Biology subjects

Fernandes, F. A. N.

Publications and source records attributed to Fernandes, F. A. N..

6 recordsLinked to original sources

bioRxiv2025

Early-life stress as a potential resilience factor in king penguins

Climate change-driven environmental variability can cause species to shift their phenology, leading to an increase in phenological mismatches. Whether the fitness cost of such mismatches can be mitigated by plasticity remains poorly understood, especially in the context of wild populations. In this study, we quantified responses to phenological mismatch using capture-mark-recapture, morphological, and transcriptomic data from king penguins hatching during and after the seasonal peak of food availability (i.e., born early and late in the breeding season, respectively). Late-born chicks showed reduced survival to fledging and fledged smaller and in slightly poorer body condition than chicks born during the seasonal peak in food availability. Yet post-fledging return rates did not differ between late- and early-born chicks, suggesting that early-life stress was effectively mitigated among individuals that survived to fledging, with no detectable carryover effects in the first years post-fledging. Genes involved in oxidative stress response, homeostasis, and nutrient-sensing were differentially expressed between early- and late-born chicks, consistent with stress mitigation and homeostasis maintenance at the expense of growth in late-born chicks. A co-expression gene cluster whose primary expression pattern was associated with hatching phenology also contained central genes involved in stress mitigation. Together, our findings highlight molecular mechanisms that buffer the fitness costs of phenological mismatch, which may allow species to tolerate mismatch to some extent. Despite higher winter mortality of individuals born in mismatch, the plastic response observed in survivors hints towards molecular adaptations that could allow species resilience under increasing climate-driven environmental unpredictability.

genomics↗

bioRxiv2025

A chromosome-level genome of the King penguin (Aptenodytes patagonicus): an emerging model-in-the-wild for studying evolution

The King penguin (Aptenodytes patagonicus) is an iconic species of the Southern Ocean and is currently being developed as a model-in-the-wild for understanding evolution. We present a high-quality, haplotype-resolved 1.35 Gb chromosome-level genome of an adult female King penguin - Pen/Se-guin - from the Crozet Archipelago, assembled using PacBio HiFi long-read sequencing and Hi-C proximity data. 94.93% of the assembly is assigned to 34 chromosomes (32 autosomes, plus the Z and W chromosomes), with a BUSCO completeness of 97.2%, a k-mer completeness of 99.7%, and a quality value (QV) of 63.8. We also assembled a circularised mitogenome (20,520 bp), which includes the avian tandem duplication (TD). Annotation of repetitive sequences revealed that 16.3% of the genome comprises repetitive elements, with LINEs being the most abundant transposable element class (5.6%). Gene prediction using an extensive multi-tissue RNA-seq dataset resulted in 18,081 predicted protein-coding genes, of which 17,081 were functionally annotated, with a BUSCO completeness of 98.4% and an OMArk completeness of 97.3%. The presented assembly substantially improves the quality of a previous draft genome, showing a 28-fold increase in assembly contiguity and a significantly improved genome annotation, exceeding the standards of the Earth BioGenome Project (EBP) and Vertebrate Genomes Project (VGP). This high-quality genome will enable ongoing and future studies harnessing the King penguin as a model-in-the-wild to test hypotheses on the genotype-to-fitness link, ageing, life-history trait evolution, and adaptation.

genomics↗

bioRxiv2023

Gene expression shifts in Emperor penguin adaptation to the extreme Antarctic environment

Gene expression can accelerate ecological divergence by rapidly tweaking the response of an organism to novel environments, with more divergent environments exerting stronger selection and supposedly, requiring faster adaptive responses. Organisms adapted to extreme environments provide ideal systems to test this hypothesis, particularly when compared to related species with milder ecological niches. The Emperor penguin (Aptenodytes forsteri) is the only warm-blooded vertebrate breeding in the harsh Antarctic winter, in stark contrast with the less cold-adapted sister species, the King penguin (A. patagonicus). Assembling the first de novo transcriptomes and analysing multi-tissue (brain, kidney, liver, muscle, skin) RNAseq data from natural populations of both species, we quantified the shifts in tissue-enhanced genes, co-expression gene networks, and differentially expressed genes characterising Emperor penguin adaptation to extreme Antarctic ecology. Our analyses revealed the crucial role played by muscle and liver in temperature homeostasis, fasting and whole-body energy metabolism (glucose/insulin regulation, lipid metabolism, fatty acid beta-oxidation, and blood coagulation). Repatterning at the regulatory level appears as more important in the brain of the Emperor penguin, showing the lowest signature of differential gene expression but the largest co-expression gene network shift. Nevertheless, over-expressed genes related to mTOR signalling in the brain and the liver support their central role in cold and fasting responses. Besides contributing to understanding the genetics underlying complex traits, like body energy reservoir management, our results provide a first insight into the role of gene expression in adaptation to one of the most extreme environmental conditions endured by an endotherm.

