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PubMed2026

A targetable dependency on nonsense-mediated decay for cellular homeostasis and immune control in small cell lung cancer.

Small cell lung cancer (SCLC) is one of the most aggressive malignancies, characterized by rapid metastatic dissemination and poor overall survival. Despite harboring excessive alterations, expectedly resulting in immunogenic neoantigens, patients with SCLC remain largely refractory to immunotherapy. We found abundant frameshift mutations in SCLC, regarded as highly immunogenic, counterbalanced by a hyperactive nonsense-mediated decay (NMD) pathway, responsible for frameshift-mRNA degradation. NMD activity correlated with tumor mutational burden (TMB) across cancers, suggesting that SCLC and other TMBhigh cancers may depend on NMD to limit the accumulation of mutation-derived byproducts in order to maintain cellular homeostasis and evade immune recognition. In TMBhigh SCLC models, inhibition of NMD impaired cell proliferation and induced ER stress-dependent apoptosis due to the accumulation of misfolded proteins. Genetic and pharmacological NMD inhibition in vivo effectively controlled TMBhigh tumor growth without overt toxicity. By integrating genome and transcriptome sequencing with MHC-I immunopeptidomics and functional in vitro and in vivo assays, we identified that NMD inhibition boosted neoantigen expression and presentation by tumor cells and increased T cell recognition, thus enhancing overall tumor immunogenicity and further improving immunotherapy efficacy in vivo. Our work shows that SCLC - as a TMBhigh cancer - relies on NMD for survival and immune escape, uncovering a novel TMB-dependent tractable vulnerability for this devastating disease.

Humans↗

PubMed2026

Integrative multi-omics and single-cell analysis identifies EGFR pathway activation and metabolic reprogramming as potential synthetic lethal vulnerabilities in resistance to the FGFR inhibitor AZD4547.

BACKGROUND: Although fibroblast growth factor receptor (FGFR) inhibitors (FGFRi) have demonstrated clinical promise, the inevitable emergence of acquired resistance remains a critical bottleneck, severely compromising their long-term clinical efficacy. The pan-cancer molecular landscape and heterogeneous mechanisms driving this resistance, ranging from genetic alterations to dynamic network rewiring, remain poorly understood. METHODS: We integrated large-scale pharmacogenomic profiling of the FGFR inhibitor AZD4547 from the GDSC2 and PRISM databases with single-cell RNA sequencing to dissect the multi-omics landscape of FGFRi resistance across 312 cell lines from 8 cancer types. This multi-omics framework was further extended by machine learning modeling and systematic synthetic lethality screening to uncover actionable therapeutic targets. In vitro viability assays and western blot analysis were subsequently conducted to experimentally evaluate the predicted FGFR-EGFR synthetic lethality. RESULTS: Our dual-database analysis unveiled a multi-dimensional atlas of FGFRi resistance. We identified cancer-specific genomic drivers, such as ELF4 amplification in glioblastoma, alongside key transcriptomic markers including UCP2 and FSCN1, highlighting a shift towards metabolic reprogramming and epithelial-mesenchymal transition (EMT). Single-cell analysis unveiled that resistance is linked to the heterogeneous enrichment of baseline subpopulations characterized by distinct metaprograms, including cell-cycle dysregulation. Furthermore, a random forest model built on a LASSO-derived transcriptomic signature was constructed, demonstrating promising predictive capability for AZD4547 sensitivity (mean test-set AUC = 0.73, 95% CI [0.63, 0.80]); the signature generalized well to erdafitinib but showed limited transferability to some other FGFR inhibitors (e.g. pemigatinib, BGJ398). Most notably, our synthetic lethal screening revealed a convergent reliance on compensatory RTK signaling (specifically EGFR pathway enrichment) and downstream MAPK/PI3K cascades in resistant phenotypes, providing converging computational evidence for EGFR pathway activation as an adaptive bypass mechanism. This predicted synthetic lethality was experimentally supported in two FGFR-dependent cell line models (RT112 and CCLP1), in which combined FGFR-EGFR inhibition produced marked synergistic antiproliferative effects. CONCLUSIONS: This study establishes a comprehensive multi-omics atlas of resistance to the FGFR inhibitor AZD4547, delineating convergent mechanisms of metabolic reprogramming and EGFR-mediated bypass signaling. Our findings characterize the resistance as a dynamic network rewiring and nominate rational combination strategies to overcome this therapeutic bottleneck. While FGFR-EGFR co-inhibition is experimentally supported, metabolic co-targeting remains a computationally derived, hypothesis-generating strategy.

