[Qualitative and quantitative analysis of the TCR chain Vbeta transcriptom. Summary of the lecture by J. P. Soulillou].
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Complementarity Determining Regions↗
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Data mining of the transcriptome of Plasmodium falciparum: the pentose phosphate pathway and ancillary processes.
The general paradigm that emerges from the analysis of the transcriptome of the malaria parasite Plasmodium falciparum is that the expression clusters of genes that code for enzymes engaged in the same cellular function is coordinated. Here the consistency of this perception is examined by analysing specific pathways that metabolically-linked. The pentose phosphate pathway (PPP) is a fundamental element of cell biochemistry since it is the major pathway for the recycling of NADP+ to NADPH and for the production of ribose-5-phosphate that is needed for the synthesis of nucleotides. The function of PPP depends on the synthesis of NADP+ and thiamine pyrophosphate, a co-enzyme of the PPP enzyme transketolase. In this essay, the transcription of gene coding for enzymes involved in the PPP, thiamine and NAD(P)+ syntheses are analysed. The genes coding for two essential enzymes in these pathways, transaldolase and NAD+ kinase could not be found in the genome of P. falciparum. It is found that the transcription of the genes of each pathway is not always coordinated and there is usually a gene whose transcription sets the latest time for the full deployment of the pathway's activity. The activity of PPP seems to involve only the oxidative arm of PPP that is geared for maximal NADP+ reduction and ribose-5-phosphate production during the early stages of parasite development. The synthesis of thiamine diphosphate is predicted to occur much later than the expression of transketolase. Later in the parasite cycle, the non-oxidative arm of PPP that can use fructose-6-phosphate and glyceraldehyde-3-phosphate supplied by glycolysis, becomes fully deployed allowing to maximize the production of ribose-5-phosphate. These discrepancies require direct biochemical investigations to test the activities of the various enzymes in the developing parasite. Notably, several transcripts of PPP enzyme-coding genes display biphasic pattern of transcription unlike most transcripts that peak only once during the parasite cycle. The physiological meaning of this pattern requires further investigation.
Animals↗
An algorithmic framework for genome-wide modeling and analysis of translation networks.
The sequencing of genomes of several organisms and advances in high throughput technologies for transcriptome and proteome analysis has allowed detailed mechanistic studies of transcription and translation using mathematical frameworks that allow integration of both sequence-specific and kinetic properties of these fundamental cellular processes. To understand how perturbations in mRNA levels affect the synthesis of individual proteins within a large protein synthesis network, we consider here a genome-scale codon-wide model of the translation machinery with explicit description of the processes of initiation, elongation, and termination. The mechanistic codon-wide description of the translation process and the large number of mRNAs competing for resources, such as ribosomes, requires the use of novel efficient algorithmic approaches. We have developed such an efficient algorithmic framework for genome-scale models of protein synthesis. The mathematical and computational framework was applied to the analysis of the sensitivity of a translation network to perturbation in the rate constants and in the mRNA levels in the system. Our studies suggest that the highest specific protein synthesis rate (protein synthesis rate per mRNA molecule) is achieved when translation is elongation-limited. We find that the mRNA species with the highest number of actively translating ribosomes exerts maximum control on the synthesis of every protein, and the response of protein synthesis rates to mRNA expression variation is a function of the strength of initiation of translation at different mRNA species. Such quantitative understanding of the sensitivity of protein synthesis to the variation of mRNA expression can provide insights into cellular robustness mechanisms and guide the design of protein production systems.
Algorithms↗
Molecular portrait of high productivity in recombinant NS0 cells.
