Distinct transcriptional profiles of maternal and fetal placental macrophages at term are associated with gravidity

Maternal intervillous monocytes (MIMs) and fetal Hofbauer cells (HBCs) are myeloid-derived immune cells at the maternal-fetal interface. Little is known regarding the molecular phenotypes and roles of these distinct monocyte/macrophage populations. Here, we used RNA sequencing to investigate the transcriptional profiles of MIMs and HBCs in six normal term pregnancies. Our analyses revealed distinct transcriptomes of MIMs and HBCs. Genes involved in differentiation and cell organization pathways were more highly expressed in MIMs vs. HBCs. In contrast, HBCs had higher expression of genes involved in inflammatory responses and cell surface receptor signaling. Maternal gravidity influenced monocyte programming, as expression of pro-inflammatory molecules was significantly higher in MIMs from multigravidas compared to primigravidas. In HBCs, multigravidas displayed enrichment of gene pathways involved in cell-cell signaling and differentiation. In summary, our results demonstrated that MIMs and HBCs have highly divergent transcriptional signatures, reflecting their distinct origins, locations, functions, and roles in inflammatory responses. Our data further suggested that maternal gravidity influences the gene signatures of MIMs and HBCs, potentially modulating the interplay between tolerance and trained immunity. The phenomenon of reproductive immune memory may play a novel role in the differential susceptibility of primigravidas to pregnancy complications.

Dynamic evaluation of blood immune cells predictive of response to immune checkpoint inhibitors in NSCLC by multicolor spectrum flow cytometry

Introduction

Immune checkpoint inhibitors (ICIs) only benefit a subset of cancer patients, underlining the need for predictive biomarkers for patient selection. Given the limitations of tumor tissue availability, flow cytometry of peripheral blood mononuclear cells (PBMCs) is considered a noninvasive method for immune monitoring. This study explores the use of spectrum flow cytometry, which allows a more comprehensive analysis of a greater number of markers using fewer immune cells, to identify potential blood immune biomarkers and monitor ICI treatment in non-small-cell lung cancer (NSCLC) patients.

Methods

PBMCs were collected from 14 non-small-cell lung cancer (NSCLC) patients before and after ICI treatment and 4 healthy human donors. Using spectrum flow cytometry, 24 immune cell markers were simultaneously monitored using only 1 million PBMCs. The results were also compared with those from clinical flow cytometry and bulk RNA sequencing analysis.

Results

Our findings showed that the measurement of CD4+ and CD8+ T cells by spectrum flow cytometry matched well with those by clinical flow cytometry (Pearson R ranging from 0.75 to 0.95) and bulk RNA sequencing analysis (R=0.80, P=1.3 x 10-4). A lower frequency of CD4+ central memory cells before treatment was associated with a longer median progression-free survival (PFS) [Not reached (NR) vs. 5 months; hazard ratio (HR)=8.1, 95% confidence interval (CI) 1.5-42, P=0.01]. A higher frequency of CD4-CD8- double-negative (DN) T cells was associated with a longer PFS (NR vs. 4.45 months; HR=11.1, 95% CI 2.2-55.0, P=0.003). ICIs significantly changed the frequency of cytotoxic CD8+PD1+ T cells, DN T cells, CD16+CD56dim and CD16+CD56- natural killer (NK) cells, and CD14+HLDRhigh and CD11c+HLADR + monocytes. Of these immune cell subtypes, an increase in the frequency of CD16+CD56dim NK cells and CD14+HLADRhigh monocytes after treatment compared to before treatment were associated with a longer PFS (NR vs. 5 months, HR=5.4, 95% CI 1.1-25.7, P=0.03; 7.8 vs. 3.8 months, HR=5.7, 95% CI 169 1.0-31.7, P=0.04), respectively.

Conclusion

Our preliminary findings suggest that the use of multicolor spectrum flow cytometry helps identify potential blood immune biomarkers for ICI treatment, which warrants further validation.

Longitudinal profiling in patients undergoing cardiac surgery reveals postoperative changes in DNA methylation

BACKGROUND

Cardiac surgery and cardiopulmonary bypass induce a substantial immune and inflammatory response, the overactivation of which is associated with significant pulmonary, cardiovascular, and neurologic complications. Commensurate with the immune and inflammatory response are changes in the heart and vasculature itself, which together drive postoperative complications through mechanisms that are poorly understood. Longitudinal DNA methylation profiling has the potential to identify changes in gene regulatory mechanisms that are secondary to surgery and to identify molecular processes that predict and/or cause postoperative complications. In this study, we measure DNA methylation in preoperative and postoperative whole blood samples from 96 patients undergoing cardiac surgery on cardiopulmonary bypass.

