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Micro serial analysis of gene expression in normal human choroid and retinal pigment epithelial transcriptomes.

PURPOSE: To investigate the gene expression profile of the normal human choroid/retinal pigment epithelium(RPE) tissues. METHODS: Micro serial analysis of gene expression (Micro SAGE) was performed. A SAGE library was constructed from 110 microg of total RNA of normal human choroid/RPE tissue, and cloned tag concatemers transformed to E.coli were sequenced. The sequence data were analyzed by SAGE software and matched to GenBank and UniGene public databases. The sequence data were also compared with the choroid/RPE cDNA library of NEIBank. RESULTS: A total of 12 070 tags were sequenced; 3627 tags were unique. Of these 3627 tags, 2508 tags were encoded genes and 1119 tags were unknown tags in the UniGene database. The most frequently expressed tag was TCCCTATTAA, but the gene corresponding to this tag has not been identified yet. Other frequently expressed tags encoded a tissue inhibitor of matrix metalloproteinase 3, insulin-like growth factor binding protein-related protein 1, and transthyretin. These genes are notably different, with high expression frequencies when compared to the cDNA library of NEIBank. CONCLUSIONS: This gene expression profile of the normal human choroid/RPE tissue should provide further understanding of the biological function of the choroid and the pathogenesis of diseases in which the choroid and RPE play a role, such as choroidal neovascularization.

Aged↗

PubMed2026

Whole-transcriptome-scale isoform-resolved spatial imaging of single cells in tissues.

Cell and tissue functions arise from complex interactions among numerous genes, and a systematic understanding of these functions requires isoform-resolved transcriptomic analysis of single cells with high spatial resolution. Here, we introduce an in situ RNA amplification method and its integration with multiplexed error-robust fluorescence in situ hybridization (MERFISH) to detect short RNA sequences and enable whole-transcriptome-scale, isoform-resolved spatial transcriptomics of individual cells in intact tissues. Using this approach, we imaged ∼33,000 distinct RNAs-including ∼23,000 genes and ∼10,000 isoforms-in the mouse brain. Our data enabled systematic analyses of region- and cell-type-specific gene programs and ligand-receptor-based cell-cell communications. These data further revealed rich spatial diversity and cell-type specificity in isoform usage across numerous genes, as well as brain structures particularly rich in isoform specificity. We anticipate broad application of this method for characterizing the molecular and cellular basis of tissue functions, unlocking previously inaccessible discoveries in cell and organismal biology.

Animals↗

PubMed2026

A High-Resolution Stereo-Seq Spatial Transcriptomic Resource for Adult Holstein Cattle Liver.

The bovine liver is a highly compartmentalized organ that plays essential roles in continuous gluconeogenesis and nitrogen recycling; however, its spatial molecular architecture has remained largely uncharacterized due to the limitations of traditional bulk and single-cell approaches. To address this gap, Spatial Enhanced Resolution Omics-sequencing (Stereo-seq) was utilized to generate a subcellular-resolution (500 nm) transcriptomic map of an adult Holstein cattle liver, and a refined reference-guided workflow was implemented to overcome standard annotation limitations in livestock. Raw sequencing data were processed using the Stereo-seq Analysis Workflow and analyzed with Stereopy, Seurat, SingleR, and reference-guided workflows. Spatial aggregation was evaluated at Bin20, Bin50, Bin100, Bin150, and Bin200. Increasing bin size increased molecular identifier counts and detected-gene complexity while progressively reducing spatial granularity. Bin50, corresponding to 50 × 50 DNA nanoballs and an approximate nominal footprint of 25 × 25 µm, was therefore selected as a practical intermediate aggregation level for the primary analyses. Quality-control assessment, Leiden clustering, UMAP visualization, reference-based cell-type annotation, cluster-marker analysis, and spatial mapping of canonical hepatic genes demonstrated preservation of biologically interpretable liver transcriptional organization. Raw sequencing data processed spatial matrices, annotated objects, and analysis code are publicly available to support reanalysis and computational benchmarking. In summary, we present a Stereo-seq spatial transcriptomic resource generated from liver tissue of an adult Holstein cow. This initial resource provides a valuable foundation for future studies of bovine liver biology, comparative genomics, and the spatial basis of livestock health and production traits.

