PubMed Health⌕ Search

SEARCH · PubMed Health

Results for “comparative transcriptomics”

Explore indexed PubMed citations for clinical trials, systematic reviews and public health research. Read source abstracts and follow each citation to its original PubMed record.

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 793 records · Page 44Linked to original sources

Distinct transcriptional and epigenomic programs define Hofbauer cells in term placenta.

Hofbauer cells (HBCs) are fetal macrophages located in the placenta that contribute to antimicrobial defense, angiogenesis, tissue remodeling, and metabolic processes within the chorionic villi. Although their roles in placental biology are increasingly recognized, the mechanisms that regulate HBC identity and function are not yet fully defined. This study aimed to define the core transcriptomic and epigenomic features of HBCs in term placentas and to examine their capacity for transcriptional responsiveness and phenotypic variation. Using chromatin accessibility profiling and bulk RNA-seq, we found that HBCs exhibit a unique gene expression and chromatin accessibility profile compared with other fetal and adult macrophages. We identified a coordinated transcriptional network involving nuclear receptors (NRs) NR4A1-3, the glucocorticoid receptor, and RFX family members (RFX1, RFX2, RFX5) that appears to shape HBC identity, particularly through pathways linked to lipid metabolism and angiogenesis. Although exploratory in nature, in vitro stimulation studies showed that HBCs exhibited increased transcriptional activity in response to combined IL-4 and rosiglitazone treatment, including induction of the lipid transporter CD36. Mass cytometry analysis revealed surface markers indicative of both immature and mature macrophage states. These results together indicate that HBCs are a distinct and diverse population of macrophages with a specialized, adaptable regulatory program in the human placenta.

Female↗

Data analysis methods for detection of differential protein expression in two-dimensional gel electrophoresis.

The recent development of microarray technology has led statisticians and bioinformaticians to develop new statistical methodologies for comparing different biological samples. The objective is to identify a small number of differentially expressed genes from among thousands. In quantitative proteomics, analysis of protein expression using two-dimensional gel electrophoresis shows some similarities with transcriptomic studies. Thus, the goal of this study was to evaluate different data analysis methodologies widely used in array analysis using different proteomic data sets of hundreds of proteins. Even with few replications, the significance analysis of microarrays method appeared to be more powerful than the Student's t test in truly declaring differentially expressed proteins. This procedure will avoid wasting time due to false positives and losing information with false negatives.

Animals↗

SAGE identification of differentiation responsive genes in P19 embryonic cells induced to form cardiomyocytes in vitro.

Transcriptome profiling facilitates the identification of developmentally regulated genes. To quantify the functionally active genome of P19 embryonic carcinoma (EC) cells induced to form cardiomyocytes, we employed serial analysis of gene expression (SAGE) to sequence and compare a total of 171,735 SAGE tags from three libraries (undifferentiated P19 EC cells, differentiation days 3 + 0.5 and 3 + 3.0). After in vitro differentiation, only 3.1% of the gene products demonstrated significant (P < 0.05) changes in expression. The most highly significant changes (P < 0.01) involved altered expression of 410 genes encoding predominantly transcription factors, differentiation factors and growth regulators. Quantitative polymerase chain reaction analysis and in situ hybridization revealed five growth regulators (Dlk1, Igfbp5, Hmga2, Podxl and Ptn) and two unknown ESTs with expression profiles similar to known cardiac transcription factors, implicating these growth regulators in cardiac differentiation. These SAGE libraries thus serve as a reference resource for understanding the role of differentiation-dependent genes in embryonic stem cell models induced to form cardiomyocytes in vitro.

Animals↗

Stochastic epigenetic mutation profiles as biomarkers of clinical activity in juvenile idiopathic arthritis: a multi-omic machine learning approach for gene prioritization.

