White blood cell differentials enrich whole blood expression data in the context of acute cardiac allograft rejection

Progress and challenge for computational quantification of tissue immune cells

Tissue immune cells have long been recognized as important regulators for the maintenance of balance in the body system. Quantification of the abundance of different immune cells will provide enhanced understanding of the correlation between immune cells and normal or abnormal situations. Currently, computational methods to predict tissue immune cell compositions from bulk transcriptomes have been largely developed. Therefore, summarizing the advantages and disadvantages is appropriate. In addition, an examination of the challenges and possible solutions for these computational models will assist the development of this field. The common hypothesis of these models is that the expression of signature genes for immune cell types might represent the proportion of immune cells that contribute to the tissue transcriptome. In general, we grouped all reported tools into three groups, including reference-free, reference-based scoring and reference-based deconvolution methods. In this review, a summary of all the currently reported computational immune cell quantification tools and their applications, limitations, and perspectives are presented. Furthermore, some critical problems are found that have limited the performance and application of these models, including inadequate immune cell type, the collinearity problem, the impact of the tissue environment on the immune cell expression level, and the deficiency of standard datasets for model validation. To address these issues, tissue specific training datasets that include all known immune cells, a hierarchical computational framework, and benchmark datasets including both tissue expression profiles and the abundances of all the immune cells are proposed to further promote the development of this field.

Identification of Candidate Biomarkers for Transplant Rejection from Transcriptome Data: A Systematic Review

BACKGROUND

Traditional methods for rejection control in transplanted patients are considered invasive, risky, and prone to sampling errors. Using molecular biomarkers as an alternative protocol to biopsies, for monitoring rejection may help to mitigate some of these problems, increasing the survival rates and well-being of patients. Recent advances in omics technologies provide an opportunity for screening new molecular biomarkers to identify those with clinical utility.

OBJECTIVE

This systematic literature review (SLR) aimed to summarize existing evidence derived from large-scale expression profiling regarding differentially expressed mRNA and miRNA in graft rejection, highlighting potential molecular biomarkers in transplantation.

METHODS

The study was conducted following PRISMA methodology and the BiSLR guide for performing SLR in bioinformatics. PubMed, ScienceDirect, and EMBASE were searched for publications from January 2001 to January 2018, and studies (i) aiming at the identification of transplant rejection biomarkers, (ii) including human subjects, and (iii) applying methodologies for differential expression analysis from large-scale expression profiling were considered eligible. Differential expression patterns reported for genes and miRNAs in rejection were summarized from both cross-organ and organ-specific perspectives, and pathways enrichment analysis was performed for candidate biomarkers to interrogate their functional role in transplant rejection.

RESULTS

A total of 821 references were collected, resulting in 604 studies after removal of duplicates. After application of inclusion and exclusion criteria, 33 studies were included in our analysis. Among the 1517 genes and 174 miRNAs identifed, CXCL9, CXCL10, STAT1, hsa-miR-142-3p, and hsa-miR-155 appeared to be particularly promising as biomarkers in transplantation, with an increased expression associated with transplant rejection in multiple organs. In addition, hsa-miR-28-5p was consistently decreased in samples taken from rejected organs.

CONCLUSION

Despite the need for further research to fill existing knowledge gaps, transcriptomic technologies have a relevant role in the discovery of accurate biomarkers for transplant rejection diagnostics. Studies have reported consistent evidence of differential expression associated with transplant rejection, although issues such as experimental heterogeneity hinder a more systematic characterization of observed molecular changes. Special attention has been giving to large-scale mRNA expression profiling in rejection, whereas there is still room for improvements in the characterization of miRnome in this condition.

PROSPERO REGISTRATION NUMBER

CRD42018083321.

HEARTBiT: A Transcriptomic Signature for Excluding Acute Cellular Rejection in Adult Heart Allograft Patients

BACKGROUND

Nine mRNA transcripts associated with acute cellular rejection (ACR) in previous microarray studies were ported to the clinically amenable NanoString nCounter platform. Here we report the diagnostic performance of the resulting blood test to exclude ACR in heart allograft recipients: HEARTBiT.

