Skip to main content

Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

  • Article
  • Published:

Microviridae bacteriophages influence behavioural hallmarks of food addiction via tryptophan and tyrosine signalling pathways

Nature Metabolism volume 6, pages 2157–2186 (2024)Cite this article

Subjects

Abstract

Food addiction contributes to the obesity pandemic, but the connection between how the gut microbiome is linked to food addiction remains largely unclear. Here we show that Microviridae bacteriophages, particularly Gokushovirus WZ-2015a, are associated with food addiction and obesity across multiple human cohorts. Further analyses reveal that food addiction and Gokushovirus are linked to serotonin and dopamine metabolism. Mice receiving faecal microbiota and viral transplantation from human donors with the highest Gokushovirus load exhibit increased food addiction along with changes in tryptophan, serotonin and dopamine metabolism in different regions of the brain, together with alterations in dopamine receptors. Mechanistically, targeted tryptophan analysis shows lower anthranilic acid (AA) concentrations associated with Gokushovirus. AA supplementation in mice decreases food addiction and alters pathways related to the cycle of neurotransmitter synthesis release. In Drosophila, AA regulates feeding behaviour and addiction-like ethanol preference. In summary, this study proposes that bacteriophages in the gut microbiome contribute to regulating food addiction by modulating tryptophan and tyrosine metabolism.

Access through your institution
Buy or subscribe

This is a preview of subscription content, access via your institution

Access options

Access through your institution

Buy this article

  • Purchase on SpringerLink
  • Instant access to full article PDF
Buy now

Prices may be subject to local taxes which are calculated during checkout

Fig. 1: Associations of food addiction with obesity and the gut microbiota.
Fig. 2: Associations of Microviridae and Gokushovirus WZ-2015a with obesity, inhibitory control and whole-brain resting-state networks.
Fig. 3: Associations of food addiction and Gokushovirus WZ-2015a with microbial functionality (metagenomics and metabolomics).
Fig. 4: Transplantation of microbiota and virus from human donors with presence of Gokushovirus WZ-2015a induces food addiction in mice.
Fig. 5: Metabolome and transcriptome of mice nucleus accumbens and medial prefrontal cortex associated to donor’s presence or absence of Gokushovirus WZ-2015a.
Fig. 6: Associations of obesity, tryptophan-related metabolites and microbial genes with the Gokushovirus WZ-2015a.
Fig. 7: Associations of AA supplementation and food addiction in mice.
Fig. 8: Associations of AA supplementation in feeding and addiction-like behaviours in Drosophila and a second FMT from human donors with the presence of Gokushovirus.

Similar content being viewed by others

Data availability

The datasets that support the findings of the study are available from the corresponding author upon reasonable request. The raw metagenomics sequence data derived from human samples in the IRONMET-CGM, IRONMET, Aging Imageomics and Health Imageomics cohorts have been deposited in the European Nucleotide Archive under project numbers PRJEB58106, PRJEB39631, PRJEB52682 and PRJEB61680, respectively. The raw metagenomics sequence data from the second FMT experiment have also been deposited in the European Nucleotide Archive under project number PRJEB39631. The source data for the mice and Drosophila studies can be found in Supplementary Tables 70–75.

References

  1. Blüher, M. Obesity: global epidemiology and pathogenesis. Nat. Rev. Endocrinol. 15, 288–298 (2019).

    PubMed  Google Scholar 

  2. Praxedes, D. R. S. et al. Prevalence of food addiction determined by the Yale Food Addiction Scale and associated factors: a systematic review with meta-analysis. Eur. Eat. Disord. Rev. 30, 85–95 (2022).

    Article  PubMed  Google Scholar 

  3. Gupta, A., Osadchiy, V. & Mayer, E. A. Brain-gut-microbiome interactions in obesity and food addiction. Nat. Rev. Gastroenterol. Hepatol. 17, 655–672 (2020).

    PubMed  PubMed Central  Google Scholar 

  4. Vasiliu, O. Current status of evidence for a new diagnosis: food addiction-a literature review. Front. Psychiatry 12, 824936 (2022).

    PubMed  PubMed Central  Google Scholar 

  5. Schulte, E. M., Potenza, M. N. & Gearhardt, A. N. A commentary on the ‘eating addiction’ versus ‘food addiction’ perspectives on addictive-like food consumption. Appetite 115, 9–15 (2017).

    PubMed  Google Scholar 

  6. Randolph, T. The descriptive features of food addiction; addictive eating and drinking. Q. J. Stud. Alcohol 17, 198–224 (1956).

    CAS  PubMed  Google Scholar 

  7. American Psychiatric Association. Diagnostic and Statistical Manual of Mental Disorders (DSM-V) (American Psychiatric Association, 2013).

  8. Gearhardt, A. N., Corbin, W. R. & Brownell, K. D. Development of the Yale Food Addiction Scale version 2.0. Psychol. Addict. Behav. 30, 113–121 (2016).

    PubMed  Google Scholar 

  9. Martín-García, E., Domingo-Rodriguez, L. & Maldonado, R. An operant conditioning model combined with a chemogenetic approach to study the neurobiology of food addiction in mice. Bio. Protoc. 10, e3777 (2020).

    PubMed  PubMed Central  Google Scholar 

  10. Domingo-Rodriguez, L. et al. A specific prelimbic-nucleus accumbens pathway controls resilience versus vulnerability to food addiction. Nat. Commun. 11, 782 (2020).

    CAS  PubMed  PubMed Central  Google Scholar 

  11. Cryan, J. F. et al. The microbiota-gut-brain axis. Physiol. Rev. 99, 1877–2013 (2019).

    CAS  PubMed  Google Scholar 

  12. Bäckhed, F., Manchester, J. K., Semenkovich, C. F. & Gordon, J. I. Mechanisms underlying the resistance to diet-induced obesity in germ-free mice. Proc. Natl Acad. Sci. USA 104, 979–984 (2007).

    PubMed  PubMed Central  Google Scholar 

  13. Ridaura, V. K. et al. Gut microbiota from twins discordant for obesity modulate metabolism in mice. Science 341, 1241214 (2013).

    PubMed  Google Scholar 

  14. Boulangé, C. L., Neves, A. L., Chilloux, J., Nicholson, J. K. & Dumas, M.-E. Impact of the gut microbiota on inflammation, obesity, and metabolic disease. Genome Med. 8, 42 (2016).

    PubMed  PubMed Central  Google Scholar 

  15. García-Cabrerizo, R., Carbia, C., O´Riordan, K. J., Schellekens, H. & Cryan, J. F. Microbiota-gut-brain axis as a regulator of reward processes. J. Neurochem. 157, 1495–1524 (2021).

    PubMed  Google Scholar 

  16. Novelle, M. G. Decoding the role of gut-microbiome in the food addiction paradigm. Int. J. Environ. Res. Public Health 18, 6825 (2021).

