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1.
  • Dracheva, Elena, et al. (author)
  • In Silico Identification of Potential Thyroid Hormone System Disruptors among Chemicals in Human Serum and Chemicals with a High Exposure Index
  • 2022
  • In: Environmental Science and Technology. - : American Chemical Society (ACS). - 0013-936X .- 1520-5851. ; 56:12, s. 8363-8372
  • Journal article (peer-reviewed)abstract
    • Data on toxic effects are at large missing the prevailing understanding of the risks of industrial chemicals. Thyroid hormone (TH) system disruption includes interferences of the life cycle of the thyroid hormones and may occur in various organs. In the current study, high-throughput screening data available for 14 putative molecular initiating events of adverse outcome pathways, related to disruption of the TH system, were used to develop 19 in silico models for identification of potential thyroid hormone system-disrupting chemicals. The conformal prediction framework with the underlying Random Forest was used as a wrapper for the models allowing for setting the desired confidence level and controlling the error rate of predictions. The trained models were then applied to two different databases: (i) an in-house database comprising xenobiotics identified in human blood and ii) currently used chemicals registered in the Swedish Product Register, which have been predicted to have a high exposure index to consumers. The application of these models showed that among currently used chemicals, fewer were overall predicted as active compared to chemicals identified in human blood. Chemicals of specific concern for TH disruption were identified from both databases based on their predicted activity.
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2.
  • Golosovskaia, Elena, 1993- (author)
  • Development of in silico methods to aid chemical risk assessment : focusing on kinetic interactions in mixtures
  • 2024
  • Doctoral thesis (other academic/artistic)abstract
    • The environment and biota are constantly exposed to numerous chemicals through contaminated food, soil, water, and air. These chemicals can be taken up and distributed to reach sensitive tissues where they may cause various effects. Many of these chemicals lack data on their environmental and human health effects. Traditional toxicological tests relying on animal experiments are today being phased out in favor of cell-based and computational methods for early hazard detection and exposure assessment. This thesis focuses on developing computational tools for various stages of chemical risk assessment with a particular focus on bisphenols and per- and polyfluoroalkyl substances (PFAS). In Paper I, quantitative structure-activity relationship (QSAR) models covering molecular targets of the thyroid hormone (TH) system were developed and applied to two data sets to prioritize chemicals of concern for detailed toxicological studies. In Papers II and III, experimental and computational approaches were combined to study toxicokinetics and maternal transfer in zebrafish. Our main focus was to study potential mixture effects on administration, distribution, metabolism, and elimination (ADME) processes, i.e., to reveal if co-exposed chemicals impact each other’s ADME. Physiologically based kinetic (PBK) mixture models were developed to allow translation of external exposure concentrations into tissue concentrations and modelling plausible mechanisms of chemical interactions in a mixture.Main findings of this thesis are summarized as follows:• Application of QSAR models (Paper I) to two chemical inventories revealed that chemicals found in human blood could induce a large iirange of pathways in the TH system whereas chemicals used in Sweden with predicted high exposure index to consumers showed a lower likelihood to induce TH pathways.• Two zebrafish experiments (Paper II and Paper III) did not reveal statistically significant mixture effects on ADME of chemicals.• In Paper II, a PBK mixture model for PFAS accounting for competitive plasma protein binding was developed. The model demonstrated good predictive performance. Competitive plasma protein binding did not affect the predicted internal concentrations.• In Paper III we developed a binary PBK model parametrized for two bisphenols and PFOS showing that competitive plasma protein binding has an effect on ADME of bisphenols at PFOS concentrations at μg/L levels. At these levels internal concentrations of bisphenols were shown to decrease, implying that PFOS outcompeted bisphenols from studied plasma proteins resulting in higher excretion rates.Developed QSAR models showed good predictive power and the ability to identify and prioritize chemicals of concern with confidence. Additionally, PBK models aid in hypotheses testing and predicting exposure concentrations at which co-exposed chemicals could potentially influence each other’s ADME properties. These tools will provide overall early tier data on exposure and effects using non-testing methods in assessment of risks of chemicals. 
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3.
  • Jonsson, Frida, et al. (author)
  • Mutations in Collagen, Type XVII, Alpha 1 (COL17A1) Cause Epithelial Recurrent Erosion Dystrophy (ERED)
  • 2015
  • In: Human Mutation. - : John Wiley & Sons. - 1059-7794 .- 1098-1004. ; 36:4, s. 463-473
  • Journal article (peer-reviewed)abstract
    • Corneal dystrophies are a clinically and genetically heterogeneous group of inherited disorders that bilaterally affect corneal transparency. They are defined according to the corneal layer affected and by their genetic cause. In this study, we identified a dominantly inherited epithelial recurrent erosion dystrophy (ERED)-like disease that is common in northern Sweden. Whole-exome sequencing resulted in the identification of a novel mutation, c.2816C>T, p.T939I, in the COL17A1 gene, which encodes collagen type XVII alpha 1. The variant segregated with disease in a genealogically expanded pedigree dating back 200 years. We also investigated a unique COL17A1 synonymous variant, c.3156C>T, identified in a previously reported unrelated dominant ERED-like family linked to a locus on chromosome 10q23-q24 encompassing COL17A1. We show that this variant introduces a cryptic donor site resulting in aberrant pre-mRNA splicing and is highly likely to be pathogenic. Bi-allelic COL17A1 mutations have previously been associated with a recessive skin disorder, junctional epidermolysis bullosa, with recurrent corneal erosions being reported in some cases. Our findings implicate presumed gain-of-function COL17A1 mutations causing dominantly inherited ERED and improve understanding of the underlying pathology.
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  • Andersson-Evelönn, Emma, 1983-, et al. (author)
  • Combining epigenetic and clinicopathological variables improves specificity in prognostic prediction in clear cell renal cell carcinoma
  • 2020
  • In: Journal of Translational Medicine. - : Springer Science and Business Media LLC. - 1479-5876. ; 18:1
  • Journal article (peer-reviewed)abstract
    • Background: Metastasized clear cell renal cell carcinoma (ccRCC) is associated with a poor prognosis. Almost one-third of patients with non-metastatic tumors at diagnosis will later progress with metastatic disease. These patients need to be identified already at diagnosis, to undertake closer follow up and/or adjuvant treatment. Today, clinicopathological variables are used to risk classify patients, but molecular biomarkers are needed to improve risk classification to identify the high-risk patients which will benefit most from modern adjuvant therapies. Interestingly, DNA methylation profiling has emerged as a promising prognostic biomarker in ccRCC. This study aimed to derive a model for prediction of tumor progression after nephrectomy in non-metastatic ccRCC by combining DNA methylation profiling with clinicopathological variables.Methods: A novel cluster analysis approach (Directed Cluster Analysis) was used to identify molecular biomarkers from genome-wide methylation array data. These novel DNA methylation biomarkers, together with previously identified CpG-site biomarkers and clinicopathological variables, were used to derive predictive classifiers for tumor progression.Results: The “triple classifier” which included both novel and previously identified DNA methylation biomarkers together with clinicopathological variables predicted tumor progression more accurately than the currently used Mayo scoring system, by increasing the specificity from 50% in Mayo to 64% in our triple classifier at 85% fixed sensitivity. The cumulative incidence of progress (pCIP5yr) was 7.5% in low-risk vs 44.7% in high-risk in M0 patients classified by the triple classifier at diagnosis.Conclusions: The triple classifier panel that combines clinicopathological variables with genome-wide methylation data has the potential to improve specificity in prognosis prediction for patients with non-metastatic ccRCC.
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7.
  • Andersson, Henrik, et al. (author)
  • T A microarray analysis of the murine macrophage response to infection with Francisella tularensis LVS
  • 2006
  • In: Journal of Medical Microbiology. - : Microbiology Society. - 0022-2615 .- 1473-5644. ; 55:8, s. 1023-1033
  • Journal article (peer-reviewed)abstract
    • The response of cells of the mouse macrophage cell line J774 to infection with Francisella tularensis LVS was analysed by means of a DNA microarray representing approximately 18 500 genes (20 600 clones). The adaptive response was modest at all time points, and at most, 81 clones were differentially regulated from the time point of uptake of bacteria (0 min) up to 240 min later. For all five time points, 229 clones fulfilled the criteria of being differentially regulated, i.e. the ratio between infected versus non-infected cells was at least 1.7-fold up- or down-regulated and P <0.05. It was found that many of the differentially regulated genes are known to respond to stress in general and to oxidative stress specifically. However, at 120 min it was observed that genes that lead to depletion of glutathione were upregulated. Possibly, this was a result of mechanisms induced by F. tularensis. Generally, there was a conspicuous lack of inflammatory responses and, for example, although tumour necrosis factor alpha (TNF-α) was upregulated at 0 min, a significant down-regulation was noted at all subsequent time points. When cells were treated with an inhibitor of inducible nitric oxide synthase (iNOS) or the antioxidant N-acetylcysteine (NAC), the infection-induced cytopathogenic effect was significantly inhibited. Together, the results suggest that F. tularensis LVS infection confers an oxidative stress upon the target cells and that many of the host-defence mechanisms appear to be intended to counteract this stress. The infection is characterized by a very modest inflammatory response.
