Explore the vast range of titles available.

ObjectivesSystemic lupus erythematosus (SLE) is an autoimmune disease with extensive heterogeneity in disease presentation between patients, which is likely due to an underlying molecular diversity. Here, we aimed at elucidating the genetic aetiology of SLE from the immunity pathway level to the single variant level, and stratify patients with SLE into distinguishable molecular subgroups, which could inform treatment choices in SLE.MethodsWe undertook a pathway-centred approach, using sequencing of immunological pathway genes. Altogether 1832 candidate genes were analysed in 958 Swedish patients with SLE and 1026 healthy individuals. Aggregate and single variant association testing was performed, and we generated pathway polygenic risk scores (PRS).ResultsWe identified two main independent pathways involved in SLE susceptibility: T lymphocyte differentiation and innate immunity, characterised by HLA and interferon, respectively. Pathway PRS defined pathways in individual patients, who on average were positive for seven pathways. We found that SLE organ damage was more pronounced in patients positive for the T or B cell receptor signalling pathways. Further, pathway PRS-based clustering allowed stratification of patients into four groups with different risk score profiles. Studying sets of genes with priors for involvement in SLE, we observed an aggregate common variant contribution to SLE at genes previously reported for monogenic SLE as well as at interferonopathy genes.ConclusionsOur results show that pathway risk scores have the potential to stratify patients with SLE beyond clinical manifestations into molecular subsets, which may have implications for clinical follow-up and therapy selection.

The numerous physiological and phenotypic differences between the sexes, as well as the disparity between male and female reproductive interests, result in sexual conflicts, which are often manifested at the genomic level. Sexually antagonistic genes benefit one sex at the expense of the other and experience strong pressure to evolve male‐ and female‐specific expression patterns to resolve sexual conflicts and maximize fitness for both sexes. Sex‐biased gene expression has recently been demonstrated for much of the metazoan transcriptome, suggesting that many loci are sexually antagonistic. However, many coding regions function in multiple processes throughout the organism. This pleiotropy increases the complexity of selection for any given gene, which in turn may obscure sex‐specific selective pressures and hamper the evolution of sex‐biased gene expression. Here we use microarray gene expression data, in conjunction with data on transcript abundance from expressed sequence tag libraries, to demonstrate that loci with sex‐biased gene expression are significantly less pleiotropic than unbiased genes. This relationship was independent of sex chromosome gene dosage effects, and the results were concordant across two study organisms, chicken and mouse. These results suggest that the resolution of sexually antagonistic gene expression is determined by the evolutionary constraints acting on any given antagonistic locus.

Objective Systemic lupus erythematosus (SLE) predominantly affects women, but previous studies suggest that men with SLE present a more severe disease phenotype. In this study, we investigated a large and well-characterized patient group with the aim of identifying sex differences in disease manifestations, with a special focus on renal involvement. Methods We studied a Swedish multi-center SLE cohort including 1226 patients (1060 women and 166 men) with a mean follow-up time of 15.8 ± 13.4 years. Demographic data, disease manifestations including ACR criteria, serology and renal histopathology were investigated. Renal outcome and mortality were analyzed in subcohorts. Results Female SLE patients presented more often with malar rash ( p  < 0.0001), photosensitivity ( p  < 0.0001), oral ulcers ( p  = 0.01), and arthritis ( p  = 0.007). Male patients on the other hand presented more often with serositis ( p  = 0.0003), renal disorder ( p  < 0.0001), and immunologic disorder ( p  = 0.04) by the ACR definitions. With regard to renal involvement, women were diagnosed with nephritis at an earlier age ( p  = 0.006), while men with SLE had an overall higher risk for progression into end-stage renal disease (ESRD) with a hazard ratio (HR) of 5.1 (95% CI, 2.1–12.5). The mortality rate among men with SLE and nephritis compared with women was HR 1.7 (95% CI, 0.8–3.8). Conclusion SLE shows significant sex-specific features, whereby men are affected by a more severe disease with regard to both renal and extra-renal manifestations. Additionally, men are at a higher risk of developing ESRD which may require an increased awareness and monitoring in clinical practice.

Autoimmune Addison’s disease (AAD) is the predominating cause of primary adrenal failure. Despite its high heritability, the rarity of disease has long made candidate-gene studies the only feasible methodology for genetic studies. Here we conducted a comprehensive reinvestigation of suggested AAD risk loci and more than 1800 candidate genes with associated regulatory elements in 479 patients with AAD and 2394 controls. Our analysis enabled us to replicate many risk variants, but several other previously suggested risk variants failed confirmation. By exploring the full set of 1800 candidate genes, we further identified common variation in the autoimmune regulator ( AIRE ) as a novel risk locus associated to sporadic AAD in our study. Our findings not only confirm that multiple loci are associated with disease risk, but also show to what extent the multiple risk loci jointly associate to AAD. In total, risk loci discovered to date only explain about 7% of variance in liability to AAD in our study population.

