Long noncoding RNA LIRIL2R modulates FOXP3 levels and suppressive function of human CD4+ regulatory T cells by regulating IL2RA.

Regulatory T cells (Tregs) are central in controlling immune responses, and dysregulation of their function can lead to autoimmune disorders or cancer. Despite extensive studies on Tregs, the basis of epigenetic regulation of human Treg development and function is incompletely understood. Long intergenic noncoding RNAs (lincRNA)s are important for shaping and maintaining the epigenetic landscape in different cell types. In this study, we identified a gene on the chromosome 6p25.3 locus, encoding a lincRNA, that was up-regulated during early differentiation of human Tregs. The lincRNA regulated the expression of interleukin-2 receptor alpha (IL2RA), and we named it the lincRNA regulator of IL2RA (LIRIL2R). Through transcriptomics, epigenomics, and proteomics analysis of LIRIL2R-deficient Tregs, coupled with global profiling of LIRIL2R binding sites using chromatin isolation by RNA purification, followed by sequencing, we identified IL2RA as a target of LIRIL2R. This nuclear lincRNA binds upstream of the IL2RA locus and regulates its epigenetic landscape and transcription. CRISPR-mediated deletion of the LIRIL2R-bound region at the IL2RA locus resulted in reduced IL2RA expression. Notably, LIRIL2R deficiency led to reduced expression of Treg-signature genes (e.g., FOXP3, CTLA4, and PDCD1), upregulation of genes associated with effector T cells (e.g., SATB1 and GATA3), and loss of Treg-mediated suppression.

A proximal enhancer regulates RORA expression during early human Th17 cell differentiation.

Gene regulatory elements, such as enhancers, greatly influence cell identity by tuning the transcriptional activity of specific cell types. Dynamics of enhancer landscape during early human Th17 cell differentiation remains incompletely understood. Leveraging ATAC-seq-based profiling of chromatin accessibility and comprehensive analysis of key histone marks, we identified a repertoire of enhancers that potentially exert control over the fate specification of Th17 cells. We found 23 SNPs associated with autoimmune diseases within Th17-enhancers that precisely overlapped with the binding sites of transcription factors actively engaged in T-cell functions. Among the Th17-specific enhancers, we identified an enhancer in the intron of RORA and demonstrated that this enhancer positively regulates RORA transcription. Moreover, CRISPR-Cas9-mediated deletion of a transcription factor binding site-rich region within the identified RORA enhancer confirmed its role in regulating RORA transcription. These findings provide insights into the potential mechanism by which the RORA enhancer orchestrates Th17 differentiation.

Proteomics screening after pediatric allogenic hematopoietic stem cell transplantation reveals an association between increased expression of inhibitory receptor FCRL6 on γδ T cells and cytomegalovirus reactivation.

We studied the associations between inflammation-related proteins in circulation and complications after pediatric allogenic hematopoietic stem cell transplantation (HSCT), to reveal proteomic signatures or individual soluble proteins associated with specific complications after HSCT. We used a proteomics method called Proximity Extension Assay to repeatedly measure 180 different proteins together with clinical variables, cellular immune reconstitution and blood viral copy numbers in 27 children (1-18 years of age) during a 2-year follow-up after allogenic HSCT. Protein profile analysis was performed using unsupervised hierarchical clustering and a regression-based method, while the Bonferroni-corrected Mann-Whitney U-test was used for time point-specific comparison of individual proteins against outcome. At 6 months after allogenic HSCT, we could identify a protein profile pattern associated with occurrence of the complications such as chronic graft-versus-host disease, viral infections, relapse and death. When protein markers were analyzed separately, the plasma concentration of the inhibitory and cytotoxic T-cell surface protein FCRL6 (Fc receptor-like 6) was higher in patients with cytomegalovirus (CMV) viremia [log2-fold change 1.5 (P = 0.00099), 2.5 (P = 0.00035) and 2.2 (P = 0.045) at time points 6, 12 and 24 months]. Flow cytometry confirmed that FCRL6 expression was higher in innate-like γδ T cells, indicating that these cells are involved in controlling CMV reactivation in HSCT recipients. In conclusion, the potentially druggable FCRL6 receptor on cytotoxic T cells appears to have a role in controlling CMV viremia after HSCT. Furthermore, our results suggest that system-level analysis is a useful addition to the studying of single biomarkers in allogenic HSCT.

