A targeted sequencing extension for transcript genotyping in single-cell transcriptomics.

As no existing methods within the single-cell RNA sequencing repertoire combine genotyping of specific genomic loci with high throughput, we evaluated a straightforward, targeted sequencing approach as an extension to high-throughput droplet-based single-cell RNA sequencing. Overlaying standard gene expression data with transcript level genotype information provides a strategy to study the impact of genetic variants. Here, we describe this targeted sequencing extension, explain how to process the data and evaluate how technical parameters such as amount of input cDNA, number of amplification rounds, and sequencing depth influence the number of transcripts detected. Finally, we demonstrate how targeted sequencing can be used in two contexts: (1) simultaneous investigation of the presence of a somatic variant and its potential impact on the transcriptome of affected cells and (2) evaluation of allele-specific expression of a germline variant in ad hoc cell subsets. Through these and other comparable applications, our targeted sequencing extension has the potential to improve our understanding of functional effects caused by genetic variation.

Correction: Schroyen et al. Neuroinflammation and Its Association with Cognition, Neuronal Markers and Peripheral Inflammation after Chemotherapy for Breast Cancer. Cancers 2021, 13, 4198.

In the original publication [...].

Genetics of multiple sclerosis: lessons from polygenicity.

Large-scale mapping studies have identified 236 independent genetic variants associated with an increased risk of multiple sclerosis. However, none of these variants are found exclusively in patients with multiple sclerosis. They are located throughout the genome, including 32 independent variants in the MHC and one on the X chromosome. Most variants are non-coding and seem to act through cell-specific effects on gene expression and splicing. The likely functions of these variants implicate both adaptive and innate immune cells in the pathogenesis of multiple sclerosis, provide pivotal biological insight into the causes and mechanisms of multiple sclerosis, and some of the variants implicated in multiple sclerosis also mediate risk of other autoimmune and inflammatory diseases. Genetics offers an approach to showing causality for environmental factors, through Mendelian randomisation. No single variant is necessary or sufficient to cause multiple sclerosis; instead, each increases total risk in an additive manner. This combined contribution from many genetic factors to disease risk, or polygenicity, has important consequences for how we interpret the epidemiology of multiple sclerosis and how we counsel patients on risk and prognosis. Ongoing efforts are focused on increasing cohort sizes, increasing diversity and detailed characterisation of study populations, and translating these associations into an understanding of the biology of multiple sclerosis.

Body Mass Index, Interleukin-6 Signaling and Multiple Sclerosis: A Mendelian Randomization Study.

Objectives: We explored whether genetically predicted increased body mass index (BMI) modulates multiple sclerosis (MS) risk through interleukin-6 (IL-6) signaling. Methods: We performed a two-sample Mendelian randomization (MR) study using multiple genome-wide association studies (GWAS) datasets for BMI, IL-6 signaling, IL-6 levels and c-reactive protein (CRP) levels as exposures and estimated their effects on risk of MS from GWAS data from the International Multiple Sclerosis Genetics Consortium (IMSGC) in 14,802 MS cases and 26,703 controls. Results: In univariable MR analyses, genetically predicted increased BMI and IL-6 signaling were associated with higher risk of MS (BMI: odds ratio (OR) = 1.30, 95% confidence interval (CI) = 1.15-1.47, p = 3.76 × 10-5; IL-6 signaling: OR = 1.51, 95% CI = 1.11-2.04, p = 0.01). Furthermore, higher BMI was associated with increased IL-6 signaling (ß = 0.37, 95% CI = 0.32,0.41, p = 1.58 × 10-65). In multivariable MR analyses, the effect of IL-6 signaling on MS risk remained after adjusting for BMI (OR = 1.36, 95% CI = 1.11-1.68, p = 0.003) and higher BMI remained associated with an increased risk for MS after adjustment for IL-6 signaling (OR = 1.16, 95% CI =1.00-1.34, p = 0.046). The proportion of the effect of BMI on MS mediated by IL-6 signaling corresponded to 43% (95% CI = 25%-54%). In contrast to IL-6 signaling, there was little evidence for an effect of serum IL-6 levels or CRP levels on risk of MS. Conclusion: In this study, we identified IL-6 signaling as a major mediator of the association between BMI and risk of MS. Further explorations of pathways underlying the association between BMI and MS are required and will, together with our findings, improve the understanding of MS biology and potentially lead to improved opportunities for targeted prevention strategies.

