Sven Kjellström

ESR1 mutations are a driver of endocrine therapy resistance in breast cancer, however their impact on protein signaling pathways and clinical outcomes remains poorly understood. We analyzed the transcriptomic, proteomic, and phosphoproteomic landscape of tumors harboring ESR1 mutations. Here we show that ESR1 mutated cancers exhibit a higher mutational burden associated with increased cell proliferation, and display enhanced activation of the mTOR pathway via MTORC1, suggesting an ER-driven amplification of existing oncogenic mechanisms. Analysis of an independent proteomic dataset from breast cancer cell lines further supported a convergence between MTORC1 and ER signaling programs. Integrative multi-omic data analysis incorporating features related to tumor grade, MTORC1 signaling, and ESR1 mutations, enabled the development of a predictive signature that distinguishes patients with ESR1 mutation and identified those at high risk of recurrence. This study provides insights into the mechanisms underlying tumor aggressiveness and poor clinical outcome modulated by ESR1 mutations and highlights the potential of proteogenomic approaches to identify signaling pathways and molecular signatures.

Donation after circulatory death (DCD) has significantly increased the number of organs potentially available for transplantation. Livers are more vulnerable than most other solid organs to warm ischemia in the DCD process. The injury induced by warm ischemic time (WIT) is further exacerbated during cold ischemia and reperfusion, resulting in tissue damage caused by the production of reactive oxygen species. These are not only cytotoxic but also perpetuate mitochondrial dysfunction and cell death. To improve the utilization of livers from uncontrolled DCD (uDCD) or controlled DCD (cDCD) donors with prolonged WIT, new strategies to mitigate WIT must be developed.Ex vivo normothermic machine perfusion (NMP) has increased DCD organ utilization and improved the assessment of the viability of organs before transplantation. NMP could also serve as a platform for isolated treatment of organs prior to transplant. An innovative approach to mitigate organ injury is to treat damaged livers with mitochondrial transplantation (MTx). In this study, we tested the efficacy of xenogeneic mitochondrial administration to restore the function of porcine DCD livers with two hours of warm ischemia. DCD livers were explanted and connected to NMP, where mitochondrial transplantation was administered as a bolus dose in the portal vein and hepatic artery at the start of perfusion. After four hours of NMP, treated livers demonstrated a significant increase in bile production, improved bile quality, and restored cytoarchitecture.To further assess the transplantation suitability of these livers post-NMP, they were connected to cross-circulation (CC) with a recipient pig. After six hours of CC, untreated livers became oedematous, exhibited significant aspartate aminotransferase elevations in the bile, and showed cellular degeneration, including near-complete loss of Kupffer cells. In contrast, livers treated with MTx maintained function across CC and were comparable to healthy controls. These data validate the efficacy of MTx in restoring function in DCD livers exposed to prolonged ischaemic times, presenting a promising approach to expand the donor pool.

Recently, mass spectrometry based peptidomics studies have proven useful in the identification of biomarkers and bioactive peptide-based therapeutics. Here, we present a dataset comprised of temporal wound fluid peptidomics data from highly defined porcine models. Wound fluids from porcine wounds infected with Staphylococcus aureus and Pseudomonas aeruginosa, and uninfected controls, were sampled at different timepoints of the infection. Peptides were extracted from the samples, followed by liquid chromatography tandem mass spectrometry analysis in data dependent acquisition mode. The resulting spectra and searched files have been deposited to online repositories and made easily accessible to enable further investigations of the infected and uninfected wound fluid peptidome.

This study showcases an integrative mass spectrometry-based strategy combining systems antigenomics and systems serology to characterize human antibodies in clinical samples. This strategy involves using antibodies circulating in plasma to affinity-enrich antigenic proteins in biochemically fractionated pools of bacterial proteins, followed by their identification and quantification using mass spectrometry. A selected subset of the identified antigens is then expressed recombinantly to isolate antigen-specific IgG, followed by characterization of the structural and functional properties of these antibodies. We focused on Group A streptococcus (GAS), a major human pathogen lacking an approved vaccine. The data shows that both healthy and GAS-infected individuals have circulating IgG against conserved streptococcal proteins, including toxins and virulence factors. The antigenic breadth of these antibodies remains relatively constant across healthy individuals but changes considerably in GAS bacteremia. Moreover, antigen-specific IgG analysis reveals individual variation in titers, subclass distributions, and Fc-signaling capacity, despite similar epitope and Fc-glycosylation patterns. Finally, we show that GAS antibodies may cross-react with Streptococcus dysgalactiae (SD), a bacterial pathogen that occupies similar niches and causes comparable infections. Collectively, our results highlight the complexity of GAS-specific antibody responses and the versatility of our methodology to characterize immune responses to bacterial pathogens.

