Whole blood transcriptomics identifies subclasses of pediatric septic shock.

BACKGROUND: Sepsis is a highly heterogeneous syndrome, which has hindered the development of effective therapies. This has prompted investigators to develop a precision medicine approach aimed at identifying biologically homogenous subgroups of patients with septic shock and critical illnesses. Transcriptomic analysis can identify subclasses derived from differences in underlying pathophysiological processes that may provide the basis for new targeted therapies. The goal of this study was to elucidate pathophysiological pathways and identify pediatric septic shock subclasses based on whole blood RNA expression profiles. METHODS: The subjects were critically ill children with cardiopulmonary failure who were a part of a prospective randomized insulin titration trial to treat hyperglycemia. Genome-wide expression profiling was conducted using RNA sequencing from whole blood samples obtained from 46 children with septic shock and 52 mechanically ventilated noninfected controls without shock. Patients with septic shock were allocated to subclasses based on hierarchical clustering of gene expression profiles, and we then compared clinical characteristics, plasma inflammatory markers, cell compositions using GEDIT, and immune repertoires using Imrep between the two subclasses. RESULTS: Patients with septic shock depicted alterations in innate and adaptive immune pathways. Among patients with septic shock, we identified two subtypes based on gene expression patterns. Compared with Subclass 2, Subclass 1 was characterized by upregulation of innate immunity pathways and downregulation of adaptive immunity pathways. Subclass 1 had significantly worse clinical outcomes despite the two classes having similar illness severity on initial clinical presentation. Subclass 1 had elevated levels of plasma inflammatory cytokines and endothelial injury biomarkers and demonstrated decreased percentages of CD4 T cells and B cells and less diverse T cell receptor repertoires. CONCLUSIONS: Two subclasses of pediatric septic shock patients were discovered through genome-wide expression profiling based on whole blood RNA sequencing with major biological and clinical differences. Trial Registration This is a secondary analysis of data generated as part of the observational CAF-PINT ancillary of the HALF-PINT study (NCT01565941). Registered March 29, 2012.

Amnion responses to intrauterine inflammation and effects of inhibition of TNF signaling in preterm Rhesus macaque.

Intrauterine infection/inflammation (IUI) is a frequent complication of pregnancy leading to preterm labor and fetal inflammation. How inflammation is modulated at the maternal-fetal interface is unresolved. We compared transcriptomics of amnion (a fetal tissue in contact with amniotic fluid) in a preterm Rhesus macaque model of IUI induced by lipopolysaccharide with human cohorts of chorioamnionitis. Bulk RNA sequencing (RNA-seq) amnion transcriptomic profiles were remarkably similar in both Rhesus and human subjects and revealed that induction of key labor-mediating genes such as IL1 and IL6 was dependent on nuclear factor κB (NF-κB) signaling and reversed by the anti-tumor necrosis factor (TNF) antibody Adalimumab. Inhibition of collagen biosynthesis by IUI was partially restored by Adalimumab. Interestingly, single-cell transcriptomics, flow cytometry, and immunohistology demonstrated that a subset of amnion mesenchymal cells (AMCs) increase CD14 and other myeloid cell markers during IUI both in the human and Rhesus macaque. Our data suggest that CD14+ AMCs represent activated AMCs at the maternal-fetal interface.

Whole Blood Transcriptomics Identifies Subclasses of Pediatric Septic Shock.

