Deconvoluting post-transplant immunity: cell subset-specific mapping reveals pathways for activation and expansion of memory T, monocytes and B cells

The Magnitude and Functionality of SARS-CoV-2 Reactive Cellular and Humoral Immunity in Transplant Population Is Similar to the General Population Despite Immunosuppression

BACKGROUND

The ability of transplant (Tx) patients to generate a protective antiviral response under immunosuppression is pivotal in COVID-19 infection. However, analysis of immunity against SARS-CoV-2 is currently lacking.

METHODS

Here, we analyzed T cell immunity directed against SARS-CoV-2 spike-, membrane-, and nucleocapsid-protein by flow cytometry and spike-specific neutralizing antibodies in 10 Tx in comparison to 26 nonimmunosuppressed (non-Tx) COVID-19 patients.

RESULTS

Tx patients (7 renal, 1 lung, and 2 combined pancreas-kidney Txs) were recruited in this study during the acute phase of COVID-19 with a median time after SARS-CoV-2-positivity of 3 and 4 d for non-Tx and Tx patients, respectively. Despite immunosuppression, we detected antiviral CD4+ T cell-response in 90% of Tx patients. SARS-CoV-2-reactive CD4+ T cells produced multiple proinflammatory cytokines, indicating their potential protective capacity. Neutralizing antibody titers did not differ between groups. SARS-CoV-2-reactive CD8+ T cells targeting membrane- and spike-protein were lower in Tx patients, albeit without statistical significance. However, frequencies of anti-nucleocapsid-protein-reactive, and anti-SARS-CoV-2 polyfunctional CD8+ T cells, were similar between patient cohorts. Tx patients showed features of a prematurely aged adaptive immune system, but equal frequencies of SARS-CoV-2-reactive memory T cells.

CONCLUSIONS

In conclusion, a polyfunctional T cell immunity directed against SARS-CoV-2 proteins as well as neutralizing antibodies can be generated in Tx patients despite immunosuppression. In comparison to nonimmunosuppressed patients, no differences in humoral and cellular antiviral-immunity were found. Our data presenting the ability to generate SARS-CoV-2-specific immunity in immunosuppressed patients have implications for the handling of SARS-CoV-2-infected Tx patients and raise hopes for effective vaccination in this cohort.

Development of an optimized method for processing peripheral blood mononuclear cells for 1H-nuclear magnetic resonance-based metabolomic profiling

Human peripheral blood mononuclear cells (PBMCs) are part of the innate and adaptive immune system, and form a critical interface between both systems. Studying the metabolic profile of PBMC could provide valuable information about the response to pathogens, toxins or cancer, the detection of drug toxicity, in drug discovery and cell replacement therapy. The primary purpose of this study was to develop an improved processing method for PBMCs metabolomic profiling with nuclear magnetic resonance (NMR) spectroscopy. To this end, an experimental design was applied to develop an alternative method to process PBMCs at low concentrations. The design included the isolation of PBMCs from the whole blood of four different volunteers, of whom 27 cell samples were processed by two different techniques for quenching and extraction of metabolites: a traditional one using organic solvents and an alternative one employing a high-intensity ultrasound probe, the latter with a variation that includes the use of deproteinizing filters. Finally, all the samples were characterized by 1H-NMR and the metabolomic profiles were compared by the method. As a result, two new methods for PBMCs processing, called Ultrasound Method (UM) and Ultrasound and Ultrafiltration Method (UUM), are described and compared to the Folch Method (FM), which is the standard protocol for extracting metabolites from cell samples. We found that UM and UUM were superior to FM in terms of sensitivity, processing time, spectrum quality, amount of identifiable, quantifiable metabolites and reproducibility.

