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jueves, 30 de noviembre de 2023

La vida se abre camino

 La vida se abre camino: ¿cómo evoluciona una bacteria diseñada en el laboratorio con un genoma mínimo? Se puede simplificar el genoma hasta lo esencial, pero eso no detiene a la evolución

La vida se abre camino

Estudian cómo evoluciona una bacteria diseñada en el laboratorio con un genoma mínimo

Quizá recuerdes esta frase mítica: “Si algo nos ha enseñado la historia de la evolución es que la vida no puede contenerse. La vida se libera, se extiende a través de nuevos territorios y rompe las barreras dolorosamente, incluso peligrosamente, … la vida se abre camino”. La dijo Ian Malcolm, el personaje de Jeff Goldblum, en la película de ciencia ficción Jurassic Park de 1993.

En una célula, a lo largo del tiempo, se van acumulando cambios o mutaciones en el genoma, que, si suponen alguna ventaja, son seleccionadas y pasan a la descendencia. Con el tiempo, las células pueden adquirir nuevas funciones y/o adaptarse a nuevos ambientes. La variabilidad genética y la selección natural son la base de la evolución celular. Para que la evolución “funcione” es necesario que la célula posea partes del genoma “redundantes” o extra que puedan acumular esas mutaciones, sin afectar a su viabilidad. Los genes esenciales para la supervivencia de la célula se mantienen (una mutación en un gen esencial podría llegar a ser letal para la célula), mientras que otras partes del genoma que no sean esenciales pueden actuar como fuentes de variabilidad y evolución. Así es cómo pensábamos que funcionaba la evolución y la selección natural… hasta ahora.

La vida, ¿siempre se abre camino? ¿qué es antes la vida o la evolución?

Ahora los investigadores han estudiado cómo se enfrenta a las fuerzas de la evolución una célula mínima modificada con el genoma más pequeño que existe, en comparación con la célula original de la cual deriva. Veamos cómo lo han hecho.

Hace ya varios años, investigadores del J. Craig Venter Institute en California diseñaron y sintetizaron el primer genoma bacteriano mínimo. Para ello, emplearon la bacteria Mycoplasma mycoides, un patógeno del ganado y pequeños rumiantes que puede causarles una enfermedad respiratoria. El género Mycoplasma son bacterias muy pequeñas (0,2-0,3 micras) que carece de pared celular y con un genoma también muy pequeño. En este trabajo eliminaron el 47% de los 901 genes del genoma natural de Mycoplasma mycoides, reduciendo el genoma al conjunto de genes más pequeño necesarios para la vida autónoma de una célula. Con 473 genes, el genoma mínimo artificial de esta bacteria, que pasó a denominarse M. mycoides JCVI-syn3B, es el genoma más pequeño de un organismo autónomo de vida libre. Es decir, con un solo gen menos, esa bacteria ya no puede desarrollarse. M. mycoides JCVI-syn3B es, por tanto, una versión sintética minimizada de la bacteria M. mycoides. Curiosamente, 149 de esos 473 genes tenían una función biológica desconocida. (Para que te hagas una idea, otras bacterias como Bacillus subtilis o Escherichia coli tiene entre 4.000 y 5.000 genes).

Aunque M. mycoides JCVI-syn3B podía crecer y dividirse en condiciones de laboratorio, los investigadores querían saber cómo respondería a la evolución a lo largo del tiempo esta célula artificial con un genoma mínimo. Como hemos comentado, cada gen en su genoma es esencial, por lo que la mayoría de los cambios o mutaciones afectarían a funciones esenciales y, en principio, serian letales. Se podría pensar que en esta célula mínima no hay margen para mutaciones, lo que limitaría su capacidad de evolucionar. Este microorganismo mínimo debería tener en teoría una muy baja capacidad de adaptación. Pero ¿qué sucede si dejas que este organismo se reproduzca por sí mismo a lo largo de muchas generaciones? Con el tiempo, ¿se seleccionarían algunas mutaciones?, ¿mejoraría su capacidad de adaptación?

Dicho y hecho. Los investigadores han dejado evolucionar a M. mycoides JCVI-syn3B durante 2.000 generaciones para ver qué sucedía (algunos han hecho el cálculo y dos mil generaciones bacterianas equivalen aproximadamente 40.000 años de evolución humana). Y lo que sucede es «mucho». Algo que parece ser una constante evolutiva es que, a menor tamaño del genoma, mayor es la tasa de mutación, y M. mycoides JCVI-syn3B mostró la tasa más alta de mutación jamás medida. Esto en realidad tiene sentido, porque en el proceso que llevó a su síntesis se eliminaron los genes necesarios para corregir los errores en la replicación del ADN y reparar mutaciones.

Además, el 80% de las mutaciones en M. mycoides JCVI-syn3B fueron puntuales, cambios de un solo nucleótido. Sin embargo, a diferencia de su progenitor, las células mínimas de M. mycoides JCVI-syn3B mostraron una preferencia por los cambios de Guanina a Citosina y de Adenina a Timina (por el contario en el M. mycoides original las mutaciones son en la otra dirección, de Citosina a Guanina y de Timina a Adenina).

Al comparar las células al cabo de 2.000 generaciones con las del inicio, encontraron que las células mínimas M. mycoides JCVI-syn3B se adaptaron aproximadamente un 40% más rápido, pero terminaron al mismo nivel que las M. mycoides originales. Parece ser que simplificar el genoma no debilitó a la célula como para que no pudiera adaptarse. Lo que sí cambiaron fueron varios genes necesarios para la biosíntesis de los lípidos, lo que puede estar relacionado con cambios en la membrana celular.