Many important therapeutic proteins are produced in recombinant mammalian cells. Upon the introduction of the product gene, the isolated clones typically exhibit a wide range of productivity and high producers are subsequently selected for use in production. Using DNA microarray, two-dimensional gel electrophoresis (2DE), and iTRAQ as global surveying tools, we examined the transcriptome and proteome profiles of 11 lines of NS0 cells producing the same antibody molecule. Genes that are significantly differentially expressed between high and low producer groups statistically fall into a number of functional classes. Their distribution among the functional classes differs somewhat between transcriptomic and proteomic results. Overall, a high degree of consistency between transcriptome and proteome analysis are seen, although some genes exhibiting inconsistent trends between transcript and protein levels were observed as expected. In a novel approach, functional gene networks were retrieved using computational pathway analysis tools and their association with productivity was tested by physiological comprehension of the possible pathways involved in high recombinant protein production. Network analysis indicates that protein synthesis pathways were altered in high producers at both transcriptome and proteome levels, whereas the effect on cell growth/death pathways was more prominent only at the transcript level. The results suggest a common mechanism entailing the alteration of protein synthesis and cell growth control networks leading to high productivity. However, alternate routes with different sets of genes may be invoked to give rise to the same mechanistic outcomes. Such systematic approaches, combining transcriptomic and proteomic tools to examine high and low producers of recombinant mammalian cells will greatly enhance our capability to rationally design high producer cells. This work is a first step towards shedding a new light on the global physiological landscape of hyper productivity of recombinant cells.
Animals↗
[Integrating obtained knowledge from transcriptome data by a new framework for data analysis].
Microarray analyses facilitate the investigation of quantitative information coded in the genome by measuring transcriptome, which records the decoded information from the genome. The state of a cell and differences from other states can be studied through genome information, by comparing one set of transcriptome data to other sets. Clearly, those data should be shared and compared with researchers, and the knowledge should be integrated. Unfortunately, at present data comparisons in microarray analyses are quite difficult; the accuracy as well as the reproducibility is low. The difficulties are originated from data analyses methods. Data comparison requires an intelligent framework, such as that discussed by philosopher Sir Karl R Popper. Frameworks for microarray analyses have been developed by many efforts of bioinformatitians. The frameworks currently used are being inspected and critically discussed. By checking the mathematical models that form the practical frameworks, arbitrariness such as the lack of falsifiability has been pointed out. The paradigm in this field of analyses is also criticized by disagreement with the scientific standard, and it is shown as the origin of errors in analyses. The excessive numbers of frameworks produced in an ad hoc manner has also been criticized, since the existence of so many allows researchers to select different frameworks, discussions beyond frameworks are always difficult. A new framework that uses a parametric model is introduced with an explanation of the bases of the framework and the process of testing. Additionally, differences of obtained results by these frameworks are presented using GeneChip data, in stability of log-ratio measurements and reproducibility of analyses. The possibility of artificial decoding of genome information by an extended framework is also discussed.
Gene Expression Profiling↗
Transcriptomic responses to developmental temperature in two field-collected Spodoptera exigua populations from Korea.
The beet armyworm, Spodoptera exigua, is a polyphagous insect whose development and seasonal occurrence are strongly influenced by temperature. However, transcriptomic responses to developmental thermal regimes remain insufficiently characterized in field-collected populations. In this study, we compared two Korean field-collected populations of S. exigua: a Haenam population collected in May and initially maintained at 15 ± 1 °C (HN), and a Jeju population collected in July and initially maintained at 27 ± 1 °C (JJ). F1 larvae from each population were reared under three fluctuating developmental temperature regimes: low (15-21 °C), middle (21-27 °C), and high (27-33 °C), followed by RNA-seq analysis. Differential expression analysis revealed population-associated variation in transcriptomic responses across developmental temperatures. HN exhibited a larger number of differentially expressed genes under the high-temperature regime, suggesting stronger transcriptomic sensitivity to elevated developmental temperature. Functional enrichment analyses identified population-associated differences in pathways related to heat response, oxidative metabolism, cytoskeletal organization, cuticle-associated processes, lipid metabolism, and immune-related functions. In JJ, heat-response and cuticle-related expression patterns were more prominent under warmer developmental conditions, whereas HN showed broader changes in stress- and metabolism-associated pathways under high temperature. Overall, this study provides a comparative transcriptomic analysis of two field-collected S. exigua populations under different developmental temperature regimes and identifies RNA-seq-based molecular response patterns associated with population-specific thermal response profiles.
