Measuring differential gene expression by short read sequencing: quantitative comparison to 2-channel gene expression microarrays

TFAM is an autophagy receptor that limits inflammation by binding to cytoplasmic mitochondrial DNA

When cells are stressed, DNA from energy-producing mitochondria can leak out and drive inflammatory immune responses if not cleared. Cells employ a quality control system called autophagy to specifically degrade damaged components. We discovered that mitochondrial transcription factor A (TFAM)-a protein that binds mitochondrial DNA (mtDNA)-helps to eliminate leaked mtDNA by interacting with the autophagy protein LC3 through an autolysosomal pathway (we term this nucleoid-phagy). TFAM contains a molecular zip code called the LC3 interacting region (LIR) motif that enables this binding. Although mutating TFAM's LIR motif did not affect its normal mitochondrial functions, more mtDNA accumulated in the cell cytoplasm, activating inflammatory signalling pathways. Thus, TFAM mediates autophagic removal of leaked mtDNA to restrict inflammation. Identifying this mechanism advances understanding of how cells exploit autophagy machinery to selectively target and degrade inflammatory mtDNA. These findings could inform research on diseases involving mitochondrial damage and inflammation.

Elongation rate of RNA polymerase II affects pausing patterns across 3' UTRs

Yeast mRNAs are polyadenylated at multiple sites in their 3' untranslated regions (3' UTRs), and poly(A) site usage is regulated by the rate of transcriptional elongation by RNA polymerase II (Pol II). Slow Pol II derivatives favor upstream poly(A) sites, and fast Pol II derivatives favor downstream poly(A) sites. Transcriptional elongation and polyadenylation are linked at the nucleotide level, presumably reflecting Pol II dwell time at each residue that influences the level of polyadenylation. Here, we investigate the effect of Pol II elongation rate on pausing patterns and the relationship between Pol II pause sites and poly(A) sites within 3' UTRs. Mutations that affect Pol II elongation rate alter sequence preferences at pause sites within 3' UTRs, and pausing preferences differ between 3' UTRs and coding regions. In addition, sequences immediately flanking the pause sites show preferences that are largely independent of Pol II speed. In wild-type cells, poly(A) sites are preferentially located < 50 nucleotides upstream from Pol II pause sites, but this spatial relationship is diminished in cells harboring Pol II speed mutants. Based on a random forest classifier, Pol II pause sites are modestly predicted by the distance to poly(A) sites but are better predicted by the chromatin landscape in Pol II speed derivatives. Transcriptional regulatory proteins can influence the relationship between Pol II pausing and polyadenylation but in a manner distinct from Pol II elongation rate derivatives. These results indicate a complex relationship between Pol II pausing and polyadenylation.

A multi-omics dataset of human transcriptome and proteome stable reference

The development of high-throughput omics technology has greatly promoted the development of biomedicine. However, the poor reproducibility of omics techniques limits their application. It is necessary to use standard reference materials of complex RNAs or proteins to test and calibrate the accuracy and reproducibility of omics workflows. The transcriptome and proteome of most cell lines shift during culturing, which limits their applicability as standard samples. In this study, we demonstrated that the human hepatocellular cell line MHCC97H has a very stable transcriptome (r = 0.983~0.997) and proteome (r = 0.966~0.988 for data-dependent acquisition, r = 0.970~0.994 for data-independent acquisition) after 9 subculturing generations, which allows this steady standard sample to be consistently produced on an industrial scale in long term. Moreover, this stability was maintained across labs and platforms. In sum, our study provides omics standard reference material and reference datasets for transcriptomic and proteomics research. This helps to further standardize the workflow and data quality of omics techniques and thus promotes the application of omics technology in precision medicine.

Toxic effects of the wastewater produced by underwater hull cleaning equipment on the copepod Tigriopus japonicus

Unmanaged disposal of wastewater produced by underwater hull cleaning equipment (WHCE) is suspected to induce toxic effects to marine organisms because wastewater contains several anti-fouling compounds. To investigate the effects of WHCE on marine copepod, we examined the toxicity on life parameters (e.g. mortality, development, and fecundity) and gene expression changes of Tigriopus japonicus as model organism. Significant mortality and developmental time changes were observed in response to wastewater. No significant differences in fecundity were observed. Transcriptional profiling with differentially expressed genes from WHCE exposed T. japonicus showed WHCE may induce genotoxicity associated genes and pathways. In addition, potentially neurotoxic effects were evident following exposure to WHCE. The findings suggest that wastewater released during hull cleaning should be managed to reduce physiological and molecular deleterious effects in marine organisms.

Differentially expressed miRNAs and potential therapeutic targets for asthenospermia

Asthenozoospermia is detected in 40% of infertile men, and characterised by low sperm motility. MicroRNAs (miRNAs) play essential roles in spermatogenesis, but little is known regarding the function of seminal plasma miRNAs in asthenozoospermia. In this study, we collected seminal plasma samples from patients with asthenospermia and healthy men and employed high-throughput sequence technology to identify differentially expressed miRNAs. Thirteen altered miRNAs were confirmed by qRT-PCR. Six of these miRNAs were upregulated, and seven were downregulated. Five of the miRNAs (hsa-miR-34c-5p, hsa-miR-34b-5p, hsa-miR-146b-5p, hsa-miR-449a and has-miR-765) had been characterised previously, and eight of the others (miR-5000-3p, miR-4289, miR-6514-3p, miR-6882-5p and miR-6739-5p, miR-135a-5p, miR-509-3p and miR-196b-5p) were identified in asthenospermia for the first time in this study. These miRNAs were significantly associated with PI3K-Akt signaling pathway, MAPK signaling pathway, HIF-1 signaling pathway and FoxO signaling pathway. The identified dysregulated miRNA may be the key to the development of new and enhanced diagnosis and prognosis technologies for asthenospermia, and may also provide new therapeutic possibilities in the field of personalised medicine.

Autoactivation of Translation Causes the Bloom of Prorocentrum donghaiense in Harmful Algal Blooms

Harmful algal blooms (HABs) are symptomatic of ecosystem imbalance, leading to major worldwide marine natural disasters, and seriously threaten the human health. Some HAB algae's exceptional genome size prohibited the genomic investigations on molecular mechanisms, for example, Prorocentrum. This study performed translatome sequencing (RNC-seq) for Prorocentrum donghaiense to assemble the translatome reference sequences on appropriate cost to enable the global molecular study at translatome and proteome levels. By analyzing the translatome and proteome of P. donghaiense in phosphor-rich, phosphor-deficient, and phosphor-restored media, we found massive up-regulation of energy and material production pathways in phosphor-rich conditions that enables autoactivation of translation, which is the key to its exponential growth in HABs. To break down the autoactivation, we demonstrated that mild translation delay using very low concentrations of cycloheximide efficiently controls the blooming without harming other aquatic organisms and humans. Our result provides a novel hint for controlling HABs and demonstrated the RNC-seq as an economic strategy on investigating functions of organisms with large and unknown genomes.