BACKGROUND: Juvenile idiopathic arthritis (JIA) is a rare autoimmune disease arising from a complex interplay between genetic and environmental factors. Epigenetic modifications such as DNA methylation (DNAm) have been described as potential mediators in gene-environment interactions, contributing to immune system dysregulation. Emerging evidence suggests that DNAm profiles also predict therapeutic responses in autoimmune diseases. This study aims to identify epigenetic biomarkers and epigenetic-driven gene expression changes associated with JIA clinical activity. METHODS: We reanalyzed a publicly available dataset of 44 JIA patients, with whole-genome DNAm and gene expression from CD4&#x2009;+&#x2009;T cells measured at two points: at anti-TNF therapy withdrawal (T0) and eight months later (Tend). At Tend, 30 patients maintained inactive disease (ID) while 14 did not (NO ID). We investigated differences between ID and NO ID patients in the epigenetic mutation load and various epigenetic clocks through linear regression models, and prioritized genomic regions with significantly higher number of epimutations in NO ID patients through machine learning. RESULTS: We found a higher mutation load in NO ID than ID patients, both at T0 and at Tend, with the differences at Tend reaching statistical significance (p&#x2009;=&#x2009;0.02). In contrast, we found no evidence of association between epigenetic clocks and JIA clinical activity. Using a multi-omic approach, we identified a List of candidate epigenetically-driven differentially expressed genes, 80 up-regulated and 77 down-regulated, in NO ID patients. Finally, comparing our candidate gene list with the Connectivity Map database, we identified new candidate potential therapeutic targets. Key findings were validated in independent datasets: DNAm profiles from CD4&#x2009;+&#x2009;T cells (56 JIA patients, 57 controls) and transcriptomic data from PBMCs of JIA patients with active or inactive disease, confirming dysregulation of pathways such as TNF-&#x3b1; signaling via NF-kB and TGF-&#x3b2; signaling among others. CONCLUSIONS: We described a significant association of epigenetic mutations with JIA clinical activity, indicating that epigenetic changes might precede clinical symptoms and may serve as biomarkers for early disease monitoring. Further, our results shed light on biomolecular mechanisms of JIA, supporting the development of more effective treatments.

Humans↗

Convergent evolution of gene expression in two high-toothed stickleback populations.

Changes in developmental gene regulatory networks enable evolved changes in morphology. These changes can be in cis regulatory elements that act in an allele-specific manner, or changes to the overall trans regulatory environment that interacts with cis regulatory sequences. Here we address several questions about the evolution of gene expression accompanying a convergently evolved constructive morphological trait, increases in tooth number in two independently derived freshwater populations of threespine stickleback fish (Gasterosteus aculeatus). Are convergently evolved cis and/or trans changes in gene expression associated with convergently evolved morphological evolution? Do cis or trans regulatory changes contribute more to gene expression changes accompanying an evolved morphological gain trait? Transcriptome data from dental tissue of ancestral low-toothed and two independently derived high-toothed stickleback populations revealed significantly shared gene expression changes that have convergently evolved in the two high-toothed populations. Comparing cis and trans regulatory changes using phased gene expression data from F1 hybrids, we found that trans regulatory changes were predominant and more likely to be shared among both high-toothed populations. In contrast, while cis regulatory changes have evolved in both high-toothed populations, overall these changes were distinct and not shared among high-toothed populations. Together these data suggest that a convergently evolved trait can occur through genetically distinct regulatory changes that converge on similar trans regulatory environments.

Alleles↗

Comprehensive characterization of the genes in AP2/ERF family and their involvement in salt-alkali stress response during Nelumbo nucifera seed germination.

Nelumbo nucifera Gaertn. is an economically and ecologically important aquatic plant, but its growth and productivity are severely constrained by soil salinization and alkalization. AP2/ERF transcription factors are key regulators of plant abiotic stress responses; however, their roles in salt-alkali tolerance in N. nucifera remain largely unclear. In this study, we performed a genome-wide identification and characterization of the AP2/ERF gene family in N. nucifera, followed by phylogenetic, structural, and physicochemical analyses. A total of 101 AP2/ERF genes were identified and classified into five subfamilies, showing both evolutionary conservation and species-specific divergence compared with Arabidopsis thaliana. Physiological analyses during seed germination under salt-alkali stress revealed significant changes in malondialdehyde content, proline accumulation, and antioxidant enzyme activities, suggesting activation of oxidative stress defense and osmotic adjustment mechanisms. Transcriptome profiling of seedlings treated with 150 mM salt-alkali solution for 5 and 10 days identified 7,350 differentially expressed genes, including 29 AP2/ERF members responsive to stress. Among them, 13 genes, including AP2-9, ERF23, ERF15, ERF31, ERF34, and DREB21, were consistently upregulated under both treatments, indicating their potential roles in stress adaptation. qRT-PCR validation further confirmed the sustained upregulation of key genes AP2-9, ERF23, ERF34, and DREB21, consistent with transcriptome data. Overall, this study provides the first comprehensive overview of the AP2/ERF gene family in N. nucifera and identifies candidate regulators involved in salt-alkali stress responses, offering valuable insights into the molecular mechanisms of stress adaptation and potential genetic resources for breeding salt-alkali tolerant aquatic plants.

