Gene Selection Research Articles

A New Differential Gene Expression Based Simulated Annealing for Solving Gene Selection Problem: A Case Study on Eosinophilic Esophagitis and Few Other Gastro-intestinal Diseases.

Identifying the set of genes collectively responsible for causing a disease from differential gene expression data is called gene selection problem. Though many complex methodologies have been applied to solve gene selection, formulated as an optimization problem, this study introduces a new simple, efficient, and biologically plausible solution procedure where the collective power of the targeted gene set to discriminate between diseased and normal gene expression profiles was focused. It uses Simulated Annealing to solve the underlying optimization problem and termed here as Differential Gene Expression Based Simulated Annealing (DGESA). The Ranked Variance (RV) method has been applied to prioritize genes to form reference set to compare with the outcome of DGESA. In a case study on Eosinophilic Esophagitis (EoE) and other gastrointestinal diseases, RV identified the top 40 high-variance genes, overlapping with disease-causing genes from DGESA. DGESA identified 40 gene pathways each for EoE, Crohn's Disease (CD), and Ulcerative Colitis (UC), with 10 genes for EoE, 8 for CD, and 7 for UC confirmed in literature. For EoE, confirmed genes include KRT79, CRISP2, IL36G, SPRR2B, SPRR2D, and SPRR2E. For CD, validated genes are NPDC1, SLC2A4RG, LGALS8, CDKN1A, XAF1, and CYBA. For UC, confirmed genes include TRAF3, BAG6, CCDC80, CDC42SE2, and HSPA9. RV and DGESA effectively elucidate molecular signatures in gastrointestinal diseases. Validating genes like SPRR2B, SPRR2D, SPRR2E, and STAT6 for EoE demonstrates DGESA's efficacy, highlighting potential targets for future research.

Immune-related gene characterization and biological mechanisms in major depressive disorder revealed based on transcriptomics and network pharmacology

BackgroundMajor depressive disorder (MDD) is a severe psychiatric disorder characterized by complex etiology, with genetic determinants that are not fully understood. The objective of this study was to investigate the pathogenesis of MDD and to explore its association with the immune system by identifying hub biomarkers using bioinformatics analyses and examining immune infiltrates in human autopsy samples.MethodsGene microarray data were obtained from the Gene Expression Omnibus (GEO) datasets GSE32280, GSE76826, GSE98793, and GSE39653. Our approach included differential expression analysis, weighted gene co-expression network analysis (WGCNA), and protein-protein interaction (PPI) network analysis to identify hub genes associated with MDD. Subsequently, gene ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG), Cytoscape plugin CluGO, and Gene Set Enrichment Analysis (GSEA) were utilized to identify immune-related genes. The final selection of immune-related hub genes was determined through the least absolute shrinkage and selection operator (Lasso) regression analysis and PPI analysis. Immune cell infiltration in MDD patients was analyzed using CIBERSORT, and correlation analysis was performed between key immune cells and genes. The diagnostic accuracy of the identified hub genes was evaluated using receiver operating characteristic (ROC) curve analysis. Furthermore, we conducted a study involving 10 MDD patients and 10 healthy controls (HCs) meeting specific criteria to assess the expression levels of these hub genes in their peripheral blood mononuclear cells (PBMCs). The Herbal Ingredient Target Database (HIT) was employed to screen for herbal components that target these genes, potentially identifying novel therapeutic agents.ResultsA total of 159 down-regulated and 51 up-regulated genes were identified for further analysis. WGCNA revealed 12 co-expression modules, with modules “darked”, “darkurquoise” and “light yellow” showing significant positive associations with MDD. Functional enrichment pathway analysis indicated that these differential genes were associated with immune functions. Integration of differential and immune-related gene analysis identified 21 common genes. The Lasso algorithm confirmed 4 hub genes as potential biomarkers for MDD. GSEA analysis suggested that these genes may be involved in biological processes such as protein export, RNA degradation, and fc gamma r mediated cytotoxis. Pathway enrichment analysis identified three highly enriched immune-related pathways associated with the 4 hub genes. ROC curve analysis indicated that these hub genes possess good diagnostic value. Quantitative reverse transcription-polymerase chain reaction (RT-qPCR) demonstrated significant expression differences of these hub genes in PBMCs between MDD patients and HCs. Immune infiltration analysis revealed significant correlations between immune cells, including Mast cells resting, T cells CD8, NK cells resting, and Neutrophils, which were significantly correlated with the hub genes expression. HIT identified one herb target related to IL7R and 14 targets related to TLR2.ConclusionsThe study identified four immune-related hub genes (TLR2, RETN, HP, and IL7R) in MDD that may impact the diagnosis and treatment of the disorder. By leveraging the GEO database, our findings contribute to the understanding of the relationship between MDD and immunity, presenting potential therapeutic targets.

