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.

Advertisement

Translational Psychiatry
  • View all journals
  • Search
  • My Account Login
  • Content Explore content
  • About the journal
  • Publish with us
  • Sign up for alerts
  • RSS feed
  1. nature
  2. translational psychiatry
  3. articles
  4. article
MKRN1 as a prioritized drug target for postpartum depression: evidence from druggable proteome profiling and multi-layer validation
Download PDF
Download PDF
  • Article
  • Open access
  • Published: 10 February 2026

MKRN1 as a prioritized drug target for postpartum depression: evidence from druggable proteome profiling and multi-layer validation

  • Tingting Jia1,2 na1,
  • Chengsong Yuan1 na1,
  • Shiyi Hu1,
  • Liangyue Xie1,
  • Andi Liu1,
  • Fengqin Qin3,
  • Yongji He4 &
  • …
  • Chengcheng Zhang  ORCID: orcid.org/0000-0002-2992-62911 

Translational Psychiatry , Article number:  (2026) Cite this article

  • 7 Altmetric

  • Metrics details

We are providing an unedited version of this manuscript to give early access to its findings. Before final publication, the manuscript will undergo further editing. Please note there may be errors present which affect the content, and all legal disclaimers apply.

Subjects

  • Biomarkers
  • Genomics

Abstract

Postpartum depression (PPD) is a significant global health concern affecting women, yet effective and innovative therapeutic targets remain limited. Although genome-wide association studies (GWAS) have identified genetic risk loci, their underlying mechanisms and translational potential remain poorly understood. Therefore, we integrated PPD GWAS data with protein quantitative trait loci from two independent datasets to identify risk genes through proteome-wide association studies (PWAS). Validation was performed using colocalization analysis and Mendelian randomization (MR). To assess the safety of genes as drug targets, phenome-wide MR (Phe-MR) was conducted using the UK Biobank disease data. Finally, we performed gene methylation analysis in PPD patients, alongside validation of expression in key brain regions including anterior cingulate gyrus (AnCg), dorsolateral prefrontal cortex, and nucleus accumbens, as well as in peripheral blood (whole blood and leukocytes), across depressive patients and chronic mild stress mice. Co-expression enrichment was used to identify biological pathways associated with risk genes. PWAS and colocalization analysis identified MKRN1 and CCDC92 as overlapping risk genes, with MKRN1 validated in MR. Phe-MR showed non-significant association between MKRN1 dysregulation and disease beyond depression and mood disorders, suggesting minimal off-target effects. Methylation analysis in PPD patients’ blood revealed significant hypomethylation of MKRN1, consistent with expression analysis that confirmed its upregulation in AnCg and as a biomarker in blood. Enrichment analysis indicated MKRN1 involvement in immune–inflammatory pathways. Our study identified MKRN1 as a therapeutic target for PPD, integrating multi-omics evidence from genomics, proteomics, and druggable proteome profiling, and offering a promising path for targeted treatments.

Similar content being viewed by others

Genome-wide Mendelian randomization identifies actionable novel drug targets for psychiatric disorders

Article 16 September 2022

Using phenotype risk scores to enhance gene discovery for generalized anxiety disorder and posttraumatic stress disorder

Article 18 February 2022

Identifying causal genes for depression via integration of the proteome and transcriptome from brain and blood

Article 16 March 2022

Data availability

The data used in the study can be accessed and downloaded from original studies [40, 45,46,47,48, 59,60,61,62,63].

References

  1. American Psychiatric Association, American Psychiatric Association (eds). Diagnostic and statistical manual of mental disorders: DSM-5. 5th ed. Washington, D.C: American Psychiatric Association, 2013.

  2. Stewart DE, Robertson E, Dennis C-L, Grace SL, Wallington T Postpartum depression: Literature review of risk factors and interventions. Tor Univ Health Netw Women’s Health Program Tor Public Health 2003; 1–289.

  3. O’Hara MW, McCabe JE. Postpartum depression: current status and future directions. Annu Rev Clin Psychol. 2013;9:379–407.

    Google Scholar 

  4. Horáková A, Němcová H, Mohr P, Sebela A. Structural, functional, and metabolic signatures of postpartum depression: a systematic review. Front Psychiatry. 2022;13:1044995.

    Google Scholar 

  5. Pearlstein T, Howard M, Salisbury A, Zlotnick C. Postpartum depression. Am J Obstet Gynecol. 2009;200:357–64.

    Google Scholar 

  6. Tebeka S, Gauld C, Belzeaux R, Peyre H, Dubertret C. Major depressive episode and postpartum depression: A network analysis comparison on the IGEDEPP cohort. Eur Psychiatry J Assoc Eur Psychiatr. 2023;66:e51.

    Google Scholar 

  7. Saharoy R, Potdukhe A, Wanjari M, Taksande AB. Postpartum Depression and Maternal Care: Exploring the Complex Effects on Mothers and Infants. Cureus. 2023;15:e41381.