RESULTS

While the vast majority of DNA methylation is unchanged by surgery after accounting for changes in cell-type composition, we identify several loci with statistically significant postoperative changes in methylation. Additionally, two of these loci are associated with new-onset postoperative atrial fibrillation, a significant complication after cardiac surgery. Paired statistical analysis, use of FACS data to support sufficient control of cell-type heterogeneity, and measurement of IL6 levels in a subset of patients add rigor to this analysis, allowing us to distinguish cell-type variability from actual changes in methylation.

CONCLUSIONS

This study identifies significant changes in DNA methylation that occur immediately after cardiac surgery and demonstrates that these acute alterations in DNA methylation have the granularity to identify processes associated with major postoperative complications. This research also establishes methods for controlling for cell-type variability in a large human cohort that may be useful to deploy in other longitudinal studies of epigenetic marks in the setting of acute and chronic disease.

Systematic evaluation of transcriptomics-based deconvolution methods and references using thousands of clinical samples

Estimating cell type composition of blood and tissue samples is a biological challenge relevant in both laboratory studies and clinical care. In recent years, a number of computational tools have been developed to estimate cell type abundance using gene expression data. Although these tools use a variety of approaches, they all leverage expression profiles from purified cell types to evaluate the cell type composition within samples. In this study, we compare 12 cell type quantification tools and evaluate their performance while using each of 10 separate reference profiles. Specifically, we have run each tool on over 4000 samples with known cell type proportions, spanning both immune and stromal cell types. A total of 12 of these represent in vitro synthetic mixtures and 300 represent in silico synthetic mixtures prepared using single-cell data. A final 3728 clinical samples have been collected from the Framingham cohort, for which cell populations have been quantified using electrical impedance cell counting. When tools are applied to the Framingham dataset, the tool Estimating the Proportions of Immune and Cancer cells (EPIC) produces the highest correlation, whereas Gene Expression Deconvolution Interactive Tool (GEDIT) produces the lowest error. The best tool for other datasets is varied, but CIBERSORT and GEDIT most consistently produce accurate results. We find that optimal reference depends on the tool used, and report suggested references to be used with each tool. Most tools return results within minutes, but on large datasets runtimes for CIBERSORT can exceed hours or even days. We conclude that deconvolution methods are capable of returning high-quality results, but that proper reference selection is critical.

Human DNA methylation signatures differentiate persistent from resolving MRSA bacteremia

Persistent methicillin-resistant Staphylococcus aureus (MRSA) bacteremia is life threatening and occurs in up to 30% of MRSA bacteremia cases despite appropriate antimicrobial therapy. Isolates of MRSA that cause antibiotic-persistent methicillin-resistant S. aureus bacteremia (APMB) typically have in vitro antibiotic susceptibilities equivalent to those causing antibiotic-resolving methicillin-resistant S. aureus bacteremia (ARMB). Thus, persistence reflects host-pathogen interactions occurring uniquely in context of antibiotic therapy in vivo. However, host factors and mechanisms involved in APMB remain unclear. We compared DNA methylomes in circulating immune cells from patients experiencing APMB vs. ARMB. Overall, methylation signatures diverged in the distinct patient cohorts. Differentially methylated sites intensified proximate to transcription factor binding sites, primarily in enhancer regions. In APMB patients, significant hypomethylation was observed in binding sites for CCAAT enhancer binding protein-β (C/EBPβ) and signal transducer/activator of transcription 1 (STAT1). In contrast, hypomethylation in ARMB patients localized to glucocorticoid receptor and histone acetyltransferase p300 binding sites. These distinct methylation signatures were enriched in neutrophils and achieved a mean area under the curve of 0.85 when used to predict APMB using a classification model. These findings validated by targeted bisulfite sequencing (TBS-seq) differentiate epigenotypes in patients experiencing APMB vs. ARMB and suggest a risk stratification strategy for antibiotic persistence in patients treated for MRSA bacteremia.

The Gene Expression Deconvolution Interactive Tool (GEDIT): accurate cell type quantification from gene expression data

Background

The cell type composition of heterogeneous tissue samples can be a critical variable in both clinical and laboratory settings. However, current experimental methods of cell type quantification (e.g., cell flow cytometry) are costly, time consuming and have potential to introduce bias. Computational approaches that use expression data to infer cell type abundance offer an alternative solution. While these methods have gained popularity, most fail to produce accurate predictions for the full range of platforms currently used by researchers or for the wide variety of tissue types often studied.

