Högskolan i Skövde

Background: Exfoliative glaucoma (XFG) is characterized by elevated intraocular pressure (IOP) caused by fibrous deposits obstructing aqueous humor drainage, traditionally emphasizing mechanical factors. In this study, we aim to investigate the relationships between clinical parameters and autoimmune activity in exfoliative glaucoma (XFG) patients, and identify patterns of autoantibody reactivity among patient subgroups with differing levels of overall autoimmune activity.

Methods: We analyzed serum autoantibody profiles against 92 antigens in 116 XFG patients using an xMAP suspension bead array. Spearman correlation analysis and PERMANOVA were applied to evaluate relationships between autoantibody reactivity and clinical parameters in glaucoma management. Hierarchical clustering was used to identify clusters of patients based on overall autoantibody reactivity levels, and network analysis using shortest path mapping was utilized to explore potential connections among the proteins associated with the antigens used in this study and other proteins not targeted in this study.

Results: Significant positive correlations were found between the clinical parameter SE and autoantibodies targeting the proteins LOXL3, CYP39A1, and HYOU1. Clustering analysis revealed distinct subgroups of patients with differing overall autoantibody reactivity levels, notably showing significant negative correlations between broad autoantibody profiles and CCT exclusively within the subgroup characterized by higher autoimmune activity. Network analysis identified CTNNB1 as a prominent multi-path intermediary connecting disparate proteins, highlighting potential common regulatory pathways.

Conclusions: This study identifies novel correlations between clinical parameters and autoimmune activity in XFG, suggesting refractive changes and corneal structural characteristics may be influenced by specific immune mechanisms. These findings underscore the value of integrating immunological profiling with clinical glaucoma assessments, potentially enhancing patient stratification and identifying possible therapeutic targets. 

Chronic lymphocytic leukemia (CLL) is divided into unmutated (UM-CLL) and mutated (M-CLL) subtypes depending on somatic hypermutation (SHM) frequency in their immunoglobulin heavy chain V (IGHV) region. We previously demonstrated that CD27^bright memory B cells (MBCs) are germinal center (GC)-dependent with higher mutation rate, whereas CD27^dull MBCs accumulate fewer mutations and originate independently from the GC. We conducted a meta-transcriptomic analysis on bulk RNA data from 116 individuals combining four CLL cohorts and healthy B cell subsets (naïve, CD27^dull and CD27^bright MBCs) to decipher the transcriptional and mechanistic functions of CLL subtypes. CD27^bright MBCs showed more transcriptional similarity to M-CLL rather than UM-CLL. Functional enrichment analysis revealed that LPL, ZNF667 and ZNF667-AS1 are potential informative biomarkers for stratification of CLL subtypes. They are part of the mechanistic regulatory pathways of CLL pathology through cholesterol and Epithelial Mesenchymal Transition (EMT) regulation. We applied markers for the GC B-cell substages to map in silico the CLL cohorts to their potential GC B cell counterpart. UM-CLL represented transcriptional mimicry to an early intermediary GC substage whereas M-CLL mimicked later substages in the GC. This could potentially explain the IGHV mutational status of M-CLL as well as hypothesize that CLL subtypes could derive from a GC-dependent pathway.

This book is more than just a book – it is a full course designed as an interactive guide for beginners in multivariate analysis. Combining theoretical videos with practical examples in R, it offers readers a unique blend of theory, practice, and application in biology and medicine. In an era where data-driven insights shape every field, mastering multivariate statistics and machine learning techniques has never been more essential. Each chapter links directly to videos, which explain the theoretical foundations of the statistical or machine learning methods in a basic way. Following each video, readers will find R code that replicates the analyses presented in the videos, empowering them to see real-world applications in action. Many exercises are included, allowing the readers to test their understanding of each concept through hands-on practice. The book covers a comprehensive range of essential topics in multivariate statistics and machine learning, including fundamentals of matrix operations, multivariate plotting, and correlation, as well as methods for multivariate data analysis such as multivariate analysis of variance (MANOVA), principal component analysis (PCA), clustering, decision trees, discriminant analysis, random forest, partial least squares (PLS), canonical correlation analysis (CCA) and survival analysis. It also includes two case studies that reproduce the multivariate analyses in two scientific papers related to drug discovery and biomarker identification. By integrating videos with practical coding examples, this text makes complex topics accessible for beginners. The interactive learning approach ensures that readers not only grasp the statistical theories and machine learning concepts but also gain the confidence to apply them effectively in real-world scenarios.