Environmental risk factors in multiple sclerosis: bridging Mendelian randomization and observational studies.

Multiple sclerosis (MS) is a complex disease with both genetic variants and environmental factors involved in disease susceptibility. The main environmental risk factors associated with MS in observational studies include obesity, vitamin D deficiency, Epstein-Barr virus infection and smoking. As modifying these environmental and lifestyle factors may enable prevention, it is important to pinpoint causal links between these factors and MS. Leveraging genetics through the Mendelian randomization (MR) paradigm is an elegant way to inform prevention strategies in MS. In this review, we summarize MR studies regarding the impact of environmental factors on MS susceptibility, thereby paying attention to quality criteria which will aid readers in interpreting any MR studies. We draw parallels and differences with observational studies and randomized controlled trials and look forward to the challenges that such work presents going forward.

Effects of Vitamin D and Body Mass Index on Disease Risk and Relapse Hazard in Multiple Sclerosis: A Mendelian Randomization Study.

BACKGROUND AND OBJECTIVES: Decreased vitamin D levels and obesity are associated with an increased risk for multiple sclerosis (MS). However, whether they also affect the disease course after onset remains unclear. With larger data sets now available, we used Mendelian randomization (MR) to determine whether serum 25-hydroxyvitamin D (25OHD) and body mass index (BMI) are causally associated with MS risk and, moving beyond susceptibility toward heterogeneity, with relapse hazard. METHODS: We used genetic variants from 4 distinct genome-wide association studies (GWASs) for serum 25OHD in up to 416,247 individuals and for BMI from a GWAS in 681,275 individuals. Applying 2-sample MR, we examined associations of 25OHD and BMI with the risk of MS, with summary statistics from the International Multiple Sclerosis Genetics Consortium GWAS in 14,802 MS cases and 26,703 controls. In addition, we examined associations with relapse hazard, with data from our GWAS in 506 MS cases. RESULTS: A 1-SD increase in genetically predicted natural-log transformed 25OHD levels decreased odds of MS up to 28% (95% CI: 12%-40%, p = 0.001) and decreased hazard for a relapse occurring up to 43% (95% CI: 15%-61%, p = 0.006). A 1-SD increase in genetically predicted BMI, corresponding to roughly 5 kg/m2, increased risk for MS with 30% (95% CI: 15%-47%, p = 3.76 × 10-5). On the contrary, we did not find evidence for a causal role of higher BMI with an increased hazard for occurrence of a relapse. DISCUSSION: This study supports causal effects of genetically predicted serum 25OHD concentrations and BMI on risk of MS. In contrast, serum 25OHD but not BMI is significantly associated with relapse hazard after onset. These findings might offer clinical implications for both prevention and treatment.

Natural Killer Cells in Multiple Sclerosis: Entering the Stage.

Studies investigating the immunopathology of multiple sclerosis (MS) have largely focused on adaptive T and B lymphocytes. However, in recent years there has been an increased interest in the contribution of innate immune cells, amongst which the natural killer (NK) cells. Apart from their canonical role of controlling viral infections, cell stress and malignancies, NK cells are increasingly being recognized for their modulating effect on the adaptive immune system, both in health and autoimmune disease. From different lines of research there is now evidence that NK cells contribute to MS immunopathology. In this review, we provide an overview of studies that have investigated the role of NK cells in the pathogenesis of MS by use of the experimental autoimmune encephalomyelitis (EAE) animal model, MS genetics or through ex vivo and in vitro work into the immunology of MS patients. With the advent of modern hypothesis-free technologies such as single-cell transcriptomics, we are exposing an unexpected NK cell heterogeneity, increasingly blurring the boundaries between adaptive and innate immunity. We conclude that unravelling this heterogeneity, as well as the mechanistic link between innate and adaptive immune cell functions will lay the foundation for the use of NK cells as prognostic tools and therapeutic targets in MS and a myriad of other currently uncurable autoimmune disorders.

