Method For Gene Expression Analysis Research Articles

Identification and validation of stable reference genes for RT-qPCR analyses of Kobresia littledalei seedlings

BackgroundKobreisa littledalei, belonging to the Cyperaceae family is the first Kobresia species with a reference genome and the most dominant species in Qinghai-Tibet Plateau alpine meadows. It has several resistance genes which could be used to breed improved crop varieties. Reverse Transcription Quantitative Real-Time Polymerase Chain Reaction (RT-qPCR) is a popular and accurate gene expression analysis method. Its reliability depends on the expression levels of reference genes, which vary by species, tissues and environments. However, K.littledalei lacks a stable and normalized reference gene for RT-qPCR analysis.ResultsThe stability of 13 potential reference genes was tested and the stable reference genes were selected for RT-qPCR normalization for the expression analysis in the different tissues of K. littledalei under two abiotic stresses (salt and drought) and two hormonal treatments (abscisic acid (ABA) and gibberellin (GA)). Five algorithms were used to assess the stability of putative reference genes. The results showed a variation amongst the methods, and the same reference genes showed tissue expression differences under the same conditions. The stability of combining two reference genes was better than a single one. The expression levels of ACTIN were stable in leaves and stems under normal conditions, in leaves under drought stress and in roots under ABA treatment. The expression of glyceraldehyde-3-phosphate dehydrogenase (GAPDH) expression was stable in the roots under the control conditions and salt stress and in stems exposed to drought stress. Expression levels of superoxide dismutase (SOD) were stable in stems of ABA-treated plants and in the roots under drought stress. Moreover, RPL6 expression was stable in the leaves and stems under salt stress and in the stems of the GA-treated plants. EF1-alpha expression was stable in leaves under ABA and GA treatments. The expression levels of 28 S were stable in the roots under GA treatment. In general, ACTIN and GAPDH could be employed as housekeeping genes for K. littledalei under different treatments.ConclusionThis study identified the best RT-qPCR reference genes for different K. littledalei tissues under five experimental conditions. ACTIN and GAPDH genes can be employed as the ideal housekeeping genes for expression analysis under different conditions. This is the first study to investigate the stable reference genes for normalized gene expression analysis of K. littledalei under different conditions. The results could aid molecular biology and gene function research on Kobresia and other related species.

Identification of Hub Genes in Liver Hepatocellular Carcinoma Based on Weighted Gene Co-expression Network Analysis.

Liver hepatocellular carcinoma (LIHC) is a malignant cancer with high incidence and poor prognosis. To investigate the correlation between hub genes and progression of LIHC and to provided potential prognostic markers and therapy targets for LIHC. Our study mainly used The Cancer Genome Atlas (TCGA) LIHC database and the gene expression profiles of GSE54236 from the Gene Expression Omnibus (GEO) to explore the differential co-expression genes between LIHC and normal tissues. The differential co-expression genes were extracted by Weighted Gene Co-expression Network Analysis (WGCNA) and differential gene expression analysis methods. The Genetic Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) were carried out to annotate the function of differential genes. Then the hub genes were validated using protein-protein interaction (PPI) network. And the expression level and prognostic analysis were performed. The probable associations between the expression of hub genes and both tumor purity and infiltration of immune cells were explored by TIMER. A total of 68 differential co-expression genes were extracted. These genes were mainly enriched in complement activation (biological process), collagen trimer (cellular component), carbohydrate binding and receptor ligand activity (molecular function) and cytokine - cytokine receptor interaction. Then we demonstrated that the 10 hub genes (CFP, CLEC1B, CLEC4G, CLEC4M, FCN2, FCN3, PAMR1 and TIMD4) were weakly expressed in LIHC tissues, the qRT-PCR results of clinical samples showed that six genes were significantly downregulated in LIHC patients compared with adjacent tissues. Worse overall survival (OS) and disease-free survival (DFS) in LIHC patients were associated with the lower expression of CFP, CLEC1B, FCN3 and TIMD4. Ten hub genes had positive association with tumor purity. CFP, CLEC1B, FCN3 and TIMD4 could serve as novel potential molecular targets for prognosis prediction in LIHC.

