A genome-wide functional genomics approach uncovers genetic determinants of immune phenotypes in type 1 diabetes
Curation statements for this article:-
Curated by eLife
Evaluation Summary:
This study examines genetic and non-genetic factors influencing immune responses in type 1 diabetes Key findings are: 1) age and season affect immune cell traits and cytokine production upon stimulation; 2) certain genetic variants that determine susceptibility to T1D significantly affect T cell composition, notably the CCR region that is associated with CCR5+ regulatory T cells; and 3) 15 genetic loci that influence immune responses in T1D, most of which have not been seen previously in healthy populations. The results suggest mechanisms of T1D-specific genetic regulation.
(This preprint has been reviewed by eLife. We include the public reviews from the reviewers here; the authors also receive private feedback with suggested changes to the manuscript. The reviewers remained anonymous to the authors.)
This article has been Reviewed by the following groups
Listed in
- Evaluated articles (eLife)
- @motwani_kartik's saved articles (motwani_kartik)
Abstract
The large inter-individual variability in immune-cell composition and function determines immune responses in general and susceptibility o immune-mediated diseases in particular. While much has been learned about the genetic variants relevant for type 1 diabetes (T1D), the pathophysiological mechanisms through which these variations exert their effects remain unknown.
Methods:
Blood samples were collected from 243 patients with T1D of Dutch descent. We applied genetic association analysis on >200 immune-cell traits and >100 cytokine production profiles in response to stimuli measured to identify genetic determinants of immune function, and compared the results obtained in T1D to healthy controls.
Results:
Genetic variants that determine susceptibility to T1D significantly affect T cell composition. Specifically, the CCR5+ regulatory T cells associate with T1D through the CCR region, suggesting a shared genetic regulation. Genome-wide quantitative trait loci (QTLs) mapping analysis of immune traits revealed 15 genetic loci that influence immune responses in T1D, including 12 that have never been reported in healthy population studies, implying a disease-specific genetic regulation.
Conclusions:
This study provides new insights into the genetic factors that affect immunological responses in T1D.
Funding:
This work was supported by an ERC starting grant (no. 948207) and a Radboud University Medical Centre Hypatia grant (2018) to YL and an ERC advanced grant (no. 833247) and a Spinoza grant of the Netherlands Association for Scientific Research to MGN CT received funding from the Perspectief Biomarker Development Center Research Programme, which is (partly) financed by the Netherlands Organisation for Scientific Research (NWO). AJ was funded by a grant from the European Foundation for the Study of Diabetes (EFSD/AZ Macrovascular Programme 2015). XC was supported by the China Scholarship Council (201706040081).
Article activity feed
-
-
Author Response:
Evaluation Summary:
This study examines genetic and non-genetic factors influencing immune responses in type 1 diabetes Key findings are: 1) age and season affect immune cell traits and cytokine production upon stimulation; 2) certain genetic variants that determine susceptibility to T1D significantly affect T cell composition, notably the CCR region that is associated with CCR5+ regulatory T cells; and 3) 15 genetic loci that influence immune responses in T1D, most of which have not been seen previously in healthy populations. The results suggest mechanisms of T1D-specific genetic regulation.
We thank the reviewer for the appreciation of our data quality and approach. We have tried to bring more focus in the conclusions, partly by taking out the whole non-genetic section.
Reviewer #1 (Public Review):
Strengths …
Author Response:
Evaluation Summary:
This study examines genetic and non-genetic factors influencing immune responses in type 1 diabetes Key findings are: 1) age and season affect immune cell traits and cytokine production upon stimulation; 2) certain genetic variants that determine susceptibility to T1D significantly affect T cell composition, notably the CCR region that is associated with CCR5+ regulatory T cells; and 3) 15 genetic loci that influence immune responses in T1D, most of which have not been seen previously in healthy populations. The results suggest mechanisms of T1D-specific genetic regulation.
We thank the reviewer for the appreciation of our data quality and approach. We have tried to bring more focus in the conclusions, partly by taking out the whole non-genetic section.
