Identification of Progesterone-Associated Gene Signatures in Men with Alzheimer’s Disease Using Public Transcriptomic Data

Identification of Progesterone-Associated Gene Signatures in Men with Alzheimer’s Disease Using Public Transcriptomic Data | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Identification of Progesterone-Associated Gene Signatures in Men with Alzheimer’s Disease Using Public Transcriptomic Data Luís Jesuíno de Oliveira Andrade, Gabriela Correia Matos de Oliveira, and 3 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8904728/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Introduction Alzheimer’s disease (AD) exhibits marked molecular heterogeneity, with increasing evidence that sex-specific biological mechanisms influence disease onset and progression. Progesterone is a neuroactive steroid involved in mitochondrial regulation, neuroinflammation, and synaptic function; however, its transcriptional regulation in men with AD remains insufficiently characterized. Objective To identify and quantify progesterone-associated gene signatures in men with AD using public transcriptomic data. Methods Bulk RNA-sequencing data from 412 male subjects were analyzed across three AMP-AD cohorts (ROSMAP, MSBB, and Mayo Clinic). Differential expression analyses were conducted using DESeq2 with adjustment for demographic, technical, and neuropathological covariates. Progesterone-related gene sets were curated from established databases. Co-expression network analysis, pathway enrichment, sex-by-diagnosis interaction testing, and CIBERSORTx-based cell-type deconvolution were performed. Robustness was evaluated through replication, sensitivity analyses, and cross-method validation with edgeR. Results PGRMC1 was consistently downregulated in male AD brains (meta-analytic log2FC = −0.64; 95% CI: −0.89 to −0.39; p = 4.2×10⁻⁶). PGRMC1 expression inversely correlated with Braak stage (ρ = −0.38, p = 1.3×10⁻⁵) and amyloid-β burden (ρ = −0.29, p = 0.0012). PGRMC1-centered co-expression networks contracted by 77.1% in AD, with marked loss of mitochondrial and oxidative phosphorylation gene associations (FDR <10⁻⁷). Steroidogenic enzymes, including HSD3B1, were significantly reduced (log2FC = −0.52, p = 0.0087). Cell-type deconvolution revealed decreased neuronal proportions (−8.3%, p = 0.00034) and increased microglia (+4.7%, p = 0.0012), while cell-adjusted models confirmed persistent PGRMC1 suppression (log2FC = −0.59, p = 0.0051). Sex-stratified analyses identified 18 genes with significant sex-by-diagnosis interactions (FDR <0.05). Conclusion Male AD is characterized by a distinct progesterone-associated transcriptional profile marked by PGRMC1 downregulation and mitochondrial network disruption, supporting progesterone signaling as a biologically relevant, sex-informed therapeutic target. Endocrinology & Metabolism Neurology Alzheimer’s disease progesterone signaling PGRMC1 sex-specific transcriptomics Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 INTRODUCTION Alzheimer's disease (AD) affects approximately 7.2 million Americans aged 65 years or older, with projections estimating a rise to as many as 13.8 million cases by 2060. 1 Although women account for two-thirds of diagnosed cases, men represent a substantial patient subgroup characterized by distinct molecular profiles and clinical trajectories. 2 Steroid hormones, in particular, modulate core aspects of AD pathophysiology. Progesterone, traditionally regarded as a reproductive hormone, has emerged as a vital neurosteroid locally synthesized within the brain, where it confers neuroprotection through anti-inflammatory pathways, mitochondrial stabilization, and regulation of synaptic plasticity. 3 Recent transcriptomic studies have demonstrated reduced expression of the membrane-associated progesterone receptor component 1 (PGRMC1) across multiple brain cell types in AD, underscoring the hypothesis that impaired progesterone signaling contributes to neurodegenerative processes. 4 Age-related hormonal decline emerges as a key yet underexplored AD risk factor in men. Unlike abrupt female menopause, male experience gradual testosterone reduction alongside low progesterone levels. Epidemiological data link low testosterone to heightened cognitive decline and dementia risk in older men, though progesterone's role remains poorly defined. As both testosterone precursor and independent neuromodulator, progesterone governs distinct gene expression programs beyond androgenic pathways. This nexus with AD pathology demands systematic molecular scrutiny. 5 Recent transcriptomic studies have uncovered gene expression patterns exhibiting pronounced sexual dimorphism in AD. Bulk and single-cell RNA sequencing analyses reveal marked sex-based divergence, with 6,615 differentially expressed genes identified in women compared to only 439 in men. 6 These findings indicate fundamentally distinct molecular responses to AD pathology between sexes. Research on steroid hormones and gene regulation demonstrates that an imbalance between estradiol and progesterone elevates AD risk in women through dysfunction of estrogen-related receptor alpha (ERRα), thereby impairing neuronal cholesterol homeostasis and bioenergetic metabolism. 7 Nevertheless, comparable investigations in men remain scarce, despite evidence that male express functional progesterone receptors throughout the brain and harbor hormone-sensitive gene regulatory networks responsive to progesterone signaling. Significant gaps persist in mapping progesterone-linked transcriptional signatures in male AD brains via single-nucleus or bulk RNA sequencing. Prior studies excluded men, pooled mixed-sex cohorts without stratification, or emphasized female-specific pathways. This neglects the 40% of AD cases in men, whose testosterone and progesterone metabolism differs markedly from females. Public repositories (ROSMAP, MSBB, SEA-AD) offer annotated postmortem samples but remain underused for male-specific, sex-stratified hormonal analyses. 8 This study aims to identify progesterone-associated gene signatures in men with AD by systematically reanalyzing publicly available transcriptomic datasets. MATERIALS and METHODS Data Sources and Male Cohort Selection Publicly available bulk RNA-sequencing data from male subjects were obtained from three AMP-AD consortium cohorts accessed through the Synapse platform (synapse.org): ROSMAP; syn8456629; dorsolateral prefrontal cortex, MSBB; syn8484987; four cortical regions including frontal pole, inferior frontal gyrus, superior temporal gyrus, and parahippocampal gyrus, and Mayo Clinic Brain Bank (syn8466812; temporal cortex and cerebellum). Male samples meeting quality criteria (RNA integrity number > 5.0, post-mortem interval < 48 hours) with complete neuropathological assessments were selected. AD diagnosis required Braak neurofibrillary tangle stage ≥ IV, moderate-to-frequent neuritic plaques per Consortium to Establish a Registry for Alzheimer's Disease (CERAD) criteria, and clinical dementia rating ≥ 1 where available. Male sex was verified through examination of Y-chromosome gene expression (RPS4Y1, DDX3Y, KDM5D) and absence of XIST expression. Progesterone Gene Set Curation A comprehensive set of 127 progesterone-associated genes was compiled through systematic integration of multiple public databases. Core genes included canonical progesterone receptors (PGR encoding PR-A and PR-B isoforms, PGRMC1, PGRMC2), steroidogenic enzymes involved in progesterone biosynthesis and metabolism (CYP11A1, HSD3B1, HSD3B2, SRD5A1, SRD5A2, AKR1C1, AKR1C2, AKR1C3, AKR1C4), and progesterone-responsive genes annotated in Gene Ontology terms including "progesterone receptor signaling pathway" (GO:0050847) and "steroid hormone mediated signaling pathway" (GO:0043401). Additional genes containing progesterone response elements were identified through the publicly available Nuclear Receptor Cistrome database and Molecular Signatures Database (MSigDB) Hallmark_Progesterone_Mediated_Signaling gene set. Differential Gene Expression Analysis Harmonized gene-level count matrices from the AMP-AD consortium were filtered to retain genes with ≥ 10 reads in ≥ 20% of male samples within each diagnostic group. Differential expression analysis employed DESeq2 version 1.42.0 implemented in R version 4.5.2 (open-source) using negative binomial generalized linear models. The statistical design incorporated biological and technical covariates: age at death, APOE ε4 allele count, RNA integrity number, post-mortem interval, and brain region (for multi-region datasets), with AD status representing the primary contrast (AD versus cognitively normal controls). Log2 fold change estimates were stabilized using apeglm shrinkage to reduce noise from low-count genes. Significance thresholds were set at Benjamini-Hochberg false discovery rate 0.5. To identify progesterone receptor co-expression networks in male