    Google Scholar 

  8. Nasreen H-E, Kabir ZN, Forsell Y, Edhborg M. Impact of maternal depressive symptoms and infant temperament on early infant growth and motor development: results from a population based study in Bangladesh. J Affect Disord. 2013;146:254–61.

    Google Scholar 

  9. Azak S. Maternal depression and sex differences shape the infants’ trajectories of cognitive development. Infant Behav Dev. 2012;35:803–14.

    Google Scholar 

  10. Feldman R, Granat A, Pariente C, Kanety H, Kuint J, Gilboa-Schechtman E. Maternal depression and anxiety across the postpartum year and infant social engagement, fear regulation, and stress reactivity. J Am Acad Child Adolesc Psychiatry. 2009;48:919–27.

    Google Scholar 

  11. Moore Simas TA, Whelan A, Byatt N. Postpartum depression-new screening recommendations and treatments. JAMA. 2023;330:2295–6.

    Google Scholar 

  12. Cardaci V, Carminati M, Tondello M, Pecorino B, Serretti A, Zanardi R. Understanding and treating postpartum depression: a narrative review. Int Clin Psychopharmacol. 2025;40:127–37. https://doi.org/10.1097/YIC.0000000000000560.

    Google Scholar 

  13. Dennis C-L, Singla DR, Brown HK, Savel K, Clark CT, Grigoriadis S, et al. Postpartum depression: a clinical review of impact and current treatment solutions. Drugs. 2024;84:645–59.

    Google Scholar 

  14. Weissman AM, Levy BT, Hartz AJ, Bentler S, Donohue M, Ellingrod VL, et al. Pooled analysis of antidepressant levels in lactating mothers, breast milk, and nursing infants. Am J Psychiatry. 2004;161:1066–78.

    Google Scholar 

  15. McDonald M, Alhusen J. A review of treatments and clinical guidelines for perinatal depression. J Perinat Neonatal Nurs. 2022;36:233.

    Google Scholar 

  16. O’Hara MW, Stuart S, Gorman LL, Wenzel A. Efficacy of interpersonal psychotherapy for postpartum depression. Arch Gen Psychiatry. 2000;57:1039–45.

    Google Scholar 

  17. Pearlstein TB, Zlotnick C, Battle CL, Stuart S, O’Hara MW, Price AB, et al. Patient choice of treatment for postpartum depression: a pilot study. Arch Womens Ment Health. 2006;9:303–8.

    Google Scholar 

  18. Li X, Takahashi N, Narita A, Nakamura Y, Sakurai-Yageta M, Murakami K, et al. Identification of risk loci for postpartum depression in a genome-wide association study. Psychiatry Clin Neurosci. 2024;78:712–20. https://doi.org/10.1111/pcn.13731.

    Google Scholar 

  19. Tebeka S, Gloaguen E, Mullaert J, He Q, Boland A, Deleuze J-F, et al. Genome-wide association study of early-onset and late-onset postpartum depression: the IGEDEPP prospective study. Eur Psychiatry J Assoc Eur Psychiatr. 2024;67:1–36.

    Google Scholar 

  20. Lewis CM, Knight J. Introduction to genetic association studies. Cold Spring Harb Protoc. 2012;2012:297–306.

    Google Scholar 

  21. Brandes N, Linial N, Linial M. PWAS: proteome-wide association study-linking genes and phenotypes by functional variation in proteins. Genome Biol. 2020;21:173.

    Google Scholar 

  22. Hu T, Parrish RL, Dai Q, Buchman AS, Tasaki S, Bennett DA, et al. Omnibus proteome-wide association study identifies 43 risk genes for alzheimer disease dementia. Am J Hum Genet. 2024;111:1848–63.

    Google Scholar 

  23. Wei W, Zhang H, Cheng B, Qin X, He D, Zhang N, et al. Identification of novel functional brain proteins for treatment-resistant schizophrenia: based on a proteome-wide association study. Eur Psychiatry J Assoc Eur Psychiatr. 2023;66:e33.

    Google Scholar 

  24. Emdin CA, Khera AV, Kathiresan S. Mendelian randomization. JAMA. 2017;318:1925–6.

    Google Scholar 

  25. Wingo TS, Gerasimov ES, Liu Y, Duong DM, Vattathil SM, Lori A, et al. Integrating human brain proteomes with genome-wide association data implicates novel proteins in post-traumatic stress disorder. Mol Psychiatry. 2022;27:3075–84.

    Google Scholar 

  26. Gu X, Dou M, Yuan M, Zhang W. Identifying novel proteins underlying loneliness by integrating GWAS summary data with human brain proteomes. Neuropsychopharmacol Off Publ Am Coll Neuropsychopharmacol. 2023;48:1087–97.

    Google Scholar 

  27. Zhao H, Liu Y, Zhang X, Liao Y, Zhang H, Han X, et al. Identifying novel proteins for suicide attempt by integrating proteomes from brain and blood with genome-wide association data. Neuropsychopharmacol Off Publ Am Coll Neuropsychopharmacol. 2024;49:1255–65.

