Dynamics in Transcriptomics: Advancements in RNA-seq Time Course and Downstream Analysis

Drought Response in the Transcriptome and Ionome of Wild and Domesticated Lablab purpureus L. Sweet, an Underutilized Legume

Hunger remains a prevalent issue worldwide, and with a changing climate, it is expected to become an even greater problem that our food systems are not adapted to. There is therefore a need to investigate strategies to fortify our foods and food systems. Underutilized crops are farmed regionally, are often adapted to stresses, including droughts, and have great nutritional profiles, potentially being key for food security. One of these crops, Lablab purpureus L Sweet, or lablab, is a legume grown for humans or as fodder and shows remarkable drought tolerance. Understanding of lablab's molecular responses to drought and drought's effects on its nutritional qualities is limited and affects breeding potential. Using transcriptomics at three time points, changes in gene expression in response to drought were investigated in wild and domesticated lablab. The effect of drought on the elemental profile of lablab leaves was investigated using ionomics to assess drought's impact on nutritional quality. Differences in drought response between wild and domesticated lablab accessions were revealed, which were mainly due to differences in the expression of genes related to phosphorus metabolic response, cell wall organization, and cellular signaling. The leaves of wild and domesticated lablab accessions differed significantly in their elemental concentrations, with wild accessions having higher protein, zinc, and iron concentrations. Drought affected the concentration of some elements, with potential implications for the use of lablab under different environments. Overall, this study is an important first step in understanding drought response in lablab with implications for breeding and improvement of drought-tolerant lablab.

Time-/dose- series transcriptome data analysis and traditional Chinese medicine treatment of pneumoconiosis

Pneumoconiosis' pathogenesis is still unclear and specific drugs for its treatment are lacking. Analysis of series transcriptome data often uses a single comparison method, and there are few reports on using such data to predict the treatment of pneumoconiosis with traditional Chinese medicine (TCM). Here, we proposed a new method for analyzing series transcriptomic data, series difference analysis (SDA), and applied it to pneumoconiosis. By comparison with 5 gene sets including existing pneumoconiosis-related genes and gene set functional enrichment analysis, we demonstrated that the new method was not inferior to two existing traditional analysis methods. Furthermore, based on the TCM-drug target interaction network, we predicted the TCM corresponding to the common pneumoconiosis-related genes obtained by multiple methods, and combined them with the high-frequency TCM for its treatment obtained through literature mining to form a new TCM formula for it. After feeding it to pneumoconiosis modeling mice for two months, compared with the untreated group, the coat color, mental state and tissue sections of the mice in the treated group were markedly improved, indicating that the new TCM formula has a certain efficacy. Our study provides new insights into method development for series transcriptomic data analysis and treatment of pneumoconiosis.

Inference of Dynamic Growth Regulatory Network in Cancer Using High-Throughput Transcriptomic Data

Growth is regulated by gene expression variation at different developmental stages of biological processes such as cell differentiation, disease progression, or drug response. In cancer, a stage-specific regulatory model constructed to infer the dynamic expression changes in genes contributing to tissue growth or proliferation is referred as a dynamic growth regulatory network (dGRN). Over the past decade, gene expression data has been widely used for reconstructing dGRN by computing correlations between the differentially expressed genes (DEGs). A wide variety of pipelines are available to construct the GRNs using DEGs and the choice of a particular method or tool depends on the nature of the study. In this protocol, we have outlined a step-by-step guide for the analysis of DEGs using RNA-Seq data, beginning from data acquisition, pre-processing, mapping to reference genome, and construction of a correlation-based co-expression network to further downstream analysis. We have also outlined the steps for the inclusion of publicly available interaction/regulation information into the dGRN followed by relevant topological inferences. This tutorial has been designed in a way that early researchers can refer to for an easy and comprehensive glimpse of methodologies used in the inference of dGRN using transcriptomics data.

Carotenoids in Health as Studied by Omics-Related Endpoints

Carotenoids have been associated with risk reduction for several chronic diseases, including the association of their dietary intake/circulating levels with reduced incidence of obesity, type 2 diabetes, certain types of cancer, and even lower total mortality. In addition to some carotenoids constituting vitamin A precursors, they are implicated in potential antioxidant effects and pathways related to inflammation and oxidative stress, including transcription factors such as nuclear factor κB and nuclear factor erythroid 2-related factor 2. Carotenoids and metabolites may also interact with nuclear receptors, mainly retinoic acid receptor/retinoid X receptor and peroxisome proliferator-activated receptors, which play a role in the immune system and cellular differentiation. Therefore, a large number of downstream targets are likely influenced by carotenoids, including but not limited to genes and proteins implicated in oxidative stress and inflammation, antioxidation, and cellular differentiation processes. Furthermore, recent studies also propose an association between carotenoid intake and gut microbiota. While all these endpoints could be individually assessed, a more complete/integrative way to determine a multitude of health-related aspects of carotenoids includes (multi)omics-related techniques, especially transcriptomics, proteomics, lipidomics, and metabolomics, as well as metagenomics, measured in a variety of biospecimens including plasma, urine, stool, white blood cells, or other tissue cellular extracts. In this review, we highlight the use of omics technologies to assess health-related effects of carotenoids in mammalian organisms and models.

TrACES of time: Transcriptomic analyses for the contextualization of evidential stains - Identification of RNA markers for estimating time-of-day of bloodstain deposition

Obtaining forensically relevant information beyond who deposited a biological stain on how and under which circumstances it was deposited is a question of increasing importance in forensic molecular biology. In the past few years, several studies have been produced on the potential of gene expression analysis to deliver relevant contextualizing information, e.g. on nature and condition of a stain as well as aspects of stain deposition timing. However, previous attempts to predict the time-of-day of sample deposition were all based on and thus limited by previously described diurnal oscillators. Herein, we newly approached this goal by applying current sequencing technologies and statistical methods to identify novel candidate markers for forensic time-of-day predictions from whole transcriptome analyses. To this purpose, we collected whole blood samples from ten individuals at eight different time points throughout the day, performed whole transcriptome sequencing and applied biostatistical algorithms to identify 81 mRNA markers with significantly differential expression as candidates to predict the time of day. In addition, we performed qPCR analysis to assess the characteristics of a subset of 13 candidate predictors in dried and aged blood stains. While we demonstrated the general possibility of using the selected candidate markers to predict time-of-day of sample deposition, we also observed notable variation between different donors and storage conditions, highlighting the relevance of employing accurate quantification methods in combination with robust normalization procedures.This study's results are foundational and may be built upon when developing a targeted assay for time-of-day predictions from forensic blood samples in the future.

