Biology subjects

Cantarel, B. L.

Publications and source records attributed to Cantarel, B. L..

7 recordsLinked to original sources

bioRxiv2025

On the ancestry and evolution of the extinct dire wolf

Dire wolves (Aenocyon dirus) are extinct predators of Pleistocene North America. Although phenotypically similar to living wolves (Canis lupus), dire wolves have yet to be placed confidently in the canid family tree. We generated 3.4x and 12.8x paleogenomes from two well-preserved dire wolves dating to > 13,000 and > 72,000 years ago, and estimated consensus species trees for these and 10 canid species. Our results revealed that [~]2/3 of dire wolf ancestry is derived from a lineage sister to the clade comprising the gray wolf, coyote, and dhole, and the remaining [~]1/3 from a lineage near the base of Canini diversity. We identified 80 genes evolving under diversifying selection in dire wolves. Our results underscore the power of paleogenomes to resolve long-standing taxonomic questions and contribute to growing evidence of the role of post-speciation gene flow as an evolutionary force.

evolutionary biology↗

bioRxiv2025

Multiplex-edited mice recapitulate woolly mammoth hair phenotypes

The woolly mammoth (Mammuthus primigenius) possessed a thick woolly coat and other cold-adaptive traits that enabled survival in harsh arctic environments. Current de-extinction efforts focus on genetically modifying the closely related Asian elephant to express woolly mammoth traits. In this study, we establish a multiplex-edited mouse model with modifications in genes associated with hair morphology and lipid metabolism, enabling insights into traits involved in developing woolly hair textures. Our optimized workflows achieved high editing efficiencies and produced genetically modified mice with simultaneous editing of up to seven different genes. Selected modifications include loss-of-function mutations in Fgf5, Tgm3, and Fam83g, among others. The resulting mice display exaggerated hair phenotypes including curly, textured coats, and golden-brown hair. This study advances methods of rapid establishment of complex genetic models. These approaches inform de-extinction efforts and research involving the genetic basis of mammalian hair development.

genetics↗

bioRxiv2024

Standardized Nomenclature and Reporting for PacBio HiFi Sequencing and Analysis of rAAV Gene Therapy Vectors

Despite recombinant adeno-associated viruses (rAAVs) being the leading platform for gene therapy, there is a lack of standardized computational analysis methods and reporting to assess the contents of each capsid through long-read sequencing. PacBios highly accurate long-read HiFi sequencing enables comprehensive characterization of AAV genomes but requires bioinformatics expertise for analyzing, interpreting and comparing the results. To address this need and improve the understanding of functional viral payloads, our working group established standardized nomenclature and reporting for long-read sequencing data of rAAV vectors. The working group recommendations cover critical quality attributes (CQAs) related to vector purity (full-length vs. fragmented genomes) and identification of contaminants (host DNA, plasmid DNA). Our data analyses of de novo manufacturing runs by the recommended protocol revealed specificity of full and partially filled capsids and high-resolution characterization of partial/truncated vector species. Finally, we provide an open-source software implementing this standardized AAV analysis and reporting to promote transparency, facilitate data comparability, and improve rAAV vector design and quality control.

bioinformatics↗

bioRxiv2024

DERIVATION OF ELEPHANT INDUCED PLURIPOTENT STEM CELLS

The crisis of biodiversity loss in the anthropogenic era requires new tools for studying non-model organisms. Elephants, for example, are both an endangered species and excellent models studying complex phenotypes like size, social behavior, and longevity, but they remain severely understudied. Here we report the first derivation of elephant (Elephas maximus) induced pluripotent stem cells (emiPSCs) achieved via a two-step process of chemical-media induction and colony selection, followed by overexpression of elephant transcription factors OCT4, SOX2, KLF4, MYC {+/-} NANOG and LIN28A, and modulation of the TP53 pathway. Since the seminal discovery of reprogramming by Shinya Yamanaka, iPSCs from many species including the functionally extinct northern white rhinocerous have been reported, but emiPSCs have remained elusive. While for multiple species the reprogramming protocol was adopted with little changes compared to model organisms like mouse and human, our emiPSC protocol requires a longer timeline and inhibition of TP53 expansion genes that are hypothesized to confer unique cancer resistance in elephants. iPSCs unlock tremendous potential to explore cell fate determination, cell and tissue development, cell therapies, drug screening, disease modeling, cancer development, gametogenesis and beyond to further our understanding of this iconic megafauna. This study opens new frontiers in advanced non-model organism cellular models for genetic rescue and conservation.

cell biology↗

bioRxiv2023

FLAG: Find, Label Annotate Genomes, a fully automated tool for genome gene structural and functional annotation of highly fragmented non-model species