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10.
  • Andersson, H., et al. (author)
  • Transcriptional profiling of the peripheral blood response during tularemia
  • 2006
  • In: Genes and Immunity. - : Springer Science and Business Media LLC. - 1466-4879 .- 1476-5470. ; 7:6, s. 503-513
  • Journal article (peer-reviewed)abstract
    • Tularemia is a febrile disease caused by the highly contagious bacterium Francisella tularensis. We undertook an analysis of the transcriptional response in peripheral blood during the course of ulceroglandular tularemia by use of Affymetrix microarrays comprising 14,500 genes. Samples were obtained from seven individuals at five occasions during 2 weeks after the first hospital visit and convalescent samples 3 months later. In total, 265 genes were differentially expressed, 95 of which at more than one time point. The differential expression was verified with real-time quantitative polymerase chain reaction for 36 genes (R(2)=0.590). The most prominent changes were noted in samples drawn on days 2-3 and a considerable proportion of the upregulated genes appeared to represent an interferon-gamma-induced response and also a proapoptotic response. Genes involved in the generation of innate and acquired immune responses were found to be downregulated, presumably a pathogen-induced event. A logistic regression analysis revealed that seven genes were good predictors of the early phase of tularemia. This is the first description of the transcriptional host response to ulceroglandular tularemia and the study has identified gene subsets relevant to the pathogenesis of the disease and subsets that may serve as early diagnostic biomarkers.
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11.
  • Axelsson, Ulrika, et al. (author)
  • A multicenter study investigating the molecular fingerprint of psychological resilience in breast cancer patients : Study protocol of the SCAN-B resilience study
  • 2018
  • In: BMC Cancer. - : Springer Science and Business Media LLC. - 1471-2407. ; 18:1
  • Journal article (peer-reviewed)abstract
    • Background: Individual patients differ in their psychological response when receiving a cancer diagnosis, in this case breast cancer. Given the same disease burden, some patients master the situation well, while others experience a great deal of stress, depression and lowered quality of life. Patients with high psychological resilience are likely to experience fewer stress reactions and better adapt to and manage the life threat and the demanding treatment that follows the diagnosis. If this phenomenon of mastering difficult situations is reflected also in biomolecular processes is not much studied, nor has its capacity for impacting the cancer prognosis been addressed. This project specifically aims, for the first time, to investigate how a breast cancer patient's psychological resilience is coupled to biomolecular parameters using advanced "omics" and, as a secondary aim, whether it relates to prognosis and quality of life one year after diagnosis. Method: The study population consists of newly diagnosed breast cancer patients enrolled in the Sweden Cancerome Analysis Network - Breast (SCAN-B) at four hospitals in Sweden. At the time of cancer diagnosis, the patient fills out the standardized method to measure psychological resilience, the "Connor-Davidson Resilience scale" (CD-RISC), the quality of life measure SF-36, as well as providing social and socioeconomic variables. In addition, one blood sample is collected. At the one-year follow-up, the patient will be subjected to the same assessments, and we also collect information regarding smoking, exercise habits, and BMI, as well as patients' trust in the treatment and their satisfaction with the care and treatment. Discussion: This explorative hypothesis-generating project will pave the way for larger validation studies, potentially leading to a standardized method of measuring psychological resilience as an important parameter in cancer care. Revealing the body-mind interaction, in terms of psychological resilience and quality of life, will herald the development of truly personalized psychosocial care and cancer intervention treatment strategies. Trial registration: This is a retrospectively registered trial at ClinicalTrials.gov, ID: NCT03430492on February 6, 2018.
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12.
  • Baeckdahl, Jesper, et al. (author)
  • Spatial mapping reveals human adipocyte subpopulations with distinct sensitivities to insulin
  • 2021
  • In: Cell Metabolism. - : Elsevier BV. - 1550-4131 .- 1932-7420. ; 33:9, s. 1869-
  • Journal article (peer-reviewed)abstract
    • The contribution of cellular heterogeneity and architecture to white adipose tissue (WAT) function is poorly understood. Herein, we combined spatially resolved transcriptional profiling with single-cell RNA sequencing and image analyses to map human WAT composition and structure. This identified 18 cell classes with unique propensities to form spatially organized homo-and heterotypic clusters. Of these, three constituted mature adipocytes that were similar in size, but distinct in their spatial arrangements and transcriptional profiles. Based on marker genes, we termed these Adipo(LEP), Adipo(PLIN), and Adipo(SAA). We confirmed, in independent datasets, that their respective gene profiles associated differently with both adipocyte and whole-body insulin sensitivity. Corroborating our observations, insulin stimulation in vivo by hyperinsulinemic-euglycemic clamp showed that only Adipo(PLIN) displayed a transcriptional response to insulin. Altogether, by mining this multimodal resource we identify that human WAT is composed of three classes of mature adipocytes, only one of which is insulin responsive.
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13.
  • Bayisa, Fekadu, et al. (author)
  • Large-scale modelling and forecasting of ambulance calls in northern Sweden using spatio-temporal log-Gaussian Cox processes
  • 2020
  • In: Spatial Statistics. - : Elsevier BV. - 2211-6753. ; 39
  • Journal article (peer-reviewed)abstract
    • Although ambulance call data typically come in the form of spatio-temporal point patterns, point process-based modelling approaches presented in the literature are scarce. In this paper, we study a unique set of Swedish spatio-temporal ambulance call data, which consist of the spatial (GPS) locations of the calls (within the four northernmost regions of Sweden) and the associated days of occurrence of the calls (January 1, 2014-December 31, 2018). Motivated by the nature of the data, we here employ log-Gaussian Cox processes (LGCPs) for the spatiotemporal modelling and forecasting of the calls. To this end, we propose a K-means clustering based bandwidth selection method for the kernel estimation of the spatial component of the separable spatio-temporal intensity function. The temporal component of the intensity function is modelled by means of Poisson regression, using different calendar covariates, and the spatiotemporal random field component of the random intensity of the LGCP is fitted using the Metropolis-adjusted Langevin algorithm. Spatial hot-spots have been found in the south-eastern part of the study region, where most people in the region live and our fitted model/forecasts manage to capture this behaviour quite well. Also, there is a significant association between the expected number of calls and the day-of-the-week, and the season-ofthe-year. A non-parametric second-order analysis indicates that LGCPs seem to be reasonable models for the data. Finally, we find that the fitted forecasts generate simulated future spatial event patterns which quite well resemble the actual future data. (C) 2020 The Author(s). Published by Elsevier B.V.
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14.
  • Bayisa, Fekadu, et al. (author)
  • Regularised Semi-parametric Composite Likelihood Intensity Modelling of a Swedish Spatial Ambulance Call Point Pattern
  • 2023
  • In: Journal of Agricultural Biological and Environmental Statistics. - : Springer. - 1085-7117 .- 1537-2693. ; 28, s. 664-83
  • Journal article (peer-reviewed)abstract
    • Motivated by the development of optimal dispatching strategies for prehospital resources, we model the spatial distribution of ambulance call events in the Swedish municipality Skelleftea during 2014-2018 in order to identify important spatial covariates and discern hotspot regions. Our large-scale multivariate data point pattern of call events consists of spatial locations and marks containing the associated priority levels and sex labels. The covariates used are related to road network coverage, population density, and socio-economic status. For each marginal point pattern, we model the associated intensity function by means of a log-linear function of the covariates and their interaction terms, in combination with lasso-like elastic-net regularized composite/Poisson process likelihood estimation. This enables variable selection and collinearity adjustment as well as reduction of variance inflation from overfitting and bias from underfitting. To incorporate mobility adjustment, reflecting people's movement patterns, we also include a nonparametric (kernel) intensity estimate as an additional covariate. The kernel intensity estimation performed here exploits a new heuristic bandwidth selection algorithm. We discover that hotspot regions occur along dense parts of the road network. A mean absolute error evaluation of the fitted model indicates that it is suitable for designing prehospital resource dispatching strategies. Supplementary materials accompanying this paper appear online.