Results

We present the Gene Expression Deconvolution Interactive Tool (GEDIT), a flexible tool that utilizes gene expression data to accurately predict cell type abundances. Using both simulated and experimental data, we extensively evaluate the performance of GEDIT and demonstrate that it returns robust results under a wide variety of conditions. These conditions include multiple platforms (microarray and RNA-seq), tissue types (blood and stromal), and species (human and mouse). Finally, we provide reference data from 8 sources spanning a broad range of stromal and hematopoietic types in both human and mouse. GEDIT also accepts user-submitted reference data, thus allowing the estimation of any cell type or subtype, provided that reference data are available.

Conclusions

GEDIT is a powerful method for evaluating the cell type composition of tissue samples and provides excellent accuracy and versatility compared to similar tools. The reference database provided here also allows users to obtain estimates for a wide variety of tissue samples without having to provide their own data.

Effect of the p53 P72R Polymorphism on Mutant TP53 Allele Selection in Human Cancer

BACKGROUND

TP53 mutations occur in more than 50% of cancers. We sought to determine the effect of the intragenic P72R SNP (rs1042522) on the oncogenic properties of mutant p53.

METHODS

P72R allelic selection in tumors was determined from genotype calls and a Gaussian distributed mixture model. The SNP effect on mutant p53 was determined in p53-negative cancer cell lines. RNA-sequencing, chromatin immunoprecipitation, and survival analysis were performed to describe the SNP effect. All statistical tests were 2-sided.

RESULTS

Among 409 patients with germline heterozygous P72R SNP who harbored somatic mutations in TP53, we observed a selection bias against missense TP53 mutants encoding the P72 SNP (P = 1.64 x 10-13). Exogenously expressed hotspot p53 mutants with the P72 SNP were negatively selected in cancer cells. Gene expression analyses showed the enrichment of p53 pathway genes and inflammatory genes in cancer cells transduced with mutants encoding P72 SNP. Immune gene signature is enriched in patients harboring missense TP53 mutations with homozygous P72 SNP. These patients have improved overall survival as compared to those with the R72 SNP (P = 0.04).

CONCLUSION

This is the largest study demonstrating a selection against the P72 SNP.  Missense p53 mutants with the P72 SNP retain partial wild type tumor-suppressive functions, which may explain the selection bias against P72 SNP across cancer types. Ovarian cancer patients with the P72 SNP have a better prognosis than with the R72 SNP. Our study describes a previously unknown role through which the rs1042522 SNP modifies tumor suppressor activities of mutant p53 in patients.

Profiling immunoglobulin repertoires across multiple human tissues using RNA sequencing

Profiling immunoglobulin (Ig) receptor repertoires with specialized assays can be cost-ineffective and time-consuming. Here we report ImReP, a computational method for rapid and accurate profiling of the Ig repertoire, including the complementary-determining region 3 (CDR3), using regular RNA sequencing data such as those from 8,555 samples across 53 tissues types from 544 individuals in the Genotype-Tissue Expression (GTEx v6) project. Using ImReP and GTEx v6 data, we generate a collection of 3.6 million Ig sequences, termed the atlas of immunoglobulin repertoires (TAIR), across a broad range of tissue types that often do not have reported Ig repertoires information. Moreover, the flow of Ig clonotypes and inter-tissue repertoire similarities across immune-related tissues are also evaluated. In summary, TAIR is one of the largest collections of CDR3 sequences and tissue types, and should serve as an important resource for studying immunological diseases.

Information on immune receptor repertoire provides important insights on disease progression and therapy development, but can be expensive and time-consuming to obtain. Here the authors report ImReP, a computational method that can extract detailed immune repertoire information from existing tissue-specific RNA sequencing data.

Vitamin A Metabolism by Dendritic Cells Triggers an Antimicrobial Response against Mycobacterium tuberculosis

