Model-based clustering for RNA-seq data

MBCdeg4: A modified clustering-based method for identifying differentially expressed genes from RNA-seq data

RNA-seq is a commonly employed methodology for the measurement of transcriptomes, particularly for the identification of differentially expressed genes (DEGs) between different conditions or groups. In a previous report, we described a clustering-based method for identifying DEGs, designated MBCdeg1 and MBCdeg2. and a modified version, MBCdeg3. This study presents a further improved version, designated MBCdeg4. The four versions of MBCdeg employ an R package, designated MBCluster.Seq, internally. The sole distinction between them is the manner of data normalization. MBCdeg4 employs normalization factors derived from a robust normalization algorithm, designated as DEGES. Seven competing methods were compared: the four versions of MBCdeg and three conventional R packages (edgeR, DESeq2, and TCC). MBCdeg4 demonstrated superior performance in a multitude of simulation scenarios involving RNA-seq count data. Therefore, MBCdeg4 is recommended for use in preference to the earlier versions, MBCdeg1-3.•MBCdeg4 is a method for both identification and classification of DEGs from RNA-seq count data.•MBCdeg4 is available as an R function and performs well in a wide variety of simulation scenarios.

Overexpression of the RNA-binding protein NrdA affects global gene expression and secondary metabolism in Aspergillus species

RNA-binding protein Nrd1 plays a role in RNA polymerase II transcription termination. In this study, we showed that the orthologous NrdA is important in global mRNA expression and secondary metabolism in Aspergillus species. We constructed an nrdA conditional expression strain using the Tet-On system in Aspergillus luchuenesis mut. kawachii. Downregulation of nrdA caused a severe growth defect, indicating that NrdA is essential for the proliferation of A. kawachii. Parallel RNA-sequencing and RNA immunoprecipitation-sequencing analysis identified potential NrdA-interacting transcripts, corresponding to 32% of the predicted protein-coding genes of A. kawachii. Subsequent gene ontology analysis suggested that overexpression of NrdA affects the production of secondary metabolites. To clarify this, we constructed Aspergillus nidulans, Aspergillus fumigatus, and Aspergillus oryzae strains overexpressing NrdA in the early developmental stage. Overexpression of NrdA reduced the production of sterigmatocystin and penicillin in A. nidulans, as well as that of helvolic acid and pyripyropene A in A. fumigatus. Moreover, it increased the production of kojic acid and reduced the production of penicillin in A. oryzae. These effects were accompanied by almost consistent changes in the mRNA levels of relevant genes. Collectively, these results suggest that NrdA is the essential RNA-binding protein, which plays a significant role in global gene expression and secondary metabolism in Aspergillus species.IMPORTANCENrd1, a component of the Nrd1-Nab3-Sen1 complex, is an essential RNA-binding protein involved in transcriptional termination in yeast. However, its role in filamentous fungi has not been studied. In this study, we characterized an orthologous NrdA in the Aspergillus species, identified potential NrdA-interacting mRNA, and investigated the effect of overexpression of NrdA on mRNA expression in Aspergillus luchuensis mut. kawachii. The results indicated that NrdA controls global gene expression involved in versatile metabolic pathways, including the secondary metabolic process, at least in the early developmental stage. We demonstrated that NrdA overexpression significantly affected the production of secondary metabolites in Aspergillus nidulans, Aspergillus oryzae, and Aspergillus fumigatus. Our findings are of importance to the fungal research community because the secondary metabolism is an industrially and clinically important aspect for the Aspergillus species.

Multiparametric MRI along with machine learning predicts prognosis and treatment response in pediatric low-grade glioma

Pediatric low-grade gliomas (pLGGs) exhibit heterogeneous prognoses and variable responses to treatment, leading to tumor progression and adverse outcomes in cases where complete resection is unachievable. Early prediction of treatment responsiveness and suitability for immunotherapy has the potential to improve clinical management and outcomes. Here, we present a radiogenomic analysis of pLGGs, integrating MRI and RNA sequencing data. We identify three immunologically distinct clusters, with one group characterized by increased immune activity and poorer prognosis, indicating potential benefit from immunotherapies. We develop a radiomic signature that predicts these immune profiles with over 80% accuracy. Furthermore, our clinicoradiomic model predicts progression-free survival and correlates with treatment response. We also identify genetic variants and transcriptomic pathways associated with progression risk, highlighting links to tumor growth and immune response. This radiogenomic study in pLGGs provides a framework for the identification of high-risk patients who may benefit from targeted therapies.

Sexual reproduction during diatom bloom

Phytoplankton supports food webs in all aquatic ecosystems. Ecological studies highlighted the links between environmental variables and species successions in situ. However, the role of life cycle characteristics on phytoplankton community dynamics remains poorly characterized. In diatoms, sexual reproduction creates new genetic combinations and prevents excessive cell size miniaturization. It has been extensively studied in vitro but seldom in the natural environment. Here, analyzing metatranscriptomic data in the light of the expression patterns previously characterized in vitro, we identified a synchronized and transient sexual reproduction event during a bloom of the toxic diatom species Pseudo-nitzschia australis. Despite the complexity of environmental conditions encountered in situ, sexual reproduction appeared to be the strongest differential gene expression signal that occurred during the bloom. The potential link between environmental conditions and the initiation of sexual reproduction remain to be determined, but sexual reproduction probably had a major impact on the bloom dynamic.

Maintenance of R-loop structures by phosphorylated hTERT preserves genome integrity

As aberrant accumulation of RNA-DNA hybrids (R-loops) causes DNA damage and genome instability, cells express regulators of R-loop structures. Here we report that RNA-dependent RNA polymerase (RdRP) activity of human telomerase reverse transcriptase (hTERT) regulates R-loop formation. We found that the phosphorylated form of hTERT (p-hTERT) exhibits RdRP activity in nuclear speckles both in telomerase-positive cells and telomerase-negative cells with alternative lengthening of telomeres (ALT) activity. The p-hTERT did not associate with telomerase RNA component in nuclear speckles but, instead, with TERRA RNAs to resolve R-loops. Targeting of the TERT gene in ALT cells ablated RdRP activity and impaired tumour growth. Using a genome-scale CRISPR loss-of-function screen, we identified Fanconi anaemia/BRCA genes as synthetic lethal partners of hTERT RdRP. Inactivation of RdRP and Fanconi anaemia/BRCA genes caused accumulation of R-loop structures and DNA damage. These findings indicate that RdRP activity of p-hTERT guards against genome instability by removing R-loop structures.

Enhanced clustering-based differential expression analysis method for RNA-seq data

RNA-seq is a tool for measuring gene expression and is commonly used to identify differentially expressed genes (DEGs). Gene clustering has been widely used to classify DEGs with similar expression patterns, but rarely used to identify DEGs themselves. We recently reported that the clustering-based method (called MBCdeg1 and 2) for identifying DEGs has great potential. However, these methods left room for improvement. This study reports on the improvement (named MBCdeg3). We compared a total of six competing methods: three conventional R packages (edgeR, DESeq2, and TCC) and three versions of MBCdeg (i.e., MBCdeg1, 2, and 3) corresponding to three different normalization algorithms. As MBCdeg3 performs well in many simulation scenarios of RNA-seq count data, MBCdeg3 replaces MBCdeg1 and 2 in our previous report. •MBCdeg3 is a method for both identification and classification of DEGs from RNA-seq count data.•MBCdeg3 is available as a function of R, which is common in the field of expression analysis.•MBCdeg3 performs well in a variety of simulation scenarios for RNA-seq count data.

Solar urticaria involves rapid mast cell STAT3 activation and neutrophil recruitment, with FcεRI as an upstream regulator

BACKGROUND

Solar urticaria is a rare photodermatosis characterized by rapid-onset sunlight-induced urticaria, but its pathophysiology is not well understood.

OBJECTIVE

We sought to define cutaneous cellular and molecular events in the evolution of solar urticaria following its initiation by solar-simulated UV radiation (SSR) and compare with healthy controls (HC).

