Diversity of respiratory parameters and metabolic adaptation to low oxygen tension in mesenchymal stromal cells

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.CC BY-NC-ND 4.0Corresponding author: Department of Surgical Sciences, Uppsala University, 751 85, Uppsala, Sweden. E-mail address: [email protected] (K.-H. Grinnemo).Available online 3 February 2022, Version of Record 5 February 2022The project was funded by Karolinska Institute-Mayo Clinic Collaborative Grant 2013; The Swedish Research Council young investigator: 2013–3590; Stockholm county; The Swedish Research Council; The Family Erling-Persson Foundation; ERC-2018-AdG (834860 EYELETS); Uppsala county; Uppsala County Association against Heart and Lung Diseases; and Higher Education of the Russian Federation (agreement no. 075-15-2020-899).</p

A cell topography-based mechanism for ligand discrimination by the T cell receptor.

The T cell receptor (TCR) initiates the elimination of pathogens and tumors by T cells. To avoid damage to the host, the receptor must be capable of discriminating between wild-type and mutated self and nonself peptide ligands presented by host cells. Exactly how the TCR does this is unknown. In resting T cells, the TCR is largely unphosphorylated due to the dominance of phosphatases over the kinases expressed at the cell surface. However, when agonist peptides are presented to the TCR by major histocompatibility complex proteins expressed by antigen-presenting cells (APCs), very fast receptor triggering, i.e., TCR phosphorylation, occurs. Recent work suggests that this depends on the local exclusion of the phosphatases from regions of contact of the T cells with the APCs. Here, we developed and tested a quantitative treatment of receptor triggering reliant only on TCR dwell time in phosphatase-depleted cell contacts constrained in area by cell topography. Using the model and experimentally derived parameters, we found that ligand discrimination likely depends crucially on individual contacts being ∼200 nm in radius, matching the dimensions of the surface protrusions used by T cells to interrogate their targets. The model not only correctly predicted the relative signaling potencies of known agonists and nonagonists but also achieved this in the absence of kinetic proofreading. Our work provides a simple, quantitative, and predictive molecular framework for understanding why TCR triggering is so selective and fast and reveals that, for some receptors, cell topography likely influences signaling outcomes.This work was funded by The Wellcome Trust, the UK Medical Research Council, the UK Biotechnology and Biological Sciences Research Council and Cancer Research UK. We thank the Wolfson Imaging Centre, University of Oxford, for access to their microscope facility. We would like to thank the Wellcome Trust for the Sir Henry Dale Fellowship of R.A.F. (WT101609MA), the Royal Society for the University Research Fellowship of S.F.L. (UF120277) and acknowledge a GSK Professorship (D.K.). We are also grateful to Doug Tischer (UCSF, US) and Muaz Rushdi (Georgia Tech, US) for their critical comments on the manuscript

Utvärdering av olika klustringsmetoder för läkemedelsdata

Huvudsyftet med denna studie var att utvärdera olika klustringsmetoder för att analysera data hämtade från en läkemedelsstudie där cytokinprofiler hade genererats från 23 olika läkemedel. Hierarkisk klustring användes eftersom antal kluster inte var förutbestämt. Olika distansmått och s.k. länkfunktioner utvärderades för hierarkisk klustring. Utvärderingen av de olika distansmåtten visade att Pearsons korrelationskoefficient lämpade sig bäst vid klustring av de olika läkemedlen eftersom likheter i mönster var viktigare än de faktiska mätvärdena. Även fyra länkfunktioner för att slå samman kluster utvärderades. Den länkfunktion som beräknade medelavståndet mellan objektens kluster visade sig vara den optimala metoden baserat på robusthet och korrelation mellan avstånden i dendrogramet och avstånden i distansmatrisen. Genom att använda hierarkisk klustring baserat på Pearsons korrelationskoefficient och medelavstånd så kunde ett antal intressanta läkemedelsgrupper identifieras. De s.k. JAK-inhibitorerna grupperades i ett distinkt kluster medan calcineurin inhibitorerna återfanns i ett robust kluster tillsammans med proteinkinasinhibitorer. Denna studie visar att klustring av läkemedel baserat på cytokinprofiler kan erbjuda viktig information som beslutstöd för framtida projekt inom läkemedelsutveckling, samt att avstånd baserade på Pearsons korrelationskoefficient och att en länkfunktion som beräknar medelavstånd lämpar sig bäst för den här typen av data.  The aim of this study was to evaluate different hierarchical clustering techniques for data obtained from a study where cytokine profiles had been generated for 23 different drugs. Both distance metrics and linkage functions were evaluated. The evaluation of the distance metrics showed that the Pearson correlation coefficient was the most appropriate distance metric since similarity in patterns of the profiles was more important than similarity based on the actual values. Out of the four evaluated linkage functions: single, complete, average and Ward’s, the average linkage function was the best clustering method based on the cophenetic correlation and the bootstrap probability value. Using the Pearson correlation clustering with the average linkage function, the JAK inhibitors were successfully clustered and the calcineurin inhibitors were found in a robust cluster together with protein kinase inhibitors. This study indicates that cytokine profiles from drugs may provide valuable information where similar drugs can be found in the same clusters. In addition, the study shows that the Pearson correlation coefficient and the average linkage functions were the most appropriate distance metric and linkage function, respectively, for this type of data

