Time-course Gene Expression Research Articles

Roles of Pbp1, Mkt1, and Dhh1 in the regulation of gene expression in the medium containing non-fermentative carbon sources.

Pbp1, a yeast ortholog of human ataxin-2, is important for cell growth in the medium containing non-fermentable carbon sources. We had reported that Pbp1 regulates expression of genes related to glycogenesis via transcriptional regulation and genes related to mitochondrial function through mRNA stability control. To further analyze the role of Pbp1 in gene expression, we first examined the time course of gene expression after transfer from YPD medium containing glucose to YPGlyLac medium containing glycerol and lactate. At 12 h after transfer to YPGlyLac medium, the pbp1∆ mutant showed decreased expression of genes related to mitochondrial function but no decrease in expression of glycogenesis-related genes. We also examined a role of the Pbp1-binding factor, Mkt1. The mkt1∆ mutant, like the pbp1∆ mutant, showed slow growth on YPGlyLac plate and reduced expression of genes related to mitochondrial function. Furthermore, we found that mutation of DHH1 gene encoding a decapping activator exacerbated the growth of the pbp1∆ mutant on YPGlyLac plate. The dhh1∆ mutant showed reduced expression of genes related to mitochondrial function. These results indicate that Pbp1 and Mkt1 regulate the expression of genes related to mitochondrial function and that the decapping activator Dhh1 also regulates the expression of those genes.

P-426 Comprehensive study of the endometrial early/mid secretory phase during natural cycles reveals conserved molecular dynamics involved in the window of implantation

Abstract Study question Is there a common transcriptomic signature in the endometrial early/mid secretory phase of healthy women in natural cycles? Summary answer We observed a conserved transcriptomic regulation of the endometrium around the window of implantation (WOI), consistent across donors, and not influenced by the biopsy procedure. What is known already Extensive research has been conducted on the gene expression changes that characterize the WOI, aiming to identify signals that indicate its onset. However, much of this work has utilized whole-transcriptome techniques and has involved IVF patients, who require luteal phase support. The few studies performed in natural cycles often relied on restricted methods, such as qPCR that examines only a few targets simultaneously. As such, an extensive transcriptomics study in volunteer donors in true natural cycles is lacking. This may provide deeper insights into the physiological changes during the WOI in young women driven by the endogenous effects of progesterone. Study design, size, duration 34 healthy donors were enrolled. Each donor provided one endometrial sample, on a day between LH + 2 and LH + 9 during their natural cycle. A minimum of two and a maximum of five donors were allocated to each day. Ovulation was detected via ecographic inspection and hormonal determination of LH/E2/P4. LH + 0 was established when the LH peak was detected (>2.8 times basal levels), together with a subsequenc decrease in E2 (-30%) and increase in P4 (>1.6ng/ml) Participants/materials, setting, methods When the biopsy was of sufficient size, we separated it in two parts and extracted RNA separately to assess concordance between different areas of the same biopsy. RNAseq libraries were prepared with NEBNextUltraII Directional RNA LibraryPrep and sequenced on a NovaSeq6000. Fastq files were aligned with STAR to GRCh38. Principal component analyses (PCA) were created with plotPCA, gene expression time course analysis was performed with DEseq2, and pseudotime analysis was performed using the slingshot package. Main results and the role of chance Of the 34 donors, we obtained good quality RNAseq data (minimum 20 million reads) from 41 samples, with 27 donors having one sample sequenced, while 2 independent samples from the same biopsy were obtained from 7 volunteers. Analysing same-biopsy concordance via PCA revealed a great degree of similarity between all the pairs, which clustered close to each other. Samples from different donors collected on the same day also showed a good clustering, apart from a single day LH + 4 sample. Pseudotime analysis revealed a distinct trajectory in transcriptomic profiles changing progressively from day LH + 2 to LH + 9. This analysis clearly delineated three phases: LH + 2 to LH + 5, LH + 6 to LH + 7, and LH + 8 to LH + 9. Accordingly, we categorized these into pre-receptive, receptive and post receptive groups, and conducted a time course experiment. We identified a total of 3,311 genes with varying expression across these three timepoints, primarily associated with progesterone signalling, metabolism and cell-to-cell communication. Based on their expression patterns, we isolated a subset of 538 genes which increased their expression during the receptive phase only. These genes could represent novel markers for a more in-depth characterization of the WOI. Limitations, reasons for caution The samples analysed are in bulk, and the genes identified could be expressed either in the stroma or in the epithelial compartment. These findings are not applicable to artificial cycles, as the absence of the corpus luteum and varying levels of exogenous progesterone administered may change the transcriptomic signatures. Wider implications of the findings Our study revealed good concordance of whole transcriptome profiles of donor’s endometria according to cycle progression. This may lead to the identification of novel markers involved in the regulation of the WOI that may not have been found in previous studies performed on artificial cycles. Trial registration number not applicable

