Standardized whole-blood transcriptional profiling enables the deconvolution of complex induced immune responses

Figura como autor también el Milieu Intérieur ConsortiumSystems approaches for the study of immune signaling pathways have been traditionally based on purified cells or cultured lines. However, in vivo responses involve the coordinated action of multiple cell types, which interact to establish an inflammatory microenvironment. We employed standardized whole-blood stimulation systems to test the hypothesis that responses to Toll-like receptor ligands or whole microbes can be defined by the transcriptional signatures of key cytokines. We found 44 genes, identified using Support Vector Machine learning, that captured the diversity of complex innate immune responses with improved segregation between distinct stimuli. Furthermore, we used donor variability to identify shared inter-cellular pathways and trace cytokine loops involved in gene expression. This provides strategies for dimension reduction of large datasets and deconvolution of innate immune responses applicable for characterizing immunomodulatory molecules. Moreover, we provide an interactive R-Shiny application with healthy donor reference values for induced inflammatory genes

A gene-expression profiling score for prediction of outcome in patients with follicular lymphoma: a retrospective training and validation analysis in three international cohorts

Patients with follicular lymphoma (FL) have heterogeneous outcomes. Predictor models able to distinguish, at diagnosis, patients at high versus low risk of progression are still needed. A training set of fresh-frozen tumour biopsies was prospectively obtained from 160 untreated patients with high-tumour-burden follicular lymphoma enrolled in the phase 3 randomised PRIMA trial, in which rituximab maintenance was evaluated after rituximab plus chemotherapy induction (median follow-up 6·6 years [IQR 6·0-7·0]). RNA of sufficient quality was obtained for 149 of 160 cases, and Affymetrix U133 Plus 2.0 microarrays were used for gene-expression profiling. We did a multivariate Cox regression analysis to identify genes with expression levels associated with progression-free survival independently of maintenance treatment in a subgroup of 134 randomised patients. Expression levels from 95 curated genes were then determined by digital expression profiling (NanoString technology) in 53 formalin-fixed paraffin-embedded samples of the training set to compare the technical reproducibility of expression levels for each gene between technologies. Genes with high correlation (>0·75) were included in an L2-penalised Cox model adjusted on rituximab maintenance to build a predictive score for progression-free survival. The model was validated using NanoString technology to digitally quantify gene expression in 488 formalin-fixed, paraffin-embedded samples from three independent international patient cohorts from the PRIMA trial (n=178; distinct from the training cohort), the University of Iowa/Mayo Clinic Lymphoma SPORE project (n=201), and the Barcelona Hospital Clinic (n=109). All tissue samples consisted of pretreatment diagnostic biopsies and were confirmed as follicular lymphoma grade 1-3a. The patients were all treated with regimens containing rituximab and chemotherapy, possibly followed by either rituximab maintenance or ibritumomab-tiuxetan consolidation. We determined an optimum threshold on the score to predict patients at low risk and high risk of progression. The model, including the multigene score and the threshold, was initially evaluated in the three validation cohorts separately. The sensitivity and specificity of the score for the prediction of the risk of lymphoma progression at 2 years were assessed on the combined validation cohorts. FINDINGS: In the training cohort, the expression levels of 395 genes were associated with a risk of progression. 23 genes reflecting both B-cell biology and tumour microenvironment with correlation coefficients greater than 0·75 between the two technologies and sample types were retained to build a predictive model that identified a population at an increased risk of progression (p<0·0001). In a multivariate Cox model for progression-free survival adjusted on rituximab maintenance treatment and Follicular Lymphoma International Prognostic Index 1 (FLIPI-1) score, this predictor independently predicted progression (adjusted hazard ratio [aHR] of the high-risk group compared with the low-risk group 3·68, 95% CI 2·19-6·17 [p<0·0001]). The 5-year progression-free survival was 26% (95% CI 16-43) in the high-risk group and 73% (64-83) in the low-risk group. The predictor performances were confirmed in each of the individual validation cohorts (aHR comparing high-risk to low-risk groups 2·57 [95% CI 1·65-4·01] in cohort 1; 2·12 [1·32-3·39] in cohort 2; and 2·11 [1·01-4·41] in cohort 3). In the combined validation cohort, the median progression-free survival was 3·1 years (95% CI 2·4-4·8) in the high-risk group and 10·8 years (10·1-not reached) in the low-risk group (p<0·0001). The risk of lymphoma progression at 2 years was 38% (95% CI 29-46) in the high-risk group and 19% (15-24) in the low-risk group. In a multivariate analysis, the score predicted progression-free survival independently of anti-CD20 maintenance treatment and of the FLIPI score (aHR for the combined cohort 2·30, 95% CI 1·72-3·07). INTERPRETATION: We developed and validated a robust 23-gene expression-based predictor of progression-free survival that is applicable to routinely available formalin-fixed, paraffin-embedded tumour biopsies from patients with follicular lymphoma at time of diagnosis. Applying this score could allow individualised therapy for patients according to their risk category