RNA-Seq-Based Transcriptome Analysis of Nitric Oxide Scavenging Response in Neurospora crassa

While the biological role of naturally occurring nitric oxide (NO) in filamentous fungi has been uncovered, the underlying molecular regulatory networks remain unclear. In this study, we conducted an analysis of transcriptome profiles to investigate the initial stages of understanding these NO regulatory networks in Neurospora crassa, a well-established model filamentous fungus. Utilizing RNA sequencing, differential gene expression screening, and various functional analyses, our findings revealed that the removal of intracellular NO resulted in the differential transcription of 424 genes. Notably, the majority of these differentially expressed genes were functionally linked to processes associated with carbohydrate and amino acid metabolism. Furthermore, our analysis highlighted the prevalence of four specific protein domains (zinc finger C2H2, PLCYc, PLCXc, and SH3) in the encoded proteins of these differentially expressed genes. Through protein-protein interaction network analysis, we identified eight hub genes with substantial interaction connectivity, with mss-4 and gel-3 emerging as possibly major responsive genes during NO scavenging, particularly influencing vegetative growth. Additionally, our study unveiled that NO scavenging led to the inhibition of gene transcription related to a protein complex associated with ribosome biogenesis. Overall, our investigation suggests that endogenously produced NO in N. crassa likely governs the transcription of genes responsible for protein complexes involved in carbohydrate and amino acid metabolism, as well as ribosomal biogenesis, ultimately impacting the growth and development of hyphae.

Next-generation sequencing reveals altered gene expression and enriched pathways in triple-negative breast cancer cells treated with oleuropein and oleocanthal

Triple-negative breast cancer (TNBC) is a subtype of breast cancer characterized by poor prognosis and limited treatment options. Oleuropein and oleocanthal are bioactive chemicals found in extra-virgin olive oil; they have been shown to have anti-cancer potential. In this study, we examined the inhibitory effects of these two natural compounds, on MDA-MB-231 and MDA-MB-468 TNBC cell lines. The human TNBC MDA-MB-231 and MDA-MB-468 cell lines were treated with oleuropein or oleocanthal at ranging concentrations for 48 h. After determining the optimum concentration to reach IC50, using the sulforhodamine B assay, total RNA was extracted after 12, 24, and 48 h from treated and untreated cells. Poly(A)-RNA selection was conducted, followed by library construction and RNA sequencing. Differential gene expression (DEG) analysis was performed to identify DEGs between treated and untreated cells. Pathway analysis was carried out using the KEGG and GO databases. Oleuropein and oleocanthal considerably reduced the proliferation of TNBC cells, with oleocanthal having a slightly stronger effect than oleuropein. Furthermore, multi-time series RNA sequencing showed that the expression profile of TNBC cells was significantly altered after treatment with these compounds, with temporal dynamics and groups of genes consistently affected at all time points. Pathway analysis revealed several significant pathways associated with TNBC, including cell death, apoptotic process, programmed cell death, response to stress, mitotic cell cycle process, cell division, and cancer progression. Our findings suggest that oleuropein and oleocanthal have potential therapeutic benefits for TNBC and can be further investigated as alternative treatment options.

Gene Functional Networks from Time Expression Profiles: A Constructive Approach Demonstrated in Chili Pepper (Capsicum annuum L.)

Gene co-expression networks are powerful tools to understand functional interactions between genes. However, large co-expression networks are difficult to interpret and do not guarantee that the relations found will be true for different genotypes. Statistically verified time expression profiles give information about significant changes in expressions through time, and genes with highly correlated time expression profiles, which are annotated in the same biological process, are likely to be functionally connected. A method to obtain robust networks of functionally related genes will be useful to understand the complexity of the transcriptome, leading to biologically relevant insights. We present an algorithm to construct gene functional networks for genes annotated in a given biological process or other aspects of interest. We assume that there are genome-wide time expression profiles for a set of representative genotypes of the species of interest. The method is based on the correlation of time expression profiles, bound by a set of thresholds that assure both, a given false discovery rate, and the discard of correlation outliers. The novelty of the method consists in that a gene expression relation must be repeatedly found in a given set of independent genotypes to be considered valid. This automatically discards relations particular to specific genotypes, assuring a network robustness, which can be set a priori. Additionally, we present an algorithm to find transcription factors candidates for regulating hub genes within a network. The algorithms are demonstrated with data from a large experiment studying gene expression during the development of the fruit in a diverse set of chili pepper genotypes. The algorithm is implemented and demonstrated in a new version of the publicly available R package "Salsa" (version 1.0).

Harnessing introns to monitor gene expression

A number of new technologies have utilized synthetic RNAs which leverage the cell’s RNA splicing machinery to drive expression of gene products. Now a new study reports a technique to dynamically and non-invasively monitor gene expression by embedding reporters within introns contained in the parent gene.

Improved 93-11 Genome and Time-Course Transcriptome Expand Resources for Rice Genomics

In 2002, the first crop genome was published using the rice cultivar 93-11, which is the progenitor of the first super-hybrid rice. The genome sequence has served as a reference genome for the indica cultivars, but the assembly has not been updated. In this study, we update the 93-11 genome assembly to a gap-less sequence using ultra-depth single molecule real-time (SMRT) reads, Hi-C sequencing, reference-guided, and gap-closing approach. The differences in the genome collinearity and gene content between the 93-11 and the Nipponbare reference genomes confirmed to map the indica cultivar sequencing data to the 93-11 genome, instead of the reference. Furthermore, time-course transcriptome data showed that the expression pattern was consistently correlated with the stages of seed development. Alternative splicing of starch synthesis-related genes and genomic variations of waxy make it a novel resource for targeted breeding. Collectively, the updated high quality 93-11 genome assembly can improve the understanding of the genome structures and functions of Oryza groups in molecular breeding programs.

Identify differential genes and cell subclusters from time-series scRNA-seq data using scTITANS

Time-series single-cell RNA sequencing (scRNA-seq) provides a breakthrough in modern biology by enabling researchers to profile and study the dynamics of genes and cells based on samples obtained from multiple time points at an individual cell resolution. However, cell asynchrony and an additional dimension of multiple time points raises challenges in the effective use of time-series scRNA-seq data for identifying genes and cell subclusters that vary over time. However, no effective tools are available. Here, we propose scTITANS (https://github.com/ZJUFanLab/scTITANS), a method that takes full advantage of individual cells from all time points at the same time by correcting cell asynchrony using pseudotime from trajectory inference analysis. By introducing a time-dependent covariate based on time-series analysis method, scTITANS performed well in identifying differentially expressed genes and cell subclusters from time-series scRNA-seq data based on several example datasets. Compared to current attempts, scTITANS is more accurate, quantitative, and capable of dealing with heterogeneity among cells and making full use of the timing information hidden in biological processes. When extended to broader research areas, scTITANS will bring new breakthroughs in studies with time-series single cell RNA sequencing data.

Mechanism underlying the toxicity of the microplastic fibre transfer in the sea cucumber Apostichopus japonicus

Microscopic plastic particles (0.1 µm-5 mm) are widespread hazardous pollutants, and microfibres (MFs) are their dominant shape in habitats. Previous field and laboratory studies have demonstrated that MFs enter the coelomic fluid of sea cucumbers from the water through the respiratory tree. However, the possible mechanism underlying the toxicity of this process is not well understood. Herein, RNA-Seq was used to examine the responses of the respiratory tree during the MF transfer process in the sea cucumber Apostichopus japonicus. Polyester synthetic MFs were used, and the number of transferred MFs was controlled to the amount reported from the field. The results showed that the MFs altered gene expression as the transfer process increased. The top genes regulated by MF transfer were mainly related to metabolic processes and signal transduction pathways, with upregulated genes following low MF transfer and downregulated genes following high MF transfer. Functional enrichment analysis revealed the pathways in which differentially expressed genes were enriched under different MF transfer scenarios. The transcriptomic findings were further supported by histological observations, which revealed injury and loss of cell components. This study contributes to understanding the effects of MFs in a valuable echinoderm species through transcriptomic and histological examinations.

