Biology subjects

Hernando, J.

Publications and source records attributed to Hernando, J..

3 recordsLinked to original sources

bioRxiv2026

From Splicing to Survival: Understanding the Molecular Basis of Prognostic Differences in advanced Small Intestine Neuroendocrine Tumors

Small intestine neuroendocrine tumors (SI-NETs) represent a challenging neoplasm due to their high rate of metastasis and the limited availability of effective therapies. Recent evidence has unveiled altered splicing as an influential player in tumor progression, linked to patient prognosis, which provides a powerful source of novel biomarkers and therapeutic targets in cancer. Here, we conducted a comprehensive analysis regarding the possible connection of the spliceosomic landscape of metastatic SI-NETs with patient survival. Initial analyses revealed a distinctive pattern of splicing events between the two conditions, short (< 5 years, n = 17 patients), vs. long survival ([≥] 5 years, n = 26 patients), with strong discrimination capacity. Furthermore, numerous key splicing events were associated with relevant clinical parameters in SI-NETs. The observed impairments in spliceosomic parameters led us to interrogate the integrity of the splicing machinery. This revealed striking changes in the expression of a high proportion of core spliceosome components and splicing factors between short vs. longsurvival groups, suggesting a possible mechanistic link with the distinct profile of splicing events observed. Among them, CPSF2, a core component of the CPSF complex, stands out as its higher expression is associated with worse survival and vascular and perineural invasion. Interestingly, the shifts potentially generated by this factor supported a possible role in tumor plasticity oriented to higher tumor aggressiveness and treatment resistance. In sum, this study offers the first detailed snapshot of the status of the spliceosomic landscape in SI-NETs and its molecular linkage to patient survival, thus offering a new window towards identification of novel candidate biomarkers and/or therapeutic targets.

cancer biology↗

bioRxiv2019

The SONATA Data Format for Efficient Description of Large-Scale Network Models

Increasing availability of comprehensive experimental datasets and of high-performance computing resources are driving rapid growth in scale, complexity, and biological realism of computational models in neuroscience. To support construction and simulation, as well as sharing of such large-scale models, a broadly applicable, flexible, and high-performance data format is necessary. To address this need, we have developed the Scalable Open Network Architecture TemplAte (SONATA) data format. It is designed for memory and computational efficiency and works across multiple platforms. The format represents neuronal circuits and simulation inputs and outputs via standardized files and provides much flexibility for adding new conventions or extensions. SONATA is used in multiple modeling and visualization tools, and we also provide reference Application Programming Interfaces and model examples to catalyze further adoption. SONATA format is free and open for the community to use and build upon with the goal of enabling efficient model building, sharing, and reproducibility.

neuroscience↗

bioRxiv2017

Reconstruction and visualization of large-scale volumetric models of neocortical circuits for physically-plausible in silico optical studies

Background We present a software workflow capable of building large scale, highly detailed and realistic volumetric models of neocortical circuits from the morphological skeletons of their digitally reconstructed neurons. The limitations of the existing approaches for creating those models are explained, and then, a multi-stage pipeline is discussed to overcome those limitations. Starting from the neuronal morphologies, we create smooth piecewise watertight polygonal models that can be efficiently utilized to synthesize continuous and plausible volumetric models of the neurons with solid voxelization. The somata of the neurons are reconstructed on a physically-plausible basis relying on the physics engine in Blender.\n\nResults Our pipeline is applied to create 55 exemplar neurons representing the various morphological types that are reconstructed from the somatsensory cortex of a juvenile rat. The pipeline is then used to reconstruct a volumetric slice of a cortical circuit model that contains [~]210,000 neurons. The applicability of our pipeline to create highly realistic volumetric models of neocortical circuits is demonstrated with an in silico imaging experiment that simulates tissue visualization with brightfield microscopy. The results were evaluated with a group of domain experts to address their demands and also to extend the workflow based on their feedback.\n\nConclusion A systematic workflow is presented to create large scale synthetic tissue models of the neocortical circuitry. This workflow is fundamental to enlarge the scale of in silico neuroscientific optical experiments from several tens of cubic micrometers to a few cubic millimeters.

neuroscience↗