Transcriptomic analysis defines gestation-specific programs of fetal liver maturation in premature sheep

Transcriptomic analysis defines gestation-specific programs of fetal liver maturation in premature sheep | 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 Article Transcriptomic analysis defines gestation-specific programs of fetal liver maturation in premature sheep Hideyuki Ikeda, Shimpei Watanabe, Kantarou Sahara, Noriyoshi Mochii, and 14 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9094102/v1 This work is licensed under a CC BY 4.0 License Status: Under Revision Version 1 posted 11 You are reading this latest preprint version Abstract Background: Fetal liver maturation is essential for postnatal metabolic, synthetic, detoxification, and immune function. Disruption of this process by preterm birth contributes to hypoglycemia, impaired detoxification, and immune immaturity. However, the transcriptional programs governing hepatic maturation during mid-to-late gestation remain incompletely defined. We addressed this gap using a translationally relevant ovine model. Methods: Bulk RNA sequencing was performed on ovine fetal livers collected at gestational days 100, 124, and 144 (term = 150). Differential expression analysis, gene set variation analysis (GSVA), and weighted gene co-expression network analysis (WGCNA) were integrated to characterize temporal pathway dynamics, transcriptional modules, and regulatory architecture. Results: Global transcriptomic profiles segregated clearly by gestational age. Advancing gestation was associated with a coordinated transition from proliferative and hematopoietic programs toward metabolic, biosynthetic, detoxification, and immune competence pathways. Late gestation showed increased activity of gluconeogenesis, fatty acid β-oxidation, bile acid metabolism, xenobiotic detoxification, the urea cycle, and complement/coagulation pathways, together with suppression of cell-cycle programs. WGCNA identified two major maturation-associated modules and highlighted conserved candidate regulators, including STAT3, HNF4G, CEBPB, KLF6, NR3C1, and RORA, linking metabolic reprogramming with stress-adaptive signaling. Conclusions: These findings define a systems-level transcriptomic reference of fetal liver maturation across gestation and provide a framework for investigating how perinatal conditions or therapeutic interventions may alter hepatic developmental trajectories, questions that are difficult to address directly in human fetuses due to limited access to fetal liver tissue. Biological sciences/Developmental biology Biological sciences/Genetics Biological sciences/Molecular biology Liver development Fetal liver maturation Preterm liver physiology Gestational transcriptomics Gene Set Variation Analysis (GSVA) Gene co-expression network (WGCNA) Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 [Introduction] The fetal liver is a multifunctional organ that undergoes profound structural and physiological transitions during gestation. In addition to serving as the principal site of hematopoiesis in early and mid-gestation, the liver progressively acquires the metabolic, synthetic, and detoxification capacities required for postnatal survival.[ 1 , 2 ] These functions include regulation of glucose and lipid homeostasis, urea cycle activity, bile acid synthesis and transport, production of coagulation and complement factors, xenobiotic metabolism, and immune responsiveness.[ 3 ] In humans, many of these pathways mature rapidly between mid-to-late gestation, and the extent of their development at birth critically influences neonatal complications, including hypoglycaemia, hyperbilirubinaemia, coagulopathy, susceptibility to infection, and drug metabolism capacity.[ 4 , 5 ] Despite the clear importance of these processes to newborn health, the transcriptional programs responsible for coordinating fetal liver growth and functional maturation remain incompletely defined in humans and have not been systematically characterised in relevant large-animal models. This knowledge gap is critical for understanding the differential susceptibility of the developing fetus to prenatal interventions (e.g., steroids, progesterone) and how their impact may vary depending on the gestational age at which they are administered. Early stages of hepatic development, including hepatoblast specification, lineage commitment, and spatial organization, have been extensively studied using rodent embryonic models and stem cell–based differentiation systems.[ 6 – 8 ] These approaches have identified key transcription factor networks governing early liver organogenesis. However, comprehensive investigation of hepatic maturation beyond mid-gestation, which is most relevant to preterm birth and neonatal intensive care, remains limited. Moreover, rodents differ substantially from humans in gestational length, timing of hepatic functional maturation, and perinatal physiology, constraining their translational relevance.[ 8 ] The short gestation of rodents also limits modelling of prolonged prenatal exposures and their cumulative effects on organ maturation. This limitation highlights the need for experimental models with gestational timelines more comparable to humans to better understand organ maturation and the long-term impact of prenatal interventions. In contrast, the sheep is a well-established perinatal large-animal model for the study of human pregnancy that closely recapitulates human fetal development, including patterns of liver growth, endocrine maturation, bile acid metabolism, and perinatal metabolic adaptation.[ 9 ] Accordingly, ovine models have been widely employed in translational perinatal research, including studies associated with intrauterine inflammation,[ 10 , 11 ] intrauterine growth restriction,[ 12 ] artificial placenta support,[ 13 – 20 ] and antenatal corticosteroid therapy.[ 21 – 27 ] Despite their extensive use, a systematic time-course transcriptomic reference of normal fetal liver development across mid-to-late gestation has not yet been developed using the sheep model. Here, we aimed to generate a gestational transcriptomic reference of normal fetal liver maturation across mid-to-late gestation by performing bulk RNA sequencing of ovine fetal livers at three key developmental stages (gestational day [GD] 100, 124, and 144; term = 150), corresponding approximately to the human transition from ~ 24 weeks’ gestation to term. Moving beyond gene-level analysis, we applied an integrated analytical framework combining differential gene expression analysis, gene set variation analysis (GSVA)–based pathway activity profiling, and weighted gene co-expression network analysis (WGCNA) to resolve coordinated temporal changes in hepatic function and regulatory network architecture. This reference framework provides a systems-level view of normal fetal liver maturation and establishes a critical foundation for mechanistic studies of hepatic dysfunction in preterm birth and perinatal disease. [Results] Overview of Bulk RNA sequencing Analysis and Sample Clustering Bulk RNA sequencing was performed on fetal sheep livers collected at GD100, GD124, and GD144, representing developmental stages equivalent to approximately 24 weeks’ gestation through term in humans (Table 1, Figure 1A). To elucidate the time course of transcriptional programs underlying fetal liver maturation, we applied an integrated, multi-phase RNA sequencing analytical strategy. Differential expression analysis identified genes exhibiting gestational variation. GSVA quantified dynamic shifts in pathway-level activity across developmental stages, enabling functional interpretation beyond individual gene changes. WGCNA further delineated coordinated gene modules and their eigengenes, capturing structured transcriptional programs associated with progressive liver maturation. Collectively, this integrative approach provided complementary gene-level and systems-level insights into stage-dependent functional maturation of the fetal liver (Figure 1B). Unsupervised analyses demonstrated clear stage-dependent transcriptomic segregation. Hierarchical clustering of variance-stabilized expression profiles revealed distinct grouping of GD100, GD124, and GD144 samples, with no intermixing across gestational ages (Figure 1C). Principal component analysis (PCA) corroborated