Global patterns of plumage color evolution in island-living passeriform birds.

Island environments have the potential to change evolutionary trajectories of morphological traits in species relative to their mainland counterparts due to habitat and resource differences, or by reductions in the intensity of social or sexual selection. Latitude, island size, and isolation may further influence trait evolution through biases in colonization rates. We used a global dataset of passerine plumage color as a model group to identify selective pressures driving morphological evolution of island animals using phylogenetically-controlled analyses. We calculated chromaticity values from red and blue scores extracted from images of the majority of Passeriformes and tested these against the factors hypothesized to influence color evolution. In contrast to predictions based on sexual and social selection theory, we found consistent changes in island female color (lower red and higher blue chromaticity), but no change in males. Instead, island size and distance from mainland and other islands influenced color in both sexes, reinforcing the importance of island physiognomy in shaping evolutionary processes. Interactions between ecological factors and latitude also consistently influenced color for both sexes, supporting a latitudinal gradient hypothesis. Finally, patterns of color evolution varied among families, indicating taxon-specific microevolutionary processes in driving color evolution. Our results show island residency influences color evolution differently between sexes, but the patterns in both sexes are tempered by ecological, island characteristics, and phylogenetic effects that further vary in their importance among families. The key role of environmental factors in shaping bird plumage on islands further suggests a reduced importance of sexual and social factors in driving color evolution.

An updated gene regulatory network reconstruction of multidrug-resistant Pseudomonas aeruginosa CCBH4851.

BACKGROUND: Healthcare-associated infections due to multidrug-resistant (MDR) bacteria such as Pseudomonas aeruginosa are significant public health issues worldwide. A system biology approach can help understand bacterial behaviour and provide novel ways to identify potential therapeutic targets and develop new drugs. Gene regulatory networks (GRN) are examples of in silico representation of interaction between regulatory genes and their targets. OBJECTIVES: In this work, we update the MDR P. aeruginosa CCBH4851 GRN reconstruction and analyse and discuss its structural properties. METHODS: We based this study on the gene orthology inference methodology using the reciprocal best hit method. The P. aeruginosa CCBH4851 genome and GRN, published in 2019, and the P. aeruginosa PAO1 GRN, published in 2020, were used for this update reconstruction process. FINDINGS: Our result is a GRN with a greater number of regulatory genes, target genes, and interactions compared to the previous networks, and its structural properties are consistent with the complexity of biological networks and the biological features of P. aeruginosa. MAIN CONCLUSIONS: Here, we present the largest and most complete version of P. aeruginosa GRN published to this date, to the best of our knowledge.

An updated gene regulatory network reconstruction of multidrug-resistant Pseudomonas aeruginosa CCBH4851

BACKGROUND Healthcare-associated infections due to multidrug-resistant (MDR) bacteria such as Pseudomonas aeruginosa are significant public health issues worldwide. A system biology approach can help understand bacterial behaviour and provide novel ways to identify potential therapeutic targets and develop new drugs. Gene regulatory networks (GRN) are examples of in silico representation of interaction between regulatory genes and their targets. OBJECTIVES In this work, we update the MDR P. aeruginosa CCBH4851 GRN reconstruction and analyse and discuss its structural properties. METHODS We based this study on the gene orthology inference methodology using the reciprocal best hit method. The P. aeruginosa CCBH4851 genome and GRN, published in 2019, and the P. aeruginosa PAO1 GRN, published in 2020, were used for this update reconstruction process. FINDINGS Our result is a GRN with a greater number of regulatory genes, target genes, and interactions compared to the previous networks, and its structural properties are consistent with the complexity of biological networks and the biological features of P. aeruginosa. MAIN CONCLUSIONS Here, we present the largest and most complete version of P. aeruginosa GRN published to this date, to the best of our knowledge.

A Systematic Strategy to Find Potential Therapeutic Targets for Pseudomonas aeruginosa Using Integrated Computational Models.

