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At least 433 records · Page 24Linked to original sources

DNA microarrays in pediatric cancer.

Childhood cancer, like all cancer, is at heart a genetic disease. Consequently, fundamental understanding of the oncogenic process is likely to be beneficially addressed by genetic methodology. Current methods have largely focused on single-gene defects, like chimeric genes, which are present in many sarcomas and leukemias. Real understanding is more likely to derive from a genome-wide analysis of these malignancies. Recent technologic advances have made it possible to simultaneously assess the entire expressed gene profile, or transcriptome, of a given cancer. Foremost among these methods is gene expression profiling using DNA microarrays. Two basic approaches predominate: spotted arrays and photolithography arrays. Regardless of the method, the resulting information can be used to create disease profiles, but only if appropriate bioinformatic solutions are employed. Common analytic approaches include two-way expression comparisons, or scatter analyses; outlier gene analysis, to identify significantly dysregulated genes; dendrogram analyses, as pioneered by Eisen; cluster analyses to identify diagnostic or biologic groups; and various forms of functional analyses to identify relevant genes and biologic pathways. Studies of both adult and pediatric cancer have demonstrated the feasibility of such analyses to identify both diagnostic and prognostic groups of tumors. Acute childhood leukemias have been grouped into myelogenous and lymphoid, and even B- and T-cell subsets. Breast cancer prognostic groups have been identified on the basis of a small subset of expressed genes. In addition, preliminary data on childhood sarcomas appear to identify both diagnostic and prognostic subsets. Specifically, embryonal rhabdomyosarcoma could be distinguished from alveolar rhabdomyosarcoma, and even morphologically mixed embryonal and alveolar rhabdomyosarcoma showed similar gene expression profiles in both histologies. Further, collaborative studies using clustering analyses appear to identify prognostic groups of diverse sarcomas. Larger institutional and cooperative group studies are currently underway to validate these preliminary findings.

Animals↗

Systematic Analysis of Tumor Microenvironment Using IOBR.

The Immuno-Oncology Biological Research (IOBR) package is an R-based analysis tool for exploring the tumor microenvironment (TME) and its influence on anti-tumor immunity. Built for high-throughput data-spanning both transcriptomic and genomic profiles-IOBR integrates six analytical modules, including transcriptomic data preprocessing, TME profiling, TME pattern identification, ligand-receptor interaction analysis, genome-TME interaction assessment, and visualization. In this chapter, we walk through a multi-omics workflow using example datasets, illustrating data preparation, distribution analyses, result interpretation, and graphical output. IOBR is open source and is available at https://github.com/IOBR/IOBR and a detailed GitBook ( https://iobr.github.io/book/ ) offers a complete manual and analysis guide for each function.

Tumor Microenvironment↗

Phosphorylation and stabilization of HURP by Aurora-A: implication of HURP as a transforming target of Aurora-A.

Aurora-A, a mitotic serine/threonine kinase with oncogene characteristics, has recently drawn intense attention because of its association with the development of human cancers and its relationship with mitotic progression. Using the gene expression profiles of Aurora-A as a template to search for and compare transcriptome expression profiles in publicly accessible microarray data sets, we identified HURP (encodes hepatoma upregulated protein) as one of the best Aurora-A-correlated genes. Empirical validation indicates that HURP has several characteristics in common with Aurora-A. These two genes have similar expression patterns in hepatocellular carcinoma, liver regeneration after partial hepatectomy, and cell cycle progression and across a variety of tissues and cell lines. Moreover, Aurora-A phosphorylated HURP in vitro and in vivo. Ectopic expression of either the catalytically inactive form of Aurora-A or the HURP-4P mutant, in which the Aurora-A phosphorylation sites were replaced with Ala, resulted in HURP instability and complex disassembly. In addition, HURP-wild-type stable transfectants were capable of growing in low-serum environments whereas HURP-4P grew poorly under low-serum conditions and failed to proliferate. These studies together support the view that the ability to integrate evidence derived from microarray studies into biochemical analyses may ultimately augment our predictive power when analyzing the potential role of poorly characterized proteins. While this combined approach was simply an initial attempt to answer a range of complex biological questions, our findings do suggest that HURP is a potential oncogenic target of Aurora-A.

Amino Acid Sequence↗

Modulation of multigene expression and proteome profiles by chemopreventive agents.

