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Results for “transcriptomic profiling”

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PubMed2026

A contextual activity score (CAS) for inferring ADAR-associated transcriptional activity across RNA-seq, single-cell, and spatial transcriptomics.

BACKGROUND AND OBJECTIVE: Adenosine-to-inosine RNA editing, catalyzed by Adenosine Deaminases Acting on RNA (ADARs), is a widespread modification involved in neural function, immune regulation, and cancer. The Alu Editing Index (AEI) is the standard metric to estimate ADAR activity but requires raw sequencing reads and is poorly suited for single-cell and spatial transcriptomic data. This study aimed to develop an alternative framework for inferring ADAR-associated transcriptional activity from gene expression data across diverse transcriptomic technologies. METHODS: We developed the Contextual Activity Score (CAS), a framework based on transcriptional signatures from ADAR perturbation experiments. Context-specific signatures were generated for human neurons, mouse neurons, and cancer models to infer ADAR1 and ADAR2 activity. CAS was computed from normalized gene expression matrices using regulon-based enrichment analysis. Performance was evaluated by comparing with the Alu Editing Index across bulk RNA sequencing datasets, simulated sequencing depths, and library preparation protocols. RESULTS: CAS showed strong concordance with the Alu Editing Index across multiple datasets, while remaining robust to reduced sequencing depth and different library protocols. Unlike the Alu Editing Index, CAS can be applied to single-cell and spatial transcriptomic data and enables the independent assessment of ADAR2 activity. In cancer and neuronal contexts, CAS captured biologically meaningful variations in ADAR-associated transcriptional activity at sample, cell-type, and spatial levels. CONCLUSION: CAS provides a scalable approach applicable across multiple RNA-seq protocols for estimating ADAR-associated transcriptional activity using gene expression data. This method, implemented in an open-source R package for broad adoption, expands the ability to study ADAR-associated transcriptional activity across transcriptomic modalities where direct editing quantification is challenging, such as single-cell and spatial transcriptomics.

Adenosine Deaminase↗

PubMed2025

Spatial Genomic Approaches to Investigate HOX Genes in Mouse Brain Tissues.

Spatial transcriptomic tools are an upcoming and powerful way to investigate targeted gene expression patterns within tissues. These tools offer the unique advantage of visualizing and understanding gene expression while preserving tissue integrity, thereby maintaining the spatial context of genes. Curio is a robust spatial transcriptomic tool that facilitates high throughput comprehensive spatial gene expression analysis across the entir e transcriptome with high efficiency. Here, we present a bioinformatics protocol for performing whole transcriptome gene expression analysis of mouse brain tissue using Curio. Specifically, we demonstrate using computational techniques to visualize expression patterns of various HOX genes in the mouse brain.

Animals↗

PubMed2024

Transcriptomic analysis reveals key molecular signatures across recovery phases of hemorrhagic fever with renal syndrome.

BACKGROUND: Hemorrhagic fever with renal syndrome (HFRS), a life-threatening zoonosis caused by hantavirus, poses significant mortality risks and lacks specific treatments. This study aimed to delineate the transcriptomic alterations during the recovery phases of HFRS. METHODS: RNA sequencing was employed to analyze the transcriptomic alterations in peripheral blood mononuclear cells from HFRS patients across the oliguric phase (OP), diuretic phase (DP), and convalescent phase (CP). Twelve differentially expressed genes (DEGs) were validated using quantitative real-time PCR in larger sample sets. RESULTS: Our analysis revealed pronounced transcriptomic differences between DP and OP, with 38 DEGs showing consistent expression changes across all three phases. Notably, immune checkpoint genes like CD83 and NR4A1 demonstrated a monotonic increase, in contrast to a monotonic decrease observed in antiviral and immunomodulatory genes, including IFI27 and RNASE2. Furthermore, this research elucidates a sustained attenuation of immune responses across three phases, alongside an upregulation of pathways related to tissue repair and regeneration. CONCLUSION: Our research reveals the transcriptomic shifts during the recovery phases of HFRS, illuminating key genes and pathways that may serve as biomarkers for disease progression and recovery.