Frequent somatic mosaicism in T lymphocyte subsets in individuals with and without multiple sclerosis.

Background: Somatic variants are variations in an individual's genome acquired after the zygotic stadium and result from mitotic errors or not (fully) repaired DNA damage. Objectives: To investigate whether somatic mosaicism in T lymphocyte subsets is enriched early in multiple sclerosis (MS). Methods: We identified somatic variants with variant allele fractions ≥1% across the whole exome in CD4+ and CD8+ T lymphocytes of 21 treatment-naive MS patients with <5 years of disease duration and 16 partially age-matched healthy controls. We investigated the known somatic STAT3 variant p.Y640F in peripheral blood in a larger cohort of 446 MS patients and 259 controls. Results: All subjects carried 1-142 variants in CD4+ or CD8+ T lymphocytes. Variants were more common, more abundant, and increased with age in CD8+ T lymphocytes. Somatic variants were common in the genes DNMT3A and especially STAT3. Overall, the presence or abundance of somatic variants, including the STAT3 p.Y640F variant, did not differ between MS patients and controls. Conclusions: Somatic variation in T lymphocyte subsets is widespread in both control individuals and MS patients. Somatic mosaicism in T lymphocyte subsets is not enriched in early MS and thus unlikely to contribute to MS risk, but future research needs to address whether a subset of variants influences disease susceptibility.

Human immune diversity: from evolution to modernity.

The extreme diversity of the human immune system, forged and maintained throughout evolutionary history, provides a potent defense against opportunistic pathogens. At the same time, this immune variation is the substrate upon which a plethora of immune-associated diseases develop. Genetic analysis suggests that thousands of individually weak loci together drive up to half of the observed immune variation. Intense selection maintains this genetic diversity, even selecting for the introgressed Neanderthal or Denisovan alleles that have reintroduced variation lost during the out-of-Africa migration. Variations in age, sex, diet, environmental exposure, and microbiome each potentially explain the residual variation, with proof-of-concept studies demonstrating both plausible mechanisms and correlative associations. The confounding interaction of many of these variables currently makes it difficult to assign definitive contributions. Here, we review the current state of play in the field, identify the key unknowns in the causality of immune variation, and identify the multidisciplinary pathways toward an improved understanding.

Neuroinflammation and Its Association with Cognition, Neuronal Markers and Peripheral Inflammation after Chemotherapy for Breast Cancer.

To uncover mechanisms underlying chemotherapy-induced cognitive impairment in breast cancer, we studied new biomarkers of neuroinflammation and neuronal survival. This cohort study included 74 women (47 ± 10 years) from 22 October 2017 until 20 August 2020. Nineteen chemotherapy-treated and 18 chemotherapy-naïve patients with breast cancer were assessed one month after the completion of surgery and/or chemotherapy, and 37 healthy controls were included. Assessments included neuropsychological testing, questionnaires, blood sampling for 17 inflammatory and two neuronal survival markers (neurofilament light-chain (NfL), and brain-derived neurotrophic factor (BDNF) and PET-MR neuroimaging. To investigate neuroinflammation, translocator protein (TSPO) [18F]DPA714-PET-MR was acquired for 15 participants per group, and evaluated by volume of distribution normalized to the cerebellum. Chemotherapy-treated patients showed higher TSPO expression, indicative for neuroinflammation, in the occipital and parietal lobe when compared to healthy controls or chemotherapy-naïve patients. After partial-volume correction, differences with healthy controls persisted (pFWE < 0.05). Additionally, compared to healthy- or chemotherapy-naïve controls, cognitive impairment (17-22%) and altered levels in blood markers (F ≥ 3.7, p ≤ 0.031) were found in chemotherapy-treated patients. NfL, an axonal damage marker, was particularly sensitive in differentiating groups (F = 105, p = 4.2 × 10 -21), with levels 20-fold higher in chemotherapy-treated patients. Lastly, in chemotherapy-treated patients alone, higher local TSPO expression was associated with worse cognitive performance, higher blood levels of BDNF/NfL, and decreased fiber cross-section in the corpus callosum (pFWE < 0.05). These findings suggest that increased neuroinflammation is associated with chemotherapy-related cognitive impairment in breast cancer. Additionally, NfL could be a useful biomarker to assess neurotoxic effects of anticancer chemotherapies.