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.

Introducing the novel Cytoscape app TimeNexus to analyze time-series data using temporal MultiLayer Networks (tMLNs)

Integrating -omics data with biological networks such as protein-protein interaction networks is a popular and useful approach to interpret expression changes of genes in changing conditions, and to identify relevant cellular pathways, active subnetworks or network communities. Yet, most -omics data integration tools are restricted to static networks and therefore cannot easily be used for analyzing time-series data. Determining regulations or exploring the network structure over time requires time-dependent networks which incorporate time as one component in their structure. Here, we present a method to project time-series data on sequential layers of a multilayer network, thus creating a temporal multilayer network (tMLN). We implemented this method as a Cytoscape app we named TimeNexus. TimeNexus allows to easily create, manage and visualize temporal multilayer networks starting from a combination of node and edge tables carrying the information on the temporal network structure. To allow further analysis of the tMLN, TimeNexus creates and passes on regular Cytoscape networks in form of static versions of the tMLN in three different ways: (i) over the entire set of layers, (ii) over two consecutive layers at a time, (iii) or on one single layer at a time. We combined TimeNexus with the Cytoscape apps PathLinker and AnatApp/ANAT to extract active subnetworks from tMLNs. To test the usability of our app, we applied TimeNexus together with PathLinker or ANAT on temporal expression data of the yeast cell cycle and were able to identify active subnetworks relevant for different cell cycle phases. We furthermore used TimeNexus on our own temporal expression data from a mouse pain assay inducing hindpaw inflammation and detected active subnetworks relevant for an inflammatory response to injury, including immune response, cell stress response and regulation of apoptosis. TimeNexus is freely available from the Cytoscape app store at https://apps.cytoscape.org/apps/TimeNexus .

Dense time-course gene expression profiling of the Drosophila melanogaster innate immune response

BACKGROUND

Immune responses need to be initiated rapidly, and maintained as needed, to prevent establishment and growth of infections. At the same time, resources need to be balanced with other physiological processes. On the level of transcription, studies have shown that this balancing act is reflected in tight control of the initiation kinetics and shutdown dynamics of specific immune genes.

RESULTS

To investigate genome-wide expression dynamics and trade-offs after infection at a high temporal resolution, we performed an RNA-seq time course on D. melanogaster with 20 time points post Imd stimulation. A combination of methods, including spline fitting, cluster analysis, and Granger causality inference, allowed detailed dissection of expression profiles, lead-lag interactions, and functional annotation of genes through guilt-by-association. We identified Imd-responsive genes and co-expressed, less well characterized genes, with an immediate-early response and sustained up-regulation up to 5 days after stimulation. In contrast, stress response and Toll-responsive genes, among which were Bomanins, demonstrated early and transient responses. We further observed a strong trade-off with metabolic genes, which strikingly recovered to pre-infection levels before the immune response was fully resolved.

CONCLUSIONS

This high-dimensional dataset enabled the comprehensive study of immune response dynamics through the parallel application of multiple temporal data analysis methods. The well annotated data set should also serve as a useful resource for further investigation of the D. melanogaster innate immune response, and for the development of methods for analysis of a post-stress transcriptional response time-series at whole-genome scale.

Transcriptome Analyses Throughout Chili Pepper Fruit Development Reveal Novel Insights into the Domestication Process

Chili pepper (Capsicum spp.) is an important crop, as well as a model for fruit development studies and domestication. Here, we performed a time-course experiment to estimate standardized gene expression profiles with respect to fruit development for six domesticated and four wild chili pepper ancestors. We sampled the transcriptomes every 10 days from flowering to fruit maturity, and found that the mean standardized expression profiles for domesticated and wild accessions significantly differed. The mean standardized expression was higher and peaked earlier for domesticated vs. wild genotypes, particularly for genes involved in the cell cycle that ultimately control fruit size. We postulate that these gene expression changes are driven by selection pressures during domestication and show a robust network of cell cycle genes with a time shift in expression, which explains some of the differences between domesticated and wild phenotypes.

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.

RNA-Sequencing Analysis Pipeline for Prognostic Marker Identification in Cancer

Sequencing analysis finds many applications in various fields of biology from comparative genomics to clinical research. Recent studies, using high-throughput sequencing method, has generated terabytes of data. It is challenging to interpret and draw a meaningful conclusion without the proper understanding of various steps involved in the analysis of such data. This chapter deals with the pipeline to be followed to process the raw RNA sequencing (RNA-Seq) reads, align, assemble, and quantify them in order to draw significant clinical conclusions from them.

High-resolution temporal transcriptome sequencing unravels ERF and WRKY as the master players in the regulatory networks underlying sesame responses to waterlogging and recovery

Major crops are generally sensitive to waterlogging, but our limited understanding of the waterlogging gene regulatory network hinders the efforts to develop waterlogging-tolerant cultivars. We generated high-resolution temporal transcriptome data from root of two contrasting sesame genotypes over a 48 h period waterlogging and drainage treatments. Three distinct chronological transcriptional phases were identified, including the early-waterlogging, late-waterlogging and drainage responses. We identified 47 genes representing the core waterlogging-responsive genes. Waterlogging/drainage-induced transcriptional changes were mainly driven by ERF and WRKY transcription factors (TF). The major difference between the two genotypes resides in the early transcriptional phase. A chronological transcriptional network model predicting putative causal regulations between TFs and downstream waterlogging-responsive genes was constructed and some interactions were validated through yeast one-hybrid assay. Overall, this study unveils the architecture and dynamic regulation of the waterlogging/drainage response in a non-model crop and helps formulate new hypotheses on stress sensing, signaling and sophisticated adaptive responses.

Identification and characterization of dynamically regulated hepatitis-related genes in a concanavalin A-induced liver injury model

Background: Concanavalin A (ConA)-induced liver damage of mice is a well-established murine model mimicking the human autoimmune hepatitis (AIH). However, the pathogenic genes of the liver injury remain to be revealed.

Methods: Using time-series liver transcriptome, top dynamic genes were inferred from a set of segmented regression models, and cross-checked by weighted correlation network analysis (WGCNA). AIH murine models created by ConA were used to verify the in vivo effect of these genes.

Results: We identified 115 top dynamic genes, of which most were overlapped with the hub genes determined by WGCNA. The expression of several top dynamic genes including Cd63, Saa3, Slc10a1, Nrxn1, Ugt2a3, were verified in vivo. Further, Cluster determinant 63 (Cd63) knockdown in mice treated with ConA showed significantly less liver pathology and inflammation as well as higher survival rates than the corresponding controls.

Conclusion: We have identified the top dynamic genes related to the process of acute liver injury, and highlighted a targeted strategy for Cd63 might have utility for the protection of hepatocellular damage.

