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  • 1. Bengtsson, Anders A.
    et al.
    Trygg, Johan
    Umeå University, Faculty of Science and Technology, Department of Chemistry.
    Wuttge, Dirk M.
    Sturfelt, Gunnar
    Theander, Elke
    Donten, Magdalena
    Moritz, Thomas
    Sennbro, Carl-Johan
    Torell, Frida
    Umeå University, Faculty of Science and Technology, Department of Chemistry.
    Lood, Christian
    Surowiec, Izabella
    Umeå University, Faculty of Science and Technology, Department of Chemistry.
    Rännar, Stefan
    Lundstedt, Torbjörn
    Metabolic Profiling of Systemic Lupus Erythematosus and Comparison with Primary Sjögren’s Syndrome and Systemic Sclerosis2016In: PLOS ONE, E-ISSN 1932-6203, Vol. 11, no 7, article id e0159384Article in journal (Refereed)
    Abstract [en]

    Systemic lupus erythematosus (SLE) is a chronic inflammatory autoimmune disease which can affect most organ systems including skin, joints and the kidney. Clinically, SLE is a heterogeneous disease and shares features of several other rheumatic diseases, in particular primary Sjögrens syndrome (pSS) and systemic sclerosis (SSc), why it is difficult to diag- nose The pathogenesis of SLE is not completely understood, partly due to the heterogeneity of the disease. This study demonstrates that metabolomics can be used as a tool for improved diagnosis of SLE compared to other similar autoimmune diseases. We observed differences in metabolic profiles with a classification specificity above 67% in the comparison of SLE with pSS, SSc and a matched group of healthy individuals. Selected metabolites were also significantly different between studied diseases. Biochemical pathway analysis was conducted to gain understanding of underlying pathways involved in the SLE pathogenesis. We found an increased oxidative activity in SLE, supported by increased xanthine oxidase activity and an increased turnover in the urea cycle. The most discriminatory metabolite observed was tryptophan, with decreased levels in SLE patients compared to control groups. Changes of tryptophan levels were related to changes in the activity of the aromatic amino acid decarboxylase (AADC) and/or to activation of the kynurenine pathway. 

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  • 2. Dhillon, Sundeep S.
    et al.
    Torell, Frida
    Umeå University, Faculty of Science and Technology, Department of Chemistry. Accelerator Lab (ACL), Karlsruhe Institute of Technology, Karlsruhe, Germany; AcureOmics, Umeå, Sweden.
    Donten, Magdalena
    Lundstedt-Enkel, Katrin
    Bennett, Kate
    Raennar, Stefan
    Trygg, Johan
    Umeå University, Faculty of Science and Technology, Department of Chemistry. AcureOmics, Umeå, Sweden.
    Lundstedt, Torbjorn
    Metabolic profiling of zebrafish embryo development from blastula period to early larval stages2019In: PLOS ONE, E-ISSN 1932-6203, Vol. 14, no 5, article id e0213661Article in journal (Refereed)
    Abstract [en]

    The zebrafish embryo is a popular model for drug screening, disease modelling and molecular genetics. In this study, samples were obtained from zebrafish at different developmental stages. The stages that were chosen were 3/4, 4/5, 24, 48, 72 and 96 hours post fertilization (hpf). Each sample included fifty embryos. The samples were analysed using gas chromatography time-of-flight mass spectrometry (GC-TOF-MS). Principle component analysis (PCA) was applied to get an overview of the data and orthogonal projection to latent structure discriminant analysis (OPLS-DA) was utilised to discriminate between the developmental stages. In this way, changes in metabolite profiles during vertebrate development could be identified. Using a GC-TOF-MS metabolomics approach it was found that nucleotides and metabolic fuel (glucose) were elevated at early stages of embryogenesis, whereas at later stages amino acids and intermediates in the Krebs cycle were abundant. This agrees with zebrafish developmental biology, as organs such as the liver and pancreas develop at later stages. Thus, metabolomics of zebrafish embryos offers a unique opportunity to investigate large scale changes in metabolic processes during important developmental stages in vertebrate development. In terms of stability of the metabolic profile and viability of the embryos, it was concluded at 72 hpf was a suitable time point for the use of zebrafish as a model system in numerous scientific applications.

