Biology subjects

Mariette, J.

Publications and source records attributed to Mariette, J..

4 recordsLinked to original sources

bioRxiv2023

Purchases dominate the carbon footprint of research laboratories

Despite increasing interest for the carbon footprint of higher education institutions, little is known about the carbon footprint associated to research activities. Air travel and attendance to conferences concentrate recent data and debates but purchases have attracted little attention. Here we develop a hybrid method to estimate the greenhouse gas emissions (GHG) associated to research purchases. To do so, we combine macroe-conomic databases, research-centered companies footprints and life-cycle assesments to construct a public database of monetary emission factors (EF) for research purchases. We apply it to estimate the purchases emissions of a hundred of research laboratories in France, belonging to the Labos 1point5 network and gathering more than 20000 staff, from all disciplines. We find that purchases dominate laboratory emissions, accounting for more than 50% of emissions, with a median of 2.7 t CO2e/pers, which is 3 to 4-fold the separate contribution from travel, commutes and heating. Median electricity emissions are 5-fold lower in our dataset of laboratories using low carbon electricity but they become preponderant for high carbon electricity mixes (3.5 t CO2e/pers). Purchases emissions are very heterogeneous among laboratories and are linearly correlated with budget, with an average carbon intensity of 0.31 {+/-} 0.07 kg CO2e/{euro} and differences between research domains. Finally, we quantify the effect of a series of demand-driven mitigation strategies obtaining up to -20 % in total emissions (-40 % in purchases emissions), suggesting that effectively reducing the carbon footprint of research activities calls for systemic changes. Significance statementResearch activities are recently interrogating their contribution to global warming, mainly through the impact of air travel but neglecting the emissions embodied in scientific purchases. However, goods and services used in a research laboratory emit greenhouse gases when they are produced. Here we construct a public and robust database of emission factors to quantify purchases emissions in a laboratory and we use it to assess emissions from a hundred of laboratories in France, from all disciplines. We find that purchases emissions represent half of the of the 6.3 t CO2e/pers per year emitted on average per laboratory. Emissions, however, vary greatly between laboratories and disciplines and an analysis of mitigation strategies shows that decreasing demand may significantly reduce purchases emissions.

scientific communication and education↗

bioRxiv2021

Nuclear HMGB1 protects from non-alcoholic fatty liver diseases through negative regulation of liver X receptor

Dysregulations of lipid metabolism in the liver may trigger steatosis progression leading to potentially severe clinical consequences such as non-alcoholic fatty liver diseases (NAFLD). Molecular mechanisms underlying liver lipogenesis are very complex and fine-tuned by chromatin dynamics and the activity of multiple key transcription factors. Here, we demonstrate that the nuclear factor HMGB1 acts as a strong repressor of liver lipogenesis during metabolic stress in NAFLD. Mice with liver-specific Hmgb1-deficiency display exacerbated liver steatosis and hepatic insulin resistance when subjected to a high-fat diet or after fasting/refeeding. Global transcriptome and functional analysis revealed that the deletion of Hmgb1 gene enhances LXR activity resulting in increased lipogenesis. HMGB1 repression is not mediated through nucleosome landscape re-organization but rather via a preferential DNA occupation in region carrying genes regulated by LXR. Together these findings suggest that hepatocellular HMGB1 protects from liver steatosis development. HMGB1 may constitute a new attractive option to therapeutically target LXR axis during NAFLD.

pathology↗

bioRxiv2021

An open-source tool to assess the carbon footprint of research

The scrutiny over the carbon footprint of academics has increased rapidly in the last few years. This has resulted in a series of publications providing various estimates of the carbon footprint of one or several research activities, principally at the scale of a university or a research center or, more recently, a field of research. The variety of tools or methodologies - on which these estimates rely - unfortunately prevents from any direct comparison because of the sensitivity of carbon footprint assessments to variations in the scope and to key parameters such as emission factors. In an effort to enabling a robust comparison of research carbon footprints across institutions, contexts or disciplines, we present an open-source web application, GES 1point5 designed to estimate the carbon footprint of a department, research lab or team in any country of the world with a transparent and common methodology. The current version of GES 1point5, open-source and freely available, takes into account the most common and often predominant emission sources in research labs: buildings, digital devices, commuting, and professional travel. GES 1point5 is developed by an interdisciplinary team of scientists from several public research institutions in France as part of the Labos 1point5 project. GES 1point5 is therefore presently tailored for the French context but can be adjusted to any national contexts by adjusting the values of emission factors. The versatility and usability of the software have been empirically validated by its adoption by several hundred research labs in France over the last 18 months. In addition to enabling the estimation and monitoring of greenhouse gas (GHG) emissions at the scale of a research lab, GES 1point5 is designed to aggregate the data entered by the labs and the corresponding GHG emissions estimates into a comprehensive database. GES 1point5 can therefore allow to (i) identify robust determinants of the carbon footprint of research activities across a network of research labs (ii) estimate the carbon footprint of research at the national scale. A preliminary analysis of the carbon footprint of more than one hundred laboratories is presented to illustrate the potential of the approach. While assessments of carbon footprints are often externalized onto extension services and proprietary softwares, GES 1point5 is designed as a hands-on, pedagogic and transparent tool for research labs to monitor and reduce their own carbon footprint. This internalization has strong positive co-benefits for academics in terms of awareness and empowerment. We further expect that international dissemination of GES 1point5 will contribute to establishing a global understanding of the drivers of the research carbon footprint worldwide and an identification of the levers to decrease it. Availability and implementationGES 1point5 is available online at http://labos1point5.org/ges-1point5 and its source code can be downloaded from the GitLab platform at https://framagit.org/labos1point5/l1p5-vuejs.

scientific communication and education↗

bioRxiv2017

Integrating TARA Oceans Datasets Using Unsupervised Multiple Kernel Learning

Recent high-throughput sequencing advances have expanded the breadth of available omics datasets and the integrated analysis of multiple datasets obtained on the same samples has allowed to gain important insights in a wide range of applications. However, the integration of various sources of information remains a challenge for systems biology since produced datasets are often of heterogeneous types, with the need of developing generic methods to take their different specificities into account.\n\nWe propose a multiple kernel framework that allows to integrate multiple datasets of various types into a single exploratory analysis. Several solutions are provided to learn either a consensus meta-kernel or a meta-kernel that preserves the original topology of the datasets. We applied our framework to analyse two public multi-omics datasets. First, the multiple metagenomic datasets, collected during the TARA Oceans expedition, was explored to demonstrate that our method is able to retrieve previous findings in a single KPCA as well as to provide a new image of the sample structures when a larger number of datasets are included in the analysis. To perform this analysis, a generic procedure is also proposed to improve the interpretability of the kernel PCA in regards with the original data. Second, the multi-omics breast cancer datasets, provided by The Cancer Genome Atlas, is analysed using a kernel Self-Organizing Maps with both single and multi-omics strategies. The comparison of this two approaches demonstrates the benefit of our integration method to improve the representation of the studied biological system.\n\nProposed methods are available in the R package mixKernel, released on CRAN. It is fully compatible with the mixOmics package and a tutorial describing the approach can be found on mixOmics web site http://mixomics.org/mixkernel/.

bioinformatics↗