METHODS

Blood samples for transcriptomic profiling were collected during routine post-transplantation monitoring in 8 Canadian transplant centres participating in the Biomarkers in Transplantation initiative, a large (n = 1622) prospective observational study conducted between 2009 and 2014. All adult cardiac transplant patients were invited to participate (median age = 56 [17 to 71]). The reference standard for rejection status was histopathology grading of tissue from endomyocardial biopsy (EMB). All locally graded ISHLT ≥ 2R rejection samples were selected for analysis (n = 36). ISHLT 1R (n = 38) and 0R (n = 86) samples were randomly selected to create a cohort approximately matched for site, age, sex, and days post-transplantation, with a focus on early time points (median days post-transplant = 42 [7 to 506]).

RESULTS

ISHLT ≥ 2R rejection was confirmed by EMB in 18 and excluded in 92 samples in the test set. HEARTBiT achieved 47% specificity (95% confidence interval [CI], 36%-57%) given ≥ 90% sensitivity, with a corresponding area under the receiver operating characteristic curve of 0.69 (95% CI, 0.56-0.81).

CONCLUSIONS

HEARTBiT's diagnostic performance compares favourably to the only currently approved minimally invasive diagnostic test to rule out ACR, AlloMap (CareDx, Brisbane, CA) and may be used to inform care decisions in the first 2 months post-transplantation, when AlloMap is not approved, and most ACR episodes occur.

Enumerateblood - an R package to estimate the cellular composition of whole blood from Affymetrix Gene ST gene expression profiles

Background

Measuring genome-wide changes in transcript abundance in circulating peripheral whole blood is a useful way to study disease pathobiology and may help elucidate the molecular mechanisms of disease, or discovery of useful disease biomarkers. The sensitivity and interpretability of analyses carried out in this complex tissue, however, are significantly affected by its dynamic cellular heterogeneity. It is therefore desirable to quantify this heterogeneity, either to account for it or to better model interactions that may be present between the abundance of certain transcripts, specific cell types and the indication under study. Accurate enumeration of the many component cell types that make up peripheral whole blood can further complicate the sample collection process, however, and result in additional costs. Many approaches have been developed to infer the composition of a sample from high-dimensional transcriptomic and, more recently, epigenetic data. These approaches rely on the availability of isolated expression profiles for the cell types to be enumerated. These profiles are platform-specific, suitable datasets are rare, and generating them is expensive. No such dataset exists on the Affymetrix Gene ST platform.

Results

We present ‘Enumerateblood’, a freely-available and open source R package that exposes a multi-response Gaussian model capable of accurately predicting the composition of peripheral whole blood samples from Affymetrix Gene ST expression profiles, outperforming other current methods when applied to Gene ST data.

Conclusions

‘Enumerateblood’ significantly improves our ability to study disease pathobiology from whole blood gene expression assayed on the popular Affymetrix Gene ST platform by allowing a more complete study of the various components of this complex tissue without the need for additional data collection. Future use of the model may allow for novel insights to be generated from the ~400 Affymetrix Gene ST blood gene expression datasets currently available on the Gene Expression Omnibus (GEO) website.

Electronic supplementary material

The online version of this article (doi:10.1186/s12864-016-3460-1) contains supplementary material, which is available to authorized users.

Multicenter Analysis of Immune Biomarkers and Heart Transplant Outcomes: Results of the Clinical Trials in Organ Transplantation-05 Study