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  • Belyaev, Yu.K., et al. (author)
  • On Non-Parametric Estimation of Poission Point Processes Related to Failure Stresses of Fibres
  • 2000
  • Reports (other academic/artistic)abstract
    • We consider statistical analysis of the reliability of fibres. The problem is to estimate the distribution law of random failure stresses of fibres (i.e. the critical level of stresses that destroy fibres) by using data obtained in a special kind of test, where several fibres are tested until they break. All new pieces resulting from this test will also be tested, if they are long enough. The test ends when all the remaining pieces are too short to be tested further. We refer to these as binary tree structured tests. We assume that the cumulative hazard function (c.h.f.) of the failure stresses of these fibres is continuous, and that the fibres are statistically identical. Under these assumptions we obtain, as the number of tested fibres increases, a strongly consistent Nelson-Aalen type estimator of the c.h.f. The functional central limit resampling theorem in Skorohod space is proved. It justifies the possibility of using resampling for estimating the accuracy of these estimators. The theorem shows that resampling can be used to asymptotically consistently estimate distribution laws of continuous functionals of the random deviations between the estimator and the true c.h.f.. For example, resampling can be used to estimate the distribution law of the maximum distance between estimators and estimands. Numerical examples suggest that resampling works well for a moderate number of tested fibres.
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17.
  • Black, Rebecca Mae, et al. (author)
  • Proteomic analysis reveals dexamethasone rescues matrix breakdown but not anabolic dysregulation in a cartilage injury model
  • 2020
  • In: Osteoarthritis and Cartilage Open. - : Elsevier BV. - 2665-9131. ; 2:4
  • Journal article (peer-reviewed)abstract
    • Objectives: In this exploratory study, we used discovery proteomics to follow the release of proteins from bovine knee articular cartilage in response to mechanical injury and cytokine treatment. We also studied the effect of the glucocorticoid Dexamethasone (Dex) on these responses.Design: Bovine cartilage explants were treated with either cytokines alone (10 ng/ml TNFα, 20 ng/ml IL-6, 100 ng/ml sIL-6R), a single compressive mechanical injury, cytokines and injury, or no treatment, and cultured in serum-free DMEM supplemented with 1% ITS for 22 days. All samples were incubated with or without addition of 100 nM Dex. Mass spectrometry and western blot analyses were performed on medium samples for the identification and quantification of released proteins.Results: We identified 500 unique proteins present in all three biological replicates. Many proteins involved in the catabolic response of cartilage degradation had increased release after inflammatory stress. Dex rescued many of these catabolic effects. The release of some proteins involved in anabolic and chondroprotective processes was inconsistent, indicating differential effects on processes that may protect cartilage from injury. Dex restored only a small fraction of these to the control state, while others had their effects exacerbated by Dex exposure.Conclusions: We identified proteins that were released upon cytokine treatment which could be potential biomarkers of the inflammatory contribution to cartilage degradation. We also demonstrated the imperfect rescue of Dex on the effects of cartilage degradation, with many catabolic factors being reduced, while other anabolic or chondroprotective processes were not.
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  • Chen, Changchun, et al. (author)
  • Elongator Complex Influences Telomeric Gene Silencing and DNA Damage Response by Its Role in Wobble Uridine tRNA Modification
  • 2011
  • In: PLoS genetics. - : Public Library of Science. - 1553-7404. ; 7:9, s. e1002258-
  • Journal article (peer-reviewed)abstract
    • Elongator complex is required for formation of the side chains at position 5 of modified nucleosides 5-carbamoylmethyluridine (ncm(5)U(34)), 5-methoxycarbonylmethyluridine (mcm(5)U(34)), and 5-methoxycarbonylmethyl-2-thiouridine (mcm(5)s(2)U(34)) at wobble position in tRNA. These modified nucleosides are important for efficient decoding during translation. In a recent publication, Elongator complex was implicated to participate in telomeric gene silencing and DNA damage response by interacting with proliferating cell nuclear antigen (PCNA). Here we show that elevated levels of tRNA(Lys) (s(2) ) (UUU), tRNA(Gln) (s(2) ) (UUG), and tRNA(Glu) (s(2) ) (UUC), which in a wild-type background contain the mcm(5)s(2)U nucleoside at position 34, suppress the defects in telomeric gene silencing and DNA damage response observed in the Elongator mutants. We also found that the reported differences in telomeric gene silencing and DNA damage response of various elp3 alleles correlated with the levels of modified nucleosides at U(34). Defects in telomeric gene silencing and DNA damage response are also observed in strains with the tuc2Δ mutation, which abolish the formation of the 2-thio group of the mcm(5)s(2)U nucleoside in tRNA(Lys) (mcm(5) (s(2) ) (UUU) ), tRNA(Gln) (mcm(5) (s(2) ) (UUG) ), and tRNA(Glu) (mcm(5) (s(2) ) (UUC) ). These observations show that Elongator complex does not directly participate in telomeric gene silencing and DNA damage response, but rather that modified nucleosides at U(34) are important for efficient expression of gene products involved in these processes. Consistent with this notion, we found that expression of Sir4, a silent information regulator required for assembly of silent chromatin at telomeres, was decreased in the elp3Δ mutants.
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  • De Pascalis, Roberto, et al. (author)
  • A panel of correlates predicts vaccine-induced protection of rats against respiratory challenge with virulent Francisella tularensis
  • 2018
  • In: PLOS ONE. - : Public Library of Science. - 1932-6203. ; 13:5
  • Journal article (peer-reviewed)abstract
    • There are no defined correlates of protection for any intracellular pathogen, including the bacterium Francisella tularensis, which causes tularemia. Evaluating vaccine efficacy against sporadic diseases like tularemia using field trials is problematic, and therefore alternative strategies to test vaccine candidates like the Francisella Live Vaccine Strain (LVS), such as testing in animals and applying correlate measurements, are needed. Recently, we described a promising correlate strategy that predicted the degree of vaccine-induced protection in mice given parenteral challenges, primarily when using an attenuated Francisella strain. Here, we demonstrate that using peripheral blood lymphocytes (PBLs) in this approach predicts LVS-mediated protection against respiratory challenge of Fischer 344 rats with fully virulent F. tularensis, with exceptional sensitivity and specificity. Rats were vaccinated with a panel of LVS-derived vaccines and subsequently given lethal respiratory challenges with Type A F. tularensis. In parallel, PBLs from vaccinated rats were evaluated for their functional ability to control intramacrophage Francisella growth in in vitro co-culture assays. PBLs recovered from co-cultures were also evaluated for relative gene expression using a large panel of genes identified in murine studies. In vitro control of LVS intramacrophage replication reflected the hierarchy of protection. Further, despite variability between individuals, 22 genes were significantly more up-regulated in PBLs from rats vaccinated with LVS compared to those from rats vaccinated with the variant LVS-R or heat killed LVS, which were poorly protective. These genes included IFN-gamma, IL-21, NOS2, LTA, T-bet, IL-12rβ2, and CCL5. Most importantly, combining quantifications of intramacrophage growth control with 5-7 gene expression levels using multivariate analyses discriminated protected from non-protected individuals with greater than 95% sensitivity and specificity. The results therefore support translation of this approach to non-human primates and people to evaluate new vaccines against Francisella and other intracellular pathogens.