Restringir el genoma puede tener consecuencias en el tamaño celular

Otro cambio significativo tiene que ver con el tamaño celular. Mientras que las células originales de M. mycoides aumentaron su diámetro en un 85% y su volumen en 10 veces a lo largo de las 2.000 generaciones, las células mínimas de M. mycoides JCVI-syn3B no cambiaron de tamaño durante todo el experimento. Quizá esto tiene que ver con el control celular de la relación superficie/volumen: al aumentar de tamaño tienes más espacio para almacenar proteínas, lípidos y nutrientes, pero empeora la relación superficie/volumen y se dificulta el transporte de nutrientes dentro y fuera de la célula. Las células mínimas quizá sean incapaces de transportar suficientes nutrientes para construir una célula más grande.

Se podría pensar que la reducción del genoma en una célula mínima podría llevar a la extinción a lo largo de las generaciones. Pero no es eso lo que ha ocurrido. El que las funciones celulares básicas se mantengan a lo largo del tiempo es importante cuando se utilizan este tipo de células mínimas en biotecnología. La selección natural durante el crecimiento prolongado en el laboratorio (2.000 generaciones) compensó cualquier efecto perjudicial de la reducción del genoma. Por tanto, se puede simplificar el genoma celular hasta lo esencial, pero eso no detiene a la evolución. La célula mínima sintética puede evolucionar tan rápido como una célula normal. Esto demuestra la tremenda capacidad que tiene los seres vivos para adaptarse, incluso cuando poseen un genoma artificial con el mínimo número de genes necesarios para sobrevivir.

Este artículo me ha recordado una conversación con un buen amigo que un día me preguntó ¿Qué es antes la vida o la evolución? Sin dudarlo contesté: La evolución, sin evolución no hay vida. Después de leer este artículo… tengo dudas.

Evolution of a minimal cell. R. Z. Moger-Reischer, y col. Nature. 2023. 620:122–127.

Design and synthesis of a minimal bacterial genome. Clyde A. Hutchison III, y col. SCIENCE. 2016. 351(6280).

https://microbioblog.es/la-vida-se-abre-camino

 

 

lunes, 27 de noviembre de 2023

#Quantum / chip cuántico de 5 qubits

 #Quantum La informática, una nueva tecnología con un gran potencial transformador, está llegando a nuestras vidas más rápido de lo que esperábamos y, en menos de una década, podría transformar por completo la forma en que procesamos la información.

 Es una tecnología disruptiva con aplicaciones inusuales en la industria, las finanzas, la criptografía, la salud y la investigación, entre muchos otros campos. 

Para conocerlo de primera mano, nuestros alumnos del EMBA de Esade han visitado hoy un ordenador cuántico en nuestra asignatura optativa con el profesor Josep Maria Martorell Rodón. El dispositivo, con un chip cuántico de 5 qubits, está operativo en el laboratorio Qilimanjaro Quantum Tech del Instituto de Física de Altas Energías (IFAE) y es, de hecho, el primer ordenador cuántico ubicado en territorio español. Qilimanjaro es una spin-off de la Universitat de Barcelona, el IFAE y el Barcelona Supercomputing Center

 

 

 


 

En Esade estamos convencidos de que las grandes oportunidades de gestión para las próximas décadas se encontrarán en la frontera entre la ciencia y la sociedad.

domingo, 26 de noviembre de 2023

Un ácido graso propio de carne roja y la leche, identificado en Nature como un potente anticancerígeno

 Un ácido graso propio de carne roja y la leche, identificado en Nature como un potente anticancerígeno

Trans-vaccenic acid reprograms CD8+ T cells and anti-tumour immunity

Diet-derived nutrients are inextricably linked to human physiology by providing energy and biosynthetic building blocks and by functioning as regulatory molecules. However, the mechanisms by which circulating nutrients in the human body influence specific physiological processes remain largely unknown. Here we use a blood nutrient compound library-based screening approach to demonstrate that dietary trans-vaccenic acid (TVA) directly promotes effector CD8+ T cell function and anti-tumour immunity in vivo. TVA is the predominant form of trans-fatty acids enriched in human milk, but the human body cannot produce TVA endogenously1. Circulating TVA in humans is mainly from ruminant-derived foods including beef, lamb and dairy products such as milk and butter2,3, but only around 19% or 12% of dietary TVA is converted to rumenic acid by humans or mice, respectively4,5. Mechanistically, TVA inactivates the cell-surface receptor GPR43, an immunomodulatory G protein-coupled receptor activated by its short-chain fatty acid ligands6-8. TVA thus antagonizes the short-chain fatty acid agonists of GPR43, leading to activation of the cAMP-PKA-CREB axis for enhanced CD8+ T cell function. These findings reveal that diet-derived TVA represents a mechanism for host-extrinsic reprogramming of CD8+ T cells as opposed to the intrahost gut microbiota-derived short-chain fatty acids. TVA thus has translational potential for the treatment of tumours.

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References

    1. Jamiol-Milc, D., Stachowska, E., Janus, T., Barcz, A. & Chlubek, D. Trans fatty acids (elaidic and vaccenic) in the human milk. Pomeranian J. Life Sci. 61, 58–63 (2015). - PubMed
    1. Te Morenga, L. & Montez, J. M. Health effects of saturated and trans-fatty acid intake in children and adolescents: Systematic review and meta-analysis. PLoS ONE 12, e0186672 (2017). - DOI
    1. Sommerfeld, M. Trans unsaturated fatty acids in natural products and processed foods. Prog. Lipid Res. 22, 221–233 (1983). - PubMed DOI
    1. Turpeinen, A. M. et al. Bioconversion of vaccenic acid to conjugated linoleic acid in humans. Am. J. Clin. Nutr. 76, 504–510 (2002). - PubMed DOI
    1. Santora, J. E., Palmquist, D. L. & Roehrig, K. L. Trans-vaccenic acid is desaturated to conjugated linoleic acid in mice. J. Nutr. 130, 208–215 (2000). - PubMed DOI
  1. Trans-vaccenic acid reprograms CD8+ T cells and anti-tumour immunity - PubMed (nih.gov)

lunes, 20 de noviembre de 2023

Human microbiota is a reservoir of SARS-CoV-2 advantageous mutations

 El #SARSCoV2 roba mutaciones de las bacterias de nuestro cuerpo para reforzar su ataque y evadir nuestro sistema inmunológico.