Benzamides↗

PubMed2024

Decoding the trajectory of antibiotic resistance genes in saline and alkaline soils: Insights from different fertilization regimes.

The soil salinity and alkalinity play an important role in the occurrence and proliferation of antibiotic resistance genes (ARGs). Yet, little is known the underlying mechanism by which soil salinity and alkalinity affect antibiotic resistance evolution. Here we investigated the ARGs variation in soil salinity and alkalinity environments created by different fertilization, and explored the biological mechanisms that salinity and alkalinity alter the evolutionary paradigm of antibiotic resistance. The results showed the soil treated by organic fertilizer exhibited a low salinity, neutral level (TSD 239.20 μS/cm, pH 7.17). The ARG abundance in the OF treatment was the highest, keeping an average of 67.83 TPM. Beside the effect of direct input of organic fertilizer at the beginning, it was important to note that, ARGs abundance during planting showed significant correlations with pH and electric conductivity. We observed that changes in microbial survival strategies under different salinity and alkalinity conditions further affected ARG hosts abundance. Indoor experiments demonstrated that there was a survival trade-off between the growth of resistant bacteria and the evolution of antibiotic resistance in salinity and alkalinity environments. Meta-genomic and Meta-transcriptomic analysis consistently demonstrated bacterial antibiotic resistance was primarily associated with pyruvate, energy and lipid metabolic pathways. The functional gene related to salinity and alkalinity, like cysH, cysK, plsB and plsC showed negative correlations with MDR. Prokaryotic transcription assays validated these relations. This study well explains the prevalence of soil ARGs after different fertilization regimes and will give a deeper understanding for the effect of soil salinity and alkalinity on antibiotic resistance evolution.

Soil↗

PubMed2026

A-to-I RNA editing remodels 5'-UTR initiation codons to tune translational output.

A-to-I RNA editing is a prevalent post-transcriptional modification in higher eukaryotes that converts adenosine to inosine within RNA molecules. Because inosine is interpreted as guanosine during translation, editing can alter codon identity and potentially influence translation initiation signals. Here, we examined whether A-to-I editing within the 5' untranslated region (5'-UTR) can remodel upstream initiation codons and thereby tune downstream translation. Using luciferase-based reporter systems, we show that AUA-to-AUI editing generates an initiation-competent inosine-containing codon, whereas AUG-to-IUG editing markedly attenuates initiation and can relieve uORF-mediated repression. Quantitative in vitro and cellular assays establish the initiation hierarchy AUA < AUI < AUG, with IUG exhibiting strongly reduced initiation efficiency. Importantly, AUI-mediated upstream initiation did not behave like a canonical AUG-initiated uORF in the tested contexts; its effect on downstream ORF translation was modest and context-dependent. Transcriptome-wide bioinformatic analysis identified endogenous human transcripts whose 5'-UTRs harbor editing sites compatible with initiation-codon gain or attenuation. Reporter validation using native 5'-UTR sequences supports the possibility that editing-dependent initiation-codon remodeling can tune translational output in living cells, particularly through AUG-to-IUG-mediated derepression. Together, these findings establish a reporter-based framework in which A-to-I editing can remodel 5'-UTR initiation codons, while highlighting the need for endogenous protein-level and native-locus validation to determine physiological relevance.

RNA Editing↗

PubMed2025

A Multi-omics Exploration Revealing SLIT2 as a Prime Therapeutic Target for Peripheral Facial Paralysis: Integrating Single-Cell Transcriptomics and Plasma Proteome Data.