An age-independent gene signature for monitoring acute rejection in kidney transplantation

Acute rejection (AR) remains a significant problem that negatively impacts long-term renal allograft survival. Numerous therapies are used to prevent AR that differ by center and recipient age. This variability confounds diagnostic methods. Methods: To develop an age-independent gene signature for AR effective across a broad array of immunosuppressive regimens, we compiled kidney transplant biopsy (n=1091) and peripheral blood (n=392) gene expression profiles from 12 independent public datasets. After removing genes differentially expressed in pediatric and adult patients, we compared gene expression profiles from biopsy and peripheral blood samples of patients with AR to those who were stable (STA), using Mann-Whitney U Tests with validation in independent testing datasets. We confirmed this signature in pediatric and adult patients (42 AR and 47 STA) from our institutional biorepository. Results: We identified a novel age-independent gene network that identified AR from both kidney and blood samples. We developed a 90-probe set signature targeting 76 genes that differentiated AR from STA and found an 8 gene subset (DIP2C, ENOSF1, FBXO21, KCTD6, PDXDC1, REXO2, HLA-E, and RAB31) that was associated with AR. Conclusion: We used publicly available datasets to create a gene signature of AR that identified AR irrespective of immunosuppression regimen or recipient age. This study highlights a novel model to screen and validate biomarkers across multiple treatment regimens.

Synstable Fusion: A Network-Based Algorithm for Estimating Driver Genes in Fusion Structures

Gene fusion structure is a class of common somatic mutational events in cancer genomes, which are often formed by chromosomal mutations. Identifying the driver gene(s) in a fusion structure is important for many downstream analyses and it contributes to clinical practices. Existing computational approaches have prioritized the importance of oncogenes by incorporating prior knowledge from gene networks. However, different methods sometimes suffer different weaknesses when handling gene fusion data due to multiple issues such as fusion gene representation, network integration, and the effectiveness of the evaluation algorithms. In this paper, Synstable Fusion (SYN), an algorithm for computationally evaluating the fusion genes, is proposed. This algorithm uses network-based strategy by incorporating gene networks as prior information, but estimates the driver genes according to the destructiveness hypothesis. This hypothesis balances the two popular evaluation strategies in the existing studies, thereby providing more comprehensive results. A machine learning framework is introduced to integrate multiple networks and further solve the conflicting results from different networks. In addition, a synchronous stability model is established to reduce the computational complexity of the evaluation algorithm. To evaluate the proposed algorithm, we conduct a series of experiments on both artificial and real datasets. The results demonstrate that the proposed algorithm performs well on different configurations and is robust when altering the internal parameter settings.

Identifying low-grade cellular rejection after heart transplantation in children by using gene expression profiling

Endomyocardial biopsy (EMB) remains the gold standard for detecting rejection after heart transplantation but is costly and invasive. This study aims to distinguish no rejection (0R) from low-grade rejection (1R/2R) after heart transplantation in children by using global gene expression profiling in blood. A total of 106 blood samples with corresponding EMB from 18 children who underwent heart transplantation from 2011 to 2014 were analyzed (18 baseline/pretransplantation samples, 88 EMB samples). Corresponding rejection grades for each blood sample were 0R in 39% (34/88), 1R in 51% (45/88), and 2R in 10% (9/88). mRNA from each sample was sequenced. Differential expression analysis was performed at the gene level. A k-nearest neighbor (kNN) analysis was applied to the most differentially expressed (DE) genes to identify rejection after transplantation. Mean age at transplantation was 10.0 ± 5.4 yr. Expression of B cell and T cell receptor sequences was used to measure the effect of posttransplantation immunosuppression. Follow-up samples had lower levels of immunoglobulin gene families compared with pretransplantation ( P < 3E-5) (lower numbers of activated B cells). T cell receptor alpha and beta gene families had decreased expression in 0R samples compared with pretransplantation ( P < 4E-5) but recovered to near baseline levels in 1R/2R samples. kNN using the most DE gene (MKS1) and k = 9 nearest neighbors correctly identified 83% (73/88) of 1R/2R compared with 0R by leave-one-out cross validation. Using a genomic approach we can distinguish low-grade cellular allograft rejection (1R/2R) from no rejection (0R) after heart transplantation in children despite a wide age range.

