Enhanced symbolic regression to infer biochemical network models

Biological systems can be represented as complex networks illustrating the relationships and connections among biochemical species. Complex networks can uncover vital information regarding specific pathways or network bottlenecks, helping to reveal novel discoveries relevant to a variety of applications. Biological networks, however, are often highly interconnected and non-linear in nature making development of a comprehensive model challenging. Large amounts of data can be acquired to elucidate specific pathways, but deducing the entire network topology requires more rigorous computational techniques. There are in silico techniques, including evolutionary algorithms, to predict network topologies using information from experimental data. Biological networks can be decomposed into a system of differential equations under mass action kinetics assumptions describing the rate of change of the various biochemical species in the network. Symbolic regression can be used to generate a system of equations from acquired data. Please click Additional Files below to see the full abstract

De Man et al.: Activity tracker validation Sensoria: A Journal of Mind, Brain & Culture Validity and inter-device reliability of dominant and non-dominant wrist worn activity trackers in suburban walking

Abstract Wearable activity trackers have become a popular way for general and athletic populations to measure daily physical activity and rest patterns. The validity and reliability of step count is often unknown for these devices

Inbred Mouse Populations Exhibit Intergenerational Changes in Intestinal Microbiota Composition and Function Following Introduction to a Facility

This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.Inbred mice are used to investigate many aspects of human physiology, including susceptibility to disease and response to therapies. Despite increasing evidence that the composition and function of the murine intestinal microbiota can substantially influence a broad range of experimental outcomes, relatively little is known about microbiome dynamics within experimental mouse populations. We investigated changes in the intestinal microbiome between C57BL/6J mice spanning six generations (assessed at generations 1, 2, 3, and 6), following their introduction to a stringently controlled facility. Fecal microbiota composition and function were assessed by 16S rRNA gene amplicon sequencing and liquid chromatography mass spectrometry, respectively. Significant divergence of the intestinal microbiota between founder and second generation mice, as well as continuing inter-generational variance, was observed. Bacterial taxa whose relative abundance changed significantly through time included Akkermansia, Turicibacter, and Bifidobacterium (p < 0.05), all of which are recognized as having the potential to substantially influence host physiology. Shifts in microbiota composition were mirrored by corresponding differences in the fecal metabolome (r = 0.57, p = 0.0001), with notable differences in levels of tryptophan pathway metabolites and amino acids, including glutamine, glutamate and aspartate. We related the magnitude of changes in the intestinal microbiota and metabolome characteristics during acclimation to those observed between populations housed in separate facilities, which differed in regards to husbandry, barrier conditions and dietary intake. The microbiome variance reported here has implications for experimental reproducibility, and as a consequence, experimental design and the interpretation of research outcomes across wide range of contexts

Integrative analysis of the plasma proteome and polygenic risk of cardiometabolic diseases.

Cardiometabolic diseases are frequently polygenic in architecture, comprising a large number of risk alleles with small effects spread across the genome1-3. Polygenic scores (PGS) aggregate these into a metric representing an individual's genetic predisposition to disease. PGS have shown promise for early risk prediction4-7 and there is an open question as to whether PGS can also be used to understand disease biology8. Here, we demonstrate that cardiometabolic disease PGS can be used to elucidate the proteins underlying disease pathogenesis. In 3,087 healthy individuals, we found that PGS for coronary artery disease, type 2 diabetes, chronic kidney disease and ischaemic stroke are associated with the levels of 49 plasma proteins. Associations were polygenic in architecture, largely independent of cis and trans protein quantitative trait loci and present for proteins without quantitative trait loci. Over a follow-up of 7.7 years, 28 of these proteins associated with future myocardial infarction or type 2 diabetes events, 16 of which were mediators between polygenic risk and incident disease. Twelve of these were druggable targets with therapeutic potential. Our results demonstrate the potential for PGS to uncover causal disease biology and targets with therapeutic potential, including those that may be missed by approaches utilizing information at a single locus

Southern Ocean Action Plan (2021-2030) in support of the United Nations Decade of Ocean Science for Sustainable Development

In 2017, the United Nations proclaimed a Decade of Ocean Science for Sustainable Development (hereafter referred to as the UN Ocean Decade) from 2021 until 2030 to support efforts to reverse the cycle of decline in ocean health. To achieve this ambitious goal, this initiative aims to gather ocean stakeholders worldwide behind a common framework that will ensure ocean science can fully support countries in creating improved conditions for sustainable development of the world’s oceans. The initiative strives to strengthen the international cooperation needed to develop the scientific research and innovative technologies that can connect ocean science with the needs of society at the global scale. Based on the recommendations in the Implementation Plan of the United Nations Decade of Ocean Science for Sustainable Development (Version 2.0, July 2021), the Southern Ocean community engaged in a stakeholder - oriented process to develop the Southern Ocean Action Plan. The Southern Ocean process engaged a broad community, which includes the scientific research community, the business and industry sector, and governance and management bodies. As part of this global effort, the Southern Ocean Task Force identified the needs of the Southern Ocean community to address the challenges related to the unique environmental characteristics and governance structure of the Southern Ocean. Through this community-driven process, we identified synergies within the Southern Ocean community and beyond in order to elaborate an Action Plan that provides a framework for Southern Ocean stakeholders to formulate and develop tangible actions and deliverables that support the UN Ocean Decade vision. Through the publication of this Action Plan, the Southern Ocean Task Force aims to mobilise the Southern Ocean community and inspire all stakeholders to seek engagement and leverage opportunities to deliver innovative solutions that maintain and foster the unique conditions of the Southern Ocean. This framework provides an initial roadmap to strengthen links between science, industry and policy, as well as to encourage internationally collaborative activities in order to address existing gaps in our knowledge and data coverage

Global estimates of mortality associated with long-term exposure to outdoor fine particulate matter.