Hemorrhagic Fever with Renal Syndrome↗

PubMed2026

Exploration and experimental verification of triaptosis-related prognostic genes and cells in gastric cancer.

BACKGROUND: Triaptosis is a recently characterized form of programmed cell death with unclear implications in cancer. This study aimed to investigate the prognostic significance and biological relevance of triaptosis in gastric cancer (GC). METHODS: Transcriptomic and clinical data from TCGA-STAD and GSE62254, and single-cell RNA sequencing data from GSE183904 were analyzed. Triaptosis-related gene (TRG) scores were calculated using single-sample gene set enrichment analysis. Differentially expressed genes identified in TRG-score and GC-versus-normal comparisons underwent functional enrichment, Cox regression, and least absolute shrinkage and selection operator regression to develop an externally validated signature. Immune profiles, pathway activity, somatic mutations, tumor mutational burden (TMB), predicted drug sensitivity, and clinical features were compared by risk group. Single-cell analyses assessed TRG activity, prognostic gene expression, cell-cell communication, and pseudotime. Reverse transcription-quantitative PCR and Western blotting assessed mRNA expression and protein levels, respectively. RESULTS: A TRG-based prognostic model comprising ASPN, GRB14, and VTN was developed and externally validated, effectively distinguishing patients into two distinct risk groups with notably different survival outcomes. mRNA expression of all three genes and their protein levels were significantly higher in SGC-7901 cells than in GES-1 cells. High-risk patients had higher stromal scores and distinct immune profiles; 15 immune cell types differed between groups. Single-cell analysis revealed fibroblasts and pericytes among high-TRG-active cell types. Prognostic genes were significantly overexpressed in fibroblasts, which also showed high TRG activity. Fibroblasts demonstrated enhanced communication with pericytes, whereas tumor-derived fibroblasts showed weaker communication with macrophages, indicating immune microenvironment remodeling. CONCLUSION: The three-gene prognostic signature predicted GC prognosis and was associated with distinct immune and genomic features, suggesting potential value for risk stratification and personalized treatment.

Humans↗

PubMed2025

Personalizing chemotherapy drug selection using a novel transcriptomic chemogram.

Gene signatures predictive of chemotherapeutic response have the potential to extend the reach of precision medicine by allowing oncologists to optimize treatment for individuals. Most published predictive signatures are only capable of predicting response for individual drugs, but most chemotherapy regimens utilize combinations of different agents. We propose a unified framework, called the chemogram, that uses predictive signatures to rank the relative predicted sensitivity of different drugs for individual tumors. Using this approach, providers could efficiently screen against many therapeutics to optimize chemotherapy at any time, whether it be for a treatment-naive tumor or a chemo-resistant tumor requiring a new treatment strategy. To demonstrate the utility of the chemogram, we used predictive signatures (extracted from a previously established method) in our framework to rank predicted sensitivity among drugs within cell lines. We then compared the rank order of predicted and observed response against each drug. Across most cancer types, chemogram-generated predictions were more accurate than predictions made by randomly generated gene signatures, signatures extracted from differential expression alone, and was comparable to another established method of drug response prediction. Our framework demonstrates the ability of transcriptomic signatures to not only predict chemotherapeutic response, but also correctly assign rankings of drug sensitivity on an individual basis. Additionally, scaling the chemogram to include more drugs does not compromise accuracy.

Humans↗

PubMed2026

Transcriptomic analysis provides molecular insights into the innate immune defense of Mactra veneriformis against Vibrio alginolyticus infection.

