A survey of best practices for RNA-seq data analysis

Prognostic significance of Ki-67 in assessing the risk of progression, relapse or metastasis in pheochromocytomas and paragangliomas

INTRODUCTION

Since the Fourth edition of the WHO classification, PPGLs have been recognized for their metastatic potential, though no clear features can accurately predict this behavior. The prognostic value of Ki-67 in assessing the risk of progression, relapse, or metastasis in PPGLs remains debated.

METHODS

This cohort study included 501 patients diagnosed with PPGLs at the First Hospital of Jilin University between 2000 and 2022, with clinical data, treatment details, pathological indicators, and germline gene test results collected. Bulk sequencing was performed on formalin-fixed paraffin-embedded (FFPE) primary tumor samples from 87 patients. Progression-free survival (PFS) was analyzed using multivariable Cox regression.

RESULTS

Among the 119 enrolled patients with PPGLs, the average age was 45.7 ± 14.0 years, and the median follow-up time was 46 months. A significant finding was the high expression of CDK1, a gene known to be significantly associated with the metastatic risk of PPGLs, in samples with Ki-67 ≥ 3% (p < 0.0001). More importantly, patients with PPGLs and a Ki-67 level ≥ 3% had a 3.59-fold higher risk of progression, relapse or metastasis compared to those with Ki-67 < 3% (HR = 4.59, 95% CI: 1.06-11.95), after adjusting for all confounding factors. In the composite model, the addition of Ki-67 enhanced the predictive ability of the combined model of SDHB, primary site, tumor size, and invade neighboring tissue (AUC = 0.888, 95% CI: 0.808-0.967 vs. AUC = 0.874, 95% CI: 0.783-0.965).

CONCLUSION

A Ki-67 level ≥ 3% is associated with an increased risk of progression, relapse or metastasis in patients with PPGLs.

Dynamics of cytosolic and organellar gene transcripts in wild and cultivated genotypes of pigeon pea due to simulated herbivory

Pigeon pea (Cajanus cajan), widely grown in India, suffers significant yield losses due to pod borers (Helicoverpa armigera and Maruca vitrata). Therefore, studying the host resistance mechanism is pivotal for crop improvement. In this study, we conducted transcriptome analysis on two wild-type (WT) Cajanus scarabaeoides accessions (ICP-15761 and ICP-15738) having high levels of resistance to pod borers and two cultivated C. cajan genotypes, ICPL-332 (moderately resistant) and ICPL-87 (susceptible), following simulated herbivory with H. armigera oral secretions (OS). Differential gene expression analysis identified 3573 and 4677 differentially expressed genes (DEGs) in ICP-15761 and ICP-15738, whereas 4149 and 3639 DEGs were documented in ICPL-332 and ICPL-87, respectively. Genes related to chloroplast biogenesis, photosynthesis, and chlorophyll metabolism exhibited significant differential expression, indicating chloroplast reprogramming under simulated herbivory. Significant upregulation of key defense genes, including chitinases and cysteine proteases, in C. scarabaeoides accessions highlighted robust defense pathway activation. A genotype-specific shift in transcription factors, phytohormones, and calcium signaling-related gene expression was noted. Higher levels of expression of aspartic proteinases and pathogenesis-related proteins in cultivated genotypes suggesting adaptive evolutionary traits. This is a novel insight on molecular mechanism of defense in a wild type, C. scarabaeoides and cultivated genotypes of pigeon pea under simulated herbivory. The information on cytosolic and organellar gene changes in pigeon pea due to H. armigera OS mediated-simulated herbivory may help develop pigeon pea varieties that are resistant to pod borer infestations.

Development of a microRNA-Based age estimation model using whole-blood microRNA expression profiling

Age estimation is a critical aspect of human identification. Traditional methods, reliant on morphological examinations, are often suitable for living subjects. However, there are relatively few studies on age estimation based on biological samples, such as blood. Recent advancements have concentrated on DNA methylation for forensic age prediction. However, to explore further possibilities, this study investigated microRNAs (miRNAs) as alternative molecular markers for age estimation. Peripheral blood samples from 127 healthy individuals were analyzed for miRNA expression using small RNA sequencing. Lasso regression selected 103 candidate miRNAs, and Shapley additive explanations (SHAP) analysis identified 38 key miRNAs significant for age prediction. Five machine learning models were developed, with the elastic net model achieving the best performance (MAE of 4.08 years) on the testing set, surpassing current miRNA age estimation results. Additionally, we observed significant changes in the expression levels of miRNAs in healthy individuals aged 48-52 years. This study demonstrated the potential of blood miRNA biomarkers in age prediction and provides a set of miRNA markers for developing more accurate age prediction methods.

Transcriptomic profiles reveal hormonal regulation of sugar-induced stolon initiation in potato

Potato (Solanum tuberosum L.) is one of the world's most important non-cereal food crops, with stolon development playing a crucial role in determining tuber yield. While some studies have examined the effects of sugars on potato stolon growth, their influence-particularly that of sucrose-on early stolon development remains unclear. Furthermore, the regulatory role of plant hormones in this process has yet to be established. Using a combination of in vitro culture, transcriptomics, gene expression analysis, and biochemical approaches, we investigated the contribution of sucrose (3% or 8%) on potato seedling stem nodes and stolon initials through phenotypic observation, RNA sequencing (RNA-seq), comparison of expression patterns, and hormone quantification. Firstly, compared to other types of sugars, we found that high concentrations of sucrose were the most effective in inducing stolon initial formation in potato seedlings. Furthermore, RNA-seq data showed that high sucrose levels significantly up-regulated the expression of genes involved in sugar metabolism and plant hormone metabolism. Additionally, the development of stem nodes and stolon initials under high sucrose conditions was also closely linked to hormone metabolism. Notably, high sucrose concentrations contributed to stem node and stolon initial development by modulating the IAA, CK, and GA signaling pathways. Based on the endogenous hormone measurement, and exogenous hormone application, together with heterologous overexpression of a potato Auxin response factor 9 (StARF9), we concluded that the early development of potato stolons was regulated by plant hormones, particularly auxin. In summary, this study elucidates the hormonal regulation of stolon initiation under high sucrose concentrations, offering a theoretical foundation and potential targets for in vitro culture and genetic improvement of potato.

High expression of HECW1 is associated with the poor prognosis and cancer progression of gastric cancer

BACKGROUND

The E3 ubiquitin ligase HECW1 was found to be involved in ubiquitination modifications during malignant progression of multiple tumors. However, the prognostic role of HECW1 expression in gastric cancer (GC) remains unclear.

METHODS

The Tumor Immunoassay Resource (TIMER2.0) system evaluated the association of HECW1 with tumor-infiltrating lymphocytes in carcinomas. The UALCAN assessed HECW1 mRNA expression levels in GC tissues and examined their associations with clinicopathological characteristics. The Kaplan Meier-plotter analyzed the effect of HECW1 on the survival of GC patients. The cBioPortal retrieved information about genetic variants in HECW1 gene. Protein‒protein interaction (PPI) networks associated with HECW1 were explored using the STRING database. The functional effects of HECW1 on GC cells were evaluated through proliferation (Cell Counting Kit-8), apoptosis (Flow cytometry), and migration (Transwell and wound healing assays). The RNA-Seq was applied to explore the underlying mechanisms.

RESULTS

HECW1 demonstrated significant overexpression in GC tumor tissues, correlating with adverse clinical outcomes. Clinically, elevated HECW1 expression exhibited an inverse association with tumor-infiltrating CD8+ T lymphocytes while demonstrating a positive correlation with macrophages, DCs, and neutrophils infiltration, suggesting its potential involvement in tumor immune evasion mechanisms. Functional validation revealed that HECW1 knockdown markedly suppressed GC cell proliferation and migratory capacity, concurrently promoting apoptotic cell death. Mechanistic investigations identified that HECW1 exerts its oncogenic effects through dysregulation of the Hippo signaling pathway, with its silencing effectively attenuating tumor progression via pathway modulation.

CONCLUSIONS

HECW1 upregulation is significantly associated with poor prognosis and immune infiltration in GC patients, emphasizing its potential as a prognostic biomarker.

Worm Perturb-Seq: massively parallel whole-animal RNAi and RNA-seq

Transcriptomes provide highly informative molecular phenotypes that, combined with gene perturbation, can connect genotype to phenotype. An ultimate goal is to perturb every gene and measure transcriptome changes, however, this is challenging, especially in whole animals. Here, we present 'Worm Perturb-Seq (WPS)', a method that provides high-resolution RNA-sequencing profiles for hundreds of replicate perturbations at a time in living animals. WPS introduces multiple experimental advances combining strengths of Caenhorhabditis elegans genetics and multiplexed RNA-sequencing with a novel analytical framework, EmpirDE. EmpirDE leverages the unique power of large transcriptomic datasets and improves statistical rigor by using gene-specific empirical null distributions to identify DEGs. We apply WPS to 103 nuclear hormone receptors (NHRs) and find a striking 'pairwise modularity' in which pairs of NHRs regulate shared target genes. We envision the advances of WPS to be useful not only for C. elegans, but broadly for other models, including human cells.

