Tool For Analysis Of Gene Expression Research Articles

Gene count estimation with pytximport enables reproducible analysis of bulk RNA sequencing data in Python.

Transcript quantification tools efficiently map bulk RNA sequencing reads to reference transcriptomes. However, their output consists of transcript count estimates that are subject to multiple biases and cannot be readily used with existing differential gene expression analysis tools in Python.Here we present pytximport, a Python implementation of the tximport R package that supports a variety of input formats, different modes of bias correction, inferential replicates, gene-level summarization of transcript counts, transcript-level exports, transcript-to-gene mapping generation and optional filtering of transcripts by biotype. pytximport is part of the scverse ecosystem of open-source Python software packages for omics analyses and includes both a Python as well as a command-line interface.With pytximport, we propose a bulk RNA sequencing analysis workflow based on Bioconda and scverse ecosystem packages, ensuring reproducible analyses through Snakemake rules. We apply this pipeline to a publicly available RNA-sequencing dataset, demonstrating how pytximport enables the creation of Python-centric workflows capable of providing insights into transcriptomic alterations. pytximport is licensed under the GNU General Public License version 3. The source code is available at https://github.com/complextissue/pytximport and via Zenodo with DOI: 10.5281/zenodo.13907917. A related Snakemake workflow is available through GitHub at https://github.com/complextissue/snakemake-bulk-rna-seq-workflow and Zenodo with DOI: 10.5281/zenodo.12713811. Documentation and a vignette for new users are available at: https://pytximport.readthedocs.io. Supplementary Material is available at Bioinformatics online.

A high-throughput gene expression analysis software tool for developmental time series and gene signature analysis of human cardiomyocyte differentiation

Publicly available high-throughput gene expression data enable the investigation of biological processes by the scientific community. Although several bioinformatics tools offer methodologies for basic differential gene expression analysis, difficulties arise in the analysis of multiple sample groups comprising a developmental time series, especially when the identification and classification of unique gene expression patterns is the primary goal of the study. Data analysis using these tools requires programming experience, which limits the accessibility of these tools to the broader community. To streamline developmental time-series investigations, we created the Developmental Gene Expression Analysis (devGEA) tool. This environment can be implemented locally or via web browsers to expedite differential gene expression analysis. This tool provides gene signature determination methods that can classify differentially expressed genes based on their correlation with gene expression patterns. devGEA was used to characterize cardiac development gene expression signatures from high-throughput RNA-seq datasets profiling small-molecule directed cardiomyocyte differentiation of human pluripotent stem cell lines (hiPSCs). After pre-processing, discrete gene expression criteria-based expected changes were used to classify the genes into developmental signatures. Several cardiomyocyte differentiation markers and candidate cardiac genes representing different developmental signatures were experimentally validated using the GIBCOTM hiPSC line. This method was then compared to a gene signature correlation approach that classified expressed genes based on their degree of similarity with key cardiac developmental signatures representing the stages of cardiomyocyte differentiation. Therefore, devGEA provides a robust workflow for investigator-driven analysis of developmental time series, allowing for the identification of differentially expressed genes and gene signatures for extensive experimental investigation. We also introduced a method for classifying genes by their correlation with genes or developmental patterns of interest. Our correlation-based method takes advantage of a priori knowledge of an experiment and is conceptually simpler than unsupervised clustering approaches.

Exploring the effects of matrix metalloproteinase-13 on the malignant biological behavior of tongue squamous cell carcinoma via the TNF signaling pathway based on bioinformatics methods.