Ordena a la maquinaria replicativa de nuestras células q injerte mutaciones bacterianas en sí misma, produciendo ARN virales quiméricos

Human microbiota is a reservoir of SARS-CoV-2 advantageous mutations

SARS-CoV-2 mutations are rapidly emerging, in particular advantageous mutations in the spike (S) protein, which either increase transmissibility or lead to immune escape, are posing a major challenge to pandemic prevention and treatment. However, how the virus acquires a high number of advantageous mutations in a short time remains a mystery. Here, we show that the human microbiota may contribute to mutations in variants of concern (VOCs). We identified a mutation and adjacent 6 amino acids (aa) in a viral mutation fragment (VMF) and searched for homologous fragments (HFs) in the National Center for Biotechnology Information (NCBI) database. Among the approximate 8000 HFs obtained, 61 mutations in S and other outer membrane proteins were found in bacteria, accounting for 62% of all mutation sources, which is a 12-fold higher than the natural variable proportion. Approximately 70% of these bacterial species belong to the human microbiota, are primarily distributed in the gut or lung and exhibit a composition pattern similar to that of COVID-19 patients. Importantly, SARS-CoV-2 RNA-dependent RNA polymerase (RdRp) replicates corresponding bacterial mRNAs harboring mutations, producing chimeric RNAs. Collectively, SARS-CoV-2 may acquire mutations from the human microbiota, resulting in alterations in the binding sites or antigenic determinants of the original virus. Our study sheds light on the evolving mutational mechanisms of SARS-CoV-2.



viernes, 17 de noviembre de 2023

En qué consiste la primera terapia por edición genética aprobada en el mundo

 

En qué consiste la primera terapia por edición genética aprobada en el mundo

Fue autorizada en el Reino Unido y se basa en el trabajo de las científicas que ganaron el Premio Nobel de Química en 2020. Cómo funciona y cuáles son los desafíos

 

la vitamina B12 desempeña un papel clave en la reprogramación celular y la regeneración de tejidos

 Estudios en ratones realizados por científicos del IRB de Barcelona han demostrado que la vitamina B12 desempeña un papel clave en la reprogramación celular y la regeneración de tejidos

Vitamin B12 is a limiting factor for induced cellular plasticity and tissue repair

Abstract

Transient reprogramming by the expression of OCT4, SOX2, KLF4 and MYC (OSKM) is a therapeutic strategy for tissue regeneration and rejuvenation, but little is known about its metabolic requirements. Here we show that OSKM reprogramming in mice causes a global depletion of vitamin B12 and molecular hallmarks of methionine starvation. Supplementation with vitamin B12 increases the efficiency of reprogramming both in mice and in cultured cells, the latter indicating a cell-intrinsic effect. We show that the epigenetic mark H3K36me3, which prevents illegitimate initiation of transcription outside promoters (cryptic transcription), is sensitive to vitamin B12 levels, providing evidence for a link between B12 levels, H3K36 methylation, transcriptional fidelity and efficient reprogramming. Vitamin B12 supplementation also accelerates tissue repair in a model of ulcerative colitis. We conclude that vitamin B12, through its key role in one-carbon metabolism and epigenetic dynamics, improves the efficiency of in vivo reprogramming and tissue repair.

Main

Cellular reprogramming consists of the loss of differentiated cell identity followed by the acquisition of embryonic stem pluripotency, which can be achieved by the simultaneous expression of the transcription factors OCT4, SOX2, KLF4 and MYC (OSKM; in mice encoded by Pou5f1, Sox2, Klf4 and Myc, respectively)1. During recent years, it has become evident that this process involves intermediate states in which cells acquire various degrees of plasticity and differentiation potential, which may have broad implications in regenerative medicine and organ repair (reviewed in ref. 2). Continuous expression of OSKM in mice can recapitulate full reprogramming to pluripotency, a process that culminates with the generation of teratomas3. Interestingly, transient expression of OSKM leads to molecular and physiological features of rejuvenation, including an enhanced capacity for tissue regeneration4,5,6,7,8,9. Nevertheless, in vivo reprogramming via OSKM remains a poorly understood process, with low efficiency and high risks, including teratoma and cancer development3,10,11. Thus, we sought to unravel new molecular mechanisms of in vivo reprogramming that could be harnessed to manipulate cell plasticity and tissue repair.

Given the unique metabolic requirements of in vitro reprogramming12,13, we hypothesized that unique metabolic requirements may also operate during in vivo reprogramming. As a new approach, we considered the gut microbiota as a commensal community in metabolic equilibrium with its host. Indeed, the microbiota is sensitive to perturbations in host physiology, capable of adapting and rewiring itself based on nutrient availability and depletion14, a process known as the host–gut microbiota metabolic interaction15. We reasoned that analysis and manipulation of the microbiota could provide new insights into the metabolic requirements of in vivo reprogramming.

