Performance comparison of two microarray platforms to assess differential gene expression in human monocyte and macrophage cells

Lipid raft-disrupting miltefosine preferentially induces the death of colorectal cancer stem-like cells

BACKGROUND

Lipid rafts (LRs), cholesterol-enriched microdomains on cell membranes, are increasingly viewed as signalling platforms governing critical facets of cancer progression. The phenotype of cancer stem-like cells (CSCs) presents significant hurdles for successful cancer treatment, and the expression of several CSC markers is associated with LR integrity. However, LR implications in CSCs remain unclear.

METHODS

This study evaluated the biological and molecular functions of LRs in colorectal cancer (CRC) by using an LR-disrupting alkylphospholipid (APL) drug, miltefosine. The mechanistic role of miltefosine in CSC inhibition was examined through normal or tumour intestinal mouse organoid, human CRC cell, CRC xenograft and miltefosine treatment gene expression profile analyses.

RESULTS

Miltefosine suppresses CSC populations and their self-renewal activities in CRC cells, a CSC-targeting effect leading to irreversible disruption of tumour-initiating potential in vivo. Mechanistically, miltefosine reduced the expression of a set of genes, leading to stem cell death. Among them, miltefosine transcriptionally inhibited checkpoint kinase 1 (CHEK1), indicating that LR integrity is essential for CHEK1 expression regulation. In isolated CD44high CSCs, we found that CSCs exhibited stronger therapy resistance than non-CSC counterparts by preventing cell death through CHEK1-mediated cell cycle checkpoints. However, inhibition of the LR/CHEK1 axis by miltefosine released cell cycle checkpoints, forcing CSCs to enter inappropriate mitosis with accumulated DNA damage and resulting in catastrophic cell death.

CONCLUSION

Our findings underscore the therapeutic potential of LR-targeting APLs for CRC treatment that overcomes the therapy-resistant phenotype of CSCs, highlighting the importance of the LR/CHEK1 axis as a novel mechanism of APLs.

Serum amyloid P component is an essential element of resistance against Aspergillus fumigatus

Serum amyloid P component (SAP, also known as Pentraxin 2; APCS gene) is a component of the humoral arm of innate immunity involved in resistance to bacterial infection and regulation of tissue remodeling. Here we investigate the role of SAP in antifungal resistance. Apcs^-/- mice show enhanced susceptibility to A. fumigatus infection. Murine and human SAP bound conidia, activate the complement cascade and enhance phagocytosis by neutrophils. Apcs^-/- mice are defective in vivo in terms of recruitment of neutrophils and phagocytosis in the lungs. Opsonic activity of SAP is dependent on the classical pathway of complement activation. In immunosuppressed mice, SAP administration protects hosts against A. fumigatus infection and death. In the context of a study of hematopoietic stem-cell transplantation, genetic variation in the human APCS gene is associated with susceptibility to invasive pulmonary aspergillosis. Thus, SAP is a fluid phase pattern recognition molecule essential for resistance against A. fumigatus.

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.

Mitochondrial oxidative stress by Lon-PYCR1 maintains an immunosuppressive tumor microenvironment that promotes cancer progression and metastasis

Mitochondrial Lon is a chaperone protein whose upregulation increases the production of mitochondrial reactive oxygen species (ROS). However, there is a lack of information in detail on how mitochondrial Lon regulates cancer metastasis through ROS production in the tumor microenvironment (TME). Our results show that elevated Lon promotes epithelial-mesenchymal transition (EMT) via ROS-dependent p38 and NF-κB-signaling. We further identified pyrroline-5-carboxylate reductase 1 (PYCR1) as a client of chaperone Lon, which induces mitochondrial ROS and EMT by Lon. Mitochondrial Lon induces ROS-dependent production of inflammatory cytokines, such as TGF-β, IL-6, IL-13, and VEGF-A, which consequently activates EMT, angiogenesis, and M2 macrophage polarization. In addition, Lon expression is induced upon the activation and M2 polarization of macrophages, which further promotes M2 macrophages to enhance the immunosuppressive microenvironment and metastatic behaviors in the TME. This raises the possibility that manipulation of the mitochondrial redox balance in the TME may serve as a therapeutic strategy to improve T cell function in cancer immunotherapy.

Working Towards a Blood-Derived Gene Expression Biomarker Specific for Alzheimer's Disease

BACKGROUND

The typical approach to identify blood-derived gene expression signatures as a biomarker for Alzheimer's disease (AD) have relied on training classification models using AD and healthy controls only. This may inadvertently result in the identification of markers for general illness rather than being disease-specific.

OBJECTIVE

Investigate whether incorporating additional related disorders in the classification model development process can lead to the discovery of an AD-specific gene expression signature.

METHODS

Two types of XGBoost classification models were developed. The first used 160 AD and 127 healthy controls and the second used the same 160 AD with 6,318 upsampled mixed controls consisting of Parkinson's disease, multiple sclerosis, amyotrophic lateral sclerosis, bipolar disorder, schizophrenia, coronary artery disease, rheumatoid arthritis, chronic obstructive pulmonary disease, and cognitively healthy subjects. Both classification models were evaluated in an independent cohort consisting of 127 AD and 687 mixed controls.