Background: Sepsis is a highly heterogeneous syndrome, that has hindered the development of effective therapies. This has prompted investigators to develop a precision medicine approach aimed at identifying biologically homogenous subgroups of patients with septic shock and critical illnesses. Transcriptomic analysis can identify subclasses derived from differences in underlying pathophysiological processes that may provide the basis for new targeted therapies. The goal of this study was to elucidate pathophysiological pathways and identify pediatric septic shock subclasses based on whole blood RNA expression profiles. Methods: The subjects were critically ill children with cardiopulmonary failure who were a part of a prospective randomized insulin titration trial to treat hyperglycemia. Genome-wide expression profiling was conducted using RNA-sequencing from whole blood samples obtained from 46 children with septic shock and 52 mechanically ventilated noninfected controls without shock. Patients with septic shock were allocated to subclasses based on hierarchical clustering of gene expression profiles, and we then compared clinical characteristics, plasma inflammatory markers, cell compositions using GEDIT, and immune repertoires using Imrep between the two subclasses. Results: Patients with septic shock depicted alterations in innate and adaptive immune pathways. Among patients with septic shock, we identified two subtypes based on gene expression patterns. Compared with Subclass 2, Subclass 1 was characterized by upregulation of innate immunity pathways and downregulation of adaptive immunity pathways. Subclass 1 had significantly worse clinical outcomes despite the two classes having similar illness severity on initial clinical presentation. Subclass 1 had elevated levels of plasma inflammatory cytokines and endothelial injury biomarkers and demonstrated decreased percentages of CD4 T cells and B cells, and less diverse T-Cell receptor repertoires. Conclusions: Two subclasses of pediatric septic shock patients were discovered through genome-wide expression profiling based on whole blood RNA sequencing with major biological and clinical differences. Trial Registration: This is a secondary analysis of data generated as part of the observational CAF PINT ancillary of the HALF PINT study (NCT01565941). Registered 29 March 2012.

Rigorous benchmarking of T-cell receptor repertoire profiling methods for cancer RNA sequencing.

The ability to identify and track T-cell receptor (TCR) sequences from patient samples is becoming central to the field of cancer research and immunotherapy. Tracking genetically engineered T cells expressing TCRs that target specific tumor antigens is important to determine the persistence of these cells and quantify tumor responses. The available high-throughput method to profile TCR repertoires is generally referred to as TCR sequencing (TCR-Seq). However, the available TCR-Seq data are limited compared with RNA sequencing (RNA-Seq). In this paper, we have benchmarked the ability of RNA-Seq-based methods to profile TCR repertoires by examining 19 bulk RNA-Seq samples across 4 cancer cohorts including both T-cell-rich and T-cell-poor tissue types. We have performed a comprehensive evaluation of the existing RNA-Seq-based repertoire profiling methods using targeted TCR-Seq as the gold standard. We also highlighted scenarios under which the RNA-Seq approach is suitable and can provide comparable accuracy to the TCR-Seq approach. Our results show that RNA-Seq-based methods are able to effectively capture the clonotypes and estimate the diversity of TCR repertoires, as well as provide relative frequencies of clonotypes in T-cell-rich tissues and low-diversity repertoires. However, RNA-Seq-based TCR profiling methods have limited power in T-cell-poor tissues, especially in highly diverse repertoires of T-cell-poor tissues. The results of our benchmarking provide an additional appealing argument to incorporate RNA-Seq into the immune repertoire screening of cancer patients as it offers broader knowledge into the transcriptomic changes that exceed the limited information provided by TCR-Seq.

Mapping human haematopoietic stem cells from haemogenic endothelium to birth.

The ontogeny of human haematopoietic stem cells (HSCs) is poorly defined owing to the inability to identify HSCs as they emerge and mature at different haematopoietic sites1. Here we created a single-cell transcriptome map of human haematopoietic tissues from the first trimester to birth and found that the HSC signature RUNX1+HOXA9+MLLT3+MECOM+HLF+SPINK2+ distinguishes HSCs from progenitors throughout gestation. In addition to the aorta-gonad-mesonephros region, nascent HSCs populated the placenta and yolk sac before colonizing the liver at 6 weeks. A comparison of HSCs at different maturation stages revealed the establishment of HSC transcription factor machinery after the emergence of HSCs, whereas their surface phenotype evolved throughout development. The HSC transition to the liver marked a molecular shift evidenced by suppression of surface antigens reflecting nascent HSC identity, and acquisition of the HSC maturity markers CD133 (encoded by PROM1) and HLA-DR. HSC origin was tracked to ALDH1A1+KCNK17+ haemogenic endothelial cells, which arose from an IL33+ALDH1A1+ arterial endothelial subset termed pre-haemogenic endothelial cells. Using spatial transcriptomics and immunofluorescence, we visualized this process in ventrally located intra-aortic haematopoietic clusters. The in vivo map of human HSC ontogeny validated the generation of aorta-gonad-mesonephros-like definitive haematopoietic stem and progenitor cells from human pluripotent stem cells, and serves as a guide to improve their maturation to functional HSCs.