Analysis of transcriptome and proteome profiles by microarray technologies provides a formidable, new tool in cancer chemoprevention research. An ideal chemopreventive agent should not excessively alter per se the basal make-up of multigene expression and protein synthesis and should at the same time be able to attenuate alterations induced by risk factors. In order to validate this working hypothesis, we previously performed a series of studies in animal models using the thiol N-acetyl-l-cysteine (NAC) and the nonsteroidal antiinflammatory drug sulindac. We report herein the results of new studies evaluating modulation of DNA adduct levels and expression of 4858 genes in lung and liver of Sprague-Dawley rats, either unexposed or exposed to environmental cigarette smoke (ECS). The tested chemopreventive agents included NAC, oltipraz (OPZ), 5,6-benzoflavone (5,6-BF), phenethyl isothiocyanate (PEITC), and indole 3-carbinol (I3C). Combinations of OPZ with NAC and of PEITC with I3C were also assayed. Excepting OPZ, all treatments inhibited by at least 50% the formation of bulky DNA adducts in the lung of ECS-exposed rats. Hierarchical cluster analysis and principal component analysis allowed us to classify the agents according to their influence on basal gene expression and their ability to attenuate ECS-induced transcriptome alterations. PEITC and I3C were the most effective but the least safe agents. 5,6-BF displayed intermediate patterns. OPZ was poorly effective in lung and considerably altered the basal gene expression in liver. NAC had a medium efficacy and was the safest agent, as also supported by the analysis of 518 proteins in rat lung.

Acetylcysteine↗

Single-nucleus profiling reveals a core disease signature and cell type-specific vulnerabilities in early Rett syndrome.

Rett syndrome (RTT) is an X-linked neurological disorder caused by MECP2 mutations, creating distinct cellular environments in females (mosaic) versus males (nonmosaic). Despite female patients representing most cases, how mosaicism contributes molecularly to RTT pathogenesis, particularly in presymptomatic stages, remains poorly understood. To address this question, we profiled hippocampal transcriptomes of young female and male RTT mice using bulk and single-nucleus RNA sequencing. We identified a core disease signature of consistently dysregulated genes only in MeCP2- cells across RTT models. Moreover, we uncovered non-cell autonomous effects exclusively in female MeCP2+ excitatory neurons, suggesting that these circuits are more vulnerable early in the mosaic RTT environment. The single-nuclei data also revealed an underappreciated MeCP2- interneuron subtype that had the most transcriptional dysregulation in both male and female RTT hippocampi. Together, these data highlight the different effects of MeCP2 loss on excitatory and inhibitory circuits between the mosaic and nonmosaic environments in early RTT pathogenesis.

Rett Syndrome↗

A novel method for the construction of genome wide transcriptome maps.

Expression profiling by cDNA-AFLP is commonly used to display the transcriptome of a specific tissue, treatment or developmental stage. In this paper, cDNA-AFLP has been used to study transcripts expressed in segregating populations from Arabidopsis thaliana and potato (Solanum tuberosum). The genetic differences between the offspring genotypes are thus visualized as polymorphisms in the transcriptome. We show that polymorphic transcripts can be used as genetic markers and allow the construction of a linkage map. The resulting map shows that, in contrast to genomic markers, the transcriptome-derived markers did not cluster in particular areas of the chromosome, and that cDNA-AFLP markers are targeted specifically to transcriptionally active regions. The cDNA-AFLP markers used in mapping are derived from DNA polymorphisms in transcripts, rather than differences in expression regulation. The high potential of transcriptome markers as opposed to (anonymous) genomic markers for applications in genetic analyses, marker-assisted breeding and map-based cloning is discussed.

Arabidopsis↗

Venomics of the Arabian saw-scaled viper (Echis coloratus) through transcriptome-guided proteomics and in vitro functional profiling.