Identification of the etiology, molecular mechanisms, and carcinogenic pathways of tongue squamous cell carcinoma (TSCC) is crucial for developing new diagnostic and therapeutic strategies. This study used bioinformatics methods to identify key genes in TSCC and explored the potential functions and pathway mechanisms related to the malignant biological behavior of TSCC. Gene chip data sets (i.e., GSE13601 and GSE34106) containing the data of both TSCC patients and normal control subjects were selected from the Gene Expression Omnibus (GEO) database. Using a gene expression analysis tool (GEO2R) of the GEO database, the differentially expressed genes (DEGs) were identified using the following criteria: |log fold change| >1, and P<0.05. The GEO2R tool was also used to select the upregulated DEGs in the chip candidates based on a P value <0.05. A Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis, Gene Ontology (GO) function analysis, and a protein-protein interaction (PPI) network analysis were then conducted. The results were displayed using R language packages, including volcano plots, Venn diagrams, heatmaps, and enriched pathway bubble charts. Genes from the MalaCards database were compared with the candidate genes, and a thorough review of the literature was conducted to determine the clinical significance of these genes. Finally, feature gene-directed chemical drugs or targeted drugs were predicted using the Comparative Toxicogenomics Database (CTD). In total, 767 upregulated DEGs were identified from GSE13601 and 695 from GSE34106. By intersecting the upregulated DEGs from both data sets using a Venn diagram, 100 DEGs related to TSCC were identified. The enrichment analysis of the KEGG signaling pathways identified the majority of the pathways associated with the upregulated DEGs, including the Toll-like receptor signaling pathway, the extracellular matrix-receptor interaction, the tumor necrosis factor (TNF) signaling pathway, cytokine-cytokine receptor interaction, the chemokine signaling pathway, the interlukin-17 signaling pathway, and natural killer cell-mediated cytotoxicity. The PPI network and module analyses of the shared DEGs ultimately resulted in five clusters and 55 candidate genes. A further intersection analysis of the TSCC-related genes in the MalaCards database via a Venn diagram identified three important shared DEGs; that is, matrix metalloproteinase-1 (MMP1), MMP9, and MMP13. In the CTD, seven drugs related to MMP13 were identified for treating tongue tumors. This study identified key genes and signaling pathways involved in TSCC and thus extended understandings of the molecular mechanisms that underlie the development and progression of TSCC. Additionally, this study showed that MMP13 may influence the malignant biological behavior of TSCC through the TNF signaling pathway. This finding could provide a theoretical basis for research into early differential diagnosis and targeted treatment.

Screening for biomarkers of tuberous sclerosis complex-associated epilepsy: a bioinformatics analysis.

The optimal biomarkers for early diagnosis, treatment, and prognosis of tuberous sclerosis complex (TSC)-associated epilepsy are not yet clear. This study identifies the crucial genes involved in the pathophysiology of TSC-associated epilepsy via a bioinformatics analysis. These genes may serve as novel therapeutic targets. Gene chip data sets (GSE62019 and GSE16969) comprising the data of patients with TSC-associated epilepsy and healthy control participants were obtained from the Gene Expression Omnibus (GEO) database. Differentially expressed genes (DEGs) in the GEO database were identified using the GEO2R gene expression analysis tool. Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway, Gene Ontology function, and protein-protein interaction (PPI) network analyses were then conducted. The results were analyzed using R language, and are presented in volcano plots, Venn diagrams, heatmaps, and enrichment pathway bubble charts. A gene set enrichment analysis (GSEA), was conducted to examine the KEGG pathways and crucial genes linked to TSC-associated epilepsy. The potential genes were compared with the genes listed in the Online Mendelian Inheritance in Man (OMIM) database and analyzed against the literature to determine their clinical significance. Finally, the expression of the key genes in the TSC-associated epilepsy mice cerebral cortex was examined through immunohistochemical staining. The intersection of the GSE62019 and GSE16969 data sets revealed 151 commonly upregulated DEGs. The KEGG enrichment analysis indicated that these DEGs affected the occurrence and development of TSC-associated epilepsy by modulating complement and coagulation cascades, glycosaminoglycans in cancer, and extracellular matrix-receptor interactions. Four high-scoring clusters emerged, and podoplanin (PDPN) was identified as a key gene through the construction of a PPI network of the common DEGs using the Cytoscape software. A GSEA of the DEGs revealed that the common DEG PDPN was enriched in both data sets in pathways related to platelet activation, aggregation, and the glycoprotein VI (GPVI)-mediated activation cascade. Immunohistochemical staining revealed a significant elevation in PDPN expression in the cerebral cortex of mice with TSC-associated epilepsy. Conversely, the control group mice did not display any significantly positive neurons. The discovery of these crucial genes and signaling pathways extends understanding of the molecular processes underlying the development of TSC-associated epilepsy. Additionally, our findings may provide a theoretical basis for research into targeted clinical treatments.