Serum Hydroxysteroid (17beta) dehydrogenase 1 concentration in pregnant women correlates with pregnancy-associated plasma protein a but does not serve as an independent marker for preeclampsia.

Hydroxysteroid (17beta) dehydrogenase 1 (HSD17B1) is a steroid synthetic enzyme expressed in ovarian granulosa cells and placental syncytiotrophoblasts. Here, HSD17B1 serum concentration was measured with a validated immuno assay during pregnancy at three time points (12-14, 18-20 and 26-28 weeks of gestation). The concentration increased 2.5-fold (p < 0.0001) and 1.7-fold (p = 0.0019) during the follow-up period for control women and women who later developed preeclampsia (PE), respectively, and a significant difference was observed at weeks 26-28 (p = 0.0266). HSD17B1 concentration at all the three time points positively correlated with serum PAPPA measured at the first time point (first time point r = 0.38, p = 1.1x10-10; second time point r = 0.27, p = 5.9x10-6 and third timepoint r = 0.26, p = 2.3x10-5). No correlation was observed between HSD17B1 and placental growth factor (PLGF). Serum HSD17B1, furthermore, negatively correlated with the mother's weight and body mass index (BMI), mirroring the pattern observed for PAPPA. The univariable logistic regression identified a weak association between HSD17B1 at 26-28 weeks and later development of PE (P = 0.04). Also, the best multivariable model obtained using penalized logistic regression with stable iterative variable selection at 26-28 weeks included HSD17B1, together with PLGF, PAPPA and the mother's BMI. While the area under the ROC curve of the model was higher than that of the adjusted PLGF, the difference was not statistically significant. In summary, the serum concentration of HSD17B1 correlated with PAPPA, another protein expressed in syncytiotrophoblasts, and with mother's weight and BMI but could not be considered as an independent marker for PE.

Benchmarking methods for detecting differential states between conditions from multi-subject single-cell RNA-seq data.

Single-cell RNA-sequencing (scRNA-seq) enables researchers to quantify transcriptomes of thousands of cells simultaneously and study transcriptomic changes between cells. scRNA-seq datasets increasingly include multisubject, multicondition experiments to investigate cell-type-specific differential states (DS) between conditions. This can be performed by first identifying the cell types in all the subjects and then by performing a DS analysis between the conditions within each cell type. Naïve single-cell DS analysis methods that treat cells statistically independent are subject to false positives in the presence of variation between biological replicates, an issue known as the pseudoreplicate bias. While several methods have already been introduced to carry out the statistical testing in multisubject scRNA-seq analysis, comparisons that include all these methods are currently lacking. Here, we performed a comprehensive comparison of 18 methods for the identification of DS changes between conditions from multisubject scRNA-seq data. Our results suggest that the pseudobulk methods performed generally best. Both pseudobulks and mixed models that model the subjects as a random effect were superior compared with the naïve single-cell methods that do not model the subjects in any way. While the naïve models achieved higher sensitivity than the pseudobulk methods and the mixed models, they were subject to a high number of false positives. In addition, accounting for subjects through latent variable modeling did not improve the performance of the naïve methods.

PhosPiR: an automated phosphoproteomic pipeline in R.

Large-scale phosphoproteome profiling using mass spectrometry (MS) provides functional insight that is crucial for disease biology and drug discovery. However, extracting biological understanding from these data is an arduous task requiring multiple analysis platforms that are not adapted for automated high-dimensional data analysis. Here, we introduce an integrated pipeline that combines several R packages to extract high-level biological understanding from large-scale phosphoproteomic data by seamless integration with existing databases and knowledge resources. In a single run, PhosPiR provides data clean-up, fast data overview, multiple statistical testing, differential expression analysis, phosphosite annotation and translation across species, multilevel enrichment analyses, proteome-wide kinase activity and substrate mapping and network hub analysis. Data output includes graphical formats such as heatmap, box-, volcano- and circos-plots. This resource is designed to assist proteome-wide data mining of pathophysiological mechanism without a need for programming knowledge.

