RNA-seq: an assessment of technical reproducibility and comparison with gene expression arrays

The complete mitochondrial genome of Ophiocordyceps gracilis and its comparison with related species

In this study, the complete mitochondrial genome of O. gracilis was sequenced and assembled before being compared with related species. As the second largest mitogenome reported in the family Ophiocordycipitaceae, the mitogenome of O. gracilis (voucher OG201301) is a circular DNA molecule of 134,288 bp that contains numerous introns and longer intergenomic regions. UCA was detected as anticodon in tRNA-Sec of O. gracilis, while comparative mitogenome analysis of nine Ophiocordycipitaceae fungi indicated that the order and contents of PCGs and rRNA genes were considerably conserved and could descend from a common ancestor in Ophiocordycipitaceae. In addition, the expansion of mitochondrial organization, introns, gene length, and order of O. gracilis were determined to be similar to those of O. sinensis, which indicated common mechanisms underlying adaptive evolution in O. gracilis and O. sinensis. Based on the mitochondrial gene dataset (15 PCGs and 2 RNA genes), a close genetic relationship between O. gracilis and O. sinensis was revealed through phylogenetic analysis. This study is the first to investigate the molecular evolution, phylogenetic pattern, and genetic structure characteristics of mitogenome in O. gracilis. Based on the obtained results, the mitogenome of O. gracilis can increase understanding of the genetic diversity and evolution of cordycipitoid fungi.

An integrated approach to identify bimodal genes associated with prognosis in câncer

Bimodal gene expression (where a gene expression distribution has two maxima) is associated with phenotypic diversity in different biological systems. A critical issue, thus, is the integration of expression and phenotype data to identify genuine associations. Here, we developed tools that allow both: i) the identification of genes with bimodal gene expression and ii) their association with prognosis in cancer patients from The Cancer Genome Atlas (TCGA). Bimodality was observed for 554 genes in expression data from 25 tumor types. Furthermore, 96 of these genes presented different prognosis when patients belonging to the two expression peaks were compared. The software to execute the method and the corresponding documentation are available at the Data access section.

Adipose gene expression profiles reveal insights into the adaptation of northern Eurasian semi-domestic reindeer (Rangifer tarandus)

Reindeer (Rangifer tarandus) are semi-domesticated animals adapted to the challenging conditions of northern Eurasia. Adipose tissues play a crucial role in northern animals by altering gene expression in their tissues to regulate energy homoeostasis and thermogenic activity. Here, we perform transcriptome profiling by RNA sequencing of adipose tissues from three different anatomical depots: metacarpal (bone marrow), perirenal, and prescapular fat in Finnish and Even reindeer (in Sakha) during spring and winter. A total of 16,212 genes are expressed in our data. Gene expression profiles in metacarpal tissue are distinct from perirenal and prescapular adipose tissues. Notably, metacarpal adipose tissue appears to have a significant role in the regulation of the energy metabolism of reindeer in spring when their nutritional condition is poor after winter. During spring, genes associated with the immune system are upregulated in the perirenal and prescapular adipose tissue. Blood and tissue parameters reflecting general physiological and metabolic status show less seasonal variation in Even reindeer than in Finnish reindeer. This study identifies candidate genes potentially involved in immune response, fat deposition, and energy metabolism and provides new information on the mechanisms by which reindeer adapt to harsh arctic conditions.

Cancer DEIso: An integrative analysis platform for investigating differentially expressed gene-level and isoform-level human cancer markers

Transcript isoforms regulated by alternative splicing can substantially impact carcinogenesis, leading to a need to obtain clues for both gene differential expression and malfunctions of isoform distributions in cancer studies. The Cancer Genome Atlas (TCGA) project was launched in 2008 to collect cancer-related genome mutation raw data from the population. While many repositories tried to add insights into the raw data in TCGA, no existing database provides both comprehensive gene-level and isoform-level cancer stage marker investigation and survival analysis. We constructed Cancer DEIso to facilitate in-depth analyses for both gene-level and isoform-level human cancer studies. Patient RNA-seq data, sample sheets, patient clinical data, and human genome datasets were collected and processed in Cancer DEIso. And four functions to search differentially expressed genes/isoforms between cancer stages were implemented: (i) Search potential gene/isoform markers for a specified cancer type and its two stages; (ii) Search potentially induced cancer types and stages for a gene/isoform; (iii) Expression survival analysis on a given gene/isoform for some cancer; (iv) Gene/isoform stage expression comparison visualization. As an example, we demonstrate that Cancer DEIso can indicate potential colorectal cancer isoform diagnostic markers that are not easily detected when only gene-level expressions are considered. Cancer DEIso is available at http://cosbi4.ee.ncku.edu.tw/DEIso/.

Reproducibility of mass spectrometry based metabolomics data

BACKGROUND

Assessing the reproducibility of measurements is an important first step for improving the reliability of downstream analyses of high-throughput metabolomics experiments. We define a metabolite to be reproducible when it demonstrates consistency across replicate experiments. Similarly, metabolites which are not consistent across replicates can be labeled as irreproducible. In this work, we introduce and evaluate the use (Ma)ximum (R)ank (R)eproducibility (MaRR) to examine reproducibility in mass spectrometry-based metabolomics experiments. We examine reproducibility across technical or biological samples in three different mass spectrometry metabolomics (MS-Metabolomics) data sets.

RESULTS

We apply MaRR, a nonparametric approach that detects the change from reproducible to irreproducible signals using a maximal rank statistic. The advantage of using MaRR over model-based methods that it does not make parametric assumptions on the underlying distributions or dependence structures of reproducible metabolites. Using three MS Metabolomics data sets generated in the multi-center Genetic Epidemiology of Chronic Obstructive Pulmonary Disease (COPD) study, we applied the MaRR procedure after data processing to explore reproducibility across technical or biological samples. Under realistic settings of MS-Metabolomics data, the MaRR procedure effectively controls the False Discovery Rate (FDR) when there was a gradual reduction in correlation between replicate pairs for less highly ranked signals. Simulation studies also show that the MaRR procedure tends to have high power for detecting reproducible metabolites in most situations except for smaller values of proportion of reproducible metabolites. Bias (i.e., the difference between the estimated and the true value of reproducible signal proportions) values for simulations are also close to zero. The results reported from the real data show a higher level of reproducibility for technical replicates compared to biological replicates across all the three different datasets. In summary, we demonstrate that the MaRR procedure application can be adapted to various experimental designs, and that the nonparametric approach performs consistently well.

CONCLUSIONS

This research was motivated by reproducibility, which has proven to be a major obstacle in the use of genomic findings to advance clinical practice. In this paper, we developed a data-driven approach to assess the reproducibility of MS-Metabolomics data sets. The methods described in this paper are implemented in the open-source R package marr, which is freely available from Bioconductor at http://bioconductor.org/packages/marr .

RNA-seq profiling reveals PBMC RNA as a potential biomarker for hepatocellular carcinoma

Hepatocellular carcinoma (HCC) is one of the most common malignant tumors and has extremely high morbidity and mortality. Although many existing studies have focused on the identification of biomarkers, little information has been uncovered regarding the PBMC RNA profile of HCC. We attempted to create a profile throughout using expression of peripheral blood mononuclear cell (PBMC) RNA using RNA-seq technology and compared the transcriptome between HCC patients and healthy controls. Seventeen patients and 17 matched healthy controls were included in this study, and PBMC RNA was sequenced from all samples. Sequencing data were analyzed using bioinformatics tools, and quantitative reverse transcription PCR (qRT-PCR) was used for selected validation of DEGs. A total of 1,578 dysregulated genes were found in the PBMC samples, including 1,334 upregulated genes and 244 downregulated genes. GO enrichment and KEGG studies revealed that HCC is closely linked to differentially expressed genes (DEGs) implicated in the immune response. Expression of 6 selected genes (SELENBP1, SLC4A1, SLC26A8, HSPA8P4, CALM1, and RPL7p24) was confirmed by qRT-PCR, and higher sensitivity and specificity were obtained by ROC analysis of the 6 genes. CALM1 was found to gradually decrease as tumors enlarged. Nearly the opposite expression modes were obtained when compared to tumor sequencing data. Immune cell populations exhibited significant differences between HCC and controls. These findings suggest a potential biomarker for the diagnosis of HCC. This study provides new perspectives for liver cancer development and possible future successful clinical diagnosis.