AP2/ERF transcription factors↗

Structural variant discovery and diagnostic impact in rare diseases from short-read and long-read sequencing.

Rare diseases collectively affect 1 in 10 individuals, yet current genetic testing fails to identify a causal variant for most cases. At present, cytogenetic methods and/or sequencing approaches such as exome (ES) or short-read genome sequencing (srGS) represent the state-of-the-art for comprehensive clinical discovery of sequence and structural variants (SVs), including copy number variants, balanced SVs, complex SVs, and tandem repeats (TRs). Recently, long-read genome sequencing (lrGS), coupled with multiomics data, has presented great promise to resolve variation in genomic regions recalcitrant to characterization by srGS such as highly repetitive simple repeat sequences and segmental duplications. However, there are few guidelines to enable clinical interpretation of genetic variation in these highly repetitive genomic regions, and the enthusiasm of the field in adopting lrGS has made it difficult to assess the true added diagnostic yield of this technology due to widely variable and inconsistently applied analytic pipelines and variable degrees of pre-screening by ES or srGS. Here, we investigated the contribution of SVs to rare diseases using srGS as a front-line strategy when paired with highly sensitive SV discovery and evaluate the added diagnostic yield of incorporating lrGS for a subset of cases. Our srGS analysis encompassed 1,462 families (3,450 individuals) recruited through the Broad Institute Center for Mendelian Genetics and the Genomics Research to Elucidate the Genetics of Rare Diseases (GREGoR) programs. Diagnostic SVs were identified in 5.4% of cases (79/1,462), of which 80% were uniquely detectable by srGS compared to standard cytogenetic techniques. For 96 families (including 10 families with a heterozygous variant observed in a known recessive gene of clinical relevance), we performed lrGS with methylation profiling, as well as long-read transcriptomic analyses in a subset of 20 trios. Analyses with lrGS yielded over 25,000 SVs per genome, 63% of which were not captured by srGS, along with an additional ~200 rare SNV/indels per genome not previously captured and 12 differentially methylated regions per genome. Among these, we identified only one diagnostic variant not interpreted by srGS, an apparently mosaic de novo SNV in CASK that was absent in the srGS callset due to allelic imbalance. No new diagnoses were supported by long-read transcriptomics or episignatures. In this well characterized rare disease cohort, the added diagnostic yield was thus 1.04% (1/96 families). Following a systematic literature review of prior lrGS studies, we find that most reported diagnoses were detectable by srGS and that our added diagnostic yield is consistent with those prior studies. These studies emphasize the significant impact of comprehensive SV discovery in rare disease cases and further demonstrate the power for increased discovery of novel genomic variation and episignatures from lrGS. Nonetheless, they also serve to temper expectations of dramatic diagnostic advances in rare disease patients until there is more extensive annotation of the functional and clinical impact of all coding and noncoding variation uniquely accessible to lrGS with extensive reference databases spanning highly repetitive genomic sequencing that could be enabled by this transformative technology.

Journal Article↗

In vitro expansion of human mesenchymal stem cells: choice of serum is a determinant of cell proliferation, differentiation, gene expression, and transcriptome stability.