Cell-connectivity-guided trajectory inference from single-cell data.

MOTIVATION: Single-cell RNA-sequencing enables cell-level investigation of cell differentiation, which can be modelled using trajectory inference methods. While tremendous effort has been put into designing these methods, inferring accurate trajectories automatically remains difficult. Therefore, the standard approach involves testing different trajectory inference methods and picking the trajectory giving the most biologically sensible model. As the default parameters are often suboptimal, their tuning requires methodological expertise. RESULTS: We introduce Totem, an open-source, easy-to-use R package designed to facilitate inference of tree-shaped trajectories from single-cell data. Totem generates a large number of clustering results, estimates their topologies as minimum spanning trees, and uses them to measure the connectivity of the cells. Besides automatic selection of an appropriate trajectory, cell connectivity enables to visually pinpoint branching points and milestones relevant to the trajectory. Furthermore, testing different trajectories with Totem is fast, easy, and does not require in-depth methodological knowledge. AVAILABILITY AND IMPLEMENTATION: Totem is available as an R package at https://github.com/elolab/Totem.

Deep Learning Enables Automatic Detection of Joint Damage Progression in Rheumatoid Arthritis-Model Development and External Validation.

OBJECTIVES: Although deep learning has demonstrated substantial potential in automatic quantification of joint damage in rheumatoid arthritis (RA), evidence for detecting longitudinal changes at an individual patient level is lacking. Here, we introduce and externally validate our automated RA scoring algorithm (AuRA), and demonstrate its utility for monitoring radiographic progression in a real-world setting. METHODS: The algorithm, originally developed during the Rheumatoid Arthritis 2-Dialogue for Reverse Engineering Assessment and Methods (RA2-DREAM) challenge, was trained to predict expert-curated Sharp-van der Heijde total scores in hand and foot radiographs from two previous clinical studies (n = 367). We externally validated AuRA against data (n = 205) from Turku University Hospital and compared the performance against two top-performing RA2-DREAM solutions. Finally, for 54 patients, we extracted additional radiograph sets from another control visit to the clinic (average time interval of 4.6 years). RESULTS: In the external validation cohort, with a root-mean-square-error (RMSE) of 23.6, AuRA outperformed both top-performing RA2-DREAM algorithms (RMSEs 35.0 and 35.6). The improved performance was explained mostly by lower errors at higher expert-assessed scores. The longitudinal changes predicted by our algorithm were significantly correlated with changes in expert-assessed scores (Pearson's R = 0.74, p< 0.001). CONCLUSION: AuRA had the best external validation performance and demonstrated potential for detecting longitudinal changes in joint damage. Available in https://hub.docker.com/r/elolab/aura, our algorithm can easily be applied for automatic detection of radiographic progression in the future, reducing the need for laborious manual scoring.

Multi-omics analysis reveals drivers of loss of ß-cell function after newly diagnosed autoimmune type 1 diabetes: An INNODIA multicenter study.

AIMS: Heterogeneity in the rate of ß-cell loss in newly diagnosed type 1 diabetes patients is poorly understood and creates a barrier to designing and interpreting disease-modifying clinical trials. Integrative analyses of baseline multi-omics data obtained after the diagnosis of type 1 diabetes may provide mechanistic insight into the diverse rates of disease progression after type 1 diabetes diagnosis. METHODS: We collected samples in a pan-European consortium that enabled the concerted analysis of five different omics modalities in data from 97 newly diagnosed patients. In this study, we used Multi-Omics Factor Analysis to identify molecular signatures correlating with post-diagnosis decline in ß-cell mass measured as fasting C-peptide. RESULTS: Two molecular signatures were significantly correlated with fasting C-peptide levels. One signature showed a correlation to neutrophil degranulation, cytokine signalling, lymphoid and non-lymphoid cell interactions and G-protein coupled receptor signalling events that were inversely associated with a rapid decline in ß-cell function. The second signature was related to translation and viral infection was inversely associated with change in ß-cell function. In addition, the immunomics data revealed a Natural Killer cell signature associated with rapid ß-cell decline. CONCLUSIONS: Features that differ between individuals with slow and rapid decline in ß-cell mass could be valuable in staging and prediction of the rate of disease progression and thus enable smarter (shorter and smaller) trial designs for disease modifying therapies as well as offering biomarkers of therapeutic effect.