Mactra veneriformis is an economically important bivalve mollusc in China, but its aquaculture is frequently threatened by Vibrio infections, particularly Vibrio alginolyticus. To investigate the molecular immune response of M. veneriformis to V. alginolyticus, we performed RNA-seq analysis of hepatopancreatic tissues collected at 48 h post-infection, the peak mortality time point, with PBS-injected individuals used as controls. Infection with V. alginolyticus caused severe histopathological damage in the hepatopancreas and resulted in a cumulative mortality of 53.3% over 14 d, compared with 3.3% in the control group. Transcriptomic analysis identified 2623 differentially expressed genes (DEGs), including 1585 significantly up-regulated genes and 1038 down-regulated genes. KEGG enrichment analysis demonstrated that DEGs were significantly enriched in immune related and metabolism pathways, including the JAK-STAT signaling pathway, RIG-I-like receptor (RLR) signaling pathway, and cytochrome P450 (CYP450) signaling pathway. Collectively, these findings revealed candidate immune related genes (tlr3, tlr5, myd88, nfkb1, il-17d, and ifi44l), a putative TLR-MyD88-NF-κB signaling axis, and KEGG signaling pathways, including JAK-STAT, RLR and CYP450, that may be involved in the innate immune response of M. veneriformis to V. alginolyticus infection. These results provide a transcriptomic basis for understanding host-pathogen interactions in this species and highlight candidate genes and pathways for future functional validation and potential application in disease-resistance breeding.

Animals↗

PubMed2025

Revealing cancer driver genes through integrative transcriptomic and epigenomic analyses with Moonlight.

Cancer involves dynamic changes caused by (epi)genetic alterations such as mutations or abnormal DNA methylation patterns which occur in cancer driver genes. These driver genes are divided into oncogenes and tumor suppressors depending on their function and mechanism of action. Discovering driver genes in different cancer (sub)types is important not only for increasing current understanding of carcinogenesis but also from prognostic and therapeutic perspectives. We have previously developed a framework called Moonlight which uses a systems biology multi-omics approach for prediction of driver genes. Here, we present an important development in Moonlight2 by incorporating a DNA methylation layer which provides epigenetic evidence for deregulated expression profiles of driver genes. To this end, we present a novel functionality called Gene Methylation Analysis (GMA) which investigates abnormal DNA methylation patterns to predict driver genes. This is achieved by integrating the tool EpiMix which is designed to detect such aberrant DNA methylation patterns in a cohort of patients and further couples these patterns with gene expression changes. To showcase GMA, we applied it to three cancer (sub)types (basal-like breast cancer, lung adenocarcinoma, and thyroid carcinoma) where we discovered 33, 190, and 263 epigenetically driven genes, respectively. A subset of these driver genes had prognostic effects with expression levels significantly affecting survival of the patients. Moreover, a subset of the driver genes demonstrated therapeutic potential as drug targets. This study provides a framework for exploring the driving forces behind cancer and provides novel insights into the landscape of three cancer sub(types) by integrating gene expression and methylation data.

Humans↗

PubMed2026

Bacteria and phage consortia modulate cecal SCFA production and host metabolism to enhance feed efficiency in ducks.

BACKGROUND: The gut microbiota influences poultry health, nutrition, feed efficiency (FE), and overall productivity. However, the relationship between gut microbes, including bacteria and phages, and FE in ducks remains underexplored. To address this, we integrated cecal 16S amplicon, metagenome, microbiota-derived short-chain fatty acids (SCFAs) profiling, liver transcriptome, and serum metabolome data to illustrate the contribution of the gut microbiome (bacteria and viruses) to duck FE. RESULTS: We reconstructed viral genomes and prokaryotic metagenome-assembled genomes (MAGs) and annotated their genes using comprehensive databases. Prokaryotic hosts of viruses were also predicted to understand virus-host dynamics within the gut ecosystem. Our results revealed that high-FE ducks have higher concentration of propionate and butyrate in cecum compared with low-FE ducks. The metagenome sequencing revealed distinct cecal microbiota profiles between two groups, with increased relative abundance of representative SCFA producers, especially Paraprevotella sp905215575 and Bacteroides sp944322345, and enhanced SCFA-biosynthesis pathways in high-FE ducks. Virome genome assembly identified two phages encoding auxiliary metabolic genes (AMGs) involved in pyruvate metabolism, enhancing nutrient availability for host bacteria to produce SCFAs (e.g., temperate phage-encoded pyruvate phosphate dikinase) or exploiting host central metabolic pathways for viral replication (e.g., lytic phage-encoded formate C-acetyltransferase). Furthermore, these representative SCFA-producing bacteria and phage consortia were associated with serum metabolites (including L-histidine and 4-hydroxydecanedioylcarnitine) linked to duck FE. CONCLUSION: Collectively, these findings provide novel insights into the gut microbial factors regulating FE in ducks, offering potential strategies to optimize poultry nutrition and productivity. Video Abstract.

