Biology subjects

Nielsen, J. B.

Publications and source records attributed to Nielsen, J. B..

17 recordsLinked to original sources

bioRxiv2026

Spinal cord population activity lacks rotational dynamics during an alternating isometric task in macaque

Recent studies report rotational population dynamics in spinal cord neuronal activity during rhythmic movements, suggesting computational principles shared with motor cortex. Here we show that primate cervical spinal cord activity does not exhibit rotational dynamics during an alternating single-joint isometric wrist task, instead it displays low-dimensional alternating population patterns. Positive controls confirm presence of rotational structure in motor cortex activity during the same task, indicating distinct computational strategies across the motor axis. Cortical neurons with post-spike effects on motoneurons had activity with dynamics resembling cortical rather than spinal populations.

neuroscience↗

bioRxiv2025

Wearable MEG data recorded during human stepping

Non-invasive spatiotemporal imaging of brain activity during large-scale, whole body movement is a significant methodological challenge for the field of movement neuroscience. Here, we present a dataset recorded using a new imaging modality - optically-pumped magnetoencephalography (OP-MEG) - to record brain activity during human stepping. Participants (n=3) performed a visually guided stepping task requiring precise foot placement while dual-axis and triaxial OP-MEG and leg muscle activity (electromyography, EMG) were recorded. The dataset also includes a structural MRI for each participant and foot kinematics. We validate the fidelity of the OPM data by showing movement-related beta band desynchronization source localised to the primary motor cortex. This multimodal dataset offers a resource for methodological development and testing for OPM data (e.g., movement-related interference rejection), within-subject analyses, and exploratory analyses to generate hypotheses for further work on the neural control of human stepping.

neuroscience↗

bioRxiv2020

Characterization and Engineering of Streptomyces griseofuscus DSM 40191 as a Potential Host for Heterologous Expression of Biosynthetic Gene Clusters

Streptomyces griseofuscus DSM 40191 is a fast growing Streptomyces strain that remains largely underexplored as a heterologous host. Here, we report the genome mining of S. griseofuscus, followed by the detailed exploration of its phenotype, including production of native secondary metabolites and ability to utilise carbon, nitrogen, sulphur and phosphorus sources. Furthermore, several routes for genetic engineering of S. griseofuscus were explored, including use of GusA-based vectors, CRISPR-Cas9 and CRISPR-cBEST-mediated knockouts. Using CRISPR-BEST technology, core genes of 4 biosynthetic gene clusters (BGCs) that are situated on the chromosome arms were inactivated and the outcomes of the inactivations were tested. Two out of the three native plasmids were cured using CRISPR-Cas9 technology, leading to the generation of strain S. griseofuscus DEL1. DEL1 was further modified by full deletion of a pentamycin BGC and an unknown NRPS BGC, leading to the generation of strain DEL2, lacking approx. 500 kbp of the genome, which corresponds to a 5,19% genome reduction. Sequencing confirmed that DEL2 does not bear any crucial off-target effects or rearrangements in its genome. It can be characterized by faster growth and inability to produce three main native metabolites of S. griseofuscus: lankacidin, lankamycin, pentamycin and their derivatives. To test the ability of DEL2 to heterologously produce secondary metabolites, the actinorhodin BGC was used. We were able to confirm the production of actinorhodin by both S. griseofuscus wild type and DEL2. We believe that this strain will serve as a good chassis for heterologous expression of BGCs. ImportanceThe rise of antibacterial resistance calls on the development of the next generation of antibiotics, majority of which are derived from natural compounds, produced by actinomycetes. The manipulation, refactoring and expression of BGCs coding for such natural products is a promising approach in secondary metabolite discovery. Thus, the development of a versatile panel of heterologous hosts for the expression of BGCs is essential. We believe that first-to-date systematic, detailed characterisation of S. griseofuscus, a highly promising chassis strain, will not only facilitate the further development of this particular strain, but also will set a blueprint for characterisation of other potential hosts.

microbiology↗

bioRxiv2020

A light trap and computer vision system to detect and classify live moths (Lepidoptera) using tracking and deep learning