In vitro versus in vivo models of kidney fibrosis: Time-course experimental design is crucial to avoid misinterpretations of gene expression data

Background:

In vitro models are common tools in nephrology research. However, their validity has rarely been scrutinized.

Materials and Methods:

Considering the critical role of transforming growth factor (TGF)-β and hypoxia pathways in kidney fibrosis, kidney-derived cells were exposed to TGF-β and/or hypoxic conditions and the expression levels of some genes related to these two signaling pathways were quantified in a time-course manner. Furthermore, a unilateral ureteral obstruction mouse model was generated, and the expressions of the same genes were assessed.

Results:

In all in vitro experimental groups, the expression of the genes was noisy with no consistent pattern. However, in the animal model, TGF-β pathway-related genes demonstrated considerable overexpression in the ureteral obstruction group compared with the sham controls. Interestingly, hypoxia pathway genes had prominent fluctuations with very similar patterns in both animal groups, suggesting a periodical pattern not affected by the intervention.

Conclusion:

The findings of this study suggest that in vitro findings should be interpreted cautiously and if possible are substituted or supported by animal models that are more consistent and reliable. Furthermore, we underscore the importance of time-course evaluation of both case and control groups in gene expression studies to avoid misconceptions caused by gene expression noise or intrinsic rhythms.

Time-course RNA-seq analysis provides an improved understanding of gene regulation during the formation of nodule-like structures in rice

Using a time-course RNA-seq analysis we identified transcriptomic changes during formation of nodule-like structures (NLS) in rice and compared rice RNA-seq dataset with a nodule transcriptome dataset in Medicago truncatula. Plant hormones can induce the formation of nodule-like structures (NLS) in plant roots even in the absence of bacteria. These structures can be induced in roots of both legumes and non-legumes. Moreover, nitrogen-fixing bacteria can recognize and colonize these root structures. Therefore, identifying the genetic switches controlling the NLS organogenesis program in crops, especially cereals, can have important agricultural implications. Our recent study evaluated the transcriptomic response occurring in rice roots during NLS formation, 7 days post-treatment (dpt) with auxin, 2,4-D. In this current study, we investigated the regulation of gene expression occurring in rice roots at different stages of NLS formation: early (1-dpt) and late (14-dpt). At 1-dpt and 14-dpt, we identified 1662 and 1986 differentially expressed genes (DEGs), respectively. Gene ontology enrichment analysis revealed that the dataset was enriched with genes involved in auxin response and signaling; and in anatomical structure development and morphogenesis. Next, we compared the gene expression profiles across the three time points (1-, 7-, and 14-dpt) and identified genes that were uniquely or commonly differentially expressed at all three time points. We compared our rice RNA-seq dataset with a nodule transcriptome dataset in Medicago truncatula. This analysis revealed there is some amount of overlap between the molecular mechanisms governing nodulation and NLS formation. We also identified that some key nodulation genes were not expressed in rice roots during NLS formation. We validated the expression pattern of several genes via reverse transcriptase polymerase chain reaction (RT-PCR). The DEGs identified in this dataset may serve as a useful resource for future studies to characterize the genetic pathways controlling NLS formation in cereals.

Development of genetic quality tests for good manufacturing practice-compliant induced pluripotent stem cells and their derivatives

Although human induced pluripotent stem cell (hiPSC) lines are karyotypically normal, they retain the potential for mutation in the genome. Accordingly, intensive and relevant quality controls for clinical-grade hiPSCs remain imperative. As a conceptual approach, we performed RNA-seq-based broad-range genetic quality tests on GMP-compliant human leucocyte antigen (HLA)-homozygous hiPSCs and their derivatives under postdistribution conditions to investigate whether sequencing data could provide a basis for future quality control. We found differences in the degree of single-nucleotide polymorphism (SNP) occurring in cells cultured at three collaborating institutes. However, the cells cultured at each centre showed similar trends, in which more SNPs occurred in late-passage hiPSCs than in early-passage hiPSCs after differentiation. In eSNP karyotyping analysis, none of the predicted copy number variations (CNVs) were identified, which confirmed the results of SNP chip-based CNV analysis. HLA genotyping analysis revealed that each cell line was homozygous for HLA-A, HLA-B, and DRB1 and heterozygous for HLA-DPB type. Gene expression profiling showed a similar differentiation ability of early- and late-passage hiPSCs into cardiomyocyte-like, hepatic-like, and neuronal cell types. However, time-course analysis identified five clusters showing different patterns of gene expression, which were mainly related to the immune response. In conclusion, RNA-seq analysis appears to offer an informative genetic quality testing approach for such cell types and allows the early screening of candidate hiPSC seed stocks for clinical use by facilitating safety and potential risk evaluation.

Ocean acidification promotes broad transcriptomic responses in marine metazoans: a literature survey

For nearly a decade, the metazoan-focused research community has explored the impacts of ocean acidification (OA) on marine animals, noting that changes in ocean chemistry can impact calcification, metabolism, acid-base regulation, stress response and behavior in organisms that hold high ecological and economic value. Because OA interacts with several key physiological processes in marine organisms, transcriptomics has become a widely-used method to characterize whole organism responses on a molecular level as well as inform mechanisms that explain changes in phenotypes observed in response to OA. In the past decade, there has been a notable rise in studies that examine transcriptomic responses to OA in marine metazoans, and here we attempt to summarize key findings across these studies. We find that organisms vary dramatically in their transcriptomic responses to pH although common patterns are often observed, including shifts in acid-base ion regulation, metabolic processes, calcification and stress response mechanisms. We also see a rise in transcriptomic studies examining organismal response to OA in a multi-stressor context, often reporting synergistic effects of OA and temperature. In addition, there is an increase in studies that use transcriptomics to examine the evolutionary potential of organisms to adapt to OA conditions in the future through population and transgenerational experiments. Overall, the literature reveals complex organismal responses to OA, in which some organisms will face more dramatic consequences than others. This will have wide-reaching impacts on ocean communities and ecosystems as a whole.

Lag penalized weighted correlation for time series clustering

BACKGROUND

The similarity or distance measure used for clustering can generate intuitive and interpretable clusters when it is tailored to the unique characteristics of the data. In time series datasets generated with high-throughput biological assays, measurements such as gene expression levels or protein phosphorylation intensities are collected sequentially over time, and the similarity score should capture this special temporal structure.

RESULTS

We propose a clustering similarity measure called Lag Penalized Weighted Correlation (LPWC) to group pairs of time series that exhibit closely-related behaviors over time, even if the timing is not perfectly synchronized. LPWC aligns time series profiles to identify common temporal patterns. It down-weights aligned profiles based on the length of the temporal lags that are introduced. We demonstrate the advantages of LPWC versus existing time series and general clustering algorithms. In a simulated dataset based on the biologically-motivated impulse model, LPWC is the only method to recover the true clusters for almost all simulated genes. LPWC also identifies clusters with distinct temporal patterns in our yeast osmotic stress response and axolotl limb regeneration case studies.