Animals↗

PubMed2025

NMFProfiler: a multi-omics integration method for samples stratified in groups.

MOTIVATION: The development of high-throughput sequencing enabled the massive production of "omics" data for various applications in biology. By analyzing simultaneously paired datasets collected on the same samples, integrative statistical approaches allow researchers to get a global picture of such systems and to highlight existing relationships between various molecular types and levels. Here, we introduce NMFProfiler, an integrative supervised NMF that accounts for the stratification of samples into groups of biological interest. RESULTS: NMFProfiler was shown to successfully extract signatures characterizing groups with performances comparable to or better than state-of-the-art approaches. In particular, NMFProfiler was used in a clinical study on atopic dermatitis (AD) and to analyze a multi-omic cancer dataset. In the first case, it successfully identified signatures combining known AD protein biomarkers and novel transcriptomic biomarkers. In addition, it was also able to extract signatures significantly associated to cancer survival. AVAILABILITY AND IMPLEMENTATION: NMFProfiler is released as a Python package, NMFProfiler (v0.3.0), available on PyPI.

Humans↗

PubMed2006

Molecular phenotype of zebrafish ovarian follicle by serial analysis of gene expression and proteomic profiling, and comparison with the transcriptomes of other animals.

BACKGROUND: The ability of an oocyte to develop into a viable embryo depends on the accumulation of specific maternal information and molecules, such as RNAs and proteins. A serial analysis of gene expression (SAGE) was carried out in parallel with proteomic analysis on fully-grown ovarian follicles from zebrafish (Danio rerio). The data obtained were compared with ovary/follicle/egg molecular phenotypes of other animals, published or available in public sequence databases. RESULTS: Sequencing of 27,486 SAGE tags identified 11,399 different ones, including 3,329 tags with an occurrence superior to one. Fifty-eight genes were expressed at over 0.15% of the total population and represented 17.34% of the mRNA population identified. The three most expressed transcripts were a rhamnose-binding lectin, beta-actin 2, and a transcribed locus similar to the H2B histone family. Comparison with the large-scale expressed sequence tags sequencing approach revealed highly expressed transcripts that were not previously known to be expressed at high levels in fish ovaries, like the short-sized polarized metallothionein 2 transcript. A higher sensitivity for the detection of transcripts with a characterized maternal genetic contribution was also demonstrated compared to large-scale sequencing of cDNA libraries. Ferritin heavy polypeptide 1, heat shock protein 90-beta, lactate dehydrogenase B4, beta-actin isoforms, tubulin beta 2, ATP synthase subunit 9, together with 40 S ribosomal protein S27a, were common highly-expressed transcripts of vertebrate ovary/unfertilized egg. Comparison of transcriptome and proteome data revealed that transcript levels provide little predictive value with respect to the extent of protein abundance. All the proteins identified by proteomic analysis of fully-grown zebrafish follicles had at least one transcript counterpart, with two exceptions: eosinophil chemotactic cytokine and nothepsin. CONCLUSION: This study provides a complete sequence data set of maternal mRNA stored in zebrafish germ cells at the end of oogenesis. This catalogue contains highly-expressed transcripts that are part of a vertebrate ovarian expressed gene signature. Comparison of transcriptome and proteome data identified downregulated transcripts or proteins potentially incorporated in the oocyte by endocytosis. The molecular phenotype described provides groundwork for future experimental approaches aimed at identifying functionally important stored maternal transcripts and proteins involved in oogenesis and early stages of embryo development.

