Biology subjects

de Lima Camillo, L. P.

Publications and source records attributed to de Lima Camillo, L. P..

5 recordsLinked to original sources

bioRxiv2025

A single factor for safer cellular rejuvenation

Ageing is a key driver of the major diseases afflicting the modern world. Slowing or reversing the ageing process would therefore drive significant and broad benefits to human health. Previously, the Yamanaka factors (OCT4, SOX2, KLF4, with or without c-MYC: OSK(M)) have been shown to rejuvenate cells based on accurate predictors of age known as epigenetic clocks. Unfortunately, OSK(M) induces dangerous pluripotency pathways, making it unsuitable for therapeutic use. To overcome this therapeutic barrier, we screened for novel factors by optimising directly for age reversal rather than for pluripotency. We trained a transcriptomic ageing clock, unhindered by the low throughput of bulk DNA methylation assays, to enable a screen of unprecedented scale and granularity. Our platform identified SB000, the first single gene intervention to rejuvenate cells from multiple germ layers with efficacy rivalling the Yamanaka factors. Cells rejuvenated by SB000 retain their somatic identity, without evidence of pluripotency or loss of function. These results reveal that decoupling pluripotency from cell rejuvenation does not remove the ability to rejuvenate multiple cell types. This discovery paves the way for cell rejuvenation therapeutics that can be broadly applied across age-driven diseases. HighlightsO_LISB000 drives multi-omic rejuvenation in human fibroblasts, as evidenced by substantial reversal of numerous epigenetic clocks, lowered single-cell transcriptomic age, and decreased senescence-associated gene expression. C_LIO_LIIn contrast to OSK(M), SB000 treatment maintains transcriptomic and functional measures of fibroblast identity without the activation of pluripotency. C_LIO_LISB000 rejuvenation generalises to keratinocytes, cells from another germ layer, with potency matching or surpassing OSK(M). C_LI

cell biology↗

bioRxiv2024

Single-Cell Temporal Atlas of Myeloid Cells in the Live Haemorrhagic Brain

Innate immune cells contribute to both secondary brain injury and repair following intracerebral hemorrhage (ICH). However, the specific signaling pathways that govern initial inflammatory and subsequent reparative myeloid programs in living patients remain poorly understood. To better characterize mononuclear phagocyte cell changes over time, we generated a single-cell transcriptomic dataset of paired hematoma clot evacuates and peripheral blood samples from 10 patients following ICH (5 - 290 hours). We identified distinct populations of activated and TNF-low microglia, as well as a unique, highly activated population of CD14+ monocytes in the hematoma. Perturbation analysis identified TNF signaling as the primary driver of hematoma monocyte activation. Custom temporal trajectory analysis using single-cell foundation model embeddings found that this TNF response in monocytes was transient, peaking early after hemorrhage and decreasing over the following 48 hours as monocytes shifted to reparative transcriptional programs. Transiently activated microglia emerged as the likely acute source of TNF, signaling through monocyte TNFR2. Surprisingly, acute TNF signaling in CD14+ monocytes was also associated with better severity-adjusted neurological outcomes both in our cohort and an independent validation cohort. These findings suggest acute TNF signaling between activated microglia and hematoma-associated monocytes, particularly through TNFR2, may contribute to recovery following ICH. O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=139 SRC="FIGDIR/small/630187v2_ufig1.gif" ALT="Figure 1"> View larger version (41K): org.highwire.dtl.DTLVardef@ff4fa0org.highwire.dtl.DTLVardef@1c4cc5borg.highwire.dtl.DTLVardef@1449761org.highwire.dtl.DTLVardef@2e7ea6_HPS_FORMAT_FIGEXP M_FIG C_FIG

