Normalization Of Gene Expression Data Research Articles

Selection of reference genes for normalizing gene expression data across seasons in spermatozoa of water buffalo (Bubalus bubalis).

Selection of the most stably expressed reference genes is key to monitoring accurate target gene expression across any tissue or cell type. The mRNA in spermatozoa stores valuable information related to changes in spermatogenesis due to variations in environmental conditions, especially during heat stress, which affects various sperm functions. Semen quality in buffalo bulls is significantly influenced by the seasons. In the study, a panel of nine genes was evaluated to identify the most stably expressed internal control gene (ICG) for the normalization of real-time gene expression data generated across various seasons for Murrah buffalo bulls' spermatozoa. Sperm cells were purified from the semen samples collected during different seasons, with temperature-humidity index (THI) ranging from 80.80 ± 1.47 (hot summer) to 55.88 ± 1.98 (winter), using the BoviPure™ gradient purification method. The RNA isolated from the purified spermatozoa fraction was quality checked prior to reverse transcription and subjected to qPCR (quantitative real-time PCR) based expression analysis. An automated 'endoGene' pipeline was employed to apply the geNorm, NormFinder, and BestKeeper algorithms for data analysis. The result indicated that GAPDH and PP1A were the most stably expressed among the gene panel, whereas ATPSF1 and ACTB were the two least stable expressed reference genes. Further, the most suitable ICGs identified were validated by normalization of real time expression data of heat stress and sperm quality genes, HSFY2 and AKAP4, respectively. The genes identified would help in generating the most reliable results for the expression profiling of the genes dictating sperm quality and heat stress cope-up mechanism in buffalo spermatozoa, collected during different seasons.

Abstract 2859: The Single-cell Pediatric Cancer Atlas: Open-source data and tools for single-cell transcriptomics of pediatric tumors

Abstract The Single-cell Pediatric Cancer Atlas (ScPCA) Portal (https://scpca.alexslemonade.org/), developed and maintained by the Childhood Cancer Data Lab, is an open-source data resource for single-cell and single-nuclei RNA sequencing data of pediatric tumors. Originally comprised of data from 10 projects funded by Alex’s Lemonade Stand Foundation, the Portal currently contains summarized gene expression data for over 500 samples from a diverse set of over 50 types of cancers and is growing to include community-contributed datasets. In addition to gene expression data from single-cell and single-nuclei RNA sequencing, the Portal holds data obtained from bulk RNA sequencing, spatial transcriptomics, and feature barcoding methods, such as CITE-seq and cell hashing. ScPCA data are available for download in formats ready for downstream analysis, such as SingleCellExperiment or AnnData objects. Objects include raw counts and normalized gene expression data, PCA and UMAP coordinates, unsupervised clustering assignments, and cell type annotations. Additionally, all downloads include two summary reports for each library. The quality control report is a general overview, including processing information, summary statistics, and general visualizations of cell metrics. The cell type annotation report includes an overview of cell type annotation, comparisons among cell type annotation methods, and diagnostic plots to assess annotation quality. Comprehensive documentation about data processing and the contents of files on the portal, including a guide to getting started working with an ScPCA dataset, can be found at scpca.readthedocs.io. All data on the Portal were uniformly processed using scpca-nf, a Nextflow-based open-source pipeline developed by the Childhood Cancer Data Lab. The scpca-nf workflow uses alevin-fry for fast and efficient processing of all data currently available on the portal, including single-cell RNA-seq data and any associated CITE-seq or cell hash data, spatial transcriptomics data, and bulk RNA sequencing. The workflow and associated documentation are freely available at https://github.com/AlexsLemonade/scpca-nf, allowing researchers to leverage this pipeline for their own datasets. Providing an open-source workflow has allowed researchers to process their own single-cell or single-nuclei datasets for their own research. Furthermore, any data sets processed with scpca-nf are then eligible for inclusion on the ScPCA Portal. The continuous growth of the ScPCA Portal will help pediatric cancer researchers spend less time finding and processing data and more time answering their pressing research questions. Citation Format: Allegra G. Hawkins, Joshua A. Shapiro, Stephanie J. Spielman, David S. Mejia, Deepashree Venkatesh Prasad, Nozomi Ichihara, Arkadii Yakovets, Kurt G. Wheeler, Chanté J. Bethell, Steven M. Foltz, Jennifer O'Malley, Casey S. Greene, Jaclyn N. Taroni. The Single-cell Pediatric Cancer Atlas: Open-source data and tools for single-cell transcriptomics of pediatric tumors [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 2859.