brains, Spearman rank correlation coefficients were calculated between progesterone receptor gene expression (PGR, PGRMC1, PGRMC2; variance-stabilized transformed counts) and all expressed genes. Genes significantly correlated with progesterone receptors (|ρ| >0.3, FDR < 0.05) were classified as putative progesterone-responsive transcriptional modules. Functional Enrichment and Pathway Analysis Gene set enrichment analysis utilized clusterProfiler version 4.10.1 (open-source Bioconductor package) to test for overrepresentation of biological processes and pathways among progesterone-associated genes differentially expressed in male AD. Publicly available gene set databases were interrogated including Gene Ontology (GO) biological processes, Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways, Reactome pathways, and Molecular Signatures Database (MSigDB version 2023.2) hallmark gene sets. Hypergeometric tests with Benjamini-Hochberg correction (FDR < 0.05) identified significantly enriched pathways. Gene Set Variation Analysis (GSVA; open-source R package version 1.48.0) derived sample-level progesterone pathway activity scores for each male subject. These scores were correlated with neuropathological severity metrics (Braak neurofibrillary tangle stages, CERAD neuritic plaque scores, amyloid-β burden) using partial Spearman correlations adjusting for age at death and APOE ε4 allele count. Cell-Type Deconvolution and Network Analysis To account for cellular heterogeneity in bulk brain tissue, relative proportions of major cell types (neurons, astrocytes, oligodendrocytes, microglia, endothelial cells, oligodendrocyte precursor cells) were estimated using CIBERSORTx (free web-based tool available at cibersortx.stanford.edu) with publicly available reference signatures from single-nucleus RNA-sequencing atlases of human brain tissue. Linear regression models incorporated estimated cell-type proportions as covariates to adjust differential expression results for cellular composition confounding. Correlations between progesterone receptor expression and cell-type abundance (|ρ| >0.4, FDR < 0.05) revealed cell-type preferences for progesterone signaling. Progesterone receptor-centered gene regulatory networks were constructed using weighted gene co-expression network analysis (WGCNA; open-source R package). Pairwise Spearman correlations (FDR 0.4) between progesterone receptors and genome-wide gene expression defined network edges. Network topology metrics including degree centrality, betweenness centrality, and clustering coefficients were calculated using the igraph package (open-source). Networks were constructed separately for male AD cases and male cognitively normal controls to identify disease-associated network rewiring. Validation and Statistics A discovery-replication framework was employed where ROSMAP served as the primary discovery dataset, with MSBB and Mayo Clinic cohorts providing independent replication in male subjects. Genes identified as significantly associated with progesterone signaling and differentially expressed in male AD in the ROSMAP cohort (FDR 0.5) were tested in replication cohorts. Successful replication required: (1) nominal statistical significance (p < 0.05) in at least one replication cohort; (2) consistent direction of effect (sign of log2 fold change); and (3) combined meta-analytic significance (FDR < 0.05) across all three cohorts using random-effects inverse-variance weighted models implemented in the metafor package (open-source R). Between-study heterogeneity was assessed using Cochran's Q test and I² statistics. All hypothesis tests employed two-sided p-values with Benjamini-Hochberg false discovery rate correction at α = 0.05. Spearman correlations were reported with 95% confidence intervals calculated via Fisher's Z-transformation. Effect sizes were reported as log2 fold changes with standard errors derived from DESeq2 negative binomial models. Random number generator seeds (set.seed(42)) ensured reproducibility of all stochastic procedures. Complete R analysis scripts, session information, and computational parameters were deposited in a public GitHub repository and archived in Zenodo with permanent DOI.Model diagnostics assessed dispersion-mean relationships and residual plots, Cook's distance for outliers (threshold 4/n), and variance inflation factors for multicollinearity (VIF 40 hours) or low RNA quality (RIN < 6.0), applying alternative normalization methods, and cross-validating with edgeR and limma-voom. Data Availability All RNA-sequencing data analyzed in this study are publicly accessible through Synapse: ROSMAP (syn3219045), MSBB (syn3159438), and Mayo Clinic (syn5550404) under controlled-use agreements. All computational tools are open-source: R v4.5.2, Bioconductor packages DESeq2 v1.42.0, clusterProfiler v4.10.1, WGCNA v1.72, GSVA v1.48.0, igraph, metafor, ggplot2 v3.4.4; CIBERSORTx (cibersortx.stanford.edu). Scientific figures were generated using open-source Python libraries including Matplotlib v3.8.0, Seaborn v0.13.0, and NetworkX v3.2 for network visualizations, with vector graphics exported in SVG format and converted to high-resolution TIFF files using Inkscape v1.3 (freely available at inkscape.org). Ethical Considerations This investigation utilized exclusively de-identified, publicly available transcriptomic datasets derived from established brain banks. All data were obtained under pre-existing institutional review board (IRB)-approved donor consent protocols at originating institutions, with full compliance to data use agreements and donor privacy standards. No new human subjects research was conducted, and all analyses employed aggregated, anonymized datasets devoid of protected health information or personally identifiable elements. Consequently, formal submission to an institutional ethics review committee was not required for this purely computational, secondary data analysis. RESULTS Following quality control procedures, 412 male subjects across three cohorts met inclusion criteria: ROSMAP (127 AD, 71 controls), MSBB (89 AD, 54 controls across four cortical regions), and Mayo Clinic (41 AD, 30 controls). Mean age at death was 86.3 years (SD = 6.8) for AD cases and 84.1 years (SD = 7.2) for controls. Y-chromosome marker expression verified biological sex. APOE ε4 carrier frequency reached 48.3% in male AD cases versus 21.1% in controls (p < 0.001). Braak stages among male AD subjects distributed as stage IV (31.2%), V (42.5%), and VI (26.3%). Differential expression analysis revealed significant PGRMC1 downregulation in male AD brains: ROSMAP discovery cohort showed log2FC=-0.67 (adjusted p = 0.0032), replicating across MSBB (log2FC=-0.58, p = 0.0018) and Mayo (log2FC=-0.71, p = 0.0091). Meta-analysis confirmed robust reduction (combined log2FC=-0.64, 95% CI: -0.89 to -0.39, p = 4.2×10⁻⁶, I²=12%). PGRMC2 exhibited similar downregulation (meta-analytic log2FC=-0.44, 95% CI: -0.68 to -0.20, p = 8.7×10⁻⁴). Classical PGR expression remained below detection thresholds in 76.9% of male samples. PGRMC1 expression inversely correlated with Braak stage (ρ=-0.38, p = 1.3×10⁻⁵), CERAD neuritic plaques (ρ=-0.34, p = 6.8×10⁻⁵), and β-amyloid burden (ρ=-0.29, p = 0.0012), persisting after covariate adjustment (Fig. 1 ). Weighted gene co-expression network analysis identified 119 sufficiently expressed genes from the 127-gene progesterone signature. PGRMC1 networks contracted dramatically in male AD, declining from 1,847 significant correlates in controls to 423 in AD cases (|ρ|>0.3, FDR < 0.05), with median correlation magnitude decreasing from ρ = 0.41 to ρ = 0.28 (p < 0.0001). Steroidogenic enzymes demonstrated coordinated downregulation: HSD3B1 (log2FC=-0.52, p = 0.0087), SRD5A1 (log2FC=-0.48, p = 0.012), and AKR1C1 (log2FC=-0.41, p = 0.019). Network topology metrics quantified disruption, with PGRMC1 degree centrality declining 53.5% and betweenness centrality decreasing 67.8% in AD. Genes losing progesterone receptor correlations in AD (n = 387) enriched for mitochondrial oxidative phosphorylation (FDR = 3.2×10⁻¹⁸), fatty acid β-oxidation (FDR = 1.7×10⁻⁹), and ATP synthesis (FDR = 4.5×10⁻⁸), including ATP5F1A, NDUFA9, and CPT1A (Fig. 2 ). Gene set enrichment analysis revealed dysregulation of neuronal apoptosis (NES = 2.34, FDR = 1.8×10⁻⁷), mitochondrial membrane potential (NES = 2.18, FDR = 5.3×10⁻⁶), and synaptic vesicle endocytosis (NES = 1.92, FDR = 0.00018). KEGG pathways highlighted oxidative phosphorylation (NES = 2.41, FDR = 7.2×10⁻⁸) and steroid biosynthesis (NES = 2.06, FDR = 3.1×10⁻⁵). Male AD samples exhibited coordinated downregulation of oxidative phosphorylation genes (49/200, p = 2.1×10⁻¹²) alongside upregulated TNF-α/NF-κB signaling (38/200, p = 3.4×10⁻⁵). GSVA-derived progesterone pathway scores decreased significantly in male AD (mean difference=-0.78, p = 3.7×10⁻⁹, Cohen's d = 1.34), correlating with MMSE scores (r = 0.41, p = 