Standardized Whole-Blood Transcriptional Profiling Enables the Deconvolution of Complex Induced Immune Responses

SummarySystems approaches for the study of immune signaling pathways have been traditionally based on purified cells or cultured lines. However, in vivo responses involve the coordinated action of multiple cell types, which interact to establish an inflammatory microenvironment. We employed standardized whole-blood stimulation systems to test the hypothesis that responses to Toll-like receptor ligands or whole microbes can be defined by the transcriptional signatures of key cytokines. We found 44 genes, identified using Support Vector Machine learning, that captured the diversity of complex innate immune responses with improved segregation between distinct stimuli. Furthermore, we used donor variability to identify shared inter-cellular pathways and trace cytokine loops involved in gene expression. This provides strategies for dimension reduction of large datasets and deconvolution of innate immune responses applicable for characterizing immunomodulatory molecules. Moreover, we provide an interactive R-Shiny application with healthy donor reference values for induced inflammatory genes

Genome-wide identification of genic and intergenic neuronal DNA regions bound by Tau protein under physiological and stress conditions

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Early born neurons are abnormally positioned in the doublecortin knockout hippocampus

International audienceHuman doublecortin (DCX) mutations are associated with severe brain malformations leading to aberrant neuron positioning (heterotopia), intellectual disability and epilepsy. The Dcx protein plays a key role in neuronal migration, and hippocampal pyramidal neurons in Dcx knockout (KO) mice are disorganized. The single CA3 pyramidal cell layer observed in wild type (WT) is present as two abnormal layers in the KO, and CA3 KO pyramidal neurons are more excitable than WT. Dcx KO mice also exhibit spontaneous epileptic activity originating in the hippocampus. It is unknown, however, how hyperexcitability arises and why two CA3 layers are observed.Transcriptome analyses were performed to search for perturbed postnatal gene expression, comparing Dcx KO CA3 pyramidal cell layers with WT. Gene expression changes common to both KO layers indicated mitochondria and Golgi apparatus anomalies, as well as increased cell stress. Intriguingly, gene expression analyses also suggested that the KO layers differ significantly from each other, particularly in terms of maturity. Layer-specific molecular markers and BrdU birthdating to mark the final positions of neurons born at distinct timepoints revealed inverted layering of the CA3 region in Dcx KO animals. Notably, many early-born ‘outer boundary’ neurons are located in an inner position in the Dcx KO CA3, superficial to other pyramidal neurons. This abnormal positioning likely affects cell morphology and connectivity, influencing network function. Dissecting this Dcx KO phenotype sheds light on coordinated developmental mechanisms of neuronal subpopulations, as well as gene expression patterns contributing to a bi-layered malformation associated with epilepsy

Standardized Whole-Blood Transcriptional Profiling Enables the Deconvolution of Complex Induced Immune Responses.

International audienceSystems approaches for the study of immune signaling pathways have been traditionally based on purified cells or cultured lines. However, in vivo responses involve the coordinated action of multiple cell types, which interact to establish an inflammatory microenvironment. We employed standardized whole-blood stimulation systems to test the hypothesis that responses to Toll-like receptor ligands or whole microbes can be defined by the transcriptional signatures of key cytokines. We found 44 genes, identified using Support Vector Machine learning, that captured the diversity of complex innate immune responses with improved segregation between distinct stimuli. Furthermore, we used donor variability to identify shared inter-cellular pathways and trace cytokine loops involved in gene expression. This provides strategies for dimension reduction of large datasets and deconvolution of innate immune responses applicable for characterizing immunomodulatory molecules. Moreover, we provide an interactive R-Shiny application with healthy donor reference values for induced inflammatory genes

Relevance of the TRIAP1/p53 axis in colon cancer cell proliferation and adaptation to glutamine deprivation