Animals↗

PubMed2003

Molecular analysis of cancer using DNA and protein microarrays.

In conclusion, array-based technologies have emerged that contribute to profiling tissues at the genomic, transcriptomic and proteomic levels. Analytical tools are needed to mine the vast amount of data generated. Ultimately the molecular analysis of cancer at a genome and proteome scale will allow better classification of disease and tailored individualized therapy for individual patients.

Biotechnology↗

PubMed2026

Mining the sHSP20 (small heat-shock protein) gene family in finger millet (Eleusine coracana (L.) Gaertn.): structural, evolutionary and predicted abiotic-stress-responsive insights.

Small heat-shock proteins (sHSPs, the HSP20 family) are ATP-independent molecular chaperones that hold partially unfolded substrates and protect the proteome during heat and other abiotic stresses; every member is defined by a conserved α-crystallin domain (ACD). Finger millet (Eleusine coracana) is a climate-resilient, calcium-rich allotetraploid cereal of the semi-arid tropics whose HSP20 repertoire had not been catalogued. The present study is an entirely computational (in silico) analysis of the chromosome-scale reference genome of finger millet (NCBI GenBank assembly GCA_032690845.1, cultivar KNE 796-S). Mining the predicted proteome with the ACD profile (Pfam PF00011) and confirming every candidate by NCBI CD-search recovered 76 non-redundant ACD-bearing HSP20 genes (EcHSP20-1-EcHSP20-76). Based on phylogeny and TargetP-predicted localization, the members were classified into ten subfamilies: seven cytosolic/nuclear classes (C-I to C-VII, 60 members) together with chloroplastic (11), mitochondrial (3) and endoplasmic-reticulum (2) groups. The proteins ranged from 110 to 355 amino acids (12.1-39.2 kDa) with theoretical pI of 4.85-9.69. The 76 loci were distributed over 14 of the 18 chromosomes and were conspicuously absent from chromosomes 8 A, 8B, 9 A and 9B, with pronounced clustering on chromosomes 1, 2, 3 and 6. Duplication analysis detected 149 paralogous pairs (49 homoeologous, 80 segmental/dispersed and 18 tandem); 147 of 148 pairs for which substitution rates could be calculated returned Ka/Ks < 1 (mean 0.20), indicating strong purifying selection consistent with retention after whole-genome/allopolyploid duplication. Promoter analysis (PlantCARE) revealed enrichment of abscisic-acid-responsive (ABRE), MYB/MYC drought-related, STRE, DRE, low-temperature (LTR) and methyl-jasmonate/salicylic-acid elements, whereas canonical heat-shock elements (HSE) were not recovered. Expression profiling against a public drought transcriptome (SRP081350) showed that about half of the genes (39 of 76) are transcribed in leaf tissue, the expressed fraction being dominated by the cytosolic class C-I. This first finger-millet HSP20 catalogue provides a verified, reproducible framework and nominates computationally predicted candidate genes for future functional work on thermotolerance in cereals.

Allotetraploid↗

PubMed2004

Adaptation of enzymes to temperature: searching for basic "strategies".

