Untethered muscle tracking using magnetomicrometry

Muscle tissue drives nearly all movement in the animal kingdom, providing power, mobility, and dexterity. Technologies for measuring muscle tissue motion, such as sonomicrometry, fluoromicrometry, and ultrasound, have significantly advanced our understanding of biomechanics. Yet, the field lacks the ability to monitor muscle tissue motion for animal behavior outside the lab. Towards addressing this issue, we previously introduced magnetomicrometry, a method that uses magnetic beads to wirelessly monitor muscle tissue length changes, and we validated magnetomicrometry via tightly-controlled in situ testing. In this study we validate the accuracy of magnetomicrometry against fluoromicrometry during untethered running in an in vivo turkey model. We demonstrate real-time muscle tissue length tracking of the freely-moving turkeys executing various motor activities, including ramp ascent and descent, vertical ascent and descent, and free roaming movement. Given the demonstrated capacity of magnetomicrometry to track muscle movement in untethered animals, we feel that this technique will enable new scientific explorations and an improved understanding of muscle function.</jats:p

Genomic correlates of glatiramer acetate adverse cardiovascular effects lead to a novel locus mediating coronary risk

Glatiramer acetate is used therapeutically in multiple sclerosis but also known for adverse effects including elevated coronary artery disease (CAD) risk. The mechanisms underlying the cardiovascular side effects of the medication are unclear. Here, we made use of the chromosomal variation in the genes that are known to be affected by glatiramer treatment. Focusing on genes and gene products reported by drug-gene interaction database to interact with glatiramer acetate we explored a large meta-analysis on CAD genome-wide association studies aiming firstly, to investigate whether variants in these genes also affect cardiovascular risk and secondly, to identify new CAD risk genes. We traced association signals in a 200-kb region around genomic positions of genes interacting with glatiramer in up to 60 801 CAD cases and 123 504 controls. We validated the identified association in additional 21 934 CAD cases and 76 087 controls. We identified three new CAD risk alleles within the TGFB1 region on chromosome 19 that independently affect CAD risk. The lead SNP rs12459996 was genome-wide significantly associated with CAD in the extended meta-analysis (odds ratio 1.09, p = 1.58×10-12). The other two SNPs at the locus were not in linkage disequilibrium with the lead SNP and by a conditional analysis showed p-values of 4.05 × 10-10 and 2.21 × 10-6. Thus, studying genes reported to interact with glatiramer acetate we identified genetic variants that concordantly with the drug increase the risk of CAD. Of these, TGFB1 displayed signal for association. Indeed, the gene has been associated with CAD previously in both in vivo and in vitro studies. Here we establish genome-wide significant association with CAD in large human samples.This work was supported by grants from the Fondation Leducq (CADgenomics: Understanding CAD Genes, 12CVD02), the German Federal Ministry of Education and Research (BMBF) within the framework of the e:Med research and funding concept (e:AtheroSysMed, grant 01ZX1313A-2014 and SysInflame, grant 01ZX1306A), and the European Union Seventh Framework Programme FP7/2007-2013 under grant agreement no HEALTH-F2-2013-601456 (CVgenes-at-target). Further grants were received from the DFG as part of the Sonderforschungsbereich CRC 1123 (B2). T.K. was supported by a DZHK Rotation Grant. I.B. was supported by the Deutsche Forschungsgemeinschaft (DFG) cluster of excellence ‘Inflammation at Interfaces’. F.W.A. is supported by a Dekker scholarship-Junior Staff Member 2014T001 - Netherlands Heart Foundation and UCL Hospitals NIHR Biomedical Research Centre

Implicating genes, pleiotropy, and sexual dimorphism at blood lipid loci through multi-ancestry meta-analysis

