Biology subjects

Bastolla, U.

Publications and source records attributed to Bastolla, U..

5 recordsLinked to original sources

bioRxiv2024

The topography of DNA replication origins in Eukarya: GGN clusters, landmark nucleosomes, CDC6 and G4 structures

BackgroundWe recently identified tandem repeats of GGN triplets as a sequence motif strongly enriched in the DNA replication origins (ORIs) of the Arabidopsis thaliana genome, where it correlates with the ORI strength quantified through short nascent strands sequencing. ResultsHere we show that clusters of four or more GGN repeats separated by short tracts (<7 nucleotides) are present in more than 65% ORIs of six model eukaryotic organisms: Leishmania major, Arabidopsis thaliana, Caenorhabditis elegans, Drosophila melanogaster, Mus musculus and Homo sapiens. The measured percentages vary with the experimental technique adopted for ORI determination. For all studied techniques and organisms, ORIs are significantly prone to be located within or near strong GGN clusters, although these are neither necessary nor sufficient for ORI activation. Interestingly, for same experimental technique, the association between ORIs and GGN clusters was stronger al later developmental stages for all organisms for which we could perform this comparison. In accordance with a biophysical model of nucleosome positioning, the GGN clusters strongly favor nucleosome occupancy. At the same time, both GGN clusters and ORIs occur frequently within 1kb from nucleosome-depleted regions (NDR). We propose a structural model based on chromatin secondary structure in which the NDR and the well-positioned nucleosome at the GGN are close in space, which may favor functional interactions. We hypothesize that the presence of GGN at ORIs arose in part for promoting the above nucleosome organization Moreover, at least in Arabidopsis, the replication protein CDC6 is very strongly associated with GGN clusters. NMR experiments showed that clusters of at least four GGN can form G-quadruplex (G4) in vitro. Our data support the view that GGN clusters are formed through the interplay of mutational processes (GC skew at ORIs plus triplet expansion), and that similar mutational processes, i.e. AT skew at transcription start sites (TSS) and ORIs, might facilitate the formation of the NDR, thus favoring the evolvability of chromatin. ConclusionsGGN clusters are easily evolvable sequence motifs enriched at ORIs of eukaryotic genomes. They favor G4 secondary structure and a nucleosome organization that may unify the apparent discrepancy between ORIs of higher eukaryotes and yeast.

genomics↗

bioRxiv2023

Structure and stability constrained substitution models outperform traditional substitution models used for evolutionary inference

In previous studies, we presented site-specific substitution models of protein evolution based on selection on the folding stability of the native state (Stab-CPE), which predict more realistically the evolutionary variability across protein sites. However, those Stab-CPE present qualitative differences from observed data, probably because they ignore changes in the native structure, despite empirical studies suggesting that conservation of the native structure is a stronger selective force than selection on folding stability. Here we present novel structurally constrained substitution models (Str-CPE) based on Julian Echaves model of the structural change due to a mutation as the linear response of the protein to a perturbation and on the explicit model of the perturbation generated by a specific amino-acid mutation. Compared to our previous Stab-CPE models, the novel Str-CPE models are more stringent (they predict lower sequence entropy and substitution rate), provide higher likelihood to multiple sequence alignments (MSA) that include one or more known structures, and better predict the observed conservation across sites. The models that combine Str-CPE and Stab-CPE models are even more stringent and fit the empirical MSAs better. We refer collectively to our models as structure and stability constrained substitution models (SSCPE). Importantly in comparison to the traditional empirical substitution models, the SSCPE models infer phylogenetic trees of distantly related proteins more similar to reference trees based on structural information. We implemented the SSCPE models in the program SSCPE.pl, freely available at https://github.com/ugobas/SSCPE, which infers phylogenetic trees under the SS-CPE models with the program RAxML-NG from a concatenated alignment and a list of protein structures that overlap with it.

evolutionary biology↗

bioRxiv2023

PC_sim: An integrated measure of protein sequence and structure similarity for improved alignments and evolutionary inference