The pervasive influence of temperature on biological systems necessitates a suite of temperature--compensatory adaptations that span all levels of biological organization--from behavior to fine-scale molecular structure. Beginning about 50 years ago, physiological studies conducted with whole organisms or isolated tissues, by such pioneers of comparative thermal physiology as V.Ya. Alexandrov, T.H. Bullock, F.E.J. Fry, H. Precht, C.L. Prosser, and P.F. Scholander, began to document in detail the abilities of ectothermic animals to sustain relatively similar rates of metabolic activity at widely different temperatures of adaptation or acclimation. These studies naturally led to investigation of the roles played by enzymatic proteins in metabolic temperature compensation. Peter Hochachka's laboratory became an epicenter of this new focus in comparative physiology. The studies of the enzyme lactate dehydrogenase (LDH) that he initiated as a PhD student at Duke University in the mid-1960s and continued for several years at the University of British Columbia laid much of the foundation for subsequent studies of protein adaptation to temperature. Studies of orthologs of LDH have revealed the importance of conserving kinetic properties (catalytic rate constants (kcat) and Michaelis-Menten constants (Km) and structural stability during adaptation to temperature, and recently have identified the types of amino acid substitutions causing this adaptive variation. The roles of pH and low-molecular-mass organic solutes (osmolytes) in conserving the functional and structural properties of enzymes also have been elucidated using LDH. These studies, begun in Peter Hochachka's laboratory almost 40 years ago, have been instrumental in the development of a conceptual framework for the study of biochemical adaptation, a field whose origin can be traced largely to his creative influences. This framework emphasizes the complementary roles of three "strategies" of adaptation: (1) changes in amino acid sequence that cause adaptive variation in the kinetic properties and stabilities of proteins, (2) shifts in concentrations of proteins, which are mediated through changes in gene expression and protein turnover; and (3) changes in the milieu in which proteins function, which conserve the intrinsic properties of proteins established by their primary structure and modulate protein activity in response to physiological needs. This theoretical framework has helped guide research in adaptational biochemistry for many years and now stands poised to play a critical role in the post-genomic era, as physiologists grapple with the challenge of integrating the wealth of new data on gene sequences (genome), gene expression (transcriptome and proteome), and metabolic profiles (metabolome) into a realistic physiological context that takes into account the evolutionary histories and environmental relationships of species.

Adaptation, Physiological↗

PubMed2005

Gene expression perturbation in vitro--a growing case for three-dimensional (3D) culture systems.

Cells grown in vitro are dramatically perturbed by their new microenvironment. Analyses of genome-wide gene expression levels offer a first glance at which genes and pathways are affected in cell lines as compared to their tissues of origins. We have summarized available gene expression data and review how cell lines adapt to in vitro environments, to what degree they express markers of their tissues of origins and discuss how cells grown in three-dimensional (3D) cultures may have more physiological interactions with neighbouring cells and extracellular matrix. We will also discuss the interplay between malignant cells and stroma present in tumours but lacking in cell lines and how these differences might affect gene expression comparisons of cell lines to tumours. A model simulating impact of stromal cells on gene expression profiles is presented. Understanding the transcriptomes of cells grown in 2D and 3D cultures and how they compare to those of in vivo cells are important for improving cell line model systems and for the reconstituting of tissues in vitro.

Cell Adhesion↗

PubMed2026

A ribozyme ligase that requires a 3' terminal phosphate on its RNA substrate.

Ribozymes likely played essential roles in catalyzing metabolic processes and facilitating genome replication in primordial RNA-based life. In vitro evolution has allowed us to expand the biochemical capabilities of RNA, especially new ribozyme chemistries. Here, we report the serendipitous discovery of ribozyme ligases that catalyze the attack of the 2'-hydroxyl group of an RNA substrate on its own 5'-triphosphate group, but only when the substrate possesses a 3'-phosphate vicinal to its nucleophilic 2'-hydroxyl group. The ligases' requirement for a 3'-phosphate group on its substrate resembles enzymatic mechanisms found in protein-based RNA repair pathways. We propose that ribozyme-catalyzed ligation of 3'-phosphorylated RNA could have provided pathways for RNA repair in primordial cells. We demonstrate that these ribozymes ligate specifically to 3'-phosphorylated RNA present in a heterogeneous mixture of cellular RNAs. We further show that these ribozymes can capture cleaved RNAs with 3'-phosphate and 2'-3'-cyclic phosphate termini, enabling us to selectively amplify the captured RNAs. These results demonstrate their potential utility as enrichment reagents for profiling RNA cleavage products in transcriptomics studies. Our findings not only report a new catalytic reactivity in RNA but also provide insights into ribozyme evolution, primordial RNA repair, and potential applications in RNA sequencing.