these findings, with the first principal component (PC1) accounting for the largest proportion of variance and aligning with gestational progression, indicating that developmental stage is the dominant driver of transcriptomic variability (Figure 1D). Together, these results confirm robust and orderly transcriptional transitions underlying fetal liver maturation. Differentially Expressed Genes Analysis Volcano plots for each pairwise gestational comparison (GD124 vs. GD100, GD144 vs. GD124, and GD144 vs. GD100) revealed extensive sets of differentially expressed genes (DEGs) meeting predefined significance thresholds (FDR < 0.05 and |log2FC| ≥ 1) (Figure 2A–C). To identify genes exhibiting consistent temporal regulation, we focused on DEGs commonly upregulated or downregulated in both GD124 and GD144 relative to GD100, as visualized by Venn diagrams (Figure 2D, 2G). A total of 915 genes showed shared upregulation, whereas 1,163 genes demonstrated shared downregulation across advancing gestation. Functional enrichment analysis of commonly upregulated genes revealed significant overrepresentation of Gene Ontology (GO) Biological Process terms related to amino acid, lipid, and organic acid catabolism, bile acid transport and secretion, xenobiotic and detoxification processes, nutrient sensing, immune activation, wound healing, and coagulation (Figure 2E). The Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis further highlighted complement and coagulation cascades, cytokine–cytokine receptor interaction, Toll-like receptor signaling, bile secretion, and amino acid and urea metabolism (Figure 2F). Together, these enrichment patterns indicate progressive maturation of hepatic metabolic, innate immune, and synthetic functions toward late gestation, and demonstrate that key hepatic functions do not emerge abruptly at birth but develop in a coordinated and stage-specific manner during gestation, providing a biological framework to interpret how disruptions in late gestation could have lasting metabolic and immunological consequences after birth. In contrast, commonly downregulated genes were strongly enriched for GO Biological Process terms associated with organelle fission, nuclear division, chromosome segregation, and mitotic cell-cycle transitions (Figure 2H). KEGG pathway analysis identified significant enrichment of Cell cycle, DNA replication, p53 signaling, and multiple DNA repair pathways, including mismatch repair, homologous recombination, base excision repair, and Fanconi anemia pathways (Figure 2I). These coordinated changes indicate suppression of proliferative and genome maintenance programs with advancing gestation. Collectively, these findings demonstrate a developmental shift from proliferative growth programs at mid-gestation toward functional metabolic specialization and immune competence, defining the transition from growth to functional readiness as term approaches. These changes identify critical windows of metabolic and immune maturation and inform how timing of disruption may shape distinct outcomes. Gene Set Variation Analysis GSVA revealed broad and coordinated changes in hepatic pathway activity across GD100, GD124, and GD144. GSVA scores for selected functional pathways are summarized in a heatmap (Figure 3A; see Supplementary Table S1 online), with pairwise comparisons displayed as bar plots (Figure 3B–D). Overall, most metabolic pathways and hepatic synthetic functions showed prominent developmental changes, including nutrient and energy metabolism, coagulation, and complement, with robust stepwise upregulation across gestation. Glucose metabolism showed a clear developmental transition. Gluconeogenesis and glycogen synthesis were progressively upregulated, particularly between GD124 and GD144, whereas glycolytic pathways were downregulated (Figure 3C). This reciprocal pattern indicates a shift from glucose utilization toward glucose production and storage capacity as gestation advances. Lipid metabolic pathways also demonstrated progressive activation. Between GD100 and GD124, enrichment was primarily observed in sphingolipid metabolism, suggesting early maturation of membrane lipid composition (Figure 3B). In contrast, the transition from GD124 to GD144 was characterized by broader upregulation of mitochondrial fatty acid β-oxidation, triglyceride metabolism, and adipogenesis pathways (Figure 3C), reflecting establishment of mitochondrial energy production, lipid storage, and lipid remodeling capacities in preparation for postnatal life. Ketone body and phospholipid metabolism showed minimal gestational variation, suggesting limited functional engagement during fetal life. Detoxification and nitrogen/ammonia metabolism were progressively enhanced across gestation. Cytochrome P450 and Phase II conjugation pathways involved in drug and xenobiotic metabolism, together with branched-chain amino acid catabolism and the urea cycle, were robustly and significantly upregulated in a stepwise manner across gestation, indicating maturation of hepatic detoxification and ammonia clearance capacity. Bile acid metabolism showed progressive activation, whereas bile transport pathways increased predominantly between GD100 and GD124 with minimal subsequent change, potentially reflecting partial reliance on maternal–placental support prior to postnatal enterohepatic circulation. Cholesterol biosynthesis pathways showed no significant developmental changes; however, LXR (NR1H3/NR1H2)-associated regulatory pathways involved in cholesterol handling were progressively upregulated. Developmental and morphogenetic signaling pathways, including Notch and TGF-β signaling, angiogenesis, and epithelial–mesenchymal transition, were enriched toward late gestation, supporting differentiation, extracellular matrix remodeling, and vascular maturation. In contrast, proliferative programs, including E2F targets, G2/M checkpoint signaling, and mitotic spindle assembly, were highest at GD100 and markedly suppressed by GD144. Together, these coordinated reciprocal changes indicate a developmental transition from proliferative expansion at mid-gestation toward metabolic specialization, structural remodeling, and functional maturation as term approaches. Weighted Gene Co-expression Network Analysis a. Module Identification and Temporal Patterns (Figure 4 A-C) WGCNA was performed using the top 10,000 most variable genes. Hierarchical clustering of genes based on pairwise expression correlations, followed by dynamic tree cutting, identified six modules of tightly co-expressed genes representing coordinated transcriptional programs (Figure 4A). PCA showed coherent clustering of genes within each module, whereas genes lacking consistent co-expression were assigned to the grey module (Figure 4B). Module eigengene (ME) trajectories revealed distinct gestational patterns (Figure 4C). The turquoise (n = 2,643) and yellow (n = 1,249) modules exhibited progressive increases from GD100 to GD144, whereas the brown (n = 1,896) and blue (n = 2,282) modules declined across gestation. The green module (n = 387) demonstrated a transient peak at GD124. These coordinated module-level dynamics indicate structured transcriptional remodeling during fetal liver maturation. b. Functional Enrichment of WGCNA Modules with GO Biological Process and KEGG (Figure 4 D, E) The turquoise module showed a progressive increase in ME values across gestation, consistent with coordinated activation of broad catabolic and metabolic maturation programs toward late gestation. GO Biological Process enrichment highlighted organic acid, lipid, and small-molecule catabolic processes, as well as multiple amino acid catabolic and metabolic pathways. KEGG pathway analysis demonstrated significant enrichment of peroxisome, carbon metabolism, bile secretion, tryptophan metabolism, branched-chain amino acid (valine, leucine, and isoleucine) degradation, together with complement and coagulation cascades. Collectively, these findings indicate that the turquoise module represents progressive metabolic specialization, enhanced detoxification capacity, and maturation of hepatic plasma protein synthetic function. The yellow module displayed increased ME values from GD124 to GD144. GO Biological Process highlighted broad pathways related to energy metabolism, including fatty acid β-oxidation, hexose and