CONCLUSIONS

LPWC achieves both of its time series clustering goals. It groups time series with correlated changes over time, even if those patterns occur earlier or later in some of the time series. In addition, it refrains from introducing large shifts in time when searching for temporal patterns by applying a lag penalty. The LPWC R package is available at https://github.com/gitter-lab/LPWC and CRAN under a MIT license.

Uncovering extensive post-translation regulation during human cell cycle progression by integrative multi-'omics analysis

BACKGROUND

Analysis of high-throughput multi-'omics interactions across the hierarchy of expression has wide interest in making inferences with regard to biological function and biomarker discovery. Expression levels across different scales are determined by robust synthesis, regulation and degradation processes, and hence transcript (mRNA) measurements made by microarray/RNA-Seq only show modest correlation with corresponding protein levels.

RESULTS

In this work we are interested in quantitative modelling of correlation across such gene products. Building on recent work, we develop computational models spanning transcript, translation and protein levels at different stages of the H. sapiens cell cycle. We enhance this analysis by incorporating 25+ sequence-derived features which are likely determinants of cellular protein concentration and quantitatively select for relevant features, producing a vast dataset with thousands of genes. We reveal insights into the complex interplay between expression levels across time, using machine learning methods to highlight outliers with respect to such models as proteins associated with post-translationally regulated modes of action.

CONCLUSIONS

We uncover quantitative separation between modified and degraded proteins that have roles in cell cycle regulation, chromatin remodelling and protein catabolism according to Gene Ontology; and highlight the opportunities for providing biological insights in future model systems.

Acute systemic inflammatory response to lipopolysaccharide stimulation in pigs divergently selected for residual feed intake

Background

It is unclear whether improving feed efficiency by selection for low residual feed intake (RFI) compromises pigs’ immunocompetence. Here, we aimed at investigating whether pig lines divergently selected for RFI had different inflammatory responses to lipopolysaccharide (LPS) exposure, regarding to clinical presentations and transcriptomic changes in peripheral blood cells.

Results

LPS injection induced acute systemic inflammation in both the low-RFI and high-RFI line (n = 8 per line). At 4 h post injection (hpi), the low-RFI line had a significantly lower (p = 0.0075) mean rectal temperature compared to the high-RFI line. However, no significant differences in complete blood count or levels of several plasma cytokines were detected between the two lines. Profiling blood transcriptomes at 0, 2, 6, and 24 hpi by RNA-sequencing revealed that LPS induced dramatic transcriptional changes, with 6296 genes differentially expressed at at least one time point post injection relative to baseline in at least one line (n = 4 per line) (|log₂(fold change)| ≥ log₂(1.2); q < 0.05). Furthermore, applying the same cutoffs, we detected 334 genes differentially expressed between the two lines at at least one time point, including 33 genes differentially expressed between the two lines at baseline. But no significant line-by-time interaction effects were detected. Genes involved in protein translation, defense response, immune response, and signaling were enriched in different co-expression clusters of genes responsive to LPS stimulation. The two lines were largely similar in their peripheral blood transcriptomic responses to LPS stimulation at the pathway level, although the low-RFI line had a slightly lower level of inflammatory response than the high-RFI line from 2 to 6 hpi and a slightly higher level of inflammatory response than the high-RFI line at 24 hpi.

Conclusions

The pig lines divergently selected for RFI had a largely similar response to LPS stimulation. However, the low-RFI line had a relatively lower-level, but longer-lasting, inflammatory response compared to the high-RFI line. Our results suggest selection for feed efficient pigs does not significantly compromise a pig’s acute systemic inflammatory response to LPS, although slight differences in intensity and duration may occur.

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.

A Multivariate Negative-Binomial Model with Random Effects for Differential Gene-Expression Analysis of Correlated mRNA Sequencing Data

Experimental designs such as matched-pair or longitudinal studies yield mRNA sequencing (mRNA-Seq) counts that are correlated across samples. Most of the approaches for the analysis of correlated mRNA-Seq data are restricted to a specific design and/or balanced data only (with the same number of samples in each group). We propose a model that is applicable to the analysis of correlated mRNA-Seq data of different types: paired, clustered, longitudinal, or others. Any combination of explanatory variables, as well as unbalanced data, can be processed within the proposed modeling framework. The model assumes that exon counts of a particular gene of an individual sample jointly follow a multivariate negative-binomial distribution. Additional correlation between exon counts obtained for, for example, individual samples within the same pair or cluster, is taken into account by including into the model a cluster-level normally distributed random effect. An interesting feature of the model is that it provides explicit expression for marginal correlation between exon counts at different levels. The performance of the model is evaluated by using a simulation study and an analysis of two real-life data sets: a paired mRNA-Seq experiment for 24 patients with clear-cell renal-cell carcinoma and a longitudinal mRNA-Seq experiment for 29 patients with Lyme disease.

Molecular Systems Biology of Neurodevelopmental Disorders, Rett Syndrome as an Archetype

Neurodevelopmental disorders represent a challenging biological and medical problem due to their genetic and phenotypic complexity. In many cases, we lack the comprehensive understanding of disease mechanisms necessary for targeted therapeutic development. One key component that could improve both mechanistic understanding and clinical trial design is reliable molecular biomarkers. Presently, no objective biological markers exist to evaluate most neurodevelopmental disorders. Here, we discuss how systems biology and "omic" approaches can address the mechanistic and biomarker limitations in these afflictions. We present heuristic principles for testing the potential of systems biology to identify mechanisms and biomarkers of disease in the example of Rett syndrome, a neurodevelopmental disorder caused by a well-defined monogenic defect in methyl-CpG-binding protein 2 (MECP2). We propose that such an approach can not only aid in monitoring clinical disease severity but also provide a measure of target engagement in clinical trials. By deepening our understanding of the "big picture" of systems biology, this approach could even help generate hypotheses for drug development programs, hopefully resulting in new treatments for these devastating conditions.

A Prunus persica genome-wide RNA-seq approach uncovers major differences in the transcriptome among chilling injury sensitive and non-sensitive varieties

Chilling injury represents a major constrain for crops productivity. Prunus persica, one of the most relevant rosacea crops, have early season varieties that are resistant to chilling injury, in contrast to late season varieties, which display chilling symptoms such as mealiness (dry, sandy fruit mesocarp) after prolonged storage at chilling temperatures. To uncover the molecular processes related to the ability of early varieties to withstand mealiness, postharvest and genome-wide RNA-seq assessments were performed in two early and two late varieties. Differences in juice content and ethylene biosynthesis were detected among early and late season fruits that became mealy after exposed to prolonged chilling. Principal component and data distribution analysis revealed that cold-stored late variety fruit displayed an exacerbated and unique transcriptome profile when compared to any other postharvest condition. A differential expression analysis performed using an empirical Bayes mixture modeling approach followed by co-expression and functional enrichment analysis uncover processes related to ethylene, lipids, cell wall, carotenoids and DNA metabolism, light response, and plastid homeostasis associated to the susceptibility or resistance of P. persica varieties to chilling stress. Several of the genes related to these processes are in quantitative trait loci (QTL) associated to mealiness in P. persica. Together, these analyses exemplify how P. persica can be used as a model for studying chilling stress in plants.

