Approach For Gene Expression Analysis Research Articles

Evaluation of Collection and Processing Conditions for Gene Expression Analysis Using Human Myeloid Cells.

Background: The population of blast cells among peripheral blood mononuclear cells (PBMCs) obtained from patients is a desirable specimen for analyzing gene expression in diseases including acute myeloid leukemia. Although the enrichment of blast cells often needs to be performed at a central laboratory, acceptable conditions for sample transport from clinical sites remain to be established. Methods: We evaluated storage temperature, duration, and tube type before initiating sample processing for the analysis of cluster of differentiation (CD)33+ myeloid cells among PBMCs as an alternative to CD34+/CD33+ blast cells. Results: CD33+ myeloid cells were successfully purified by MACS. The cell viability and the RNA integrity were sustained during storage up to 48 hours before sample processing. Storage at 4°C had minimal effects on gene expression, whereas storage at room temperature induced the senescence pathway, characterized by the expression of stress-inducible genes. A CPT tube was also better than an ethylenediaminetetraacetic acid tube for minimizing gene expression change. Conclusions: Our study provided important clues for establishing a sample handling approach for gene expression analysis with purified cell fractions from human PBMCs. To keep the variation of gene expression to a minimum, samples should be delivered at 4°C within 48 hours before processing.

The Gene Expression Classifier ALLCatchR Identifies B-cell Precursor ALL Subtypes and Underlying Developmental Trajectories Across Age.

Current classifications (World Health Organization-HAEM5/ICC) define up to 26 molecular B-cell precursor acute lymphoblastic leukemia (BCP-ALL) disease subtypes by genomic driver aberrations and corresponding gene expression signatures. Identification of driver aberrations by transcriptome sequencing (RNA-Seq) is well established, while systematic approaches for gene expression analysis are less advanced. Therefore, we developed ALLCatchR, a machine learning-based classifier using RNA-Seq gene expression data to allocate BCP-ALL samples to all 21 gene expression-defined molecular subtypes. Trained on n = 1869 transcriptome profiles with established subtype definitions (4 cohorts; 55% pediatric / 45% adult), ALLCatchR allowed subtype allocation in 3 independent hold-out cohorts (n = 1018; 75% pediatric / 25% adult) with 95.7% accuracy (averaged sensitivity across subtypes: 91.1% / specificity: 99.8%). High-confidence predictions were achieved in 83.7% of samples with 98.9% accuracy. Only 1.2% of samples remained unclassified. ALLCatchR outperformed existing tools and identified novel driver candidates in previously unassigned samples. Additional modules provided predictions of samples blast counts, patient's sex, and immunophenotype, allowing the imputation in cases where these information are missing. We established a novel RNA-Seq reference of human B-lymphopoiesis using 7 FACS-sorted progenitor stages from healthy bone marrow donors. Implementation in ALLCatchR enabled projection of BCP-ALL samples to this trajectory. This identified shared proximity patterns of BCP-ALL subtypes to normal lymphopoiesis stages, extending immunophenotypic classifications with a novel framework for developmental comparisons of BCP-ALL. ALLCatchR enables RNA-Seq routine application for BCP-ALL diagnostics with systematic gene expression analysis for accurate subtype allocation and novel insights into underlying developmental trajectories.

A Survey on Automated Leukemia Detection and Classification: Exploring Image Processing and Gene Expression Analysis Approaches

A Survey on Automated Leukemia Detection and Classification: Exploring Image Processing and Gene Expression Analysis Approaches

Deciphering the Role of Phosphoglycerate Kinase 1 in Polycystic Ovarian Syndrome using Differential Gene Expression Analysis Approach