Pseudomonas aeruginosa is an opportunistic human pathogen that has been a constant global health problem due to its ability to cause infection at different body sites and its resistance to a broad spectrum of clinically available antibiotics. The World Health Organization classified multidrug-resistant Pseudomonas aeruginosa among the top-ranked organisms that require urgent research and development of effective therapeutic options. Several approaches have been taken to achieve these goals, but they all depend on discovering potential drug targets. The large amount of data obtained from sequencing technologies has been used to create computational models of organisms, which provide a powerful tool for better understanding their biological behavior. In the present work, we applied a method to integrate transcriptome data with genome-scale metabolic networks of Pseudomonas aeruginosa. We submitted both metabolic and integrated models to dynamic simulations and compared their performance with published in vitro growth curves. In addition, we used these models to identify potential therapeutic targets and compared the results to analyze the assumption that computational models enriched with biological measurements can provide more selective and (or) specific predictions. Our results demonstrate that dynamic simulations from integrated models result in more accurate growth curves and flux distribution more coherent with biological observations. Moreover, identifying drug targets from integrated models is more selective as the predicted genes were a subset of those found in the metabolic models. Our analysis resulted in the identification of 26 non-host homologous targets. Among them, we highlighted five top-ranked genes based on lesser conservation with the human microbiome. Overall, some of the genes identified in this work have already been proposed by different approaches and (or) are already investigated as targets to antimicrobial compounds, reinforcing the benefit of using integrated models as a starting point to selecting biologically relevant therapeutic targets.

Evolution of altitudinal migration in passerines is linked to diet.

Bird migration is typically associated with a latitudinal movement from north to south and vice versa. However, many bird species migrate seasonally with an upslope or downslope movement in a process termed altitudinal migration. Globally, 830 of the 6,579 Passeriformes species are considered altitudinal migrants and this pattern has emerged multiple times across 77 families of this order. Recent work has indicated an association between altitudinal migration and diet, but none have looked at diet as a potential evolutionary driver. Here, we investigated potential evolutionary drivers of altitudinal migration in passerines around the world by using phylogenetic comparative methods. We tested for evolutionary associations between altitudinal migration and foraging guild and primary habitat preference in passerines species worldwide. Our results indicate that foraging guild is evolutionarily associated with altitudinal migration, but this relationship varies across zoogeographical regions. In the Nearctic, herbivorous and omnivorous species are associated with altitudinal migration, while only omnivorous species are associated with altitudinal migration in the Palearctic. Habitat was not strongly linked to the evolution of altitudinal migration. While our results point to diet as a potentially important driver of altitudinal migration, the evolution of this behavior is complex and certainly driven by multiple factors. Altitudinal migration varies in its use (for breeding or molting), within a species, population, and even at the individual level. As such, the evolution of altitudinal migration is likely driven by an ensemble of factors, but this study provides a beginning framework for understanding the evolution of this complex behavior.

Vulnerability of a killer whale social network to disease outbreaks.

Emerging infectious diseases are among the main threats to conservation of biological diversity. A crucial task facing epidemiologists is to predict the vulnerability of populations of endangered animals to disease outbreaks. In this context, the network structure of social interactions within animal populations may affect disease spreading. However, endangered animal populations are often small and to investigate the dynamics of small networks is a difficult task. Using network theory, we show that the social structure of an endangered population of mammal-eating killer whales is vulnerable to disease outbreaks. This feature was found to be a consequence of the combined effects of the topology and strength of social links among individuals. Our results uncover a serious challenge for conservation of the species and its ecosystem. In addition, this study shows that the network approach can be useful to study dynamical processes in very small networks.

Nonuniversality in the pair contact process with diffusion.

We study the static and dynamic behavior of the one dimensional pair contact process with diffusion. Several critical exponents are found to vary with the diffusion rate, while the order-parameter moment ratio m=rho(2);/rho;(2) grows logarithmically with system size. The anomalous behavior of m is traced to a violation of scaling in the order parameter probability density, which in turn reflects the presence of two distinct sectors, one purely diffusive, the other reactive, within the active phase. Studies restricted to the reactive sector yield precise estimates for exponents beta and nu( perpendicular ), and confirm finite size scaling. We also determine the value m(c)=1.334 for the parity-conserving universality class in one dimension.