Developing a 'personalome' for precision medicine: emerging methods that compute interpretable effect sizes from single-subject transcriptomes

The development of computational methods capable of analyzing -omics data at the individual level is critical for the success of precision medicine. Although unprecedented opportunities now exist to gather data on an individual's -omics profile ('personalome'), interpreting and extracting meaningful information from single-subject -omics remain underdeveloped, particularly for quantitative non-sequence measurements, including complete transcriptome or proteome expression and metabolite abundance. Conventional bioinformatics approaches have largely been designed for making population-level inferences about 'average' disease processes; thus, they may not adequately capture and describe individual variability. Novel approaches intended to exploit a variety of -omics data are required for identifying individualized signals for meaningful interpretation. In this review-intended for biomedical researchers, computational biologists and bioinformaticians-we survey emerging computational and translational informatics methods capable of constructing a single subject's 'personalome' for predicting clinical outcomes or therapeutic responses, with an emphasis on methods that provide interpretable readouts. Key points: (i) the single-subject analytics of the transcriptome shows the greatest development to date and, (ii) the methods were all validated in simulations, cross-validations or independent retrospective data sets. This survey uncovers a growing field that offers numerous opportunities for the development of novel validation methods and opens the door for future studies focusing on the interpretation of comprehensive 'personalomes' through the integration of multiple -omics, providing valuable insights into individual patient outcomes and treatments.

Improved Algal Toxicity Test System for Robust Omics-Driven Mode-of-Action Discovery in Chlamydomonas reinhardtii

Algae are key components of aquatic food chains. Consequently, they are internationally recognised test species for the environmental safety assessment of chemicals. However, existing algal toxicity test guidelines are not yet optimized to discover molecular modes of action, which require highly-replicated and carefully controlled experiments. Here, we set out to develop a robust, miniaturised and scalable Chlamydomonas reinhardtii toxicity testing approach tailored to meet these demands. We primarily investigated the benefits of synchronised cultures for molecular studies, and of exposure designs that restrict chemical volatilisation yet yield sufficient algal biomass for omics analyses. Flow cytometry and direct-infusion mass spectrometry metabolomics revealed significant and time-resolved changes in sample composition of synchronised cultures. Synchronised cultures in sealed glass vials achieved adequate growth rates at previously unachievably-high inoculation cell densities, with minimal pH drift and negligible chemical loss over 24-h exposures. Algal exposures to a volatile test compound (chlorobenzene) yielded relatively high reproducibility of metabolic phenotypes over experimental repeats. This experimental test system extends existing toxicity testing formats to allow highly-replicated, omics-driven, mode-of-action discovery.

Personalized medicine-concepts, technologies, and applications in inflammatory skin diseases

The current state, tools, and applications of personalized medicine with special emphasis on inflammatory skin diseases like psoriasis and atopic dermatitis are discussed. Inflammatory pathways are outlined as well as potential targets for monoclonal antibodies and small-molecule inhibitors.

Dynamic modeling and network approaches for omics time course data: overview of computational approaches and applications

Inferring networks and dynamics of genes, proteins, cells and other biological entities from high-throughput biological omics data is a central and challenging issue in computational and systems biology. This is essential for understanding the complexity of human health, disease susceptibility and pathogenesis for Predictive, Preventive, Personalized and Participatory (P4) system and precision medicine. The delineation of the possible interactions of all genes/proteins in a genome/proteome is a task for which conventional experimental techniques are ill suited. Urgently needed are rapid and inexpensive computational and statistical methods that can identify interacting candidate disease genes or drug targets out of thousands that can be further investigated or validated by experimentations. Moreover, identifying biological dynamic systems, and simultaneously estimating the important kinetic structural and functional parameters, which may not be experimentally accessible could be important directions for drug-disease-gene network studies. In this article, we present an overview and comparison of recent developments of dynamic modeling and network approaches for time-course omics data, and their applications to various biological systems, health conditions and disease statuses. Moreover, various data reduction and analytical schemes ranging from mathematical to computational to statistical methods are compared including their merits, drawbacks and limitations. The most recent software, associated web resources and other potentials for the compared methods are also presented and discussed in detail.

Extensive reprogramming of the nascent transcriptome during iPSC to hepatocyte differentiation

Hepatocyte-like cells (HLCs) derived from induced pluripotent stem cells (iPSCs) provide a renewable source of cells for drug discovery, disease modelling and cell-based therapies. Here, by using GRO-Seq we provide the first genome-wide analysis of the nascent RNAs in iPSCs, HLCs and primary hepatocytes to extend our understanding of the transcriptional changes occurring during hepatic differentiation process. We demonstrate that a large fraction of hepatocyte-specific genes are regulated at transcriptional level and identify hundreds of differentially expressed non-coding RNAs (ncRNAs), including primary miRNAs (pri-miRNAs) and long non-coding RNAs (lncRNAs). Differentiation induced alternative transcription start site (TSS) usage between the cell types as evidenced for miR-221/222 and miR-3613/15a/16-1 clusters. We demonstrate that lncRNAs and coding genes are tightly co-expressed and could thus be co-regulated. Finally, we identified sets of transcriptional regulators that might drive transcriptional changes during hepatocyte differentiation. These included RARG, E2F1, SP1 and FOXH1, which were associated with the down-regulated transcripts, and hepatocyte-specific TFs such as FOXA1, FOXA2, HNF1B, HNF4A and CEBPA, as well as RXR, PPAR, AP-1, JUNB, JUND and BATF, which were associated with up-regulated transcripts. In summary, this study clarifies the role of regulatory ncRNAs and TFs in differentiation of HLCs from iPSCs.

Trendy: segmented regression analysis of expression dynamics in high-throughput ordered profiling experiments

BACKGROUND

High-throughput expression profiling experiments with ordered conditions (e.g. time-course or spatial-course) are becoming more common for studying detailed differentiation processes or spatial patterns. Identifying dynamic changes at both the individual gene and whole transcriptome level can provide important insights about genes, pathways, and critical time points.

RESULTS

We present an R package, Trendy, which utilizes segmented regression models to simultaneously characterize each gene's expression pattern and summarize overall dynamic activity in ordered condition experiments. For each gene, Trendy finds the optimal segmented regression model and provides the location and direction of dynamic changes in expression. We demonstrate the utility of Trendy to provide biologically relevant results on both microarray and RNA-sequencing (RNA-seq) datasets.

CONCLUSIONS

Trendy is a flexible R package which characterizes gene-specific expression patterns and summarizes changes of global dynamics over ordered conditions. Trendy is freely available on Bioconductor with a full vignette at https://bioconductor.org/packages/release/bioc/html/Trendy.html .