RESULTS

The AD versus healthy control models resulted in an average 48.7% sensitivity (95% CI = 34.7-64.6), 41.9% specificity (95% CI = 26.8-54.3), 13.6% PPV (95% CI = 9.9-18.5), and 81.1% NPV (95% CI = 73.3-87.7). In contrast, the mixed control models resulted in an average of 40.8% sensitivity (95% CI = 27.5-52.0), 95.3% specificity (95% CI = 93.3-97.1), 61.4% PPV (95% CI = 53.8-69.6), and 89.7% NPV (95% CI = 87.8-91.4).

CONCLUSIONS

This early work demonstrates the value of incorporating additional related disorders into the classification model developmental process, which can result in models with improved ability to distinguish AD from a heterogeneous aging population. However, further improvement to the sensitivity of the test is still required.

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.

Review and Meta-Analyses of TAAR1 Expression in the Immune System and Cancers

Since its discovery in 2001, the major focus of TAAR1 research has been on its role in monoaminergic regulation, drug-induced reward and psychiatric conditions. More recently, TAAR1 expression and functionality in immune system regulation and immune cell activation has become a topic of emerging interest. Here, we review the immunologically-relevant TAAR1 literature and incorporate open-source expression and cancer survival data meta-analyses. We provide strong evidence for TAAR1 expression in the immune system and cancers revealed through NCBI GEO datamining and discuss its regulation in a spectrum of immune cell types as well as in numerous cancers. We discuss connections and logical directions for further study of TAAR1 in immunological function, and its potential role as a mediator or modulator of immune dysregulation, immunological effects of psychostimulant drugs of abuse, and cancer progression.

The Pathway To Establishing HIV Latency Is Critical to How Latency Is Maintained and Reversed

HIV infection requires lifelong antiretroviral therapy because of the persistence of latently infected CD4⁺ T cells. The induction of virus expression from latently infected cells occurs following T cell receptor (TCR) activation, but not all latently infected cells respond to TCR stimulation. We compared two models of latently infected cells using an enhanced green fluorescent protein (EGFP) reporter virus to infect CCL19-treated resting CD4⁺ (rCD4⁺) T cells (preactivation latency) or activated CD4⁺ T cells that returned to a resting state (postactivation latency). We isolated latently infected cells by sorting for EGFP-negative (EGFP^-) cells after infection. These cells were cultured with antivirals and stimulated with anti-CD3/anti-CD28, mitogens, and latency-reversing agents (LRAs) and cocultured with monocytes and anti-CD3. Spontaneous EGFP expression was more frequent in postactivation than in preactivation latency. Stimulation of latently infected cells with monocytes/anti-CD3 resulted in an increase in EGFP expression compared to that for unstimulated controls using the preactivation latency model but led to a reduction in EGFP expression in the postactivation latency model. The reduced EGFP expression was not associated with reductions in the levels of viral DNA or T cell proliferation but depended on direct contact between monocytes and T cells. Monocytes added to the postactivation latency model during the establishment of latency reduced spontaneous virus expression, suggesting that monocyte-T cell interactions at an early time point postinfection can maintain HIV latency. This direct comparison of pre- and postactivation latency suggests that effective strategies needed to reverse latency will depend on how latency is established.IMPORTANCE One strategy being evaluated to eliminate latently infected cells that persist in HIV-infected individuals on antiretroviral therapy (ART) is to activate HIV expression or production with the goal of inducing virus-mediated cytolysis or immune-mediated clearance of infected cells. The gold standard for the activation of latent virus is T cell receptor stimulation with anti-CD3/anti-CD28. However, this stimulus activates only a small proportion of latently infected cells. We show clear differences in the responses of latently infected cells to activating stimuli based on how latent infection is established, an observation that may potentially explain the persistence of noninduced intact proviruses in HIV-infected individuals on ART.

Monocyte alterations in rheumatoid arthritis are dominated by preterm release from bone marrow and prominent triggering in the joint

OBJECTIVE

Rheumatoid arthritis (RA) accompanies infiltration and activation of monocytes in inflamed joints. We investigated dominant alterations of RA monocytes in bone marrow (BM), blood and inflamed joints.

METHODS

CD14⁺ cells from BM and peripheral blood (PB) of patients with RA and osteoarthritis (OA) were profiled with GeneChip microarrays. Detailed functional analysis was performed with reference transcriptomes of BM precursors, monocyte blood subsets, monocyte activation and mobilisation. Cytometric profiling determined monocyte subsets of CD14⁺⁺CD16^-, CD14⁺⁺CD16⁺ and CD14⁺CD16⁺ cells in BM, PB and synovial fluid (SF) and ELISAs quantified the release of activation markers into SF and serum.

