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PubMed2006

Large-scale histological analysis of leaf mutants using two simple leaf observation methods: identification of novel genetic pathways governing the size and shape of leaves.

Observations of cellular organization are essential in understanding the mechanisms underlying leaf morphogenesis. These observations require several preparative steps, such as fixation and clearing of organs, and such procedures are time-consuming and labor-intensive for large-scale analyses. Thus, we have developed simple methods for the observation of leaf epidermal and mesophyll cells. To visualize the epidermis, a gel cast was made of the leaf surface, which was then observed under a light microscope. To visualize the leaf mesophyll cells, leaves were immersed in a solution containing Triton X-100, briefly centrifuged, and then viewed under a light microscope. These methods allowed us to conduct a histological phenome analysis for a large number of known and newly isolated leaf-shape/size mutants of Arabidopsis thaliana by measuring various parameters, including cell number, size, and distribution of cells within a leaf blade. Mutants showed changes in leaf size caused by specific increases or decreases in the number and/or size of cells. In addition, altered cell distributions in the leaf blade were observed, resulting from increases or decreases in the number of cells along the proximo-distal or medio-lateral axis, or recruitment of cells along a particular axis at the expense of other leaf parts. These results provide a phenomic view of the cellular behavior involved in organ size control and leaf-shape patterning.

Arabidopsis↗

PubMed2004

Characterization of blood pressure and morphological traits in cardiovascular-related organs in 13 different inbred mouse strains.

To better understand the contributions of various genetic backgrounds to complex quantitative phenotypes, we have measured several quantitative traits of cardiovascular interest [i.e., systolic blood pressure, weight (corrected by body weight) of several cardiac compartments and adrenals and kidneys, and histological correlates for kidneys and adrenals] in male and female mice from 13 different inbred strains. We selected strains so that each major genealogical group would be represented and to conform to priorities set by the Mouse Phenome Database project. Interstrain comparisons of phenotypes made it possible to identify strains that displayed values that belonged to either the low or the high end of the interstrain variance for quantitative traits, such as systolic blood pressure, body weight, left ventricular weight, and/or adrenocortical structure. For instance, both male and female C3H/HeJ and A/J mice displayed either low systolic blood pressure or low cardiac ventricular mass, respectively, and male C57BL6/J displayed low adrenal weight. Likewise, intersex comparisons made it possible to identify phenotypic values that were sexually dimorphic for some of the same traits. For instance, female AKR/J mice had relatively higher body weight and systolic blood pressure values than their male counterparts, perhaps constituting an animal model of the metabolic X syndrome. These strain- and sex-specific features will be of value both for future genetic and/or developmental studies and for the development of new animal models that will help in the generation of mechanistic hypotheses. All data have been deposited to the Mouse Phenome Database for future integration with the Mouse Genome Database and can be further analyzed and compared with tools available on the site.

Adrenal Glands↗

PubMed2025

Risk of Cardiovascular Disease Mortality in Patients With Diagnosed Cancer and Associated Genetic and Proteomic Mechanisms: A UK Biobank-Based Cohort Study.

BACKGROUND: Previous studies have identified a link between cancer and cardiovascular disease; however, the underlying genetic and proteomic mechanisms remain unclear. Therefore, this study aimed to investigate the association between cancer diagnosis and cardiovascular mortality and to explore the potential mechanisms involved. METHODS: A total of 379 944 participants without cardiovascular disease at baseline, including 65 047 individuals with cancer, were recruited from the UK Biobank database. The primary end point was cardiovascular death. Multivariate Cox regression was performed to evaluate the risk of cardiovascular death in populations with and without cancer. Genome-wide association studies, phenome-wide association studies, and proteomic analyses were applied to investigate the underlying genetic and proteomic mechanisms. RESULTS: Multivariate Cox regression analysis showed an increased risk of cardiovascular death in the group with cancer (hazard ratio, 1.50 [95% CI, 1.40-1.61]) after multivariable adjustment. Proteomic analysis confirmed a strong association between cancer and cardiovascular disease, primarily involving pathways related to complement and coagulation cascades, and various inflammatory processes. In contrast, genome-wide association studies and phenome-wide association studies revealed only a limited number of shared genetic variations between cancer and cardiovascular conditions, such as hypertension and cardiac dysrhythmias. CONCLUSIONS: Cardiovascular risk is increased in patients with cancer and may be related to altered expression of inflammation- and coagulation-related proteins. In clinical practice, it is recommended to emphasize the management of endocrine, kidney, and inflammation-related risk factors in the population with cancer.

