during stress acclimation. Instead, failure to repress rESR transcripts reduces polysome association of induced transcripts, delaying production of their proteins. Our results suggest that stressed cells alleviate competition for translation factors by removing mRNAs and ribosomes from the translating pool, directing translational capacity toward induced transcripts to accelerate protein production.

Transcriptional and temporal response of Populus stems to gravi-stimulation

Plants modify development in response to external stimuli, to produce new growth that is appropriate for environmental conditions. For example, gravi-stimulation of leaning branches in angiosperm trees results in modifications of wood development, to produce tension wood that pulls leaning stems upright. Here, we use gravi-stimulation and tension wood response to dissect the temporal changes in gene expression underlying wood formation in Populus stems. Using time-series analysis of seven time points over a 14-d experiment, we identified 8,919 genes that were differentially expressed between tension wood (upper) and opposite wood (lower) sides of leaning stems. Clustering of differentially expressed genes showed four major transcriptional responses, including gene clusters whose transcript levels were associated with two types of tissue-specific impulse responses that peaked at about 24-48 h, and gene clusters with sustained changes in transcript levels that persisted until the end of the 14-d experiment. Functional enrichment analysis of those clusters suggests they reflect temporal changes in pathways associated with hormone regulation, protein localization, cell wall biosynthesis and epigenetic processes. Time-series analysis of gene expression is an underutilized approach for dissecting complex developmental responses in plants, and can reveal gene clusters and mechanisms influencing development.