Identification of novel gene expression patterns and pathways involved in PARP-1 inhibitor resistance

US-FDA has approved PARP-1 inhibitors (Talazoparib, Olaparib, Rucaparib, and Niraparib) as the first line of treatment for many cancer types (e.g., breast, ovarian, pancreatic, and prostate) caused by mutations in breast cancer gene 1 and 2 (BRCA1/2). However, developing resistance to PARP-1 inhibitors is a major concern, which limits therapeutic effectiveness. In the present study, we identified novel gene signatures implicated in developing resistance to Olaparib. Meta-analysis was performed on publicly available RNA-Seq data related to ovarian and breast cancers from the GEO (Gene Expression Omnibus) database. Differential gene expression analysis, gene ontology, KEGG pathway enrichment, and protein-protein interaction (PPI) networking analyses were performed. A total of 139 Common DEGs (Differentially Expressed Genes) were identified, comprising 69 and 70 genes that were upregulated and downregulated respectively. KEGG Pathways "P53 signaling pathway" and "Positive regulation of developmental process(BP)", "endoplasmic reticulum lumen(CC)," and "growth factor binding(MF)", were found to be potentially associated with Olaparib resistance. Five hub genes were identified using PPI networking of which FN1, CCN2, and JUN may play a significant role in the development of Olaparib resistance and could be promising therapeutic and diagnostic biomarkers for dealing with Olaparib resistance in BRCA1/2 mutant breast and ovarian cancer.

Study on the mechanism of acteoside in treating purinomycin aminonucleoside-induced chronic glomerulonephritis in childhood rats based on Cxcr4-PI3K-Akt-eNOS axis

The diagnosis and treatment of chronic glomerulonephritis (CGN) during childhood pose distinct challenges. Acteoside (ACT) is the primary active ingredient extracted from the leaves of Rehmannia glutinosa by our research group, accounting for 2.15 %, which possess multiple biological activities, especially for nephropathy treatment. However, its mechanism intervention on CGN in children remains obscure. In this study, we established a model of purinomycin aminonucleoside (PAN)-induced CGN in childhood rats to assess the potential therapeutic effect and underlying mechanisms of ACT. Leveraging network pharmacology and multi-omics technology, we delved into the effects and mechanisms of ACT intervention on CGN. And these findings were further corroborated through qRT-PCR, western blot and targeted metabolomics. Our results demonstrated that ACT had significantly efficient in the treatment of CGN in childhood rats by improving the key indicators and pathological changes. Further, ACT could significantly regulate differences in endogenous small molecules and genes based on non-target metabolomics and transcriptomics. Meanwhile, target capture analysis found the crucial targets of ACT treatment in CGN. Integrated analysis of multi-omics study indicated that PI3K/Akt signaling pathway and its downstream amino acid metabolism were significantly enriched, hinting at the essential regulatory pathway for ACT in treating of CGN. Finally, through qRT-PCR, western blot and targeted metabolomics, it was verified that ACT could ameliorate CGN through Cxcr4-PI3K-Akt-eNOS signaling pathway, thereby regulating amino acid metabolism. The collective results were consistent with those of multi-omics analysis. Our study illuminated that ACT had notable curative effect on CGN rats, and preliminarily elucidated its mechanism of action. Our research will provide solid basis for the treatment of chronic glomerulonephritis in children with ACT and developing it into innovative traditional Chinese medicine.

Utilizing Nanopore direct RNA sequencing of blood from patients with sepsis for discovery of co- and post-transcriptional disease biomarkers

BACKGROUND

RNA sequencing of whole blood has been increasingly employed to find transcriptomic signatures of disease states. These studies traditionally utilize short-read sequencing of cDNA, missing important aspects of RNA expression such as differential isoform abundance and poly(A) tail length variation.

METHODS

We used Oxford Nanopore Technologies sequencing to sequence native mRNA extracted from whole blood from 12 patients with definite bacterial and viral sepsis and compared with results from matching Illumina short-read cDNA sequencing data. Additionally, we explored poly(A) tail length variation, novel transcript identification, and differential transcript usage.

RESULTS

The correlation of gene count data between Illumina cDNA- and Nanopore RNA-sequencing strongly depended on the choice of analysis pipeline; NanoCount for Nanopore and Kallisto for Illumina data yielded the highest mean Pearson's correlation of 0.927 at the gene level and 0.736 at the transcript isoform level. We identified 2 genes with differential polyadenylation, 9 genes with differential expression and 4 genes with differential transcript usage between bacterial and viral infection. Gene ontology gene set enrichment analysis of poly(A) tail length revealed enrichment of long tails in mRNA of genes involved in signaling and short tails in oxidoreductase molecular functions. Additionally, we detected 240 non-artifactual novel transcript isoforms.

CONCLUSIONS

Nanopore RNA- and Illumina cDNA-gene counts are strongly correlated, indicating that both platforms are suitable for discovery and validation of gene count biomarkers. Nanopore direct RNA-seq provides additional advantages by uncovering additional post- and co-transcriptional biomarkers, such as poly(A) tail length variation and transcript isoform usage.

TempO-seq and RNA-seq gene expression levels are highly correlated for most genes: A comparison using 39 human cell lines

Recent advances in transcriptomics technologies allow for whole transcriptome gene expression profiling using targeted sequencing techniques, which is becoming increasingly popular due to logistical ease of data acquisition and analysis. As data from these targeted sequencing platforms accumulates, it is important to evaluate their similarity to traditional whole transcriptome RNA-seq. Thus, we evaluated the comparability of TempO-seq data from cell lysates to traditional RNA-Seq from purified RNA using baseline gene expression profiles. First, two TempO-seq data sets that were generated several months apart at different read depths were compared for six human cell lines. The average Pearson correlation was 0.93 (95% CI: 0.90-0.96) and principal component analysis (PCA) showed that these two TempO-seq data sets were highly reproducible and could be combined. Next, TempO-seq data was compared to RNA-Seq data for 39 human cell lines. The log2 normalized expression data for 19,290 genes within both platforms were well correlated between TempO-seq and RNA-seq (Pearson correlation 0.77, 95% CI: 0.76-0.78), and the majority of genes (15,480 genes, 80%) had concordant gene expression levels. PCA showed a platform divergence, but this was readily resolved by calculating relative log2 expression (RLE) of genes compared to the average expression across cell lines in each platform. Application of gene ontology analysis revealed that ontologies associated with histone and ribosomal functions were enriched for the 20% of genes with non-concordant expression levels (3,810 genes). On the other hand, gene ontologies annotated to cellular structure functions were enriched for genes with concordant expression levels between the platforms. In conclusion, we found TempO-seq baseline expression data to be reproducible at different read depths and found TempO-seq RLE data from lysed cells to be comparable to RNA-seq RLE data from purified RNA across 39 cell lines, even though the datasets were generated by different laboratories using different cell stocks.

Expert recommendations to standardize transcriptomic analysis in inflammatory bowel disease clinical trials

BACKGROUND AND AIMS

Substantial methodological and reporting heterogeneity confounds the interpretation and generalizability of transcriptomic data for inflammatory bowel disease (IBD) studies. We aimed to develop recommendations to standardize transcriptomic research in clinical trials.

METHODS

A 2-part study was undertaken. A systematic review identified reports of transcriptomic analyses utilizing samples from IBD clinical trials. Studies that used global RNA assay platforms were included. Data regarding study design, methodological approaches, and reporting of transcriptomic research were extracted. The systematic review results informed a modified Research and Development/University of California Los Angeles appropriateness methodology process and the development of survey statements focused on topics with substantial methodological heterogeneity. A panel of 16 IBD translational researchers and gastroenterologists rated the appropriateness of survey statements in 2 rounds.

RESULTS

The systematic review identified 37 reports that included transcriptomic analyses of samples from IBD patients. The appropriateness of 416 statements was rated by 15 panellists in the first survey. The final survey included 305 statements, of which 14 panellists rated 75% appropriate, 1% inappropriate, and 24% uncertain. The panel determined that transcriptomic analysis for multiple research objectives was appropriate at most phases of clinical development in patients with active disease. Recommendations regarding study sample size; biopsy number, location, preservation, and storage; and data analysis and reporting were also generated.

CONCLUSION

The persistence of existing methodologic heterogeneity may continue to limit the value of transcriptomic research in IBD. This study provides expert recommendations to address and overcome these discrepancies and foster the inclusion of this research in clinical development.

Autonomous Self-Evolving Research on Biomedical Data: The DREAM Paradigm

In contemporary biomedical research, the efficiency of data-driven methodologies is constrained by large data volumes, the complexity of tool selection, and limited human resources. To address these challenges, a Data-dRiven self-Evolving Autonomous systeM (DREAM) is developed as the first fully autonomous biomedical research system capable of independently conducting scientific investigations without human intervention. DREAM autonomously formulates and evolves scientific questions, configures computational environments, and performs result evaluation and validation. Unlike existing semi-autonomous systems, DREAM operates without manual intervention and is validated in real-world biomedical scenarios. It exceeds the average performance of top scientists in question generation, achieves a higher success rate in environment configuration than experienced human researchers, and uncovers novel scientific findings. In the context of the Framingham Heart Study, it demonstrated an efficiency that is over 10 000 times greater than that of average scientists. As a fully autonomous, self-evolving system, DREAM offers a robust and efficient solution for accelerating biomedical discovery and advancing other data-driven scientific disciplines.

