Gabriel Arantes dos Santos

Young scientist: bioinformatics, molecular biology, and genomics in cancer and aging. PhD in Science, Master's in Genetics, Bachelor's in Biomedical Sciences.

[Gabriel Arantes dos Santos @GarantesSantos]

I'm thrilled to share my latest publication and the first publication of my postdoc! It's a privilege to be part of the @GalanteLab

[galante_lab @GalanteLab]

We are excited to share our latest paper, where we review the role of endogenous retroviruses in our genomes.
Read the full story of "Endogenous retroviruses in aging and cancer: from genomic defense to oncogenic activation" here: https://t.co/mMNKTsJy1X

[Gabriel Arantes dos Santos @GarantesSantos]

@jpsenescence Really interesting work Pedro; makes me wonder if a similar approach could bring new insights in bulk RNA‑seq data. The immune cell upregulation is especially intriguing; maybe it’s a response to a frailer, inflammatory state rather than a driver of ageing. Very cool, congrats!

[João Pedro de Magalhães @jpsenescence]

Have we been looking at RNA changes during ageing the wrong way? 🧬

In our new study we use a largely overlooked technique called spike-in normalisation to investigate age-related changes in transcripts across mouse cell types.

A thread 🧵

1/6

https://t.co/jgmelVQUO3

[galante_lab @GalanteLab]

Are pseudogenes just "genomic fossils"? Our latest preprint says no! By analyzing 244 high-quality vertebrate genomes from VGP, we show that processed pseudogenes (retrocopies) are actually dynamic substrates for evolutionary innovation. Full study here: https://t.co/NCoe57hMRh

[Alexandr Rakitko @AlexRakitko]

🧬Possibly one of the most important genetics preprints of 2026 has just been published: the flagship paper of the Vertebrate Genomes Project.

An ambitious effort to generate near-complete and near-error-free reference genomes for all living vertebrate species.

The authors present 816 high-quality reference genomes covering around 95% of major vertebrate evolutionary lineages. Most assemblies are at chromosome-level quality and some are fully diploid telomere-to-telomere genomes.

Using this resource the authors reconstructed the genome of the last common ancestor of vertebrates, studied the evolution of sex chromosomes, explored 3D genome organization and compared epigenetic landscapes across hundreds of species.

This dataset will likely become a foundational platform for evolutionary biology and comparative genomics.

https://t.co/MuwxZD5bZd

#VGP #evolution #ComparativeGenomics #vertebrates #genomes

[Gabriel Arantes dos Santos @GarantesSantos]

Really "cool" paper that suggests biological aging promotes carcinogenesis. They also provide evidence that we're aging faster and getting cancer earlier 🥲🥲
We need to understand aging to (also) fight cancer!

https://t.co/0VkcAjQNW0

[João Pedro de Magalhães @jpsenescence]

Can we use machine learning to predict longevity drugs? 💊

In our updated preprint led by @AlekseyVBelikov we trained ML models on compounds in the DrugAge database that extend lifespan in mice. Features associated with longevity drugs included receptors for neurotransmitters, metabolic hormones and sex hormones.

We then used our top-performing models to screen DrugBank for new potential longevity drugs. Identified clusters included compounds targeting IGF1 and insulin receptors, beta adrenergic receptors, carbonic anhydrases, dopamine and serotonin receptors.

In collaboration with @scrippsresearch we tested 22 predicted compounds in C. elegans and found that 6 of them extended lifespan: dihydroergotamine, mianserin, bromocriptine, voxtalisib, bms-754807 and solifenacine.

With our collaborators @UniKent we also created a public web server with our top models.

Feedback welcome.

https://t.co/6Dm44NqIv4

[galante_lab @GalanteLab]

Excited to share high-quality genomes for sloths and anteaters and some amazing mammalian biology findings! 🦥🧬 https://t.co/FeZQOyYwnL

[Aging Science News @AgingBiology]

An inflammatory gene set driven epigenetic clock tracks down disease progression and rejuvenation
https://t.co/UVEyJPrOyo

[João Pedro de Magalhães @jpsenescence]

Gene expression can be used to predict age and mortality 🚨

Impressive study from @gladyshev_lab @harvardmed using RNA-seq data to develop accurate transcriptomic clocks across mammalian species (inc. humans) and tissues to predict age, lifespan and mortality. A great example of the power of big data in aging research.

Major processes changing with age include inflammation, mitochondrial function, epigenetic regulation, cell cycle (inc. markers of cellular senescence) and extracellular matrix remodeling.

These clocks will have applications in personalised medicine, drug discovery and clinical trials. They also suggest some degree of coordination of aging changes.

