Inducing methylation

Nutrition, Exercise and Epigenetics: Ageing Interventions

Epigenetics refers to an inheritable but reversible phenomenon that changes gene expression without altering the underlying DNA sequence. Thus, it is a change in phenotype without a change in genotype. The field of epigenetics is quickly growing especially because environmental and lifestyle factors can epigenetically interact with genes and determine an individual’s susceptibility to disease. Interestingly, aging is associated with substantial changes in epigenetic phenomena. Aging induces global DNA hypomethylation and gene-specific DNA hypermethylation due to the altered expression of DNA methyltransferases (DNMTs).

The evidence of the impact of epigenetics on aging is growing. And nutrition plays a key role on epigenetics through the life course. Thus, there are crucial reasons to focus on nutrition early in life.

It is clear that epigenetic alterations caused by aging may provide a milieu that can develop age-associated diseases such as cancer, cardiovascular diseases, neurocognitive diseases and metabolic diseases. Nutrition is one of the most important environmental factors that can modify epigenetic phenomena. Therefore, one might speculate that nutrition may delay the age-associated epigenetic change and possibly reverse the aberrant epigenetic phenomena that can cause age-associated diseases. Indeed, many nutrients and bioactive food components, which can affect one-carbon metabolism that can regulate methylation of DNA and histone or directly inhibit epigenetic modifying enzymes, are showing promising results in delaying the aging process and preventing age-associated diseases through epigenetic mechanisms.

And beyond nutrition, there is exercise. This is what this book explains and it shows the foundations for better health. If it's "only" an issue of regulating methylation...where are the incentives?

Brandant com un saltamartí

Epigenetic protein families: a new frontier for drug discovery

Si ja sabem que al costat de la genòmica hi tenim la proteòmica i la metabolòmica, i que per sobre encara hi tenim l'epigenètica (que literalment vol dir més enllà de la genètica), ara a Nature ens expliquen les diferent famílies i el que representen per al futur de la recerca en nous medicaments.
L'article de revisió és d'aquells que em guardaré perquè si fins ara s'explicava la importància de l'epigènetica i com el paradigma genòmic de la predestinació brandava com un saltamartí, calia posar ordre a les idees. Però també perquè explica amb tot detall com:

Epigenetic regulation of gene expression is a dynamic and reversible process that establishes normal cellular phenotypes but also contributes to human diseases. At the molecular level, epigenetic regulation involves hierarchical covalent modification of DNA and the proteins that package DNA, such as histones. Here, we review the key protein families that mediate epigenetic signalling through the acetylation and methylation of histones, including histone deacetylases, protein methyltransferases, lysine demethylases, bromodomain-containing proteins and proteins that bind to methylated histones. These protein families are emerging as druggable classes of enzymes and druggable classes of protein–protein interaction domains.

L'explicació inicial m'ha semblat un resum útil:

 Although all cells in an organism inherit the same genetic material, the ability of cells to maintain the unique physical characteristics and biological functions of specific tissues and organs is due to heritable differences in the packaging of DNA and chromatin. These differences dictate distinct cellular gene expression programmes but do not involve changes in the underlying DNA sequence of the organism. Thus, epigenetics (which literally means ‘above genetics’) underpins the fundamental basis of human physiology. Importantly, the epigenetic state of a cell is malleable; it evolves in an ordered manner during the cellular differentiation and development of an organism, and epigenetic changes are responsible for cellular plasticity that enables cellular reprogramming and response to the environment. Because epigenetic mechanisms are responsible for the integration of environmental cues at the cellular level, they have an important role in diseases related to diet, lifestyle, early life experience and environmental exposure to toxins1. Thus, epigenetics is of therapeutic relevance in multiple diseases such as cancer, inflammation, metabolic disease and neuropsychiatric disorders, as well as in  regenerative medicine

Així doncs, ens trobem davant un horitzó de noves descobertes que es va configurant i que explica en bona part perquè s'ha tardat més d'una dècada en traslladar el projecte genoma humà  cap a aplicacions terapèutiques àmplies. Però també s'obre un nou interrogant sobre a seguretat dels modificadors epigenètics dels medicaments. La forma com caldrà avaluar-ho suposarà més exigència al regulador i una necessitat de transparència de la caixa negra encara més gran. Seguirem atents,  perquè per ara ja se li ha girat feina amb els inhibidors HDAC, per al limfoma cutani de cèl.lules T, el primer d'aquests medicaments.