Reviewer #1 (Public Review):
Strengths of the manuscript include the important research question addressed, the robust functional genomics methodology used, the relatively large sample size, and the translational implications of the study findings that pinpointed new potential drug targets in autoimmune diabetes. Weaknesses include the analysis of immune responses at a certain time point that may not represent the dynamic immune phenotype of the disease over time, the testing of immune responses in peripheral blood mononuclear cells (PBMC's) that may not represent the islet infiltrating immune cells that cause autoimmune diabetes, using generic stimulants to activate PBMC's instead of beta-cell autoantigens, and that the QTL analysis may not be relevant to the etiology of autoimmune diabetes as it identified QTLs associated with immune cell proportion and cytokine production, but these do not necessarily influence the development of autoimmune diabetes.
We thank the reviewer for the fair assessment of our manuscript. We fully agree with the reviewer that the study in relevant tissue at different time points could be very important for understanding type 1 diabetes, however, tissue immunity could be partly reflected by the changes in circulating level of immune cells and cytokine production capacity, since the islet infiltrating immune cells do originate from circulating blood cells. We have modified the manuscript by adding more discussion about this topic.
“The data presented in our study are generated from PBMC. While these likely reflect overall immune function, some immune cell types may not be captured and all over the findings refer to changes in circulating factors that may not necessarily reflect changes occurring in relevant immune organs, such as pancreatic islets, gut or lymph nodes. Still, islet infiltrating immune cells do originate from circulating blood cells, and circulating chemokines/cytokines are important in activating and recruiting immune cells. Hence, the circulating level of immune cells and cytokine production capacity is probably relevant for local tissue immunity.”
Reviewer #2 (Public Review):
This manuscript presents data collected from two cohorts of individuals, one including patients with type 1 diabetes, the other encompassing non-diabetic persons. Of note, the cohorts are not contemporary and samples from the two groups were collected several years apart (2013/14 for controls, 2016/17 for the diabetic group). This is not an issue for any genetic comparisons. However, comparing immune phenotypes in non-contemporary cohorts, particularly with respect to seasonal variations as the authors attempt in some of their analyses, is not useful as it lacks the rigor of collecting samples under identical conditions.
We thank the reviewer for raising this point. In order to focus on “genetics part” as suggested by the reviewers, we have taken the non-genetic associations, including seasonal effects and age, completely out of the paper and have rewritten the paper accordingly. Hence also one figure was removed, the others were renumbered.
This caveat aside, the overall aim of the study was to compare the function of immune cells, with a focus on the distribution of various cell populations and their cytokine secretion, between individuals with and without type 1 diabetes. Many of the analyses are difficult to interpret because the authors use measures and correlations for which the rationale is not well explained and whose presentation in the rather busy figures lacks detailed descriptions. There is no doubt that the authors amassed a substantial amount of data in what appears to be an ambitious study of hundreds of blood samples. However, the authors do not do their data justice by failing to present it in a easily comprehensible and interpretable data. Much of the description of the results makes the assumption that readers are familiar with the very particular way the authors analyzed the data (e.g. refering to parental and grandparental percentages, where it is entirely unclear what the authors are refering to).
We thank the reviewer for the suggestions. We have modified the figure legends by adding more information to explain the results and help the readers to acquire a better understanding. We have included a supplementary table S6 describing how parental and grandparental percentages were defined and the immune cell gating method could be found in our previous study (Aguirre-Gamboa et al., 2016) and in Figure 1 - Figure supplement 2.
Many of the observations presented are trivial and could have been omitted from the manuscript, for example showing that the immune system acquires more memory lymphocytes as people age, with no apparent difference between the groups studied. The fact that our immune system gets more experienced as we age is both unsurprising and a well known phenomenon. Similarly, the correlations between immune cells and cytokine secretion compared between groups yield no discernable differences and this could have been summarized much more succinctly in the interest of clarity. The more interesting data relating to gene variations that appear to impact immune phenotypes could have been given more weight in the overall manuscript to better describe them and discuss possible implications.
In sum, this is a manuscript with a very large data set whose presentation lacks focus on the key points that would emphasize the novelty of the findings put forward by the authors. As such, it is not very accessible to a general readership.
We thank the reviewer for the comments and suggestions. We agree with the reviewer that the genetic regulation is probably the most interesting and novel, hence we have modified the manuscript to focus more on genetics.
-
-
Evaluation Summary:
This study examines genetic and non-genetic factors influencing immune responses in type 1 diabetes Key findings are: 1) age and season affect immune cell traits and cytokine production upon stimulation; 2) certain genetic variants that determine susceptibility to T1D significantly affect T cell composition, notably the CCR region that is associated with CCR5+ regulatory T cells; and 3) 15 genetic loci that influence immune responses in T1D, most of which have not been seen previously in healthy populations. The results suggest mechanisms of T1D-specific genetic regulation.