Metabolic Dysregulation and Its Role in Postoperative Pain among Knee Osteoarthritis Patients.

The inflammation observed in joint tissues is linked to pain caused by the production of proinflammatory cytokines. Since the biosynthesis of cytokines requires energy, their production is supported by extensive metabolic conversions of carbohydrates and fatty acids, which could lead to a disruption in cellular homeostasis. Therefore, this study aimed to investigate the association between POP development and disturbances in energy metabolic conversions, focusing on carbohydrate and fatty acid metabolism. Peripheral blood samples were collected from 26 healthy subjects and 50 patients with end-stage OA before joint replacement surgery. All implants were validated by orthopedic surgeons, and patients with OA demonstrated no inherent abnormalities to cause pain from other reasons than OA disease, such as malalignment, aseptic loosening, or excessive bleeding. Pain levels were assessed before surgery using the visual analogue scale (VAS) and neuropathic pain questionnaires, DN4 and PainDETECT. Functional activity was evaluated using the Western Ontario and McMaster Universities Osteoarthritis Index (WOMAC). Three and six months after surgery, pain indices according to a VAS of 30 mm or higher were considered. Total RNA isolated from whole blood was analyzed using quantitative real-time RT-PCR (qRT-PCR) for the expression of genes related to carbohydrate and fatty acid metabolism. Protein levels of the examined genes were measured using an ELISA in the peripheral blood mononuclear cells (PBMCs). We used qRT-PCR because it is the most sensitive and reliable method for gene expression analysis, while an ELISA was used to confirm our qRT-PCR results. Among the study cohort, 17 patients who reported POP demonstrated significantly higher (p < 0.05) expressions of the genes PKM2, LDH, SDH, UCP2, CPT1A, and ACLY compared to pain-free patients with KOA. Receiver-operating characteristic (ROC) curve analyses confirmed the association between these gene expressions and pain development post-arthroplasty. A principle component analysis identified the prognostic values of ACLY, CPT1A, AMPK, SDHB, Caspase 3, and IL-1β gene expressions for POP development in the examined subjects. These findings suggest that the disturbances in energy metabolism, as observed in the PBMCs of patients with end-stage KOA before arthroplasty, may contribute to POP development. An understanding of these metabolic processes could provide insights into the pathogenesis of KOA. Additionally, our findings can be used in a clinical setting to predict POP development in end-stage patients with KOA before arthroplasty.

Relative gene expression analysis of catalase in environmental RNA from Japanese medaka exposed to toxic chemicals

AbstractEnvironmental RNA (eRNA) has the potential as a non‐invasive tool for assessing the physiological status of macro‐organisms, yet the quantitative method for relative gene expression analysis is still underexplored. To bridge this gap, this study introduces a relative quantification method for eRNA, employing quantitative PCR (qPCR) to evaluate the expression of the antioxidant gene, catalase (cat), in the Japanese medaka Oryzias latipes exposed to two toxic chemicals for 96 h: chlorpyrifos (CPS) and carbamazepine. Our results showed that, in eRNA, genes frequently used as a reference for tissue or cells in conventional expression analysis correlated with each other. Also, one of them (elfa) exhibited less variability, showing its potential suitability as a reference gene in eRNA analyses. Additionally, cat expression levels in eRNA increased with increasing CPS concentrations, in a concentration‐response manner. These results suggest the promising potential of qPCR applications for eRNA in the monitoring of an organism's health and response to environmental changes. However, we observed disparities in gene expression levels of cat and beta‐actin (actb) between eRNA and whole fish body, indicating eRNA might have a biased origin. Further research is needed to uncover the origin of eRNA and determine the limitations and applicability domain of eRNA analysis.

Single-cell RNA Sequencing Analysis Reveals the Role of Cancer-associated Fibroblasts in Skin Melanoma.