Insect monitoring methods are typically very time consuming and involves substantial investment in species identification following manual trapping in the field. Insect traps are often only serviced weekly resulting in low temporal resolution of the monitoring data, which hampers the ecological interpretation. This paper presents a portable computer vision system capable of attracting and detecting live insects. More specifically, the paper proposes detection and classification of species by recording images of live individuals attracted to a light trap. An Automated Moth Trap (AMT) with multiple light sources and a camera was designed to attract and monitor live insects during twilight and night hours. A computer vision algorithm referred to as Moth Classification and Counting (MCC), based on deep learning analysis of the captured images, tracked and counted the number of insects and identified moth species. Observations over 48 nights resulted in the capture of more than 250,000 images with an average of 5,675 images per night. A customized convolutional neural network was trained on 2,000 labelled images of live moths represented by eight different species, achieving a high validation F1-score of 0.93. The algorithm measured an average classification and tracking F1-score of 0.71 and a tracking detection rate of 0.79. Overall, the proposed computer vision system and algorithm showed promising results as a low-cost solution for non-destructive and automatic monitoring of moths.

ecology↗

bioRxiv2019

Predictive engineering and optimization of tryptophan metabolism in yeast through a combination of mechanistic and machine learning models

In combination with advanced mechanistic modeling and the generation of high-quality multi-dimensional data sets, machine learning is becoming an integral part of understanding and engineering living systems. Here we show that mechanistic and machine learning models can complement each other and be used in a combined approach to enable accurate genotype-to-phenotype predictions. We use a genome-scale model to pinpoint engineering targets and produce a large combinatorial library of metabolic pathway designs with different promoters which, once phenotyped, provide the basis for machine learning algorithms to be trained and used for new design recommendations. The approach enables successful forward engineering of aromatic amino acid metabolism in yeast, with the new recommended designs improving tryptophan production by up to 17% compared to the best designs used for algorithm training, and ultimately producing a total increase of 106% in tryptophan accumulation compared to optimized reference designs. Based on a single high-throughput data-generation iteration, this study highlights the power of combining mechanistic and machine learning models to enhance their predictive power and effectively direct metabolic engineering efforts.

bioengineering↗

bioRxiv2019

Directed connectivity between primary and premotor areas underlying ankle force control in young and older adults

The control of ankle muscle force is an integral component of walking and postural control. Aging impairs the ability to produce force steadily and accurately, which can compromise functional capacity and quality of life. Here, we hypothesized that reduced force control in older adults would be associated with altered cortico-cortical communication within a network comprising the primary motor area (M1), the premotor cortex (PMC), parietal, and prefrontal regions. We examined electroencephalographic (EEG) responses from fifteen younger (20-26 yr) and fifteen older (65-73 yr) participants during a unilateral dorsiflexion force-tracing task. Dynamic Causal Modelling (DCM) and Parametric Empirical Bayes (PEB) were used to investigate how directed connectivity between contralateral M1, PMC, parietal, and prefrontal regions was related to age group and precision in force production. DCM and PEB analyses revealed that the strength of connections between PMC and M1 were related to ankle force precision and differed by age group. For young adults, bidirectional PMC-M1 coupling was negatively related to task performance: stronger backward M1-PMC and forward PMC-M1 coupling was associated with worse force precision. The older group exhibited deviations from this pattern. For the PMC to M1 coupling, there were no age-group differences in coupling strength; however, within the older group, stronger coupling was associated with better performance. For the M1 to PMC coupling, older adults followed the same pattern as young adults - with stronger coupling accompanied by worse performance - but coupling strength was lower than in the young group. Our results suggest that bidirectional M1-PMC communication is related to precision in ankle force production and that this relationship changes with aging. We argue that the observed age-related differences reflect compensatory mechanisms whereby older adults maintain performance in the face of declines in the sensorimotor system.

neuroscience↗

bioRxiv2019

Pathway engineering in yeast for synthesizing the complex polyketide bikaverin

Fungal polyketides display remarkable structural diversity and bioactivity, and therefore the biosynthesis and engineering of this large class of molecules is therapeutically significant. Here, we successfully recoded, constructed and characterized the biosynthetic pathway of the formation of bikaverin, a tetracyclic polyketide with antibiotic, antifungal and anticancer properties, in S. cerevisiae. We used a green fluorescent protein (GFP) tagging strategy to identify the low expression of Bik1 (polyketide synthase) as the bottleneck step in the pathway, and a promoter exchange strategy to increase expression of Bik1 and bikaverin yield. To further increase product yield, we used an enzyme-fusion strategy to couple the monooxygenase (Bik2) and methyltransferase (Bik3) to efficiently channel intermediates between modifying enzymes, leading to a dramatic improvement of Bikaverin yield of nearly 60-fold. This study demonstrates that the biosynthesis of complex polyketides biosynthesis can be established and efficiently engineered in S. cerevisiae, highlighting the great potential for natural product synthesis and large-scale fermentation in yeast.

