Cross-platform prediction of gene expression signatures

Using proteomic and transcriptomic data to assess activation of intracellular molecular pathways

Analysis of molecular pathway activation is the recent instrument that helps to quantize activities of various intracellular signaling, structural, DNA synthesis and repair, and biochemical processes. This may have a deep impact in fundamental research, bioindustry, and medicine. Unlike gene ontology analyses and numerous qualitative methods that can establish whether a pathway is affected in principle, the quantitative approach has the advantage of exactly measuring the extent of a pathway up/downregulation. This results in emergence of a new generation of molecular biomarkers-pathway activation levels, which reflect concentration changes of all measurable pathway components. The input data can be the high-throughput proteomic or transcriptomic profiles, and the output numbers take both positive and negative values and positively reflect overall pathway activation. Due to their nature, the pathway activation levels are more robust biomarkers compared to the individual gene products/protein levels. Here, we review the current knowledge of the quantitative gene expression interrogation methods and their applications for the molecular pathway quantization. We consider enclosed bioinformatic algorithms and their applications for solving real-world problems. Besides a plethora of applications in basic life sciences, the quantitative pathway analysis can improve molecular design and clinical investigations in pharmaceutical industry, can help finding new active biotechnological components and can significantly contribute to the progressive evolution of personalized medicine. In addition to the theoretical principles and concepts, we also propose publicly available software for the use of large-scale protein/RNA expression data to assess the human pathway activation levels.

Persistent expression of microRNA-125a targets is required to induce murine hematopoietic stem cell repopulating activity

MicroRNAs (miRs) are small noncoding RNAs that regulate gene expression posttranscriptionally by binding to the 3' untranslated regions of their target mRNAs. The evolutionarily conserved microRNA-125a (miR-125a) is highly expressed in both murine and human hematopoietic stem cells (HSCs), and previous studies have found that miR-125 strongly enhances self-renewal of HSCs and progenitors. In this study we explored whether temporary overexpression of miR-125a would be sufficient to permanently increase HSC self-renewal or, rather, whether persistent overexpression of miR-125a is required. We used three complementary in vivo approaches to reversibly enforce expression of miR-125a in murine HSCs. Additionally, we interrogated the underlying molecular mechanisms responsible for the functional changes that occur in HSCs on overexpression of miR-125a. Our data indicate that continuous expression of miR-125a is required to enhance HSC activity. Our molecular analysis confirms changes in pathways that explain the characteristics of miR-125a overexpressing HSCs. Moreover, it provides several novel putative miR-125a targets, but also highlights the complex molecular changes that collectively lead to enhanced HSC function.

Molecular intrinsic versus clinical subtyping in breast cancer: A comprehensive review

Breast cancer is a heterogeneous disease manifesting diversity at the molecular, histological and clinical level. The development of breast cancer classification was centered on informing clinical decisions. The current approach to the classification of breast cancer, which categorizes this disease into clinical subtypes based on the detection of estrogen receptor, progesterone receptor, human epidermal growth factor receptor 2, and proliferation marker Ki67, is not ideal. This is manifested as a heterogeneity of therapeutic responses and outcomes within the clinical subtypes. The newer classification model, based on gene expression profiling (intrinsic subtyping) informs about transcriptional responses downstream from IHC single markers, revealing deeper appreciation for the disease heterogeneity and capturing tumor biology in a more comprehensive way than an expression of a single protein or gene alone. While accumulating evidences suggest that intrinsic subtypes provide clinically relevant information beyond clinical surrogates, it is imperative to establish whether the current conventional immunohistochemistry-based clinical subtyping approach could be improved by gene expression profiling and if this approach has a potential to translate into clinical practice.

Unlocking the transcriptomic potential of formalin-fixed paraffin embedded clinical tissues: comparison of gene expression profiling approaches

BACKGROUND

High-throughput transcriptomics has matured into a very well established and widely utilised research tool over the last two decades. Clinical datasets generated on a range of different platforms continue to be deposited in public repositories provide an ever-growing, valuable resource for reanalysis. Cost and tissue availability normally preclude processing samples across multiple technologies, making it challenging to directly evaluate performance and whether data from different platforms can be reliably compared or integrated.

