AcureOmics AB

Umeå, Sweden

AcureOmics AB

Umeå, Sweden
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Agency: European Commission | Branch: FP7 | Program: CP-FP | Phase: HEALTH.2012.1.4-1 | Award Amount: 8.01M | Year: 2013

In renal allograft recipients, 10-year graft survival has not improved over the past decades. Histological examination of graft biopsies has long been the gold standard to confirm graft injuries, but biopsies are invasive and histological grading is not very robust. There is thus a need for robust, non-invasive methods to predict and diagnose acute and chronic graft lesions, to improve patient treatment, quality of life and long-term graft survival. Also, there is an unmet need for better understanding of the immune and non-immune mechanisms of interstitial fibrosis /tubular atrophy and graft loss. Combining all the skills required to build upon previous findings, BIOMARGIN will offer such opportunities in renal transplantation by integrating several omics approaches (mRNA, miRNA, peptides, proteins, lipids and metabolites) in blood, graft tissue and urine, in a well thought out, multistage discovery-to-validation translational programme, following the highest European ethics and regulatory requirements, as well as quality controls and quality assessments at all clinical and analytical steps. It is probably one of the first programmes to pursue such an integrated and systematic research approach. BIOMARGIN aims to: (i) discover, select and validate blood and/or urine biomarkers of renal allograft lesions in adult and pediatric kidney transplant recipients; (ii) provide renal transplant physicians with non-invasive, robust diagnostic tests and interpretation algorithms enabling closer, more accurate, more predictive and/or less invasive monitoring of transplanted patients; (iii) help to avoid or diminish the use of biopsies and improve patient treatment, quality of life and long-term graft survival; (iv) help understand the mechanisms involved in the allograft injury processes which, combined with mass spectrometry imaging should offer pathologists new molecular targets and tools for renal graft biopsy analysis.

Agency: European Commission | Branch: FP7 | Program: CP-IP | Phase: HEALTH.2013.2.1.1-1 | Award Amount: 16.16M | Year: 2013

Recently intense research identified around 4,000 single nucleotide polymorphisms (SNPs) associated with human age related diseases such as metabolic disorders. Despite their highly significant association to pathology, the functional role of these genetic variants is, in most cases, yet to be elucidated. The evolutionary distance of most animal models from humans represents a major limitation for the functional validation of these SNPs. To overcome these difficulties, HUMAN will generate mouse models carrying human hepatocytes or pancreatic cells from either primary cells (hepatocytes) or induced pluripotent stem cells (iPSCs). This innovative approach offers the unique possibility of studying function of genetic risk variants associated with metabolic diseases in an integrated living system (the mouse body), but within human-derived organs, i.e. liver and pancreas. iPSCs used to generate hepatocytes and cells will derive from extreme phenotypes, i.e. patients affected by severe metabolic diseases such as type 2 diabetes (T2D) or subjects selected for exceptional healthy longevity (subjects over 105 years and offspring of nonagenarian sibships) all fully clinically and metabolically characterised and genotyped; they will be selected according to the best combination of risk and protective alleles. We will test the effect of different nutritional regimes (e.g. high fat diet, caloric restriction), to disentangle the complex molecular mechanisms and circuitry across organs (e.g. hypothalamus-liver axis) which lead to pathology. HUMAN associates a core of outstanding basic research institutions to leading European biotech SMEs, and has the capability to produce at least 500 humanised mice. HUMAN will generate iPSCs biobanks and comprehensively manage all associated information. HUMAN is uniquely situated to drive innovation towards a better knowledge of the genetic basis of human metabolic diseases, thereby contributing to healthier aging of European citizens.

Agency: European Commission | Branch: H2020 | Program: RIA | Phase: PHC-14-2015 | Award Amount: 6.00M | Year: 2016

The goal of BATCure is to advance the development of new therapeutic options for a group of rare lysosomal diseases - neuronal ceroid lipofuscinoses (NCL) or Batten disease. There are > thousand affected across Europe, with a combined incidence of c.1:100 000. The NCLs are devastating and debilitating genetic disorders that mainly affect children, who suffer progressive dementia and motor decline, visual failure and epilepsy, leading to a long period of complete dependence on others, and eventually a premature death. Existing palliative treatment can reduce, but does not eliminate, the burden of seizures and the progressively worsening effects on the whole body due to decreasing CNS influence and control. There are no curative treatments in the clinic for any type of NCL. We will follow a novel integrated strategy to identify specific gene and small molecule treatments for three genetic types of Batten disease that include the most prevalent world-wide, juvenile CLN3 disease, and in southern and mediterranean Europe, CLN6 and CLN7 diseases. To develop new therapies for these 3 types of Batten disease, BATCure will: 1. Create new models, tools and technologies for developing and testing therapies 2. Further delineate disease biology and gene function to identify new therapeutic target pathways utilising yeast and pluripotent stem cell models 3. Identify biochemical therapeutic target pathways, facilitate effective evaluation of preclinical therapies and improve diagnostics 4. Extend a comprehensive natural history beyond the brain to include cardiology, the spinal cord, PNS, psychiatric and metabolic changes 5. Identify new and repurpose existing small molecule therapy 6. Triage new compound treatments in zebrafish, a high-throughput small vertebrate model 7. Deliver and monitor new treatments using mouse models 8. Provide a novel mechanism to involve patients and their families to inform and fully contribute to therapy development and prepare for clinical trials