    Google Scholar 

  28. Batt MM, Duffy KA, Novick AM, Metcalf CA, Epperson CN. Is postpartum depression different from depression occurring outside of the perinatal period? a review of the evidence. Focus Am Psychiatr Publ. 2020;18:106–19.

    Google Scholar 

  29. Bloch M, Daly RC, Rubinow DR. Endocrine factors in the etiology of postpartum depression. Compr Psychiatry. 2003;44:234–46.

    Google Scholar 

  30. Brummelte S, Galea LAM. Depression during pregnancy and postpartum: contribution of stress and ovarian hormones. Prog Neuropsychopharmacol Biol Psychiatry. 2010;34:766–76.

    Google Scholar 

  31. Brummelte S, Galea LAM. Postpartum depression: Etiology, treatment and consequences for maternal care. Horm Behav. 2016;77:153–66.

    Google Scholar 

  32. Agrawal I, Mehendale AM, Malhotra R. Risk factors of postpartum depression. Cureus. 2022;14:e30898.

    Google Scholar 

  33. Donofry SD, Roecklein KA, Wildes JE, Miller MA, Erickson KI. Alterations in emotion generation and regulation neurocircuitry in depression and eating disorders: a comparative review of structural and functional neuroimaging studies. Neurosci Biobehav Rev. 2016;68:911–27.

    Google Scholar 

  34. Tian H, Wang Z, Meng Y, Geng L, Lian H, Shi Z, et al. Neural mechanisms underlying cognitive impairment in depression and cognitive benefits of exercise intervention. Behav Brain Res. 2025;476:115218.

    Google Scholar 

  35. Bhandage AK, Cunningham JL, Jin Z, Shen Q, Bongiovanni S, Korol SV, et al. Depression, GABA, and age correlate with plasma levels of inflammatory markers. Int J Mol Sci. 2019;20:6172.

    Google Scholar 

  36. Costi S, Morris LS, Collins A, Fernandez NF, Patel M, Xie H, et al. Peripheral immune cell reactivity and neural response to reward in patients with depression and anhedonia. Transl Psychiatry. 2021;11:565.

    Google Scholar 

  37. Iga J, Ueno S, Ohmori T. Molecular assessment of depression from mRNAs in the peripheral leukocytes. Ann Med. 2008;40:336–42. https://doi.org/10.1080/07853890802082088.

    Google Scholar 

  38. Rupanagunta GP, Nandave M, Rawat D, Upadhyay J, Rashid S, Ansari MN. Postpartum depression: aetiology, pathogenesis and the role of nutrients and dietary supplements in prevention and management. Saudi Pharm J SPJ. 2023;31:1274–93.

    Google Scholar 

  39. Hattori N, Liu Y-Y, Ushijima T. DNA Methylation Analysis. Methods Mol Biol Clifton NJ. 2023;2691:165–83.

    Google Scholar 

  40. Guintivano J, Arad M, Gould TD, Payne JL, Kaminsky ZA. Antenatal prediction of postpartum depression with blood DNA methylation biomarkers. Mol Psychiatry. 2013;19:560.

    Google Scholar 

  41. Osborne L, Clive M, Kimmel M, Gispen F, Guintivano J, Brown T, et al. Replication of epigenetic postpartum depression biomarkers and variation with hormone levels. Neuropsychopharmacology. 2015;41:1648.

    Google Scholar 

  42. Stoffel EC, Craft RM. Ovarian hormone withdrawal-induced “depression” in female rats. Physiol Behav. 2004;83:505–13.

    Google Scholar 

  43. Suda S, Segi-Nishida E, Newton SS, Duman RS. A postpartum model in rat: behavioral and gene expression changes induced by ovarian steroid deprivation. Biol Psychiatry. 2008;64:311.

    Google Scholar 

  44. Fei F, Chen Z, Tao Y, Jiang X, Xu X, Ma Y, et al. Comparison of CUMS at different pregnancy stages, maternal separation, and their effects on offspring in postpartum depression mouse models. Heliyon. 2024;10:e35363.

    Google Scholar 

  45. Guintivano J, Byrne EM, Kiewa J, Yao S, Bauer AE, Aberg KA, et al. Meta-Analyses of genome-wide association studies for postpartum depression. Am J Psychiatry. 2023;180:884–95.

    Google Scholar 

  46. Bennett DA, Buchman AS, Boyle PA, Barnes LL, Wilson RS, Schneider JA. Religious orders study and rush memory and aging project. J Alzheimers Dis JAD. 2018;64:S161–S189.

    Google Scholar 

  47. Beach TG, Adler CH, Sue LI, Serrano G, Shill HA, Walker DG, et al. Arizona study of aging and neurodegenerative disorders and brain and body donation program. Neuropathol Off J Jpn Soc Neuropathol. 2015;35:354–89.

    Google Scholar 

  48. Wingo AP, Liu Y, Gerasimov ES, Gockley J, Logsdon BA, Duong DM, et al. Integrating human brain proteomes with genome-wide association data implicates new proteins in Alzheimer’s disease pathogenesis. Nat Genet. 2021;53:143–6.