RNA-Seq Provides New Insights into the Gene Expression Changes in Azoarcus olearius BH72 under Nitrogen-Deficient and Replete Conditions beyond the Nitrogen Fixation Process

Azoarcus olearius BH72 is an endophyte capable of biological nitrogen fixation (BNF) and of supplying nitrogen to its host plant. Our previous microarray approach provided insights into the transcriptome of strain BH72 under N₂-fixation in comparison to ammonium-grown conditions, which already indicated the induction of genes not related to the BNF process. Due to the known limitations of the technique, we might have missed additional differentially expressed genes (DEGs). Thus, we used directional RNA-Seq to better comprehend the transcriptional landscape under these growth conditions. RNA-Seq detected almost 24% of the annotated genes to be regulated, twice the amount identified by microarray. In addition to confirming entire regulated operons containing known DEGs, the new approach detected the induction of genes involved in carbon metabolism and flagellar and twitching motility. This may support N₂-fixation by increasing energy production and by finding suitable microaerobic niches. On the other hand, energy expenditures were reduced by suppressing translation and vitamin biosynthesis. Nonetheless, strain BH72 does not appear to be content with N₂-fixation but is primed for alternative economic N-sources, such as nitrate, urea or amino acids; a strong gene induction of machineries for their uptake and assimilation was detected. RNA-Seq has thus provided a better understanding of a lifestyle under limiting nitrogen sources by elucidating hitherto unknown regulated processes.

Leaf Transcriptome Analysis of Broomcorn Millet Uncovers Key Genes and Pathways in Response to Sporisorium destruens

Broomcorn millet (Panicum miliaceum L.) affected by smut (caused by the pathogen Sporisorium destruens) has reduced production yields and quality. Determining the tolerance of broomcorn millet varieties is essential for smut control. This study focuses on the differences in the phenotypes, physiological characteristics, and transcriptomes of resistant and susceptible broomcorn millet varieties under Sporisorium destruens stress. In diseased broomcorn millet, the plant height and stem diameter were reduced, while the number of nodes increased. After infection, the activities of superoxide dismutase and peroxidase decreased, and malondialdehyde and relative chlorophyll content (SPAD) decreased. Transcriptome analysis showed 514 and 5452 differentially expressed genes (DEGs) in the resistant and susceptible varieties, respectively. The Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis of DEGs showed that pathways related to plant disease resistance, such as phenylpropanoid biosynthesis, plant–pathogen interaction, and plant hormone signal transduction, were significantly enriched. In addition, the transcriptome changes of cluster leaves and normal leaves in diseased broomcorn millet were analysed. Gene ontology and KEGG enrichment analyses indicated that photosynthesis played an important role in both varieties. These findings lay a foundation for future research on the molecular mechanism of the interaction between broomcorn millet and Sporisorium destruens.

Joint Modeling of RNAseq and Radiomics Data for Glioma Molecular Characterization and Prediction

RNA sequencing (RNAseq) is a recent technology that profiles gene expression by measuring the relative frequency of the RNAseq reads. RNAseq read counts data is increasingly used in oncologic care and while radiology features (radiomics) have also been gaining utility in radiology practice such as disease diagnosis, monitoring, and treatment planning. However, contemporary literature lacks appropriate RNA-radiomics (henceforth, radiogenomics ) joint modeling where RNAseq distribution is adaptive and also preserves the nature of RNAseq read counts data for glioma grading and prediction. The Negative Binomial (NB) distribution may be useful to model RNAseq read counts data that addresses potential shortcomings. In this study, we propose a novel radiogenomics-NB model for glioma grading and prediction. Our radiogenomics-NB model is developed based on differentially expressed RNAseq and selected radiomics/volumetric features which characterize tumor volume and sub-regions. The NB distribution is fitted to RNAseq counts data, and a log-linear regression model is assumed to link between the estimated NB mean and radiomics. Three radiogenomics-NB molecular mutation models (e.g., IDH mutation, 1p/19q codeletion, and ATRX mutation) are investigated. Additionally, we explore gender-specific effects on the radiogenomics-NB models. Finally, we compare the performance of the proposed three mutation prediction radiogenomics-NB models with different well-known methods in the literature: Negative Binomial Linear Discriminant Analysis (NBLDA), differentially expressed RNAseq with Random Forest (RF-genomics), radiomics and differentially expressed RNAseq with Random Forest (RF-radiogenomics), and Voom-based count transformation combined with the nearest shrinkage classifier (VoomNSC). Our analysis shows that the proposed radiogenomics-NB model significantly outperforms (ANOVA test, p < 0.05) for prediction of IDH and ATRX mutations and offers similar performance for prediction of 1p/19q codeletion, when compared to the competing models in the literature, respectively.

Rapid Transient Transcriptional Adaptation to Hypergravity in Jurkat T Cells Revealed by Comparative Analysis of Microarray and RNA-Seq Data

Cellular responses to micro- and hypergravity are rapid and complex and appear within the first few seconds of exposure. Transcriptomic analyses are a valuable tool to analyze these genome-wide cellular alterations. For a better understanding of the cellular dynamics upon altered gravity exposure, it is important to compare different time points. However, since most of the experiments are designed as endpoint measurements, the combination of cross-experiment meta-studies is inevitable. Microarray and RNA-Seq analyses are two of the main methods to study transcriptomics. In the field of altered gravity research, both methods are frequently used. However, the generation of these data sets is difficult and time-consuming and therefore the number of available data sets in this research field is limited. In this study, we investigated the comparability of microarray and RNA-Seq data and applied the results to a comparison of the transcriptomics dynamics between the hypergravity conditions during two real flight platforms and a centrifuge experiment to identify temporal adaptation processes. We performed a comparative study on an Affymetrix HTA2.0 microarray and a paired-end RNA-Seq data set originating from the same Jurkat T cell RNA samples from a short-term hypergravity experiment. The overall agreeability was high, with better sensitivity of the RNA-Seq analysis. The microarray data set showed weaknesses on the level of single upregulated genes, likely due to its normalization approach. On an aggregated level of biotypes, chromosomal distribution, and gene sets, both technologies performed equally well. The microarray showed better performance on the detection of altered gravity-related splicing events. We found that all initially altered transcripts fully adapted after 15 min to hypergravity and concluded that the altered gene expression response to hypergravity is transient and fully reversible. Based on the combined multiple-platform meta-analysis, we could demonstrate rapid transcriptional adaptation to hypergravity, the differential expression of the ATPase subunits ATP6V1A and ATP6V1D, and the cluster of differentiation (CD) molecules CD1E, CD2AP, CD46, CD47, CD53, CD69, CD96, CD164, and CD226 in hypergravity. We could experimentally demonstrate that it is possible to develop methodological evidence for the meta-analysis of individual data.

Analysis of saponins detoxification genes in Ilyonectria mors-panacis G3B inducing root rot of Panax notoginseng by RNA-Seq

Saponins are kinds of antifungal compounds produced by Panax notoginseng to resist invasion by pathogens. Ilyonectria mors-panacis G3B was the dominant pathogen inducing root rot of P. notoginseng, and the abilities to detoxify saponins were the key to infect P. notoginseng successfully. To research the molecular mechanisms of detoxifying saponins in I. mors-panacis G3B, we used high-throughput RNA-Seq to identify 557 and 1519 differential expression genes (DEGs) in I. mors-panacis G3B with saponins treatments for 4H (Hours) and 12H (Hours) compared with no saponins treatments, respectively. Among these DEGs, we found 93 genes which were simultaneously highly expressed in I. mors-panacis G3B with saponins treatments for 4H and 12H, they mainly belong to genes encoding transporters, glycoside hydrolases, oxidation-reduction enzymes, transcription factors and so on. In addition, there were 21 putative PHI (Pathogen-Host Interaction) genes out of those 93 up-regulated genes. In this report, we analyzed virulence-associated genes in I. mors-panacis G3B which may be related to detoxifying saponins to infect P. notoginseng successfully. They provided an excellent starting point for in-depth study on pathogenicity of I. mors-panacis G3B and developed appropriate root rot disease management strategies in the future.