Re-analysis of mobile mRNA datasets raises questions about the extent of long-distance mRNA communication

Short-read RNA-seq studies of grafted plants have led to the proposal that thousands of messenger RNAs (mRNAs) move over long distances between plant tissues1-7, potentially acting as signals8-12. Transport of mRNAs between cells and tissues has been shown to play a role in several physiological and developmental processes in plants, such as tuberization13, leaf development14 and meristem maintenance15; yet for most mobile mRNAs, the biological relevance of transport remains to be determined16-19. Here we perform a meta-analysis of existing mobile mRNA datasets and examine the associated bioinformatic pipelines. Taking technological noise, biological variation, potential contamination and incomplete genome assemblies into account, we find that a high percentage of currently annotated graft-mobile transcripts are left without statistical support from available RNA-seq data. This meta-analysis challenges the findings of previous studies and current views on mRNA communication.

Multimodal discovery of bavachinin A: A natural FLT3 agonist for treating thrombocytopenia

BACKGROUND

Radiation-induced thrombocytopenia (RIT) poses a serious risk to patients with cancer undergoing radiotherapy and leads to hemorrhage and mortality. Unfortunately, effective treatment options for RIT are currently limited.

PURPOSE

This study aimed to discover active compound from Fructus Psoraleae, a traditional Chinese medicine recognized for its hemostatic properties, and to elucidate its mechanism of action in the treatment of RIT.

METHODS

The efficacy of Fructus Psoraleae in treating thrombocytopenia was assessed using network pharmacology. A drug-screening model was built using a naive Bayes algorithm to determine the effective compounds in Fructus Psoraleae. Giemsa staining and flow cytometry were used to evaluate the effects of bavachinin A on megakaryocytes (MK) differentiation. RIT and thrombopoietin (TPO) receptor (c-MPL) knockout (c-MPL-/-) mice were used to assess the therapeutic efficacy of bavachinin A in mitigating thrombocytopenia. Tg (cd41:eGFP) zebrafish were used to investigate the effect of bavachinin A on thrombopoiesis. RNA sequencing (RNA-seq), molecular docking simulations, molecular dynamics simulations, drug affinity responsive target stability assay (DARTS), and biolayer interferometry (BLI) were used to elucidate the molecular mechanisms of action of bavachinin A against thrombocytopenia.

RESULTS

In silico analysis using a drug screening model identified bavachinin A as promising candidate compound derived from Fructus Psoraleae. In vitro experiments demonstrated that Bavachinin A induced MK differentiation. In vivo experiments revealed that bavachinin A augmented platelet levels and improved coagulation in RIT mice, facilitated megakaryopoiesis and platelet levels in c-MPL-/- mice, and accelerated thrombopoiesis in zebrafish. Furthermore, RNA-seq, molecular docking simulations, molecular dynamics simulations, DARTS, and BLI demonstrated that bavachinin A bound directly to fms-like tyrosine kinase 3 (FLT3). Notably, blocking FLT3 or phosphoinositide 3-kinase (PI3K)/protein kinase B (Akt) pathway hindered bavachinin-A-induced MK differentiation. However, repressing the TPO/c-MPL signaling pathway had no significant effect.

CONCLUSION

Bavachinin A promotes MK differentiation and thrombopoiesis by directly binding to FLT3 and activating PI3K/Akt signaling. Importantly, this effect was not dependent on the conventional TPO/c-MPL signaling pathway. This study underscores the translational potential of bavachinin A as a promising novel therapeutic for thrombocytopenia, offering novel insights into TPO-independent mechanisms of thrombopoiesis and establishing a robust multimodal approach for drug discovery.

DeepQA: A Unified Transcriptome-Based Aging Clock Using Deep Neural Networks

Understanding the complex biological process of aging is of great value, especially as it can help develop therapeutics to prolong healthy life. Predicting biological age from gene expression data has shown to be an effective means to quantify aging of a subject, and to identify molecular and cellular biomarkers of aging. A typical approach for estimating biological age, adopted by almost all existing aging clocks, is to train machine learning models only on healthy subjects, but to infer on both healthy and unhealthy subjects. However, the inherent bias in this approach results in inaccurate biological age as shown in this study. Moreover, almost all existing transcriptome-based aging clocks were built around an inefficient procedure of gene selection followed by conventional machine learning models such as elastic nets, linear discriminant analysis etc. To address these limitations, we proposed DeepQA, a unified aging clock based on mixture of experts. Unlike existing methods, DeepQA is equipped with a specially designed Hinge-Mean-Absolute-Error (Hinge-MAE) loss so that it can train on both healthy and unhealthy subjects of multiple cohorts to reduce the bias of inferring biological age of unhealthy subjects. Our experiments showed that DeepQA significantly outperformed existing methods for biological age estimation on both healthy and unhealthy subjects. In addition, our method avoids the inefficient exhaustive search of genes, and provides a novel means to identify genes activated in aging prediction, alternative to such as differential gene expression analysis.

Genomic and transcriptomic sequencing in prostate cancer

PURPOSE OF REVIEW

Genomic and transcriptomic sequencing technologies have revolutionized our ability to characterize prostate cancer at the molecular level. The underlying premise of next-generation sequencing technologies and their current and evolving applications in prostate cancer management are provided in the review.

RECENT FINDINGS

Improved methodologies are allowing timely sequencing of the coding regions or both the coding and noncoding regions of the genome to help identify potential mutations and structural variations in the prostate cancer genome, some of which are currently also targetable therapeutically. DNA microarray- based differential gene expression has been supplanted by RNA sequencing (RNA-seq), which not only allows for more accurate quantitation but also nucleotide-level resolution to investigate the entire transcriptome, including alternative gene spliced transcripts and noncoding RNA transcripts, whose full clinical implications have yet to be fully understood and realized. Gene classifier platforms that predict risk of recurrence or metastasis are being incorporated into prostate cancer management algorithms. In the appropriate clinical context, not only somatic but also germline mutation testing is being recommended.

SUMMARY

Continued clinical integration of sequencing technologies and ongoing research will lead to improved understanding of prostate cancer biology and prostate cancer treatment.

Differential expression reveals inflammatory response and oxidative stress genes in dentin caries

OBJECTIVE

This study employs RNA-Seq to investigate differentially expressed genes involved in extracellular matrix (ECM) degradation, focusing on collagenases (MMP-2 and MMP-9) and their inhibitors (TIMP-1 and TIMP-2).

DESIGN

Total RNA from caries and caries-free teeth was extracted from pulp, predentin, and dentin. Samples were sequenced using Illumina® technology. Quality validation was done with FASTQC, and low-quality bases were removed using TRIMMOMATIC. Reads were aligned using SALMON against the human transcriptome (CHR38), followed by quantification using Transcripts Per Million. Differential gene expression analysis was conducted using DESeq2 (FDR < 0.05, |log2FC| ≥ 1). Functional enrichment analyses employed Gene Ontology and KEGG databases.

RESULTS

Sequencing produced 16-37 million reads per sample, with an average alignment rate of 88.08 %. A total of 334 differentially expressed genes (DEGs) were identified: 195 upregulated and 139 downregulated. Upregulated genes included SAA1 (log2FC = 2.3, p-adj = 0.001) and ORM1 (log2FC = 2.0, p-adj = 0.002), associated with inflammation. MMP-9 was significantly downregulated (log2FC = -1.8, p-adj = 0.003), while MMP-2 showed higher expression in decayed tissues. TIMP-1 expression increased in decayed dentin; TIMP-2 was upregulated in both decayed and caries-free dentin. Protein interaction analysis identified EGFR and metallothioneins as key acute-phase proteins.

CONCLUSIONS

This study reveals the role of inflammatory and oxidative stress-related genes in dentin caries and shows disruption in the ECM degradation-repair balance. Increased MMP-2 and TIMP-1 expression suggests a compensatory response. MMP activity may serve as a therapeutic target to enhance tissue resilience and slow caries progression.

LncRNA SLNCR phenocopies the E2F1 DNA binding site to promote melanoma progression

The long non-coding RNA SLNCR and the transcription factor E2F1 are known melanoma oncogenes. We show that SLNCR binds to E2F1 to promote the proliferation, invasion, and migration of melanoma cells from the bloodstream into the lungs. Blocking SLNCR-E2F1 complex formation without reducing the levels of either SLNCR or E2F1 prevents lung extravasation in mice. A 60-nt fragment of SLNCR contains two RNA analogs of the E2F1 DNA binding site (BS) in opposite orientations and can form a hairpin RNA that phenocopies the E2F1 DNA BS. Molecular dynamics (MD) simulations and biochemical experiments indicate that this fragment of SLNCR binds to the E2F1 DNA-binding domain more effectively than the E2F1 DNA BS. MD simulations predict higher affinity for DNA-E2F1 complex formation but faster kinetics and a greater number of RNA-amino acid contacts for the RNA-E2F1 complex, suggesting that RNA binding to E2F1 is more kinetically favorable.