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  • De Pascalis, Roberto, et al. (author)
  • Models Derived from In Vitro Analyses of Spleen, Liver, and Lung Leukocyte Functions Predict Vaccine Efficacy against the Francisella tularensis Live Vaccine Strain (LVS)
  • 2014
  • In: mBio. - 2161-2129 .- 2150-7511. ; 5:2
  • Journal article (peer-reviewed)abstract
    • Currently, there are no licensed vaccines and no correlates of protection against Francisella tularensis, which causes tularemia. We recently demonstrated that measuring in vitro control of intramacrophage bacterial growth by murine F. tularensis-immune splenocytes, as well as transcriptional analyses, discriminated Francisella vaccines of different efficacies. Further, we identified potential correlates of protection against systemic challenge. Here, we extended this approach by studying leukocytes derived from lungs and livers of mice immunized by parenteral and respiratory routes with F. tularensis vaccines. Liver and lung leukocytes derived from intradermally and intranasally vaccinated mice controlled in vitro Francisella Live Vaccine Strain (LVS) intramacrophage replication in patterns similar to those of splenocytes. Gene expression analyses of potential correlates also revealed similar patterns in liver cells and splenocytes. In some cases (e. g., tumor necrosis factor alpha [TNF-alpha], interleukin 22 [IL-22], and granulocyte-macrophage colony-stimulating factor [GM-CSF]), liver cells exhibited even higher relative gene expression, whereas fewer genes exhibited differential expression in lung cells. In contrast with their strong ability to control LVS replication, splenocytes from intranasally vaccinated mice expressed few genes with a hierarchy of expression similar to that of splenocytes from intradermally vaccinated mice. Thus, the relative levels of gene expression vary between cell types from different organs and by vaccination route. Most importantly, because studies comparing cell sources and routes of vaccination supported the predictive validity of this coculture and gene quantification approach, we combined in vitro LVS replication with gene expression data to develop analytical models that discriminated between vaccine groups and successfully predicted the degree of vaccine efficacy. Thus, this strategy remains a promising means of identifying and quantifying correlative T cell responses.IMPORTANCEIdentifying and quantifying correlates of protection is especially challenging for intracellular bacteria, including Francisella tularensis. F. tularensis is classified as a category A bioterrorism agent, and no vaccines have been licensed in the United States, but tularemia is a rare disease. Therefore, clinical trials to test promising vaccines are impractical. In this report, we further evaluated a novel approach to developing correlates by assessing T cell immune responses in lungs and livers of differentially vaccinated mice; these nonprofessional immune tissues are colonized by Francisella. The relative degree of vaccine efficacy against systemic challenge was reflected by the ability of immune T cells, particularly liver T cells, to control the intramacrophage replication of bacteria in vitro and by relative gene expression of several immunological mediators. We therefore developed analytical models that combined bacterial replication data and gene expression data. Several resulting models provided excellent discrimination between vaccines of different efficacies.
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21.
  • Degerman, Sofie, et al. (author)
  • Immortalization of T-Cells Is Accompanied by Gradual Changes in CpG Methylation Resulting in a Profile Resembling a Subset of T-Cell Leukemias
  • 2014
  • In: Neoplasia. - : Elsevier BV. - 1522-8002 .- 1476-5586. ; 16:7, s. 606-615
  • Journal article (peer-reviewed)abstract
    • We have previously described gene expression changes during spontaneous immortalization of T-cells, thereby identifying cellular processes important for cell growth crisis escape and unlimited proliferation. Here, we analyze the same model to investigate the role of genome-wide methylation in the immortalization process at different time points pre-crisis and post-crisis using high-resolution arrays. We show that over time in culture there is an overall accumulation of methylation alterations, with preferential increased methylation close to transcription start sites (TSSs), islands, and shore regions. Methylation and gene expression alterations did not correlate for the majority of genes, but for the fraction that correlated, gain of methylation close to TSS was associated with decreased gene expression. Interestingly, the pattern of CpG site methylation observed in immortal T-cell cultures was similar to clinical T-cell acute lymphoblastic leukemia (T-ALL) samples classified as CpG island methylator phenotype positive. These sites were highly overrepresented by polycomb target genes and involved in developmental, cell adhesion, and cell signaling processes. The presence of non-random methylation events in in vitro immortalized T-cell cultures and diagnostic T-ALL samples indicates altered methylation of CpG sites with a possible role in malignant hematopoiesis.
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22.
  • Del Peso-Santos, Teresa, et al. (author)
  • Pr is a member of a restricted class of σ70-dependent promoters that lack a recognizable -10 element
  • 2012
  • In: Nucleic Acids Research. - : Oxford University Press (OUP). - 0305-1048 .- 1362-4962. ; 40:22, s. 11308-11320
  • Journal article (peer-reviewed)abstract
    • The Pr promoter is the first verified member of a class of bacterial σ(70)-promoters that only possess a single match to consensus within its -10 element. In its native context, the activity of this promoter determines the ability of Pseudomonas putida CF600 to degrade phenolic compounds, which provides proof-of-principle for the significance of such promoters. Lack of identity within the -10 element leads to non-detection of Pr-like promoters by current search engines, because of their bias for detection of the -10 motif. Here, we report a mutagenesis analysis of Pr that reveals strict sequence requirements for its activity that includes an essential -15 element and preservation of non-consensus bases within its -35 and -10 elements. We found that highly similar promoters control plasmid- and chromosomally- encoded phenol degradative systems in various Pseudomonads. However, using a purpose-designed promoter-search algorithm and activity analysis of potential candidate promoters, no bona fide Pr-like promoter could be found in the entire genome of P. putida KT2440. Hence, Pr-like σ(70)-promoters, which have the potential to be a widely distributed class of previously unrecognized promoters, are in fact highly restricted and remain in a class of their own.
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  • Desvars, Amélie, 1980-, et al. (author)
  • Epidemiology and Ecology of Tularemia in Sweden, 1984-2012
  • 2015
  • In: Emerging Infectious Diseases. - : Centers for Disease Control and Prevention (CDC). - 1080-6040 .- 1080-6059. ; 21:1, s. 32-39
  • Journal article (peer-reviewed)abstract
    • The zoonotic disease tularemia is endemic in large areas of the Northern Hemisphere, but research is lacking on patterns of spatial distribution and connections with ecologic factors. To describe the spatial epidemiology of and identify ecologic risk factors for tularemia incidence in Sweden, we analyzed surveillance data collected over 29 years (1984-2012). A total of 4,830 cases were notified, of which 3,524 met all study inclusion criteria. From the first to the second half of the study period, mean incidence increased 10-fold, from 0.26/100,000 persons during 1984-1998 to 2.47/100,000 persons during 1999 2012 (p<0.001). The incidence of tularemia was higher than expected in the boreal and alpine ecologic regions (p<0.001), and incidence was positively correlated with the presence of lakes and rivers (p<0.001). These results provide a comprehensive epidemiologic description of tularemia in Sweden and illustrate that incidence is higher in locations near lakes and rivers.
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25.
  • Desvars-Larrive, Amélie, et al. (author)
  • High-risk regions and outbreak modelling of tularemia in humans
  • 2017
  • In: Epidemiology and Infection. - : CAMBRIDGE UNIV PRESS. - 0950-2688 .- 1469-4409. ; 145:3, s. 482-490
  • Journal article (peer-reviewed)abstract
    • Sweden reports large and variable numbers of human tularemia cases, but the high-risk regions are anecdotally defined and factors explaining annual variations are poorly understood. Here, high-risk regions were identified by spatial cluster analysis on disease surveillance data for 1984-2012. Negative binomial regression with five previously validated predictors (including predicted mosquito abundance and predictors based on local weather data) was used to model the annual number of tularemia cases within the high-risk regions. Seven high-risk regions were identified with annual incidences of 3.8-44 cases/100 000 inhabitants, accounting for 56.4% of the tularemia cases but only 9.3% of Sweden's population. For all high-risk regions, most cases occurred between July and September. The regression models explained the annual variation of tularemia cases within most high-risk regions and discriminated between years with and without outbreaks. In conclusion, tularemia in Sweden is concentrated in a few high-risk regions and shows high annual and seasonal variations. We present reproducible methods for identifying tularemia high-risk regions and modelling tularemia cases within these regions. The results may help health authorities to target populations at risk and lay the foundation for developing an early warning system for outbreaks.
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26.