Selection of reference genes for quantitative real-time PCR analysis in exogenous hormone-treated Lycoris aurea.

Selection of reference genes for quantitative real-time PCR analysis in exogenous hormone-treated Lycoris aurea.

Identification of reference genes for gene expression assessment in Avena sativa under biotic stress triggered by Blumeria graminis

A repeatable and reliable reverse transcription quantitative PCR (RT-qPCR) experiment depends upon proper reference genes (RGs) selection. This study aims to examine the expression stability of nine candidate RGs for the Avena sativa – Blumeria graminis experimental setup. B. graminis causes powdery mildew - the most devastating and economically important fungal disease of crops worldwide. RGs were evaluated in Pm3 and Pm4 oat differential lines and the susceptible cultivar Fuchs during compatible and incompatible interactions with different pathotypes of Blumeria graminis f. sp. avenae in six-time points post inoculation. The identification of genes exhibiting high expression stability was done by four algorithms (geNorm, NormFinder, BestKeeper and deltaCt). The results indicated that regardless of the analysed group, two most stable RGs are required for data normalization. The most sufficient RGs combination was HNR (heterogeneous nuclear ribonucleoprotein 27 C) + EIF4A (eukaryotic initiation factor 4 A‑3). ARF (ADP‑ribosylation factor) could also be pondered as demonstrating high expression stability. These genes can be considered universal candidates for RT-qPCR normalization to study interaction with B. graminis as well as Puccinia coronata and Puccinia graminis, as confirmed by our previous research. The worst candidate for data standardisation was TUA (α- tubulin). To our best knowledge, this is the first report regarding RGs’ selection in this pathosystem. Identified RGs are proper normalisation candidates for gene expression studies in the A. sativa infected by B. graminis as well as other related pathogens.

RGIE: A Gene Selection Method Related to Radiotherapy Resistance in Head and Neck Squamous Cell Carcinoma.

Head and Neck Squamous Cell Carcinoma (HNSCC) is a malignant tumor with a high degree of malignancy, invasiveness, and metastasis rate. Radiotherapy, as an important adjuvant therapy for HNSCC, can reduce the postoperative recurrence rate and improve the survival rate. Identifying the genes related to HNSCC radiotherapy resistance (HNSCC-RR) is helpful in the search for potential therapeutic targets. However, identifying radiotherapy resistance-related genes from tens of thousands of genes is a challenging task. While interactions between genes are important for elucidating complex biological processes, the large number of genes makes the computation of gene interactions infeasible. We propose a gene selection algorithm, RGIE, which is based on ReliefF, Gene Network Inference with Ensemble of Trees (GENIE3) and Feature Elimination. ReliefF was used to select a feature subset that is discriminative for HNSCC-RR, GENIE3 constructed a gene regulatory network based on this subset to analyze the regulatory relationship among genes, and feature elimination was used to remove redundant and noisy features. Nine genes (SPAG1, FIGN, NUBPL, CHMP5, TCF7L2, COQ10B, BSDC1, ZFPM1, GRPEL1) were identified and used to identify HNSCC-RR, which achieved performances of 0.9730, 0.9679, 0.9767, and 0.9885 in terms of accuracy, precision, recall, and AUC, respectively. Finally, qRT-PCR validated the differential expression of the nine signature genes in cell lines (SCC9, SCC9-RR). RGIE is effective in screening genes related to HNSCC-RR. This approach may help guide clinical treatment modalities for patients and develop potential treatments.