VarSCAT: A computational tool for sequence context annotations of genomic variants.

The sequence contexts of genomic variants play important roles in understanding biological significances of variants and potential sequencing related variant calling issues. However, methods for assessing the diverse sequence contexts of genomic variants such as tandem repeats and unambiguous annotations have been limited. Herein, we describe the Variant Sequence Context Annotation Tool (VarSCAT) for annotating the sequence contexts of genomic variants, including breakpoint ambiguities, flanking bases of variants, wildtype/mutated DNA sequences, variant nomenclatures, distances between adjacent variants, tandem repeat regions, and custom annotation with user customizable options. Our analyses demonstrate that VarSCAT is more versatile and customizable than the currently available methods or strategies for annotating variants in short tandem repeat (STR) regions or insertions and deletions (indels) with breakpoint ambiguity. Variant sequence context annotations of high-confidence human variant sets with VarSCAT revealed that more than 75% of all human individual germline and clinically relevant indels have breakpoint ambiguities. Moreover, we illustrate that more than 80% of human individual germline small variants in STR regions are indels and that the sizes of these indels correlated with STR motif sizes. VarSCAT is available from https://github.com/elolab/VarSCAT.

A systematic comparison of FOSL1, FOSL2 and BATF-mediated transcriptional regulation during early human Th17 differentiation.

Th17 cells are essential for protection against extracellular pathogens, but their aberrant activity can cause autoimmunity. Molecular mechanisms that dictate Th17 cell-differentiation have been extensively studied using mouse models. However, species-specific differences underscore the need to validate these findings in human. Here, we characterized the human-specific roles of three AP-1 transcription factors, FOSL1, FOSL2 and BATF, during early stages of Th17 differentiation. Our results demonstrate that FOSL1 and FOSL2 co-repress Th17 fate-specification, whereas BATF promotes the Th17 lineage. Strikingly, FOSL1 was found to play different roles in human and mouse. Genome-wide binding analysis indicated that FOSL1, FOSL2 and BATF share occupancy over regulatory regions of genes involved in Th17 lineage commitment. These AP-1 factors also share their protein interacting partners, which suggests mechanisms for their functional interplay. Our study further reveals that the genomic binding sites of FOSL1, FOSL2 and BATF harbour hundreds of autoimmune disease-linked SNPs. We show that many of these SNPs alter the ability of these transcription factors to bind DNA. Our findings thus provide critical insights into AP-1-mediated regulation of human Th17-fate and associated pathologies.

SMG6 localizes to the chromatoid body and shapes the male germ cell transcriptome to drive spermatogenesis.

Nonsense-mediated RNA decay (NMD) is a highly conserved and selective RNA turnover pathway that depends on the endonuclease SMG6. Here, we show that SMG6 is essential for male germ cell differentiation in mice. Germ-cell conditional knockout (cKO) of Smg6 induces extensive transcriptome misregulation, including a failure to eliminate meiotically expressed transcripts in early haploid cells, and accumulation of NMD target mRNAs with long 3' untranslated regions (UTRs). Loss of SMG6 in the male germline results in complete arrest of spermatogenesis at the early haploid cell stage. We find that SMG6 is strikingly enriched in the chromatoid body (CB), a specialized cytoplasmic granule in male germ cells also harboring PIWI-interacting RNAs (piRNAs) and the piRNA-binding protein PIWIL1. This raises the possibility that SMG6 and the piRNA pathway function together, which is supported by several findings, including that Piwil1-KO mice phenocopy Smg6-cKO mice and that SMG6 and PIWIL1 co-regulate many genes in round spermatids. Together, our results demonstrate that SMG6 is an essential regulator of the male germline transcriptome, and highlight the CB as a molecular platform coordinating RNA regulatory pathways to control sperm production and fertility.

Targeted serum proteomics of longitudinal samples from newly diagnosed youth with type 1 diabetes distinguishes markers of disease and C-peptide trajectory.

