Biology subjects

Rouillard, A.

Publications and source records attributed to Rouillard, A..

3 recordsLinked to original sources

bioRxiv2025

Ancient metagenomics reveals subglacial microbiomes driven by oxygen availability

Beneath Earths glaciers and ice sheets lies an aquatic realm where ice, water, rock, and microbial life interact, driving chemical reactions that can collectively influence the global carbon cycle, polar oceans, and climate. Efforts to describe subglacial microbiomes have been limited by the challenge of cleanly drilling through hundreds of meters of ice, such that only a few sites have ever been directly sampled. Here we use ancient metagenomics to present the first spatiotemporal characterization of subglacial bacteria and archaea. We extracted DNA from 25 subglacial precipitate samples, sedimentary accumulations of minerals that form in subglacial waters prior to exposure on the surface. The precipitates studied here formed between 16,000 and 570,000 years ago beneath the Antarctic and Laurentide Ice Sheets. We show that postmortem DNA damage patterns can reliably distinguish between ancient subglacial and modern surface taxa, and that this approach can enable reconstruction of subglacial microbiomes across poles and ice ages. Our analysis suggests that subglacial microbiomes are dominated by chemolithoautotrophs, ultra-small microbes, and taxa closely related to those found in deep subsurface or extreme cold and hypersaline environments. These microbiomes split into two distinct clusters distinguished by oxygen availability and redox conditions, irrespective of geography or age. Geochemical measurements of subglacial redox state, measured either indirectly via precipitate calcite Fe and Mn concentrations or directly via water reduction potential, reproduce these same two clusters exactly. Our findings describe how subglacial water redox states are held in balance by microbes, hydrology, and oxygen input from fresh subglacial meltwater, that we interpret to be controlled by the ice sheet response to past climate variations.

genomics↗

bioRxiv2021

Ancient sedimentary DNA shows rapid post-glacial colonisation of Iceland followed by relatively stable vegetation until Landnam

Understanding patterns of colonisation is important for explaining both the distribution of single species and anticipating how ecosystems may respond to global warming. Insular flora may be especially vulnerable because oceans represent severe dispersal barriers. Here we analyse two lake sediment cores from Iceland for ancient sedimentary DNA to infer patterns of colonisation and Holocene vegetation development. Our cores from lakes Torfdalsvatn and Nykurvatn span the last c. 12,000 cal. yr BP and c. 8600 cal. yr BP, respectively. With near-centennial resolution, we identified a total of 191 plant taxa, with 152 taxa identified in the sedimentary record of Torfdalsvatn and 172 plant taxa in the sedimentary record of Nykurvatn. The terrestrial vegetation at Torfdalsvatn was first dominated by bryophytes, arctic herbs such as Saxifraga spp. and grasses. Around 10,100 cal. yr BP, a massive immigration of new taxa was observed, and shrubs and dwarf shrubs became common whereas aquatic macrophytes became dominant. At Nykurvatn, all dominant taxa occurred already in the earliest samples; shrubs and dwarf shrubs were more abundant at this site than at Torfdalsvatn. There was an overall steep increase both in the local and regional species pool until 8000 cal. yr BP, by which time [3/4] of all taxa identified had arrived. In the period 4500-1000 cal. yr BP, a few new taxa of bryophytes, graminoids and forbs are identified. The last millennium, after human settlement of the island (Landnam), is characterised by a sudden disappearance of Juniperus communis, but also reappearance of some high arctic forbs and dwarf shrubs. Notable immigration during the Holocene coincides with periods of dense sea-ice cover, and we hypothesise that this may have acted as a dispersal vector. Thus, although ongoing climate change might provide a suitable habitat in Iceland for a large range of species only found in the neighbouring regions today, the reduction of sea ice may in fact limit the natural colonisation of new plant species.

ecology↗

bioRxiv2017

Systematic interrogation of diverse Omic data reveals interpretable, robust, and generalizable transcriptomic features of clinically successful therapeutic targets

Target selection is the first and pivotal step in drug discovery. An incorrect choice may not manifest itself for many years after hundreds of millions of research dollars have been spent. We collected a set of 332 targets that succeeded or failed in phase III clinical trials, and explored whether Omic features describing the target genes could predict clinical success. We obtained features from the recently published comprehensive resource: Harmonizome. Nineteen features appeared to be significantly correlated with phase III clinical trial outcomes, but only 4 passed validation schemes that used bootstrapping or modified permutation tests to assess feature robustness and generalizability while accounting for target class selection bias. We also used classifiers to perform multivariate feature selection and found that classifiers with a single feature performed as well in cross-validation as classifiers with more features (AUROC=0.57 and AUPR=0.81). The two predominantly selected features were mean mRNA expression across tissues and standard deviation of expression across tissues, where successful targets tended to have lower mean expression and higher expression variance than failed targets. This finding supports the conventional wisdom that it is favorable for a target to be present in the tissue(s) affected by a disease and absent from other tissues. Overall, our results suggest that it is feasible to construct a model integrating interpretable target features to inform target selection. We anticipate deeper insights and better models in the future, as researchers can reuse the data we have provided to improve methods for handling sample biases and learn more informative features. Code, documentation, and data for this study have been deposited on GitHub at https://github.com/arouillard/omic-features-successful-targets.\n\nAUTHOR SUMMARYDrug discovery often begins with a hypothesis that changing the abundance or activity of a target--a biological molecule, usually a protein--will cure a disease or ameliorate its symptoms. Whether a target hypothesis translates into a successful therapy depends in part on the characteristics of the target, but it is not completely understood which target characteristics are important for success. We sought to answer this question with a supervised machine learning approach. We obtained outcomes of target hypotheses tested in clinical trials, scoring targets as successful or failed, and then obtained thousands of features (i.e. properties or characteristics) of targets from dozens of biological datasets. We statistically tested which features differed between successful and failed targets, and built a computational model that used these features to predict success or failure of targets in clinical trials. We found that successful targets tended to have more variable mRNA abundance from tissue to tissue and lower average abundance across tissues than failed targets. Thus, it is probably favorable for a target to be present in the tissue(s) affected by a disease and absent from other tissues. Our work demonstrates the feasibility of predicting clinical trial outcomes from target features.

bioinformatics↗