Biology subjects

Guerrero, R.

Publications and source records attributed to Guerrero, R..

5 recordsLinked to original sources

bioRxiv2025

A network perspective on the evolution of hybrid incompatibilities

Theory predicts that hybrid incompatibilities accumulate faster than linearly with genetic divergence, a phenomenon known as the snowball effect. While this prediction is mathematically robust under simplifying assumptions, accumulating evidence suggests that the structure of gene interaction networks can alter both the rate and organization of incompatibility evolution. Here, we extend classic DMI models with a network approach, equating the assumptions of the Orr model with a complete graph of gene interactions. We simulate the evolution of hybrid incompatibilities under different gene interaction networks and evaluate the effects of network density, topology, and substitution model. We find that network density strongly governs the rate of DMI accumulation, particularly under models permitting multiple substitutions per locus, while network topology shapes the agglomeration of incompatibilities into large, connected clusters. Substitution rate heterogeneity, especially when anti-correlated with node degree, further suppresses both accumulation and clustering. These results highlight that while the snowball effect remains qualitatively valid, the structure and evolution of the incompatibility network exhibit nontrivial departures from previous expectations, with implications for observable quantities in empirical systems. Our findings underscore the importance of incorporating genomic architecture and network constraints into models of speciation.

evolutionary biology↗

bioRxiv2020

Diminutive, degraded but dissimilar: Wolbachia genomes from filarial nematodes do not conform to a single paradigm

Wolbachia are alpha-proteobacteria symbionts infecting a large range of arthropod species and two different families of nematodes. Interestingly, these endosymbionts are able to induce diverse phenotypes in their hosts: they are reproductive parasites within many arthropods, nutritional mutualists within some insects and obligate mutualists within their filarial nematode hosts. Defining Wolbachia "species" is controversial and so they are commonly classified into 16 different phylogenetic lineages, termed supergroups, named A to S. However, available genomic data remains limited and not representative of the full Wolbachia diversity; indeed, of the 24 complete genomes and 55 draft genomes of Wolbachia available to date, 84% belong to supergroups A and B, exclusively composed of Wolbachia from arthropods. For the current study, we took advantage of a recently developed DNA enrichment method to produce four complete genomes and two draft genomes of Wolbachia from filarial nematodes. Two complete genomes, wCtub and wDcau, are the smallest Wolbachia genomes sequenced to date (863,988bp and 863,427bp, respectively), as well as the first genomes representing supergroup J. These genomes confirm the validity of this supergroup, a controversial clade due to weaknesses of the multi-locus system typing (MLST) approach. We also produced the first draft Wolbachia genome from a supergroup F filarial nematode representative (wMhie), two genomes from supergroup D (wLsig and wLbra) and the complete genome of wDimm from supergroup C. Our new data confirm the paradigm of smaller Wolbachia genomes from filarial nematodes containing low levels of transposable elements and the absence of intact bacteriophage sequences, unlike many Wolbachia from arthropods, where both are more abundant. However, we observe differences among the Wolbachia genomes from filarial nematodes: no global co-evolutionary pattern, strong synteny between supergroup C and supergroup J Wolbachia, and more transposable elements observed in supergroup D Wolbachia compared to the other supergroups. Metabolic pathway analysis indicates several highly conserved pathways (haem and nucleotide biosynthesis for example) as opposed to more variable pathways, such as vitamin B biosynthesis, which might be specific to certain host-symbiont associations. Overall, there appears to be no single Wolbachia- filarial nematode pattern of co-evolution or symbiotic relationship. Graphical abstract O_FIG_DISPLAY_L [Figure 1] M_FIG_DISPLAY C_FIG_DISPLAY RepositoriesData generated are available in GenBank: BioProject PRJNA593581; BioSample SAMN13482485 for wLsig, Wolbachia endosymbiont of Litomosoides sigmodontis (genome: CP046577); Biosample SAMN15190311 for the nematode host Litomosoides sigmodontis (genome: JABVXW000000000); BioSample SAMN13482488 for wDimm, Wolbachia endosymbiont of Dirofilaria (D.) immitis (genome: CP046578); Biosample SAMN15190314 for the nematode host Dirofilaria (D.) immitis (genome: JABVXT000000000); BioSample SAMN13482046 for wCtub, Wolbachia endosymbiont of Cruorifilaria tuberocauda (genome: CP046579); Biosample SAMN15190313 for the nematode host Cruorifilaria tuberocauda (genome: JABVXU000000000); BioSample SAMN13482057 for wDcau, Wolbachia endosymbiont of Dipetalonema caudispina (genome: CP046580); Biosample SAMN15190312 for the nematode host Dipetalonema caudispina (genome: JABVXV000000000); BioSample SAMN13482459 for wLbra, Wolbachia endosymbiont of Litomosoides brasiliensis (genome: WQM000000000); Biosample SAMN15190311 for the nematode host Litomosoides brasiliensis (genome: JABVXW000000000); BioSample SAMN13482487 for wMhie, Wolbachia endosymbiont of Madathamugadia hiepei (genome: WQMP00000000); Biosample SAMN15190315 for the nematode host Madathamugadia hiepei (genome: JABVXS000000000). The raw data are available in GenBank as Sequence Read Archive (SRA): SRR10903008 to SRR10903010; SRR10902913 to SRR10902914; SRR10900508 to SRR10900511; SRR10898805 to SRR10898806. Data summaryThe authors confirm all supporting data, code and protocols have been provided within the article or through supplementary data files. Eleven Supplementary tables and two supplementary files are available with the online version of this article. Impact StatementWolbachia are endosymbiotic bacteria infecting a large range of arthropod species and two different families of nematodes, characterized by causing diverse phenotypes in their hosts, ranging from reproductive parasitism to mutualism. While available Wolbachia genomic data are increasing, they are not representative of the full Wolbachia diversity; indeed, 84% of Wolbachia genomes available on the NCBI database to date belong to the two main studied clades (supergroups A and B, exclusively composed of Wolbachia from arthropods). The present study presents the assembly and analysis of four complete genomes and two draft genomes of Wolbachia from filarial nematodes. Our genomics comparisons confirm the paradigm that smaller Wolbachia genomes from filarial nematodes contain low levels of transposable elements and the absence of intact bacteriophage sequences, unlike many Wolbachia from arthropods. However, data show disparities among the Wolbachia genomes from filarial nematodes: no single pattern of co-evolution, stronger synteny between some clades (supergroups C and supergroup J) and more transposable elements in another clade (supergroup D). Metabolic pathway analysis indicates both highly conserved and more variable pathways, such as vitamin B biosynthesis, which might be specific to certain host-symbiont associations. Overall, there appears to be no single Wolbachia-filarial nematode pattern of symbiotic relationship.