Transcriptomic prediction of breeding values in loblolly pine

Phenotypic variation in forest trees can be partitioned into subsets controlled by genetic variation and by environmental factors, and heritability expressed as the proportion of total phenotypic variation attributed to genetic variation. Applied tree breeding programs can use matrices of relationships, based either on recorded pedigrees in structured breeding populations or on genotypes of molecular genetic markers, to model genetic covariation among related individuals and predict genetic values for individuals for whom no phenotypic measurements are available. This study tests the hypothesis that genetic covariation among individuals of similar genetic value will be reflected in shared patterns of gene expression or shared sequence variation in expressed genes. We collected gene expression data by high-throughput sequencing of RNA isolated from pooled seedlings from parents of known genetic value, and compared alternative approaches to data analysis to test this hypothesis. Selection of specific sets of transcripts increased the predictive power of models over that observed using all transcripts or SNPs. Models using information of both transcript levels and SNP variation showed increased predictive accuracy relative to models using only SNPs or transcript levels. Known pedigree relationships are not required for this approach to modeling genetic variation, so it has potential to allow broader application of genetic covariance modeling to natural populations of forest trees.

Determining Antimicrobial Resistance in the Plastisphere: Lower Risks of Nonbiodegradable vs Higher Risks of Biodegradable Microplastics

The plastisphere is a potential contributor to global antimicrobial resistance (AMR), posing potential threats to public and environmental health. However, comprehensively quantifying the contribution of microplastics with different biodegradability to AMR is lacking. In this study, we systematically quantified AMR risk mediated by biodegradable and nonbiodegradable microplastics using abundance-based methods and a custom AMR risk ranking framework that includes antimicrobial resistance genes (ARGs) abundance, mobility, and host pathogenicity. Our results demonstrated that biodegradable microplastics exhibited higher AMR risk compared to that of nonbiodegradable plastics. Key resistance genes, including those for multidrug, bacitracin, and aminoglycoside resistance, were predominant. Machine learning analysis identified cell motility as the most significant signature associated with AMR risk, highlighting its potential role in promoting ARGs dissemination. In addition, biodegradable microplastics promoted oxidative stress and SOS responses, which likely enhanced horizontal gene transfer (HGT) and AMR. Metagenome-assembled genomes (MAGs) analysis uncovered the colocalization of microplastic degradation genes, ARGs, and virulence factors (VFs), further supporting the elevated risk in biodegradable plastisphere. The proximity of ARGs to mobile genetic elements (MGEs) suggests that microplastic degradation processes might favor ARGs mobility. These findings would contribute critical insights into AMR dissemination in the plastisphere, emphasizing the need for integrated environmental and public health strategies under the context of One Health.

Transcriptomic Biomarkers in Blood Indicative of the Administration of Recombinant Human Erythropoietin to Thoroughbred Horses

Erythropoiesis-stimulating agents (ESAs) continue to be a significant threat to the integrity of human and equine sports. Besides conventional direct testing, monitoring the biomarkers associated with the effects of ESAs may provide a complementary approach via indirect detection to enhance doping control. In this study, we applied RNA-sequencing (RNA-seq) to discover blood RNA biomarkers in Thoroughbred horses after administration with a long-acting form of recombinant human erythropoietin (rhEPO), methoxy polyethylene glycol epoetin beta, Mircera®. A single subcutaneous administration of Mircera® at ~ 4.2 μg/kg was effective in elevating haematocrit, haemoglobin and erythrocyte levels to varying extents in as early as 4 days post-administration in all three horses, which persisted for 40 days post-administration (the last sample collected). RNA-seq was applied to analyse blood transcriptomic changes. Differential gene expression analysis has allowed the identification of 46 genes that showed dramatic and temporary upregulation at 4-11 days after Mircera® administration. STRING analysis has identified the functional enrichment of 15 genes important for erythropoiesis and erythrocyte function, supporting the idea of an increased release into the peripheral circulation of residual RNA-containing reticulocytes after rhEPO exposure, which would otherwise mature normally inside the bone marrow in horses. Machine learning of blood transcriptomes has enabled the discrimination of samples with or without Mircera administration. Therefore, our study has provided new insights into the biological mechanism of erythropoiesis caused by rhEPO administration in horses and has provided evidence supporting the control of misuse of ESAs by monitoring the equine blood transcriptome.

Identification and validation of a novel autophagy-related biomarker in obstructive sleep apnea syndrome

STUDY OBJECTIVES

Obstructive sleep apnea syndrome is closely associated with tumor growth. Chronic intermittent hypoxia promotes autophagy and is related to malignant tumor development. However, the role of autophagy in obstructive sleep apnea syndrome progression remains unclear.

METHODS

obstructive sleep apnea syndrome datasets (GSE135917 and GSE38792) from Gene Expression Omnibus were analyzed to identify differentially expressed genes and autophagy-related differentially expressed genes. Gene Ontology, Kyoto Encyclopedia of Genes and Genomes, and gene set enrichment analysis were conducted, and a protein-protein interaction network identified hub genes. Colorectal cancer datasets from The Cancer Genome Atlas were used for differential expression and survival analyses, along with gene set enrichment analysis and immune infiltration analysis. Chronic intermittent hypoxia-induced autophagy and oxidative stress were investigated in Sprague-Dawley rats using reactive oxygen species assays. Hub genes were validated in rats and obstructive sleep apnea syndrome patient samples.

RESULTS

Gene set enrichment analysis revealed significant differences in autophagy-related gene expression among obstructive sleep apnea syndrome patients. Hub genes ATG5, CASP1, MAPK8, EIF4G1, and TANK-binding kinase 1 were identified, with ATG5 and TANK-binding kinase 1 validated. Autophagy-related differentially expressed genes were predominantly upregulated in colorectal cancer tissues. TANK-binding kinase 1 expression in colorectal cancer patients was associated with enhanced sensitivity to immunotherapy and CD8 + T cell, macrophage, and regulatory T cell infiltration, potentially influencing the immune microenvironment. The animal experiments showed that chronic intermittent hypoxia increased reactive oxygen species levels, suggesting that chronic intermittent hypoxia plays a role in autophagy. TANK-binding kinase 1 expression was significantly higher in obstructive sleep apnea syndrome patients than in controls, and continuous positive airway pressure did not alter TANK-binding kinase 1 levels.

CONCLUSIONS

This study is the first to describe the potential contribution of TANK-binding kinase 1 to the development of obstructive sleep apnea syndrome and its potential as a novel biomarker and potential therapeutic target for obstructive sleep apnea syndrome.

Assessing concordance between RNA-Seq and NanoString technologies in Ebola-infected nonhuman primates using machine learning

This study evaluates the concordance between RNA sequencing (RNA-Seq) and NanoString technologies for gene expression analysis in non-human primates (NHPs) infected with Ebola virus (EBOV). A detailed comparison of both platforms revealed a strong correlation, with Spearman coefficients for 56 out of 62 samples ranging from 0.78 to 0.88. The mean and median coefficients were 0.83 and 0.85, respectively. Bland-Altman analysis confirmed high consistency across most measurements, with values falling within the 95% limits of agreement. Using a machine learning approach with the Supervised Magnitude-Altitude Scoring (SMAS) method trained on NanoString data, OAS1 was identified as a key gene signature for distinguishing RT-qPCR positive from negative samples. Remarkably, when used as the sole predictor in a logistic regression model, OAS1 maintained its predictive power on RNA-Seq data from the same cohort of EBOV-infected NHPs, achieving 100% accuracy in distinguishing infected from non-infected samples. OAS1 was also tested in a completely independent held-out test set, consisting of human monocyte-derived dendritic cells (DC) isolated and infected with different strains of the Ebola virus: wild-type (wt), VP35m, VP24m, along with a double mutant VP35m & VP24m, and again demonstrated a 100% accuracy rate in differentiating EBOV-infected from mock-infected samples, confirming its effectiveness as a predictive marker across diverse experimental setups and virus strains. Further differential expression analysis across both platforms identified 12 common genes (including ISG15, OAS1, IFI44, IFI27, IFIT2, IFIT3, IFI44L, MX1, MX2, OAS2, RSAD2, and OASL) that showed the highest levels of statistical significance and biological relevance. Gene Ontology (GO) analysis confirmed the involvement of these genes in key immune and viral infection pathways, highlighting their importance in EBOV infection. RNA-Seq uniquely identified genes such as CASP5, USP18, and DDX60, which are important in immune regulation and antiviral defense and were not detected by NanoString, demonstrating the broader detection capabilities of RNA-Seq. This study indicates a very strong agreement between RNA-Seq and NanoString platforms in gene expression analysis, with RNA-Seq displaying broader capabilities in identifying gene signatures.

The Mechanism of Xuanyu Tongjing Decoction Regulating NOD/NFκB Pathway to Inhibit Ectopic Tissue Inflammation to Reduce Ovarian Damage in Rats with Ovarian Endometriosis

Introduction

In traditional Chinese medicine texts, Xuanyu Tongjing Decoction (XYTJD) is a prescribed remedy for premenstrual belly pain and dysmenorrhea. It is currently routinely used to treat ovarian endometriosis (OEM) with good outcomes.

Aim

In order to investigate the underlying processes of Xuanyu Tongjing Decoction in treating OEM inflammation and reducing ovarian damage.

Methods

We created a rat model of OEM and carried out transcriptome sequencing. Batch molecular docking technique in conjunction with Ultra-high-performance liquid chromatography-quadrupole-time-of-flight-high-resolution mass spectrometry was used to screen the main active components in Xuanyu Tongjing Decoction.