Objective: Subsets of CD21−/low memory B cells (MBCs), including double-negative (DN, CD27−IgD−) and Tbet+CD11c+ cells, are expanded in chronic inflammatory diseases. In rheumatoid arthritis (RA), CD21−/low MBCs correlate with joint destruction. However, whether this is due to the Tbet+CD11c+ subset, its function and pathogenic contribution to RA are unknown. This study aims to investigate the association between CD21−/lowTbet+CD11c+ MBCs and joint destruction as well as other clinical parameters and to elucidate their functional properties in patients with untreated RA (uRA). Methods: Clinical observations were combined with flow cytometry (n = 36) and single-cell RNA sequencing (scRNA-seq) and V(D)J sequencing (n = 4) of peripheral blood (PB) MBCs from patients with uRA. The transcriptome of circulating Tbet+CD11c+ MBCs was compared with scRNA-seq data of synovial B cells. In vitro coculture of Tbet+CD11c+ B cells with T cells was used to assess costimulatory capacity. Results: CD21−/lowTbet+CD11c+ MBCs in PB correlated with bone destruction but no other clinical parameters analyzed. The Tbet+CD11c+ MBCs have undergone clonal expansion and express somatically mutated V genes. Gene expression analysis of these cells identified a unique signature of more than 150 up-regulated genes associated with antigen presentation functions, including B cell receptor activation and clathrin-mediated antigen internalization; regulation of actin filaments, endosomes, and lysosomes; antigen processing, loading, presentation, and costimulation; a transcriptome mirrored in their synovial tissue counterparts. In vitro, Tbet+CD11c+ B cells induced retinoic acid receptor–related orphan nuclear receptor γT expression in CD4+ T cells, thereby polarizing to Th17 cells, a T cell subset critical for osteoclastogenesis and associated with bone destruction. Conclusion: This study suggests that Tbet+CD11c+ MBCs contribute to the pathogenesis of RA by promoting bone destruction through antigen presentation, T cell activation, and Th17 polarization. (Figure presented.). 

Background: Exfoliative glaucoma (XFG) is a subtype of open-angle glaucoma characterized by distinctive extracellular fibrils and a yet unknown pathogenesis potentially involving immune-related factors. The aim of this exploratory study was to identify biomarkers for XFG using data from autoimmunity profiling performed on blood samples from a Scandinavian cohort of patients. Methods: Autoantibody screening was analyzed against 258 different protein fragments in blood samples taken from 30 patients diagnosed with XFG and 30 healthy donors. The 258 protein fragments were selected based on a preliminary study performed on 3072 randomly selected antigens and antigens associated with the eye. The “limma” package was used to perform moderated t-tests on the proteomic data to identify differentially expressed reactivity between the groups. Results: Multiple associated genes were highlighted as possible biomarker candidates including FUT2, CDH5, and the LOX family genes. Using seven variables, our binary logistic regression model was able to classify the cases from the controls with an AUC of 0.85, and our reduced model using only one variable corresponding to the FUT2 gene provided an AUC of 0.75, based on LOOCV. Furthermore, over-representation gene analysis was performed to identify pathways that were associated with antigens differentially bound to self-antibodies. This highlighted the enrichment of pathways related to collagen fibril formation and the regulatory molecules mir-3176 and mir-876-5p. Conclusions: This study suggests several potential biomarkers that may be useful in developing further models of the pathology of XFG. In particular, CDH5, FUT2, and the LOX family seem to have a relationship which merits additional exploration. 

BACKGROUND: The rapidly growing area of sequencing technologies, and more specifically bacterial whole-genome sequencing, could offer applications in clinical microbiology, including species identification of bacteria, prediction of genetic antibiotic susceptibility and virulence genes simultaneously. To accomplish the aforementioned points, the commercial cloud-based platform, 1928 platform (1928 Diagnostics, Gothenburg, Sweden) was benchmarked against an in-house developed bioinformatic pipeline as well as to reference methods in the clinical laboratory.

METHODS: Whole-genome sequencing data retrieved from 264 Staphylococcus aureus isolates using the Illumina HiSeq X next-generation sequencing technology was used. The S. aureus isolates were collected during a prospective observational study of community-onset severe sepsis and septic shock in adults at Skaraborg Hospital, in the western region of Sweden. The collected isolates were characterized according to accredited laboratory methods i.e., species identification by MALDI-TOF MS analysis and phenotypic antibiotic susceptibility testing (AST) by following the EUCAST guidelines. Concordance between laboratory methods and bioinformatic tools, as well as concordance between the bioinformatic tools was assessed by calculating the percent of agreement.

RESULTS: There was an overall high agreement between predicted genotypic AST and phenotypic AST results, 98.0% (989/1006, 95% CI 97.3-99.0). Nevertheless, the 1928 platform delivered predicted genotypic AST results with lower very major error rates but somewhat higher major error rates compared to the in-house pipeline. There were differences in processing times i.e., minutes versus hours, where the 1928 platform delivered the results faster. Furthermore, the bioinformatic workflows showed overall 99.4% (1267/1275, 95% CI 98.7-99.7) agreement in genetic prediction of the virulence gene characteristics and overall 97.9% (231/236, 95% CI 95.0-99.2%) agreement in predicting the sequence types (ST) of the S. aureus isolates.

CONCLUSIONS: Altogether, the benchmarking disclosed that both bioinformatic workflows are able to deliver results with high accuracy aiding diagnostics of severe infections caused by S. aureus. It also illustrates the need of international agreement on quality control and metrics to facilitate standardization of analytical approaches for whole-genome sequencing based predictions.