Polycystic Ovarian Syndrome (PCOS) is perhaps the most common Metabolic, Endocrine disorder characterized in females before menopause. PCOS majorly elevates Androgen (AR) levels and irregularities in menstrual cycles or can be morphologically seen as multiple cysts in the ovary. Patients with PCOS are more likely to develop obesity, cardiovascular disease, as well as insulin resistance. The etiology of this disease is still not known, but research suggests it can be due to environmental factors, lifestyle, or diet. According to a recent study, the glycolytic enzyme Phosphoglycerate Kinase 1 (PGK1) has an effective role in PCOS patients as it binds with AR, which is high gets expressed in PCOS patients. Thus, it can be a main causable factor for PCOS patients. The granulosa cells of the ovary provide nutrients to oocytes for development. The energy to the oocytes comes via cycle glycolysis. In PCOS patients, these granulosa cells show degradation. As a result, there may be a malfunction in the energy supply via granulosa cells, with Phosphoglycerate Kinase 1(PGK1) being one of the key glycolysis enzymes. And as the high levels of AR remain in proximity with disordered follicle development in PCOS. The actual significance of AR in the fertility of PCOS patients is still not known. Thus, we can have a close look at Androgen binding Receptors (ARs) and the role of PGK1. In this research, we analyzed single-cell RNA sequence data from Gene Expression Omnibus (GEO), including data from control Dihydrotestosterone (DHT) and PGK1-DHT of PCOS patients generated by deep sequencing in triplicate. Further, we explain the transcriptomic dynamics by co-expression network analysis and evaluate the differences between PCA and limmavoom analysis to discover which genes are differentially expressed. By calculating the p-value and performing gene ontology (GO) enrichment analysis, we were able to identify multiple genes with greater expression levels in the PGK1-DHT samples of PCOS patients, including insulin-related, glycolytic, tumor-associated, and apoptotic genes. We also carried out Functional enrichment and gene co-expression network analysis. Its biological, molecular, and cellular domain lies in the intracellular membrane-bound organelle. Gene co-expression network and functional enrichment found significant enrichment and highly differentially expressed genes in the intracellular cellular domain.

System biology approaches identified novel biomarkers and their signaling pathways involved in renal cell carcinoma with different human diseases.

Renal cell carcinoma (RCC) is a type of cancer that develops in the renal epithelium of the kidney. It is responsible for approximately 3% of adult malignancies, and 90-95% of neoplasms originate from the kidney. Advances in tumor diagnosis, innovative immune therapeutics, and checkpoint inhibitors-based treatment options improved the survival rate of patients with RCC accompanied by different risk factors. RCC patients with diabetes, hepatitis C virus (HCV), or obesity (OB) may have a comorbidity, and finding the risk factor for better clinical treatment is an urgent issue. Therefore, the study focused on network-based gene expression analysis approaches to learning the impact of RCC on other comorbidities associated with the disease. The study found critical genetic factors and signal transduction pathways that share pathophysiology and commonly use dysregulated genes of the illness. Initially, the study identified 385 up-regulated genes and 338 down-regulated genes involved with RCC. OB, chronic kidney disease (CKD), type 2 diabetes (T2D), and HCV significantly shared 28, 14, 5, and 3 genes, respectively. RCC shared one down-regulated gene versican (VCAN) with OB and HCV and one down-regulated gene oxidase homolog 2 (LOXL2) with OB and CKD. Interestingly, most of the shared pathways were linked with metabolism. The study also identified six prospective biomarkers, signaling pathways, and numerous critical regulatory and associated drug candidates for the disease. We believe that the discovery will help explain these diseases' complicated interplay and aid in developing novel therapeutic targets and drug candidates.

A Bioinformatics View on Acute Myeloid Leukemia Surface Molecules by Combined Bayesian and ABC Analysis.

"Big omics data" provoke the challenge of extracting meaningful information with clinical benefit. Here, we propose a two-step approach, an initial unsupervised inspection of the structure of the high dimensional data followed by supervised analysis of gene expression levels, to reconstruct the surface patterns on different subtypes of acute myeloid leukemia (AML). First, Bayesian methodology was used, focusing on surface molecules encoded by cluster of differentiation (CD) genes to assess whether AML is a homogeneous group or segregates into clusters. Gene expressions of 390 patient samples measured using microarray technology and 150 samples measured via RNA-Seq were compared. Beyond acute promyelocytic leukemia (APL), a well-known AML subentity, the remaining AML samples were separated into two distinct subgroups. Next, we investigated which CD molecules would best distinguish each AML subgroup against APL, and validated discriminative molecules of both datasets by searching the scientific literature. Surprisingly, a comparison of both omics analyses revealed that CD339 was the only overlapping gene differentially regulated in APL and other AML subtypes. In summary, our two-step approach for gene expression analysis revealed two previously unknown subgroup distinctions in AML based on surface molecule expression, which may guide the differentiation of subentities in a given clinical-diagnostic context.