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  • 3.
    Surowiec, Izabella
    et al.
    Umeå University, Faculty of Science and Technology, Department of Chemistry.
    Johansson, Erik
    Sartorius Stedim Data Analytics AB, Umeå, Sweden.
    Torell, Frida
    Umeå University, Faculty of Science and Technology, Department of Chemistry.
    Idborg, Helena
    Rheumatology Unit, Department of Medicine, Solna, Karolinska Institutet, Karolinska University Hospital, Stockholm, Sweden.
    Gunnarsson, Iva
    Rheumatology Unit, Department of Medicine, Solna, Karolinska Institutet, Karolinska University Hospital, Stockholm, Sweden.
    Svenungsson, Elisabet
    Rheumatology Unit, Department of Medicine, Solna, Karolinska Institutet, Karolinska University Hospital, Stockholm, Sweden.
    Jakobsson, Per-Johan
    Rheumatology Unit, Department of Medicine, Solna, Karolinska Institutet, Karolinska University Hospital, Stockholm, Sweden.
    Trygg, Johan
    Umeå University, Faculty of Science and Technology, Department of Chemistry. Sartorius Stedim Data Analytics AB, Umeå, Sweden.
    Multivariate strategy for the sample selection and integration of multi-batch data in metabolomics2017In: Metabolomics, ISSN 1573-3882, E-ISSN 1573-3890, Vol. 13, no 10, article id 114Article in journal (Refereed)
    Abstract [en]

    Introduction Availability of large cohorts of samples with related metadata provides scientists with extensive material for studies. At the same time, recent development of modern high-throughput 'omics' technologies, including metabolomics, has resulted in the potential for analysis of large sample sizes. Representative subset selection becomes critical for selection of samples from bigger cohorts and their division into analytical batches. This especially holds true when relative quantification of compound levels is used.

    Objectives We present a multivariate strategy for representative sample selection and integration of results from multi-batch experiments in metabolomics.

    Methods Multivariate characterization was applied for design of experiment based sample selection and subsequent subdivision into four analytical batches which were analyzed on different days by metabolomics profiling using gas-chromatography time-of-flight mass spectrometry (GC-TOFMS). For each batch OPLS-DA (R) was used and its p(corr) vectors were averaged to obtain combined metabolic profile. Jackknifed standard errors were used to calculate confidence intervals for each metabolite in the average p(corr) profile.

    Results A combined, representative metabolic profile describing differences between systemic lupus erythematosus (SLE) patients and controls was obtained and used for elucidation of metabolic pathways that could be disturbed in SLE.

    Conclusion Design of experiment based representative sample selection ensured diversity and minimized bias that could be introduced at this step. Combined metabolic profile enabled unified analysis and interpretation.

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  • 4.
    Torell, Frida
    Umeå University, Faculty of Medicine, Department of Integrative Medical Biology (IMB), Physiology.
    Evaluation of stretch reflex synergies in the upper limb using principal component analysis (PCA)2023In: PLOS ONE, E-ISSN 1932-6203, Vol. 18, no 10, article id e0292807Article in journal (Refereed)
    Abstract [en]

    The dynamic nature of movement and muscle activation emphasizes the importance of a sound experimental design. To ensure that an experiment determines what we intend, the design must be carefully evaluated. Before analyzing data, it is imperative to limit the number of outliers, biases, and skewness. In the present study, a simple center-out experiment was performed by 16 healthy volunteers. The experiment included three load conditions, two preparatory delays, two perturbations, and four targets placed along a diagonal path on a 2D plane. While the participants performed the tasks, the activity of seven arm muscles were monitored using surface electromyography (EMG). Principal component analysis (PCA) was used to evaluate the study design, identify muscle synergies, and assess the effects of individual quirks. With PCA, we can identify the trials that trigger stretch reflexes and pinpoint muscle synergies. The posterior deltoid, triceps long head, and brachioradialis were engaged when targets were in the direction of muscle shortening and the perturbation was applied in the opposite direction. Similarly, the pectoralis and anterior deltoid were engaged when the targets were in the direction of muscle shortening and the perturbation was applied in the opposite direction. The stretch reflexes were not triggered when the perturbation brought the hand in the direction of, or into the target, except if the muscle was preloaded. The use of PCA was also proven valuable when evaluating participant performance. While individual quirks are to be expected, failure to perform trials as expected can adversely affect the study results.

