Biology subjects

Simon, N.

Publications and source records attributed to Simon, N..

25 records · Page 2Linked to original sources

bioRxiv2021

Combining genotypes and T cell receptor distributions to infer genetic loci determining V(D)J recombination probabilities

Every T cell receptor (TCR) repertoire is shaped by a complex probabilistic tangle of genetically determined biases and immune exposures. T cells combine a random V(D)J recombination process with a selection process to generate highly diverse and functional TCRs. The extent to which an individuals genetic background is associated with their resulting TCR repertoire diversity has yet to be fully explored. Using a previously published repertoire sequencing dataset paired with high-resolution genome-wide genotyping from a large human cohort, we infer specific genetic loci associated with V(D)J recombination probabilities using genome-wide association inference. We show that V(D)J gene usage profiles are associated with variation in the TCRB locus and, specifically for the functional TCR repertoire, variation in the major histocompatibility complex locus. Further, we identify specific variations in the genes encoding the Artemis protein and the TdT protein to be associated with biasing junctional nucleotide deletion and N-insertion, respectively. These results refine our understanding of genetically-determined TCR repertoire biases by confirming and extending previous studies on the genetic determinants of V(D)J gene usage and providing the first examples of trans genetic variants which are associated with modifying junctional diversity. Together, these insights lay the groundwork for further explorations into how immune responses vary between individuals.

immunology↗

bioRxiv2021

Seasonal temporal dynamics of marine protists communities in tidally mixed coastal waters

Major seasonal community reorganizations and associated biomass variations are landmarks of plankton ecology. However, the processes determining marine species and community turnover rates have not been fully elucidated so far. Here, we analyse patterns of planktonic protist community succession in temperate latitudes, based on quantitative taxonomic data from both microscopy counts and ribosomal DNA metabarcoding from plankton samples collected biweekly over 8 years (2009-2016) at the SOMLIT-Astan station (Roscoff, Western English Channel). Considering the temporal structure of community dynamics (creating temporal correlation), we elucidated the recurrent seasonal pattern of the dominant species and OTUs (rDNA-derived taxa) that drive annual plankton successions. The use of morphological and molecular analyses in combination allowed us to assess absolute species abundance while improving taxonomic resolution, and revealed a greater diversity. Overall, our results underpinned a protist community characterised by a seasonal structure, which is supported by the dominant OTUs. We detected that some were partly benthic as a result of the intense tidal mixing typical of the French coasts in the English Channel. While the occurrence of these microorganisms is driven by the physical and biogeochemical conditions of the environment, internal community processes, such as the complex network of biotic interactions, also play a key role in shaping protist communities.

ecology↗

bioRxiv2020

An interbacterial DNA deaminase toxin directly mutagenizes surviving target populations

When bacterial cells come in contact, antagonism mediated by the delivery of toxins frequently ensues. The potential for such encounters to have long-term beneficial consequences in recipient cells has not been investigated. Here we examined the effects of intoxication by DddA, a cytosine deaminase delivered via the type VI secretion system (T6SS) of Burkholderia cenocepacia. Despite its killing potential, we observed that several bacterial species resist DddA and instead accumulate mutations installed by the toxin, indicating that even in the absence of killing, interbacterial toxins can have profound consequences on target populations. Investigation of additional toxins from the deaminase superfamily revealed that mutagenic activity is a common feature of these proteins, including a representative we show targets single-stranded DNA and displays a markedly divergent structure. Our findings suggest that a surprising consequence of antagonistic interactions between bacteria could be the promotion of adaptation via the action of directly mutagenic toxins.

microbiology↗

bioRxiv2020

A thermal trade-off between viral production and degradation drives phytoplankton-virus population dynamics

Marine viruses interact with their microbial hosts in dynamic environments shaped by variations in abiotic factors, including temperature. However, the impacts of temperature on viral infection of phytoplankton are not well understood. Here we coupled mathematical modeling with experimental datasets to explore the effect of temperature on three Micromonas-prasinovirus pairs. Our model shows the negative consequences of high temperatures on infection and suggests a temperature-dependent threshold between viral production and degradation. Modeling long-term dynamics in environments with different average temperatures revealed the potential for long-term host-virus coexistence, epidemic free, or habitat loss states. Hence, we generalized our model to global sea surface temperature of present and future seas and show that climate change may influence virus-host dynamics differently depending on the virus-host pair. Our study suggests that temperature-dependent changes in the infectivity of virus particles may lead to shifts in virus-host habitats in warmer oceans, analogous to projected changes in the habitats of macro- and micro-organisms.