The complex dynamics of protein expression in plasma during hyperacute HIV-1 infection and its relation to acute retroviral syndrome, viral control, and disease progression are largely unknown. Here, we quantify 1293 blood plasma proteins from 157 longitudinally linked plasma samples collected before, during, and after hyperacute HIV-1 infection of 54 participants from four sub-Saharan African countries. Six distinct longitudinal expression profiles are identified, of which four demonstrate a consistent decrease in protein levels following HIV-1 infection. Proteins involved in inflammatory responses, immune regulation, and cell motility are significantly altered during the transition from pre-infection to one month post-infection. Specifically, decreased ZYX and SCGB1A1 levels, and increased LILRA3 levels are associated with increased risk of acute retroviral syndrome; increased NAPA and RAN levels, and decreased ITIH4 levels with viral control; and increased HPN, PRKCB, and ITGB3 levels with increased risk of disease progression. Overall, this study provides insight into early host responses in hyperacute HIV-1 infection, and present potential biomarkers and mechanisms linked to HIV-1 disease progression and viral load.

Recent advances in mass spectrometry-based peptidomics have catalyzed the identification and quantification of thousands of endogenous peptides across diverse biological systems. However, the vast peptidomic landscape generated by proteolytic processing poses several challenges for downstream analyses and limits the comparability of clinical samples. Here, we present an algorithm that aggregates peptides into peptide clusters, reducing the dimensionality of peptidomics data, improving the definition of protease cut sites, enhancing inter-sample comparability, and enabling the implementation of large-scale data analysis methods akin to those employed in other omics fields. We showcase the algorithm by performing large-scale quantitative analysis of wound fluid peptidomes of highly defined porcine wound infections and human clinical non-healing wounds. This revealed signature phenotype-specific peptide regions and proteolytic activity at the earliest stages of bacterial colonization. We validated the method on the urinary peptidome of type 1 diabetics which revealed potential subgroups and improved classification accuracy.

Recent advances in mass spectrometry-based peptidomics has catalyzed the identification and quantification of thousands endogenous peptides across diverse biological systems. However, the large theoretical peptidomic landscape and high proportion of missing values poses several challenges for downstream analyses and limits the comparability of clinical samples. Here, we present a generalizable computational workflow that clusters peptides with overlapping sequences to reduce the dimensionality of peptidomic data, improve the definition of protease cut-sites, enhance inter-sample comparability, and enable the implementation of reliable and large-scale data analysis methods akin to those employed in other omics fields. We showcase the algorithm by performing large-scale quantitative analysis of wound fluid peptidomes of highly defined porcine wounds and human clinical non-healing wounds. The analysis revealed signature phenotype-specific peptide regions reflecting pathogen-specific proteolytic activity at the earliest stages of colonization, resulting in novel class of potential peptide cluster-based biomarkers.

There is a clinical need for conceptually new treatments that target the excessive activation of inflammatory pathways during systemic infection. Thrombin-derived C-terminal peptides (TCPs) are endogenous anti-infective immunomodulators interfering with CD14-mediated TLR-dependent immune responses. Here we describe the development of a peptide-based compound for systemic use, sHVF18, expressing the evolutionarily conserved innate structural fold of natural TCPs. Using a combination of structure- and in silico-based design, nuclear magnetic resonance spectroscopy, biophysics, mass spectrometry, cellular, and in vivo studies, we here elucidate the structure, CD14 interactions, protease stability, transcriptome profiling, and therapeutic efficacy of sHVF18. The designed peptide displays a conformationally stabilized, protease resistant active innate fold and targets the LPS-binding groove of CD14. In vivo, it shows therapeutic efficacy in experimental models of endotoxin shock in mice and pigs and increases survival in mouse models of systemic polymicrobial infection. The results provide a drug class based on Nature´s own anti-infective principles.

Finding new sustainable means of diagnosing and treating diseases is one of the most pressing issues of our time. In recent years, several endogenous peptides have been found to be both excellent biomarkers for many diseases and to possess important physiological roles which may be utilized in treatments. The detection of peptides has been facilitated by the rapid development of biological mass spectrometry and now the combination of fast and sensitive high resolution MS instruments and stable nano HP-LC equipment sequences thousands of peptides in one single experiment. In most research conducted with these advanced systems, proteolytically cleaved proteins are analyzed and the specific peptides are identified by software dedicated for protein quantification using different proteomics workflows. Analysis of endogenous peptides with peptidomics workflows also benefit from the novel sensitive and advanced instrumentation, however, the generated peptidomic data is vast and subsequently laborious to visualize and examine, creating a bottleneck in the analysis. Therefore, we have created Peptimetric, an application designed to allow researchers to investigate and discover differences between peptidomic samples. Peptimetric allows the user to dynamically and interactively investigate the proteins, peptides, and some general characteristics of multiple samples, and is available as a web application at https://peptimetric.herokuapp.com. To illustrate the utility of Peptimetric, we’ve applied it to a peptidomic dataset of 15 urine samples from diabetic patients and corresponding data from healthy subjects.