AIMS/HYPOTHESIS: There is a growing need for markers that could help indicate the decline in beta cell function and recognise the need and efficacy of intervention in type 1 diabetes. Measurements of suitably selected serum markers could potentially provide a non-invasive and easily applicable solution to this challenge. Accordingly, we evaluated a broad panel of proteins previously associated with type 1 diabetes in serum from newly diagnosed individuals during the first year from diagnosis. To uncover associations with beta cell function, comparisons were made between these targeted proteomics measurements and changes in fasting C-peptide levels. To further distinguish proteins linked with the disease status, comparisons were made with measurements of the protein targets in age- and sex-matched autoantibody-negative unaffected family members (UFMs). METHODS: Selected reaction monitoring (SRM) mass spectrometry analyses of serum, targeting 85 type 1 diabetes-associated proteins, were made. Sera from individuals diagnosed under 18 years (n=86) were drawn within 6 weeks of diagnosis and at 3, 6 and 12 months afterwards (288 samples in total). The SRM data were compared with fasting C-peptide/glucose data, which was interpreted as a measure of beta cell function. The protein data were further compared with cross-sectional SRM measurements from UFMs (n=194). RESULTS: Eleven proteins had statistically significant associations with fasting C-peptide/glucose. Of these, apolipoprotein L1 and glutathione peroxidase 3 (GPX3) displayed the strongest positive and inverse associations, respectively. Changes in GPX3 levels during the first year after diagnosis indicated future fasting C-peptide/glucose levels. In addition, differences in the levels of 13 proteins were observed between the individuals with type 1 diabetes and the matched UFMs. These included GPX3, transthyretin, prothrombin, apolipoprotein C1 and members of the IGF family. CONCLUSIONS/INTERPRETATION: The association of several targeted proteins with fasting C-peptide/glucose levels in the first year after diagnosis suggests their connection with the underlying changes accompanying alterations in beta cell function in type 1 diabetes. Moreover, the direction of change in GPX3 during the first year was indicative of subsequent fasting C-peptide/glucose levels, and supports further investigation of this and other serum protein measurements in future studies of beta cell function in type 1 diabetes.

scShaper: an ensemble method for fast and accurate linear trajectory inference from single-cell RNA-seq data.

MOTIVATION: Computational models are needed to infer a representation of the cells, i.e. a trajectory, from single-cell RNA-sequencing data that model cell differentiation during a dynamic process. Although many trajectory inference methods exist, their performance varies greatly depending on the dataset and hence there is a need to establish more accurate, better generalizable methods. RESULTS: We introduce scShaper, a new trajectory inference method that enables accurate linear trajectory inference. The ensemble approach of scShaper generates a continuous smooth pseudotime based on a set of discrete pseudotimes. We demonstrate that scShaper is able to infer accurate trajectories for a variety of trigonometric trajectories, including many for which the commonly used principal curves method fails. A comprehensive benchmarking with state-of-the-art methods revealed that scShaper achieved superior accuracy of the cell ordering and, in particular, the differentially expressed genes. Moreover, scShaper is a fast method with few hyperparameters, making it a promising alternative to the principal curves method for linear pseudotemporal ordering. AVAILABILITY AND IMPLEMENTATION: scShaper is available as an R package at https://github.com/elolab/scshaper. The test data are available at https://doi.org/10.5281/zenodo.5734488. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Tool evaluation for the detection of variably sized indels from next generation whole genome and targeted sequencing data.

Insertions and deletions (indels) in human genomes are associated with a wide range of phenotypes, including various clinical disorders. High-throughput, next generation sequencing (NGS) technologies enable the detection of short genetic variants, such as single nucleotide variants (SNVs) and indels. However, the variant calling accuracy for indels remains considerably lower than for SNVs. Here we present a comparative study of the performance of variant calling tools for indel calling, evaluated with a wide repertoire of NGS datasets. While there is no single optimal tool to suit all circumstances, our results demonstrate that the choice of variant calling tool greatly impacts the precision and recall of indel calling. Furthermore, to reliably detect indels, it is essential to choose NGS technologies that offer a long read length and high coverage coupled with specific variant calling tools.

Polymorphism in interferon alpha/beta receptor contributes to glucocorticoid response and outcome of ARDS and COVID-19.

