Context matters:the power of single-cell analyses in identifying context-dependent effects on gene expression in blood immune cells

The human immune system is a complex system that we still do not fully understand. No two humans react in the same way to attacks by bacteria, viruses or fungi. Factors such as genetics, the type of pathogen or previous exposure to the pathogen may explain this diversity in response. Single-cell RNA sequencing (scRNA-seq) is a new technique that enables us to study the gene expression of each cell individually, allowing us to study immune diversity in much greater detail. This increased resolution helps us discern how disease-associated genetic variants actually contribute to disease. In this thesis, I studied the relation between disease-associated genetic variants and gene expression levels in the context of different cell types and pathogen exposures in order to gain insight into the working mechanisms of these variants. For many variants we learnt in which cell types and under which pathogen exposures they affect gene expression, and we were even able to identify changes in gene co-expression, suggesting that disease-associated variants change how our genes interact with each other. With the single-cell field being so new, much of my work was showing the feasibility of using scRNA-seq to study the interplay between genetics and gene expression. To set up future research, we created guidelines for these analyses and established a consortium that brings together many major scientists in the field to enable large-scale studies across an even wider variety of contexts. This final work helps inform current and future large-scale scRNA-seq research

A Brief Adherence Intervention that Improved Glycemic Control: Mediation by Patterns of Adherence

This study examined whether longitudinal adherence profiles mediated the relationship between a brief adherence intervention and glycemic control among patients with type 2 diabetes. Adherence was assessed using the Medication Event Monitoring System. Longitudinal analysis via growth curve mixture modeling was carried out to classify patients according to patterns of adherence to oral hypoglycemic agents. Hemoglobin A1c assays were used to measure glycemic control as the clinical outcome. Across the whole sample, longitudinal adherence profiles mediated 35.2% (13.2, 81.0%) of the effect of a brief adherence intervention on glycemic control [from odds ratio (OR) = 8.48, 95% confidence interval (CI) (3.24, 22.2) to 4.00, 95% CI (1.34, 11.93)]. Our results suggest that patients in the intervention had better glycemic control largely due to their greater likelihood of adherence to oral hypoglycemic agents

Patterns of Adherence to Oral Hypoglycemic Agents and Glucose Control among Primary Care Patients with Type 2 Diabetes

Researchers sought to examine whether there are patterns of oral hypoglycemic-agent adherence among primary-care patients with type 2 diabetes that are related to patient characteristics and clinical outcomes. Longitudinal analysis via growth curve mixture modeling was carried out to classify 180 patients who participated in an adherence intervention according to patterns of adherence to oral hypoglycemic agents across 12 weeks. Three patterns of change in adherence were identified: adherent, increasing adherence, and nonadherent. Global cognition and intervention condition were associated with pattern of change in adherence (p \u3c .05). Patients with an increasing adherence pattern were more likely to have an Hemoglobin A1c) \u3c 7%; adjusted odds ratio = 14.52, 95% CI (2.54, 82.99) at 12 weeks, in comparison with patients with the nonadherent pattern. Identification of patients with type 2 diabetes at risk of nonadherence is important for clinical prognosis and the development and delivery of interventions

Patterns of Adherence to Oral Hypoglycemic Agents and Glucose Control among Primary Care Patients with Type 2 Diabetes

Researchers sought to examine whether there are patterns of oral hypoglycemic-agent adherence among primary-care patients with type 2 diabetes that are related to patient characteristics and clinical outcomes. Longitudinal analysis via growth curve mixture modeling was carried out to classify 180 patients who participated in an adherence intervention according to patterns of adherence to oral hypoglycemic agents across 12 weeks. Three patterns of change in adherence were identified: adherent, increasing adherence, and nonadherent. Global cognition and intervention condition were associated with pattern of change in adherence (p \u3c .05). Patients with an increasing adherence pattern were more likely to have an Hemoglobin A1c) \u3c 7%; adjusted odds ratio = 14.52, 95% CI (2.54, 82.99) at 12 weeks, in comparison with patients with the nonadherent pattern. Identification of patients with type 2 diabetes at risk of nonadherence is important for clinical prognosis and the development and delivery of interventions