Human bone marrow mesenchymal stem cells (hMSCs) represent an appealing source of adult stem cells for cell therapy and tissue engineering, as they are easily obtained and expanded while maintaining their multilineage differentiation potential. All current protocols for in vitro culture of hMSCs include fetal bovine serum (FBS) as nutritional supplement. FBS is an undesirable additive to cells that are expanded for therapeutic purposes in humans because the use of FBS carries the risk of transmitting viral and prion diseases and proteins that may initiate xenogeneic immune responses. In the present study, we have therefore investigated if autologous serum (AS) or allogeneic human serum (alloHS) could replace FBS for the expansion of hMSCs in vitro. We discovered that the choice of serum affected hMSCs at several different levels. First, hMSCs in AS proliferated markedly faster than hMSCs in FBS, whereas use of alloHS resulted in hMSC growth arrest and death. Second, hMSCs in FBS differentiated more rapidly toward mesenchymal lineages compared with hMSCs in AS. Interestingly, genome-wide microarray analysis identified several transcripts involved in cell cycle and differentiation that were differentially regulated between hMSCs in FBS and AS. Finally, several transcripts, including some involved in cell cycle inhibition, were upregulated in hMSCs in FBS at a late passage, whereas the hMSC transcriptome in AS was remarkably stable. Thus, hMSCs may be expanded rapidly and with stable gene expression in AS in the absence of growth factors, whereas FBS induces a more differentiated and less stable transcriptional profile.

Animals↗

Initial transcriptome and proteome analyses of low culture temperature-induced expression in CHO cells producing erythropoietin.

Low culture temperature is known to enhance the specific productivity of Chinese hamster ovary (CHO) cells expressing erythropoietin (EPO) (LGE10-9-27). Genomic and proteomic approaches were taken to better understand the intracellular responses of these CHO cells resulting from use of low culture temperature (33 degrees C). For transcriptome analysis, commercially available rat and mouse cDNA microarrays were used. The data obtained from the rat and mouse cDNA chips were only somewhat informative in understanding the gene expression profile of CHO cells because of their different sequence homologies with CHO transcriptomes. Overall, transcriptome analysis revealed that low culture temperature could lead to changes in gene expression in various cellular processes such as metabolism, transport, and signaling pathways. Proteome analysis was carried out using 2-D PAGE. Based on spot intensity, 60 high intensity protein spots, from a total of more than 800, were chosen for MS analysis. Forty of the 60 protein spots, which represent 26 different kinds of proteins, were identified by MALDI-TOF-MS and validated by MS/MS. Compared to the reference temperature (37 degrees C), the expression levels of seven proteins (PDI, vimentin, NDK B, ERp57, RIKEN cDNA, phosphoglycerate kinase, and heat shock cognate 71 kDa protein) were increased over twofold at 33 degrees C and those of two proteins (HSP90-beta and EF2) were decreased over twofold at 33 degrees C. Taken together, the results demonstrate the potential of combined analysis of transcriptome and proteome analyses as a tool for the systematic comprehension of cellular mechanisms in CHO cells.

Animals↗

Anopheles gambiae immune responses to human and rodent Plasmodium parasite species.

Transmission of malaria is dependent on the successful completion of the Plasmodium lifecycle in the Anopheles vector. Major obstacles are encountered in the midgut tissue, where most parasites are killed by the mosquito's immune system. In the present study, DNA microarray analyses have been used to compare Anopheles gambiae responses to invasion of the midgut epithelium by the ookinete stage of the human pathogen Plasmodium falciparum and the rodent experimental model pathogen P. berghei. Invasion by P. berghei had a more profound impact on the mosquito transcriptome, including a variety of functional gene classes, while P. falciparum elicited a broader immune response at the gene transcript level. Ingestion of human malaria-infected blood lacking invasive ookinetes also induced a variety of immune genes, including several anti-Plasmodium factors. Twelve selected genes were assessed for effect on infection with both parasite species and bacteria using RNAi gene silencing assays, and seven of these genes were found to influence mosquito resistance to both parasite species. An MD2-like receptor, AgMDL1, and an immunolectin, FBN39, showed specificity in regulating only resistance to P. falciparum, while the antimicrobial peptide gambicin and a novel putative short secreted peptide, IRSP5, were more specific for defense against the rodent parasite P. berghei. While all the genes that affected Plasmodium development also influenced mosquito resistance to bacterial infection, four of the antimicrobial genes had no effect on Plasmodium development. Our study shows that the impact of P. falciparum and P. berghei infection on A. gambiae biology at the gene transcript level is quite diverse, and the defense against the two Plasmodium species is mediated by antimicrobial factors with both universal and Plasmodium-species specific activities. Furthermore, our data indicate that the mosquito is capable of sensing infected blood constituents in the absence of invading ookinetes, thereby inducing anti-Plasmodium immune responses.