Humans↗

PubMed2026

Integration of Genome-Wide Association Studies With Single-Cell and Bulk Expression Quantitative Trait Locus to Identify Stroke Susceptibility Genes.

BACKGROUND: Previous studies have integrated genome-wide association studies with expression quantitative trait locus (eQTL) data from bulk tissues to identify stroke susceptibility genes. However, eQTL data exhibit high cell-type specificity, and genetic variants may have distinct effects across stroke subtypes. METHODS: We applied the summary-data-based Mendelian randomization (MR) method to integrate eQTL data from 7 brain cell types with genome-wide association studies data for 5 stroke phenotypes (stroke, ischemic stroke, cardioembolic stroke, large artery stroke, and small vessel stroke). Results were compared with summary-data-based MR using eQTL data from 49 tissues in the Genotype-Tissue Expression project. Robustness of significant single-cell summary-data-based MR associations was assessed via MR and colocalization analyses. Further evaluations included single-cell RNA-seq differential expression, protein-protein interaction, druggability, and phenome-wide association studies. RESULTS: Single-cell summary-data-based MR identified many novel significant genes not detected using bulk tissue eQTL data. Validated associations revealed 2 stroke risk genes (LRCH1, ICA1L), 3 stroke protective genes (AHI1, LYRM9, CENPQ), 2 large artery stroke risk genes (LIPA, ELL), and 1 ischemic stroke protective gene (CENPQ). Single-cell RNA-seq showed significantly increased LIPA expression in mouse stroke samples compared with controls. Protein-protein interaction and druggability analyses, along with phenome-wide association studies, prioritized LIPA and LRCH1 as potential therapeutic targets for stroke while indicating possible adverse effects. CONCLUSIONS: Integrating single-cell eQTL with stroke-subtype genome-wide association studies uncovers novel cell-type-specific causal genes and highlights promising therapeutic targets, advancing understanding of stroke pathogenesis.

Genome-Wide Association Study↗

PubMed2026

Integrative proteogenomic and observational analysis identifies potential biomarkers for latent autoimmune diabetes in adults.

BACKGROUND: Latent autoimmune diabetes in adults (LADA) shares core genetic and immunological features with type 1 diabetes (T1D) but is frequently misdiagnosed as type 2 diabetes (T2D). With few biomarkers for its timely diagnosis and management, this study integrated proteome-wide Mendelian randomisation (MR) and observational clinical analysis to identify potential LADA biomarkers. METHODS: We performed proteome-wide MR using cis-protein quantitative trait loci (cis-pQTLs) for 1,389 plasma proteins from the deCODE study (n = 35,559) and genome-wide association study (GWAS) data for LADA (2,634 cases and 5,947 controls, European ancestry). Robustness was enhanced via multiple sensitivity analyses. Pathway enrichment analysis, druggability evaluation, phenome-wide MR, and interaction analyses were performed to investigate the clinical relevance and biological context of candidate proteins. Candidate proteins were further evaluated using enzyme-linked immunosorbent assays in a matched Chinese clinical study (n = 241) to assess their discriminative ability for LADA. RESULTS: Proteome-wide MR and colocalisation analyses indicated associations between genetically predicted plasma levels of C-X-C motif chemokine ligand 10 (CXCL10; OR [95% CI] per 1-SD increase in protein levels: 5.49 [1.74,17.32]), serum amyloid A1 (SAA1; 1.28 [1.14,1.45]), and SAA2 (1.22 [1.11,1.34]) with LADA risk. Replication, multi-tissue eQTL, and multivariable MR supported CXCL10's association. Druggability evaluation suggested CXCL10 as a drug target under investigation, and phenome-wide MR of 1,006 diseases and traits indicated no major safety concerns for CXCL10 as a potential biomarker. In the observational clinical study, CXCL10 differentiated LADA from healthy controls (area under the receiver operating characteristic curve [ROC-AUC]: 0.889; precision-recall area under the curve [PR-AUC]: 0.919) and T2D (ROC-AUC: 0.838; PR-AUC: 0.921), with both models showing adequate calibration. CONCLUSIONS: This study suggests that CXCL10 is a putative biomarker associated with LADA, demonstrating discriminative ability to distinguish LADA from T2D in an observational clinical cohort. These findings contribute to understanding the autoimmune molecular aetiology of LADA and support its diagnostic potential in resolving the clinical ambiguity between LADA and T2D.