    Google Scholar 

  49. Gusev A, Ko A, Shi H, Bhatia G, Chung W, Penninx BWJH, et al. Integrative approaches for large-scale transcriptome-wide association studies. Nat Genet. 2016;48:245–52.

    Google Scholar 

  50. Giambartolomei C, Vukcevic D, Schadt EE, Franke L, Hingorani AD, Wallace C, et al. Bayesian test for colocalisation between pairs of genetic association studies using summary statistics. PLoS Genet. 2014;10:e1004383.

    Google Scholar 

  51. Zhu Z, Zhang F, Hu H, Bakshi A, Robinson MR, Powell JE, et al. Integration of summary data from GWAS and eQTL studies predicts complex trait gene targets. Nat Genet. 2016;48:481–7.

    Google Scholar 

  52. Liu J, Xu Y, Liu Y, Zhu Y, Li X. Associations between type 1 diabetes and autoimmune skin diseases: Mendelian randomization analysis. Heliyon. 2024;10:e32781.

    Google Scholar 

  53. Guo Y, Yan J. Association between tobacco smoke exposure and depression: the NHANES 2005-2018 and Mendelian randomization study. Arch Public Health. 2024;82:100.

    Google Scholar 

  54. Chauquet S, Zhu Z, O’Donovan MC, Walters JTR, Wray NR, Shah S. Association of antihypertensive drug target genes with psychiatric disorders: a mendelian randomization study. JAMA Psychiatry. 2021;78:623–31.

    Google Scholar 

  55. Zhou W, Nielsen JB, Fritsche LG, Dey R, Gabrielsen ME, Wolford BN, et al. Efficiently controlling for case-control imbalance and sample relatedness in large-scale genetic association studies. Nat Genet. 2018;50:1335–41.

    Google Scholar 

  56. Akoglu H. User’s guide to correlation coefficients. Turk J Emerg Med. 2018;18:91–93.

    Google Scholar 

  57. Kovács K, Farkas Z, Bajić D, Kalapis D, Daraba A, Almási K, et al. Suboptimal global transcriptional response increases the harmful effects of loss-of-function mutations. Mol Biol Evol. 2021;38:1137–50.

    Google Scholar 

  58. Fernandez-Fernandez J, Jiménez-Treviño L, Andreo-Jover J, Ayad-Ahmed W, Bascarán TB, Canal-Rivero M, et al. Network analysis of influential risk factors in adolescent suicide attempters. Child Adolesc Psychiatry Ment Health. 2024;18:152.

    Google Scholar 

  59. Ramaker RC, Bowling KM, Lasseigne BN, Hagenauer MH, Hardigan AA, Davis NS, et al. Post-mortem molecular profiling of three psychiatric disorders. Genome Med. 2017;9:72.

    Google Scholar 

  60. Hervé M, Bergon A, Le Guisquet A-M, Leman S, Consoloni J-L, Fernandez-Nunez N, et al. Translational identification of transcriptional signatures of major depression and antidepressant response. Front Mol Neurosci. 2017;10:248.

    Google Scholar 

  61. Leday GGR, Vértes PE, Richardson S, Greene JR, Regan T, Khan S, et al. Replicable and coupled changes in innate and adaptive immune gene expression in two case-control studies of blood microarrays in major depressive disorder. Biol Psychiatry. 2018;83:70–80.

    Google Scholar 

  62. Miyata S, Kurachi M, Okano Y, Sakurai N, Kobayashi A, Harada K, et al. Blood transcriptomic markers in patients with late-onset major depressive disorder. PLoS One. 2016;11:e0150262.

    Google Scholar 

  63. Miyata S, Kurachi M, Sakurai N, Yanagawa Y, Ishizaki Y, Mikuni M, et al. Gene expression alterations in the medial prefrontal cortex and blood cells in a mouse model of depression during menopause. Heliyon. 2016;2:e00222.

    Google Scholar 

  64. Evangelista JE, Clarke DJB, Xie Z, Lachmann A, Jeon M, Chen K, et al. SigCom LINCS: data and metadata search engine for a million gene expression signatures. Nucleic Acids Res. 2022;50:W697–W709.

    Google Scholar 

  65. Lachmann A, Torre D, Keenan AB, Jagodnik KM, Lee HJ, Wang L, et al. Massive mining of publicly available RNA-seq data from human and mouse. Nat Commun. 2018;9:1366.

    Google Scholar 

  66. Wu T, Hu E, Xu S, Chen M, Guo P, Dai Z, et al. clusterProfiler 4.0: A universal enrichment tool for interpreting omics data. Innov. 2021;2:100141.

    Google Scholar 

  67. Gray TA, Hernandez L, Carey AH, Schaldach MA, Smithwick MJ, Rus K, et al. The ancient source of a distinct gene family encoding proteins featuring RING and C(3)H zinc-finger motifs with abundant expression in developing brain and nervous system. Genomics. 2000;66:76–86.

    Google Scholar 

  68. Zarrei M, Burton CL, Engchuan W, Young EJ, Higginbotham EJ, MacDonald JR, et al. A large data resource of genomic copy number variation across neurodevelopmental disorders. NPJ Genomic Med. 2019;4:26.