METHODS

Cutaneous biopsy specimens were taken from unexposed skin and skin exposed to a single low (physiologic) dose of SSR at 30 minutes, 3 hours, and 24 hours after exposure in 6 patients with solar urticaria and 6 HC. Biopsy specimens were assessed by immunohistochemistry and bulk RNA-sequencing analysis.

RESULTS

In solar urticaria specimens, there was enrichment of several innate immune pathways, with striking early involvement of neutrophils, which was not observed in HC. Multiple proinflammatory cytokine and chemokine genes were upregulated (including IL20, IL6, and CXCL8) or identified as upstream regulators (including TNF, IL-1β, and IFN-γ). IgE and FcεRI were identified as upstream regulators, and phosphorylated signal transducer and activator of transcription 3 expression in mast cells was increased in solar urticaria at 30 minutes and 3 hours after SSR exposure, suggesting a mechanism of mast cell activation. Clinical resolution of solar urticaria by 24 hours mirrored resolution of inflammatory gene signature profiles. Comparison with available datasets of chronic spontaneous urticaria showed transcriptomic similarities relating to immune activation, but several transcripts were identified solely in solar urticaria, including CXCL8 and CSF2/3.

CONCLUSIONS

Solar urticaria is characterized by rapid signal transducer and activator of transcription 3 activation in mast cells and involvement of multiple chemotactic and innate inflammatory pathways, with FcεRI engagement indicated as an early event.

A clustering procedure for three-way RNA sequencing data using data transformations and matrix-variate Gaussian mixture models

RNA sequencing of time-course experiments results in three-way count data where the dimensions are the genes, the time points and the biological units. Clustering RNA-seq data allows to extract groups of co-expressed genes over time. After standardisation, the normalised counts of individual genes across time points and biological units have similar properties as compositional data. We propose the following procedure to suitably cluster three-way RNA-seq data: (1) pre-process the RNA-seq data by calculating the normalised expression profiles, (2) transform the data using the additive log ratio transform to map the composition in the D-part Aitchison simplex to a D - 1 -dimensional Euclidean vector, (3) cluster the transformed RNA-seq data using matrix-variate Gaussian mixture models and (4) assess the quality of the overall cluster solution and of individual clusters based on cluster separation in the transformed space using density-based silhouette information and on compactness of the cluster in the original space using cluster maps as a suitable visualisation. The proposed procedure is illustrated on RNA-seq data from fission yeast and results are also compared to an analogous two-way approach after flattening out the biological units.

The WRKY46-MYC2 module plays a critical role in E-2-hexenal-induced anti-herbivore responses by promoting flavonoid accumulation

Volatile organic compounds (VOCs) play key roles in plant-plant communication, especially in response to pest attack. E-2-hexenal is an important component of VOCs, but it is unclear whether it can induce endogenous plant resistance to insects. Here, we show that E-2-hexenal activates early signaling events in Arabidopsis (Arabidopsis thaliana) mesophyll cells, including an H2O2 burst at the plasma membrane, the directed flow of calcium ions, and an increase in cytosolic calcium concentration. Treatment of wild-type Arabidopsis plants with E-2-hexenal increases their resistance when challenged with the diamondback moth Plutella xylostella L., and this phenomenon is largely lost in the wrky46 mutant. Mechanistically, E-2-hexenal induces the expression of WRKY46 and MYC2, and the physical interaction of their encoded proteins was verified by yeast two-hybrid, firefly luciferase complementation imaging, and in vitro pull-down assays. The WRKY46-MYC2 complex directly binds to the promoter of RBOHD to promote its expression, as demonstrated by luciferase reporter, yeast one-hybrid, chromatin immunoprecipitation, and electrophoretic mobility shift assays. This module also positively regulates the expression of E-2-hexenal-induced naringenin biosynthesis genes (TT4 and CHIL) and the accumulation of total flavonoids, thereby modulating plant tolerance to insects. Together, our results highlight an important role for the WRKY46-MYC2 module in the E-2-hexenal-induced defense response of Arabidopsis, providing new insights into the mechanisms by which VOCs trigger plant defense responses.

Clustering microbiome data using mixtures of logistic normal multinomial models

Discrete data such as counts of microbiome taxa resulting from next-generation sequencing are routinely encountered in bioinformatics. Taxa count data in microbiome studies are typically high-dimensional, over-dispersed, and can only reveal relative abundance therefore being treated as compositional. Analyzing compositional data presents many challenges because they are restricted to a simplex. In a logistic normal multinomial model, the relative abundance is mapped from a simplex to a latent variable that exists on the real Euclidean space using the additive log-ratio transformation. While a logistic normal multinomial approach brings flexibility for modeling the data, it comes with a heavy computational cost as the parameter estimation typically relies on Bayesian techniques. In this paper, we develop a novel mixture of logistic normal multinomial models for clustering microbiome data. Additionally, we utilize an efficient framework for parameter estimation using variational Gaussian approximations (VGA). Adopting a variational Gaussian approximation for the posterior of the latent variable reduces the computational overhead substantially. The proposed method is illustrated on simulated and real datasets.

Statistical analysis of high-dimensional biomedical data: a gentle introduction to analytical goals, common approaches and challenges

BACKGROUND

In high-dimensional data (HDD) settings, the number of variables associated with each observation is very large. Prominent examples of HDD in biomedical research include omics data with a large number of variables such as many measurements across the genome, proteome, or metabolome, as well as electronic health records data that have large numbers of variables recorded for each patient. The statistical analysis of such data requires knowledge and experience, sometimes of complex methods adapted to the respective research questions.

METHODS

Advances in statistical methodology and machine learning methods offer new opportunities for innovative analyses of HDD, but at the same time require a deeper understanding of some fundamental statistical concepts. Topic group TG9 "High-dimensional data" of the STRATOS (STRengthening Analytical Thinking for Observational Studies) initiative provides guidance for the analysis of observational studies, addressing particular statistical challenges and opportunities for the analysis of studies involving HDD. In this overview, we discuss key aspects of HDD analysis to provide a gentle introduction for non-statisticians and for classically trained statisticians with little experience specific to HDD.

RESULTS

The paper is organized with respect to subtopics that are most relevant for the analysis of HDD, in particular initial data analysis, exploratory data analysis, multiple testing, and prediction. For each subtopic, main analytical goals in HDD settings are outlined. For each of these goals, basic explanations for some commonly used analysis methods are provided. Situations are identified where traditional statistical methods cannot, or should not, be used in the HDD setting, or where adequate analytic tools are still lacking. Many key references are provided.

CONCLUSIONS

This review aims to provide a solid statistical foundation for researchers, including statisticians and non-statisticians, who are new to research with HDD or simply want to better evaluate and understand the results of HDD analyses.

Identifying novel regulators of placental development using time-series transcriptome data

The placenta serves as a connection between the mother and the fetus during pregnancy, providing the fetus with oxygen, nutrients, and growth hormones. However, the regulatory mechanisms and dynamic gene interaction networks underlying early placental development are understudied. Here, we generated RNA-sequencing data from mouse fetal placenta at embryonic days 7.5, 8.5, and 9.5 to identify genes with timepoint-specific expression, then inferred gene interaction networks to analyze highly connected network modules. We determined that timepoint-specific gene network modules were associated with distinct developmental processes, and with similar expression profiles to specific human placental cell populations. From each module, we identified hub genes and their direct neighboring genes, which were predicted to govern placental functions. We confirmed that four novel candidate regulators identified through our analyses regulate cell migration in the HTR-8/SVneo cell line. Overall, we predicted several novel regulators of placental development expressed in specific placental cell types using network analysis of bulk RNA-sequencing data. Our findings and analysis approaches will be valuable for future studies investigating the transcriptional landscape of early development.