International audienceHuman TRIAP1 (TP53-regulated inhibitor of apoptosis 1; also known as p53CSV for p53-inducible cell survival factor) is the homolog of yeast Mdm35, a well-known chaperone that interacts with the Ups/PRELI family proteins and participates in the intramitochondrial transfer of lipids for the synthesis of cardiolipin (CL) and phosphatidylethanolamine. Although recent reports indicate that TRIAP1 is a prosurvival factor abnormally overexpressed in various types of cancer, knowledge about its molecular and metabolic function in human cells is still elusive. It is therefore critical to understand the metabolic and proliferative advantages that TRIAP1 expression provides to cancer cells. Here, in a colorectal cancer cell model, we report that the expression of TRIAP1 supports cancer cell proliferation and tumorigenesis. Depletion of TRIAP1 perturbed the mitochondrial ultrastructure, without a major impact on CL levels and mitochondrial activity. TRIAP1 depletion caused extramitochondrial perturbations resulting in changes in the endoplasmic reticulum-dependent lipid homeostasis and induction of a p53-mediated stress response. Furthermore, we observed that TRIAP1 depletion conferred a robust p53-mediated resistance to the metabolic stress caused by glutamine deprivation. These findings highlight the importance of TRIAP1 in tumorigenesis and indicate that the loss of TRIAP1 has extramitochondrial consequences that could impact on the metabolic plasticity of cancer cells and their response to conditions of nutrient deprivation

Distinctive roles of age, sex, and genetics in shaping transcriptional variation of human immune responses to microbial challenges.

The contribution of host genetic and nongenetic factors to immunological differences in humans remains largely undefined. Here, we generated bacterial-, fungal-, and viral-induced immune transcriptional profiles in an age- and sex-balanced cohort of 1,000 healthy individuals and searched for the determinants of immune response variation. We found that age and sex affected the transcriptional response of most immune-related genes, with age effects being more stimulus-specific relative to sex effects, which were largely shared across conditions. Although specific cell populations mediated the effects of age and sex on gene expression, including CD8 &lt;sup&gt;+&lt;/sup&gt; T cells for age and CD4 &lt;sup&gt;+&lt;/sup&gt; T cells and monocytes for sex, we detected a direct effect of these intrinsic factors for the majority of immune genes. The mapping of expression quantitative trait loci (eQTLs) revealed that genetic factors had a stronger effect on immune gene regulation than age and sex, yet they affected a smaller number of genes. Importantly, we identified numerous genetic variants that manifested their regulatory effects exclusively on immune stimulation, including a Candida albicans-specific master regulator at the CR1 locus. These response eQTLs were enriched in disease-associated variants, particularly for autoimmune and inflammatory disorders, indicating that differences in disease risk may result from regulatory variants exerting their effects only in the presence of immune stress. Together, this study quantifies the respective effects of age, sex, genetics, and cellular heterogeneity on the interindividual variability of immune responses and constitutes a valuable resource for further exploration in the context of different infection risks or disease outcomes

Distinctive roles of age, sex, and genetics in shaping transcriptional variation of human immune responses to microbial challenges

The contribution of host genetic and nongenetic factors to immunological differences in humans remains largely undefined. Here, we generated bacterial-, fungal-, and viral-induced immune transcriptional profiles in an age- and sex-balanced cohort of 1,000 healthy individuals and searched for the determinants of immune response variation. We found that age and sex affected the transcriptional response of most immune-related genes, with age effects being more stimulus-specific relative to sex effects, which were largely shared across conditions. Although specific cell populations mediated the effects of age and sex on gene expression, including CD8+ T cells for age and CD4+ T cells and monocytes for sex, we detected a direct effect of these intrinsic factors for the majority of immune genes. The mapping of expression quantitative trait loci (eQTLs) revealed that genetic factors had a stronger effect on immune gene regulation than age and sex, yet they affected a smaller number of genes. Importantly, we identified numerous genetic variants that manifested their regulatory effects exclusively on immune stimulation, including a Candida albicans-specific master regulator at the CR1 locus. These response eQTLs were enriched in disease-associated variants, particularly for autoimmune and inflammatory disorders, indicating that differences in disease risk may result from regulatory variants exerting their effects only in the presence of immune stress. Together, this study quantifies the respective effects of age, sex, genetics, and cellular heterogeneity on the interindividual variability of immune responses and constitutes a valuable resource for further exploration in the context of different infection risks or disease outcomes