Integrating factor analysis and a transgenic mouse model to reveal a peripheral blood predictor of breast tumors

Optimal Supervised Reduction of High Dimensional Transcription Data

The plight of navigating high-dimensional transcription datasets remains a persistent problem. This problem is further amplified for complex disorders, such as cancer as these disorders are often multigenic traits with multiple subsets of genes collectively affecting the type, stage, and severity of the trait. We are often faced with a trade off between reducing the dimensionality of our datasets and maintaining the integrity of our data. To accomplish both tasks simultaneously for very high dimensional transcriptome for complex multigenic traits, we propose a new supervised technique, Class Separation Transformation (CST). CST accomplishes both tasks simultaneously by significantly reducing the dimensionality of the input space into a one-dimensional transformed space that provides optimal separation between the differing classes. Furthermore, CST offers an means of explainable ML, as it computes the relative importance of each feature for its contribution to class distinction, which can thus lead to deeper insights and discovery. We compare our method with existing state-of-the-art methods using both real and synthetic datasets, demonstrating that CST is the more accurate, robust, scalable, and computationally advantageous technique relative to existing methods. Code used in this paper is available on https://github.com/richiebailey74/CST.

MYC-driven synthesis of Siglec ligands is a glycoimmune checkpoint

The Siglecs (sialic acid-binding immunoglobulin-like lectins) are glycoimmune checkpoint receptors that suppress immune cell activation upon engagement of cognate sialoglycan ligands. The cellular drivers underlying Siglec ligand production on cancer cells are poorly understood. We find the MYC oncogene causally regulates Siglec ligand production to enable tumor immune evasion. A combination of glycomics and RNA-sequencing of mouse tumors revealed the MYC oncogene controls expression of the sialyltransferase St6galnac4 and induces a glycan known as disialyl-T. Using in vivo models and primary human leukemias, we find that disialyl-T functions as a "don't eat me" signal by engaging macrophage Siglec-E in mice or the human ortholog Siglec-7, thereby preventing cancer cell clearance. Combined high expression of MYC and ST6GALNAC4 identifies patients with high-risk cancers and reduced tumor myeloid infiltration. MYC therefore regulates glycosylation to enable tumor immune evasion. We conclude that disialyl-T is a glycoimmune checkpoint ligand. Thus, disialyl-T is a candidate for antibody-based checkpoint blockade, and the disialyl-T synthase ST6GALNAC4 is a potential enzyme target for small molecule-mediated immune therapy.

ATF3 Reprograms the Bone Marrow Niche in Response to Early Breast Cancer Transformation

Cancer is a systemic disease able to reprogram the bone marrow (BM) niche towards a protumorigenic state. The impact of cancer on specific BM subpopulations can qualitatively differ according to the signals released by the tumor, which can vary on the basis of the tissue of origin. Using a spontaneous model of mammary carcinoma, we identified BM mesenchymal stem cells (MSC) as the first sensors of distal cancer cells and key mediators of BM reprogramming. Through the release of IL1B, BM MSCs induced transcriptional upregulation and nuclear translocation of the activating transcription factor 3 (ATF3) in hematopoietic stem cells. ATF3 in turn promoted the formation of myeloid progenitor clusters and sustained myeloid cell differentiation. Deletion of Atf3 specifically in the myeloid compartment reduced circulating monocytes and blocked their differentiation into tumor-associated macrophages. In the peripheral blood, the association of ATF3 expression in CD14+ mononuclear cells with the expansion CD11b+ population was able to discriminate between women with malignant or benign conditions at early diagnosis. Overall, this study identifies the IL1B/ATF3 signaling pathway in the BM as a functional step toward the establishment of a tumor-promoting emergency myelopoiesis, suggesting that ATF3 could be tested in a clinical setting as a circulating marker of early transformation and offering the rationale for testing the therapeutic benefits of IL1B inhibition in patients with breast cancer. Significance: Bone marrow mesenchymal stem cells respond to early breast tumorigenesis by upregulating IL1B to promote ATF3 expression in hematopoietic stem cells and to induce myeloid cell differentiation that supports tumor development.

