Biology subjects

Castillo, D.

Publications and source records attributed to Castillo, D..

10 recordsLinked to original sources

bioRxiv2026

Laser scanning identifies large trees as a major source of uncertainty in mangrove carbon accounting

BackgroundMangrove forests are crucial ecosystems which support biodiversity, protect coastlines and store vast amounts of carbon. Mangrove conservation and protection rely on accurate carbon accounting to unlock investment. However, the allometric equations underpinning these carbon estimates remain poorly constrained, particularly for the large trees. MethodsWe used terrestrial laser scanning (TLS) to estimate the biomass of 187 mangrove stems across Suriname, Panama, Colombia and Jamaica, including 84 stems >20 cm DBH. TLS-derived biomass estimates were used to evaluate local, regional and pantropical allometric equations. ResultsMost diameter-based allometric equations underestimated biomass by 8-65%. Equations additionally incorporating tree height performed better, but still underestimated biomass by 12-16% on average. Applying alternative allometries to a representative mangrove inventory from Panama produced biomass estimates ranging from 80 to 200 Mg ha-{superscript 1}, demonstrating that allometric uncertainty alone can generate more than a two-fold difference in estimated carbon stocks. ConclusionsCurrent allometric equations systematically underestimate the biomass of large mangrove trees and are therefore likely to underestimate mangrove carbon stocks. TLS provides a practical, non-destructive approach for expanding biomass datasets and improving allometric equations. Reducing allometric uncertainty should be a priority for strengthening blue carbon accounting and mangrove conservation.

ecology↗

bioRxiv2026

Origin-1: a generative AI platform for de novo antibody design against novel epitopes

0Generative artificial intelligence has advanced antibody discovery, yet de novo design of therapeutic antibodies against targets with "zero-prior" epitopes remains a fundamental challenge. We define "zero-prior" epitopes as target sites lacking structural data from any reported antibody-antigen or protein-protein complex involving the target. Here we present Origin-1, a generative AI platform that overcomes this by integrating epitope-conditioned all-atom structure generation, paired complementarity determining region sequence design, and a specialized co-folding-based scoring protocol to select antibody designs predicted to be high-confidence, specific binders with favorable developability. We evaluated Origin-1 on a panel of ten targets selected to have no available protein-protein complex structures and minimal homology ([≤]60% sequence identity) to proteins with known complexes, creating stringent design conditions. In fewer than one hundred design attempts per target, we identified developable, specific antibodies, validated across multiple biophysical and developability assays, for four targets: COL6A3, AZGP1, CHI3L2, and IL36RA, with functional inhibition demonstrated for IL36RA. Cryogenic electron microscopy confirmed the atomic accuracy of our designs, revealing complexes that closely matched the computational models with high structural fidelity (3.0-3.3 [A] resolution; 0.83-0.91 DockQ). Furthermore, we employed AI-guided affinity maturation to optimize a de novo antibody binder against IL36RA, producing functional antagonists with sub-nanomolar affinities and a top EC50 of 12.3 nM. These results demonstrate a framework for targeting epitopes without structural precedent, expanding the programmable therapeutic antibody landscape.

molecular biology↗

bioRxiv2024

Unraveling an unknown diversity of archaeal and bacterial tetraether membrane lipid producers in a euxinic marine system

Bacterial membrane lipids have been traditionally defined as fatty acids (FAs) bilayers linked through ester bonds, while those of Archaea as ether-linked isoprenoids forming bilayers or monolayers of membrane spanning lipids (MSLs) known as isoprenoidal glycerol dialkyl glycerol tetraethers (isoGDGTs). This paradigm has been challenged with the discovery of branched GDGTs (brGDGTs), membrane spanning ether-bound branched alkyl FAs, that are of bacterial origin but whose specific producers in the environment are often unknown. The limited number of available microbial cultures restricts the knowledge of the biological sources of membrane lipids, which in turn limits their potential applicability as biomarkers. To address this limitation, we detected membrane lipids in the Black Sea using high resolution accurate mass/mass spectrometry and inferred their potential producers by targeting lipid biosynthetic pathways encoded on the metagenome, in metagenome-assembled genomes and unbinned scaffolds. We also detected brGDGTs and overly branched GDGTs in the suboxic and euxinic waters, which are potentially attributed, to members of the Planctomycetota, Cloacimonadota, Desulfobacterota, Chloroflexota, Actinobacteria and Myxococcota--all anaerobic microorganisms. These results open a new chapter in the use of specific brGDGTs as biomarkers of anoxic conditions in marine settings and of the role of these membrane lipids in microbial adaptation.