Time-course and muscle-specific gene expression of matrix metalloproteinases and inflammatory cytokines in response to acute treadmill exercise in rats.

The extracellular matrix (ECM) of skeletal muscle plays a pivotal role in tissue repair and growth, and its remodeling tightly regulated by matrix metalloproteinases (MMPs), tissue inhibitors of metalloproteinases (TIMPs), and inflammatory cytokines. This study aimed to investigate changes in the mRNA expression of MMPs (Mmp-2 and Mmp-14), TIMPs (Timp-1 and Timp-2), and inflammatory cytokines (Il-1β, Tnf-α, and Tgfβ1) in the soleus (SOL) and extensor digitorum longus (EDL) muscles of rats following acute treadmill exercise. Additionally, muscle morphology was examined using hematoxylin and eosin (H&E) staining. Male rats were subjected to acute treadmill exercise at 25m/min for 60min with a %0 slope. The mRNA expression of ECM components and muscle morphology in the SOL and EDL were assessed in both sedentary and exercise groups at various time points (immediately (0) and 1, 3, 6, 12, and 24h post-exercise). Our results revealed a muscle-specific response, with early upregulation of the mRNA expression of Mmp-2, Mmp-14, Timp-1, Timp-2, Il-1β, and Tnf-α observed in the SOL compared to the EDL. A decrease in Tgfβ1 mRNA expression was evident in the SOL at all post-exercise time points. Conversely, Tgfβ1 mRNA expression increased at 0 and 3h post-exercise in the EDL. Histological analysis also revealed earlier cell infiltration in the SOL than in the EDL following acute exercise. Our results highlight how acute exercise modulates ECM components and muscle structure differently in the SOL and EDL muscles, leading to distinct muscle-specific responses.

Real time changes in the expression of eicosanoid synthesizing enzymes during inflammation

Immune responses during inflammation involve complex, well-coordinated lipid signaling pathways. Eicosanoids are a class of lipid signaling molecules derived from polyunsaturated fatty acids such as arachidonic acid and constitute a major network that controls inflammation and its subsequent resolution. Arachidonic acid is metabolized by enzymes in three different pathways to form a variety of lipid metabolites that can be either pro- or anti-inflammatory. Therefore, an understanding of the time-dependent gene expression, lipid metabolite profiles and cytokine profiles during the initial inflammatory response is necessary, as it will allow for the design of time-dependent therapeutics. Herein, we investigate the multi-level regulation of this process. After stimulating RAW 264.7 cells, a mouse-derived macrophage cell line commonly used to examine inflammatory responses, we examine the gene expression of 44 relevant lipid metabolizing enzymes from the different eicosanoid synthesizing classes. We also measure the formation of lipid metabolites and production of cytokines at selected time points. Results reveal a dynamic relationship between the time-course of inflammation dependent gene expression of the three eicosanoid synthesizing enzymes.

Charting cellular differentiation trajectories with Ricci flow

Complex biological processes, such as cellular differentiation, require intricate rewiring of intra-cellular signalling networks. Previous characterisations revealed a raised network entropy underlies less differentiated and malignant cell states. A connection between entropy and Ricci curvature led to applications of discrete curvatures to biological networks. However, predicting dynamic biological network rewiring remains an open problem. Here we apply Ricci curvature and Ricci flow to biological network rewiring. By investigating the relationship between network entropy and Forman-Ricci curvature, theoretically and empirically on single-cell RNA-sequencing data, we demonstrate that the two measures do not always positively correlate, as previously suggested, and provide complementary rather than interchangeable information. We next employ Ricci flow to derive network rewiring trajectories from stem cells to differentiated cells, accurately predicting true intermediate time points in gene expression time courses. In summary, we present a differential geometry toolkit for understanding dynamic network rewiring during cellular differentiation and cancer.