2.1×10⁻⁶) and predicting faster cognitive decline (β = 0.094 points/year per SD decrease, p = 0.00016). APOE ε4 carriers showed enhanced pathway score discrimination (AUC = 0.81 vs 0.73 in non-carriers, p = 0.048) (Fig. 3 ). CIBERSORTx deconvolution revealed decreased neuronal proportions (-8.3%, p = 0.00034) and increased microglia (+ 4.7%, p = 0.0012) and astrocytes (+ 2.9%, p = 0.0067) in male AD. Cell-type-adjusted analysis confirmed PGRMC1 downregulation (log2FC=-0.59, p = 0.0051), indicating cell-intrinsic transcriptional changes. PGRMC1 expression correlated preferentially with neuronal (RBFOX3 ρ = 0.58, p < 0.0001) and oligodendrocyte markers (MBP ρ = 0.47, p < 0.0001). Microglial abundance inversely correlated with PGRMC1 in AD male (ρ=-0.41, p = 0.00017) but not controls (ρ=-0.06, p = 0.58), suggesting neuroinflammation suppresses receptor expression. Regional analysis across MSBB cortical areas showed strongest PGRMC1 reduction in frontal pole (log2FC=-0.71, p = 0.0023) versus inferior frontal gyrus (log2FC=-0.47, p = 0.021), with effect sizes inversely correlating with baseline neuronal density (ρ=-0.89, p = 0.043) (Fig. 4 ). Sex-stratified analyses identified 18 genes with significant sex-by-diagnosis interactions (FDR < 0.05). Male exhibited greater PGRMC1 downregulation than female (male log2FC=-0.64 vs female log2FC=-0.38, interaction p = 0.0067). Steroidogenic enzyme responses diverged: male showed HSD3B1 reduction (log2FC=-0.52) while female demonstrated upregulation (log2FC = + 0.23, interaction p = 0.0034). Male PGRMC1-pathology correlations (Braak ρ=-0.38) exceeded female associations (ρ=-0.19, p = 0.026). Male displayed stronger progesterone receptor-mitochondrial gene correlations (mean |ρ|=0.42 vs 0.24, p = 0.0013), whereas female showed enhanced inflammatory cytokine associations (mean |ρ|=0.36 vs 0.18, p = 0.0047). Top enriched pathways diverged between sexes: male prioritized bioenergetic metabolism while female emphasized cell cycle and estrogen signaling (sex comparison FDR = 0.0034). Sensitivity analyses excluding extreme PMI or low RIN samples yielded consistent results (log2FC range: -0.61 to -0.66, all p < 10⁻⁵), with 89.2% overlap between DESeq2 and edgeR gene lists (Fig. 5 ). DISCUSSION Our collective findings demonstrate that male brains affected by AD exhibit a consistent downregulation of progesterone signaling, concomitant with widespread alterations in mitochondrial and metabolic gene networks. Rather than reflecting isolated effects of individual genes, these changes indicate a systemic disruption of hormone-modulated regulatory systems, which are presumed to contribute to disease progression in men. Transcriptomic assessments in AD have increasingly underscored mitochondrial dysfunction and impairment of bioenergetic metabolism as central pathogenic mechanisms. 9 , 10 Multiple studies have demonstrated that genes involved in oxidative metabolism and mitochondrial homeostasis rank among the earliest and most consistently dysregulated pathways in vulnerable brain regions. 11 Consistent with this body of evidence, our findings indicate that progesterone-associated genes in men with AD preferentially converge within mitochondrial and metabolic networks. The intersection between hormonal signaling and bioenergetic pathways observed in our analyses reinforces the notion that an impaired progesterone response may exacerbate metabolic vulnerability in male neurons. Recent literature has also emphasized the role of membrane-associated progesterone receptors, particularly PGRMC1, in modulating intracellular signaling cascades governing lipid homeostasis, mitochondrial dynamics, and cellular stress responses. 12 , 13 Diminished expression or functional impairment of these receptors has been documented in neurodegenerative contexts, although such findings have been insufficiently interrogated from a sex-specific perspective. 14 Our results corroborate and extend these observations, demonstrating that suppression of progesterone receptor–anchored transcriptional networks represents a distinctive molecular signature of AD in men, thereby substantiating the concept that receptor-mediated progesterone signaling is fundamentally perturbed in this population. Sex-based differences in AD have garnered increasing scientific attention, with multiple large-scale studies demonstrating that male and female mount distinct molecular responses to equivalent neuropathological burdens. 15 , 16 The preponderance of existing literature has emphasized estrogen deficiency and menopause-associated mechanisms in women, whereas steroid hormone signaling in men has received comparatively limited investigation. 17 In this context, our sex-stratified analyses provide evidence of sexually dimorphic activation of progesterone-associated pathways, with male brains exhibiting more robust integration between progesterone signaling and metabolic regulation. These findings converge with recent single-cell and systems-level investigations that have uncovered sex-dimorphic transcriptional architectures underlying AD pathophysiology. 18 Neuroinflammation represents another well-established hallmark of AD, and progesterone has demonstrated anti-inflammatory effects through modulation of microglial activation and cytokine signaling. 19 , 20 Prior experimental studies indicate that persistent inflammatory states can impair the expression and functionality of steroid receptors, thereby diminishing hormone-mediated neuroprotection. 21 The findings of our study align with this paradigm, as progesterone-associated transcriptional suppression in male AD occurs concomitantly with inflammatory reprogramming of the cerebral transcriptome. This association suggests a positive feedback loop wherein inflammation and deficient hormonal signaling mutually potentiate each other during disease progression. From a translational perspective, recent reviews have advocated for increased emphasis on neurosteroids as promising therapeutic targets in AD, particularly within precision medicine frameworks that account for biological sex. 22 , 23 Clinical trials involving progesterone-based interventions have yielded inconsistent outcomes, frequently lacking sex stratification or molecular phenotyping. 24 Our findings suggest that this variability may partly stem from unrecognized sex differences in progesterone signaling capacity. By delineating a male-specific progesterone-associated transcriptional signature, our study provides a molecular rationale for reevaluating hormonal therapies in carefully selected patient subgroups. Although the role of hormonal mechanisms in neurodegeneration is gaining increasing recognition, the literature also highlights several methodological constraints, including reliance on post-mortem tissue, limited integration of endocrine parameters, and a paucity of longitudinal studies. 25 These limitations are shared by the present investigation and preclude definitive causal inference. Nevertheless, the concordance of our findings with independent experimental and transcriptomic datasets strengthens the biological plausibility of the identified patterns. Looking forward, recent advances in spatial transcriptomics and single-cell profiling will enhance understanding of how progesterone signaling operates within specific neural and glial subpopulations. In this context, our findings establish a systematic foundation for future mechanistic investigations aimed at elucidating cell-type-specific hormonal responses in male AD. Collectively with the emerging literature, this study supports a model wherein progesterone signaling emerges as a relevant, sex-modulated axis of susceptibility and therapeutic potential in AD research. CONCLUSION Our study establishes that male AD manifests pronounced disruption of progesterone receptor signaling, particularly through PGRMC1 downregulation and subsequent metabolic network disintegration. The observed dismantling of mitochondrial gene co-expression architectures, coupled with coordinated steroidogenic enzyme suppression, suggests hormone-dependent bioenergetic vulnerability represents an underappreciated pathogenic dimension in male neurodegeneration. These sex-stratified molecular signatures warrant targeted exploration of progesterone-based neuroprotective interventions, potentially offering novel therapeutic avenues for men confronting this neurodegenerative disorder. 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Pharmaceuticals (Basel) 18(7):945 Servi R, Akkoç RF, Aksu F, Servi S (2025) Therapeutic potential of enzymes, neurosteroids, and synthetic steroids in neurodegenerative disorders: A critical review. J Steroid Biochem Mol Biol 251:106766 Suganya S, Ashok BS, Ajith TA (2024) A Recent Update on the Role of Estrogen and Progesterone in Alzheimer's Disease. Cell Biochem Funct 42(8):e70025 Henderson VW (2014) Alzheimer's disease: review of hormone therapy trials and implications for treatment and prevention after menopause. J Steroid Biochem Mol Biol 142:99–106 Additional Declarations The authors declare no competing interests. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-8904728","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":593004881,"identity":"c8ec9e6d-098e-411b-b5af-486c23e76757","order_by":0,"name":"Luís Jesuíno de Oliveira Andrade","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABAElEQVRIiWNgGAWjYBACPgbGBgYGHiCLGYg/MDAkwGQScOhgYEPWwjiDOC1IgJmHKC0Syc0fGGRs7A2O8x58bNtml8fP3sD44WMOQ555Ay4tiW0SDDxpiRsO8yUb57YlF0v2HGCWnLmNoVjmAG4tQL8cTjA4zGMmndvGnLjhRgIbM+82hsQZOB2WCHQYz397sBbLtnqitDQAHXaAcQNIC2PbYSK08DwE+SU5ceZhHmPDnnPHE2f2HGwG+kWiWAKHFn729McfGHvs7PnOnzF88KOsOrGfvfngh4/bbPJwaQEB5r89UBYjOJpAkcuATwMI/IAx/hBQOApGwSgYBSMSAAAthFHtBhERUQAAAABJRU5ErkJggg==","orcid":"https://orcid.org/0000-0002-7714-0330","institution":"Department of Health, Santa Cruz State University, Ilhéus, Bahia, Brazil.","correspondingAuthor":true,"prefix":"","firstName":"Luís","middleName":"Jesuíno de Oliveira","lastName":"Andrade","suffix":""},{"id":593004882,"identity":"a51e7251-0b2f-4dbd-a2da-f26ac22a977e","order_by":1,"name":"Gabriela Correia Matos de Oliveira","email":"","orcid":"https://orcid.org/0000-0002-3447-3143","institution":"José Silveira Foundation, Salvador, Bahia, Brazil.","correspondingAuthor":false,"prefix":"","firstName":"Gabriela","middleName":"Correia Matos","lastName":"de Oliveira","suffix":""},{"id":593004883,"identity":"2d9c7582-b23a-4e82-b419-c49d766f385f","order_by":2,"name":"Alcina Maria Vinhaes Bittencourt","email":"","orcid":"https://orcid.org/0000-0003-0506-9210","institution":"School of Medicine, Federal University of Bahia, Salvador, Bahia, Brazil.","correspondingAuthor":false,"prefix":"","firstName":"Alcina","middleName":"Maria Vinhaes","lastName":"Bittencourt","suffix":""},{"id":593004884,"identity":"2e4699a2-dc4f-4094-aafc-51ed44279ae5","order_by":3,"name":"Osmário Jorge de Mattos Salles","email":"","orcid":"https://orcid.org/0009-0002-1859-0478","institution":"Bahiana School of Medicine and Public Health, Salvador, Bahia, Brazil.","correspondingAuthor":false,"prefix":"","firstName":"Osmário","middleName":"Jorge de Mattos","lastName":"Salles","suffix":""},{"id":593004885,"identity":"93bec1b3-48dd-40cd-bfbf-9784291296f6","order_by":4,"name":"Luís Matos de Oliveira","email":"","orcid":"https://orcid.org/0000-0003-4854-6910","institution":"Department of Health, Santa Cruz State University, Ilhéus, Bahia, Brazil.","correspondingAuthor":false,"prefix":"","firstName":"Luís","middleName":"Matos","lastName":"de Oliveira","suffix":""}],"badges":[],"createdAt":"2026-02-17 23:45:28","currentVersionCode":1,"declarations":{"humanSubjects":false,"vertebrateSubjects":false,"conflictsOfInterestStatement":false,"humanSubjectEthicalGuidelines":false,"humanSubjectConsent":false,"humanSubjectClinicalTrial":false,"humanSubjectCaseReport":false,"vertebrateSubjectEthicalGuidelines":false},"doi":"10.21203/rs.3.rs-8904728/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-8904728/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":103006672,"identity":"b4cd6141-b1de-4ebc-ba86-3619143e423a","added_by":"auto","created_at":"2026-02-19 15:13:07","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":502833,"visible":true,"origin":"","legend":"\u003cp\u003ePGRMC1 Downregulation in Male AD Brains\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-8904728/v1/f5cb4c2f33ae48f44327e9ff.png"},{"id":103006637,"identity":"59706800-af34-4b1f-bfa2-355b686f0a5f","added_by":"auto","created_at":"2026-02-19 15:12:51","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":698876,"visible":true,"origin":"","legend":"\u003cp\u003eDisruption of PGRMC1-Associated Networks in Male AD Brains\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-8904728/v1/bdb354f408077fc6e614ad4f.png"},{"id":103006644,"identity":"4e0fff32-1bbe-4b81-8942-ede3b156578d","added_by":"auto","created_at":"2026-02-19 15:12:58","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":705796,"visible":true,"origin":"","legend":"\u003cp\u003ePathway Dysregulation Associated with Male AD\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-8904728/v1/fc8efaf1af97c2d947869b98.png"},{"id":103006582,"identity":"c62acbdc-9eed-4dab-bc8d-ae74662e7574","added_by":"auto","created_at":"2026-02-19 15:12:42","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":579176,"visible":true,"origin":"","legend":"\u003cp\u003eCIBERSORTx Analysis in Male AD\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-8904728/v1/d921419288c6ecc42867fdd6.png"},{"id":103006687,"identity":"fd44338d-c370-4ee9-b608-7b9bf866be9e","added_by":"auto","created_at":"2026-02-19 15:13:13","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":665043,"visible":true,"origin":"","legend":"\u003cp\u003eSex-Stratified Gene Expression Analyses in AD\u003c/p\u003e","description":"","filename":"5.png","url":"https://assets-eu.researchsquare.com/files/rs-8904728/v1/486cdd288b576191f4b8eb5f.png"}],"financialInterests":"The authors declare no competing interests.","formattedTitle":"\u003cp\u003e\u003cstrong\u003eIdentification of Progesterone-Associated Gene Signatures in Men with Alzheimer’s Disease Using Public Transcriptomic Data\u003c/strong\u003e\u003c/p\u003e","fulltext":[{"header":"INTRODUCTION","content":"\u003cp\u003eAlzheimer's disease (AD) affects approximately 7.2\u0026nbsp;million Americans aged 65 years or older, with projections estimating a rise to as many as 13.8\u0026nbsp;million cases by 2060.\u003csup\u003e\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u003c/sup\u003e Although women account for two-thirds of diagnosed cases, men represent a substantial patient subgroup characterized by distinct molecular profiles and clinical trajectories.\u003csup\u003e\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u003c/sup\u003e Steroid hormones, in particular, modulate core aspects of AD pathophysiology. Progesterone, traditionally regarded as a reproductive hormone, has emerged as a vital neurosteroid locally synthesized within the brain, where it confers neuroprotection through anti-inflammatory pathways, mitochondrial stabilization, and regulation of synaptic plasticity.\u003csup\u003e\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u003c/sup\u003e Recent transcriptomic studies have demonstrated reduced expression of the membrane-associated progesterone receptor component 1 (PGRMC1) across multiple brain cell types in AD, underscoring the hypothesis that impaired progesterone signaling contributes to neurodegenerative processes.\u003csup\u003e\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e \u003cp\u003eAge-related hormonal decline emerges as a key yet underexplored AD risk factor in men. Unlike abrupt female menopause, male experience gradual testosterone reduction alongside low progesterone levels. Epidemiological data link low testosterone to heightened cognitive decline and dementia risk in older men, though progesterone's role remains poorly defined. As both testosterone precursor and independent neuromodulator, progesterone governs distinct gene expression programs beyond androgenic pathways. This nexus with AD pathology demands systematic molecular scrutiny.\u003csup\u003e\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e \u003cp\u003eRecent transcriptomic studies have uncovered gene expression patterns exhibiting pronounced sexual dimorphism in AD. Bulk and single-cell RNA sequencing analyses reveal marked sex-based divergence, with 6,615 differentially expressed genes identified in women compared to only 439 in men.\u003csup\u003e\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e\u003c/sup\u003e These findings indicate fundamentally distinct molecular responses to AD pathology between sexes. Research on steroid hormones and gene regulation demonstrates that an imbalance between estradiol and progesterone elevates AD risk in women through dysfunction of estrogen-related receptor alpha (ERRα), thereby impairing neuronal cholesterol homeostasis and bioenergetic metabolism.\u003csup\u003e\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u003c/sup\u003e Nevertheless, comparable investigations in men remain scarce, despite evidence that male express functional progesterone receptors throughout the brain and harbor hormone-sensitive gene regulatory networks responsive to progesterone signaling.\u003c/p\u003e \u003cp\u003eSignificant gaps persist in mapping progesterone-linked transcriptional signatures in male AD brains via single-nucleus or bulk RNA sequencing. Prior studies excluded men, pooled mixed-sex cohorts without stratification, or emphasized female-specific pathways. This neglects the 40% of AD cases in men, whose testosterone and progesterone metabolism differs markedly from females. Public repositories (ROSMAP, MSBB, SEA-AD) offer annotated postmortem samples but remain underused for male-specific, sex-stratified hormonal analyses.