    PubMed  PubMed Central  Google Scholar 

  17. Hsu, B. B. et al. Dynamic modulation of the gut microbiota and metabolome by bacteriophages in a mouse model. Cell Host Microbe 25, 803–814.e5 (2019).

    CAS  PubMed  PubMed Central  Google Scholar 

  18. David, L. A. et al. Diet rapidly and reproducibly alters the human gut microbiome. Nature 505, 559–563 (2014).

    CAS  PubMed  Google Scholar 

  19. Minot, S. et al. The human gut virome: inter-individual variation and dynamic response to diet. Genome Res. 21, 1616–1625 (2011).

    CAS  PubMed  PubMed Central  Google Scholar 

  20. Shkoporov, A. N. et al. The human gut virome is highly diverse, stable, and individual specific. Cell Host Microbe 26, 527–541.e5 (2019).

    CAS  PubMed  Google Scholar 

  21. Schulfer, A. et al. Fecal viral community responses to high-fat diet in mice. mSphere 5, e00833-19 (2020).

    PubMed  PubMed Central  Google Scholar 

  22. Ross, R. et al. Waist circumference as a vital sign in clinical practice: a consensus statement from the IAS and ICCR Working Group on Visceral Obesity. Nat. Rev. Endocrinol. 16, 177–189 (2020).

    PubMed  PubMed Central  Google Scholar 

  23. Schulte, E. M., Grilo, C. M. & Gearhardt, A. N. Shared and unique mechanisms underlying binge eating disorder and addictive disorders. Clin. Psychol. Rev. 44, 125–139 (2016).

    PubMed  PubMed Central  Google Scholar 

  24. Adams, R. C., Sedgmond, J., Maizey, L., Chambers, C. D. & Lawrence, N. S. Food addiction: implications for the diagnosis and treatment of overeating. Nutrients 11, 2086 (2019).

    PubMed  PubMed Central  Google Scholar 

  25. Roux, S. et al. Minimum information about an uncultivated virus genome (MIUViG). Nat. Biotechnol. 37, 29–37 (2019).

    CAS  PubMed  Google Scholar 

  26. Fan, S. et al. Microbiota-gut-brain axis drives overeating disorders. Cell Metab. 35, 2011–2027.e7 (2023).

    CAS  PubMed  Google Scholar 

  27. Lechner, W. V., Sidhu, N. K., Kittaneh, A. A. & Anand, A. Interventions with potential to target executive function deficits in addiction: current state of the literature. Curr. Opin. Psychol. 30, 24–28 (2019).

    PubMed  Google Scholar 

  28. Luijten, M., Schellekens, A. F., Kühn, S., MacHielse, M. W. J. & Sescousse, G. Disruption of reward processing in addiction: an image-based meta-analysis of functional magnetic resonance imaging studies. JAMA Psychiatry 74, 387–398 (2017).

    PubMed  Google Scholar 

  29. Escrichs, A. et al. Whole-brain dynamics in aging: disruptions in functional connectivity and the role of the rich club. Cereb. Cortex 31, 2466–2481 (2021).

    PubMed  Google Scholar 

  30. Huckins, J. F. et al. Reward-related regions form a preferentially coupled system at rest. Hum. Brain Mapp. 40, 361–376 (2019).

    PubMed  Google Scholar 

  31. Zilverstand, A., Huang, A. S., Alia-Klein, N. & Goldstein, R. Z. Neuroimaging impaired response inhibition and salience attribution in human drug addiction: a systematic review. Neuron 98, 886–903 (2018).

    CAS  PubMed  PubMed Central  Google Scholar 

  32. Volkow, N. D., Wise, R. A. & Baler, R. The dopamine motive system: implications for drug and food addiction. Nat. Rev. Neurosci. 18, 741–752 (2017).

    CAS  PubMed  Google Scholar 

  33. Hellyer, P. J., Scott, G., Shanahan, M., Sharp, D. J. & Leech, R. Cognitive flexibility through metastable neural dynamics is disrupted by damage to the structural connectome. J. Neurosci. 35, 9050–9063 (2015).

    CAS  PubMed  PubMed Central  Google Scholar 

  34. Sternberg, D. E., Heninger, G. R. & Both, R. H. Plasma homovanillic acid as an index of brain dopamine metabolism: enhancement with debrisoquin. Life Sci. 32, 2447–2452 (1983).

    CAS  PubMed  Google Scholar 

  35. Alex, K. D. & Pehek, E. A. Pharmacologic mechanisms of serotonergic regulation of dopamine neurotransmission. Pharmacol. Ther. 113, 296–320 (2007).

    CAS  PubMed  Google Scholar 

  36. Everitt, B. J. Neural and psychological mechanisms underlying compulsive drug seeking habits and drug memories–indications for novel treatments of addiction. Eur. J. Neurosci. 40, 2163–2182 (2014).

    PubMed  PubMed Central  Google Scholar 

  37. Volkow, N. D., Michaelides, M. & Baler, R. The neuroscience of drug reward and addiction. Physiol. Rev. 99, 2115–2140 (2019).

    CAS  PubMed  PubMed Central  Google Scholar 

  38. Volkow, N. D., Fowler, J. S., Wang, G. J., Swanson, J. M. & Telang, F. Dopamine in drug abuse and addiction: results of imaging studies and treatment implications. Arch. Neurol. 64, 1575–1579 (2007).

    PubMed  Google Scholar 

  39. Everitt, B. J. et al. Review. Neural mechanisms underlying the vulnerability to develop compulsive drug-seeking habits and addiction. Philos. Trans. R. Soc. Lond. B Biol. Sci. 363, 3125–3135 (2008).

    PubMed  PubMed Central  Google Scholar 

  40. Goldstein, R. Z. & Volkow, N. D. Dysfunction of the prefrontal cortex in addiction: neuroimaging findings and clinical implications. Nat. Rev. Neurosci. 12, 652–669 (2011).

    CAS  PubMed  PubMed Central  Google Scholar 

  41. Weygandt, M. et al. Impulse control in the dorsolateral prefrontal cortex counteracts post-diet weight regain in obesity. Neuroimage 109, 318–327 (2015).

    PubMed  Google Scholar 

  42. Riga, D. et al. Optogenetic dissection of medial prefrontal cortex circuitry. Front. Syst. Neurosci. 8, 230 (2014).

    PubMed  PubMed Central  Google Scholar 

  43. Wang, G. J. et al. Brain dopamine and obesity. Lancet 357, 354–357 (2001).

    CAS  PubMed  Google Scholar 

  44. Volkow, N. D. et al. Low dopamine striatal D2 receptors are associated with prefrontal metabolism in obese subjects: possible contributing factors. Neuroimage 42, 1537–1543 (2008).

    PubMed  Google Scholar 

  45. Pfaffly, J. et al. Leptin increases striatal dopamine D2 receptor binding in leptin-deficient obese (ob/ob) mice. Synapse 64, 503–510 (2010).

  46. Johnson, P. M. & Kenny, P. J. Dopamine D2 receptors in addiction-like reward dysfunction and compulsive eating in obese rats. Nat. Neurosci. 13, 635–641 (2010).