microbiology↗

bioRxiv2024

The genomic and cultural diversity of the Inka Qhapaq hucha ceremony in Chile and Argentina

The South American archaeological record has ample evidence of the socio-cultural dynamism of human populations in the past. This has also been supported through the analysis of ancient genomes, by showing evidence of gene flow across the region. While the extent of these signals is yet to be tested, the growing number of ancient genomes allows for more fine-scaled hypotheses to be evaluated. In this study, we assessed the genetic diversity of individuals associated with the Inka ritual, Qhapaq hucha. As part of this ceremony, one or more individuals were buried with Inka and local-style offerings on mountain summits along the Andes, leaving a very distinctive record. Using paleogenomic tools, we analyzed three individuals: two newly-generated genomes from El Plomo Mountain (Chile) and El Toro Mountain (Argentina), and a previously published genome from Argentina (Aconcagua Mountain). Our results reveal a complex demographic scenario with each of the individuals showing different genetic affinities. Furthermore, while two individuals showed genetic similarities with present-day and ancient populations from the southern region of the Inka empire, the third individual may have undertaken long-distance movement. The genetic diversity we observed between individuals from similar cultural contexts supports the highly diverse strategies the Inka implemented while incorporating new territories. More broadly, this research contributes to our growing understanding of the population dynamics in the Andes by discussing the implications and temporality of population movements in the region.

genomics↗

bioRxiv2023

In vitro validated antibody design against multiple therapeutic antigens using generative inverse folding

Deep learning approaches have demonstrated the ability to design protein sequences given backbone structures [1, 2, 3, 4, 5]. While these approaches have been applied in silico to designing antibody complementarity-determining regions (CDRs), they have yet to be validated in vitro for designing antibody binders, which is the true measure of success for antibody design. Here we describe IgDesign, a deep learning method for antibody CDR design, and demonstrate its robustness with successful binder design for 8 therapeutic antigens. The model is tasked with designing heavy chain CDR3 (HCDR3) or all three heavy chain CDRs (HCDR123) using native backbone structures of antibody-antigen complexes, along with the antigen and antibody framework (FWR) sequences as context. For each of the 8 antigens, we design 100 HCDR3s and 100 HCDR123s, scaffold them into the native antibodys variable region, and screen them for binding against the antigen using surface plasmon resonance (SPR). As a baseline, we screen 100 HCDR3s taken from the models training set and paired with the native HCDR1 and HCDR2. We observe that both HCDR3 design and HCDR123 design outperform this HCDR3-only baseline. IgDesign is the first experimentally validated antibody inverse folding model. It can design antibody binders to multiple therapeutic antigens with high success rates and, in some cases, improved affinities over clinically validated reference antibodies. Antibody inverse folding has applications to both de novo antibody design and lead optimization, making IgDesign a valuable tool for accelerating drug development and enabling therapeutic design. The data generated in this study serve as a useful benchmark of diverse antibody-antigen interactions. We use this data to benchmark self-consistency RMSD (scRMSD), using ABodyBuilder2 [6], ABodyBuilder3 [7], and ESMFold [8], as a metric for assessing binding. We open source the code for IgDesign and the SPR datasets. Code and datasets can be found at https://github.com/AbSciBio/igdesign.

synthetic biology↗

bioRxiv2020

3D reconstruction of genomic regions from sparse interaction data

Chromosome Conformation Capture (3C) technologies measure the interaction frequency between pairs of chromatin regions within the nucleus in a cell or a population of cells. Some of these 3C technologies retrieve interactions involving non-contiguous sets of loci, resulting in sparse interaction matrices. One of such 3C technologies is Promoter Capture Hi-C (pcHi-C) that is tailored to probe only interactions involving gene promoters. As such, pcHi-C provides sparse interaction matrices that are suitable to characterise short- and long-range enhancer-promoter interactions. Here, we introduce a new method to reconstruct the chromatin structural (3D) organisation from sparse 3C-based datasets such as pcHi-C. Our method allows for data normalisation, detection of significant interactions, and reconstruction of the full 3D organisation of the genomic region despite of the data sparseness. Specifically, it produces reliable reconstructions, in line with the ones obtained from dense interaction matrices, with as low as the 2-3% of the data from the matrix. Furthermore, the method is sensitive enough to detect cell-type-specific 3D organisational features such as the formation of different networks of active gene communities.