PS. I si voleu una perspectiva diferent, consulteu aquest article.

PS. Millor no saber-ho. Ja ho vaig explicar fa temps i ara ho trobareu al WSJ. Cal conèixer la teva predisposició a l'Alzheimer mitjançant un test genètic? de què et servirà. Podeu llegir aquí una història real que em confirma el que deia.

PS. I si l'altre dia teníem una galleda d'aigua freda, ara en tenim una altra de calenta. 23andMe acaba d'obtenir una patent genòmica als USA. I precisament ho fa utilitzant arsenal de dades epigenètiques. Després de cinc anys sense beneficis i sense haver apostat per les patents, han trobat un forat i el podrien fer més gran. Cal estar atents.

In “Round Hill” (1977), the light is a harsh glare, enveloping five languid bathers in the Caribbean in a self-contained, enclosed moment of time and place. The figure in the foreground turns away from us, so we see only the back of his head; the others are self-absorbed, expressions hidden behind sunglasses.

Alex Katz: Give Me Tomorrow’, Tate St Ives to September 23, www.tate.org.uk 

A controversial view of epigenetic inheritance

A critical view on transgenerational epigenetic inheritance in humans

A new article in Nature suggest that more evidence is needed to ascertain the role of epigenetic inheritance.

Even if the molecular mechanisms exist to transmit epigenetic information across generations in humans, it is very likely that the transgenerational transmission of culture by communication, imitation, teaching and learning surpasses the effects of epigenetic inheritance and our ability to detect this phenomenon. Cultural inheritance has certainly had an adaptive role in the evolution of our species, but the evidence for transgenerational epigenetic inheritance, as laid out above, is not (yet) conclusive. 

Let's wait for new evidence...

DNA methylation assays as epigenetic biomarkers

Quantitative comparison of DNA methylation assays for biomarker development and clinical applications

A new milestone has been achieved in Medicine. Tracking epigenetic alterations is crucial to understand a disease. However, epigenetic biomarkers are needed to assess such changes. Its precision (sensitivity-specifity) is  paramount for its clinical application. Now a group of international researchers has certified its performance (partially). Have a look at this Nature article:

Genome-wide mapping and analysis of DNA methylation has become feasible for patient cohorts with thousands of samples, and epigenome-wide association studies have been conducted for numerous biomedically relevant phenotypes. To translate relevant epigenome associations into clinically useful biomarkers, it is necessary to select a manageable set of highly informative genomic regions, to target these loci with DNA methylation assays that are sufficiently fast, cheap, robust and widely available to be useful for routine clinical diagnostics, and to confirm their predictive value in large validation cohorts.

Among its conclusions I would like to highlight three of them:

(i) Absolute DNA methylation assays are the method of choice when validating DNA methylation differences in large cohorts, and they are also an excellent technology for developing epigenetic biomarkers.

(ii) Relative DNA methylation assays are not a good replacement for absolute assays. However, experiences of scientists in the contributing laboratories suggest that carefully selected, designed and validated relative assays can cost-effectively detect minimal  races of methylated DNA against an excess of unmethylated DNA.

(iii) Global DNA methylation assays suffer from noisy data and divergent results between technologies. Locus-specific assays (possibly combined with prediction) provide a more robust alternative

That's it. Very soon will see the epigenetic biomarkers in routine clinical use. And afterwards,  epigenetic drugs and treatments. Then, we'll confirm that the promise of precision medicine is a reality. The implications for medicine as a scientific discipline and clinical decision making are huge, and specifically, healthcare organizations will need to adapt to new knowledge and technologies.