Mechanism of fibroblasts in skin melanoma (SKME) revealed by single-cell RNA sequencing data. SKME is responsible for more than 80% of skin-related cancer deaths. Cancer-associated fibroblasts (CAFs) generate inflammatory factors, growth factors and extracellular matrix proteins to facilitate cancer cell growth, metastasis, drug resistance and immune exclusion. However, molecular mechanisms of CAFs in SKME are still lacking. Our goal was to reveal the role of CAFs in SKME. We downloaded the single-cell RNA sequencing (scRNA-seq) dataset from the Gene Expression Omnibus (GSE215120) database. Then, the Seurat package was applied to analyze the single-cell atlas of SKME data, and cell subsets were annotated with the CellMarker database. The molecular mechanisms of CAFs in SKME were disclosed via differential gene expression and enrichment analysis, Cellchat and SCENIC methods. Using scRNA-seq data, three SKME cases were used and downscaled and clustered to identify 11 cell subgroups and 5 CAF subsets. The enrichment of highly expressed genes among the 5 CAF subsets suggests that cell migration-inducing hyaluronan-binding protein (CEMIP) + fibroblasts and naked cuticle homolog 1 (NKD1) + fibroblasts were closely associated with epithelial to mesenchymal transition. Cellchat analysis revealed that CAF subpopulations promoted melanocyte proliferation through Jagged1 (JAG1)-Notch homolog 1 (NOTCH1), JAG1-NOTCH3 and migration through pleiotrophin (PTN)-syndecan-3 (SDC3) receptor-ligand pairs. The SCENIC analysis identified that most of the transcription factors in each CAF subpopulation played a certain role in the metastasis of melanoma and were highly expressed in metastatic SKME samples. Specifically, we observed that CEMIP+ fibroblasts and NKD1+ fibroblasts had potential roles in participating in immune therapy resistance. Collectively, we uncovered a single-- cell atlas of SKME and revealed the molecular mechanisms of CAFs in SKME development, providing a base for immune therapy and prognosis assessment. Our study reveals that 5 CAFs in SKME have a promoting effect on melanocyte proliferation and metastasis. More importantly, CEMIP+ fibroblasts and NKD1+ fibroblasts displayed close connections with immune therapy resistance. These findings help provide a good basis for future immune therapy and prognosis assessment targeting CAFs in SKME.

Quantitative Real-Time PCR Method to Evaluate Gene Expression in Zebrafish Embryos.

The fast zebrafish embryonic development offers an opportunity for the study of genes crucial for developmental processes. Several genes associated with human diseases have orthologs in zebrafish. Enhanced comprehension of a gene's function can be achieved by knowing when and where it is expressed. This knowledge also makes it possible to alter a gene-by-gene knockdown in a time- and place-specific manner. Moreover, gene expression analyses contribute greatly to teratogenicity studies in zebrafish embryos. Therefore, the importance of examining the differences between the expressions of these genes has increased day by day. The incorporation of reverse transcription (RT) as the initial step prior to thermal cycling in quantitative real-time polymerase chain reaction (RT-qPCR) has made a significant contribution to RNA research. RT-qPCR is the gold standard and an effective method for gene expression analysis. Quick readout, high sensitivity, reproducibility, as well as high potential throughput, along with reliable quantification, are just a few advantages of RT-qPCR. However, there are drawbacks to its application, such as RNA's inherent variability, impurities during RNA extraction, and variations in reverse transcription and PCR efficiencies. Implementing a precise normalization technique is crucial to account for these inaccuracies. In this chapter, the protocol for gene expression analysis by RT-qPCR for zebrafish embryos is explained.

ANALISIS GEN KOMPARATIF KARSINOMA SEL SKUAMOSA PARU-PARU ANTARA INDIVIDU MEROKOK DAN TIDAK MEROKOK [Comparative Gene Analysis of Squamous Cell Lung Carcinoma Between Smoking and Non-smoking Individuals]