Zoo: Selecting Transcriptomic and Methylomic Biomarkers by Ensembling Animal-Inspired Swarm Intelligence Feature Selection Algorithms

Biological omics data such as transcriptomes and methylomes have the inherent “large p small n” paradigm, i.e., the number of features is much larger than that of the samples. A feature selection (FS) algorithm selects a subset of the transcriptomic or methylomic biomarkers in order to build a better prediction model. The hidden patterns in the FS solution space make it challenging to achieve a feature subset with satisfying prediction performances. Swarm intelligence (SI) algorithms mimic the target searching behaviors of various animals and have demonstrated promising capabilities in selecting features with good machine learning performances. Our study revealed that different SI-based feature selection algorithms contributed complementary searching capabilities in the FS solution space, and their collaboration generated a better feature subset than the individual SI feature selection algorithms. Nine SI-based feature selection algorithms were integrated to vote for the selected features, which were further refined by the dynamic recursive feature elimination framework. In most cases, the proposed Zoo algorithm outperformed the existing feature selection algorithms on transcriptomics and methylomics datasets.

Platform-independent approach for cancer detection from gene expression profiles of peripheral blood cells

Peripheral blood gene expression intensity-based methods for distinguishing healthy individuals from cancer patients are limited by sensitivity to batch effects and data normalization and variability between expression profiling assays. To improve the robustness and precision of blood gene expression-based tumour detection, it is necessary to perform molecular diagnostic tests using a more stable approach. Taking breast cancer as an example, we propose a machine learning-based framework that distinguishes breast cancer patients from healthy subjects by pairwise rank transformation of gene expression intensity in each sample. We showed the diagnostic potential of the method by performing RNA-seq for 37 peripheral blood samples from breast cancer patients and by collecting RNA-seq data from healthy donors in Genotype-Tissue Expression project and microarray mRNA expression datasets in Gene Expression Omnibus. The framework was insensitive to experimental batch effects and data normalization, and it can be simultaneously applied to new sample prediction.

A dynamic recursive feature elimination framework (dRFE) to further refine a set of OMIC biomarkers

MOTIVATION

A feature selection algorithm may select the subset of features with the best associations with the class labels. The recursive feature elimination (RFE) is a heuristic feature screening framework and has been widely used to select the biological OMIC biomarkers. This study proposed a dynamic recursive feature elimination (dRFE) framework with more flexible feature elimination operations. The proposed dRFE was comprehensively compared with 11 existing feature selection algorithms and five classifiers on the eight difficult transcriptome datasets from a previous study, the ten newly collected transcriptome datasets and the five methylome datasets.

RESULTS

The experimental data suggested that the regular RFE framework did not perform well, and dRFE outperformed the existing feature selection algorithms in most cases. The dRFE-detected features achieved Acc=1.0000 for the two methylome datasets GSE53045 and GSE66695. The best prediction accuracies of the dRFE-detected features were 0.9259, 0.9424, and 0.8601 for the other three methylome datasets GSE74845, GSE103186, and GSE80970, respectively. Four transcriptome datasets received Acc=1.0000 using the dRFE-detected features, and the prediction accuracies for the other six newly collected transcriptome datasets were between 0.6301 and 0.9917.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Quantitative STAU2 measurement in lymphocytes for breast cancer risk assessment