In vivo reprogramming is dependent on the microbiota

To study modulators of in vivo reprogramming, we used a previously described mouse model in which doxycycline drives systemic, inducible OSKM expression3,9,16,17. On a short timescale (7 days), OSKM induction causes focal regions of abnormal tissue architecture, correlating with the appearance of rare NANOG-positive cells (a marker of embryonic pluripotency) predominantly in the pancreas, colon and stomach3. We first asked whether the microbiota was important for in vivo reprogramming by disrupting it with a commonly used, broad-spectrum cocktail of antibiotics (ABX): ampicillin, metronidazole, neomycin and vancomycin18. We administered ABX for 3 weeks before and during the 7 days of OSKM induction (Fig. 1a). We noted that mice treated with ABX had very low levels of serum doxycycline (Extended Data Fig. 1a), therefore precluding the induction of OSKM in organs beyond the gastrointestinal tract (Extended Data Fig. 1b). Nevertheless, doxycycline efficiently induced OSKM in the colon and stomach in the presence of ABX (Extended Data Fig. 1b). Strikingly, despite strong transgene induction, reprogramming was significantly reduced in the colon and stomach of ABX-treated mice (Fig. 1b and Extended Data Fig. 1c). Reduction in reprogramming was also reflected in the reduced abundance of SCA1-positive and KRT14-positive cells (Fig. 1b and Extended Data Fig. 1c), markers of early and advanced stages of intermediate in vivo reprogramming, respectively19. Consistent with low levels of reprogramming, ABX-treated mice lost significantly less weight than mice with normal levels of reprogramming (Extended Data Fig. 1d). These results indicate that the microbiota is critical for the successful reprogramming of tissues in vivo.

Fig. 1: In vivo OSKM reprogramming requires the gut microbiota and is enhanced by vitamin B12 supplementation.
figure 1

a, Mice were pretreated with an antibiotic cocktail administered in the drinking water for 3 weeks (ABX) before, and during, 7 d of doxycycline administration (doxy), with or without vitamin B12 supplementation according to the schematic. b, Representative histology images and quantification of a blinded histological score, SCA1 staining and KRT14 staining. n = 4 mice (WT; 3 M 1 F), n = 8 (OSKM + doxy; 4 M 4 F), n = 11 (OSKM + doxy + ABX 4 M 7 F); a representative subset of animals was analysed for SCA1 and KRT14. Scale bar, 100 µm. c, GO pathway analysis of differentially abundant microbial gene signatures in the metagenome sequencing of stool samples. Changes in microbial gene abundance between day 7 and day 0 were compared in a subset of WT (n = 4; 2 M 2 F) and OSKM (n = 4; 2 M 2 F) mice from b. See Supplementary Table 1 for complete gene list. The overlap between GO terms and the 200 most differentially depleted or enriched genes was scored using standard hypergeometric tests and GO terms above a threshold of 30% FDR are shown (for all GO terms, see Supplementary Table 2). Processes marked with an asterisk are directly related to cobalamin metabolism. Dashed line indicates 5% FDR cut-off. d, Serum holoTC (biologically available vitamin B12) levels as measured by ADVIA immunoassay in untreated mice or WT and OSKM mice treated with doxycycline for 7 d. n = 14 mice (untreated; 6 M 8 F), n = 11 (doxy WT doxy; 7 M 4 F), n = 12 (doxy OSKM doxy; 6 M 6 F). e, OSKM mice received vitamin B12 supplementation co-administered with doxycycline as indicated and representative images and quantification are shown for the indicated markers in the pancreas. Mice marked by an open circle received both B12 and folate (B9) supplementation (not significant (NS) difference for B12 versus B12 + folate; see text for details). n = 5 mice (OSKM; 2 M 3 F), n = 10 mice (OSKM + B12; 4 M 6 F); a representative subset of n = 5 animals per group was analysed for SCA1 and KRT14. Scale bars, 100 µm. Bar graphs represent the average ± s.d.; ****P < 0.0001 by two-tailed Student’s t-test.

Source data

In vivo reprogramming causes microbial dysbiosis

Given the profound impact that disruption of the microbiota had on in vivo reprogramming, we reasoned that a functional analysis of microbial changes during this process could illuminate previously unknown requirements for reprogramming. To this end, we isolated bacterial DNA from paired stool samples of both OSKM-expressing mice and wild-type (WT) littermate control mice before and after 7 days of doxycycline treatment, and performed shotgun metagenome sequencing20 (Extended Data Fig. 2a–c and Supplementary Tables 1 and 2). In both WT and OSKM mice, the microbial diversity as measured by the Shannon index decreased following 7 days of doxycycline treatment, with the most profound loss of diversity occurring in reprogrammed mice (Extended Data Fig. 2a). At a genus level, reprogrammed mice were characterized by a relative expansion of Chlamydia, Bacteriodes and Alistipes spp. and a relative contraction of Muribaculaceae spp. (Extended Data Fig. 2b). Muribaculaceae have been reported to contract during inflammatory colonic injury21, which shares features with in vivo reprogramming including inflammation and loss of differentiated cell identity22. Alistipes on the other hand, have been reported to promote colonic interleukin (IL)-6 production23, which is an important mediator of in vivo reprogramming16.

In vivo reprogramming reduces systemic vitamin B12 levels

Our whole-genome approach allowed us to investigate changes not only in bacterial species abundance, but also in gene composition and ontology groups, which could uncover pathways relevant to reprogramming. Remarkably, we found that microbial gene modules related to the biosynthesis and metabolism of cobalamin (vitamin B12) dominated the bacterial Gene Ontology (GO) groups altered during reprogramming (Fig. 1c and Supplementary Table 2). Under conditions of disrupted cobalamin bioavailability, competition for vitamins can shift the relative abundance of cobalamin-producing and cobalamin-utilizing bacteria in a process referred to as ‘corrinoid remodelling’14,24. We found microbial changes consistent with this phenomenon in reprogramming: the few genera of bacteria able to synthesize B12 (~20 genera)25 were generally enriched in OSKM mice after 7 days of doxycycline, with Proteus, Escherichia and Salmonella being most significantly enriched among the B12 synthesizers (Extended Data Fig. 2c and Supplementary Table 2).