Exploration of the biological mechanisms of CENPA as an oncogene in glioma: Screening based on cancer functional status

AbstractGlioma is the most common primary tumour in central nervous system, characterized by high invasiveness, a high recurrence rate and extremely poor prognosis. Machine learning based on cancer functional state helps to combine multi‐omics methods to screen for key gene, such as CENPA, that influences the phenotype of glioma and patients' prognosis. Based on 14 CFS, glioma was divided into three subtypes. Bioinformatics and machine learning methods were utilized to develop an enhanced prognostic prediction signature based on three subtypes. We selected CENPA as a hub biomarker and conducted in vitro experiments such as IHC, western blot, Coip, transwell, cck8, flow cytometry, scratch assay, qPCR, AlphaFold, MOE and in vivo experiments. We identified three subtypes of glioma based on the 14 CFS. The C subtype exhibits poor clinical outcomes, increased carbohydrate and nucleotide metabolism, high infiltration of immune cells, high CNV and tumour mutation burden (p < 0.05). The differential expression of gene between three subtypes were used to construct a novel signature with improved performance in prognostic prediction via machine learning. CENPA was selected as the hub gene, in vitro experiments such as ihc, western blot and qPCR showed that CENPA had high expression in tissues and cell lines (p < 0.05). The scratch assay, edu, cck8, flow cytometry and transwell after CENPA knockdown or overexpression had significant effects on the functions of glioma. Meanwhile, CENPA was regulated by EZH2 and influenced downstream wnt pathway, affecting phosphorylation of two sites, Ser675 and Ser552, on β‐catenin. The effect of CENPA knockdown was reversed by drug CHIR‐99021. Animal experiments indicated that the tumour volume of control and overexpression group increased faster, especially the overexpression group, which was significantly faster (p < 0.001). Machine learning based on CFS is beneficial for the selection of key genes and disease assessment. In glioma, CENPA is positively correlated with WHO grading at both the gene and protein levels, and high CENPA affects patients' poor prognosis. Regulating CENPA can affect functions of glioma, and these phenomena may act through the EZH2/CENPA/β‐catenin signalling axis. CENPA knockdown can be reversed by the drug CHIR‐99021. CENPA may become one of the therapeutic targets in glioma.

Multiomics reveals tumor microenvironment remodeling in locally advanced gastric and gastroesophageal junction cancer following neoadjuvant immunotherapy and chemotherapy

BackgroundPerioperative chemotherapy is the standard of care for patients with locally advanced gastric and gastroesophageal junction cancer. Recent evidence demonstrated the addition of programmed cell death protein 1 (PD-1) inhibitors...

SNMF: Integrated Learning of Mutational Signatures and Prediction of DNA Repair Deficiencies.

Many tumours show deficiencies in DNA damage response (DDR), which influence tumorigenesis and progression, but also expose vulnerabilities with therapeutic potential. Assessing which patients might benefit from DDR-targeting therapy requires knowledge of tumour DDR deficiency status, with mutational signatures reportedly better predictors than loss of function mutations in select genes. However, signatures are identified independently using unsupervised learning, and therefore not optimised to distinguish between different pathway or gene deficiencies. We propose SNMF, a supervised non-negative matrix factorisation that jointly optimises the learning of signatures: (1) shared across samples, and (2) predictive of DDR deficiency. We applied SNMF to mutation profiles of human induced pluripotent cell lines carrying gene knockouts linked to three DDR pathways. The SNMF model achieved high accuracy (0.971) and learned more complete signatures of the DDR status of a sample, further discerning distinct mechanisms within a pathway. Cell line SNMF signatures recapitulated tumour-derived COSMIC signatures and predicted DDR pathway deficiency of TCGA tumours with high recall, suggesting that SNMF-like models can leverage libraries of induced DDR deficiencies to decipher intricate DDR signatures underlying patient tumours. https://github.com/joanagoncalveslab/SNMF .