monosaccharide metabolism, amino acid metabolism, extracellular matrix organization, and regulation of body fluid levels among enriched GO terms. KEGG pathway analysis further identified glycolysis/gluconeogenesis, amino acid and cofactor biosynthesis, and regulatory signaling pathways including PPAR, HIF-1, and ECM–receptor interaction. Together, these enrichment patterns suggest coordinated metabolic regulation and structural remodeling associated with late-gestation functional maturation and potential preparation for postnatal circulatory adaptation. In contrast, the brown and blue modules exhibited progressively declining ME values from GD100 to GD144, reflecting transcriptional programs that are predominant during early gestation. The brown module was strongly enriched for proliferative processes. GO Biological Process terms included chromosome segregation, nuclear division, DNA replication, and cell cycle phase transitions, indicating active mitotic progression. Complementary KEGG pathway enrichment identified Cell cycle, DNA replication, Fanconi anemia pathway, homologous recombination, mismatch repair, and oocyte meiosis, collectively highlighting coordinated regulation of cell division and genome replication machinery. The blue module was enriched for pathways related to nuclear transport, genome maintenance, and RNA processing. GO Biological Process terms such as mRNA transport, nucleocytoplasmic transport, double-strand break repair, and RNA localization reflected regulatory and DNA repair functions. Consistently, KEGG pathway analysis identified nucleocytoplasmic transport, base excision repair, Fanconi anemia pathway, mRNA surveillance, and RNA degradation, highlighting molecular quality-control systems that preserve transcriptional fidelity and genome integrity. Together, these enrichment patterns indicate that the brown module represents core proliferative cell-cycle programs, whereas the blue module captures complementary regulatory and maintenance mechanisms that support sustained hepatocyte proliferation during early fetal liver development. c. Functional Annotation of WGCNA Modules by Gene Set–Based Composition Integration of WGCNA modules with GSVA-derived pathway gene sets linked co-expression network architecture to hepatic functional pathways, thereby defining how coordinated transcriptional programs contribute to fetal liver maturation (Figure 5A; see Supplementary Table S2 online). The turquoise module emerged as the dominant maturation-associated module in fetal liver, capturing the largest proportion of genes across diverse liver-related functional gene sets. This module exhibited consistently elevated ME values across gestation, indicating a sustained and coordinated transcriptional program underlying core hepatic functions. Functional gene sets enriched within this module included cholesterol and bile acid metabolism, xenobiotic detoxification, urea-cycle activity, serum protein synthesis, vitamin and cofactor metabolism, immune signaling, stress response, developmental pathways, and morphogenesis. Together, these findings indicate that the turquoise module represents a central metabolic and biosynthetic axis of fetal liver maturation. The yellow module displayed a more stage-restricted profile, with increased representation in late gestation. It was enriched for gene sets related to gluconeogenesis and oxidative phosphorylation, consistent with establishment of endogenous glucose-producing capacity. In addition, it captured specialized lipid metabolic pathways including peroxisomal and very-long-chain fatty acid oxidation, reflecting preparation for neonatal lipid-dependent energy utilization. These patterns indicate that the yellow module supports metabolic refinement and energetic adaptation during late gestation. In contrast, the brown module was strongly enriched for cell cycle–related pathways and hematopoietic heme synthesis programs, consistent with the early fetal liver’s dual role as a proliferative and hematopoietic organ. The declining representation of these pathways across gestation reflects progressive resolution of hepatic hematopoiesis and proliferative expansion. The blue module shared early-gestation characteristics, with enrichment of pathways related to cellular growth, genome maintenance, glycolysis, and the pentose phosphate pathway, consistent with a biosynthetic metabolic state supporting rapid tissue expansion. d. Transcription Factor – Hub gene – Pathway networks The turquoise and yellow modules were prioritized as key regulatory modules in fetal liver maturation based on their eigengene trajectories, strong concordance with GSVA-derived pathway activities, and consistent GO and KEGG enrichment profiles. These modules were closely associated with major hepatic functions including energy metabolism, bile acid synthesis and transport, detoxification, plasma protein synthesis, and nutrient handling, indicating that they represent core transcriptional programs underlying gestational liver development. To further delineate the regulatory architecture and identify key regulatory drivers underlying fetal hepatic functional maturation, integrated transcription factor–hub gene–pathway networks were constructed. The turquoise module represented a highly integrated metabolic program. Hub genes were enriched for oxidative phosphorylation (ALDH6A1, HAO1), mitochondrial and peroxisomal lipid β-oxidation (ACOX1, ACAT1, ECHDC1), glycogen and glucose metabolism (SLC2A2, UGP2, FBP1), cholesterol homeostasis (INSIG2, LRP6, APOH), bile acid synthesis and transport (PLG, ITIH4, APOH), and vitamin and iron–heme metabolism (MMADHC, HSDL2, BHMT) (Figure 5B). Five transcription factors, NR3C1, HNF4G, NFKB1, ATF6, and SNAI2, were identified as putative central regulators, integrating metabolic, stress-responsive, and developmental signals. Pathway-level network mapping demonstrated convergence of these transcription factors (TFs) on hub genes, centered functional axes governing mitochondrial energy production, lipid utilization, xenobiotic detoxification, complement and coagulation factor synthesis, and micronutrient handling, indicating that the turquoise module coordinates the establishment of metabolic and biosynthetic capacity required for fetal liver maturation. The yellow module captured a complementary metabolic and stress-adaptive program. Hub genes were involved in mitochondrial energy production (SLC25A4, ETFDH, PDHB), fatty-acid and lipid catabolism (ACOX2, ACSL1, CPT1A), glucose regulation and glycogen mobilization (PDK4, PPP1R3B, PYGL), bile acid and cholesterol handling (ABCG8, ABCA1), amino-acid and nitrogen metabolism (ASS1, GLS2, GNMT), oxidative and inflammatory stress control (XDH, DUSP1, NFKBIA, IL1RN), and cell-survival pathways (MCL1, GADD45B) (Figure 5C). Five transcription factors, STAT3, RORA, CEBPB, FOSL2, and KLF6, were identified as key regulators linking cytokine/JAK–STAT activity, inflammatory signaling, lipid metabolism, and developmental transcriptional remodeling. Pathway mapping showed that these TFs converge on major functional routes, including fatty-acid β-oxidation, peroxisome/mitochondrial lipid metabolism, gluconeogenesis, Wnt/β-catenin signaling, cytokine/JAK–STAT pathways, apoptosis regulation, and immune–metabolic stress responses, indicating that the yellow module governs the metabolic flexibility and stress resilience required for late-gestation hepatic maturation. [Discussion] This study establishes a gestational transcriptomic reference of normal fetal liver maturation in sheep spanning mid-to-late gestation, corresponding to the clinically relevant human preterm window. By integrating differential gene expression analysis with GSVA-based pathway activity profiling,[ 28 – 30 ] and WGCNA network analysis,[ 31 , 32 ] we show that fetal liver development is organized into coordinated, time-resolved transcriptional programs rather than isolated gene-level changes (Figs. 2 – 4 ). Two dominant co-expression modules defined the developmental trajectory (Figs. 4 – 5 ). The turquoise module progressively increased across gestation and encompassed core hepatic metabolic, synthetic, and detoxification functions, whereas the yellow module showed preferential enrichment in late gestation and captured metabolic refinement and stress-adaptive programs. In contrast, the brown and blue modules declined with advancing gestation and were enriched for proliferative and genome-maintenance pathways, consistent with the progressive resolution of early proliferative and hematopoietic functions. Energy metabolism followed a structured developmental trajectory characterized by progressive activation of amino-acid catabolism, mitochondrial oxidative phosphorylation, and urea-cycle pathways, followed by late-gestation induction of gluconeogenesis and fatty-acid β-oxidation accompanied by reciprocal suppression of glycolysis. This pattern closely parallels human fetal liver maturation and reflects preparation for postnatal metabolic autonomy following loss of placental nutrient supply.