The Effect of Tacrolimus and Mycophenolic Acid on CD14+ Monocyte Activation and Function

Monocytes and macrophages play key roles in many disease states, including cellular and humoral rejection after solid organ transplantation (SOT). To suppress alloimmunity after SOT, immunosuppressive drug therapy is necessary. However, little is known about the effects of the immunosuppressive drugs tacrolimus and mycophenolic acid (MPA) on monocyte activation and function. Here, the effect of these immunosuppressants on monocytes was investigated by measuring phosphorylation of three intracellular signaling proteins which all have a major role in monocyte function: p38MAPK, ERK and Akt. In addition, biological functions downstream of these signaling pathways were studied, including cytokine production, phagocytosis and differentiation into macrophages. To this end, blood samples from healthy volunteers were spiked with diverse concentrations of tacrolimus and MPA. Tacrolimus (200 ng/ml) inhibited phosphorylation of p38MAPK by 30% (mean) in CD14+ monocytes which was significantly less than in activated CD3+ T cells (max 60%; p < 0.05). This immunosuppressive agent also partly inhibited p-AKT (14%). MPA, at a therapeutic concentration showed the strongest effect on p-AKT (27% inhibition). p-ERK was inhibited with a maximum of 15% after spiking with either tacrolimus or MPA. The production of IL-1β and phagocytosis by monocytes were not affected by tacrolimus concentrations, whereas MPA did inhibit IL-1β production by 50%. Monocyte/macrophage polarization was shifted to an M2-like phenotype in the presence of tacrolimus, while MPA increased the expression of M2 surface markers, including CD163 and CD200R, on M1 macrophages. These results show that tacrolimus and MPA do not strongly affect monocyte function, apart from a change in macrophage polarization, to a clinically relevant degree.

Gene expression analysis of TIL rich HPV-driven head and neck tumors reveals a distinct B-cell signature when compared to HPV independent tumors

Human papilloma virus (HPV)-associated head and neck squamous cell carcinoma (HNSCC) has a better prognosis than it's HPV negative (HPV(-)) counterpart. This may be due to the higher numbers of tumor-infiltrating lymphocytes (TILs) in HPV positive (HPV(+)) tumors. RNA-Sequencing (RNA-Seq) was used to evaluate whether the differences in clinical behaviour simply reflect a numerical difference in TILs or whether there is a fundamental behavioural difference between TILs in these two settings. Thirty-nine HNSCC tumors were scored for TIL density by immunohistochemistry. After the removal of 16 TILlow tumors, RNA-Seq analysis was performed on 23 TILhigh/med tumors (HPV(+) n=10 and HPV(-) n=13). Using EdgeR, differentially expressed genes (DEG) were identified. Immune subset analysis was performed using Functional Analysis of Individual RNA-Seq/ Microarray Expression (FAIME) and immune gene RNA transcript count analysis. In total, 1,634 DEGs were identified, with a dominant immune signature observed in HPV(+) tumors. After normalizing the expression profiles to account for differences in B- and T-cell number, 437 significantly DEGs remained. A B-cell associated signature distinguished HPV(+) from HPV(-) tumors, and included the DEGs CD200, GGA2, ADAM28, STAG3, SPIB, VCAM1, BCL2 and ICOSLG; the immune signal relative to T-cells was qualitatively similar between TILs of both tumor cohorts. Our findings were validated and confirmed in two independent cohorts using TCGA data and tumor-infiltrating B-cells from additional HPV(+) HNSCC patients. A B-cell associated signal segregated tumors relative to HPV status. Our data suggests that the role of B-cells in the adaptive immune response to HPV(+) HNSCC requires re-assessment.

Profiling networks of distinct immune-cells in tumors

Background

It is now clearly evident that cancer outcome and response to therapy is guided by diverse immune-cell activity in tumors. Presently, a key challenge is to comprehensively identify networks of distinct immune-cell signatures present in complex tissue, at higher-resolution and at various stages of differentiation, activation or function. This is particularly so for closely related immune-cells with diminutive, yet critical, differences.