Animals↗

Large-scale analysis of gene expression: methods and application to the kidney.

Characterization of tissue-specific gene expression profiles, or transcriptomes, may serve two purposes: a) establishing relationships between cell transcriptomes and functions (i.e. molecular and physiological phenotypes) under physiological and pathophysiological conditions serves to elucidate gene functions, and b) determination of the totality of genes expressed in a cell seems a prerequisite for understanding cell functions, because the properties of proteins vary with their environment. Sophisticated methods are now available for transcriptome analysis. They are based on serial, partial sequencing of cDNAs (sequencing of expressed sequenced tags (ESTs) and serial analysis of gene expression (SAGE)), or on parallel hybridization of labeled cDNAs to specific probes immobilized on a grid (macro- and microarrays and DNA chips). Some methods were designed specifically to compare gene expression under different conditions (substractive hybridization, glass microarrays). However, all these methods require several microg of mRNA as starting material, making impossible, in most tissues, to analyse gene expression in homogeneous cell populations. To get around this limitation, we developed a scaled-down SAGE method (SAGE adaptation to downsized extracts: SADE) in our laboratory. SAGE is based on the following: a) each cDNA is characterized by a 10-bp informative sequence called tag, b) the information from several transcripts is condensed into a single DNA molecule by concatenation of several tags, c) sequencing of individual clones from the library of concatemers, computer analysis of sequences and interrogation of sequence databases allow quantitative gene expression profiling. Applied to microdissected mouse nephron segments, SADE made it possible to determine segment-specific transcriptomes.

Animals↗

De novo clustering of large long-read transcriptome datasets with isONclust3.

MOTIVATION: Long-read sequencing techniques can sequence transcripts from end to end, greatly improving our ability to study the transcription process. Although there are several well-established tools for long-read transcriptome analysis, most are reference-based. This limits the analysis of organisms without high-quality reference genomes and samples or genes with high variability (e.g. cancer samples or some gene families). In such settings, analysis using a reference-free method is favorable. The computational problem of clustering long reads by region of common origin is well-established for reference-free transcriptome analysis pipelines. Such clustering enables large datasets to be split roughly by gene family and, therefore, an independent analysis of each cluster. There exist tools for this. However, none of those tools can efficiently process the large amount of reads that are now generated by long-read sequencing technologies. RESULTS: We present isONclust3, an improved algorithm over isONclust and isONclust2, to cluster massive long-read transcriptome datasets into gene families. Like isONclust, isONclust3 represents each cluster with a set of minimizers. However, unlike other approaches, isONclust3 dynamically updates the cluster representation during clustering by adding high-confidence minimizers from new reads assigned to the cluster and employs an iterative cluster-merging step. We show that isONclust3 yields results with higher or comparable quality to state-of-the-art algorithms but is 10-100 times faster on large datasets. Also, using a 256&#x2009;Gb computing node, isONclust3 was the only tool that could cluster 37 million PacBio reads, which is a typical throughput of the recent PacBio Revio sequencing machine. AVAILABILITY AND IMPLEMENTATION: https://github.com/aljpetri/isONclust3.

Algorithms↗

Advances in the diagnosis and classification of B-ALL: comparative insights from updated guidelines.

Accurate molecular classification is essential for diagnosis, risk stratification, and treatment selection in B-cell lymphoblastic leukemia (B-ALL). In this study, we performed a comprehensive, real-world reclassification of 1015 consecutively diagnosed B-ALL patients using the fifth edition of the World Health Organization Classification of Haematolymphoid Tumours (WHO-HAEM5) and the International Consensus Classification (ICC). An integrative genomic strategy that combined whole transcriptome sequencing, fusion detection, mutational analysis, and cytogenetics enabled reclassification according to both the WHO-HAEM5 and ICC frameworks, thereby substantially reducing the proportion of unclassifiable B-ALL from 41.9% (2016 WHO revision [WHO-HAEM4R]) to 15.9% (WHO-HAEM5) and 11.9% (ICC). Distinct clinical and prognostic features were identified across newly defined subtypes. Multivariable analysis confirmed that this genomic classification is a robust, independent predictor of survival after adjusting for age, minimal residual disease status, and transplant intervention. Specifically, HLF-rearranged and MEF2D-rearranged B-ALL conferred a persistently poor prognosis across all age groups despite allogeneic hematopoietic stem cell transplantation, highlighting an urgent need for novel therapeutic strategies. Gene expression profiling resolved cryptic subtypes, including ETV6::RUNX1-like, ZNF384-rearranged-like, and BCR::ABL1-like B-ALL, and uncovered diagnostic ambiguity in patients with concurrent lesions. In addition, we report emerging high-risk groups, including IDH1/2- and ZEB2 Q1072-mutated B-ALL, that may warrant recognition as distinct molecular entities. Our findings demonstrate the clinical use of integrative transcriptomic profiling in refining B-ALL taxonomy in guiding risk-adapted therapies and informing future revisions of diagnostic standards. This study supports the incorporation of high-throughput molecular diagnostics into routine leukemia classification and precision treatment planning.