About one-third of oestrogen receptor alpha-positive breast cancer patients treated with tamoxifen relapse. Here we identify the nuclear receptor retinoic acid receptor alpha as a marker of tamoxifen resistance. Using quantitative mass spectrometry-based proteomics, we show that retinoic acid receptor alpha protein networks and levels differ in a tamoxifen-sensitive (MCF7) and a tamoxifen-resistant (LCC2) cell line. High intratumoural retinoic acid receptor alpha protein levels also correlate with reduced relapse-free survival in oestrogen receptor alpha-positive breast cancer patients treated with adjuvant tamoxifen solely. A similar retinoic acid receptor alpha expression pattern is seen in a comparable independent patient cohort. An oestrogen receptor alpha and retinoic acid receptor alpha ligand screening reveals that tamoxifen-resistant LCC2 cells have increased sensitivity to retinoic acid receptor alpha ligands and are less sensitive to oestrogen receptor alpha ligands compared with MCF7 cells. Our data indicate that retinoic acid receptor alpha may be a novel therapeutic target and a predictive factor for oestrogen receptor alpha-positive breast cancer patients treated with adjuvant tamoxifen. Many patients with breast cancer develop resistance to the drug tamoxifen and relapse. Here Johansson et al . identify the nuclear receptor retinoic acid receptor alpha (RARA) as a marker of tamoxifen resistance and show that RARA expression correlates negatively with relapse-free survival of patients.

The powerful pressures of sexual and natural selection associated with species recognition and reproduction are thought to manifest in a faster rate of evolution in sex-biased genes, an effect that has been documented particularly for male-biased genes expressed in the reproductive tract. However, little is known about the rate of evolution for genes involved in sexually dimorphic behaviors, which often form the neurological basis of intrasexual competition and mate choice. We used microarray data, designed to uncover sex-biased expression patterns in embryonic chicken brain, in conjunction with data on the rate of sequence evolution for >4,000 coding regions aligned between chicken and zebra finch in order to study the role of selection in governing the molecular evolution for sex-biased and unbiased genes. Surprisingly, we found that female-biased genes, defined across a range of cutoff values, show a higher rate of functional evolution than both male-biased and unbiased genes. Autosomal male-biased genes evolve at a similar rate as unbiased genes. Sex-specific genomic properties, such as heterogeneity in genomic distribution and GC content, and codon usage bias for sex-biased classes fail to explain this surprising result, suggesting that selective pressures may be acting differently on the male and female brain. [PUBLICATION ABSTRACT]

Background There is a vast need to find clinically applicable protein biomarkers as support in cancer diagnosis and tumour classification. In proteomics research, a number of methods can be used to obtain systemic information on protein and pathway level on cells and tissues. One fundamental tool in analysing protein expression has been two-dimensional gel electrophoresis (2DE). Several cancer 2DE studies have reported partially redundant lists of differently expressed proteins. To be able to further extract valuable information from existing 2DE data, the power of a multivariate meta-analysis will be evaluated in this work. Results We here demonstrate a multivariate meta-analysis of 2DE proteomics data from human prostate and colon tumours. We developed a bioinformatic workflow for identifying common patterns over two tumour types. This included dealing with pre-processing of data and handling of missing values followed by the development of a multivariate Partial Least Squares (PLS) model for prediction and variable selection. The variable selection was based on the variables performance in the PLS model in combination with stability in the validation. The PLS model development and variable selection was rigorously evaluated using a double cross-validation scheme. The most stable variables from a bootstrap validation gave a mean prediction success of 93% when predicting left out test sets on models discriminating between normal and tumour tissue, common for the two tumour types. The analysis conducted in this study identified 14 proteins with a common trend between the tumour types prostate and colon, i.e. the same expression profile between normal and tumour samples. Conclusions The workflow for meta-analysis developed in this study enabled the finding of a common protein profile for two malign tumour types, which was not possible to identify when analysing the data sets separately.