Systematic identification and validation of the reference genes from 447 transcriptome datasets of moso bamboo (Phyllostachys edulis)

Bamboo was one of the first plants to be cultivated in China and is widely used in industry and daily life. The study of gene function has become an important part of bamboo breeding, whereas quantitative real-time PCR (qRT-PCR) is a powerful tool for gene expression analysis. The accuracy of qRT-PCR results largely depends on suitable reference genes. In this study, a transcriptome-wide identification of reference genes was conducted based on 447 transcriptome datasets, comprising 200 tissue samples, 107 treated samples, and 140 samples from various moso bamboo (Phyllostachys edulis) forms. A total of 3444, 1013, and 3962 stably expressed genes were identified from these three groups, respectively. Functional enrichment analysis revealed significant enrichment of these genes in pathways, including the spliceosome, proteasome, and oxidative phosphorylation. Eight candidate genes (ADPRE, GAPDH, TRX, TUBA, NRP, MBF, UNK, and CAM1), were selected for qRT-PCR validation using 112 samples. To assess their stability, five statistical methods (geNorm, NormFinder, BestKeeper, Delta-Ct, and RefFinder) were employed. The most suitable reference genes were ADPRE and GAPDH for different tissues, GAPDH and CAM1 for different treatments, and GAPDH and TRX for various moso bamboo forms. Overall, ADPRE and GAPDH were the most stable reference genes across all conditions, while TUBA and TRX were the least stable reference genes. In addition, a significant negative correlation was found between the Ct values of RT-qPCR and the log2TPM values from the transcriptome data (Ct = −1.534x + 37.221), providing a potential method for estimating gene expression levels. The identified reference genes, particularly ADPRE and GAPDH, provide a robust set of references for gene expression studies in moso bamboo.

Focused cancer pathway analysis revealed unique therapeutic targets in retinoblastoma.

Retinoblastoma (RB) is a pediatric cancer of the eye that occurs in 1/15000 live births worldwide. Albeit RB is initiated by the inactivation of RB1 gene, the disease progression relies largely on transcriptional alterations. Therefore, evaluating gene expression is vital to unveil the therapeutic targets in RB management. In this study, we employed an RT2 Profiler™ PCR array for a focused analysis of 84 cancer-specific genes in RB. An interaction network was built with gene expression data to identify the dysregulated pathways in RB. The key transcript alterations identified in 13 tumors by RT2 Profiler™ PCR array was further validated in 15 tumors by independent RT-qPCR. Out of 84 cancer-specific genes, 68 were dysregulated in RB tumors. Among the 68 genes, 23 were chosen for further analysis based on statistical significance and abundance across multiple tumors. Pathway analysis of altered genes showed the frequent perturbations of cell cycle, angiogenesis and apoptotic pathways in RB. Notably, upregulation of MCM2, MKI67, PGF, WEE1, CDC20 and downregulation of COX5A were found in all the tumors. Western blot confirmed the dysregulation of identified targets at protein levels as well. These alterations were more prominent in invasive RB, correlating with the disease pathogenesis. Our molecular analysis thus identified the potential therapeutic targets for improving retinoblastoma treatment. We also suggest that PCR array can be used as a tool for rapid and cost-effective gene expression analysis.

Time- and Region-Specific Selection of Reference Genes in the Rat Brain in the Lithium-Pilocarpine Model of Acquired Temporal Lobe Epilepsy.

Reverse transcription followed by quantitative polymerase chain reaction (RT-qPCR) is a commonly used tool for gene expression analysis. The selection of stably expressed reference genes is required for accurate normalization. The aim of this study was to identify the optimal reference genes for RT-qPCR normalization in various brain regions of rats at different stages of the lithium-pilocarpine model of acquired epilepsy. We tested the expression stability of nine housekeeping genes commonly used as reference genes in brain research: Actb, Gapdh, B2m, Rpl13a, Sdha, Ppia, Hprt1, Pgk1, and Ywhaz. Based on four standard algorithms (geNorm, NormFinder, BestKeeper, and comparative delta-Ct), we found that after pilocarpine-induced status epilepticus, the stability of the tested reference genes varied significantly between brain regions and depended on time after epileptogenesis induction (3 and 7 days in the latent phase, and 2 months in the chronic phase of the model). Pgk1 and Ywhaz were the most stable, while Actb, Sdha, and B2m demonstrated the lowest stability in the analyzed brain areas. We revealed time- and region-specific changes in the mRNA expression of the housekeeping genes B2m, Actb, Sdha, Rpl13a, Gapdh, Hprt1, and Sdha. These changes were more pronounced in the hippocampal region during the latent phase of the model and are thought to be related to epileptogenesis. Thus, RT-qPCR analysis of mRNA expression in acquired epilepsy models requires careful selection of reference genes depending on the brain region and time of analysis. For the time course study of epileptogenesis in the rat lithium-pilocarpine model, we recommend the use of the Pgk1 and Ywhaz genes.