Humans↗

PubMed2003

Macrophage proteomic fingerprinting predicts HIV-1-associated cognitive impairment.

BACKGROUND: Specific proteins produced from monocytes may be linked to the pathogenesis and aid in the diagnosis of HIV-1-associated dementia (HAD). OBJECTIVE: The authors assessed whether a diagnostic phenomic protein profile could be obtained from monocyte-derived macrophages (MDM) from HIV-1-infected patients with cognitive impairment. METHODS: Twenty-one HIV-1-infected Hispanic women and 10 seronegative controls matched by age and sex were followed at the University of Puerto Rico Medical Sciences Campus, where neuropsychological, immune, and viral parameters were tested. Monocytes were recovered by Percoll gradient centrifugation from peripheral blood mononuclear cells. MDM lysates were prepared after 7 days of cultivation and protein profiles analyzed by surface enhanced laser desorption/ionization (SELDI)-time of flight (TOF) ProteinChip tests. Classification trees were prepared for statistical analyses. RESULTS: A total of 177 protein peaks from 2 to 80 kDa were evaluated in 31 patient MDM lysates by SELDI-TOF ProteinChip assays. Select protein peaks, at 5028 and 4320 Da, separated HIV-1-infected from HIV-1-seronegative subjects with a sensitivity of 100% and a specificity of 80%. Thirty-eight peaks were used to differentiate HIV-1-infected subjects with and without cognitive impairment. A 4348 Da protein separated the two groups with a sensitivity of 100% and a specificity of 75%. CONCLUSIONS: The identification of unique phenomic MDM profiles from cognitively impaired HIV-1-infected patients supports the hypothesis that changes in monocyte function parallel the development of HAD.

AIDS Dementia Complex↗

PubMed2001

[Is disease grounded in the mind, the spirit or communication? Pathology as a leading path in learning about disease].

What is the reason, why pathology could command a dominating position as a central teaching discipline in the last 150 years? Furthermore: can this position be maintained in the future? Pathology takes its definitive stand in the philosophic confrontation of "Mind" versus "Matter". It guards the dogma of Morgagni: every disease is reflected by its typical morphological substrate. As long as this dogma is guarded and defended by the discipline of pathology, we will stay at the helm of teaching in medicine. Recently information as a third reality (besides mind and matter) has come into existence. Information is defined here as genomic information specifically the differentially activated functional genomics. In extension to the old dogma of Morgagni we recognize now an extension to "functional" diseases where we do not (yet) find a pathological anatomical manifestation. In these cases "information" is defined as differences in physical states. They can be converted to information in the sense of meaning and purpose by communication. Pathology provides the bridge between functional genomics and morphological manifestations of diseases (phenomics). The future of pathology is based on the amalgamation of the old and reliable Morgagni-Virchow dogma with the new informational dogma. If pathology provides sense, meaning and significance to phenomics, morphology in relation to functional genomics it will retain and even enlarge its influence as the most important teaching discipline in medicine.