Humans↗

Whole-transcriptome RNA sequencing and ceRNA network analyses provide novel insights into the antibacterial immune response of Hippocampus abdominalis against Vibrio harveyi.

Long non-coding RNAs (lncRNAs) stand as newly-arisen molecular types that exert regulatory effects, able to operate as competitive endogenous RNAs (ceRNAs) to engage microRNAs (miRNAs) in interaction, resulting in the recovery of target mRNA expression and activity. Increasing evidences indicate that the ceRNA network affects various biological processes in mammals, including development, cellular differentiation, metabolism, immune response, and disease pathogenesis. In teleost fish, the lncRNA-miRNA-mRNA regulatory networks have been reported occasionally. However, up to now, the roles of lncRNAs in the big-belly seahorse (Hippocampus abdominalis) remains unclear. In this study, we reported for the first time, via whole-transcriptome RNA sequencing, the lncRNA mediated ceRNA regulatory network in Vibrio harveyi-infected H. abdominalis. A total of 4197 differentially expressed mRNAs (DE-mRNAs), 1317 DE-lncRNAs, and 183 DE-miRNAs were identified. Furthermore, the crosstalk between miRNAs and lncRNAs as well as between miRNAs and mRNAs was inferred based on the negative correlations between miRNAs and their target lncRNAs/mRNAs. A core immune associated lncRNA-miRNA-mRNA putative regulatory network was thus constructed, comprising 211 lncRNA-miRNA and 224 mRNA-miRNA pairs. In conclusion, our findings provide an integrative overview of the ceRNA regulatory networks on the underlying immune responses to V. harveyi infection in the big-belly seahorse, and offer a solid theoretical foundation for the comparative immunological research of teleost fish.

Animals↗

Oncogenic Mutations and Tumor Microenvironment Alterations in Diffuse Large B-Cell Lymphoma With Bulky Disease.

BACKGROUND: Bulky disease represents a clinically aggressive subset of diffuse large B-cell lymphoma (DLBCL) associated with adverse clinical outcomes. The aim of this study was to investigate the influence of oncogenic mutations and tumor microenvironment alterations on bulky disease in DLBCL. METHODS: We analyzed a cohort of 939 patients with newly diagnosed DLBCL. Using DNA (n&#x2009;=&#x2009;934) and RNA (n&#x2009;=&#x2009;524) sequencing, we compared oncogenic mutations and tumor microenvironment (TME) alterations based on tumor diameter, with cutoff values at 5.0&#x2009;cm and 10.0&#x2009;cm. Further stratification by mutations in key genes (CD58, STAT6, EBF1) correlated with tumor diameter revealed distinct transcriptomic and immunologic profiles. Subsequent single-cell RNA sequencing, guided by these mutational signatures, resolved the cellular heterogeneity within the TME. RESULTS: Integrative analysis revealed that tumor diameter correlated with increased incidence of mutations in CD58, STAT6, and EBF1; adverse genetic subtypes such as EZB-like MYC+ and TP53Mut; activation of oncogenic pathways (JAK/STAT, BCR, PI3K, and MYC); and an immunosuppressive tumor microenvironment. Notably, immune checkpoint molecules varied across the bulky stages, with CTLA-4, TIGIT, ICOS, and CD28 expression inversely correlated with tumor diameter, while CD70 and 4-1BBL expression positively correlated. Single-cell RNA sequencing further revealed mutation-specific tumor microenvironment insights. CD58-mutated tumor exhibited a profoundly immune-deserted microenvironment dominated by malignant B cells with minimal immune infiltration, whereas STAT6-mutated tumor was associated with increased fibroblasts and CD4&#x2009;+&#x2009;T cells, particularly regulatory T cells (Treg) and Th1-like cells; EBF1-mutated tumor was characterized by increased proportions of malignant B cells. CONCLUSIONS: Collectively, our findings highlight the biological complexity of bulky disease, identifying candidate molecular targets and providing a biological framework for future therapeutic hypothesis generation in this clinically aggressive subset of DLBCL.