Selection and validation of reference genes for qRT-PCR normalization in dayflower (Commelina communis) based on the transcriptome profiling

BackgroundDayflower (Commelina communis), a widely invasive weed, thrives well under a variety of abiotic stresses, including drought and herbicides, and harms the growth of crops such as maize and soybean. Gene expression in dayflower is an important but understudied area due to the lack of reliable reference genes.ResultsFifteen candidate reference genes, which are common reference genes and homologous to those used in other plants, were selected through RNA-seq datasets of dayflower. The expression stability of these screened reference genes was evaluated under three abiotic stresses (drought, herbicide and copper) and in five organs (roots, stems, leaves, flowers and seeds) using five commonly used software programs (geNorm, NormFinder, BestKeeper, ΔCt and RefFinder). The results showed that API5 and SAND had the highest stability in stems, while SAND, EF1A and API5 had the highest stability in roots. Moreover, SAND, ALDH113 and API5 were most stably expressed under copper stress, and EF1A, SAND and API5 were most stable under drought stress. SAND was consistently the most stably expressed gene in both the organs and all samples. Notably, The SAND gene ranked among the top three in terms of stability in all abiotic treatments and in various organs. This result indicates that the SAND gene is suitable for qRT-PCR experimentation in diverse tissues and under multiple (drought, herbicide and copper) abiotic stress conditions in dayflower.ConclusionThis study identified the most stably expressed reference genes under three abiotic stresses and in five organs of dayflower, and SAND showed high expression stability under various experimental conditions, making it a reliable reference gene for gene expression analysis experiments under different conditions in dayflower. This study will enhance the precision of the qRT-PCR quantification of candidate genes related to the adaptation significance of dayflower.

Considering severity in the design of reproductive genetic carrier screening programs: screening for severe conditions.

Reproductive genetic carrier screening (RGCS) provides information about people's chance of having children with certain genetic conditions, to inform reproductive decision making. RGCS at population scale requires a robust and streamlined program that is purposively designed and formally implemented to ensure equity and consistency. There are many considerations in selecting conditions, genes and variants for inclusion in RGCS, with severity of the genetic condition a key criterion. However, the concept of severity is complex and often underspecified in available guidelines. Severity is often determined in relation to other contextual features and can be experienced differently by individuals who all have the same condition. While some genetic conditions are unambiguously considered severe, there are many factors that contribute to how severe a condition is perceived to be (and by whom), and perspectives will vary. In this paper, we analyse why severity is an important criterion when selecting conditions, genes or variants to be included in RGCS. We suggest that screening programs should be oriented more towards variants and genes associated with severe conditions. We discuss the importance of taking a practical approach to gene selection in a carrier screening program when presenting the offer at population scale.

Ear to the ground! socioeconomic status, environmental stress, and the neural substrate of selective attention

We reanalyzed data originally published by Berman and Friedman (1995), who recorded event related potentials (ERPs) while children and adults with low, medium, and high socioeconomic status (SES) detected oddball auditory targets (tones and consonant–vowel sequences) among distractors. The ERP differential measuring how much attention was allocated to the targets vs. distractors increased significantly with SES, independently of age. To explain these findings, we integrate the ear to the ground hypothesis and the socioenvironmental epigenetic stress approach. According to the ear to the ground hypothesis, frequent and prolonged environmental uncertainty and hazard induce low-SES children to learn adaptive over-vigilance, attending disproportionately to stimuli that are currently irrelevant but may quickly become relevant and thus require an immediate survival response. Socioenvironmental epigenetic stress refers to the bidirectional interaction between a stress-inducing environment and the impact and expression of transgenerational gene selections in low-SES contexts. Because low-SES individuals are historically under and misrepresented in research, the proposed framework contributes to increase our understanding of how socioeconomic and environmental conditions may affect neurocognitive development. This offers significant points of entry for future interventions and policies targeting macrosocial settings (i.e., education and the justice system) and microsocial ontogenetic settings (i.e., individuals and families).