[ 33 , 34 ] In parallel, bile acid synthesis and xenobiotic metabolism matured progressively across gestation, consistent with developmental induction of cytochrome P450 pathways, whereas bile transport systems exhibited more limited gestational activation, suggesting continued reliance on maternal–placental clearance mechanisms during fetal life.[ 18 , 35 , 36 ] Complement, coagulation, and plasma protein synthesis pathways were coordinately upregulated, aligning with developmental acquisition of hepatic synthetic capacity and IL-6/STAT3-mediated acute-phase regulation.[ 37 , 38 ] Network analysis further identified conserved transcriptional regulators including STAT3,[ 39 ] HNF4G,[ 40 ] CEBPB,[ 41 ] KLF6,[ 42 ] NR3C1,[ 43 ] RORA,[ 44 ] NFKB1,[ 45 ] and ATF6[ 46 ], which are established drivers of hepatocyte maturation, metabolic reprogramming, and stress adaptation in mammalian systems. The convergence of these regulators within gestationally dynamic gene modules provides mechanistic support for translational alignment between ovine and human fetal liver development. Beyond these mechanistic insights, the findings may also help contextualize clinical vulnerability associated with preterm birth and guide future translational studies. For example, the staged shift toward gluconeogenesis and lipid oxidation provides a molecular framework for the vulnerability of preterm infants, especially those born at very early gestations, to hypoglycemia, impaired lipid utilization, and nitrogen imbalance,[ 4 , 5 ] because preterm birth may interrupt coordinated “fuel switching” before full metabolic autonomy is achieved. Similarly, progressive maturation of bile acid metabolism and detoxification pathways supports clinical observations that hyperbilirubinemia and altered drug metabolism in preterm infants reflect incomplete development of both metabolic enzymes and transport systems.[ 35 , 36 ] Developmental activation of complement, coagulation, and immune-metabolic signaling further suggests that hepatic acute-phase competence is physiologically acquired across gestation. Immaturity of IL-6/STAT3-, NF-κB-, and ATF6-dependent programs may therefore contribute to reduced inflammatory responsiveness and immune resilience in preterm neonates.[ 37 , 38 , 45 – 48 ] Collectively, these trajectories provide a reference framework for interpreting hepatic vulnerability in prematurity and contextualizing molecular effects of antenatal steroids,[ 21 – 27 ] intrauterine inflammation,[ 10 , 11 ] growth restriction,[ 12 ] and artificial placenta support.[ 13 – 20 ] This systems-level reference framework also enables mechanistic comparison of pathological states and clinical interventions against a defined baseline of normal maturation, where deviations in module-level trajectories may help distinguish delayed maturation from maladaptive reprogramming. Several limitations should be considered. First, bulk RNA sequencing precluded cell-type–specific resolution of hepatocytes, endothelial cells, Kupffer cells, and hematopoietic populations. Second, although sheep closely model human perinatal physiology,[ 9 ] species differences in gestational timing and endocrine regulation may limit direct extrapolation. Third, transcriptomic activation does not necessarily equate to functional protein activity; therefore, complementary proteomic, metabolomic, and functional studies will be required to determine how these transcriptional programs translate into metabolic capacity and pathway flux. Fourth, the analysis was restricted to three gestational time points, and finer temporal sampling may reveal additional transitional inflection points during liver maturation. Finally, network-based inference identifies putative regulatory drivers but does not establish direct causal relationships. An additional limitation regarding clinical translation is that although the ovine model closely approximates human gestational physiology and developmental timing, species-specific differences in placentation, endocrine regulation, immune ontogeny, and hepatic gene expression may influence the extent to which these findings can be directly extrapolated to humans. Transcriptomic patterns may not fully reflect post-transcriptional regulation or functional enzymatic activity, and environmental exposures in controlled experimental settings differ from the complexity of human pregnancies. Consequently, validation in human tissues and complementary experimental systems will be necessary before definitive clinical application can be established. Nevertheless, the concordance among module eigengene trajectories, pathway enrichment patterns, and conserved transcriptional regulators supports the robustness and biological coherence of the developmental framework described. Overall, fetal liver maturation proceeds through coordinated, network-level transcriptional programs that evolve systematically across gestation, transitioning from proliferative/hematopoietic dominance to progressive establishment of metabolic, synthetic, detoxification, immune, and stress-adaptive capacities. These trajectories closely recapitulate patterns described in human fetal liver development and appear to be governed by conserved transcriptional regulators. By defining gestational module dynamics and pathway architecture in a translational large-animal model, this study provides a systems-level developmental reference for interpreting hepatic immaturity in preterm infants and for evaluating how perinatal conditions and therapeutic interventions influence hepatic developmental trajectories. [Methods] Animal Work All procedures were conducted in accordance with the ARRIVE guidelines and approved by the Animal Ethics Committee of the University of Western Australia (RA/3/100/1378). Merino ewes carrying singleton fetuses were studied in Perth, Western Australia. Delivery and Tissue Collection Fetuses were delivered at GD100, GD124, and GD144 (term ~ GD150), corresponding approximately to 24, 32, and 37 human weeks’ gestation (Table 1 , Fig. 1 A). Ewes were anesthetized with an intravenous bolus of midazolam (0.5 mg/kg) and ketamine (10 mg/kg), followed by immediate laparotomy and fetal delivery to minimize anesthetic exposure. While under anesthesia, ewes were euthanized using pentobarbital (160 mg/kg). Immediately after delivery, fetuses were euthanized using pentobarbital. Fetal livers were promptly excised, sectioned, snap-frozen in liquid nitrogen, and stored at − 80°C until RNA extraction. RNA extraction and Bulk RNA sequencing A total of 18 fetal liver samples (n = 6 per gestational age group) were initially collected for bulk RNA sequencing. Total RNA was extracted using the RNeasy Plus Mini Kit (Qiagen), and RNA integrity was assessed with the Agilent RNA 6000 Nano assay. Following quality control (RNA integrity number [RIN] > 7) and exclusion of one major outlier identified by PCA, 15 samples were retained for downstream analysis (GD100, n = 4; GD124, n = 5; GD144, n = 6) (Table 1 ). Library preparation and 3′ directional bulk RNA sequencing (~ 30 million reads/sample) were performed by Novogene Singapore. Reads were aligned using the DRAGEN RNA Pipeline v3.8.4 (Illumina) against the Rambouillet sheep reference genome (ARS-UI_Ramb_v3.0). To reduce rRNA contamination, the 18S–5.8S–28S rRNA locus (Chr2: 250,088,714–250,098,250) was designated as a decoy region. The mean alignment rate was 97.35%, with 1.95% of reads mapping to rRNA and 0.69% unmapped reads. Ortholog mapping was performed using biomaRt (v2.62.0) with Ensembl annotations to map Ovis aries genes to their Homo sapiens orthologs, where necessary for downstream pathway analyses. Differentially Expressed Gene (DEG) Analysis DEG analysis was performed using edgeR (v3.42.4) and limma-voom (v3.56.2). Low-expression genes were filtered, normalized with TMM, and variance-stabilized with voom. DEGs across the three gestational contrasts were evaluated, and genes with an adjusted p value (FDR) < 0.05 and |log₂FC| ≥ 1 were considered significant. Gene Set Variation Analysis (GSVA) Gene Set Variation Analysis (GSVA; v1.50.1) was performed on gene expression values using the Poisson kernel without gene set size restrictions to compute GSVA scores.