Peripheral facial paralysis (PFP) is a common neurological disorder characterized by facial-nerve dysfunction. Identifying therapeutic targets and understanding the molecular and cellular mechanisms underlying PFP are crucial for developing effective treatment strategies. This study combined Mendelian randomization (MR) analysis and single-cell RNA sequencing (scRNA-seq) to explore potential therapeutic candidates and their roles in PFP pathophysiology. The MR analysis included 1925 publicly available plasma protein cis-heritability instruments. Instrumental variables were selected for MR analysis to identify plasma proteins associated with PFP, followed by colocalization analysis to evaluate shared genetic variants between the identified proteins and PFP. After the initial identification of plasma proteins associated with Bell's palsy using MR analysis, a rat model of facial-nerve injury was established to further dissect underlying mechanisms at cellular and molecular levels. Using scRNA-seq technology, we delved deeply into cellular Heterogeneity and dynamic changes in gene expression in the facial-nerve nucleus tissues under both injured and control conditions, thereby achieving a systematic study ranging from macroscopic genetic associations to microscopic cellular functions. Finally, expression patterns were preliminarily validated by performing in vitro immunofluorescence analysis on the facial-nerve nucleus samples of SD rats. The MR analysis results identified 30 plasma proteins significantly associated with PFP, with nine target genes showing differential expression in the scRNA-seq data. Colocalization analysis demonstrated that slit guidance Ligand 2 (SLIT2), semaphorin 4D (SEMA4D), EGF containing fibulin extracellular matrix protein 1 (EFEMP1), and sprouty related EVH1 domain containing 2 (SPRED2) shared causal variants with PFP. SLIT2 was highly expressed in the microglia and inhibitory neurons in the experimental group, whereas SEMA4D showed elevated expression across multiple glial cell types in the same group. In contrast, EFEMP1 and SPRED2 showed distinct expression patterns in fibroblasts and oligodendrocytes. The role of SLIT2 has been previously well-documented in many central nervous system diseases. However, for the first time, this study detected SLIT2 alteration after facial-nerve injury. Altered intercellular signaling, particularly enhanced SLIT2-ROBO signaling between neurons and glial cells, was observed in the PFP group. Pseudotime analysis revealed dynamic SLIT2 expression during microglia and inhibitory neuron differentiation, mirroring changes in ROBO1 expression. Immunofluorescence analysis of rat facial-nerve nucleus samples verified that SLIT2 protein levels were significantly increased in the facial-nerve nuclei of injured samples. In conclusion, despite the fact that this study is primarily founded on animal models and despite notable differences existing between animals and humans in terms of the facial motor nucleus, this study successfully identified SLIT2 as potential therapeutic targets for PFP. The SLIT2-ROBO axis stands out as a particularly promising candidate. SLIT2 may play a role in modulating neuroimmune interactions and promoting nerve repair. These findings provide a foundation for future clinical studies and targeted interventions to enhance recovery from PFP. Future research should focus on human sample validation to enhance clinical translation.

Animals↗

PubMed2002

A microarray-assisted screen for potential Hap1 and Rox1 target genes in Saccharomyces cerevisiae.

Saccharomyces cerevisiae adapts to altered oxygen availability by differentially expressing a number of genes. Under aerobic conditions oxygen control of gene expression is exerted through the activator Hap1 and the repressor Rox1. The Hap1 transcription factor senses cellular heme status and increases expression of aerobic genes in response to oxygen. The repression of hypoxic genes under normoxic conditions results from Hap1-mediated activation of ROX1 transcription. To allow the identification of additional Hap1 and Rox1 target genes, genome-wide expression was analysed in aerobically, chemostat-cultivated hap1 and rox1 null mutants. The microarray results show that deletion of HAP1 causes a lower transcript level of 51 genes. Transcription of 40 genes was increased in rox1 mutant cells compared to wild-type cells. Combining these results with our previously described transcriptome data of aerobically and anaerobically grown cells and with computational analysis of the promoters identified 24 genes that are potentially regulated by Hap1, and 38 genes satisfied the criteria of being direct targets of Rox1. In addition, this work provides further evidence that Rox1 controls transcription of anaerobic genes through repression under normoxic conditions.