Squamous cell lung carcinoma (SCC) is a form of non-small cell lung cancer that commonly arises in the primary airway. The development of SCC is closely linked to changes in squamous cells that line the airways, primarily caused by exposure to tobacco smoke. To gain insights into SCC, bioinformatics techniques have been employed to detect biomarkers and analyze gene expression patterns, utilizing data from the Cancer Genome Atlas (TCGA) database, which was preprocessed for analysis. By employing DESeq2, a differential gene expression analysis method, identified genes showed significant variations in expression between smoking and non-smoking groups among the 11,530 genes examined. Notably, five genes, namely CT45A1, GCGR, TPTE, ABCC2, and PI16, were found to play a significant role in tumor development and were susceptible to under- or over-expression due to smoking. The majority of these genes were found to be underexpressed rather than overexpressed. These identified genes hold potential as biomarkers for tumor development and exhibit a strong correlation between smoking history and the development of SCC. However, a limitation encountered during this analysis was the unavailability of data from normal non-tumor patients, which could have facilitated a more comprehensive analysis of differential gene expression. Furthermore, this research gives a deeper implementation regarding the molecular mechanisms and genomics underlying SCC development, identifies differentially expressed genes associated with SCC and smoking history, and highlights potential biomarkers that warrant further investigation.

Uncovering hub genes in sepsis through bioinformatics analysis.

In-depth studies on the mechanisms of pathogenesis of sepsis and diagnostic biomarkers in the early stages may be the key to developing individualized and effective treatment strategies. This study aimed to identify sepsis-related hub genes and evaluate their diagnostic reliability. The gene expression profiles of GSE4607 and GSE131761 were obtained from the Gene Expression Omnibus. Differentially co-expressed genes between the sepsis and control groups were screened. Single-sample gene set enrichment analysis and gene set variation analysis were performed to investigate the biological functions of the hub genes. A receiver operating characteristic curve was used to evaluate diagnostic value. Datasets GSE154918 and GSE185263 were used as external validation datasets to verify the reliability of the hub genes. Four differentially co-expressed genes, FAM89A, FFAR3, G0S2, and FGF13, were extracted using a weighted gene co-expression network analysis and differential gene expression analysis methods. These 4 genes were upregulated in the sepsis group and were distinct from those in the controls. Moreover, the receiver operating characteristic curves of the 4 genes exhibited considerable diagnostic value in discriminating septic blood samples from those of the non-septic control group. The reliability and consistency of these 4 genes were externally validated. Single-sample gene set enrichment analysis and gene set variation analysis analyses indicated that the 4 hub genes were significantly correlated with the regulation of immunity and metabolism in sepsis. The identified FAM89A, FFAR3, G0S2, and FGF13 genes may help elucidate the molecular mechanisms underlying sepsis and drive the introduction of new biomarkers to advance diagnosis and treatment.

Identification of Accurate Reference Genes for qRT-PCR Analysis of Gene Expression in Eremochloa ophiuroides under Multiple Stresses of Phosphorus Deficiency and/or Aluminum Toxicity.

Centipedegrass (Eremochloa ophiuroides (Munro.) Hack.) is a species originating in China and is an excellent warm-season turfgrass. As a native species in southern China, it is naturally distributed in the phosphorus-deficient and aluminum-toxic acid soil areas. It is important to research the molecular mechanism of centipedegrass responses to phosphorus-deficiency and/or aluminum-toxicity stress. Quantitative Real-Time PCR (qRT-PCR) is a common method for gene expression analysis, and the accuracy of qRT-PCR results depends heavily on the stability of internal reference genes. However, there are still no reported stable and effective reference genes for qRT-PCR analysis of target genes under the acid-soil-related stresses in different organs of centipedegrass. For scientific rigor, the gene used as a reference for any plant species and/or any stress conditions should be first systematically screened and evaluated. This study is the first to provide a group of reliable reference genes to quantify the expression levels of functional genes of Eremochloa ophiuroides under multiple stresses of P deficiency and/or aluminum toxicity. In this study, centipedegrass seedlings of the acid-soil-resistant strain 'E041' and acid-soil-sensitive strain 'E089' were used for qRT-PCR analysis. A total of 11 candidate reference genes (ACT, TUB, GAPDH, TIP41, CACS, HNR, EP, EF1α, EIF4α, PP2A and actin) were detected by qRT-PCR technology, and the stability of candidate genes was evaluated with the combination of four internal stability analysis software programs. The candidate reference genes exhibited differential stability of expression in roots, stems and leaves under phosphorus-deficiency and/or aluminum-toxicity stress. On the whole, the results showed that GAPDH, TIP41 and HNR were the most stable in the total of samples. In addition, for different tissues under various stresses, the selected reference genes were also different. CACS and PP2A were identified as two stable reference genes in roots through all three stress treatments (phosphate deficiency, aluminum toxicity, and the multiple stress treatment of aluminum toxicity and phosphate deficiency). Moreover, CACS was also stable as a reference gene in roots under each treatment (phosphate deficiency, aluminum toxicity, or multiple stresses of aluminum toxicity and phosphate deficiency). In stems under all three stress treatments, GAPDH and EIF4α were the most stable reference genes; for leaves, PP2A and TIP41 showed the two highest rankings in all three stress treatments. Finally, qRT-PCR analysis of the expression patterns of the target gene ALMT1 was performed to verify the selected reference genes. The application of the reference genes identified as internal controls for qRT-PCR analysis will enable accurate analysis of the target gene expression levels and expression patterns in centipedegrass under acid-soil-related stresses.