RESULTS

Investigation of genes differentially expressed between RA and OA monocytes with reference transcriptomes revealed gene patterns of early myeloid precursors in RA-BM and late myeloid precursors along with reduced terminal differentiation to CD14⁺CD16⁺monocytes in RA-PB. Patterns associated with tumor necrosis factor/lipopolysaccharide (TNF/LPS) stimulation were weak and more pronounced in RA-PB than RA-BM. Cytometric phenotyping of cells in BM, blood and SF disclosed differences related to monocyte subsets and confirmed the reduced frequency of terminally differentiated CD14⁺CD16⁺monocytes in RA-PB. Monocyte activation in SF was characterised by the predominance of CD14⁺⁺CD16⁺⁺CD163⁺HLA-DR⁺ cells and elevated concentrations of sCD14, sCD163 and S100P.

CONCLUSION

Patterns of less mature and less differentiated RA-BM and RA-PB monocytes suggest increased turnover with accelerated monocytopoiesis, BM egress and migration into inflamed joints. Predominant activation in the joint indicates the action of local and primary stimuli, which may also promote adaptive immune triggering through monocytes, potentially leading to new diagnostic and therapeutic strategies.

The cell-cell interaction between tumor-associated macrophages and small cell lung cancer cells is involved in tumor progression via STAT3 activation

OBJECTIVES

Small cell lung cancer (SCLC) is an aggressive tumor with a poor prognosis. It is well known that various stromal cells, including macrophages, play a role in tumor progression in several types of malignant tumors; however, the significance of tumor-associated macrophages (TAMs) in SCLC has not been fully elucidated. Signal transducer and activator of transcription 3 (STAT3) is a molecule well-known to be related to tumor progression. In the present study, we investigated the relationship of TAMs and SCLC cells to test the hypothesis that TAMs induce tumor progression in SCLC via STAT3 activation.

MATERIALS AND METHODS

We performed immunohistochemical analysis using surgically resected tumor specimens and in vitro co-culture experiments using human SCLC cell lines and human monocyte-derived macrophages.

RESULTS

We first demonstrated via immunostaining that STAT3 activation in tumor cells was predominantly observed in the peripheral areas of tumor nests existing near TAMs in stroma. The indirect co-culture of SCLC cells and macrophages induced STAT3 activation in both cell types, and macrophage-derived culture supernatant (CS) significantly activated STAT3 in SCLC cells. Macrophage-derived CS induced tumor cell proliferation and invasion via STAT3 activation. In addition, chemo-resistance and sphere formation were also increased by macrophage-derived CS. Macrophage-derived interleukin-6 and CC chemokine ligand 4 (CCL4/MIP-1β) were suggested to be associated with STAT3 activation in SCLC cells. CS-induced STAT3 activation in SCLC cells was suppressed by anti-IL-6 receptor antibody, but not by anti-CCL4/MIP-1β antibody.

CONCLUSION

These results suggest that TAMs are likely involved in SCLC progression via STAT3 activation and TAM-derived IL-6 is indicated to be one of molecules related to STAT3 activation in SCLC cells. Thus, the cell-cell interaction between TAMs and SCLC cells might be a target for therapy.

Integrative analysis of methylome and transcriptome in human blood identifies extensive sex- and immune cell-specific differentially methylated regions

The relationship between DNA methylation and gene expression is complex and elusive. To further elucidate these relations, we performed an integrative analysis of the methylome and transcriptome of 4 circulating immune cell subsets (B cells, monocytes, CD4(+), and CD8(+) T cells) from healthy females. Additionally, in light of the known sex bias in the prevalence of several immune-mediated diseases, the female datasets were compared with similar public available male data sets. Immune cell-specific differentially methylated regions (DMRs) were found to be highly similar between sexes, with an average correlation coefficient of 0.82; however, numerous sex-specific DMRs, shared by the cell subsets, were identified, mainly on autosomal chromosomes. This provides a list of highly interesting candidate genes to be studied in disorders with sexual dimorphism, such as autoimmune diseases. Immune cell-specific DMRs were mainly located in the gene body and intergenic region, distant from CpG islands but overlapping with enhancer elements, indicating that distal regulatory elements are important in immune cell specificity. In contrast, sex-specific DMRs were overrepresented in CpG islands, suggesting that the epigenetic regulatory mechanisms of sex and immune cell specificity may differ. Both positive and, more frequently, negative correlations between subset-specific expression and methylation were observed, and cell-specific DMRs of both interactions were associated with similar biological pathways, while sex-specific DMRs were linked to networks of early development or estrogen receptor and immune-related molecules. Our findings of immune cell- and sex-specific methylome and transcriptome profiles provide novel insight on their complex regulatory interactions and may particularly contribute to research of immune-mediated diseases.

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.