Anaerobiosis↗

PubMed2025

Parabacteroides goldsteinii mitigates parkinsonism in LRRK2 mutant mice by reducing neuroinflammation through Gut-Brain axis.

INTRODUCTION: Alterations in the gut microbiota accompanied by intestinal inflammation are early features of Parkinson's disease (PD). Mutations in the leucine-rich repeat kinase 2 (LRRK2) gene represent a common genetic risk factor for PD and inflammatory bowel disease. Parabacteroides goldsteinii has been reported to alleviate intestinal and systemic inflammation. However, whether modulation of the gut microenvironment at early disease stage can attenuate PD progression remains unclear. OBJECTIVE: To investigate the impact of P. goldsteinii colonization prior to the onset of motor dysfunction on PD progression. METHODS: We established a germ-free PD mouse model carrying the LRRK2 G2019S mutation and administered P. goldsteinii orally at the pre-symptomatic stage to evaluate its effects on motor performance and PD-related neuropathology. Spatial and bulk RNA transcriptomic analyses of brain tissue, together with cytokine profiling, were conducted to assess central changes. To investigate gut immunomodulatory mechanisms, we performed intestinal bulk and single-cell RNA sequencing, spectral flow cytometry as well as cellular bioenergetic analyses. RESULTS: Germ-free conditions partially alleviated PD-like phenotypes in LRRK2 G2019S mice. Colonization with P. goldsteinii at 5-months of age, prior to motor symptom onset, further improved locomotor performance, reduced neuronal α-synuclein aggregations, and mitigated microglial activation and dopaminergic neurodegeneration. Neuroprotection was mediated through enhanced noncanonical neuronal IL-12 receptor-dependent neurotrophic support without activating the canonical STAT4 phosphorylation pathway, along with suppression of microglial activation and downregulation of LRRK2 kinase activity. At the intestinal level, P. goldsteinii suppressed TLR4-driven inflammation, expanded anti-inflammatory intraepithelial CD4+CD8αα+ T cells, promoted dendritic cell and macrophage differentiation, upregulated epithelial tight-junction genes, and improved mitochondrial bioenergetics in intestinal cells. CONCLUSION: P. goldsteinii colonization attenuates the progression of LRRK2-associated parkinsonism by restoring intestinal homeostasis and reducing neuroinflammation. These findings underscore the therapeutic potential of modulating the gut-immune-brain axis during the prodromal stage of PD.

Animals↗

PubMed2026

COVID-19 multi-omics reveal organ-specific responses and biomarkers.

OBJECTIVE: Post-COVID-19 syndrome is characterised by persistent immune dysfunction and multi-organ sequelae. This study aimed to characterise the systemic blood molecular landscape induced by SARS-CoV-2 infection and identify prognostic markers linked to skeletal muscle mass loss, a key driver of poor outcomes. METHODS: We enrolled 30 healthy controls and 307 COVID-19 patients, collecting 422 plasma samples for integrated proteomic and metabolomic profiling to investigate organ-specific molecular alterations in COVID-19. RESULTS: We comprehensively mapped the molecular landscape of COVID-19, encompassing immune, tissue-specific, and metabolic perturbations, and delineated their interactions. Focusing on organ-damage-related molecular patterns associated with disease progression and mortality, we found that skeletal muscle mass loss contributed to poor clinical outcomes of COVID-19 (p < 0.0001). Dysregulated arginine metabolism emerged as a key metabolic signature in fatal COVID-19 cases, with GLUL, GOT1, and citrulline showing significant correlation with skeletal muscle mass loss. Longitudinal analyses further revealed that reduced citrulline levels underlie the poor outcome of COVID-19 patients with muscle mass loss. These findings were robustly supported through multiple approaches: Mendelian randomization confirmed causal relationships between citrulline depletion, sarcopenia/fat-free mass loss, and COVID-19 mortality (p < 0.05), transcriptomic analyses of SARS-CoV-2-infected golden hamsters (GSE231910) provided additional support in enrichment of arginine biosynthesis (FDR < 0.05), and in vitro experiments further demonstrated that citrulline depletion promotes pro-inflammatory M1 macrophage polarisation — a key immunological feature of critical COVID-19. Leveraging these insights, we developed a skeletal muscle loss-specific prognostic prediction model for COVID-19 using GLUL, GOT1, and citrulline. This model effectively stratified patients into high- and low-risk groups (p = 0.035). CONCLUSION: Our study advances the understanding of COVID-19-induced organ pathophysiology and provides a foundation for developing targeted therapeutic strategies for post-COVID sequelae.

