Biology subjects

Duarte, L.

Publications and source records attributed to Duarte, L..

8 recordsLinked to original sources

bioRxiv2023

Cancer associated fibroblasts mitigate the efficacy of the combination treatment of chemotherapy and BCL-xL targeting in triple negative breast cancer cells

Triple negative breast cancers (TNBC) present a poor prognosis primarily due to their resistance to chemotherapy. This resistance is understood to associate with elevated expression of certain antiapoptotic members within the proteins of the BCL-2 family (namely BCL-xL, MCL-1 and BCL-2). These regulate cell death by inhibiting pro-apoptotic protein activation through binding and sequestration and they can be selectively antagonized by BH3 mimetics. Yet the individual influences of BCL-xL, MCL-1, and BCL-2 on the sensitivity of TNBC cells to chemotherapy, and their regulation by cancer-associated fibroblasts (CAFs), major components of the tumor stroma and key contributors to therapy resistance remain to be delineated. By utilizing engineered TNBC cell line MDA-MB-231 deficient in BCL-2 family proteins and BH3 mimetics, we show that BCL-xL and MCL-1 promote cancer cell survival by compensatory mechanisms. This cell line shows limited sensitivity to chemotherapy, in line with the clinical resistance observed in TNBC patients. We elucidate that BCL-xL plays a pivotal role in therapy response, as its depletion or pharmacological inhibition heightened chemotherapy effectiveness. Moreover, BCL-xL expression is associated to chemotherapy resistance in patient-derived tumoroids where its pharmacological inhibition enhances ex vivo response to chemotherapy. In a co-culture model of cancer cells and CAFs, we observe that even in a context where BCL-xL inhibition renders cancer cells more susceptible to chemotherapy, those in contact with CAFs display reduced sensitivity to chemotherapy. Thus CAFs exert a profound pro-survival effect in breast cancer cells, even in a setting highly favoring cell death through combined chemotherapy and inhibition of the main actor of chemoresistance, BCL-xL.

cancer biology↗

bioRxiv2021

There and Back to the Present: An Evolutionary Tale on Biological Diversity

Ecologists often agree on the importance of macroevolution for niche-mediated distribution of biological diversity along environmental gradients. Yet, macroevolutionary diversification and dispersal in time and space generate uneven geographic distribution of phylogenetic pools, which affects the imprint let by macroevolution on local species pools. In this article we introduce an individual-based simulation approach coupled to Approximate Bayesian Computation (ABC) that allows to parameterize the adaptation rate of species niche positions along the evolution of a monophyletic lineage, and the intensity of dispersal limitation, associated with the distribution of biological diversity between assemblages potentially connected by dispersal (metacommunity). The analytical tool was implemented in an R package called mcfly. We evaluated the statistical performance of the analytical framework using simulated datasets, which confirmed the suitability of the analysis to estimate adaptation rate and dispersal limitation parameters. Further, we evaluated the role played by niche evolution and dispersal limitation on species diversity distribution of Phyllostomidae bats across the Neotropics. The framework proposed here shed light on the links between niche evolution, dispersal limitation and the distribution of biological diversity, and thereby improved our understanding of evolutionary imprints on ecological patterns. Perhaps more importantly, it offers new possibilities for solving the eco-evolutionary puzzle.

ecology↗

bioRxiv2021

The Hidden Side Of Diversity: Effects Of Imperfect Detection On Multiple Dimensions Of Biodiversity