Network Activity Evaluation Reveals Significant Gene Regulatory Architectures during SARS-CoV-2 Viral Infection from Dynamic scRNA-seq Data.

The key to understand COVID-19 caused by SARS-CoV-2, which has caused massive deaths worldwide, is to reveal the gene activities at molecular level. Single-cell RNA-sequencing (scRNA-seq) technology allows us to capture gene expression at high resolution, thereby delineating cell-specific gene regulatory network (GRN). Network activity refers to the degree of consistency between GRN architectures and gene expression profiles in a specific condition or cellular microenvironment. Currently, numerous experimentally determined molecular interactions, including regulatory relationships closely related to SARS-CoV-2 infection, are documented in knowledge-bases. However, GRN activity is closely related to the cell dynamic environment and the heterogeneity of cell clusters. Therefore, to evaluate the consistency of GRN with gene expression profiles, we propose a single-cell Network Activity Evaluation framework, called scNAE. First, scNAE performs ODE modeling of time-course gene expression data. Then, the loss function with regularization penalty terms is constructed for formulating GRN inference rules from transcriptomic data. Furthermore, we have devised a rapid-convergence alternating direction method of multipliers to solve the regularized and constrained programs. Finally, an empirical P-value is derived based on a permutation statistical testing procedure to quantify the likelihood significance of the network matching with the data. The efficiency and advantage of scNAE have also been demonstrated by extensive numerical experiments, which can clearly depict the dynamic responses underlying GRN architectures triggered by the infection of SARS-CoV-2 in cells. The code and data of scNAE are available at https://github.com/zpliulab/scNAE.

Comprehensive time-course gene expression evaluation of high-risk beef cattle to establish immunological characteristics associated with undifferentiated bovine respiratory disease.

Bovine respiratory disease (BRD) remains the leading infectious disease in beef cattle production systems. Host gene expression upon facility arrival may indicate risk of BRD development and severity. However, a time-course approach would better define how BRD development influences immunological and inflammatory responses after disease occurrences. Here, we evaluated whole blood transcriptomes of high-risk beef cattle at three time points to elucidate BRD-associated host response. Sequenced jugular whole blood mRNA from 36 cattle (2015: n = 9; 2017: n = 27) across three time points (n = 100 samples; days [D]0, D28, and D63) were processed through ARS-UCD1.2 reference-guided assembly (HISAT2/Stringtie2). Samples were categorized into BRD-severity cohorts (Healthy, n = 14; Treated 1, n = 11; Treated 2+, n = 11) via frequency of antimicrobial clinical treatment. Assessment of gene expression patterns over time within each BRD cohort was modeled through an autoregressive hidden Markov model (EBSeq-HMM; posterior probability ≥ 0.5, FDR < 0.01). Mixed-effects negative binomial models (glmmSeq; FDR < 0.05) and edgeR (FDR < 0.10) identified differentially expressed genes between and across cohorts overtime. A total of 2,580, 2,216, and 2,381 genes were dynamically expressed across time in Healthy, Treated 1, and Treated 2+ cattle, respectively. Genes involved in the production of specialized resolving mediators (SPMs) decreased at D28 and then increased by D63 across all three cohorts. Accordingly, SPM production and alternative complement were differentially expressed between Healthy and Treated 2+ at D0, but not statistically different between the three groups by D63. Magnitude, but not directionality, of gene expression related to SPM production, alternative complement, and innate immune response signified Healthy and Treated 2+ cattle. Differences in gene expression at D63 across the three groups were related to oxygen binding and carrier activity, natural killer cell-mediated cytotoxicity, cathelicidin production, and neutrophil degranulation, possibly indicating prolonged airway pathology and inflammation weeks after clinical treatment for BRD. These findings indicate genomic mechanisms indicative of BRD development and severity over time.