Animals↗
Optics-free spatial genomics for mapping mammalian brain aging by IRISeq.
Spatial transcriptomics has emerged as a transformative approach for in situ mapping of cellular heterogeneity and interactions, yet existing methods often compromise throughput, cost and tissue coverage. Here we introduce Imaging Reconstruction using Indexed Sequencing (IRISeq): an optics-free, cost-effective platform that leverages spatial interaction mapping by indexed sequencing to profile tissues at adjustable sizes and resolutions (5-50 µm). We applied IRISeq to map gene expression across more than 70 coronal sections from both adult and aged mouse brains, including wild-type and two lymphocyte-deficient models (Rag1 and Prkdc mutants) and generated more than 460,000 spatial transcriptome profiles. Our integrated analysis with 783,264 single-cell transcriptomes revealed region-specific aging signatures that are lymphocyte dependent, notably a downregulation of interferon signaling and inflammation in ventricular regions upon lymphocyte depletion, alongside mutant-specific upregulation of senescence pathways. Furthermore, lymphocyte deficiency was linked to preserved abundance of ependymal cells that line the brain's ventricles and to distinct microglial state dynamics, highlighting a key role for lymphocytes in driving inflammatory processes during brain aging. Overall, IRISeq provides a high-throughput and cost-effective solution for spatially resolved transcriptomic profiling, opening new avenues for elucidating region-specific cellular mechanisms underlying aging and identifying potential therapeutic targets to preserve brain homeostasis.
Animals↗
Single-cell and spatial transcriptomic technologies for lung cancer tumor microenvironment analysis.
Lung cancer remains one of the leading causes of cancer-related mortality worldwide; beyond its rising incidence, its marked molecular heterogeneity and complex tumor microenvironment (TME) hinder treatment response and drive resistance, contributing directly to its high mortality rate. Single-cell RNA sequencing (scRNA-seq) and spatial transcriptomics (ST) provide complementary approaches for dissecting these features. scRNA-seq enables high-resolution analysis of cellular diversity and transcriptional states but requires tissue dissociation and therefore loses spatial context. In contrast, ST preserves tissue architecture and provides insights into how gene-expression programs within the TME are organized, although no currently available spatial platform combines whole-transcriptome coverage with true single-cell resolution over large tissue areas. Together, these technologies have enabled detailed mapping of tumor, immune and stromal populations and of their spatial interactions, revealing functionally distinct cellular niches that contribute to immune evasion, metastasis and response to therapy. In this narrative review we organize the primary literature around a single question, how spatially structured cellular ecosystems, rather than individual cell types, determine therapeutic response and resistance in lung cancer - and we explicitly separate observations that are reproducible across independent cohorts and platforms from those that remain confined to single studies. We further summarize the technical, analytical and logistic barriers that currently prevent spatially resolved signatures from entering routine diagnostic pathology. Understanding dysregulated pathways and spatially constrained intercellular communication within the TME helps identify candidate biomarkers and may support the identification of therapeutic approaches directed at tumor-intrinsic programs as well as at microenvironment-driven resistance mechanisms.
Cell-cell communication↗
A small cationic probe for accurate, punctate discovery of RNA tertiary structure.
RNA molecules fold into intricate three-dimensional tertiary structures that are central to their biological functions. Yet reliably discovering new motifs that form true tertiary interactions remains a major challenge. Here we show that RNA tertiary folding occasionally generates electronegative motifs that react selectively with the small, positively-charged probe trimethyloxonium (TMO). Sites with enhanced reactivity to TMO, compared with the neutral reagent dimethyl sulfate (DMS), are indicative of tertiary structure and define T-sites. These positions share a structural signature in which a reactive nucleobase is adjacent to non-bridging phosphate oxygens, creating localized regions of negative charge. T-sites consistently map to the cores of higher-order structural interactions and functional centers across diverse RNAs, including distinct states in conformational ensembles. In the 10,723-nt dengue virus genome, three strong T-sites were detected, each within a complex structure required for viral replication. Cation-based covalent chemistry enables high-confidence discovery and analysis of functional RNA tertiary motifs across long and complex RNAs, opening new opportunities for transcriptome-wide structural analysis.