RNA, Catalytic↗

PubMed2026

Mapping convergent regulators of melanoma drug resistance by PerturbFate.

High-throughput genomic studies have uncovered associations between diverse genetic alterations and disease phenotypes. However, elucidating how perturbations in functionally disparate genes give rise to convergent cellular states remains challenging. Here we present PerturbFate, a high-throughput, cost-effective, combinatorial-indexing single-cell platform that enables systematic interrogation of massively parallel CRISPR interference1 perturbations across the full spectrum of gene regulation, from chromatin remodelling and nascent transcription to steady-state transcriptomic phenotypes. Using PerturbFate, we profiled more than 300,000 cultured melanoma cells to characterize multimodal phenotypic and gene regulatory responses to perturbations in more than 140 vemurafenib resistance-associated genes. We uncovered a shared dedifferentiated cell state marked by convergent cooperative transcription factor activities across diverse genetic perturbations. We further dissected phenotypic responses to perturbations in Mediator complex components, linking module-specific biochemical properties to convergent transcriptional activations. We identified common regulatory nodes that drive similar phenotypic outcomes across distinct genetic perturbations. We also delineated how perturbations in functionally unrelated genes reshape cell state. Thus, PerturbFate establishes a versatile platform for identifying key molecular regulators by anchoring multimodal regulatory dynamics to disease-relevant phenotypes.

Humans↗

PubMed2026

A nucleolar stress gene signature enables quantitative scoring across multi-omics contexts.

The nucleolus is essential for ribosome biogenesis and cellular homeostasis, and its dysfunction can induce nucleolar stress, a process implicated in cancer and other diseases. However, nucleolar stress is commonly inferred from morphological changes or a limited set of functional assays, and quantitative approaches based on gene expression profiles remain lacking. Here, we integrate literature curation with multi-dataset screening to define a nucleolar stress gene signature and develop a nucleolar stress score (NuS) applicable to bulk transcriptomics, single-cell transcriptomics, proteomics, and spatial transcriptomics. Using this framework, we show in colorectal cancer models that oxaliplatin induces nucleolar stress, suppresses nascent rRNA synthesis, and activates p53 signaling, whereas these responses are attenuated in oxaliplatin-resistant cells. Combined with a ribosome biogenesis activity score (RiboSis), NuS captures related but distinct dimensions of nucleolar function and stratifies tumors into functional states associated with clinical outcomes. NuS-based analysis of perturbational transcriptomes further prioritizes compounds with putative nucleolar stress-inducing activity. Collectively, this study provides a quantitative framework for evaluating nucleolar stress and illustrates its applications in disease stratification and drug mechanism discovery.

Cell Nucleolus↗

PubMed2025

Dynamic changes in chromosome and nuclear architecture during maturation of normal and ALS C9orf72 motor neurons.

We have investigated changes in chromosome conformation, nuclear organization, and transcription during differentiation and maturation of control and mutant motor neurons harboring hexanucleotide expansions in the C9orf72 gene that cause amyotrophic lateral sclerosis (ALS). Using an in vitro reprogramming, differentiation and neural maturation protocol, we obtained highly purified populations of post-mitotic motor neurons for both normal and diseased cells. As expected, as fibroblasts are reprogrammed into iPSCs, and as iPSCs differentiate into motor neurons, chromatin accessibility, chromosome conformation, and nuclear organization change along with large-scale alterations in transcriptional profiles. We find that the transcriptome changes extensively during the first three weeks of post-mitotic neuronal maturation, with thousands of genes changing expression, but then is relatively stable for the next three weeks. In contrast, chromosome conformation and nuclear organization continue to change over the entire 6-week maturation period: chromosome territoriality increases, long-range interactions along chromosomes decrease, compartmentalization strength increases, and centromeres and telomeres increasingly cluster. In motor neurons derived from ALS patients such changes in chromosome conformation were much reduced. Chromatin accessibility changes also showed delayed maturation. The transcriptome in these cells matured relatively normally but with notable changes in expression of genes involved in lipid, sterol and mitochondrial function. We conclude that neural maturation is associated with large scale post-mitotic changes in gene expression, chromosome conformation and nuclear organization, and that these processes are defective in motor neurons derived from ALS patients carrying C9orf72 hexanucleotide repeat expansions.