Comprehensive RNA-sequencing analysis of colorectal cancer in a Korean cohort

Considering the recent increase in the number of colorectal cancer (CRC) cases in South Korea, we aimed to clarify the molecular characteristics of CRC unique to the Korean population. To gain insights into the complexities of CRC and promote the exchange of critical data, RNA-sequencing (RNA-Seq) analysis was performed to reveal the molecular mechanisms that drive the development and progression of CRC; this analysis is critical for developing effective treatment strategies. We performed RNA-Seq analysis of CRC and adjacent normal tissue samples from 214 Korean participants (comprising a total of 381 including 169 normal and 212 tumor samples) to investigate differential gene expression between the groups. We identified 19,575 genes expressed in CRC and normal tissues, with 3,830 differentially expressed genes (DEGs) between the groups. Functional annotation analysis revealed that the upregulated DEGs were significantly enriched in pathways related to the cell cycle, DNA replication, and IL-17, whereas the downregulated DEGs were enriched in metabolic pathways. We also analyzed the relationship between clinical information and subtypes using the Consensus Molecular Subtype (CMS) classification. Furthermore, we compared groups clustered within our dataset to CMS groups and performed additional analysis of the methylation data between DEGs and CMS groups to provide comprehensive biological insights from various perspectives. Our study provides valuable insights into the molecular mechanisms underlying CRC in Korean patients and serves as a platform for identifying potential target genes for this disease. The raw data and processed results have been deposited in a public repository for further analysis and exploration. Data availability statementThe RNA-Seq data have been deposited in the Korean Nucleotide Archive under the accession number KAP230608 (https://www.kobic.re.kr/kona/search_bioproject?bioproject_id=KAP230608#none). Clinical data, preprocessed gene quantification (RSEM), normalized gene expression data (log2CPM), and downstream analysis results have been deposited in Zenodo (https://doi.org/10.5281/zenodo.8333650). Further information and requests for experimental resources should be directed to and will be fulfilled by the corresponding authors, Kil-yong Lee (cyboryee@hanmail.net), Jin Ok Yang (joy@kribb.re.kr), and Young-Joon Kim (yjkim@yonsei.ac.kr).

An angiogenesis-associated gene-based signature predicting prognosis and immunotherapy efficacy of head and neck squamous cell carcinoma patients

ObjectivesTo develop a model that can assist in the diagnosis and prediction of prognosis for head and neck squamous cell carcinoma (HNSCC).Materials and methodsData from TCGA and GEO databases were used to generate normalized gene expression data. Consensus Cluster Plus was used for cluster analysis and the relationship between angiogenesis-associated gene (AAG) expression patterns, clinical characteristics and survival was examined. Support vector machine (SVM) and least absolute shrinkage and selection operator (LASSO) analyzes and multiple logistic regression analyzes were performed to determine the diagnostic model, and a prognostic nomogram was constructed using univariate and multivariate Cox regression analyses. ESTIMATE, XCELL, TIMER, QUANTISEQ, MCPCOUNTER, EPIC, CIBERSORT-ABS, CIBERSORT algorithms were used to assess the immune microenvironment of HNSCC patients. In addition, gene set enrichment analysis, treatment sensitivity analysis, and AAGs mutation studies were performed. Finally, we also performed immunohistochemistry (IHC) staining in the tissue samples.ResultsWe classified HNSCC patients into subtypes based on differences in AAG expression from TCGA and GEO databases. There are differences in clinical features, TME, and immune-related gene expression between two subgroups. We constructed a HNSCC diagnostic model based on nine AAGs, which has good sensitivity and specificity. After further screening, we constructed a prognostic risk signature for HNSCC based on six AAGs. The constructed risk score had a good independent prognostic significance, and it was further constructed into a prognostic nomogram together with age and stage. Different prognostic risk groups have differences in immune microenvironment, drug sensitivity, gene enrichment and gene mutation.ConclusionWe have constructed a diagnostic and prognostic model for HNSCC based on AAG, which has good performance. The constructed prognostic risk score is closely related to tumor immune microenvironment and immunotherapy response.

Machine learning extracts marks of thiamine's role in cold acclimation in the transcriptome of Vitis vinifera.