Neighborhood Social Environment and Patterns of Adherence to Oral Hypoglycemic Agents among Patients with Type 2 Diabetes Mellitus

This study examined whether neighborhood social environment was related to patterns of adherence to oral hypoglycemic agents among primary care patients with type 2 diabetes mellitus. Residents in neighborhoods with high social affluence, high residential stability, and high neighborhood advantage, compared to residents in neighborhoods with one or no high features present, were significantly more likely to have an adherent pattern compared to a nonadherent pattern. Neighborhood social environment may influence patterns of adherence. Reliance on a multilevel contextual framework, extending beyond the individual, to promote diabetic self-management activities may be essential for notable public health improvements

Causal factors of work-related chemical eye injuries reported to the Dutch Poisons Information Center

This study investigated the circumstances of chemical occupational eye exposures reported to the Dutch Poisons Information Center. During a 1-year prospective study, data were collected through a telephone survey of 132 victims of acute occupational eye exposure. Victims were often exposed to industrial products (35%) or cleaning products (27%). Most patients developed no or mild symptoms. Organizational factors (such as lack of work instructions (52%)), and personal factors (such as time pressure and fatigue (50%), and not adequately using personal protective equipment (PPE, 14%), were the main causes of occupational eye exposures. Exposure often occurred during cleaning activities (34%) and personal factors were reported more often during cleaning (67%) than during other work activities (41%). Data from Poison Control Centers are a valuable source of information, enabling the identification of risk factors for chemical occupational eye exposure. This study shows that personal factors like time pressure and fatigue play a significant role, although personal factors may be related to organizational issues such as poor communication. Therefore, risk mitigation strategies should focus on technical, organizational, and personal factors. The need to follow work instructions and proper use of PPE should also have a prominent place in the education and training of workers

Integrating GWAS with bulk and single-cell RNA-sequencing reveals a role for LY86 in the anti-Candida host response

Contains fulltext : 220669.pdf (publisher's version ) (Open Access)Candida bloodstream infection, i.e. candidemia, is the most frequently encountered life-threatening fungal infection worldwide, with mortality rates up to almost 50%. In the majority of candidemia cases, Candida albicans is responsible. Worryingly, a global increase in the number of patients who are susceptible to infection (e.g. immunocompromised patients), has led to a rise in the incidence of candidemia in the last few decades. Therefore, a better understanding of the anti-Candida host response is essential to overcome this poor prognosis and to lower disease incidence. Here, we integrated genome-wide association studies with bulk and single-cell transcriptomic analyses of immune cells stimulated with Candida albicans to further our understanding of the anti-Candida host response. We show that differential expression analysis upon Candida stimulation in single-cell expression data can reveal the important cell types involved in the host response against Candida. This confirmed the known major role of monocytes, but more interestingly, also uncovered an important role for NK cells. Moreover, combining the power of bulk RNA-seq with the high resolution of single-cell RNA-seq data led to the identification of 27 Candida-response QTLs and revealed the cell types potentially involved herein. Integration of these response QTLs with a GWAS on candidemia susceptibility uncovered a potential new role for LY86 in candidemia susceptibility. Finally, experimental follow-up confirmed that LY86 knockdown results in reduced monocyte migration towards the chemokine MCP-1, thereby implying that this reduced migration may underlie the increased susceptibility to candidemia. Altogether, our integrative systems genetics approach identifies previously unknown mechanisms underlying the immune response to Candida infection

Single-cell RNA-sequencing of peripheral blood mononuclear cells reveals widespread, context-specific gene expression regulation upon pathogenic exposure