Animals↗

PubMed2026

Association Between Ticagrelor and Glucose Homeostasis Regulation: Insights from Genetic and Transcriptomic Analyses.

Emerging evidence has demonstrated the additional therapeutic benefits of ticagrelor in acute coronary syndrome (ACS) patients with diabetes. However, the underlying mechanisms of this association remain elusive. Mendelian randomization (MR) analysis using genome-wide association study (GWAS) data on ticagrelor, plasma proteomics and type 2 diabetes was employed to identify causal mediator proteins. RNA sequencing (RNA-seq) of ticagrelor-treated HepG2 cells revealed the molecular pathways regulating glucose metabolism. Genetically proxied ticagrelor was significantly associated with a reduced risk of diabetes (OR = 0.859, 95% CI: 0.783-0.934, P = 7.98E-05), and 24.41% of this effect was mediated by upregulation of BDH2 protein. In vitro experiments confirmed the enhanced effect of ticagrelor on glucose consumption. Transcriptome analysis revealed that mitochondrial respiratory chain transfer and oxidative phosphorylation (OXPHOS) were significantly enriched, and genes related to ATP biosynthesis were significantly upregulated. These findings highlight the non-platelet function of ticagrelor in maintaining glucose homeostasis, providing insights into potential drug repurposing in the future.

Humans↗

PubMed2005

Gene expression profile of activated microglia under conditions associated with dopamine neuronal damage.

Microglia are the resident antigen-presenting cells within the central nervous system (CNS), and they serve immune-like functions in protecting the brain against injury and invading pathogens. By contrast, activated microglia can secrete numerous reactants that damage neurons. The pathogenesis of various neurodegenerative diseases has been associated with microglial activation, but the signaling pathways that program a neuronally protective or destructive phenotype in microglia are not known. To increase the understanding of microglial activation, microarray analysis was used to profile the transcriptome of BV-2 microglial cells after activation. Microglia were activated by lipopolysaccharide, the HIV neurotoxic protein TAT, and dopamine quinone, each of which has been linked to dopamine neuronal damage. We identified 210 of 9882 genes whose expression was differentially regulated by all activators (116 increased and 94 decreased in expression). Gene ontology analysis assigned up-regulated genes to a number of specific biological processes and molecular functions, including immune response, inflammation, and cytokine/chemokine activity. Genes down-regulated in expression contribute to conditions that are permissive of microglial migration, lowered adhesion to matrix, lessened phagocytosis, and reduction in receptors that oppose chemotaxis and inflammation. These results elaborate a broad profile of microglial genes whose expression is altered by conditions associated with both neurodegenerative diseases and microglial activation.

Animals↗

PubMed2025

Transcriptomic analysis at 48 h postmortem: a proof of concept for the identification of biomarkers to estimate time since death.

BACKGROUND: The postmortem interval (PMI) refers to the time elapsed between an individual's death and the examination of the body. Tissues undergo a sequence of anatomical changes following death, which are routinely used to estimate the PMI. METHODS: To determine if these anatomical changes are associated with identifiable genomic adaptations that could characterize the PMI more accurately, we analyzed the rat skeletal muscle transcriptome at 0 and 48 h postmortem using Clariom™ S arrays. This study investigates whether specific transcriptomic changes correlate with PMI progression, offering a potential molecular tool to complement established anatomical methods. RESULTS: A total of 3,873 differentially expressed mRNAs were identified, of which 2,787 downregulated and 1,086 upregulated transcripts. The most significantly downregulated mRNA was Tnni1 (FC = -30.95, p = 1 × 10-3), while the most upregulated were mt-ATP6, mt-ATP8, and mt-CO3 (FC > 7.78, p < 1.36 × 10-12). Gene ontology (GO) enrichment analyses revealed that mRNAs upregulated at 48 h in the PMI were primarily associated with vascular and endothelial processes, including nitric oxide transport and angiogenesis. Conversely, downregulated mRNAs were linked to mitochondrial activity and cellular metabolism, reflecting both a transient vascular response and metabolic pathway shutdown in the rat skeletal muscle. CONCLUSION: Our results demonstrate significant transcriptomic changes at 48 h postmortem, highlighting specific genes and biological pathways that may serve as candidate biomarkers for PMI estimation.