Design of Large-Scale Reporter Construct Arrays for Dynamic, Live Cell Systems Biology

Dynamic systems biology aims to identify the molecular mechanisms governing cell fate decisions through the analysis of living cells. Large scale molecular information from living cells can be obtained from reporter constructs that provide activities for either individual transcription factors or multiple factors binding to the full promoter following CRISPR/Cas9 directed insertion of luciferase. In this report, we investigated the design criteria to obtain reporters that are specific and responsive to transcription factor (TF) binding and the integration of TF binding activity with genetic reporter activity. The design of TF reporters was investigated for the impact of consensus binding site spacing sequence and off-target binding on the reporter sensitivity using a library of 25 SMAD3 activity reporters with spacers of random composition and length. A spacer was necessary to quantify activity changes after TGFβ stimulation. TF binding site prediction algorithms (BEEML, FIMO and DeepBind) were used to predict off-target binding, and nonresponsiveness to a SMAD3 reporter was correlated with a predicted competitive binding of constitutively active p53. The network of activity of the SMAD3 reporter was inferred from measurements of TF reporter library, and connected with large-scale genetic reporter activity measurements. The integration of TF and genetic reporters identified the major hubs directing responses to TGFβ, and this method provided a systems-level algorithm to investigate cell signaling.

A threshold model for receptor tyrosine kinase signaling specificity and cell fate determination

Upon ligand engagement, the single-pass transmembrane receptor tyrosine kinases (RTKs) dimerize to transmit qualitatively and quantitatively different intracellular signals that alter the transcriptional landscape and thereby determine the cellular response. The molecular mechanisms underlying these fundamental events are not well understood. Considering recent insights into the structural biology of fibroblast growth factor signaling, we propose a threshold model for RTK signaling specificity in which quantitative differences in the strength/longevity of ligand-induced receptor dimers on the cell surface lead to quantitative differences in the phosphorylation of activation loop (A-loop) tyrosines as well as qualitative differences in the phosphorylation of tyrosines mediating substrate recruitment. In this model, quantitative differences on A-loop tyrosine phosphorylation result in gradations in kinase activation, leading to the generation of intracellular signals of varying amplitude/duration. In contrast, qualitative differences in the pattern of tyrosine phosphorylation on the receptor result in the recruitment/activation of distinct substrates/intracellular pathways. Commensurate with both the dynamics of the intracellular signal and the types of intracellular pathways activated, unique transcriptional signatures are established. Our model provides a framework for engineering clinically useful ligands that can tune receptor dimerization stability so as to bias the cellular transcriptome to achieve a desired cellular output.

Insight into Genes Regulating Postharvest Aflatoxin Contamination of Tetraploid Peanut from Transcriptional Profiling

Postharvest aflatoxin contamination is a challenging issue that affects peanut quality. Aflatoxin is produced by fungi belonging to the Aspergilli group, and is known as an acutely toxic, carcinogenic, and immune-suppressing class of mycotoxins. Evidence for several host genetic factors that may impact aflatoxin contamination has been reported, e.g., genes for lipoxygenase (PnLOX1 and PnLOX2/PnLOX3 that showed either positive or negative regulation with Aspergillus infection), reactive oxygen species, and WRKY (highly associated with or differentially expressed upon infection of maize with Aspergillus flavus); however, their roles remain unclear. Therefore, we conducted an RNA-sequencing experiment to differentiate gene response to the infection by A. flavus between resistant (ICG 1471) and susceptible (Florida-07) cultivated peanut genotypes. The gene expression profiling analysis was designed to reveal differentially expressed genes in response to the infection (infected vs. mock-treated seeds). In addition, the differential expression of the fungal genes was profiled. The study revealed the complexity of the interaction between the fungus and peanut seeds as the expression of a large number of genes was altered, including some in the process of plant defense to aflatoxin accumulation. Analysis of the experimental data with "keggseq," a novel designed tool for Kyoto Encyclopedia of Genes and Genomes enrichment analysis, showed the importance of α-linolenic acid metabolism, protein processing in the endoplasmic reticulum, spliceosome, and carbon fixation and metabolism pathways in conditioning resistance to aflatoxin accumulation. In addition, coexpression network analysis was carried out to reveal the correlation of gene expression among peanut and fungal genes. The results showed the importance of WRKY, toll/Interleukin1 receptor-nucleotide binding site leucine-rich repeat (TIR-NBS-LRR), ethylene, and heat shock proteins in the resistance mechanism.

Optimization of an RNA-Seq Differential Gene Expression Analysis Depending on Biological Replicate Number and Library Size

RNA-Seq is a widely used technology that allows an efficient genome-wide quantification of gene expressions for, for example, differential expression (DE) analysis. After a brief review of the main issues, methods and tools related to the DE analysis of RNA-Seq data, this article focuses on the impact of both the replicate number and library size in such analyses. While the main drawback of previous relevant studies is the lack of generality, we conducted both an analysis of a two-condition experiment (with eight biological replicates per condition) to compare the results with previous benchmark studies, and a meta-analysis of 17 experiments with up to 18 biological conditions, eight biological replicates and 100 million (M) reads per sample. As a global trend, we concluded that the replicate number has a larger impact than the library size on the power of the DE analysis, except for low-expressed genes, for which both parameters seem to have the same impact. Our study also provides new insights for practitioners aiming to enhance their experimental designs. For instance, by analyzing both the sensitivity and specificity of the DE analysis, we showed that the optimal threshold to control the false discovery rate (FDR) is approximately 2^-r, where r is the replicate number. Furthermore, we showed that the false positive rate (FPR) is rather well controlled by all three studied R packages: DESeq, DESeq2, and edgeR. We also analyzed the impact of both the replicate number and library size on gene ontology (GO) enrichment analysis. Interestingly, we concluded that increases in the replicate number and library size tend to enhance the sensitivity and specificity, respectively, of the GO analysis. Finally, we recommend to RNA-Seq practitioners the production of a pilot data set to strictly analyze the power of their experimental design, or the use of a public data set, which should be similar to the data set they will obtain. For individuals working on tomato research, on the basis of the meta-analysis, we recommend at least four biological replicates per condition and 20 M reads per sample to be almost sure of obtaining about 1000 DE genes if they exist.

Designing and interpreting 'multi-omic' experiments that may change our understanding of biology

Most biological mechanisms involve more than one type of biomolecule, and hence operate not solely at the level of either genome, transcriptome, proteome, metabolome or ionome. Datasets resulting from single-omic analysis are rapidly increasing in throughput and quality, rendering multi-omic studies feasible. These should offer a comprehensive, structured and interactive overview of a biological mechanism. However, combining single-omic datasets in a meaningful manner has so far proved challenging, and the discovery of new biological information lags behind expectation. One reason is that experiments conducted in different laboratories can typically not to be combined without restriction. Second, the interpretation of multi-omic datasets represents a significant challenge by nature, as the biological datasets are heterogeneous not only for technical, but also for biological, chemical, and physical reasons. Here, multi-layer network theory and methods of artificial intelligence might contribute to solve these problems. For the efficient application of machine learning however, biological datasets need to become more systematic, more precise - and much larger. We conclude our review with basic guidelines for the successful set-up of a multi-omic experiment.

Transcriptome profiling of aging Drosophila photoreceptors reveals gene expression trends that correlate with visual senescence

BACKGROUND

Aging is associated with functional decline of neurons and increased incidence of both neurodegenerative and ocular disease. Photoreceptor neurons in Drosophila melanogaster provide a powerful model for studying the molecular changes involved in functional senescence of neurons since decreased visual behavior precedes retinal degeneration. Here, we sought to identify gene expression changes and the genomic features of differentially regulated genes in photoreceptors that contribute to visual senescence.