Reference gene selection for real-time qPCR in European flounder (Platichthys flesus) using organ-specific RNA-seq data

BackgroundThe European flounder is readily chosen as an experimental subject and model in physiological and ecotoxicological studies mostly because of its adaptability to laboratory conditions. Many studies utilise a quantitative PCR (qPCR) approach to ascertain the expression of target genes under experimental conditions. Such an approach relies heavily on the selection of reference genes with stable expression. Yet certain housekeeping genes are commonly used in this role, often without due consideration of their overall expression patterns. Therefore, new approaches should be developed to identify stable reference genes for a given species and to expand the general pool of genes suitable for the reference in qPCR analysis.Methods and resultsHere RNA-seq data of nine flounder organs led to identify four candidate genes of the most stable expression. It was achieved by differential expression analysis and tritoconstrictor script. Specific primers were designed for the complete ORF as well as for qPCR analysis. RT-qPCR efficiencies were tested on ORF amplicon templates. Most of the genes tested showed good amplification in a wide range of template dilutions (107-101), with a correlation coefficient (R2) ranging from 0.991 to 0.998 and a consistent efficiency (E) (Sybr Green I staining and TaqMan molecular probe).ConclusionsThe proposed approach based on differential expression analysis and a new bioinformatic tool is an appropriate selection method of candidates for reference genes in qPCR. The proposed approach, combining differential expression analysis with a new bioinformatics tool, provides an effective method for selecting reference gene candidates for qPCR. As a result, we can propose four genes (polr2f, yif1a, sf3b6, uba52), each with a set of validated primers, as suitable for consideration as reference genes in qPCR analysis in European flounder, an emerging model species.

Transcriptome-based analysis of the molecular mechanism of recombinant protein expression in Periplaneta americana cells.

The Insect Cell-Baculovirus Expression Vector System (IC-BEVS) is widely used for the generation of a variety of gene products, including proteins, vaccines, and gene therapy vectors; however, it has some limitations, including a constrained host range and low protein yields. In a previous study, we established the RIRI-PA1 cell line, which was derived from Periplaneta americana. This cell line is susceptible to Autographa californica multiple nucleopolyhedrovirus (AcMNPV) infection, which results in a higher yield production of recombinant protein within a short post-infection period of 24-48 h compared to the commonly used engineered cell line Sf21. To elucidate the basis for this phenomenon, we used RNA sequencing and transcriptome analysis of RIRI-PA1 and Sf21 cells infected with AcMNPV-GFP at 24, 72, and 168 h post-infection. Differentially expressed genes (DEGs) were identified in both cell lines. GO, eggNOG, and KEGG annotation analyses were used to identify DEGs and select candidate genes that could regulate recombinant protein expression. The results indicated a significant link between ribosomal pathway regulation and recombinant protein expression. After 24 h of AcMNPV-GFP infection, relatively high levels of protein were produced in RIRI-PA1 cells compared to Sf21 cells, which exhibited lesser enrichment of ribosomal protein-related DEGs (7 : 12). Moreover, a correlation was observed in the gene expression patterns between AcMNPV-GFP infection and recombinant protein synthesis, including genes associated with the ribosome, Toll and Imd signaling, and the cytochrome P450 pathway. Overall, our findings suggested that the ribosomal pathway might be more involved in regulation of protein expression during the early stages of RIRI-PA1 infection. The mechanisms underlying this process could have potential future applications in engineering cell modifications to reduce production time for recombinant proteins and to promote the use of IC-BEVS.

Machine learning-based identification of general transcriptional predictors for plant disease.

This study investigated the generalizability of Arabidopsis thaliana immune responses across diverse pathogens, including Botrytis cinerea, Sclerotinia sclerotiorum, and Pseudomonas syringae, using a data-driven, machine learning approach. Machine learning models were trained to predict disease development from early transcriptional responses. Feature selection techniques based on network science and topology were used to train models employing only a fraction of the transcriptome. Machine learning models trained on one pathosystem where then validated by predicting disease development in new pathosystems. The identified feature selection gene sets were enriched for pathways related to biotic, abiotic, and stress responses, though the specific genes involved differed between feature sets. This suggests common immune responses to diverse pathogens that operate via different gene sets. The study demonstrates that machine learning can uncover both established and novel components of the plant's immune response, offering insights into disease resistance mechanisms. These predictive models highlight the potential to advance our understanding of multigenic outcomes in plant immunity and can be further refined for applications in disease prediction.