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  • 5.
    Torell, Frida
    Umeå University, Faculty of Science and Technology, Department of Chemistry.
    Multivariate data analysis of metabolomic multi-tissue samples2020Doctoral thesis, comprehensive summary (Other academic)
    Abstract [en]

    Multi-tissue metabolomics involves characterisation of the metabolome of several tissue types. The metabolome consists of small chemical entities of low molecular weight called metabolites, which are constantly produced and interchanged through a vast variety of biochemical reactions occurring throughout living organisms. Metabolome alterations can be attributed to genetics, environment, and diseases. We used gas chromatography timeof-flight mass spectrometry (GC TOF-MS) to characterise the metabolome of mouse organ samples: gut, kidney, liver, muscle, pancreas and plasma. Samples were obtained from wild-type mice and mice carrying a mutation in the hepatocyte nuclear factor 1b (HNF1b) gene, referred to as MODY5/RCAD (for maturity onset diabetes of the young 5/renal cysts and diabetes syndrome) mice. MODY is a class of hereditary diabetes mellitus, and MODY5 is caused by mutations in HNF1B, resulting in a wide range of manifestations, including renal diseases, kidney and genitourinary malformation, and elevation of liver enzymes. Today, MODY5 in humans is diagnosed using genetic tests, and varying referral rates and manifestations have resulted in misdiagnosis. Our main focus was therefore to increase understanding of the metabolism associated with MODY5/RCAD by studying the metabolic profiles of individual organs and plasma (Paper I) from MODY5/RCAD mutant and wildtype mice. The mouse model displayed an overall metabolic pattern consistent with the presumed outcome of the mutation in humans, making the MODY5/RCAD model suitable for studies of HNF1B-associated diseases. An understanding of metabolite origin would be beneficial for understanding the plasma profile associated with MODY5/RCAD. We used hierarchical modelling to provide an understanding of metabolite origin by detecting how metabolites from the organs contributed to the plasma metabolic profile (Paper II). Both specific and overall organ metabolite contributions to the plasma metabolic profile were studied. Further exploration of the dataset involved study of its innate variation using joint and unique multiblock analysis (JUMBA; Paper III). In addition, we explored the effects of improper sample handling for metabolomic multi-tissue data, and we studied the similarities and differences in the responses to thawing between organ tissues (Paper IV) and plasma samples (Paper V), thus identifying metabolic profiles that could indicate compromised samples. These profiles could be beneficial for large-scale collaborations that involve sample exposure to unsuitable conditions. Altogether, we have contributed to an increased understanding of the MODY5/RCAD multi-tissue metabolomic dataset and worked up protocols and strategies for how small datasets should be handled.

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  • 6.
    Torell, Frida
    et al.
    Umeå University, Faculty of Science and Technology, Department of Chemistry. Accelerator Lab (ACL), Karlsruhe Institute of Technology, Karlsruhe 76344, Germany.
    Bennet, Kate
    Cereghini, Silvia
    Fabre, Mélanie
    Rännar, Stefan
    Lundstedt-Enkel, Katrin
    Moritz, Thomas
    Haumaitre, Cécile
    Trygg, Johan
    Umeå University, Faculty of Science and Technology, Department of Chemistry.
    Lundstedt, Torbjörn
    Metabolic Profiling of Multiorgan Samples: Evaluation of MODY5/RCAD Mutant Mice2018In: Journal of Proteome Research, ISSN 1535-3893, E-ISSN 1535-3907, Vol. 17, no 7, p. 2293-2306Article in journal (Refereed)
    Abstract [en]

    In the present study, we performed a metabolomics analysis to evaluate a MODY5/RCAD mouse mutant line as a potential model for HNF1B-associated diseases. Gas chromatography time-of-flight mass spectrometry (GC-TOF-MS) of gut, kidney, liver, muscle, pancreas, and plasma samples uncovered the tissue specific metabolite distribution. Orthogonal projections to latent structures discriminant analysis (OPLS-DA) was used to identify the differences between MODY5/RCAD and wild-type mice in each of the tissues. The differences included, for example, increased levels of amino acids in the kidneys and reduced levels of fatty acids in the muscles of the MODY5/RCAD mice. Interestingly, campesterol was found in higher concentrations in the MODY5/RCAD mice, with a four-fold and three-fold increase in kidneys and pancreas, respectively. As expected, the MODY5/RCAD mice displayed signs of impaired renal function in addition to disturbed liver lipid metabolism, with increased lipid and fatty acid accumulation in the liver. From a metabolomics perspective, the MODY5/RCAD model was proven to display a metabolic pattern similar to what would be suspected in HNF1B-associated diseases. These findings were in line with the presumed outcome of the mutation based on the different anatomy and function of the tissues as well as the effect of the mutation on development.