MUREN: a robust and multi-reference approach of RNA-seq transcript normalization

Background

Normalization of RNA-seq data aims at identifying biological expression differentiation between samples by removing the effects of unwanted confounding factors. Explicitly or implicitly, the justification of normalization requires a set of housekeeping genes. However, the existence of housekeeping genes common for a very large collection of samples, especially under a wide range of conditions, is questionable.

Results

We propose to carry out pairwise normalization with respect to multiple references, selected from representative samples. Then the pairwise intermediates are integrated based on a linear model that adjusts the reference effects. Motivated by the notion of housekeeping genes and their statistical counterparts, we adopt the robust least trimmed squares regression in pairwise normalization. The proposed method (MUREN) is compared with other existing tools on some standard data sets. The goodness of normalization emphasizes on preserving possible asymmetric differentiation, whose biological significance is exemplified by a single cell data of cell cycle. MUREN is implemented as an R package. The code under license GPL-3 is available on the github platform: github.com/hippo-yf/MUREN and on the conda platform: anaconda.org/hippo-yf/r-muren.

Conclusions

MUREN performs the RNA-seq normalization using a two-step statistical regression induced from a general principle. We propose that the densities of pairwise differentiations are used to evaluate the goodness of normalization. MUREN adjusts the mode of differentiation toward zero while preserving the skewness due to biological asymmetric differentiation. Moreover, by robustly integrating pre-normalized counts with respect to multiple references, MUREN is immune to individual outlier samples.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12859-021-04288-0.

Identification of pathological transcription in autosomal dominant polycystic kidney disease epithelia

Autosomal dominant polycystic kidney disease (ADPKD) affects more than 12 million people worldwide. Mutations in PKD1 and PKD2 cause cyst formation through unknown mechanisms. To unravel the pathogenic mechanisms in ADPKD, multiple studies have investigated transcriptional mis-regulation in cystic kidneys from patients and mouse models, and numerous dysregulated genes and pathways have been described. Yet, the concordance between studies has been rather limited. Furthermore, the cellular and genetic diversity in cystic kidneys has hampered the identification of mis-expressed genes in kidney epithelial cells with homozygous PKD mutations, which are critical to identify polycystin-dependent pathways. Here we performed transcriptomic analyses of Pkd1- and Pkd2-deficient mIMCD3 kidney epithelial cells followed by a meta-analysis to integrate all published ADPKD transcriptomic data sets. Based on the hypothesis that Pkd1 and Pkd2 operate in a common pathway, we first determined transcripts that are differentially regulated by both genes. RNA sequencing of genome-edited ADPKD kidney epithelial cells identified 178 genes that are concordantly regulated by Pkd1 and Pkd2. Subsequent integration of existing transcriptomic studies confirmed 31 previously described genes and identified 61 novel genes regulated by Pkd1 and Pkd2. Cluster analyses then linked Pkd1 and Pkd2 to mRNA splicing, specific factors of epithelial mesenchymal transition, post-translational protein modification and epithelial cell differentiation, including CD34, CDH2, CSF2RA, DLX5, HOXC9, PIK3R1, PLCB1 and TLR6. Taken together, this model-based integrative analysis of transcriptomic alterations in ADPKD annotated a conserved core transcriptomic profile and identified novel candidate genes for further experimental studies.

TransRef enables accurate transcriptome assembly by redefining accurate neo-splicing graphs

RNA-seq technology is widely employed in various research areas related to transcriptome analyses, and the identification of all the expressed transcripts from short sequencing reads presents a considerable computational challenge. In this study, we introduce TransRef, a new computational algorithm for accurate transcriptome assembly by redefining a novel graph model, the neo-splicing graph, and then iteratively applying a constrained dynamic programming to reconstruct all the expressed transcripts for each graph. When TransRef is utilized to analyze both real and simulated datasets, its performance is notably better than those of several state-of-the-art assemblers, including StringTie2, Cufflinks and Scallop. In particular, the performance of TransRef is notably strong in identifying novel transcripts and transcripts with low-expression levels, while the other assemblers are less effective.

Vesicular hand eczema transcriptome analysis provides insights into its pathophysiology

Hand eczema is a common inflammatory skin condition of the hands whose pathogenesis is largely unknown. More insight and knowledge of the disease on a more fundamental level might lead to a better understanding of the biological processes involved, which could provide possible new treatment strategies. We aimed to profile the transcriptome of lesional palmar epidermal skin of patients suffering from vesicular hand eczema using RNA‐sequencing. RNA‐sequencing was performed to identify differentially expressed genes in lesional vs. non‐lesional palmar epidermal skin from a group of patients with vesicular hand eczema compared to healthy controls. Comprehensive real‐time quantitative PCR analyses and immunohistochemistry were used for validation of candidate genes and protein profiles for vesicular hand eczema. Overall, a significant and high expression of genes/proteins involved in keratinocyte host defense and inflammation was found in lesional skin. Furthermore, we detected several molecules, both up or downregulated in lesional skin, which are involved in epidermal differentiation. Immune signalling genes were found to be upregulated in lesional skin, albeit with relatively low expression levels. Non‐lesional patient skin showed no significant differences compared to healthy control skin. Lesional vesicular hand eczema skin shows a distinct expression profile compared to non‐lesional skin and healthy control skin. Notably, the overall results indicate a large overlap between vesicular hand eczema and earlier reported atopic dermatitis lesional transcriptome profiles, which suggests that treatments for atopic dermatitis could also be effective in (vesicular) hand eczema.

Comparative Transcriptomics Reveals Distinct Patterns of Gene Expression Conservation through Vertebrate Embryogenesis

Despite life's diversity, studies of variation often remind us of our shared evolutionary past. Abundant genome sequencing and analyses of gene regulatory networks illustrate that genes and entire pathways are conserved, reused, and elaborated in the evolution of diversity. Predating these discoveries, 19th-century embryologists observed that though morphology at birth varies tremendously, certain stages of vertebrate embryogenesis appear remarkably similar across vertebrates. In the mid to late 20th century, anatomical variability of early and late-stage embryos and conservation of mid-stages embryos (the "phylotypic" stage) was named the hourglass model of diversification. This model has found mixed support in recent analyses comparing gene expression across species possibly owing to differences in species, embryonic stages, and gene sets compared. We compare 186 microarray and RNA-seq data sets covering embryogenesis in six vertebrate species. We use an unbiased clustering approach to group stages of embryogenesis by transcriptomic similarity and ask whether gene expression similarity of clustered embryonic stages deviates from a null expectation. We characterize expression conservation patterns of each gene at each evolutionary node after correcting for phylogenetic nonindependence. We find significant enrichment of genes exhibiting early conservation, hourglass, late conservation patterns in both microarray and RNA-seq data sets. Enrichment of genes showing patterned conservation through embryogenesis indicates diversification of embryogenesis may be temporally constrained. However, the circumstances under which each pattern emerges remain unknown and require both broad evolutionary sampling and systematic examination of embryogenesis across species.

Short-term high fat diet alters genes associated with metabolic and vascular dysfunction during adolescence in rats: a pilot study

Background

Diet-induced metabolic dysfunction precedes multiple disease states including diabetes, heart disease, and vascular dysfunction. The critical role of the vasculature in disease progression is established, yet the details of how gene expression changes in early cardiovascular disease remain an enigma. The objective of the current pilot project was to evaluate whether a quantitative assessment of gene expression within the aorta of six-week old healthy male Sprague-Dawley rats compared to those exhibiting symptoms of metabolic dysfunction could reveal potential mediators of vascular dysfunction.

Methods

RNA was extracted from the aorta of eight rats from a larger experiment; four animals fed a high-fat diet (HFD) known to induce symptoms of metabolic dysfunction (hypertension, increased adiposity, fasting hyperglycemia) and four age-matched healthy animals fed a standard chow diet (CHOW). The bioinformatic workflow included Gene Ontology (GO) biological process enrichment and network analyses.

Results

The resulting network contained genes relevant to physiological processes including fat and protein metabolism, oxygen transport, hormone regulation, vascular regulation, thermoregulation, and circadian rhythm. The majority of differentially regulated genes were downregulated, including several associated with circadian clock function. In contrast, leptin and 3-hydroxy-3-methylglutaryl-CoA synthase 2 (Hmgcs2) were notably upregulated. Leptin is involved in several major energy balance signaling pathways and Hmgcs2 is a mitochondrial enzyme that catalyzes the first reaction of ketogenesis.