Recent advances in long-read sequencing technologies and the efforts of projects aimed at increasing the universe of sequenced reference genomes have led to a growth in the number of whole genomes sequenced for non-model organisms. Still, 81% of the over 36,000 unique publicly available eukaryotic genomes in the NCBI database lack gene structure annotations (1). While there are many open-source tools available for each step in the annotation process, many of these tools are designed for chromosomal assemblies with available transcript data from the same or very closely related organisms. Here we present "Find, Label, Annotate Genomes" (FLAG), a fully automated genome annotation workflow. FLAG (i) works on any computing environment, (ii) runs automatically without initial training data, (iii) generates structural and functional annotations, (iv) performs accurately with fragmented genomes, (v) does not require species-specific extrinsic evidence (transcript sequences) and (vi) includes quality control steps to evaluate annotation completeness. We compared the gene annotations generated by FLAG and publicly available annotations from 12 eukaryotic organisms, including 1 plant, 8 living animals, and 1 extinct animal. In fragmented genomes, FLAG annotations provided an average of 18% increase in complete BUSCO scores and 15x lower error rate for the predicted number of protein-coding genes when compared to published BRAKER2 annotations. With FLAG-Refiner further improved results by decreasing the error rate to 17x lower when compared to published BRAKER2 annotations. In high-quality model organisms, FLAG demonstrates comparable results to those of the NCBI EGAP pipeline, underscoring its robustness and accuracy in gene prediction across diverse taxa and assembly qualities.

bioinformatics↗

bioRxiv2020

Interpretable deep learning of label-free live cell images uncovers functional hallmarks of highly-metastatic melanoma

Deep convolutional neural networks have emerged as a powerful technique to identify hidden patterns in complex cell imaging data. However, these machine learning techniques are often criticized as uninterpretable "black-boxes" - lacking the ability to provide meaningful explanations for the cell properties that drive the machines prediction. Here, we demonstrate that the latent features extracted from label-free live cell images by an adversarial auto-encoding deep convolutional neural network capture subtle details of cell appearance that allow classification of melanoma cell states, including the metastatic efficiency of seven patient-derived xenograft models that reflect clinical outcome. Although trained exclusively on patient-derived xenograft models, the same classifier also predicted the metastatic efficiency of immortalized melanoma cell lines suggesting that the latent features capture properties that are specifically associated with the metastatic potential of a melanoma cell regardless of its origin. We used the autoencoder to generate "in-silico" cell images that amplified the cellular features driving the classifier of metastatic efficiency. These images unveiled pseudopodial extensions and increased light scattering as functional hallmarks of metastatic cells. We validated this interpretation by analyzing experimental image time-lapse sequences in which melanoma cells spontaneously transitioned between states indicative of low and high metastatic efficiency. Together, this data is an example of how the application of Artificial Intelligence supports the identification of processes that are essential for the execution of complex integrated cell functions but are too subtle to be identified by a human expert.

cell biology↗

bioRxiv2017

Biocuration as an undergraduate training experience: Improving the annotation of the insect vector of Citrus greening disease

The Asian citrus psyllid (Diaphorina citri Kuwayama) is the insect vector of the bacterium Candidatus Liberibacter asiaticus (CLas), the pathogen associated with citrus Huanglongbing (HLB, citrus greening). HLB threatens citrus production worldwide. Suppression or reduction of the insect vector using chemical insecticides has been the primary method to inhibit the spread of citrus greening disease. Accurate structural and functional annotation of the Asian citrus psyllid genome, as well as a clear understanding of the interactions between the insect and CLas, are required for development of new molecular-based HLB control methods. A draft assembly of the D. citri genome has been generated and annotated with automated pipelines. However, knowledge transfer from well-curated reference genomes such as that of Drosophila melanogaster to newly sequenced ones is challenging due to the complexity and diversity of insect genomes. To identify and improve gene models as potential targets for pest control, we manually curated several gene families with a focus on genes that have key functional roles in D. citri biology and CLas interactions. This community effort produced 530 manually curated gene models across developmental, physiological, RNAi regulatory, and immunity-related pathways. As previously shown in the pea aphid, RNAi machinery genes putatively involved in the microRNA pathway have been specifically duplicated. A comprehensive transcriptome enabled us to identify a number of gene families that are either missing or misassembled in the draft genome. In order to develop biocuration as a training experience, we included undergraduate and graduate students from multiple institutions, as well as experienced annotators from the insect genomics research community. The resulting gene set (OGS v1.0) combines both automatically predicted and manually curated gene models. All data are available on https://citrusgreening.org/.

genomics↗