evolutionary biology↗

bioRxiv2023

Gene expression is the main driver of purifying selection in large penguin populations

Purifying selection is the most pervasive type of selection, as it constantly removes deleterious mutations arising in populations, directly scaling with population size. Highly expressed genes appear to accumulate fewer deleterious mutations between divergent species lineages (known as E-R anticorrelation), pointing towards gene expression as an additional driver of purifying selection. However, estimates of the effect of gene expression on segregating deleterious variants in natural populations are scarce, as is an understanding of the relative contribution of population size and gene expression to purifying selection. Here, we analyse genomic and transcriptomic data from two natural populations of closely related sister species with different demographic histories, the Emperor penguin (Aptenodytes forsteri) and the King penguin (A. patagonicus), and show that purifying selection at the population-level depends on gene expression rate, resulting in very high selection coefficients at highly expressed genes. Leveraging realistic forward simulations, we estimate that the top 10% of the most highly expressed genes in a genome experience a selection pressure corresponding to an average selection coefficient of -0.1, which decreases to a selection coefficient of -0.01 for the top 50%. Gene expression rate can be regarded as a fundamental parameter of protein evolution in natural populations, maintaining selection effective even at small population size. We suggest it could be used as a proxy for gene selection coefficients, which are notoriously difficult to derive in non-model species under real-world conditions.

evolutionary biology↗

bioRxiv2023

RFIDeep: Unfolding the Potential of Deep Learning forRadio-Frequency Identification

O_LIAutomatic monitoring of wildlife is becoming a critical tool in the field of ecology. In particular, Radio-Frequency IDentification (RFID) is now a widespread technology to assess the phenology, breeding, and survival of many species. While RFID produces massive datasets, no established fast and accurate methods are yet available for this type of data processing. Deep learning approaches have been used to overcome similar problems in other scientific fields and hence might hold the potential to overcome these analytical challenges and unlock the full potential of RFID studies. C_LIO_LIWe present a deep learning workflow, coined "RFIDeep", to derive ecological features, such as breeding status and outcome, from RFID mark-recapture data. To demonstrate the performance of RFIDeep with complex datasets, we used a long-term automatic monitoring of a long-lived seabird that breeds in densely packed colonies, hence with many daily entries and exits. C_LIO_LITo determine individual breeding status and phenology and for each breeding season, we first developed a one-dimensional convolution neural network (1D-CNN) architecture. Second, to account for variance in breeding phenology and technical limitations of field data acquisition, we built a new data augmentation step mimicking a shift in breeding dates and missing RFID detections, a common issue with RFIDs. Third, to identify the segments of the breeding activity used during classification, we also included a visualisation tool, which allows users to understand what is usually considered a "black box" step of deep learning. With these three steps, we achieved a high accuracy for all breeding parameters: breeding status accuracy = 96.3%; phenological accuracy = 86.9%; breeding success accuracy = 97.3%. C_LIO_LIRFIDeep has unfolded the potential of artificial intelligence for tracking changes in animal populations, multiplying the benefit of automated mark-recapture monitoring of undisturbed wildlife populations. RFIDeep is an open source code to facilitate the use, adaptation, or enhancement of RFID data in a wide variety of species. In addition to a tremendous time saving for analyzing these large datasets, our study shows the capacities of CNN models to autonomously detect ecologically meaningful patterns in data through visualisation techniques, which are seldom used in ecology. C_LI

ecology↗

bioRxiv2021

Selection-driven adaptation to the extreme Antarctic environment in the Emperor penguin

The eco-evolutionary history of penguins is profoundly influenced by their shift from temperate to cold environments. Breeding only in Antarctica during the winter, the Emperor penguin appears as an extreme outcome of this process, with unique features related to insulation, heat production and energy management. However, whether this species actually diverged from a less cold-adapted ancestor, thus more similar in ecology to its sister species, the King penguin, is still an open question. As the Antarctic niche shift likely resulted in vast changes in selective pressure experienced by the Emperor penguin, the identification and relative quantification of the genomic signatures of selection, unique to each of these sister species, could answer this question. Applying a suite of phylogeny-based methods on 7,651 orthologous gene alignments of seven penguins and 13 other birds, we identified a set of candidate genes showing significantly different selection regimes either in the Emperor or in the King penguin lineage. Our comparative approach unveils a more pervasive selection shift in the Emperor penguin, supporting the hypothesis that its extreme cold adaptation is a derived state from a more King penguin-like ecology. Among the candidate genes under selection in the Emperor penguin, four genes (TRPM8, LEPR, CRB1, and SFI1) were identified before in other cold adapted vertebrates, while, on the other hand, 161 genes can be assigned to functional pathways relevant to cold adaptation (e.g., cardiovascular system, lipid, fatty acid and glucose metabolism, insulation, etc.). Our results show that extreme cold adaptation in the Emperor penguin largely involved unique genetic options which, however, affect metabolic and physiological traits common to other cold-adapted homeotherms.

evolutionary biology↗