Although mammograms play a key role in early breast cancer detection, the test is not applicable to all women, for example, women under the age of 40. The development of a noninvasive blood test with high sensitivity and accessibility will improve the effectiveness of breast cancer screening programmes. Secretory factors released from cancer cells can induce the expression of certain genes in a large number of white blood cells (WBCs). Therefore, cancer-dependent proteins in WBCs can be used as tumour markers with high sensitivity. Five proteins (LMAN1, AZI2, STAU2, MMP9 and PLOD1) from a systemic analysis of a variety of array data of breast cancer patients were subjected to immunofluorescence staining to evaluate the presence of fixed WBCs on 96-well plates from 363 healthy females and 358 female breast cancer patients. The results revealed that the average fluorescence intensity of anti-STAU2 and the percentage of STAU2-positive T and B lymphocytes in breast cancer patients (110.50 ± 23.38 and 61.87 ± 12.44, respectively) were significantly increased compared with those in healthy females (56.47 ± 32.03 and 33.02 ± 18.10, respectively) (p = 3.56 × 10^-71, odds ratio = 24.59, 95% CI = 16.64-36.34). The effect of secreted molecules from breast cancer cells was proven by the increase in STAU2 intensity in PBMCs cocultured with MCF-7 and T47D cells at 48 h (p = 0.0289). The test demonstrated 98.32%, 82.96%, and 48.32% sensitivity and 56.47%, 83.47%, and 98.62% specificity in correlation with the percentage of STAU2-positive cells at 40, 53.34 and 63.38, respectively. We also demonstrated how to use the STAU2 test for the assessment of risk in women under the age of 40. STAU2 is a novel breast cancer marker that can be assessed by quantitative immunofluorescence staining of fixed WBCs that are transportable at room temperature via mail, representing a useful risk assessment tool for women without access to mammograms.

A web-based automated machine learning platform to analyze liquid biopsy data

Liquid biopsy (LB) technologies continue to improve in sensitivity, specificity, and multiplexing and can measure an ever growing library of disease biomarkers. However, clinical interpretation of the increasingly large sets of data these technologies generate remains a challenge. Machine learning is a popular approach to discover and detect signatures of disease. However, limited machine learning expertise in the LB field has kept the discipline from fully leveraging these tools and risks improper analyses and irreproducible results. In this paper, we develop a web-based automated machine learning tool tailored specifically for LB, where machine learning models can be built without the user's input. We also incorporate a differential privacy algorithm, designed to limit the effects of overfitting that can arise from users iteratively developing a panel with feedback from our platform. We validate our approach by performing a meta-analysis on 11 published LB datasets, and found that we had similar or better performance compared to those reported in the literature. Moreover, we show that our platform's performance improved when incorporating information from prior LB datasets, suggesting that this approach can continue to improve with increased access to LB data. Finally, we show that by using our platform the results achieved in the literature can be matched using 40% of the number of subjects in the training set, potentially reducing study cost and time. This self-improving and overfitting-resistant automatic machine learning platform provides a new standard that can be used to validate machine learning works in the LB field.

Untangling the effects of cellular composition on coexpression analysis

Coexpression analysis is widely used for inferring regulatory networks, predicting gene function, and interpretation of transcriptome profiling studies, based on methods such as clustering. The majority of such studies use data collected from bulk tissue, where the effects of cellular composition present a potential confound. However, the impact of composition on coexpression analysis has not been studied in detail. Here, we examine this issue for the case of human RNA analysis. Focusing on brain tissue, we found that, for most genes, differences in expression levels across cell types account for a large fraction of the variance of their measured RNA levels (median R 2 = 0.68). We then show that genes that have similar expression patterns across cell types will have correlated RNA levels in bulk tissue, due to the effect of variation in cellular composition. We demonstrate that much of the coexpression and the formation of coexpression clusters can be attributed to this effect for both brain and blood transcriptomes. For brain, we further show how this composition-induced coexpression masks underlying intra-cell-type coexpression observed in single-cell data. An attempt to correct for composition yielded mixed results. Our conclusion is that the dominant coexpression signal in brain, blood, and, likely, other complex tissues can be attributed to cellular compositional effects, rather than intra-cell-type regulatory relationships. These results have implications for the relevance and interpretation of coexpression analysis.

Analyzing gene expression data for pediatric and adult cancer diagnosis using logic learning machine and standard supervised methods

Background

Logic Learning Machine (LLM) is an innovative method of supervised analysis capable of constructing models based on simple and intelligible rules.

In this investigation the performance of LLM in classifying patients with cancer was evaluated using a set of eight publicly available gene expression databases for cancer diagnosis.