Treatment-Induced BAFF Expression and B Cell Biology in Multiple Sclerosis.

Although fingolimod and interferon-ß are two mechanistically different multiple sclerosis (MS) treatments, they both induce B cell activating factor (BAFF) and shift the B cell pool towards a regulatory phenotype. However, whether there is a shared mechanism between both treatments in how they influence the B cell compartment remains elusive. In this study, we collected a cross-sectional study population of 112 MS patients (41 untreated, 42 interferon-ß, 29 fingolimod) and determined B cell subsets, cell-surface and RNA expression of BAFF-receptor (BAFF-R) and transmembrane activator and cyclophilin ligand interactor (TACI) as well as plasma and/or RNA levels of BAFF, BAFF splice forms and interleukin-10 (IL-10) and -35 (IL-35). We added an in vitro B cell culture with four stimulus conditions (Medium, CpG, BAFF and CpG+BAFF) for untreated and interferon-ß treated patients including measurement of intracellular IL-10 levels. Our flow experiments showed that interferon-ß and fingolimod induced BAFF protein and mRNA expression (P ≤ 3.15 x 10-4) without disproportional change in the antagonizing splice form. Protein BAFF correlated with an increase in transitional B cells (P = 5.70 x 10-6), decrease in switched B cells (P = 3.29 x 10-4), and reduction in B cell-surface BAFF-R expression (P = 2.70 x 10-10), both on TACI-positive and -negative cells. TACI and BAFF-R RNA levels remained unaltered. RNA, plasma and in vitro experiments demonstrated that BAFF was not associated with increased IL-10 and IL-35 levels. In conclusion, treatment-induced BAFF correlates with a shift towards transitional B cells which are enriched for cells with an immunoregulatory function. However, BAFF does not directly influence the expression of the immunoregulatory cytokines IL-10 and IL-35. Furthermore, the post-translational mechanism of BAFF-induced BAFF-R cell surface loss was TACI-independent. These observations put the failure of pharmaceutical anti-BAFF strategies in perspective and provide insights for targeted B cell therapies.

Genetic Variation in WNT9B Increases Relapse Hazard in Multiple Sclerosis.

OBJECTIVE: Many multiple sclerosis (MS) genetic susceptibility variants have been identified, but understanding disease heterogeneity remains a key challenge. Relapses are a core feature of MS and a common primary outcome of clinical trials, with prevention of relapses benefiting patients immediately and potentially limiting long-term disability accrual. We aim to identify genetic variation associated with relapse hazard in MS by analyzing the largest study population to date. METHODS: We performed a genomewide association study (GWAS) in a discovery cohort and investigated the genomewide significant variants in a replication cohort. Combining both cohorts, we captured a total of 2,231 relapses occurring before the start of any immunomodulatory treatment in 991 patients. For assessing time to relapse, we applied a survival analysis utilizing Cox proportional hazards models. We also investigated the association between MS genetic risk scores and relapse hazard and performed a gene ontology pathway analysis. RESULTS: The low-frequency genetic variant rs11871306 within WNT9B reached genomewide significance in predicting relapse hazard and replicated (meta-analysis hazard ratio (HR) = 2.15, 95% confidence interval (CI) = 1.70-2.78, p = 2.07 × 10-10 ). A pathway analysis identified an association of the pathway "response to vitamin D" with relapse hazard (p = 4.33 × 10-6 ). The MS genetic risk scores, however, were not associated with relapse hazard. INTERPRETATION: Genetic factors underlying disease heterogeneity differ from variants associated with MS susceptibility. Our findings imply that genetic variation within the Wnt signaling and vitamin D pathways contributes to differences in relapse occurrence. The present study highlights these cross-talking pathways as potential modulators of MS disease activity. ANN NEUROL 2021;89:884-894.