synthetic biology↗

bioRxiv2019

Is disrupted sleep a risk factor for Alzheimers disease? Evidence from a two-sample Mendelian randomization analysis

INTRODUCTIONIt is established that Alzheimers disease (AD) patients experience sleep disruption. However, it remains unknown whether disruption in the quantity, quality or timing of sleep is a risk factor for the onset of AD.\n\nMETHODSMendelian randomization (MR) was used to estimate the causal effect of self-reported and accelerometer-measured sleep parameters (chronotype, duration, fragmentation, insomnia, daytime napping and daytime sleepiness) on AD risk.\n\nRESULTSOverall, there was little evidence that sleep traits affect the risk of AD. There was some evidence to suggest that self-reported daytime napping was associated with lower AD risk (odds ratio [OR]: 0.70, 95% confidence interval [CI]: 0.50 to 0.99). Some other sleep traits (accelerometer-measured eveningness and sleep duration, and self-reported daytime sleepiness) had ORs for AD risk of a similar magnitude to daytime napping, but were less precisely estimated.\n\nDISCUSSONOur findings provide tentative evidence that daytime napping may reduce AD risk. However, findings should be replicated using independent samples.

epidemiology↗

bioRxiv2019

Loss-of-function genomic variants with impact on liver-related blood traits highlight potential therapeutic targets for cardiovascular disease

Cardiovascular diseases (CVD), and in particular cerebrovascular and ischemic heart diseases, are leading causes of death globally.1 Lowering circulating lipids is an important treatment strategy to reduce risk.2,3 However, some pharmaceutical mechanisms of reducing CVD may increase risk of fatty liver disease or other metabolic disorders.4,5,6 To identify potential novel therapeutic targets, which may reduce risk of CVD without increasing risk of metabolic disease, we focused on the simultaneous evaluation of quantitative traits related to liver function and CVD. Using a combination of low-coverage (5x) whole-genome sequencing and targeted genotyping, deep genotype imputation based on the TOPMed reference panel7, and genome-wide association study (GWAS) meta-analysis, we analyzed 12 liver-related blood traits (including liver enzymes, blood lipids, and markers of iron metabolism) in up to 203,476 people from three population-based cohorts of different ancestries. We identified 88 likely causal protein-altering variants that were associated with one or more liver-related blood traits. We identified several loss-of-function (LoF) variants reducing low-density lipoprotein cholesterol (LDL-C) or risk of CVD without increased risk of liver disease or diabetes, including variants in known lipid genes (e.g. APOB, LPL). A novel LoF variant, ZNF529:p.K405X, was associated with decreased levels of LDL-C (P=1.3x10-8) but demonstrated no association with liver enzymes or non-fasting blood glucose levels. Silencing of ZNF529 in human hepatocytes resulted in upregulation of LDL receptor (LDLR) and increased LDL-C uptake in the cells, suggesting that inhibition of ZNF529 or its gene product could be used for treating hypercholesterolemia and hence reduce the risk of CVD. Taken together, we demonstrate that simultaneous consideration of multiple phenotypes and a focus on rare protein-altering variants may identify promising therapeutic targets.

genomics↗

bioRxiv2019

Scalable generalized linear mixed model for region-based association tests in large biobanks and cohorts

With very large sample sizes, population-based cohorts and biobanks provide an exciting opportunity to identify genetic components of complex traits. To analyze rare variants, gene or region-based multiple variant aggregate tests are commonly used to increase association test power. However, due to the substantial computation cost, existing region-based rare variant tests cannot analyze hundreds of thousands of samples while accounting for confounders, such as population stratification and sample relatedness. Here we propose a scalable generalized mixed model region-based association test that can handle large sample sizes and accounts for unbalanced case-control ratios for binary traits. This method, SAIGE-GENE, utilizes state-of-the-art optimization strategies to reduce computational and memory cost, and hence is applicable to exome-wide and genome-wide region-based analysis for hundreds of thousands of samples. Through the analysis of the HUNT study of 69,716 Norwegian samples and the UK Biobank data of 408,910 White British samples, we show that SAIGE-GENE can efficiently analyze large sample data (N > 400,000) with type I error rates well controlled.

genetics↗

bioRxiv2018

Genome-wide association analysis of excessive daytime sleepiness identifies 42 loci that suggest phenotypic subgroups