Important aims of cancer proteomics include gaining better understanding of cancer biology and identifying cancer biomarkers. Mass spectrometry (MS) based shotgun proteomics allow for identification and quantification of thousands of proteins in complex human samples. However, proteomics discovery research in clinical material faces many challenges. The biological differences between groups are often expected to be rather small, at the same time the human proteome is highly complex and there is large biological variation between clinical samples. To be able to extract meaningful results from proteomics data derived from biological and clinical material, care has to be taken to all the critical steps in the data analysis workflow. First of all we need to have robust methods to extract good quality data. A proper statistical analysis is then of outmost importance, taking into account risks of over- fitting and false positives. In addition, we also need system based approaches to relate the data to clinical and biological questions.The main goal of this thesis was to generate robust methods for selection of key proteins, networks and pathways relevant for answering biological and clinical questions. The work includes development and evaluation of workflows for quantitative analysis of proteomics data.In paper I, a multivariate meta-analysis workflow was developed to link existing proteomics data from human colon and prostate tumours. The aim was to identify proteins distinguishing between normal and tumour samples independent of tissue origin, as well as to find unique markers. The bioinformatics workflow for meta- analysis developed in this study enabled the finding of a common protein profile for the two malign tumour types, which was not possible when analysing the data sets separately. The purpose of paper II was to generate a basis for the decision of what protein quantities are reliable and find a way for accurate and precise protein quantification. We developed a methodology for improved protein quantification in shotgun proteomics and introduced a way to assess quantification for proteins with few peptides. The experimental design and developed algorithms decreased the relative protein quantification error in the analysis of complex biological samples. In paper III, we presented SpliceVista, a tool for splice variant identification and visualization based on MS proteomics data. SpliceVista identifies splice variant specific peptides and provides the possibility to perform splice variant specific quantitative analysis. SpliceVista was applied in two experimental datasets to exemplify its capability of detecting differentially expressed splice variants at the protein level. The aim of paper IV was to develop a network based analysis workflow for proteomics data to identify protein subnetworks with different activity between groups of samples. The methodology, which is based on a multivariate model directed by the network, was applied to several of our clinical mass spectrometry datasets. The output from the subnetwork analysis was functional subunits of proteins, rather than a collection of sparse proteins, which were shown to more readily provide a model of the biological mechanisms studied, and thus aid in the biological interpretation.

Most patients treated with immune checkpoint blockade (ICB) do not have durable treatment responses. Therefore, there is a critical need to identify early non-invasive biomarkers of response. We performed plasma proteomic analysis (>700 proteins) at three timepoints on 174 metastatic melanoma patients treated with ICB. We leverage independent training and testing cohorts to build a predictor of immunotherapy response that outperforms several tissue-based approaches. We found 217 differentially expressed proteins between ICB responders (R) and non-responders (NR), including a co-regulated module of proteins enriched in certain NR patients. By analyzing single-cell RNA-sequencing data of tumor biopsies from 32 patients, we dissected the relative contribution of cells in the tumor to proteins in circulation. The majority of proteins in the co-regulated NR module derived from tumor and myeloid cells. Amongst myeloid cells, we identified a subset of tumor-associated macrophages (TAMs) with a suppressive phenotype that expressed high levels of the co-regulated NR module, thus suggesting they are key drivers of non-response signatures. Together, our data demonstrates the utility of plasma proteomics in biomarker discovery and in understanding the biology of host response to tumors. Competing Interest Statement AM has served a consultant/advisory role for Third Rock Ventures, Asher Biotherapeutics, Abata Therapeutics, Flare Therapeutics, venBio Partners, BioNTech, Rheos Medicines and Checkmate Pharmaceuticals, is an equity holder in Asher Biotherapeutics and Abata Therapeutics, and has a sponsored research agreement with Bristol-Myers Squibb and Olink Proteomics. RWJ. is a member of the advisory board for and has a financial interest in Xsphera Biosciences Inc., a company focused on using ex vivo profiling technology to deliver functional, precision immune-oncology solutions for patients, providers, and drug development companies. The interests of RWJ were reviewed and are managed by Massachusetts General Hospital and Partners HealthCare in accordance with their conflict-of-interest policies. SJK has served a consultant/advisory role for Astellas, Daiichi-Sankyo, Merck, Bristol Myers Squibb, Eli Lilly, Sanofi-Aventis, Natera, and AstraZeneca. SJK reports stock ownership in Turning Point Therapeutics. All other authors have no disclosures. RJS has served as a consultant/advisory role for BMS, Merck, Pfizer and Novartis. KTF serves on the Board of Directors of Clovis Oncology, Strata Oncology, Kinnate, Checkmate Pharmaceuticals, and Scorpion Therapeutics; Scientific Advisory Boards of PIC Therapeutics, Apricity, Tvardi, ALX Oncology, xCures, Monopteros, Vibliome, and Soley Therapeutics, and is a consultant to Takeda, Novartis and Transcode Therapeutics. NH has sponsored research agreements with BMS, holds equity in BioNTech, and is a consultant for Related Sciences. GMB has sponsored research agreements with Olink Proteomics, InterVenn Biosciences, Palleon Pharmaceuticals. GMB is on scientific advisory boards for Merck, Novartis, Nektar Therapeutics, Iovance, and Ankyra Therapeutics, and consults for Merck and Ankyra Therapeutics.