  • Dihge, Looket, et al. (author)
  • Artificial neural network models to predict nodal status in clinically node-negative breast cancer
  • 2019
  • In: BMC Cancer. - : Springer Science and Business Media LLC. - 1471-2407. ; 19:1
  • Journal article (other academic/artistic)abstract
    • Background: Sentinel lymph node biopsy (SLNB) is standard staging procedure for nodal status in breast cancer, but lacks therapeutic benefit for patients with benign sentinel nodes. For patients with positive sentinel nodes, individualized surgical strategies are applied depending on the extent of nodal involvement. Preoperative prediction of nodal status is thus important for individualizing axillary surgery avoiding unnecessary surgery. We aimed to predict nodal status in clinically node-negative breast cancer and identify candidates for SLNB omission by including patient-related and pathological characteristics into artificial neural network (ANN) models. Methods: Patients with primary breast cancer were consecutively included between January 1, 2009 and December 31, 2012 in a prospectively maintained pathology database. Clinical- and radiological data were extracted from patient's files and only clinically node-negative patients constituted the final study cohort. ANN-based models for nodal prediction were constructed including 15 risk variables for nodal status. Area under the receiver operating characteristic curve (AUC) and Hosmer-Lemeshow goodness-of-fit test (HL) were used to assess performance and calibration of three predictive ANN-based models for no lymph node metastasis (N0), metastases in 1-3 lymph nodes (N1) and metastases in ≥ 4 lymph nodes (N2). Linear regression models for nodal prediction were calculated for comparison. Results: Eight hundred patients (N0, n = 514; N1, n = 232; N2, n = 54) were included. Internally validated AUCs for N0 versus N+ was 0.740 (95% CI = 0.723-0.758); median HL was 9.869 (P = 0.274), for N1 versus N0, 0.705 (95% CI = 0.686-0.724; median HL: 7.421; P = 0.492) and for N2 versus N0 and N1, 0.747 (95% CI = 0.728-0.765; median HL: 9.220; P = 0.324). Tumor size and vascular invasion were top-ranked predictors of all three end-points, followed by estrogen receptor status and lobular cancer for prediction of N2. For each end-point, ANN models showed better discriminatory performance than multivariable logistic regression models. Accepting a false negative rate (FNR) of 10% for predicting N0 by the ANN model, SLNB could have been abstained in 27.25% of patients with clinically node-negative axilla. Conclusions: In this retrospective study, ANN showed promising result as decision-supporting tools for estimating nodal disease. If prospectively validated, patients least likely to have nodal metastasis could be spared SLNB using predictive models. Trial registration: Registered in the ISRCTN registry with study ID ISRCTN14341750. Date of registration 23/11/2018. Retrospectively registered.
  •  
27.
  • Eneslätt, Kjell, et al. (author)
  • Persistence of cell-mediated immunity three decades after vaccination with the live vaccine strain of Francisella tularensis
  • 2011
  • In: European Journal of Immunology. - Weinheim : Wiley-VCH Verlagsgesellschaft. - 0014-2980 .- 1521-4141. ; 41:4, s. 974-980
  • Journal article (peer-reviewed)abstract
    • The efficacy of many vaccines against intracellular bacteria depends on the generation of cell-mediated immunity, but studies to determine the duration of immunity are usually confounded by re-exposure. The causative agent of tularemia, Francisella tularensis, is rare in most areas and, therefore, tularemia vaccination is an interesting model for studies of the longevity of vaccine-induced cell-mediated immunity. Here, lymphocyte proliferation and cytokine production in response to F. tularensis were assayed in two groups of 16 individuals, vaccinated 1-3 or 27-34 years previously. As compared to naïve individuals, vaccinees of both groups showed higher proliferative responses and, out of 17 cytokines assayed, higher levels of MIP-1β, IFN-γ, IL-10, and IL-5 in response to recall stimulation. The responses were very similar in the two groups of vaccinees. A statistical model was developed to predict the immune status of the individuals and by use of two parameters, proliferative responses and levels of IFN-γ, 91.1% of the individuals were correctly classified. Using flow cytometry analysis, we demonstrated that during recall stimulation, expression of IFN-γ by CD4(+) CCR7(+) , CD4(+) CD62L(+) , CD8(+) CCR7(+) , and CD8(+) CD62L(+) cells significantly increased in samples from vaccinated donors. In conclusion, cell-mediated immunity was found to persist three decades after tularemia vaccination without evidence of decline.
  •  
28.
  • Eneslätt, Kjell, et al. (author)
  • Vaccine-mediated mechanisms controlling replication of Francisella tularensis in human peripheral blood mononuclear cells using a co-culture system
  • 2018
  • In: Frontiers in Cellular and Infection Microbiology. - : Frontiers Media S.A.. - 2235-2988. ; 8
  • Journal article (peer-reviewed)abstract
    • Cell-mediated immunity (CMI) is normally required for efficient protection against intracellular infections, however, identification of correlates is challenging and they are generally lacking. Francisella tularensis is a highly virulent, facultative intracellular bacterium and CMI is critically required for protection against the pathogen, but how this is effectuated in humans is poorly understood. To understand the protective mechanisms, we established an in vitro co-culture assay to identify how control of infection of F. tularensis is accomplished by human cells and hypothesized that the model will mimic in vivo immune mechanisms. Non-adherent peripheral blood mononuclear cells (PBMCs) were expanded with antigen and added to cultures with adherent PBMC infected with the human vaccine strain, LVS, or the highly virulent SCHU S4 strain. Intracellular numbers of F. tularensis was followed for 72 h and secreted and intracellular cytokines were analyzed. Addition of PBMC expanded from naïve individuals, i.e., those with no record of immunization to F. tularensis, generally resulted in little or no control of intracellular bacterial growth, whereas addition of PBMC from a majority of F. tularensis-immune individuals executed static and sometimes cidal effects on intracellular bacteria. Regardless of infecting strain, statistical differences between the two groups were significant, P < 0.05. Secretion of 11 cytokines was analyzed after 72 h of infection and significant differences with regard to secretion of IFN-γ, TNF, and MIP-1β was observed between immune and naïve individuals for LVS-infected cultures. Also, in LVS-infected cultures, CD4 T cells from vaccinees, but not CD8 T cells, showed significantly higher expression of IFN-γ, MIP-1β, TNF, and CD107a than cells from naïve individuals. The co-culture system appears to identify correlates of immunity that are relevant for the understanding of mechanisms of the protective host immunity to F. tularensis.
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29.
  • Evengård, Birgitta, 1952-, et al. (author)
  • Healthy ecosystems for human and animal health : Science diplomacy for responsible development in the Arctic
  • 2021
  • In: Polar Record. - : Cambridges Institutes Press. - 0032-2474 .- 1475-3057. ; 57
  • Journal article (peer-reviewed)abstract
    • Climate warming is occurring most rapidly in the Arctic, which is both a sentinel and a driver of further global change. Ecosystems and human societies are already affected by warming. Permafrost thaws and species are on the move, bringing pathogens and vectors to virgin areas. During a five-year project, the CLINF - a Nordic Center of Excellence, funded by the Nordic Council of Ministers, has worked with the One Health concept, integrating environmental data with human and animal disease data in predictive models and creating maps of dynamic processes affecting the spread of infectious diseases. It is shown that tularemia outbreaks can be predicted even at a regional level with a manageable level of uncertainty. To decrease uncertainty, rapid development of new and harmonised technologies and databases is needed from currently highly heterogeneous data sources. A major source of uncertainty for the future of contaminants and infectious diseases in the Arctic, however, is associated with which paths the majority of the globe chooses to follow in the future. Diplomacy is one of the most powerful tools Arctic nations have to influence these choices of other nations, supported by Arctic science and One Health approaches that recognise the interconnection between people, animals, plants and their shared environment at the local, regional, national and global levels as essential for achieving a sustainable development for both the Arctic and the globe.
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30.
  • Fahlén, Jessica, 1973-, et al. (author)
  • Bioinformatics strategies for cDNA-microarray data processing
  • 2009. - 1
  • In: Batch effects and noise in microarray experiments. - : Wiley and Sons. - 9780470741382 ; , s. 61-74
  • Book chapter (other academic/artistic)abstract
    •  Pre-processing plays a vital role in cDNA-microarray data analysis. Without proper pre-processing it is likely that the biological conclusions will be misleading. However, there are many alternatives and in order to choose a proper pre-processing procedure it is necessary to understand the effect of different methods. This chapter discusses several pre-processing steps, including image analysis, background correction, normalization, and filtering. Spike-in data are used to illustrate how different procedures affect the analytical ability to detect differentially expressed genes and estimate their regulation. The result shows that pre-processing has a major impact on both the experiment’s sensitivity andits bias. However, general recommendations are hard to give, since pre-processing consists of several actions that are highly dependent on each other. Furthermore, it is likely that pre-processing have a major impact on downstream analysis, such as clustering and classification, and pre-processing methods should be developed and evaluated with this in mind.
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31.