Identification of biomarkers that assess posttransplant risk is needed to improve long-term outcomes following heart transplantation. The Clinical Trials in Organ Transplantation (CTOT)-05 protocol was an observational, multicenter, cohort study of 200 heart transplant recipients followed for the first posttransplant year. The primary endpoint was a composite of death, graft loss/retransplantation, biopsy-proven acute rejection (BPAR), and cardiac allograft vasculopathy (CAV) as defined by intravascular ultrasound (IVUS). We serially measured anti-HLA- and auto-antibodies, angiogenic proteins, peripheral blood allo-reactivity, and peripheral blood gene expression patterns. We correlated assay results and clinical characteristics with the composite endpoint and its components. The composite endpoint was associated with older donor allografts (p < 0.03) and with recipient anti-HLA antibody (p < 0.04). Recipient CMV-negativity (regardless of donor status) was associated with BPAR (p < 0.001), and increases in plasma vascular endothelial growth factor-C (OR 20; 95%CI:1.9-218) combined with decreases in endothelin-1 (OR 0.14; 95%CI:0.02-0.97) associated with CAV. The remaining biomarkers showed no relationships with the study endpoints. While suboptimal endpoint definitions and lower than anticipated event rates were identified as potential study limitations, the results of this multicenter study do not yet support routine use of the selected assays as noninvasive approaches to detect BPAR and/or CAV following heart transplantation.

Two-stage, in silico deconvolution of the lymphocyte compartment of the peripheral whole blood transcriptome in the context of acute kidney allograft rejection

Acute rejection is a major complication of solid organ transplantation that prevents the long-term assimilation of the allograft. Various populations of lymphocytes are principal mediators of this process, infiltrating graft tissues and driving cell-mediated cytotoxicity. Understanding the lymphocyte-specific biology associated with rejection is therefore critical. Measuring genome-wide changes in transcript abundance in peripheral whole blood cells can deliver a comprehensive view of the status of the immune system. The heterogeneous nature of the tissue significantly affects the sensitivity and interpretability of traditional analyses, however. Experimental separation of cell types is an obvious solution, but is often impractical and, more worrying, may affect expression, leading to spurious results. Statistical deconvolution of the cell type-specific signal is an attractive alternative, but existing approaches still present some challenges, particularly in a clinical research setting. Obtaining time-matched sample composition to biologically interesting, phenotypically homogeneous cell sub-populations is costly and adds significant complexity to study design. We used a two-stage, in silico deconvolution approach that first predicts sample composition to biologically meaningful and homogeneous leukocyte sub-populations, and then performs cell type-specific differential expression analysis in these same sub-populations, from peripheral whole blood expression data. We applied this approach to a peripheral whole blood expression study of kidney allograft rejection. The patterns of differential composition uncovered are consistent with previous studies carried out using flow cytometry and provide a relevant biological context when interpreting cell type-specific differential expression results. We identified cell type-specific differential expression in a variety of leukocyte sub-populations at the time of rejection. The tissue-specificity of these differentially expressed probe-set lists is consistent with the originating tissue and their functional enrichment consistent with allograft rejection. Finally, we demonstrate that the strategy described here can be used to derive useful hypotheses by validating a cell type-specific ratio in an independent cohort using the nanoString nCounter assay.

Longitudinal Analysis of Whole Blood Transcriptomes to Explore Molecular Signatures Associated with Acute Renal Allograft Rejection

In this study, we explored a time course of peripheral whole blood transcriptomes from kidney transplantation patients who either experienced an acute rejection episode or did not in order to better delineate the immunological and biological processes measureable in blood leukocytes that are associated with acute renal allograft rejection. Using microarrays, we generated gene expression data from 24 acute rejectors and 24 nonrejectors. We filtered the data to obtain the most unambiguous and robustly expressing probe sets and selected a subset of patients with the clearest phenotype. We then performed a data-driven exploratory analysis using data reduction and differential gene expression analysis tools in order to reveal gene expression signatures associated with acute allograft rejection. Using a template-matching algorithm, we then expanded our analysis to include time course data, identifying genes whose expression is modulated leading up to acute rejection. We have identified molecular phenotypes associated with acute renal allograft rejection, including a significantly upregulated signature of neutrophil activation and accumulation following transplant surgery that is common to both acute rejectors and nonrejectors. Our analysis shows that this expression signature appears to stabilize over time in nonrejectors but persists in patients who go on to reject the transplanted organ. In addition, we describe an expression signature characteristic of lymphocyte activity and proliferation. This lymphocyte signature is significantly downregulated in both acute rejectors and nonrejectors following surgery; however, patients who go on to reject the organ show a persistent downregulation of this signature relative to the neutrophil signature.