However, many questions remains. Are age-related transcriptomic changes drivers of aging or merely passengers? In other words, if we normalized these aging changes back to youthful levels, would this be beneficial, detrimental, or have no effect?

And with so many aging clocks now available, how will researchers determine which ones are most biologically meaningful and clinically useful?

Link to original paper:
https://t.co/S7Cu17YqCu

My thoughts on the study:
https://t.co/xYAguwvsiu

[Gabriel Arantes dos Santos @GarantesSantos]

Evolutionary genetics of ageing.

https://t.co/C3ZV6r43N0

[Norn Group @NornGroup]

This paper exists because teams of people started an unglamorous data collection project many years ago.

The Interventions Testing Program (ITP) is a long-running project by the NIH-NIA with the goal to identify interventions that extend lifespan in mice. The ITP was not built around the analysis this paper eventually performed. Instead, the paper’s method emerged because a group of scientists had access to a sufficiently large dataset generated by the ITP and realized there were better questions to ask of it.

Computational power is scaling rapidly. What isn't scaling is the generation of biological data that requires organisms to age, populations to evolve, or longitudinal measurements to accumulate across years.

If someone starts a 10-year dataset now, by the time it matures the analytical tools available to interrogate it will be dramatically more powerful than anything we have today.

But if nobody starts the dataset, those tools have nothing to work with.

For those thinking about the overall progress of longevity, it’s now obvious that AI will be bottlenecked by the lack of high quality biological datasets.

https://t.co/mEXSgsAH3t

[João Pedro de Magalhães @jpsenescence]

Ageing isn't one disease, it's a network.

Excited to share our latest study exploring the genetic links between ageing and age-related diseases 🧬

We show how shared pleiotropic genes connect disease clusters, revealing two distinct genetic architectures: one driven by ageing-related pathways and another by immune processes.

Using machine learning, we also predict new candidate ageing genes associated with intracellular signalling and programmed cell death.

Fantastic work by PhD student Gustavo Magdaleno 👏

https://t.co/v3z1aOffQe

[Danny Arends @DannyArends]

Our paper in @Nature today 🥳 We tracked 6,438 mice from puberty to death and mapped the genetics of *when* you die, not just whether a gene associates with lifespan.

https://t.co/EoeexqJoHk

59 loci. Two decades of data. Thread 👇

#Longevity #Aging #Genetics #Healthspan

[Aging Science News @AgingBiology]

Temporal AI model predicts drivers of cell state trajectories across human aging
https://t.co/5oD9nDiWHw

[Aging Science News @AgingBiology]

Intra- and inter-organ communications in aging and cancer: Local and global spatial perspectives
https://t.co/QHOEakpjA6

[João Pedro de Magalhães @jpsenescence]

Excited to share our new preprint on multi-omics signatures of accelerated PhenoAge.

We integrated plasma proteomics and metabolomics to study individuals in UK Biobank with accelerated PhenoAge. Proteomics alone showed strong predictive power, while metabolomics enhanced biological interpretability.

Our integrated analysis revealed four axes associated with accelerated aging processes: immune-inflammatory activity, lipid-vascular processes, nutrient metabolism, and HDL/apolipoprotein signaling.

Not surprisingly, immune and inflammatory activation emerged as a dominant signature, consistent with inflammaging.

Feedback welcome!

https://t.co/oshdmtC3ON

[galante_lab @GalanteLab]

I'm pleased to share our recent review in Molecular Cancer Research, which examines the relationship between L1 activation and endocrine-related cancers. Many thanks to Rafael Loch's group for the excellent collaboration.

[João Pedro de Magalhães @jpsenescence]

🚨 The Human Ageing Genomic Resources Are at Risk

For over 20 years, our Human Ageing Genomic Resources (HAGR) have supported ageing and longevity research worldwide (200,000+ users per year, 1,000+ citations, widely used by academic labs and longevity biotech).

Due to shifting UK government funding priorities, our infrastructure funding was not renewed in 2024, and these databases are now at risk of going offline.

We are raising funds to cover basic server costs and secure their continuity while we pursue long-term funding.

If these resources have supported your research, your company, or your thinking, please consider helping sustain them.

https://t.co/bFryPX5vnS

[Markus Tschurtschenthaler @MarTschu]

Our work is now published in @Nature.

We introduce the Mouse Cancer Cell Line Atlas (MCCA) and reveal deterministic, tissue-specific rules of KRAS-driven cancer evolution.

Huge thanks to the entire team & collaborators.
https://t.co/vjiwCcHnQl

[Gabriel Arantes dos Santos @GarantesSantos]

Very cool perspective.

"Molecular evolution of animal aging."
https://t.co/OkDkh97H5z