  • Fahlén, Jessica, 1973- (author)
  • Essays on spatial point processes and bioinformatics
  • 2010
  • Doctoral thesis (other academic/artistic)abstract
    • This thesis consists of two separate parts. The first part consists of one paper and considers problems concerning spatial point processes and the second part includes three papers in the field of bioinformatics. The first part of the thesis is based on a forestry problem of estimating the number of trees in a region by using the information in an aerial photo, showing the area covered by the trees. The positions of the trees are assumed to follow either a binomial point process or a hard-core Strauss process. Furthermore, discs of equal size are used to represent the tree-crowns. We provide formulas for the expectation and the variance of the relative vacancy for both processes. The formulas are approximate for the hard-core Strauss process. Simulations indicate that the approximations are accurate.  The second part of this thesis focuses on pre-processing of microarray data. The microarray technology can be used to measure the expression of thousands of genes simultaneously in a single experiment. The technique is used to identify genes that are differentially expressed between two populations, e.g. diseased versus healthy individuals. This information can be used in several different ways, for example as diagnostic tools and in drug discovery. The microarray technique involves a number of complex experimental steps, where each step introduces variability in the data. Pre-processing aims to reduce this variation and is a crucial part of the data analysis. Paper II gives a review over several pre-processing methods. Spike-in data are used to describe how the different methods affect the sensitivity and bias of the experi­ment. An important step in pre-processing is dye-normalization. This normalization aims to re­move the systematic differences due to the use of different dyes for coloring the samples. In Paper III a novel dye-normalization, the MC-normalization, is proposed. The idea behind this normaliza­tion is to let the channels’ individual intensities determine the cor­rection, rather than the aver­age intensity which is the case for the commonly used MA-normali­zation. Spike-in data showed that  the MC-normalization reduced the bias for the differentially expressed genes compared to the MA-normalization. The standard method for preserving patient samples for diagnostic purposes is fixation in formalin followed by embedding in paraffin (FFPE). In Paper IV we used tongue-cancer micro­RNA-microarray data to study the effect of FFPE-storage. We suggest that the microRNAs are not equally affected by the storage time and propose a novel procedure to remove this bias. The procedure improves the ability of the analysis to detect differentially expressed microRNAs.
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32.
  • Fahlén, Jessica, 1973-, et al. (author)
  • MicroRNA-microarray data analysis in the precence of FFPE storage time effects
  • 2010
  • Other publication (other academic/artistic)abstract
    • Background: The standard method for preserving patient samples for diagnostic purposes is fixation in formalin followed by embedding in paraffin (FFPE). The use of FFPE blocks makes it possible to include a large number of patients in the experimental studies since millions of FFPE blocks are stored around the world. However, FFPE storage can cause degradation and modifi­cations of nucleic acids. In order to draw reliable biological conclusions it is therefore important to know what effect FFPE-storage have on the tissues and to have procedures that normalize this effect. In this paper, we study the effect that FFPE-storage has on microRNA-microarray data from tongue-cancer patients and propose a novel procedure for normalizing the bias intro­duced by FFPE-storage.Results: MicroRNA-microarray data from 21 tongue-cancer patients and 8 control patients were used. The samples were stored in FFPE blocks and had been in storage for up to 11 years. The data contained a large amount of biological relevant variation, yet the largest variation was due to the samples storage times. The storage effect was shown to be significant and some results suggested that it may be causal. Moreover, the microRNAs were unequally affected by storage and this could partially be explained by sequence characteristics. The novel normaliza­tion procedure was shown to have a large impact in the analysis ability to identify differentially expressed microRNAs between young and old cancer patients as well as between cancer and control patients. The p-values for the top microRNAs candidates were much lower for the pro­posed novel normalization compared to a standard normalization procedure which suggested that the novel normalization made the analysis more efficient.Conclusions: MicroRNA-microarray data can be seriously affected by FFPE-storage and the introduced variation cannot be removed by standard normalizations. The proposed normaliza­tion removes the bias introduced by FFPE-storage and gives higher sensitivity than the standard normalization.
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33.
  • Folkesson, Elin, et al. (author)
  • Proteomic characterization of the normal human medial meniscus body using data-independent acquisition mass spectrometry
  • 2020
  • In: Journal of Orthopaedic Research. - : Wiley. - 0736-0266 .- 1554-527X. ; 38:8, s. 1735-1745
  • Journal article (peer-reviewed)abstract
    • Recent research suggests an important role of the meniscus in the development of knee osteoarthritis. We, therefore, aimed to analyze the proteome of the normal human meniscus body, and specifically to gain new knowledge on global protein expression in the different radial zones. Medial menisci were retrieved from the right knees of 10 human cadaveric donors, from which we cut a 2 mm radial slice from the mid-portion of the meniscal body. This slice was further divided into three zones: inner, middle, and peripheral. Proteins were extracted and prepared for mass spectrometric analysis using data-independent acquisition. We performed subsequent data searches using Spectronaut Pulsar and used fixed-effect linear regression models for statistical analysis. We identified 638 proteins and after statistical analysis, we observed the greatest number of differentially expressed proteins between the inner and peripheral zones (163 proteins) and the peripheral and middle zones (136 proteins), with myocilin being the protein with the largest fold-change in both comparisons. Chondroadherin was one of eight proteins that differed between the inner and middle zones. Functional enrichment analyses showed that the peripheral one-third of the medial meniscus body differed substantially from the two more centrally located zones, which were more similar to each other. This is probably related to the higher content of cells and vascularization in the peripheral zone, whereas the middle and inner zones of the meniscal body appear to be more similar to hyaline cartilage, with high levels of extracellular matrix proteins such as aggrecan and collagen type II.
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34.
  • Freyhult, Eva, et al. (author)
  • Challenges in microarray class discovery : a comprehensive examination of normalization, gene selection and clustering
  • 2010
  • In: BMC Bioinformatics. - : BioMed Central. - 1471-2105. ; 11
  • Journal article (peer-reviewed)abstract
    • Background: Cluster analysis, and in particular hierarchical clustering, is widely used to extract information from gene expression data. The aim is to discover new classes, or sub-classes, of either individuals or genes. Performing a cluster analysis commonly involve decisions on how to; handle missing values, standardize the data and select genes. In addition, pre processing, involving various types of filtration and normalization procedures, can have an effect on the ability to discover biologically relevant classes. Here we consider cluster analysis in a broad sense and perform a comprehensive evaluation that covers several aspects of cluster analyses, including normalization.Result: We evaluated 2780 cluster analysis methods on seven publicly available 2-channel microarray data sets with common reference designs. Each cluster analysis method differed in data normalization (5 normalizations were considered), missing value imputation (2), standardization of data (2), gene selection (19) or clustering method (11). The cluster analyses are evaluated using known classes, such as cancer types, and the adjusted Rand index. The performances of the different analyses vary between the data sets and it is difficult to give general recommendations. However, normalization, gene selection and clustering method are all variables that have a significant impact on the performance. In particular, gene selection is important and it is generally necessary to include a relatively large number of genes in order to get good performance. Selecting genes with high standard deviation or using principal component analysis are shown to be the preferred gene selection methods. Hierarchical clustering using Ward's method, k-means clustering and Mclust are the clustering methods considered in this paper that achieves the highest adjusted Rand. Normalization can have a significant positive impact on the ability to cluster individuals, and there are indications that background correction is preferable, in particular if the gene selection is successful. However, this is an area that needs to be studied further in order to draw any general conclusions.Conclusions: The choice of cluster analysis, and in particular gene selection, has a large impact on the ability to cluster individuals correctly based on expression profiles. Normalization has a positive effect, but the relative performance of different normalizations is an area that needs more research. In summary, although clustering, gene selection and normalization are considered standard methods in bioinformatics, our comprehensive analysis shows that selecting the right methods, and the right combinations of methods, is far from trivial and that much is still unexplored in what is considered to be the most basic analysis of genomic data.
  •  
35.
  • Fries, Niklas, et al. (author)
  • A comparison of local explanation methods for high-dimensional industrial data : a simulation study
  • 2022
  • In: Expert systems with applications. - : Elsevier. - 0957-4174 .- 1873-6793. ; 207
  • Journal article (peer-reviewed)abstract
    • Prediction methods can be augmented by local explanation methods (LEMs) to perform root cause analysis for individual observations. But while most recent research on LEMs focus on low-dimensional problems, real-world datasets commonly have hundreds or thousands of variables. Here, we investigate how LEMs perform for high-dimensional industrial applications. Seven prediction methods (penalized logistic regression, LASSO, gradient boosting, random forest and support vector machines) and three LEMs (TreeExplainer, Kernel SHAP, and the conditional normal sampling importance (CNSI)) were combined into twelve explanation approaches. These approaches were used to compute explanations for simulated data, and real-world industrial data with simulated responses. The approaches were ranked by how well they predicted the contributions according to the true models. For the simulation experiment, the generalized linear methods provided best explanations, while gradient boosting with either TreeExplainer or CNSI, or random forest with CNSI were robust for all relationships. For the real-world experiment, TreeExplainer performed similarly, while the explanations from CNSI were significantly worse. The generalized linear models were fastest, followed by TreeExplainer, while CNSI and Kernel SHAP required several orders of magnitude more computation time. In conclusion, local explanations can be computed for high-dimensional data, but the choice of statistical tools is crucial.