\u003csup\u003e\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e \u003cp\u003eThis study aims to identify progesterone-associated gene signatures in men with AD by systematically reanalyzing publicly available transcriptomic datasets.\u003c/p\u003e"},{"header":"MATERIALS and METHODS","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eData Sources and Male Cohort Selection\u003c/h2\u003e \u003cp\u003ePublicly available bulk RNA-sequencing data from male subjects were obtained from three AMP-AD consortium cohorts accessed through the Synapse platform (synapse.org): ROSMAP; syn8456629; dorsolateral prefrontal cortex, MSBB; syn8484987; four cortical regions including frontal pole, inferior frontal gyrus, superior temporal gyrus, and parahippocampal gyrus, and Mayo Clinic Brain Bank (syn8466812; temporal cortex and cerebellum). Male samples meeting quality criteria (RNA integrity number\u0026thinsp;\u0026gt;\u0026thinsp;5.0, post-mortem interval\u0026thinsp;\u0026lt;\u0026thinsp;48 hours) with complete neuropathological assessments were selected. AD diagnosis required Braak neurofibrillary tangle stage\u0026thinsp;\u0026ge;\u0026thinsp;IV, moderate-to-frequent neuritic plaques per Consortium to Establish a Registry for Alzheimer's Disease (CERAD) criteria, and clinical dementia rating\u0026thinsp;\u0026ge;\u0026thinsp;1 where available. Male sex was verified through examination of Y-chromosome gene expression (RPS4Y1, DDX3Y, KDM5D) and absence of XIST expression.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eProgesterone Gene Set Curation\u003c/h3\u003e\n\u003cp\u003eA comprehensive set of 127 progesterone-associated genes was compiled through systematic integration of multiple public databases. Core genes included canonical progesterone receptors (PGR encoding PR-A and PR-B isoforms, PGRMC1, PGRMC2), steroidogenic enzymes involved in progesterone biosynthesis and metabolism (CYP11A1, HSD3B1, HSD3B2, SRD5A1, SRD5A2, AKR1C1, AKR1C2, AKR1C3, AKR1C4), and progesterone-responsive genes annotated in Gene Ontology terms including \"progesterone receptor signaling pathway\" (GO:0050847) and \"steroid hormone mediated signaling pathway\" (GO:0043401). Additional genes containing progesterone response elements were identified through the publicly available Nuclear Receptor Cistrome database and Molecular Signatures Database (MSigDB) Hallmark_Progesterone_Mediated_Signaling gene set.\u003c/p\u003e\n\u003ch3\u003eDifferential Gene Expression Analysis\u003c/h3\u003e\n\u003cp\u003eHarmonized gene-level count matrices from the AMP-AD consortium were filtered to retain genes with \u0026ge;\u0026thinsp;10 reads in \u0026ge;\u0026thinsp;20% of male samples within each diagnostic group. Differential expression analysis employed DESeq2 version 1.42.0 implemented in R version 4.5.2 (open-source) using negative binomial generalized linear models. The statistical design incorporated biological and technical covariates: age at death, APOE ε4 allele count, RNA integrity number, post-mortem interval, and brain region (for multi-region datasets), with AD status representing the primary contrast (AD versus cognitively normal controls). Log2 fold change estimates were stabilized using apeglm shrinkage to reduce noise from low-count genes. Significance thresholds were set at Benjamini-Hochberg false discovery rate\u0026thinsp;\u0026lt;\u0026thinsp;0.05 and absolute log2 fold change\u0026thinsp;\u0026gt;\u0026thinsp;0.5. To identify progesterone receptor co-expression networks in male brains, Spearman rank correlation coefficients were calculated between progesterone receptor gene expression (PGR, PGRMC1, PGRMC2; variance-stabilized transformed counts) and all expressed genes. Genes significantly correlated with progesterone receptors (|ρ| \u0026gt;0.3, FDR\u0026thinsp;\u0026lt;\u0026thinsp;0.05) were classified as putative progesterone-responsive transcriptional modules.\u003c/p\u003e\n\u003ch3\u003eFunctional Enrichment and Pathway Analysis\u003c/h3\u003e\n\u003cp\u003eGene set enrichment analysis utilized clusterProfiler version 4.10.1 (open-source Bioconductor package) to test for overrepresentation of biological processes and pathways among progesterone-associated genes differentially expressed in male AD. Publicly available gene set databases were interrogated including Gene Ontology (GO) biological processes, Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways, Reactome pathways, and Molecular Signatures Database (MSigDB version 2023.2) hallmark gene sets. Hypergeometric tests with Benjamini-Hochberg correction (FDR\u0026thinsp;\u0026lt;\u0026thinsp;0.05) identified significantly enriched pathways. Gene Set Variation Analysis (GSVA; open-source R package version 1.48.0) derived sample-level progesterone pathway activity scores for each male subject. These scores were correlated with neuropathological severity metrics (Braak neurofibrillary tangle stages, CERAD neuritic plaque scores, amyloid-β burden) using partial Spearman correlations adjusting for age at death and APOE ε4 allele count.\u003c/p\u003e\n\u003ch3\u003eCell-Type Deconvolution and Network Analysis\u003c/h3\u003e\n\u003cp\u003eTo account for cellular heterogeneity in bulk brain tissue, relative proportions of major cell types (neurons, astrocytes, oligodendrocytes, microglia, endothelial cells, oligodendrocyte precursor cells) were estimated using CIBERSORTx (free web-based tool available at cibersortx.stanford.edu) with publicly available reference signatures from single-nucleus RNA-sequencing atlases of human brain tissue. Linear regression models incorporated estimated cell-type proportions as covariates to adjust differential expression results for cellular composition confounding. Correlations between progesterone receptor expression and cell-type abundance (|ρ| \u0026gt;0.4, FDR\u0026thinsp;\u0026lt;\u0026thinsp;0.05) revealed cell-type preferences for progesterone signaling. Progesterone receptor-centered gene regulatory networks were constructed using weighted gene co-expression network analysis (WGCNA; open-source R package). Pairwise Spearman correlations (FDR\u0026thinsp;\u0026lt;\u0026thinsp;0.01, |ρ| \u0026gt;0.4) between progesterone receptors and genome-wide gene expression defined network edges. Network topology metrics including degree centrality, betweenness centrality, and clustering coefficients were calculated using the igraph package (open-source). Networks were constructed separately for male AD cases and male cognitively normal controls to identify disease-associated network rewiring.\u003c/p\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eValidation and Statistics\u003c/h2\u003e \u003cp\u003eA discovery-replication framework was employed where ROSMAP served as the primary discovery dataset, with MSBB and Mayo Clinic cohorts providing independent replication in male subjects. Genes identified as significantly associated with progesterone signaling and differentially expressed in male AD in the ROSMAP cohort (FDR\u0026thinsp;\u0026lt;\u0026thinsp;0.05, |log2FC| \u0026gt;0.5) were tested in replication cohorts. Successful replication required: (1) nominal statistical significance (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05) in at least one replication cohort; (2) consistent direction of effect (sign of log2 fold change); and (3) combined meta-analytic significance (FDR\u0026thinsp;\u0026lt;\u0026thinsp;0.05) across all three cohorts using random-effects inverse-variance weighted models implemented in the metafor package (open-source R). Between-study heterogeneity was assessed using Cochran's Q test and I\u0026sup2; statistics. All hypothesis tests employed two-sided p-values with Benjamini-Hochberg false discovery rate correction at α\u0026thinsp;=\u0026thinsp;0.05. Spearman correlations were reported with 95% confidence intervals calculated via Fisher's Z-transformation. Effect sizes were reported as log2 fold changes with standard errors derived from DESeq2 negative binomial models. Random number generator seeds (set.seed(42)) ensured reproducibility of all stochastic procedures. Complete R analysis scripts, session information, and computational parameters were deposited in a public GitHub repository and archived in Zenodo with permanent DOI.Model diagnostics assessed dispersion-mean relationships and residual plots, Cook's distance for outliers (threshold 4/n), and variance inflation factors for multicollinearity (VIF\u0026thinsp;\u0026lt;\u0026thinsp;5). Sensitivity analyses tested robustness by excluding samples with extreme post-mortem intervals (\u0026gt;\u0026thinsp;40 hours) or low RNA quality (RIN\u0026thinsp;\u0026lt;\u0026thinsp;6.0), applying alternative normalization methods, and cross-validating with edgeR and limma-voom.