Selecting Reliable mRNA Expression Measurements Across Platforms Improves Downstream Analysis

With increasing use of publicly available gene expression data sets, the quality of the expression data is a critical issue for downstream analysis, gene signature development, and cross-validation of data sets. Thus, identifying reliable expression measurements by leveraging multiple mRNA expression platforms is an important analytical task. In this study, we propose a statistical framework for selecting reliable measurements between platforms by modeling the correlations of mRNA expression levels using a beta-mixture model. The model-based selection provides an effective and objective way to separate good probes from probes with low quality, thereby improving the efficiency and accuracy of the analysis. The proposed method can be used to compare two microarray technologies or microarray and RNA sequencing measurements. We tested the approach in two matched profiling data sets, using microarray gene expression measurements from the same samples profiled on both Affymetrix and Illumina platforms. We also applied the algorithm to mRNA expression data to compare Affymetrix microarray data with RNA sequencing measurements. The algorithm successfully identified probes/genes with reliable measurements. Removing the unreliable measurements resulted in significant improvements for gene signature development and functional annotations.

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.

Genetic and Informatic Analyses Implicate Kif12 as a Candidate Gene within the Mpkd2 Locus That Modulates Renal Cystic Disease Severity in the Cys1cpk Mouse

We have previously mapped the interval on Chromosome 4 for a major polycystic kidney disease modifier (Mpkd) of the B6(Cg)-Cys1^cpk/J mouse model of recessive polycystic kidney disease (PKD). Informatic analyses predicted that this interval contains at least three individual renal cystic disease severity-modulating loci (Mpkd1-3). In the current study, we provide further validation of these predicted effects using a congenic mouse line carrying the entire CAST/EiJ (CAST)-derived Mpkd1-3 interval on the C57BL/6J background. We have also generated a derivative congenic line with a refined CAST-derived Mpkd1-2 interval and demonstrated its dominantly-acting disease-modulating effects (e.g., 4.2-fold increase in total cyst area; p<0.001). The relative strength of these effects allowed the use of recombinants from these crosses to fine map the Mpkd2 effects to a <14 Mbp interval that contains 92 RefSeq sequences. One of them corresponds to the previously described positional Mpkd2 candidate gene, Kif12. Among the positional Mpkd2 candidates, only expression of Kif12 correlates strongly with the expression pattern of Cys1 across multiple anatomical nephron structures and developmental time points. Also, we demonstrate that Kif12 encodes a primary cilium-associated protein. Together, these data provide genetic and informatic validation of the predicted renal cystic disease-modulating effects of Mpkd1-3 loci and implicate Kif12 as the candidate locus for Mpkd2.

Evaluation of Different Normalization and Analysis Procedures for Illumina Gene Expression Microarray Data Involving Small Changes

While Illumina microarrays can be used successfully for detecting small gene expression changes due to their high degree of technical replicability, there is little information on how different normalization and differential expression analysis strategies affect outcomes. To evaluate this, we assessed concordance across gene lists generated by applying different combinations of normalization strategy and analytical approach to two Illumina datasets with modest expression changes. In addition to using traditional statistical approaches, we also tested an approach based on combinatorial optimization. We found that the choice of both normalization strategy and analytical approach considerably affected outcomes, in some cases leading to substantial differences in gene lists and subsequent pathway analysis results. Our findings suggest that important biological phenomena may be overlooked when there is a routine practice of using only one approach to investigate all microarray datasets. Analytical artefacts of this kind are likely to be especially relevant for datasets involving small fold changes, where inherent technical variation-if not adequately minimized by effective normalization-may overshadow true biological variation. This report provides some basic guidelines for optimizing outcomes when working with Illumina datasets involving small expression changes.

Extended haplotype association study in Crohn's disease identifies a novel, Ashkenazi Jewish-specific missense mutation in the NF-κB pathway gene, HEATR3

The Ashkenazi Jewish population has a several-fold higher prevalence of Crohn’s disease compared to non-Jewish European ancestry populations and has a unique genetic history. Haplotype association is critical to Crohn’s disease etiology in this population, most notably at NOD2, in which three causal, uncommon, and conditionally independent NOD2 variants reside on a shared background haplotype. We present an analysis of extended haplotypes which showed significantly greater association to Crohn’s disease in the Ashkenazi Jewish population compared to a non-Jewish population (145 haplotypes and no haplotypes with P-value < 10⁻³, respectively). Two haplotype regions, one each on chromosomes 16 and 21, conferred increased disease risk within established Crohn’s disease loci. We performed exome sequencing of 55 Ashkenazi Jewish individuals and follow-up genotyping focused on variants in these two regions. We observed Ashkenazi Jewish-specific nominal association at R755C in TRPM2 on chromosome 21. Within the chromosome 16 region, R642S of HEATR3 and rs9922362 of BRD7 showed genome-wide significance. Expression studies of HEATR3 demonstrated a positive role in NOD2-mediated NF-κB signaling. The BRD7 signal showed conditional dependence with only the downstream rare Crohn’s disease-causal variants in NOD2, but not with the background haplotype; this elaborates NOD2 as a key illustration of synthetic association.