A sparse negative binomial mixture model for clustering RNA-seq count data

Clustering with variable selection is a challenging yet critical task for modern small-n-large-p data. Existing methods based on sparse Gaussian mixture models or sparse $K$-means provide solutions to continuous data. With the prevalence of RNA-seq technology and lack of count data modeling for clustering, the current practice is to normalize count expression data into continuous measures and apply existing models with a Gaussian assumption. In this article, we develop a negative binomial mixture model with lasso or fused lasso gene regularization to cluster samples (small $n$) with high-dimensional gene features (large $p$). A modified EM algorithm and Bayesian information criterion are used for inference and determining tuning parameters. The method is compared with existing methods using extensive simulations and two real transcriptomic applications in rat brain and breast cancer studies. The result shows the superior performance of the proposed count data model in clustering accuracy, feature selection, and biological interpretation in pathways.

Poisson hurdle model-based method for clustering microbiome features

MOTIVATION

High-throughput sequencing technologies have greatly facilitated microbiome research and have generated a large volume of microbiome data with the potential to answer key questions regarding microbiome assembly, structure and function. Cluster analysis aims to group features that behave similarly across treatments, and such grouping helps to highlight the functional relationships among features and may provide biological insights into microbiome networks. However, clustering microbiome data are challenging due to the sparsity and high dimensionality.

RESULTS

We propose a model-based clustering method based on Poisson hurdle models for sparse microbiome count data. We describe an expectation-maximization algorithm and a modified version using simulated annealing to conduct the cluster analysis. Moreover, we provide algorithms for initialization and choosing the number of clusters. Simulation results demonstrate that our proposed methods provide better clustering results than alternative methods under a variety of settings. We also apply the proposed method to a sorghum rhizosphere microbiome dataset that results in interesting biological findings.

AVAILABILITY AND IMPLEMENTATION

R package is freely available for download at https://cran.r-project.org/package=PHclust.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Production of Reactive Oxygen Species via Nanobubble Water Improves Radish Seed Water Absorption and the Expression of Aquaporin Genes

Nanobubbles (NBs) stimulate seed germination; however, the mechanism of the promotion effect of NBs remains unclear. The impact of NBs on seed water absorption was investigated; we subsequently studied the genes associated with the response of radish seeds to NB water and used RNA sequencing to generate their expression profiles, especially those of aquaporin genes. NB water significantly promoted germination. The times at which 50% of the germinating seeds achieved germination (T₅₀) for the submerged radish seeds in NB and control water were 11.6 and 17.4 h, respectively. NB water-germinated radish seeds showed a water uptake rate coefficient that was 15% higher than that of those germinated in control water. Through GO enrichment and cluster analyses, it was evident that NB water significantly increased the level of expression of the genes associated with the following activities: oxidoreductase, peroxidase, and antioxidant. Our results demonstrated that NB water increases the water uptake rate of radish seeds via two mechanisms. The NB water-produced exogenous hydroxyl radical (•OH) increases the seed coat's water permeability and enhances cell wall loosening, and NB water increases the aquaporin gene expression level of radish seeds.

Limb Mesoderm and Head Ectomesenchyme Both Express a Core Transcriptional Program During Chondrocyte Differentiation

To explain how cartilage appeared in different parts of the vertebrate body at discrete times during evolution, we hypothesize that different embryonic populations co-opted expression of a core gene regulatory network (GRN) driving chondrocyte differentiation. To test this hypothesis, laser-capture microdissection coupled with RNA-seq was used to reveal chondrocyte transcriptomes in the developing chick humerus and ceratobranchial, which are mesoderm- and neural crest-derived, respectively. During endochondral ossification, two general types of chondrocytes differentiate. Immature chondrocytes (IMM) represent the early stages of cartilage differentiation, while mature chondrocytes (MAT) undergo additional stages of differentiation, including hypertrophy and stimulating matrix mineralization and degradation. Venn diagram analyses generally revealed a high degree of conservation between chondrocyte transcriptomes of the limb and head, including SOX9, COL2A1, and ACAN expression. Typical maturation genes, such as COL10A1, IBSP, and SPP1, were upregulated in MAT compared to IMM in both limb and head chondrocytes. Gene co-expression network (GCN) analyses of limb and head chondrocyte transcriptomes estimated the core GRN governing cartilage differentiation. Two discrete portions of the GCN contained genes that were differentially expressed in limb or head chondrocytes, but these genes were enriched for biological processes related to limb/forelimb morphogenesis or neural crest-dependent processes, respectively, perhaps simply reflecting the embryonic origin of the cells. A core GRN driving cartilage differentiation in limb and head was revealed that included typical chondrocyte differentiation and maturation markers, as well as putative novel "chondrocyte" genes. Conservation of a core transcriptional program during chondrocyte differentiation in both the limb and head suggest that the same core GRN was co-opted when cartilage appeared in different regions of the skeleton during vertebrate evolution.

Key Soybean Seedlings Drought-Responsive Genes and Pathways Revealed by Comparative Transcriptome Analyses of Two Cultivars

Seedling drought stress is one of the most important constraints affecting soybean yield and quality. To unravel the molecular mechanisms under soybean drought tolerance, we conducted comprehensive comparative transcriptome analyses of drought-tolerant genotype Jindou 21 (JD) and drought-sensitive genotype Tianlong No.1 (N1) seedlings that had been exposed to drought treatment. A total of 6038 and 4112 differentially expressed genes (DEGs) were identified in drought-tolerant JD and drought-sensitive N1, respectively. Subsequent KEGG pathway analyses showed that numerous DEGs in JD are predominately involved in signal transduction pathways, including plant hormone signaling pathway, calcium signaling pathway, and MAPK signaling pathway. Interestingly, JA and BR plant hormone signal transduction pathways were found specifically participating in drought-tolerant JD. Meanwhile, the differentially expressed CPKs, CIPKs, MAPKs, and MAP3Ks of calcium and MAPK signaling pathway were only identified in JD. The number of DEGs involved in transcription factors (TFs) is larger in JD than that of in N1. Moreover, some differently expressed transcriptional factor genes were only identified in drought-tolerant JD, including FAR1, RAV, LSD1, EIL, and HB-PHD. In addition, this study suggested that JD could respond to drought stress by regulating the cell wall remodeling and stress-related protein genes such as EXPs, CALSs, CBPs, BBXs, and RD22s. JD is more drought tolerant than N1 owing to more DEGs being involved in multiple signal transduction pathways (JA, BR, calcium, MAPK signaling pathway), stress-related TFs, and proteins. The above valuable genes and pathways will deepen the understanding of the molecular mechanisms under drought stress in soybean.

Differential expression analysis using a model-based gene clustering algorithm for RNA-seq data

Background

RNA-seq is a tool for measuring gene expression and is commonly used to identify differentially expressed genes (DEGs). Gene clustering is used to classify DEGs with similar expression patterns for the subsequent analyses of data from experiments such as time-courses or multi-group comparisons. However, gene clustering has rarely been used for analyzing simple two-group data or differential expression (DE). In this study, we report that a model-based clustering algorithm implemented in an R package, MBCluster.Seq, can also be used for DE analysis.

Results

The input data originally used by MBCluster.Seq is DEGs, and the proposed method (called MBCdeg) uses all genes for the analysis. The method uses posterior probabilities of genes assigned to a cluster displaying non-DEG pattern for overall gene ranking. We compared the performance of MBCdeg with conventional R packages such as edgeR, DESeq2, and TCC that are specialized for DE analysis using simulated and real data. Our results showed that MBCdeg outperformed other methods when the proportion of DEG (P_DEG) was less than 50%. However, the DEG identification using MBCdeg was less consistent than with conventional methods. We compared the effects of different normalization algorithms using MBCdeg, and performed an analysis using MBCdeg in combination with a robust normalization algorithm (called DEGES) that was not implemented in MBCluster.Seq. The new analysis method showed greater stability than using the original MBCdeg with the default normalization algorithm.