[ 28 ] Gene sets of hepatic functions, including pathways related to glucose and lipid metabolism, bile acids, detoxification, the urea cycle, serum proteins, vitamins, iron–heme metabolism, steroid hormones, immune and stress responses, developmental signaling, morphogenesis, and the cell cycle, were compiled from MSigDB Hallmark and Reactome databases.[ 29 , 30 ] These GSVA scores were then integrated with the corresponding WGCNA module eigengenes to characterize pathway–module relationships. Weighted Gene Co-expression Network Analysis Gene co-expression networks were constructed using the top 10,000 most variable genes using WGCNA (v1.72.1),[ 31 ] after removing low-abundance transcripts and applying variance stabilizing transformation. Modules were identified by dynamic tree cutting, and module eigengenes were used to define five biologically relevant modules (turquoise, yellow, brown, blue, and green) that captured coherent temporal expression patterns; the grey module contained unassigned genes. For each module, associations with GSVA pathway scores were evaluated, and functional enrichment was performed using GO Biological Process and KEGG pathways. We generated module-specific TFs–gene–pathway networks to integrate TFs, hub genes, and functional pathway networks. Intramodular connectivity was calculated for all genes, and those with module eigengene–based connectivity (kME) ≥ 0.90 were designated as hub gene candidates. To refine hub gene selection using protein-level evidence, genes were mapped to Ovis aries STRING (v12) identifiers, and module-restricted protein–protein interaction (PPI) networks were constructed.[ 32 ] TFs were identified by intersecting module genes with a curated TF reference derived from the human DoRothEA (v1.8.0) and the human TF annotations from AnimalTFDB (v3.0). Module-specific TFs that also passed the double-filtering criteria (kME ≥ 0.90 and high STRING-PPI degree) were included in the final network models. Functional Enrichment Analysis Genes identified from the DEG analysis and WGCNA modules were subjected to Gene Ontology (GO) Biological Process (BP) enrichment analysis using the enrichGO function in clusterProfiler (v4.10.0) with the human annotation databases org.Hs.eg.db (v3.19.1) and GO.db (v3.19.1). GO terms with an FDR < 0.05 were considered statistically significant. KEGG pathway enrichment analysis was performed on the corresponding human Entrez gene IDs using the enrichKEGG function in clusterProfiler, retaining pathways with FDR < 0.05.[ 49 – 51 ] Abbreviations DEG: Differentially Expressed Gene GD: Gestational Day GO: Gene Ontology GSVA: Gene Set Variation Analysis KEGG: Kyoto Encyclopedia of Genes and Genomes kME: Module Eigengene–based connectivity PCA: Principal Component Analysis TF: Transcription Factor WGCNA: Weighted Gene Co-expression Network Analysis Declarations Author Contribution HI, MK, and HU conceived and designed the study. HI, SW, EJ, SC, YK, YS, TS, MS, NM, KS, HW, SK, SI, MS, MC, MK, and HU performed the animal studies. HI, SC, and HU conducted the laboratory experiments. HI and HU analyzed the data. HI wrote the original draft of the manuscript and figures. All authors reviewed the manuscript. Data Availability The RNA sequence datasets generated in this study have been deposited in the Gene Expression Omnibus (GEO) under accession number GSE325213. Raw sequencing data are available in the Sequence Read Archive (SRA) under BioProject accession number PRJNA1438060. Funding statement This study was supported by grants to MK from the Channel 7 Telethon Trust, the Department of Health, Government of Western Australia, the Stan Perron Charitable Foundation, the National University of Singapore (NUHSRO/2021/075), and the Ministry of Education Government of Singapore (NUHSRO/2021/109/T1/Seed-Sep/02). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. References Gordillo, M., Evans, T. & Gouon-Evans, V. Orchestrating liver development. Development 142 , 2094–108 (2015). Zorn, A. M. Liver Development . (2008). Suchy, F. J. 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Usuda, H. et al. Assessment of synthetic red cell therapy for extremely preterm ovine fetuses maintained on an artificial placenta life‐support platform. Artif. Organs 46 , 653–665 (2022). Usuda, H. et al. Artificial placenta support of extremely preterm ovine fetuses at the border of viability for up to 336 hours with maintenance of systemic circulation but reduced somatic and organ growth. Front. Physiol. 14 , (2023). Usuda, H. et al. Artificial placenta technology: History, potential and perception. Placenta 141 , 10–17 (2023). Ikeda, H. et al. Upregulation of hepatic nuclear receptors in extremely preterm ovine fetuses undergoing artificial placenta therapy. The Journal of Maternal-Fetal & Neonatal Medicine 37 , (2024). Usuda, H. et al. Pumpless arteriovenous extracorporeal membrane oxygenation for 2000 g newborns with respiratory failure: proof of principle data from a preterm lamb model. Pediatr. Res. https://doi.org/10.1038/s41390-025-04429-8 (2025) doi:10.1038/s41390-025-04429-8. Usuda, H. et al. Identification of Gestation-Specific Patterns of Physiological, Protein and Cell-Free RNA Injury Markers in a Sheep Model of Regulable Preterm Fetal Hypoxia. Reproductive Sciences 32 , 3906–3927 (2025). Usuda, H. et al. Low-dose antenatal betamethasone treatment achieves preterm lung maturation equivalent to that of the World Health Organization dexamethasone regimen but with reduced endocrine disruption in a sheep model of pregnancy. Am. J. Obstet. Gynecol. 227 , 903.e1-903.e16 (2022). Carter, S. W. D. et al. Correction: Antenatal steroids elicited neurodegenerative-associated transcriptional changes in the hippocampus of preterm fetal sheep independent of lung maturation. BMC Med. 22 , 409 (2024). Johnson, E. L. et al. Perturbation of the ovine placental transcriptome occurs at sub-therapeutic exposures to antenatal steroid therapy. Placenta 171 , 1–15 (2025). Carter, S. W. D. et al. Transdermal delivery of antenatal steroids to promote fetal lung maturation: Proof of principle data from sheep and non-human primate models. BMC Med. 23 , 452 (2025). Fee, E. L. et al. Single-nucleotide polymorphisms in dizygotic twin ovine fetuses are associated with discordant responses to antenatal steroid therapy. BMC Med. 23 , 65 (2025). Fee, E. L. et al. Respiratory benefit in preterm lambs is progressively lost when the concentration of fetal plasma betamethasone is titrated below two nanograms per milliliter. American Journal of Physiology-Lung Cellular and Molecular Physiology 325 , L628–L637 (2023). Takahashi, T. et al. Variability in the efficacy of a standardized antenatal steroid treatment was independent of maternal or fetal plasma drug levels: evidence from a sheep model of pregnancy. Am. J. Obstet. Gynecol. 223 , 921.e1-921.e10 (2020). Hänzelmann, S., Castelo, R. & Guinney, J. GSVA: gene set variation analysis for microarray and RNA-seq data. BMC Bioinformatics 14 , 7 (2013). Gillespie, M. et al. The reactome pathway knowledgebase 2022. Nucleic Acids Res. 50 , D687–D692 (2022). Liberzon, A. et al. The Molecular Signatures Database Hallmark Gene Set Collection. Cell Syst. 1 , 417–425 (2015). Langfelder, P. & Horvath, S. WGCNA: an R package for weighted correlation network analysis. BMC Bioinformatics 9 , 559 (2008). Szklarczyk, D. et al. The STRING database in 2023: protein–protein association networks and functional enrichment analyses for any sequenced genome of interest. Nucleic Acids Res. 51 , D638–D646 (2023). Wesley, B. T. et al. Single-cell atlas of human liver development reveals pathways directing hepatic cell fates. Nat. Cell Biol. 24 , 1487–1498 (2022). Giancotti, A. et al. Functions and the Emerging Role of the Foetal Liver into Regenerative Medicine. Cells 8 , 914 (2019). Hakkola, J., Tanaka, E. & Pelkonen, O. Developmental Expression of Cytochrome P450 Enzymes in Human Liver. Pharmacol. Toxicol. 