Results

To predict networks of infiltrated distinct immune-cell phenotypes at higher resolution, we explored an integrated knowledge-based approach to select immune-cell signature genes integrating not only expression enrichment across immune-cells, but also an automatic capture of relevant immune-cell signature genes from the literature. This knowledge-based approach was integrated with resources of immune-cell specific protein networks, to define signature genes of distinct immune-cell phenotypes. We demonstrate the utility of this approach by profiling signatures of distinct immune-cells, and networks of immune-cells, from metastatic melanoma patients who had undergone chemotherapy. The resultant bioinformatics strategy complements immunohistochemistry from these tumors, and predicts both tumor-killing and immunosuppressive networks of distinct immune-cells in responders and non-responders, respectively. The approach is also shown to capture differences in the immune-cell networks of BRAF versus NRAS mutated metastatic melanomas, and the dynamic changes in resistance to targeted kinase inhibitors in MAPK signalling.

Conclusions

This integrative bioinformatics approach demonstrates that capturing the protein network signatures and ratios of distinct immune-cell in the tumor microenvironment maybe an important factor in predicting response to therapy. This may serve as a computational strategy to define network signatures of distinct immune-cells to guide immuno-pathological discovery.

Electronic supplementary material

The online version of this article (doi:10.1186/s12859-016-1141-3) contains supplementary material, which is available to authorized users.

A compendium of monocyte transcriptome datasets to foster biomedical knowledge discovery

Systems-scale profiling approaches have become widely used in translational research settings. The resulting accumulation of large-scale datasets in public repositories represents a critical opportunity to promote insight and foster knowledge discovery. However, resources that can serve as an interface between biomedical researchers and such vast and heterogeneous dataset collections are needed in order to fulfill this potential. Recently, we have developed an interactive data browsing and visualization web application, the Gene Expression Browser (GXB). This tool can be used to overlay deep molecular phenotyping data with rich contextual information about analytes, samples and studies along with ancillary clinical or immunological profiling data. In this note, we describe a curated compendium of 93 public datasets generated in the context of human monocyte immunological studies, representing a total of 4,516 transcriptome profiles. Datasets were uploaded to an instance of GXB along with study description and sample annotations. Study samples were arranged in different groups. Ranked gene lists were generated based on relevant group comparisons. This resource is publicly available online at http://monocyte.gxbsidra.org/dm3/landing.gsp.

A curated compendium of monocyte transcriptome datasets of relevance to human monocyte immunobiology research

Systems-scale profiling approaches have become widely used in translational research settings. The resulting accumulation of large-scale datasets in public repositories represents a critical opportunity to promote insight and foster knowledge discovery. However, resources that can serve as an interface between biomedical researchers and such vast and heterogeneous dataset collections are needed in order to fulfill this potential. Recently, we have developed an interactive data browsing and visualization web application, the Gene Expression Browser (GXB). This tool can be used to overlay deep molecular phenotyping data with rich contextual information about analytes, samples and studies along with ancillary clinical or immunological profiling data. In this note, we describe a curated compendium of 93 public datasets generated in the context of human monocyte immunological studies, representing a total of 4,516 transcriptome profiles. Datasets were uploaded to an instance of GXB along with study description and sample annotations. Study samples were arranged in different groups. Ranked gene lists were generated based on relevant group comparisons. This resource is publicly available online at http://monocyte.gxbsidra.org/dm3/landing.gsp.

A survey of disease connections for CD4+ T cell master genes and their directly linked genes