PubMed | Acureomics AB, University of Bergen, Umeå University and University of Stavanger
Type: Journal Article | Journal: Rheumatology international | Year: 2016

Fatigue occurs in all chronic inflammatory diseases, in cancer, and in some neurological conditions. Patients often regard fatigue as one of their most debilitating problems, but currently there is no established treatment and the mechanisms that lead to and regulate fatigue are incompletely understood. Our objective was to more completely understand the physiology of this phenomenon. Twenty-four patients with rheumatoid arthritis (RA) nave to treatment with biological drugs were enrolled for the study. Fatigue was measured with a fatigue visual analogue scale (fVAS). Ethylenediaminetetraacetic acid (EDTA) plasma samples were subjected to gas chromatography-time-of-flight mass spectrometry (GC/MS-TOF)-based metabolite profiling. Obtained metabolite data were evaluated by multivariate data analysis with orthogonal projections to latent structures (OPLS) method to pinpoint metabolic changes related to fatigue severity. A significant multivariate OPLS model was obtained between the fVAS scores and the measured metabolic levels. Increasing fatigue scores were associated with a metabolic pattern characterized by down-regulation of metabolites from the urea cycle, fatty acids, tocopherols, aromatic amino acids, and hypoxanthine. Uric acid levels were increased. Apart from fatigue, we found no other disease-related variables that might be responsible for these changes. Our MS-based metabolomic approach demonstrated strong associations between fatigue and several biochemical patterns related to oxidative stress.

Agency: European Commission | Branch: FP7 | Program: MC-ITN | Phase: FP7-PEOPLE-ITN-2008 | Award Amount: 3.72M | Year: 2009

Aims: provide a cutting edge research training programme encompassing complementary approaches to the investigation of liver and pancreatic development and disease. Provide early stage researchers with a balanced mix of experience and skills in academic and industry based research. Give early stage researchers a set of transferrable skills which will improve their employment and career prospects. Objectives: 1) To provide a broad multi-disciplinary approach to liver and pancreatic development and disease which will ensure a solid foundation in research technology and methods. 2) To offer a number of multi-centre and cross-sector projects. 3) To organize regular meetings which will provide task-specific and complementary training in skills essential for career development. Implementation: 1) involvement of highly successful research leaders and groups (with expertise in different disciplines including systems biology and bioinformatics, developmental biology, genomics, genetics and epigenetics, cell biology, engineering and drug development) in design and running of this programme will ensure the cutting edge research methodology and multidisciplinary approach to training. 2) Each research project will involve minimum two partners. 3) Partners will alternate in organizing network meetings which will include laboratory courses, single-topic conferences and network workshops. 4) The experienced researchers recruited to the network will spend more time in the industrial setting and have more leadership training and responsibilities.

PubMed | Karlsruhe Institute of Technology, AcureOmics AB, French Institute of Health and Medical Research and Umeå University
Type: Journal Article | Journal: PloS one | Year: 2015

Hierarchical modelling was applied in order to identify the organs that contribute to the levels of metabolites in plasma. Plasma and organ samples from gut, kidney, liver, muscle and pancreas were obtained from mice. The samples were analysed using gas chromatography time-of-flight mass spectrometry (GC TOF-MS) at the Swedish Metabolomics centre, Ume University, Sweden. The multivariate analysis was performed by means of principal component analysis (PCA) and orthogonal projections to latent structures (OPLS). The main goal of this study was to investigate how each organ contributes to the metabolic plasma profile. This was performed using hierarchical modelling. Each organ was found to have a unique metabolic profile. The hierarchical modelling showed that the gut, kidney and liver demonstrated the greatest contribution to the metabolic pattern of plasma. For example, we found that metabolites were absorbed in the gut and transported to the plasma. The kidneys excrete branched chain amino acids (BCAAs) and fatty acids are transported in the plasma to the muscles and liver. Lactic acid was also found to be transported from the pancreas to plasma. The results indicated that hierarchical modelling can be utilized to identify the organ contribution of unknown metabolites to the metabolic profile of plasma.