Journal Article↗

PubMed2006

Integration--a key to success in the genetic dissection of complex diseases?

Complex diseases are polygenic and multifactorial. The outcome of two decades of search for the culprit genes in complex diseases involving the cardiovascular system has been less than satisfactory. Genomic studies using linkage analysis have led so far to the detection of a large number of quantitative trait loci that embed a large number of candidate genes. Transcriptomic studies using differential gene expression profiling and DNA microarrays have also generated hundreds of potential candidate genes. None of these genetic strategies has enabled researchers to reduce the number of genes to a manageable number or to identify the specific culprit genes. We recently proposed that the search for genes involved in complex diseases such as hypertension might benefit from an integration of genomics and transcriptomics as a logical alternative strategy to using either approach alone. We applied this integrated genomic-transcriptomic approach to identify the genes that are involved in the pathogenesis of hypertension in the Sabra rat model of salt susceptibility. We successfully identified seven novel candidate genes for hypertension, an outcome that could not have been achieved by genomics or transcriptomics alone.

Animals↗

PubMed2026

GenOT: generative optimal transport enables spatiotemporal interpolation and generation in cross-platform spatial transcriptomics.

Spatial transcriptomics technologies have revolutionized the analysis of spatial gene expression, yet integrating spatial information and generating data across heterogeneous samples remain challenging. We present GenOT, a generative framework combining multi-scale graph self-supervised contrastive learning with optimal transport barycenter theory for efficient cross-slice and cross-platform spatiotemporal interpolation. The core innovation of GenOT lies in introducing an optimal transport barycenter-based interpolation algorithm, which mathematically models spatial distribution differences across heterogeneous samples to reconstruct spatiotemporal gene expression dynamics. Extensive evaluations demonstrate that GenOT consistently outperforms existing approaches in spatial domain identification, cross-platform interpolation, and developmental trajectory reconstruction.

Spatial Transcriptomics↗

PubMed2026

Representation learning for multi-modal spatially resolved transcriptomics data.

MOTIVATION: Spatial transcriptomics enables in-depth molecular characterization of samples on a morphology and RNA level while preserving spatial location. Integrating the resulting multi-modal data is an unsolved problem, and developing new solutions in precision medicine depends on improved methodologies. RESULTS: We introduce AESTETIK, a convolutional deep learning model that jointly integrates spatial, transcriptomics, and morphology information to learn accurate spot representations. AESTETIK yielded substantially improved cluster assignments on widely adopted technology platforms (e.g. 10x Genomics™, NanoString™) across multiple datasets. We achieved performance enhancement on structured tissues (e.g. brain) with a 21% increase in median ARI over previous state-of-the-art methods. Notably, AESTETIK also demonstrated superior performance on cancer tissues with heterogeneous cell populations, showing a 2-fold increase in breast cancer, 79% in melanoma, and 21% in liver cancer. We expect that these advances will enable a multi-modal understanding of key biological processes. AVAILABILITY AND IMPLEMENTATION: AESTETIK is implemented in Python 3 and is available as open source software at http://www.github.com/ratschlab/aestetik. The Snakemake pipeline for reproducing the results is available at http://www.github.com/ratschlab/st-rep.

Spatial Transcriptomics↗

PubMed2002

Generation of kidney transcriptomes using serial analysis of gene expression.