Data-driven detection of critical points of phase transitions in complex systems

Detecting the critical points of phase transitions and their driver factors in complex systems from data is a very challenging task. In these regards, the dynamic network biomarker/marker (DNB) method derived from the bifurcation theory is currently very popular, but a unified criterion to pick the most appropriate DNBs is lacking. Here, we propose a giant-component-based DNB (GDNB) method inspired by the percolation theory, that directly selects the largest DNB as the transition core to reflect the progress of the transition. We test the effectiveness of this scheme to detect transitions on three distinct systems, differing in terms of interactions and transitions: Monte Carlo simulations of the 2D Ising model, molecular dynamics simulations of protein folding, and measured gene expression time course in mouse muscle regeneration. These results suggest that the GDNB method inherits all the advantages of the DNB method, while it improves the interpretability at a reduced computational complexity.

PC3T: a signature-driven predictor of chemical compounds for cellular transition

Cellular transitions hold great promise in translational medicine research. However, therapeutic applications are limited by the low efficiency and safety concerns of using transcription factors. Small molecules provide a temporal and highly tunable approach to overcome these issues. Here, we present PC3T, a computational framework to enrich molecules that induce desired cellular transitions, and PC3T was able to consistently enrich small molecules that had been experimentally validated in both bulk and single-cell datasets. We then predicted small molecule reprogramming of fibroblasts into hepatic progenitor-like cells (HPLCs). The converted cells exhibited epithelial cell-like morphology and HPLC-like gene expression pattern. Hepatic functions were also observed, such as glycogen storage and lipid accumulation. Finally, we collected and manually curated a cell state transition resource containing 224 time-course gene expression datasets and 153 cell types. Our framework, together with the data resource, is freely available at http://pc3t.idrug.net.cn/. We believe that PC3T is a powerful tool to promote chemical-induced cell state transitions.

A MACHINE LEARNING MODEL DERIVED FROM ANALYSIS OF TIME-COURSE GENE-EXPRESSION DATASETS REVEALS TEMPORALLY STABLE GENE MARKERS PREDICTIVE OF SEPSIS MORTALITY.

Sepsis is associated with significant mortality and morbidity among critically ill patients admitted to intensive care units and represents a major health challenge globally. Given the significant clinical and biological heterogeneity among patients and the dynamic nature of the host immune response, identifying those at high risk of poor outcomes remains a critical challenge. Here, we performed secondary analysis of publicly available time-series gene-expression datasets from peripheral blood of patients admitted to the intensive care unit to elucidate temporally stable gene-expression markers between sepsis survivors and nonsurvivors. Using a limited set of genes that were determined to be temporally stable, we derived a dynamical model using a Support Vector Machine classifier to accurately predict the mortality of sepsis patients. Our model had robust performance in a test dataset, where patients' transcriptome was sampled at alternate time points, with an area under the curve of 0.89 (95% CI, 0.82-0.96) upon 5-fold cross-validation. We also identified 7 potential biomarkers of sepsis mortality (STAT5A, CX3CR1, LCP1, SNRPG, RPS27L, LSM5, SHCBP1) that require future validation. Pending prospective testing, our model may be used to identify sepsis patients with high risk of mortality accounting for the dynamic nature of the disease and with potential therapeutic implications.

Chromosome-level genome assembly of Quercus variabilis provides insights into the molecular mechanism of cork thickness

Quercus variabilis is a deciduous woody species with high ecological and economic value, and is a major source of cork in East Asia. Cork from thick softwood sheets have higher commercial value than those from thin sheets. It is extremely difficult to genetically improve Q. variabilis to produce high quality softwood due to the lack of genomic information. Here, we present a high-quality chromosomal genome assembly for Q. variabilis with length of 791,89 Mb and 54,606 predicted genes. Comparative analysis of protein sequences of Q. variabilis with 11 other species revealed that specific and expanded gene families were significantly enriched in the "fatty acid biosynthesis" pathway in Q. variabilis, which may contribute to the formation of its unique cork. Based on weighted correlation network analysis of time-course (i.e., five important developmental ages) gene expression data in thick-cork versus thin-cork genotypes of Q. variabilis, we identified one co-expression gene module associated with the thick-cork trait. Within this co-expression gene module, 10 hub genes were associated with suberin biosynthesis. Furthermore, we identified a total of 198 suberin biosynthesis-related new candidate genes that were up-regulated in trees with a thick cork layer relative to those with a thin cork layer. Also, we found that some genes related to cell expansion and cell division were highly expressed in trees with a thick cork layer. Collectively, our results revealed that two metabolic pathways (i.e., suberin biosynthesis, fatty acid biosynthesis), along with other genes involved in cell expansion, cell division, and transcriptional regulation, were associated with the thick-cork trait in Q. variabilis, providing insights into the molecular basis of cork development and knowledge for informing genetic improvement of cork thickness in Q. variabilis and closely related species.