Quantitative MRI phenotypes capture biological heterogeneity in multiple sclerosis patients.

Magnetization transfer ratio (MTR) and brain volumetric imaging are (semi-)quantitative MRI markers capturing demyelination, axonal degeneration and/or inflammation. However, factors shaping variation in these traits are largely unknown. In this study, we collected a longitudinal cohort of 33 multiple sclerosis (MS) patients and extended it cross-sectionally to 213. We measured MTR in lesions, normal-appearing white matter (NAWM), normal-appearing grey matter (NAGM) and total brain, grey matter, white matter and lesion volume. We also calculated the polygenic MS risk score. Longitudinally, inter-patient differences at inclusion and intra-patient changes during follow-up together explained > 70% of variance in MRI, with inter-patient differences at inclusion being the predominant source of variance. Cross-sectionally, we observed a moderate correlation of MTR between NAGM and NAWM and, less pronounced, with lesions. Age and gender explained about 30% of variance in total brain and grey matter volume. However, they contributed less than 10% to variance in MTR measures. There were no significant associations between MRI traits and the genetic risk score. In conclusion, (semi-)quantitative MRI traits change with ongoing disease activity but this change is modest in comparison to pre-existing inter-patient differences. These traits reflect individual variation in biological processes, which appear different from those involved in genetic MS susceptibility.

Smoking and multiple sclerosis risk: a Mendelian randomization study.

BACKGROUND: Striking changes in the demographic pattern of multiple sclerosis (MS) strongly indicate an influence of modifiable exposures, which lend themselves well to intervention. It is important to pinpoint which of the many environmental, lifestyle, and sociodemographic changes that have occurred over the past decades, such as higher smoking and obesity rates, are responsible. Mendelian randomization (MR) is an elegant tool to overcome limitations inherent to observational studies and leverage human genetics to inform prevention strategies in MS. METHODS: We use genetic variants from the largest genome-wide association study for smoking phenotypes (initiation: N = 378, heaviness: N = 55, lifetime smoking: N = 126) and body mass index (BMI, N = 656) and apply these as instrumental variables in a two-sample MR analysis to the most recent meta-analysis for MS. We adjust for the genetic correlation between smoking and BMI in a multivariable MR. RESULTS: In univariable and multivariable MR, smoking does not have an effect on MS risk nor explains part of the association between BMI and MS risk. In contrast, in both analyses each standard deviation increase in BMI, corresponding to roughly 5 kg/m2 units, confers a 30% increase in MS risk. CONCLUSION: Despite observational studies repeatedly reporting an association between smoking and increased risk for MS, MR analyses on smoking phenotypes and MS risk could not confirm a causal relationship. This is in contrast with BMI, where observational studies and MR agree on a causal contribution. The reasons for the discrepancy between observational studies and our MR study concerning smoking and MS require further investigation.

CHIT1 at Diagnosis Reflects Long-Term Multiple Sclerosis Disease Activity.