Longitudinal analysis of whole blood transcriptomes to explore molecular signatures associated with acute renal allograft rejection

In this study, we explored a time course of peripheral whole blood transcriptomes from kidney transplantation patients who either experienced an acute rejection episode or did not in order to better delineate the immunological and biological processes measureable in blood leukocytes that are associated with acute renal allograft rejection. Using microarrays, we generated gene expression data from 24 acute rejectors and 24 nonrejectors. We filtered the data to obtain the most unambiguous and robustly expressing probe sets and selected a subset of patients with the clearest phenotype. We then performed a data-driven exploratory analysis using data reduction and differential gene expression analysis tools in order to reveal gene expression signatures associated with acute allograft rejection. Using a template-matching algorithm, we then expanded our analysis to include time course data, identifying genes whose expression is modulated leading up to acute rejection. We have identified molecular phenotypes associated with acute renal allograft rejection, including a significantly upregulated signature of neutrophil activation and accumulation following transplant surgery that is common to both acute rejectors and nonrejectors. Our analysis shows that this expression signature appears to stabilize over time in nonrejectors but persists in patients who go on to reject the transplanted organ. In addition, we describe an expression signature characteristic of lymphocyte activity and proliferation. This lymphocyte signature is significantly downregulated in both acute rejectors and nonrejectors following surgery; however, patients who go on to reject the organ show a persistent downregulation of this signature relative to the neutrophil signature.

Computational deconvolution: extracting cell type-specific information from heterogeneous samples

The quanta unit of the immune system is the cell, yet analyzed samples are often heterogeneous with respect to cell subsets which can mislead result interpretation. Experimentally, researchers face a difficult choice whether to profile heterogeneous samples with the ensuing confounding effects, or a priori focus on a few cell subsets of interest, potentially limiting new discoveries. An attractive alternative solution is to extract cell subset-specific information directly from heterogeneous samples via computational deconvolution techniques, thereby capturing both cell-centered and whole system level context. Such approaches are capable of unraveling novel biology, undetectable otherwise. Here we review the present state of available deconvolution techniques, their advantages and limitations, with a focus on blood expression data and immunological studies in general.

Decreased miR-192 expression in peripheral blood of asthmatic individuals undergoing an allergen inhalation challenge

Background

MicroRNAs are small non-coding RNAs that regulate gene expression at the post-transcriptional level. While they have been implicated in various diseases, the profile changes in allergen inhalation challenge are not clarified in human. We aimed to evaluate changes in the microRNA profiles in the peripheral blood of asthmatic subjects undergoing allergen inhalation challenge.

Results

Seven mild asthmatic subjects participated in the allergen inhalation challenge. In addition, four healthy control subjects (HCs) were recruited. MicroRNA profiles in peripheral blood samples (pre-challenge and 2 hours post-challenge) were measured by the NanoString nCounter assay to determine changes in miRNA levels as these asthmatic subjects underwent an allergen inhalation challenge. One common miRNA, miR-192, was significantly expressed in both comparisons; HCs vs. pre-challenge and pre- vs. post-challenge, showing that miR-192 was significantly under-expressed in asthmatics compared to HCs and decreased in post-challenge at an FDR of 1%. Cell-specific statistical deconvolution attributed miR-192 expression in whole blood to PBMCs. MiR-192 was technically validated using real-time reverse transcription-quantitative polymerase chain reaction (RT-qPCR) showing that the level in asthmatics (pre-challenge) was significantly lower than HCs and that post-challenge was significantly lower than pre-challenge. The normalized relative miR-192 expression quantified using RT-qPCR specific to PBMCs was also validated. Ontology enrichment and canonical pathway analyses for target genes suggested several functions and pathways involved in immune response and cell cycle.

Conclusions

The miRNA profile in peripheral blood was altered after allergen inhalation challenge. Change in miR-192 levels may be implicated in asthma mechanisms. These results suggest that allergen inhalation challenge is a suitable method to characterize peripheral miRNA profiles and potentially elucidate the mechanism of human asthma.