The observed changes in the gut microbiota could be indicative of a systemic deficit in B12, affecting not only the microbiota but also the entire physiology of the host. To test this, we examined systemic vitamin B12 levels in the serum during reprogramming, which were significantly reduced in OSKM mice after 7 days of doxycycline administration (Fig. 1d). The liver is one of the organs with the greatest demand for vitamin B12 (ref. 26) and, as such, is sensitive to B12 deficiency27. In rodents, this manifests as depletion of phosphatidylcholines (PCs)28, which are produced in large quantities by the liver in a B12-dependent manner. We saw that PCs were significantly reduced in the serum of reprogrammed mice as compared to WT mice treated with doxycycline (Extended Data Fig. 3a). Importantly, the liver of OSKM mice does not exhibit histological changes after 7 days of doxycycline9, making the reduction in PCs unlikely to reflect liver dysfunction as a result of reprogramming. The kidney is another organ that is refractory to reprogramming in our mouse model3; however, we did observe a significant depletion of vitamin B12 from the proximal tubules during reprogramming (Extended Data Fig. 3b). The kidney is the primary site of B12 concentration and storage in rodents, from where it is released for use by other organs upon systemic deficiency27,29,30,31. Collectively, these results suggest that vitamin B12 becomes systemically depleted during in vivo reprogramming, affecting both the colonic microbiota and the host.

Vitamin B12 supplementation improves in vivo reprogramming

Given the systemic reduction of vitamin B12 during in vivo reprogramming, we hypothesized that B12 supplementation could enhance reprogramming under normal conditions (that is, in the absence of ABX). Indeed, vitamin B12 supplementation significantly improved in vivo reprogramming in the pancreas, colon and stomach, as evaluated by the extent of histological dysplasia and SCA1 or KRT14 levels (Fig. 1e and Extended Data Fig. 3c–e). B12 also increased the number of NANOG+ cells, a marker of full pluripotency, in the pancreas (Fig. 1e and Extended Data Fig. 3f). B12 administration did not affect transgene expression (Extended Data Fig. 3g). Even after B12 supplementation, we could not detect histological evidence of reprogramming in the kidney (Extended Data Fig. 3h). However, we did observe a significant increase of vitamin B12 stores within the kidney after supplementation (Extended Data Fig. 3b), indicating that B12 absorption, distribution and storage were occurring normally in the reprogrammed mice.

We also wondered if B12 supplementation could rescue the reprogramming defect of ABX-treated mice. Interestingly, B12 supplementation was able to partially rescue reprogramming in the colon (Extended Data Fig. 3c–e). This supports the concept that an important role of the microbiota during murine reprogramming is to increase the dietary supply of B12 through coprophagy. Another B vitamin that is partly supplied by the microbiota in rodents and humans is vitamin B9 (folate)32, which is functionally related to B12 (ref. 33). However, co-supplementation of B12 and B9 was indistinguishable from B12 alone (Fig. 1e and Extended Data Fig. 3c,f,g). Collectively, these results demonstrate that vitamin B12 is a limiting factor for in vivo reprogramming.

One-carbon metabolism drives vitamin B12 demand during reprogramming

In both humans and mice, vitamin B12 is used as a cofactor by only two enzymes: methionine synthase (MS) and methylmalonyl-CoA mutase (MUT)26. MS uses B12 as a cofactor to regenerate methionine (Met) from homocysteine (Hcy), forming an integral part of one-carbon (1C) metabolism (Fig. 2a). Met is used to synthesize S-adenosylmethionine (SAM), the universal methyl donor for all methylation reactions33. The nuclear-encoded mitochondrial enzyme MUT uses B12 as a cofactor for the catabolism of branched-chain amino acids via isomerization of methylmalonyl-CoA to succinyl-CoA, for use in the tricarboxylic acid cycle (Extended Data Fig. 4a)26.

Fig. 2: Tissues undergoing in vivo reprogramming exhibit an increased demand of 1C metabolism.
figure 2

a, Summary of the mammalian folate and methionine cycles (1C metabolism) and the transsulfuration pathway. Enzymes are marked in green. Coenzyme vitamin B12 is marked in red. DHF, dihydrofolate; THF, tetrahydrofolate; MTs, methyltransferases; ACHY, adenosylhomocysteinase; CSE, cystathionine gamma-lyase; DMG, dimethylglycine; Ser, serine; Thr, threonine; Gly, glycine. Figure adapted from ref. 33, Springer Nature Limited. b, Changes in metabolic pathways during reprogramming. MetaboAnalyst (4.0)91 was used to assess the annotated metabolites identified in the serum of paired OSKM mice (n = 6; 3 M 3 F) at day 5 versus day 0 of doxycycline treatment (serum was collected repeatedly from the same mice). Colour gradient from white to red indicates the P value; red is most significant. Gly/Ser/Thr metabolism (KEGG map00260) is highlighted. See Supplementary Table 3 for all metabolites, pathways and scores. c, Fold change (FC) of SAM/Met ratio detected by mass spectrometry from b on the indicated days. P values represent significant difference between OSKM and WT mice. d, Levels of MS (encoded by Mtr) by immunoblot and RT–qPCR in the pancreas (upper) and kidney (lower) from WT (n = 4; 3 M 1 F) and OSKM (n = 8; 4 M 4 F) mice treated with doxycycline for 7 d. Representative mice are shown in the immunoblot. e, Previously published RNA-seq data16 from the pancreas (highly prone to reprogramming; green) and kidney (refractory to reprogramming; orange) of OSKM-Cdk2na/(low or absent reprogramming) and OSKM-Tp53−/− (high reprogramming) mice were used to perform GSEA against a published signature (MsigDB: M13537) of Met deprivation39. f, WT and OSKM mice (n = 5 per group; 5 M) were treated with doxycycline and a bolus of vitamin B12 as shown in the schematic. Met levels were measured in the indicated serum samples by mass spectrometry. Only n = 4 WT (day 0) and OSKM (day 6) mice are represented, as the blood volume was insufficient. Welch’s two-sample t-test was used to evaluate differences between groups on day 6. Bar graphs represent the average ± s.d.; P values determined by a two-tailed Student’s t-test.