PS. Neuroepigenetics: DNA methylation and memory

Epigenetics contribution to clarify disease mechanisms

Epigenetics at the Crossroads of Genes and the Environment

You may find an updated definition of epigenetics in this JAMA article:

 Epigenetics refers to information transmitted during cell division other than the DNA sequence per se, and it is the language that distinguishes stem cells from
somatic cells, one organ from another, and even identical twins from each other. Examples include (1) DNA methylation, a covalent modification of the nucleotide cytosine, that is copied during cell division at CpG dinucleotides by the maintenance enzyme DNA methyltransferase I; (2) posttranslational modifications of nucleosome proteins about which the DNA double helix is wrapped; and (3) the density of  nucleosomes and higher-order packaging of chromatin within the nucleus, including its relationship to the nuclear lamina.

If this is so, why is the message of predictive genetics so widespread?. I've insisted on this issue before.

 The field of epigenetics and epigenetic epidemiology have much to do to improve measurement of epigenetic marks, inform natural variation in such marks, and the biological and population level relationships between genes, environment, and epigenetics. This is an important emerging area as it holds promise for better risk prediction in precision medicine as well as for clarification of disease mechanisms among the existing opaque landscape only partially informed by traditional genetic and environmental studies to date.

 A short and relevant article that provides hints for further reading.

PS. Epigenetic phenomena, from Nature.

Approaching the golden age of epigenomics and epitranscriptomics

A new twist on epigenetics

If epigenomics is crucial to discard the genetic predestination paradigm, now we can add a new 'omics to the paradigm: epitranscriptomics. Last February, Nature published interesting news related to recent scientific developments:

The epigenome helps to explain how cells with identical DNA can develop into the multitude of specialized types that make up different tissues. The marks help cells in the heart, for example, maintain their identity and not turn into neurons or fat cells. Misplaced epigenetic marks are often found in cancerous cells.

 Chuan He and Tao Pan are two researchers that have been working on new ways of controlling gene expression

He and others have shown that a methyl group attached to adenine, one of the four bases in RNA, has crucial roles in cell differentiation, and may contribute to cancer, obesity and more. In 2015, He’s lab and two other teams uncovered the same chemical mark on adenine bases in DNA (methyl marks had previously been found only on cytosine), suggesting that the epigenome may be even richer than previously imagined.

The team had shown for the first time that RNA methylation was reversible, just like the marks found on DNA and histones.

Methylated adenine bases are the focus of research on gene expression.

The meta-informational challenge of molecular data

The future of DNA sequencing

Where does DNA sequencing goes from here?. Nowadays, this is an appropriate question to pose.  The answer appears in an article in an interesting article in Nature.

Now, geneticists would like to have DNA sequences for everyone on Earth, and from every cell in every tissue at every developmental stage (including epigenetic modifications), in health and in disease. They would also like to get comprehensive gene-expression patterns by sequencing the complementary DNA copies of messenger RNA molecules.

In a mere 40 years, the central goal of putting molecular data about cells to practical use has changed from an informational challenge to a meta-informational one. Take clinical applications of genome-sequence data. It may soon be possible to use DNA sequencing routinely to analyse body fluids obtained for any clinical purpose. But only a vast amount of well-organized data about the multi-year medical histories of millions of people will provide the meta-information needed to establish when to ignore such data and when to act on them.

El llenguatge de la vida

 

L'Eric Topol ens ofereix un resum d'alta qualitat per entendre el moment que viu la ciència mitjançant la intel·ligència artificial. 

Impressionant. Aquesta és la llista de grans models de llenguatge en ciències de la vida (LLLMs):

1. Evo. This model was trained with 2.7 million evolutionary diverse organisms (prokaryotes—without a nucleus, and bacteriophages) representing 300 billion nucleotides to serve as a foundation model (with 7 billion parameters) for DNA language, predicting function of DNA, essentiality of a gene, impact of variants, and DNA sequence or function, and CRISPR-Cas prediction. It’s multimodal, cutting across protein-RNA and protein-DNA design.

   Figure below from accompanying perspective by Christina Theodoris.

2. Human Cell Atlas A collection of publications from this herculean effort involving 3,000 scientists, 1,700 institutions, and 100 countries, mapping 62 million cells (on the way to 1 billion), with 20 new papers that can be found here. We have about 37 trillion cells in our body and until fairly recently it was thought there were about 200 cell types. That was way off—-now we know there are over 5,000.