Communication↗

PubMed2025

Predicting natural variation in the yeast phenotypic landscape with machine learning.

Most organismal traits result from the complex interplay of many genetic and environmental factors, making their prediction difficult. Here, we used machine learning (ML) models to explore phenotype predictions for 223 traits measured across 1011 genome-sequenced Saccharomyces cerevisiae strains isolated worldwide. We benchmarked a ML pipeline with multiple linear and non-linear models to predict phenotypes from genotypes and gene expression, and determined gradient boosting machines as the best-performing model. Gene function disruption scores and gene presence/absence emerged as best predictors, suggesting a considerable contribution of the accessory genome in controlling phenotypes. The prediction accuracy broadly varied among phenotypes, with stress resistance being easier to predict compared to growth across nutrients. ML identified relevant genomic features linked to phenotypes, including high-impact variants with established relationships to phenotypes, despite these being rare in the population. Near-perfect accuracies were achieved when other phenomics data mostly in similar conditions were used, suggesting that useful information can be conveyed across phenotypes. Overall, our study underscores the power of ML to interpret the functional outcome of genetic variants.

Genetic Variation↗

PubMed2026

Systematic Identification of Therapeutic Targets and Repurposed Drugs for Stroke: From Genome Causal Analysis to Multilevel Validation.

BACKGROUND: Stroke is a severe cerebrovascular disease characterized by narrow time windows and complications. This study aimed to identify novel drug targets and repurposed drugs for stroke. METHODS: This study used expression quantitative trait loci data from druggable genes in brain and blood as instrumental variables. Mendelian randomization, colocalization, and phenome-wide Mendelian randomization were applied to evaluate causal relationships and potential side effects, with stroke and ischemic stroke as primary outcomes. Preclinical validation used oxygen-glucose deprivation/reperfusion and middle cerebral artery occlusion/reperfusion models. Pharmacological and behavioral assessments evaluated the therapeutic potential of candidate targets and drugs. Additionally, proteomic sequencing was performed following GGCX (γ-glutamyl carboxylase) overexpression to explore its biological functions. RESULTS: Elevated GGCX expression in brain and blood was potentially causally associated with reduced risk of stroke and ischemic stroke, supported by colocalization evidence, although potential cardiovascular risks could not be excluded. Drug repositioning identified ifenprodil as a candidate agent that reduced infarction volume, improved motor and cognitive functions, and reversed GGCX downregulation in mice. Ifenprodil treatment and GGCX overexpression alleviated oxygen-glucose deprivation/reperfusion-induced injury and upregulated GGCX expression. Mechanistically, GGCX conferred neuroprotection by regulating protein homeostasis, suppressing inflammation, promoting metabolic recovery, and modulating nuclear transcriptional regulation. CONCLUSIONS: This study established a potential causal link between GGCX and stroke risk, particularly ischemic stroke. GGCX represents a promising therapeutic target for ischemic stroke. Targeted GGCX expression upregulation and drug repurposing, particularly ifenprodil, may offer novel therapeutic avenues. Further validation is warranted to assess clinical efficacy and safety.

Animals↗

PubMed2026

AI-integrated digital breeding for crop improvement.

Crop breeding increasingly depends on the effective integration and interpretation of large, heterogeneous datasets spanning genomic, phenotypic, multi-omics, and environmental layers. Conventional breeding approaches are often insufficient to capture the complex relationships among these data or to support timely selection decisions. Digital breeding can help address this limitation by complementing field experimentation, mixed models, and genomic prediction with the integration of biological data and computational prediction throughout the breeding process. In particular, the rapid advancement of artificial intelligence (AI) has improved the analysis of high-dimensional datasets and broadened its application to trait prediction, selection, and breeding design. Here, we review recent developments in AI-enabled digital breeding, encompassing genomic, phenomic, and multi-omics data generation and analysis, predictive modeling, explainable and generative AI, and data-driven breeding decision support. We further discuss emerging AI applications, their current contributions to crop research and breeding, and the major considerations affecting their reliable and practical implementation. Collectively, this review provides a structured understanding of the roles of AI across the digital breeding process and offers guidance for future methodological development and practical application in crop improvement.

artificial intelligence↗

PubMed2001

Protein functions and biological contexts.

