Biology subjects

Benitez, D.

Publications and source records attributed to Benitez, D..

3 recordsLinked to original sources

bioRxiv2026

Genotype-phenotype mapping identifies fetal-like CD55+ immunoregulatory cancer cells as mediators of immune escape in colorectal cancer

Immune evasion is a defining feature of advanced colorectal cancer (CRC), yet the cellular mechanisms linking cancer cell states to immune suppression remain poorly understood. To systematically map tumour-immune interactions in advanced CRC, we modelled recurrent human CRC mutations using a multiplex CRISPR-based genetically engineered mouse model platform. The resulting 20 models recapitulate key stages, genetic routes and histopathological features of human CRC, while single-cell transcriptomics reveals extensive disease complexity across models. Profiling of the epithelial tumour compartment identified a population of fetal-like immunoregulatory cancer cells (IRCs) characterized by interferon-{gamma} and MAPK activity. IRCs are marked by expression of the membrane-bound complement regulatory protein CD55. Mechanistically, CD55 protects IRCs from immune surveillance and complement-mediated lysis. Together, these findings reveal a cancer cell-immune interface that promotes immune evasion in advanced CRC.

cancer biology↗

bioRxiv2024

ARTIC RSV amplicon sequencing reveals global RSV genotype dynamics.

Respiratory syncytial virus (RSV) is a leading cause of lower respiratory tract infections (LTRIs) in young children and adults over 65, contributing significantly to global healthcare burdens. With the recent approval of multiple pharmacological interventions for RSV, there is an increased demand for efficient, high-throughput sequencing methods to monitor RSV genetic diversity and any potential impact these interventions may have. Here we introduce two novel amplicon-based sequencing schemes designed for RSV A and B, optimised for integration with widespread existing ARTIC sequencing workflows. We demonstrate that these primer schemes can produce high quality genomes from RSV samples across the globe, with eight laboratories in five countries generating complete genomes on both Nanopore and Illumina sequencing platforms. The ability to effectively multiplex these RSV A and B primer schemes, enables streamlined, high-throughput sequencing without prior subtyping. Furthermore, these results provide a snapshot of the circulating diversity of RSV. Phylogenetic analysis of the 882 samples sequenced for this study suggests only minimal geographic clustering of RSV sequences, underscoring the global nature of RSV spread. It also highlights the distinct lineage dynamics seen between RSV A and B. This study represents an advancement in RSV genomics, providing robust tools for global sequencing efforts aimed at tracking RSV evolution and assessing the efficacy of new therapeutic interventions both rapidly and at scale.

genomics↗

bioRxiv2022

Automatic identification of intestinal parasites in reptiles using microscopic stool images and convolutional neural networks

Captive environments trigger the propagation and multiplication of parasites among different reptile species, thus weakening their immune response and causing infections and diseases. Technological advances of convolutional neural networks have opened a new field for detecting and classifying diseases which have shown great potential to overcome the shortcomings of manual detection performed by experts. Therefore, we propose an approach to identify six captive reptiles parasitic agents (Ophionyssus natricis, Blastocystis sp, Oxiurdo egg, Rhytidoides similis, Strongyloides, Taenia) from a microscope stool images dataset. Towards this end, we first use an image segmentation stage to detect the parasite within the image, which combines the Contrast Limited Adaptive Histogram Equalization (CLAHE) technique, the OTSU binarization method, and morphological operations. Then, we carry out a classification stage through Mobilenet CNN under a transfer learning scheme. This method was validated on a stool image dataset containing 3616 images data samples and 26 videos from the six parasites mentioned above. The results obtained indicate that our transfer learning-based approach can learn a helpful representation from the dataset. We obtained an average accuracy of 98.66% across the six parasitic agent classes, which statistically outperformed, at a 95% confidence level, a custom CNN trained from scratch.

zoology↗