The escalating challenge of climate change has underscored the critical need to understand cold defense mechanisms in cultivated grapevine Vitis vinifera. Temperature variations can affect the growth and overall health of vine. We used Self Organizing Maps machine learning method to analyze gene expression data from leaves of five Vitis vinifera cultivars each treated by four different temperature conditions. The algorithm generated sample-specific "portraits" of the normalized gene expression data, revealing distinct patterns related to the temperature conditions applied. Our analysis unveiled a connection with vitamin B1 (thiamine) biosynthesis, suggesting a link between temperature regulation and thiamine metabolism, in agreement with thiamine related stress response established in Arabidopsis before. Furthermore, we found that epigenetic mechanisms play a crucial role in regulating the expression of stress-responsive genes at low temperatures in grapevines. Application of Self Organizing Maps portrayal to vine transcriptomics identified modules of coregulated genes triggered under cold stress. Our machine learning approach provides a promising option for transcriptomics studies in plants.

Evaluation and validation of reference genes for RT-qPCR gene expression in Naegleria gruberi

Naegleria gruberi is a free-living amoeboflagellate commonly found in freshwater and in soils around the world. It is a non-pathogenic relative of Naegleria fowleri, which is the etiologic agent of Primary Amoebic Meningoencephalitis (PAM). PAM occurs world-wide and it is considered a rare disease, but its fatality rate is high (96%) mainly because of delay in initiation of treatment due to misdiagnosis and lack of a specific treatment. The analysis of gene expression by quantitative real-time PCR in N. gruberi could be a highly efficient means to understand the pathogenicity of N. fowleri and also to find drug targets. Accurate RT-qPCR analysis requires correct normalization of gene expression data using reference genes (RG), whose expression should be constant under different experimental conditions. In this study, six genes, representing the most frequently used housekeeping genes, were selected for evaluation as reference genes in N. gruberi. The expression and stability of these genes was evaluated employing four algorithms (geNorm, NormFinder, BestKeeper and RefFinder). This work shows significant variations of the stability of RGs depending on the algorithms employed and on the experimental conditions (i.e. logarithmic, stationary, heat-shock and oxidative stress). The geNorm, NormFinder and RefFinder analysis of all the experimental conditions in combination revealed that ACT and G6PD were the most stable RGs. While BestKeeper analysis showed that 18S and TBP were the most stable RGs. Moreover, normalization of HSP90 gene expression with the most stable RGs resulted in an upregulation whereas when the normalization was done with the unstable RGs, the gene expression was not reliable. Hence, the implications of this study are relevant to gene expression studies in N. gruberi.

Uniformly shaped harmonization combines human transcriptomic data from different platforms while retaining their biological properties and differential gene expression patterns.

Introduction: Co-normalization of RNA profiles obtained using different experimental platforms and protocols opens avenue for comprehensive comparison of relevant features like differentially expressed genes associated with disease. Currently, most of bioinformatic tools enable normalization in a flexible format that depends on the individual datasets under analysis. Thus, the output data of such normalizations will be poorly compatible with each other. Recently we proposed a new approach to gene expression data normalization termed Shambhala which returns harmonized data in a uniform shape, where every expression profile is transformed into a pre-defined universal format. We previously showed that following shambhalization of human RNA profiles, overall tissue-specific clustering features are strongly retained while platform-specific clustering is dramatically reduced. Methods: Here, we tested Shambhala performance in retention of fold-change gene expression features and other functional characteristics of gene clusters such as pathway activation levels and predicted cancer drug activity scores. Results: Using 6,793 cancer and 11,135 normal tissue gene expression profiles from the literature and experimental datasets, we applied twelve performance criteria for different versions of Shambhala and other methods of transcriptomic harmonization with flexible output data format. Such criteria dealt with the biological type classifiers, hierarchical clustering, correlation/regression properties, stability of drug efficiency scores, and data quality for using machine learning classifiers. Discussion: Shambhala-2 harmonizer demonstrated the best results with the close to 1 correlation and linear regression coefficients for the comparison of training vs validation datasets and more than two times lesser instability for calculation of drug efficiency scores compared to other methods.

Performance and Advancement of the Kidney Solid Organ Response Test.