COVID-19↗

PubMed2026

OsICL-associated metabolic reprogramming during dehydration in rice is regulated by ABA and modulated by ACC and its metabolites.

Drought coordinates hormonal, transcriptional, and metabolic reprogramming, but how abscisic acid (ABA) and 1-aminocyclopropane-1-carboxylic acid (ACC) jointly shape cereal dehydration responses remains unclear. We integrated hormone profiling, transcriptome and promoter analyses, synthetic promoter assays, and metabolite profiling in rice. ABA and ACC contents increased markedly in rice shoots under moderate soil water deficit. Combined ABA + ACC treatment showed larger absolute overlaps with dehydration-responsive genes than either ABA or ACC treatment alone in shoots; in roots, this pattern was observed for induced but not repressed genes. Promoters of dehydration- and ABA-inducible genes were enriched in ACGT-core motifs, including a CGTACG core preferentially embedded in ACGTACGT, designated the eXtended ACGT box (Xbox). Multimerised Xbox conferred transcriptional induction under soil water deficit and in response to ABA. OsICL was induced under soil water deficit and by ABA or ACC; in shoots, combined ABA + ACC treatment produced the highest mean transcript accumulation. OsICL overexpression and knockout lines showed altered organic-acid, sugar, and amino-acid profiles, particularly under soil water deficit, but several metabolites changed in the same direction in both line classes. These findings support an ABA-centred, ACC-modulated model of dehydration-responsive transcription and associate OsICL regulation with broader, condition-dependent changes in primary metabolism.

Oryza sativa↗

PubMed2017

Genome-wide analysis suggests a differential microRNA signature associated with normal and diabetic human corneal limbus.

Small non-coding RNAs, in particular microRNAs (miRNAs), regulate fine-tuning of gene expression and can impact a wide range of biological processes. However, their roles in normal and diseased limbal epithelial stem cells (LESC) remain unknown. Using deep sequencing analysis, we investigated miRNA expression profiles in central and limbal regions of normal and diabetic human corneas. We identified differentially expressed miRNAs in limbus vs. central cornea in normal and diabetic (DM) corneas including both type 1 (T1DM/IDDM) and type 2 (T2DM/NIDDM) diabetes. Some miRNAs such as miR-10b that was upregulated in limbus vs. central cornea and in diabetic vs. normal limbus also showed significant increase in T1DM vs. T2DM limbus. Overexpression of miR-10b increased Ki-67 staining in human organ-cultured corneas and proliferation rate in cultured corneal epithelial cells. MiR-10b transfected human organ-cultured corneas showed downregulation of PAX6 and DKK1 and upregulation of keratin 17 protein expression levels. In summary, we report for the first time differential miRNA signatures of T1DM and T2DM corneal limbus harboring LESC and show that miR-10b could be involved in the LESC maintenance and/or their early differentiation. Furthermore, miR-10b upregulation may be an important mechanism of corneal diabetic alterations especially in the T1DM patients.

Adult↗

PubMed2003

The transcriptome and its translation during recovery from cell cycle arrest in Saccharomyces cerevisiae.

