Biology subjects

Hütt, M.-T.

Publications and source records attributed to Hütt, M.-T..

4 recordsLinked to original sources

bioRxiv2025

Arctic defaunation initiated a cascade of mammal-plant interaction shift through dispersal dynamics

Amid accelerating defaunation, it remains unclear whether the loss of large mammals can trigger ecosystem collapse and through which functional pathways. Using sedimentary ancient metagenomics, we identify an overlooked role of large mammal defaunation in helping drive the collapse of the glacial mammoth-steppe. Network analyses identified rewiring of mammal interactions during the late glacial supporting cascading effects. Crucially, megafaunal-mediated long-distance seed dispersal buffered vegetation turnover during periods of rapid climate warming but weakened with progressive losses of vital dispersal interactions leading to the steppe-tundras transition to open woodland in the early Holocene. Trophic interactions did not offset turnover and became dominant afterwards with the more stable climate. We identified resilient interactions that may support recovery, suggesting the potential of rewilding to restore Arctic ecosystems. One-sentence abstractAncient metagenomics reveals disrupted long-distance seed dispersal helped drive mammoth-steppe collapse and informs Arctic rewilding efforts.

ecology↗

bioRxiv2024

Binary Brains: How Excitable Dynamics Simplify Neural Connectomes

Fiber networks connecting different brain regions are the structural foundation of brain dynamics and function. Recent studies have provided detailed characterizations of neural connectomes with weighted connections. However, the topological analysis of weighted networks still has conceptual and practical challenges. Consequently, many investigations of neural networks are performed on binarized networks, and the functional impact of unweighted versus weighted networks is unclear. Here we show, for the widespread case of excitable dynamics, that the excitation patterns observed in weighted and unweighted networks are nearly identical, if an appropriate network threshold is selected. We generalize this observation to different excitable models, and formally predict the network threshold from the intrinsic model features. The network-binarizing capacity of excitable dynamics suggests that neural activity patterns may primarily depend on the strongest structural connections. Our findings have practical advantages in terms of the computational cost of representing and analyzing complex networks. There are also fundamental implications for the computational simulation of connectivity-based brain dynamics and the computational function of diverse other systems governed by excitable dynamics such as artificial neural networks.

neuroscience↗

bioRxiv2021

Contribution of 3D genome topological domains to genetic risk of cancers

Genome-wide association studies have identified statistical associations between various diseases, including cancers, and a large number of single-nucleotide polymorphisms (SNPs). However, they provide no direct explanation of the mechanisms underlying the association. Based on the recent discovery that changes in 3-dimensional genome organization may have functional consequences on gene regulation favoring diseases, we investigated systematically the genome-wide distribution of disease-associated SNPs with respect to a specific feature of 3D genome organization: topologically-associating domains (TADs) and their borders. For each of 449 diseases, we tested whether the associated SNPs are present in TAD borders more often than observed by chance, where chance (i.e. the null model in statistical terms) corresponds to the same number of pointwise loci drawn at random either in the entire genome, or in the entire set of disease-associated SNPs listed in the GWAS catalog. Our analysis shows that a fraction of diseases display such a preferential location of their risk loci. Moreover, cancers are relatively more frequent among these diseases, and this predominance is generally enhanced when considering only intergenic SNPs. The structure of SNP-based diseasome networks confirms that TAD border enrichment in risk loci differ between cancers and non-cancer diseases. Different TAD border enrichments are observed in embryonic stem cells and differentiated cells, which agrees with an evolution along embryogenesis of the 3D genome organization into topological domains. Our results suggest that, for certain diseases, part of the genetic risk lies in a local genetic variation affecting the genome partitioning in topologically-insulated domains. Investigating this possible contribution to genetic risk is particularly relevant in cancers. This study thus opens a way of interpreting genome-wide association studies, by distinguishing two types of disease-associated SNPs: one with a direct effect on an individual gene, the other acting in interplay with 3D genome organization. Author summaryGenome-wide association studies comparing patients and healthy subjects have evidenced correlations between diseases and the presence of pointwise genetic variations known as single-nucleotide polymorphisms (SNPs). We exploit and extend this statistical analysis by investigating the location of risk loci, i.e. disease-associated SNPs, with respect to the 3D organization of the genome into spatially-insulated domains, the topologically-associating domains (TADs). We show that for certain diseases, mostly cancers, their associated risk loci are preferentially located in the borders of these topological domains. The predominance of cancers among these diseases is confirmed and even enhanced when considering only intergenic SNPs. A different enrichment behavior is observed in embryonic stem cells and derived cell lines at an early developmental stage, presumably due to the not fully mature TAD structure in these cells. Overall, our results show that genome variations in specific TAD borders may increase the risk of developing certain diseases, especially cancers. Our work underlines the importance of considering the genetic risk loci within their 3D genomic context, and suggests a role of 3D genome partitioning into topological domains in the genetic risk which differs between cancers and non-cancer diseases.

genetics↗

bioRxiv2018

Topological Reinforcement as a Principle of Modularity Emergence in Brain Networks

Modularity is a ubiquitous topological feature of structural brain networks at various scales. While a variety of potential mechanisms have been proposed, the fundamental principles by which modularity emerges in neural networks remain elusive. We tackle this question with a plasticity model of neural networks derived from a purely topological perspective. Our topological reinforcement model acts enhancing the topological overlap between nodes, iteratively connecting a randomly selected node to a non-neighbor with the highest topological overlap, while pruning another network link at random. This rule reliably evolves synthetic random networks toward a modular architecture. Such final modular structure reflects initial proto-modules, thus allowing to predict the modules of the evolved graph. Subsequently, we show that this topological selection principle might be biologically implemented as a Hebbian rule. Concretely, we explore a simple model of excitable dynamics, where the plasticity rule acts based on the functional connectivity between nodes represented by co-activations. Results produced by the activity-based model are consistent with the ones from the purely topological rule, showing a consistent final network configuration. Our findings suggest that the selective reinforcement of topological overlap may be a fundamental mechanism by which brain networks evolve toward modular structure.

neuroscience↗