    CAS  PubMed  PubMed Central  Google Scholar 

  47. Dong, T. S. et al. A distinct brain-gut-microbiome profile exists for females with obesity and food addiction. Obesity 28, 1477–1486 (2020).

    CAS  PubMed  Google Scholar 

  48. Kursa, M. B. & Rudnicki, W. R. Feature selection with the boruta package. J. Stat. Softw. 36, 1–13 (2010).

    Google Scholar 

  49. Lundberg, S. M. et al. From local explanations to global understanding with explainable AI for trees. Nat. Mach. Intell. 2, 56–67 (2020).

    PubMed  PubMed Central  Google Scholar 

  50. Dodd, D. et al. A gut bacterial pathway metabolizes aromatic amino acids into nine circulating metabolites. Nature 551, 648–652 (2017).

    CAS  PubMed  PubMed Central  Google Scholar 

  51. Negatu, D. A. et al. Gut microbiota metabolite indole propionic acid targets tryptophan biosynthesis in Mycobacterium tuberculosis. mBio 10, e02781-18 (2019).

    PubMed  PubMed Central  Google Scholar 

  52. Shaw, C., Hess, M. & Weimer, B. C. Microbial-derived tryptophan metabolites and their role in neurological disease: anthranilic acid and anthranilic acid derivatives. Microorganisms 11, 1825 (2023).

    CAS  PubMed  PubMed Central  Google Scholar 

  53. Scofield, M. D. et al. The nucleus accumbens: mechanisms of addiction across drug classes reflect the importance of glutamate homeostasis. Pharmacol. Rev. 68, 816–871 (2016).

    CAS  PubMed  PubMed Central  Google Scholar 

  54. Kalivas, P. W. The glutamate homeostasis hypothesis of addiction. Nat. Rev. Neurosci. 10, 561–572 (2009).

    CAS  PubMed  Google Scholar 

  55. Fetcho, R. N. et al. A stress-sensitive frontostriatal circuit supporting effortful reward-seeking behavior. Neuron 112, 473–487.e4 (2024).

    CAS  PubMed  Google Scholar 

  56. Biria, M. et al. Cortical glutamate and GABA are related to compulsive behaviour in individuals with obsessive compulsive disorder and healthy controls. Nat. Commun. 14, 3324 (2023).

    CAS  PubMed  PubMed Central  Google Scholar 

  57. Shyu, C., Chavez, S., Boileau, I. & Le Foll, B. Quantifying GABA in addiction: a review of proton magnetic resonance spectroscopy studies. Brain Sci. 12, 918 (2022).

    CAS  PubMed  PubMed Central  Google Scholar 

  58. Ducker, G. S. & Rabinowitz, J. D. One-carbon metabolism in health and disease. Cell Metab. 25, 27–42 (2017).

    CAS  PubMed  Google Scholar 

  59. Fanet, H., Capuron, L., Castanon, N., Calon, F. & Vancassel, S. Tetrahydrobioterin (BH4) pathway: from metabolism to neuropsychiatry. Curr. Neuropharmacol. 19, 591–609 (2021).

    CAS  PubMed  PubMed Central  Google Scholar 

  60. Fussi, F., Savoldi, F. & Curti, M. Identification of N-carboxyethyl γ-aminobutyric acid in bovine brain and human cerebrospinal fluid. Neurosci. Lett. 77, 308–310 (1987).

    CAS  PubMed  Google Scholar 

  61. McFarland, K., Lapish, C. C. & Kalivas, P. W. Prefrontal glutamate release into the core of the nucleus accumbens mediates cocaine-induced reinstatement of drug-seeking behavior. J. Neurosci. 23, 3531–3537 (2003).

    CAS  PubMed  PubMed Central  Google Scholar 

  62. Moulin, T. C. et al. Transient administration of dopaminergic precursor causes inheritable overfeeding behavior in young Drosophila melanogaster adults. Brain Sci. 10, 1–10 (2020).

    Google Scholar 

  63. Kaun, K. R., Devineni, A. V. & Heberlein, U. Drosophila melanogaster as a model to study drug addiction. Hum. Genet. 131, 959–975 (2012).

    CAS  PubMed  PubMed Central  Google Scholar 

  64. Devineni, A. V. & Heberlein, U. Preferential ethanol consumption in Drosophila models features of addiction. Curr. Biol. 19, 2126–2132 (2009).

    CAS  PubMed  PubMed Central  Google Scholar 

  65. Chvilicek, M. M., Titos, I. & Rothenfluh, A. The neurotransmitters involved in Drosophila alcohol-induced behaviors. Front. Behav. Neurosci. 14, 607700 (2020).

    CAS  PubMed  PubMed Central  Google Scholar 

  66. Sokoloff, P. & Le Foll, B. The dopamine D3 receptor, a quarter century later. Eur. J. Neurosci. 45, 2–19 (2017).

    PubMed  Google Scholar 

  67. Boileau, I. et al. Higher binding of the dopamine D3 receptor-preferring ligand [11C]-(+)-propyl-hexahydro-naphtho-oxazin in methamphetamine polydrug users: a positron emission tomography study. J. Neurosci. 32, 1353–1359 (2012).

  68. Payer, D. E. et al. Heightened D3 dopamine receptor levels in cocaine dependence and contributions to the addiction behavioral phenotype: a positron emission tomography study with [11C]-+-PHNO. Neuropsychopharmacology 39, 311–318 (2014).

    PubMed  Google Scholar 

  69. Glickstein, S. B., DeSteno, D. A., Hof, P. R. & Schmauss, C. Mice lacking dopamine D2 and D3 receptors exhibit differential activation of prefrontal cortical neurons during tasks requiring attention. Cereb. Cortex 15, 1016–1024 (2005).

    PubMed  Google Scholar 

  70. Puig, J. et al. The aging imageomics study: rationale, design and baseline characteristics of the study population. Mech. Ageing Dev. 189, 111257 (2020).

    PubMed  Google Scholar 

  71. Granero, R. et al. Validation of the Spanish Version of the Yale Food Addiction Scale 2.0 (YFAS 2.0) and clinical correlates in a sample of eating disorder, gambling disorder, and healthy control participants. Front. Psychiatry 9, 208 (2018).

    PubMed  PubMed Central  Google Scholar 

  72. Torrubia, R., Ávila, C., Moltó, J. & Caseras, X. The Sensitivity to Punishment and Sensitivity to Reward Questionnaire (SPSRQ) as a measure of Gray’s anxiety and impulsivity dimensions. Pers. Individ. Dif. 31, 837–862 (2001).

    Google Scholar 

  73. Corr, P. J., Deyoung, C. G. & Mcnaughton, N. Motivation and personality: a neuropsychological perspective. Soc. Personal Psychol. Compass. 7, 158–175 (2013).