  • 7.
    Torell, Frida
    et al.
    Umeå University, Faculty of Science and Technology, Department of Chemistry.
    Bennett, Kate
    Cereghini, Silvia
    Raennar, Stefan
    Lundstedt-Enkel, Katrin
    Moritz, Thomas
    Haumaitre, Cecile
    Trygg, Johan
    Umeå University, Faculty of Science and Technology, Department of Chemistry.
    Lundstedt, Torbjoern
    Multi-Organ Contribution to the Metabolic Plasma Profile Using Hierarchical Modelling2015In: PLOS ONE, E-ISSN 1932-6203, Vol. 10, no 6, article id e0129260Article in journal (Refereed)
    Abstract [en]

    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, Umea 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.

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  • 8.
    Torell, Frida
    et al.
    Umeå University, Faculty of Science and Technology, Department of Chemistry.
    Bennett, Kate
    AcureOmics AB, Umeå, Sweden.
    Cereghini, Silvia
    Paris, France.
    Rännar, Stefan
    AcureOmics AB, Umeå, Sweden.
    Lundstedt-Enkel, Katrin
    AcureOmics AB, Umeå, Sweden; Uppsala, Sweden.
    Moritz, Thomas
    AcureOmics AB, Umeå, Sweden.
    Haumaitre, Cecile
    Paris, France.
    Trygg, Johan
    Umeå University, Faculty of Science and Technology, Department of Chemistry.
    Lundstedt, Torbjörn
    AcureOmics AB, Umeå, Sweden.
    Tissue sample stability: thawing effect on multi-organ samples2016In: Metabolomics, ISSN 1573-3882, E-ISSN 1573-3890, Vol. 12, no 2, article id 19Article in journal (Refereed)
    Abstract [en]

    Correct handling of samples is essential in metabolomic studies. Improper handling and prolonged storage of samples has unwanted effects on the metabolite levels. The aim of this study was to identify the effects that thawing has on different organ samples. Organ samples from gut, kidney, liver, muscle and pancreas were analyzed for a number of endogenous metabolites in an untargeted metabolomics approach, using gas chromatography time of flight mass spectrometry at the Swedish Metabolomics Centre, Umeå University, Sweden. Multivariate data analysis was performed by means of principal component analysis and orthogonal projection to latent structures discriminant analysis. The results showed that the metabolic changes caused by thawing were almost identical for all organs. As expected, there was a marked increase in overall metabolite levels after thawing, caused by increased protein and cell degradation. Cholesterol was one of the eight metabolites found to be decreased in the thawed samples in all organ groups. The results also indicated that the muscles are less susceptible to oxidation compared to the rest of the organ samples.

  • 9.
    Torell, Frida
    et al.
    Umeå University, Faculty of Science and Technology, Department of Chemistry. Karlsruhe Institute of Technology, Karlsruhe, Germany.
    Bennett, Kate
    Rännar, Stefan
    Lundstedt-Enkel, Katrin
    Lundstedt, Torbjörn
    Trygg, Johan
    Umeå University, Faculty of Science and Technology, Department of Chemistry.
    The effects of thawing on the plasma metabolome: evaluating differences between thawed plasma and multi-organ samples2017In: Metabolomics, ISSN 1573-3882, E-ISSN 1573-3890, Vol. 13, no 6, article id 66Article in journal (Refereed)
    Abstract [en]

    Introduction: Post-collection handling, storage and transportation can affect the quality of blood samples. Pre-analytical biases can easily be introduced and can jeopardize accurate profiling of the plasma metabolome. Consequently, a mouse study must be carefully planned in order to avoid any kind of bias that can be introduced, in order not to compromise the outcome of the study. The storage and shipment of the samples should be made in such a way that the freeze–thaw cycles are kept to a minimum. In order to keep the latent effects on the stability of the blood metabolome to a minimum it is essential to study the effect that the post-collection and pre-analytical error have on the metabolome. Objectives: The aim of this study was to investigate the effects of thawing on the metabolic profiles of different sample types. Methods: In the present study, a metabolomics approach was utilized to obtain a thawing profile of plasma samples obtained on three different days of experiment. The plasma samples were collected from the tail on day 1 and 3, while retro-orbital sampling was used on day 5. The samples were analysed using gas chromatography time-of-flight mass spectrometry (GC TOF-MS). Results: The thawed plasma samples were found to be characterized by higher levels of amino acids, fatty acids, glycerol metabolites and purine and pyrimidine metabolites as a result of protein degradation, cell degradation and increased phospholipase activity. The consensus profile was thereafter compared to the previously published study comparing thawing profiles of tissue samples from gut, kidney, liver, muscle and pancreas. Conclusions: The comparison between thawed organ samples and thawed plasma samples indicate that the organ samples are more sensitive to thawing, however thawing still affected all investigated sample types.