Extralymphocytic flexible immune recognition: a new angle on inflammation and aging

Longstanding immunological dogma holds that flexible immune recognition, which forms the mechanistic basis of adaptive immunity, is strictly confined to the lymphocyte lineage. In higher vertebrates, flexible immune recognition is represented by recombinatorial antigen receptors of enormous diversity known as immunoglobulins, expressed by B lymphocytes, and the T cell receptor (TCR), expressed by T lymphocytes. The recent discovery of recombinatorial immune receptors that are structurally based on the TCR (referred to as TCR-like immunoreceptors, "TCRL") in myeloid phagocytes such as neutrophils and monocytes/macrophages now challenges the lymphocentric paradigm of flexible immunity. Here, we introduce the emerging concept of "extralymphocytic flexible immune recognition" and discuss its implications for inflammation and aging.

Short-term erythropoietin treatment does not substantially modulate monocyte transcriptomes of patients with combined heart and renal failure

BACKGROUND

Combined heart and renal failure is associated with high cardiovascular morbidity and mortality. Anti-oxidant and anti-inflammatory, non-hematopoietic effects of erythropoietin (EPO) treatment have been proposed. Monocytes may act as biosensors of the systemic environment. We hypothesized that monocyte transcriptomes of patients with cardiorenal syndrome (CRS) reflect the pathophysiology of the CRS and respond to short-term EPO treatment at a recommended dose for treatment of renal anemia.

METHODS

Patients with CRS and anemia (n = 18) included in the EPOCARES trial were matched to healthy controls (n = 12). Patients were randomized to receive 50 IU/kg/week EPO or not. RNA from CD14(+)-monocytes was subjected to genome wide expression analysis (Illumina) at baseline and 18 days (3 EPO injections) after enrolment. Transcriptomes from patients were compared to healthy controls and effect of EPO treatment was evaluated within patients.

RESULTS

In CRS patients, expression of 471 genes, including inflammation and oxidative stress related genes was different from healthy controls. Cluster analysis did not separate patients from healthy controls. The 6 patients with the highest hsCRP levels had more differentially expressed genes than the 6 patients with the lowest hsCRP levels. Analysis of the variation in log(2) ratios of all individual 18 patients indicated that 4 of the 18 patients were different from the controls, whereas the other 14 were quite similar. After short-term EPO treatment, every patient clustered to his or her own baseline transcriptome. Two week EPO administration only marginally affected expression profiles on average, however, individual gene responses were variable.

CONCLUSIONS

In stable, treated CRS patients with mild anemia, monocyte transcriptomes were modestly altered, and indicated imprints of inflammation and oxidative stress. EPO treatment with a fixed dose has hematopoietic effects, had no appreciable beneficial actions on monocyte transcription profiles, however, could also not be associated with undesirable transcriptional responses.

On the horizon: flexible immune recognition outside lymphocytes

Since decades there is consensus among immunologists that in jawless and jawed vertebrates flexible immune recognition is strictly confined to the lymphoid lineage. In jawed vertebrates the adaptive immune system is represented by two lineages of lymphocytes, B cells and T cells that express recombinatorial antigen receptors of enormous diversity known as immunoglobulins and the T cell receptor (TCR). The recent identification of recombined immune receptors that are structurally based on the TCR in subpopulations of neutrophils and eosinophils (referred to here as TCR-like immunoreceptors, "TCRL") provides unexpected evidence for the existence of flexible host defense mechanisms beyond the realm of lymphocytes. Consistent with this, subpopulations of monocytes and macrophages from humans and mice now have also been shown to constitutively express recombined TCR-like immunoreceptors. Available in vitro evidence suggests that the TCRL in macrophages may exert functions as facilitators of phagocytosis and self-recruitment. More importantly, our recent findings that the macrophage-TCRL is implicated in granuloma formation in tuberculosis and the neutrophil-TCRL is associated with autoimmune hemolytic anemia establish for the first time a link between myeloid recombinatorial immune receptors and clinical disease. The discovery of recombined TCR-like immune receptors in granulocytes and macrophages extends the principle of combinatorial immune recognition to phagocytic cells. Conceptually, this unifies the two hitherto disparate cardinal features of innate and adaptive immunity, phagocytic capacity and recombinatorial immune recognition on a common cellular platform. Moreover, it strongly suggests that flexible host defense in vertebrates may operate on a broader cellular basis than currently thought.

REGγ is associated with multiple oncogenic pathways in human cancers

Background

Recent studies suggest a role of the proteasome activator, REGγ, in cancer progression. Since there are limited numbers of known REGγ targets, it is not known which cancers and pathways are associated with REGγ.