Genome-wide association studies and other genetic analyses have identified a large number of genes and variants implicating a variety of disease etiological mechanisms. It is imperative for the study of human diseases to put these genetic findings into a coherent functional context. Here we use system biology tools to examine disease connections of five master genes for CD4+ T cell subtypes (TBX21, GATA3, RORC, BCL6, and FOXP3). We compiled a list of genes functionally interacting (protein-protein interaction, or by acting in the same pathway) with the master genes, then we surveyed the disease connections, either by experimental evidence or by genetic association. Embryonic lethal genes (also known as essential genes) are over-represented in master genes and their interacting genes (55% versus 40% in other genes). Transcription factors are significantly enriched among genes interacting with the master genes (63% versus 10% in other genes). Predicted haploinsufficiency is a feature of most these genes. Disease-connected genes are enriched in this list of genes: 42% of these genes have a disease connection according to Online Mendelian Inheritance in Man (OMIM) (versus 23% in other genes), and 74% are associated with some diseases or phenotype in a Genome Wide Association Study (GWAS) (versus 43% in other genes). Seemingly, not all of the diseases connected to genes surveyed were immune related, which may indicate pleiotropic functions of the master regulator genes and associated genes.

Immune cell subsets and their gene expression profiles from human PBMC isolated by Vacutainer Cell Preparation Tube (CPT™) and standard density gradient

Background

High quality genetic material is an essential pre-requisite when analyzing gene expression using microarray technology. Peripheral blood mononuclear cells (PBMC) are frequently used for genomic analyses, but several factors can affect the integrity of nucleic acids prior to their extraction, including the methods of PBMC collection and isolation. Due to the lack of the relevant data published, we compared the Ficoll-Paque density gradient centrifugation and BD Vacutainer cell preparation tube (CPT) protocols to determine if either method offered a distinct advantage in preparation of PBMC-derived immune cell subsets for their use in gene expression analysis. We evaluated the yield and purity of immune cell subpopulations isolated from PBMC derived by both methods, the quantity and quality of extracted nucleic acids, and compared gene expression in PBMC and individual immune cell types from Ficoll and CPT isolation protocols using Affymetrix microarrays.

Results

The mean yield and viability of fresh PBMC acquired by the CPT method (1.16 × 10⁶ cells/ml, 93.3 %) were compatible to those obtained with Ficoll (1.34 × 10⁶ cells/ml, 97.2 %). No differences in the mean purity, recovery, and viability of CD19+ (B cells), CD8+ (cytotoxic T cells), CD4+ (helper T cell) and CD14+ (monocytes) positively selected from CPT- or Ficoll-isolated PBMC were found. Similar quantities of high quality RNA and DNA were extracted from PBMC and immune cells obtained by both methods. Finally, the PBMC isolation methods tested did not impact subsequent recovery and purity of individual immune cell subsets and, importantly, their gene expression profiles.

Conclusions

Our findings demonstrate that the CPT and Ficoll PBMC isolation protocols do not differ in their ability to purify high quality immune cell subpopulations. Since there was no difference in the gene expression profiles between immune cells obtained by these two methods, the Ficoll isolation can be substituted by the CPT protocol without conceding phenotypic changes of immune cells and compromising the gene expression studies. Given that the CPT protocol is less elaborate, minimizes cells’ handling and processing time, this method offers a significant operating advantage, especially in large-scale clinical studies aiming at dissecting gene expression in PBMC and PBMC-derived immune cell subpopulations.

Electronic supplementary material

The online version of this article (doi:10.1186/s12865-015-0113-0) contains supplementary material, which is available to authorized users.

Toxicity mechanisms identification via gene set enrichment analysis of time-series toxicogenomics data: impact of time and concentration

The advance in high-throughput "toxicogenomics" technologies, which allows for concurrent monitoring of cellular responses globally upon exposure to chemical toxicants, presents promises for next-generation toxicity assessment. It is recognized that cellular responses to toxicants have a highly dynamic nature, and exhibit both temporal complexity and dose-response shifts. Most current gene enrichment or pathway analysis lack the recognition of the inherent correlation within time series data, and may potentially miss important pathways or yield biased and inconsistent results that ignore dynamic patterns and time-sensitivity. In this study, we investigated the application of two score metrics for GSEA (gene set enrichment analysis) to rank the genes that consider the temporal gene expression profile. One applies a novel time series CPCA (common principal components analysis) to generate scores for genes based on their contributions to the common temporal variation among treatments for a given chemical at different concentrations. Another one employs an integrated altered gene expression quantifier-TELI (transcriptional effect level index) that integrates altered gene expression magnitude over the exposure time. By comparing the GSEA results using two different ranking metrics for examining the dynamic responses of reporter cells treated with various dose levels of three model toxicants, mitomycin C, hydrogen peroxide, and lead nitrate, the analysis identified and revealed different toxicity mechanisms of these chemicals that exhibit chemical-specific, as well as time-aware and dose-sensitive nature. The ability, advantages, and disadvantages of varying ranking metrics were discussed. These findings support the notion that toxicity bioassays should account for the cells' complex dynamic responses, thereby implying that both data acquisition and data analysis should look beyond simple traditional end point responses.