genomics↗

bioRxiv2018

Limited One-time Sampling Irregularity Age Map (LOTS-IAM): Automatic Unsupervised Detection of Brain White Matter Abnormalities in Structural Magnetic Resonance Images

We present a complete study of limited one-time sampling irregularity map (LOTS-IM), a fully automatic unsupervised approach to extract brain tissue irregularities in magnetic resonance images (MRI), including its application and evaluation for quantitative assessment of white matter hyperintensities (WMH) of presumed vascular origin and assessing multiple sclerosis (MS) lesion progression. LOTS-IM is unique compared to similar other methods because it yields irregularity map (IM) which represents WMH as irregularity values, not probability values, and retains the original MRIs texture information. We tested and compared the usage of IM for WMH segmentation on T2-FLAIR MRI with various methods, including the well established unsupervised WMH segmentation Lesion Growth Algorithm from the public toolbox Lesion Segmentation Toolbox (LST-LGA), conventional supervised machine learning schemes and state-of-the-art supervised deep neural networks. In our experiments, LOTS-IM outperformed unsupervised method LST-LGA, both in performance and processing speed, thanks to the limited one-time sampling scheme and its implementation on GPU. Our method also outperformed supervised conventional machine learning algorithms (i.e., support vector machine (SVM) and random forest (RF)) and deep neural networks algorithms (i.e., deep Boltzmann machine (DBM) and convolutional encoder network (CEN)), while yielding comparable results to the convolutional neural network schemes that rank top of the algorithms developed up to date for this purpose (i.e., UResNet and UNet). The high sensitivity of IM on depicting signal change deems suitable for assessing MS progression, although care must be taken with signal changes not reflective of a true pathology.

neuroscience↗

bioRxiv2017

Speciation as a sieve for ancestral polymorphism

Studying the process of speciation using patterns of genomic divergence between species requires that we understand the determinants of genetic diversity within species. Because sequence diversity in an ancestral population determines the starting point from which divergent populations accumulate differences (Gillespie & Langley 1979), any evolutionary forces that shape diversity within species can have a large impact on measures of divergence between species. These forces include those both decreasing (e.g., selective sweeps (Begun et al. 2007; Cruickshank & Hahn 2014) or background selection (Phung et al. 2016)) and increasing variation (e.g., balancing selection (Charlesworth 2006)). Selection can increase diversity by favoring the maintenance of polymorphism via overdominance, frequency dependence, and heterogeneous selection. Nevertheless, balanced polymorphisms are co ...

evolutionary biology↗

bioRxiv2016

Topological features of gene regulatory networks predict patterns of natural diversity in environmental response

Molecular interactions affect the evolution of complex traits. For instance, adaptation may be constrained by pleiotropic or epistatic effects, both of which will be reflected in the structure of molecular interaction networks. To date, empirical studies investigating the role of molecular interactions in phenotypic evolution have been idiosyncratic, offering no clear patterns. Here, we investigated the network topology of genes putatively involved in local adaptation to two abiotic stressors--drought and cold--in Arabidopsis thaliana. Our findings suggest that the gene-interaction topologies for both cold and drought stress response are non-random, with genes that show genetic variation in drought response (GxE) being significantly more peripheral and cold response genes being significantly more central than genes not involved in either response. We suggest that the observed topologies reflect different constraints on the genetic pathways involved in the assayed phenotypes. The approach presented here may inform predictive models linking genetic variation in molecular signaling networks with phenotypic variation, specifically traits involved in environmental response.\n\nSignificance StatementOur study focuses on genes whose transcriptional activity exhibits genetic variation in response to the environment, or \"GxE.\" GxE is a widely observed phenomenon of critical importance to understanding the genotype-to-phenotype map, the evolution of natural populations, medical genetics, population response to climate change, and agricultural improvement. We investigated expression GxE in plant responses to two abiotic cues: cold and drought. We found that genes showing genetically variable response to cold stress are centrally located in regulatory networks whereas genes showing genetically variable response to drought stress are peripherally located in regulatory networks. This result suggests that selection is presented with vastly different mutational landscapes for shaping evolutionary or breeding response to these two important climatic factors

evolutionary biology↗