Telomere-to-telomere genome assembly of a male goat reveals variants associated with cashmere traits

A complete goat (Capra hircus) reference genome enhances analyses of genetic variation, thus providing insights into domestication and selection in goats and related species. Here, we assemble a telomere-to-telomere (T2T) gap-free genome (2.86 Gb) from a cashmere goat (T2T-goat1.0), including a Y chromosome of 20.96 Mb. With a base accuracy of >99.999%, T2T-goat1.0 corrects numerous genome-wide structural and base errors in previous assemblies and adds 288.5 Mb of previously unresolved regions and 446 newly assembled genes to the reference genome. We sequence the genomes of five representative goat breeds for PacBio reads, and use T2T-goat1.0 as a reference to identify a total of 63,417 structural variations (SVs) with up to 4711 (7.42%) in the previously unresolved regions. T2T-goat1.0 was applied in population analyses of global wild and domestic goats, which revealed 32,419 SVs and 25,397,794 SNPs, including 870 SVs and 545,026 SNPs in the previously unresolved regions. Also, our analyses reveal a set of selective variants and genes associated with domestication (e.g., NKG2D and ABCC4) and cashmere traits (e.g., ABCC4 and ASIP).

Genome-Wide Identification of the PAP Gene Family in Potatoes and Its Expression Response to Phosphorus-Solubilizing Bacteria, Freezing Stresses, and Phytohormonal Regulation

Purple acid phosphatases (PAPs) are multifunctional proteins implicated in various aspects of plant biology, including flowering, seed development, senescence, carbon metabolism, and the response to biotic and abiotic stresses. Although PAPs have been reported in potatoes, the gene features and expression under phosphorus-solubilizing bacteria and freezing stresses remain unclear. In the present study, we identified 29 StPAPs with molecular weights ranging from 27.96 to 85.40 kDa, which were classified into three distinct groups based on a phylogenetic analysis. Integrating gene chromosomal localization, gene duplication events, intergenic collinearity, and gene selection pressure analysis, we found that the expansion of the potato PAP gene family was primarily driven by genome-wide duplication events, with the majority of the PAP genes undergoing purifying selection. Promoter cis-acting element analysis and RNA-Seq data revealed that a total of 89 cis-acting elements were associated with plant hormones, and the seven StPAP genes for low phosphorous response were associated with responses to abscisic acid, gibberellin, and abiotic stresses. Tissue expression pattern analyses indicated that the expression of StPAP genes were regulated in response to varying phosphorus levels. A co-expression network analysis identified eight StPAP genes involved in symbiosis with phosphorus-solubilizing bacteria, and seven StPAP genes exhibited significantly higher expression in response to freezing stress and abiotic stresses (drought, heat, darkness, and N/K/Ca/Fe/Mg/Zn deficiencies). In conclusion, the StPAP may synergistically modulate phytohormone levels in response to stress by regulating plant phosphorus homeostasis; StPAP12 and abscisic acid are key phosphorus-regulated genes and phytohormones in response to freezing stress. This study represents the first report to demonstrating the responsiveness of StPAPs to phytohormones, revealing a potential new function for these genes in plant stress responses and nutrient management. These findings provide novel insights into the multifaceted roles of PAPs in plant adaptation and stress tolerance.

Remodeling of Il4-Il13-Il5 locus underlies selective gene expression.

The type 2 cytokines, interleukin (IL)-4, IL-13 and IL-5 reside within a multigene cluster. Both innate (ILC2) and adaptive T helper 2 (TH2) lymphocytes secrete type 2 cytokines with diverse production spectra. Using transcription factor footprint and chromatin accessibility, we systemically cataloged regulatory elements (REs) denoted as SHS-I/II, KHS-I/II, +6.5kbIl13, 5HS-I(a, b, c, d, e), 5HS-II and 5HS-III(a, b, c) across the extended Il4-Il13-Il5 locus in mice. Physical proximities among REs were coordinately remodeled in three-dimensional space after cell activation, leading to divergent compartmentalization of Il4, Il13 and Il5 with varied combinations of REs. Deletions of REs revealed no single RE solely accounted for selective regulation of a given cytokine in vivo. Instead, individual RE differentially contribute to proper genomic positioning of REs and target genes. RE deletions resulted in context-dependent dysregulation of cytokine expression and immune response in tissue. Thus, signal-dependent remodeling of three-dimensional configuration underlies divergent cytokine outputs from the type 2 loci.