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36.
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37.
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38.
  • Fries, Niklas, et al. (author)
  • Data-driven process adjustment policies for quality improvement
  • 2024
  • In: Expert systems with applications. - : Elsevier. - 0957-4174 .- 1873-6793. ; 237
  • Journal article (peer-reviewed)abstract
    • Common objectives in machine learning research are to predict the output quality of manufacturing processes, to perform root cause analysis in case of reduced quality, and to propose intervention strategies. The cost of reduced quality must be weighed against the cost of the interventions, which depend on required downtime, personnel costs, and material costs. Furthermore, there is a risk of false negatives, i.e., failure to identify the true root causes, or false positives, i.e., adjustments that further reduce the quality. A policy for process adjustments describes when and where to perform interventions, and we say that a policy is worthwhile if it reduces the expected operational cost. In this paper, we describe a data-driven alarm and root cause analysis framework, that given a predictive and explanatory model trained on high-dimensional process and quality data, can be used to search for a worthwhile adjustment policy. The framework was evaluated on large-scale simulated process and quality data. We find that worthwhile adjustment policies can be derived also for problems with a large number of explanatory variables. Interestingly, the performance of the adjustment policies is almost exclusively driven by the quality of the model fits. Based on these results, we discuss key areas of future research, and how worthwhile adjustment policies can be implemented in real world applications.
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39.
  • Fries, Niklas, 1991- (author)
  • Data-driven quality management using explainable machine learning and adaptive control limits
  • 2023
  • Doctoral thesis (other academic/artistic)abstract
    • In industrial applications, the objective of statistical quality management is to achieve quality guarantees through the efficient and effective application of statistical methods. Historically, quality management has been characterized by a systematic monitoring of critical quality characteristics, accompanied by manual and experience-based root cause analysis in case of an observed decline in quality. Machine learning researchers have suggested that recent improvements in digitization, including sensor technology, computational power, and algorithmic developments, should enable more systematic approaches to root cause analysis.In this thesis, we explore the potential of data-driven approaches to quality management. This exploration is performed with consideration to an envisioned end product which consists of an automated data collection and curation system, a predictive and explanatory model trained on historical process and quality data, and an automated alarm system that predicts a decline in quality and suggests worthwhile interventions. The research questions investigated in this thesis relate to which statistical methods are relevant for the implementation of the product, how their reliability can be assessed, and whether there are knowledge gaps that prevent this implementation.This thesis consists of four papers: In Paper I, we simulated various types of process-like data in order to investigate how several dataset properties affect the choice of methods for quality prediction. These properties include the number of predictors, their distribution and correlation structure, and their relationships with the response. In Paper II, we reused the simulation method from Paper I to simulate multiple types of datasets, and used them to compare local explanation methods by evaluating them against a ground truth.In Paper III, we outlined a framework for an automated process adjustment system based on a predictive and explanatory model trained on historical data. Next, given a relative cost between reduced quality and process adjustments, we described a method for searching for a worthwhile adjustment policy. Several simulation experiments were performed to demonstrate how to evaluate such a policy.In Paper IV, we described three ways to evaluate local explanation methods on real-world data, where no ground truth is available for comparison. Additionally, we described four methods for decorrelation and dimension reduction, and describe the respective tradeoffs. These methods were evaluated on real-world process and quality data from the paint shop of the Volvo Trucks cab factory in Umeå, Sweden.During the work on this thesis, two significant knowledge gaps were identified: The first gap is a lack of best practices for data collection and quality control, preprocessing, and model selection. The other gap is that although there are many promising leads for how to explain the predictions of machine learning models, there is still an absence of generally accepted definitions for what constitutes an explanation, and a lack of methods for evaluating the reliability of such explanations.
  •  
40.
  • Griffin, Robert M., et al. (author)
  • The Shared Genome Is a Pervasive Constraint on the Evolution of Sex-Biased Gene Expression
  • 2013
  • In: Molecular biology and evolution. - : Oxford University Press (OUP). - 0737-4038 .- 1537-1719. ; 30:9, s. 2168-2176
  • Journal article (peer-reviewed)abstract
    • Males and females share most of their genomes, and differences between the sexes can therefore not evolve through sequence divergence in protein coding genes. Sexual dimorphism is instead restricted to occur through sex-specific expression and splicing of gene products. Evolution of sexual dimorphism through these mechanisms should, however, also be constrained when the sexes share the genetic architecture for regulation of gene expression. Despite these obstacles, sexual dimorphism is prevalent in the animal kingdom and commonly evolves rapidly. Here, we ask whether the genetic architecture of gene expression is plastic and easily molded by sex-specific selection, or if sexual dimorphism evolves rapidly despite pervasive genetic constraint. To address this question, we explore the relationship between the intersexual genetic correlation for gene expression (r(MF)), which captures how independently genes are regulated in the sexes, and the evolution of sex-biased gene expression. Using transcriptome data from Drosophila melanogaster, we find that most genes have a high r(MF) and that genes currently exposed to sexually antagonistic selection have a higher average r(MF) than other genes. We further show that genes with a high r(MF) have less pronounced sex-biased gene expression than genes with a low r(MF) within D. melanogaster and that the strength of the r(MF) in D. melanogaster predicts the degree to which the sex bias of a gene's expression has changed between D. melanogaster and six other species in the Drosophila genus. In sum, our results show that a shared genome constrains both short- and long-term evolution of sexual dimorphism.
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41.
  • Ignatov, Dmitriy, et al. (author)
  • An mRNA-mRNA Interaction Couples Expression of a Virulence Factor and Its Chaperone in Listeria monocytogenes
  • 2020
  • In: Cell Reports. - : cell press. - 2211-1247. ; 30:12, s. 4027-
  • Journal article (peer-reviewed)abstract
    • Bacterial pathogens often employ RNA regulatory elements located in the 5' untranslated regions (UTRs) to control gene expression. Using a comparative structural analysis, we examine the structure of 5' UTRs at a global scale in the pathogenic bacterium Listeria monocytogenes under different conditions. In addition to discovering an RNA thermoswitch and detecting simultaneous interaction of ribosomes and small RNAs with mRNA, we identify structural changes in the 5' UTR of an mRNA encoding the post-translocation chaperone PrsA2 during infection conditions. We demonstrate that the 5' UTR of the prsA2 mRNA base pairs with the 3' UTR of the full-length hly mRNA encoding listeriolysin O, thus preventing RNase J1-mediated degradation of the prsA2 transcript. Mutants lacking the hly-prsA2 interaction exhibit reduced virulence properties. This work highlights an additional level of RNA regulation, where the mRNA encoding a chaperone is stabilized by the mRNA encoding its substrate.
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42.
  • Kellgren, Therese, et al. (author)
  • Completed genome and emergence scenario of the multidrug-resistant nosocomial pathogen Staphylococcus epidermidis ST215
  • 2024
  • In: BMC Microbiology. - : BioMed Central (BMC). - 1471-2180. ; 24:1
  • Journal article (peer-reviewed)abstract
    • Background: A multidrug-resistant lineage of Staphylococcus epidermidis named ST215 is a common cause of prosthetic joint infections and other deep surgical site infections in Northern Europe, but is not present elsewhere. The increasing resistance among S. epidermidis strains is a global concern. We used whole-genome sequencing to characterize ST215 from healthcare settings.Results: We completed the genome of a ST215 isolate from a Swedish hospital using short and long reads, resulting in a circular 2,676,787 bp chromosome and a 2,326 bp plasmid. The new ST215 genome was placed in phylogenetic context using 1,361 finished public S. epidermidis reference genomes. We generated 10 additional short-read ST215 genomes and 11 short-read genomes of ST2, which is another common multidrug-resistant lineage at the same hospital. We studied recombination’s role in the evolution of ST2 and ST215, and found multiple recombination events averaging 30–50 kb. By comparing the results of antimicrobial susceptibility testing for 31 antimicrobial drugs with the genome content encoding antimicrobial resistance in the ST215 and ST2 isolates, we found highly similar resistance traits between the isolates, with 22 resistance genes being shared between all the ST215 and ST2 genomes. The ST215 genome contained 29 genes that were historically identified as virulence genes of S. epidermidis ST2. We established that in the nucleotide sequence stretches identified as recombination events, virulence genes were overrepresented in ST215, while antibiotic resistance genes were overrepresented in ST2.Conclusions: This study features the extensive antibiotic resistance and virulence gene content in ST215 genomes. ST215 and ST2 lineages have similarly evolved, acquiring resistance and virulence through genomic recombination. The results highlight the threat of new multidrug-resistant S. epidermidis lineages emerging in healthcare settings.