Overexpression of SQUALENE SYNTHASE Reduces Nicotiana benthamiana Resistance against Phytophthora infestans

Plant triterpenoids play a critical role in plant resistance against Phytophthora infestans de Bary, the causal pathogen of potato and tomato late blight. However, different triterpenoids could have contrasting functions on plant resistance against P. infestans. In this study, we targeted the key biosynthetic gene of all plant triterpenoids, SQUALENE SYNTHASE (SQS), to examine the function of this gene in plant-P. infestans interactions. A post-inoculation, time-course gene expression analysis revealed that SQS expression was induced in Nicotiana benthamiana but was transiently suppressed in Solanum lycopersicum. Consistent with the host-specific changes in SQS expression, concentrations of major triterpenoid compounds were only induced in S. lycopersicum. A stable overexpression of SQS in N. benthamiana reduced plant resistance against P. infestans and induced the hyperaccumulation of stigmasterol. A comparative transcriptomics analysis of the transgenic lines showed that diverse plant physiological processes were influenced by SQS overexpression, suggesting that phytosterol content regulation may not be the sole mechanism through which SQS promotes plant susceptibility towards P. infestans. This study provides experimental evidence for the host-specific transcriptional regulation and function of SQS in plant interactions with P. infestans, offering a novel perspective in examining the quantitative disease resistance against late blight.

Recording of cellular physiological histories along optically readable self-assembling protein chains

Observing cellular physiological histories is key to understanding normal and disease-related processes. Here we describe expression recording islands—a fully genetically encoded approach that enables both continual digital recording of biological information within cells and subsequent high-throughput readout in fixed cells. The information is stored in growing intracellular protein chains made of self-assembling subunits, human-designed filament-forming proteins bearing different epitope tags that each correspond to a different cellular state or function (for example, gene expression downstream of neural activity or pharmacological exposure), allowing the physiological history to be read out along the ordered subunits of protein chains with conventional optical microscopy. We use expression recording islands to record gene expression timecourse downstream of specific pharmacological and physiological stimuli in cultured neurons and in living mouse brain, with a time resolution of a fraction of a day, over periods of days to weeks.

Liver vs. spleen: Time course of organ-dependent immune gene expression in an LPS-stimulated toad (Rhinella diptycha).

The inflammatory response comprises highly orchestrated events that are conserved amongst vertebrate groups. Hepatic and splenic cytokines are major mediators of the systemic inflammatory processes. However, the liver is still neglected as an immune organ in amphibians. This study reports organ-dependent gene expression using an anuran model. We tracked mRNA levels of immune proteins [C1s (subcomponent S of the complement protein 1), IFN-γ, IL-1β, IL-6, and IL-10] at four time-points (1h, 3h, 6h, and 18h post-injection) in spleens and livers of intraperitoneal LPS-challenged (2mg/kg) adult male toads (Rhinella diptycha) using independent samples. We found acute C1s up-regulation in the liver 1h post-injection, with no treatment effect in the spleen. The LPS injection did not show any effect in splenic IFN-γ gene expression while eliciting only a marginal effect in the hepatic tissue. IL-1β was up-regulated in both organs, with the liver initially displaying early expression (1h and 3h) and the spleen taking over late expression (18h). Both organs exhibited similar patterns for IL-6, with early up-regulation (1h and 3h) and late peak (18h). Although IL-10 was early detected and up-regulated only in the liver, both organs showed up-regulation in 6h and 18h post-injection. Our results show an exclusive hepatic prominence in complement protein expression during the acute-phase response. Furthermore, hepatic pro-inflammatory cytokine expression was more pronounced in earliest time-points, while the spleen offers a slower and more consistent response overall. Our data provide an organ-integrative outlook into the initial hours of the inflammation in amphibians, confirming the liver's pivotal role as a regulator in the acute-phase of the inflammatory response in amphibians.