RNA electrostatics↗
Alterations in the ovarian transcriptome during primordial follicle assembly and development.
The assembly of the developmentally arrested primordial follicle and subsequent transition to the primary follicle are poorly understood processes critical to ovarian biology. Abnormal primordial follicle development can lead to pathologies such as premature ovarian failure. The current study used a genome-wide expression profile to investigate primordial follicle assembly and development. Rat ovaries with predominantly unassembled, primordial, or primary follicles were obtained. RNA from these ovaries was hybridized to rat microarray gene chips, and the gene expression (i.e., ovarian transcriptome) was compared between the developmental stages. Analysis of the ovarian transcriptome demonstrated 148 genes up-regulated and 50 genes down-regulated between the unassembled and primordial follicle stages. Observations demonstrate 80 genes up-regulated and 44 genes down-regulated between the primordial and primary follicle stages. The analysis demonstrated 2332 genes common among the three developmental stages, 146 genes specific for the unassembled follicles, 94 genes specific for the primordial follicles, and 151 genes specific for the primary follicles. Steroidogenic genes are up-regulated between unassembled and primordial follicles, and then many are again down-regulated between primordial and primary follicles. The hormones inhibin and Mullerian inhibitory substance (MIS) display a similar pattern of expression with the highest levels of mRNA in the primordial follicles. Several novel unknown genes that had dramatic changes in expression during primordial follicle development were also identified. Gene families/clusters identified that were up-regulated from unassembled to primordial follicles include growth factors and signal transduction gene clusters, whereas a down-regulated gene family was the synaptonemal complex genes associated with meiosis. Gene families/clusters that were up-regulated between primordial and primary follicles included immune response genes, metabolic enzymes, and proteases, whereas down-regulated gene families include the globulin genes and some steroidogenic genes. The expression of several growth factors changed during primordial follicle development, including vascular endothelial growth factor and insulin-like growth factor II. Elucidation of how these changes in gene expression coordinate primordial follicle assembly and the primordial to primary follicle transition provides a better understanding of these critical biological processes and allows selection of candidate regulatory factors for further investigation.
Animals↗
Tiling microarray analysis of rice chromosome 10 to identify the transcriptome and relate its expression to chromosomal architecture.
BACKGROUND: Sequencing and annotation of the genome of rice (Oryza sativa) have generated gene models in numbers that top all other fully sequenced species, with many lacking recognizable sequence homology to known genes. Experimental evaluation of these gene models and identification of new models will facilitate rice genome annotation and the application of this knowledge to other more complex cereal genomes. RESULTS: We report here an analysis of the chromosome 10 transcriptome of the two major rice subspecies, japonica and indica, using oligonucleotide tiling microarrays. This analysis detected expression of approximately three-quarters of the gene models without previous experimental evidence in both subspecies. Cloning and sequence analysis of the previously unsupported models suggests that the predicted gene structure of nearly half of those models needs improvement. Coupled with comparative gene model mapping, the tiling microarray analysis identified 549 new models for the japonica chromosome, representing an 18% increase in the annotated protein-coding capacity. Furthermore, an asymmetric distribution of genome elements along the chromosome was found that coincides with the cytological definition of the heterochromatin and euchromatin domains. The heterochromatin domain appears to associate with distinct chromosome level transcriptional activities under normal and stress conditions. CONCLUSION: These results demonstrated the utility of genome tiling microarray in evaluating annotated rice gene models and in identifying novel transcriptional units. The tiling microarray sanalysis further revealed a chromosome-wide transcription pattern that suggests a role for transposable element-enriched heterochromatin in shaping global transcription in response to environmental changes in rice.
Chromosomes, Plant↗
Towards dissecting nutrient metabolism in plants: a systems biology case study on sulphur metabolism.