Funding Information: GMP, PN, and CW are supported by NHLBI R01HL127564. GMP and PN are supported by R01HL142711. AG acknowledge support from the Wellcome Trust (201543/B/16/Z), European Union Seventh Framework Programme FP7/2007–2013 under grant agreement no. HEALTH-F2-2013–601456 (CVGenes@Target) & the TriPartite Immunometabolism Consortium [TrIC]-Novo Nordisk Foundation’s Grant number NNF15CC0018486. JMM is supported by American Diabetes Association Innovative and Clinical Translational Award 1–19-ICTS-068. SR was supported by the Academy of Finland Center of Excellence in Complex Disease Genetics (Grant No 312062), the Finnish Foundation for Cardiovascular Research, the Sigrid Juselius Foundation, and University of Helsinki HiLIFE Fellow and Grand Challenge grants. EW was supported by the Finnish innovation fund Sitra (EW) and Finska Läkaresällskapet. CNS was supported by American Heart Association Postdoctoral Fellowships 15POST24470131 and 17POST33650016. Charles N Rotimi is supported by Z01HG200362. Zhe Wang, Michael H Preuss, and Ruth JF Loos are supported by R01HL142302. NJT is a Wellcome Trust Investigator (202802/Z/16/Z), is the PI of the Avon Longitudinal Study of Parents and Children (MRC & WT 217065/Z/19/Z), is supported by the University of Bristol NIHR Biomedical Research Centre (BRC-1215–2001) and the MRC Integrative Epidemiology Unit (MC_UU_00011), and works within the CRUK Integrative Cancer Epidemiology Programme (C18281/A19169). Ruth E Mitchell is a member of the MRC Integrative Epidemiology Unit at the University of Bristol funded by the MRC (MC_UU_00011/1). Simon Haworth is supported by the UK National Institute for Health Research Academic Clinical Fellowship. Paul S. de Vries was supported by American Heart Association grant number 18CDA34110116. Julia Ramierz acknowledges support by the People Programme of the European Union’s Seventh Framework Programme grant n° 608765 and Marie Sklodowska-Curie grant n° 786833. Maria Sabater-Lleal is supported by a Miguel Servet contract from the ISCIII Spanish Health Institute (CP17/00142) and co-financed by the European Social Fund. Jian Yang is funded by the Westlake Education Foundation. Olga Giannakopoulou has received funding from the British Heart Foundation (BHF) (FS/14/66/3129). CHARGE Consortium cohorts were supported by R01HL105756. Study-specific acknowledgements are available in the Additional file : Supplementary Note. The views expressed in this manuscript are those of the authors and do not necessarily represent the views of the National Heart, Lung, and Blood Institute; the National Institutes of Health; or the U.S. Department of Health and Human Services. Publisher Copyright: © 2022, The Author(s).Background: Genetic variants within nearly 1000 loci are known to contribute to modulation of blood lipid levels. However, the biological pathways underlying these associations are frequently unknown, limiting understanding of these findings and hindering downstream translational efforts such as drug target discovery. Results: To expand our understanding of the underlying biological pathways and mechanisms controlling blood lipid levels, we leverage a large multi-ancestry meta-analysis (N = 1,654,960) of blood lipids to prioritize putative causal genes for 2286 lipid associations using six gene prediction approaches. Using phenome-wide association (PheWAS) scans, we identify relationships of genetically predicted lipid levels to other diseases and conditions. We confirm known pleiotropic associations with cardiovascular phenotypes and determine novel associations, notably with cholelithiasis risk. We perform sex-stratified GWAS meta-analysis of lipid levels and show that 3–5% of autosomal lipid-associated loci demonstrate sex-biased effects. Finally, we report 21 novel lipid loci identified on the X chromosome. Many of the sex-biased autosomal and X chromosome lipid loci show pleiotropic associations with sex hormones, emphasizing the role of hormone regulation in lipid metabolism. Conclusions: Taken together, our findings provide insights into the biological mechanisms through which associated variants lead to altered lipid levels and potentially cardiovascular disease risk.Peer reviewe

Measuring the Impact of Burn Injury on the Parent-Reported Health Outcomes of Children 1 to 5 Years: A Conceptual Framework for Development of the Preschool Life Impact Burn Recovery Evaluation Profile CAT.

Due to the rapid developmental growth in preschool-aged children, more precise measurement of the effects of burns on child health outcomes is needed. Expanding upon the Shriners Hospitals for Children/American Burn Association Burn Outcome Questionnaire 0 to 5 (BOQ0-5), we developed a conceptual framework describing domains important in assessing recovery from burn injury among preschool-aged children (1-5 years). We developed a working conceptual framework based on the BOQ0-5, the National Research Council and Institute of Medicine's Model of Child Health, and the World Health Organization's International Classification of Functioning, Disability, and Health for Children and Youth. We iteratively refined our framework based on a literature review, focus groups, interviews, and expert consensus meetings. Data were qualitatively analyzed using methods informed by grounded theory. We reviewed 95 pediatric assessments, conducted two clinician focus groups and six parent interviews, and consulted with 23 clinician experts. Three child health outcome domains emerged from our analysis: symptoms, functioning, and family. The symptoms domain describes parents' perceptions of their child's pain, skin-related discomfort, and fatigue. The functioning domain describes children's physical functioning (gross and fine motor function), psychological functioning (internalizing, externalizing, and dysregulation behavior; trauma; toileting; resilience), communication and language development (receiving and producing meaning), and social functioning (connecting with family/peers, friendships, and play). The family domain describes family psychological and routine functioning outcomes

Emergent achievement segregation in freshmenlearning community networks

A common assumption about Freshmen Learning Communities (FLCs) is that academic relationships contribute to students’ success. This study investigates how students inlearning communities connect with fellow students for friendship and academic support. Longitudinal social network data across the first year, collected from 95 Dutch students in eight FLCs, measure both social and academic relational choices within and beyond the FLCs. Using stochastic actor-based models, the study tests two competing hypotheses. The alignment hypothesis states that students connect with their similar-achieving friends for both academic and social support, leading to an alignment of both types of networks over time. In contrast, the duality hypothesis states dissimilarity between academic support networks and friendship networks: students should connect with better-achieving fellow students for academic support and to more similar peers for friendship. The data support the alignment hypothesis but not the duality hypothesis; in addition, they show evidence of achievement segregation in FLCs: the higher the students’ achievement level, the more they connect with other students for both academic support and friendship, relating in particular to peers with a similarly high achievement level. The results suggest that lower-achieving students are excluded from the support provided by higher achieving students and instead ask similar lower achievers for support. They thus cannot benefit optimally from the academic integration FLC offer. The article concludes with recommendations of how to support students in an FLC so that they can reach optimal achievement potential