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43.
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44.
  • Kellgren, Therese, 1983- (author)
  • Hidden patterns that matter : statistical methods for analysis of DNA and RNA data
  • 2020
  • Doctoral thesis (other academic/artistic)abstract
    • Understanding how the genetic variations can affect characteristics and function of organisms can help researchers and medical doctors to detect genetic alterations that cause disease and reveal genes that causes antibiotic resistance. The opportunities and progress associated with such data come however with challenges related to statistical analysis. It is only by using properly designed and employed tools, that we can extract the information about hidden patterns. In this thesis we present three types of such analysis. First, the genetic variant in the gene COL17A1 that causes corneal dystrophy with recurrent erosions is reveled. By studying Next-generation sequencing data, the order of the nucleotides in the DNAsequence was be obtained, which enabled us to detect interesting variants in the genome. Further, we present results of an experimental design study with the aim to make the best selection from a family that is affected by an inherited disease. In second part of the work, we analyzed a novel antibiotic resistance Staphylococcus epidermidis clone that is only found in northern Europe. By investigating its genetic data, we revealed similarities to a world known antibiotic resistance clone. As a result, the antibiotic resistance profile is established from the DNA sequences. Finally, we also focus on the challenges related to the abundance of genetic data from different sources. The increasing number of public gene expression datasets gives us opportunity to increase our understanding by using information from multiple sources simultaneously. Naturally, this requires merging independent datasets together. However, when doing so, the technical and biological variation in the joined data increases. We present a pre-processing method to construct gene co-expression networks from a large diverse gene-expression dataset.
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45.
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46.
  • Kheir, Sahar M., et al. (author)
  • Results of application of the ISPD guidelines to the management of peritoneal dialysis in a single center in Sudan
  • 2017
  • In: Journal of Infection and Public Health. - : ELSEVIER SCIENCE LONDON. - 1876-0341 .- 1876-035X. ; 10:3, s. 348-352
  • Journal article (peer-reviewed)abstract
    • The culture negative peritonitis in Sudan 2010 was 46% exceeding 20% of the recommended ISPD (International Society for Peritoneal Dialysis) guidelines. This study reports an update after applying the standard ISPD protocol. The routine method was replaced by ISPD protocol. The culture negative rate using the ISPD guidelines dropped from 46% in the year 2010, to 39% in the year 2011, to 5% in the 2012 and to zero percent in the year 2013. Bacterial and fungal species represent (86.76%) and (13.23%) of infection and most isolates showed low resistance rate to antibiotics. Touch contamination added significantly (p = 0.0006) to the risk of contracting Peritonitis. The risk of contracting Peritonitis was 1.53 times higher in the group exposed by touch contamination. None of the other risk factors contributed significantly to Peritonitis. The study highlights the importance of implementing high hygiene practice. (C) 2016 King Saud Bin Abdulaziz University for Health Sciences. Published by Elsevier Limited. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).
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47.
  • Kurtz, Sherry L., et al. (author)
  • Transcriptional signatures measured in whole blood correlate with protection against tuberculosis in inbred and outbred mice
  • 2023
  • In: PLOS ONE. - : Public Library of Science (PLoS). - 1932-6203. ; 18:8
  • Journal article (peer-reviewed)abstract
    • Although BCG has been used for almost 100 years to immunize against Mycobacterium tuberculosis, TB remains a global public health threat. Numerous clinical trials are underway studying novel vaccine candidates and strategies to improve or replace BCG, but vaccine development still lacks a well-defined set of immune correlates to predict vaccine-induced protection against tuberculosis. This study aimed to address this gap by examining transcriptional responses to BCG vaccination in C57BL/6 inbred mice, coupled with protection studies using Diversity Outbred mice. We evaluated relative gene expression in blood obtained from vaccinated mice, because blood is easily accessible, and data can be translated to human studies. We first determined that the average peak time after vaccination is 14 days for gene expression of a small subset of immune-related genes in inbred mice. We then performed global transcriptomic analyses using whole blood samples obtained two weeks after mice were vaccinated with BCG. Using comparative bioinformatic analyses and qRT-PCR validation, we developed a working correlate panel of 18 genes that were highly correlated with administration of BCG but not heat-killed BCG. We then tested this gene panel using BCG-vaccinated Diversity Outbred mice and revealed associations between the expression of a subset of genes and disease outcomes after aerosol challenge with M. tuberculosis. These data therefore demonstrate that blood-based transcriptional immune correlates measured within a few weeks after vaccination can be derived to predict protection against M. tuberculosis, even in outbred populations.
  •  
48.
  • Kurtz, Sherry L., et al. (author)
  • Whole genome profiling refines a panel of correlates to predict vaccine efficacy against Mycobacterium tuberculosis
  • 2020
  • In: Tuberculosis. - : Elsevier. - 1472-9792 .- 1873-281X. ; 120
  • Journal article (peer-reviewed)abstract
    • New vaccines are needed to combat the public health threat posed by M. tuberculosis (M. tb), but no correlates have been defined to aid vaccine development. Using mouse models, we previously developed an in vitro system that measures the ability of M. tb-immune lymphocytes to control bacterial replication during co-culture with M. tb-infected macrophages. We demonstrated that the degree of in vitro growth control by lymphocytes from mice given vaccines of varying efficacy reflected the relative degree of in vivo protection against lethal challenge. Further, using targeted analyses of gene expression in lymphocytes recovered from co-cultures, we found mediators whose relative expression also correlated with in vitro and in vivo outcomes. Here we advanced those findings by employing genome-wide expression analyses. We first screened splenocytes recovered from co-cultures by microarray, revealing additional genes whose expression correlated with protection. After applying pathway analyses to down-select gene candidates, we used both splenocytes and peripheral blood lymphocytes to validate microarray findings by qRT-PCR. We then subjected data from top candidates to rigorous statistical analyses. Resulting correlate candidates, including CXCL9, IFN-γ, and CCL5, significantly predicted protection with high specificity. These findings therefore refine and extend a panel of relevant immune correlates to advance vaccine development.
  •  
49.
  • Källberg, David, et al. (author)
  • A moment-distance hybrid method for estimating a mixture of two symmetric densities
  • 2018
  • In: Modern Stochastics: Theory and Applications. - 2351-6054. ; 5:1, s. 1-36
  • Journal article (peer-reviewed)abstract
    • In clustering of high-dimensional data a variable selection is commonly applied to obtain an accurate grouping of the samples. For two-class problems this selection may be carried out by fitting a mixture distribution to each variable. We propose a hybrid method for estimating a parametric mixture of two symmetric densities. The estimator combines the method of moments with the minimum distance approach. An evaluation study including both extensive simulations and gene expression data from acute leukemia patients shows that the hybrid method outperforms a maximum-likelihood estimator in model-based clustering. The hybrid estimator is flexible and performs well also under imprecise model assumptions, suggesting that it is robust and suited for real problems.
  •  
50.
  • Källberg, David, 1982-, et al. (author)
  • Comparison of Methods for Feature Selection in Clustering of High-Dimensional RNA-Sequencing Data to Identify Cancer Subtypes
  • 2021
  • In: Frontiers in Genetics. - : Frontiers Media S.A.. - 1664-8021. ; 12
  • Journal article (peer-reviewed)abstract
    • Cancer subtype identification is important to facilitate cancer diagnosis and select effective treatments. Clustering of cancer patients based on high-dimensional RNA-sequencing data can be used to detect novel subtypes, but only a subset of the features (e.g., genes) contains information related to the cancer subtype. Therefore, it is reasonable to assume that the clustering should be based on a set of carefully selected features rather than all features. Several feature selection methods have been proposed, but how and when to use these methods are still poorly understood. Thirteen feature selection methods were evaluated on four human cancer data sets, all with known subtypes (gold standards), which were only used for evaluation. The methods were characterized by considering mean expression and standard deviation (SD) of the selected genes, the overlap with other methods and their clustering performance, obtained comparing the clustering result with the gold standard using the adjusted Rand index (ARI). The results were compared to a supervised approach as a positive control and two negative controls in which either a random selection of genes or all genes were included. For all data sets, the best feature selection approach outperformed the negative control and for two data sets the gain was substantial with ARI increasing from (−0.01, 0.39) to (0.66, 0.72), respectively. No feature selection method completely outperformed the others but using the dip-rest statistic to select 1000 genes was overall a good choice. The commonly used approach, where genes with the highest SDs are selected, did not perform well in our study.
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