BACKGROUND: The use of glucocorticoids has given contradictory results for treating acute respiratory distress syndrome (ARDS). The use of intravenous Interferon beta (IFN ß) for the treatment of ARDS was recently tested in a phase III ARDS trial (INTEREST), in which more than half of the patients simultaneously received glucocorticoids. Trial results showed deleterious effects of glucocorticoids when administered together with IFN ß, and therefore, we aimed at finding the reason behind this. METHODS: We first sequenced the genes encoding the IFN α/ß receptor of the patients, who participated in the INTEREST study (ClinicalTrials.gov Identifier:  NCT02622724 , November 24, 2015) in which the patients were randomized to receive an intravenous injection of IFN ß-1a (144 patients) or placebo (152 patients). Genetic background was analyzed against clinical outcome, concomitant medication, and pro-inflammatory cytokine levels. Thereafter, we tested the influence of the genetic background on IFN α/ß receptor expression in lung organ cultures and whether, it has any effect on transcription factors STAT1 and STAT2 involved in IFN signaling. RESULTS: We found a novel disease association of a SNP rs9984273, which is situated in the interferon α/ß receptor subunit 2 (IFNAR2) gene in an area corresponding to a binding motif of the glucocorticoid receptor (GR). The minor allele of SNP rs9984273 associates with higher IFNAR expression, more rapid decrease of IFN γ and interleukin-6 (IL-6) levels and better outcome in IFN ß treated patients with ARDS, while the major allele associates with a poor outcome especially under concomitant IFN ß and glucocorticoid treatment. Moreover, the minor allele of rs9984273 associates with a less severe form of coronavirus diseases (COVID-19) according to the COVID-19 Host Genetics Initiative database. CONCLUSIONS: The distribution of this SNP within clinical study arms may explain the contradictory results of multiple ARDS studies and outcomes in COVID-19 concerning type I IFN signaling and glucocorticoids.

A novel easy-to-use index to predict institutionalization and death in older population - a 10-year population-based follow-up study.

BACKGROUND: Various indexes have been developed to estimate the risk for mortality, institutionalization, and other adverse outcomes for older people. Most indexes are based on a large number of clinical or laboratory parameters. An index based on only a few parameters would be more practical to use in every-day clinical practice. Our aim was to create an index to predict the risk for mortality and institutionalization with as few parameters as possible without compromising their predictive ability. METHODS: A prospective study with a 10-year follow-up period. Thirty-six clinical and fourteen laboratory parameters were combined to form an index. Cox regression model was used to analyze the association of the index with institutionalization and mortality. A backward statistical method was used to reduce the number of parameters to form an easy-to-use index for predicting institutionalization and mortality. RESULTS: The mean age of the participants (n = 1172) was 73.1 (SD 6.6, range 64‒97) years. Altogether, 149 (14%) subjects were institutionalized, and 413 (35%) subjects deceased during the follow-up. Institutionalization and mortality rates increased as index scores increased both for the large 50-parameter combined index and for the reduced indexes. After a backward variable selection in the Cox regression model, three clinical parameters remained in the index to predict institutionalization and six clinical and three laboratory parameters in the index to predict mortality. The reduced indexes showed a slightly better predictive value for both institutionalization and mortality compared to the full index. CONCLUSIONS: A large index with fifty parameters included many unimportant parameters that did not increase its predictive value, and therefore could be replaced with a reduced index with only a few carefully chosen parameters, that were individually associated with institutionalization or death.

Risk factors for revision due to prosthetic joint infection following total knee arthroplasty based on 62,087 knees in the Finnish Arthroplasty Register from 2014 to 2020.