The availability of a rough draft of the predicted human proteome allows an evaluation of the extent to which the predicted and biochemical functions of proteins are in alignment, and the roles of different technologies and approaches to understanding human diseases and instantiating therapeutics. Microarray technologies at the transcriptomic and proteomic levels can be high throughput and excellent for diagnostic purposes, but their informational outputs are inferior in quality to those emerging from the co- and post-translational levels and from antibody-based molecular anatomy. It is now abundantly clear that data transfer between the transcriptome and proteome is not straightforward, and that increasing emphasis needs to be placed on pure proteomic approaches at the structural, quantitative, cell biological and phenomic levels, with special focus on embryogenic and foetal processes. Finally, the precision genetic engineering that is required to evaluate the functional significance of context-dependent protein interactions underpinned by post-translational modifications and proteolytic cleavage events, is still too time consuming and rudimentary to be implemented on a large scale in the mouse, and basic principles and first order networks will need to be sorted out in even simpler model systems such as Drosophila.

Databases, Protein↗

PubMed2025

Cross-Phenotype Genome-Wide Association Study on the Shared Genetic Susceptibility to Systemic Sclerosis and Primary Biliary Cholangitis.

OBJECTIVE: An increased risk of primary biliary cholangitis (PBC) has been reported in patients with systemic sclerosis (SSc). Our study aims to investigate the shared genetic susceptibility between the two disorders and to define candidate causal genes using cross-phenotype genome-wide association study (GWAS) meta-analysis. METHODS: We performed cross-phenotype GWAS meta-analysis and Bayesian colocalization analysis for patients with SSc and patients with PBC. We performed both genome-wide and locus-based analysis, including tissue and pathway enrichment analyses, fine-mapping, Bayesian colocalization analyses with expression quantitative trait loci and protein quantitative trait loci (pQTL) datasets, and phenome-wide association studies. Finally, we used an integrative approach to prioritize candidate causal genes from the novel loci. RESULTS: We detected a strong genetic correlation between SSc and PBC (global genetic correlation = 0.84, P = 1.7 × 10-6). In the cross-phenotype GWAS meta-analysis, we identified 44 nonhuman leukocyte antigens loci that reached genome-wide significance (P < 5 × 10-8). Evidence of shared causal variants between patients with SSc and patients with PBC was found for nine loci, five of which were novel. Integrating multiple sources of evidence, we prioritized CD40, ERAP1, PLD4, SPPL3, and CCDC113 as novel candidate causal genes. The CD40 risk locus colocalized with trans-pQTLs of multiple plasma proteins involved in B cell function. CONCLUSION: Our study supports a strong shared genetic susceptibility between SSc and PBC. Using cross-phenotype analyses, we have prioritized several novel candidate causal genes and pathways for these disorders.

Humans↗

PubMedDate not supplied

Linear genetics, non-linear epigenetics: complementary approaches to understanding complex diseases.

Recent discoveries and rediscoveries in molecular and cell biology, in population and evolutionary biology, and in disease natural history raise new doubts about the ability of genetic analysis alone to predict multifactorial (polygenic) human diseases and other complex phenotypes. These doubts serve to redirect our attention to epigenetic regulation as a second informational system in parallel with the genome. Epigenetic regulation is now viewed by many biologists as a process that includes mechanisms capable of constraining the genome and providing for new patterns of gene expression. Epigenetic networks, both intra- and inter-cellular, provide a basis for nonlinear and chaotic views of cellular and tissue level differentiation and organization, and thus provide a more dynamic approach to understanding the creation of complex phenotypes, even from isogeneic conditions. The reality of regulatory networks within cells inserted, as it were, between genome and phenome, also helps explain the difficulties now encountered when prediction and diagnosis of complex disease omits epigenetic considerations and depends entirely on gene causality.