The kidney solid organ response test (kSORT) has been investigated for the prediction of acute rejection in kidney transplant recipients with conflicting results. We aimed to investigate if the kSORT assay score is associated with rejection or immune quiescence. The blinded association between rejection and kSORT > 9 were investigated. Optimization of kSORT prediction was evaluated after unblinding to determine the optimal prediction cutoff value of kSORT score. Additionally, the predictive capability of the kSORT gene set was assessed using blinded normalized gene expression data from microarray (Affymetrix) and qPCR assays. Of the 95 blood samples analyzed, 18 patients had blood samples before transplant, 77 patients after transplant and 71 had clinically indicated biopsies of which 15 biopsies showed acute rejection and 16 showed chronic active antibody-mediated rejection. When 31 patients with rejection were compared to the remaining 64 patients, positive predictive value (PPV) was 54.29% and negative predictive value (NPV) was 75% when stratified using a kSORT score > 9, and PPV was 57.89% and NPV was 78.95% when stratified using a kSORT score > 5. Using the kSORT assay for detection of rejection showed an area under the curve value of 0.71. Microarray data improved prediction accuracy with PPV of 53% and NPV of 84% compared to qPCR results (PPV and NPV were 36% and 66%), respectively. The kSORT assay has the potential to be used as a predictive tool for active rejection and/or immune quiescence, but additional studies will be useful in improving and refining the kSORT assay, in particular the prediction algorithm.

Drought and Oxidative Stress in Flax (Linum usitatissimum L.) Entails Harnessing Non-Canonical Reference Gene for Precise Quantification of qRT-PCR Gene Expression.

Flax (Linum usitatissimum L.) is a self-pollinating, annual, diploid crop grown for multi-utility purposes for its quality oil, shining bast fiber, and industrial solvent. Being a cool (Rabi) season crop, it is affected by unprecedented climatic changes such as high temperature, drought, and associated oxidative stress that, globally, impede its growth, production, and productivity. To precisely assess the imperative changes that are inflicted by drought and associated oxidative stress, gene expression profiling of predominant drought-responsive genes (AREB, DREB/CBF, and ARR) was carried out by qRT-PCR. Nevertheless, for normalization/quantification of data obtained from qRT-PCR results, a stable reference gene is mandatory. Here, we evaluated a panel of four reference genes (Actin, EF1a, ETIF5A, and UBQ) and assessed their suitability as stable reference genes for the normalization of gene expression data obtained during drought-induced oxidative stress in flax. Taking together, from the canonical expression of the proposed reference genes in three different genotypes, we report that EF1a as a stand-alone and EF1a and ETIF5A in tandem are suitable reference genes to be used for the real-time visualization of cellular impact of drought and oxidative stress on flax.

Abstract 2050: Molecular subclassification of NCI PDMR breast cancer models using PAM50 gene expression signature

Abstract Background: Breast cancer is the second most common cancer in women. In 2022, it accounted for 15% of total new cancer cases and is the number four cause of death among all cancer types. To benefit from precision medicine, distinguishing molecular subtypes for prognosis and treatment in a clinical setting is essential. While intrinsic subtype classification from NGS results of patients is well established, the approach has not been comprehensively described for patient-derived xenograft (PDX) models, which have been shown to be powerful in translational research. The National Cancer Institute's Patient-Derived Models Repository (NCI PDMR; https://pdmr.cancer.gov) provides rich information in developing the method. Materials and Methods: Normalized gene expression data of breast cancer PDX and patient specimens (originators) were extracted using tximport and DESeq2 based on RNA-seq analysis. The immunohistochemistry (IHC) was used to determine the status of ER, PR and HER2 receptor expression in these tumor specimens. The PAM50 classification was performed by the R package Genefu. For further analysis, the PAM50 centroids for all 5 subtypes were also obtained from Genefu. Results: Using the RNA-seq data from 43 PDX models (180 PDX samples, 4~6 samples/model), we were able to predict subtypes at the model level based on the PAM50 method: There are 1 Luminal A subtypes, 5 Luminal B; 6 Her2; 30 Basal and 1 Normal, which encompasses the whole spectrum of PAM50. Thirty originators were also included and there are 8 Luminal A, 9 Luminal B, 2 Her2 and 11 Basal. With the matched 11 originators and the PDX models, 91% of their predicted subtypes are identical; 0.80 Cohen’s kappa was obtained, indicating high inter-rater agreement. We also described subsequent analysis with IHC data-based subtypes. For the 10 originators having IHC-based subtypes, 90% agreement was observed; for 24 PDX models with IHC data, 88% was observed. Of all the 180 PDX samples, 33 of the 43 PDX models (77%) have consistent predicted PAM50 molecular subtypes across different passages and lineages. Within the discordant samples, we observed cases such as a mixture of luminal B and Basal, which can be reasonably interpreted by AR positive signal from IHC. The discrepancy encourages further PDX subclassification from the Basal subtype. Conclusions: Using our high-throughput gene expression profiles from many patients and samples from patient derived models, we have demonstrated the feasibility of applying classic PAM50 classification algorithm, which was originally developed with microarray data, to be able to recognize the expression signals from our RNA-seq data. Overall, this study should set a primer for the identification of PDX-based subtypes, starting from breast cancer. Citation Format: Peter I. Wu, Lindsay Dutko, Shahanawaz Jiwani, Li Chen, Biswajit Das, Ting-Chia Chang, Yvonne A. Evrard, Chris A. Karlovich, Alyssa Chapman, Brandie Fullmer, Ashley Hayes, Ruth Thornton, Nikitha Nair, Kelly Benauer, Gloryvee Rivera, Thomas Forbes, John Carter, Suzanne Borgel, Tiffanie Miner, Chelsea McGlynn, Justine Mills, Shannon Uzelac, Tia Shearer, Lauren Hicks, Michelle Norris, Carley Border, Sergio Alcoser, Thomas Walsh, Michael Mullendore, Michelle Eugeni, Dianne Newton, Melinda G. Hollingshead, P. M. Williams, James H. Doroshow. Molecular subclassification of NCI PDMR breast cancer models using PAM50 gene expression signature [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 1 (Regular and Invited Abstracts); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(7_Suppl):Abstract nr 2050.