Conclusion

Together, these data describe changes in gene expression within the aortic wall of HFD rats with early metabolic dysfunction and highlight potential pathways and signaling intermediates that may impact the development of early vascular dysfunction.

Three dimensional modeling of biologically relevant fluid shear stress in human renal tubule cells mimics in vivo transcriptional profiles

The kidney proximal tubule is the primary site for solute reabsorption, secretion and where kidney diseases can originate, including drug-induced toxicity. Two-dimensional cell culture systems of the human proximal tubule cells (hPTCs) are often used to study these processes. However, these systems fail to model the interplay between filtrate flow, fluid shear stress (FSS), and functionality essential for understanding renal diseases and drug toxicity. The impact of FSS exposure on gene expression and effects of FSS at differing rates on gene expression in hPTCs has not been thoroughly investigated. Here, we performed RNA-sequencing of human RPTEC/TERT1 cells in a microfluidic chip-based 3D model to determine transcriptomic changes. We measured transcriptional changes following treatment of cells in this device at three different fluidic shear stress. We observed that FSS changes the expression of PTC-specific genes and impacted genes previously associated with renal diseases in genome-wide association studies (GWAS). At a physiological FSS level, we observed cell morphology, enhanced polarization, presence of cilia, and transport functions using albumin reabsorption via endocytosis and efflux transport. Here, we present a dynamic view of hPTCs response to FSS with increasing fluidic shear stress conditions and provide insight into hPTCs cellular function under biologically relevant conditions.

Ethanol extract of Ardisiae Japonicae Herba inhibits hepatoma carcinoma cell proliferation in vitro through regulating lipid metabolism

Objective

The aim of this study is to discover the possible working mechanisms of Ardisiae Japonicae Herba (AJH) on hepatoma carcinoma (HCC).

Methods

In this study, ethanol extract of AJH was prepared and used to treat HCC cell in vitro. Furthermore, a genomic wide RNA sequencing (RNA-seq) was performed to screen deregulated genes in HCC cells after the treatment of AJH extract. The gene and protein expression related to lipid metabolism in HCC cells were also investigated to validate the results obtained from RNA-seq.

Results

AJH extract could inhibit HCC cell proliferation in vitro. RNA-seq analysis has identified 1,601 differentially expressed genes (DEGs, fold change ≥ 2.0 or fold change ≤ 0.5, P < 0.05) in HCC after AJH extract treatment, which included 225 up-regulated genes and 1,376 down-regulated genes. KEGG pathway analysis of DEGs demonstrated that lipid metabolism was a potential pathway related to AJH treatment. In agreement with the RNA-seq data, qPCR and Western-blot analysis indicated that expression of genes and proteins related to lipid metabolism (SREBP1, ACC, ACLY and FASN) were significantly down-regulated in AJH treatment group as compared with the control group. Furthermore, AJH extract could also decrease lipid contents and cellular free fatty acid levels in HCC cells.

Conclusion

Ethanol extract of AJH could inhibit HCC cell proliferation in vitro, the possible mechanism may be related to the inhibition of lipid metabolism.

BP4RNAseq: a babysitter package for retrospective and newly generated RNA-seq data analyses using both alignment-based and alignment-free quantification method

SUMMARY

Processing raw reads of RNA-sequencing (RNA-seq) data, no matter public or newly sequenced data, involves a lot of specialized tools and technical configurations that are often unfamiliar and time-consuming to learn for non-bioinformatics researchers. Here, we develop the R package BP4RNAseq, which integrates the state-of-art tools from both alignment-based and alignment-free quantification workflows. The BP4RNAseq package is a highly automated tool using an optimized pipeline to improve the sensitivity and accuracy of RNA-seq analyses. It can take only two non-technical parameters and output six formatted gene expression quantification at gene and transcript levels. The package applies to both retrospective and newly generated bulk RNA-seq data analyses and is also applicable for single-cell RNA-seq analyses. It, therefore, greatly facilitates the application of RNA-seq.

AVAILABILITY AND IMPLEMENTATION

The BP4RNAseq package for R and its documentation are freely available at https://github.com/sunshanwen/BP4RNAseq.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

A scaling-free minimum enclosing ball method to detect differentially expressed genes for RNA-seq data

Background

Identifying differentially expressed genes between the same or different species is an urgent demand for biological and medical research. For RNA-seq data, systematic technical effects and different sequencing depths are usually encountered when conducting experiments. Normalization is regarded as an essential step in the discovery of biologically important changes in expression. The present methods usually involve normalization of the data with a scaling factor, followed by detection of significant genes. However, more than one scaling factor may exist because of the complexity of real data. Consequently, methods that normalize data by a single scaling factor may deliver suboptimal performance or may not even work.The development of modern machine learning techniques has provided a new perspective regarding discrimination between differentially expressed (DE) and non-DE genes. However, in reality, the non-DE genes comprise only a small set and may contain housekeeping genes (in same species) or conserved orthologous genes (in different species). Therefore, the process of detecting DE genes can be formulated as a one-class classification problem, where only non-DE genes are observed, while DE genes are completely absent from the training data.

Results

In this study, we transform the problem to an outlier detection problem by treating DE genes as outliers, and we propose a scaling-free minimum enclosing ball (SFMEB) method to construct a smallest possible ball to contain the known non-DE genes in a feature space. The genes outside the minimum enclosing ball can then be naturally considered to be DE genes. Compared with the existing methods, the proposed SFMEB method does not require data normalization, which is particularly attractive when the RNA-seq data include more than one scaling factor. Furthermore, the SFMEB method could be easily extended to different species without normalization.

Conclusions

Simulation studies demonstrate that the SFMEB method works well in a wide range of settings, especially when the data are heterogeneous or biological replicates. Analysis of the real data also supports the conclusion that the SFMEB method outperforms other existing competitors. The R package of the proposed method is available at https://bioconductor.org/packages/MEB.

Supplementary Information

The online version contains supplementary material available at (10.1186/s12864-021-07790-0).

Bayesian Modeling of Spatial Molecular Profiling Data via Gaussian Process

MOTIVATION

The location, timing, and abundance of gene expression (both mRNA and proteins) within a tissue define the molecular mechanisms of cell functions. Recent technology breakthroughs in spatial molecular profiling, including imaging-based technologies and sequencing-based technologies, have enabled the comprehensive molecular characterization of single cells while preserving their spatial and morphological contexts. This new bioinformatics scenario calls for effective and robust computational methods to identify genes with spatial patterns.

RESULTS

We represent a novel Bayesian hierarchical model to analyze spatial transcriptomics data, with several unique characteristics. It models the zero-inflated and over-dispersed counts by deploying a zero-inflated negative binomial model that greatly increases model stability and robustness. Besides, the Bayesian inference framework allows us to borrow strength in parameter estimation in a de novo fashion. As a result, the proposed model shows competitive performances in accuracy and robustness over existing methods in both simulation studies and two real data applications.

AVAILABILITY

The related R/C ++ source code is available at https://github.com/Minzhe/BOOST-GP.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Transcriptional regulator XtgS is involved in iron transition and attenuates the virulence of Streptococcus agalactiae

Streptococcus agalactiae (GBS) is an important pathogen that has increasingly received attention for its role in invasive infections and its broad host range. Research on the regulation of gene expression could illuminate GBS pathogenesis. We previously identified a novel transcriptional regulator XtgS, which is a negative regulator of GBS pathogenicity. Here, we demonstrate that XtgS overexpression significantly attenuated GBS virulence in zebrafish infection tests, and XtgS indirectly downregulated the transcription of two iron transport systems based on the results of transcriptomic analysis, electrophoretic mobility shift assays (EMSAs) and lacZ fusion assays. Subsequent studies verified that the inactivation of iron transport system 1 resulted in GBS excessive iron accumulation and attenuated virulence. Thus, we infer that the downregulation of iron transport system 1 caused by XtgS overexpression probably attenuates bacterial virulence, which partially clarifies the mechanism by which XtgS alleviates the pathogenesis. These findings provide new insights into the relationship between exogenous transcriptional regulation and bacterial virulence.