Explainable Machine Learning Models Using Robust Cancer Biomarkers Identification from Paired Differential Gene Expression.

In oncology, there is a critical need for robust biomarkers that can be easily translated into the clinic. We introduce a novel approach using paired differential gene expression analysis for biological feature selection in machine learning models, enhancing robustness and interpretability while accounting for patient variability. This method compares primary tumor tissue with the same patient's healthy tissue, improving gene selection by eliminating individual-specific artifacts. A focus on carcinoma was selected due to its prevalence and the availability of the data; we aim to identify biomarkers involved in general carcinoma progression, including less-researched types. Our findings identified 27 pivotal genes that can distinguish between healthy and carcinoma tissue, even in unseen carcinoma types. Additionally, the panel could precisely identify the tissue-of-origin in the eight carcinoma types used in the discovery phase. Notably, in a proof of concept, the model accurately identified the primary tissue origin in metastatic samples despite limited sample availability. Functional annotation reveals these genes' involvement in cancer hallmarks, detecting subtle variations across carcinoma types. We propose paired differential gene expression analysis as a reference method for the discovering of robust biomarkers.

Phased chromosome-level genome provides insights into the molecular adaptation for migratory lifestyle and population diversity for Pacific saury, Cololabis saira

The Pacific saury (Cololabis saira) is a pelagic fish commonly found in the North Pacific Ocean. Its population diversity and migratory lifestyle have long captured global attention. Despite the inherent complexity of the C. saira genome, characterized by extremely high heterozygosity, we successfully assembled a phased chromosome-level genome. The genome analysis revealed the expansion and natural selection of numerous functional genes, likely contributing to its enduring and extensive migratory lifestyle. Notably, gpr35 and igh genes showed significant expansion in the C. saira genome, potentially associated with regulating the immune response against environmental parasites and pathogens. Moreover, genes involved in DNA repair/replication and peroxisome function, including atm, ercc6, pex14, and pex16, displayed evidence of positive selection. Based on genome-sequencing of 80 individuals from eight sampling sites, we demonstrated that the genomic divergence among C. saira populations is relatively low. However, the sampling sites could be grouped into two distinct clusters, roughly corresponding to the migratory route of C. saira. This suggests a possible genome-wide divergence for C. saira within the open ocean region. Furthermore, the trmu gene, responsible for controlling otolith development and sharpness, exhibited differentiation between the two groups, consistent with previously reported differences in otolith morphology. This study has provided a reference genome and insights into the evolution, ecology, and conservation of Pacific saury and closely-related species.

Robust reference gene selection in Norway spruce: essential for real-time quantitative PCR across different tissue, stress and developmental conditions

Accurate gene expression analysis in Norway spruce (Picea abies) under diverse stress conditions requires the identification of stable reference genes for normalization. Notably, the literature lacks reports on suitable reference genes in Norway spruce. Here, we aimed to address this gap by identifying suitable reference genes for quantitative real-time PCR in Norway spruce across various stress conditions (drought, heat, pathogen infection) in seedlings, tissues (needle, phloem, root), and developmental stages (seedlings, mature trees). We evaluated the stability of 15 candidate reference genes and assessed their expression stability using five statistical algorithms (ΔCt, geNorm, NormFinder, BestKeeper, and RefFinder). Our results highlight ubiquitin-protein ligase (SP1), conserved oligomeric Golgi complex (COG7), and tubby-like F-box protein (TULP6) as the most stable reference genes, while succinate dehydrogenase (SDH5) and heat shock protein 90 (HSP90) were the least stable under various experimental conditions. COG7 and TULP6 are novel candidate reference genes reported for the first time. The expression stability of the identified reference genes was further validated using dehydrin-like protein 5 (PaDhn5) under drought conditions in Norway spruce. Pairwise variation analysis suggests that two reference genes were sufficient to normalize gene expression across all sample sets. This study provides a comprehensive analysis of reference gene stability under different experimental conditions and a catalog of genes for each condition, facilitating future functional genomic research in Norway spruce and related conifers.