BACKGROUND AND PURPOSE: Periprosthetic joint infection (PJI) is the commonest reason for revision after total knee arthroplasty (TKA). We assessed the risk factors for revision due to PJI following TKA based on the Finnish Arthroplasty Register (FAR). PATIENTS AND METHODS: We analyzed 62,087 primary condylar TKAs registered between June 2014 and February 2020 with revision for PJI as the endpoint. Cox proportional hazards regression was used to estimate hazard ratios (HR) with 95% confidence intervals (CI) for the first PJI revision using 25 potential patient- and surgical-related risk factors as covariates. RESULTS: 484 knees were revised for the first time during the first postoperative year because of PJI. The HRs for revision due to PJI in unadjusted analysis were 0.5 (0.4-0.6) for female sex, 0.7 (0.6-1.0) for BMI 25-29, and 1.6 (1.1-2.5) for BMI > 40 compared with BMI < 25, 4.0 (1.3-12) for preoperative fracture diagnosis compared with osteoarthritis, and 0.7 (0.5-0.9) for use of an antimicrobial incise drape. In adjusted analysis the HRs were 2.2 (1.4-3.5) for ASA class III-IV compared with class I, 1.7 (1.4-2.1) for intraoperative bleeding ≥ 100 mL, 1.4 (1.2-1.8) for use of a drain, 0.7 (0.5-1.0) for short duration of operation of 45-59 minutes, and 1.7 (1.3-2.3) for long operation duration > 120 min compared with 60-89 minutes, and 1.3 (1.0-1.8) for use of general anesthesia. CONCLUSION: We found increased risk for revision due to PJI when no incise drape was used. The use of drainage also increased the risk. Specializing in performing TKA reduces operative time and thereby also the PJI rate.

Early DNA methylation changes in children developing beta cell autoimmunity at a young age.

AIMS/HYPOTHESIS: Type 1 diabetes is a chronic autoimmune disease of complex aetiology, including a potential role for epigenetic regulation. Previous epigenomic studies focused mainly on clinically diagnosed individuals. The aim of the study was to assess early DNA methylation changes associated with type 1 diabetes already before the diagnosis or even before the appearance of autoantibodies. METHODS: Reduced representation bisulphite sequencing (RRBS) was applied to study DNA methylation in purified CD4+ T cell, CD8+ T cell and CD4-CD8- cell fractions of 226 peripheral blood mononuclear cell samples longitudinally collected from seven type 1 diabetes-specific autoantibody-positive individuals and control individuals matched for age, sex, HLA risk and place of birth. We also explored correlations between DNA methylation and gene expression using RNA sequencing data from the same samples. Technical validation of RRBS results was performed using pyrosequencing. RESULTS: We identified 79, 56 and 45 differentially methylated regions in CD4+ T cells, CD8+ T cells and CD4-CD8- cell fractions, respectively, between type 1 diabetes-specific autoantibody-positive individuals and control participants. The analysis of pre-seroconversion samples identified DNA methylation signatures at the very early stage of disease, including differential methylation at the promoter of IRF5 in CD4+ T cells. Further, we validated RRBS results using pyrosequencing at the following CpG sites: chr19:18118304 in the promoter of ARRDC2; chr21:47307815 in the intron of PCBP3; and chr14:81128398 in the intergenic region near TRAF3 in CD4+ T cells. CONCLUSIONS/INTERPRETATION: These preliminary results provide novel insights into cell type-specific differential epigenetic regulation of genes, which may contribute to type 1 diabetes pathogenesis at the very early stage of disease development. Should these findings be validated, they may serve as a potential signature useful for disease prediction and management.

Umbilical cord blood DNA methylation in children who later develop type 1 diabetes.

AIMS/HYPOTHESIS: Distinct DNA methylation patterns have recently been observed to precede type 1 diabetes in whole blood collected from young children. Our aim was to determine whether perinatal DNA methylation is associated with later progression to type 1 diabetes. METHODS: Reduced representation bisulphite sequencing (RRBS) analysis was performed on umbilical cord blood samples collected within the Finnish Type 1 Diabetes Prediction and Prevention (DIPP) Study. Children later diagnosed with type 1 diabetes and/or who tested positive for multiple islet autoantibodies (n = 43) were compared with control individuals (n = 79) who remained autoantibody-negative throughout the DIPP follow-up until 15 years of age. Potential confounding factors related to the pregnancy and the mother were included in the analysis. RESULTS: No differences in the umbilical cord blood methylation patterns were observed between the cases and controls at a false discovery rate <0.05. CONCLUSIONS/INTERPRETATION: Based on our results, differences between children who progress to type 1 diabetes and those who remain healthy throughout childhood are not yet present in the perinatal DNA methylome. However, we cannot exclude the possibility that such differences would be found in a larger dataset.