Animals↗

PubMed2024

Protocol to decode the role of transcriptionally active microbes in SARS-CoV-2-positive patients using an RNA-seq-based approach.

The elucidation of the role of microorganisms in human infections has been hindered by difficulties using conventional culture-based techniques. Here, we present a protocol for the investigation of transcriptionally active microbes (TAMs) using an RNA sequencing (RNA-seq)-based approach. We describe the steps for RNA isolation, viral genome sequencing, RNA-seq library preparation, and metatranscriptomic and transcriptomic analysis. This protocol permits a comprehensive evaluation of TAMs' contributions to the differential severity of infectious diseases, with a particular focus on diseases such as COVID-19. For complete details on the use and execution of this protocol, please refer to Devi et al.1.

Humans↗

PubMed2026

Identification and characterization of the HSP gene family in the Chinese giant salamander: Expression patterns under combined environmental stress.

BACKGROUND: The Chinese giant salamander (Andrias davidianus) is a critically endangered living fossil species that is highly sensitive to changes in water temperature. However, systematic studies on the heat shock protein (HSP) gene family and its response mechanisms to environmental stress in this species remain limited. This study utilized transcriptome data from captive-bred salamanders exposed to combined temperature and pathogen stress. Bioinformatics tools were employed to identify the HSP gene family of A. davidianus (AndHSP) and to analyze their evolution, structure, and function, thereby revealing their regulatory mechanisms in response to environmental stress. RESULTS: A total of 72 AndHSPs were identified and classified into five subfamilies. Phylogenetic analysis revealed that each subfamily is evolutionarily conserved and functionally related. Gene expression analysis demonstrated that pathogen infection induced the expression of AndHSPs, and elevated temperature significantly intensified this response. Nine key differentially expressed genes were identified, predominantly from the AndHSP70 subfamily, with AndHSP70-18 exhibiting rapid heat-induced expression. Tissue-specific analysis showed high expression of AndHSP60 in the spleen. A qPCR validation confirmed the reliability of the transcriptome expression results. CONCLUSIONS: This study presents the first systematic identification of the AndHSP gene family and elucidates its cooperative stress response mechanisms under combined temperature and pathogen stress. These findings provide a molecular basis for understanding the species' environmental adaptation and have important implications for its conservation and artificial breeding.

Animals↗

PubMed2026

Protocol to predict gene expression from transcriptomic data using PREDICT.

Linking DNA sequence variation to context-specific transcriptional programs is a critical challenge in regulatory genomics, especially for non-model organisms. Here, we present PREDICT, a modular Python package for discovering cis-regulatory elements and transcription factor binding motifs. We describe steps to identify enriched k-mers from differentially expressed genes, map them to known motifs, quantify their impact on gene expression, and visualize motif co-occurrences. PREDICT provides a robust, k-mer-based approach to uncover regulatory logic in diverse genomic systems. For complete details on the use and execution of this protocol, please refer to Yen et al. and Liu et al.1,2.

Gene Expression Profiling↗

PubMed2026

Multi-Omics Integration Identifies a Five-Gene Metabolic Signature With Experimental Validation in Clear Cell Renal Cell Carcinoma.