A tail-based test to detect differential expression in RNA-sequencing data

RNA sequencing data have been abundantly generated in biomedical research for biomarker discovery and other studies. Such data at the exon level are usually heavily tailed and correlated. Conventional statistical tests based on the mean or median difference for differential expression likely suffer from low power when the between-group difference occurs mostly in the upper or lower tail of the distribution of gene expression. We propose a tail-based test to make comparisons between groups in terms of a specific distribution area rather than a single location. The proposed test, which is derived from quantile regression, adjusts for covariates and accounts for within-sample dependence among the exons through a specified correlation structure. Through Monte Carlo simulation studies, we show that the proposed test is generally more powerful and robust in detecting differential expression than commonly used tests based on the mean or a single quantile. An application to TCGA lung adenocarcinoma data demonstrates the promise of the proposed method in terms of biomarker discovery.

Skipping of exon 10 in Axl pre-mRNA regulated by PTBP1 mediates invasion and metastasis process of liver cancer cells

The Axl gene is known to encode for a receptor tyrosine kinase involved in the metastasis process of cancer. In this study, we investigated the underlying molecular mechanism of Axl alternative splicing. Methods: The expression levels of PTBP1 in hepatocellular carcinoma (HCC) tissues were obtained from TCGA samples and cell lines. The effect of Axl-L, Axl-S, and PTBP1 on cell growth, migration, invasion tumor formation, and metastasis of liver cancer cells were measured by cell proliferation, wound-healing, invasion, xenograft tumor formation, and metastasis. Interaction between PTBP1 and Axl was explored using cross-link immunoprecipitation, RNA pull-down assays and RNA immunoprecipitation assays. Results: Knockdown of the PTBP1 and exon 10 skipping isoform of Axl (Axl-S), led to impaired invasion and metastasis in hepatoma cells. Immunoprecipitation results indicated that Axl-S protein binds more robustly with Gas6 ligand than Axl-L (exon 10 including) and is more capable of promoting phosphorylation of ERK and AKT proteins. Furthermore, cross-link immunoprecipitation and RNA-pulldown assays revealed that PTBP1 binds to the polypyrimidine sequence(TCCTCTCTGTCCTTTCTTC) on Axl-Intron 9. MS2-GFP-IP experiments demonstrated that PTBP1 competes with U2AF2 for binding to the aforementioned polypyrimidine sequence, thereby inhibiting alternative splicing and ultimately promoting Axl-S production. Conclusion: Our results highlight the biological significance of Axl-S and PTBP1 in tumor metastasis, and show that PTBP1 affects the invasion and metastasis of hepatoma cells by modulating the alternative splicing of Axl exon 10.

De novo transcriptome analysis of the mussel Perna viridis after exposure to the toxic dinoflagellate Prorocentrum lima

Diarrheic shellfish poisoning (DSP) toxins are produced by harmful microalgae and accumulate in bivalve mollusks, causing various toxicity. These toxic effects appear to abate with increasing DSP concentration and longer exposure time, however, the underlying mechanisms remain unclear. To explore the underlying molecular mechanisms, de novo transcriptome analysis of the digestive gland of Perna viridis was performed after Prorocentrum lima exposure. RNA-seq analysis showed that 1886 and 237 genes were up- and down-regulated, respectively after 6 h exposure to P. lima, while 265 genes were up-regulated and 217 genes were down-regulated after 96 h compared to the control. These differentially expressed genes mainly involved in Nrf2 signing pathways, immune stress, apoptosis and cytoskeleton, etc. Combined with qPCR results, we speculated that the mussel P. viridis might mainly rely on glutathione S-transferase (GST) and ABC transporters to counteract DSP toxins during short-term exposure. However, longer exposure of P. lima could activate the Nrf2 signaling pathway and inhibitors of apoptosis protein (IAP), which in turn reduced the damage of DSP toxins to the mussel. DSP toxins could induce cytoskeleton destabilization and had some negative impact on the immune system of bivalves. Collectively, our findings uncovered the crucial molecular mechanisms and the regulatory metabolic nodes that underpin the defense mechanism of bivalves against DSP toxins and also advanced our current understanding of bivalve defense mechanisms.

Transcriptome Analysis Reveals the Molecular Mechanisms Underlying Adenosine Biosynthesis in Anamorph Strain of Caterpillar Fungus

Caterpillar fungus is a well-known fungal Chinese medicine. To reveal molecular changes during early and late stages of adenosine biosynthesis, transcriptome analysis was performed with the anamorph strain of caterpillar fungus. A total of 2,764 differentially expressed genes (DEGs) were identified (p ≤ 0.05, |log₂ Ratio| ≥ 1), of which 1,737 were up-regulated and 1,027 were down-regulated. Gene expression profiling on 4–10 d revealed a distinct shift in expression of the purine metabolism pathway. Differential expression of 17 selected DEGs which involved in purine metabolism (map00230) were validated by qPCR, and the expression trends were consistent with the RNA-Seq results. Subsequently, the predicted adenosine biosynthesis pathway combined with qPCR and gene expression data of RNA-Seq indicated that the increased adenosine accumulation is a result of down-regulation of ndk, ADK, and APRT genes combined with up-regulation of AK gene. This study will be valuable for understanding the molecular mechanisms of the adenosine biosynthesis in caterpillar fungus.

A hidden human proteome encoded by 'non-coding' genes

It has been a long debate whether the 98% ‘non-coding’ fraction of human genome can encode functional proteins besides short peptides. With full-length translating mRNA sequencing and ribosome profiling, we found that up to 3330 long non-coding RNAs (lncRNAs) were bound to ribosomes with active translation elongation. With shotgun proteomics, 308 lncRNA-encoded new proteins were detected. A total of 207 unique peptides of these new proteins were verified by multiple reaction monitoring (MRM) and/or parallel reaction monitoring (PRM); and 10 new proteins were verified by immunoblotting. We found that these new proteins deviated from the canonical proteins with various physical and chemical properties, and emerged mostly in primates during evolution. We further deduced the protein functions by the assays of translation efficiency, RNA folding and intracellular localizations. As the new protein UBAP1-AST6 is localized in the nucleoli and is preferentially expressed by lung cancer cell lines, we biologically verified that it has a function associated with cell proliferation. In sum, we experimentally evidenced a hidden human functional proteome encoded by purported lncRNAs, suggesting a resource for annotating new human proteins.

Plasma extracellular vesicle‑packaged microRNAs as candidate diagnostic biomarkers for early‑stage breast cancer

Extracellular vesicle-packaged microRNAs (miRNAs) are a class of circulating miRNAs located in the plasma that are packaged into extracellular vesicles. The present study examined the expression profiles of extracellular vesicles and tissue miRNAs with the aim of investigating the miRNA signatures in early-stage breast cancer. The present study identified and compared the extracellular vesicle-packaged miRNA expression signature and tissue miRNA expression signature from healthy individuals (n=10) and patients with early-stage breast cancer (n=12). A total of five miRNAs, including miRNA-375, miRNA-24-2-5p, miRNA-548b-5p, miRNA-655-3P and miRNA-376b-5p, were synchronized in extracellular vesicles and tissues of the breast cancer group when compared with the healthy group. The highest area under the curve (AUC) for a single miRNA was achieved with miRNA-548b-5p [AUC=0.785; 95% confidence interval (CI)=0.585–0.984; P=0.022]. The highest overall AUC was achieved by the combination of miRNA-375, miRNA-655-3p, miRNA-548b-5p and miRNA-24-2-5p (AUC=0.808; 95% CI=0.629–0.986; P=0.013). The Kaplan-Meier curves and log test analysis results of these five miRNAs, especially those for miRNA-548b-5p, were partly consistent with the hypothesis. Two miRNAs (miRNA-548b-5p and miRNA-376b-5p) were positively associated with patient survival, while two miRNAs (miRNA-375 and miRNA-24-2-5p) were negatively associated with patient survival. The present study provided a set of plasma extracellular vesicle-packaged miRNA-based biomarkers for the diagnosis of early-stage breast cancer.