Genomic and proteomic fingerprints of acute rejection in peripheral blood and urine

Acute dysfunction of a kidney transplant can be the result of many different etiologies and an allograft biopsy is frequently necessary to diagnose acute rejection. This invasive procedure, while generally safe, is time consuming, costly and inconvenient. We summarize recent advances in genomic and proteomic techniques using peripheral blood and urine for the diagnosis of acute rejection. While much progress has been made, validation of these new molecular tests in the clinical setting is still required.

Effects of immunosuppression on circulating adeno-associated virus capsid-specific T cells in humans

In humans adeno-associated virus (AAV)-mediated gene transfer is followed by expansion of AAV capsid-specific T cells, evidence of cell damage, and loss of transgene product expression, implicating immunological rejection of vector-transduced cells, which may be prevented by immunosuppressive drugs. We undertook this study to assess the effect of immunosuppression (IS) used for organ transplantation on immune responses to AAV capsid antigens. Recipients of liver or kidney transplants were tested before and 4 weeks after induction of IS in comparison with matched samples from healthy human adults and an additional cohort with comorbid conditions similar to those of the transplant patients. Our data show that transplant patients and comorbid control subjects have markedly higher frequencies of circulating AAV capsid-specific T cells compared with healthy adults. On average, IS resulted in a reduction of AAV-specific CD4⁺ T cells, whereas numbers of circulating CD8⁺ effector and central memory T cells tended to increase. Independent of the type of transplant or the IS regimens, the trend of AAV capsid-specific T cell responses after drug treatment varied; in some patients responses were unaffected whereas others showed decreases or even pronounced increases, casting doubt on the usefulness of prophylactic IS for AAV vector recipients.

MicroRNA regulation of T-lymphocyte immunity: modulation of molecular networks responsible for T-cell activation, differentiation, and development

MicroRNAs (miRNA) are a class of small non-coding RNAs that constitute an essential and evolutionarily conserved mechanism for post-transcriptional gene regulation. Multiple miRNAs have been described to play key roles in T-lymphocyte development, differentiation, and function. In this review, we highlight the current literature regarding the differential expression of miRNAs in various models of murine and human T-cell biology. We emphasize mechanistic understandings of miRNA regulation of thymocyte development, T-cell activation, and differentiation into effector and memory subsets. We describe the participation of miRNAs in complex regulatory circuits shaping T-cell proteomes in a context-dependent manner. It is striking that some miRNAs regulate multiple processes, while others only appear in limited functional contexts. It is also evident that the expression and function of specific miRNAs can differ between murine and human systems. Ultimately, it is not always correct to simplify the complex events of T-cell biology into a model driven by only one or two master regulator miRNAs. In reality, T-cell activation and differentiation involve the expression of multiple miRNAs with many mRNA targets; thus, the true extent of miRNA regulation of T-cell biology is likely far more vast than currently appreciated.

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.

Systems analysis of high-throughput data

Modern high-throughput assays yield detailed characterizations of the genomic, transcriptomic, and proteomic states of biological samples, enabling us to probe the molecular mechanisms that regulate hematopoiesis or give rise to hematological disorders. At the same time, the high dimensionality of the data and the complex nature of biological interaction networks present significant analytical challenges in identifying causal variations and modeling the underlying systems biology. In addition to identifying significantly disregulated genes and proteins, integrative analysis approaches that allow the investigation of these single genes within a functional context are required. This chapter presents a survey of current computational approaches for the statistical analysis of high-dimensional data and the development of systems-level models of cellular signaling and regulation. Specifically, we focus on multi-gene analysis methods and the integration of expression data with domain knowledge (such as biological pathways) and other gene-wise information (e.g.,  sequence or methylation data) to identify novel functional modules in the complex cellular interaction network.

