Proteòmica plasmàtica

 Atlas of the plasma proteome in health and disease in 53,026 adults

Hem arribat a desxifrar el 93% del Proteoma humà. Aquesta és una fita molt rellevant. I si ja havia parlat algunes vegades de l'Atles Cel·lular, ara toca fixar-nos amb el proteoma.

El més important és relacionar les proteïnes amb malalties i trets de salut. Això és el que s'ha presentat recentment a Cell i aquí resumeixo les 27 pàgines amb IA. 

Aquest article presenta un estudi proteòmic a gran escala que relaciona 2.920 proteïnes plasmàtiques amb 406 malalties prevalents, 660 malalties incidentals i 986 trets relacionats amb la salut en 53.026 adults del Biobanc del Regne Unit. L'estudi pretén crear un atles proteòmic complet per a la medicina de precisió, identificant biomarcadors per al diagnòstic i la predicció de malalties, i també possibles objectius terapèutics i oportunitats de reposicionament de fàrmacs.

A continuació es detallen les troballes i els mètodes principals de l'estudi:

**Associacions proteïna-malaltia:** Es van identificar **168.100 associacions proteïna-malaltia** i **554.488 associacions proteïna-trets**. Més de 650 proteïnes es van compartir entre almenys 50 malalties, i més de 1.000 van mostrar heterogeneïtat per sexe i edat. Algunes proteïnes van mostrar un potencial prometedor en la discriminació de malalties (àrea sota la corba [AUC] > 0,80 en 183 malalties).

**Anàlisi de regressió:** Es van utilitzar models de regressió logística per a les malalties prevalents i models de regressió de riscos proporcionals de Cox per a les malalties incidentals per investigar la relació entre els nivells de proteïnes plasmàtiques i les malalties.

**Proteïnes causals:** Mitjançant la integració de dades del locus de trets quantitatius de proteïnes (pQTL), es van determinar **474 proteïnes causals**, cosa que va comportar **37 oportunitats de reposicionament de fàrmacs i 26 possibles objectius terapèutics** amb perfils de seguretat favorables.

**Pleiotropia:** La majoria de les proteïnes van mostrar associacions multifenotípiques, amb algunes com la **GDF15** i la família del **TNF** que es van associar amb moltes malalties. Per exemple, **GDF15** es va associar amb la majoria de les malalties, incloses 205 prevalents i 397 incidents.

**Anàlisi de subgrups:** Es van dur a terme anàlisis de subgrups per sexe i edat per revelar associacions específiques. Es van trobar 37.979 i 22.911 associacions específiques per sexe en les associacions proteïna-malaltia incidentals i prevalents, respectivament.

**Anàlisi de sensibilitat:** Les anàlisis de sensibilitat van revelar que la majoria de les associacions proteïna-malaltia van seguir sent significatives després d'ajustar per la comorbiditat.

**Funcions biològiques:** L'anàlisi d'enriquiment funcional va revelar que les vies relacionades amb el sistema immunitari s'enriqueixen principalment en diverses malalties. Les vies relacionades amb la unió de TNF als seus receptors fisiològics van ser les més freqüents entre les malalties.

**Classificació de malalties:** Es va aplicar una agrupació jeràrquica per agrupar les 660 malalties en 40 grups basats en les magnituds de les associacions proteïna-malaltia. Les malalties amb similituds es van agrupar i van mostrar característiques biològiques singulars.

**Diagnòstic i predicció:** La integració de les proteïnes plasmàtiques a models demogràfics va millorar significativament la precisió diagnòstica i predictiva de moltes malalties.

**Randomització mendeliana (MR):** L'anàlisi MR va identificar proteïnes causals que contribueixen a la patogènesi de les malalties i proteïnes que poden ser conseqüència de determinades malalties. Per exemple, la proteïna **GDF15** es va associar causalment amb diverses malalties autoimmunes.

**Validació i reposicionament d'objectius terapèutics:** Es va identificar que moltes proteïnes associades a la malaltia coincidien amb el genoma utilitzable per a fàrmacs, cosa que indicava possibles objectius terapèutics. Es van descobrir **37 oportunitats de reposicionament de fàrmacs** per a 25 objectius terapèutics establerts.

**Interfície web:** Es va desenvolupar una eina web interactiva per explorar els resultats.

Aquest estudi proporciona un recurs de gran escala per a la investigació futura sobre el paper de les proteïnes en la patogènesi, detecció, diagnòstic i tractament de les malalties humanes. A més, l'estudi destaca el potencial de la proteòmica plasmàtica per a la implementació de la medicina de precisió.

PS Alphafold3, la gran eina.