Proteomics Links Ubiquitin Chain Topology Change to Transcription Factor Activation

A surprising complexity of ubiquitin signaling has emerged with identification of different ubiquitin chain topologies. However, mechanisms of how the diverse ubiquitin codes control biological processes remain poorly understood. Here, we use quantitative whole-proteome mass spectrometry to identify yeast proteins that are regulated by lysine 11 (K11)-linked ubiquitin chains. The entire Met4 pathway, which links cell proliferation with sulfur amino acid metabolism, was significantly affected by K11 chains and selected for mechanistic studies. Previously, we demonstrated that a K48-linked ubiquitin chain represses the transcription factor Met4. Here, we show that efficient Met4 activation requires a K11-linked topology. Mechanistically, our results propose that the K48 chain binds to a topology-selective tandem ubiquitin binding region in Met4 and competes with binding of the basal transcription machinery to the same region. The change to K11-enriched chain architecture releases this competition and permits binding of the basal transcription complex to activate transcription.

TranslatomeDB: a comprehensive database and cloud-based analysis platform for translatome sequencing data

Translation is a key regulatory step, linking transcriptome and proteome. Two major methods of translatome investigations are RNC-seq (sequencing of translating mRNA) and Ribo-seq (ribosome profiling). To facilitate the investigation of translation, we built a comprehensive database TranslatomeDB (http://www.translatomedb.net/) which provides collection and integrated analysis of published and user-generated translatome sequencing data. The current version includes 2453 Ribo-seq, 10 RNC-seq and their 1394 corresponding mRNA-seq datasets in 13 species. The database emphasizes the analysis functions in addition to the dataset collections. Differential gene expression (DGE) analysis can be performed between any two datasets of same species and type, both on transcriptome and translatome levels. The translation indices translation ratios, elongation velocity index and translational efficiency can be calculated to quantitatively evaluate translational initiation efficiency and elongation velocity, respectively. All datasets were analyzed using a unified, robust, accurate and experimentally-verifiable pipeline based on the FANSe3 mapping algorithm and edgeR for DGE analyzes. TranslatomeDB also allows users to upload their own datasets and utilize the identical unified pipeline to analyze their data. We believe that our TranslatomeDB is a comprehensive platform and knowledgebase on translatome and proteome research, releasing the biologists from complex searching, analyzing and comparing huge sequencing data without needing local computational power.

Multifaceted Stoichiometry Control of Bacterial Operons Revealed by Deep Proteome Quantification

More than half of the protein-coding genes in bacteria are organized in polycistronic operons composed of two or more genes. It remains under debate whether the operon organization maintains the stoichiometric expression of the genes within an operon. In this study, we performed a label-free data-independent acquisition hyper reaction monitoring mass-spectrometry (HRM-MS) experiment to quantify the Escherichia coli proteome in exponential phase and quantified 93.6% of the cytosolic proteins, covering 67.9% and 56.0% of the translating polycistronic operons in BW25113 and MG1655 strains, respectively. We found that the translational regulation contributes largely to the proteome complexity: the shorter operons tend to be more tightly controlled for stoichiometry than longer operons; the operons which mainly code for complexes is more tightly controlled for stoichiometry than the operons which mainly code for metabolic pathways. The gene interval (distance between adjacent genes in one operon) may serve as a regulatory factor for stoichiometry. The catalytic efficiency might be a driving force for differential expression of enzymes encoded in one operon. These results illustrated the multifaceted nature of the operon regulation: the operon unified transcriptional level and gene-specific translational level. This multi-level regulation benefits the host by optimizing the efficiency of the productivity of metabolic pathways and maintenance of different types of protein complexes.

Maize factors ZmUBP15, ZmUBP16 and ZmUBP19 play important roles for plants to tolerance the cadmium stress and salt stress

Ubiquitin-Specific Protease16 (UBP16) has been described involved in cadmium stress and salt stress in Arabidopsis, however nothing is known about the functions of its homologs in maize. In this study, we investigate the functions of ZmUBP15, ZmUBP16 and ZmUBP19, three Arabidopsis UBP16 homologs in maize. Our results indicate that ZmUBP15, ZmUBP16 and ZmUBP19 are ubiquitously expressed throughout plant development, and ZmUBP15, ZmUBP16 and ZmUBP19 proteins are mainly localized in plasma membrane. Complementation analyses show that over-expression of ZmUBP15 or ZmUBP16 can rescue the defective phenotype of ubp16-1 in cadmium stress. In addition, over-expression of ZmUBP15, ZmUBP16 or ZmUBP19 can increase the plant tolerance to cadmium stress. These results indicate that ZmUBP15, ZmUBP16 and ZmUBP19 are required for plant to tolerance the cadmium stress. Consistent with this point, cadmium-related genes are markedly up-regulated in seedlings over-expressing ZmUBP15, ZmUBP16 or ZmUBP19. Furthermore, our data indicate that ZmUBP15, ZmUBP16 and ZmUBP19 partially rescue the salt-stress phenotype of ubp16-1. Thus, our research uncover the functions of three novel maize proteins, ZmUBP15, ZmUBP16 and ZmUBP19, which are required for plants in response to cadmium stress and salt stress.

Enhancing co-translational folding of heterologous protein by deleting non-essential ribosomal proteins in Pichia pastoris

BACKGROUND

Translational regulation played an important role in the correct folding of heterologous proteins to form bioactive conformations during biogenesis. Translational pausing coordinates protein translation and co-translational folding. Decelerating translation elongation speed has been shown to improve the soluble protein yield when expressing heterologous proteins in industrial expression hosts. However, rational redesign of translational pausing via synonymous mutations may not be feasible in many cases. Our goal was to develop a general and convenient strategy to improve heterologous protein synthesis in Pichia pastoris without mutating the expressed genes.

RESULTS

Here, a large-scale deletion library of ribosomal protein (RP) genes was constructed for heterologous protein expression in Pichia pastoris, and 59% (16/27) RP deletants have significantly increased heterologous protein yield. This is due to the delay of 60S subunit assembly by deleting non-essential ribosomal protein genes or 60S subunit processing factors, thus globally decreased the translation elongation speed and improved the co-translational folding, without perturbing the relative transcription level and translation initiation.

CONCLUSION

Global decrease in the translation elongation speed by RP deletion enhanced co-translational folding efficiency of nascent chains and decreased protein aggregates to improve heterologous protein yield. A potential expression platform for efficient pharmaceutical proteins and industrial enzymes production was provided without synonymous mutation.