Pseudomonas aeruginosa transcriptome analysis of metal restriction in ex vivo cystic fibrosis sputum.

Chronic Pseudomonas aeruginosa lung infections are a feature of cystic fibrosis (CF) that many patients experience even with the advent of highly effective modulator therapies. Identifying factors that impact P. aeruginosa in the CF lung could yield novel strategies to eradicate infection or otherwise improve outcomes. To complement published P. aeruginosa studies using laboratory models or RNA isolated from sputum, we analyzed transcripts of strain PAO1 after incubation in sputum from different CF donors prior to RNA extraction. We compared PAO1 gene expression in this "spike-in" sputum model to that for P. aeruginosa grown in synthetic cystic fibrosis sputum medium to determine key genes, which are among the most differentially expressed or most highly expressed. Using the key genes, gene sets with correlated expression were determined using the gene expression analysis tool eADAGE. Gene sets were used to analyze the activity of specific pathways in P. aeruginosa grown in sputum from different individuals. Gene sets that we found to be more active in sputum showed similar activation in published data that included P. aeruginosa RNA isolated from sputum relative to corresponding in vitro reference cultures. In the ex vivo samples, P. aeruginosa had increased levels of genes related to zinc and iron acquisition which were suppressed by metal amendment of sputum. We also found a significant correlation between expression of the H1-type VI secretion system and CFTR corrector use by the sputum donor. An ex vivo sputum model or synthetic sputum medium formulation that imposes metal restriction may enhance future CF-related studies.IMPORTANCEIdentifying the gene expression programs used by Pseudomonas aeruginosa to colonize the lungs of people with cystic fibrosis (CF) will illuminate new therapeutic strategies. To capture these transcriptional programs, we cultured the common P. aeruginosa laboratory strain PAO1 in expectorated sputum from CF patient donors. Through bioinformatic analysis, we defined sets of genes that are more transcriptionally active in real CF sputum compared to a synthetic cystic fibrosis sputum medium. Many of the most differentially active gene sets contained genes related to metal acquisition, suggesting that these gene sets play an active role in scavenging for metals in the CF lung environment which may be inadequately represented in some models. Future studies of P. aeruginosa transcript abundance in CF may benefit from the use of an expectorated sputum model or media supplemented with factors that induce metal restriction.

Introducing the f0% method: a reliable and accurate approach for qPCR analysis

BackgroundqPCR is a widely used technique in scientific research as a basic tool in gene expression analysis. Classically, the quantitative endpoint of qPCR is the threshold cycle (CT) that ignores differences in amplification efficiency among many other drawbacks. While other methods have been developed to analyze qPCR results, none has statistically proven to perform better than the CT method. Therefore, we aimed to develop a new qPCR analysis method that overcomes the limitations of the CT method. Our f0% [eff naught percent] method depends on a modified flexible sigmoid function to fit the amplification curve with a linear part to subtract the background noise. Then, the initial fluorescence is estimated and reported as a percentage of the predicted maximum fluorescence (f0%).ResultsThe performance of the new f0% method was compared against the CT method along with another two outstanding methods—LinRegPCR and Cy0. The comparison regarded absolute and relative quantifications and used 20 dilution curves obtained from 7 different datasets that utilize different DNA-binding dyes. In the case of absolute quantification, f0% reduced CV%, variance, and absolute relative error by 1.66, 2.78, and 1.8 folds relative to CT; and by 1.65, 2.61, and 1.71 folds relative to LinRegPCR, respectively. While, regarding relative quantification, f0% reduced CV% by 1.76, 1.55, and 1.25 folds and variance by 3.13, 2.31, and 1.57 folds regarding CT, LinRegPCR, and Cy0, respectively. Finally, f0% reduced the absolute relative error caused by LinRegPCR by 1.83 folds.ConclusionsWe recommend using the f0% method to analyze and report qPCR results based on its reported advantages. Finally, to simplify the usage of the f0% method, it was implemented in a macro-enabled Excel file with a user manual located on https://github.com/Mahmoud0Gamal/F0-perc/releases.

Dex-Benchmark: datasets and code to evaluate algorithms for transcriptomics data analysis.