Conclusions

MBCdeg with DEGES normalization can be used in the identification of DEGs when the P_DEG is relatively low. As the method is based on gene clustering, the DE result includes information on which expression pattern the gene belongs to. The new method may be useful for the analysis of time-course and multi-group data, where the classification of expression patterns is often required.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12859-021-04438-4.

The Metabolic Landscape of Thymic T Cell Development In Vivo and In Vitro

Although metabolic pathways have been shown to control differentiation and activation in peripheral T cells, metabolic studies on thymic T cell development are still lacking, especially in human tissue. In this study, we use transcriptomics and extracellular flux analyses to investigate the metabolic profiles of primary thymic and in vitro-derived mouse and human thymocytes. Core metabolic pathways, specifically glycolysis and oxidative phosphorylation, undergo dramatic changes between the double-negative (DN), double-positive (DP), and mature single-positive (SP) stages in murine and human thymus. Remarkably, despite the absence of the complex multicellular thymic microenvironment, in vitro murine and human T cell development recapitulated the coordinated decrease in glycolytic and oxidative phosphorylation activity between the DN and DP stages seen in primary thymus. Moreover, by inducing in vitro T cell differentiation from Rag1-/- mouse bone marrow, we show that reduced metabolic activity at the DP stage is independent of TCR rearrangement. Thus, our findings suggest that highly conserved metabolic transitions are critical for thymic T cell development.

Transcriptional regulation of N6-methyladenosine orchestrates sex-dimorphic metabolic traits

Males and females exhibit striking differences in the prevalence of metabolic traits including hepatic steatosis, a key driver of cardiometabolic morbidity and mortality. RNA methylation is a widespread regulatory mechanism of transcript turnover. Here, we show that presence of the RNA modification N6-methyladenosine (m6A) triages lipogenic transcripts for degradation and guards against hepatic triglyceride accumulation. In male but not female mice, this protective checkpoint stalls under lipid-rich conditions. Loss of m6A control in male livers increases hepatic triglyceride stores, leading to a more 'feminized' hepatic lipid composition. Crucially, liver-specific deletion of the m6A complex protein Mettl14 from male and female mice significantly diminishes sex-specific differences in steatosis. We further surmise that the m6A installing machinery is subject to transcriptional control by the sex-responsive BCL6-STAT5 axis in response to dietary conditions. These data show that m6A is essential for precise and synchronized control of lipogenic enzyme activity and provide insights into the molecular basis for the existence of sex-specific differences in hepatic lipid traits.

Reproductive developmental transcriptome analysis of Tripidium ravennae (Poaceae)

BACKGROUND

Tripidium ravennae is a cold-hardy, diploid species in the sugarcane complex (Poaceae subtribe Saccharinae) with considerable potential as a genetic resource for developing improved bioenergy and ornamental grasses. An improved understanding of the genetic regulation of reproductive processes (e.g., floral induction, inflorescence development, and seed development) will enable future applications of precision breeding and gene editing of floral and seed development. In particular, the ability to silence reproductive processes would allow for developing seedless forms of valuable but potentially invasive plants. The objective of this research was to characterize the gene expression environment of reproductive development in T. ravennae.

RESULTS

During the early phases of inflorescence development, multiple key canonical floral integrators and pathways were identified. Annotations of type II subfamily of MADS-box transcription factors, in particular, were over-represented in the GO enrichment analyses and tests for differential expression (FDR p-value < 0.05). The differential expression of floral integrators observed in the early phases of inflorescence development diminished prior to inflorescence determinacy regulation. Differential expression analysis did not identify many unique genes at mid-inflorescence development stages, though typical biological processes involved in plant growth and development expressed abundantly. The increase in inflorescence determinacy regulatory elements and putative homeotic floral development unigenes at mid-inflorescence development coincided with the expression of multiple meiosis annotations and multicellular organism developmental processes. Analysis of seed development identified multiple unigenes involved in oxidative-reductive processes.

CONCLUSION

Reproduction in grasses is a dynamic system involving the sequential coordination of complex gene regulatory networks and developmental processes. This research identified differentially expressed transcripts associated with floral induction, inflorescence development, and seed development in T. ravennae. These results provide insights into the molecular regulation of reproductive development and provide a foundation for future investigations and analyses, including genome annotation, functional genomics characterization, gene family evolutionary studies, comparative genomics, and precision breeding.

Best practices on the differential expression analysis of multi-species RNA-seq

Advances in transcriptome sequencing allow for simultaneous interrogation of differentially expressed genes from multiple species originating from a single RNA sample, termed dual or multi-species transcriptomics. Compared to single-species differential expression analysis, the design of multi-species differential expression experiments must account for the relative abundances of each organism of interest within the sample, often requiring enrichment methods and yielding differences in total read counts across samples. The analysis of multi-species transcriptomics datasets requires modifications to the alignment, quantification, and downstream analysis steps compared to the single-species analysis pipelines. We describe best practices for multi-species transcriptomics and differential gene expression.

Mitochondrial genotype alters the impact of rapamycin on the transcriptional response to nutrients in Drosophila

BACKGROUND

In addition to their well characterized role in cellular energy production, new evidence has revealed the involvement of mitochondria in diverse signaling pathways that regulate a broad array of cellular functions. The mitochondrial genome (mtDNA) encodes essential components of the oxidative phosphorylation (OXPHOS) pathway whose expression must be coordinated with the components transcribed from the nuclear genome. Mitochondrial dysfunction is associated with disorders including cancer and neurodegenerative diseases, yet the role of the complex interactions between the mitochondrial and nuclear genomes are poorly understood.

RESULTS

Using a Drosophila model in which alternative mtDNAs are present on a common nuclear background, we studied the effects of this altered mitonuclear communication on the transcriptomic response to altered nutrient status. Adult flies with the 'native' and 'disrupted' genotypes were re-fed following brief starvation, with or without exposure to rapamycin, the cognate inhibitor of the nutrient-sensing target of rapamycin (TOR). RNAseq showed that alternative mtDNA genotypes affect the temporal transcriptional response to nutrients in a rapamycin-dependent manner. Pathways most greatly affected were OXPHOS, protein metabolism and fatty acid metabolism. A distinct set of testis-specific genes was also differentially regulated in the experiment.

CONCLUSIONS

Many of the differentially expressed genes between alternative mitonuclear genotypes have no direct interaction with mtDNA gene products, suggesting that the mtDNA genotype contributes to retrograde signaling from mitochondria to the nucleus. The interaction of mitochondrial genotype (mtDNA) with rapamycin treatment identifies new links between mitochondria and the nutrient-sensing mTORC1 (mechanistic target of rapamycin complex 1) signaling pathway.

Light Harvesting-like Protein 3 Interacts with Phytoene Synthase and Is Necessary for Carotenoid and Chlorophyll Biosynthesis in Rice

BACKGROUND

Carotenoid biosynthesis is essential for the generation of photosynthetic pigments, phytohormone production, and flower color development. The light harvesting like 3 (LIL3) protein, which belongs to the light-harvesting complex protein family in photosystems, interacts with geranylgeranyl reductase (GGR) and protochlorophyllide oxidoreductase (POR) both of which are known to regulate terpenoid and chlorophyll biosynthesis, respectively, in both rice and Arabidopsis.

RESULTS

In our study, a CRISPR-Cas9 generated 4-bp deletion mutant oslil3 showed aberrant chloroplast development, growth defects, low fertility rates and reduced pigment contents. A comparative transcriptomic analysis of oslil3 suggested that differentially expressed genes (DEGs) involved in photosynthesis, cell wall modification, primary and secondary metabolism are differentially regulated in the mutant. Protein-protein interaction assays indicated that LIL3 interacts with phytoene synthase (PSY) and in addition the gene expression of PSY genes are regulated by LIL3. Subcellular localization of LIL3 and PSY suggested that both are thylakoid membrane anchored proteins in the chloroplast. We suggest that LIL3 directly interacts with PSY to regulate carotenoid biosynthesis.