Validation of stable reference genes in peripheral blood mononuclear cells for expression studies involving vector-borne haemoparasitic diseases in bovines

Normalization of gene expression data using appropriate reference genes is critical to diminish any technical bias in an experiment involving quantitative real-time PCR (qPCR). To the best of our knowledge, this is the first report offering a systematic assessment of 14 potential reference genes (RPLP0, ACTB, RPS28, YWHAZ, SDHA, PPIA, RPS9, RPS15, UXT, GAPDH, B2M, BACH1, HMBS, and PPIB) for the identification of the most stable normalizers for qPCR of target genes in peripheral blood mononuclear cells (PBMCs) of bovines for vector-borne haemoparasitic diseases such as anaplasmosis, babesiosis, theileriosis, and trypanosomiasis. A total of 38 blood samples were collected from healthy as well as diseased cattle and buffaloes representing different haemoparasitic diseases. RNA isolated from the PBMCs was subjected to qPCR for the 14 prospective internal control genes. The comprehensive ranking of the genes was accomplished by the RefFinder tool that integrates the results of three algorithms (geNorm, NormFinder, and BestKeeper) and the comparative CT method. RPS15, B2M, and GAPDH were ranked to be the most stable genes, whereas, PPIA and HMBS emerged to be the least suitable genes. Validation of the selected reference genes by the qPCR analysis of two immunity genes, ISG15 and GPX7 was congruent with the observations of this study. We recommend that a panel of three reference genes including RPS15, B2M, and GAPDH could prove useful in delineating the transcriptional landscape of PBMCs for vector-borne haemoparasitic diseases in bovines.

Exogenous spike-in mouse RNAs for accurate differential gene expression analysis in barley using RT-qPCR.

Reverse transcriptase quantitative polymerase chain reaction (RT-qPCR) followed by the 2-ΔΔCt method is the most common way to measure transcript levels for relative gene expression assays. The quality of an RT-qPCR assay is dependent upon the identification and validation of reference genes to normalize gene expression data. The so-called housekeeping genes are commonly used as internal reference genes because they are assumed to be ubiquitously expressed at stable levels. Commonly, researchers do not validate their reference genes but rely on historical reference genes or previously validated genes from an unrelated experiment. Using previously validated reference genes to assess gene expression changes occurring during malting resulted in extensive variability. Therefore, a new method was tested and validated to circumvent the use of internal reference genes. Total mouse RNA was chosen as the external reference RNA and a suite of primer sets to putatively stable mouse genes was created to identify stably expressed genes for use as an external reference gene. cDNA was created by co-amplifying total mouse RNA, as an RNA spike-in, and barley RNA. When using the external reference genes to normalize malting gene expression data, standard deviations were significantly reduced and significant differences in transcript abundance were observed, whereas when using the internal reference genes, standard deviations were larger with no significant differences seen. Furthermore, external reference genes were more accurate at assessing expression levels in malting and developing grains, whereas the internal reference genes overestimated abundance in developing grains and underestimated abundance in malting grains.