Chronic renal disease initiation and progression remain incompletely understood. Genomewide expression monitoring should clarify the mechanisms which cause progressive renal disease by determining how clusters of genes coordinately change their activity. Serial analysis of gene expression (SAGE) is a technique of expression profiling which permits simultaneous and quantitative analysis of 9- to 13-bp sequence tags that correspond to unique mRNAs. Key principles of the technique are use of PCR in a manner to minimize distortion and serial concatenation of tags which facilitates sequencing and permits identification of many expressed genes in a single cDNA molecule. Tags are extracted from many concatenated sequences, counted using software, and identified by comparison with existing gene databases. In aggregate, gene expression profiles generated from a tag library comprise a transcriptome which represents a comprehensive and quantitative profile of genes expressed at the time of analysis. These global snapshots of gene expression patterns can better define basic cell biology and provide insights into disease pathogenesis by simultaneously determining the net consequences of gene-gene and gene-environment interactions on expression of thousands of genes. Rather than applying a priori assumptions (i.e., hypothesis testing), transcriptome analysis is hypothesis generating and requires no prior knowledge of gene expression. SAGE kidney transcriptomes, from normal animals and animals with progressive kidney disease, are being produced and can be analyzed for novel pathogenetic mechanisms. The use of SAGE and other genomic and proteomic tools should result in a better understanding of kidney disease pathogenesis and in identification of new therapeutic targets.

Animals↗

PubMed2026

Dual-transcriptomic analysis of human nasal transcriptome and microbiome reveals host-bacteria associations in symptomatic respiratory infection.

BACKGROUND: The human nasopharynx is colonized by a diverse community of commensal microbiota linked to many respiratory diseases, yet their associations with the host remain unclear. RESULTS: In this study, we introduced a dual-transcriptomics analysis strategy, which can characterize the host transcriptome and microbiome from nasal samples simultaneously. We applied this workflow to a local SARS-CoV-2 cohort with 76 asymptomatic infected patients, among whom 52 (68.42%) developed symptomatic infection during a 1-week follow-up period. Nasal swabs were collected from all 76 patients at enrollment and from 73 patients at one-week later follow-up. We detected a median of 8.94% reads that did not map to the human genome across all 149 samples, among which around half (median 49.68%) were successfully mapped to microbiome genome. Meta-transcriptomic analysis detected significantly higher SARS-related coronavirus loads in samples from the symptomatic group at enrollment (P = 0.004), and both groups showed decreased loads one week later (symptomatic, P = 0.001; asymptomatic, P = 0.035). Compared with benchmarking 16 S rRNA sequencing on 53 samples, our computational strategy showed high correlation of relative abundance in all top 20 genera (median Rho = 0.90, Pmax < 0.001). A total of 670 bacteria species were identified to show a relative abundance ≥ 0.01% in at least 10% samples. Differential abundance analysis identified 76 species (DASs) from six phyla with significantly decreased abundance in samples from the symptomatic group (log2(fold change or FC) < -1 and adjusted P < 0.05) compared to the asymptomatic group at enrollment. Integrating these symptom-associated DASs with host's gene expression using an expression quantitative trait bacteria (eQTB) model, we found 45 symptom-associated DASs identified at enrollment were significantly associated with one to 14 genes (adjusted P < 0.05). GSEA showed a series of symptom-associated DASs were significantly correlated with pathways related to olfactory function, keratinocyte differentiation, and DNA methylation. CONCLUSIONS: In summary, our dual-transcriptomic analysis strategy effectively characterized host-microbiome associations, offering insights into microbial contributions to respiratory diseases.

Humans↗

PubMed2004

Preparation of planar retinal specimens: verification by histology, mRNA profiling, and proteome analysis.