Astrocyte heterogeneity within white matter tracts and a unique subpopulation of optic nerve head astrocytes.

Much of what we know about astrocyte form and function is derived from the study of gray matter protoplasmic astrocytes, whereas white matter fibrous astrocytes remain relatively unexplored. Here, we used the ribotag approach to isolate ribosome-associated mRNA and investigated the transcriptome of uninjured fibrous astrocytes from three regions: unmyelinated optic nerve head, myelinated optic nerve proper, and corpus callosum. Astrocytes from each region were transcriptionally distinct and we identified region-specific astrocyte genes and pathways. Energy metabolism, particularly oxidative phosphorylation and mitochondrial protein translation emerged as key differentiators of astrocyte populations. Optic nerve astrocytes expressed higher levels of neuroinflammatory pathways than corpus callosum astrocytes and we further identified CARTPT as a new marker of optic nerve head astrocytes. These previously uncharacterized transcriptional profiles of white matter astrocyte types reveal their functional diversity and a greater heterogeneity than previously appreciated.

DynDeepDRIM: a dynamic deep learning model to infer direct regulatory interactions using time-course single-cell gene expression data.

Time-course single-cell RNA sequencing (scRNA-seq) data have been widely used to explore dynamic changes in gene expression of transcription factors (TFs) and their target genes. This information is useful to reconstruct cell-type-specific gene regulatory networks (GRNs). However, the existing tools are commonly designed to analyze either time-course bulk gene expression data or static scRNA-seq data via pseudo-time cell ordering. A few methods successfully utilize the information from multiple time points while also considering the characteristics of scRNA-seq data. We proposed dynDeepDRIM, a novel deep learning model to reconstruct GRNs using time-course scRNA-seq data. It represents the joint expression of a gene pair as an image and utilizes the image of the target TF-gene pair and the ones of the potential neighbors to reconstruct GRNs from time-course scRNA-seq data. dynDeepDRIM can effectively remove the transitive TF-gene interactions by considering neighborhood context and model the gene expression dynamics using high-dimensional tensors. We compared dynDeepDRIM with six GRN reconstruction methods on both simulation and four real time-course scRNA-seq data. dynDeepDRIM achieved substantially better performance than the other methods in inferring TF-gene interactions and eliminated the false positives effectively. We also applied dynDeepDRIM to annotate gene functions and found it achieved evidently better performance than the other tools due to considering the neighbor genes.

Comparative single-cell transcriptional atlases of Babesia species reveal conserved and species-specific expression profiles.

Babesia is a genus of apicomplexan parasites that infect red blood cells in vertebrate hosts. Pathology occurs during rapid replication cycles in the asexual blood stage of infection. Current knowledge of Babesia replication cycle progression and regulation is limited and relies mostly on comparative studies with related parasites. Due to limitations in synchronizing Babesia parasites, fine-scale time-course transcriptomic resources are not readily available. Single-cell transcriptomics provides a powerful unbiased alternative for profiling asynchronous cell populations. Here, we applied single-cell RNA sequencing to 3 Babesia species (B. divergens, B. bovis, and B. bigemina). We used analytical approaches and algorithms to map the replication cycle and construct pseudo-synchronized time-course gene expression profiles. We identify clusters of co-expressed genes showing “just-in-time” expression profiles, with gradually cascading peaks throughout asexual development. Moreover, clustering analysis of reconstructed gene curves reveals coordinated timing of peak expression in epigenetic markers and transcription factors. Using a regularized Gaussian graphical model, we reconstructed co-expression networks and identified conserved and species-specific nodes. Motif analysis of a co-expression interactome of AP2 transcription factors identified specific motifs previously reported to play a role in DNA replication in Plasmodium species. Finally, we present an interactive web application to visualize and interactively explore the datasets.

APRIL Drives a Coordinated but Diverse Response as a Foundation for Plasma Cell Longevity.