OBJECTIVE: Evidence for a role of microglia in the pathogenesis of multiple sclerosis (MS) is growing. We investigated association of microglial markers at time of diagnostic lumbar puncture (LP) with different aspects of disease activity (relapses, disability, magnetic resonance imaging parameters) up to 6 years later in a cohort of 143 patients. METHODS: In cerebrospinal fluid (CSF), we measured 3 macrophage and microglia-related proteins, chitotriosidase (CHIT1), chitinase-3-like protein 1 (CHI3L1 or YKL-40), and soluble triggering receptor expressed on myeloid cells 2 (sTREM2), as well as a marker of neuronal damage, neurofilament light chain (NfL), using enzyme-linked immunosorbent assay and electrochemiluminescence. We investigated the same microglia-related markers in publicly available RNA expression data from postmortem brain tissue. RESULTS: CHIT1 levels at diagnostic LP correlated with 2 aspects of long-term disease activity after correction for multiple testing. First, CHIT1 increased with reduced tissue integrity in lesions at a median 3 years later (p = 9.6E-04). Second, CHIT1 reflected disease severity at a median 5 years later (p = 1.2E-04). Together with known clinical covariates, CHIT1 levels explained 12% and 27% of variance in these 2 measures, respectively, and were able to distinguish slow and fast disability progression (area under the curve = 85%). CHIT1 was the best discriminator of chronic active versus chronic inactive lesions and the only marker correlated with NfL (r = 0.3, p = 0.0019). Associations with disease activity were, however, independent of NfL. INTERPRETATION: CHIT1 CSF levels measured during the diagnostic LP reflect microglial activation early on in MS and can be considered a valuable prognostic biomarker for future disease activity. ANN NEUROL 2020;87:633-645.

Machine learning identifies an immunological pattern associated with multiple juvenile idiopathic arthritis subtypes.

OBJECTIVES: Juvenile idiopathic arthritis (JIA) is the most common class of childhood rheumatic diseases, with distinct disease subsets that may have diverging pathophysiological origins. Both adaptive and innate immune processes have been proposed as primary drivers, which may account for the observed clinical heterogeneity, but few high-depth studies have been performed. METHODS: Here we profiled the adaptive immune system of 85 patients with JIA and 43 age-matched controls with indepth flow cytometry and machine learning approaches. RESULTS: Immune profiling identified immunological changes in patients with JIA. This immune signature was shared across a broad spectrum of childhood inflammatory diseases. The immune signature was identified in clinically distinct subsets of JIA, but was accentuated in patients with systemic JIA and those patients with active disease. Despite the extensive overlap in the immunological spectrum exhibited by healthy children and patients with JIA, machine learning analysis of the data set proved capable of discriminating patients with JIA from healthy controls with ~90% accuracy. CONCLUSIONS: These results pave the way for large-scale immune phenotyping longitudinal studies of JIA. The ability to discriminate between patients with JIA and healthy individuals provides proof of principle for the use of machine learning to identify immune signatures that are predictive to treatment response group.

Myoglobinopathy is an adult-onset autosomal dominant myopathy with characteristic sarcoplasmic inclusions.

Myoglobin, encoded by MB, is a small cytoplasmic globular hemoprotein highly expressed in cardiac myocytes and oxidative skeletal myofibers. Myoglobin binds O2, facilitates its intracellular transport and serves as a controller of nitric oxide and reactive oxygen species. Here, we identify a recurrent c.292C>T (p.His98Tyr) substitution in MB in fourteen members of six European families suffering from an autosomal dominant progressive myopathy with highly characteristic sarcoplasmic inclusions in skeletal and cardiac muscle. Myoglobinopathy manifests in adulthood with proximal and axial weakness that progresses to involve distal muscles and causes respiratory and cardiac failure. Biochemical characterization reveals that the mutant myoglobin has altered O2 binding, exhibits a faster heme dissociation rate and has a lower reduction potential compared to wild-type myoglobin. Preliminary studies show that mutant myoglobin may result in elevated superoxide levels at the cellular level. These data define a recognizable muscle disease associated with MB mutation.