bioinformatics↗

bioRxiv2019

Bioactive compounds from Chrysosporium multifidum, a fungus isolated from Hermetia illucens gut microbiota

The gut microbiota of insects contains a wide range of organisms that protect them against the attack of pathogens by releasing various types of bioactive compounds. In the present study, we report the isolation and identification of the fungus Chrysosporium multifidum as a component of the microbiota from the larval gut of Hermetia illucens. Extract from the broth culture of C. multifidum showed moderate activity on a strain of methicillin-resistant Staphylococcus aureus (MRSA). The bioguided isolation of the extract resulted in the characterization of six -pyrone derivatives (1-6) and one diketopiperazines (7), among them 5,6-dihydro-4-methoxy-6-(1-oxopentyl)-2H-pyran-2-one (4) showed the best activity (IC50 = 11.4 {+/-} 0.7 {micro}g/ml and MIC = 62.5 g/ml).

microbiology↗

bioRxiv2019

Dynamic simulations of transcriptional control during cell reprogramming reveal spatial chromatin caging

Chromosome structure is a crucial regulatory factor for a wide range of nuclear processes. Chromosome Conformation Capture (3C)-based experiments combined with computational modelling are pivotal for unveiling 3D chromosome structure. Here, we introduce TADdyn, a new tool that integrates time-course 3C data, restraint-based modelling, and molecular dynamics to simulate the structural rearrangements of genomic loci in a completely data-driven way. We applied TADdyn on in-situ Hi-C time-course experiments studying the reprogramming of murine B cells to pluripotent cells, and characterized the structural rearrangements that take place upon changes in the transcriptional state of 11 genomic loci. TADdyn simulations show that structural cages form around the transcription starting site of active loci to stabilize their dynamics, by initiating (hit) and maintaining (stick) interactions with regulatory regions. Consistent findings with TADdyn for all loci under study suggest that this hit-and-stick mechanism may represent a general mechanism to trigger and stabilize transcription.

bioinformatics↗

bioRxiv2019

MuGVRE. A virtual research environment for 3D/4D genomics

Multiscale Genomics (MuG) Virtual Research Environment (MuGVRE) is a cloud-based computational infrastructure created to support the deployment of software tools addressing the various levels of analysis in 3D/4D genomics. Integrated tools tackle needs ranging from high computationally demanding applications (e.g. molecular dynamics simulations) to high-throughput data analysis applications (like the processing of next generation sequencing). The MuG Infrastructure is based on openNebula cloud systems implemented at the Institute for research in Biomedicine, and the Barcelona Supercomputing Center, and has specific interfaces for users and developers. Interoperability of the tools included in MuGVRE is maintained through a rich set of metadata allowing the system to associate tools and data in a transparent manner. Execution scheduling is based in a traditional queueing system to handle demand peaks in applications of fixed needs, and an elastic and multi-scale programming model (pyCOMPSs, controlled by the PMES scheduler), for complex workflows requiring distributed or multi-scale executions schemes. MuGVRE is available at https://vre.multiscalegenomics.eu and documentation and general information at https://www.multiscalegenomics.eu. The infrastructure is open and freely accessible.

bioinformatics↗

bioRxiv2017

Binless normalization of Hi-C data provides significant interaction and difference detection independently of resolution

3C-like experiments, such as 4C or Hi-C, have been fundamental in understanding genome organization. Thanks to these technologies, it is now known, for example, that Topologically Associating Domains (TADs) and chromatin loops are implicated in the dynamic interplay of gene activation and repression, and their disruption can have dramatic effects on embryonic development. To make their detection easier, scientists have endeavored into deeper sequencing to mechanically increase the chances to detect weaker signals such as chromatin loops. Part of this mindset can be attributed to the limitations of existing software: the analysis of Hi-C experiments is both statistically and computationally demanding. Here, we devise a new way to represent Hi-C data, which leads to a more detailed classification of paired-end reads and, ultimately, to a new normalization and interaction detection method. Unlike any other, Binless is resolution-agnostic, and adapts to the quality and quantity of available data. We demonstrate its capacities to call interactions and differences and make the software freely available.

bioinformatics↗