BACKGROUND: Clear cell renal cell carcinoma (ccRCC) is hallmarked by profound metabolic reprogramming; however, its intricate crosstalk with the tumor immune microenvironment (TIME) and its clinical ramifications remain inadequately elucidated. This study aims to systematically decipher the metabolic-immune interplay in ccRCC through multi-omics integration, with the goal of identifying robust prognostic biomarkers and actionable therapeutic vulnerabilities. AIMS: This study aims to systematically decipher the metabolic-immune interplay in clear cell renal cell carcinoma (ccRCC) through multi‑omics integration, and to identify robust prognostic biomarkers and actionable therapeutic vulnerabilities that can inform precision risk stratification and individualized treatment strategies. METHODS: We integrated bulk transcriptomic, genomic, and clinical data from multiple ccRCC cohorts. Differential expression and functional enrichment analyses were performed to characterize metabolic pathway alterations. Mendelian randomization (MR) was employed to infer causal relationships between metabolic disorders and ccRCC risk. A machine learning-based prognostic framework, incorporating SHAP (SHapley Additive exPlanations) for feature interpretability, was constructed and rigorously validated. TIME heterogeneity was dissected using deconvolution algorithms, while drug sensitivity, tumor mutation burden (TMB), and TIDE scores were utilized to assess therapeutic responses and immune evasion. Candidate gene function was evaluated through in vitro gain- and loss-of-function assays, with expression validated via TCGA, HPA, western blot, and qRT-PCR. RESULTS: Enrichment analysis identified coordinated dysregulation in lipid metabolism, energy homeostasis, and hypoxia response pathways. MR analysis confirmed lipid metabolism disorders as a causal risk factor for ccRCC. Our machine-learning model, centered on five core SHAP-identified features (SUCLA2, ACAT1, PC, SUCLG1, and HMGCS2), demonstrated superior predictive accuracy over conventional clinical staging. Immune profiling unveiled dichotomous TIME states: the low-risk group retained active immune surveillance, whereas the high-risk group was enriched with immunosuppressive subsets. Drug sensitivity screening pinpointed LY2109761 and carmustine as high-risk-specific candidate agents. Furthermore, TMB and TIDE analyses stratified high-risk patients displaying genomic instability and immune evasion phenotypes. Functionally, SUCLA2 knockdown significantly enhanced ccRCC cell proliferation and invasion, while its overexpression suppressed these malignant phenotypes, corroborating its tumor-suppressive role. Expression patterns of the hub genes were consistently validated across multi-level datasets and experimental assays. CONCLUSION: This study establishes a precision oncology framework for ccRCC by functionally linking metabolic biomarkers, immunophenotypes, and stratified therapeutic strategies. Importantly, we identify SUCLA2 as a potential functional tumor suppressor and a promising target for further mechanistic and translational investigation.

Humans↗

PubMed2005

Overlaps in the transcriptional profiles of Medicago truncatula roots inoculated with two different Glomus fungi provide insights into the genetic program activated during arbuscular mycorrhiza.

Arbuscular mycorrhiza (AM) is a widespread symbiotic association between plants and fungal microsymbionts that supports plant development under nutrient-limiting and various stress conditions. In this study, we focused on the overlapping genetic program activated by two commonly studied microsymbionts in addition to identifying AM-related genes. We thus applied 16,086 probe microarrays to profile the transcriptome of the model legume Medicago truncatula during interactions with Glomus mosseae and Glomus intraradices and specified a total of 201 plant genes as significantly coinduced at least 2-fold, with more than 160 being reported as AM induced for the first time. Several hundred genes were additionally up-regulated during a sole interaction, indicating that the plant genetic program activated in AM to some extent depends on the colonizing microsymbiont. Genes induced during both interactions specified AM-related nitrate, ion, and sugar transporters, enzymes involved in secondary metabolism, proteases, and Kunitz-type protease inhibitors. Furthermore, coinduced genes encoded receptor kinases and other components of signal transduction pathways as well as AM-induced transcriptional regulators, thus reflecting changes in signaling. By the use of reporter gene expression, we demonstrated that one member of the AM-induced gene family encoding blue copper binding proteins (MtBcp1) was both specifically and strongly up-regulated in arbuscule-containing regions of mycorrhizal roots. A comparison of the AM expression profiles to those of nitrogen-fixing root nodules suggested only a limited overlap between the genetic programs orchestrating root endosymbioses.

Carrier Proteins↗