LLM accuracy was assessed by summary ROC curve (sROC) analysis and estimated by the area under an sROC curve (sAUC). Its performance was compared in cross validation with that of standard supervised methods, namely: decision tree, artificial neural network, support vector machine (SVM) and k-nearest neighbor classifier.

Results

LLM showed an excellent accuracy (sAUC = 0.99, 95%CI: 0.98–1.0) and outperformed any other method except SVM.

Conclusions

LLM is a new powerful tool for the analysis of gene expression data for cancer diagnosis. Simple rules generated by LLM could contribute to a better understanding of cancer biology, potentially addressing therapeutic approaches.

Complete deconvolution of cellular mixtures based on linearity of transcriptional signatures

Changes in bulk transcriptional profiles of heterogeneous samples often reflect changes in proportions of individual cell types. Several robust techniques have been developed to dissect the composition of such mixed samples given transcriptional signatures of the pure components or their proportions. These approaches are insufficient, however, in situations when no information about individual mixture components is available. This problem is known as the  complete deconvolution problem, where the composition is revealed without any a priori knowledge about cell types and their proportions. Here, we identify a previously unrecognized property of tissue-specific genes - their mutual linearity - and use it to reveal the structure of the topological space of mixed transcriptional profiles and provide a noise-robust approach to the complete deconvolution problem. Furthermore, our analysis reveals systematic bias of all deconvolution techniques due to differences in cell size or RNA-content, and we demonstrate how to address this bias at the experimental design level.

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.

Comprehensive tissue-specific gene set enrichment analysis and transcription factor analysis of breast cancer by integrating 14 gene expression datasets

Breast cancer is the most commonly diagnosed malignancy in women. Several key genes and pathways have been proven to correlate with breast cancer pathology. This study sought to explore the differences in key transcription factors (TFs), transcriptional regulation networks and dysregulated pathways in different tissues in breast cancer. We employed 14 breast cancer datasets from NCBI-GEO and performed an integrated analysis in three different tissues including breast, blood and saliva. The results showed that there were eight genes (CEBPD, EGR1, EGR2, EGR3, FOS, FOSB, ID1 and NFIL3) down-regulated in breast tissue but up-regulated in blood tissue. Furthermore, we identified several unreported tissue-specific TFs that may contribute to breast cancer, including ATOH8, DMRT2, TBX15 and ZNF367. The dysregulation of these TFs damaged lipid metabolism, development, cell adhesion, proliferation, differentiation and metastasis processes. Among these pathways, the breast tissue showed the most serious impairment and the blood tissue showed a relatively moderate damage, whereas the saliva tissue was almost unaffected. This study could be helpful for future biomarker discovery, drug design, and therapeutic and predictive applications in breast cancers.

Identification of molecular alterations in leukocytes from gene expression profiles of peripheral whole blood of Alzheimer's disease

Blood-based test has been considered as a promising way to diagnose and study Alzheimer's disease (AD). However, the changed proportions of the leukocytes under disease states could confound the aberrant expression signals observed in mixed-cell blood samples. We have previously proposed a method, Ref-REO, to detect the leukocyte specific expression alterations from mixed-cell blood samples. In this study, by applying Ref-REO, we detect 42 and 45 differentially expressed genes (DEGs) between AD and normal peripheral whole blood (PWB) samples in two datasets, respectively. These DEGs are mainly associated with AD-associated functions such as Wnt signaling pathways and mitochondrion dysfunctions. They are also reproducible in AD brain tissue, and tend to interact with the reported AD-associated biomarkers and overlap with targets of AD-associated PWB miRNAs. Moreover, they are closely associated with aging and have severer expression alterations in the younger adults with AD. Finally, diagnostic signatures are constructed from these leukocyte specific alterations, whose area under the curve (AUC) for predicting AD is higher than 0.73 in the two AD PWB datasets. In conclusion, gene expression alterations in leukocytes could be extracted from AD PWB samples, which are closely associated with AD progression, and used as a diagnostic signature of AD.