Animals↗

PubMedDate not supplied

What have two decades of laboratory life-history evolution studies on Drosophila melanogaster taught us?

A series of laboratory selection experiments on Drosophila melanogaster over the past two decades has provided insights into the specifics of life-history tradeoffs in the species and greatly refined our understanding of how ecology and genetics interact in life-history evolution. Much of what has been learnt from these studies about the subtlety of the microevolutionary process also has significant implications for experimental design and inference in organismal biology beyond life-history evolution, as well as for studies of evolution in the wild. Here we review work on the ecology and evolution of life-histories in laboratory populations of D. melanogaster, emphasizing how environmental effects on life-history-related traits can influence evolutionary change. We discuss life-history tradeoffs - many unexpected - revealed by selection experiments, and also highlight recent work that underscores the importance to life-history evolution of cross-generation and cross-life-stage effects and interactions, sexual antagonism and sexual dimorphism, population dynamics, and the possible role of biological clocks in timing life-history events. Finally, we discuss some of the limitations of typical selection experiments, and how these limitations might be transcended in the future by a combination of more elaborate and realistic selection experiments, developmental evolutionary biology, and the emerging discipline of phenomics.

Adaptation, Physiological↗

PubMed2002

Plant functional genomics.

Functional genome analysis of plants has entered the high-throughput stage. The complete genome information from key species such as Arabidopsis thaliana and rice is now available and will further boost the application of a range of new technologies to functional plant gene analysis. To broadly assign functions to unknown genes, different fast and multiparallel approaches are currently used and developed. These new technologies are based on known methods but are adapted and improved to accommodate for comprehensive, large-scale gene analysis, i.e. such techniques are novel in the sense that their design allows researchers to analyse many genes at the same time and at an unprecedented pace. Such methods allow analysis of the different constituents of the cell that help to deduce gene function, namely the transcripts, proteins and metabolites. Similarly the phenotypic variations of entire mutant collections can now be analysed in a much faster and more efficient way than before. The different methodologies have developed to form their own fields within the functional genomics technological platform and are termed transcriptomics, proteomics, metabolomics and phenomics. Gene function, however, cannot solely be inferred by using only one such approach. Rather, it is only by bringing together all the information collected by different functional genomic tools that one will be able to unequivocally assign functions to unknown plant genes. This review focuses on current technical developments and their impact on the field of plant functional genomics. The lower plant Physcomitrella is introduced as a new model system for gene function analysis, owing to its high rate of homologous recombination.

Databases, Factual↗

PubMed2026

Engineering cold stress resilience in capsicum annuum through functional genomics and precision breeding.

This review synthesizes the molecular mechanisms of cold tolerance in pepper, integrating multi-omics data,genome editing, and precision breeding strategies to accelerate the development of cold-resilient cultivars. Cold stress is a significant environmental factor that affects the growth, productivity, and fruit quality of Capsicum annuum by impairing membrane integrity photosynthesis and cellular redox homeostasis. Although pepper has several endogenous cold-responsive regulators such as CaNAC035 and CabHLH035, along with antioxidant defense systems, its cold tolerance remains limited due to low transcriptional activation of key regulators, functional redundancy among cold-responsive genes, and the polygenicity of cold tolerance. These complexities, combined with low genetic diversity and linkage drag, have hindered the improvement of cold-resistant cultivars through conventional breeding. This review brings together the recent progress in understanding the molecular mechanisms of cold stress perception, signal transduction, transcriptional regulation, metabolic reprogramming, and phytohormone interactions in pepper. Precision Breeding 2.0 is a new innovation that combines the integration of multi-omics-based target identification with next-generation genome-editing techniques, allowing precise and multiplex engineering of complex and interconnected regulatory networks instead of single genes. We cover new approaches such as engineering the DREB/CBF pathway, allele-specific editing and targeted disruption of negative regulators to enhance the pathway(s) involved in cold response. Moreover, we propose a roadmap for integration of transcriptomics, proteomics, metabolomics, high-throughput phenomics, and speed breeding to accelerate the identification, validation, and deployment of superior alleles to boost cold tolerance. This review provides a foundation for developing climate-resilient pepper cultivars by connecting functional genomics with precision genome engineering approaches to maintain productivity under variable environmental conditions.