Analysis of gene expression from human breastmilk cells: A comparison between low and high producers, and the influence of anxiety and depression on milk production, gene expression and bacterial production.

BACKGROUND: Breastmilk is considered the gold standard of infant nutrition. Many mothers have difficulty with breastfeeding and over 50% of women stop due to perceived low production. AIMS AND METHODS: Our study compared gene expression in 8 samples of low and high producers of milk. All subjects were administered GAD-7 and PHQ-9 questionnaires. Low-producers were all found to have more depression and anxiety compared to high-producers. RESULTS: We did not find significant differences between gene expression between low and high milk producers. Only 5 of 8 samples contained a significant number of human cells. We did find differences in the amount of various bacterial populations. CONCLUSION: Our results indicate that gene expression in breastmilk is complicated by collection methods. We recommend that even though some women produced less than 600 ml of milk over a 24-hour period of time, due to the nature of the bacteria found in milk they try to breastfeed as much as they can for the health benefits of their infants. the rich bacterial diversity in all patients including the low producers strongly suggests that even women producing lesser quantities of milk confer their children numerous benefits by breastfeeding them.

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.

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.

Cell type-specific genetic regulation of gene expression across human tissues.

The Genotype-Tissue Expression (GTEx) project has identified expression and splicing quantitative trait loci in cis (QTLs) for the majority of genes across a wide range of human tissues. However, the functional characterization of these QTLs has been limited by the heterogeneous cellular composition of GTEx tissue samples. We mapped interactions between computational estimates of cell type abundance and genotype to identify cell type-interaction QTLs for seven cell types and show that cell type-interaction expression QTLs (eQTLs) provide finer resolution to tissue specificity than bulk tissue cis-eQTLs. Analyses of genetic associations with 87 complex traits show a contribution from cell type-interaction QTLs and enables the discovery of hundreds of previously unidentified colocalized loci that are masked in bulk tissue.

Activation of the HIF1α/PFKFB3 stress response pathway in beta cells in type 1 diabetes.

AIMS/HYPOTHESIS: The conserved hypoxia inducible factor 1 α (HIF1α) injury-response pro-survival pathway has recently been implicated in early beta cell dysfunction but slow beta cell loss in type 2 diabetes. We hypothesised that the unexplained prolonged prediabetes phase in type 1 diabetes may also be, in part, due to activation of the HIF1α signalling pathway. METHODS: RNA sequencing (RNA-Seq) data from human islets with type 1 diabetes or after cytokine exposure in vitro was evaluated for activation of HIF1α targets. This was corroborated by immunostaining human pancreases from individuals with type 1 diabetes for 6-phosphofructo-2-kinase/fructose-2,6-biphosphatase 3 (PFKFB3), the key effector of HIF1α-mediated metabolic remodelling, and by western blotting of islets and INS-1 832/13 cells exposed to cytokines implicated in type 1 diabetes. RESULTS: HIF1α signalling is activated (p = 4.5 × 10-9) in islets from individuals with type 1 diabetes, and in human islets exposed in vitro to cytokines implicated in type 1 diabetes (p = 1.1 × 10-14). Expression of PFKFB3 is increased fivefold (p < 0.01) in beta cells in type 1 diabetes and in human and rat islets exposed to cytokines that induced increased lactate production. HIF1α attenuates cytokine-induced cell death in beta cells. CONCLUSIONS/INTERPRETATION: The conserved pro-survival HIF1α-mediated injury-response signalling is activated in beta cells in type 1 diabetes and likely contributes to the relatively slow rate of beta cell loss at the expense of early defective glucose-induced insulin secretion.

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/ß-catenin signaling genes were significantly mutated in all colorectal cancer subtypes, and activated WNT/ß-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.