Complete genome sequences together with high throughput technologies have made comprehensive characterizations of gene expression patterns possible. While genome-wide measurement of mRNA levels was one of the first applications of these advances, other important aspects of gene expression are also amenable to a genomic approach, for example, the translation of message into protein. Earlier we reported a high throughput technology for simultaneously studying mRNA level and translation, which we termed translation state array analysis, or TSAA. The current studies test the proposition that TSAA can identify novel instances of translation regulation at the genome-wide level. As a biological model, cultures of Saccharomyces cerevisiae were cell cycle-arrested using either alpha-factor or the temperature-sensitive cdc15-2 allele. Forty-eight mRNAs were found to change significantly in translation state following release from alpha-factor arrest, including genes involved in pheromone response and cell cycle arrest such as BAR1, SST2, and FAR1. After the shift of the cdc15-2 strain from 37 degrees C to 25 degrees C, 54 mRNAs were altered in translation state, including the products of the stress genes HSP82, HSC82, and SSA2. Thus, regulation at the translational level seems to play a significant role in the response of yeast cells to external physical or biological cues. In contrast, surprisingly few genes were found to be translationally controlled as cells progressed through the cell cycle. Additional refinements of TSAA should allow characterization of both transcriptional and translational regulatory networks on a genomic scale, providing an additional layer of information that can be integrated into models of system biology and function.

Cell Cycle↗

PubMed2026

CARS1 as a Prognostic Biomarker and Candidate Therapeutic Vulnerability in Hepatocellular Carcinoma: Insights Into Tumor Progression and the Immune Microenvironment.

BACKGROUND: Cysteinyl-tRNA synthetase 1 (CARS1) has been included in ferroptosis-related prognostic signatures, but its clinicopathological relevance, cellular functions, and relationship with the immune microenvironment in hepatocellular carcinoma (HCC) remain incompletely characterized. METHODS: Transcriptomic and clinical data from The Cancer Genome Atlas Liver Hepatocellular Carcinoma (TCGA-LIHC) dataset were integrated with corresponding data from an institutional HCC tissue cohort of 60 patients. CARS1 expression was evaluated by immunohistochemistry, and immune infiltration was examined using single-sample gene-set enrichment analysis (ssGSEA) and multiplex immunofluorescence, as well as by analyzing public single-cell datasets. The effects of CARS1 depletion were evaluated in MHCC97H and Hep3B cells using Cell Counting Kit-8 (CCK-8) assays, cell-cycle profiling, wound-healing assays, Transwell migration assays, western blotting, and erlotinib-sensitivity assays. RESULTS: CARS1 expression was elevated in HCC and was associated with adverse clinicopathological features and poor overall survival. Quantitative immunohistochemistry confirmed elevated CARS1 protein expression in tumor tissues. CARS1 depletion inhibited cell proliferation, altered cell-cycle distribution, impaired migration, and enhanced in vitro sensitivity to erlotinib. High CARS1 expression was also associated with increased infiltration of Th2-like immune cells. CONCLUSIONS: Elevated CARS1 expression is associated with an adverse biological and immune phenotype in HCC. These clinical, histopathological, and loss-of-function findings support further investigation of CARS1 as a prognostic marker and candidate therapeutic target in HCC, although additional mechanistic and in vivo validation is required.

Humans↗

PubMed2002

High glucose-altered gene expression in mesangial cells. Actin-regulatory protein gene expression is triggered by oxidative stress and cytoskeletal disassembly.

High extracellular glucose plays a pivotal role in the pathophysiology of diabetic nephropathy. Here we report 200 genes, identified using suppression-subtractive hybridization, that are differentially expressed when human mesangial cells are propagated in high ambient glucose in vitro. The major functional classes of genes identified included modulators and products of extracellular matrix protein metabolism, regulators of cell growth and turnover, and a cohort of actin cytoskeleton regulatory proteins. Actin cytoskeletal disassembly is a prominent feature of diabetic nephropathy. The induction of actin cytoskeleton regulatory gene expression by high glucose was attenuated by the inhibitor of reactive oxygen species generation, carbonyl cyanide m-chlorophenylhydrazone but not by the protein kinase C inhibitor GF 109203X and was not mimicked by the addition of transforming growth factor beta. Enhanced expression of actin cytoskeleton regulatory genes was also observed following disruption of the mesangial cell actin cytoskeleton by cytochalasin D. In aggregate, these results suggest that the induction of genes encoding actin cytoskeleton regulatory proteins (a) is a prominent component of the mesangial cell transcriptomic response in diabetic nephropathy and (b) is dependent on oxidative stress, is independent of protein kinase C and transforming growth factor-beta, and represents an adaptive response to actin cytoskeleton disassembly.