    Google Scholar 

  74. Golden, C. A Manual for the Clinical and Experimental Use of the Stroop Color and Word Test (Stoelting, 1978).

  75. Whiteside, S. P. & Lynam, D. R. The Five Factor Model and impulsivity: using a structural model of personality to understand impulsivity. Pers. Individ. Dif. 30, 669–689 (2001).

    Google Scholar 

  76. Cándido, A., Orduña, E., Perales, J. C., Verdejo-García, A. & Billieux, J. Validation of a short Spanish version of the UPPS-P impulsive behaviour scale. Trastor. Adict. 14, 73–78 (2012).

    Google Scholar 

  77. Chen, S., Zhou, Y., Chen, Y. & Gu, J. fastp: an ultra-fast all-in-one FASTQ preprocessor. Bioinformatics 34, i884–i890 (2018).

    PubMed  PubMed Central  Google Scholar 

  78. Langmead, B. & Salzberg, S. L. Fast gapped-read alignment with Bowtie 2. Nat. Methods 9, 357–359 (2012).

    CAS  PubMed  PubMed Central  Google Scholar 

  79. Tamames, J. & Puente-Sánchez, F. SqueezeMeta, a highly portable, fully automatic metagenomic analysis pipeline. Front. Microbiol. 9, 3349 (2019).

    PubMed  PubMed Central  Google Scholar 

  80. Li, D., Liu, C. M., Luo, R., Sadakane, K. & Lam, T. W. MEGAHIT: an ultra-fast single-node solution for large and complex metagenomics assembly via succinct de Bruijn graph. Bioinformatics 31, 1674–1676 (2015).

    CAS  PubMed  Google Scholar 

  81. Hyatt, D. et al. Prodigal: prokaryotic gene recognition and translation initiation site identification. BMC Bioinform. 11, 119 (2010).

    Google Scholar 

  82. Buchfink, B., Reuter, K. & Drost, H. G. Sensitive protein alignments at tree-of-life scale using DIAMOND. Nat. Methods 18, 366–368 (2021).

    CAS  PubMed  PubMed Central  Google Scholar 

  83. Herwig, R., Hardt, C., Lienhard, M. & Kamburov, A. Analyzing and interpreting genome data at the network level with ConsensusPathDB. Nat. Protoc. 11, 1889–1907 (2016).

    CAS  PubMed  Google Scholar 

  84. Schmieder, R. & Edwards, R. Quality control and preprocessing of metagenomic datasets. Bioinformatics 27, 863–864 (2011).

    CAS  PubMed  PubMed Central  Google Scholar 

  85. Magoc, T. & Salzberg, S. L. FLASH: fast length adjustment of short reads to improve genome assemblies. Bioinformatics 27, 2957–2963 (2011).

    CAS  PubMed  PubMed Central  Google Scholar 

  86. Menzel, P., Ng, K. L. & Krogh, A. Fast and sensitive taxonomic classification for metagenomics with Kaiju. Nat. Commun. 7, 11257 (2016).

    CAS  PubMed  PubMed Central  Google Scholar 

  87. Mills, R., Rozanov, M., Lomsadze, A., Tatusova, T. & Borodovsky, M. Improving gene annotation of complete viral genomes. Nucleic Acids Res. 31, 7041–7055 (2003).

    CAS  PubMed  PubMed Central  Google Scholar 

  88. Camacho, C. et al. BLAST+: architecture and applications. BMC Bioinform. 10, 421 (2009).

    Google Scholar 

  89. Moraru, C., Varsani, A. & Kropinski, A. M. VIRIDIC—A novel tool to calculate the intergenomic similarities of prokaryote-infecting viruses. Viruses 12, 1268 (2020).

    CAS  PubMed  PubMed Central  Google Scholar 

  90. Bolduc, B. et al. vConTACT: an iVirus tool to classify double-stranded DNA viruses that infect Archaea and Bacteria. PeerJ 5, e3243 (2017).

    PubMed  PubMed Central  Google Scholar 

  91. Martinez-Hernandez, F. et al. Single-virus genomics reveals hidden cosmopolitan and abundant viruses. Nat. Commun. 8, 15892 (2017).

    CAS  PubMed  PubMed Central  Google Scholar 

  92. Shmakov, S. A., Wolf, Y. I., Savitskaya, E., Severinov, K. V. & Koonin, E. V. Mapping CRISPR spaceromes reveals vast host-specific viromes of prokaryotes. Commun. Biol. 3, 321 (2020).

    CAS  PubMed  PubMed Central  Google Scholar 

  93. Mayneris-Perxachs, J. et al. Caudovirales bacteriophages are associated with improved executive function and memory in flies, mice, and humans. Cell Host Microbe 30, 340–356.e8 (2022).

    CAS  PubMed  Google Scholar 

  94. Lin, H. & Peddada, S. D. Analysis of compositions of microbiomes with bias correction. Nat. Commun. 11, 3514 (2020).

    CAS  PubMed  PubMed Central  Google Scholar 

  95. Zhou, C., Wang, H., Zhao, H. & Wang, T. fastANCOM: a fast method for analysis of compositions of microbiomes. Bioinformatics 38, 2039–2041 (2022).

    CAS  PubMed  Google Scholar 

  96. Yang, L. & Chen, J. A comprehensive evaluation of microbial differential abundance analysis methods: current status and potential solutions. Microbiome 10, 130 (2022).

    CAS  PubMed  PubMed Central  Google Scholar 

  97. Carvajal-Rodríguez, A., de Uña-Alvarez, J. & Rolán-Alvarez, E. A new multitest correction (SGoF) that increases its statistical power when increasing the number of tests. BMC Bioinform. 10, 209 (2009).

    Google Scholar 

  98. Wu, T. et al. clusterProfiler 4.0: a universal enrichment tool for interpreting omics data. Innovation 2, 100141 (2021).

    CAS  PubMed  PubMed Central  Google Scholar 

  99. Fernandes, A. D. et al. Unifying the analysis of high-throughput sequencing datasets: characterizing RNA-seq, 16S rRNA gene sequencing and selective growth experiments by compositional data analysis. Microbiome 2, 15 (2014).

    PubMed  PubMed Central  Google Scholar 

  100. Ritchie, M. E. et al. Limma powers differential expression analyses for RNA-sequencing and microarray studies. Nucleic Acids Res. 43, e47 (2015).

    PubMed  PubMed Central  Google Scholar 

  101. Robinson, M. D., McCarthy, D. J. & Smyth, G. K. edgeR: a Bioconductor package for differential expression analysis of digital gene expression data. Bioinformatics 26, 139–140 (2010).

    CAS  PubMed  Google Scholar 

  102. Szklarczyk, D. et al. STRING v11: protein-protein association networks with increased coverage, supporting functional discovery in genome-wide experimental datasets. Nucleic Acids Res. 47, D607–D613 (2019).

    CAS  PubMed  Google Scholar 

  103. Kamburov, A., Stelzl, U., Lehrach, H. & Herwig, R. The ConsensusPathDB interaction database: 2013 Update. Nucleic Acids Res. 41, D793–D800 (2013).