Evaluation of MCT1, MCT4 and CD147 Genes in Peripheral Blood Cells of Breast Cancer Patients and Their Potential Use as Diagnostic and Prognostic Markers

Background: Patients with breast cancer—the deadliest cancer among women—are at constant risk of developing metastasis. Oxidative stress and hypoxia are common feature of tumor cells that can proliferate even in a resultant metabolic acidosis. Despite the low extracellular pH, intracellular pH of tumor cells remains relatively normal, or even more alkaline due to the action of a membrane protein family known as monocarboxylate transporters (MCTs). The objective of this study was to verify the diagnostic and prognostic value of MCT1, MCT4 and CD147 in tumor and peripheral blood samples of patients with breast cancer undergoing chemotherapic treatment. Methods: Differential expression of MCT1, MCT4 and CD147 obtained by qPCR was determined by 2^−ΔΔCq method between biological samples (tumor and serial samples of peripheral) of patients (n = 125) and healthy women (n = 25). Results: tumor samples with higher histological grades have shown higher expression of these markers; this higher expression was also observed in blood samples obtained at diagnosis of patients when compared to healthy women and in patients with positive progression of the disease (metastasis development). Conclusion: markers studied here could be a promising strategy in routine laboratory evaluations as breast cancer diagnosis and prognosis.

Integrated Bioinformatics Approach Reveals Crosstalk Between Tumor Stroma and Peripheral Blood Mononuclear Cells in Breast Cancer

Breast cancer is now the leading cause of cancer death in women worldwide. Cancer progression is driven not only by cancer cell intrinsic alterations and interactions with tumor microenvironment, but also by systemic effects. Integration of multiple profiling data may provide insights into the underlying molecular mechanisms of complex systemic processes. We performed a bioinformatic analysis of two public available microarray datasets for breast tumor stroma and peripheral blood mononuclear cells, featuring integrated transcriptomics data, protein-protein interactions (PPIs) and protein subcellular localization, to identify genes and biological pathways that contribute to dialogue between tumor stroma and the peripheral circulation. Genes of the integrin family as well as CXCR4 proved to be hub nodes of the crosstalk network and may play an important role in response to stroma-derived chemoattractants. This study pointed to potential for development of therapeutic strategies that target systemic signals travelling through the circulation and interdict tumor cell recruitment.

GRACOMICS: software for graphical comparison of multiple results with omics data

Background

Analysis of large-scale omics data has become more and more challenging due to high dimensionality. More complex analysis methods and tools are required to handle such data. While many methods already exist, those methods often produce different results. To help users obtain more appropriate results (i.e. candidate genes), we propose a tool, GRACOMICS that compares numerous analysis results visually in a more systematic way; this enables the users to easily interpret the results more comfortably.

Results

GRACOMICS has the ability to visualize multiple analysis results interactively. We developed GRACOMICS to provide instantaneous results (plots and tables), corresponding to user-defined threshold values, since there are yet no other up-to-date omics data visualization tools that provide such features. In our analysis, we successfully employed two types of omics data: transcriptomic data (microarray and RNA-seq data) and genomic data (SNP chip and NGS data).

Conclusions

GRACOMICS is a graphical user interface (GUI)-based program written in Java for cross-platform computing environments, and can be applied to compare analysis results for any type of large-scale omics data. This tool can be useful for biologists to identify genes commonly found by intersected statistical methods, for further experimental validation.

Electronic supplementary material

The online version of this article (doi:10.1186/s12864-015-1461-0) contains supplementary material, which is available to authorized users.