Reconstruction of circRNA-miRNA-mRNA associated ceRNA networks reveal functional circRNAs in intracerebral hemorrhage

Circular RNA (circRNA), a novel class of noncoding RNAs, has been used extensively to complement transcriptome remodeling in the central nervous system, although the genomic coverage provided has rarely been studied in intracerebral hemorrhage (ICH) and is limited and fails to provide a detailed picture of the cerebral transcriptome landscape. Here, we described sequencing-based transcriptome profiling, providing comprehensive analysis of cerebral circRNA, messenger RNA (mRNA) and microRNA (miRNA) expression in ICH rats. In the study, male Sprague-Dawley rats were subjected to ICH, and next-generation sequencing of RNAs isolated from non-hemorrhagic (Sham) and hemorrhagic (ICH) rat brain samples collected 7 (early phase) and 28 (chronic phase) days after insults, was conducted. Bioinformatics analysis was performed to determine miRNA binding sites and gene ontology of circRNAs, target genes of miRNAs, as well as biological functions of mRNAs, altered after ICH. These analyses revealed different expression profiles of circRNAs, mRNAs and miRNAs in day-7 and day-28 ICH groups, respectively, compared with the Sham. In addition, the expression signature of circRNAs was more sensitive to disease progression than that of mRNAs or miRNAs. Further analysis suggested two temporally specific circRNA-miRNA-mRNA networks based on the competitive endogenous RNA theory, which had profound impacts on brain activities after ICH. In summary, these results suggested an important role for circRNAs in the pathogenesis of ICH and in reverse remodeling based on self-protection support, providing deep insights into diverse possibilities for ICH therapy through targeting circRNAs.

Separating measurement and expression models clarifies confusion in single-cell RNA sequencing analysis

The high proportion of zeros in typical single-cell RNA sequencing datasets has led to widespread but inconsistent use of terminology such as dropout and missing data. Here, we argue that much of this terminology is unhelpful and confusing, and outline simple ideas to help to reduce confusion. These include: (1) observed single-cell RNA sequencing counts reflect both true gene expression levels and measurement error, and carefully distinguishing between these contributions helps to clarify thinking; and (2) method development should start with a Poisson measurement model, rather than more complex models, because it is simple and generally consistent with existing data. We outline how several existing methods can be viewed within this framework and highlight how these methods differ in their assumptions about expression variation. We also illustrate how our perspective helps to address questions of biological interest, such as whether messenger RNA expression levels are multimodal among cells.

Predominantly inverse modulation of gene expression in genomically unbalanced disomic haploid maize

The phenotypic consequences of the addition or subtraction of part of a chromosome is more severe than changing the dosage of the whole genome. By crossing diploid trisomies to a haploid inducer, we identified 17 distal segmental haploid disomies that cover ∼80% of the maize genome. Disomic haploids provide a level of genomic imbalance that is not ordinarily achievable in multicellular eukaryotes, allowing the impact to be stronger and more easily studied. Transcriptome size estimates revealed that a few disomies inversely modulate most of the transcriptome. Based on RNA sequencing, the expression levels of genes located on the varied chromosome arms (cis) in disomies ranged from being proportional to chromosomal dosage (dosage effect) to showing dosage compensation with no expression change with dosage. For genes not located on the varied chromosome arm (trans), an obvious trans-acting effect can be observed, with the majority showing a decreased modulation (inverse effect). The extent of dosage compensation of varied cis genes correlates with the extent of trans inverse effects across the 17 genomic regions studied. The results also have implications for the role of stoichiometry in gene expression, the control of quantitative traits, and the evolution of dosage-sensitive genes.

Identification of differentially expressed genes in diabetic kidney disease by RNA-Seq analysis of venous blood platelets

Diabetic kidney disease (DKD) is the leading cause of end‐stage renal disease. However, because of shared complications between DKD and chronic kidney disease (CKD), the description and characterization of DKD remain ambiguous in the clinic, hindering the diagnosis and treatment of early‐stage DKD patients. Although estimated glomerular filtration rate and albuminuria are well‐established biomarkers of DKD, early‐stage DKD is rarely accompanied by a high estimated glomerular filtration rate, and thus there is a need for new sensitive biomarkers. Transcriptome profiling of kidney tissue has been reported previously, although RNA sequencing (RNA‐Seq) analysis of the venous blood platelets in DKD patients has not yet been described. In the present study, we performed RNA‐Seq analysis of venous blood platelets from three patients with CKD, five patients with DKD and 10 healthy controls, and compared the results with a CKD‐related microarray dataset. In total, 2097 genes with differential transcript levels were identified in platelets of DKD patients and healthy controls, and 462 genes with differential transcript levels were identified in platelets of DKD patients and CKD patients. Through Kyoto Encyclopedia of Genes and Genomes pathway enrichment analysis, we selected 11 pathways, from which nine potential biomarkers (IL‐1B, CD‐38, CSF1R, PPARG, NR1H3, DDO, HDC, DPYS and CAD) were identified. Furthermore, by comparing the RNA‐Seq results with the GSE30566 dataset, we found that the biomarker KCND3 was the only up‐regulated gene in DKD patients. These biomarkers may have potential application for the therapy and diagnosis of DKD, as well aid in determining the mechanisms underlying DKD.

Multi-omics integration strategies for animal epigenetic studies - A review

Genome-wide studies provide considerable insights into the genetic background of animals; however, the inheritance of several heritable factors cannot be elucidated. Epigenetics explains these heritabilities, including those of genes influenced by environmental factors. Knowledge of the mechanisms underlying epigenetics enables understanding the processes of gene regulation through interactions with the environment. Recently developed next-generation sequencing (NGS) technologies help understand the interactional changes in epigenetic mechanisms. There are large sets of NGS data available; however, the integrative data analysis approaches still have limitations with regard to reliably interpreting the epigenetic changes. This review focuses on the epigenetic mechanisms and profiling methods and multi-omics integration methods that can provide comprehensive biological insights in animal genetic studies.

Identification of transcriptional subtypes in lung adenocarcinoma and squamous cell carcinoma through integrative analysis of microarray and RNA sequencing data

Classification of tumors into subtypes can inform personalized approaches to treatment including the choice of targeted therapies. The two most common lung cancer histological subtypes, lung adenocarcinoma and lung squamous cell carcinoma, have been previously divided into transcriptional subtypes using microarray data, and corresponding signatures were subsequently used to classify RNA-seq data. Cross-platform unsupervised classification facilitates the identification of robust transcriptional subtypes by combining vast amounts of publicly available microarray and RNA-seq data. However, cross-platform classification is challenging because of intrinsic differences in data generated using the two gene expression profiling technologies. In this report, we show that robust gene expression subtypes can be identified in integrated data representing over 3500 normal and tumor lung samples profiled using two widely used platforms, Affymetrix HG-U133 Plus 2.0 Array and Illumina HiSeq RNA sequencing. We tested and analyzed consensus clustering for 384 combinations of data processing methods. The agreement between subtypes identified in single-platform and cross-platform normalized data was then evaluated using a variety of statistics. Results show that unsupervised learning can be achieved with combined microarray and RNA-seq data using selected preprocessing, cross-platform normalization, and unsupervised feature selection methods. Our analysis confirmed three lung adenocarcinoma transcriptional subtypes, but only two consistent subtypes in squamous cell carcinoma, as opposed to four subtypes previously identified. Further analysis showed that tumor subtypes were associated with distinct patterns of genomic alterations in genes coding for therapeutic targets. Importantly, by integrating quantitative proteomics data, we were able to identify tumor subtype biomarkers that effectively classify samples on the basis of both gene and protein expression. This study provides the basis for further integrative data analysis across gene and protein expression profiling platforms.