82 , 209–217 (1998). van Groen, B. D. et al. Proteomics of human liver membrane transporters: a focus on fetuses and newborn infants. European Journal of Pharmaceutical Sciences 124 , 217–227 (2018). Bode, J. G., Albrecht, U., Häussinger, D., Heinrich, P. C. & Schaper, F. Hepatic acute phase proteins – Regulation by IL-6- and IL-1-type cytokines involving STAT3 and its crosstalk with NF-κB-dependent signaling. Eur. J. Cell Biol. 91 , 496–505 (2012). Tanaka, T., Narazaki, M. & Kishimoto, T. IL-6 in Inflammation, Immunity, and Disease. Cold Spring Harb. Perspect. Biol. 6 , a016295–a016295 (2014). Kamiya, A., Kinoshita, T. & Miyajima, A. Oncostatin M and hepatocyte growth factor induce hepatic maturation via distinct signaling pathways. FEBS Lett. 492 , 90–94 (2001). Sasaki, S. et al. Induction of Hepatic Metabolic Functions by a Novel Variant of Hepatocyte Nuclear Factor 4γ. Mol. Cell. Biol. 38 , (2018). Jakobsen, J. S. et al. Temporal mapping of CEBPA and CEBPB binding during liver regeneration reveals dynamic occupancy and specific regulatory codes for homeostatic and cell cycle gene batteries. Genome Res. 23 , 592–603 (2013). Oates, A. C. et al. The zebrafish klf gene family. Blood 98 , 1792–1801 (2001). Quinn, M. A., McCalla, A., He, B., Xu, X. & Cidlowski, J. A. Silencing of maternal hepatic glucocorticoid receptor is essential for normal fetal development in mice. Commun. Biol. 2 , 104 (2019). Kim, K. et al. RORα controls hepatic lipid homeostasis via negative regulation of PPARγ transcriptional network. Nat. Commun. 8 , 162 (2017). Zarate, M. A., Nguyen, L. M., De Dios, R. K., Zheng, L. & Wright, C. J. Maturation of the Acute Hepatic TLR4/NF-κB Mediated Innate Immune Response Is p65 Dependent in Mice. Front. Immunol. 11 , (2020). Lee, E.-J. et al. Mutations in unfolded protein response regulator ATF6 cause hearing and vision loss syndrome. Journal of Clinical Investigation 135 , (2025). Suo, C. et al. Mapping the developing human immune system across organs. Science (1979). 376 , (2022). Wu, J. & Kaufman, R. J. From acute ER stress to physiological roles of the Unfolded Protein Response. Cell Death Differ. 13 , 374–384 (2006). Kanehisa, M. KEGG: Kyoto Encyclopedia of Genes and Genomes. Nucleic Acids Res. 28 , 27–30 (2000). Kanehisa, M. Toward understanding the origin and evolution of cellular organisms. Protein Science 28 , 1947–1951 (2019). Kanehisa, M., Furumichi, M., Sato, Y., Matsuura, Y. & Ishiguro-Watanabe, M. KEGG: biological systems database as a model of the real world. Nucleic Acids Res. 53 , D672–D677 (2025). Table Table 1. General characteristics of fetal lambs at delivery Group n Sex (Male / Female) Gestational day at delivery (days) Birth weight (kg) Gestational day 100 (67%) 4 2 / 2 99.5 ± 0.6 1.07 ± 0.06 Gestational day 124 (83%) 5 3 / 2 123.4 ± 0.5 2.99 ± 0.47 Gestational day 144 (96%) 6 3 / 3 144.0 ± 0.0 5.37 ± 0.42 Data are presented as mean ± standard deviation (SD). Percentages indicate gestational age relative to full term (150 days). Additional Declarations No competing interests reported. Supplementary Files SupplementaryS1.csv SupplementaryS2.csv Cite Share Download PDF Status: Under Revision Version 1 posted Editorial decision: Revision requested 06 May, 2026 Reviews received at journal 05 May, 2026 Reviewers agreed at journal 05 May, 2026 Reviews received at journal 15 Apr, 2026 Reviewers agreed at journal 28 Mar, 2026 Reviewers agreed at journal 23 Mar, 2026 Reviewers invited by journal 20 Mar, 2026 Editor assigned by journal 20 Mar, 2026 Editor invited by journal 20 Mar, 2026 Submission checks completed at journal 19 Mar, 2026 First submitted to journal 19 Mar, 2026 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. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. 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2","display":"","copyAsset":false,"role":"figure","size":13363859,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eDifferential expression and functional enrichment of genes across fetal liver development\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"Figure2.png","url":"https://assets-eu.researchsquare.com/files/rs-9094102/v1/23c2f0ec412978a3f7901988.png"},{"id":105405476,"identity":"a8fb1788-423f-4b2d-94d5-de34fd282bf1","added_by":"auto","created_at":"2026-03-25 16:14:11","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":10318514,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eGene set variation analysis (GSVA)-based pathway dynamics across fetal liver development\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"Figure3.png","url":"https://assets-eu.researchsquare.com/files/rs-9094102/v1/63101a042229244ed1a4d549.png"},{"id":105405479,"identity":"c8f1a332-f781-481c-bc58-664b2bdfe86f","added_by":"auto","created_at":"2026-03-25 16:14:11","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":11904906,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eWeighted Gene Co-expression Network Analysis (WGCNA) of gestationally regulated co-expression modules in fetal liver\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"Figure4.png","url":"https://assets-eu.researchsquare.com/files/rs-9094102/v1/9116de45da0a1d5c6686ee30.png"},{"id":105405481,"identity":"837f2d35-03b9-4a9c-a132-2292aa82288f","added_by":"auto","created_at":"2026-03-25 16:14:11","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":17646547,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eIntegrated WGCNA–GSVA framework defining functional composition and regulatory networks of fetal liver modules\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"Figure5.png","url":"https://assets-eu.researchsquare.com/files/rs-9094102/v1/a20a08607a8ecac77db96658.png"},{"id":105570345,"identity":"16ac336a-4034-47b4-a9f0-1cf16b462c16","added_by":"auto","created_at":"2026-03-27 13:16:12","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":52049982,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-9094102/v1/0e88ee09-213e-4509-8f9c-19b61d663266.pdf"},{"id":105405474,"identity":"f221344e-74fd-408b-b1a0-27be57aa6d6f","added_by":"auto","created_at":"2026-03-25 16:14:11","extension":"csv","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":16815,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryS1.csv","url":"https://assets-eu.researchsquare.com/files/rs-9094102/v1/31b6873bd98194433d458482.csv"},{"id":105405475,"identity":"35e123f1-5d88-4172-a09a-18b85ddbdac9","added_by":"auto","created_at":"2026-03-25 16:14:11","extension":"csv","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":7115,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryS2.csv","url":"https://assets-eu.researchsquare.com/files/rs-9094102/v1/1bf2a23a4aaead0d0ec15f0d.csv"}],"financialInterests":"No competing interests reported.","formattedTitle":"Transcriptomic analysis defines gestation-specific programs of fetal liver maturation in premature sheep","fulltext":[{"header":"[Introduction]","content":"\u003cp\u003eThe fetal liver is a multifunctional organ that undergoes profound structural and physiological transitions during gestation. In addition to serving as the principal site of hematopoiesis in early and mid-gestation, the liver progressively acquires the metabolic, synthetic, and detoxification capacities required for postnatal survival.[\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e] These functions include regulation of glucose and lipid homeostasis, urea cycle activity, bile acid synthesis and transport, production of coagulation and complement factors, xenobiotic metabolism, and immune responsiveness.[\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e] In humans, many of these pathways mature rapidly between mid-to-late gestation, and the extent of their development at birth critically influences neonatal complications, including hypoglycaemia, hyperbilirubinaemia, coagulopathy, susceptibility to infection, and drug metabolism capacity.