Capsicum↗

PubMed2002

Inbred strain variation in lung function.

The purpose of the present study was to determine the strain-specific phenotype variance of lung function parameters among common inbred laboratory mouse strains. In accordance with the "Mouse Phenome Project" run by The Jackson Laboratory (http://www.jax.org/phenome), lung volumes, lung mechanics, and diffusing capacity of 16 males and 16 females of the strains C3H/HeJ, BALB/cByJ, C57B1/6J, A/J, FVB/J, 129SV/ImJ, and SWR/J were determined in a standardized manner. The defined respiratory maneuvers for lung function testing were performed with a custom-made, computer-controlled servo-ventilator in anesthetized animals. Sex differences within the strains were found in most (83%) of the absolute lung function parameters. Usually, normalization to body or lung size completely compensates for the observed gender differences. There was great diversity between strains for all of the lung function parameters studied; for example, the total lung capacity as well as the pulmonary diffusing capacity for carbon monoxide varied by 50% and the static lung compliance by a factor of almost two among the strains. Little, but statistically significant variability was detectable for the dead space volume and the respiratory system resistance. There was no clear-cut evidence for any strain exhibiting either the smallest or the largest values for all parameters studied, suggesting that there were no simple allometric relationships of lung size between the strains. Well-established genealogical relationships among strains were not constantly reflected in phenotype similarities of pulmonary function. Therefore, these data strongly support heritable genetic traits for pulmonary function. Moreover, it constitutes a basis for further genetic lung function-related studies.

Animals↗

PubMed2026

Cross-tissue Mendelian randomization prioritizes RAB27B as a brain-derived candidate protein for postpartum depression.

OBJECTIVE: Postpartum depression (PPD) is one of the most common and debilitating complications of childbirth, yet the candidate proteins linking genetic risk to disease remain poorly defined. Building on recent genome-wide association studies (GWAS), we sought to integrate cross-tissue proteogenomic data to identify candidate proteins for PPD and explore therapeutic opportunities. METHODS: We conducted two-sample Mendelian randomization (MR) using genome-wide significant cis-protein QTLs from brain (n = 608 proteins), cerebrospinal fluid (CSF; n = 214), and plasma (n = 612). PPD summary statistics were obtained from FinnGen R8 (13,657 cases, 236,178 controls) and replicated in an independent GWAS. Phenome-wide association (PheWAS) was used to assess pleiotropy. Potential therapeutic targets were evaluated through DSigDB drug repurposing, molecular docking, and molecular dynamics simulations. RESULTS: Among all proteins tested, RAB27B was the only brain-derived protein surpassing Bonferroni correction (OR = 1.60; 95% CI: 1.30-1.96; P = 6.6 × 10⁻⁶), whereas no significant proteins were identified in CSF or plasma. This association was replicated in an independent GWAS (OR = 1.27; 95% CI: 1.02-1.58; P = 0.037). PheWAS identified no pleiotropic associations at genome-wide significance. In silico drug repurposing identified pregnenolone as a candidate ligand with computationally predicted stable binding to RAB27B, providing a starting point for future experimental validation. CONCLUSION: This study provides the first cross-tissue proteogenomic evidence that RAB27B is a brain-derived, reproducible candidate protein genetically associated with PPD. By extending GWAS signals to functional protein-level mechanisms and therapeutic inference, our findings nominate RAB27B and pregnenolone as promising directions for postpartum psychiatric research.

Humans↗