Identification of biomarker candidates for exfoliative glaucoma from autoimmunity profiling

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. CC BY 4.0 DEED© 2024, The Author(s).Correspondence Address: R. Potter; Sahlgrenska Academy, Gothenburg University, Gothenburg, Sweden; email: [email protected] access funding provided by University of Gothenburg. This study was funded by Synskadades Vänner Skaraborg (SVS).</p

Sequence-based genotyping of extra-intestinal pathogenic Escherichia coli isolates from patients with suspected community-onset sepsis, Sweden

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.CC BY 4.0Available online 17 October 2022Corresponding author: Diana TilevikThis research received no external financial support.</p

Genotypic Characterization of Clinical Klebsiella spp. Isolates Collected From Patients With Suspected Community-Onset Sepsis, Sweden

Klebsiella is a genus of Gram-negative bacteria known to be opportunistic pathogens that may cause a variety of infections in humans. Highly drug-resistant Klebsiella species, especially K. pneumoniae, have emerged rapidly and are becoming a major concern in clinical management. Although K. pneumoniae is considered the most important pathogen within the genus, the true clinical significance of the other species is likely underrecognized due to the inability of conventional microbiological methods to distinguish between the species leading to high rates of misidentification. Bacterial whole-genome sequencing (WGS) enables precise species identification and characterization that other technologies do not allow. Herein, we have characterized the diversity and traits of Klebsiella spp. in community-onset infections by WGS of clinical isolates (n = 105) collected during a prospective sepsis study in Sweden. The sequencing revealed that 32 of the 82 isolates (39.0%) initially identified as K. pneumoniae with routine microbiological methods based on cultures followed by matrix-assisted laser desorption-time of flight mass spectrometry (MALDI-TOF MS) had been misidentified. Of these, 23 were identified as Klebsiella variicola and nine as other members of the K. pneumoniae complex. Comparisons of the number of resistance genes showed that significantly fewer resistance genes were detected in Klebsiella oxytoca compared to K. pneumoniae and K. variicola (both values of p &lt; 0.001). Moreover, a high proportion of the isolates within the K. pneumoniae complex were predicted to be genotypically multidrug-resistant (MDR; 79/84, 94.0%) in contrast to K. oxytoca (3/16, 18.8%) and Klebsiella michiganensis (0/4, 0.0%). All isolates predicted as genotypically MDR were found to harbor the combination of β-lactam, fosfomycin, and quinolone resistance markers. Multi-locus sequence typing (MLST) revealed a high diversity of sequence types among the Klebsiella spp. with ST14 (10.0%) and ST5429 (10.0%) as the most prevalent ones for K. pneumoniae, ST146 for K. variicola (12.0%), and ST176 for K. oxytoca (25.0%). In conclusion, the results from this study highlight the importance of using high-resolution genotypic methods for identification and characterization of clinical Klebsiella spp. isolates. Our findings indicate that infections caused by other members of the K. pneumoniae complex than K. pneumoniae are a more common clinical problem than previously described, mainly due to high rates of misidentifications.CC BY 4.0Correspondence: Diana Tilevik [email protected]</p