Common variable immune deficiency (CVID) is a heterogeneous disorder characterized by recurrent infections, low levels of serum immunoglobulins, and impaired vaccine responses. Autoimmune manifestations are common, but B cell central and peripheral selection mechanisms in CVID are incompletely understood. Here, we find that receptor editing, a measure of central tolerance, is increased in transitional B cells from CVID patients and that these cells have a higher immunoglobulin κ:λ ratio in CVID patients with autoimmune manifestations than in those with infection only. Contrariwise, the selection pressure in the germinal center on CD27bright memory B cells is decreased in CVID patients with autoimmune manifestations. Finally, functionally, T cell-dependent activation showed that naive B cells in CVID patients are badly equipped for activation and induction of mismatch repair genes. We conclude that central tolerance is functional whereas peripheral selection is defective in CVID patients with autoimmune manifestations, which could underpin the development of autoimmunity. 

B and T cells are integral parts of the immune system and are implicated in many diseases, e.g. autoimmunity. Towards understanding the biology of B and T cells and subsets thereof, their transcriptomes can be analyzed using single-cell RNA sequencing. In some studies, the V(D)J transcripts encoding the variable regions of the B- and T-cell antigen receptors have been removed before the analyses. However, a systematic analysis of the effects of including versus excluding these genes is currently lacking. We have investigated the effects of these transcripts on unsupervised clustering and down-stream analyses of single-cell RNA sequencing data from B and T cells. We found that exclusion of the B-/T-cell receptor genes prior to unsupervised clustering resulted in clusters that represented biologically meaningful subsets, such as subsets of memory B and memory T cells. Furthermore, pseudo-time and trajectory inference analyses of early B-lineage cells resulted in a developmental pathway from progenitor to immature B cells. In contrast, when the B-/T-cell receptor genes were not removed, with the PCs used for clustering consisting of up to 70% V-genes, this resulted in some clusters being defined exclusively by V-gene segments. These did not represent biologically meaningful subsets; for instance in the early B-lineage cells, these clusters contained cells representing all developmental stages. Thus, in studies of B and T cells, to derive biologically meaningful results, it is imperative to remove the gene sequences that encode B- and T-cell receptors.

Objective

Cell metabolism has been shown to play an active role in regulation of stemness and fate decision. In order to identify favorable culture conditions for mesenchymal stromal cells (MSCs) prior to transplantation, this study aimed to characterize the metabolic function of MSCs from different developmental stages in response to different oxygen tension during expansion.

Materials and methods

We cultured human fetal cardiac MSCs and human adult bone-marrow MSCs for a week under hypoxia (3% O2) and normoxia (20% O2). We performed mitochondrial characterization and assessed oxygen consumption- and extracellular acidification-rates (OCR and ECAR) in addition to oxygen-sensitive respiration and mitochondrial complex activities, using both the Seahorse and Oroboros systems.

Results

Adult and fetal MSCs displayed similar basal respiration and mitochondrial amount, however fetal MSCs had lower spare respiratory capacity and apparent coupling efficiency. Fetal MSCs expanded in either hypoxia or normoxia demonstrated similar acidification rates, while adult MSCs downregulated their aerobic glycolysis in normoxia. Acute decrease in oxygen tension caused a higher respiratory inhibition in adult compared to fetal MSCs. In both sources of MSCs, minor changes in complex activities in normoxic and hypoxic cultures were found.

Conclusions

In contrast to adult MSCs, fetal MSCs displayed similar respiration and aerobic glycolysis at different O2 culture concentrations during expansion. Adult MSCs adjusted their respiration to glycolytic activities, depending on the culture conditions thus displaying a more mature metabolic function. These findings are relevant for establishing optimal in vitro culturing conditions, with the aim to maximize engraftment and therapeutic outcome.

Extra-intestinal pathogenic Escherichia coli (ExPEC) strains are responsible for a large number of human infections globally. The management of infections caused by ExPEC has been complicated by the emergence of antimicrobial resistance, most importantly the increasing recognition of isolates producing extended-spectrum β-lactamases (ESBL). Herein, we used whole-genome sequencing (WGS) on ExPEC isolates for a comprehensive genotypic characterization. Twenty-one ExPEC isolates, nine with and 12 without ESBL-production, from 16 patients with suspected sepsis were sequenced on an Illumina MiSeq platform. Analysis of WGS data was performed with widely used bioinformatics software and tools for genotypic characterization of the isolates. A higher number of plasmids, virulence and resistance genes were observed in the ESBL-producing isolates than the non-ESBL-producing, although not statistically significant due to the low sample size. All nine ESBL-producing ExPEC isolates presented with at least one bla gene, as did three of the 12 without ESBL-production. Multi-locus sequence typing analysis revealed a diversity of sequence types whereas phylogroup A prevailed among isolates both with and without ESBL-production. In conclusion, this limited study shows that analysis of WGS data can be used for genotypic characterization of ExPEC isolates to obtain in-depth information of clinical relevance.