On the identification of circulating tumor cells in breast cancer

Breast cancer is a highly heterogeneous disease and very common among western women. The main cause of death is not the primary tumor but its metastases at distant sites, such as lymph nodes and other organs (preferentially lung, liver, and bones). The study of circulating tumor cells (CTCs) in peripheral blood resulting from tumor cell invasion and intravascular filtration highlights their crucial role concerning tumor aggressiveness and metastasis. Genomic research regarding CTCs monitoring for breast cancer is limited due to the lack of indicative genes for their detection and isolation. Instead of direct CTC detection, in our study, we focus on the identification of factors in peripheral blood that can indirectly reveal the presence of such cells. Using selected publicly available breast cancer and peripheral blood microarray datasets, we follow a two-step elimination procedure for the identification of several discriminant factors. Our procedure facilitates the identification of major genes involved in breast cancer pathology, which are also indicative of CTCs presence.

Biomarkers from biosimulations: Transcriptome-To-Reactome™ Technology for individualized medicine

We validated a model of the TGF-β signaling pathway using reactions from Reactome. Using a patentpending technique, gene expression profiles from individual patients are used to determine model parameters. Gene expression profiles from 45 women, normal, or benign tumor and malignant breast cancer were used as training and validating sets for assessing clinical sensitivity and specificity. Biomarkers were identified from the biosimulation results using sensitivity analyses and derivative properties from the model. A membrane signaling marker had sensitivity of 80% and specificity of 60%; while a nuclear transcription factor marker had sensitivity of 80% and specificity of 90% to predict malignancy. Use of Fagan's nomogram increased probability from 7.5% for positive mammogram to 39% with positive results of the biosimulation for the nuclear marker. Our technology will allow researchers to identify and develop biomarkers and assist clinicians in diagnostic and treatment decision making.

Understanding disease mechanisms with models of signaling pathway activities

BACKGROUND

Understanding the aspects of the cell functionality that account for disease or drug action mechanisms is one of the main challenges in the analysis of genomic data and is on the basis of the future implementation of precision medicine.

RESULTS

Here we propose a simple probabilistic model in which signaling pathways are separated into elementary sub-pathways or signal transmission circuits (which ultimately trigger cell functions) and then transforms gene expression measurements into probabilities of activation of such signal transmission circuits. Using this model, differential activation of such circuits between biological conditions can be estimated. Thus, circuit activation statuses can be interpreted as biomarkers that discriminate among the compared conditions. This type of mechanism-based biomarkers accounts for cell functional activities and can easily be associated to disease or drug action mechanisms. The accuracy of the proposed model is demonstrated with simulations and real datasets.

CONCLUSIONS

The proposed model provides detailed information that enables the interpretation disease mechanisms as a consequence of the complex combinations of altered gene expression values. Moreover, it offers a framework for suggesting possible ways of therapeutic intervention in a pathologically perturbed system.

Network-based Prediction of Cancer under Genetic Storm

Classification of cancer patients using traditional methods is a challenging task in the medical practice. Owing to rapid advances in microarray technologies, currently expression levels of thousands of genes from individual cancer patients can be measured. The classification of cancer patients by supervised statistical learning algorithms using the gene expression datasets provides an alternative to the traditional methods. Here we present a new network-based supervised classification technique, namely the NBC method. We compare NBC to five traditional classification techniques (support vector machines (SVM), k-nearest neighbor (kNN), naïve Bayes (NB), C4.5, and random forest (RF)) using 50–300 genes selected by five feature selection methods. Our results on five large cancer datasets demonstrate that NBC method outperforms traditional classification techniques. Our analysis suggests that using symmetrical uncertainty (SU) feature selection method with NBC method provides the most accurate classification strategy. Finally, in-depth analysis of the correlation-based co-expression networks chosen by our network-based classifier in different cancer classes shows that there are drastic changes in the network models of different cancer types.