TISSUES 2.0: an integrative web resource on mammalian tissue expression

Physiological and molecular similarities between organisms make it possible to translate findings from simpler experimental systems—model organisms—into more complex ones, such as human. This translation facilitates the understanding of biological processes under normal or disease conditions. Researchers aiming to identify the similarities and differences between organisms at the molecular level need resources collecting multi-organism tissue expression data. We have developed a database of gene–tissue associations in human, mouse, rat and pig by integrating multiple sources of evidence: transcriptomics covering all four species and proteomics (human only), manually curated and mined from the scientific literature. Through a scoring scheme, these associations are made comparable across all sources of evidence and across organisms. Furthermore, the scoring produces a confidence score assigned to each of the associations. The TISSUES database (version 2.0) is publicly accessible through a user-friendly web interface and as part of the STRING app for Cytoscape. In addition, we analyzed the agreement between datasets, across and within organisms, and identified that the agreement is mainly affected by the quality of the datasets rather than by the technologies used or organisms compared.

Database URL: http://tissues.jensenlab.org/

Integrated genomic analyses in PDX model reveal a cyclin-dependent kinase inhibitor Palbociclib as a novel candidate drug for nasopharyngeal carcinoma

Background

Patient-derived xenograft (PDX) tumor model has become a new approach in identifying druggable tumor mutations, screening and evaluating personalized cancer drugs based on the mutated targets.

Methods

We established five nasopharyngeal carcinoma (NPC) PDXs in mouse model. Subsequently, whole-exome sequencing (WES) and genomic mutation analyses were performed to search for genetic alterations for new drug targets. Potential drugs were applied in two NPC PDX mice model to assess their anti-cancer activities. RNA sequencing and transcriptomic analysis were performed in one NPC PDX mice to correlate with the efficacy of the anti-cancer drugs.

Results

A relative high incident rate of copy number variations (CNVs) of cell cycle-associated genes. Among the five NPC-PDXs, three had cyclin D1 (CCND1) amplification while four had cyclin-dependent kinase inhibitor CDKN2A deletion. Furthermore, CCND1 overexpression was observed in > 90% FFPE clinical metastatic NPC tumors (87/91) and was associated with poor outcomes. CNV analysis disclosed that plasma CCND1/CDKN2A ratio is correlated with EBV DNA load in NPC patients’ plasma and could serve as a screening test to select potential CDK4/6 inhibitor treatment candidates. Based on our NPC PDX model and RNA sequencing, Palbociclib, a cyclin-dependent kinase inhibitor, proved to have anti-tumor effects by inducing G1 arrest. One NPC patient with liver metastatic was treated with Palbociclib, had stable disease response and a drop in Epstein Barr virus (EBV) EBV titer.

Conclusions

Our integrated information of sequencing-based genomic studies and tumor transcriptomes with drug treatment in NPC-PDX models provided guidelines for personalized precision treatments and revealed a cyclin-dependent kinase inhibitor Palbociclib as a novel candidate drug for NPC.

Electronic supplementary material

The online version of this article (10.1186/s13046-018-0873-5) contains supplementary material, which is available to authorized users.

Modeling and analysis of RNA-seq data: a review from a statistical perspective

BACKGROUND

Since the invention of next-generation RNA sequencing (RNA-seq) technologies, they have become a powerful tool to study the presence and quantity of RNA molecules in biological samples and have revolutionized transcriptomic studies. The analysis of RNA-seq data at four different levels (samples, genes, transcripts, and exons) involve multiple statistical and computational questions, some of which remain challenging up to date.

RESULTS

We review RNA-seq analysis tools at the sample, gene, transcript, and exon levels from a statistical perspective. We also highlight the biological and statistical questions of most practical considerations.

CONCLUSIONS

The development of statistical and computational methods for analyzing RNA-seq data has made significant advances in the past decade. However, methods developed to answer the same biological question often rely on diverse statistical models and exhibit different performance under different scenarios. This review discusses and compares multiple commonly used statistical models regarding their assumptions, in the hope of helping users select appropriate methods as needed, as well as assisting developers for future method development.

Transcriptome analysis of phosphorus stress responsiveness in the seedlings of Dongxiang wild rice (Oryza rufipogon Griff.)

Background

Low phosphorus availability is a major factor restricting rice growth. Dongxiang wild rice (Oryza rufipogon Griff.) has many useful genes lacking in cultivated rice, including stress resistance to phosphorus deficiency, cold, salt and drought, which is considered to be a precious germplasm resource for rice breeding. However, the molecular mechanism of regulation of phosphorus deficiency tolerance is not clear.

Results

In this study, cDNA libraries were constructed from the leaf and root tissues of phosphorus stressed and untreated Dongxiang wild rice seedlings, and transcriptome sequencing was performed with the goal of elucidating the molecular mechanisms involved in phosphorus stress response. The results indicated that 1184 transcripts were differentially expressed in the leaves (323 up-regulated and 861 down-regulated) and 986 transcripts were differentially expressed in the roots (756 up-regulated and 230 down-regulated). 43 genes were up-regulated both in leaves and roots, 38 genes were up-regulated in roots but down-regulated in leaves, and only 2 genes were down-regulated in roots but up-regulated in leaves. Among these differentially expressed genes, the detection of many transcription factors and functional genes demonstrated that multiple regulatory pathways were involved in phosphorus deficiency tolerance. Meanwhile, the differentially expressed genes were also annotated with gene ontology terms and key pathways via functional classification and Kyoto Encyclopedia of Gene and Genomes pathway mapping, respectively. A set of the most important candidate genes was then identified by combining the differentially expressed genes found in the present study with previously identified phosphorus deficiency tolerance quantitative trait loci.

Conclusion

The present work provides abundant genomic information for functional dissection of the phosphorus deficiency resistance of Dongxiang wild rice, which will be help to understand the biological regulatory mechanisms of phosphorus deficiency tolerance in Dongxiang wild rice.

Electronic supplementary material

The online version of this article (10.1186/s40659-018-0155-x) contains supplementary material, which is available to authorized users.

Differential gene expression analysis in ageing muscle and drug discovery perspectives

Identifying therapeutic target genes represents the key step in functional genomics-based therapies. Within this context, the disease heterogeneity, the exogenous factors and the complexity of genomic structure and function represent important challenges. The functional genomics aims to overcome such obstacles via identifying the gene functions and therefore highlight disease-causing genes as therapeutic targets. Genomic technologies promise to reshape the research on ageing muscle, exercise response and drug discovery. Herein, we describe the functional genomics strategies, mainly differential gene expression methods microarray, serial analysis of gene expression (SAGE), massively parallel signature sequence (MPSS), RNA sequencing (RNA seq), representational difference analysis (RDA), and suppression subtractive hybridization (SSH). Furthermore, we review these illustrative approaches that have been used to discover new therapeutic targets for some complex diseases along with the application of these tools to study the modulation of the skeletal muscle transcriptome.