Systematic analysis of inflammation and pain pathways in a mouse model of gout.

Gout is a prevalent and painful inflammatory arthritis, and its global burden continues to rise. Intense pain induced by gout attacks is a major complication of gout. However, systematic studies of gout inflammation and pain are lacking. Using a monosodium urate (MSU) crystal-induced gout model, we performed genome-wide transcriptome analysis of the inflamed ankle joint, dorsal root ganglion (DRG), and spinal cord of gouty mice. Our results revealed important transcriptional changes, including highly elevated inflammation and broad activation of immune pathways in both the joint and the nervous system, in gouty mice. Integrated analysis showed that there was a remarkable overlap between our RNAseq and human genome-wide association study (GWAS) of gout; for example, the risk gene, stanniocalcin-1 (STC1) showed significant upregulation in all three tissues. Interestingly, when compared to the transcriptomes of human osteoarthritis (OA) and rheumatoid arthritis (RA) joint tissues, we identified significant upregulation of cAMP/cyclic nucleotide-mediated signaling shared between gouty mice and human OA with high knee pain, which may provide excellent drug targets to relieve gout pain. Furthermore, we investigated the common and distinct transcriptomic features of gouty, inflammatory pain, and neuropathic pain mouse models in their DRG and spinal cord tissues. Moreover, we discovered distinct sets of genes with significant differential alternative splicing or differential transcript usage in each tissue, which were largely not detected by conventional differential gene expression analysis approaches. Based on these results, our study provided a more accurate and comprehensive depiction of transcriptomic alterations related to gout inflammation and pain.

Structural Valve Deterioration Is Linked to Increased Immune Infiltrate and Chemokine Expression.

We aim to investigate whether structural valve deterioration (SVD) of bioprosthetic xenogenic tissue heart valves (XTHVs) is associated with increased immune cell infiltration and whether co-expression of several chemokines correlates with this increase in immune infiltrate. Explanted XTHVs from patients undergoing redo valve replacement for SVD were obtained. Immunohistochemical, microscopic, and gene expression analysis approaches were used. XTHVs (n = 37) were obtained from 32 patients (mean 67.7years) after a mean time of 11.6years post-implantation. Significantly increased immune cellular infiltration was observed in the explanted SVD valves for all immune cell types examined, including T cells, macrophages, B cells, neutrophils, and plasma cells, compared to non-SVD controls. Furthermore, a significantly increased chemokine gradient in explanted SVD valves accompanied immune cell infiltration. These data suggest the development of SVD is associated with a significantly increased burden of immune cellular infiltrate correlated to the induction of a chemokine gradient around the XHTV, representing chronic immune rejection.Graphical abstract Proposed interaction between innate and adaptive immunity leading to the development of structural valve deterioration in xenogenic tissue heart valves.

Abstract P2-11-20: Endocrine resistance in breast cancer: A dynamic gene expression analysis approach reveals potential new responsible genes