Actins↗

PubMed2003

Genes duplicated by polyploidy show unequal contributions to the transcriptome and organ-specific reciprocal silencing.

Most eukaryotes have genomes that exhibit high levels of gene redundancy, much of which seems to have arisen from one or more cycles of genome doubling. Polyploidy has been particularly prominent during flowering plant evolution, yielding duplicated genes (homoeologs) whose expression may be retained or lost either as an immediate consequence of polyploidization or on an evolutionary timescale. Expression of 40 homoeologous gene pairs was assayed by cDNA-single-stranded conformation polymorphism in natural (1- to 2-million-yr-old) and synthetic tetraploid cotton (Gossypium) to determine whether homoeologous gene pairs are expressed at equal levels after polyploid formation. Silencing or unequal expression of one homoeolog was documented for 10 of 40 genes examined in ovules of Gossypium hirsutum. Assays of homoeolog expression in 10 organs revealed variable expression levels and silencing, depending on the gene and organ examined. Remarkably, silencing and biased expression of some gene pairs are reciprocal and developmentally regulated, with one homoeolog showing silencing in some organs and the other being silenced in other organs, suggesting rapid subfunctionalization. Duplicate gene expression was examined in additional natural polyploids to characterize the pace at which expression alteration evolves. Analysis of a synthetic tetraploid revealed homoeolog expression and silencing patterns that sometimes mirrored those of the natural tetraploid. Both long-term and immediate responses to polyploidization were implicated. Data suggest that some silencing events are epigenetically induced during the allopolyploidization process.

DNA, Plant↗

PubMed2025

Methodologies for Mitochondrial Omic Profiling During Spaceflight.

To be able to understand how spaceflight can affect human biology, there is a need for maximizing the amount of information that can be obtained from experiments flown to space. Recently there has been an influx of data obtained from astronauts through multi-omics approaches based on both governmental and commercial spaceflight missions. In addition to data from humans, mitochondrial specific data is gathered for other experiments from rodents and other organisms that are flown in space. This data has started to universally demonstrate that mitochondrial dysfunction is the key regulator associated with increasing health risks associated with spaceflight. This mitochondrial dysfunction can have influence downstream on immune suppression, inflammation, circadian rhythm issues, and more. Due to the space environment, standard methodologies have to be altered for performing mitochondrial specific analysis and in general sample collection for omics. To perform mitochondrial specific analysis and data collection from samples flown to space we will outline the current sample collection methods, processing of the samples, and specific analysis. Specifically we will highlight the different mitochondrial methodologies and challenges involved with research associated with spaceflight.

Space Flight↗

PubMed2026

Pathomics-based machine learning models for predicting METTL5 expression and prognosis in lung adenocarcinoma.