METHODS

This study describes our experiences of nine new and established mRNA profiling techniques including Lexogen QuantSeq, Qiagen QiaSeq, BioSpyder TempO-Seq, Ion AmpliSeq, Nanostring, Affymetrix Clariom S or U133A, Illumina BeadChip and RNA-seq of formalin-fixed paraffin embedded (FFPE) and fresh frozen (FF) sequential patient-matched breast tumour samples.

RESULTS

The number of genes represented and reliability varied between the platforms, but overall all methods provided data which were largely comparable. Crucially we found that it is possible to integrate data for combined analyses across FFPE/FF and platforms using established batch correction methods as required to increase cohort sizes. However, some platforms appear to be better suited to FFPE samples, particularly archival material.

CONCLUSIONS

Overall, we illustrate that technology selection is a balance between required resolution, sample quality, availability and cost.

Sequential analysis of myocardial gene expression with phenotypic change: Use of cross-platform concordance to strengthen biologic relevance

Objectives

To investigate the biologic relevance of cross-platform concordant changes in gene expression in intact human failing/hypertrophied ventricular myocardium undergoing reverse remodeling.

Background

Information is lacking on genes and networks involved in remodeled human LVs, and in the associated investigative best practices.

Methods

We measured mRNA expression in ventricular septal endomyocardial biopsies from 47 idiopathic dilated cardiomyopathy patients, at baseline and after 3–12 months of β-blocker treatment to effect left ventricular (LV) reverse remodeling as measured by ejection fraction (LVEF). Cross-platform gene expression change concordance was investigated in reverse remodeling Responders (R) and Nonresponders (NR) using 3 platforms (RT-qPCR, microarray, and RNA-Seq) and two cohorts (All 47 subjects (A-S) and a 12 patient “Super-Responder” (S-R) subset of A-S).

Results

For 50 prespecified candidate genes, in A-S mRNA expression 2 platform concordance (C_cpT), but not single platform change, was directly related to reverse remodeling, indicating C_cpT has biologic significance. Candidate genes yielded a C_cpT (PCR/microarray) of 62% for Responder vs. Nonresponder (R/NR) change from baseline analysis in A-S, and ranged from 38% to 100% in S-R for PCR/microarray/RNA-Seq 2 platform comparisons. Global gene C_cpT measured by microarray/RNA-Seq was less than for candidate genes, in S-R R/NR 17.5% vs. 38% (P = 0.036). For S-R global gene expression changes, both cross-cohort concordance (C_ccT) and C_cpT yielded markedly greater values for an R/NR vs. an R-only analysis (by 22 fold for C_ccT and 7 fold for C_cpT). Pathway analysis of concordant global changes for R/NR in S-R revealed signals for downregulation of multiple phosphoinositide canonical pathways, plus expected evidence of a β₁-adrenergic receptor gene network including enhanced Ca²⁺ signaling.

Conclusions

Two-platform concordant change in candidate gene expression is associated with LV biologic effects, and global expression concordant changes are best identified in an R/NR design that can yield novel information.

RNA sequencing for research and diagnostics in clinical oncology

Molecular diagnostics is becoming one of the major drivers of personalized oncology. With hundreds of different approved anticancer drugs and regimens of their administration, selecting the proper treatment for a patient is at least nontrivial task. This is especially sound for the cases of recurrent and metastatic cancers where the standard lines of therapy failed. Recent trials demonstrated that mutation assays have a strong limitation in personalized selection of therapeutics, consequently, most of the drugs cannot be ranked and only a small percentage of patients can benefit from the screening. Other approaches are, therefore, needed to address a problem of finding proper targeted therapies. The analysis of RNA expression (transcriptomic) profiles presents a reasonable solution because transcriptomics stands a few steps closer to tumor phenotype than the genome analysis. Several recent studies pioneered using transcriptomics for practical oncology and showed truly encouraging clinical results. The possibility of directly measuring of expression levels of molecular drugs' targets and profiling activation of the relevant molecular pathways enables personalized prioritizing for all types of molecular-targeted therapies. RNA sequencing is the most robust tool for the high throughput quantitative transcriptomics. Its use, potentials, and limitations for the clinical oncology will be reviewed here along with the technical aspects such as optimal types of biosamples, RNA sequencing profile normalization, quality controls and several levels of data analysis.