Advantages of RNA-seq compared to RNA microarrays for transcriptome profiling of anterior cruciate ligament tears

Microarrays and RNA-seq are at the forefront of high throughput transcriptome analyses. Since these methodologies are based on different principles, there are concerns about the concordance of data between the two techniques. The concordance of RNA-seq and microarrays for genome-wide analysis of differential gene expression has not been rigorously assessed in clinically derived ligament tissues. To demonstrate the concordance between RNA-seq and microarrays and to assess potential benefits of RNA-seq over microarrays, we assessed differences in transcript expression in anterior cruciate ligament (ACL) tissues based on time-from-injury. ACL remnants were collected from patients with an ACL tear at the time of ACL reconstruction. RNA prepared from torn ACL remnants was subjected to Agilent microarrays (N = 24) and RNA-seq (N = 8). The correlation of biological replicates in RNA-seq and microarrays data was similar (0.98 vs. 0.97), demonstrating that each platform has high internal reproducibility. Correlations between the RNA-seq data and the individual microarrays were low, but correlations between the RNA-seq values and the geometric mean of the microarrays values were moderate. The cross-platform concordance for differentially expressed transcripts or enriched pathways was linearly correlated (r = 0.64). RNA-Seq was superior in detecting low abundance transcripts and differentiating biologically critical isoforms. Additional independent validation of transcript expression was undertaken using microfluidic PCR for selected genes. PCR data showed 100% concordance (in expression pattern) with RNA-seq and microarrays data. These findings demonstrate that RNA-seq has advantages over microarrays for transcriptome profiling of ligament tissues when available and affordable. Furthermore, these findings are likely transferable to other musculoskeletal tissues where tissue collection is challenging and cells are in low abundance. © 2017 Orthopaedic Research Society. Published by Wiley Periodicals, Inc. J Orthop Res 36:484-497, 2018.

Integrative radiogenomic analysis for multicentric radiophenotype in glioblastoma

We postulated that multicentric glioblastoma (GBM) represents more invasiveness form than solitary GBM and has their own genomic characteristics. From May 2004 to June 2010 we retrospectively identified 51 treatment-naïve GBM patients with available clinical information from the Samsung Medical Center data registry. Multicentricity of the tumor was defined as the presence of multiple foci on the T1 contrast enhancement of MR images or having high signal for multiple lesions without contiguity of each other on the FLAIR image. Kaplan-Meier survival analysis demonstrated that multicentric GBM had worse prognosis than solitary GBM (median, 16.03 vs. 20.57 months, p < 0.05). Copy number variation (CNV) analysis revealed there was an increase in 11 regions, and a decrease in 17 regions, in the multicentric GBM. Gene expression profiling identified 738 genes to be increased and 623 genes to be decreased in the multicentric radiophenotype (p < 0.001). Integration of the CNV and expression datasets identified twelve representative genes: CPM, LANCL2, LAMP1, GAS6, DCUN1D2, CDK4, AGAP2, TSPAN33, PDLIM1, CLDN12, and GTPBP10 having high correlation across CNV, gene expression and patient outcome. Network and enrichment analyses showed that the multicentric tumor had elevated fibrotic signaling pathways compared with a more proliferative and mitogenic signal in the solitary tumors. Noninvasive radiological imaging together with integrative radiogenomic analysis can provide an important tool in helping to advance personalized therapy for the more clinically aggressive subset of GBM.

Proteogenomics in Aid of Host-Pathogen Interaction Studies: A Bacterial Perspective

By providing useful tools to study host-pathogen interactions, next-generation omics has recently enabled the study of gene expression changes in both pathogen and infected host simultaneously. However, since great discriminative power is required to study pathogen and host simultaneously throughout the infection process, the depth of quantitative gene expression profiling has proven to be unsatisfactory when focusing on bacterial pathogens, thus preferentially requiring specific strategies or the development of novel methodologies based on complementary omics approaches. In this review, we focus on the difficulties encountered when making use of proteogenomics approaches to study bacterial pathogenesis. In addition, we review different omics strategies (i.e., transcriptomics, proteomics and secretomics) and their applications for studying interactions of pathogens with their host.

A comprehensive simulation study on classification of RNA-Seq data

RNA sequencing (RNA-Seq) is a powerful technique for the gene-expression profiling of organisms that uses the capabilities of next-generation sequencing technologies. Developing gene-expression-based classification algorithms is an emerging powerful method for diagnosis, disease classification and monitoring at molecular level, as well as providing potential markers of diseases. Most of the statistical methods proposed for the classification of gene-expression data are either based on a continuous scale (eg. microarray data) or require a normal distribution assumption. Hence, these methods cannot be directly applied to RNA-Seq data since they violate both data structure and distributional assumptions. However, it is possible to apply these algorithms with appropriate modifications to RNA-Seq data. One way is to develop count-based classifiers, such as Poisson linear discriminant analysis and negative binomial linear discriminant analysis. Another way is to bring the data closer to microarrays and apply microarray-based classifiers. In this study, we compared several classifiers including PLDA with and without power transformation, NBLDA, single SVM, bagging SVM (bagSVM), classification and regression trees (CART), and random forests (RF). We also examined the effect of several parameters such as overdispersion, sample size, number of genes, number of classes, differential-expression rate, and the transformation method on model performances. A comprehensive simulation study is conducted and the results are compared with the results of two miRNA and two mRNA experimental datasets. The results revealed that increasing the sample size, differential-expression rate and decreasing the dispersion parameter and number of groups lead to an increase in classification accuracy. Similar with differential-expression studies, the classification of RNA-Seq data requires careful attention when handling data overdispersion. We conclude that, as a count-based classifier, the power transformed PLDA and, as a microarray-based classifier, vst or rlog transformed RF and SVM classifiers may be a good choice for classification. An R/BIOCONDUCTOR package, MLSeq, is freely available at https://www.bioconductor.org/packages/release/bioc/html/MLSeq.html.

Low-cost, Low-bias and Low-input RNA-seq with High Experimental Verifiability based on Semiconductor Sequencing

Low-input RNA-seq is powerful to represent the gene expression profiles with limited number of cells, especially when single-cell variations are not the aim. However, pre-amplification-based and molecule index-based library construction methods boost bias or require higher throughput. Here we demonstrate a simple, low-cost, low-bias and low-input RNA-seq with ion torrent semiconductor sequencing (LIEA RNA-seq). We also developed highly accurate and error-tolerant spliced mapping algorithm FANSe2splice to accurately map the single-ended reads to the reference genome with better experimental verifiability than the previous spliced mappers. Combining the experimental and computational advancements, our solution is comparable with the bulk mRNA-seq in quantification, reliably detects splice junctions and minimizes the bias with much less mappable reads.

Sexually Dimorphic Gene Expression Associated with Growth and Reproduction of Tongue Sole (Cynoglossus semilaevis) Revealed by Brain Transcriptome Analysis

In this study, we performed a comprehensive analysis of the transcriptome of one- and two-year-old male and female brains of Cynoglossus semilaevis by high-throughput Illumina sequencing. A total of 77,066 transcripts, corresponding to 21,475 unigenes, were obtained with a N50 value of 4349 bp. Of these unigenes, 33 genes were found to have significant differential expression and potentially associated with growth, from which 18 genes were down-regulated and 12 genes were up-regulated in two-year-old males, most of these genes had no significant differences in expression among one-year-old males and females and two-year-old females. A similar analysis was conducted to look for genes associated with reproduction; 25 genes were identified, among them, five genes were found to be down regulated and 20 genes up regulated in two-year-old males, again, most of the genes had no significant expression differences among the other three. The performance of up regulated genes in Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis was significantly different between two-year-old males and females. Males had a high gene expression in genetic information processing, while female’s highly expressed genes were mainly enriched on organismal systems. Our work identified a set of sex-biased genes potentially associated with growth and reproduction that might be the candidate factors affecting sexual dimorphism of tongue sole, laying the foundation to understand the complex process of sex determination of this economic valuable species.