BACKGROUND: METTL5, an N6-methyladenosine (m6A) RNA methyltransferase, has been implicated in tumor progression, but its prognostic value and non-invasive prediction in lung adenocarcinoma (LUAD) remain unclear. This study aimed to develop a pathomics-based machine learning model to predict METTL5 expression from histopathological images and evaluate its prognostic significance in LUAD. METHODS: A total of 327 LUAD patients from The Cancer Genome Atlas (TCGA) with matched hematoxylin and eosin (H&E) slides, transcriptomic, and clinical data were included and randomly divided into training and validation sets (7:3). Quantitative histopathological features were extracted using PyRadiomics. Feature selection was performed via maximum relevance minimum redundancy (mRMR) and recursive feature elimination (RFE), followed by construction of a Gradient Boosting Machine (GBM) model. A pathomics score (PS) was generated to assess prognostic relevance. Survival analyses, gene set variation analysis (GSVA), tumor mutational burden (TMB), immune infiltration analysis, and in vitro functional assays were conducted. RESULTS: METTL5 overexpression was independently associated with poor overall survival [hazard ratio (HR) =1.637, P=0.007]. The model achieved good predictive performance [area under the curve (AUC) =0.847 in the training set and 0.752 in the validation set]. High PS was significantly associated with worse survival and remained an independent prognostic factor (HR =1.563, P=0.03). Elevated PS correlated with altered metabolic pathways, increased TMB, and immune microenvironment changes. METTL5 knockdown reduced proliferation, migration, invasion, and epithelial-mesenchymal transition (EMT) in A549 cells. CONCLUSIONS: The pathomics-based model accurately predicts METTL5 expression and provides prognostic stratification in LUAD, supporting its potential as a practical imaging-derived biomarker.

Methyltransferase-like 5↗

PubMed2026

PathMED: an R toolkit for single-sample molecular scoring and machine learning with omics data.

MOTIVATION: Molecular scoring is a popular approach for studying pathway-level functional alterations with omics data. Using molecular scores for tasks such as single-sample molecular characterisation, phenotype prediction or disease stratification has several advantages compared to using omics data directly. Molecular scores provide biological interpretability and are more generalisable across datasets, facilitating data integration and machine learning applications. However, numerous scoring methods are available through different software packages, and currently there is a lack of tools to easily use these scores for model training and prediction. RESULTS: We developed pathMED, an R/Bioconductor package that unifies various scoring methods in a simple framework. Furthermore, pathMED also contains a machine learning module to train and test models that use the calculated molecular scores to predict clinical outcomes. We demonstrate some of its potential applications in three use cases using public omics data. We showed the generalisability of machine learning models trained on transcriptomic scores in predicting clinical outcomes when deploying on proteomic scores. We also demonstrated the application of transcriptomics scores in predicting breast cancer treatment response and identifying pathways strongly associated to tumour biology and treatment response. Finally, we demonstrated the benefit of integrating a novel gene set dissection step into the analysis pipeline to resolve disease heterogeneity at the pathway level. AVAILABILITY: PathMED is freely available in the Bioconductor repository (https://bioconductor.org/packages/release/bioc/html/pathMED.html). Code to reproduce the analyses is publicly available at https://github.com/GENyO-BioInformatics/pathMED_article.

Software↗

PubMed2025

MPAC: a computational framework for inferring pathway activities from multi-omic data.

Fully capturing cellular state requires examining genomic, epigenomic, transcriptomic, proteomic, and other assays for a biological sample and comprehensive computational modeling to reason with the complex and sometimes conflicting measurements. Modeling these so-called multi-omic data is especially beneficial in disease analysis, where observations across omic data types may reveal unexpected patient groupings and inform clinical outcomes and treatments. We present Multi-omic Pathway Analysis of Cells (MPAC), a computational framework that interprets multi-omic data through prior knowledge from biological pathways. MPAC leverages network relationships encoded in pathways through a factor graph to infer consensus activity levels for proteins and associated pathway entities from multi-omic data, runs permutation testing to eliminate spurious activity predictions, and groups biological samples by pathway activities to allow identifying and prioritizing proteins with potential clinical relevance, e.g., associated with patient prognosis. Using DNA copy number alteration and RNA-seq data from head and neck squamous cell carcinoma patients from The Cancer Genome Atlas as an example, we demonstrate that MPAC predicts a patient subgroup related to immune responses not identified by analysis with either input omic data type alone. Key proteins identified via this subgroup have pathway activities related to clinical outcome as well as immune cell compositions. Our MPAC R package, available at https://bioconductor.org/packages/MPAC, enables similar multi-omic analyses on new datasets.

Journal Article↗