Abstract While ER-targeted endocrine therapies are the most successful therapies for ER-positive breast cancer, tumor recurrence and endocrine-resistance happens in many of the cases. Despite efforts, the underlying genes and mechanisms of this acquired resistance remain largely unknown. Here, we utilized a recently developed pipeline for dynamic gene regulatory network analysis1to re-analyze the publically available datasets related to endocrine resistance in breast cancer in order to find new target genes related to endocrine resistance. The pipeline exploits the time-resolved expression pattern of genes to identify dynamic responsive genes (DRG) and group them into multiple modules based on the similarity in their dynamic activities, which leads us to identify more significant genes compared with previous gene expression analysis models that solely focus on expression value of the genes. Here, we analyzed a dataset of time-resolved gene expression during MCF-7 cell transition to endocrine-resistant cells by long-term deprivation of estrogen, which is an accepted model of endocrine resistance2. The dataset is publically available as GES20361 in the Gene Expression Omnibus (GEO) database. Based on their dynamic expression pattern over time, our method of analysis grouped genes into three main modules. Interestingly, the majority of well-studied genes in the literature related to endocrine resistance are part of one of these modules, while there are many genes such as GABPA, TFDP1, MCM7, MDC1, and FOXC1 that are potential novel targets for treatments against endocrine resistance. Module One included genes related to endocrine resistance, mainly those related to cell cycle, such as CCNA1, CCNA2, CCND1, and CDK family, and DNA replication and repair, such as MDC1 and PARP1, as well as EGFR. Consistent with the previous literature on endocrine resistant breast cancer cells, these genes all showed similar up-regulated expression patterns over time. Furthermore, our model confirmed the identified genes shown in the previous paper2 (POLD1, TERF1, RAF1, and E2F1) as part of Module One and as important genes in endocrine resistance regulation. Module Two was enriched for genes related to PI3K/AKT/mTOR signaling pathway, while Module Three mostly contained tyrosine kinase related genes, which not only confirms the previous findings related to the importance of these genes in acquiring endocrine resistance, but also indicates the difference between the expression patterns of these genes compare to cell cycle genes and those in Module One. Our analysis revealed that the genes in Module One continued their up-regulating trend beyond day 90, when the marked transition toward resistance happens and resistance phenotype starts to arise, while genes in Module Two got up-regulated before the 90 days and down-regulated afterward, suggesting different roles from the genes in Module One. Lastly, we studied the gene regulatory networks, transcription factors, and miRNAs upstream of the identified genes, and discovered several interesting targets, such as MiR-193b and MiR-16, which have integral parts in the regulation of our identified genes and thus can be of great importance toward endocrine resistance.

Inter-genus gene expression analysis in livestock fibroblasts using reference gene validation based upon a multi-species primer set.

Quantitative reverse transcription PCR (RT-qPCR) remains as an accurate approach for gene expression analysis but requires labor-intensive validation of reference genes using species-specific primers. To ease such demand, the aim was to design and test a multi-species primer set to validate reference genes for inter-genus RT-qPCR gene expression analysis. Primers were designed for ten housekeeping genes using transcript sequences of various livestock species. All ten gene transcripts were detected by RT-PCR in Bos taurus (cattle), Bubalus bubalis (buffaloes), Capra hircus (goats), and Ovis aries (sheep) cDNA. Primer efficiency was attained for eight reference genes using B. taurus—O. aries fibroblast cDNA (95.54–98.39%). The RT-qPCR data normalization was carried out for B. taurus vs. O. aries relative gene expression using Bestkeeper, GeNorm, Norm-finder, Delta CT method, and RefFinder algorithms. Validation of inter-genus RT-qPCR showed up-regulation of TLR4 and ZFX gene transcripts in B. taurus fibroblasts, irrespectively of normalization conditions (two, three, or four reference genes). In silico search in mammalian transcriptomes showed that the multi-species primer set is expected to amplify transcripts of at least two distinct loci in 114 species, and 79 species would be covered by six or more primers. Hence, a multi-species primer set allows for inter-genus gene expression analysis between O. aries and B. taurus fibroblasts and further reveals species-specific gene transcript abundance of key transcription factors.

The Use of Informed Priors in Biclustering of Gene Expression with the Hierarchical Dirichlet Process.

We motivate and describe the application of Hierarchical Dirichlet Process (HDP) models to the "soft" biclustering of gene expression data, in which we obtain modules (biclusters) where the affiliation of genes and samples with the modules are weighted, instead of being hard memberships. As a distinct contribution, we propose a method which HDP is informed with prior beliefs, significantly increasing the quality of the biclustering in terms of both the correctness of the number of modules inferred, and the precision of these modules, especially when evidence is sparse. We outline two such informed priors; one based on co-expression relationships inherent in the data, the other based on an externally provided regulatory network. We validate these results and compare the performance of our approach to Weighted Gene Correlation Network Analysis (WGCNA), another model that features weighted modules. We have, to this end, performed experiments on semi-synthetic data. The results show that HDP, with the addition of a well-informed prior, is able to capture the correct number of modules with increased accuracy. Furthermore, the model becomes robust to changes in the strength of the prior. We conclude by discussing these results and the benefits provided by our approach for gene expression analysis and network validation.