O_LIStudies on ecological communities often address patterns of species distribution and abundance, but few consider uncertainty in counts of both species and individuals when computing diversity measures. C_LIO_LIWe evaluated the extent to which imperfect detection may influence patterns of taxonomic, functional, and phylogenetic diversity in ecological communities. C_LIO_LIWe estimated the true abundance of fruit-feeding butterflies sampled in canopy and understory strata in a subtropical forest. We compared the diversity values calculated by observed and estimated abundance data through the hidden diversity framework. This framework evaluates the deviation of observed diversity when compared with diversities derived from estimated true abundances, and if such deviation represents a bias or a noise in the observed diversity pattern. C_LIO_LIThe hidden diversity values differed between strata for all diversity measures, except for functional richness. The taxonomic measure was the only one where we observed an inversion of the most diverse stratum when imperfect detection was included. Regarding phylogenetic and functional measures, the strata showed distinct responses to imperfect detection, despite the tendency to overestimate observed diversity. While the understory showed noise for the phylogenetic measure, since the observed pattern was maintained, the canopy had biased diversity for the functional metric. This bias occurred since no significant differences were found between strata for observed diversity, but rather for estimated diversity, with the canopy being more clustered. C_LIO_LIWe demonstrate that ignore imperfect detection may lead to unrealistic estimates of diversity and hence, to erroneous interpretations of patterns and processes that structure biological communities. For fruit-feeding butterflies, according to their phylogenetic position or functional traits, the undetected individuals triggered different responses in the relationship of the diversity measures to the environmental factor. This highlights the importance to evaluate and include the uncertainty in species detectability before calculating biodiversity measures to describe communities. C_LI

ecology↗

bioRxiv2020

Potential mammalian species for investigating the past connections between Amazonia and the Atlantic Forest

Much evidence suggests that Amazonia and the Atlantic Forest were connected through at least three dispersion routes in the past: the northeast route, the central route, and the southeast-northwest route. According to previous studies, the southeast-northwest route would have been the most frequently used. However, few studies have assessed the use of these routes based on multiple species. Here we present a compilation of potential mammal species that could have dispersed between the two forest regions to investigate these connections. We evaluate the geographic distributions of mammals occurring in both Amazonia and the Atlantic Forest and the likely connective routes between these forests. We classified the species per habitat occupancy (strict forest specialists, species that prefer forest, or generalists) and compiled the genetic data available for each species to evaluate their potential for phylogeographic studies focusing on genetic exchange between the two forest regions. We found 127 mammalian species occurring in both Amazonia and the Atlantic Forest for which significant genetic data was available. Hence, highlighting their potential for phylogeographic studies investigating the past connections between the two forests. Differently from what was previously proposed, the northeast route showed evidence of past use by more mammal species than the remaining two routes. The central route would have been the second most important in terms of species. Our results show the potential of using mammal species to investigate and bring new insights about the past connections between Amazonia and the Atlantic Forest.

ecology↗

bioRxiv2020

naturaList: a package to classify occurrence records in levels of confidence in species identification

O_LIThere is a big volume of occurrence records available in biodiversity databases, but researchers should guarantee its quality before use it in scientific studies. A problem that might compromise the quality of occurrence data is species misidentification. We address this issue by presenting naturaList, a R package designed to classify species occurrence data according to identification reliability. C_LIO_LInaturaList allows to classify species occurrences up to six levels of confidence in species identification, and to filter occurrence data accordingly. The highest level of confidence is assigned to records identified by a specialist, whose name must be provided by the user. The other five levels of confidence are derived from the occurrence data. We demonstrate naturaList functions using occurrences of Alsophila setosa, a tree fern species from Atlantic Forest, as example. We classified and filtered data in grid cells in order to maintain only the highest-level records in each cell. Then we selected only those records classified in the two highest levels of confidence. C_LIO_LIFrom 323 occurrences of Alsophila setosa displaying geographic coordinates, 69 (21%) were identified by a specialist. After filtering the highest-level records inside grid cells, 102 records remained. From these grid cell filtered data, 38 occurrences (37%) were classified into the highest confidence level. Three records were removed using an interactive map module, due to falling in sea sites or outside the native range size of the species. Since we selected only records classified in the two highest levels of confidence, the final dataset contained 94 occurrence records. C_LIO_LInaturaList guarantees the reproducibility of occurrence data processing and cleaning. Macroecologists, biogeographers and taxonomists might benefit from using naturaList package to evaluate the quality of species identification in occurrence data and by identify sites that need evaluation of taxonomic classification of species. C_LI