CONCLUSION

This study reveals a new role of LIL3 in regulating pigment biosynthesis through interaction with the rate limiting enzyme PSY in carotenoid biosynthesis in rice presenting it as a putative target for genetic manipulation of pigment biosynthesis pathways in crop plants.

MODEL-BASED FEATURE SELECTION AND CLUSTERING OF RNA-SEQ DATA FOR UNSUPERVISED SUBTYPE DISCOVERY

Clustering is a form of unsupervised learning that aims to uncover latent groups within data based on similarity across a set of features. A common application of this in biomedical research is in delineating novel cancer subtypes from patient gene expression data, given a set of informative genes. However, it is typically unknown a priori what genes may be informative in discriminating between clusters, and what the optimal number of clusters are. Few methods exist for performing unsupervised clustering of RNA-seq samples, and none currently adjust for between-sample global normalization factors, select cluster-discriminatory genes, or account for potential confounding variables during clustering. To address these issues, we propose the Feature Selection and Clustering of RNA-seq (FSCseq): a model-based clustering algorithm that utilizes a finite mixture of regression (FMR) model and the quadratic penalty method with a Smoothly-Clipped Absolute Deviation (SCAD) penalty. The maximization is done by a penalized Classification EM algorithm, allowing us to include normalization factors and confounders in our modeling framework. Given the fitted model, our framework allows for subtype prediction in new patients via posterior probabilities of cluster membership, even in the presence of batch effects. Based on simulations and real data analysis, we show the advantages of our method relative to competing approaches.

Novel and Transgressive Salinity Tolerance in Recombinant Inbred Lines of Rice Created by Physiological Coupling-Uncoupling and Network Rewiring Effects

The phenomenon of transgressive segregation, where a small minority of recombinants are outliers relative to the range of parental phenotypes, is commonly observed in plant breeding populations. While this phenomenon has been attributed to complementation and epistatic effects, the physiological and developmental synergism involved have not been fully illuminated by the QTL mapping approach alone, especially for stress-adaptive traits involving highly complex interactions. By systems-level profiling of the IR29 × Pokkali recombinant inbred population of rice, we addressed the hypothesis that novel salinity tolerance phenotypes are created by reconfigured physiological networks due to positive or negative coupling-uncoupling of developmental and physiological attributes of each parent. Real-time growth and hyperspectral profiling distinguished the transgressive individuals in terms of stress penalty to growth. Non-parental network signatures that led to either optimal or non-optimal integration of developmental with stress-related mechanisms were evident at the macro-physiological, biochemical, metabolic, and transcriptomic levels. Large positive net gain in super-tolerant progeny was due to ideal complementation of beneficial traits while shedding antagonistic traits. Super-sensitivity was explained by the stacking of multiple antagonistic traits and loss of major beneficial traits. The synergism uncovered by the phenomics approach in this study supports the modern views of the Omnigenic Theory, emphasizing the synergy or lack thereof between core and peripheral components. This study also supports a breeding paradigm rooted on genomic modeling from multi-dimensional genetic, physiological, and phenotypic profiles to create novel adaptive traits for new crop varieties of the 21st century.

FADU: a Quantification Tool for Prokaryotic Transcriptomic Analyses

Most currently available quantification tools for transcriptomics analyses have been designed for human data sets, in which full-length transcript sequences, including the untranslated regions, are well annotated. In most prokaryotic systems, full-length transcript sequences have yet to be characterized, leading to prokaryotic transcriptomics analyses being performed based on only the coding sequences.

Quantification tools for RNA sequencing (RNA-Seq) analyses are often designed and tested using human transcriptomics data sets, in which full-length transcript sequences are well annotated. For prokaryotic transcriptomics experiments, full-length transcript sequences are seldom known, and coding sequences must instead be used for quantification steps in RNA-Seq analyses. However, operons confound accurate quantification of coding sequences since a single transcript does not necessarily equate to a single gene. Here, we introduce FADU (Feature Aggregate Depth Utility), a quantification tool designed specifically for prokaryotic RNA-Seq analyses. FADU assigns partial count values proportional to the length of the fragment overlapping the target feature. To assess the ability of FADU to quantify genes in prokaryotic transcriptomics analyses, we compared its performance to those of eXpress, featureCounts, HTSeq, kallisto, and Salmon across three paired-end read data sets of (i) Ehrlichia chaffeensis, (ii) Escherichia coli, and (iii) the Wolbachia endosymbiont wBm. Across each of the three data sets, we find that FADU can more accurately quantify operonic genes by deriving proportional counts for multigene fragments within operons. FADU is available at https://github.com/IGS/FADU.

IMPORTANCE Most currently available quantification tools for transcriptomics analyses have been designed for human data sets, in which full-length transcript sequences, including the untranslated regions, are well annotated. In most prokaryotic systems, full-length transcript sequences have yet to be characterized, leading to prokaryotic transcriptomics analyses being performed based on only the coding sequences. In contrast to eukaryotes, prokaryotes contain polycistronic transcripts, and when genes are quantified based on coding sequences instead of transcript sequences, this leads to an increased abundance of improperly assigned ambiguous multigene fragments, specifically those mapping to multiple genes in operons. Here, we describe FADU, a quantification tool for prokaryotic RNA-Seq analyses designed to assign proportional counts with the purpose of better quantifying operonic genes while minimizing the pitfalls associated with improperly assigning fragment counts from ambiguous transcripts.

Integrative Generalized Convex Clustering Optimization and Feature Selection for Mixed Multi-View Data

In mixed multi-view data, multiple sets of diverse features are measured on the same set of samples. By integrating all available data sources, we seek to discover common group structure among the samples that may be hidden in individualistic cluster analyses of a single data view. While several techniques for such integrative clustering have been explored, we propose and develop a convex formalization that enjoys strong empirical performance and inherits the mathematical properties of increasingly popular convex clustering methods. Specifically, our Integrative Generalized Convex Clustering Optimization (iGecco) method employs different convex distances, losses, or divergences for each of the different data views with a joint convex fusion penalty that leads to common groups. Additionally, integrating mixed multi-view data is often challenging when each data source is high-dimensional. To perform feature selection in such scenarios, we develop an adaptive shifted group-lasso penalty that selects features by shrinking them towards their loss-specific centers. Our so-called iGecco+ approach selects features from each data view that are best for determining the groups, often leading to improved integrative clustering. To solve our problem, we develop a new type of generalized multi-block ADMM algorithm using sub-problem approximations that more efficiently fits our model for big data sets. Through a series of numerical experiments and real data examples on text mining and genomics, we show that iGecco+ achieves superior empirical performance for high-dimensional mixed multi-view data.

Multi-assignment clustering: Machine learning from a biological perspective

A common approach for analyzing large-scale molecular data is to cluster objects sharing similar characteristics. This assumes that genes with highly similar expression profiles are likely participating in a common molecular process. Biological systems are extremely complex and challenging to understand, with proteins having multiple functions that sometimes need to be activated or expressed in a time-dependent manner. Thus, the strategies applied for clustering of these molecules into groups are of key importance for translation of data to biologically interpretable findings. Here we implemented a multi-assignment clustering (MAsC) approach that allows molecules to be assigned to multiple clusters, rather than single ones as in commonly used clustering techniques. When applied to high-throughput transcriptomics data, MAsC increased power of the downstream pathway analysis and allowed identification of pathways with high biological relevance to the experimental setting and the biological systems studied. Multi-assignment clustering also reduced noise in the clustering partition by excluding genes with a low correlation to all of the resulting clusters. Together, these findings suggest that our methodology facilitates translation of large-scale molecular data into biological knowledge. The method is made available as an R package on GitLab (https://gitlab.com/wolftower/masc).