Excessive daytime sleepiness (EDS) affects 10-20% of the population and is associated with substantial functional deficits. We identified 42 loci for self-reported EDS in GWAS of 452,071 individuals from the UK Biobank, with enrichment for genes expressed in brain tissues and in neuronal transmission pathways. We confirmed the aggregate effect of a genetic risk score of 42 SNPs on EDS in independent Scandinavian cohorts and on other sleep disorders (restless leg syndrome, insomnia) and sleep traits (duration, chronotype, accelerometer-derived sleep efficiency and daytime naps or inactivity). Strong genetic correlations were also seen with obesity, coronary heart disease, psychiatric diseases, cognitive traits and reproductive ageing. EDS variants clustered into two predominant composite phenotypes - sleep propensity and sleep fragmentation - with the former showing stronger evidence for enriched expression in central nervous system tissues, suggesting two unique mechanistic pathways. Mendelian randomization analysis indicated that higher BMI is causally associated with EDS risk, but EDS does not appear to causally influence BMI.

genomics↗

bioRxiv2018

Sex-specific and pleiotropic effects underlying kidney function identified from GWAS meta-analysis

Chronic Kidney Disease (CKD) is a growing health burden currently affecting 10-15% of adults worldwide. Estimated glomerular filtration rate (eGFR) as a marker of kidney function is commonly used to diagnose CKD. Previous genome-wide association study (GWAS) meta-analyses of CKD and eGFR or related phenotypes have identified a number of variants associated with kidney function, but these only explain a fraction of the variability in kidney phenotypes attributed to genetic components. To extend these studies, we analyzed data from the Nord-Trondelag Health Study (HUNT), which is more densely imputed than previous studies, and performed a GWAS meta-analysis of eGFR with publicly available summary statistics, more than doubling the sample size of previous meta-analyses. We identified 147 loci (53 novel loci) associated with eGFR, including genes involved in transcriptional regulation, kidney development, cellular signaling, metabolism, and solute transport. Moreover, genes at these loci show enriched expression in urogenital tissues and highlight gene sets known to play a role in kidney function. In addition, sex-stratified analysis identified three regions (prioritized genes: PPM1J, MCL1, and SLC47A1) with more significant effects in women than men. Using genetic risk scores constructed from these eGFR meta-analysis results, we show that associated variants are generally predictive of CKD but improve detection only modestly compared with other known clinical risk factors. Collectively, these results yield additional insight into the genetic factors underlying kidney function and progression to CKD.

genomics↗

bioRxiv2018

Evidence of a common causal relationship between body mass index and inflammatory skin disease: a Mendelian Randomization study

ObjectivePsoriasis and eczema are common inflammatory skin diseases that have been reported to be associated with obesity. However, causality has not yet been established. We aimed to investigate the possible causal relationship between body mass index (BMI) and psoriasis or eczema.\n\nMethodsFollowing a review of published epidemiological evidence of the association between obesity and either psoriasis or eczema, Mendelian Randomization (MR) was used to test for a causal relationship between BMI and these inflammatory skin conditions. We used a genetic instrument comprising 97 single nucleotide polymorphisms (SNPs) associated with BMI. One-sample MR was conducted using individual-level data (401,508 individuals) from the UK Biobank and the Nord-Trondelag Health Study (HUNT), Norway. Two-sample MR was performed with summary-level data (731,021 individuals) from published BMI, psoriasis and eczema GWAS. The one-sample and two-sample MR estimates were meta-analysed using a fixed effect model. To explore the reverse causal direction, MR analysis with genetic instruments comprising variants from recent genome-wide analyses for psoriasis and eczema were used to test if inflammatory skin disease has a causal effect on BMI.\n\nResultsPublished observational data show an association of greater BMI with both psoriasis and eczema case status. The observational associations were confirmed in UK Biobank and HUNT datasets. MR analyses provide evidence that higher BMI causally increases the odds of psoriasis (by 53% per 5 units higher BMI; OR= 1.09 (1.06 to 1.12) per 1 kg/m2; P=4.67x10-9) and eczema (by 8% per 5 units higher BMI; OR=1.02 (1.00 to 1.03) per 1 kg/m2; P=0.09). When investigating causality in the opposite direction, MR estimates provide little evidence for an effect of either psoriasis or eczema influencing BMI.\n\nConclusionOur study, using genetic variants as instrumental variables for BMI, shows that higher BMI leads to a higher risk of inflammatory skin disease. The causal relationship was stronger for psoriasis than eczema. Therapies and life-style interventions aimed at controlling BMI or targeting the mechanisms linking obesity with skin inflammation may offer an opportunity for the prevention or treatment of these common skin diseases.