MotivationEvolutionary inferences depend crucially on the quality of multiple sequence alignments (MSA), which is problematic for distantly related proteins. Since protein structure is more conserved than protein sequence, it seems natural to use structure alignments for distant homologs. However, structure alignments may not be suitable for inferring evolutionary relationships at the sequence level. ResultsHere we investigate the mutual relationships between four protein similarity measures that depend on sequence and structure (fraction of aligned residues, sequence similarity, fraction of superimposed backbones and contact overlap) and the corresponding alignments. Changes in protein sequences and structures are intimately correlated, but our results suggest that no individual measure can provide a complete and unbiased picture of changes in protein sequences and structure. Therefore, we propose a new hybrid measure of protein sequence and structure similarity based on Principal Components (PC_sim). Starting from an MSA, we obtain modified pairwise alignments (PA) based on PC_sim, and from them we construct a new MSA based on the maximal cliques of the PA graph. These alignments yield larger protein similarities and agree better with the Balibase "reference" MSA and with consensus MSA than alignments that target individual similarity measures. Moreover, PC_sim is associated with a divergence measure that correlates strongest with divergences obtained from individual similarities, which suggests that it can infer more accurate evolutionary divergences for the reconstruction of phylogenetic trees with distance methods. Availabilityhttps://github.com/ugobas/Evol_div Contactubastolla@cbm.csic.es

bioinformatics↗

bioRxiv2022

Cross-biome microbial networks reveal functional redundancy and suggest genome reduction through functional complementarity

Microbial communities are complex and dynamic entities, and their structure arises from the interplay of a multitude of factors, including the interactions of microorganisms with each other and with the environment. Since each extant community has a unique eco-evolutionary history, it might appear that contingency rather than general rules govern their assembly. In spite of this, there is evidence that some general assembly principles exist, at least to a certain extent. In this work, we sought to identify those principles by performing a cross-study, cross-biome meta-analysis of microbial occurrence data in more than 5,000 samples from ten different environmental groups. We adopted a novel algorithm that allows the same taxa to aggregate with different partners in different habitats, capturing the complexity of interactions inherent to natural microbial communities. We tried to decouple function from phylogeny, the environment, and genome size, in order to provide an unbiased characterization of phylogenetic and functional redundancy in environmental microbial assemblages. We then examined the phylogenetic and functional composition of the resulting inferred communities, and searched for global patterns of assembly both at the community level and in individual metabolic pathways. Our analysis of the resulting microbial assemblages highlighted that environmental communities are more functionally redundant than expected by chance. This effect is greater for communities appearing in more than one environment, suggesting a link between functional redundancy and environmental adaptation. In spite of this, certain pathways are observed in fewer taxa than expected by chance, suggesting the presence of auxotrophy, and presumably cooperation among community members, which is supported by our analysis of amino acid biosynthesis pathways. Furthermore, this hypothetical cooperation may play a role in genome reduction, since we observed a negative relationship between the size of bacterial genomes and the number of taxa of the community they belong to. Overall, our results provide a global characterization of environmental microbial communities, and offer design principles for engineering robust bacterial communities.

microbiology↗

bioRxiv2018

Differences in firing efficiency, chromatin and transcription underlie the developmental plasticity of Arabidopsis originome

Eukaryotic genome replication depends on thousands of DNA replication origins (ORIs) that constitute the originome. A major challenge is to learn ORI biology in multicellular organisms in the context of growing organs to understand their developmental plasticity. We have determined the originome and chromatin landscape of Arabidopsis thaliana at two stages of postembryonic development. ORIs associate with multiple chromatin signatures including TSS but also regulatory regions and heterochromatin, where ORIs colocalize with retrotransposons. In addition, quantitative analysis of ORI activity led us to conclude that strong ORIs have high GC content and clusters of GGN trinucleotides. Development primarily influences ORI firing strength rather than ORI location. ORIs that preferentially fire at early developmental stages colocalize with GC-rich heterochromatin whereas at later stages with transcribed genes, perhaps as a consequence of changes in chromatin features associated with developmental processes. Our study provides the originome of an organism at the postembryo stage that should allow us to study ORI biology in response to development, environment and mutations with a quantitative approach. In a wider scope, the computational strategies developed here can be transferred to other eukaryotic systems.

plant biology↗