Exposure to ambient fine particulate matter (PM2.5) is a major global health concern. Quantitative estimates of attributable mortality are based on disease-specific hazard ratio models that incorporate risk information from multiple PM2.5 sources (outdoor and indoor air pollution from use of solid fuels and secondhand and active smoking), requiring assumptions about equivalent exposure and toxicity. We relax these contentious assumptions by constructing a PM2.5-mortality hazard ratio function based only on cohort studies of outdoor air pollution that covers the global exposure range. We modeled the shape of the association between PM2.5 and nonaccidental mortality using data from 41 cohorts from 16 countries-the Global Exposure Mortality Model (GEMM). We then constructed GEMMs for five specific causes of death examined by the global burden of disease (GBD). The GEMM predicts 8.9 million [95% confidence interval (CI): 7.5-10.3] deaths in 2015, a figure 30% larger than that predicted by the sum of deaths among the five specific causes (6.9; 95% CI: 4.9-8.5) and 120% larger than the risk function used in the GBD (4.0; 95% CI: 3.3-4.8). Differences between the GEMM and GBD risk functions are larger for a 20% reduction in concentrations, with the GEMM predicting 220% higher excess deaths. These results suggest that PM2.5 exposure may be related to additional causes of death than the five considered by the GBD and that incorporation of risk information from other, nonoutdoor, particle sources leads to underestimation of disease burden, especially at higher concentrations

Polar Data Forum IV – An Ocean of Opportunities

This paper reports on the Hackathon Sessions organised at the Polar Data Forum IV (PDF IV) (20–24 September 2021), during which 351 participants from 50 different countries discussed collaboratively about the latest developments in polar data management. The 4th edition of the PDF hosted lively discussions on (i) best practices for polar data management, (ii) data policy, (ii) documenting data flows into aggregators, (iv) data interoperability, (v) polar federated search, (vi) semantics and vocabularies, (vii) Virtual Research Environments (VREs), and (viii) new polar technologies. This paper provides an overview of the organisational aspects of PDF IV and summarises the polar data objectives and outcomes by describing the conclusions drawn from the Hackathon Sessions

Loci influencing blood pressure identified using a cardiovascular gene-centric array

Blood pressure (BP) is a heritable determinant of risk for cardiovascular disease (CVD). To investigate genetic associations with systolic BP (SBP), diastolic BP (DBP), mean arterial pressure (MAP) and pulse pressure (PP), we genotyped 50 000 single-nucleotide polymorphisms (SNPs) that capture variation in 2100 candidate genes for cardiovascular phenotypes in 61 619 individuals of European ancestry from cohort studies in the USA and Europe. We identified novel associations between rs347591 and SBP (chromosome 3p25.3, in an intron of HRH1) and between rs2169137 and DBP (chromosome1q32.1 in an intron of MDM4) and between rs2014408 and SBP (chromosome 11p15 in an intron of SOX6), previously reported to be associated with MAP. We also confirmed 10 previously known loci associated with SBP, DBP, MAP or PP (ADRB1, ATP2B1, SH2B3/ATXN2, CSK, CYP17A1, FURIN, HFE, LSP1, MTHFR, SOX6) at array-wide significance (P 2.4 10(6)). We then replicated these associations in an independent set of 65 886 individuals of European ancestry. The findings from expression QTL (eQTL) analysis showed associations of SNPs in the MDM4 region with MDM4 expression. We did not find any evidence of association of the two novel SNPs in MDM4 and HRH1 with sequelae of high BP including coronary artery disease (CAD), left ventricular hypertrophy (LVH) or stroke. In summary, we identified two novel loci associated with BP and confirmed multiple previously reported associations. Our findings extend our understanding of genes involved in BP regulation, some of which may eventually provide new targets for therapeutic intervention.</p

Trans-ancestry meta-analyses identify rare and common variants associated with blood pressure and hypertension

High blood pressure is a major risk factor for cardiovascular disease and premature death. However, there is limited knowledge on specific causal genes and pathways. To better understand the genetics of blood pressure, we genotyped 242,296 rare, low-frequency and common genetic variants in up to ~192,000 individuals, and used ~155,063 samples for independent replication. We identified 31 novel blood pressure or hypertension associated genetic regions in the general population, including three rare missense variants in RBM47, COL21A1 and RRAS with larger effects (>1.5mmHg/allele) than common variants. Multiple rare, nonsense and missense variant associations were found in A2ML1 and a low-frequency nonsense variant in ENPEP was identified. Our data extend the spectrum of allelic variation underlying blood pressure traits and hypertension, provide new insights into the pathophysiology of hypertension and indicate new targets for clinical intervention