Selection of Reference Genes for Transcription Studies Considering Co-Regulation and Average Transcriptional Stability: Case Study on Adventitious Root Induction in Olive (Olea europaea L.) Microshoots

Selection of reference genes (RGs) for normalization of PCR-gene expression data includes two crucial steps: determination of the between-sample transcriptionally more stable genes, and subsequent choosing of the most suitable genes as internal controls. Both steps can be carried out through generally accepted strategies, each having different strengths and weaknesses. The present study proposes reinforcement of the normalization of gene expression data by integrating analytical revision at critical steps of those accepted procedures. In vitro olive adventitious rooting was used as an experimental system. Candidate RGs were ranked according to transcriptional stability according to several methods. An algorithm of one of these programs (GeNorm) was adapted to allow for partial automatization of RG selection for any strategy of transcriptional-gene stability ordering. In order to choose the more appropriate set of RGs, the achieved results were analytically revised, with special emphasis on biasing effects such as co-regulation. The obtained putative RG sets were also tested for cases restricted to fewer variables. The set formed by the genes H2B, OUB and ACT is valid for normalization in transcriptional studies on olive microshoot rooting when comparing treatments, time points and assays. Such internal reference is now available for wider expression studies on any target gene in similar biological systems. The overall methodology aims to constitute a guide for general application.

A novel model based on immune-related genes for differentiating biliary atresia from other cholestatic diseases

Based on a public gene expression database, this study established the immune-related genetic model that distinguished BA from other cholestasis diseases (DC) for the first time. We explored the molecular mechanism of BA based on the gene model. The BA microarray dataset GSE46960, containing BA, other cause of intrahepatic cholestasis than biliary atresia and normal liver gene expression data, was downloaded from the Gene Expression Omnibus (GEO) database. We performed a comprehensive bioinformatics analysis to establish and validate an immune-related gene model and subsequently identified hub genes as biomarkers associated with the molecular mechanisms of BA. To assess the model's performance for separating BA from other cholestasis diseases, we used receiver operating characteristic (ROC) curves and the area under the curve (AUC) of the ROC. Independent datasets GSE69948 and GSE122340 were used for the validation process. The model was built using eight immune-related genes, including EDN1, HAMP, SAA1, SPP1, ANKRD1, MMP7, TACSTD2, and UCA1. In the GSE46960 and validation group, it presented excellent results, and the prediction accuracy of BA in comparison to other cholestasis diseases was good. Functional enrichment analysis revealed significant immunological differences between BA and other cholestatic diseases. Finally, we found that the TNFα-NF-κB pathway is associated with EDN1 gene expression and may explain fibrosis progression, which may become a new therapeutic target. In summary, we have successfully constructed an immune-related gene model that can distinguish BA from other cholestatic diseases, while identifying the hub gene. Our exploration of immune genes provides new clues for the early diagnosis, molecular mechanism, and clinical treatment of biliary atresia.

Selection and validation of reference genes for qPCR analysis of differentiation and maturation of THP-1 cells into M1 macrophage-like cells.

For cell-based assays studying monocytes and macrophages, the immortalized monocyte cell line THP-1 is widely used and stimulated with phorbol 12-myristate 13-acetate, lipopolysaccharide (LPS) and/or interferon-γ (IFN-γ), after which it differentiates and polarizes into proinflammatory M1-like macrophages. For the quantification of this and the effect of different factors affecting these processes, the expression levels of various maturation markers are determined using reverse transcription-quantitative PCR. For this purpose, stably expressed reference genes are crucial. However, no studies evaluating the stability of reference genes in THP-1 cells stimulated with LPS and IFN-γ have been performed. Therefore, this paper describes the selection of the most used reference genes [RPL37A (ribosomal protein L37a), GAPDH (glyceraldehyde-3-phosphate dehydrogenase), UBC (ubiquitin C), B2M (0β2-microbulin), ACTB (β-actin) and PPIA (cyclophilin A)], the in silico primer design, the analysis and the validation of these in accordance with the MIQE (Minimum Information for Publication of Quantitative Real-Time PCR Experiments) guidelines and more recent recommendations for the validation of the stability of reference genes. Using the RefFinder platform, including the four most popular algorithms for reference gene validation, the Delta CT, BestKeeper, NormFinder and geNorm, we find the reference genes GAPDH and UBC to be the most stable. Furthermore, we demonstrate that the normalization of gene expression data using the least stable reference genes, ACTB and B2M, dramatically affects the interpretation of experimental data. Taken together, it is vital to validate the stability of reference genes under the specific experimental conditions used when utilizing the THP-1 monocyte model system.

