Biology subjects

Mueller, J.

Publications and source records attributed to Mueller, J..

77 records · Page 5Linked to original sources

bioRxiv2019

Abiotic environmental adaptation in vertebrates is characterized by functional genomic constraint

Understanding the genomic basis of adaptation to different abiotic environments is important for understanding organismal responses to current short-term environmental fluctuations. Using functional and comparative genomics approaches, we here investigated whether genomic adaptation to a set of environmental parameters is contingent across vertebrate genomes or, alternatively, contains an element of evolutionary constraint that would be evident through recurrent involvement of specific subsets of genes and functions in adaptation to similar environments. We first identified 200 genes with signatures of selection from transcriptomes of 24 species of lacertid lizards with known adaptations in preferred temperature, correlated with thermal environment experienced by these lizards in their range. In order to discern genes adapting to climate from other selective factors, we then performed a meta-analysis of 1100 genes with signatures of selection obtained from-omics studies in vertebrate species adapted to different abiotic environments. We found that this gene set formed a tightly connected interactome which was to 23% enriched in predicted functions of adaptation to climate and to 18% involved in organismal stress response. We found a much higher degree of recurrent use of identical genes (43.6%) and functional similarity than expected by chance, and no clear division between genes used in ectotherm and endotherm physiological strategies. 171 out of 200 genes of Lacertidae were part of this network, indicating that a comparative genomic approach can help to disentangle genes functionally related to adaptation to different abiotic environments from other selective factors. These results furthermore highlight an important role of genomic constraint in adaptation to the abiotic environment, and narrows the set of candidate markers to be used in future research on environmental adaptability related to climate change.\n\nSignificance Statement / Nontechnical summaryWhile the convergent evolution of phenotypes in similar environments is a well-studied phenomenon, the genomic basis of such common phenotypes and physiologies is still enigmatic. The prevalent notion is that re-use of the same genes to adapt to similar environments in different species is about as likely as winning the lottery - but organismal systems are also, to some extent, comparable between different species such as man and fruit fly through shared genes and gene functions. In this paper, we test whether constraint or contingency is more prevalent in genomic adaptation of vertebrates to aspects of their abiotic environment. We find evidence for strong functional constraint and stress responsiveness of the genes involved, which might help understand how currently experienced stress under changing climates may result in future adaptation.

evolutionary biology↗

bioRxiv2019

Identifying hetero-protein complexes in the nuclear envelope

The nucleus is delineated by the nuclear envelope (NE), which is a double membrane barrier composed of the inner and outer nuclear membranes as well as a ~40 nm wide lumen. In addition to its barrier function, the NE acts as a critical signaling node for a variety of cellular processes which are mediated by protein complexes within this subcellular compartment. While fluorescence fluctuation spectroscopy (FFS) is a powerful tool for characterizing protein complexes in living cells, it was recently demonstrated that conventional FFS methods are not suitable for applications in the NE because of the presence of slow nuclear membrane undulations. We previously addressed this challenge by developing time-shifted mean-segmented Q (tsMSQ) analysis and applied it to successfully characterize protein homo-oligomerization in the NE. However, many NE complexes, such as the linker of the nucleoskeleton and cytoskeleton (LINC) complex, are formed by heterotypic interactions, which single-color tsMSQ is unable to characterize. Here, we describe the development of dual-color (DC) tsMSQ to analyze NE hetero-protein complexes built from proteins that carry two spectrally distinct fluorescent labels. Experiments performed on model systems demonstrate that DC tsMSQ properly identifies hetero-protein complexes and their stoichiometry in the NE by accounting for spectral crosstalk and local volume fluctuations. Finally, we applied DC tsMSQ to study the assembly of the LINC complex, a hetero-protein complex composed of Klarsicht/ANC-1/SYNE homology (KASH) and Sad1/UNC-84 (SUN) proteins, in the NE of living cells. Using DC tsMSQ, we demonstrate the ability of the SUN protein SUN2 and the KASH protein nesprin-2 to form a hetero-complex in vivo. Our results are consistent with previously published in vitro studies and demonstrate the utility of the DC tsMSQ technique for characterizing NE hetero-protein complexes.\n\nStatement of SignificanceProtein complexes found within the nuclear envelope (NE) play a vital role in regulating cellular functions ranging from gene expression to cellular movement. However, the assembly state of these complexes within their native environment remains poorly understood, which is compounded by a general lack of fluorescence techniques suitable for quantifying the oligomeric state of NE protein complexes. This study aims at addressing this issue by introducing dual-color time-shifted mean-segmented Q analysis as a fluorescence fluctuation method specifically designed to identify the average oligomeric state of hetero-protein complexes within the NE of living cells.

