Biology subjects

Reyes, F.

Publications and source records attributed to Reyes, F..

8 recordsLinked to original sources

bioRxiv2025

Self-Organizing Neural Networks in Organoids Reveal Principles of Forebrain Circuit Assembly

The mouse cortex is a canonical model for studying how functional neural networks emerge, yet it remains unclear which topological features arise from intrinsic cellular organization versus external regional cues. Mouse forebrain organoids provide a powerful system to investigate these intrinsic mechanisms. We generated dorsal (DF) and ventral (VF) forebrain organoids from mouse pluripotent stem cells and tracked their development using longitudinal electrophysiology. DF organoids showed progressively stronger network-wide correlations, while VF organoids developed more refined activity patterns, enhanced small-world topology, and increased modular organization. These differences emerged without extrinsic inputs and may be driven by the increased generation of Pvalb+ interneurons in VF organoids. Our findings demonstrate how variations in cellular composition influence the self-organization of neural circuits, establishing mouse forebrain organoids as a tractable platform to study how neuronal populations shape cortical network architecture.

neuroscience↗

bioRxiv2025

HIPPIE: A Multimodal Deep Learning Model for Electrophysiological Classification of Neurons

Extracellular electrophysiological recordings present unique computational challenges for neuronal classification due to noise, technical variability, and batch effects across experimental systems. We introduce HIPPIE (High-dimensional Interpretation of Physiological Patterns In Extracellular recordings), a deep learning framework that combines self-supervised pretraining on unlabeled datasets with supervised fine-tuning to classify neurons from extracellular recordings. Using conditional convolutional joint autoencoders, HIPPIE learns robust, technology-adjusted representations of waveforms and spiking dynamics. This model can be applied to electrophysiological classification and clustering across diverse biological cultures and technologies. We validated HIPPIE on both in vivo mouse recordings and in vitro brain slices, where it demonstrated superior performance over other unsupervised methods in cell-type discrimination and aligned closely with anatomically defined classes. Its latent space organizes neurons along electrophysiological gradients, while enabling batch and individual corrected alignment of recordings across experiments. HIPPIE establishes a general framework for systematically decoding neuronal diversity in native and engineered systems.

neuroscience↗

bioRxiv2023

Establishment of a screening platform based on human coronavirus OC43 for the identification of microbial natural products with antiviral activity

Human coronaviruses (HCoVs) cause respiratory tract infections and are of great importance due to the recent severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) pandemic. Human betacoronavirus OC43 (HCoV-OC43) is an adequate surrogate for SARS-CoV-2 because it infects the human respiratory system, presents a comparable biology, and is transmitted in a similar way. Its use is advantageous since it only requires biosafety level (BSL)-2 infrastructure which minimizes costs and biosafety associated limitations. In this report, we describe a high-throughput screening (HTS) platform to identify compounds that inhibit the propagation of HCoV-OC43. Optimization of assays based on inhibition of the cytopathic effect and virus immunodetection with a specific antibody, has provided a robust methodology for the screening of a selection of microbial natural product extracts from the Fundacion MEDINA collection. Using this approach, a subset of 1280 extracts has been explored. Of these, upon hit confirmation and early LC-MS dereplication, 10 extracts were identified that contain potential new compounds. In addition, we report on the novel antiviral activity of some previously described natural products whose presence in bioactive extracts was confirmed by LC/MS analysis. IMPORTANCEThe COVID-19 pandemic has revealed the lack of effective treatments against betacoronaviruses and the urgent need for new broad-spectrum antivirals. Natural products are a valuable source of bioactive compounds with pharmaceutical potential that may lead to the discovery of new antiviral agents. Specifically, compared to conventional synthetic molecules, microbial natural extracts possess a unique and vast chemical diversity and are amenable to large-scale production. The implementation of a high-throughput screening platform using the betacoronavirus OC43 in a human cell line infection model has provided proof of concept of the approach and has allowed for the rapid and efficient evaluation of 1280 microbial extracts. The identification of several active compounds validates the potential of the platform for the search for new compounds with antiviral capacity.

microbiology↗

bioRxiv2022

NGSEP 4: Efficient and Accurate Identification of Orthogroups and Whole Genome Alignment

Whole-genome alignment allows researchers to understand the genomic structure and variations among the genomes. Approaches based on direct pairwise comparisons of DNA sequences require large computational capacities. As a consequence, pipelines combining tools for orthologous gene identification and synteny have been developed. In this manuscript, we present the latest functionalities implemented in NGSEP 4, to identify orthogroups and perform whole genome alignments. NGSEP implements functionalities for identification of clusters of homologus genes, synteny analysis and whole genome alignment, and visualization. Our results showed that the NGSEP algorithm for ortholog identification has competitive accuracy and better efficiency in comparison to commonly used tools. The implementation also includes a visualization of the whole genome alignment based on synteny of the orthogroups that were identified, and a reconstruction of the pangenome based on frequencies of the orthogroups among the genomes. Finally, our software includes a new graphical user interface. We expect that these new developments will be very useful for several studies in evolutionary biology and population genomics.

bioinformatics↗

bioRxiv2021

Identification of the 216kbp gene cluster and structure elucidation of gargantulides B and C, new complex 52-membered macrolides from Amycolatopsis sp.