   One of the foundation models built is Single-Cell (SC) SCimilarity, which acts as a nearest neighbor analysis for identifying a cell type, and includes perturbation markers for cells (Figure below). Other foundation models used in this initiative are scGPT, GeneFormeR, SC Foundation, and universal cell embedding. Collectively, this effort has been called th “Periodic Table of Cells” or a Wikipedia for cells and is fully open-source. Among so many new publications, a couple of notable outputs from the blitz of new reports include the finding of cancer-like (aneuploid) changes in 3% of normal breast tissue, representing clones of rare cells and metaplasia of gut tissue in people with inflammatory bowel disease.

3. BOLTZ-1 This is a fully open-source model akin to AlphaFold 3, with similar state-of-the-art performance, for democratizing protein-molecular interactions as summarized above (for AlphaFold 3). Unlike AlphaFold 3 which is only available to the research community, this foundation model is open to all. It also has some tweaks incorporated beyond AlphaFold 3, as noted in the preprint.

4. RhoFold For accurate 3D RNA structure prediction, pre-trained on almost 24 million RNA sequences, superior to all existing models (as shown below for one example).

5. EVOLVEPro A large language protein model combined with a regression model for genome editing, antibody binding and many more applications for evolving proteins, all representing a jump forward for the field of A.I. guided protein engineering.

6. PocketGen A model dedicated to defining the atomic structure of protein regions for their ligand interactions, surpassing all previous models for this purpose.

   

7. MassiveFold A version of AlphaFold that does predictions in parallel, enabling a marked reduction of computing time from several months to hours

8. RhoDesign From the same team that produced RhoFold, but this model is for efficient design of RNA aptamers that can be used for diagnostics or as a drug therapy.

9. MethylGPT Built upon scGPT architecture, trained on over 225,000 samples, it captures and can reconstruct almost 50,000 relevant methylation CpG sites which help in predicting diseases and gauging the impact of interventions (see graphic below).

   

10. CpGPT Trained on more than 100,000 samples, it is the optimal model to date fo predicting biological (epigenetic) age, imputing missing data, and understanding biology of methylation patterns.

11. PIONEER A deep learning pipeline dedicated to the protein-protein interactome, identifying almost 600 protein-protein interactions (PPIs) from 11,000 exome sequencing across 33 types of cancer, leading to the capability of prediction which PPIs are associated with survival. (This was published 24 October, the only one not in November on the list!)

Al KBR, Cartier-Bresson

I per aquí aprop, mentrestant anem venent privadament la recerca inicial finançada públicament i perdem l'oremus, mentre alguns se n'aprofiten. Desgavell perfectament dissenyat.

IA pertot arreu

Multimodal biomedical AI

En aquest blog he anat explicant darrerament els avenços en la intel·ligència artificial i els podeu trobar en aquestes entrades. La innovació és tan ràpida que costa molt estar atent al que està passant. Per aquest motiu us suggereixo una ullada a l'article que parla de la intel·ligència artificial multimodal en biomedicina, recullo una frase:

The development of multimodal AI models that incorporate data across modalities—including biosensors, genetic, epigenetic, proteomic, microbiome, metabolomic, imaging, text, clinical, social determinants and environmental data—is poised to partially bridge this gap and enable broad applications that include individualized medicine, integrated, real-time pandemic surveillance, digital clinical trials and virtual health coaches (Fig. 1). In this Review, we explore the opportunities for such multimodal datasets in healthcare; we then discuss the key challenges and promising strategies for overcoming these. 

I un gràfic: 

 

i el que són els foundation models:

Bommasani R et al, arXiv, July 2022

i si voleu saber l'última informació sobre "multimodal ai", la trobareu aquí.

I jo em pregunto, hi ha algú aquí aprop que estigui a l'aguaït de tot això? Es tant important que convindria no perdre passada i em temo que fem tard.

PD. Avui  a WSJ i al NYT 

"This is going to change everything about how we do everything. I think that it represents mankind’s greatest invention to date"