Pilar Aymerich. Anys 70 BCN

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.

El cos humà com un ecosistema cel·lular (2)

 A ‘Wikipedia for cells’: researchers get an updated look at the Human Cell Atlas, and it’s remarkable

Cellular atlases are unlocking the mysteries of the human body

The Human Cell Atlas: towards a first draft atlas

Figure 1 | Human cellular atlases. a, The Human Cell Atlas project aims to create cellular maps of human organs and tissues throughout life, and in health and disease. Cells are isolated from tissues during different stages of development and from cell-based organ models (organoids). Cell types and states can be captured using single-cell profiling techniques, mainly transcriptomics (using RNA transcripts to examine gene expression) but also other methods such as epigenomics (examining regulation of gene expression by assessing modifications to DNA and histone proteins). Sophisticated computational analyses are used to classify cell types and integrate information from different data types. b, Cellular atlases can be used to make inferences about human biology: spatial transcriptomics enables cells of defined type to be mapped to the tissue from which they originated, so that tissue architecture can be examined; cell differentiation and maturation during developmental processes can be traced; interactions between cells of different types can be inferred; and comparisons can be made between healthy and diseased tissue.

El cos humà com un ecosistema cel·lular

 Impact of the Human Cell Atlas on medicine

El projecte d'Atles de les cèl·lules comença a oferir els seus fruits. Si amb el genoma la seqüenciació del DNA era la tecnologia cabdal, ara per a estudiar les cèl·lules la transcriptòmica ha estat clau (estudi de totes les mol·lècules de RNA d'una cèl·lula, per tant genoma i epigenoma). L'avenç a hores d'ara és notable i així s'explica a Nature.

En moltes ocasions anteriors ja he explicat el paper de l'epigenètica, però ens faltava l'Atles, saber quants tipus de cèl·lules té el cos humà i com el mRNA codifica proteïnes. Ara s'està avançant molt ràpidament. El canvi a la recerca de medicaments i teràpies pot ser crucial.

PD. Avui, Atul Gawande

 

AI everywhere (10)

 Atlas of AI. Power, Politics, and the Planetary Costs of Artificial Intelligence

Artificial intelligence is not an objective, universal, or neutral computational technique that makes determinations without human direction. Its systems are embedded in social, political, cultural, and economic worlds, shaped by humans, institutions, and imperatives that determine what they do and how they do it. They are designed to discriminate, to amplify hierarchies, and to encode narrow classifications. When applied in social contexts such as policing, the court system, health care, and education, they can reproduce, optimize, and amplify existing structural inequalities. This is no accident: AI systems are built to see and intervene in the world in ways that primarily benefit the states, institutions, and corporations that they serve. In this sense, AI systems are expressions of power that emerge from wider economic and political forces, created to increase profits and centralize control for those who wield them. But this is not how the story of artificial intelligence is typically told.

The standard accounts of AI often center on a kind of algorithmic exceptionalism—the idea that because AI systems can perform uncanny feats of computation, they must be smarter and more objective than their flawed human creators.

 

Medical practice variation in oncology

 Atlas de variaciones en cirugía oncológica.

Why there is still so much variation in medical practice?

Some details inside this atlas.

Aliza Nisenbaum

Mapping genetic regulation

The GTEx Consortium atlas of genetic regulatory effects across human tissues

A great step in research:

The GTEx v8 data release represents a deep survey of both intra- and interindividual transcriptome variation across a large number of tissues. With 838 donors and 15,201 samples—approximately twice the size of the v6 release used in the previous set of GTEx Consortium papers—we have created a comprehensive resource of genetic variants that influence gene expression and splicing in cis. This substantially expands and updates the GTEx catalog of sQTLs, doubles the number of eGenes per tissue, and saturates the discovery of eQTLs with greater than twofold effect sizes in ~40 tissues. The fine-mapping data of GTEx cis-eQTLs provide a set of thousands of likely causal functional variants. While trans-QTL discovery and the characterization of sex- and population-specific genetic effects are still limited by sample size, analyses of the v8 data provide important insights into each.

Fig. 1 Sample and data types in the GTEx v8 study.

(A) Illustration of the 54 tissue types examined (including 11 distinct brain regions and two cell lines), with sample numbers from genotyped donors in parentheses and color coding indicated in the adjacent circles. Tissues with 70 or more samples were included in QTL analyses. (B) Illustration of the core data types used throughout the study. Gene expression and splicing were quantified from bulk RNA-seq of heterogeneous tissue samples, and local and distal genetic effects (cis-QTLs and trans-QTLs, respectively) were quantified across individuals for each tissue.