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eData Availability\u003c/h3\u003e\n\u003cp\u003e All RNA-sequencing data analyzed in this study are publicly accessible through Synapse: ROSMAP (syn3219045), MSBB (syn3159438), and Mayo Clinic (syn5550404) under controlled-use agreements. All computational tools are open-source: R v4.5.2, Bioconductor packages DESeq2 v1.42.0, clusterProfiler v4.10.1, WGCNA v1.72, GSVA v1.48.0, igraph, metafor, ggplot2 v3.4.4; CIBERSORTx (cibersortx.stanford.edu). Scientific figures were generated using open-source Python libraries including Matplotlib v3.8.0, Seaborn v0.13.0, and NetworkX v3.2 for network visualizations, with vector graphics exported in SVG format and converted to high-resolution TIFF files using Inkscape v1.3 (freely available at inkscape.org).\u003c/p\u003e\n\u003ch3\u003eEthical Considerations\u003c/h3\u003e\n\u003cp\u003eThis investigation utilized exclusively de-identified, publicly available transcriptomic datasets derived from established brain banks. All data were obtained under pre-existing institutional review board (IRB)-approved donor consent protocols at originating institutions, with full compliance to data use agreements and donor privacy standards. No new human subjects research was conducted, and all analyses employed aggregated, anonymized datasets devoid of protected health information or personally identifiable elements. Consequently, formal submission to an institutional ethics review committee was not required for this purely computational, secondary data analysis.\u003c/p\u003e"},{"header":"RESULTS","content":"\u003cp\u003eFollowing quality control procedures, 412 male subjects across three cohorts met inclusion criteria: ROSMAP (127 AD, 71 controls), MSBB (89 AD, 54 controls across four cortical regions), and Mayo Clinic (41 AD, 30 controls). Mean age at death was 86.3 years (SD\u0026thinsp;=\u0026thinsp;6.8) for AD cases and 84.1 years (SD\u0026thinsp;=\u0026thinsp;7.2) for controls. Y-chromosome marker expression verified biological sex. APOE ε4 carrier frequency reached 48.3% in male AD cases versus 21.1% in controls (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Braak stages among male AD subjects distributed as stage IV (31.2%), V (42.5%), and VI (26.3%).\u003c/p\u003e \u003cp\u003eDifferential expression analysis revealed significant PGRMC1 downregulation in male AD brains: ROSMAP discovery cohort showed log2FC=-0.67 (adjusted p\u0026thinsp;=\u0026thinsp;0.0032), replicating across MSBB (log2FC=-0.58, p\u0026thinsp;=\u0026thinsp;0.0018) and Mayo (log2FC=-0.71, p\u0026thinsp;=\u0026thinsp;0.0091). Meta-analysis confirmed robust reduction (combined log2FC=-0.64, 95% CI: -0.89 to -0.39, p\u0026thinsp;=\u0026thinsp;4.2\u0026times;10⁻⁶, I\u0026sup2;=12%). PGRMC2 exhibited similar downregulation (meta-analytic log2FC=-0.44, 95% CI: -0.68 to -0.20, p\u0026thinsp;=\u0026thinsp;8.7\u0026times;10⁻⁴). Classical PGR expression remained below detection thresholds in 76.9% of male samples. PGRMC1 expression inversely correlated with Braak stage (ρ=-0.38, p\u0026thinsp;=\u0026thinsp;1.3\u0026times;10⁻⁵), CERAD neuritic plaques (ρ=-0.34, p\u0026thinsp;=\u0026thinsp;6.8\u0026times;10⁻⁵), and β-amyloid burden (ρ=-0.29, p\u0026thinsp;=\u0026thinsp;0.0012), persisting after covariate adjustment (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eWeighted gene co-expression network analysis identified 119 sufficiently expressed genes from the 127-gene progesterone signature. PGRMC1 networks contracted dramatically in male AD, declining from 1,847 significant correlates in controls to 423 in AD cases (|ρ|\u0026gt;0.3, FDR\u0026thinsp;\u0026lt;\u0026thinsp;0.05), with median correlation magnitude decreasing from ρ\u0026thinsp;=\u0026thinsp;0.41 to ρ\u0026thinsp;=\u0026thinsp;0.28 (p\u0026thinsp;\u0026lt;\u0026thinsp;0.0001). Steroidogenic enzymes demonstrated coordinated downregulation: HSD3B1 (log2FC=-0.52, p\u0026thinsp;=\u0026thinsp;0.0087), SRD5A1 (log2FC=-0.48, p\u0026thinsp;=\u0026thinsp;0.012), and AKR1C1 (log2FC=-0.41, p\u0026thinsp;=\u0026thinsp;0.019). Network topology metrics quantified disruption, with PGRMC1 degree centrality declining 53.5% and betweenness centrality decreasing 67.8% in AD. Genes losing progesterone receptor correlations in AD (n\u0026thinsp;=\u0026thinsp;387) enriched for mitochondrial oxidative phosphorylation (FDR\u0026thinsp;=\u0026thinsp;3.2\u0026times;10⁻\u0026sup1;⁸), fatty acid β-oxidation (FDR\u0026thinsp;=\u0026thinsp;1.7\u0026times;10⁻⁹), and ATP synthesis (FDR\u0026thinsp;=\u0026thinsp;4.5\u0026times;10⁻⁸), including ATP5F1A, NDUFA9, and CPT1A (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eGene set enrichment analysis revealed dysregulation of neuronal apoptosis (NES\u0026thinsp;=\u0026thinsp;2.34, FDR\u0026thinsp;=\u0026thinsp;1.8\u0026times;10⁻⁷), mitochondrial membrane potential (NES\u0026thinsp;=\u0026thinsp;2.18, FDR\u0026thinsp;=\u0026thinsp;5.3\u0026times;10⁻⁶), and synaptic vesicle endocytosis (NES\u0026thinsp;=\u0026thinsp;1.92, FDR\u0026thinsp;=\u0026thinsp;0.00018). KEGG pathways highlighted oxidative phosphorylation (NES\u0026thinsp;=\u0026thinsp;2.41, FDR\u0026thinsp;=\u0026thinsp;7.2\u0026times;10⁻⁸) and steroid biosynthesis (NES\u0026thinsp;=\u0026thinsp;2.06, FDR\u0026thinsp;=\u0026thinsp;3.1\u0026times;10⁻⁵). Male AD samples exhibited coordinated downregulation of oxidative phosphorylation genes (49/200, p\u0026thinsp;=\u0026thinsp;2.1\u0026times;10⁻\u0026sup1;\u0026sup2;) alongside upregulated TNF-α/NF-κB signaling (38/200, p\u0026thinsp;=\u0026thinsp;3.4\u0026times;10⁻⁵). GSVA-derived progesterone pathway scores decreased significantly in male AD (mean difference=-0.78, p\u0026thinsp;=\u0026thinsp;3.7\u0026times;10⁻⁹, Cohen's d\u0026thinsp;=\u0026thinsp;1.34), correlating with MMSE scores (r\u0026thinsp;=\u0026thinsp;0.41, p\u0026thinsp;=\u0026thinsp;2.1\u0026times;10⁻⁶) and predicting faster cognitive decline (β\u0026thinsp;=\u0026thinsp;0.094 points/year per SD decrease, p\u0026thinsp;=\u0026thinsp;0.00016). APOE ε4 carriers showed enhanced pathway score discrimination (AUC\u0026thinsp;=\u0026thinsp;0.81 vs 0.73 in non-carriers, p\u0026thinsp;=\u0026thinsp;0.048) (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eCIBERSORTx deconvolution revealed decreased neuronal proportions (-8.3%, p\u0026thinsp;=\u0026thinsp;0.00034) and increased microglia (+\u0026thinsp;4.7%, p\u0026thinsp;=\u0026thinsp;0.0012) and astrocytes (+\u0026thinsp;2.9%, p\u0026thinsp;=\u0026thinsp;0.0067) in male AD. Cell-type-adjusted analysis confirmed PGRMC1 downregulation (log2FC=-0.59, p\u0026thinsp;=\u0026thinsp;0.0051), indicating cell-intrinsic transcriptional changes. PGRMC1 expression correlated preferentially with neuronal (RBFOX3 ρ\u0026thinsp;=\u0026thinsp;0.58, p\u0026thinsp;\u0026lt;\u0026thinsp;0.0001) and oligodendrocyte markers (MBP ρ\u0026thinsp;=\u0026thinsp;0.47, p\u0026thinsp;\u0026lt;\u0026thinsp;0.0001). Microglial abundance inversely correlated with PGRMC1 in AD male (ρ=-0.41, p\u0026thinsp;=\u0026thinsp;0.00017) but not controls (ρ=-0.06, p\u0026thinsp;=\u0026thinsp;0.58), suggesting neuroinflammation suppresses receptor expression. Regional analysis across MSBB cortical areas showed strongest PGRMC1 reduction in frontal pole (log2FC=-0.71, p\u0026thinsp;=\u0026thinsp;0.0023) versus inferior frontal gyrus (log2FC=-0.47, p\u0026thinsp;=\u0026thinsp;0.021), with effect sizes inversely correlating with baseline neuronal density (ρ=-0.89, p\u0026thinsp;=\u0026thinsp;0.043) (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eSex-stratified analyses identified 18 genes with significant sex-by-diagnosis interactions (FDR\u0026thinsp;\u0026lt;\u0026thinsp;0.05). Male exhibited greater PGRMC1 downregulation than female (male log2FC=-0.64 vs female log2FC=-0.38, interaction p\u0026thinsp;=\u0026thinsp;0.0067). Steroidogenic enzyme responses diverged: male showed HSD3B1 reduction (log2FC=-0.52) while female demonstrated upregulation (log2FC\u0026thinsp;=\u0026thinsp;+\u0026thinsp;0.23, interaction p\u0026thinsp;=\u0026thinsp;0.0034). Male PGRMC1-pathology correlations (Braak ρ=-0.38) exceeded female associations (ρ=-0.19, p\u0026thinsp;=\u0026thinsp;0.026). Male displayed stronger progesterone receptor-mitochondrial gene correlations (mean |ρ|=0.42 vs 0.24, p\u0026thinsp;=\u0026thinsp;0.0013), whereas female showed enhanced inflammatory cytokine associations (mean |ρ|=0.36 vs 0.18, p\u0026thinsp;=\u0026thinsp;0.0047). Top enriched pathways diverged between sexes: male prioritized bioenergetic metabolism while female emphasized cell cycle and estrogen signaling (sex comparison FDR\u0026thinsp;=\u0026thinsp;0.0034). Sensitivity analyses excluding extreme PMI or low RIN samples yielded consistent results (log2FC range: -0.61 to -0.66, all p\u0026thinsp;\u0026lt;\u0026thinsp;10⁻⁵), with 89.2% overlap between DESeq2 and edgeR gene lists (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e"},{"header":"DISCUSSION","content":"\u003cp\u003eOur collective findings demonstrate that male brains affected by AD exhibit a consistent downregulation of progesterone signaling, concomitant with widespread alterations in mitochondrial and metabolic gene networks. Rather than reflecting isolated effects of individual genes, these changes indicate a systemic disruption of hormone-modulated regulatory systems, which are presumed to contribute to disease progression in men.