A genomics analysis on sulphur metabolism has been conducted at the level of transcriptomics and metabolomics. The analysis of these data after applying bioinformatic tools is to reveal novel findings. The findings are discussed and the knowledge obtained from comparable analyses on sulphur metabolism and other plant nutrient genomic studies is reviewed. The analysis of the response of the transcriptome and metabolome to sulphur deprivation in the growth medium provides a tool set for the analysis of comparable genomics studies of other nutrients. The goal of this 'sulphobolomics' (i.e. sulphur genomics and metabolome analysis) approach, and of other investigations, is to describe in a holistic way the biochemical, molecular, and physiological response of a plant to nutrient starvation, here sulphate, or, more generally, to alterations and imbalances in nutrient availability. Eventually, this analysis will provide a case study for a systems biology approach.
Arabidopsis↗
Single-cell expression quantitative trait locus Mendelian randomization reveals immune cell-specific causal regulatory networks and actionable targets in polycystic ovary syndrome.
ObjectiveTo systematically investigate whether the pathogenesis of polycystic ovary syndrome (PCOS) is causally related to dysregulated gene expression in specific immune cell subsets, and to evaluate the potential of these causal genes as actionable drug targets.MethodsThis study employed a two-sample Mendelian randomization (MR) framework using publicly available genome-wide association study (GWAS) summary statistics. The participant data included 797 PCOS cases and 140,558 controls (no direct patient recruitment was involved). Instrumental variables were derived from high-resolution immune cell-specific single-cell expression quantitative trait locus (sc-eQTL) data (OneK1K project) across 14 immune cell types. Primary analyses utilized the inverse-variance weighted (IVW) method. Shared causal variants were validated using Bayesian colocalization. Phenome-wide association analysis (PheWAS), external transcriptomic dataset validation (GSE8157), and DrugBank database screening were conducted for pleiotropy assessment and drug repositioning.ResultsMR analysis revealed genome-wide significant causal associations for GLIPR1 in non-classical monocytes (Mono NC) and XBP1 in CD4+ effector memory T cells (CD4 ET) with PCOS risk. Higher GLIPR1 expression was associated with a decreased PCOS risk (OR = 0.669, P = 4.34×10-6), whereas higher XBP1 expression was associated with an increased risk (OR = 1.406, P = 9.53×10-8). Colocalization analysis confirmed that GLIPR1 shares a causal variant with PCOS (PP.H4 = 96.73%). PheWAS and external validation confirmed the safety profile and significant upregulation (P = 0.03) of GLIPR1. Drug repositioning identified SOT-107, a Phase III protein therapy drug, as a potential interacting agent for GLIPR1.ConclusionsThis sc-eQTL MR study reveals immune cell-specific causal regulatory networks in PCOS. GLIPR1 in non-classical monocytes represents a high-confidence protective target, while XBP1 provides suggestive evidence for immune-mediated pathogenesis. The candidate drug SOT-107 highlights theoretical repositioning opportunities, though rigorous preclinical validation remains required.
Female↗
The mouse genome sequence--the end of the tail, or just the beginning?
The recent flurry of papers on the mouse genome includes the description of the full genome assembly, analysis of the mouse transcriptome, the origin of interstrain variation, initial analysis of conserved non-coding regions and high-throughput expression analysis of a subset of genes. Each illustrates how the availability of the genome sequence will change the way mouse biologists do business in future.
Alternative Splicing↗
Additional paper: computational resources for metabolomics.
Metabolomics, a comprehensive extension of traditional targeted metabolite analysis, has recently attracted much attention as the biological jigsaw puzzle's missing piece that can complement transcriptome and proteome analysis. This tutorial survey introduces practical web resources with special emphasis on the computational aspects involved in processing and navigating metabolome data. The introduced materials are also accessible from the author's web directory (Atomic Reconstruction of Metabolism or ARM).
Algorithms↗
Dual genetic loci and flavonoid metabolism orchestrate fruiting body coloration in Flammulina filiformis: a multi-omic roadmap for fungal pigmentation.