Identification and Validation of Reference Genes for Expression Analysis in Nitrogen-Fixing Bacteria under Environmental Stress.

Reference genes, also referred to as housekeeping genes (HKGs), play an important role in gene expression analysis by serving as an internal control. These HKGs are usually involved in basic cellular functions and their expression should remain at relatively constant levels. Quantitative reverse transcription-polymerase chain reaction (qRT-PCR) has been used to measure gene expression. Since the normalization of gene expression data depends on baseline expression of HKGs, it is important to identify and verify true HKGs for the qRT-PCR analysis. The goal of this study is to identify and confirm HKGs in Bradyrhizobium diazoefficiens, a nitrogen fixing bacterium which forms a symbiotic relationship with soybean. By revealing such HKGs, the normalization of gene expression would be more robust, reliable, and consistent. Here, we analyzed previous gene expression data for B. diazoefficiens under multiple environmental conditions. As a result, we identified seven constitutively expressed genes among 8453 genes across all conditions. Their fold-change values were within a range of −1.25-fold < x < 1.25-fold. We adopted GeNorm, NormFinder, and comparative ∆Ct methods to rank the seven candidate genes based on their expression stability. To validate these potential HKGs, we measured their expression in various experimental conditions, such as heat, pH, and heavy metal stress. The HKGs that were found in B. diazoefficiens were also applied in closely related species by identifying their homologs.

Validation of superior reference genes in mouse submandibular glands under developmental and functional regeneration states.

Saliva is crucial for lubricating the mouth and aiding in food digestion. However, the occurrence of oral dysfunction, such as xerostomia, dysphagia or oral infection can markedly reduce the quality of life of affected individuals. The major salivary glands include the submandibular gland (SMG), and sublingual and parotid glands; they are the larger glands in mammals that produce the majority of the saliva. The SMG serves as an effective model for the study of branching morphogenesis and functional regeneration. In order to better understand the key dynamic gene expression patterns during salivary gland development and functional regeneration, it is crucial to search for a panel of reliable reference genes. The present study thus aimed to identify superior reference genes to normalize gene expression data in the SMG under states of development and functional regeneration. First, the developmental SMG samples were harvested from mice in the embryonic and post-natal periods. Functional regeneration samples from a ductal ligation/de-ligation model were obtained at several stages. A total of 12 reference genes (Actb, Actg1, Ubc, Uba1, Uba52, Ube2c, Tuba1a, Tuba1b, Tubb5, H2afy, H2afx and Gapdh) from 430 candidates involving tubulin, histone, actin, ubiquitin and GAPDH family members were screened via transcriptome sequencing (RNA-seq) analysis. RT-qPCR (SYBR-Green) and western blot analysis were then used to semi-quantitatively assess gene and protein expression. The stability of expression was evaluated using the ΔCq, geNorm, BestKeeper, NormFinder and RefFinder methods and software. Actg1 exhibited the highest stability in the SMG developmental stage, while Tubb5 was recommended as the most stable reference gene for the SMG regenerative stage. In summary, the present study provides evidence-based selections for superior reference genes in the SMG during the stages of development and functional regeneration.

Normalization of gene expression data revisited: the three viewpoints of the transcriptome in human skeletal muscle undergoing load-induced hypertrophy and why they matter