Gargantulides B and C, two new and highly complex 52-membered glycosylated macrolactones, were isolated from Amycolatopsis sp. strain CA-230715 during an antibacterial screening campaign. The structures of these giant macrolides were elucidated by 2D NMR spectroscopy and shown to be related to gargantulide A, although containing additional {beta}-glucopyranose and/or -arabinofuranose monosaccharides separately attached to their backbones. Genome sequencing allowed the identification of a strikingly large 216 kbp biosynthetic gene cluster, among the largest type I PKS clusters described so far, and the proposal of a biosynthetic pathway for gargantulides A-C. Additionally, genes putatively responsible for the biosynthesis of the amino sugar {beta}-3,6-deoxy-3-methylamino glucose, reported exclusively in gargantulide macrolides, were also found in the cluster and described in this work. The absolute configurations of gargantulides B and C were assigned based on a combination of NMR and bioinformatics analysis of ketoreductase and enoylreductase domains within the multimodular type I PKS. Furthermore, the absolute stereochemistry of the related macrolide gargantulide A has now been revised and completed. Gargantulides B and C display potent antibacterial activity against a set of drug-resistant Gram-positive bacteria and moderate activity against the clinically relevant Gram-negative pathogen Acinetobacter baumannii.

microbiology↗

bioRxiv2020

One pathway, two cyclic pentapeptides: heterologous expression of BE-18257 A-C and pentaminomycins A-E from Streptomyces cacaoi CA-170360

1.The strain Streptomyces cacaoi CA-170360 produces the cyclic pentapeptides pentaminomycins A-E and BE-18257 A-C, two families of cyclopeptides synthesized by two nonribosomal peptide synthetases encoded in tandem within the same biosynthetic gene cluster. In this work, we have cloned and confirmed the heterologous expression of this biosynthetic gene cluster, demonstrating that each of the nonribosomal peptide synthetases present in the cluster is involved in the biosynthesis of each group of cyclopeptides. In addition, we discuss the involvement of a stand-alone enzyme belonging to the Penicillin Binding Protein family in the release and macrocyclization of the peptides.

molecular biology↗

bioRxiv2019

Genome-wide association analysis for resistance to infectious pancreatic necrosis virus identifies candidate genes involved in viral replication and immune response in rainbow trout (Oncorhynchus mykiss)

Infectious pancreatic necrosis (IPN) is a viral disease with considerable negative impact on the rainbow trout (Oncorhynchus mykiss) aquaculture industry. The aim of the present work was to detect genomic regions that explain resistance to infectious pancreatic necrosis virus (IPNV) in rainbow trout. A total of 2,278 fish from 58 full-sib families were challenged with IPNV. Of the challenged fish, 768 individuals were genotyped (488 resistant and 280 susceptible), using a 57K single nucleotide polymorphisms (SNPs) panel Axiom(R), Affymetrix(R). A genome-wide association study (GWAS) was performed using the phenotypes time to death (TD) and binary survival (BS), along with the genotypes of the challenged fish using a Bayesian model (Bayes C). Heritabilities for resistance to IPNV estimated using pedigree information, were 0.39 and 0.32 for TD and BS, respectively. Heritabilities for resistance to IPNV estimated using genomic information, were 0.50 and 0.54 for TD and BS, respectively. The Bayesian GWAS detected a SNP located on chromosome 5 explaining 18% of the genetic variance for TD. A SNP located on chromosome 23 was detected explaining 9% of the genetic variance for BS. The proximity of Sentrin-specific protease 5 (SENP5) to a significant SNP makes it a candidate gene for resistance against IPNV. However, the moderate-low proportion of variance explained by the detected marker leads to the conclusion that the incorporation of all genomic information, through genomic selection, would be the most appropriate approach to accelerate genetic progress for the improvement of resistance against IPNV in rainbow trout.

genomics↗

bioRxiv2018

MuSCA: a multi-scale model to explore carbon allocation in plants

Background and aimsCarbon allocation in plants is usually represented at a specific spatial scale, peculiar to each model. This makes the results obtained by different models, and the impact of their scale of representation, difficult to compare. In this work we developed a Multi Scale Carbon Allocation model (MuSCA) that can be applied at different, user-defined, topological scales of a plant, and used to assess the impact of each spatial scale on simulated results and computation time.\n\nMethodsModel multi-scale consistency and behavior were tested by applications on three realistic apple tree structures. Carbon allocation was computed at five spatial scales, spanning from the metamer (the finest scale, used as a reference) up to 1st order branches, and for different values of a sap friction coefficient. Fruit dry mass increments were compared across spatial scales and with field data.\n\nKey ResultsThe model showed physiological coherence in representing competition for carbon assimilates. Results from intermediate values of the friction parameter best fitted the field data. For these, fruit growth simulated at the metamer scale (considered as a reference) differed from about 1% at growth unit scale up to 35% at first order branch scale. Generally, the coarser the spatial scale the more fruit growth diverged from the reference and the lower the obtained within-tree fruit growth variability. Coherence in the carbon allocated across scales was also differently impacted, depending on the tree structure considered. Decreasing the topological resolution reduced computation time up to four orders of magnitude.\n\nConclusionsMuSCA revealed that the topological scale has a major influence on the simulation of carbon allocation, suggesting that this factor should be carefully evaluated when using different carbon allocation models or comparing their results. Trades-off between computation time and prediction accuracy can be evaluated by changing topological scales.

plant biology↗