Analysis of transcriptome difference between rapid-growing and slow-growing in Penaeus vannamei

Penaeus vannamei is the principle cultured shrimp species in China. However, with the increase of culture density, the growth difference between individuals is also expanding. Here, we make use of RNA-seq to study the growth mechanisms of P. vannamei. After 120 days, we examined the transcriptomes of rapid-growing individuals (RG) and slow-growing individuals (SG). A total of 2116 and 176 differentially expressed genes (DEGs) were found in SG and RG, respectively. Moreover, the main DEGs are opsin, heat shock protein (HSP), actin, myosin, superoxide dismutase (SOD), cuticle protein, and chitinase. GO analysis further revealed that the DEGs were enriched in biological processes significantly, such as "sensory perception," "sensory perception of light stimulus," "response to stimulus," and "response to stress." Additionally, KEGG enrichment analysis showed that the DEGs were mainly enriched in "pentose and glucuronate interconversions," "amino sugar and nucleotide sugar metabolism," "glycophospholipid biosynthesis," and "glutathione metabolism." Interestingly, the upstream genes in the ecdysone signaling pathway, including molting inhibition hormone (MIH) and crustacean hyperglycemic hormone (CHH), did not differ significantly between RG and SG, which suggests that the cause for the inconsistent growth performance is due to the stress levels rather than the ecdysone signal pathway. In summary, this work provides data that will be useful for future studies on shrimp growth and development.

Integration of Transformative Platforms for the Discovery of Causative Genes in Cardiovascular Diseases

Cardiovascular diseases are the leading cause of morbidity and mortality worldwide. Genome-wide association studies (GWAS) are powerful epidemiological tools to find genes and variants associated with cardiovascular diseases while follow-up biological studies allow to better understand the etiology and mechanisms of disease and assign causality. Improved methodologies and reduced costs have allowed wider use of bulk and single-cell RNA sequencing, human-induced pluripotent stem cells, organoids, metabolomics, epigenomics, and novel animal models in conjunction with GWAS. In this review, we feature recent advancements relevant to cardiovascular diseases arising from the integration of genetic findings with multiple enabling technologies within multidisciplinary teams to highlight the solidifying transformative potential of this approach. Well-designed workflows integrating different platforms are greatly improving and accelerating the unraveling and understanding of complex disease processes while promoting an effective way to find better drug targets, improve drug design and repurposing, and provide insight towards a more personalized clinical practice.

Simultaneous profiling of Arabidopsis thaliana and Vibrio vulnificus MO6-24/O transcriptomes by dual RNA-seq analysis

We previously demonstrated that a marine bacterial pathogen Vibrio vulnificus isolated from sea foods modulated gene expression levels and defense responses of a land plant Arabidopsis thaliana. Although the interaction between V. vulnificus and A. thaliana was verified under artificial and greenhouse conditions, the simultaneous changes in host and pathogen transcriptomes remained obscure. In this study, we simultaneously analyzed the transcriptome of V. vulnificus MO6-24/O and A. thaliana by dual RNA-sequencing analysis. Disease symptoms appeared at 5 and 7 days post-inoculation in vitro and post-infiltration in planta, respectively. A total of 31, 128, 303, 219, and 130 differentially expressed genes (DEGs) were identified in V. vulnificus MO6-24/O at 3, 6, 12, 24, and 48 h post-infiltration. Out of these, 14 genes involved in the virulence and pathogenicity of V. vulnificus MO6 were characterized. These genes were clustered into six categories, including adherence, antiphagocytosis, chemotaxis and motility, iron uptake, toxin and secretion system. In plant side, the bacterium DEGs potentially played a pivotal role in activating pattern recognition receptors (PRRs)-mediated defense responses. A. thaliana genes related to PRRs, reactive oxygen species burst, mitogen-activated protein kinase cascade induction, salicylic acid, jasmonic acid, ethylene, abscisic acid, auxin, gibberellin, and cytokinin were highly induced by V. vulnificus MO6-24/O challenge. Taken together, our results indicate that the sophisticated communication between a marine bacterial pathogen V. vulnificus and A. thaliana occurs. It is the first report demonstration that V. vulnificus actively modulates its virulence factors and potential host immune regulator in a land plant species.

Investigating Molecular Determinants of Cancer Cell Resistance to Ionizing Radiation Through an Integrative Bioinformatics Approach

Eradication of cancer cells through exposure to high doses of ionizing radiation (IR) is a widely used therapeutic strategy in the clinical setting. However, in many cases, cancer cells can develop remarkable resistance to radiation. Radioresistance represents a prominent obstacle in the effective treatment of cancer. Therefore, elucidation of the molecular mechanisms and pathways related to radioresistance in cancer cells is of paramount importance. In the present study, an integrative bioinformatics approach was applied to three publicly available RNA sequencing and microarray transcriptome datasets of human cancer cells of different tissue origins treated with ionizing radiation. These data were investigated in order to identify genes with a significantly altered expression between radioresistant and corresponding radiosensitive cancer cells. Through rigorous statistical and biological analyses, 36 genes were identified as potential biomarkers of radioresistance. These genes, which are primarily implicated in DNA damage repair, oxidative stress, cell pro-survival, and apoptotic pathways, could serve as potential diagnostic/prognostic markers cancer cell resistance to radiation treatment, as well as for therapy outcome and cancer patient survival. In addition, our findings could be potentially utilized in the laboratory and clinical setting for enhancing cancer cell susceptibility to radiation therapy protocols.

Maize endosperm development

Recent breakthroughs in transcriptome analysis and gene characterization have provided valuable resources and information about the maize endosperm developmental program. The high temporal-resolution transcriptome analysis has yielded unprecedented access to information about the genetic control of seed development. Detailed spatial transcriptome analysis using laser-capture microdissection has revealed the expression patterns of specific populations of genes in the four major endosperm compartments: the basal endosperm transfer layer (BETL), aleurone layer (AL), starchy endosperm (SE), and embryo-surrounding region (ESR). Although the overall picture of the transcriptional regulatory network of endosperm development remains fragmentary, there have been some exciting advances, such as the identification of OPAQUE11 (O11) as a central hub of the maize endosperm regulatory network connecting endosperm development, nutrient metabolism, and stress responses, and the discovery that the endosperm adjacent to scutellum (EAS) serves as a dynamic interface for endosperm-embryo crosstalk. In addition, several genes that function in BETL development, AL differentiation, and the endosperm cell cycle have been identified, such as ZmSWEET4c, Thk1, and Dek15, respectively. Here, we focus on current advances in understanding the molecular factors involved in BETL, AL, SE, ESR, and EAS development, including the specific transcriptional regulatory networks that function in each compartment during endosperm development.

Comparison of genome-wide gene expression profiling by RNA Sequencing versus microarray in bronchial biopsies of COPD patients before and after inhaled corticosteroid treatment: does it provide new insights?

More DEGs are detected by RNA-Seq than microarrays in COPD lung biopsies and are associated with immunological pathways. Performing bulk tissue cell-type deconvolution in microarray lung samples, using the SVR method, reflects RNA-Seq results. https://bit.ly/2N8sY3s.

Comparative RNA-Seq Transcriptome Analysis on Pulmonary Inflammation in a Mouse Model of Asthma-COPD Overlap Syndrome

Asthma–chronic obstructive pulmonary disease (COPD) overlap (ACO) is a severe clinical syndrome characterized to describe patients with both asthma and COPD clinical characteristics, which has posed a serious threat to patients’ quality of life and life safety. However, there are many difficulties and uncertainties in its diagnosis and treatment in clinic; especially, its animal model has not been fully and thoroughly established, and the evaluation of therapeutic drugs is still in its infancy. Here, we used ovalbumin (OVA), lipopolysaccharide (LPS), and smoke costimulation to establish an ACO mouse model and then used RNA-seq technology to detect gene expression in mouse lung tissue. The results showed that ACO mice showed an overlap syndrome of asthma and COPD in lung histological changes and the levels of inflammatory cytokines in bronchoalveolar lavage fluid. The RNA-seq analysis results showed that 6,324 differentially expressed genes (DEGs) were screened between the ACO group and the control group, of which 2,717 (42.7%) were downregulated, and 3,607 (57.3%) were upregulated. Metascape analysis results showed that in the ACO model we established, due to the damage of the respiratory system, the accumulated diseased tissue involves lung, spleen, blood, bone marrow, thymus, etc. It has certain characteristics of pneumonia, pulmonary fibrosis, and chronic obstructive airway disease, lung tumors, rheumatoid arthritis. Gene Ontology and Kyoto Encyclopedia of Genes and Genomes analysis showed that DEGs were enriched in inflammation, immune system activation and imbalance, cell proliferation, and adhesion migration, and the upstream signaling pathways of inflammation were mainly affected by HLA-DRA, SYK, CTLA4, VAV1, NRAS, and JAK3. In short, our research established a mouse model that can better simulate the clinicopathological characteristics of ACO and suggested the foundations in elucidating the molecular mechanisms for pulmonary inflammation and fibrosis in ACO. This work may help further research and contribute substantially to prevention and clinical treatment of ACO in the future.