Not just differential gene expression but also differential gene regulation in immune cells account for individual differences in the immune response. Authors show here by single-cell RNA-sequencing of peripheral blood mononuclear cells from a large cohort of genetically diverse individuals that gene expression and regulatory changes in these cells depend on the context of and interactions between cell types, genetics, type of pathogen and time after exposure. The host's gene expression and gene regulatory response to pathogen exposure can be influenced by a combination of the host's genetic background, the type of and exposure time to pathogens. Here we provide a detailed dissection of this using single-cell RNA-sequencing of 1.3M peripheral blood mononuclear cells from 120 individuals, longitudinally exposed to three different pathogens. These analyses indicate that cell-type-specificity is a more prominent factor than pathogen-specificity regarding contexts that affect how genetics influences gene expression (i.e., eQTL) and co-expression (i.e., co-expression QTL). In monocytes, the strongest responder to pathogen stimulations, 71.4% of the genetic variants whose effect on gene expression is influenced by pathogen exposure (i.e., response QTL) also affect the co-expression between genes. This indicates widespread, context-specific changes in gene expression level and its regulation that are driven by genetics. Pathway analysis on the CLEC12A gene that exemplifies cell-type-, exposure-time- and genetic-background-dependent co-expression interactions, shows enrichment of the interferon (IFN) pathway specifically at 3-h post-exposure in monocytes. Similar genetic background-dependent association between IFN activity and CLEC12A co-expression patterns is confirmed in systemic lupus erythematosus by in silico analysis, which implies that CLEC12A might be an IFN-regulated gene. Altogether, this study highlights the importance of context for gaining a better understanding of the mechanisms of gene regulation in health and disease

Effective questionnaire-based prediction models for type 2 diabetes across several ethnicities:a model development and validation study

Background: Type 2 diabetes disproportionately affects individuals of non-White ethnicity through a complex interaction of multiple factors. Therefore, early disease detection and prediction are essential and require tools that can be deployed on a large scale. We aimed to tackle this problem by developing questionnaire-based prediction models for type 2 diabetes prevalence and incidence for multiple ethnicities.Methods: In this proof of principle analysis, logistic regression models to predict type 2 diabetes prevalence and incidence, using questionnaire-only variables reflecting health state and lifestyle, were trained on the White population of the UK Biobank (n = 472,696 total, aged 37–73 years, data collected 2006–2010) and validated in five other ethnicities (n = 29,811 total) and externally in Lifelines (n = 168,205 total, aged 0–93 years, collected between 2006 and 2013). In total, 631,748 individuals were included for prevalence prediction and 67,083 individuals for the eight-year incidence prediction. Type 2 diabetes prevalence in the UK Biobank ranged between 6% in the White population to 23.3% in the South Asian population, while in Lifelines, the prevalence was 1.9%. Predictive accuracy was evaluated using the area under the receiver operating characteristic curve (AUC), and a detailed sensitivity analysis was conducted to assess potential clinical utility. We compared the questionnaire-only models to models containing physical measurements and biomarkers as well as to clinical non-laboratory type 2 diabetes risk tools and conducted a reclassification analysis.Findings: Our algorithms accurately predicted type 2 diabetes prevalence (AUC = 0.901) and eight-year incidence (AUC = 0.873) in the White UK Biobank population. Both models replicated well in the Lifelines external validation, with AUCs of 0.917 and 0.817 for prevalence and incidence, respectively. Both models performed consistently well across different ethnicities, with AUCs of 0.855–0.894 for prevalence and 0.819–0.883 for incidence. These models generally outperformed two clinically validated non-laboratory tools and correctly reclassified >3,000 additional cases. Model performance improved with the addition of blood biomarkers but not with the addition of physical measurements.Interpretation: Our findings suggest that easy-to-implement, questionnaire-based models could be used to predict prevalent and incident type 2 diabetes with high accuracy across several ethnicities, providing a highly scalable solution for population-wide risk stratification. Future work should determine the effectiveness of these models in identifying undiagnosed type 2 diabetes, validated in cohorts of different populations and ethnic representation.Funding: University Medical Center Groningen