Systems vaccinology: its promise and challenge for HIV vaccine development

PURPOSE OF REVIEW

The use of systems biology approaches to understand and predict vaccine-induced immunity promises to revolutionize vaccinology. For centuries vaccines were developed empirically, with very little understanding of the mechanisms by which they mediate protective immunity. The so-called systems vaccinology approach employs high-throughput technologies (e.g. microarrays, RNA-seq and mass spectrometry-based proteomics and metabolomics) and computational modeling to describe the complex interactions between all the parts of immune system, with a view to elucidating new biological rules capable of predicting the behavior of the system.

RECENT FINDINGS

Systems biology successfully applied to yellow-fever and influenza vaccines has led to the discovery of signatures that predict vaccine immunogenicity, and promises to advance basic immunology research by providing novel mechanistic insights about immune regulation. However a major challenge of systems vaccinology concerns the analyses and interpretation of the large and noisy data sets generated by high-throughput techniques. Overcoming these issues, we envision that systems vaccinology will have a potential impact on vaccine development, including HIV vaccines.

SUMMARY

High-throughput technologies allow the investigation of vaccine-induced immune responses at system and molecular levels. These are currently being used to unravel new molecular insights about the immune system, and are on the verge of being integrated into clinical trials to enable rational vaccine design and development.

Ten years of pathway analysis: current approaches and outstanding challenges

Pathway analysis has become the first choice for gaining insight into the underlying biology of differentially expressed genes and proteins, as it reduces complexity and has increased explanatory power. We discuss the evolution of knowledge base–driven pathway analysis over its first decade, distinctly divided into three generations. We also discuss the limitations that are specific to each generation, and how they are addressed by successive generations of methods. We identify a number of annotation challenges that must be addressed to enable development of the next generation of pathway analysis methods. Furthermore, we identify a number of methodological challenges that the next generation of methods must tackle to take advantage of the technological advances in genomics and proteomics in order to improve specificity, sensitivity, and relevance of pathway analysis.

Transcriptional and epigenetic mechanisms of addiction

Investigations of long-term changes in brain structure and function that accompany chronic exposure to drugs of abuse suggest that alterations in gene regulation contribute substantially to the addictive phenotype. Here, we review multiple mechanisms by which drugs alter the transcriptional potential of genes. These mechanisms range from the mobilization or repression of the transcriptional machinery - including the transcription factors ΔFOSB, cyclic AMP-responsive element binding protein (CREB) and nuclear factor-κB (NF-κB) - to epigenetics - including alterations in the accessibility of genes within their native chromatin structure induced by histone tail modifications and DNA methylation, and the regulation of gene expression by non-coding RNAs. Increasing evidence implicates these various mechanisms of gene regulation in the lasting changes that drugs of abuse induce in the brain, and offers novel inroads for addiction therapy.

Strategies for aggregating gene expression data: the collapseRows R function

Background

Genomic and other high dimensional analyses often require one to summarize multiple related variables by a single representative. This task is also variously referred to as collapsing, combining, reducing, or aggregating variables. Examples include summarizing several probe measurements corresponding to a single gene, representing the expression profiles of a co-expression module by a single expression profile, and aggregating cell-type marker information to de-convolute expression data. Several standard statistical summary techniques can be used, but network methods also provide useful alternative methods to find representatives. Currently few collapsing functions are developed and widely applied.