(This preprint has been reviewed by eLife. We include the public reviews from the reviewers here; the authors also receive private feedback with suggested changes to the manuscript. The reviewers remained anonymous to the authors.)
-
Reviewer #1 (Public Review):
Strengths of the manuscript include the important research question addressed, the robust functional genomics methodology used, the relatively large sample size, and the translational implications of the study findings that pinpointed new potential drug targets in autoimmune diabetes. Weaknesses include the analysis of immune responses at a certain time point that may not represent the dynamic immune phenotype of the disease over time, the testing of immune responses in peripheral blood mononuclear cells (PBMC's) that may not represent the islet infiltrating immune cells that cause autoimmune diabetes, using generic stimulants to activate PBMC's instead of beta-cell autoantigens, and that the QTL analysis may not be relevant to the etiology of autoimmune diabetes as it identified QTLs associated with immune …
Reviewer #1 (Public Review):
Strengths of the manuscript include the important research question addressed, the robust functional genomics methodology used, the relatively large sample size, and the translational implications of the study findings that pinpointed new potential drug targets in autoimmune diabetes. Weaknesses include the analysis of immune responses at a certain time point that may not represent the dynamic immune phenotype of the disease over time, the testing of immune responses in peripheral blood mononuclear cells (PBMC's) that may not represent the islet infiltrating immune cells that cause autoimmune diabetes, using generic stimulants to activate PBMC's instead of beta-cell autoantigens, and that the QTL analysis may not be relevant to the etiology of autoimmune diabetes as it identified QTLs associated with immune cell proportion and cytokine production, but these do not necessarily influence the development of autoimmune diabetes.
-
Reviewer #2 (Public Review):
This manuscript presents data collected from two cohorts of individuals, one including patients with type 1 diabetes, the other encompassing non-diabetic persons. Of note, the cohorts are not contemporary and samples from the two groups were collected several years apart (2013/14 for controls, 2016/17 for the diabetic group). This is not an issue for any genetic comparisons. However, comparing immune phenotypes in non-contemporary cohorts, particularly with respect to seasonal variations as the authors attempt in some of their analyses, is not useful as it lacks the rigor of collecting samples under identical conditions. This caveat aside, the overall aim of the study was to compare the function of immune cells, with a focus on the distribution of various cell populations and their cytokine secretion, …
Reviewer #2 (Public Review):
This manuscript presents data collected from two cohorts of individuals, one including patients with type 1 diabetes, the other encompassing non-diabetic persons. Of note, the cohorts are not contemporary and samples from the two groups were collected several years apart (2013/14 for controls, 2016/17 for the diabetic group). This is not an issue for any genetic comparisons. However, comparing immune phenotypes in non-contemporary cohorts, particularly with respect to seasonal variations as the authors attempt in some of their analyses, is not useful as it lacks the rigor of collecting samples under identical conditions. This caveat aside, the overall aim of the study was to compare the function of immune cells, with a focus on the distribution of various cell populations and their cytokine secretion, between individuals with and without type 1 diabetes. Many of the analyses are difficult to interpret because the authors use measures and correlations for which the rationale is not well explained and whose presentation in the rather busy figures lacks detailed descriptions. There is no doubt that the authors amassed a substantial amount of data in what appears to be an ambitious study of hundreds of blood samples. However, the authors do not do their data justice by failing to present it in a easily comprehensible and interpretable data. Much of the description of the results makes the assumption that readers are familiar with the very particular way the authors analyzed the data (e.g. refering to parental and grandparental percentages, where it is entirely unclear what the authors are refering to).
Many of the observations presented are trivial and could have been omitted from the manuscript, for example showing that the immune system acquires more memory lymphocytes as people age, with no apparent difference between the groups studied. The fact that our immune system gets more experienced as we age is both unsurprising and a well known phenomenon. Similarly, the correlations between immune cells and cytokine secretion compared between groups yield no discernable differences and this could have been summarized much more succinctly in the interest of clarity. The more interesting data relating to gene variations that appear to impact immune phenotypes could have been given more weight in the overall manuscript to better describe them and discuss possible implications.
In sum, this is a manuscript with a very large data set whose presentation lacks focus on the key points that would emphasize the novelty of the findings put forward by the authors. As such, it is not very accessible to a general readership.
-