[\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e] Despite the clear importance of these processes to newborn health, the transcriptional programs responsible for coordinating fetal liver growth and functional maturation remain incompletely defined in humans and have not been systematically characterised in relevant large-animal models. This knowledge gap is critical for understanding the differential susceptibility of the developing fetus to prenatal interventions (e.g., steroids, progesterone) and how their impact may vary depending on the gestational age at which they are administered.\u003c/p\u003e \u003cp\u003eEarly stages of hepatic development, including hepatoblast specification, lineage commitment, and spatial organization, have been extensively studied using rodent embryonic models and stem cell\u0026ndash;based differentiation systems.[\u003cspan additionalcitationids=\"CR7\" citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e] These approaches have identified key transcription factor networks governing early liver organogenesis. However, comprehensive investigation of hepatic maturation beyond mid-gestation, which is most relevant to preterm birth and neonatal intensive care, remains limited. Moreover, rodents differ substantially from humans in gestational length, timing of hepatic functional maturation, and perinatal physiology, constraining their translational relevance.[\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e] The short gestation of rodents also limits modelling of prolonged prenatal exposures and their cumulative effects on organ maturation. This limitation highlights the need for experimental models with gestational timelines more comparable to humans to better understand organ maturation and the long-term impact of prenatal interventions.\u003c/p\u003e \u003cp\u003eIn contrast, the sheep is a well-established perinatal large-animal model for the study of human pregnancy that closely recapitulates human fetal development, including patterns of liver growth, endocrine maturation, bile acid metabolism, and perinatal metabolic adaptation.[\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e] Accordingly, ovine models have been widely employed in translational perinatal research, including studies associated with intrauterine inflammation,[\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e] intrauterine growth restriction,[\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e] artificial placenta support,[\u003cspan additionalcitationids=\"CR14 CR15 CR16 CR17 CR18 CR19\" citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e] and antenatal corticosteroid therapy.[\u003cspan additionalcitationids=\"CR22 CR23 CR24 CR25 CR26\" citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e] Despite their extensive use, a systematic time-course transcriptomic reference of normal fetal liver development across mid-to-late gestation has not yet been developed using the sheep model.\u003c/p\u003e \u003cp\u003eHere, we aimed to generate a gestational transcriptomic reference of normal fetal liver maturation across mid-to-late gestation by performing bulk RNA sequencing of ovine fetal livers at three key developmental stages (gestational day [GD] 100, 124, and 144; term\u0026thinsp;=\u0026thinsp;150), corresponding approximately to the human transition from ~\u0026thinsp;24 weeks\u0026rsquo; gestation to term. Moving beyond gene-level analysis, we applied an integrated analytical framework combining differential gene expression analysis, gene set variation analysis (GSVA)\u0026ndash;based pathway activity profiling, and weighted gene co-expression network analysis (WGCNA) to resolve coordinated temporal changes in hepatic function and regulatory network architecture. This reference framework provides a systems-level view of normal fetal liver maturation and establishes a critical foundation for mechanistic studies of hepatic dysfunction in preterm birth and perinatal disease.\u003c/p\u003e"},{"header":"[Results]","content":"\u003cp\u003e\u003cstrong\u003eOverview of Bulk RNA sequencing Analysis and Sample Clustering\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eBulk RNA sequencing was performed on fetal sheep livers collected at GD100, GD124, and GD144, representing developmental stages equivalent to approximately 24 weeks\u0026rsquo; gestation through term in humans (Table 1, Figure 1A).\u003c/p\u003e\n\u003cp\u003eTo elucidate the time course of transcriptional programs underlying fetal liver maturation, we applied an integrated, multi-phase RNA sequencing analytical strategy. Differential expression analysis identified genes exhibiting gestational variation. GSVA quantified dynamic shifts in pathway-level activity across developmental stages, enabling functional interpretation beyond individual gene changes. WGCNA further delineated coordinated gene modules and their eigengenes, capturing structured transcriptional programs associated with progressive liver maturation. Collectively, this integrative approach provided complementary gene-level and systems-level insights into stage-dependent functional maturation of the fetal liver (Figure 1B).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eUnsupervised analyses demonstrated clear stage-dependent transcriptomic segregation. Hierarchical clustering of variance-stabilized expression profiles revealed distinct grouping of GD100, GD124, and GD144 samples, with no intermixing across gestational ages (Figure 1C). Principal component analysis (PCA) corroborated these findings, with the first principal component (PC1) accounting for the largest proportion of variance and aligning with gestational progression, indicating that developmental stage is the dominant driver of transcriptomic variability (Figure 1D). Together, these results confirm robust and orderly transcriptional transitions underlying fetal liver maturation.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDifferentially Expressed Genes Analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eVolcano plots for each pairwise gestational comparison (GD124 vs. GD100, GD144 vs. GD124, and GD144 vs. GD100) revealed extensive sets of differentially expressed genes (DEGs) meeting predefined significance thresholds (FDR \u0026lt; 0.05 and |log2FC| \u0026ge; 1) (Figure 2A\u0026ndash;C).\u003c/p\u003e\n\u003cp\u003eTo identify genes exhibiting consistent temporal regulation, we focused on DEGs commonly upregulated or downregulated in both GD124 and GD144 relative to GD100, as visualized by Venn diagrams (Figure 2D, 2G). A total of 915 genes showed shared upregulation, whereas 1,163 genes demonstrated shared downregulation across advancing gestation.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eFunctional enrichment analysis of commonly upregulated genes revealed significant overrepresentation of Gene Ontology (GO) Biological Process terms related to amino acid, lipid, and organic acid catabolism, bile acid transport and secretion, xenobiotic and detoxification processes, nutrient sensing, immune activation, wound healing, and coagulation (Figure 2E). The Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis further highlighted complement and coagulation cascades, cytokine\u0026ndash;cytokine receptor interaction, Toll-like receptor signaling, bile secretion, and amino acid and urea metabolism (Figure 2F). Together, these enrichment patterns indicate progressive maturation of hepatic metabolic, innate immune, and synthetic functions toward late gestation, and demonstrate that key hepatic functions do not emerge abruptly at birth but develop in a coordinated and stage-specific manner during gestation, providing a biological framework to interpret how disruptions in late gestation could have lasting metabolic and immunological consequences after birth. In contrast, commonly downregulated genes were strongly enriched for GO Biological Process terms associated with organelle fission, nuclear division, chromosome segregation, and mitotic cell-cycle transitions (Figure 2H). KEGG pathway analysis identified significant enrichment of Cell cycle, DNA replication, p53 signaling, and multiple DNA repair pathways, including mismatch repair, homologous recombination, base excision repair, and Fanconi anemia pathways (Figure 2I). These coordinated changes indicate suppression of proliferative and genome maintenance programs with advancing gestation.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eCollectively, these findings demonstrate a developmental shift from proliferative growth programs at mid-gestation toward functional metabolic specialization and immune competence, defining the transition from growth to functional readiness as term approaches. These changes identify critical windows of metabolic and immune maturation and inform how timing of disruption may shape distinct outcomes.