RNA-seq RNAaccess identified as the preferred method for gene expression analysis of low quality FFPE samples.

Clinical tumor tissues that are preserved as formalin-fixed paraffin-embedded (FFPE) samples result in extensive cross-linking, fragmentation, and chemical modification of RNA, posing significant challenges for RNA-seq-based gene expression profiling. This study sought to define an optimal RNA-seq protocol for FFPE samples. We employed a common RNA extraction method and then compared RNA-seq library preparation protocols including RNAaccess, RiboZero and PolyA in terms of sequencing quality and concordance of gene expression using FFPE and case-matched fresh-frozen (FF) triple-negative breast cancer (TNBC) tissues. We found that RNAaccess, a method based on exome capture, produced the most concordant results. Applying RNAaccess to FFPE gastric cancer tissues, we established a minimum RNA DV200 requirement of 10% and a RNA input amount of 10ng that generated highly reproducible gene expression data. Lastly, we demonstrated that RNAaccess and NanoString platforms produced highly concordant expression profiles from FFPE samples for shared genes; however, RNA-seq may be preferred for clinical biomarker discovery work because of the broader coverage of the transcriptome. Taken together, these results support the selection of RNA-seq RNAaccess method for gene expression profiling of FFPE samples. The minimum requirements for RNA quality and input established here may allow for inclusion of clinical FFPE samples of sub-optimal quality in gene expression analyses and ultimately increasing the statistical power of such analyses.

Optimized method for differential gene expression analysis in non-model species: Case of Cedrela odorata L.

The following protocol introduces a targeted methodological approach of differential gene expression analysis, which is particularly beneficial in the context of non-model species. While we acknowledge that biological complexity often involves the interplay of multiple genes in any given biological response our method provides a strategy to streamline this complexity, enabling researchers to focus on a more manageable subset of genes of interest. In this context, red cedar transcriptome (Cedrela odorata L.) and known or hypothetical genes related to the response to herbivory were used as reference. The protocol key points are:•Implementation of a transcriptome thinning process to eliminate redundant and non-coding sequences, optimizing the analysis and reducing processing time.•Use of a custom gene database to identify and retain coding sequences with high precision.•Focus on specific genes of interest, allowing a more targeted analysis for specific experimental conditions.This approach holds particular value for pilot studies, research with limited resources, or when rapid identification and validation of candidate genes are needed in species without a reference genome.

Post-COVID symptoms are associated with endotypes reflecting poor inflammatory and hemostatic modulation.