Methods

REGγ protein expressions in four different cancers were investigated by immunohistochemistry (IHC) analysis. Following NCBI Gene Expression Omnibus (GEO) database search, microarray platform validation, differential expressions of REGγ in corresponding cancers were statistically analyzed. Genes highly correlated with REGγ were defined based on Pearson's correlation coefficient. Functional links were estimated by Ingenuity Core analysis. Finally, validation was performed by RT-PCR analysis in established cancer cell lines and IHC in human colon cancer tissues

Results

Here, we demonstrate overexpression of REGγ in four different cancer types by micro-tissue array analysis. Using meta-analysis of publicly available microarray databases and biological studies, we verified elevated REGγ gene expression in the four types of cancers and identified genes significantly correlated with REGγ expression, including genes in p53, Myc pathways, and multiple other cancer-related pathways. The predicted correlations were largely consistent with quantitative RT-PCR analysis.

Conclusions

This study provides us novel insights in REGγ gene expression profiles and its link to multiple cancer-related pathways in cancers. Our results indicate potentially important pathogenic roles of REGγ in multiple cancer types and implicate REGγ as a putative cancer marker.

SIGNATURE: a workbench for gene expression signature analysis

Background

The biological phenotype of a cell, such as a characteristic visual image or behavior, reflects activities derived from the expression of collections of genes. As such, an ability to measure the expression of these genes provides an opportunity to develop more precise and varied sets of phenotypes. However, to use this approach requires computational methods that are difficult to implement and apply, and thus there is a critical need for intelligent software tools that can reduce the technical burden of the analysis. Tools for gene expression analyses are unusually difficult to implement in a user-friendly way because their application requires a combination of biological data curation, statistical computational methods, and database expertise.

Results

We have developed SIGNATURE, a web-based resource that simplifies gene expression signature analysis by providing software, data, and protocols to perform the analysis successfully. This resource uses Bayesian methods for processing gene expression data coupled with a curated database of gene expression signatures, all carried out within a GenePattern web interface for easy use and access.

Conclusions

SIGNATURE is available for public use at http://genepattern.genome.duke.edu/signature/.

Multiple platform assessment of the EGF dependent transcriptome by microarray and deep tag sequencing analysis

Background

Epidermal Growth Factor (EGF) is a key regulatory growth factor activating many processes relevant to normal development and disease, affecting cell proliferation and survival. Here we use a combined approach to study the EGF dependent transcriptome of HeLa cells by using multiple long oligonucleotide based microarray platforms (from Agilent, Operon, and Illumina) in combination with digital gene expression profiling (DGE) with the Illumina Genome Analyzer.

Results

By applying a procedure for cross-platform data meta-analysis based on RankProd and GlobalAncova tests, we establish a well validated gene set with transcript levels altered after EGF treatment. We use this robust gene list to build higher order networks of gene interaction by interconnecting associated networks, supporting and extending the important role of the EGF signaling pathway in cancer. In addition, we find an entirely new set of genes previously unrelated to the currently accepted EGF associated cellular functions.

Conclusions

We propose that the use of global genomic cross-validation derived from high content technologies (microarrays or deep sequencing) can be used to generate more reliable datasets. This approach should help to improve the confidence of downstream in silico functional inference analyses based on high content data.

Cross-platform comparison of microarray data using order restricted inference

MOTIVATION

Titration experiments measuring the gene expression from two different tissues, along with total RNA mixtures of the pure samples, are frequently used for quality evaluation of microarray technologies. Such a design implies that the true mRNA expression of each gene, is either constant or follows a monotonic trend between the mixtures, applying itself to the use of order restricted inference procedures. Exploiting only the postulated monotonicity of titration designs, we propose three statistical analysis methods for the validation of high-throughput genetic data and corresponding preprocessing techniques.

RESULTS

Our methods allow for inference of accuracy, repeatability and cross-platform agreement, with minimal required assumptions regarding the underlying data generating process. Therefore, they are readily applicable to all sorts of genetic high-throughput data independent of the degree of preprocessing. An application to the EMERALD dataset was used to demonstrate how our methods provide a rich spectrum of easily interpretable quality metrics and allow the comparison of different microarray technologies and normalization methods. The results are on par with previous work, but provide additional new insights that cast doubt on the utility of popular preprocessing techniques, specifically concerning the EMERALD projects dataset.

AVAILABILITY

All datasets are available on EBI's ArrayExpress web site http://www.ebi.ac.uk/microarray-as/ae/) under accession numbers E-TABM-536, E-TABM-554 and E-TABM-555. Source code implemented in C and R is available at: http://statistics.msi.meduniwien.ac.at/float/cross_platform/. Methods for testing and variance decomposition have been made available in the R-package orQA, which can be downloaded and installed from CRAN http://cran.r-project.org.

Selection of reliable reference genes during THP-1 monocyte differentiation into macrophages

BACKGROUND

Reliable reference genes are a vital prerequisite for any functional study employing quantitative real-time RT-PCR (RT-qPCR) for analyzing gene expression. Yet a proper selection and assessment of the chosen reference genes is only rarely included into a study. To date, no reference genes have been validated for differentiation of THP-1 monocytes. Here we report on the selection of validated reference genes during differentiation of THP-1 monocytes into macrophages induced by phorbol 12-myristate 13-acetate (PMA).