Cluster-Separated Classification Approach for Gene Expression Analysis

Cluster-Separated Classification Approach for Gene Expression Analysis

Cluster-Separated Classification Approach for Gene Expression Analysis

Cluster-Separated Classification Approach for Gene Expression Analysis

Microscale Gene Expression Analysis of Tumor-Associated Macrophages

Macrophages, apart from being the key effector cells of the innate immune system, also play critical roles during the development and progression of various complex diseases, including cancer. Tumor-associated macrophages, infiltrate tumors during different stages of cancer progression to regulate motility, invasion, and intravasation to metastatic sites. Macrophages can exist in different polarization states associated with unique function in tumors. Since tumor-associated macrophages constitute a very small proportion of tumor cells, analysis of gene expression pattern using normal extraction buffer-based methods remains a challenging task. Therefore, it is imperative to develop low-throughput strategies to investigate transcriptional regulations from a small number of immune cells. Here, we describe an efficient, sensitive, and cost-effective approach for gene expression analysis of a small number of fluorescence-activated sorted tumor-associated macrophages. Our analyses from the different number of stable, primary, and sorted macrophages suggest 5,000 cells is an optimal number for performing quantitative, real-time PCR analysis of multiple genes. Our studies could detect expression of macrophage-specific genes from cultured primary macrophages, and FACS-sorted macrophages from different biological tissues without introducing biases in comparative gene expression ratios. In conclusion, our kit-based method for quantitative gene expression analysis from a small number of cells found in biological tissues will provide an opportunity to study cell-specific, transcriptional changes.

Multifaceted remodeling by vitamin C boosts sensitivity of Mycobacterium tuberculosis subpopulations to combination treatment by anti-tubercular drugs

Bacterial dormancy is a major impediment to the eradication of tuberculosis (TB), because currently used drugs primarily target actively replicating bacteria. Therefore, decoding of the critical survival pathways in dormant tubercle bacilli is a research priority to formulate new approaches for killing these bacteria. Employing a network-based gene expression analysis approach, we demonstrate that redox active vitamin C (vit C) triggers a multifaceted and robust adaptation response in Mycobacterium tuberculosis (Mtb) involving ~ 67% of the genome. Vit C-adapted bacteria display well-described features of dormancy, including growth stasis and progression to a viable but non-culturable (VBNC) state, loss of acid-fastness and reduction in length, dissipation of reductive stress through triglyceride (TAG) accumulation, protective response to oxidative stress, and tolerance to first line TB drugs. VBNC bacteria are reactivatable upon removal of vit C and they recover drug susceptibility properties. Vit C synergizes with pyrazinamide, a unique TB drug with sterilizing activity, to kill dormant and replicating bacteria, negating any tolerance to rifampicin and isoniazid in combination treatment in both in-vitro and intracellular infection models. Finally, the vit C multi-stress redox models described here also offer a unique opportunity for concurrent screening of compounds/combinations active against heterogeneous subpopulations of Mtb. These findings suggest a novel strategy of vit C adjunctive therapy by modulating bacterial physiology for enhanced efficacy of combination chemotherapy with existing drugs, and also possible synergies to guide new therapeutic combinations towards accelerating TB treatment.

High Throughput Sequencing-Based Approaches for Gene Expression Analysis.

Next-generation sequencing has emerged as the method of choice to answer fundamental questions in biology. The massively parallel sequencing technology for RNA-Seq analysis enables better understanding of gene expression patterns in model and nonmodel organisms. Sequencing per se has reached the stage of commodity level while analyzing and interpreting huge amount of data has been a significant challenge. This chapter is aimed at discussing the complexities involved in sequencing and analysis, and tries to simplify sequencing based gene expression analysis. Biologists and experimental scientists were kept in mind while discussing the methods and analysis workflow.

Characterization of epithelial-mesenchymal transition intermediate/hybrid phenotypes associated to resistance to EGFR inhibitors in non-small cell lung cancer cell lines.

Increasing evidence points to a key role played by epithelial-mesenchymal transition (EMT) in cancer progression and drug resistance. In this study, we used wet and in silico approaches to investigate whether EMT phenotypes are associated to resistance to target therapy in a non-small cell lung cancer model system harboring activating mutations of the epidermal growth factor receptor. The combination of different analysis techniques allowed us to describe intermediate/hybrid and complete EMT phenotypes respectively in HCC827- and HCC4006-derived drug-resistant human cancer cell lines. Interestingly, intermediate/hybrid EMT phenotypes, a collective cell migration and increased stem-like ability associate to resistance to the epidermal growth factor receptor inhibitor, erlotinib, in HCC827 derived cell lines. Moreover, the use of three complementary approaches for gene expression analysis supported the identification of a small EMT-related gene list, which may have otherwise been overlooked by standard stand-alone methods for gene expression analysis.

