Biology subjects

Schuh, L.

Publications and source records attributed to Schuh, L..

8 recordsLinked to original sources

bioRxiv2026

Estimating the Presence and Abundance of Aedes Albopictus in Europe Using Neural Networks

Aedes albopictus is an invasive mosquito species transmitting dengue, chikungunya, Zika and other arboviruses. Its ongoing geographical expansion across Europe, driven by climate change, trade, human mobility and urbanisation, is increasing the risk of vector-borne disease transmission in previously unaffected regions. Accurate mapping of mosquito distribution and abundance is essential for timely and targeted vector control and public health preparedness. Here we present AIedes, a neural network framework that predicts both the presence and weekly abundance of Ae. albopictus across Europe using climate variables alone. The model reproduces the large-scale spatial distribution of the species and captures fine-grained spatiotemporal variation in egg-laying activity. To support reproducible evaluation, the framework is trained and validated on newly harmonised, large-scale surveillance and climate datasets, which we release together with the model and code. These resources provide a consistent benchmark for the comparison of vector distribution and abundance models and enable extension to other regions and species where comparable data are available.

ecology↗

bioRxiv2024

Standardising a microbiome pipeline for body fluid identification from complex crime scene stains

BackgroundRecent advances in next-generation sequencing have opened up new possibilities for utilizing the human microbiome in various fields, including forensics. Researchers have capitalized on the site-specific microbial communities found in different parts of the body to identify body fluids from biological evidence. Despite promising results, microbiome-based methods have not yet been fully integrated into forensic practice due to the lack of standardized protocols and systematic testing of methods on forensically relevant samples. Our study addresses critical decisions in establishing these protocols, focusing on bioinformatics choices and the use of machine learning to present microbiome results in court for forensically relevant and challenging samples. ResultsWe propose using Operational Taxonomic Units (OTUs) for read data processing and creating heterogeneous training datasets for training a random forest classifier. Our classifier incorporates six forensically relevant classes: saliva, semen, hand skin, penile skin, urine, and vaginal/menstrual fluid. Across these classes, our classifier achieved a high weighted average F1 score of 0.89. Systematic testing on mixed-source samples and underwear revealed reliable detection of at least one component of the mixture and the identification of vaginal fluid from underwear substrates. Additionally, when investigating the sexually shared microbiome (sexome) of heterosexual couples, our classifier shows promising results for the inference of sexual activity. ConclusionIn our study, we recommend the use of a novel random forest classifier trained on a heterogenous dataset for obtaining predictions from samples mimicking forensic evidence. We also highlight the potential of the sexome for assessing the nature of sexual activities in forensic investigations, while delineating areas that warrant further research. Furthermore, we underscore key considerations when presenting machine learning results for classifying mixed-source samples.

bioinformatics↗

bioRxiv2022

Gal1 repression memory in budding yeast

Cells must continuously adapt to changing environments and, thus, have evolved mechanisms allowing them to respond to repeated stimuli. For example, faster gene induction upon a repeated stimulus aids adaptation - a process known as reinduction memory. However, whether such a memory exists for gene repression is unclear. Here, we studied gene repression across repeated carbon source shifts in over 2,500 single Saccharomyces cerevisiae cells. By monitoring the expression of a carbon source-responsive gene, galactokinase 1 (Gal1), and mathematical modeling, we discovered repression memory at the population and single-cell level. Using a repressor model to estimate single-cell repression parameters, we show that repression memory is due to a shortened repression delay, the estimated time gap between carbon source shift and Gal1 expression termination, upon the repeated carbon source shift. Additionally, we show that cells lacking Elp6 display a gain-of-repression-memory phenotype characterized by a stronger decrease in repression delay between two consecutive carbon source shifts. Collectively, our study provides the first quantitative description of repression memory in single cells.

systems biology↗

bioRxiv2021

Allelic correlation is a marker of tradeoffs between barriers to transmission of expression variability and signal responsiveness in genetic networks

Accurately functioning genetic networks should be responsive to signals but prevent transmission of stochastic bursts of expression. Existing data in mammalian cells suggests that such transcriptional "noise" is transmitted by some genes and not others, suggesting that noise transmission is tunable, perhaps at the expense of other signal processing capabilities.However, systematic claims about noise transmission in genetic networks have been limited by the inability to directly measure noise transmission. Here we build a mathematical framework capable of modeling allelic correlation and noise transmission. We find that allelic correlation and noise transmission correspond across a broad range of model parameters and network architectures. We further find that limiting noise transmission comes with the trade-off of being unresponsive to signals, and that within the parameter regimes that are responsive to signals, there is a further trade-off between response time and basal noise transmission. Using a published allele specific single cell RNA-sequencing dataset, we found that genes with high allelic odds ratios are enriched for cell-type specific functions, and that within multiple signaling pathways, factors which are upstream in the pathway have higher allelic odds ratios than downstream factors. Overall, our findings suggest that some degree of noise transmission is required to be responsive to signals, but that minimization of noise transmission can be accomplished by trading-off for a slower response time.