Eliasson M.,Umeå University | Rannar S.,Umeå University | Madsen R.,Umeå University | Donten M.A.,Umeå University | And 6 more authors.
Analytical Chemistry | Year: 2012

A strategy for optimizing LC-MS metabolomics data processing is proposed. We applied this strategy on the XCMS open source package written in R on both human and plant biology data. The strategy is a sequential design of experiments (DoE) based on a dilution series from a pooled sample and a measure of correlation between diluted concentrations and integrated peak areas. The reliability index metric, used to define peak quality, simultaneously favors reliable peaks and disfavors unreliable peaks using a weighted ratio between peaks with high and low response linearity. DoE optimization resulted in the case studies in more than 57% improvement in the reliability index compared to the use of the default settings. The proposed strategy can be applied to any other data processing software involving parameters to be tuned, e.g., MZmine 2. It can also be fully automated and used as a module in a complete metabolomics data processing pipeline. © 2012 American Chemical Society.

Pinto R.C.,Umeå University | Stenlund H.,Umeå University | Hertzberg M.,SweTree Technologies AB | Lundstedt T.,AcureOmics AB | And 2 more authors.
Analytica Chimica Acta | Year: 2011

To find and ascertain phenotypic differences, minimal variation between biological replicates is always desired. Variation between the replicates can originate from genetic transformation but also from environmental effects in the greenhouse. Design of experiments (DoE) has been used in field trials for many years and proven its value but is underused within functional genomics including greenhouse experiments. We propose a strategy to estimate the effect of environmental factors with the ultimate goal of minimizing variation between biological replicates, based on DoE. DoE can be analyzed in many ways. We present a graphical solution together with solutions based on classical statistics as well as the newly developed OPLS methodology. In this study, we used DoE to evaluate the influence of plant specific factors (plant size, shoot type, plant quality, and amount of fertilizer) and rotation of plant positions on height and section area of 135 cloned wild type poplar trees grown in the greenhouse. Statistical analysis revealed that plant position was the main contributor to variability among biological replicates and applying a plant rotation scheme could reduce this variation. © 2010 Elsevier B.V.

Madsen R.,Umeå University | Lundstedt T.,AcureOmics AB | Lundstedt T.,Uppsala University | Trygg J.,Umeå University
Analytica Chimica Acta | Year: 2010

Metabolomics is a post genomic research field concerned with developing methods for analysis of low molecular weight compounds in biological systems, such as cells, organs or organisms. Analyzing metabolic differences between unperturbed and perturbed systems, such as healthy volunteers and patients with a disease, can lead to insights into the underlying pathology. In metabolomics analysis, large amounts of data are routinely produced in order to characterize samples. The use of multivariate data analysis techniques and chemometrics is a commonly used strategy for obtaining reliable results. Metabolomics have been applied in different fields such as disease diagnosis, toxicology, plant science and pharmaceutical and environmental research. In this review we take a closer look at the chemometric methods used and the available results within the field of disease diagnosis. We will first present some current strategies for performing metabolomics studies, especially regarding disease diagnosis. The main focus will be on data analysis strategies and validation of multivariate models, since there are many pitfalls in this regard. Further, we highlight the most interesting metabolomics publications and discuss these in detail; additional studies are mentioned as a reference for the interested reader. A general trend is an increased focus on biological interpretation rather than merely the ability to classify samples. In the conclusions, the general trends and some recommendations for improving metabolomics data analysis are provided. © 2009 Elsevier B.V. All rights reserved.

Madsen R.,Umeå University | Rantapaa-Dahlqvist S.,Umeå University | Lundstedt T.,AcureOmics AB | Moritz T.,Swedish University of Agricultural Sciences | Trygg J.,Umeå University
Journal of Proteome Research | Year: 2012

Assessment of disease activity in patients with rheumatoid arthritis (RA) is of importance in the evaluation of treatment. The most important measure of disease activity is the Disease Activity Score counted in 28 joints (DAS28). In this study, we evaluated whether metabolic profiling could complement current measures of disease activity. Fifty-six patients, in two separate studies, were followed for two years after commencing anti-TNF therapy. DAS28 was assessed, and metabolic profiles were recorded at defined time points. Correlations between metabolic profile and DAS28 scores were analyzed using multivariate statistics. The metabolic responses to lowering DAS28 scores varied in different patients but could predict DAS28 scores at the individual and subgroup level models. The erythrocyte sedimentation rate (ESR) component in DAS28 was most correlated to the metabolite data, pointing to inflammation as the primary effect driving metabolic profile changes. Patients with RA had differing metabolic response to changes in DAS28 following anti-TNF therapy. This suggests that discovery of new metabolic biomarkers for disease activity will derive from studies at the individual and subgroup level. Increased inflammation, measured as ESR, was the main common effect seen in metabolic profiles from periods associated with high DAS28. © 2012 American Chemical Society.

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