Many tools and algorithms are available for analyzing transcriptomics data. These include algorithms for performing sequence alignment, data normalization and imputation, clustering, identifying differentially expressed genes, and performing gene set enrichment analysis. To make the best choice about which tools to use, objective benchmarks can be developed to compare the quality of different algorithms to extract biological knowledge maximally and accurately from these data. The Dexamethasone Benchmark (Dex-Benchmark) resource aims to fill this need by providing the community with datasets and code templates for benchmarking different gene expression analysis tools and algorithms. The resource provides access to a collection of curated RNA-seq, L1000, and ChIP-seq data from dexamethasone treatment as well as genetic perturbations of its known targets. In addition, the website provides Jupyter Notebooks that use these pre-processed curated datasets to demonstrate how to benchmark the different steps in gene expression analysis. By comparing two independent data sources and data types with some expected concordance, we can assess which tools and algorithms best recover such associations. To demonstrate the usefulness of the resource for discovering novel drug targets, we applied it to optimize data processing strategies for the chemical perturbations and CRISPR single gene knockouts from the L1000 transcriptomics data from the Library of Integrated Network Cellular Signatures (LINCS) program, with a focus on understudied proteins from the Illuminating the Druggable Genome (IDG) program. Overall, the Dex-Benchmark resource can be utilized to assess the quality of transcriptomics and other related bioinformatics data analysis workflows. The resource is available from: https://maayanlab.github.io/dex-benchmark.

Prediction of target genes in community-acquired pneumonia based on the bioinformatics method.

To screen the related genes of community-acquired pneumonia (CAP) by bioinformatics technology, and to analyze the clinical value of key genes. Gene chip data sets containing CAP patients and normal controls were screened from the Gene Expression Omnibus (GEO) database. The downregulated differentially expressed genes (DEGs) were screened using a gene expression analysis tool (GEO2R). Simultaneously, gene set enrichment analysis (GSEA) was used to explore the Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway and core genes related to CAP. The candidate genes were then intersected with the genes reported in Online Mendelian Inheritance in Man (OMIM), and the clinical value of these candidate genes was examined based on a literature search. Finally, the clinical data of the CAP patients were retrospectively analyzed. Detect the type of pathogenic bacteria in bronchial-alveolar lavage fluid (BALF) using metagenomics next-generation sequencing (mNGS) high throughput sequencing technology, and detect the expression of key genes through liquid based cell immunohistochemistry to analyze the correlation between pathogenic bacteria and key genes. Through the intersection of Venn diagrams, 175 co-expressed downregulated DEGs related to CAP were identified. A total of 4 candidate genes, including ICOS, IL7R, ITK, and ZAP70, were obtained by constructing the protein mutual aid network and conducting a module analysis of the common differentially expressed genes. The core genes in the GSEA enrichment pathways were intersected with the CAP-related genes reported in the relevant literature retrieved from the OMIM database. In the Venn diagram, two genes that coexist with OMIM include IL7R and PIK3R1. After considering our findings and the relevant literature, we determined that the key gene related to the occurrence and development of CAP was IL7R. The mNGS detected 13 kinds of bacteria, 4 kinds of fungi, and 2 kinds of viruses. Based on immunohistochemical results, it was found that there were relatively more bacteria detected in the IL7R high expression group. The identification of the key gene IL7R and the related signaling pathways extend understanding of the pathogenesis of CAP and provide a theoretical basis for clinical targeted therapy research.

VIGET: A web portal for study of vaccine-induced host responses based on Reactome pathways and ImmPort data.

Host responses to vaccines are complex but important to investigate. To facilitate the study, we have developed a tool called Vaccine Induced Gene Expression Analysis Tool (VIGET), with the aim to provide an interactive online tool for users to efficiently and robustly analyze the host immune response gene expression data collected in the ImmPort/GEO databases. VIGET allows users to select vaccines, choose ImmPort studies, set up analysis models by choosing confounding variables and two groups of samples having different vaccination times, and then perform differential expression analysis to select genes for pathway enrichment analysis and functional interaction network construction using the Reactome's web services. VIGET provides features for users to compare results from two analyses, facilitating comparative response analysis across different demographic groups. VIGET uses the Vaccine Ontology (VO) to classify various types of vaccines such as live or inactivated flu vaccines, yellow fever vaccines, etc. To showcase the utilities of VIGET, we conducted a longitudinal analysis of immune responses to yellow fever vaccines and found an intriguing complex activity response pattern of pathways in the immune system annotated in Reactome, demonstrating that VIGET is a valuable web portal that supports effective vaccine response studies using Reactome pathways and ImmPort data.

LCM-Seq for Retinal Cell Layer-Specific Responses During Optic Nerve Regeneration.