biophysics↗

bioRxiv2019

Antibody Complementarity Determining Region Design Using High-Capacity Machine Learning

The precise targeting of antibodies and other protein therapeutics is required for their proper function and the elimination of deleterious off-target effects. Often the molecular structure of a therapeutic target is unknown and randomized methods are used to design antibodies without a model that relates antibody sequence to desired properties. Here we present a machine learning method that can design human Immunoglobulin G (IgG) antibodies with target affinities that are superior to candidates from phage display panning experiments within a limited design budget. We also demonstrate that machine learning can improve target-specificity by the modular composition of models from different experimental campaigns, enabling a new integrative approach to improving target specificity. Our results suggest a new path for the discovery of therapeutic molecules by demonstrating that predictive and differentiable models of antibody binding can be learned from high-throughput experimental data without the need for target structural data.\n\nSignificanceAntibody based therapeutics must meet both affinity and specificity metrics, and existing in vitro methods for meeting these metrics are based upon randomization and empirical testing. We demonstrate that with sufficient target-specific training data machine learning can suggest novel antibody variable domain sequences that are superior to those observed during training. Our machine learning method does not require any target structural information. We further show that data from disparate antibody campaigns can be combined by machine learning to improve antibody specificity.

immunology↗

bioRxiv2018

Core components of endosomal mRNA transport are evolutionarily conserved in fungi

Active movement of mRNAs by sophisticated transport machineries determines precise spatiotemporal expression of encoded proteins. A prominent example discovered in fungi is microtubule-dependent transport via endosomes. This mode of transport was thought to be only operational in the basidiomycete Ustilago maydis. Here, we report that distinct core components are evolutionarily conserved in fungal species of distantly related phyla like Mucoromycota. Interestingly, orthologues of the key RNA-binding protein Rrm4 from the higher basidiomycete Coprinopsis cinerea and the mucoromycete Rhizophagus irregularis shuttle on endosomes in hyphae of U. maydis. Thus, endosomal mRNA transport appears to be more wide-spread than initially anticipated.\n\nHighlights- Core transport components Upa1 and Rrm4 are conserved in different fungal phyla\n- Components of the Rrm4 machinery were most likely secondarily lost in ascomycetes\n- Upa1 from Microbotryum lychnidis-dioicae is functional in U. maydis\n- Rrm4 orthologues from Basidio- and Mucoromycota shuttle in hyphae of U. maydis

microbiology↗

bioRxiv2017

Effects of population- and seed bank noise on neutral evolution and efficacy of natural selection

Population genetics models typically consider a fixed population size and a unique selection coefficient. However, population dynamics inherently generate noise in numbers of individuals and selection acts on various components of the individuals fitness. In plant species with seed banks, the size of both the above- and below-ground compartments present noise depending on seed production and the state of the seed bank. We investigate if this noise has consequences on 1) the rate of genetic drift, and 2) the efficacy of selection. We consider four variants of two-allele Moran-type models defined by combinations of presence and absence of noise in above-ground and seed bank compartments. Time scale analysis and dimension reduction methods allow us to reduce the corresponding Fokker-Planck equation to a one-dimensional diffusive Moran model. We first show that if the above-ground noise classically affects the rate of genetic drift, below-ground noise reduces the diversity storage effect of the seed bank. Second, we consider that selection can act on four different components of the plant fitness: plant or seed death rate, seed production or seed germination. Our striking result is that the efficacy of selection for seed death rate or germination rate is reduced by seed bank noise, whereas selection occurring on plant death rate or seed production is not affected. We derive the expected site-frequency spectrum reflecting this heterogeneity in selection efficacy between genes underpinning different plant fitness components. Our results highlight the importance to consider the effect of ecological noise to predict the impact of seed banks on neutral and selective evolution.

evolutionary biology↗