The Arabian saw-scaled viper (Echis coloratus) is among the snakes of highest medical relevance in the Middle East and North Africa. However, to date, its venom has been investigated in a very limited number of studies, and much remains unknown regarding its compositional and functional properties. By integrating proteotranscriptomics with bioactivity profiling, we present a comprehensive transcriptome-level catalogue of E. coloratus venom components and their associated biological activities. Our analysis identified 183 venom components belonging to 17 distinct protein families. Relative toxin abundances revealed that 92% of the venom proteome is composed of C-type lectin and C-type lectin-related protein (CTL), L-amino acid oxidase (LAAO), phospholipase A2 (PLA2), snake venom serine protease (SVSP), and snake venom metalloproteinase (SVMP), with CTL and PLA2 alone accounting for 73% of the total composition. Bioassays targeting key aspects of viperid envenomation demonstrated potent protease and PLA2 activity in a concentration-dependent manner. In contrast, Factor Xa-like, plasmin-like, and haemolytic activities were negligible. Marked cytotoxicity was observed at the highest concentration tested (i.e., 25 μg/ml) in the mammalian cell lines MDCK II and Calu-3, whereas cytotoxic effects were minimal at lower concentrations. These findings highlight the complexity and potency of E. coloratus venom, and provide a valuable foundation for improving our understanding of envenomation caused by this species.

Animals↗

Expression profiling using a hexamer-based universal microarray.

We describe a transcriptional analysis platform consisting of a universal micro-array system (UMAS) combined with an enzymatic manipulation step that is capable of generating expression profiles from any organism without requiring a priori species-specific knowledge of transcript sequences. The transcriptome is converted to cDNA and processed with restriction endonucleases to generate low-complexity pools (approximately 80-120) of equal length DNA fragments. The resulting material is amplified and detected with the UMAS system, comprising all possible 4,096 (4(6)) DNA hexamers. Ligation to the arrays yields thousands of 14-mer sequence tags. The compendium of signals from all pools in the array-of-universal arrays comprises a full-transcriptome expression profile. The technology was validated by analysis of the galactose response of Saccharomyces cerevisiae, and the resulting profiles showed excellent agreement with the literature and real-time PCR assays. The technology was also used to demonstrate expression profiling from a hybrid organism in a proof-of-concept experiment where a T-cell receptor gene was expressed in yeast.

3' Untranslated Regions↗

A critical appraisal of base-resolution m6A profiling techniques.

N6-methyladenosine (m6A) is the most prevalent internal modification in eukaryotic mRNA, influencing RNA fate and gene regulation. Early antibody-based approaches enabled transcriptome-wide profiling but lacked resolution and quantitative accuracy. Newer approaches now achieve base-resolution m6A detection using improved crosslinking, chemical or enzymatic conversion, and single-molecule sequencing. Antibody-free methods provide quantitative stoichiometry from minimal input, while nanopore direct RNA sequencing offers real-time, single-molecule readouts across entire transcriptomes. Collectively, these methods form a versatile toolkit that integrates global mapping with precise site-level analysis, advancing knowledge of context-dependent m6A regulation in physiology and disease. This review compares their principles, strengths, and limitations to guide method selection and highlight how next-generation epitranscriptomic tools are paving the way for clinical and therapeutic applications.

Humans↗

Whole genome expression profiling of the medial and lateral substantia nigra in Parkinson's disease.

We have used brain tissue from clinically well-documented and neuropathologically confirmed cases of sporadic Parkinson's disease to establish the transcriptomic expression profile of the medial and lateral substantia nigra. In addition, the superior frontal cortex was analyzed in a subset of the same cases. DNA oligonucleotide microarrays were employed, which provide whole human genome coverage. A total of 570 genes were found to be differentially regulated at a high level of significance. A large number of differentially regulated expressed sequence tags were also identified. Levels of mRNA sequences encoded by genes of key interest were validated by means of quantitative real-time polymerase chain reaction (PCR). Comparing three different normalization procedures, results based on the recently published GeneChip Robust Multi Array algorithm were found to be the most accurate predictor of real-time PCR results. Several new candidate genes which map to PARK loci are reported. In addition, the DNAJ family of chaperones is discussed in the context of Parkinson's disease pathogenesis.

Aged↗

Prior vaccination prevents overactivation of innate immune responses during COVID-19 breakthrough infection.