Results

The ectopic cyst was firmly attached to the ovary in our successfully created rat model of ovarian endometriosis. According to GSEA enrichment study, XYTJD may up-regulate pathways linked to oocyte formation in ovarian tissues and down-regulate immunological and inflammatory pathways in ectopic tissues. Rat ectopic tissues and human ectopic tissues showed a similar pattern in the expression of the NOD/NFκB pathway during the proliferative phase. In ectopic tissues of rats, XYTJD may down-regulate the NOD/NFκB pathway and suppress the expression of TNF-α and IL-1β, which are downstream inflammatory factors in this pathway. In addition, XYTJD may restore the down-regulation of cAMP/PI3K/AKT and lower the expression of apoptotic factor CASP9, endoplasmic reticulum stress protein SEC61B and antioxidant protein GSTM5 in the ovary with ectopic tissue attachment. Following identification, the three samples' intersection included 10 active compounds in total. There was a 21-component overlap in active ingredients between rat and human serum. After a preliminary virtual screening, β-Hederin, Proanthocyanidin A2, and Cimiside E were suggested to be the essential components that interfere with NOD/NFκB.

Conclusion

In rats with proliferative OEM, XYTJD may down-regulate the NOD/NFκB pathway in ectopic tissues, consequently alleviating ovarian tissue damage by reducing inflammation brought on by ectopic tissues.

Development of an alkaliptosis-related lncRNA risk model and immunotherapy target analysis in lung adenocarcinoma

Background

Lung cancer has the highest mortality rate among all cancers worldwide. Alkaliptosis is characterized by a pH-dependent form of regulated cell death. In this study, we constructed a model related to alkaliptosis-associated long non-coding RNAs (lncRNAs) and developed a prognosis-related framework, followed by the identification of potential therapeutic drugs.

Methods

The TCGA database was utilized to obtain RNA-seq-based transcriptome profiling data, clinical information, and mutation data. We conducted multivariate Cox regression analysis to identify alkaliptosis-related lncRNAs. Subsequently, we employed the training group to construct the prognostic model and utilized the testing group to validate the model's accuracy. Calibration curves were generated to illustrate the discrepancies between predicted and observed outcomes. Principal Component Analysis (PCA) was performed to investigate the distribution of LUAD patients across high- and low-risk groups. Additionally, Gene Ontology (GO) and Gene Set Enrichment Analysis (GSEA) were conducted. Immune cell infiltration and Tumor Mutational Burden (TMB) analyses were carried out using the CIBERSORT and maftools algorithms. Finally, the "oncoPredict" package was employed to predict immunotherapy sensitivity and to further forecast potential anti-tumor immune drugs. qPCR was used for experimental verification.

Results

We identified 155 alkaliptosis-related lncRNAs and determined that 5 of these lncRNAs serve as independent prognostic factors. The progression-free survival (PFS) and overall survival (OS) rates of the low-risk group were significantly higher than those of the high-risk group. The risk signature functions as a prognostic factor that is independent of other variables. Different stages (I-II and III-IV) effectively predict the survival rates of lung adenocarcinoma (LUAD) patients, and these lncRNAs can reliably forecast these signatures. GSEA revealed that processes related to chromosome segregation and immune response activation were significantly enriched in both the high- and low-risk groups. The high-risk group exhibited a lower fraction of plasma cells and a higher proportion of activated CD4 memory T cells. Additionally, the OS of the low TMB group was significantly lower compared to the high TMB group. Furthermore, drug sensitivity was significantly greater in the high-risk group than in the low-risk group. These lncRNAs may serve as biomarkers for treating LUAD patients.

Conclusion

In summary, the construction of an alkaliptosis-related lncRNA prognostic model and drug sensitivity analysis in LUAD patients provides new insights into the clinical diagnosis and treatment of advanced LUAD patients.

Automation of RNA-Seq Sample Preparation and Miniaturized Parallel Bioreactors Enable High-Throughput Differential Gene Expression Studies

A powerful strategy to accelerate bioprocess development is to complement parallel bioreactor systems with an automated approach, often achieved using liquid handling stations. The benefit of such high-throughput experiments is determined by the employed monitoring procedures. To gain a molecular understanding of the microbial production strains in miniaturized parallel single-use bioreactors, we extended the at-line monitoring procedures to transcriptome analysis in a parallel approach using RNA-Seq. To perform automated RNA-Seq experiments, we developed a sample preparation workflow consisting of at-line cell disruption by enzymatic cell lysis, total RNA extraction, nucleic acid concentration normalization, and Nanopore cDNA Library preparation. The pH-controlled aerobic batch growth of Saccharomyces cerevisiae was studied with six different carbon sources (glucose, pyruvate, fructose, galactose, sucrose, and mannose) on a 11 mL scale using 24 parallel stirred tank bioreactors integrated into a liquid handling station while performing at-line sample preparation for RNA-Seq on the same deck. With four biological replicates per condition, 24 cDNA libraries were prepared over 11.5 h. Off-line Nanopore sequencing yielded 20.97 M classified reads with a Q-score > 9. Differential gene expression analysis revealed significant differences in transcriptomic profiles when comparing growth with glucose (exponential growth) to growth with pyruvate (stress conditions), allowing identification of 674 downregulated and 709 upregulated genes. Insignificant changes in gene expression patterns were measured when comparing growth with glucose and fructose, yielding only 64 differentially expressed genes. The expected differences in cellular responses identified in this study show a promising approach for transcriptomic profiling of bioreactor cultures, providing valuable insights on a molecular level at-line in a high-throughput fashion.

Inhibition of CHI3L1 attenuates excessive autophagy in intestinal epithelial cells to reduce the severity of necrotizing enterocolitis

Neonatal necrotizing enterocolitis (NEC) is a devastating intestinal disease that primarily affects preterm infants. Unfortunately, no specific treatment for NEC is currently available, making it crucial to further investigate its underlying mechanisms. In this study, we aimed to identify the key target gene, CHI3L1, which was significantly upregulated in the intestinal tissues of both affected children and model mice from the GEO database. CHI3L1 is known to play important roles in inflammatory and immune responses, as well as in tissue damage and repair, all of which are closely associated with the development of NEC. We conducted validations at both the cellular and animal levels, demonstrating that the inhibition or knockdown of CHI3L1 significantly reduced the severity of NEC. Mechanistic investigations revealed that the knockdown of CHI3L1 inhibited the PI3K-Akt-FoxO1 signalling pathway, alleviating excessive autophagy in intestinal epithelial cells and subsequently reducing injury and inflammatory responses. Clinical studies have revealed that elevated serum CHI3L1 expression in paediatric patients is associated with both the occurrence and severity of necrotising enterocolitis NEC, demonstrating positive correlations with the Duke Abdominal Assessment Scale (DAAS), C-reactive protein (CRP), procalcitonin (PCT), red cell distribution width (RDW), and lactate dehydrogenase (LDH) levels. In conclusion, our findings confirmed a close relationship between CHI3L1 and the occurrence and severity of NEC, suggesting that it may mitigate inflammatory responses and tissue damage by alleviating excessive autophagy in intestinal epithelial cells. Therefore, targeting CHI3L1 may be an effective strategy to combat NEC.

A systematic benchmark of Nanopore long-read RNA sequencing for transcript-level analysis in human cell lines

The human genome contains instructions to transcribe more than 200,000 RNAs. However, many RNA transcripts are generated from the same gene, resulting in alternative isoforms that are highly similar and that remain difficult to quantify. To evaluate the ability to study RNA transcript expression, we profiled seven human cell lines with five different RNA-sequencing protocols, including short-read cDNA, Nanopore long-read direct RNA, amplification-free direct cDNA and PCR-amplified cDNA sequencing, and PacBio IsoSeq, with multiple spike-in controls, and additional transcriptome-wide N6-methyladenosine profiling data. We describe differences in read length, coverage, throughput and transcript expression, reporting that long-read RNA sequencing more robustly identifies major isoforms. We illustrate the value of the SG-NEx data to identify alternative isoforms, novel transcripts, fusion transcripts and N6-methyladenosine RNA modifications. Together, the SG-NEx data provide a comprehensive resource enabling the development and benchmarking of computational methods for profiling complex transcriptional events at isoform-level resolution.

Environmentally induced variation in sperm sRNAs is linked to gene expression and transposable elements in zebrafish offspring

Environmental factors affect not only paternal condition but may translate into the following generations where sperm-mediated small RNAs (sRNAs) can contribute to the transmission of paternal effects. sRNAs play a key role in the male germ line in genome maintenance and repair, and particularly in response to environmental stress and the resulting increase in transposable element (TE) activity. Here, we investigated how the social environment (high competition, low competition) of male zebrafish Danio rerio affects sRNAs in sperm and how these are linked to gene expression and TE activity in their offspring. In a first experiment, we collected sperm samples after exposing males to each social environment for 2 weeks to test for differentially expressed sperm micro- (miRNA) and piwi-interacting RNAs (piRNA). In a separate experiment, we performed in vitro fertilisations after one 2-week period using a split-clutch design to control for maternal effects and collected embryos at 24 h to test for differentially expressed genes and TEs. We developed new computational prediction tools to link sperm sRNAs with differentially expressed TEs and genes in the embryos. Our results support the idea that the molecular stress response in the male germ line has significant down-stream effects on the molecular pathways, and we provide a direct link between sRNAs, TEs and gene expression.