Atlas of RNA sequencing profiles for normal human tissues

Comprehensive analysis of molecular pathology requires a collection of reference samples representing normal tissues from healthy donors. For the available limited collections of normal tissues from postmortal donors, there is a problem of data incompatibility, as different datasets generated using different experimental platforms often cannot be merged in a single panel. Here, we constructed and deposited the gene expression database of normal human tissues based on uniformly screened original sequencing data. In total, 142 solid tissue samples representing 20 organs were taken from post-mortal human healthy donors of different age killed in road accidents no later than 36 hours after death. Blood samples were taken from 17 healthy volunteers. We then compared them with the 758 transcriptomic profiles taken from the other databases. We found that overall 463 biosamples showed tissue-specific rather than platform- or database-specific clustering and could be aggregated in a single database termed Oncobox Atlas of Normal Tissue Expression (ANTE). Our data will be useful to all those working with the analysis of human gene expression.

Shambhala: a platform-agnostic data harmonizer for gene expression data

Background

Harmonization techniques make different gene expression profiles and their sets compatible and ready for comparisons. Here we present a new bioinformatic tool termed Shambhala for harmonization of multiple human gene expression datasets obtained using different experimental methods and platforms of microarray hybridization and RNA sequencing.

Results

Unlike previously published methods enabling good quality data harmonization for only two datasets, Shambhala allows conversion of multiple datasets into the universal form suitable for further comparisons. Shambhala harmonization is based on the calibration of gene expression profiles using the auxiliary standardization dataset. Each profile is transformed to make it similar to the output of microarray hybridization platform Affymetrix Human Gene. This platform was chosen because it has the biggest number of human gene expression profiles deposited in public databases. We evaluated Shambhala ability to retain biologically important features after harmonization. The same four biological samples taken in multiple replicates were profiled independently using three and four different experimental platforms, respectively, then Shambhala-harmonized and investigated by hierarchical clustering.

Conclusion

Our results showed that unlike other frequently used methods: quantile normalization and DESeq/DESeq2 normalization, Shambhala harmonization was the only method supporting sample-specific and platform-independent biologically meaningful clustering for the data obtained from multiple experimental platforms.

Electronic supplementary material

The online version of this article (10.1186/s12859-019-2641-8) contains supplementary material, which is available to authorized users.

Phospholipase D isoforms differentially regulate leukocyte responses to acute lung injury

Phospholipase D (PLD) plays important roles in cellular responses to tissue injury that are critical to acute inflammatory diseases, such as the acute respiratory distress syndrome (ARDS). We investigated the expression of PLD isoforms and related phospholipid phosphatases in patients with ARDS, and their roles in a murine model of self-limited acute lung injury (ALI). Gene expression microarray analysis on whole blood obtained from patients that met clinical criteria for ARDS and clinically matched controls (non-ARDS) demonstrated that PLD1 gene expression was increased in patients with ARDS relative to non-ARDS and correlated with survival. In contrast, PLD2 expression was associated with mortality. In a murine model of self-resolving ALI, lung Pld1 expression increased and Pld2 expression decreased 24 h after intrabronchial acid. Total lung PLD activity was increased 24 h after injury. Pld1^-/- mice demonstrated impaired alveolar barrier function and increased tissue injury relative to WT and Pld2^-/- , whereas Pld2^-/- mice demonstrated increased recruitment of neutrophils and macrophages, and decreased tissue injury. Isoform-specific PLD inhibitors mirrored the results with isoform-specific Pld-KO mice. PLD1 gene expression knockdown in human leukocytes was associated with decreased phagocytosis by neutrophils, whereas reactive oxygen species production and phagocytosis decreased in M2-macrophages. PLD2 gene expression knockdown increased neutrophil and M2-macrophage transmigration, and increased M2-macrophage phagocytosis. These results uncovered selective regulation of PLD isoforms after ALI, and opposing effects of selective isoform knockdown on host responses and tissue injury. These findings support therapeutic strategies targeting specific PLD isoforms for the treatment of ARDS.