systems biology↗

bioRxiv2021

rasterdiv - an Information Theory tailored R package for measuring ecosystem heterogeneity from space: to the origin and back

O_LIEcosystem heterogeneity has been widely recognized as a key ecological feature, influencing several ecological functions, since it is strictly related to several ecological functions like diversity patterns and change, metapopulation dynamics, population connectivity, or gene flow. C_LIO_LIIn this paper, we present a new R package - rasterdiv - to calculate heterogeneity indices based on remotely sensed data. We also provide an ecological application at the landscape scale and demonstrate its power in revealing potentially hidden heterogeneity patterns. C_LIO_LIThe rasterdiv package allows calculating multiple indices, robustly rooted in Information Theory, and based on reproducible open source algorithms. C_LI

ecology↗

bioRxiv2020

Computational modeling reveals cell-cycle dependent kinetics of H4K20 methylation states during Xenopus embryogenesis

Histone modifications regulate chromatin architecture and thereby control gene expression. Rapid cell divisions and DNA replication however lead to a dilution of histone modifications and can thus affect chromatin mediated gene regulation So how does the cell-cycle shape the histone modification landscape, in particular during embryogenesis when a fast and precise control of cell-specific gene expression is required? We addressed this question in vivo by manipulating the cell-cycle during early Xenopus laevis embryogenesis. The global distribution of un-, mono- di- and tri-methylated histone H4K20 was measured by mass spectrometry in normal and cell-cycle arrested embryos over time. Using multi-start maximum likelihood optimization and quantitative model selection, we found that three specific methylation rate constants were required to explain the measured H4K20 methylation state kinetics. Interestingly, demethylation was found to be redundant in the cycling embryos but essential in the cell-cycle arrested embryos. Together, we present the first quantitative analysis of in vivo histone H4K20 methylation kinetics. Our computational model shows that demethylation is only essential for regulating H4K20 methylation kinetics in non-cycling cells. In rapidly dividing cells of early embryos, we predict that demethylation is dispensable, suggesting that cell-cycle mediated dilution of chromatin marks is an essential regulatory component for shaping the epigenetic landscape during early embryonic development.

bioinformatics↗

bioRxiv2019

Domain model explains propagation dynamics and stability of K27 and K36 methylation landscapes

Chromatin states must be maintained during cell proliferation to uphold cellular identity and genome integrity. Inheritance of histone modifications is central in this process. However, the histone modification landscape is challenged by incorporation of new unmodified histones during each cell cycle and the principles governing heritability remain unclear. Here, we take a quantitative computational modeling approach to describes propagation of K27 and K36 methylation states. We measure combinatorial K27 and K36 methylation patterns by quantitative mass spectrometry on subsequent generations of histones. Using model comparison, we reject active global demethylation and invoke the existence of domains defined by distinct methylation endpoints. We find that K27me3 on pre-existing histones stimulates the rate of de novo K27me3 establishment, supporting a read-write mechanism in timely chromatin restoration. Finally, we provide a detailed, quantitative picture of the mutual antagonism between K27 and K37 methylation, and propose that it stabilizes epigenetic states across cell division.

systems biology↗

bioRxiv2019

Gene networks with transcriptional bursting recapitulate rare transient coordinated expression states in cancer

SUMMARYNon-genetic transcriptional variability at the single-cell level is a potential mechanism for therapy resistance in melanoma. Specifically, rare subpopulations of melanoma cells occupy a transient pre-resistant state characterized by coordinated high expression of several genes. Importantly, these rare cells are able to survive drug treatment and develop resistance. How might these extremely rare states arise and disappear within the population? It is unclear whether the canonical stochastic models of probabilistic transcriptional pulsing can explain this behavior, or if it requires special, hitherto unidentified molecular mechanisms. Here we use mathematical modeling to show that a minimal network comprising of transcriptional bursting and interactions between genes can give rise to rare coordinated high states. We next show that although these states occur across networks of different sizes, they depend strongly on three (out of seven) model parameters and require network connectivity to be [≤] 6. Interestingly, we find that while entry into the rare coordinated high state is initiated by a long transcriptional burst that also triggers entry of other genes, the exit from it occurs through the independent inactivation of individual genes. Finally, our model predicts that increased network connectivity can lead to transcriptionally stable states, which we verify using network inference analysis of experimental data. In sum, we demonstrate that established principles of gene regulation are sufficient to describe this new class of rare cell variability and argue for its general existence in other biological contexts.

systems biology↗