Single-cell RNA sequencing: A new opportunity for retinal research

Single-cell RNA sequencing (scRNA-seq) is a technology for single-cell transcriptome analysis that can be used to characterize complex dynamics of various retinal cell types. It provides deep scrutiny into the gene expression character of diverse cell types, lending insight into all the biological processes being carried out. The scRNA-seq is an alternative to regular RNA-seq, which does not achieve cellular heterogeneity. The retina, is a part of the central nervous system (CNS) and consists of six types of neurons and several types of glial cells. Studying retinal cell heterogeneity is important for understanding retinal diseases. Currently, scRNA-seq is employed to assess retina development and retinal disease pathogenesis and has improved our understanding of the relationship between the retina, its visual pathways, and the brain. Moreover, this technology provides new ideas on the sensitivity and molecular mechanisms of cell subtypes involved in retinal-related diseases. The application of scRNA-seq technology has given us a deeper understanding of the latest advancements and challenges in retinal development and diseases. We advocate scRNA-seq as one of the important tools for developing novel therapies for retinal diseases. This article is categorized under: RNA Methods > RNA Analyses in Cells RNA in Disease and Development > RNA in Development RNA in Disease and Development > RNA in Disease.

Gene expression profile of the taimen Hucho taimen in response to acute temperature changes

The endangered cold-water fish species taimen (Hucho taimen) suffer acute temperature changes in culture and wild conditions. Understanding the effects of acute temperature changes on physiological processes of this species is essential for aquaculture practices and conservation. Liver transcriptomic profiles of the taimen (n = 24) exposed to acute temperature decrease (from 20 °C to 10 °C) and acute temperature increase (from 10 °C to 20 °C) was evaluated using high-throughput RNA-Sequencing. Samples were collected at day 0, 1, 7 and 35 in both treatments. Compared to day 0, the total numbers of differentially expressed genes (DEGs) in the taimen after acute temperature decrease were 173, 226 and 42 at day 1, 7 and 35, respectively, and the total numbers of DEGs following acute temperature increase were 260, 253 and 282 at day 1, 7 and 35, respectively. Particularly, 14 key regulatory genes were commonly found between both acute temperature treatments. Functional analysis based on the commonly identified DEGs revealed important metabolic pathways related to metabolism and immune function, suggesting a specific response mechanism of taimen against cold and heat shock. The results may assist in developing management strategies for stress mediation caused by acute temperature changes in the taimen and other cold water fish.

The case for using mapped exonic non-duplicate reads when reporting RNA-sequencing depth: examples from pediatric cancer datasets

Background

The reproducibility of gene expression measured by RNA sequencing (RNA-Seq) is dependent on the sequencing depth. While unmapped or non-exonic reads do not contribute to gene expression quantification, duplicate reads contribute to the quantification but are not informative for reproducibility. We show that mapped, exonic, non-duplicate (MEND) reads are a useful measure of reproducibility of RNA-Seq datasets used for gene expression analysis.

Findings

In bulk RNA-Seq datasets from 2,179 tumors in 48 cohorts, the fraction of reads that contribute to the reproducibility of gene expression analysis varies greatly. Unmapped reads constitute 1–77% of all reads (median [IQR], 3% [3–6%]); duplicate reads constitute 3–100% of mapped reads (median [IQR], 27% [13–43%]); and non-exonic reads constitute 4–97% of mapped, non-duplicate reads (median [IQR], 25% [16–37%]). MEND reads constitute 0–79% of total reads (median [IQR], 50% [30–61%]).

Conclusions

Because not all reads in an RNA-Seq dataset are informative for reproducibility of gene expression measurements and the fraction of reads that are informative varies, we propose reporting a dataset's sequencing depth in MEND reads, which definitively inform the reproducibility of gene expression, rather than total, mapped, or exonic reads. We provide a Docker image containing (i) the existing required tools (RSeQC, sambamba, and samblaster) and (ii) a custom script to calculate MEND reads from RNA-Seq data files. We recommend that all RNA-Seq gene expression experiments, sensitivity studies, and depth recommendations use MEND units for sequencing depth.

Super-delta2: An Enhanced Differential Expression Analysis Procedure for Multi-Group Comparisons of RNA-seq Data

MOTIVATION

We developed super-delta2, a differential gene expression analysis pipeline designed for multi-group comparisons for RNA-seq data. It includes a customized one-way ANOVA F-test and a post-hoc test for pairwise group comparisons; both are designed to work with a multivariate normalization procedure to reduce technical noise. It also includes a trimming procedure with bias-correction to obtain robust and approximately unbiased summary statistics used in these tests. We demonstrated the asymptotic applicability of super-delta2 to log-transformed read counts in RNA-seq data by large sample theory based on Negative Binomial Poisson (NBP) distribution.

RESULTS

We compared super-delta2 with three commonly used RNA-seq data analysis methods: limma/voom, edgeR, and DESeq2 using both simulated and real datasets. In all three simulation settings, super-delta2 not only achieved the best overall statistical power, but also was the only method that controlled type I error at the nominal level. When applied to a breast cancer dataset to identify differential expression pattern associated with multiple pathologic stages, super-delta2 selected more enriched pathways than other methods, which are directly linked to the underlying biological condition (breast cancer).

CONCLUSIONS

By incorporating trimming and bias-correction in the normalization step, super-delta2 was able to achieve tight control of type I error. Because the hypothesis tests are based on asymptotic normal approximation of the NBP distribution, super-delta2 does not require computationally expensive iterative optimization procedures used by methods such as edgeR and DESeq2, which occasionally have convergence issues.

AVAILABILITY

Our method is implemented in a R-package, "superdelta2", freely available at: https://github.com/fhlsjs/superdelta2.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Transcriptomic analysis provides insights into candidate genes and molecular pathways involved in growth of Manila clam Ruditapes philippinarum

Growth is one of the most important traits of aquaculture breeding programs. Understanding the mechanisms underlying growth differences between individuals can contribute to improving growth rates through more efficient breeding schemes. Ruditapes philippinarum is an economically important marine bivalve. In order to gain insights into the molecular mechanisms to growth variability in marine shellfish, we conducted the transcriptome sequencing and examined the expression differences in growth-related gene and molecular pathways involved in growth trait of R. philippinarum. In this study, we investigated the molecular and gene expression differences in fast-growing and slow-growing Manila clam and focused on the analysis of the differential expression patterns of specific genes associated with growth by RNA-seq and qPCR analysis. A total of 61 differentially expressed genes (DEGs) were captured significantly differentially expressed, and were categorized into Ras signaling pathway, hedgehog signaling pathway, AMPK signaling pathway, p53 signaling pathway, regulation of actin cytoskeleton, focal adhesion, mTOR signaling pathway, VEGF signaling pathway, and TGF-beta signaling pathway. A total of 34 growth-related genes were validated significantly and up/downregulated at fast growing and slow growing of R. philippinarum. Functional enrichment analysis revealed the insulin signaling pathway, PI3K-Akt signaling pathway, and mTOR signaling pathway play pivotal roles in molecular function and regulation of growth trait in R. philippinarum. The growth-related genes and pathways obtained here provide important insights into the molecular basis of physiological acclimation, metabolic activity, and growth variability in marine bivalves.

Requirements and reliability of AI in the medical context

The digital information age has been a catalyst in creating a renewed interest in Artificial Intelligence (AI) approaches, especially the subclass of computer algorithms that are popularly grouped into Machine Learning (ML). These methods have allowed one to go beyond limited human cognitive ability into understanding the complexity in the high dimensional data. Medical sciences have seen a steady use of these methods but have been slow in adoption to improve patient care. There are some significant impediments that have diluted this effort, which include availability of curated diverse data sets for model building, reliable human-level interpretation of these models, and reliable reproducibility of these methods for routine clinical use. Each of these aspects has several limiting conditions that need to be balanced out, considering the data/model building efforts, clinical implementation, integration cost to translational effort with minimal patient level harm, which may directly impact future clinical adoption. In this review paper, we will assess each aspect of the problem in the context of reliable use of the ML methods in oncology, as a representative study case, with the goal to safeguard utility and improve patient care in medicine in general.