At this stage in the COVID-19 pandemic, most infections are "breakthrough" infections that occur in individuals with prior severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) exposure. To refine long-term vaccine strategies against emerging variants, we examined both innate and adaptive immunity in breakthrough infections. We performed single-cell transcriptomic, proteomic, and functional profiling of primary and breakthrough infections to compare immune responses from unvaccinated and vaccinated individuals during the SARS-CoV-2 Delta wave. Breakthrough infections were characterized by a less activated transcriptomic profile in monocytes and natural killer cells, with induction of pathways limiting monocyte migratory potential and natural killer cell proliferation. Furthermore, we observed a female-specific increase in transcriptomic and proteomic activation of multiple innate immune cell subsets during breakthrough infections. These insights suggest that prior SARS-CoV-2 vaccination prevents overactivation of innate immune responses during breakthrough infections with discernible sex-specific patterns and underscore the potential of harnessing vaccines in mitigating pathologic immune responses resulting from overactivation.

Immunity, Innate↗

Genome-wide profiling of segmental-regulated transcriptomes in human epididymis using oligo microarray.

Sperm maturation during passage through the epididymis depends on regionalized gene expression which maintains the progressively changing environment within the epididymal tubule. Towards defining the genes that drive the sequential maturation of spermatozoa, we profiled regionally regulated gene expression pattern in the epididymis of a fertile young male donor using Affymetrix human genome U133 plus 2.0 microarray representing approximately the whole human genome. Over 15000 transcripts, almost one-third of the total on the array were identified in whole epididymis. Among them, 65% were detected in all three regions of the epididymis, 410 or 2.6% were present only in one region and the remaining 32.4% were distributed in two regions. Region-specific transcripts observed in caput (264), corpus (61) and cauda (81) epididymides were further classified as empirically determined reported genes or ESTs. This study revealed for the first time, the expression in human epididymis of a number of region-specific genes. The original data will be made publicly available on the Shanghai Science and Technology Database (http://www.scbit.org/human_epididymis_transcriptomes).

Adult↗

Understanding the sources of performance in deep drug response models reveals insights and improvements.

MOTIVATION: Anti-cancer drug response prediction (DRP) using cancer cell lines (CLs) is crucial in stratified medicine and drug discovery. Recently, new deep learning models for DRP have improved performance over their predecessors. However, different models use different input data types and architectures making it hard to find the source of these improvements. Here we consider published DRP models that report state-of-the-art performance predicting continuous response values. These models take chemical structures of drugs and omics profiles of CLs as input. RESULTS: By experimenting with these models and comparing with our simple baselines, we show that no performance comes from drug features, instead, performance is due to the transcriptomics CL profiles. Furthermore, we show that, depending on the testing type, much of the current reported performance is a property of the training target values. We address these limitations by creating BinaryET and BinaryCB that predict binary drug response values, guided by the hypothesis that this reduces the noise in the drug efficacy data. Thus, better aligning them with biochemistry that can be learnt from the input data. BinaryCB leverages a chemical foundation model, while BinaryET is trained from scratch using a transformer-type architecture. We show that these models learn useful chemical drug features, which is the first time this has been demonstrated for multiple testing types to our knowledge. We further show binarizing the drug response values causes the models to learn useful chemical drug features. We also show that BinaryET improves performance over BinaryCB, and the published models that report state-of-the-art performance. AVAILABILITY AND IMPLEMENTATION: Code is available from https://github.com/Nik-BB/Understanding_DRP_models.

Humans↗

Analysis of the prostate cancer cell line LNCaP transcriptome using a sequencing-by-synthesis approach.

BACKGROUND: High throughput sequencing-by-synthesis is an emerging technology that allows the rapid production of millions of bases of data. Although the sequence reads are short, they can readily be used for re-sequencing. By re-sequencing the mRNA products of a cell, one may rapidly discover polymorphisms and splice variants particular to that cell. RESULTS: We present the utility of massively parallel sequencing by synthesis for profiling the transcriptome of a human prostate cancer cell-line, LNCaP, that has been treated with the synthetic androgen, R1881. Through the generation of approximately 20 megabases (MB) of EST data, we detect transcription from over 10,000 gene loci, 25 previously undescribed alternative splicing events involving known exons, and over 1,500 high quality single nucleotide discrepancies with the reference human sequence. Further, we map nearly 10,000 ESTs to positions on the genome where no transcription is currently predicted to occur. We also characterize various obstacles with using sequencing by synthesis for transcriptome analysis and propose solutions to these problems. CONCLUSION: The use of high-throughput sequencing-by-synthesis methods for transcript profiling allows the specific and sensitive detection of many of a cell's transcripts, and also allows the discovery of high quality base discrepancies, and alternative splice variants. Thus, this technology may provide an effective means of understanding various disease states, discovering novel targets for disease treatment, and discovery of novel transcripts.