Transcriptome dynamics in the asexual cycle of the chordate Botryllus schlosseri

Background

We performed an analysis of the transcriptome during the blastogenesis of the chordate Botryllus schlosseri, focusing in particular on genes involved in cell death by apoptosis. The tunicate B. schlosseri is an ascidian forming colonies characterized by the coexistence of three blastogenetic generations: filter-feeding adults, buds on adults, and budlets on buds. Cyclically, adult tissues undergo apoptosis and are progressively resorbed and replaced by their buds originated by asexual reproduction. This is a feature of colonial tunicates, the only known chordates that can reproduce asexually.

Results

Thanks to a newly developed web-based platform (http://botryllus.cribi.unipd.it), we compared the transcriptomes of the mid-cycle, the pre-take-over, and the take-over phases of the colonial blastogenetic cycle. The platform is equipped with programs for comparative analysis and allows to select the statistical stringency. We enriched the genome annotation with 11,337 new genes; 581 transcripts were resolved as complete open reading frames, translated in silico into amino acid sequences and then aligned onto the non-redundant sequence database. Significant differentially expressed genes were classified within the gene ontology categories. Among them, we recognized genes involved in apoptosis activation, de-activation, and regulation.

Conclusions

With the current work, we contributed to the improvement of the first released B. schlosseri genome assembly and offer an overview of the transcriptome changes during the blastogenetic cycle, showing up- and down-regulated genes. These results are important for the comprehension of the events underlying colony growth and regression, cell proliferation, colony homeostasis, and competition among different generations.

Electronic supplementary material

The online version of this article (doi:10.1186/s12864-016-2598-1) contains supplementary material, which is available to authorized users.

Identification of Differentially Expressed Genes in RNA-seq Data of Arabidopsis thaliana: A Compound Distribution Approach

Gene expression is the process by which information from a gene is used in the synthesis of a functional gene product, which may be proteins. A gene is declared differentially expressed if an observed difference or change in read counts or expression levels between two experimental conditions is statistically significant. To identify differentially expressed genes between two conditions, it is important to find statistical distributional property of the data to approximate the nature of differential genes. In the present study, the focus is mainly to investigate the differential gene expression analysis for sequence data based on compound distribution model. This approach was applied in RNA-seq count data of Arabidopsis thaliana and it has been found that compound Poisson distribution is more appropriate to capture the variability as compared with Poisson distribution. Thus, fitting of appropriate distribution to gene expression data provides statistically sound cutoff values for identifying differentially expressed genes.

RNA sequencing reveals region-specific molecular mechanisms associated with epileptogenesis in a model of classical hippocampal sclerosis

We report here the first complete transcriptome analysis of the dorsal (dDG) and ventral dentate gyrus (vDG) of a rat epilepsy model presenting a hippocampal lesion with a strict resemblance to classical hippocampal sclerosis (HS). We collected the dDG and vDG by laser microdissection 15 days after electrical stimulation and performed high-throughput RNA-sequencing. There were many differentially regulated genes, some of which were specific to either of the two sub-regions in stimulated animals. Gene ontology analysis indicated an enrichment of inflammation-related processes in both sub-regions and of axonal guidance and calcium signaling processes exclusively in the vDG. There was also a differential regulation of genes encoding molecules involved in synaptic function, neural electrical activity and neuropeptides in stimulated rats. The data presented here suggests, in the time point analyzed, a remarkable interaction among several molecular components which takes place in the damaged hippocampi. Furthermore, even though similar mechanisms may function in different regions of the DG, the molecular components involved seem to be region specific.

Transcriptome Analysis of Salt Stress Responsiveness in the Seedlings of Dongxiang Wild Rice (Oryza rufipogon Griff.)

Dongxiang wild rice (Oryza rufipogon Griff.) is the progenitor of cultivated rice (Oryza sativa L.), and is well known for its superior level of tolerance against cold, drought and diseases. To date, however, little is known about the salt-tolerant character of Dongxiang wild rice. To elucidate the molecular genetic mechanisms of salt-stress tolerance in Dongxiang wild rice, the Illumina HiSeq 2000 platform was used to analyze the transcriptome profiles of the leaves and roots at the seedling stage under salt stress compared with those under normal conditions. The analysis results for the sequencing data showed that 6,867 transcripts were differentially expressed in the leaves (2,216 up-regulated and 4,651 down-regulated) and 4,988 transcripts in the roots (3,105 up-regulated and 1,883 down-regulated). Among these differentially expressed genes, the detection of many transcription factor genes demonstrated that multiple regulatory pathways were involved in salt stress tolerance. In addition, the differentially expressed genes were compared with the previous RNA-Seq analysis of salt-stress responses in cultivated rice Nipponbare, indicating the possible specific molecular mechanisms of salt-stress responses for Dongxiang wild rice. A large number of the salt-inducible genes identified in this study were co-localized onto fine-mapped salt-tolerance-related quantitative trait loci, providing candidates for gene cloning and elucidation of molecular mechanisms responsible for salt-stress tolerance in rice.

Comparison of normalization and differential expression analyses using RNA-Seq data from 726 individual Drosophila melanogaster

Background

A generally accepted approach to the analysis of RNA-Seq read count data does not yet exist. We sequenced the mRNA of 726 individuals from the Drosophila Genetic Reference Panel in order to quantify differences in gene expression among single flies. One of our experimental goals was to identify the optimal analysis approach for the detection of differential gene expression among the factors we varied in the experiment: genotype, environment, sex, and their interactions. Here we evaluate three different filtering strategies, eight normalization methods, and two statistical approaches using our data set. We assessed differential gene expression among factors and performed a statistical power analysis using the eight biological replicates per genotype, environment, and sex in our data set.

Results

We found that the most critical considerations for the analysis of RNA-Seq read count data were the normalization method, underlying data distribution assumption, and numbers of biological replicates, an observation consistent with previous RNA-Seq and microarray analysis comparisons. Some common normalization methods, such as Total Count, Quantile, and RPKM normalization, did not align the data across samples. Furthermore, analyses using the Median, Quantile, and Trimmed Mean of M-values normalization methods were sensitive to the removal of low-expressed genes from the data set. Although it is robust in many types of analysis, the normal data distribution assumption produced results vastly different than the negative binomial distribution. In addition, at least three biological replicates per condition were required in order to have sufficient statistical power to detect expression differences among the three-way interaction of genotype, environment, and sex.

Conclusions

The best analysis approach to our data was to normalize the read counts using the DESeq method and apply a generalized linear model assuming a negative binomial distribution using either edgeR or DESeq software. Genes having very low read counts were removed after normalizing the data and fitting it to the negative binomial distribution. We describe the results of this evaluation and include recommended analysis strategies for RNA-Seq read count data.

Electronic supplementary material

The online version of this article (doi:10.1186/s12864-015-2353-z) contains supplementary material, which is available to authorized users.