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.

Comparative analysis of the transcriptomes of two rice subspecies during domestication

Two subspecies of rice, Oryza sativa ssp. indica and O. sativa ssp. japonica, with reproductive isolation and differences in morphology and phenotypic differences, were established during the process of rice domestication. To understand how domestication has changed the transcriptomes of the two rice subspecies and given rise to the phenotypic differences, we obtained approximately 700 Gb RNA-Seq data from 26 indica and 25 japonica accessions, and identified 97,005 transcribed fragments and 4579 novel transcriptionally active regions. The two rice subspecies had significantly different gene expression profiles, we identified 1,357 (3.3% in all genes) differentially expressed genes (DEGs) between indica and japonica rice. Combining existing gene function studies, it is found that some of these differential genes are related to the differentiation of the two subspecies, such as grain shape and cold tolerance, etc. Functional annotation of these DEGs indicates that they are involved in cell wall biosynthesis and reproductive processes. Furthermore, compared with the non-DEGs, the DEGs from both subspecies had more 5'flanking regions with low polymorphism to divergence ratios, indicating a stronger positive selection pressure on the regulation of the DEGs. This study improves our understanding of the rice genome by comparatively analyzing the transcriptomes of indica and japonica rice and identifies DEGs those may be responsible for the reproductive isolation and phenotypic differences between the two rice subspecies.

IsoDetect: Detection of Splice Isoforms from Third Generation Long Reads Based on Short Feature Sequences

Background:

Transcriptome annotation is the basis for understanding gene structures and analysing gene expression. The transcriptome annotation of many organisms such as humans is far from incomplete, due partly to the challenge in the identification of isoforms that are produced from the same gene through alternative splicing. Third generation sequencing (TGS) reads provide unprecedented opportunity for detecting isoforms due to their long length that exceeds the length of most isoforms. One limitation of current TGS reads-based isoform detection methods is that they are exclusively based on sequence reads, without incorporating the sequence information of annotated isoforms.

Objective:

We aim to develop a method to detect isoforms by incorporating annotated isoforms.

Methods:

Based on annotated isoforms, we propose a splice isoform detection method called IsoDetect. First, the sequence at exon-exon junctions is extracted from annotated isoforms as “short feature sequences”, which is used to distinguish splice isoforms. Second, we align these feature sequences to long reads and partition long reads into groups that contain the same set of feature sequences, thereby avoiding the pair-wise comparison among the large number of long reads. Third, clustering and consensus generation are carried out based on sequence similarity. For the long reads that do not contain any short feature sequence, clustering analysis based on sequence similarity is performed to identify isoforms. Therefore, our method can detect not only known but also novel isoforms.

Result:

Tested on two datasets from Calypte anna and Zebra Finch, IsoDetect shows higher speed and good accuracies compared with four existing methods.

Conclusion:

IsoDetect may become a promising method for isoform detection.

High heterogeneity undermines generalization of differential expression results in RNA-Seq analysis

BACKGROUND

RNA sequencing (RNA-Seq) has been widely applied in oncology for monitoring transcriptome changes. However, the emerging problem that high variation of gene expression levels caused by tumor heterogeneity may affect the reproducibility of differential expression (DE) results has rarely been studied. Here, we investigated the reproducibility of DE results for any given number of biological replicates between 3 and 24 and explored why a great many differentially expressed genes (DEGs) were not reproducible.

RESULTS

Our findings demonstrate that poor reproducibility of DE results exists not only for small sample sizes, but also for relatively large sample sizes. Quite a few of the DEGs detected are specific to the samples in use, rather than genuinely differentially expressed under different conditions. Poor reproducibility of DE results is mainly caused by high variation of gene expression levels for the same gene in different samples. Even though biological variation may account for much of the high variation of gene expression levels, the effect of outlier count data also needs to be treated seriously, as outlier data severely interfere with DE analysis.

CONCLUSIONS

High heterogeneity exists not only in tumor tissue samples of each cancer type studied, but also in normal samples. High heterogeneity leads to poor reproducibility of DEGs, undermining generalization of differential expression results. Therefore, it is necessary to use large sample sizes (at least 10 if possible) in RNA-Seq experimental designs to reduce the impact of biological variability and DE results should be interpreted cautiously unless soundly validated.

Transcriptome analysis of immune-related genes in Sesarmops sinensis hepatopancreas in reaction to peptidoglycan challenge

Sesarmops sinensis is a dominant omnivorous crab species, which plays an important ecological function in salt marsh ecosystems. To better understand its immune system and immune related genes under pathogen infection, the transcriptome was analyzed by comparing the data of S. sinensis hepatopancreas stimulated by PBS and PGN. A set of assembly and annotation identified 39,039 unigenes with an average length of 1105 bp, obtaining 1300 differentially expressed genes (DEGs) in all, which included 466 remarkably up-regulated unigenes and 834 remarkably down-regulated unigenes. In addition, based on mensurable real time-polymerase chain reaction and high-throughput sequencing, several immune responsive genes were found to be markedly up-regulated under PGN stimulation. In conclusion, in addition to enriching the existing transcriptome data of S. sinensis, this study also clarified the immune response of S. sinensis to PGN stimulation, which will help us to further understand the crustacean's immune system.

Gene co-expression network analysis of Trypanosoma brucei in tsetse fly vector

Background

Trypanosoma brucei species are motile protozoan parasites that are cyclically transmitted by tsetse fly (genus Glossina) causing human sleeping sickness and nagana in livestock in sub-Saharan Africa. African trypanosomes display digenetic life cycle stages in the tsetse fly vector and in their mammalian host. Experimental work on insect-stage trypanosomes is challenging because of the difficulty in setting up successful in vitro cultures. Therefore, there is limited knowledge on the trypanosome biology during its development in the tsetse fly. Consequently, this limits the development of new strategies for blocking parasite transmission in the tsetse fly.

Methods

In this study, RNA-Seq data of insect-stage trypanosomes were used to construct a T. brucei gene co-expression network using the weighted gene co-expression analysis (WGCNA) method. The study identified significant enriched modules for genes that play key roles during the parasite’s development in tsetse fly. Furthermore, potential 3′ untranslated region (UTR) regulatory elements for genes that clustered in the same module were identified using the Finding Informative Regulatory Elements (FIRE) tool.

Results

A fraction of gene modules (12 out of 27 modules) in the constructed network were found to be enriched in functional roles associated with the cell division, protein biosynthesis, mitochondrion, and cell surface. Additionally, 12 hub genes encoding proteins such as RNA-binding protein 6 (RBP6), arginine kinase 1 (AK1), brucei alanine-rich protein (BARP), among others, were identified for the 12 significantly enriched gene modules. In addition, the potential regulatory elements located in the 3′ untranslated regions of genes within the same module were predicted.

Conclusions

The constructed gene co-expression network provides a useful resource for network-based data mining to identify candidate genes for functional studies. This will enhance understanding of the molecular mechanisms that underlie important biological processes during parasite’s development in tsetse fly. Ultimately, these findings will be key in the identification of potential molecular targets for disease control.

Current RNA-seq methodology reporting limits reproducibility

Ribonucleic acid sequencing (RNA-seq) identifies and quantifies RNA molecules from a biological sample. Transformation from raw sequencing data to meaningful gene or isoform counts requires an in silico bioinformatics pipeline. Such pipelines are modular in nature, built using selected software and biological references. Software is usually chosen and parameterized according to the sequencing protocol and biological question. However, while biological and technical noise is alleviated through replicates, biases due to the pipeline and choice of biological references are often overlooked. Here, we show that the current standard practice prevents reproducibility in RNA-seq studies by failing to specify required methodological information. Peer-reviewed articles are intended to apply currently accepted scientific and methodological standards. Inasmuch as the bias-less and optimal RNA-seq pipeline is not perfectly defined, methodological information holds a meaningful role in defining the results. This work illustrates the need for a standardized and explicit display of methodological information in RNA-seq experiments.