Peripheral blood cells inform on the presence of breast cancer: a population-based case-control study

Tumor–host interactions extend beyond the local microenvironment and cancer development largely depends on the ability of malignant cells to hijack and exploit the normal physiological processes of the host. Here, we established that many genes within peripheral blood cells show differential expression when an untreated breast cancer (BC) is present, and harnessed this fact to construct a 50-gene signature that distinguish BC patients from population-based controls. Our results were derived from a series of large datasets within our unique population-based Norwegian Women and Cancer cohort that allowed us to investigate the influence of medications and tumor characteristics on our blood-based test, and were further tested in two external datasets. Our 50-gene signature contained cytostatic signals including the specific suppression of the immune response and medications influencing transcription involved in those processes were identified as confounders. Through analysis of the biological processes differentially expressed in blood, we were able to provide a rationale as to why the systemic response of the host may be a reliable marker of BC, characterized by the underexpression of both immune-specific pathways and “universal” cell programs driven by MYC (i.e., metabolism, growth and cell cycle). In conclusion, gene expression of peripheral blood cells is markedly perturbed by the specific presence of carcinoma in the breast and these changes simultaneously engage a number of systemic cytostatic signals emerging connections with immune escape of BC.

Functional networking of human divergently paired genes (DPGs)

Divergently paired genes (DPGs), also known as bidirectional (head-to-head positioned) genes, are conserved across species and lineages, and thus deemed to be exceptional in genomic organization and functional regulation. Despite previous investigations on the features of their conservation and gene organization, the functional relationship among DPGs in a given species and lineage has not been thoroughly clarified. Here we report a network-based comprehensive analysis on human DPGs and our results indicate that the two members of the DPGs tend to participate in different biological processes while enforcing related functions as modules. Comparing to randomly paired genes as a control, the DPG pairs have a tendency to be clustered in similar “cellular components” and involved in similar “molecular functions”. The functional network bridged by DPGs consists of three major modules. The largest module includes many house-keeping genes involved in core cellular activities. This module also shows low variation in expression in both CNS (central nervous system) and non-CNS tissues. Based on analyses of disease transcriptome data, we further suggest that this particular module may play crucial roles in HIV infection and its disease mechanism.

TGF-β signaling in myeloid cells is required for tumor metastasis

TGF-β is overexpressed in advanced human cancers. It correlates with metastasis and poor prognosis. However, TGF-β functions as both a tumor suppressor and a tumor promoter. Here, we report for the first time that genetic deletion of Tgfbr2 specifically in myeloid cells (Tgfbr2(MyeKO)) significantly inhibited tumor metastasis. Reconstitution of tumor-bearing mice with Tgfbr2(MyeKO) bone marrow recapitulated the inhibited metastasis phenotype. This effect is mediated through decreased production of type II cytokines, TGF-β1, arginase 1, and inducible nitric oxide synthase, which promoted IFN-γ production and improved systemic immunity. Depletion of CD8 T cells diminished the metastasis defect in the Tgfbr2(MyeKO) mice. Consistent with animal studies, myeloid cells from patients with advanced-stage cancer showed increased TGF-β receptor II expression. Our studies show that myeloid-specific TGF-β signaling is an essential component of the metastasis-promoting puzzle of TGF-β. This is in contrast to the previously reported tumor-suppressing phenotypes in fibroblasts, epithelial cells, and T cells.

Host gene expression profiling and in vivo cytokine studies to characterize the role of linezolid and vancomycin in methicillin-resistant Staphylococcus aureus (MRSA) murine sepsis model

Linezolid (L), a potent antibiotic for Methicillin Resistant Staphylococcus aureus (MRSA), inhibits bacterial protein synthesis. By contrast, vancomycin (V) is a cell wall active agent. Here, we used a murine sepsis model to test the hypothesis that L treatment is associated with differences in bacterial and host characteristics as compared to V. Mice were injected with S. aureus USA300, and then intravenously treated with 25 mg/kg of either L or V at 2 hours post infection (hpi). In vivo alpha-hemolysin production was reduced in both L and V-treated mice compared to untreated mice but the reduction did not reach the statistical significance [P = 0.12 for L; P = 0.70 for V). PVL was significantly reduced in L-treated mice compared to untreated mice (P = 0.02). However the reduction of in vivo PVL did not reach the statistical significance in V- treated mice compared to untreated mice (P = 0.27). Both antibiotics significantly reduced IL-1β production [P = 0.001 for L; P = 0.006 for V]. IL-6 was significantly reduced with L but not V antibiotic treatment [P<0.001 for L; P = 0.11 for V]. Neither treatment significantly reduced production of TNF-α. Whole-blood gene expression profiling showed no significant effect of L and V on uninfected mice. In S. aureus-infected mice, L altered the expression of a greater number of genes than V (95 vs. 42; P = 0.001). Pathway analysis for the differentially expressed genes identified toll-like receptor signaling pathway to be common to each S. aureus-infected comparison. Expression of immunomodulatory genes like Cxcl9, Cxcl10, Il1r2, Cd14 and Nfkbia was different among the treatment groups. Glycerolipid metabolism pathway was uniquely associated with L treatment in S. aureus infection. This study demonstrates that, as compared to V, treatment with L is associated with reduced levels of toxin production, differences in host inflammatory response, and distinct host gene expression characteristics in MRSA sepsis.