Humans↗

The yeast transcriptome in aerobic and hypoxic conditions: effects of hap1, rox1, rox3 and srb10 deletions.

The transcriptome of Saccharomyces cerevisiae was screened using the high-density membrane hybridization method, under aerobic and hypoxic conditions, in wild-type and mutant backgrounds obtained by the disruption of the genes encoding the regulatory proteins Hap1, Rox1 and the Srb10 and Rox3 subunits of RNA polymerase II holoenzyme. None of the mutations studied was able to fully overcome the wild-type hypoxic response. Deletion of the hap1 gene changed the expression profiles of individual open reading frames (ORFs) under both aerobic and hypoxic conditions. Major changes associated with rox3 deletion were related to the hypoxic activation. Rox3 also caused a repressor effect (oxygen-independent) on a subset of genes related to subtelomeric proteins. With regard to the effect brought about by the deletion of rox1 and srb10, correspondence cluster analysis revealed that the transcriptome profile in aerobic conditions is very similar in the wild-type and both deletion strains. In contrast, however, differences were found during hypoxia between the subgroup formed by wild-type and the Deltarox1 deletant compared with the Deltasrb10 deletant. An analysis of selected ORFs responding to hypoxia, in association with a dependence on the regulatory factors studied, made it possible to identify the clusters that are related to different regulatory circuits.

Aerobiosis↗

Transcript mapping with high-density oligonucleotide tiling arrays.

MOTIVATION: High-density DNA tiling microarrays are a powerful tool for the characterization of complete transcriptomes. The two major analytical challenges are the segmentation of the hybridization signal along genomic coordinates to accurately determine transcript boundaries and the adjustment of the sequence-dependent response of the oligonucleotide probes to achieve quantitative comparability of the signal between different probes. RESULTS: We describe a dynamic programming algorithm for finding a globally optimal fit of a piecewise constant expression profile along genomic coordinates. We developed a probe-specific background correction and scaling method that employs empirical probe response parameters determined from reference hybridizations with no need for paired mismatch probes. This combined analysis approach allows the accurate determination of dynamical changes in transcription architectures from hybridization data and will help to study the biological significance of complex transcriptional phenomena in eukaryotic genomes. AVAILABILITY: R package tilingArray at http://www.bioconductor.org.

Algorithms↗

Changes in alternative splicing of human and mouse genes are accompanied by faster evolution of constitutive exons.

Alternative splicing is known to be an important source of protein sequence variation, but its evolutionary impact has not been explored in detail. Studying alternative splicing requires extensive sampling of the transcriptome, but new data sets based on expressed sequence tags aligned to chromosomes make it possible to study alternative splicing on a genome-wide scale. Although genes showing alternative splicing by exon skipping are conserved as compared to the genome as a whole, we find that genes where structural differences between human and mouse result in genome-specific alternatively spliced exons in one species show almost 60% greater nonsynonymous divergence in constitutive exons than genes where exon skipping is conserved. This effect is also seen for genes showing species-specific patterns of alternative splicing where gene structure is conserved. Our observations are not attributable to an inherent difference in rate of evolution between these two sets of proteins or to differences with respect to predictors of evolutionary rate such as expression level, tissue specificity, or genetic redundancy. Where genome-specific alternatively spliced exons are seen in mammals, the vast majority of skipped exons appear to be recent additions to gene structures. Furthermore, among genes with genome-specific alternatively spliced exons, the degree of nonsynonymous divergence in constitutive sequence is a function of the frequency of incorporation of these alternative exons into transcripts. These results suggest that alterations in alternative splicing pattern can have knock-on effects in terms of accelerated sequence evolution in constant regions of the protein.

Alternative Splicing↗