In Vitro Recapitulation of Murine Thymopoiesis from Single Hematopoietic Stem Cells

We report a serum-free, 3D murine artificial thymic organoid (M-ATO) system that mimics normal murine thymopoiesis with the production of all T cell stages, from early thymic progenitors to functional single-positive (CD8SP and CD4SP) TCRαβ and TCRγδ cells. RNA sequencing aligns M-ATO-derived populations with phenotypically identical primary thymocytes. M-ATOs initiated with Rag1-/- marrow produce the same differentiation block as seen in the endogenous thymus, and Notch signaling patterns in M-ATOs mirror primary thymopoiesis. M-ATOs initiated with defined hematopoietic stem cells (HSCs) and lymphoid progenitors from marrow and thymus generate each of the downstream differentiation stages, allowing the kinetics of T cell differentiation to be tracked. Remarkably, single HSCs deposited into each M-ATO generate the complete trajectory of T cell differentiation, producing diverse TCR repertoires across clones that largely match endogenous thymus. M-ATOs represent a highly reproducible and efficient experimental platform for the interrogation of clonal thymopoiesis from HSCs.

Coordinated analysis of exon and intron data reveals novel differential gene expression changes

RNA-Seq expression analysis currently relies primarily upon exon expression data. The recognized role of introns during translation, and the presence of substantial RNA-Seq counts attributable to introns, provide the rationale for the simultaneous consideration of both exon and intron data. We describe here a method for the coordinated analysis of exon and intron data by investigating their relationship within individual genes and across samples, while taking into account changes in both variability and expression level. This coordinated analysis of exon and intron data offers strong evidence for significant differences that distinguish the profiles of the exon-only expression data from the combined exon and intron data. One advantage of our proposed method, called matched change characterization for exons and introns (MEI), is its straightforward applicability to existing archived data using small modifications to standard RNA-Seq pipelines. Using MEI, we demonstrate that when data are examined for changes in variability across control and case conditions, novel differential changes can be detected. Notably, when MEI criteria were employed in the analysis of an archived data set involving polyarthritic subjects, the number of differentially expressed genes was expanded by sevenfold. More importantly, the observed changes in exon and intron variability with statistically significant false discovery rates could be traced to specific immune pathway gene networks. The application of MEI analysis provides a strategy for incorporating the significance of exon and intron variability and further developing the role of using both exons and intron sequencing counts in studies of gene regulatory processes.

Insights Into the Albinism Mechanism for Two Distinct Color Morphs of Northern Snakehead, Channa argus Through Histological and Transcriptome Analyses

The great northern snakehead (Channa argus) is one of the most important economic and conservational fish in China. In this study, the melanocytes in the skin of two distinct color morphs C. argus were investigated and compared through employment of the microscopic analysis, hematoxylin and eosin (H&E) and Masson Fontana staining. Our results demonstrated the uneven distribution of melanocytes with extremely low density and most of them were in the state of aging or death. Meanwhile, there was no obvious pigment layer and melanocytes distribution pattern found in the albino-type (AT), while the melanocytes were evenly distributed with abundance in the bicolor-type (BT). The transcriptome analysis through Illumina HiSeq sequencing showed that a total of 34.93 Gb Clean Data was obtained, and Q30 base percentage reached 92.66%. The BT and AT northern snakeheads transcriptome data included a total of 56,039,701 and 60,410,063 clean reads (n = 3), respectively. In gene expression analyses, the sample correlation coefficients (r) were ranged between 0.92 and 1.00; the contribution of PC1 and PC2 were 50.25 and 13.73% by using PCA cluster analysis, the total number of DEGs were 1024 (559 up-regulated and 465 down-regulated), and the number of annotated DEGs was 767 (COG 172, KEGG 262, GO 288, SwissProt 548, Pfam 579 and NR 765). Additionally, 46,363 ± 873 and 44,947 ± 392 single nucleotide polymorphisms (SNPs) were compiled via genetic structure analysis, respectively. Ten key pigment-related genes were screened using qRT-PCR. And all of them revealed extremely higher expression levels in the skin of BT than those of AT. This is the first study to analyze the mechanism of albino characteristics of Channa via histology and transcriptomics, and also provide the oretical and practical support for the protection and development of germplasm resources for C. argus.

The mitochondrial protein PGAM5 suppresses energy consumption in brown adipocytes by repressing expression of uncoupling protein 1

Accumulating evidence suggests that brown adipose tissue (BAT) is a potential therapeutic target for managing obesity and related diseases. PGAM family member 5, mitochondrial serine/threonine protein phosphatase (PGAM5), is a protein phosphatase that resides in the mitochondria and regulates many biological processes, including cell death, mitophagy, and immune responses. Because BAT is a mitochondria-rich tissue, we have hypothesized that PGAM5 has a physiological function in BAT. We previously reported that PGAM5-knockout (KO) mice are resistant to severe metabolic stress. Importantly, lipid accumulation is suppressed in PGAM5-KO BAT, even under unstressed conditions, raising the possibility that PGAM5 deficiency stimulates lipid consumption. However, the mechanism underlying this observation is undetermined. Here, using an array of biochemical approaches, including quantitative RT-PCR, immunoblotting, and oxygen consumption assays, we show that PGAM5 negatively regulates energy expenditure in brown adipocytes. We found that PGAM5-KO brown adipocytes have an enhanced oxygen consumption rate and increased expression of uncoupling protein 1 (UCP1), a protein that increases energy consumption in the mitochondria. Mechanistically, we found that PGAM5 phosphatase activity and intramembrane cleavage are required for suppression of UCP1 activity. Furthermore, utilizing a genome-wide siRNA screen in HeLa cells to search for regulators of PGAM5 cleavage, we identified a set of candidate genes, including phosphatidylserine decarboxylase (PISD), which catalyzes the formation of phosphatidylethanolamine at the mitochondrial membrane. Taken together, these results indicate that PGAM5 suppresses mitochondrial energy expenditure by down-regulating UCP1 expression in brown adipocytes and that its phosphatase activity and intramembrane cleavage are required for UCP1 suppression.

Model-based clustering of multi-tissue gene expression data

MOTIVATION

Recently, it has become feasible to generate large-scale, multi-tissue gene expression data, where expression profiles are obtained from multiple tissues or organs sampled from dozens to hundreds of individuals. When traditional clustering methods are applied to this type of data, important information is lost, because they either require all tissues to be analyzed independently, ignoring dependencies and similarities between tissues, or to merge tissues in a single, monolithic dataset, ignoring individual characteristics of tissues.

RESULTS

We developed a Bayesian model-based multi-tissue clustering algorithm, revamp, which can incorporate prior information on physiological tissue similarity, and which results in a set of clusters, each consisting of a core set of genes conserved across tissues as well as differential sets of genes specific to one or more subsets of tissues. Using data from seven vascular and metabolic tissues from over 100 individuals in the STockholm Atherosclerosis Gene Expression (STAGE) study, we demonstrate that multi-tissue clusters inferred by revamp are more enriched for tissue-dependent protein-protein interactions compared to alternative approaches. We further demonstrate that revamp results in easily interpretable multi-tissue gene expression associations to key coronary artery disease processes and clinical phenotypes in the STAGE individuals.

AVAILABILITY AND IMPLEMENTATION

Revamp is implemented in the Lemon-Tree software, available at https://github.com/eb00/lemon-tree.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

A model-based approach for clustering of multivariate semicontinuous data with application to dietary pattern analysis and intervention

Semicontinuous data, characterized by a sizable number of zeros and observations from a continuous distribution, are frequently encountered in health research concerning food consumptions, physical activities, medical and pharmacy claims expenditures, and many others. In analyzing such semicontinuous data, it is imperative that the excessive zeros be adequately accounted for to obtain unbiased and efficient inference. Although many methods have been proposed in the literature for the modeling and analysis of semicontinuous data, little attention has been given to clustering of semicontinuous data to identify important patterns that could be indicative of certain health outcomes or intervention effects. We propose a Bernoulli-normal mixture model for clustering of multivariate semicontinuous data and demonstrate its accuracy as compared to the well-known clustering method with the conventional normal mixture model. The proposed method is illustrated with data from a dietary intervention trial to promote healthy eating behavior among children with type 1 diabetes. In the trial, certain diabetes friendly foods (eg, total fruit, whole fruit, dark green and orange vegetables and legumes, whole grain) were only consumed by a proportion of study participants, yielding excessive zero values due to nonconsumption of the foods. Baseline foods consumptions data in the trial are used to explore preintervention dietary patterns among study participants. While the conventional normal mixture model approach fails to do so, the proposed Bernoulli-normal mixture model approach has shown to be able to identify a dietary profile that significantly differentiates the intervention effects from others, as measured by the popular healthy eating index at the end of the trial.