Source data

As a first approach to investigate B12-dependent metabolism during in vivo reprogramming, we performed untargeted serum metabolomics. The metabolic pathway with the strongest (by pathway impact) and most significant changes during reprogramming was ‘glycine (Gly), serine (Ser), threonine (Thr) metabolism’ (Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway map00260; Fig. 2b and Supplementary Table 3). These three amino acids, together with Met, feed 1C metabolism (Fig. 2a). Notably, Thr is known to be critical for the generation of SAM during in vitro reprogramming and in the maintenance of pluripotent cells12,34,35. In OSKM as compared to WT mice, we saw significant depletion of all four of these amino acids (Gly, Ser, Thr and Met), concomitant with an increase in SAM and an increase in the SAM/Met ratio, progressively over the course of reprogramming (Fig. 2c and Extended Data Fig. 4). The increased SAM/Met ratio was indicative of methylation cycle activation, which occurs during in vitro reprogramming13 and in cultured pluripotent cells12,34,35. Neither Hcy nor S-adenosyl-l-homocysteine (SAH), two important 1C metabolites, were detected via untargeted metabolomics, so we performed a separate, targeted serum metabolomic analysis. We found that the SAM/SAH ratio, known as the ‘methylation index’ because it indicates the methylation capacity of an organism36, was significantly increased during in vivo reprogramming (Extended Data Fig. 4c). We did not observe changes in Hcy (Extended Data Fig. 4c). Although Hcy accumulation can occur clinically as a result of B12 insufficiency in humans26, we observed that induced pluripotent stem (iPS) cells upregulate the expression of genes encoding two main consumers of Hcy: cystathionine beta-synthase (CBS, which initiates the transsulfuration pathway) and betaine-homocysteine methyltransferase (BHMT, which synthesizes Met using Hcy and betaine in an MS-independent manner; Fig. 2a and Extended Data Fig. 4d). In support of this, we found that serum betaine levels were significantly decreased during in vivo reprogramming (Extended Data Fig. 4e), which may serve as an additional or alternate source of Met generation.

On the other hand, methylmalonic acid (MMA), the substrate of MUT, showed no significant differences between OSKM mice and WT littermate controls treated with doxycycline (Extended Data Fig. 4b). The MMA levels, along with decreases in Mmut expression in several organs (Extended Data Fig. 4f), suggested that MUT’s enzymatic activity does not become limiting during reprogramming. It is important to note that while serum accumulation of MMA and Hcy are sensitive biomarkers of vitamin B12 deficiency in humans26, this is not the case in mice37,38.

We next asked whether the metabolic alterations related to 1C metabolism in the serum were caused by changes specifically within those tissues undergoing reprogramming. We first examined expression of MS, which was upregulated at both the protein and RNA levels in the pancreas, colon and stomach of mice undergoing reprogramming, but not in the kidney (Fig. 2d and Extended Data Fig. 5a). Expression of Cd320, the main receptor for cellular uptake of B12 (ref. 26), was significantly upregulated in the reprogramming pancreas (Extended Data Fig. 5b). We also examined a gene signature of Met deprivation39 by gene-set enrichment analysis (GSEA). As a proof of concept, we tested this signature in previously published bulk RNA-sequencing (RNA-seq) data from in vitro OSKM reprogramming40, and found it was significantly enriched in iPS cells as compared to the mouse embryonic fibroblasts (MEFs) from which the iPS cells were derived (Extended Data Fig. 5c). This is consistent with the fact that ESCs require high Met levels for self-renewal and survival34. We then tested this Met deprivation signature in previously published bulk RNA-seq data from in vivo reprogramming16. At day 7 of doxycycline treatment, Met deprivation was significantly enriched in the pancreas of mice with high levels of reprogramming as compared to mice genetically resistant to reprogramming; in contrast, there was no enrichment of this pathway in the kidney (Fig. 2e). To further validate these results, we analysed a subset of genes from the Met deprivation signature, which were among the most highly enriched by GSEA, in reprogramming tissues by quantitative PCR with reverse transcription (RT–qPCR). A total of 11 genes were assessed and, interestingly, they were broadly upregulated in the pancreas, colon and stomach—but not in the kidney—of mice expressing OSKM (Extended Data Fig. 5d). A subset of these genes was basally high in the colon due to their importance in the stem cell compartment. Importantly, B12 supplementation generally relieved the upregulation of these genes, in support of the idea that limiting B12 levels were driving the Met restriction in vivo. In the kidney, these genes were induced with B12 supplementation, likely a feedback response caused by the large influx of B12 storage into the kidney after supplementation (Extended Data Fig. 3b).

Finally, to ensure that the depletion of serum B12 levels and its associated low levels of Met were not simply caused by defective oral uptake due to the reprogramming of several digestive organs, we administered a large bolus of vitamin B12 (5 µg per mouse, 100 times the recommended dietary allowance41) on day 6 of doxycycline treatment, 1 day before euthanasia (Fig. 2f). Mice expressing OSKM had significantly higher levels of serum B12 than WT mice following the bolus (Extended Data Fig. 5e,f), which is known to occur in B12-deficient rodents27,31, and further indicated that reprogramming does not compromise oral bioavailability of B12. Strikingly, the bolus rescued the depletion of Met levels in the serum (Fig. 2f).

Together, these data suggest that tissues undergoing reprogramming are the ones driving the depletion of serum factors that feed 1C metabolism, including Met, serine, glycine, threonine, betaine and vitamin B12. B12 becomes a limiting factor, as shown by the effects of B12 supplementation in rescuing Met deprivation and promoting reprogramming.