RESULTS

The mRNA expression of 21 preselected potential reference genes was measured by RT-qPCR at several time-points over six days of PMA-induced THP-1 monocyte-to-macrophage differentiation. A ranking according to expression stability was calculated. Calculations were performed using Microsoft Excel-based applets GeNorm, NormFinder and BestKeeper. Our results indicated ACTB (β-actin) (Cq ± SD, 14.1 ± 0.3) and RPL37A (ribosomal protein L37a) (14.5 ± 0.3) as the most stable genes. While other frequently used reference genes such as GAPDH (glycereraldehyde-3-phosphate dehydrogenase) (20.8 ± 0.8) or G6PD (glucose-6-phophate dehydrogenase) (16.1 ± 1.0) were found to be not as reliable and were therefore unsuited for use as reference genes. These findings were validated by investigating mRNA expression of macrophage scavenger receptor CD36, known to be regulated during monocyte-to-macrophage differentiation. Using ACTB and RPL37A as reference genes a profound and significant regulation of CD36 could be demonstrated, while use of G6PD resulted in a much less pronounced apparent regulation of CD36.

CONCLUSION

Consequently, it is recommended to normalize any real-time PCR-based expression data obtained during THP-1 monocyte differentiation using ACTB and RPL37A.

Antagonistic regulation of macrophage phenotype by M-CSF and GM-CSF: implication in atherosclerosis

OBJECTIVES

We characterized the transcriptional profiles of GM-CSF- (GM-MØ) and M-CSF-induced macrophages (M-MØ) and investigated in situ a subset of differentially expressed genes in human and mouse atherosclerotic lesions.

METHODS AND RESULTS

Using microarrays we identified a number of genes and biological processes differentially regulated in M-MØ vs GM-MØ. By varying in culture the M-CSF/GM-CSF ratio (0-10), a spectrum of macrophage phenotypes was explored by RT-QPCR. M-CSF (10 ng/ml) stimulated expression of several genes, including selenoprotein-1 (SEPP1), stabilin-1 (STAB1) and CD163 molecule-like-1 (CD163L1) which was inhibited by a low dose of GM-CSF (1 ng/ml); M-CSF inhibited the expression of pro-platelet basic protein (PPBP) induced by GM-CSF. Combining tissue microarrays/quantitative immunohistochemistry of human aortic lesions with RT-QPCR expression data either from human carotids vs mammary non-atherosclerotic arteries or from the apoE null mice normal and atherosclerotic aortas showed that, STAB1, SEPP1 and CD163L1 (M-CSF-sensitive genes) and PPBP (GM-CSF-sensitive gene) were expressed in both human arterial and apoE null mice atherosclerotic tissues.

CONCLUSION

A balance between M-CSF vs GM-CSF defines macrophage functional polarisation and may contribute to the progression of atherosclerosis.

Gene expression profiling for discovery of novel targets in human traumatic brain injury

Several clinical trials have failed to demonstrate a significant effect on outcome following human traumatic brain injury (TBI) despite promising results obtained in preclinical animal studies. These failures may be due in part to a misinterpretation of the findings obtained in preclinical animal models of TBI, a misunderstanding of the complexity of the human response to TBI, limited knowledge about the biological pathways that interact to contribute to good and bad outcomes after brain injury, and the effects of genomic variability and environment on individual recovery. Recent publications suggest that data obtained from gene expression profiling studies of complex neurological diseases such as stroke, multiple sclerosis (MS), Alzheimer's and Parkinson's may contribute to a more informed understanding of what affects outcome following TBI. These data may help to bridge the gap between successful preclinical studies and negative clinical trials in humans to reveal novel targets for therapy. Gene expression profiling has the capability to identify biomarkers associated with response to TBI, elucidate complex genetic interactions that may play a role in outcome following TBI, and reveal biological pathways related to brain health. This review highlights the current state of the literature on gene expression profiling for neurological disease and discusses its ability to aid in unraveling the variable human response to TBI and the potential for it to offer treatment strategies in an area where we currently have limited therapeutic options primarily based on supportive care.

Gene expression reveals overlap between normal aging and Alzheimer's disease genes

Alzheimer's disease (AD) is a common cause of dementia with a strong genetic component and risk sharply increasing with age. We performed two parallel microarray experiments to independently identify genes involved in normal aging and genes involved in AD using RNA extracted from the temporal lobe of 22 late onset AD and 23 control brain donors. We found that AD is accompanied by significant changes in the expression of many genes with upregulation of genes involved in inflammation and in transcription regulation and downregulation of genes involved in neuronal functions. The changes with healthy aging involved multiple genes but were not as strong. Replicating and strengthening previous reports, we find a highly significant overlap between genes changing expression with age and those changing in AD, and we observe that those changes are most often in the same direction. This result supports an overlap between the biological processes of normal aging and susceptibility to AD and suggests that age related genes expression changes might increase the risk of developing AD.