Loss of histone macroH2A1 in hepatocellular carcinoma cells promotes paracrine-mediated chemoresistance and CD4+CD25+FoxP3+ regulatory T cells activation

Rationale: Loss of histone macroH2A1 induces appearance of cancer stem cells (CSCs)-like cells in hepatocellular carcinoma (HCC). How CSCs interact with the tumor microenvironment and the adaptive immune system is unclear. Methods: We screened aggressive human HCC for macroH2A1 and CD44 CSC marker expression. We also knocked down (KD) macroH2A1 in HCC cells, and performed integrated transcriptomic and secretomic analyses. Results: Human HCC showed low macroH2A1 and high CD44 expression compared to control tissues. MacroH2A1 KD CSC-like cells transferred paracrinally their chemoresistant properties to parental HCC cells. MacroH2A1 KD conditioned media transcriptionally reprogrammed parental HCC cells activated regulatory CD4⁺/CD25⁺/FoxP3⁺ T cells (Tregs). Conclusions: Loss of macroH2A1 in HCC cells drives cancer stem-cell propagation and evasion from immune surveillance.

Identification of Shoot Differentiation-Related Genes in Populus euphratica Oliv

De novo shoot regeneration is one of the important manifestations of cell totipotency in organogenesis, which reflects a survival strategy organism evolved when facing natural selection. Compared with tissue regeneration, and somatic embryogenesis, de novo shoot regeneration denotes a shoot regeneration process directly from detatched or injured tissues of plant. Studies on plant shoot regeneration had identified key genes mediating shoot regeneration. However, knowledge was derived from Arabidopsis; the regeneration capacity is hugely distinct among species. To achieve a comprehensive understanding of the shoot regeneration mechanism from tree species, we select four genetic lines of Populus euphratica from a natural population to be sequenced at transcriptome level. On the basis of the large difference of differentiation capacity, between the highly differentiated (HD) and low differentiated (LD) groups, the analysis of differential expression identified 4920 differentially expressed genes (DEGs), which were revealed in five groups of expression patterns by clustering analysis. Enrichment showed crucial pathways involved in regulation of regeneration difference, including “plant hormone signal transduction”, “cell differentiation”, "cellular response to auxin stimulus", and “auxin-activated signaling pathway”. The expression of nine genes reported to be associated with shoot regeneration was validated using quantitative real-time PCR (qRT-PCR). For the specificity of regeneration mechanism with P. euphratica, large amount of DEGs involved in "plant-pathogen interaction", ubiquitin-26S proteosome mediated proteolysis pathway, stress-responsive DEGs, and senescence-associated DEGs were summarized to possibly account for the differentiation difference with distinct genotypes of P. euphratica. The result in this study helps screening of key regulators in mediating the shoot differentiation. The transcriptomic characteristic in P. euphratica further enhances our understanding of key processes affecting the regeneration capacity of de novo shoots among distinct species.

A multivariate Poisson-log normal mixture model for clustering transcriptome sequencing data

Background

High-dimensional data of discrete and skewed nature is commonly encountered in high-throughput sequencing studies. Analyzing the network itself or the interplay between genes in this type of data continues to present many challenges. As data visualization techniques become cumbersome for higher dimensions and unconvincing when there is no clear separation between homogeneous subgroups within the data, cluster analysis provides an intuitive alternative. The aim of applying mixture model-based clustering in this context is to discover groups of co-expressed genes, which can shed light on biological functions and pathways of gene products.

Results

A mixture of multivariate Poisson-log normal (MPLN) model is developed for clustering of high-throughput transcriptome sequencing data. Parameter estimation is carried out using a Markov chain Monte Carlo expectation-maximization (MCMC-EM) algorithm, and information criteria are used for model selection.

Conclusions

The mixture of MPLN model is able to fit a wide range of correlation and overdispersion situations, and is suited for modeling multivariate count data from RNA sequencing studies. All scripts used for implementing the method can be found at https://github.com/anjalisilva/MPLNClust.

Electronic supplementary material

The online version of this article (10.1186/s12859-019-2916-0) contains supplementary material, which is available to authorized users.

Loss of the transcription factor RBPJ induces disease-promoting properties in brain pericytes

Sufficient vascular supply is indispensable for brain development and function, whereas dysfunctional blood vessels are associated with human diseases such as vascular malformations, stroke or neurodegeneration. Pericytes are capillary-associated mesenchymal cells that limit vascular permeability and protect the brain by preserving blood-brain barrier integrity. Loss of pericytes has been linked to neurodegenerative changes in genetically modified mice. Here, we report that postnatal inactivation of the Rbpj gene, encoding the transcription factor RBPJ, leads to alteration of cell identity markers in brain pericytes, increases local TGFβ signalling, and triggers profound changes in endothelial behaviour. These changes, which are not mimicked by pericyte ablation, imperil vascular stability and induce the acquisition of pathological landmarks associated with cerebral cavernous malformations. In adult mice, loss of Rbpj results in bigger stroke lesions upon ischemic insult. We propose that brain pericytes can acquire deleterious properties that actively enhance vascular lesion formation and promote pathogenic processes.

Pericytes are perivascular cells essential for blood-brain barrier maintenance. Here Diéguez-Hurtado et al. show that depletion of the transcription factor RBPJ in pericytes affects their molecular identity and disturbs endothelial cell behaviour, inducing the formation of vascular lesions in the brain.

Inter- and Intra-Specific Transcriptional and Phenotypic Responses of Pseudo-nitzschia under Different Nutrient Conditions

Untangling the functional basis of divergence between closely related species is a step toward understanding species dynamics within communities at both the evolutionary and ecological scales. We investigated cellular (i.e., growth, domoic acid production, and nutrient consumption) and molecular (transcriptomic analyses) responses to varying nutrient concentrations across several strains belonging to three species of the toxic diatom genus Pseudo-nitzschia. Three main results were obtained. First, strains from the same species displayed similar transcriptomic, but not necessarily cellular, responses to the experimental conditions. It showed the importance of considering intraspecific diversity to investigate functional divergence between species. Second, a major exception to the first finding was a strain recently isolated from the natural environment and displaying contrasting gene expression patterns related to cell motility and domoic acid production. This result illustrated the profound modifications that may occur when transferring a cell from the natural to the in vitro environment and asks for future studies to better understand the influence of culture duration and life cycle on expression patterns. Third, transcriptomic responses were more similar between the two species displaying similar ecology in situ, irrespective of the genetic distance. This was especially true for molecular responses related to TCA cycle, photosynthesis, and nitrogen metabolism. However, transcripts related to phosphate uptake were variable between species. It highlighted the importance of considering both overall genetic distance and ecological divergence to explain functional divergence between species.

Whole blood transcriptomic profiles can differentiate vulnerability to chronic low back pain

The mechanisms underlying the transition from acute to chronic pain remain unclear. Here, we sought to characterize the transcriptome associated with chronic low back pain as well as the transcriptome of the transition from acute to chronic low back pain. For the analysis, we compared the whole blood transcriptome of: (a) patients at the onset of low back pain who no longer had pain within 6 weeks after onset (acute) with patients who developed chronic low back pain at 6 months (chronic T5); and, (b) patients at the onset of low back pain (chronic T1) who developed chronic pain at 6 months with healthy pain-free (normal) controls. The majority of differentially expressed genes were protein coding. We illustrate a unique chronic low back pain transcriptome characterized by significant enrichment for known pain genes, extracellular matrix genes, and genes from the extended major histocompatibility complex (MHC) genomic locus. The transcriptome of the transition from acute to chronic low back pain was characterized by significant upregulation of antigen presentation pathway (MHC class I and II) genes and downregulation of mitochondrial genes associated with oxidative phosphorylation, suggesting a unique genomic signature of vulnerability to low back pain chronicity.