genetics↗

bioRxiv2018

Biological and clinical insights from genetics of insomnia symptoms

Insomnia is a common disorder linked with adverse long-term medical and psychiatric outcomes, but underlying pathophysiological processes and causal relationships with disease are poorly understood. Here we identify 57 loci for self-reported insomnia symptoms in the UK Biobank (n=453,379) and confirm their impact on self-reported insomnia symptoms in the HUNT study (n=14,923 cases, 47,610 controls), physician diagnosed insomnia in Partners Biobank (n=2,217 cases, 14,240 controls), and accelerometer-derived measures of sleep efficiency and sleep duration in the UK Biobank (n=83,726). Our results suggest enrichment of genes involved in ubiquitin-mediated proteolysis, phototransduction and muscle development pathways and of genes expressed in multiple brain regions, skeletal muscle and adrenal gland. Evidence of shared genetic factors is found between frequent insomnia symptoms and restless legs syndrome, aging, cardio-metabolic, behavioral, psychiatric and reproductive traits. Evidence is found for a possible causal link between insomnia symptoms and coronary heart disease, depressive symptoms and subjective well-being.\n\nOne Sentence SummaryWe identify 57 genomic regions associated with insomnia pointing to the involvement of phototransduction and ubiquitination and potential causal links to CAD and depression.

genomics↗

bioRxiv2018

Genome-wide association study of 1 million people identifies 111 loci for atrial fibrillation

To understand the genetic variation underlying atrial fibrillation (AF), the most common cardiac arrhythmia, we performed a genome-wide association study (GWAS) of > 1 million people, including 60,620 AF cases and 970,216 controls. We identified 163 independent risk variants at 111 loci and prioritized 165 candidate genes likely to be involved in AF. Many of the identified risk variants fall near genes where more deleterious mutations have been reported to cause serious heart defects in humans or mice (MYH6, NKX2-5, PITX2, TBC1D32, TBX5),1,2 or near genes important for striated muscle function and integrity (e.g. MYH7, PKP2, SSPN, SGCA). Experiments in rabbits with heart failure and left atrial dilation identified a heterogeneous distributed molecular switch from MYH6 to MYH7 in the left atrium, which resulted in contractile and functional heterogeneity and may predispose to initiation and maintenance of atrial arrhythmia.

genetics↗

bioRxiv2017

Genome-wide analysis yields new loci associating with aortic valve stenosis

Aortic valve stenosis (AS) is the most common valvular heart disease, characterized by a thickened and calcified valve causing left ventricular outflow obstruction. Severe AS is a significant cause of morbidity and mortality, affecting approximately 5% of those over 70 years of age1,2,3. Little is known about the genetics of AS, although recently a variant at the LPA locus4 and a rare MYH6 missense variant were found to associate with AS5. We report a large genome-wide association study (GWAS) with a follow-up in up to 7,307 AS cases and 801,073 controls. We identified two new AS loci, on chromosome 1p21 near PALMD (rs7543130; OR=1.20, P=1.2x10-22) and on chromosome 2q22 in TEX41 (rs1830321; OR=1.15, P=1.8x10-13). Rs7543130 also associates with bicuspid aortic valve (BAV) (OR=1.28, P=6.6x10-10) and aortic root diameter (P=1.30x10-8) and rs1830321 associates with BAV (OR=1.12, P=5.3x10-3 and coronary artery disease (CAD) (OR=1.05, P=9.3x10-5). These results indicate that AS is partly rooted in the same processes as cardiac development and atherosclerosis.

genetics↗

bioRxiv2017

Efficiently controlling for case-control imbalance and sample relatedness in large-scale genetic association studies

In genome-wide association studies (GWAS) for thousands of phenotypes in large biobanks, most binary traits have substantially fewer cases than controls. Both of the widely used approaches, linear mixed model and the recently proposed logistic mixed model, perform poorly - producing large type I error rates - in the analysis of phenotypes with unbalanced case-control ratios. Here we propose a scalable and accurate generalized mixed model association test that uses the saddlepoint approximation (SPA) to calibrate the distribution of score test statistics. This method, SAIGE, provides accurate p-values even when case-control ratios are extremely unbalanced. It utilizes state-of-art optimization strategies to reduce computational time and memory cost of generalized mixed model. The computation cost linearly depends on sample size, and hence can be applicable to GWAS for thousands of phenotypes by large biobanks. Through the analysis of UK Biobank data of 408,961 white British European-ancestry samples for >1400 binary phenotypes, we show that SAIGE can efficiently analyze large sample data, controlling for unbalanced case-control ratios and sample relatedness.

genomics↗