Epidemiological evidence correlates low serum vitamin A (retinol) levels with increased susceptibility to active tuberculosis (TB); however, retinol is biologically inactive and must be converted into its bioactive form, all-trans retinoic acid (ATRA). Given that ATRA triggers a Niemann-Pick type C2 (NPC2)-dependent antimicrobial response against Mycobacterium tuberculosis, we investigated the mechanism by which the immune system converts retinol into ATRA at the site of infection. We demonstrate that granulocyte-macrophage colony-stimulating factor (GM-CSF)-derived dendritic cells (DCs), but not macrophages, express enzymes in the vitamin A metabolic pathway, including aldehyde dehydrogenase 1 family, member a2 (ALDH1A2) and short-chain dehydrogenase/reductase family, member 9 (DHRS9), enzymes capable of the two-step conversion of retinol into ATRA, which is subsequently released from the cell. Additionally, mRNA and protein expression levels of ALDH1A2 and DC marker CD1B were lower in tuberculosis lung tissues than in normal lung. The conditioned medium from DCs cultured with retinol stimulated antimicrobial activity from M. tuberculosis-infected macrophages, as well as the expression of NPC2 in monocytes, which was blocked by specific inhibitors, including retinoic acid receptor inhibitor (RARi) or N,N-diethylaminobenzaldehyde (DEAB), an ALDH1A2 inhibitor. These results indicate that metabolism of vitamin A by DCs transactivates macrophage antimicrobial responses.IMPORTANCE Tuberculosis (TB) is the leading cause of death by a single infectious agent worldwide. One factor that contributes to the success of the microbe is the deficiency in immunomodulatory nutrients, such as vitamin A (retinol), which are prevalent in areas where TB is endemic. Clinical trials show that restoration of systemic retinol levels in active TB patients is ineffective in mitigating the disease; however, laboratory studies demonstrate that activation of the vitamin A pathway in Mycobacterium tuberculosis-infected macrophages triggers an antimicrobial response. Therefore, the goal of this study was to determine the link between host retinol levels and retinoic acid-mediated antimicrobial responses against M. tuberculosis By combining established in vitro models with in situ studies of lung tissue from TB patients, this study demonstrates that the innate immune system utilizes transcellular metabolism leading to activation between dendritic cells and macrophages as a means to combat the pathogen.

Dual RNA-Seq of Human Leprosy Lesions Identifies Bacterial Determinants Linked to Host Immune Response

To understand how the interaction between an intracellular bacterium and the host immune system contributes to outcome at the site of infection, we studied leprosy, a disease that forms a clinical spectrum, in which progressive infection by the intracellular bacterium Mycobacterium leprae is characterized by the production of type I IFNs and antibody production. Dual RNA-seq on patient lesions identifies two independent molecular measures of M. leprae, each of which correlates with distinct aspects of the host immune response. The fraction of bacterial transcripts, reflecting bacterial burden, correlates with a host type I IFN gene signature, known to inhibit antimicrobial responses. Second, the bacterial mRNA:rRNA ratio, reflecting bacterial viability, links bacterial heat shock proteins with the BAFF-BCMA host antibody response pathway. Our findings provide a platform for the interrogation of host and pathogen transcriptomes at the site of infection, allowing insight into mechanisms of inflammation in human disease.

Genetic Mechanisms of Immune Evasion in Colorectal Cancer

To understand the genetic drivers of immune recognition and evasion in colorectal cancer, we analyzed 1,211 colorectal cancer primary tumor samples, including 179 classified as microsatellite instability-high (MSI-high). This set includes The Cancer Genome Atlas colorectal cancer cohort of 592 samples, completed and analyzed here. MSI-high, a hypermutated, immunogenic subtype of colorectal cancer, had a high rate of significantly mutated genes in important immune-modulating pathways and in the antigen presentation machinery, including biallelic losses of B2M and HLA genes due to copy-number alterations and copy-neutral loss of heterozygosity. WNT/β-catenin signaling genes were significantly mutated in all colorectal cancer subtypes, and activated WNT/β-catenin signaling was correlated with the absence of T-cell infiltration. This large-scale genomic analysis of colorectal cancer demonstrates that MSI-high cases frequently undergo an immunoediting process that provides them with genetic events allowing immune escape despite high mutational load and frequent lymphocytic infiltration and, furthermore, that colorectal cancer tumors have genetic and methylation events associated with activated WNT signaling and T-cell exclusion.Significance: This multi-omic analysis of 1,211 colorectal cancer primary tumors reveals that it should be possible to better monitor resistance in the 15% of cases that respond to immune blockade therapy and also to use WNT signaling inhibitors to reverse immune exclusion in the 85% of cases that currently do not. Cancer Discov; 8(6); 730-49. ©2018 AACR.This article is highlighted in the In This Issue feature, p. 663.

Anti-inflammatory therapies of amyotrophic lateral sclerosis guided by immune pathways

Sporadic ALS patients display heterogeneous immune pathways in peripheral blood mononuclear cells (PBMCs). We tested nine sALS patients and one unaffected identical twin of an index case by RNA-Seq of PBMCs. The inflammatory patients (n = 3) clustered into a subset with an inflammatory Th1/Th17 signature and the non-inflammatory patients (n = 7) into another subset with a B cell signature. The inflammatory subset was remarkable for granulocyte and agranulocyte diapedesis, hepatic fibrosis, roles of cytokines and metalloproteases. The non-inflammatory subset was highlighted by degradation of vitamin E, serotonin and nucleotides, altered T cell and B cell signaling, agranulocyte diapedesis, and up regulation of B cell genes. Identification of these differentially regulated pathways in sALS patients may guide the choice of anti-inflammatory therapies.