ecology↗

bioRxiv2019

Evoregions: Mapping Shifts in Phylogenetic Turnover Across Biogeographic Regions

Biogeographic regionalization offers context to the geographical evolution of clades. The positions of bioregions inform both the spatial location of clusters in species distribution and where their most important boundaries are. Nevertheless, defining bioregions based on species distribution alone only incidentally recovers regions that are important during the evolution of the focal group. The extent to which bioregions correspond to centers of independent diversification depends on how clusters of species composition naturally reflect the radiation of single clades, which is not the case when mixed colonization occurred. Here, we showed that using phylogenetic turnover based on fuzzy sets, instead of species composition, led to more adequate detection of evolutionary important bioregions, that is, regions that truly account for the independent diversification of lineages. Mapping those evoregions in the phylogenetic tree quickly reveals the timing and location of major shifts of biogeographic regions. Moreover, evolutionary transition zones are easily mapped, and permits the recognition of regions with high phylogenetic overlap. Our results using the global radiation of rats and mice (Muroidea) recovered four evoregions--three major evolutionary arenas corresponding to the Neotropics, a Nearctic-Siberian, and a Paleotropical-Australian evoregion, and a fourth and fuzzy Afro-Palearctic evoregion. In comparison, an analysis with a method considering species distribution alone found 52 bioregions. Evoregions is a useful framework whenever the question is related to the identification of the most important centers of a groups diversification history and its evolutionary transitions zones.

ecology↗

bioRxiv2018

The dimensionality of biological diversity revisited.

Biodiversity can be represented by a variety of different dimensions (e.g. functional diversity, phylogenetic diversity, genetic diversity and taxonomic diversity). While the many different representations of biodiversity can lead to a more complete description of communities, they also lead to a degree of fragmentation of the biodiversity concept. Developing a unified measure that integrates all the dimensions of biodiversity is a theoretical solution for this problem, however, it remains an operationally utopian task. Alternatively, quantifying the dimensionality of biodiversity is a way to integrate the different dimensions of biological diversity. This concept is explored in the present study. We define the dimensionality of diversity as the number of different diversity metrics needed to effectively describe biodiversity. We provide a solution that joins the two components of dimensionality (correlation and the variation of each diversity metric) in the same framework, allowing us to quantify dimensionality. We show, through a simulation study, that considering the correlation among dimensions with the variation of each dimension offers important information that is neglected when only correlation information (the component used in the current methodology to measure dimensionality) is considered. We demonstrate how to apply the proposed framework by investigating the dimensionality of South American small mammal communities (cricetids and marsupials). Our example demonstrates that the taxonomic dimension of diversity captures most of the spatial variation among these communities. We conclude by highlighting the greater understanding of dimensionality that is obtained through this method, as well as the insights it provides into the importance of each diversity metric to the whole biodiversity space, insights which can not be obtained through the current common method.

ecology↗

bioRxiv2018

HaploVectors: an integrative analytical tool for phylogeography

Sets of local populations show different degrees of gene flow due to dispersal barriers and environmental constraints, which renders genetic composition gradients among populations (genetic turnover). Unveiling biogeographic correlates of genetic turnover is paramount for phylogeography. While some processes (genetic drift, secondary contact) may erase the historical track of genetic turnover, vicariance or ancient dispersal likely leads to genetic divergence among populations. Yet available analyses do not permit direct inference about mechanisms driving genetic turnover. We propose a novel analytical approach called genvector analysis, which fulfills this gap by decomposing genetic compositional dissimilarities between populations based on either haplotypes or other genetic data into genetic eigenvectors. Such procedure allows exploring genetic turnover among sets of local populations, and analyzing their biogeographic correlates based on null model tests. We evaluate the statistical performance of the method on simulated datasets. We also analyzed biogeographic correlates of genetic turnover of Akodon cursor in the Brazilian Atlantic Forest. Results revealed that genvector analysis is robust to discriminate biogeographic drivers of genetic turnover. For Akodon cursor analysis, we observed that while for the entire species, all predictors considered (except for elevation) explained genetic turnover, within phylogroups some factors varied their importance. Genvector analysis was demonstrated to be useful for several different purposes in phylogeography, and complementary to classic analytical tools widely used by phylogeographers, such as AMOVA or DAPC. The role of ancient versus recent biogeographic events, the relationship between morphological divergence or abiotic variables and genetic turnover can easily be investigated using genvector analysis.

ecology↗