    CAS  PubMed  Google Scholar 

  104. Shen, X., Tokoglu, F., Papademetris, X. & Constable, R. T. Groupwise whole-brain parcellation from resting-state fMRI data for network node identification. Neuroimage 82, 403–415 (2013).

    CAS  PubMed  Google Scholar 

  105. Finn, E. S. et al. Functional connectome fingerprinting: identifying individuals using patterns of brain connectivity. Nat. Neurosci. 18, 1664–1671 (2015).

    CAS  PubMed  PubMed Central  Google Scholar 

  106. Jenkinson, M., Bannister, P., Brady, M. & Smith, S. Improved optimization for the robust and accurate linear registration and motion correction of brain images. Neuroimage 17, 825–841 (2002).

    PubMed  Google Scholar 

  107. Yan, C. G. et al. A comprehensive assessment of regional variation in the impact of head micromovements on functional connectomics. Neuroimage 76, 183–201 (2013).

    PubMed  Google Scholar 

  108. Deco, G. & Kringelbach, M. L. Hierarchy of information processing in the brain: a novel ‘intrinsic ignition’ framework. Neuron 94, 961–968 (2017).

  109. Zhao, S., Li, H., Han, W., Chan, W. & Li, L. Metabolomic coverage of chemical-group-submetabolome analysis: group classification and four-channel chemical isotope labeling LC-MS. Anal. Chem. 91, 12108–12115 (2019).

    CAS  PubMed  Google Scholar 

  110. Whiley, L. et al. Ultra-high-performance liquid chromatography tandem mass spectrometry with electrospray ionization quantification of tryptophan metabolites and markers of gut health in serum and plasma-application to clinical and epidemiology cohorts. Anal. Chem. 91, 5207–5216 (2019).

    CAS  PubMed  PubMed Central  Google Scholar 

  111. Lai, Y., Liu, C. W., Chi, L., Ru, H. & Lu, K. High-resolution metabolomics of 50 neurotransmitters and tryptophan metabolites in feces, serum, and brain tissues using UHPLC-ESI-Q exactive mass spectrometry. ACS Omega 6, 8094–8103 (2021).

    CAS  PubMed  PubMed Central  Google Scholar 

  112. García-Blanco, A. et al. miRNA signatures associated with vulnerability to food addiction in mice and humans. J. Clin. Invest. 132, e156281 (2022).

    PubMed  PubMed Central  Google Scholar 

  113. Mancino, S. et al. Epigenetic and proteomic expression changes promoted by eating addictive-like behavior. Neuropsychopharmacology 40, 2788–2800 (2015).

    CAS  PubMed  PubMed Central  Google Scholar 

  114. Martín-García, E. et al. New operant model of reinstatement of food-seeking behavior in mice. Psychopharmacology 215, 49–70 (2011).

    PubMed  Google Scholar 

  115. Deroche-Gamonet, V., Belin, D. & Piazza, P. V. Evidence for addiction-like behavior in the rat. Science 305, 1014–1017 (2004). (1979).

    CAS  PubMed  Google Scholar 

  116. Paxinos, G. & Franklin, K. B. J. The Mouse Brain in Stereotaxic Coordinates (Academic Press, 1997).

  117. Dobin, A. et al. STAR: ultrafast universal RNA-seq aligner. Bioinformatics 29, 15–21 (2013).

    CAS  PubMed  Google Scholar 

  118. Liao, Y., Smyth, G. K. & Shi, W. FeatureCounts: an efficient general purpose program for assigning sequence reads to genomic features. Bioinformatics 30, 923–930 (2014).

    CAS  PubMed  Google Scholar 

  119. Castells-Nobau, A. et al. Conserved regulation of neurodevelopmental processes and behavior by FoxP in Drosophila. PLoS ONE 14, e0211652 (2019).

    CAS  PubMed  PubMed Central  Google Scholar 

  120. Wu, Q. et al. Excreta quantification (EX-Q) for longitudinal measurements of food intake in Drosophila. iScience 23, 100776 (2020).

    PubMed  Google Scholar 

  121. Shell, B. C. et al. Measurement of solid food intake in Drosophila via consumption-excretion of a dye tracer. Sci. Rep. 8, 11536 (2018).

    PubMed  PubMed Central  Google Scholar 

  122. Kubrak, O. et al. The gut hormone allatostatin C/somatostatin regulates food intake and metabolic homeostasis under nutrient stress. Nat. Commun. 13, 692 (2022).

    CAS  PubMed  PubMed Central  Google Scholar 

  123. Ja, W. W. et al. Prandiology of Drosophila and the CAFE assay. Proc. Natl Acad. Sci. USA 104, 8253–8256 (2007).

    CAS  PubMed  PubMed Central  Google Scholar 

  124. Diegelmann, S. et al. The CApillary FEeder assay measures food intake in Drosophila melanogaster. J. Vis. Exp. 2017, 55024 (2017).

    Google Scholar 

  125. Itskov, P. M. et al. Automated monitoring and quantitative analysis of feeding behaviour in Drosophila. Nat. Commun. 5, 4560 (2014).

    CAS  PubMed  Google Scholar 

Download references

Acknowledgements

This work was partially funded by Instituto de Salud Carlos III (ISCIII, Madrid, Spain) through the project PI15/01934 to J.M.F.-R. and the projects PI20/01090 and PI23/00575 (co-funded by the European Union under the European Regional Development Fund (FEDER) ‘A way to make Europe’) to J.M.-P. (HR22-00737). The Aging Imageomics Study was funded by Pla estratègic de recerca i innovació en salut 2016–2020 from Generalitat de Catalunya (reference no. SLT002/16/00250). Health Imageomics was funded by Health LivingLab operation of the Girona Healthy Region Program, which was granted by the Projectes d’Especialització i Competitivitat Territorial of the RIS3Cat and the Operative Programme of the European Regional Development Fund of Catalonia 2014–2020. This work was also supported by the Spanish ‘Ministerio de Ciencia e Innovación (MICIN), Agencia Estatal de Investigación’ (PID2020-120029GB-I00/MICIN/AEI/10.13039/501100011033, RD21/0009/0019, to R.M.; the ‘Generalitat de Catalunya, AGAUR’ (2017 SGR-669, to R.M.; the ‘ICREA-Acadèmia’ (2020, to R.M.); the ‘European Commission-DG Research’ (PainFact, H2020-SC1-2019-2-RTD-848099, QSPain Relief, H2020-SC1-2019-2-RTD-848068, to R.M.); the Spanish ‘la Caixa’ Foundation under project code LCF/PR/HR22/52420017 to R.M. and J.M.F.-R.; the Spanish ‘Instituto de Salud Carlos III, RETICS-RTA’ (RD16/0017/0020, to R.M.); the Spanish ‘Ministerio de Sanidad, Servicios Sociales e Igualdad, Plan Nacional Sobre Drogas’ (PNSD-2021I076, to R.M.; PNSD-2023I040, to E.M.G.; and Ministerio de Ciencia e Innovación (ERA-NET) PCI2021-122073-2A to E.M.G. This study has been co-financed by FEDER funds from the European Union (‘A way to build Europe’) and the Generalitat of Catalonia: Agency for Management of University and Research Grants (2021SGR00990) and Department of Health (SLT002/16/00250) to R.P. IDIBGI is a CERCA Programme/Generalitat de Catalunya. J.M.-P. and A.C.N. are funded by Instituto de Salud Carlos III (Madrid, Spain) through the Miguel Servet Program CP18/00009 and Sara Borrell Program CD20/00051 (co-funded by the European Union under the European Social Fund ‘Investing in Your Future’), respectively. L.V.-C. is funded by the Program for the Promotion of Talent and Employability (Generalitat de Catalunya) SLT017_20_000164. This study was conducted using samples and/or data from the Aging Imageomics Study, supported by the Generalitat de Catalunya through the Strategic Plan for Health Research and Innovation 2016–2020 (SLT002/16/00250). We particularly acknowledge the participants of the IDIBGI Horizontal Aging Program and the IDIBGI Biobank (Biobanc IDIBGI, B.0000872), integrated in the Platform ISCIII Biomodels and Biobanks, for their collaboration. This study was conducted using samples and/or data from the Healthy Imageomics Study, supported by the Specialization and Territorial Competitiveness Projects within the RIS3Cat and the Catalonia European Regional Development Fund (ERDF) Operational Programme 2014–2020. It was co-financed by the ERDF of the European Union under the Catalonia ERDF Operational Programme 2014–2020 and by the Diputació de Girona. Illustrations were created with Biorender.com.