Persistent symptoms after COVID-19 infection ("long COVID") negatively affects almost half of COVID-19 survivors. Despite its prevalence, its pathophysiology is poorly understood, with multiple host systems likely affected. Here, we followed patients from hospital to discharge and used a systems-biology approach to identify mechanisms of long COVID. RNA-seq was performed on whole blood collected early in hospital and 4-12 weeks after discharge from 24 adult COVID-19 patients (10 reported post-COVID symptoms after discharge). Differential gene expression analysis, pathway enrichment, and machine learning methods were used to identify underlying mechanisms for post-COVID symptom development. Compared to patients with post-COVID symptoms, patients without post-COVID symptoms had larger temporal gene expression changes associated with downregulation of inflammatory and coagulation genes over time. Patients could also be separated into three patient endotypes with differing mechanistic trajectories, which was validated in another published patient cohort. The "Resolved" endotype (lowest rate of post-COVID symptoms) had robust inflammatory and hemostatic responses in hospital that resolved after discharge. Conversely, the inflammatory/hemostatic responses of "Suppressive" and "Unresolved" endotypes (higher rates of patients with post-COVID symptoms) were persistently dampened and activated, respectively. These endotypes were accurately defined by specific blood gene expression signatures (6-7 genes) for potential clinical stratification. This study allowed analysis of long COVID whole blood transcriptomics trajectories while accounting for the issue of patient heterogeneity. Two of the three identified and externally validated endotypes ("Unresolved" and "Suppressive") were associated with higher rates of post-COVID symptoms and either persistently activated or suppressed inflammation and coagulation processes. Gene biomarkers in blood could potentially be used clinically to stratify patients into different endotypes, paving the way for personalized long COVID treatment.

Rapid, Point-of-Care Host-Based Gene Expression Diagnostics Using Giant Magnetoresistive Biosensors.

Host-based gene expression analysis is a promising tool for a broad range of clinical applications, including rapid infectious disease diagnostics and real-time disease monitoring. However, the complex instrumentation requirements and slow turnaround-times associated with traditional gene expression analysis methods have hampered their widespread adoption at the point-of-care (POC). To overcome these challenges, we have developed an automated and portable platform that utilizes polymerase chain reaction (PCR) and giant magnetoresistive (GMR) biosensors to perform rapid multiplexed, targeted gene expression analysis at the POC. As proof-of-concept, we utilized our platform to amplify and measure the expression of four genes (HERC5, HERC6, IFI27, and IFIH1) that were previously shown to be upregulated in hosts infected with influenza viruses. The compact instrument conducted highly automated PCR amplification and GMR detection to measure the expression of the four genes in multiplex, then utilized Bluetooth communication to relay results to users on a smartphone application. To validate the platform, we tested 20 cDNA samples from symptomatic patients that had been previously diagnosed as either influenza-positive or influenza-negative using a RT-PCR virology panel. A non-parametric Mann-Whitney test revealed that day 0 (day of symptom onset) gene expression was significantly different between the two groups (p < 0.0001, n = 20). Hence, we preliminarily demonstrated that our platform could accurately discriminate between symptomatic influenza and non-influenza populations based on host gene expression in ∼30 min. This study not only establishes the potential clinical utility of our proposed assay and device for influenza diagnostics but it also paves the way for broadscale and decentralized implementation of host-based gene expression diagnostics at the POC.

Optimizing identification of consensus molecular subtypes in muscle-invasive bladder cancer: a comparison of two sequencing methods and gene sets using FFPE specimens

BackgroundMolecular subtypes predict prognosis in muscle-invasive bladder cancer (MIBC) and are explored as predictive markers. To provide a common base for molecular subtyping and facilitate clinical applications, a consensus classification has been developed. However, methods to determine consensus molecular subtypes require validation, particularly when FFPE specimens are used. Here, we aimed to evaluate two gene expression analysis methods on FFPE samples and to compare reduced gene sets to classify tumors into molecular subtypes.MethodsRNA was isolated from FFPE blocks of 15 MIBC patients. Massive analysis of 3’ cDNA ends (MACE) and the HTG transcriptome panel (HTP) were used to retrieve gene expression. We used normalized, log2-transformed data to call consensus and TCGA subtypes with the consensusMIBC package for R using all available genes, a 68-gene panel (ESSEN1), and a 48-gene panel (ESSEN2).ResultsFifteen MACE-samples and 14 HTP-samples were available for molecular subtyping. The 14 samples were classified as Ba/Sq in 7 (50%), LumP in 2 (14.3%), LumU in 1 (7.1%), LumNS in 1 (7.1%), stroma-rich in 2 (14.3%) and NE-like in 1 (7.1%) case based on MACE- or HTP-derived transcriptome data. Consensus subtypes were concordant in 71% (10/14) of cases when comparing MACE with HTP data. Four cases with aberrant subtypes had a stroma-rich molecular subtype with either method. The overlap of the molecular consensus subtypes with the reduced ESSEN1 and ESSEN2 panels were 86% and 100%, respectively, with HTP data and 86% with MACE data.ConclusionDetermination of consensus molecular subtypes of MIBC from FFPE samples is feasible using various RNA sequencing methods. Inconsistent classification mainly involves the stroma-rich molecular subtype, which may be the consequence of sample heterogeneity with (stroma)-cell sampling bias and highlights the limitations of bulk RNA-based subclassification. Classification is still reliable when analysis is reduced to selected genes.

