Biology subjects

Teufel, A. I.

Publications and source records attributed to Teufel, A. I..

5 recordsLinked to original sources

bioRxiv2020

Resilience and evolvability of protein-protein interaction networks

Protein-protein interaction (PPI) networks represent complex intra-cellular protein interactions, and the presence or absence of such interactions can lead to biological changes in an organism. Recent network-based approaches have shown that a phenotypes PPI networks resilience to environmental perturbations is related to its placement in the tree of life; though we still do not know how or why certain intra-cellular factors can bring about this resilience. Here, we explore the influence of gene expression and network properties on PPI networks resilience. We use publicly-available data of PPIs for E. coli, S. cerevisiae, and H. sapiens, where we compute changes in network resilience as new nodes (proteins) are added to the networks under three node addition mechanisms--random, degree-based, and gene-expression-based attachments. By calculating the resilience of the resulting networks, we estimate the effectiveness of these node addition mechanisms. We demonstrate that adding nodes with gene-expression-based preferential attachment (as opposed to random or degree-based) preserves and can increase the original resilience of PPI network in all three species, regardless of gene expression distribution or network structure. These findings introduce a general notion of prospective resilience, which highlights the key role of network structures in understanding the evolvability of phenotypic traits.

systems biology↗

bioRxiv2019

Quantifying the relative importance of competition, predation, and environmental variation for species coexistence

1Coexistence theory and food web theory are two cornerstones of the longstanding effort to understand how species coexist. Although competition and predation are known to act simultaneously in communities, theory and empirical study of the two processes continue to be developed independently. Here, we integrate modern coexistence theory and food web theory to simultaneously quantify the relative importance of predation, competition, and environmental fluctuations for species coexistence. We first examine coexistence in a classic multi-trophic model, adding complexity to the food web using a novel machine learning approach. We then apply our framework to a parameterized rocky intertidal food web model, partitioning empirical coexistence dynamics. We find that both environmental fluctuation and variation in predation contribute substantially to species coexistence. Unexpectedly, covariation in these two forces tends to destabilize coexistence, leading to new insights about the role of bottom-up versus top-down forces in both theory and the rocky intertidal ecosystem.

ecology↗

bioRxiv2019

Humanization of yeast genes with multiple human orthologs reveals principles of functional divergence between paralogs

Despite over a billion years of evolutionary divergence, several thousand human genes possess clearly identifiable orthologs in yeast, and many have undergone lineage-specific duplications in one or both lineages. The ortholog conjecture postulates that orthologous genes between species retain ancestral functions despite divergence over vast timescales, but duplicated genes will be free to diverge in function. However, the retention of ancestral functions among co-orthologs between species and within gene families has been difficult to test experimentally at scale. In order to investigate how ancestral functions are retained or lost post-duplication, we systematically replaced hundreds of essential yeast genes with their human orthologs from gene families that have undergone lineage-specific duplications, including those with single duplications (one yeast gene to two human genes, 1:2) or higher-order expansions (1:>2) in the human lineage. We observe a variable pattern of replaceability across different ortholog classes, with an obvious trend towards differential replaceability inside gene families, rarely observing replaceability by all members of a family. We quantify the ability of various properties of the orthologs to predict replaceability, showing that in the case of 1:2 orthologs, replaceability is predicted largely by the divergence and tissue-specific expression of the human co-orthologs, i.e. the human proteins that are less diverged from their yeast counterpart and more ubiquitously expressed across human tissues more often replace their single yeast ortholog. These trends were consistent with in silico simulations demonstrating that when only one ortholog is replaceable, it tends to be the least diverged of the pair. Replaceability of yeast genes having more than two human co-orthologs was marked by retention of orthologous interactions in functional or protein networks as well as by more ancestral subcellular localization. Overall, we performed >400 human gene replaceability assays revealing 56 new human-yeast complementation pairs, thus opening up avenues to further functionally characterize these human genes in a simplified organismal context.

systems biology↗

bioRxiv2018

The many nuanced evolutionary consequences of duplicated genes

Gene duplication is seen as a major source of structural and functional divergence in genome evolution. Under the conventional models of sub- or neofunctionalizaton, functional changes arise in one of the duplicates after duplication. However, we suggest here that the presence of a duplicated gene can result in functional changes to its interacting partners. We explore this hypothesis by in-silico evolution of a heterodimer when one member of the interacting pair is duplicated. We examine how a range of selection pressures and protein structures leads to differential patterns of evolutionary divergence. We find that a surprising number of distinct evolutionary trajectories can be observed even in a simple three member system. Further, we observe that selection to correct dosage imbalance can affect the evolution of the initial function in several unexpected ways. For example, if a duplicate is under selective pressure to avoid binding its original binding partner, this can lead to changes in the binding interface of a non-duplicated interacting partner to exclude the duplicate. Hence, independent of the fate of the duplicate, its presence can impact how the original function operates. Additionally, we introduce a conceptual framework to describe how interacting partners cope with dosage imbalance after duplication. Contextualizing our results within this framework reveals that the evolutionary path taken by a duplicates interacting partners is highly stochastic in nature. Consequently, the fate of duplicate genes may not only be controlled by their own ability to accumulate mutations but also by how interacting partners cope with them.

evolutionary biology↗

bioRxiv2016

Accelerated simulation of evolutionary trajectories in origin-fixation models

We present an accelerated algorithm to forward-simulate origin--fixation models. Our algorithm requires on average only about two fitness evaluations per fixed mutation, whereas traditional algorithms require, per one fixed mutation, a number of fitness evaluations on the order of the effective population size Ne. Our accelerated algorithm yields the exact same steady state as the original algorithm but produces a different order of fixed mutations. By comparing several relevant evolutionary metrics, such as the distribution of fixed selection coefficients and the probability of reversion, we find that the two algorithms behave equivalently in many respects. However, the accelerated algorithm yields less variance in fixed selection coefficients. Notably, we are able to recover the expected amount of variance by rescaling population size, and we find a linear relationship between the rescaled population size and the population size used by the original algorithm. Considering the widespread usage of origin--fixation simulations across many areas of evolutionary biology, we introduce our accelerated algorithm as a useful tool for increasing the computational complexity of fitness functions without sacrificing much in terms of accuracy of the evolutionary simulation.

evolutionary biology↗