    Google Scholar 

  69. Dyke J, Calapre L, Beasley A, Gray E, Allcock R, Bentel J. Application of multiplex ligation-dependent probe amplification (MLPA) and low pass whole genome sequencing (LP-WGS) to the classification / characterisation of low grade glioneuronal tumours. Pathol Res Pract. 2022;229:153724.

    Google Scholar 

  70. AlShail E, Alahmari AN, Dababo AAM, Alsagob M, Al-Hindi H, Khalil H, et al. A molecular study of pediatric pilomyxoid and pilocytic astrocytomas: genome-wide copy number screening, retrospective analysis of clinicopathological features and long-term clinical outcome. Front Oncol. 2023;13:1034292.

    Google Scholar 

  71. Kim JH, Park S-M, Kang MR, Oh S-Y, Lee TH, Muller MT, et al. Ubiquitin ligase MKRN1 modulates telomere length homeostasis through a proteolysis of hTERT. Genes Dev. 2005;19:776–81.

    Google Scholar 

  72. Jones-Weinert C, Mainz L, Karlseder J. Telomere function and regulation from mouse models to human ageing and disease. Nat Rev Mol Cell Biol. 2024;26:297–313.

    Google Scholar 

  73. Ayora M, Fraguas D, Abregú-Crespo R, Recio S, Blasco MA, Moises A, et al. Leukocyte telomere length in patients with schizophrenia and related disorders: a meta-analysis of case-control studies. Mol Psychiatry. 2022;27:2968–75.

    Google Scholar 

  74. Gurvich C, Thomas N, Hudaib A-R, Van Rheenen TE, Thomas EHX, Tan EJ, et al. The relationship between cognitive clusters and telomere length in bipolar-schizophrenia spectrum disorders. Psychol Med. 2023;53:5119–26.

    Google Scholar 

  75. Salpea KD, Talmud PJ, Cooper JA, Maubaret CG, Stephens JW, Abelak K, et al. Association of telomere length with type 2 diabetes, oxidative stress and UCP2 gene variation. Atherosclerosis. 2010;209:42–50.

    Google Scholar 

  76. Nault J-C, Ningarhari M, Rebouissou S, Zucman-Rossi J. The role of telomeres and telomerase in cirrhosis and liver cancer. Nat Rev Gastroenterol Hepatol. 2019;16:544–58.

    Google Scholar 

  77. Incollingo Rodriguez AC, Polcari JJ, Nephew BC, Harris R, Zhang C, Murgatroyd C, et al. Acculturative stress, telomere length, and postpartum depression in latinx mothers. J Psychiatr Res. 2022;147:301–6.

    Google Scholar 

  78. Hartmann N, Boehner M, Groenen F, Kalb R. Telomere length of patients with major depression is shortened but independent from therapy and severity of the disease. Depress Anxiety. 2010;27:1111–6.

    Google Scholar 

  79. Pisanu C, Tsermpini EE, Skokou M, Kordou Z, Gourzis P, Assimakopoulos K, et al. Leukocyte telomere length is reduced in patients with major depressive disorder. Drug Dev Res. 2020;81:268–73.

    Google Scholar 

  80. Wang T, Liu W, Wang C, Ma X, Akhtar MF, Li Y, et al. MRKNs: gene, functions, and role in disease and infection. Front Oncol. 2022;12:862206.

    Google Scholar 

  81. Mahmood S, Evinová A, Škereňová M, Ondrejka I, Lehotský J. Association of EGF, IGFBP-3 and TP53 gene polymorphisms with major depressive disorder in slovak population. Cent Eur J Public Health. 2016;24:223–30.

    Google Scholar 

  82. Zhang H, Liu S, Qin Q, Xu Z, Qu Y, Wang Y, et al. Genetic and pharmacological inhibition of astrocytic Mysm1 Alleviates depressive-like disorders by promoting ATP production. Adv Sci Weinh Baden-Wurtt Ger. 2022;10:e2204463.

    Google Scholar 

  83. Aldossary KM, Saad Ali L, Abdallah MS, Bahaa MM, Elmasry TA, Elberri EI, et al. Corrigendum: Effect of a high dose atorvastatin as added-on therapy on symptoms and serum AMPK/NLRP3 inflammasome and IL-6/STAT3 axes in patients with major depressive disorder: randomized controlled clinical study. Front Pharmacol. 2024;15:1464358.

    Google Scholar 

  84. Bullone M, Lavoie J-P. The contribution of oxidative stress and inflamm-aging in human and equine asthma. Int J Mol Sci. 2017;18:2612.

    Google Scholar 

  85. Liu X, Fan L, Lu C, Yin S, Hu H. Functional Role of p53 in the regulation of chemical-induced oxidative stress. Oxid Med Cell Longev. 2020;2020:6039769.

    Google Scholar 

  86. Jeon S-M, Chandel NS, Hay N. AMPK regulates NADPH homeostasis to promote tumour cell survival during energy stress. Nature. 2012;485:661–5.