Identification and validation of the reference genes in the echiuran worm Urechis unicinctus based on transcriptome data

BackgroundReal-time quantitative PCR (RT-qPCR) is a crucial and widely used method for gene expression analysis. Selecting suitable reference genes is extremely important for the accuracy of RT-qPCR results. Commonly used reference genes are not always stable in various organisms or under different environmental conditions. With the increasing application of high-throughput sequencing, transcriptome analysis has become an effective method for identifying novel stable reference genes.ResultsIn this study, we identified candidate reference genes based on transcriptome data covering embryos and larvae of early development, normal adult tissues, and the hindgut under sulfide stress using the coefficient of variation (CV) method in the echiuran Urechis unicinctus, resulting in 6834 (15.82%), 7110 (16.85%) and 13880 (35.87%) candidate reference genes, respectively. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses revealed that the candidate reference genes were significantly enriched in cellular metabolic process, protein metabolic process and ribosome in early development and normal adult tissues as well as in cellular localization and endocytosis in the hindgut under sulfide stress. Subsequently, ten genes including five new candidate reference genes and five commonly used reference genes, were validated by RT-qPCR. The expression stability of the ten genes was analyzed using four methods (geNorm, NormFinder, BestKeeper, and ∆Ct). The comprehensive results indicated that the new candidate reference genes were more stable than most commonly used reference genes. The commonly used ACTB was the most unstable gene. The candidate reference genes STX12, EHMT1, and LYAG were the most stable genes in early development, normal adult tissues, and hindgut under sulfide stress, respectively. The log2(TPM) of the transcriptome data was significantly negatively correlated with the Ct values of RT-qPCR (Ct =  − 0.5405 log2(TPM) + 34.51), which made it possible to estimate the Ct value before RT-qPCR using transcriptome data.ConclusionOur study is the first to select reference genes for RT-qPCR from transcriptome data in Echiura and provides important information for future gene expression studies in U. unicinctus.

RoPE: A robust profile likelihood method for differential gene expression analysis.

Variation in RNA-Seq data creates modeling challenges for differential gene expression (DE) analysis. Statistical approaches address conventional small sample sizes and implement empirical Bayes or non-parametric tests, but frequently produce different conclusions. Increasing sample sizes enable proposal of alternative DE paradigms. Here we develop RoPE, which uses a data-driven adjustment for variation and a robust profile likelihood ratio DE test. Simulation studies show RoPE can have improved performance over existing tools as sample size increases and has the most reliable control of error rates. Application of RoPE demonstrates that an active Pseudomonas aeruginosa infection downregulates the SLC9A3 Cystic Fibrosis modifier gene.

Neonatal administration of synthetic estrogen, diethylstilbestrol to mice up-regulates inflammatory Cxclchemokines located in the 5qE1 region in the vaginal epithelium.