bioinformatics↗

bioRxiv2024

CpGPT: a Foundation Model for DNA Methylation

DNA methylation is a type of epigenetic modification that plays a significant role in development, aging, and disease. Despite extensive research, how genome-wide DNA methylation patterns collectively encode and influence complex phenotypes such as aging and disease remains difficult to characterize with conventional approaches. Foundation models are a class of machine learning model that leverage vast quantities of data to make sense of complex data types, such as genome sequences or single-cell transcriptomes. Here, we present the Cytosine-phosphate-Guanine Pretrained Transformer (CpGPT), a novel foundation model pretrained on CpGCorpus, a novel database with more than 2,000 DNA methylation datasets encompassing over 150,000 samples from diverse conditions. CpGPT lever-ages an improved transformer architecture to learn comprehensive representations of methylation patterns, allowing it to impute and reconstruct genome-wide methylation profiles from limited input data. By capturing sequence, positional, and epigenetic contexts, CpGPT outperforms specialized models when finetuned for aging-related tasks, including the state-of-the-art GrimAge2 and PCGrimAge for mortality and morbidity estimation. The model is highly adaptable and can impute beta values across different methylation platforms, tissue types, mammalian species, and even single-cell data. As a foundation model, CpGPT can be leveraged as a new tool for biological discovery in the field of epigenetics. The open-source code and model can be found at http://github.com/lucascamillomd/CpGPT. HighlightsO_LICpGPT is a novel foundation model for DNA methylation analysis, pretrained on over 2,000 datasets encompassing 150,000+ samples. C_LIO_LIThe model demonstrates strong performance in zero-shot tasks including imputation, array conversion, and reference mapping. C_LIO_LICpGPT achieves state-of-the-art results in mortality prediction and chronological age estimation. C_LI

systems biology↗

bioRxiv2023

pyaging: a Python-based compendium of GPU-optimized aging clocks

MotivationAging is intricately linked to diseases and mortality and is reflected in molecular changes across various tissues. The development and refinement of biomarkers of aging, healthspan, and lifespan using machine learning models, known as aging clocks, leverage epigenetic and other molecular signatures. Despite advancements, as noted by the Biomarkers of Aging Consortium, the field grapples with challenges, notably the lack of robust software tools for integrating and comparing these diverse models. ResultsI introduce pyaging, a comprehensive Python package, designed to bridge the gap in aging research software tools. pyaging integrates over 30 aging clocks, with plans to expand to more than 100, covering a range of molecular data types including DNA methylation, transcriptomics, histone mark ChIP-Seq, and ATAC-Seq. The package features a variety of model types, from linear and principal component models to neural networks and automatic relevance determination models. Utilizing a PyTorch-based backend for GPU acceleration, pyaging ensures rapid inference even with large datasets and complex models. The package supports multi-species analysis, currently including humans, various mammals, and C. elegans. Availability and Implementationpyaging is accessible at https://github.com/rsinghlab/pyaging. The package is structured to facilitate ease of use and integration into existing research workflows, supporting the flexible anndata data format. Supplementary InformationSupplementary materials, including detailed documentation and usage examples, are available online at the pyaging documentation site (https://pyaging.readthedocs.io/en/latest/index.html).

bioinformatics↗

bioRxiv2023

Histone mark age of human tissues and cells

BackgroundAging involves intricate epigenetic changes, with histone modifications playing a pivotal role in dynamically regulating gene expression. Our research comprehensively analyzes seven key histone modifications across various tissues to understand their behavior during human aging and formulate age prediction models. ResultsThese histone-centric prediction models exhibit remarkable accuracy and resilience against experimental and artificial noise. They showcase comparable efficacy when compared with DNA methylation age predictors through simulation experiments. Intriguingly, our gene set enrichment analysis pinpoints vital developmental pathways crucial for age prediction. Unlike in DNA methylation age predictors, genes previously recognized in animal studies as integral to aging are amongst the most important features of our models. We also introduce a pan-histone-mark, pan-tissue age predictor that operates across multiple tissues and histone marks, reinforcing that age-related epigenetic markers are not restricted to particular histone modifications. ConclusionOur findings underscore the potential of histone marks in crafting robust age predictors and shed light on the intricate tapestry of epigenetic alterations in aging.

systems biology↗