Vitamin B12 plays a cell-autonomous role in reprogramming

In vivo, vitamin B12 deficiency yields a complex phenotype because it impacts multiple cellular processes and organ functions26. Therefore, we asked whether the effect of B12 on reprogramming could be recapitulated in vitro. We first observed, in a previously published bulk RNA-seq dataset of MEFs undergoing in vitro reprogramming40, that Mtr and Cd320 were upregulated soon after OSKM induction, remaining high during the early and middle phases of reprogramming, ultimately stabilizing to levels above those measured in MEFs (Extended Data Fig. 6a). This suggests that during in vitro reprogramming there is also a high demand of B12 and Met, which we explored further using pharmacological manipulation of the Met cycle. The addition of B12 significantly increased the efficiency of iPS cell colony formation (Fig. 3a), recapitulating our observations in vivo and demonstrating a cell-intrinsic role for vitamin B12 in reprogramming. Of note, B12 supplementation increased the number of successfully formed iPS cell colonies without an obvious effect on the rate of colony formation (Extended Data Fig. 6b), and the improved efficiency was also observed in a doxycycline-free, retroviral-based reprogramming system in WT MEFs (Extended Data Fig. 6c). The B12-mediated increase in reprogramming efficiency was prevented by concomitant treatment with a methionine adenosyltransferase 2A inhibitor (MAT2Ai; Fig. 3a); MAT2A is the enzyme immediately downstream of MS, which converts Met into SAM (Fig. 2a). Moreover, directly supplementing only SAM at a high concentration34 during reprogramming significantly improved the efficiency of the process, even beyond that of B12 itself (Fig. 3a).

Fig. 3: Vitamin B12 supplementation enhances H3K36me3 and has a cell-autonomous role in reprogramming.
figure 3

a, In vitro reprogramming of MEFs with doxycycline-inducible OSKM for 10 d in the presence of doxycycline (OSKM) and/or vitamin B12, MAT2Ai, KDM4A/KDM4B inhibition (NSC), or SAM as indicated, cultured in KSR. iPS cell colonies were quantified by alkaline phosphatase staining (left) and representative images are shown (right). Each data point represents MEFs generated from an independent embryo (n = 6 OSKM, B12; n = 5 B12 + MAT2Ai, SAM; n = 3 NSC). b, Fraction of total intracellular SAM 13C-labelled at the methyl m + 1 position (Extended Data Fig. 6d), using 13C-serine as a precursor. Labelling was initiated at t = 72 h for 6 h. Data from n = 3 independent MEFs are shown. c, H3K36me3 dynamics during in vitro reprogramming. MEFs were treated with doxycycline with or without vitamin B12 as indicated. A representative immunoblot and quantification from n = 3 independent MEFs are shown. d, H3K36me3 level correlates with reprogramming efficiency in vitro. MEFs were treated as indicated and H3K36me3 levels were probed in histone extracts at day 3 after doxycycline treatment. A representative blot from n = 2 independent MEFs with similar results is shown. e,f, Expression of the H3K36 trimethyl-transferase Setd2 during in vitro (e) and in vivo pancreatic (f) reprogramming. In e, P values represent significant change from the parental MEFs (n = 3 MEFs). In f, samples were collected from WT (n = 4; 3 M 1 F) or OSKM (n = 8; 4 M 4 F) mice after 7 d of doxycycline treatment. g, H3K36me3 during in vivo reprogramming. Pancreatic tissue from OSKM mice treated with doxy (n = 5; 2 M 3 F) or doxy + B12 (n = 10; 4 M 6 F; Fig. 1) was stained for H3K36me3. Representative images are shown with dysplastic foci demarcated by red dashed lines. The mean nuclear optical density of the H3K36me3 stain is expressed as the ratio between the dysplastic region and the adjacent normal tissue for each mouse. Scale bar, 100 µm. Mice that received folate in addition to B12 are represented by open points. Graphs represent the average ± s.d.; ****P < 0.0001 by two-tailed Student’s t-test.

Source data

To directly assess the contribution of B12 supplementation to SAM generation, we performed stable isotope labelling (SIL) with 13C-labelled serine. Serine significantly decreases in the serum during in vivo reprogramming (Extended Data Fig. 4b) and can contribute as a methyl donor to 1C metabolism (Extended Data Fig. 6d). We began SIL 72 h after OSKM induction, well before iPS cell colonies are formed. Culturing cells with 13C-serine did not affect the reprogramming efficiency, nor the capacity of B12 to enhance reprogramming (Extended Data Fig. 6e). Importantly, B12 significantly stimulated the incorporation of the 13C-methyl donor group (m + 1) from serine into SAM (Fig. 3b). Collectively, these data demonstrate that B12 operates in a cell-intrinsic manner during in vitro reprogramming and that it is a limiting factor for SAM generation and successful reprogramming in vitro.

H3K36me3 is enhanced by vitamin B12 during reprogramming

sigue en  

https://www.nature.com/articles/s42255-023-00916-6

Bioinformatic analysis

RNA-seq data processing

All analyses were performed in the R programming language (version 4.0.5)96 unless otherwise stated. Stranded paired-end reads were aligned to the Mus musculus reference genome version mm10 using STAR80 with default parameters. STAR indexes were built using the ENSEMBL annotation version GRC138.97. SAM files were converted to BAM and sorted using sambamba (version 0.6.7)97. Gene counts were obtained with the featureCounts function from the Rsubread package98 with the gtf file corresponding to ENSEMBL version GRC138.97 and parameters set to: isPairedEnd = TRUE and strandSpecific = 2. Technical replicates were collapsed by adding the corresponding columns in the count matrix.

Reprogramming score

We obtained a reprogramming gene signature from published data48 and selected genes with false discovery rate (FDR) lower than 0.05 and fold change between MEF and d3-EFF larger than 2. The reprogramming score was defined as the average of all genes in the signature after scaling the rlog transformed matrix.