A synthetic estrogen, diethylstilbestrol (DES), is known to cause adult vaginal carcinoma by neonatal administration of DES to mice. However, the carcinogenic process remains unclear. By Cap Analysis of Gene Expression method, we found that neonatal DES exposure up-regulated inflammatory Cxcl chemokines 2, 3, 5, and 7 located in the 5qE1 region in the vaginal epithelium of mice 70 days after birth. When we examined the gene expressions of these genes much earlier stages, we found that neonatal DES exposure increased these Cxcl chemokine genes expression even after 17 days after birth. It implies the DES-mediated persistent activation of inflammatory genes. Intriguingly, we also detected DES-induced non-coding RNAs from a region approximately 100 kb far from the Cxcl5 gene. The non-coding RNA up-regulation by DES exposure was confirmed on the 17-day vagina and continued throughout life, which may responsible for the activation of Cxcl chemokines located in the same region, 5qE1. This study shows that neonatal administration of DES to mice causes long-lasting up-regulation of inflammatory Cxcl chemokines in the vaginal epithelium. DES-mediated inflammation may be associated with the carcinogenic process.

Detecting Embryo Developmental Potential by Single Blastomere RNA-Seq.

Recent advances in preimplantation embryo diagnostics enable a wide range of applications using single cell biopsy and molecular-based selection techniques without compromising embryo production. This study was conducted to develop a single cell embryo biopsy technique and gene expression analysis method with a very low input volume to ensure normal embryo development and to see if there are differences in gene expression profiles between day-5 biopsied bovine embryos that developed into blastocysts and embryos arrested at morula stage. Out of the 65 biopsied morulae, 32 developed to blastocysts (49.2%). Out of the 13,580 successfully annotated genes, 1204 showed a difference in mRNA expression level. Out of these, 155 genes were expressed in embryos developing to blastocysts. The pathway enrichment analysis revealed significant enrichment in "organelle biogenesis and maintenance", "mRNA splicing" and "mitochondrial translation" pathways. These findings suggest principal differences in gene expression patterns and functional networks of embryos able to reach the blastocyst stage compared to embryos arrested in development. Our preliminary data suggest that single blastomere biopsy and selected gene expression profiles at morula stage could offer additional possibilities for early preimplantation embryo selection before transfer.

Machine Learning Methods for Cancer Classification Using Gene Expression Data: A Review

Cancer is a term that denotes a group of diseases caused by the abnormal growth of cells that can spread in different parts of the body. According to the World Health Organization (WHO), cancer is the second major cause of death after cardiovascular diseases. Gene expression can play a fundamental role in the early detection of cancer, as it is indicative of the biochemical processes in tissue and cells, as well as the genetic characteristics of an organism. Deoxyribonucleic acid (DNA) microarrays and ribonucleic acid (RNA)-sequencing methods for gene expression data allow quantifying the expression levels of genes and produce valuable data for computational analysis. This study reviews recent progress in gene expression analysis for cancer classification using machine learning methods. Both conventional and deep learning-based approaches are reviewed, with an emphasis on the application of deep learning models due to their comparative advantages for identifying gene patterns that are distinctive for various types of cancers. Relevant works that employ the most commonly used deep neural network architectures are covered, including multi-layer perceptrons, as well as convolutional, recurrent, graph, and transformer networks. This survey also presents an overview of the data collection methods for gene expression analysis and lists important datasets that are commonly used for supervised machine learning for this task. Furthermore, we review pertinent techniques for feature engineering and data preprocessing that are typically used to handle the high dimensionality of gene expression data, caused by a large number of genes present in data samples. The paper concludes with a discussion of future research directions for machine learning-based gene expression analysis for cancer classification.

Host Transcriptome Analysis of Ferret Tissues Following Henipavirus Infection.

Ferrets are commonly used as experimental models of infection for a variety of viruses due to their susceptibility to human respiratory viruses and the close resemblance of pathological outcomes found in human infections. Even though ferret-specific reagents are limited, the use of ferrets as a preclinical experimental model of infection has gained considerable interest since the publication of the ferret transcriptome and draft ferret genome. These advances have made it feasible to easily perform whole-genome gene expression analysis in the ferret infection model. Here, we describe methods for genome-wide gene expression analysis using RNA sequence (RNAseq) data obtained from the lung and brain tissues obtained from experimental infections of Hendra (HeV) and Nipah (NiV) viruses in ferrets. We provide detailed methods for RNAseq and representative data for host gene expression profiles of the lung tissues that show early activation of interferon pathways and later activation of inflammation-related pathways.