PURPOSE: Elucidation of the transcriptome and proteome of the normal retina will be difficult since it is comprised of at least 55 different cell types. However the characteristic layered cellular anatomy of the retina makes it amenable to planar sectioning, enabling the generation of enriched retinal cell populations. The aim of this study was to validate a reproducible method for preparing enriched retinal layers from porcine retina. METHODS: The thicknesses of the retinal photoreceptor, inner nuclear and ganglion cell, and fiber layers were determined by routine histology of cross sections of fresh whole retina mounted on polyvinylidene difluoride (PVDF) membrane. Dissected retina (5 mm2) was placed on PVDF membrane and a series of planar cryosections corresponding to the photoreceptor and inner nuclear layer were removed leaving the ganglion cell and fiber layer which was subsequently detached from the membrane. The retinal specimens were stored at -80 degrees C. Representative planar tissue sections were sonicated in ice-chilled 40 mM ammonium bicarbonate pH 7.9 and aliquots removed for RNA extraction. Quantitative RT-PCR was used to analyze the mRNA expression of genes indicative of specific retinal layers. Ammonium bicarbonate protein extracts were centrifuged, lyophilized and prepared for direct liquid chromatography-tandem mass spectrometry (LC-MS/MS) analysis using a Waters Q-Tof Ultima. RESULTS: Histological analysis established the parameters for planar cryosectioning: photoreceptor layer (69+/-1.8 microm), outer plexiform (11+/-0.6 microm), inner nuclear layer (28+/-0.5 microm), inner plexiform, ganglion cell and fiber layer (100+/-5.3 microm). Gene expression profiling provided an independent method for validating the respective retinal preparations. For example, glial fibrillary acidic protein (GFAP) was expressed up to 21 fold higher in the inner retinal "ganglion cell enriched" fraction than in the outer retinal "photoreceptor enriched" fraction. The pattern was reversed for blue cone opsin, which was expressed up to 24 fold higher in the "photoreceptor enriched" fraction. Endogenous protein fragments indicative of each layer were identified by mass spectrometry and de novo sequence data obtained. CONCLUSIONS: Combined histological and mRNA expression profiling has confirmed the development of a reproducible method for generating validated porcine retinal layers enriched for specific cell types. Direct proteome analysis detected endogenous peptide fragments of characteristic retinal proteins. Further analysis of these enriched retinal cell preparations will facilitate a more selective investigation of the retinal transcriptome and proteome than studies of the intact retina.

Anatomy, Cross-Sectional↗

PubMed2005

Impact of haloperidol and risperidone on gene expression profile in the rat cortex.

Despite the clinical efficacy of the most thoroughly studied conventional neuroleptic agent haloperidol, and the atypical antipsychotic risperidone is well established, little information is available on their molecular effects. Recent advances in high-density DNA microarray techniques allow the possibility to analyze thousands of genes simultaneously for their differential gene expression patterns in various biological processes, and to determine mechanisms of drug action. The aim of this series of experiments was to gain experience in antipsychotic gene-expression profiling and characterize (in the parlance of genomics) the "antipsychotic transcriptome." In this prospective animal study, broad-scale gene expression profiles were characterized for brains of rats treated with antipsychotics and compared with those of sham controls. We used DNA microarrays containing 8000 sequences to measure the expression patterns of multiple genes in rat fronto-temporo-parietal cortex after intraperitoneal treatment with haloperidol or risperidone. A number of transcripts were differentially expressed between control and treated samples, of which only 36 and 89 were found to significantly differ in expression as a result of exposure to haloperidol or risperidone, respectively (P<0.05). Acutely, 13 genes were more highly expressed and 15 transcripts were found to be significantly less abundant, whereas chronically nine genes were up-regulated and none of them was repressed in haloperidol-treated cortices. Risperidone acutely induced 43 and repressed 46 genes, and chronically over-expressed 6 and down-regulated 11 transcripts. Selected genes were assayed by real-time PCR, then normalized to beta-actin. These assays confirmed the significance of the array results for all transcripts tested. Despite their differing receptor affinity and selectivity, our findings indicate that haloperidol and risperidone interfere with cell survival, neural plasticity, signal transduction, ionic homeostasis and metabolism in a similar manner.

Animals↗