A re-annotation pipeline for Illumina BeadArrays: improving the interpretation of gene expression data

Illumina BeadArrays are among the most popular and reliable platforms for gene expression profiling. However, little external scrutiny has been given to the design, selection and annotation of BeadArray probes, which is a fundamental issue in data quality and interpretation. Here we present a pipeline for the complete genomic and transcriptomic re-annotation of Illumina probe sequences, also applicable to other platforms, with its output available through a Web interface and incorporated into Bioconductor packages. We have identified several problems with the design of individual probes and we show the benefits of probe re-annotation on the analysis of BeadArray gene expression data sets. We discuss the importance of aspects such as probe coverage of individual transcripts, alternative messenger RNA splicing, single-nucleotide polymorphisms, repeat sequences, RNA degradation biases and probes targeting genomic regions with no known transcription. We conclude that many of the Illumina probes have unreliable original annotation and that our re-annotation allows analyses to focus on the good quality probes, which form the majority, and also to expand the scope of biological information that can be extracted.

Transcriptome analysis describing new immunity and defense genes in peripheral blood mononuclear cells of rheumatoid arthritis patients

Background

Large-scale gene expression profiling of peripheral blood mononuclear cells from Rheumatoid Arthritis (RA) patients could provide a molecular description that reflects the contribution of diverse cellular responses associated with this disease. The aim of our study was to identify peripheral blood gene expression profiles for RA patients, using Illumina technology, to gain insights into RA molecular mechanisms.

Methodology/Principal Findings

The Illumina Human-6v2 Expression BeadChips were used for a complete genome-wide transcript profiling of peripheral blood mononuclear cells (PBMCs) from 18 RA patients and 15 controls. Differential analysis per gene was performed with one-way analysis of variance (ANOVA) and P values were adjusted to control the False Discovery Rate (FDR<5%). Genes differentially expressed at significant level between patients and controls were analyzed using Gene Ontology (GO) in the PANTHER database to identify biological processes. A differentially expression of 339 Reference Sequence genes (238 down-regulated and 101 up-regulated) between the two groups was observed. We identified a remarkably elevated expression of a spectrum of genes involved in Immunity and Defense in PBMCs of RA patients compared to controls. This result is confirmed by GO analysis, suggesting that these genes could be activated systemically in RA. No significant down-regulated ontology groups were found. Microarray data were validated by real time PCR in a set of nine genes showing a high degree of correlation.

Conclusions/Significance

Our study highlighted several new genes that could contribute in the identification of innovative clinical biomarkers for diagnostic procedures and therapeutic interventions.

Platform dependence of inference on gene-wise and gene-set involvement in human lung development

BACKGROUND

With the recent development of microarray technologies, the comparability of gene expression data obtained from different platforms poses an important problem. We evaluated two widely used platforms, Affymetrix U133 Plus 2.0 and the Illumina HumanRef-8 v2 Expression Bead Chips, for comparability in a biological system in which changes may be subtle, namely fetal lung tissue as a function of gestational age.

RESULTS

We performed the comparison via sequence-based probe matching between the two platforms. "Significance grouping" was defined as a measure of comparability. Using both expression correlation and significance grouping as measures of comparability, we demonstrated that despite overall cross-platform differences at the single gene level, increased correlation between the two platforms was found in genes with higher expression level, higher probe overlap, and lower p-value. We also demonstrated that biological function as determined via KEGG pathways or GO categories is more consistent across platforms than single gene analysis.

CONCLUSION

We conclude that while the comparability of the platforms at the single gene level may be increased by increasing sample size, they are highly comparable ontologically even for subtle differences in a relatively small sample size. Biologically relevant inference should therefore be reproducible across laboratories using different platforms.

Microarray expression analysis of human dopaminergic neuroblastoma cells after RNA interference of SNCA--a key player in the pathogenesis of Parkinson's disease

The pre-synaptic protein alpha-synuclein is a key player in the pathogenesis of Parkinson's disease (PD). Misfolded alpha-synuclein protofibrils accumulate and serve as seed structures that cause numerous proteins in the cytoplasm of neuronal cells to aggregate into so-called Lewy bodies. Furthermore, both missense mutations and multiplications of the SNCA gene lead to autosomal dominant forms of familial PD. However, the exact biological role of alpha-synuclein in normal brains remains elusive. To gain more insight into the normal function of this protein, we evaluated changes in whole genome expression in dopaminergic neuroblastoma cells (SH-SY5Y) caused by reductions of 90% in alpha-synuclein RNA levels and of 59% in alpha-synuclein protein levels as a result of RNA interference. The expression of 361 genes was altered at least+/-1.5-fold by the RNA interference, with 82 up-regulated and 279 down-regulated. The differentially expressed gene products are involved in the regulation of transcription, cell cycle, protein degradation, apoptosis, neurogenesis, and lipid metabolism. To examine the influence of SNCA down-regulation by RNAi on apoptosis, we performed cell death assays using different stress triggers. The changes observed in the expression profile of dopaminergic neuronal cells following reduction of SNCA expression warrant studies to investigate the role of signaling cascades in familial and idiopathic PD.