Transformation and model choice for RNA-seq co-expression analysis

Although a large number of clustering algorithms have been proposed to identify groups of co-expressed genes from microarray data, the question of if and how such methods may be applied to RNA sequencing (RNA-seq) data remains unaddressed. In this work, we investigate the use of data transformations in conjunction with Gaussian mixture models for RNA-seq co-expression analyses, as well as a penalized model selection criterion to select both an appropriate transformation and number of clusters present in the data. This approach has the advantage of accounting for per-cluster correlation structures among samples, which can be strong in RNA-seq data. In addition, it provides a rigorous statistical framework for parameter estimation, an objective assessment of data transformations and number of clusters and the possibility of performing diagnostic checks on the quality and homogeneity of the identified clusters. We analyze four varied RNA-seq data sets to illustrate the use of transformations and model selection in conjunction with Gaussian mixture models. Finally, we propose a Bioconductor package coseq (co-expression of RNA-seq data) to facilitate implementation and visualization of the recommended RNA-seq co-expression analyses.

Subject level clustering using a negative binomial model for small transcriptomic studies

Background

Unsupervised clustering represents one of the most widely applied methods in analysis of high-throughput ‘omics data. A variety of unsupervised model-based or parametric clustering methods and non-parametric clustering methods have been proposed for RNA-seq count data, most of which perform well for large samples, e.g. N ≥ 500. A common issue when analyzing limited samples of RNA-seq count data is that the data follows an over-dispersed distribution, and thus a Negative Binomial likelihood model is often used. Thus, we have developed a Negative Binomial model-based (NBMB) clustering approach for application to RNA-seq studies.

Results

We have developed a Negative Binomial Model-Based (NBMB) method to cluster samples using a stochastic version of the expectation-maximization algorithm. A simulation study involving various scenarios was completed to compare the performance of NBMB to Gaussian model-based or Gaussian mixture modeling (GMM). NBMB was also applied for the clustering of two RNA-seq studies; type 2 diabetes study (N = 96) and TCGA study of ovarian cancer (N = 295). Simulation results showed that NBMB outperforms GMM applied with different transformations in majority of scenarios with limited sample size. Additionally, we found that NBMB outperformed GMM for small clusters distance regardless of sample size. Increasing total number of genes with fixed proportion of differentially expressed genes does not change the outperformance of NBMB, but improves the overall performance of GMM. Analysis of type 2 diabetes and ovarian cancer tumor data with NBMB found good agreement with the reported disease subtypes and the gene expression patterns. This method is available in an R package on CRAN named NB.MClust.

Conclusion

Use of Negative Binomial model based clustering is advisable when clustering over dispersed RNA-seq count data.

Electronic supplementary material

The online version of this article (10.1186/s12859-018-2556-9) contains supplementary material, which is available to authorized users.

Potential of Oryza officinalis to augment the cold tolerance genetic mechanisms of Oryza sativa by network complementation

Oryza officinalis is an accessible alien donor for genetic improvement of rice. Comparison across a representative panel of Oryza species showed that the wild O. officinalis and cultivated O. sativa ssp. japonica have similar cold tolerance potentials. The possibility that either distinct or similar genetic mechanisms are involved in the low temperature responses of each species was addressed by comparing their transcriptional networks. General similarities were supported by shared transcriptomic signatures indicative of equivalent metabolic, hormonal, and defense status. However, O. officinalis has maintained an elaborate cold-responsive brassinosteroid-regulated BES1-network that appeared to have been fragmented in O. sativa. BES1-network is potentially important for integrating growth-related responses with physiological adjustments and defenses through the protection of photosynthetic machinery and maintenance of stomatal aperture, oxidative defenses, and osmotic adjustment. Equivalent physiological processes are functional in O. sativa but their genetic mechanisms are under the direct control of ABA-dependent, DREB-dependent and/or oxidative-mediated networks uncoupled to BES1. While O. officinalis and O. sativa represent long periods of speciation and domestication, their comparable cold tolerance potentials involve equivalent physiological processes but distinct genetic networks. BES1-network represents a novel attribute of O. officinalis with potential applications in diversifying or complementing other mechanisms in the cultivated germplasm.

Assembling the genome of the African wild rice Oryza longistaminata by exploiting synteny in closely related Oryza species

The African wild rice species Oryza longistaminata has several beneficial traits compared to cultivated rice species, such as resistance to biotic stresses, clonal propagation via rhizomes, and increased biomass production. To facilitate breeding efforts and functional genomics studies, we de-novo assembled a high-quality, haploid-phased genome. Here, we present our assembly, with a total length of 351 Mb, of which 92.2% was anchored onto 12 chromosomes. We detected 34,389 genes and 38.1% of the genome consisted of repetitive content. We validated our assembly by a comparative linkage analysis and by examining well-characterized gene families. This genome assembly will be a useful resource to exploit beneficial alleles found in O. longistaminata. Our results also show that it is possible to generate a high-quality, functionally complete rice genome assembly from moderate SMRT read coverage by exploiting synteny in a closely related Oryza species.

Stefan Reuscher et al. assembled the genome of an African wild rice species to facilitate breeding efforts and functional genomic studies. They used SMRT sequencing, chromosomal synteny between rice species, and a linkage map to assemble the 351 Mb genome into 12 chromosomes.

Flatworm-specific transcriptional regulators promote the specification of tegumental progenitors in Schistosoma mansoni

Schistosomes infect more than 200 million people. These parasitic flatworms rely on a syncytial outer coat called the tegument to survive within the vasculature of their host. Although the tegument is pivotal for their survival, little is known about maintenance of this tissue during the decades schistosomes survive in the bloodstream. Here, we demonstrate that the tegument relies on stem cells (neoblasts) to specify fusogenic progenitors that replace tegumental cells lost to turnover. Molecular characterization of neoblasts and tegumental progenitors led to the discovery of two flatworm-specific zinc finger proteins that are essential for tegumental cell specification. These proteins are homologous to a protein essential for neoblast-driven epidermal maintenance in free-living flatworms. Therefore, we speculate that related parasites (i.e., tapeworms and flukes) employ similar strategies to control tegumental maintenance. Since parasitic flatworms infect every vertebrate species, understanding neoblast-driven tegumental maintenance could identify broad-spectrum therapeutics to fight diseases caused by these parasites.

Comprehensive transcriptional landscape of porcine cardiac and skeletal muscles reveals differences of aging

Aging significantly affects the cardiac muscle (CM) and skeletal muscles (SM). Since the aging process of CM and SM may be different, high throughput RNA sequencing was performed using CM and SM in different age conditions to evaluate the expression profiles of messenger RNA (mRNA), long non-coding RNA (lncRNA), micro RNA (miRNA), and circular (circRNA). Several mRNAs, lncRNAs, and miRNAs were highly expressed and consistently appeared in both ages in one of the two muscle tissues. Gene ontology (GO) annotation described that these genes were required for maintaining normal biological functions of CM and SM tissues. Furthermore, 26 mRNAs, 4 lncRNAs, 22 miRNAs, and 26 circRNAs were differentially expressed during cardiac muscle aging. Moreover, 81 mRNAs, 5 lncRNAs, 79 miRNAs, and 62 circRNAs were differentially expressed during aging of skeletal muscle. When comparing the expression profiles of CM and SM during aging, the senescence process in CM and SM was found to be fundamentally different. In addition, we assessed multi-group cooperative control relationships and constructed circRNA-miRNA-mRNA co-expression networks in muscular aging. In conclusion, our findings will contribute to the understanding of muscular aging and provide a foundation for future studies on the molecular mechanisms underlying muscular aging.