    Google Scholar 

  87. Wang S, Zhang M, Liang B, Xu J, Xie Z, Liu C, et al. AMPKalpha2 deletion causes aberrant expression and activation of NAD(P)H oxidase and consequent endothelial dysfunction in vivo: role of 26S proteasomes. Circ Res. 2010;106:1117–28.

    Google Scholar 

  88. Tutton S, Azzam GA, Stong N, Vladimirova O, Wiedmer A, Monteith JA, et al. Subtelomeric p53 binding prevents accumulation of DNA damage at human telomeres. EMBO J. 2016;35:193–207.

    Google Scholar 

  89. Feitosa CM, da Silva Oliveira GL, do Nascimento Cavalcante A, Morais Chaves SK, Rai M. Determination of parameters of oxidative stress in vitro models of neurodegenerative diseases-a review. Curr Clin Pharmacol. 2018;13:100–9.

    Google Scholar 

  90. Ji N, Lei M, Chen Y, Tian S, Li C, Zhang B. How oxidative stress induces depression?. ASN Neuro. 2023;15:17590914231181037.

    Google Scholar 

  91. Black CN, Bot M, Scheffer PG, Cuijpers P, Penninx BWJH. Is depression associated with increased oxidative stress? a systematic review and meta-analysis. Psychoneuroendocrinology. 2015;51:164–75.

    Google Scholar 

  92. Bhatt S, Nagappa AN, Patil CR. Role of oxidative stress in depression. Drug Discov Today. 2020;25:1270–6.

    Google Scholar 

  93. Palta P, Samuel LJ, Miller ER, Szanton SL. Depression and oxidative stress: results from a meta-analysis of observational studies. Psychosom Med. 2014;76:12–19.

    Google Scholar 

  94. Lindqvist D, Dhabhar FS, James SJ, Hough CM, Jain FA, Bersani FS, et al. Oxidative stress, inflammation and treatment response in major depression. Psychoneuroendocrinology. 2017;76:197–205.

    Google Scholar 

  95. Poletti S, Paolini M, Mazza MG, Palladini M, Furlan R, Querini PR, et al. Lower levels of glutathione in the anterior cingulate cortex associate with depressive symptoms and white matter hyperintensities in COVID-19 survivors. Eur Neuropsychopharmacol J Eur Coll Neuropsychopharmacol. 2022;61:71–77.

    Google Scholar 

  96. Tian S, Liu M, Yang C, Du W, Gao B, Li M, et al. The impact of ACTH levels on neurotransmitters and antioxidants in patients with major depressive disorder: a novel investigation. J Affect Disord. 2024;365:587–96.

    Google Scholar 

  97. Gabbay V, Mao X, Klein RG, Ely BA, Babb JS, Panzer AM, et al. Anterior cingulate cortex γ-aminobutyric acid in depressed adolescents: relationship to anhedonia. Arch Gen Psychiatry. 2012;69:139–49.

    Google Scholar 

  98. Gabbay V, Bradley KA, Mao X, Ostrover R, Kang G, Shungu DC. Anterior cingulate cortex γ-aminobutyric acid deficits in youth with depression. Transl Psychiatry. 2017;7:e1216.

    Google Scholar 

  99. Tsai GE, Ragan P, Chang R, Chen S, Linnoila VM, Coyle JT. Increased glutamatergic neurotransmission and oxidative stress after alcohol withdrawal. Am J Psychiatry. 1998;155:726–32.

    Google Scholar 

  100. Bakunina N, Pariante CM, Zunszain PA. Immune mechanisms linked to depression via oxidative stress and neuroprogression. Immunology. 2015;144:365–73.

    Google Scholar 

  101. Correia AS, Cardoso A, Vale N. Oxidative stress in depression: the link with the stress response, neuroinflammation, serotonin, neurogenesis and synaptic plasticity. Antioxidants. 2023;12:470.

    Google Scholar 

  102. Odaira-Satoh T, Nakagawasai O, Takahashi K, Shimada M, Nemoto W, Tan-No K. AMPK activation improves depression-like symptoms in olfactory bulbectomized mice by regulating microglia M1/M2 polarization in the hippocampus. Brain Behav Immun - Health. 2025;46:101008.

    Google Scholar 

  103. Wolkowitz OM, Mellon SH, Epel ES, Lin J, Dhabhar FS, Su Y, et al. Leukocyte telomere length in major depression: correlations with chronicity, inflammation and oxidative stress-preliminary findings. PLoS One. 2011;6:e17837.

    Google Scholar 

  104. Jiang Y, Cheng X, Zhao M, Zhao T, Zhang M, Shi Z, et al. Gypenoside-14 Reduces Depression via Downregulation of the Nuclear Factor Kappa B (NF-kB) Signaling Pathway on the Lipopolysaccharide (LPS)-Induced Depression Model. Pharmaceuticals. 2023;16:1152.

    Google Scholar 

  105. Ridout KK, Ridout SJ, Price LH, Sen S, Tyrka AR. Depression and telomere length: a meta-analysis. J Affect Disord. 2016;191:237–47.