Results

We introduce the R function collapseRows that implements several collapsing methods and evaluate its performance in three applications. First, we study a crucial step of the meta-analysis of microarray data: the merging of independent gene expression data sets, which may have been measured on different platforms. Toward this end, we collapse multiple microarray probes for a single gene and then merge the data by gene identifier. We find that choosing the probe with the highest average expression leads to best between-study consistency. Second, we study methods for summarizing the gene expression profiles of a co-expression module. Several gene co-expression network analysis applications show that the optimal collapsing strategy depends on the analysis goal. Third, we study aggregating the information of cell type marker genes when the aim is to predict the abundance of cell types in a tissue sample based on gene expression data ("expression deconvolution"). We apply different collapsing methods to predict cell type abundances in peripheral human blood and in mixtures of blood cell lines. Interestingly, the most accurate prediction method involves choosing the most highly connected "hub" marker gene. Finally, to facilitate biological interpretation of collapsed gene lists, we introduce the function userListEnrichment, which assesses the enrichment of gene lists for known brain and blood cell type markers, and for other published biological pathways.

Conclusions

The R function collapseRows implements several standard and network-based collapsing methods. In various genomic applications we provide evidence that both types of methods are robust and biologically relevant tools.

MicroRNA regulation of molecular networks mapped by global microRNA, mRNA, and protein expression in activated T lymphocytes

MicroRNAs (miRNAs) regulate specific immune mechanisms, but their genome-wide regulation of T lymphocyte activation is largely unknown. We performed a multidimensional functional genomics analysis to integrate genome-wide differential mRNA, miRNA, and protein expression as a function of human T lymphocyte activation and time. We surveyed expression of 420 human miRNAs in parallel with genome-wide mRNA expression. We identified a unique signature of 71 differentially expressed miRNAs, 57 of which were previously not known as regulators of immune activation. The majority of miRNAs are upregulated, mRNA expression of these target genes is downregulated, and this is a function of binding multiple miRNAs (combinatorial targeting). Our data reveal that consideration of this complex signature, rather than single miRNAs, is necessary to construct a full picture of miRNA-mediated regulation. Molecular network mapping of miRNA targets revealed the regulation of activation-induced immune signaling. In contrast, pathways populated by genes that are not miRNA targets are enriched for metabolism and biosynthesis. Finally, we specifically validated miR-155 (known) and miR-221 (novel in T lymphocytes) using locked nucleic acid inhibitors. Inhibition of these two highly upregulated miRNAs in CD4(+) T cells was shown to increase proliferation by removing suppression of four target genes linked to proliferation and survival. Thus, multiple lines of evidence link top functional networks directly to T lymphocyte immunity, underlining the value of mapping global gene, protein, and miRNA expression.

Cell subset prediction for blood genomic studies

BACKGROUND

Genome-wide transcriptional profiling of patient blood samples offers a powerful tool to investigate underlying disease mechanisms and personalized treatment decisions. Most studies are based on analysis of total peripheral blood mononuclear cells (PBMCs), a mixed population. In this case, accuracy is inherently limited since cell subset-specific differential expression of gene signatures will be diluted by RNA from other cells. While using specific PBMC subsets for transcriptional profiling would improve our ability to extract knowledge from these data, it is rarely obvious which cell subset(s) will be the most informative.

RESULTS

We have developed a computational method (Subset Prediction from Enrichment Correlation, SPEC) to predict the cellular source for a pre-defined list of genes (i.e. a gene signature) using only data from total PBMCs. SPEC does not rely on the occurrence of cell subset-specific genes in the signature, but rather takes advantage of correlations with subset-specific genes across a set of samples. Validation using multiple experimental datasets demonstrates that SPEC can accurately identify the source of a gene signature as myeloid or lymphoid, as well as differentiate between B cells, T cells, NK cells and monocytes. Using SPEC, we predict that myeloid cells are the source of the interferon-therapy response gene signature associated with HCV patients who are non-responsive to standard therapy.

CONCLUSIONS

SPEC is a powerful technique for blood genomic studies. It can help identify specific cell subsets that are important for understanding disease and therapy response. SPEC is widely applicable since only gene expression profiles from total PBMCs are required, and thus it can easily be used to mine the massive amount of existing microarray or RNA-seq data.