Adenocarcinoma↗

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↗

A Practical Workflow for Spatial Transcriptomics Data Analysis: From Data Acquisition to Advanced Analyses.

Spatial transcriptomics (ST) profiles genome-wide gene expression while preserving the two-dimensional spatial context of mRNA molecules within tissue sections, enabling studies of tissue architecture and microenvironment-associated biology. However, ST analysis remains challenging because data import, quality control, integration, deconvolution, spatial statistics, and visualization often require multiple software environments and reproducible parameter choices. This protocol presents a practical computational workflow for public ST datasets in R, beginning with data acquisition and software setup and proceeding through Seurat-based data loading, quality control, normalization, multi-sample integration, clustering, and spatially variable gene analysis. The workflow then applies complementary deconvolution strategies, including reference-guided SPOTlight analysis and unsupervised STdeconvolve topic modeling, followed by Giotto-based spatial cell-cell communication analysis and interactive region-of-interest (ROI) selection using a custom Python Dash application. By emphasizing script-based execution, explicit parameter rationales, expected outputs, and troubleshooting checkpoints, the protocol provides an adaptable framework for standard array-based ST datasets and related platforms after dataset- and platform-specific parameter evaluation.

Spatial Transcriptomics↗

Whole transcriptome amplification for gene expression profiling and development of molecular archives.

Expression profiling of clinically obtainable tumor specimens has been hindered by the need for microgram quantities of RNA. In vitro transcription (IVT)-based amplifications are most commonly used to amplify small quantities of RNA for microarray analysis. However, significant drawbacks exist with IVT-based amplification, and the need for alternative amplification methods remains. Herein, we validate whole transcriptome amplification (WTA), an exponential amplification technique that produces cDNA libraries and amplified target in 3 to 4 hours from nanogram quantities of total RNA using a combination of cDNA microarrays and quantitative polymerase chain reaction (PCR). We demonstrate that WTA material can serve as a "molecular archive" because a WTA cDNA library can be faithfully amplified through multiple rounds of PCR amplification, allowing it to serve as a bankable and distributable resource. To demonstrate applicability, WTA was combined with laser capture microdissection to profile frozen prostate tissues. Unlike most IVT-based and exponential amplification techniques, WTA does not depend on the presence of a poly-A tail. Thus, we demonstrate that WTA is compatible with artificially degraded RNA and RNA isolated from formalin-fixed paraffin-embedded tissues. Taken together, WTA represents a versatile approach to profile and archive cDNA from minute tumor samples and is compatible with partially degraded RNA.

Biological Specimen Banks↗

Multi-omics integration uncovers epigenetic control of metabolic reprogramming in triple-negative breast cancer.

Triple-negative breast cancer (TNBC) is an aggressive subtype characterized by the absence of estrogen, progesterone, and HER2 receptors, limiting effective targeted therapies. Increasing evidence suggests that metabolic reprogramming, a hallmark of TNBC progression, is driven by underlying epigenetic mechanisms such as DNA methylation. The represented study performed an integrative analysis of transcriptomic (RNA-seq) and methylome data to uncover the metabolic-epigenetic interplay in TNBC. Differential gene expression analysis using DESeq2 revealed significant dysregulation of key metabolic genes, including upregulation of genes encoding glycolytic and serine biosynthesis enzymes and downregulation of metabolic tumor suppressors. Genome-wide methylation profiling identified extensive cytosine-phosphate-guanine (CpG) hypermethylation events associated with transcriptional repression, particularly in promoter regions. Integrative analysis pinpointed a subset of metabolism-related genes exhibiting both differential expression and methylation, such as FBP1, RASSF1A, and PHGDH. Pathway enrichment analysis highlighted aberrations in glycolysis/gluconeogenesis, fatty acid metabolism, and one-carbon pathways (adjusted p&#x2009;<&#x2009;0.01). Importantly, TNBC patients with hypermethylated metabolic gene signatures displayed significantly shorter overall survival (log-rank p&#x2009;<&#x2009;0.05). These findings reveal that DNA methylation-driven metabolic dysregulation contributes to TNBC aggressiveness and may provide novel biomarkers and therapeutic targets at the metabolic-epigenetic interface.

Humans↗