    Google Scholar 

  106. Jiménez-Fernández S, Gurpegui M, Díaz-Atienza F, Pérez-Costillas L, Gerstenberg M, Correll CU. Oxidative stress and antioxidant parameters in patients with major depressive disorder compared to healthy controls before and after antidepressant treatment: results from a meta-analysis. J Clin Psychiatry. 2015;76:1658–67.

    Google Scholar 

  107. Liu T, Zhong S, Liao X, Chen J, He T, Lai S, et al. A meta-analysis of oxidative stress markers in depression. PLoS One. 2015;10:e0138904.

    Google Scholar 

  108. Rasmi Y, Shokati A, Hassan A, Aziz SG-G, Bastani S, Jalali L, et al. The role of DNA methylation in progression of neurological disorders and neurodegenerative diseases as well as the prospect of using DNA methylation inhibitors as therapeutic agents for such disorders. IBRO Neurosci Rep. 2023;14:28–37.

    Google Scholar 

  109. Grayson DR, Guidotti A. The dynamics of DNA methylation in schizophrenia and related psychiatric disorders. Neuropsychopharmacol Off Publ Am Coll Neuropsychopharmacol. 2013;38:138–66.

    Google Scholar 

  110. Moore LD, Le T, Fan G. DNA methylation and its basic function. Neuropsychopharmacology. 2013;38:23–38.

    Google Scholar 

  111. Dhar GA, Saha S, Mitra P, Nag Chaudhuri R. DNA methylation and regulation of gene expression: guardian of our health. Nucl. 2021;64:259–70.

    Google Scholar 

  112. Waring MJ, Arrowsmith J, Leach AR, Leeson PD, Mandrell S, Owen RM, et al. An analysis of the attrition of drug candidates from four major pharmaceutical companies. Nat Rev Drug Discov. 2015;14:475–86.

    Google Scholar 

  113. Kuroda M, Halfmann PJ, Hill-Batorski L, Ozawa M, Lopes TJS, Neumann G, et al. Identification of interferon-stimulated genes that attenuate Ebola virus infection. Nat Commun. 2020;11:2953.

    Google Scholar 

  114. Baranova A, Liu D, Chandhoke V, Cao H, Zhang F. Unraveling the genetic links between depression and type 2 diabetes. Prog Neuropsychopharmacol Biol Psychiatry. 2025;137:111258.

    Google Scholar 

  115. Jia N, Yin X, Zhu Z, Hou W, Yang Q, Zhu H, et al. Comprehensive transcriptome analysis and lncRNA-miRNA-mRNA establishment of schizophrenia based on induced pluripotent stem cells. Schizophr Res. 2025;281:22–29.

    Google Scholar 

  116. Freist W, Verhey JF, Rühlmann A, Gauss DH, Arnez JG. Histidyl-tRNA synthetase. Biol Chem. 1999;380:623–46.

    Google Scholar 

  117. Royer-Bertrand B, Tsouni P, Mullen P, Campos Xavier B, Mittaz Crettol L, Lobrinus AJ, et al. Peripheral neuropathy and cognitive impairment associated with a novel monoallelic HARS variant. Ann Clin Transl Neurol. 2019;6:1072–80.

    Google Scholar 

  118. Safka Brozkova D, Deconinck T, Griffin LB, Ferbert A, Haberlova J, Mazanec R, et al. Loss of function mutations in HARS cause a spectrum of inherited peripheral neuropathies. Brain J Neurol. 2015;138:2161–72.

    Google Scholar 

  119. Pierce SB, Chisholm KM, Lynch ED, Lee MK, Walsh T, Opitz JM, et al. Mutations in mitochondrial histidyl tRNA synthetase HARS2 cause ovarian dysgenesis and sensorineural hearing loss of perrault syndrome. Proc Natl Acad Sci USA. 2011;108:6543–8.

    Google Scholar 

  120. Zhang C, Yang Z, Li X, Zhao L, Guo W, Deng W, et al. Unraveling NEK4 as a potential drug target in schizophrenia and bipolar i disorder: a proteomic and genomic approach. Schizophr Bull. 2024;50:1185–96.

    Google Scholar 

Download references

Acknowledgements

We would like to thank the authors of original GWAS and pQTLs studies included in this article. All authors are grateful for participation in our research.

Funding

This work was partially supported by the Sichuan Science and Technology Program (2026NSFSC0591); the National Natural Science Foundation of China (82001413); the Key R&D Project of the Science and Technology Department of Sichuan Province (2021YFS0248); the China Postdoctoral Science Foundation (2020M673247); and the Postdoctoral Foundation of West China Hospital (2020HXBH163).