Author information

Authors and Affiliations

  1. Department of Diabetes, Endocrinology and Nutrition, Dr Josep Trueta University Hospital, Girona, Spain

    Anna Castells-Nobau, Irene Puig, Anna Motger-Albertí, Lisset de la Vega-Correa, Marisel Rosell-Díaz, María Arnoriaga-Rodríguez, José Manuel Fernández-Real & Jordi Mayneris-Perxachs

  2. Nutrition, Eumetabolism and Health Group, Girona Biomedical Research Institute (IDIBGI-CERCA), Girona, Spain

    Anna Castells-Nobau, Irene Puig, Anna Motger-Albertí, Lisset de la Vega-Correa, Marisel Rosell-Díaz, María Arnoriaga-Rodríguez & José Manuel Fernández-Real

  3. CIBER Fisiopatología de la Obesidad y Nutrición (CIBERobn), Madrid, Spain

    Anna Castells-Nobau, Irene Puig, Anna Motger-Albertí, Lisset de la Vega-Correa, Marisel Rosell-Díaz, María Arnoriaga-Rodríguez, José Manuel Fernández-Real & Jordi Mayneris-Perxachs

  4. Integrative Systems Medicine and Biology Group, Girona Biomedical Research Institute (IDIBGI-CERCA), Salt, Spain

    Anna Castells-Nobau & Jordi Mayneris-Perxachs

  5. Computational Neuroscience Group, Center for Brain and Cognition, Department of Information and Communication Technologies, Universitat Pompeu Fabra, Barcelona, Spain

    Anira Escrichs & Gustavo Deco

  6. Research Group on Health, Gender and Aging, University of Girona, Girona, Spain

    Josep Garre-Olmo

  7. Serra-Hunter Programme, Department of Nursing, University of Girona, Girona, Spain

    Josep Garre-Olmo & José Manuel Fernández-Real

  8. Department of Radiology (CDI) and IDIBAPS, Hospital Clínic de Barcelona, Barcelona, Spain

    Josep Puig

  9. Department of Medical Sciences, School of Medicine, University of Girona, Girona, Spain

    Rafael Ramos

  10. Vascular Health Research Group of Girona (ISV-Girona). Jordi Gol Institute for Primary Care Research (Institut Universitari per a la Recerca en Atenció Primària Jordi Gol I Gorina -IDIAPJGol), Red de Investigación en Cronicidad, Atención Primaria y Promoción de la Salud-RICAPPS- ISCIII, Girona, Spain

    Rafael Ramos

  11. Girona Biomedical Research Institute (IDIBGI), Dr Josep Trueta University Hospital, Catalonia, Spain

    Rafael Ramos

  12. Neuroimmunology and Multiple Sclerosis Unit, Department of Neurology, Dr Josep Trueta University Hospital. Neurodegeneration and Neuroinflammation Research Group, IDIBGI. Department of Medical Sciences, University of Girona, Girona-Salt, Spain

    Lluís Ramió-Torrentà

  13. Area of Genomics and Health, Foundation for the Promotion of Sanitary and Biomedical Research of Valencia Region (FISABIO-Public Health), Valencia, Spain

    Vicente Pérez-Brocal & Andrés Moya

  14. Biomedical Research Networking Center for Epidemiology and Public Health (CIBERESP), Madrid, Spain

    Vicente Pérez-Brocal & Andrés Moya

  15. Institute for Integrative Systems Biology (I2SysBio), University of Valencia and Spanish National Research Council (CSIC), Valencia, Spain

    Andrés Moya

  16. Department of Experimental Medicine, University of Lleida-Lleida Biomedical Research Institute (UdL-IRBLleida), Lleida, Spain

    Reinald Pamplona, Mariona Jové & Joaquim Sol

  17. Research Support Unit (USR) Lleida, Primary Care Services, Catalan Health Institute (ICS), Lleida, Spain

    Joaquim Sol

  18. Fundació Institut Universitari per a la Recerca en Atenció Primària de Salut Jordi Gol i Gurina (IDIAP JGol), Lleida, Spain

    Joaquim Sol

  19. Laboratory of Neuropharmacology, Department of Experimental and Health Sciences, Universitat Pompeu Fabra, Barcelona, Spain

    Elena Martin-Garcia & Rafael Maldonado

  20. Hospital del Mar Medical Research Institute (IMIM), Barcelona, Spain

    Elena Martin-Garcia & Rafael Maldonado

  21. Departament de Psicobiologia i Metodologia de les Ciències de la Salut, Universitat Autònoma de Barcelona, Barcelona, Spain