BackgroundThe biological relevance and accuracy of gene expression data depend on the adequacy of data normalization. This is both due to its role in resolving and accounting for technical variation and errors, and its defining role in shaping the viewpoint of biological interpretations. Still, the choice of the normalization method is often not explicitly motivated although this choice may be particularly decisive for conclusions in studies involving pronounced cellular plasticity. In this study, we highlight the consequences of using three fundamentally different modes of normalization for interpreting RNA-seq data from human skeletal muscle undergoing exercise-training-induced growth. Briefly, 25 participants conducted 12 weeks of high-load resistance training. Muscle biopsy specimens were sampled from m. vastus lateralis before, after two weeks of training (week 2) and after the intervention (week 12), and were subsequently analyzed using RNA-seq. Transcript counts were modeled as (1) per-library-size, (2) per-total-RNA, and (3) per-sample-size (per-mg-tissue).ResultInitially, the three modes of transcript modeling led to the identification of three unique sets of stable genes, which displayed differential expression profiles. Specifically, genes showing stable expression across samples in the per-library-size dataset displayed training-associated increases in per-total-RNA and per-sample-size datasets. These gene sets were then used for normalization of the entire dataset, providing transcript abundance estimates corresponding to each of the three biological viewpoints (i.e., per-library-size, per-total-RNA, and per-sample-size). The different normalization modes led to different conclusions, measured as training-associated changes in transcript expression. Briefly, for 27% and 20% of the transcripts, training was associated with changes in expression in per-total-RNA and per-sample-size scenarios, but not in the per-library-size scenario. At week 2, this led to opposite conclusions for 4% of the transcripts between per-library-size and per-sample-size datasets (↑ vs. ↓, respectively).ConclusionScientists should be explicit with their choice of normalization strategies and should interpret the results of gene expression analyses with caution. This is particularly important for data sets involving a limited number of genes or involving growing or differentiating cellular models, where the risk of biased conclusions is pronounced.

Identification of Liver Immune Microenvironment-Related Hub Genes in Liver of Biliary Atresia.

Background: Biliary atresia (BA) is one of the most common and fatal abnormalities of newborns. Increasing evidences indicated that immunology was the critical part of the etiology. This research used a public gene expression database to explore the immune microenvironment of BA liver.Methods: The gene expression profiles GSE46960, GSE159720, and GSE15235, containing BA and normal liver gene expression data, were obtained from the Expression Omnibus Gene. We applied CIBERSORTx to quantify 22 subsets of immune cells in BA liver. The differentially expressed genes (DEGs) and immune cells were used to further explore their relationship with liver fibrosis and the inflammation status of BA.Results: The expression of immune-related genes CXCL6, CXCL8, CXCL10, CCL20, IL32, TGFB2, SPP1, and SLIT2 was significantly different between BA and normal liver, among which CXCL8 was the hub gene. Six of 22 immune cell proportions were significantly different between BA and normal liver. Specifically, M0 macrophages and resting memory CD4+ T cells were upregulated in BA liver compared with normal liver. Meanwhile, monocytes, resting natural killer (NK) cells, plasma cells, and regulatory T (Treg) cells were downregulated. A further correlation analysis revealed that SLIT2 and CXCL6 owned high positive correlation coefficients with fibrosis grade, while the proportion of resting NK cells was negatively correlated. Proportions of resting CD4+ memory T cells were strongly related to the inflammation grade of BA liver.Conclusion: Biliary atresia is a disease strongly correlated with immune response. Our results might provide a clue for further exploration of BA etiology, which may promote a potential prediction model based on immune infiltration features.

Identification of reference genes for normalization of gene expression in Liposcelis entomophila (Psocoptera: Liposcelididae)

• L. entomophila is a small nuisance insect and threatens food security. • To find suitable reference genes, expression stabilities of 12 genes were tested. • The combination of ACTB and EF1a was the best reference gene for L. entomophila. • It provided a basic foundation for studies of gene expression in L. entomophila. Liposcelis entomophila is a nuisance pest that seriously threatens the safety of stored grains owing in part of pesticide resistance. To facilitate better control of L. entomophila , future studies need to focus on gene function characterization. This requires the identification of suitable reference genes (RGs) for reverse-transcription quantitative PCR (RT-qPCR), which is crucial for the normalization of target gene expression. Here, we evaluated the expression stability of 12 candidate RGs in L. entomophila across six experimental conditions (developmental stage, population, body part, phosphine fumigation, hypoxia induction, and temperature treatment). Under developmental conditions, ACTB , HSP70 , and RPL23 were the most stable RGs. Among the different populations and body parts, RPL23 , EF1a , and ACTB were the most stable RGs. Comparison of gene expression among adults under different environmental conditions revealed that at least seven RGs were fit for normalizing gene expression data. Among them, RPL23, EF1a , and ACTB showed the best fit as RGs in RT-qPCR analysis. After validation, our results demonstrated that EF1a and ACTB , as well as their combination, were the ideal RGs for L. entomophila across various experimental conditions. This study provides a comprehensive RG selection suggestion for gene expression studies of L. entomophila and will facilitate future functional genomic research on the development and tolerance of environmental factors in psocids.