MicroRNA Signatures: Illuminating Minimal Residual Disease Monitoring in Juvenile Myelomonocytic Leukemia - A Review

Juvenile myelomonocytic leukemia (JMML) is an aggressive pediatric myelodysplastic/myeloproliferative neoplasm characterized by RAS pathway mutations and significant heterogeneity. Minimal residual disease (MRD) monitoring is crucial for evaluating treatment response and predicting relapse risk. MicroRNA (miRNAs), small non-coding RNAs with pivotal roles in gene regulation, have emerged as promising biomarkers for JMML MRD detection. This review explores the mechanistic role of miRNAs in JMML pathogenesis, emphasizing their diagnostic, prognostic, and therapeutic potential. Dysregulated miRNA profiles correlate with distinct JMML subgroups and disease progression, suggesting utility in non-invasive MRD monitoring. Emerging evidence highlights miR-150-5p as a tumor suppressor targeting STAT5b and its therapeutic potential in ameliorating JMML's aberrant signaling pathways. We compare traditional MRD methods, such as flow cytometry and polymerase chain reaction (PCR), with miRNA-based techniques, underscoring the latter's superior sensitivity, specificity, and non-invasiveness. Recent advances in miRNA profiling technologies, including next-generation sequencing and digital PCR, enable precise detection of residual leukemic cells and support personalized treatment approaches. Despite significant progress, challenges persist in standardizing miRNA-based assays and validating their clinical utility. Ethical considerations, including patient privacy and informed consent, remain critical for integrating miRNA diagnostics into routine care. This review provides a comprehensive synthesis of current knowledge on miRNA signatures in JMML, illuminating their transformative potential in MRD monitoring and paving the way for innovative therapeutic strategies.

Walnut husk transcriptome dataset of codling moth (Cydia pomonella) infestation at different times

Walnuts, along with almonds, cashews, and hazelnuts, are renowned as the world's "four famous nuts," with walnuts being the foremost among them. Walnut fruit is rich in nutrients, including proteins, fats, polyphenols, sugars, phospholipids, melatonin, sterols, flavonoids, iron, zinc, manganese, and other trace elements, as well as dietary fiber. However, the codling moth poses a significant threat to walnut fruits as a major pest. Despite its importance, the transcriptomic changes in walnut husk at different times of codling moth infestation have not been fully explored. In this study, we employed the Illumina NovaSeq 6000 platform to sequence the transcriptome of walnut husk at various time points (0, 12, 24, 36, 48, and 72 hours) after codling moth infestation. The RNA-seq libraries yielded between 41,402,492 and 48,358,932 clean reads, resulting in a total of 120.34 Gb of clean data after filtering out low-quality reads. In total, 936 million reads were generated, with approximately 90% aligning uniquely to the reference genome. Differential expression analysis revealed the number of differentially expressed genes (DEGs) at each time point, including 21 genes associated with plant hormone synthesis. The results of this study provide new insights into the transcriptional changes in walnut husk induced by codling moth infestation and lay a foundation for future research on walnut husk defense mechanisms. The raw FASTQ files from this transcriptome experiment are publicly available in the NCBI Sequence Read Archive (SRA) under the BioProject accession number PRJNA1140835.

Assessment of genomic prediction capabilities of transcriptome data in a barley multi-parent RIL population

The field of genomic selection (GS) is advancing rapidly on many fronts including the utilization of multi-omics datasets with the goal to increase prediction ability (PA) and to become an integral part of an increasing number of breeding programs ensuring future food security. In this study, we used RNA sequencing (RNA-Seq) data to perform genomic prediction (GP) on three related barley RIL populations investigating the potential of increasing PA by combining genomic and transcriptomic datasets, adding whole genome sequencing (WGS) SNP data, functional parameter filtering, and empirical quality filtering. Our RNA-Seq data were generated cost-efficiently using small footprint plant cultivation, high-throughput RNA extraction, and library preparation miniaturization. We also examined the depth of the sequencing as an additional cost-saving measure. We used five-fold cross-validation to evaluate the PA of the gene expression dataset, the RNA-Seq SNP dataset, and the consensus SNP dataset between the RNA-Seq and parental WGS data, resulting in PAs between 0.73 and 0.78. The consensus SNP dataset performed best, with five out of eight traits performing significantly better compared to a 50K SNP array, which served as a benchmark. The advantage of the consensus SNP dataset was most prominent in the inter-population predictions, in which the training- and validation-set originated from different RIL sub-populations. We could therefore not only show that RNA-Seq data alone are able to predict various complex traits in barley using RIL, but also that the performance can be further increased by WGS data for which the public availability will steadily increase.

Transcriptome analysis of corpora lutea in domestic cats (Felis catus) reveals strong differences in gene expression of various hormones, hormone receptors and regulators across different developmental stages

In the domestic cat (Felis catus), the corpus luteum (CL) is the main source of progestogen during pregnancy. Here, we studied gene expression changes in different life cycle stages of the CL of pseudopregnant cats to identify potential regulatory factors. Results revealed no support for different regression substages, which were previously defined on the basis of morphological examination analysis and intraluteal hormone content, as only a very low number of differentially expressed genes and no subclusters in PCA plot were detected. By comparing the regression stage with the developmental/maintenance stage, we detected a total of 6174 differentially expressed genes in the sample set, of which 2882 were upregulated and 3292 were downregulated. The large changes in the expression levels of some genes indicate that the endocrine function of the CL may not be restricted to progesterone (P4) secretion. The findings suggest that domestic cat CLs could also be a source of adipokines such as adiponectin or APELA. The expression of these genes is highly variable and reversed between stages. The life cycle and activity of CLs seem to be regulated by different factors, as genes encoding for the hormone receptors LHCGR and PAQR5 were more highly expressed in the development/maintenance stage, in contrast to this encoding for LEPR, which is higher expressed in regression stage. For regression stage, we identified different potential ways to modulate the cholesterol level and/or P4 concentration. Furthermore, we found differences from previous studies in other species for many genes that were studied in more detail, as well as when analysing functions and pathways. Our findings support the hypothesis that different stages of the CL life cycle in domestic cats can be characterized by changes in gene regulation and that CL life cycles are partly differentially regulated between species.

Transcriptomics in the era of long-read sequencing

Transcriptome sequencing revolutionized the analysis of gene expression, providing an unbiased approach to gene detection and quantification that enabled the discovery of novel isoforms, alternative splicing events and fusion transcripts. However, although short-read sequencing technologies have surpassed the limited dynamic range of previous technologies such as microarrays, they have limitations, for example, in resolving full-length transcripts and complex isoforms. Over the past 5 years, long-read sequencing technologies have matured considerably, with improvements in instrumentation and analytical methods, enabling their application to RNA sequencing (RNA-seq). Benchmarking studies are beginning to identify the strengths and limitations of long-read RNA-seq, although there remains a need for comprehensive resources to guide newcomers through the intricacies of this approach. In this Review, we provide a comprehensive overview of the long-read RNA-seq workflow, from library preparation and sequencing challenges to core data processing, downstream analyses and emerging developments. We present an extensive inventory of experimental and analytical methods and discuss current challenges and prospects.

SOX9 Overexpression Ameliorates Metabolic Dysfunction-associated Steatohepatitis Through Activation of the AMPK Pathway

Background and Aims

The transcription factor sex-determining region Y-related high-mobility group-box gene 9 (SOX9) plays a critical role in organ development. Although SOX9 has been implicated in regulating lipid metabolism in vitro, its specific role in metabolic dysfunction-associated steatohepatitis (MASH) remains poorly understood. This study aimed to investigate the role of SOX9 in MASH pathogenesis and explored the underlying mechanisms.

Methods

MASH models were established using mice fed either a methionine- and choline-deficient (MCD) diet or a high-fat, high-fructose diet. To evaluate the effects of SOX9, hepatocyte-specific SOX9 deletion or overexpression was performed. Lipidomic analyses were conducted to assess how SOX9 influences hepatic lipid metabolism. RNA sequencing was employed to identify pathways modulated by SOX9 during MASH progression. To elucidate the mechanism further, HepG2 cells were treated with an adenosine monophosphate-activated protein kinase (AMPK) inhibitor to test whether SOX9 acts via AMPK activation.

Results

SOX9 expression was significantly elevated in hepatocytes of MASH mice. Hepatocyte-specific SOX9 deletion exacerbated MCD-induced MASH, whereas overexpression of SOX9 mitigated high-fat, high-fructose-induced MASH. Lipidomic and RNA sequencing analyses revealed that SOX9 suppresses the expression of genes associated with lipid metabolism, inflammation, and fibrosis in MCD-fed mice. Furthermore, SOX9 deletion inhibited AMPK pathway activation, while SOX9 overexpression enhanced it. Notably, administration of an AMPK inhibitor negated the protective effects of SOX9 overexpression, leading to increased lipid accumulation in HepG2 cells.

Conclusions

Our findings demonstrate that SOX9 overexpression alleviates hepatic lipid accumulation in MASH by activating the AMPK pathway. These results highlight SOX9 as a promising therapeutic target for treating MASH.