LCM-seq is a powerful tool for gene expression analysis from individual or groups of cells that can be spatially isolated. Within the visual system, retinal ganglion cells (RGCs), the cells that connect the eye to the brain through the optic nerve, reside in the retinal ganglion cell layer of the retina. This well-defined location provides a unique opportunity to harvest RNA by laser capture microdissection (LCM) from a highly enriched cell population. Using this method, it is possible to explore transcriptome-wide changes in gene expression following optic nerve injury. In the zebrafish model, this method can be used to identify molecular events driving successful optic nerve regeneration in contrast to mammals that fail to regenerate axons in the central nervous system. Here we provide a method for LCM from the different retinal layers of zebrafish following optic nerve injury and during the process of optic nerve regeneration. Purified RNA from this protocol is sufficient for RNA-seq or other downstream analysis.

Bioinformatics Analysis of Core Genes and Key Pathways in Myelodysplastic Syndrome

To screen differentially expressed gene (DEG) related to myelodysplastic syndrome (MDS) based on Gene Expression Omnibus (GEO) database, and explore the core genes and pathogenesis of MDS by analyzing the biological functions and related signaling pathways of DEG. The expression profiles of GSE4619, GSE19429, GSE58831 including MDS patients and normal controls were downloaded from GEO database. The gene expression analysis tool (GEO2R) of GEO database was used to screen DEG according to | log FC (fold change) |≥1 and P<0.01. David online database was used to annotate gene ontology function (GO). Metascape online database was used to enrich and analyze differential genes in Kyoto Encyclopedia of Genes and Genomes (KEGG). The protein-protein interaction network (PPI) was constructed by using STRING database. CytoHubba and Mcode plug-ins of Cytoscape were used to analyze the key gene clusters and hub genes. R language was used to diagnose hub genes and draw the ROC curve. GSEA enrichment analysis was performed on GSE19429 according to the expression of LEF1. A total of 74 co-DEG were identified, including 14 up-regulated genes and 60 down regulated genes. GO enrichment analysis indicated that BP of down regulated genes was mainly enriched in the transcription and regulation of RNA polymerase II promoter, negative regulation of cell proliferation, and immune response. CC of down regulated genes was mainly enriched in the nucleus, transcription factor complexes, and adhesion spots. MF was mainly enriched in protein binding, DNA binding, and β-catenin binding. KEGG pathway was enriched in primary immunodeficiency, Hippo signaling pathway, cAMP signaling pathway, transcriptional mis-regulation in cancer and hematopoietic cell lineage. BP of up-regulated genes was mainly enriched in type I interferon signaling pathway and viral response. CC was mainly enriched in cytoplasm. MF was mainly enriched in RNA binding. Ten hub genes and three important gene clusters were screened by STRING database and Cytoscape software. The functions of the three key gene clusters were closely related to immune regulation. ROC analysis showed that the hub genes had a good diagnostic significance for MDS. GSEA analysis indicated that LEF1 may affect the normal function of hematopoietic stem cells by regulating inflammatory reaction, which further revealed the pathogenesis of MDS. Bioinformatics can effectively screen the core genes and key signaling pathways of MDS, which provides a new strategy for the diagnosis and treatment of MDS.

HTG transcriptome panel (HTP): An accurate and robust tool for transcriptome-wide gene expression profiling.

e15063 Background: Analysis of transcriptome-wide gene expression has the potential to provide insights into biological pathways and molecular mechanisms that regulate disease progression and treatment response. RNA-Seq is considered the gold standard for transcriptome analysis; however, it may not perform well with older, archival formalin-fixed paraffin embedded (FFPE) tissues or low-quality/low-quantity RNA. To help researchers overcome these challenges, HTG Molecular Diagnostics developed a Research Use Only, extraction-free, targeted human transcriptome panel with an automated, proprietary web-based data analysis pipeline that provides fast, accurate, and repeatable quantitative gene expression data for 19,398 protein-coding mRNA targets from lysed FFPE tissue samples or extracted RNA (eRNA). Methods: Panel precision was evaluated by Lin’s Concordance Correlation Coefficient (Lc) using FFPE tissue lysates and eRNA from FFPE and fresh frozen tissues, including FFPE blocks that were older than ten years. The accuracy of the HTP to perform differential expression analysis was assessed by comparing it to RNA-Seq using FFPE samples from different cancer indications. The agreement of the log fold changes between the two platforms was measured by Pearson Correlation Coefficient (Pc). Additionally, a cohort of 83 breast cancer FFPE samples, that had also been evaluated for estrogen (ER) and progesterone (PR) receptor expression by immunohistochemistry (IHC), were used to determine the correlation between HTP gene expression and orthogonal IHC data. Results: The precision of the panel measured by Lc, ranged from 0.90-0.94 across multiple operators, instruments, lots, and processing days, demonstrating that the HTP is a precise and robust panel. In addition, HTP is a competitive alternate to RNA-seq (estimated log-fold changes, Pc = 0.83 between the platforms). Archival samples demonstrated high repeatability (Pc &gt; 0.9) with sample pass rates of 100% for HTP, which further confirms panel’s robust performance. Furthermore, the HTP gene expression results correlated with the IHC markers for ER (Pc = 0.92) and PR (Pc = 0.88) receptor status in a cohort of 83 breast cancer samples, which highlights the potential of the panel to replace IHC staining while providing expression data on thousands of additional genes. Conclusions: The results demonstrate the HTP is a powerful tool for comprehensive gene expression analysis that addresses several key limitations of RNA-Seq. The HTP is precise, accurate, user-friendly (avoids extraction-bias, fewer steps, automated data analysis), and efficient (quicker, requires lower amounts of sample, works well with samples of low quality and archival samples). Collectively, the data demonstrate HTP has the potential to be used for biomarker discovery and the development of clinical solutions.