Computation of cryptic transcript ratios between first and intermediate exons

Exon counts were generated using the featureCounts function with parameters: isPairedEnd = TRUE, strandSpecific = 2, GTF.featureType = exon, GTF.attrType = transcript_id, GTF.attrType.extra = gene_id, allowMultiOverlap = TRUE and useMetaFeatures = FALSE and the same GTF as for gene counts. Technical replicates were collapsed by adding the corresponding counts. For each gene, the longest annotated transcript was selected. Genes with less than four exons of RPKMs lower than exp(−2) were discarded from the analysis. Intermediate exons were defined as those from the fourth to the penultimate. A total of 9,365 genes were used to compute the ratio between the intermediate and first exons. Fold changes between untreated and B12-treated samples were computed as the ratio between the exon ratios.

Comparison of cryptic transcript ratios between conditions

Genes were separated by their expression after transcript length and library size normalization (RPKM). For each sample, we computed the median ratios for genes in each decile.

Analysis of CT in DSS time course

Data were accessed from GSE131032. Reads were processed and ratios computed as previously described. log2 ratios for all transcripts were summarized through the median by sample. Comparisons between days were performed fitting a linear model to the medians using ‘cage’ as a covariable. The function glht from the multcomp R package was used to find coefficients and P values.

Functional enrichment in genes with exon ratios affected by vitamin B12 treatment

To select genes most affected by the B12 treatment after reprogramming, we compared ratios between the doxy and MEF conditions and between the doxy and doxy + B12 conditions. Genes that increased the ratios in the first comparison (upper 25th percentile) and decreased the ratio in the second comparison (bottom 25%) were selected for functional enrichment analysis. A hypergeometric test was performed to find significant overlap between the defined gene set and the Biological Processes GO collection99.

ChIP–seq data processing

Reads were aligned to the mm10 reference genome with bowtie100 version 0.12.9 with parameters --n 2 and --m 1 to keep reads with multiple alignments in one position. SAM files were converted to BAM and sorted using sambamba version 0.6.7.

Heat maps of average coverage in gene bodies

For each sample, aligned reads were imported into R using the function scanBam from the Rsamtools package101. Whole-genome coverage was computed using the coverage function from the IRanges package102 and binned into 50-bp windows. Gene annotations were imported from Ensembl version GRCm38. The average coverage over gene bodies was computed using the normalizeToMatrix function from the EnrichedHeatmap package103 with parameters extend = 1,000, mean_mode = w0 and w = 50. Genes were filtered to coincide with those used in the exon ratio calculation from the RNA-seq data. Rows in the heat map were split by the average RNA-seq RPKM values in all samples.

Visualization of ChIP tracks

BAM files were transformed to TDF files using the count function from IGVtools (version 2.12.2)104 with parameters --z 7, --w 25 and --e 250. Visualization of TDF files was generated using IGV (version 2.9.4)105.

Analysis of human RNA-seq data

Data were accessed from GSE109142. Reads were processed and ratios computed as previously described except using the ENSEMBL GRCm38.101 human gene annotation and the hg38 genome assembly version. The log2 ratios for all transcripts were summarized through the median by sample. Comparison between diagnosis status was performed fitting a linear model to the medians with sex and the expression quantiles as covariables. The model was fitted using the lm R function and coefficients and P values with the coeff function.

Statistics and data availability

Statistical analysis and figure preparation

Unless otherwise specified, data are presented as the mean ± s.d. Statistical analysis was performed by Student’s t-test or one-way analysis of variance (ANOVA) as indicated, using GraphPad Prism v9.0.0, and specific statistical tests as indicated for each experiment for bioinformatic analyses. P values of less than 0.05 were considered as statistically significant. No statistical methods were used to predetermine sample size in the mouse studies, but our sample sizes are similar to those reported in previous publications3,9,16,17,19. Animals and data points were not excluded from analysis with the exception of the MEFs that failed to reprogram in the ChIP experiment, which is clearly detailed in the text. Mice were allocated at random to treatment groups, with attempts to balance initial body weight and sex as possible. The investigators were blinded during histological assessment of the mice; other data collection and analysis was not performed blind to the conditions of the experiments. Data distribution was assumed to be normal, but this was not formally tested. Figures were prepared using Illustrator CC 2019 (Adobe).

Reporting summary

Further information on research design is available in the Nature Portfolio Reporting Summary linked to this article.

Data availability

All sequencing data are deposited under the following Gene Expression Omnibus accessions: GSE154149, microbial genome analysis from OSKM and WT mice pretreatment and after 7 d of 1 mg ml−1 treatment with doxycycline in the drinking water; GSE200578, ChIP–seq of H3k36me3 samples of OSKM reprogramming MEFs treated with B12; GSE200579, RNA-seq samples of OSKM reprogrammable MEFs treated with or without vitamin B12; GSE232382, RNA-seq samples of OSKM reprogrammable MEFs treated with vitamin B12 and/or various compounds to modulate SAM or histone methylation. Previously published datasets that were used for analysis in the current study are: GSE131032, RNA-seq of time-course analysis of repairing murine epithelium after DSS injury; GSE109142, RNA-seq of human paediatric ulcerative colitis and normal tissue controls; GSE102518, RNA-seq of murine in vitro reprogramming in MEFs of varying genotypes; GSE77722, RNA-seq of murine in vivo reprogramming in mice of varying genotypes. Source data are provided with this paper.

 

https://www.nature.com/articles/s42255-023-00916-6

Contributions

M.K. designed and performed most experiments, contributed to bioinformatic data analysis, performed quantification of immunohistochemistry and co-wrote the paper. E.M., D.C., F.P., R.B., A.H.-H. and M.R. provided general experimental support. A.C. and C.S.-O.A. designed and performed bacterial bioinformatic analysis. C.S.-O.A. designed and performed RNA-seq and ChIP–seq bioinformatics analysis. A.J., J.C., S.D., O.Y., E.M. and G.K. performed metabolomics experiments. E.M., D.C., F.P., A.J., S.D., O.Y. and G.K. helped design and analyse metabolomics experiments. N.P. performed the histopathological study and supervised the histopathological techniques. M.S. designed and supervised the study, secured funding, analysed the data and co-wrote the paper. All authors discussed the results and commented on the paper.