RESULTS

To identify gene expression changes that could lead to visual senescence, we characterized the aging transcriptome of Drosophila sensory neurons highly enriched for photoreceptors. We profiled the nuclear transcriptome of genetically-labeled photoreceptors over a 40 day time course and identified increased expression of genes involved in stress and DNA damage response, and decreased expression of genes required for neuronal function. We further show that combinations of promoter motifs robustly identify age-regulated genes, suggesting that transcription factors are important in driving expression changes in aging photoreceptors. However, long, highly expressed and heavily spliced genes are also more likely to be downregulated with age, indicating that other mechanisms could contribute to expression changes at these genes. Lastly, we identify that circular RNAs (circRNAs) strongly increase during aging in photoreceptors.

CONCLUSIONS

Overall, we identified changes in gene expression in aging Drosophila photoreceptors that could account for visual senescence. Further, we show that genomic features predict these age-related changes, suggesting potential mechanisms that could be targeted to slow the rate of age-associated visual decline.

Influence maximization in time bounded network identifies transcription factors regulating perturbed pathways

Motivation: To understand the dynamic nature of the biological process, it is crucial to identify perturbed pathways in an altered environment and also to infer regulators that trigger the response. Current time-series analysis methods, however, are not powerful enough to identify perturbed pathways and regulators simultaneously. Widely used methods include methods to determine gene sets such as differentially expressed genes or gene clusters and these genes sets need to be further interpreted in terms of biological pathways using other tools. Most pathway analysis methods are not designed for time series data and they do not consider gene-gene influence on the time dimension.

Results: In this article, we propose a novel time-series analysis method TimeTP for determining transcription factors (TFs) regulating pathway perturbation, which narrows the focus to perturbed sub-pathways and utilizes the gene regulatory network and protein–protein interaction network to locate TFs triggering the perturbation. TimeTP first identifies perturbed sub-pathways that propagate the expression changes along the time. Starting points of the perturbed sub-pathways are mapped into the network and the most influential TFs are determined by influence maximization technique. The analysis result is visually summarized in TF-Pathway map in time clock. TimeTP was applied to PIK3CA knock-in dataset and found significant sub-pathways and their regulators relevant to the PIP3 signaling pathway.

Availability and Implementation: TimeTP is implemented in Python and available at http://biohealth.snu.ac.kr/software/TimeTP/.

Supplementary information:Supplementary data are available at Bioinformatics online.

Contact:sunkim.bioinfo@snu.ac.kr

DynOmics to identify delays and co-expression patterns across time course experiments

Dynamic changes in biological systems can be captured by measuring molecular expression from different levels (e.g., genes and proteins) across time. Integration of such data aims to identify molecules that show similar expression changes over time; such molecules may be co-regulated and thus involved in similar biological processes. Combining data sources presents a systematic approach to study molecular behaviour. It can compensate for missing data in one source, and can reduce false positives when multiple sources highlight the same pathways. However, integrative approaches must accommodate the challenges inherent in ‘omics’ data, including high-dimensionality, noise, and timing differences in expression. As current methods for identification of co-expression cannot cope with this level of complexity, we developed a novel algorithm called DynOmics. DynOmics is based on the fast Fourier transform, from which the difference in expression initiation between trajectories can be estimated. This delay can then be used to realign the trajectories and identify those which show a high degree of correlation. Through extensive simulations, we demonstrate that DynOmics is efficient and accurate compared to existing approaches. We consider two case studies highlighting its application, identifying regulatory relationships across ‘omics’ data within an organism and for comparative gene expression analysis across organisms.

Analysis of Body-wide Unfractionated Tissue Data to Identify a Core Human Endothelial Transcriptome

Endothelial cells line blood vessels and regulate hemostasis, inflammation, and blood pressure. Proteins critical for these specialized functions tend to be predominantly expressed in endothelial cells across vascular beds. Here, we present a systems approach to identify a panel of human endothelial-enriched genes using global, body-wide transcriptomics data from 124 tissue samples from 32 organs. We identified known and unknown endothelial-enriched gene transcripts and used antibody-based profiling to confirm expression across vascular beds. The majority of identified transcripts could be detected in cultured endothelial cells from various vascular beds, and we observed maintenance of relative expression in early passage cells. In summary, we describe a widely applicable method to determine cell-type-specific transcriptome profiles in a whole-organism context, based on differential abundance across tissues. We identify potential vascular drug targets or endothelial biomarkers and highlight candidates for functional studies to increase understanding of the endothelium in health and disease.

Genomic Methods Take the Plunge: Recent Advances in High-Throughput Sequencing of Marine Mammals

The dramatic increase in the application of genomic techniques to non-model organisms (NMOs) over the past decade has yielded numerous valuable contributions to evolutionary biology and ecology, many of which would not have been possible with traditional genetic markers. We review this recent progression with a particular focus on genomic studies of marine mammals, a group of taxa that represent key macroevolutionary transitions from terrestrial to marine environments and for which available genomic resources have recently undergone notable rapid growth. Genomic studies of NMOs utilize an expanding range of approaches, including whole genome sequencing, restriction site-associated DNA sequencing, array-based sequencing of single nucleotide polymorphisms and target sequence probes (e.g., exomes), and transcriptome sequencing. These approaches generate different types and quantities of data, and many can be applied with limited or no prior genomic resources, thus overcoming one traditional limitation of research on NMOs. Within marine mammals, such studies have thus far yielded significant contributions to the fields of phylogenomics and comparative genomics, as well as enabled investigations of fitness, demography, and population structure. Here we review the primary options for generating genomic data, introduce several emerging techniques, and discuss the suitability of each approach for different applications in the study of NMOs.

De Novo Transcriptome Assembly and Annotation of the Leaves and Callus of Cyclocarya Paliurus (Bata1) Iljinskaja

Cyclocarya Paliurus (Bata1) Iljinskaja contains various bioactive secondary metabolites especially in leaves, such as triterpenes, flavonoids, polysaccharides and alkaloids, and its leaves are widely used as an hyperglycemic tea in China. In the present paper, we sequenced the transcriptome of the leaves and callus of Cyclocarya Paliurus using Illumina Hiseq 4000 platform. After sequencing and de novo assembly, a total of 65,654 unigenes were generated with an N50 length of 1,244bp. Among them, 35,041 (53.37%) unigenes were annotated in NCBI Non-Redundant database, 19,453 (29.63%) unigenes were classified into Gene Ontology (GO) database, and 7,259 (11.06%) unigenes were assigned to Clusters of Orthologous Group (COG) categories. Furthermore, 11,697 (17.81%) unigenes were mapped onto 335 pathways in Kyoto Encyclopedia of Genes and Genomes (KEGG), among which 1,312 unigenes were identified to be involved in biosynthesis of secondary metabolites. In addition, a total of 11,247 putative simple sequence repeats (SSRs) were detected. This transcriptome dataset provides a comprehensive sequence resource for gene expression profiling, genetic diversity, evolution and further molecular genetics research on Cyclocarya Paliurus.