Integrative Gene Expression and Metabolic Analysis Tool IgemRNA.

Genome-scale metabolic modeling is widely used to study the impact of metabolism on the phenotype of different organisms. While substrate modeling reflects the potential distribution of carbon and other chemical elements within the model, the additional use of omics data, e.g., transcriptome, has implications when researching the genotype–phenotype responses to environmental changes. Several algorithms for transcriptome analysis using genome-scale metabolic modeling have been proposed. Still, they are restricted to specific objectives and conditions and lack flexibility, have software compatibility issues, and require advanced user skills. We classified previously published algorithms, summarized transcriptome pre-processing, integration, and analysis methods, and implemented them in the newly developed transcriptome analysis tool IgemRNA, which (1) has a user-friendly graphical interface, (2) tackles compatibility issues by combining previous data input and pre-processing algorithms in MATLAB, and (3) introduces novel algorithms for the automatic comparison of different transcriptome datasets with or without Cobra Toolbox 3.0 optimization algorithms. We used publicly available transcriptome datasets from Saccharomyces cerevisiae BY4741 and H4-S47D strains for validation. We found that IgemRNA provides a means for transcriptome and environmental data validation on biochemical network topology since the biomass function varies for different phenotypes. Our tool can detect problematic reaction constraints.

Selection and validation of reference genes for RT-qPCR analysis in the pericarp of Litchi chinensis

Real-time reverse transcription quantitative PCR (RT-qPCR) is an important tool for gene expression analysis. Suitable reference genes are the basis of accurate and reliable RT-qPCR results. Litchi (Litchi chinensis Sonn.) is a commercially important tropical and subtropical fruit, but rapid pericarp browning is a substantial negative impact on its commercial use. Reference gene validation could help in the screening for genes involved in the browning mechanism. We assessed 15 new candidate reference genes from litchi transcriptome to determine stable reference genes for RT-qPCR analysis of pericarps from different cultivars, with differing postharvest storage, and under pathogenic stress. Ct values, geNorm, Normfinder, and RefFinder algorithms, were used to identify genes with the most stable transcription. GAGA-25 was the gene with the most stable transcription for comparing different varieties of the fresh pericarp. HDAC9 was the gene with the most stable transcription for postharvest pericarp. STAM was the gene with the most stable transcription for inoculated pericarp. Of the candidate reference genes, GAGA-25 was the most stable reference gene across the complete sample set. This study evaluated reference gene stability for RT-qPCR in litchi pericarp. This work provides a foundation for using qPCR to study gene function and molecular mechanism studies of litchi pericarp browning.

Inhibition of epidermal growth factor receptor suppresses parathyroid hormone-related protein expression in tumours and ameliorates cancer-associated cachexia.