ecology↗

bioRxiv2020

seagull: lasso, group lasso and sparse-group lasso regularisation for linear regression models via proximal gradient descent

Statistical analyses of biological problems in life sciences often lead to high-dimensional linear models. To solve the corresponding system of equations, penalisation approaches are often the methods of choice. They are especially useful in case of multicollinearity which appears if the number of explanatory variables exceeds the number of observations or for some biological reason. Then, the model goodness of fit is penalised by some suitable function of interest. Prominent examples are the lasso, group lasso and sparse-group lasso. Here, we offer a fast and numerically cheap implementation of these operators via proximal gradient descent. The grid search for the penalty parameter is realised by warm starts. The step size between consecutive iterations is determined with backtracking line search. Finally, the package produces complete regularisation paths. Availability and implementationseagull is an R package that is freely available on the Comprehensive R Archive Network (CRAN; https://CRAN.R-project.org/package=seagull; vignette included). The source code is available on https://github.com/jklosa/seagull. Contactwittenburg@fbn-dummerstorf.de

bioinformatics↗

bioRxiv2019

Multidimensional analysis and detection of informative features in diffusion MRI measurements of human white matter

The white matter contains long-range connections between different brain regions and the organization of these connections holds important implications for brain function in health and disease. Tractometry uses diffusion-weighted magnetic resonance imaging (dMRI) data to quantify tissue properties (e.g. fractional anisotropy (FA), mean diffusivity (MD), etc.), along the trajectories of these connections [1]. Statistical inference from tractometry usually either (a) averages these quantities along the length of each bundle in each individual, or (b) performs analysis point-by-point along each bundle, with group comparisons or regression models computed separately for each point along every one of the bundles. These approaches are limited in their sensitivity, in the former case, or in their statistical power, in the latter. In the present work, we developed a method based on the sparse group lasso (SGL) [2] that takes into account tissue properties measured along all of the bundles, and selects informative features by enforcing sparsity, not only at the level of individual bundles, but also across the entire set of bundles and all of the measured tissue properties. The sparsity penalties for each of these constraints is identified using a nested cross-validation scheme that guards against over-fitting and simultaneously identifies the correct level of sparsity. We demonstrate the accuracy of the method in two settings: i) In a classification setting, patients with amyotrophic lateral sclerosis (ALS) are accurately distinguished from matched controls [3]. Furthermore, SGL automatically identifies FA in the corticospinal tract as important for this classification - correctly finding the parts of the white matter known to be affected by the disease. ii) In a regression setting, dMRI is used to accurately predict "brain age" [4, 5]. In this case, the weights are distributed throughout the white matter indicating that many different regions of the white matter change with development and contribute to the prediction of age. Thus, SGL makes it possible to leverage the multivariate relationship between diffusion properties measured along multiple bundles to make accurate predictions of subject characteristics while simultaneously discovering the most relevant features of the white matter for the characteristic of interest.

neuroscience↗

bioRxiv2019

Estimation of cell lineage trees by maximum-likelihood phylogenetics

CRISPR technology has enabled large-scale cell lineage tracing for complex multicellular organisms by mutating synthetic genomic barcodes during organismal development. However, these sophisticated biological tools currently use ad-hoc and outmoded computational methods to reconstruct the cell lineage tree from the mutated barcodes. Because these methods are agnostic to the biological mechanism, they are unable to take full advantage of the datas structure. We propose a statistical model for the mutation process and develop a procedure to estimate the tree topology, branch lengths, and mutation parameters by iteratively applying penalized maximum likelihood estimation. In contrast to existing techniques, our method estimates time along each branch, rather than number of mutation events, thus providing a detailed account of tissue-type differentiation. Via simulations, we demonstrate that our method is substantially more accurate than existing approaches. Our reconstructed trees also better recapitulate known aspects of zebrafish development and reproduce similar results across fish replicates.

bioinformatics↗