Epigenetic Echoes: Bridging Nature, Nurture, and Healing Across Generations

Trauma can impact individuals within a generation (intragenerational) and future generations (transgenerational) through a complex interplay of biological and environmental factors. This review explores the epigenetic mechanisms that have been correlated with the effects of trauma across generations, including DNA methylation, histone modifications, and non-coding RNAs. These mechanisms can regulate the expression of stress-related genes (such as the glucocorticoid receptor (NR3C1) and FK506 binding protein 5 (FKBP5) gene), linking trauma to biological pathways that may affect long-term stress regulation and health outcomes. Although research using model organisms has elucidated potential epigenetic mechanisms underlying the intergenerational effects of trauma, applying these findings to human populations remains challenging due to confounding variables, methodological limitations, and ethical considerations. This complexity is compounded by difficulties in establishing causality and in disentangling epigenetic influences from shared environmental factors. Emerging therapies, such as psychedelic-assisted treatments and mind-body interventions, offer promising avenues to address both the psychological and potential epigenetic aspects of trauma. However, translating these findings into effective interventions will require interdisciplinary methods and culturally sensitive approaches. Enriched environments, cultural reconnection, and psychosocial interventions have shown the potential to mitigate trauma's impacts within and across generations. By integrating biological, social, and cultural perspectives, this review highlights the critical importance of interdisciplinary frameworks in breaking cycles of trauma, fostering resilience, and advancing comprehensive healing across generations.

Cutting-edge RNA technologies to advance the understanding of learning and memory

Following the recent emergence of RNA as a therapeutic tool, and coupled with an explosion in the development of new RNA technologies, it is rapidly becoming clear that the 21st century is the era of RNA. Neuroscience as a discipline has a long history of embracing new technology to advance the understanding of brain function, particularly in the context of learning and memory. In this short review, we highlight four broad categories of emerging RNA technologies, namely: imaging, isolation, identification and manipulation, and discuss their potential to advance the fundamental understanding of how RNA impacts experience-dependent plasticity, learning, and memory.

Comprehensive guide for epigenetics and transcriptomics data quality control

Host response to environmental exposures such as pathogens and chemicals can include modifications to the epigenome and transcriptome. Improved signature discovery, including the identification of the agent and timing of exposure, has been enabled by advancements in assaying techniques to detect RNA expression, DNA base modifications, histone modifications, and chromatin accessibility. The interrogation of the epigenome and transcriptome cascade requires analyzing disparate datasets from multiple assay types, often at single-cell resolution, derived from the same biospecimen. However, there remains a paucity of rigorous quality control standards of those datasets that reflect quality assurance of the underlying assay. This guide outlines a comprehensive suite of metrics that can be used to ensure quality from 11 different epigenetics and transcriptomics assays. Recommended mitigative actions to address failed metrics are provided. The workflow presented aims to improve benchwork protocols and dataset quality to enable accurate discovery of exposure signatures.

Gene Co-Expression Analysis Reveals Functional Differences Between Early- and Late-Onset Alzheimer's Disease

The rising prevalence of Alzheimer's disease (AD), particularly among older adults, has driven increased research into its underlying mechanisms and risk factors. Aging, genetic susceptibility, and cardiovascular health are recognized contributors to AD, but how the age of onset affects disease progression remains underexplored. This study investigates the role of early- versus late-onset Alzheimer's disease (EOAD and LOAD, respectively) in shaping the trajectory of cognitive decline. Leveraging data from the Religious Orders Study and Memory and Aging Project (ROSMAP), two cohorts were established: individuals with early-onset AD and those with late-onset AD. Comprehensive analyses, including differential gene expression profiling, pathway enrichment, and gene co-expression network construction, were conducted to identify distinct molecular signatures associated with each cohort. Network modularity learning algorithms were used to discern the inner structure of co-expression networks and their related functional features. Computed network descriptors provided deeper insights into the influence of age at onset on the biological progression of AD.

Unravelling the transcriptomic characteristics of bronchoalveolar lavage in post-covid pulmonary fibrosis

BACKGROUND

Post-Covid Pulmonary Fibrosis (PCPF) has emerged as a significant global issue associated with a poor quality of life and significant morbidity. Currently, our understanding of the molecular pathways of PCPF is limited. Hence, in this study, we performed whole transcriptome sequencing of the RNA isolated from the bronchoalveolar lavage (BAL) samples of PCPF and compared it with idiopathic pulmonary fibrosis (IPF) and non-ILD (Interstitial Lung Disease) control to understand the gene expression profile and associated pathways.

METHODS

BAL samples from PCPF (n = 3), IPF (n = 3), and non-ILD Control (n = 3) (individuals with apparent healthy lung without interstitial lung disease) groups were obtained and RNA were isolated for whole transcriptomic sequencing. Differentially Expressed Genes (DEGs) were determined followed by functional enrichment analysis and qPCR validation.

RESULTS

A panel of differentially expressed genes were identified in bronchoalveolar lavage fluid cells (BALF) of PCPF as compare to control and IPF. Our analysis revealed dysregulated pathways associated with cell cycle regulation, immune responses, and neuroinflammatory processes. Real-time validation further supported these findings. The PPI network and module analysis shed light on potential biomarkers and underscore the complex interplay of molecular mechanisms in PCPF. The comparison of PCPF and IPF identified a significant downregulation of pathways that were more prominent in IPF.

CONCLUSION

This investigation provides crucial insights into the molecular mechanism of PCPF and also outlines avenues for prospective research and the development of therapeutic approaches.

Identification and characteristics of a novel CD8αα T cell subset in a refractory myasthenia gravis patient

Myasthenia gravis (MG) is an autoimmune disease that impairs neuromuscular transmission. Autoantibodies and cellular immunity mediate the immunopathology of MG, yet the mechanism of CD8+ T cells in this process remains elucidated. In this study, we discovered a novel subset of CD8αα+ T cells in the peripheral blood of an 18-year-old Chinese man diagnosed as MG, who has undergone thymectomy and persistent myasthenia crisis. Designated as CD161neg T cell, this subset was characterized by TCRαβ+CD8αα+PLZF+Vα7.2+ but notably lacked CD161, distinct from mucosal-associated invariant T (MAIT) cells known for high CD161. The patient exhibited unusually high levels of CD161neg T cells compared to other MG patients, which fluctuated with infections but not MG severity. RNA sequencing revealed that CD161neg T cells lacked the genes characteristic of mature MAIT cells including CCR6, CXCR6, ZBTB16, and IL18RAP, but expressed cytotoxic T cell-related genes GZMH and IFNG. This study shed new light on the heterogeneity and complexity of CD8αα+ T cells in MG patients with thymoma.

RNApysoforms: fast rendering interactive visualization of RNA isoform structure and expression in Python

Summary

Alternative splicing generates multiple RNA isoforms from a single gene, enriching genetic diversity and impacting gene function. Effective visualization of these isoforms and their expression patterns is crucial but challenging due to limitations in existing tools. Traditional genome browsers lack programmability, while other tools offer limited customization, produce static plots, or cannot simultaneously display structures and expression levels. RNApysoforms was developed to address these gaps by providing a Python-based package that enables concurrent visualization of RNA isoform structures and expression data. Leveraging plotly and polars libraries, it offers an interactive, customizable, and faster-rendering framework suitable for web applications, enhancing the analysis and dissemination of RNA isoform research.

Availability and implementation

RNApysoforms is a Python package available at (https://github.com/UK-SBCoA-EbbertLab/RNApysoforms) and (https://zenodo.org/records/14941190) via an open-source MIT license. It can be easily installed using the pip package installer for Python. Thorough documentation and usage vignettes are available at: https://rna-pysoforms.readthedocs.io/en/latest/.

A sort and sequence approach to dissect heterogeneity of response to a self-amplifying RNA vector in a novel human muscle cell line

Self-amplifying RNA (saRNA) is an extremely promising platform because it can produce more protein for less RNA. We used a sort and sequence approach to identify host cell factors associated with transgene expression from saRNA; the hypothesis was that cells with different expression levels would have different transcriptomes. We tested this in CDK4/hTERT immortalized human muscle cells transfected with Venezuelan equine encephalitis virus (VEEV)-derived saRNA encoding GFP. Cells with the highest expression levels had very high levels of transgene mRNA (5%-10% total reads); the cells sorted with low and negative levels of GFP protein also had detectable levels of both VEEV and GFP RNA. To understand host responses, we performed RNA sequencing. Differentially expressed gene (DEG) patterns varied with GFP expression; GFP high cells had many more DEGs, which were associated with protein synthesis and cell metabolism. Comparing profiles by an unsupervised approach revealed that negative cells expressed higher levels of cell-intrinsic immunity genes such as IFIT1, MX1, TLR3, and MyD88. To explore the role of interferon, cells were treated with the Jak inhibitor ruxolitinib. This reduced the number of DEGs, but differences between cells sorted by expression level remained. These studies demonstrate the complex interplay of factors, some immune related, affecting saRNA transgenes.

Acute and chronic pesticide exposure trigger fundamentally different molecular responses in bumble bee brains

BACKGROUND

Beneficial insects, including pollinators, encounter various pesticide exposure conditions, from brief high-concentration acute exposure to continuous low-level chronic exposure. To effectively assess the environmental risks of pesticides, it is critical to understand how different exposure schemes influence their effects. Unfortunately, this knowledge remains limited. To clarify whether different exposure schemes disrupt the physiology of pollinators in a similar manner, we exposed bumble bees to acute or chronic treatments of three different pesticides: acetamiprid, clothianidin, or sulfoxaflor. Genome-wide gene expression profiling enabled us to compare the effects of these treatments on the brain in a high-resolution manner.