Author information

Author notes

  1. These authors contributed equally: Tingting Jia, Chengsong Yuan.

Authors and Affiliations

  1. Mental Health Center and Psychiatric Laboratory, the State Key Laboratory of Biotherapy, West China Hospital of Sichuan University, Chengdu, Sichuan, China

    Tingting Jia, Chengsong Yuan, Shiyi Hu, Liangyue Xie, Andi Liu & Chengcheng Zhang

  2. Department of Gastroenterology and Hepatology and Sichuan University-University of Oxford Huaxi Joint Centre for Gastrointestinal Cancer, West China Hospital of Sichuan University, Chengdu, Sichuan, China

    Tingting Jia

  3. Department of Neurology, the 3rd Affiliated Hospital of Chengdu Medical College, Chengdu, Sichuan, China

    Fengqin Qin

  4. Clinical Trial Center, National Medical Products Administration Key Laboratory for Clinical Research and Evaluation of Innovative Drugs, West China Hospital of Sichuan University, Chengdu, Sichuan, China

    Yongji He

Authors
  1. Tingting Jia
    View author publications

    Search author on:PubMed Google Scholar

  2. Chengsong Yuan
    View author publications

    Search author on:PubMed Google Scholar

  3. Shiyi Hu
    View author publications

    Search author on:PubMed Google Scholar

  4. Liangyue Xie
    View author publications

    Search author on:PubMed Google Scholar

  5. Andi Liu
    View author publications

    Search author on:PubMed Google Scholar

  6. Fengqin Qin
    View author publications

    Search author on:PubMed Google Scholar

  7. Yongji He
    View author publications

    Search author on:PubMed Google Scholar

  8. Chengcheng Zhang
    View author publications

    Search author on:PubMed Google Scholar

Contributions

All authors are grateful for participation in our research. CZ contributed to the conception and design of the work; CZ, CY, TJ managed the literature searches and analyses. CY, SH, LX and AL contributed to visualization; TJ and CY contributed to the drafting; FQ and YH accessed and verified the data. All authors have read and agreed to the published version of the manuscript.

Corresponding author

Correspondence to Chengcheng Zhang.

Ethics declarations

Competing interests

The authors declare no competing interests and financial relationships with commercial interests.

Ethics

This study exclusively used de-identified, publicly available human and animal datasets from previously published studies. All original studies were approved by their respective named institutional review boards or ethics committees, including the Institutional Review Board of Rush University Medical Center [46], Banner Sun Health Research Institute [47], University of California, Irvine [59], Gunma University Hospital [62, 63], and CPP Sud Méditerranée II (Marseille, France) [60]. All procedures were conducted in accordance with the Declaration of Helsinki and relevant institutional and national guidelines.

Additional information

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

Supplementary information

Supplementaty Materials

Rights and permissions

Open Access This article is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License, which permits any non-commercial use, sharing, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if you modified the licensed material. You do not have permission under this licence to share adapted material derived from this article or parts of it. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by-nc-nd/4.0/.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Jia, T., Yuan, C., Hu, S. et al. MKRN1 as a prioritized drug target for postpartum depression: evidence from druggable proteome profiling and multi-layer validation. Transl Psychiatry (2026). https://doi.org/10.1038/s41398-026-03886-x

Download citation

  • Received: 11 January 2025

  • Revised: 18 December 2025

  • Accepted: 30 January 2026

  • Published: 10 February 2026

  • DOI: https://doi.org/10.1038/s41398-026-03886-x

Share this article

Anyone you share the following link with will be able to read this content:

Sorry, a shareable link is not currently available for this article.

Provided by the Springer Nature SharedIt content-sharing initiative

Download PDF

Advertisement

Explore content

  • Research articles
  • Reviews & Analysis
  • News & Comment
  • Collections
  • Sign up for alerts
  • RSS feed

About the journal

  • Journal Information
  • Open Access Publishing
  • About the Editors
  • Contact
  • For Advertisers
  • Calls for Papers
  • Press Releases

Publish with us

  • For Authors & Referees
  • Language editing services
  • Open access funding
  • Submit manuscript

Search

Advanced search

Quick links

  • Explore articles by subject
  • Find a job
  • Guide to authors
  • Editorial policies

Translational Psychiatry (Transl Psychiatry)

ISSN 2158-3188 (online)

nature.com sitemap

About Nature Portfolio

  • About us
  • Press releases
  • Press office
  • Contact us

Discover content

  • Journals A-Z
  • Articles by subject
  • protocols.io
  • Nature Index

Publishing policies

  • Nature portfolio policies
  • Open access

Author & Researcher services

  • Reprints & permissions
  • Research data
  • Language editing
  • Scientific editing
  • Nature Masterclasses
  • Research Solutions

Libraries & institutions

  • Librarian service & tools
  • Librarian portal
  • Open research
  • Recommend to library

Advertising & partnerships

  • Advertising
  • Partnerships & Services
  • Media kits
  • Branded content

Professional development

  • Nature Awards
  • Nature Careers
  • Nature Conferences

Regional websites

  • Nature Africa
  • Nature China
  • Nature India
  • Nature Japan
  • Nature Middle East
  • Privacy Policy
  • Use of cookies
  • Legal notice
  • Accessibility statement
  • Terms & Conditions
  • Your US state privacy rights
Springer Nature

© 2026 Springer Nature Limited