Ab-secreting cells survive in niche microenvironments, but cellular responses driven by particular niche signals are incompletely defined. The TNF superfamily member a proliferation-inducing ligand (APRIL) can support the maturation of transitory plasmablasts into long-lived plasma cells. In this study, we explore the biological programs established by APRIL in human plasmablasts. Under conditions allowing the maturation of ex vivo- or in vitro-generated plasmablasts, we find that APRIL drives activation of ERK, p38, and JNK, accompanied by a classical NF-κB response and activation of the AKT/FOXO1 pathway. Time-course gene expression data resolve coordinated transcriptional responses propagated via immediate early genes and NF-κB targets and converging onto modules of genes enriched for MYC targets and metabolism/cell growth-related pathways. This response is shared between APRIL and an alternate TNF superfamily member CD40L but is not a feature of alternative niche signals delivered by IFN-α or SDF1. However, APRIL and CD40L responses also diverge. CD40L drives expression of genes related to the activated B cell state whereas APRIL does not. Thus, APRIL establishes a broad foundation for plasma cell longevity with features of cellular refueling while being uncoupled from support of the B cell state.

Parasitism by the Tachinid Parasitoid Exorista japonica Leads to Suppression of Basal Metabolism and Activation of Immune Response in the Host Bombyx mori.

Simple SummaryThe dipteran parasitoid Tachinidae are important biocontrol agents and some of them are pests in sericulture. We previously have demonstrated that tachinid parasitoid Exorista japonica parasitism causes pupation defects in Bombyx mori. However, the underlying mechanism is not fully understood. In this study, we performed transcriptome analysis of the fat body of B. mori parasitized by E. japonica. We found that the host basal metabolism was inhibited whereas the immune response was activated. These results indicate that the tachinid parasitoid perturbs the basal metabolism and activates the energetically costly immunity of the host, leading to the development arrest of the host. This study provides insights into how tachinid parasitoids modify host basal metabolism and immune response for the benefit of developing parasitoid larvae.The dipteran tachinid parasitoids are important biocontrol agents, and they must survive the harsh environment and rely on the resources of the host insect to complete their larval stage. We have previously demonstrated that the parasitism by the tachinid parasitoid Exorista japonica, a pest of the silkworm, causes pupation defects in Bombyx mori. However, the underlying mechanism is not fully understood. Here, we performed transcriptome analysis of the fat body of B. mori parasitized by E. japonica. We identified 1361 differentially expressed genes, with 394 genes up-regulated and 967 genes down-regulated. The up-regulated genes were mainly associated with immune response, endocrine system and signal transduction, whereas the genes related to basal metabolism, including energy metabolism, transport and catabolism, lipid metabolism, amino acid metabolism and carbohydrate metabolism were down-regulated, indicating that the host appeared to be in poor nutritional status but active in immune response. Moreover, by time-course gene expression analysis we found that genes related to amino acid synthesis, protein degradation and lipid metabolism in B. mori at later parasitization stages were inhibited. Antimicrobial peptides including Cecropin A, Gloverin and Moricin, and an immulectin, CTL11, were induced. These results indicate that the tachinid parasitoid perturbs the basal metabolism and induces the energetically costly immunity of the host, and thus leading to incomplete larval–pupal ecdysis of the host. This study provided insights into how tachinid parasitoids modify host basal metabolism and immune response for the benefit of developing parasitoid larvae.

Multiway Sparse Distance Weighted Discrimination

Modern data often take the form of a multiway array. However, most classification methods are designed for vectors, that is, one-way arrays. Distance weighted discrimination (DWD) is a popular high-dimensional classification method that has been extended to the multiway context, with dramatic improvements in performance when data have multiway structure. However, the previous implementation of multiway DWD was restricted to classification of matrices, and did not account for sparsity. In this article, we develop a general framework for multiway classification which is applicable to any number of dimensions and any degree of sparsity. We conducted extensive simulation studies, showing that our model is robust to the degree of sparsity and improves classification accuracy when the data have multiway structure. For our motivating application, magnetic resonance spectroscopy (MRS) was used to measure the abundance of several metabolites across multiple neurological regions and across multiple time points in a mouse model of Friedreich’s ataxia, yielding a four-way data array. Our method reveals a robust and interpretable multi-region metabolomic signal that discriminates the groups of interest. We also successfully apply our method to gene expression time course data for multiple sclerosis treatment. An R implementation is available in the package MultiwayClassification at http://github.com/lockEF/MultiwayClassification. Supplementary materials for this article are available online.