Serglycin Regulates Cytoskeletal‐related Proteins Associated with Cell Motility in Breast Cancer In Vitro

Despite advances in cancer treatment, triple‐negative (ER−/PR−/HER2−) breast cancer represents a complicated subtype with no specific treatment regime. Serglycin (SRGN) is an intracellular proteoglycan which has been shown to be highly expressed in the triple‐negative MDA‐MB‐231 breast cancer cell line. In this study, we aim to decipher the molecular networks and pathways regulated by SRGN in breast cancer cells using a global gene expression analysis approach. We first analyzed the expression of SRGN in a dataset containing 1,881 breast cancer tissues from the GOBO database (http://co.bmc.lu.se/gobo/gobo.pl). For the in vitro experiments, siRNA‐mediated down‐regulation of the SRGN gene in MDA‐MB‐231 cells was performed. Transcriptomic analysis of si‐SRGN breast cancer cells was then carried out. Subsequently real time PCR for specific gene targets and expression of selected proteins by Western Blot were performed. Cell motility was determined using commercially available cell migration chambers. The Gene Set analysis from the GOBO database verified that the SRGN gene is highly expressed in basal (triple negative) breast tumors. Knockdown of the SRGN gene in MDA‐MB‐231 cells induced differential expression of 225 genes. Gene ontology analysis revealed the presence of a cluster of genes that were highly enriched in cellular processes, including cell movement. Reduced expression of SRGN was accompanied by down‐regulation of several cytoskeletal‐related genes, namely ABLIM1, LIMA1, CFL1, RAC1, RAC2 and RHOA, concomitant with decreased cell motility. Decreased expression of RAC1 and RHOA proteins were also demonstrated. The results suggest that SRGN is important in regulating actin cytoskeletal organization associated with cell migration, a crucial event during cancer metastasis.Support or Funding InformationThis work is supported by the National Medical Research Council Singapore Grant NMRC/CIRG/1370/2013.

Statistical inferences based on outliers for gene expression analysis

Discovering differential genes through the detection of outliers in samples from disease group subjects is a new and important approach for gene expression analysis. Extending the outlier mean of Chen et al. (2010a), we develop the asymptotic distributions of the outlier least squares estimator (LSE) and the outlier proportion for the linear regression model. An optimal property of the best linear outlier mean acting as an outlier estimator version of the Gauss–Markov theorem for the outlier LSE is presented. Power comparisons demonstrate that tests based on outlier estimators are competitive in detecting a shift of the parent tail distribution. An analysis of DNA microarray data from samples of pancreatic breast tumors supports the use of outlier-based methods.

ABSSeq: a new RNA-Seq analysis method based on modelling absolute expression differences.

BackgroundThe recent advances in next generation sequencing technology have made the sequencing of RNA (i.e., RNA-Seq) an extemely popular approach for gene expression analysis. Identification of significant differential expression represents a crucial initial step in these analyses, on which most subsequent inferences of biological functions are built. Yet, for identification of these subsequently analysed genes, most studies use an additional minimal threshold of differential expression that is not captured by the applied statistical procedures.ResultsHere we introduce a new analysis approach, ABSSeq, which uses a negative binomal distribution to model absolute expression differences between conditions, taking into account variations across genes and samples as well as magnitude of differences. In comparison to alternative methods, ABSSeq shows higher performance on controling type I error rate and at least a similar ability to correctly identify differentially expressed genes.ConclusionsABSSeq specifically considers the overall magnitude of expression differences, which enhances the power in detecting truly differentially expressed genes by reducing false positives at both very low and high expression level. In addition, ABSSeq offers to calculate shrinkage of fold change to facilitate gene ranking and effective outlier detection.Electronic supplementary materialThe online version of this article (doi:10.1186/s12864-016-2848-2) contains supplementary material, which is available to authorized users.