    Elena Martin-Garcia

  22. Institut de Neurociències, Universitat Autònoma de Barcelona, Barcelona, Spain

    Elena Martin-Garcia

  23. Department of Physiology, Genetics, and Microbiology, University of Alicante, Alicante, Spain

    Manuel Martinez-Garcia

  24. Multidisiciplinary Institute for Environmental Studies Ramon Margalef, University of Alicante, Alicante, Spain

    Manuel Martinez-Garcia

  25. Institucio Catalana de la Recerca i Estudis Avançats (ICREA), Barcelona, Spain

    Gustavo Deco

Authors
  1. Anna Castells-Nobau
    View author publications

    Search author on:PubMed Google Scholar

  2. Irene Puig
    View author publications

    Search author on:PubMed Google Scholar

  3. Anna Motger-Albertí
    View author publications

    Search author on:PubMed Google Scholar

  4. Lisset de la Vega-Correa
    View author publications

    Search author on:PubMed Google Scholar

  5. Marisel Rosell-Díaz
    View author publications

    Search author on:PubMed Google Scholar

  6. María Arnoriaga-Rodríguez
    View author publications

    Search author on:PubMed Google Scholar

  7. Anira Escrichs
    View author publications

    Search author on:PubMed Google Scholar

  8. Josep Garre-Olmo
    View author publications

    Search author on:PubMed Google Scholar

  9. Josep Puig
    View author publications

    Search author on:PubMed Google Scholar

  10. Rafael Ramos
    View author publications

    Search author on:PubMed Google Scholar

  11. Lluís Ramió-Torrentà
    View author publications

    Search author on:PubMed Google Scholar

  12. Vicente Pérez-Brocal
    View author publications

    Search author on:PubMed Google Scholar

  13. Andrés Moya
    View author publications

    Search author on:PubMed Google Scholar

  14. Reinald Pamplona
    View author publications

    Search author on:PubMed Google Scholar

  15. Mariona Jové
    View author publications

    Search author on:PubMed Google Scholar

  16. Joaquim Sol
    View author publications

    Search author on:PubMed Google Scholar

  17. Elena Martin-Garcia
    View author publications

    Search author on:PubMed Google Scholar

  18. Manuel Martinez-Garcia
    View author publications

    Search author on:PubMed Google Scholar

  19. Gustavo Deco
    View author publications

    Search author on:PubMed Google Scholar

  20. Rafael Maldonado
    View author publications

    Search author on:PubMed Google Scholar

  21. José Manuel Fernández-Real
    View author publications

    Search author on:PubMed Google Scholar

  22. Jordi Mayneris-Perxachs
    View author publications

    Search author on:PubMed Google Scholar

Contributions

A.C.-N. researched the data, performed the statistical analysis and wrote the manuscript; A.C.-N., L.V.-C. and I.P. performed the Drosophila experiments; E.M.-G. and R.M. supervised, performed or analysed the experiments in mice; M.R.-D., A.M.-A. and M.A.-R. performed the clinical or neuropsychological examination; V.P.-B. and A.M. contributed with the determination and analysis of the microbiota; A.E. and G.D. researched the MRI data; R.P., M.J. and J.S. performed the metabolomics analyses; M.M.-G. helped in the identification of the bacteriophage host; J.P., J.G.-O., R.R. and L.R.-T. contributed to the discussion and reviewed the manuscript. J.M.-P. and J.M.F.-R. carried out the conception and coordination of the study, performed the statistical analysis and wrote the manuscript. All authors participated in final approval of the version to be published. We thank R. Martín and C. Zapata for their technical support.

Corresponding authors

Correspondence to Rafael Maldonado, José Manuel Fernández-Real or Jordi Mayneris-Perxachs.

Ethics declarations

Competing interests

The authors declare no competinginterests.

Peer review

Peer review information

Nature Metabolism thanks Serguei Fetissov, A. Veronica Witte and the other, anonymous, reviewer(s) for their contribution to the peer review of this work. Primary Handling Editor: Yanina-Yasmin Pesch, in collaboration with the Nature Metabolism team.

Additional information

Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Extended data

Extended Data Fig. 1

Consort diagram for the discovery cohort (IRONMET-CGM). The diagram illustrates recruitment numbers and participant flow for the IRONMET-CGM study.

Extended Data Fig. 2 Gut microbiome and YFAS.

a) fastANCOM microbiome differential abundance volcano plot at the family level for YFAS controlling for age, BMI, gender, and education years as covariates in the discovery cohort IRONMET-CGM. The W-statistic represents the number of times the null hypothesis is rejected (two-sided t-test) by the analysis for a given family from all K models. Beta represents the effect size given by the coefficient of the log-linear regression model of each family against the YFAS score. Dotted lines show W-statistic quantile detection thresholds: >0.6 (red), >0.7 (light blue), >0.8 (green), >0.9 (dark blue). c–e) ANCOM-BC (two-sided Z-test using the statistic W = log2Fold Change/standard error) and b, f, g) ZicoSeq (omnibus F-statistic) microbiome differential abundance volcano plot at the family level for YFAS controlling for age, BMI, gender, and education years as covariates in b) the discovery cohort (IRONMET-CGM) c) women with obesity of the validation cohort (IRONMET) and, d, f) women and d, g) men of the discovery cohort (IRONMET-CGM). Fold change associated with a unit change in the YFAS score and log10 p values adjusted for multiple testing are plotted for each family.

Extended Data Fig. 3 Gut microbiome and YFAS components.

ANCOM-BC microbiome differential abundance (two-sided Z-test using the statistic W = log2Fold Change/standard error) volcano plot at the family level for a) YFAS in the discovery cohort (IRONMET-CGM) and b) for YFAS in the validation cohort 1 (IRONMET); c) YFAS motivation, d) YFAS persistence, e) YFAS compulsivity for the discovery cohort IRONMET-CGM; f) YFAS motivation, g) YFAS persistence, h) YFAS compulsivity for the validation cohort IRONMET. Fold change associated with a unit change in the YFAS score and log10 p values adjusted for multiple testing are plotted for each family. All analyses were performed controlling for age, waist circumference, sex (only when individuals of both sexes were included in the analysis), and education years as covariates.

Extended Data Fig. 4 Gut microbiome and YFAS in IRONMET-CGM.

a–i) ANCOM-BC microbiome differential abundance (two-sided Z-test using the statistic W = log2Fold Change/standard error) volcano plot at the phylum level for a) YFAS motivation, b) YFAS persistence, c) YFAS compulsivity for the discovery cohort IRONMET-CGM; d) YFAS motivation, e) YFAS persistence, f) YFAS compulsivity for women of the discovery cohort IRONMET-CGM; g) YFAS motivation, h) YFAS persistence, i) YFAS compulsivity for men of the discovery cohort IRONMET-CGM. Fold change associated with a unit change in the YFAS score and log10 p values adjusted for multiple testing are plotted for each family. Heat map of two-sided Spearman’s correlations between iqlr-transformed abundances of Gokushovirus WZ-2015a and YFAS, addiction-like criteria (motivation, persistence, compulsivity) and sensitivity to reward (SPSR) and punishment (NU (UPPS)) for j) women and k) men. All analysis were performed controlling for age, BMI, sex, and education years as covariates.