Appraisal of the Missing Proteins Based on the mRNAs Bound to Ribosomes

Considering the technical limitations of mass spectrometry in protein identification, the mRNAs bound to ribosomes (RNC-mRNA) are assumed to reflect the mRNAs participating in the translational process. The RNC-mRNA data are reasoned to be useful for appraising the missing proteins. A set of the multiomics data including free-mRNAs, RNC-mRNAs, and proteomes was acquired from three liver cancer cell lines. On the basis of the missing proteins in neXtProt (release 2014-09-19), the bioinformatics analysis was carried out in three phases: (1) finding how many neXtProt missing proteins have or do not have RNA-seq and/or MS/MS evidence, (2) analyzing specific physicochemical and biological properties of the missing proteins that lack both RNA-seq and MS/MS evidence, and (3) analyzing the combined properties of these missing proteins. Total of 1501 missing proteins were found by neither RNC-mRNA nor MS/MS in the three liver cancer cell lines. For these missing proteins, some are expected higher hydrophobicity, unsuitable detection, or sensory functions as properties at the protein level, while some are predicted to have nonexpressing chromatin structures on the corresponding gene level. With further integrated analysis, we could attribute 93% of them (1391/1501) to these causal factors, which result in the expression products scarcely detected by RNA-seq or MS/MS.

A Computational Pipeline for Cross-Species Analysis of RNA-seq Data Using R and Bioconductor

RNA sequencing (RNA-seq) has revolutionized transcriptome analysis through profiling the expression of thousands of genes at the same time. Systematic analysis of orthologous transcripts across species is critical for understanding the evolution of gene expression and uncovering important information in animal models of human diseases. Several computational methods have been published for analyzing gene expression between species, but they often lack crucial details and therefore cannot serve as a practical guide. Here, we present the first step-by-step protocol for cross-species RNA-seq analysis with a concise workflow that is largely based on the free open-source R language and Bioconductor packages. This protocol covers the entire process from short-read mapping, gene expression quantification, differential expression analysis to pathway enrichment. Many useful utilities for data visualization are included. This complete and easy-to-follow protocol provides hands-on guidance for users who are new to cross-species gene expression analysis.

Cross-species Transcriptomic Comparison of In Vitro and In Vivo Mammalian Neural Cells

The mammalian brain is characterized by distinct classes of cells that differ in morphology, structure, signaling, and function. Dysregulation of gene expression in these cell populations leads to various neurological disorders. Neural cells often need to be acutely purified from animal brains for research, which requires complicated procedure and specific expertise. Primary culture of these cells in vitro is a viable alternative, but the differences in gene expression of cells grown in vitro and in vivo remain unclear. Here, we cultured three major neural cell classes of rat brain (ie, neurons, astrocytes, and oligodendrocyte precursor cells [OPCs]) obtained from commercial sources. We measured transcript abundance of these cell types by RNA sequencing (RNA-seq) and compared with their counterparts acutely purified from mouse brains. Cross-species RNA-seq data analysis revealed hundreds of genes that are differentially expressed between the cultured and acutely purified cells. Astrocytes have more such genes compared to neurons and OPCs, indicating that signaling pathways are greatly perturbed in cultured astrocytes. This dataset provides a powerful resource to demonstrate the similarities and differences of biological processes in mammalian neural cells grown in vitro and in vivo at the molecular level.

Digital gene expression analysis of transcriptomes in lipopolysaccharide-induced acute respiratory distress syndrome

BACKGROUND

The mortality from acute respiratory distress syndrome (ARDS) is high, and its exact pathogenesis remains unclear, which forms a major obstacle for prevention and treatment of this disease. In the present study, we used digital gene expression (DGE) to detect the differentially expressed genes of the lung at 4h after lipopolysaccharide (LPS) exposure in a mouse model.

METHODS

Mice were treated with LPS or control saline by intratracheal instillation for 4h, and their lung tissues were collected for DGE analysis. We used a false discovery rate ≤0.001 and an absolute value of the log2 ratio≥1 as the thresholds for judging the significance of any difference in gene expression between the two members of each pair of mice.

RESULTS

We obtained 3,387,842 clean tags (i.e., after filtering to remove potentially erroneous tags) and about 84,513 corresponding distinct clean tags (i.e., types of tag). Approximately 91.20% of the clean tags could be mapped, and 82.71% could be uniquely mapped, to the reference tags, and 3.82% were unknown tags. At least 2200 differentially expressed genes were identified and analyzed for enrichment of Gene Ontology and Kyoto Encyclopedia of Genes and Genomes pathway. Twenty genes with the greatest difference in expression levels between the two members of every pair of mice were chosen. The majority of these genes are involved in signaling transduction, molecular adhesion, and metabolic pathways.

CONCLUSIONS

Using the powerful technology of DGE, we present, to our knowledge, the first in-depth transcriptomic analysis of mouse lungs after LPS exposure. We found some differentially expressed genes that might play important roles in the pathogenesis of ARDS.

The effect on the transcriptome of Anemone coronaria following infection with rust (Tranzschelia discolor)

In order to understand plant/pathogen interaction, the transcriptome of uninfected (1S) and infected (2I) plant was sequenced at 3'end by the GS FLX 454 platform. De novo assembly of high-quality reads generated 27,231 contigs leaving 37,191 singletons in the 1S and 38,393 in the 2I libraries. ESTcalc tool suggested that 71% of the transcriptome had been captured, with 99% of the genes present being represented by at least one read. Unigene annotation showed that 50.5% of the predicted translation products shared significant homology with protein sequences in GenBank. In all 253 differential transcript abundance (DTAs) were in higher abundance and 52 in lower abundance in the 2I library. 128 higher abundance DTA genes were of fungal origin and 49 were clearly plant sequences. A tBLASTn-based search of the sequences using as query the full length predicted polypeptide product of 50 R genes identified 16 R gene products. Only one R gene (PGIP) was up-regulated. The response of the plant to fungal invasion included the up-regulation of several pathogenesis related protein (PR) genes involved in JA signaling and other genes associated with defense response and down regulation of cell wall associated genes, non-race-specific disease resistance1 (NDR1) and other genes like myb, presqualene diphosphate phosphatase (PSDPase), a UDP-glycosyltransferase 74E2-like (UGT). The DTA genes identified here should provide a basis for understanding the A. coronaria/T. discolor interaction and leads for biotechnology-based disease resistance breeding.

Evaluation of body growth and immunity-related differentially expressed genes through deep RNA sequencing in the piglets of Jeju native pig and Berkshire

This study was carried out with the objective to investigate the differentially expressed genes (DEGs) between Jeju native pig (JNP) and Berkshire piglets. The RNA-Seq technique was used to investigate the transcriptomes in the fat, liver and longissimus dorsi muscle from these two breeds. Paired-end reads of the sequences that passed the quality filters were aligned to the Sus scrofa genome using tophat2 (v2.0.2). In this study, 65% of muscle, 20% of fat and 54% of liver genes showed higher expression in the piglets of JNP than in Berkshire. Gene Ontology and signaling pathways showed that immune response and lipid metabolisms were commonly enriched pathways in all three tissues. It was found that the genes pertaining to body growth and immune system are significantly (P < 0.01) more highly expressed in Berkshire piglets. DEGs explored between the piglets of the two breeds might influence the identification of the genetic markers for further breed improvement programs. Our findings provide a new perspective for understanding and identifying candidate genes that are involved in various biological functions. Moreover, transcriptome analysis makes it easier to understand the differences between genetic mechanisms of breeds.