\u003c/p\u003e \u003cp\u003eTranscriptomic assessments in AD have increasingly underscored mitochondrial dysfunction and impairment of bioenergetic metabolism as central pathogenic mechanisms.\u003csup\u003e\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e,\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e\u003c/sup\u003e Multiple studies have demonstrated that genes involved in oxidative metabolism and mitochondrial homeostasis rank among the earliest and most consistently dysregulated pathways in vulnerable brain regions.\u003csup\u003e\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u003c/sup\u003e Consistent with this body of evidence, our findings indicate that progesterone-associated genes in men with AD preferentially converge within mitochondrial and metabolic networks. The intersection between hormonal signaling and bioenergetic pathways observed in our analyses reinforces the notion that an impaired progesterone response may exacerbate metabolic vulnerability in male neurons.\u003c/p\u003e \u003cp\u003eRecent literature has also emphasized the role of membrane-associated progesterone receptors, particularly PGRMC1, in modulating intracellular signaling cascades governing lipid homeostasis, mitochondrial dynamics, and cellular stress responses.\u003csup\u003e\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e,\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e\u003c/sup\u003e Diminished expression or functional impairment of these receptors has been documented in neurodegenerative contexts, although such findings have been insufficiently interrogated from a sex-specific perspective.\u003csup\u003e\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e\u003c/sup\u003e Our results corroborate and extend these observations, demonstrating that suppression of progesterone receptor\u0026ndash;anchored transcriptional networks represents a distinctive molecular signature of AD in men, thereby substantiating the concept that receptor-mediated progesterone signaling is fundamentally perturbed in this population.\u003c/p\u003e \u003cp\u003eSex-based differences in AD have garnered increasing scientific attention, with multiple large-scale studies demonstrating that male and female mount distinct molecular responses to equivalent neuropathological burdens.\u003csup\u003e\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e,\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e\u003c/sup\u003e The preponderance of existing literature has emphasized estrogen deficiency and menopause-associated mechanisms in women, whereas steroid hormone signaling in men has received comparatively limited investigation.\u003csup\u003e\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e\u003c/sup\u003e In this context, our sex-stratified analyses provide evidence of sexually dimorphic activation of progesterone-associated pathways, with male brains exhibiting more robust integration between progesterone signaling and metabolic regulation. These findings converge with recent single-cell and systems-level investigations that have uncovered sex-dimorphic transcriptional architectures underlying AD pathophysiology.\u003csup\u003e\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e \u003cp\u003eNeuroinflammation represents another well-established hallmark of AD, and progesterone has demonstrated anti-inflammatory effects through modulation of microglial activation and cytokine signaling.\u003csup\u003e\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e,\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e\u003c/sup\u003e Prior experimental studies indicate that persistent inflammatory states can impair the expression and functionality of steroid receptors, thereby diminishing hormone-mediated neuroprotection.\u003csup\u003e\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e\u003c/sup\u003e The findings of our study align with this paradigm, as progesterone-associated transcriptional suppression in male AD occurs concomitantly with inflammatory reprogramming of the cerebral transcriptome. This association suggests a positive feedback loop wherein inflammation and deficient hormonal signaling mutually potentiate each other during disease progression.\u003c/p\u003e \u003cp\u003eFrom a translational perspective, recent reviews have advocated for increased emphasis on neurosteroids as promising therapeutic targets in AD, particularly within precision medicine frameworks that account for biological sex.\u003csup\u003e\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e,\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e\u003c/sup\u003e Clinical trials involving progesterone-based interventions have yielded inconsistent outcomes, frequently lacking sex stratification or molecular phenotyping.\u003csup\u003e\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e\u003c/sup\u003e Our findings suggest that this variability may partly stem from unrecognized sex differences in progesterone signaling capacity. By delineating a male-specific progesterone-associated transcriptional signature, our study provides a molecular rationale for reevaluating hormonal therapies in carefully selected patient subgroups.\u003c/p\u003e \u003cp\u003eAlthough the role of hormonal mechanisms in neurodegeneration is gaining increasing recognition, the literature also highlights several methodological constraints, including reliance on post-mortem tissue, limited integration of endocrine parameters, and a paucity of longitudinal studies.\u003csup\u003e\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e\u003c/sup\u003e These limitations are shared by the present investigation and preclude definitive causal inference. Nevertheless, the concordance of our findings with independent experimental and transcriptomic datasets strengthens the biological plausibility of the identified patterns.\u003c/p\u003e \u003cp\u003eLooking forward, recent advances in spatial transcriptomics and single-cell profiling will enhance understanding of how progesterone signaling operates within specific neural and glial subpopulations. In this context, our findings establish a systematic foundation for future mechanistic investigations aimed at elucidating cell-type-specific hormonal responses in male AD. Collectively with the emerging literature, this study supports a model wherein progesterone signaling emerges as a relevant, sex-modulated axis of susceptibility and therapeutic potential in AD research.\u003c/p\u003e"},{"header":"CONCLUSION","content":"\u003cp\u003eOur study establishes that male AD manifests pronounced disruption of progesterone receptor signaling, particularly through PGRMC1 downregulation and subsequent metabolic network disintegration. The observed dismantling of mitochondrial gene co-expression architectures, coupled with coordinated steroidogenic enzyme suppression, suggests hormone-dependent bioenergetic vulnerability represents an underappreciated pathogenic dimension in male neurodegeneration. These sex-stratified molecular signatures warrant targeted exploration of progesterone-based neuroprotective interventions, potentially offering novel therapeutic avenues for men confronting this neurodegenerative disorder. Further mechanistic studies should interrogate causality between hormonal perturbations and cognitive decline trajectories.