BACKGROUND: The fruiting bodies of macrofungi exhibit diverse coloration, traditionally attributed to melanin and carotenoid biosynthesis. This study is the first to reveal that flavonoids, rather than these classical pigments, are the predominant contributors to yellow pigmentation in the Flammulina filiformis. OBJECTIVE: To uncover the genetic basis and key regulatory genes involved in pigment formation in F. filiformis fruiting bodies, and to establish a model framework for studying color genetics in macrofungi. METHODS: Metabolomic profiling was conducted on yellow and white F. filiformis fruiting bodies to identify key pigment components. A segregating population was constructed, followed by integrated multi-omics analyses-including bulk segregant analysis (BSA), genome-wide association study (GWAS), and transcriptomics-to map regulatory loci and candidate genes. Functional roles were validated via genetic transformation and protein structural modeling. RESULTS: Flavonoid accumulation was identified as the biochemical hallmark of pigmented fruiting bodies. Genetic analysis revealed a dual regulatory mechanism: a qualitative locus governing pigmentation presence and a quantitative trait determining color intensity. Combined BSA and GWAS pinpointed a major locus, Ffcrs, within a recombination-suppressed region. Transcriptomic analysis identified two key regulators, Ffakr (a transcriptional activator) and Ffpal (encoding phenylalanine ammonia-lyase). Functional verification via transformation, structural modeling, and metabolite profiling in transgenic lines confirmed their essential roles in flavonoid biosynthesis and pigmentation. CONCLUSION: This study uncovers a flavonoid-based pigmentation mechanism in F. filiformis and elucidates a complex genetic architecture shaped by both qualitative and quantitative loci, providing a new paradigm for understanding pigment formation in macrofungi. The identified regulatory factors establish a molecular foundation for the precise manipulation of economically important pigmentation traits in edible mushroom.
Flavonoids↗
Spatial Genomic Approaches to Investigate HOX Genes in Mouse Brain Tissues.
Spatial transcriptomic tools are an upcoming and powerful way to investigate targeted gene expression patterns within tissues. These tools offer the unique advantage of visualizing and understanding gene expression while preserving tissue integrity, thereby maintaining the spatial context of genes. Curio is a robust spatial transcriptomic tool that facilitates high throughput comprehensive spatial gene expression analysis across the entir e transcriptome with high efficiency. Here, we present a bioinformatics protocol for performing whole transcriptome gene expression analysis of mouse brain tissue using Curio. Specifically, we demonstrate using computational techniques to visualize expression patterns of various HOX genes in the mouse brain.
Animals↗
Proteogenomic features define subtypes of mantle cell lymphoma.
Mantle cell lymphoma (MCL) is a biologically heterogeneous B-cell malignancy. Although genomics and transcriptomics have delineated parts of the MCL disease spectrum, proteomics remains largely unexplored. Here, we conducted a comprehensive proteogenomic analysis integrating genomics, transcriptomics, and proteomics on peripheral blood samples from 27 patients with MCL and 4 healthy donors to investigate the translational and posttranslational dimensions of MCL. Our study identified 1296 downregulated and 468 upregulated proteins in MCL cells. The splicing pathways were significantly upregulated at both the mRNA and protein levels, suggesting a critical role for aberrant RNA splicing in MCL pathogenesis. Integration of proteomic data with genetic aberrations revealed immunoglobulin heavy chain variable mutational status and CCND1 mutation are associated with distinctive transcriptomic and proteomic profiles, which correspond to significant differences in clinical outcomes. A multiomics molecular stratification model incorporating proteomic data showed superior predictive power for patient survival compared with single-omics models (concordance index, 0.83 vs 0.74). This study provides, to our knowledge, the first comprehensive proteogenomic profile of MCL, offering novel insights into its molecular mechanisms and clinical behavior. The identification of molecular subtypes and prognostic protein signatures underscores the potential of proteomics to guide precision medicine strategies for MCL.
Humans↗