RESULTS

There were two main findings: First, acute and chronic exposure schemes largely affected non-overlapping sets of genes. Second, different pesticides under the same exposure scheme showed more comparable effects than the same pesticide under different exposure schemes. Each exposure scheme induced a distinct gene expression profile. Acute exposure mainly caused upregulation of genes linked to the stress response mechanisms, like peroxidase and detoxification genes, while chronic exposure predominantly affected immunity and energy metabolism.

CONCLUSIONS

Our findings show that the mode of exposure is critical in determining the molecular effects of pesticides. These results signal the need for safety testing practices to better consider mode-of-exposure dependent effects and suggest that transcriptomics can support such improvements.

Unraveling cell-cell communication with NicheNet by inferring active ligands from transcriptomics data

Ligand-receptor interactions constitute a fundamental mechanism of cell-cell communication and signaling. NicheNet is a well-established computational tool that infers ligand-receptor interactions that potentially regulate gene expression changes in receiver cell populations. Whereas the original publication delves into the algorithm and validation, this paper describes a best practices workflow cultivated over four years of experience and user feedback. Starting from the input single-cell expression matrix, we describe a 'sender-agnostic' approach that considers ligands from the entire microenvironment and a 'sender-focused' approach that considers ligands only from cell populations of interest. As output, users will obtain a list of prioritized ligands and their potential target genes, along with multiple visualizations. We include further developments made in NicheNet v2, in which we have updated the data sources and implemented a downstream procedure for prioritizing cell type-specific ligand-receptor pairs. Although a standard NicheNet analysis takes <10 min to run, users often invest additional time in making decisions about the approach and parameters that best suit their biological question. This paper serves to aid in this decision-making process by describing the most appropriate workflow for common experimental designs like case-control and cell-differentiation studies. Finally, in addition to the step-by-step description of the code, we also provide wrapper functions that enable the analysis to be run in one line of code, thus tailoring the workflow to users at all levels of computational proficiency.

Systematic review and meta-analysis of bulk RNAseq studies in human Alzheimer's disease brain tissue

We systematically reviewed and meta-analyzed bulk RNA sequencing (RNAseq) studies comparing Alzheimer's disease (AD) patients to controls in human brain tissue. We searched PubMed, Web of Science, and Scopus for human brain bulk RNAseq studies, excluding re-analyses and studies limited to small RNAs or gene panels. We developed 10 criteria for quality assessment and performed a meta-analysis on three high-quality datasets. Of 3266 records, 24 qualified for the systematic review, and one study with three datasets qualified for the meta-analysis. The meta-analysis identified 571 differentially expressed genes (DEGs) in the temporal lobe and 189 in the frontal lobe, including CLU and GFAP. Pathway analysis suggested reactivation of developmental processes in the adult AD brain. Limited data availability constrained the meta-analysis. These findings underscore the need for rigorous methods in AD transcriptomic research to better identify transcriptomic changes and advance biomarker and therapeutic development. This review is registered in PROSPERO (CRD42023466522).

HIGHLIGHTS

Comprehensive review: Conducted the first systematic review and meta-analysis of bulk RNA sequencing (RNAseq) studies comparing Alzheimer's disease (AD) patients with non-demented controls using primary human brain tissue.

KEY FINDINGS

Identified 571 differentially expressed genes (DEGs) in the temporal lobe and 189 in the frontal lobe of patients with AD, revealing potential therapeutic targets. Pathway discovery: Highlighted key overlapping pathways such as "tube morphogenesis" and "neuroactive ligand-receptor interaction" that may play critical roles in AD.

QUALITY ASSESSMENT

Emphasized the importance of methodological rigor in transcriptomic studies, including quality assessment tools to guide future research in AD.

STUDY LIMITATION

Acknowledged limited access to complete data tables and lack of diversity in existing datasets, which constrained some of the analysis.

Differential gene expression analysis and machine learning identified structural, TFs, cytokine and glycoproteins, including SOX2, TOP2A, SPP1, COL1A1, and TIMP1 as potential drivers of lung cancer

BACKGROUND

Lung cancer is a primary global health concern, responsible for a considerable portion of cancer-related fatalities worldwide. Understanding its molecular complexities is crucial for identifying potential targets for treatment. The goal is to slow disease progression and intervene early to prevent the development of advanced lung cancer cases. Hence, there's an urgent need for new biomarkers that can detect lung cancer in its early stages.

METHODS

The study conducted RNA-Seq analysis of lung cancer samples from the publicly available SRA database (NCBI SRP009408), including both control and tumour samples. The genes with differential expression between tumour and healthy tissues were identified using R and Bioconductor. Machine learning (ML) techniques, Random Forest, Lasso, XGBoost, Gradient Boosting and Elastic Net were employed to pinpoint significant genes followed by classifiers, Multilayer Perceptron (MLP), Support Vector Machines (SVM) and k-Nearest Neighbours (k-NN). Gene ontology and pathway analyses were performed on the significant differentially expressed genes (DEGs). The top genes from DEG and machine learning analyses were combined for protein-protein interaction (PPI) analysis, identifying 10 hub genes essential for lung cancer progression.

RESULTS

The integrated analysis of ML and DEGs revealed the significance of specific genes in lung cancer samples, identified the top 5 upregulated genes (COL11A1, TOP2A, SULF1, DIO2, MIR196A2) and the top 5 downregulated genes (PDK4, FOSB, FLYWCH1, CYB5D2, MIR328), along with their associated genes implicated in pathways or co-expression networks were identified. Among the various algorithms employed, Random Forest and XGBoost proved effective in identifying common genes, underscoring their potential significance in lung cancer pathogenesis. The MLP exhibited the highest accuracy in classifying samples using all genes. Additionally, the protein-protein interaction (PPI) analysis identified 10 hub genes that are pivotal in lung cancer pathogenesis: COL1A1, SOX2, SPP1, THBS2, POSTN, COL5A1, COL11A1, TIMP1, TOP2A and PKP1.

CONCLUSION

The study contributes to the early prediction of lung cancer by identifying potential biomarkers that could enhance early diagnosis and pave the way for practical clinical applications in the future. Integrating DEGs and machine learning-derived significant genes for PPI analysis offers a robust approach to uncovering critical molecular targets for lung cancer treatment.

kallisto, bustools and kb-python for quantifying bulk, single-cell and single-nucleus RNA-seq

The term 'RNA-seq' refers to a collection of assays based on sequencing experiments that involve quantifying RNA species from bulk tissue, single cells or single nuclei. The kallisto, bustools and kb-python programs are free, open-source software tools for performing this analysis that together can produce gene expression quantification from raw sequencing reads. The quantifications can be individualized for multiple cells, multiple samples or both. Additionally, these tools allow gene expression values to be classified as originating from nascent RNA species or mature RNA species, making this workflow amenable to both cell-based and nucleus-based assays. This protocol describes in detail how to use kallisto and bustools in conjunction with a wrapper, kb-python, to preprocess RNA-seq data. Execution of this protocol requires basic familiarity with a command line environment. With this protocol, quantification of a moderately sized RNA-seq dataset can be completed within minutes.

Advancements in omics technologies: Molecular mechanisms of acute lung injury and acute respiratory distress syndrome (Review)

Acute lung injury (ALI)/acute respiratory distress syndrome (ARDS) is an inflammatory response arising from lung and systemic injury with diverse causes and associated with high rates of morbidity and mortality. To date, no fully effective pharmacological therapies have been established and the relevant underlying mechanisms warrant elucidation, which may be facilitated by multi‑omics technology. The present review summarizes the application of multi‑omics technology in identifying novel diagnostic markers and therapeutic strategies of ALI/ARDS as well as its pathogenesis.

Effects of ALS-associated 5'tiRNAGly-GCC on the transcriptomic and proteomic profile of primary neurons in vitro

tRNA-derived stress-induced RNAs (tiRNAs) are a new class of small non-coding RNA that have emerged as important regulators of cellular stress responses. tiRNAs are derived from specific tRNA cleavage by the stress-induced ribonuclease angiogenin (ANG). Loss-of-function mutations in the ANG gene are linked to amyotrophic lateral sclerosis (ALS), and elevated levels of specific tiRNAs were recently identified in ALS patient serum samples. However, the biological role of tiRNA production in neuronal stress responses and neurodegeneration remains largely unknown. Here, we investigated the genome-wide regulation of neuronal stress responses by a specific tiRNA, 5'tiRNAGly-GCC, which we found to be upregulated in primary neurons exposed to ALS-relevant stresses and in the spinal cord of three ALS mouse models. Whole-transcript RNA sequencing and label-free mass spectrometry on primary neurons transfected with a synthetic mimic of 5'tiRNAGly-GCC revealed predominantly downregulated RNA and protein levels, with more pronounced changes in the proteome. Over half of the downregulated mRNAs contained predicted 5'tiRNAGly-GCC binding sites, indicating that this tiRNA may silence target genes via complementary binding. On the proteome level, we observed reduction in proteins involved in translation initiation and ribosome assembly, pointing to inhibitory effects on translation. Together, these findings suggest that 5'tiRNAGly-GCC is an ALS-associated tiRNA that functions to fine-tune gene expression and supress protein synthesis as part of an ANG-induced neuronal stress response.