Extended Data Fig. 5 Gut microbiome and Stroop Colour-Word Test and fMR metastability parameters associated with Gokushovirus.

a) ANCOM-BC differential abundance (two-sided Z-test using the statistic W = log2Fold Change/standard error) plot at the family level for the Stroop Colour-Word Test – Word Colour (SCWT-WC) controlling for age, BMI, sex, and education years as covariates in men from the Aging Imageomics cohort (n = 475). Fold change associated with a unit change in the SCWT-WC score and log10 p values adjusted for multiple testing are plotted for each family. b, c) SHAP summary plot for the metastability in the resting-state networks associated with the iqlr-transformed Gokushovirus WZ-2015a levels identified through the Boruta algorithm after controlling for age, BMI, sex (only when applicable), and education level in the whole b) cohort and c) men, respectively. Each dot represents an individual sample. The X-axis represents the SHAP value: the impact of a specific resting-state network on the iqlr-transformed Gokushovirus WZ-2015a levels prediction of a specific individual. Features are sorted in decreasing order based on their overall importance for final prediction (average SHAP values shown in bold).

Extended Data Fig. 6 Metabolomics of nucleus accumbens in recipient mice according to the presence of Gokushovirus in donor’s microbiota.

a) Dot-plot of HMDB pathways enrichment in the NAc of recipient mice from metabolites significantly associated with the presence or absence (<10 counts) of Gokushovirus WZ-2015a from the human donor’s microbiota. b) Metabolite concept network depicting significant metabolites involved in the selected pathways. c) Volcano plot of metabolites identified in the DS of the recipient mice according to the presence or absence (<10 counts) of Gokushovirus WZ-2015a in the human donor’s microbiota using robust linear regression models (t-statistic based on M-estimation with Huber weighting solved using Iteratively Reweighted Least Squares) controlling for donors age, sex, BMI, and education years. Fold change and log10 p values adjusted for multiple comparisons are plotted. d) Dot-plot of KEGG enriched pathways in the DS of recipient mice from metabolites significantly associated with the presence or absence (<10 counts) of Gokushovirus WZ-2015a from the human donors microbiota. e) Metabolite concept network depicting significant metabolites involved in the selected pathways.

Extended Data Fig. 7 Genes expressed in the medial prefrontal cortex of recipient mice according to the presence of Gokushovirus in donor’s microbiota.

a) Dot-plot of KEGG enriched pathways in the mPFC of recipient mice from genes significantly downregulated with the presence or absence (<10 counts) of Gokushovirus WZ-2015a from the human donor’s microbiota. b) Dot-plot of GO biological processes enrichment analysis from genes significantly downregulated with the presence or absence (<10 counts) of Gokushovirus WZ-2015a from the human donor’s microbiota in the mPFC of recipient mice. c) Gene concept network depicting significant genes involved in the selected metabolic processes.

Extended Data Fig. 8 Microbiome families associated with the presence of food addiction and differential metabolites between the AA and control mice in the Nucleus accumbens.

Volcano plot of differential microbiome families associated with the presence of food addiction in a) all individuals (n = 147) and in b) women (n = 101) identified using ANCOM-BC (two-sided Z-test using the statistic W = log2Fold Change/standard error) controlling for age, sex (only when individuals of both sexes were included in the analysis), and waist circumference. Fold changes and p values adjusted for multiple testing are plotted. c) Dot plot of significant (q-values < 0.1) over-represented (one-sided hypergeometric test) HMDB pathways identified from significantly differential metabolites between the AA and control mice in the NAc. d) Dot plot of KEGG module-based pathway over-representation analysis (one-sided hypergeometric test) based on the molecular function significantly associated with the iqlr-transformed Gokushovirus WZ-2015a levels identified using fastANCOM (padj<0.05). e) Metabolite concept network depicting significant metabolites involved in the selected pathways.

Extended Data Fig. 9 Metabolites associated with YFAS.

a) Volcano plot of metabolites associated with YFAS identified employing robust linear regression models (t-statistic based on M-estimation with Huber weighting solved using Iteratively Reweighted Least Squares) in the IRONMET-CGM cohort adjusting for age, sex, BMI, years of education. b) Scatter-plots (two-sided partial Spearman’s rank correlation test adjusted for age, BMI, sex and education years) between 3-ethilmalic acid and YFAS residuals. Scatter-plots show tendency line with 95% confidence interval, each dote represent and independent participant. c) Dot-plot of KEGG enriched pathways (one-sided hypergeometric test) from metabolites significantly associated with YFAS in IRONMET-CGM cohort. P values were corrected for multiple testing using the Storey correction (q-values). d) Metabolite concept network depicting significant metabolites involved in the selected pathways. e) Dot plot of significantly over-represented (one-sided hypergeometric test) HMDB pathways identified from significantly differential metabolites associated with YFAS identified in the IRONMET-CGM. P values were corrected for multiple testing using the Storey correction (q-values). f) Metabolite concept network depicting significant metabolites involved in the selected pathways.

Extended Data Fig. 10 Experiments of food addiction in Drosophila.

a–c) Cumulative plot of the PI over time. The middle line represents the mean and the shading the S.E.M and Cumulative PI at 40 min of flies choosing between a 5% sucrose or 5% sucrose food + 15% ethanol. Flies expressing impTNT or TNT in the dopaminergic neurons by means of the ple-Gal4 promoter with and without dietary supplementation of AA (200 mg/L). PI ranging from −1 to +1, positive values indicate a preference for ethanol and negative values indicate preference for sucrose (PimpTNT-TNT < 0.0001). The graph represents data from a minimum of 3 independent experiments. Bar graphs represent mean with S.E.M. Significance was calculated using Mann–Whitney test (****p < 0.0001). d) Gene–gene interaction network (minimum confidence score > 0.15) constructed using differentially expressed mPFC genes (abs(logFC>1), pFDR<0.2) via the Search Tool for the Retrieval of Interacting Proteins/Genes (STRING) database. The network nodes are genes and the edges represent the predicted functional interactions. The thickness indicates the degree of confidence prediction of the interaction. e) Dot plot of GO BP, CC and MF over-representation analysis (one-sided hypergeometric test) of differentially expressed mPFC genes according to the presence or absence (<10 counts) of Gokushovirus WZ-2015a in the human donor’s microbiota. P values were corrected for multiple comparisons using the Storey correction (q-value). f) Gene-concept network depicting the connections of those genes involved in the GO biological process, cellular compartment and molecula function over-representation results. g) Gene-concept network depicting the connections of those genes involved in the KEGG-based over-representation results.

Supplementary information

Supplementary Information

IRONMET-CGM protocol.

Reporting Summary

Supplementary Tables

The file contains Supplementary Tables 1–75.

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Castells-Nobau, A., Puig, I., Motger-Albertí, A. et al. Microviridae bacteriophages influence behavioural hallmarks of food addiction via tryptophan and tyrosine signalling pathways. Nat Metab 6, 2157–2186 (2024). https://doi.org/10.1038/s42255-024-01157-x

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue date:

  • DOI: https://doi.org/10.1038/s42255-024-01157-x

This article is cited by

Search

Advanced search

Quick links

Nature Briefing

Sign up for the Nature Briefing newsletter — what matters in science, free to your inbox daily.

Get the most important science stories of the day, free in your inbox. Sign up for Nature Briefing