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  • 10.
    Torell, Frida
    et al.
    Umeå University, Faculty of Science and Technology, Department of Chemistry.
    Eketjäll, Susanna
    Idborg, Helena
    Jakobsson, Per-Johan
    Gunnarsson, Iva
    Svenungsson, Elisabet
    Trygg, Johan
    Umeå University, Faculty of Science and Technology, Department of Chemistry. Corporate Research, Sartorius AG, Göttingen, Germany.
    Cytokine Profiles in Autoantibody Defined Subgroups of Systemic Lupus Erythematosus2019In: Journal of Proteome Research, ISSN 1535-3893, E-ISSN 1535-3907, Vol. 18, no 3, p. 1208-1217Article in journal (Refereed)
    Abstract [en]

    The aim of this study was to evaluate how the cytokine profiles differed between autoantibody based subgroups of systemic lupus erythematosus (SLE). SLE is a systemic autoimmune disease, characterized by periods of flares (active disease) and remission (inactive disease). The disease can affect many organ systems, e.g., skin, joints, kidneys, heart, and the central nervous system (CNS). SLE patients often have an overproduction of cytokines, e.g., interferons, chemokines, and interleukins. The high cytokine levels are part of the systemic inflammation, which can lead to tissue injury. In the present study, SLE patients were divided into five groups based on their autoantibody profiles. We thus defined these five groups: ANA negative, antiphospholipid (aPL) positive, anti-Sm/anti-RNP positive, Sjögren’s syndrome (SS) antigen A and B positive, and patients positive for more than one type of autoantibodies (other SLE). Cytokines were measured using Mesoscale Discovery (MSD) multiplex analysis. On the basis of the cytokine data, ANA negative patients were the most deviating subgroup, with lower levels of interferon (IFN)-γ, tumor necrosis factor (TNF)-α, interleukin (IL)-12/IL-23p40, and interferon gamma-induced protein (IP)-10. Despite low cytokine levels in the ANA negative group, autoantibody profiles did not discriminate between different cytokine patterns.

  • 11.
    Torell, Frida
    et al.
    Umeå University, Faculty of Medicine, Department of Integrative Medical Biology (IMB), Physiology.
    Franklin, Sae
    Neuromuscular Diagnostics, Department of Sport and Health Sciences, Technical University of Munich, Munich, Germany.
    Franklin, David W.
    Neuromuscular Diagnostics, Department of Sport and Health Sciences, Technical University of Munich, Munich, Germany; Munich Institute of Robotics and Machine Intelligence (MIRMI), Technical University of Munich, Munich, Germany; Munich Data Science Institute (MDSI), Technical University of Munich, Munich, Germany.
    Dimitriou, Michael
    Umeå University, Faculty of Medicine, Department of Integrative Medical Biology (IMB), Physiology.
    Assistive loading promotes goal-directed tuning of stretch reflex gains2023In: eNeuro, E-ISSN 2373-2822, Vol. 10, no 2, article id ENEURO.0438-22.2023Article in journal (Refereed)
    Abstract [en]

    Voluntary movements are prepared before they are executed. Preparatory activity has been observed across the CNS and recently documented in first-order neurons of the human PNS (i.e., in muscle spindles). Changes seen in sensory organs suggest that independent modulation of stretch reflex gains may represent an impor-tant component of movement preparation. The aim of the current study was to further investigate the preparatory modulation of short-latency stretch reflex responses (SLRs) and long-latency stretch reflex responses (LLRs) of the dominant upper limb of human subjects. Specifically, we investigated how different target pa-rameters (target distance and direction) affect the preparatory tuning of stretch reflex gains in the context of goal-directed reaching, and whether any such tuning depends on preparation duration and the direction of background loads. We found that target distance produced only small variations in reflex gains. In contrast, both SLR and LLR gains were strongly modulated as a function of target direction, in a manner that facili-tated the upcoming voluntary movement. This goal-directed tuning of SLR and LLR gains was present or enhanced when the preparatory delay was sufficiently long (.250 ms) and the homonymous muscle was unloaded [i.e., when a background load was first applied in the direction of homonymous muscle action (as-sistive loading)]. The results extend further support for a relatively slow-evolving process in reach preparation that functions to modulate reflexive muscle stiffness, likely via the independent control of fusimotor neurons. Such control can augment voluntary goal-directed movement and is triggered or enhanced when the homonymous muscle is unloaded.