Benchmarking of two bioinformatic workflows for the analysis of whole-genome sequenced Staphylococcus aureus collected from patients with suspected sepsis

Abstract 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

In situ and in silico kinetic analyses of programmed cell death-1 (PD-1) receptor, programmed cell death ligands, and B7-1 protein interaction network

Programmed cell death-1 (PD-1) is an inhibitory receptor with an essential role in maintaining peripheral tolerance and is among the most promising immunotherapeutic targets for treating cancer, autoimmunity, and infectious diseases. A complete understanding of the consequences of PD-1 engagement by its ligands, PD-L1 and PD-L2, and of PD-L1 binding to B7-1 requires quantitative analysis of their interactions at the cell surface. We present here the first complete in situ kinetic analysis of the PD-1/PD-ligands/B7-1 system. Consistent with previous solution measurements, we observed higher in situ affinities for human (h) than murine (m) PD-1 interactions, stronger binding of hPD-1 to hPD-L2 than hPD-L1, and comparable binding of mPD-1 to both ligands. However, in contrast to the relatively weak solution affinities, the in situ affinities of PD-1 are as high as those of the T cell receptor for agonist pMHC and of LFA-1 (lymphocyte function-associated antigen 1) for ICAM-1 (intercellular adhesion molecule 1) but significantly lower than that of the B7-1/CTLA-4 interaction, suggesting a distinct basis for PD-1- versus CTLA-4-mediated inhibition. Notably, the in situ interactions of PD-1 are much stronger than that of B7-1 with PD-L1. Overall, the in situ affinity ranking greatly depends on the on-rate instead of the off-rate. In silico simulations predict that PD-1/PD-L1 interactions dominate at interfaces between activated T cells and mature dendritic cells and that these interactions will be highly sensitive to the dynamics of PD-L1 and PD-L2 expression. Our results provide a kinetic framework for better understanding inhibitory PD-1 activity in health and disease.CC BY 4.0</p

Single-cell RNA sequencing analyses : interference by the genes that encode the B-cell and T-cell receptors

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.CC BY-NC 4.0Corresponding author: Inga-Lill Mårtensson, Department of Rheumatology and Inflammation Research, Institute of Medicine, The Sahlgrenska Academy, University of Gothenburg, Gothenburg, Sweden. Tel.:+46(0)703640068; E-mail: [email protected] work was supported by the Swedish Research Council, grants 2018-03128 and 2021-01150 (ILM) and 2016-01576 (IG); the Swedish Cancer Foundation, grant 19 0464 (ILM); the Swedish Childhood Cancer Fund, grants PR2018-0170 and PR2020-0147 (ILM), and TJ2019-0098 (AC); Assar Gabrielsson’s Foundation, FB21-104 (AC); Patient Association for Rheumatic Diseases, R-94129 (ILM) and R-940945 (IG); ALF (agreement between the Government of Sweden and the County Councils), ALFGBG-719631 (IG); Adlerbertska stiftelsen (TS); and the IngaBritt och Arne Lundbergs Foundation (ILM, IG)</p

Spatiotemporal extracellular matrix modeling for in situ cell niche studies

Extracellular matrix (ECM) components govern a range of cell functions, such as migration, proliferation, maintenance of stemness, and differentiation. Cell niches that harbor stem-/progenitor cells, with matching ECM, have been shown in a range of organs, although their presence in the heart is still under debate. Determining niches depends on a range of in vitro and in vivo models and techniques, where animal models are powerful tools for studying cell-ECM dynamics; however, they are costly and time-consuming to use. In vitro models based on recombinant ECM proteins lack the complexity of the in vivo ECM. To address these issues, we present the spatiotemporal extracellular matrix model for studies of cell-ECM dynamics, such as cell niches. This model combines gentle decellularization and sectioning of cardiac tissue, allowing retention of a complex ECM, with recellularization and subsequent image processing using image stitching, segmentation, automatic binning, and generation of cluster maps. We have thereby developed an in situ representation of the cardiac ECM that is useful for assessment of repopulation dynamics and to study the effect of local ECM composition on phenotype preservation of reseeded mesenchymal progenitor cells. This model provides a platform for studies of organ-specific cell-ECM dynamics and identification of potential cell niches