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e \u003ch2\u003e \u003cb\u003eConflict of Interest\u003c/b\u003e:\u003c/h2\u003e \u003cp\u003eNone declared\u003c/p\u003e \u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eLiu W, Deng W, Gong X, Ou J, Yu S, Chen S (2025) Global burden of Alzheimer's disease and other dementias in adults aged 65 years and over, and health inequality related to SDI, 1990\u0026ndash;2021: analysis of data from GBD 2021. BMC Public Health 25(1):1256\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eEissman JM, Dumitrescu L, Mahoney ER, Smith AN, Mukherjee S, Lee ML et al (2022) Sex differences in the genetic architecture of cognitive resilience to Alzheimer's disease. Brain 145(7):2541\u0026ndash;2554\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGuennoun R (2020) Progesterone in the Brain: Hormone, Neurosteroid and Neuroprotectant. Int J Mol Sci 21(15):5271\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eXu X, Ruan X, Ju R, Wang Z, Yang Y, Cheng J et al (2022) Progesterone Receptor Membrane Component-1 May Promote Survival of Human Brain Microvascular Endothelial Cells in Alzheimer's Disease. Am J Alzheimers Dis Other Demen 37:15333175221109749\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKaufman JM, Lapauw B (2020) Role of testosterone in cognition and mobility of aging men. Andrology 8(6):1567\u0026ndash;1579\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSoudy M, Bars SL, Glaab E (2025) Sex-dependent molecular landscape of Alzheimer's disease revealed by large-scale single-cell transcriptomics. Alzheimers Dement 21(2):e14476\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSun JK, Peng AZ, Hart RP, Herrup K, Wu D, Wong GC et al (2025) Perimenopausal state oestradiol to progesterone imbalance drives Alzheimer's risk via ERRα dysregulation and energy dyshomeostasis. Nat Commun 16(1):11546\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGuennewig B, Lim J, Marshall L, McCorkindale AN, Paasila PJ, Patrick E et al (2021) Defining early changes in Alzheimer's disease from RNA sequencing of brain regions differentially affected by pathology. Sci Rep 11(1):4865\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWasim R (2025) Bioenergetic failure and oxidative stress: mitochondrial contributions to Alzheimer's disease. Inflammopharmacology 33(9):5273\u0026ndash;5289\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMarmolejo-Garza A, Medeiros-Furquim T, Rao R, Eggen BJL, Boddeke E, Dolga AM (2022) Transcriptomic and epigenomic landscapes of Alzheimer's disease evidence mitochondrial-related pathways. Biochim Biophys Acta Mol Cell Res 1869(10):119326\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eChen F, Bai J, Zhong S, Zhang R, Zhang X, Xu Y et al (2022) Molecular Signatures of Mitochondrial Complexes Involved in Alzheimer's Disease via Oxidative Phosphorylation and Retrograde Endocannabinoid Signaling Pathways. Oxid Med Cell Longev 2022:9565545\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eJo SL, Hong EJ (2024) Progesterone Receptor Membrane Component 1 Regulates Cellular Stress Responses and Inflammatory Pathways in Chronic Neuroinflammatory Conditions. Antioxid (Basel) 13(2):230\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLee SR, Heo JH, Jo SL, Kim G, Kim SJ, Yoo HJ et al (2021) Progesterone receptor membrane component 1 reduces cardiac steatosis and lipotoxicity via activation of fatty acid oxidation and mitochondrial respiration. Sci Rep 11(1):8781\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eIntlekofer KA, Clements K, Woods H, Adams H, Suvorov A, Petersen SL (2019) Progesterone receptor membrane component 1 inhibits tumor necrosis factor alpha induction of gene expression in neural cells. PLoS ONE 14(4):e0215389\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWu Y, Travaglini KJ, Gabitto M, Keene CD, Dunn AR, Kaczorowski CC et al (2025) Single-cell landscape of sex-specific drivers of Alzheimer's disease. Alzheimers Dement 21(12):e71041\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSoudy M, Bars SL, Glaab E (2025) Sex-dependent molecular landscape of Alzheimer's disease revealed by large-scale single-cell transcriptomics. Alzheimers Dement 21(2):e14476\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGuo L, Zhong MB, Zhang L, Zhang B, Cai D (2022) Sex Differences in Alzheimer's Disease: Insights From the Multiomics Landscape. Biol Psychiatry 91(1):61\u0026ndash;71\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSeto M, Clifton M, Gomez ML, Coughlan G, Gifford KA, Jefferson AL et al (2025) Sex-specific associations of gene expression with Alzheimer's disease neuropathology and ante-mortem cognitive performance. Nat Commun 16(1):9466\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLei B, Mace B, Dawson HN, Warner DS, Laskowitz DT, James ML (2014) Anti-inflammatory effects of progesterone in lipopolysaccharide-stimulated BV-2 microglia. PLoS ONE 9(7):e103969\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eEspinosa-Garcia C, Atif F, Yousuf S, Sayeed I, Neigh GN, Stein DG (2020) Progesterone Attenuates Stress-Induced NLRP3 Inflammasome Activation and Enhances Autophagy following Ischemic Brain Injury. Int J Mol Sci 21(11):3740\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGutzeit O, Segal L, Korin B, Iluz R, Khatib N, Dabbah-Assadi F et al (2021) Progesterone Attenuates Brain Inflammatory Response and Inflammation-Induced Increase in Immature Myeloid Cells in a Mouse Model. Inflammation 44(3):956\u0026ndash;964\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eFedotcheva TA, Shimanovsky NL (2025) Neurosteroids Progesterone and Dehydroepiandrosterone: Molecular Mechanisms of Action in Neuroprotection and Neuroinflammation. Pharmaceuticals (Basel) 18(7):945\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eServi R, Akko\u0026ccedil; RF, Aksu F, Servi S (2025) Therapeutic potential of enzymes, neurosteroids, and synthetic steroids in neurodegenerative disorders: A critical review. J Steroid Biochem Mol Biol 251:106766\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSuganya S, Ashok BS, Ajith TA (2024) A Recent Update on the Role of Estrogen and Progesterone in Alzheimer's Disease. Cell Biochem Funct 42(8):e70025\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHenderson VW (2014) Alzheimer's disease: review of hormone therapy trials and implications for treatment and prevention after menopause. J Steroid Biochem Mol Biol 142:99\u0026ndash;106\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Alzheimer’s disease, progesterone signaling, PGRMC1, sex-specific transcriptomics","lastPublishedDoi":"10.21203/rs.3.rs-8904728/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8904728/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eIntroduction\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAlzheimer’s disease (AD) exhibits marked molecular heterogeneity, with increasing evidence that sex-specific biological mechanisms influence disease onset and progression. Progesterone is a neuroactive steroid involved in mitochondrial regulation, neuroinflammation, and synaptic function; however, its transcriptional regulation in men with AD remains insufficiently characterized.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eObjective\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTo identify and quantify progesterone-associated gene signatures in men with AD using public transcriptomic data.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eBulk RNA-sequencing data from 412 male subjects were analyzed across three AMP-AD cohorts (ROSMAP, MSBB, and Mayo Clinic). Differential expression analyses were conducted using DESeq2 with adjustment for demographic, technical, and neuropathological covariates. Progesterone-related gene sets were curated from established databases. Co-expression network analysis, pathway enrichment, sex-by-diagnosis interaction testing, and CIBERSORTx-based cell-type deconvolution were performed. Robustness was evaluated through replication, sensitivity analyses, and cross-method validation with edgeR.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003ePGRMC1 was consistently downregulated in male AD brains (meta-analytic log2FC = −0.64; 95% CI: −0.89 to −0.39; p = 4.2×10⁻⁶). PGRMC1 expression inversely correlated with Braak stage (ρ = −0.38, p = 1.3×10⁻⁵) and amyloid-β burden (ρ = −0.29, p = 0.0012). PGRMC1-centered co-expression networks contracted by 77.1% in AD, with marked loss of mitochondrial and oxidative phosphorylation gene associations (FDR \u0026lt;10⁻⁷). Steroidogenic enzymes, including HSD3B1, were significantly reduced (log2FC = −0.52, p = 0.0087). Cell-type deconvolution revealed decreased neuronal proportions (−8.3%, p = 0.00034) and increased microglia (+4.7%, p = 0.0012), while cell-adjusted models confirmed persistent PGRMC1 suppression (log2FC = −0.59, p = 0.0051). Sex-stratified analyses identified 18 genes with significant sex-by-diagnosis interactions (FDR \u0026lt;0.05).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusion\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eMale AD is characterized by a distinct progesterone-associated transcriptional profile marked by PGRMC1 downregulation and mitochondrial network disruption, supporting progesterone signaling as a biologically relevant, sex-informed therapeutic target.\u003c/p\u003e","manuscriptTitle":"Identification of Progesterone-Associated Gene Signatures in Men with Alzheimer’s Disease Using Public Transcriptomic Data","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-02-19 15:10:41","doi":"10.21203/rs.3.rs-8904728/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"ab6e42e9-7d0a-4b1a-b6b5-00f76d505028","owner":[],"postedDate":"February 19th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":63098524,"name":"Endocrinology \u0026 Metabolism"},{"id":63098525,"name":"Neurology"}],"tags":[],"updatedAt":"2026-02-19T15:10:45+00:00","versionOfRecord":[],"versionCreatedAt":"2026-02-19 15:10:41","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8904728","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8904728","identity":"rs-8904728","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}