  • 12.
    Torell, Frida
    et al.
    Umeå University, Faculty of Medicine, Department of Integrative Medical Biology (IMB), Physiology.
    Franklin, Sae
    Neuromuscular Diagnostics, Department of Sport and Health Sciences, Technical University of Munich, Munich, Germany.
    Franklin, David W.
    Neuromuscular Diagnostics, Department of Sport and Health Sciences, Technical University of Munich, Munich, Germany; Munich Institute of Robotics and Machine Intelligence (MIRMI), Technical University of Munich, Munich, Germany; Munich Data Science Institute (MDSI), Technical University of Munich, Munich, Germany.
    Dimitriou, Michael
    Umeå University, Faculty of Medicine, Department of Integrative Medical Biology (IMB), Physiology.
    Goal-directed modulation of stretch reflex gains is reduced in the non-dominant upper limb2023In: European Journal of Neuroscience, ISSN 0953-816X, E-ISSN 1460-9568, Vol. 58, no 9, p. 3981-4001Article in journal (Refereed)
    Abstract [en]

    Most individuals experience their dominant arm as being more dexterous than the non-dominant arm, but the neural mechanisms underlying this asymmetry in motor behaviour are unclear. Using a delayed-reach task, we have recently demonstrated strong goal-directed tuning of stretch reflex gains in the dominant upper limb of human participants. Here, we used an equivalent experimental paradigm to address the neural mechanisms that underlie the preparation for reaching movements with the non-dominant upper limb. There were consistent effects of load, preparatory delay duration and target direction on the long latency stretch reflex. However, by comparing stretch reflex responses in the non-dominant arm with those previously documented in the dominant arm, we demonstrate that goal-directed tuning of short and long latency stretch reflexes is markedly weaker in the non-dominant limb. The results indicate that the motor performance asymmetries across the two upper limbs are partly due to the more sophisticated control of reflexive stiffness in the dominant limb, likely facilitated by the superior goal-directed control of muscle spindle receptors. Our findings therefore suggest that fusimotor control may play a role in determining performance of complex motor behaviours and support existing proposals that the dominant arm is better supplied than the non-dominant arm for executing more complex tasks, such as trajectory control.

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  • 13.
    Torell, Frida
    et al.
    Umeå University, Faculty of Science and Technology, Department of Chemistry.
    Skotare, Tomas
    Umeå University, Faculty of Science and Technology, Department of Chemistry.
    Trygg, Johan
    Umeå University, Faculty of Science and Technology, Department of Chemistry. Corporate Research, Sartorius, Umeå, Sweden.
    Application of multiblock analysis on a small metabolomic multi-tissue dataset2020In: Metabolites, ISSN 2218-1989, E-ISSN 2218-1989, Vol. 10, no 7, article id 295Article in journal (Refereed)
    Abstract [en]

    Data integration has been proven to provide valuable information. The information extracted using data integration in the form of multiblock analysis can pinpoint both common and unique trends in the different blocks. When working with small multiblock datasets the number of possible integration methods is drastically reduced. To investigate the application of multiblock analysis in cases where one has a few number of samples and a lack of statistical power, we studied a small metabolomic multiblock dataset containing six blocks (i.e., tissue types), only including common metabolites. We used a single model multiblock analysis method called the joint and unique multiblock analysis (JUMBA) and compared it to a commonly used method, concatenated principal component analysis (PCA). These methods were used to detect trends in the dataset and identify underlying factors responsible for metabolic variations. Using JUMBA, we were able to interpret the extracted components and link them to relevant biological properties. JUMBA shows how the observations are related to one another, the stability of these relationships, and to what extent each of the blocks contribute to the components. These results indicate that multiblock methods can be useful even with a small number of samples

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  • 14.
    Torell, Frida
    et al.
    Umeå University, Faculty of Science and Technology, Department of Chemistry.
    Skotare, Tomas
    Umeå University, Faculty of Science and Technology, Department of Chemistry.
    Trygg, Johan
    Umeå University, Faculty of Science and Technology, Department of Chemistry.
    Multi-Tissue Metabolomics Integration Utilising Hierarchical Modelling and Data Integration MethodsManuscript (preprint) (Other academic)
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