Gene expression-based classifiers identify Staphylococcus aureus infection in mice and humans

Staphylococcus aureus causes a spectrum of human infection. Diagnostic delays and uncertainty lead to treatment delays and inappropriate antibiotic use. A growing literature suggests the host’s inflammatory response to the pathogen represents a potential tool to improve upon current diagnostics. The hypothesis of this study is that the host responds differently to S. aureus than to E. coli infection in a quantifiable way, providing a new diagnostic avenue. This study uses Bayesian sparse factor modeling and penalized binary regression to define peripheral blood gene-expression classifiers of murine and human S. aureus infection. The murine-derived classifier distinguished S. aureus infection from healthy controls and Escherichia coli-infected mice across a range of conditions (mouse and bacterial strain, time post infection) and was validated in outbred mice (AUC>0.97). A S. aureus classifier derived from a cohort of 94 human subjects distinguished S. aureus blood stream infection (BSI) from healthy subjects (AUC 0.99) and E. coli BSI (AUC 0.84). Murine and human responses to S. aureus infection share common biological pathways, allowing the murine model to classify S. aureus BSI in humans (AUC 0.84). Both murine and human S. aureus classifiers were validated in an independent human cohort (AUC 0.95 and 0.92, respectively). The approach described here lends insight into the conserved and disparate pathways utilized by mice and humans in response to these infections. Furthermore, this study advances our understanding of S. aureus infection; the host response to it; and identifies new diagnostic and therapeutic avenues.

Intragenic ATM methylation in peripheral blood DNA as a biomarker of breast cancer risk

Few studies have evaluated the association between DNA methylation in white blood cells (WBC) and the risk of breast cancer. The evaluation of WBC DNA methylation as a biomarker of cancer risk is of particular importance as peripheral blood is often available in prospective cohorts and easier to obtain than tumor or normal tissues. Here, we used prediagnostic blood samples from three studies to analyze WBC DNA methylation of two ATM intragenic loci (ATMmvp2a and ATMmvp2b) and genome-wide DNA methylation in long interspersed nuclear element-1 (LINE1) repetitive elements. Samples were from a case-control study derived from a cohort of high-risk breast cancer families (KConFab) and nested case-control studies in two prospective cohorts: Breakthrough Generations Study (BGS) and European Prospective Investigation into Cancer and Nutrition (EPIC). Bisulfite pyrosequencing was used to quantify methylation from 640 incident cases of invasive breast cancer and 741 controls. Quintile analyses for ATMmvp2a showed an increased risk of breast cancer limited to women in the highest quintile [OR, 1.89; 95% confidence interval (CI), 1.36-2.64; P = 1.64 × 10(-4)]. We found no significant differences in estimates across studies or in analyses stratified by family history or menopausal status. However, a more consistent association was observed in younger than in older women and individually significant in KConFab and BGS, but not EPIC. We observed no differences in LINE1 or ATMmvp2b methylation between cases and controls. Together, our findings indicate that WBC DNA methylation levels at ATM could be a marker of breast cancer risk and further support the pursuit of epigenome-wide association studies of peripheral blood DNA methylation.