BackgroundLung cancer is the primary cause of cancer deaths worldwide. Activation of epidermal growth factor receptor (EGFR) leads to lung cancer progression and poor prognosis while involuntary weight loss remains a major problem. Tumour‐derived parathyroid hormone‐related protein (PTHrP) emerged as a potential mediator of cachexia. Here, we investigated the modulatory role of EGFR signalling in PTHrP (encoded by Pthlh) gene expression and the impact of this relationship on cancer cachexia.MethodsGlobal gene expression profiles of Lewis lung carcinoma (LLC) cells were analysed. Pthlh mRNA levels were measured by qRT‐PCR in LLC cells treated with EGFR ligands and tyrosine kinase inhibitors (TKIs). LLC tumour‐bearing mice received EGFR TKI erlotinib for 7 days via intraperitoneal injection or oral gavage. Tumour Pthlh mRNA, weight of fat/muscle tissue, and grip strength were assessed. RNA‐seq data from The Cancer Genome Atlas and gene expression analysis tools were used to characterize expression profiles of PTHLH and EGFR along with correlation analysis of PTHLH with EGFR and transforming growth factor alpha (TGFA) in human lung cancer and head and neck squamous carcinoma (HNSC). Survival of lung squamous cell carcinoma (LUSC) and lung adenocarcinoma (LUAD) patients with EGFR gene alterations was analysed in regard to PTHLH expression.ResultsExpression of EGFR ligands, EGFR itself, and PTHrP co‐clusters in LLC cells. Activation of EGFR signalling with its ligands significantly increases (3.8‐fold, P < 0.0005) while EGFR TKIs significantly decrease (90%, P < 0.0005) Pthlh mRNA levels in LLC cells. Pthlh mRNA drops 65–75% (P < 0.0005) in tumours upon treatment of LLC tumour‐bearing mice with erlotinib while their muscle mass and grip strength increase (9.2% P < 0.05, 23% P < 0.005, respectively) compared with tumour‐bearing control mice. PTHLH is overexpressed in tumours of LUSC (45.8‐fold, P < 0.05) and HNSC (17.5‐fold, P < 0.05) compared with normal tissue. PTHLH expression correlates with EGFR and its ligand TGFA in both cancers (LUSC: n = 745, R = 0.32, P < 0.0001 and R = 0.51, P < 0.0001; HNSC: n = 545, R = 0.34, P < 0.001 and R = 0.50, P < 0.001, respectively). High PTHLH mRNA associates with poor overall survival in LUAD patients with activating EGFR mutations (n = 40, log‐rank test, P = 0.0451).ConclusionsEpidermal growth factor receptor signalling regulates expression of cachexia mediator PTHrP. EGFR inhibition reduces PTHrP expression in LLC tumours and ameliorates cachexia in LLC tumour‐bearing mice.

Flimma: a federated and privacy-aware tool for differential gene expression analysis

Aggregating transcriptomics data across hospitals can increase sensitivity and robustness of differential expression analyses, yielding deeper clinical insights. As data exchange is often restricted by privacy legislation, meta-analyses are frequently employed to pool local results. However, the accuracy might drop if class labels are inhomogeneously distributed among cohorts. Flimma (https://exbio.wzw.tum.de/flimma/) addresses this issue by implementing the state-of-the-art workflow limma voom in a federated manner, i.e., patient data never leaves its source site. Flimma results are identical to those generated by limma voom on aggregated datasets even in imbalanced scenarios where meta-analysis approaches fail.

QTeller: a tool for comparative multi-genomic gene expression analysis.

Over the last decade, RNA-Seq whole-genome sequencing has become a widely used method for measuring and understanding transcriptome-level changes in gene expression. Since RNA-Seq is relatively inexpensive, it can be used on multiple genomes to evaluate gene expression across many different conditions, tissues and cell types. Although many tools exist to map and compare RNA-Seq at the genomics level, few web-based tools are dedicated to making data generated for individual genomic analysis accessible and reusable at a gene-level scale for comparative analysis between genes, across different genomes and meta-analyses. To address this challenge, we revamped the comparative gene expression tool qTeller to take advantage of the growing number of public RNA-Seq datasets. qTeller allows users to evaluate gene expression data in a defined genomic interval and also perform two-gene comparisons across multiple user-chosen tissues. Though previously unpublished, qTeller has been cited extensively in the scientific literature, demonstrating its importance to researchers. Our new version of qTeller now supports multiple genomes for intergenomic comparisons, and includes capabilities for both mRNA and protein abundance datasets. Other new features include support for additional data formats, modernized interface and back-end database and an optimized framework for adoption by other organisms' databases. The source code for qTeller is open-source and available through GitHub (https://github.com/Maize-Genetics-and-Genomics-Database/qTeller). A maize instance of qTeller is available at the Maize Genetics and Genomics database (MaizeGDB) (https://qteller.maizegdb.org/), where we have mapped over 200 unique datasets from GenBank across 27 maize genomes. Supplementary data are available at Bioinformatics online.