Transcriptome changes in rice (Oryza sativa L.) in response to high night temperature stress at the early milky stage

Background

Rice yield and quality are adversely affected by high temperatures, especially at night; high nighttime temperatures are more harmful to grain weight than high daytime temperatures. Unfortunately, global temperatures are consistently increasing at an alarming rate and the minimum nighttime temperature has increased three times as much as the corresponding maximum daytime temperature over the past few decades.

Results

We analyzed the transcriptome profiles for rice grain from heat-tolerant and -sensitive lines in response to high night temperatures at the early milky stage using the Illumina Sequencing method. The analysis results for the sequencing data indicated that 35 transcripts showed different expressions between heat-tolerant and -sensitive rice, and RT-qPCR analyses confirmed the expression patterns of selected transcripts. Functional analysis of the differentially expressed transcripts indicated that 21 genes have functional annotation and their functions are mainly involved in oxidation-reduction (6 genes), metabolic (7 genes), transport (4 genes), transcript regulation (2 genes), defense response (1 gene) and photosynthetic (1 gene) processes. Based on the functional annotation of the differentially expressed genes, the possible process that regulates these differentially expressed transcripts in rice grain responding to high night temperature stress at the early milky stage was further analyzed. This analysis indicated that high night temperature stress disrupts electron transport in the mitochondria, which leads to changes in the concentration of hydrogen ions in the mitochondrial and cellular matrix and influences the activity of enzymes involved in TCA and its secondary metabolism in plant cells.

Conclusions

Using Illumina sequencing technology, the differences between the transcriptomes of heat-tolerant and -sensitive rice lines in response to high night temperature stress at the early milky stage was described here for the first time. The candidate transcripts may provide genetic resources that may be useful in the improvement of heat-tolerant characters of rice. The model proposed here is based on differences in expression and transcription between two rice lines. In addition, the model may support future studies on the molecular mechanisms underlying plant responses to high night temperatures.

Electronic supplementary material

The online version of this article (doi:10.1186/s12864-015-1222-0) contains supplementary material, which is available to authorized users.

Transcript profiling reveals auxin and cytokinin signaling pathways and transcription regulation during in vitro organogenesis of Ramie (Boehmeria nivea L. Gaud)

In vitro organogenesis, one of the most common pathways leading to in vitro plant regeneration, is widely used in biotechnology and the fundamental study of plant biology. Although previous studies have constructed a complex regulatory network model for Arabidopsis in vitro organogenesis, no related study has been reported in ramie. To generate more complete observations of transcriptome content and dynamics during ramie in vitro organogenesis, we constructed a reference transcriptome library and ten digital gene expression (DGE) libraries for illumina sequencing. Approximately 111.34 million clean reads were obtained for transcriptome and the DGE libraries generated between 13.5 and 18.8 million clean reads. De novo assembly produced 43,222 unigenes and a total of 5,760 differentially expressed genes (DEGs) were filtered. Searching against the Kyoto Encyclopedia of Genes and Genomes Pathway database, 26 auxin related and 11 cytokinin related DEGs were selected for qRT-PCR validation of two ramie cultivars, which had high (Huazhu No. 5) or extremely low (Dazhuhuangbaima) shoot regeneration abilities. The results revealed differing regulation patterns of auxin and cytokinin in different genotypes. Here we report the first genome-wide gene expression profiling of in vitro organogenesis in ramie and provide an overview of transcription and phytohormone regulation during the process. Furthermore, the auxin and cytokinin related genes have distinct expression patterns in two ramie cultivars with high or extremely low shoot regeneration ability, which has given us a better understanding of the in vitro organogenesis mechanism. This result will provide a foundation for future phytohormone research and lead to improvements of the ramie regeneration system.

Genetic influences on translation in yeast

Heritable differences in gene expression between individuals are an important source of phenotypic variation. The question of how closely the effects of genetic variation on protein levels mirror those on mRNA levels remains open. Here, we addressed this question by using ribosome profiling to examine how genetic differences between two strains of the yeast S. cerevisiae affect translation. Strain differences in translation were observed for hundreds of genes. Allele specific measurements in the diploid hybrid between the two strains revealed roughly half as many cis-acting effects on translation as were observed for mRNA levels. In both the parents and the hybrid, most effects on translation were of small magnitude, such that the direction of an mRNA difference was typically reflected in a concordant footprint difference. The relative importance of cis and trans acting variation on footprint levels was similar to that for mRNA levels. There was a tendency for translation to cause larger footprint differences than expected given the respective mRNA differences. This is in contrast to translational differences between yeast species that have been reported to more often oppose than reinforce mRNA differences. Finally, we catalogued instances of premature translation termination in the two yeast strains and also found several instances where erroneous reference gene annotations lead to apparent nonsense mutations that in fact reside outside of the translated gene body. Overall, genetic influences on translation subtly modulate gene expression differences, and translation does not create strong discrepancies between genetic influences on mRNA and protein levels.

Individuals in a species differ from each other in many ways. For many traits, a fraction of this variation is genetic—it is caused by DNA sequence variants in the genome of each individual. Some of these variants influence traits by altering how much certain genes are expressed, i.e. how many mRNA and protein molecules are made in different individuals. Surprisingly, earlier work has found that the effects of genetic variants on mRNA and protein levels for the same genes appear to be very different. Many variants appeared to influence only mRNA (but not protein) levels, and vice versa. In this paper, we studied this question by using a technique called “ribosome profiling” to measure translation (the cellular process of reading mRNA molecules and synthesizing protein molecules) in two yeast strains. We found that the genetic differences between these two strains influence translation for hundreds of genes. Because most of these effects were small in magnitude, they explain at most a small fraction of the discrepancies between the effects of genetic variants on mRNA and protein levels.

Comparative transcriptomic analysis to identify differentially expressed genes in fat tissue of adult Berkshire and Jeju Native Pig using RNA-seq

Pork is a major source of animal protein for humans. The subcutaneous, intermuscular and the intramuscular fat are the factors responsible for meat quality. RNA-seq is rapidly adopted for the profiling of the transcriptomes in the studies related to gene regulation. The discovery of differentially expressed genes (DEGs) between adult animals of Jeju Native Pig (JNP) and Berkshire breeds are of particular interest for the current study. RNA-seq was used to investigate the transcriptome profiling in the fat tissue. Sequence reads were obtained from Ilumina HiSeq2000 and mapped to the pig genome using Tophat2. Total 153 DEGs were identified and 71 among the annotated genes, have BLAST matches in the non- redundant database. Metabolic, immune response and protein binding are enriched pathways in the fat tissue. In our study, biological adhesion, cellular, developmental and multicellular organismal processes in fat were up-regulated in JNP as compare to Berkshire. Multicellular organismal process, developmental process, embryonic morphogenesis and skeletal system development were the most significantly enriched terms in fat of JNP and Berkshire breeds (p = 1.17E-04, 0.044, 3.47E-04 and 4.48E-04 respectively). COL10A1, COL11A2, PDK4 and PNPLA3 genes responsible for skeletal system morphogenesis and body growth were down regulated in JNP. This study is the first statistical analysis for the detection of DEGs from RNA-seq data generated from fat tissue sample. This analysis can be used as stepping stone to understand the difference in the genetic mechanisms that might influence the identification of novel transcripts, sequence polymorphisms, isoforms and noncoding RNAs.