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eGene Set Variation Analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eGSVA revealed broad and coordinated changes in hepatic pathway activity across GD100, GD124, and GD144. GSVA scores for selected functional pathways are summarized in a heatmap (Figure 3A; see Supplementary Table S1 online), with pairwise comparisons displayed as bar plots (Figure 3B\u0026ndash;D). Overall, most metabolic pathways and hepatic synthetic functions showed prominent developmental changes, including nutrient and energy metabolism, coagulation, and complement, with robust stepwise upregulation across gestation.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eGlucose metabolism showed a clear developmental transition. Gluconeogenesis and glycogen synthesis were progressively upregulated, particularly between GD124 and GD144, whereas glycolytic pathways were downregulated (Figure 3C). This reciprocal pattern indicates a shift from glucose utilization toward glucose production and storage capacity as gestation advances.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eLipid metabolic pathways also demonstrated progressive activation. Between GD100 and GD124, enrichment was primarily observed in sphingolipid metabolism, suggesting early maturation of membrane lipid composition (Figure 3B).\u0026nbsp;In contrast, the transition from GD124 to GD144 was characterized by broader upregulation of mitochondrial fatty acid \u0026beta;-oxidation, triglyceride metabolism, and adipogenesis pathways (Figure 3C), reflecting establishment of mitochondrial energy production, lipid storage, and lipid remodeling capacities in preparation for postnatal life.\u0026nbsp;Ketone body and phospholipid metabolism showed minimal gestational variation, suggesting limited functional engagement during fetal life.\u003c/p\u003e\n\u003cp\u003eDetoxification and nitrogen/ammonia metabolism were progressively enhanced across gestation. Cytochrome P450 and Phase II conjugation pathways involved in drug and xenobiotic metabolism, together with branched-chain amino acid catabolism and the urea cycle, were robustly and significantly upregulated in a stepwise manner across gestation, indicating maturation of hepatic detoxification and ammonia clearance capacity. Bile acid metabolism showed progressive activation, whereas bile transport pathways increased predominantly between GD100 and GD124 with minimal subsequent change, potentially reflecting partial reliance on maternal\u0026ndash;placental support prior to postnatal enterohepatic circulation. Cholesterol biosynthesis pathways showed no significant developmental changes; however, LXR (NR1H3/NR1H2)-associated regulatory pathways involved in cholesterol handling were progressively upregulated.\u003c/p\u003e\n\u003cp\u003eDevelopmental and morphogenetic signaling pathways, including Notch and TGF-\u0026beta; signaling, angiogenesis, and epithelial\u0026ndash;mesenchymal transition, were enriched toward late gestation, supporting differentiation, extracellular matrix remodeling, and vascular maturation. In contrast, proliferative programs, including E2F targets, G2/M checkpoint signaling, and mitotic spindle assembly, were highest at GD100 and markedly suppressed by GD144. Together, these coordinated reciprocal changes indicate a developmental transition from proliferative expansion at mid-gestation toward metabolic specialization, structural remodeling, and functional maturation as term approaches.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eWeighted Gene Co-expression Network Analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ea. Module Identification and Temporal Patterns (Figure 4 A-C)\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWGCNA was performed using the top 10,000 most variable genes. Hierarchical clustering of genes based on pairwise expression correlations, followed by dynamic tree cutting, identified six modules of tightly co-expressed genes representing coordinated transcriptional programs (Figure 4A). PCA showed coherent clustering of genes within each module, whereas genes lacking consistent co-expression were assigned to the grey module (Figure 4B). Module eigengene (ME) trajectories revealed distinct gestational patterns (Figure 4C). The turquoise (n = 2,643) and yellow (n = 1,249) modules exhibited progressive increases from GD100 to GD144, whereas the brown (n = 1,896) and blue (n = 2,282) modules declined across gestation. \u0026nbsp;The green module (n = 387) demonstrated a transient peak at GD124. These coordinated module-level dynamics indicate structured transcriptional remodeling during fetal liver maturation.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eb. Functional Enrichment of WGCNA Modules with GO Biological Process and KEGG (Figure 4 D, E)\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe turquoise module showed a progressive increase in ME values across gestation, consistent with coordinated activation of broad catabolic and metabolic maturation programs toward late gestation. GO Biological Process enrichment highlighted organic acid, lipid, and small-molecule catabolic processes, as well as multiple amino acid catabolic and metabolic pathways. KEGG pathway analysis demonstrated significant enrichment of peroxisome, carbon metabolism, bile secretion, tryptophan metabolism, branched-chain amino acid (valine, leucine, and isoleucine) degradation, together with complement and coagulation cascades.\u0026nbsp;Collectively, these findings indicate that the turquoise module represents progressive metabolic specialization, enhanced detoxification capacity, and maturation of hepatic plasma protein synthetic function.\u003c/p\u003e\n\u003cp\u003eThe yellow module displayed increased ME values from GD124 to GD144. GO Biological Process highlighted broad pathways related to energy metabolism, including fatty acid \u0026beta;-oxidation, hexose and monosaccharide metabolism, amino acid metabolism, extracellular matrix organization, and regulation of body fluid levels among enriched GO terms. KEGG pathway analysis further identified glycolysis/gluconeogenesis, amino acid and cofactor biosynthesis, and regulatory signaling pathways including PPAR, HIF-1, and ECM\u0026ndash;receptor interaction. Together, these enrichment patterns suggest coordinated metabolic regulation and structural remodeling associated with late-gestation functional maturation and potential preparation for postnatal circulatory adaptation.\u003c/p\u003e\n\u003cp\u003eIn contrast, the brown and blue modules exhibited progressively declining ME values from GD100 to GD144, reflecting transcriptional programs that are predominant during early gestation. The brown module was strongly enriched for proliferative processes. GO Biological Process terms included chromosome segregation, nuclear division, DNA replication, and cell cycle phase transitions, indicating active mitotic progression. Complementary KEGG pathway enrichment identified Cell cycle, DNA replication, Fanconi anemia pathway, homologous recombination, mismatch repair, and oocyte meiosis, collectively highlighting coordinated regulation of cell division and genome replication machinery.\u003c/p\u003e\n\u003cp\u003eThe blue module was enriched for pathways related to nuclear transport, genome maintenance, and RNA processing. GO Biological Process terms such as mRNA transport, nucleocytoplasmic transport, double-strand break repair, and RNA localization reflected regulatory and DNA repair functions. Consistently, KEGG pathway analysis identified nucleocytoplasmic transport, base excision repair, Fanconi anemia pathway, mRNA surveillance, and RNA degradation, highlighting molecular quality-control systems that preserve transcriptional fidelity and genome integrity. Together, these enrichment patterns indicate that the brown module represents core proliferative cell-cycle programs, whereas the blue module captures complementary regulatory and maintenance mechanisms that support sustained hepatocyte proliferation during early fetal liver development.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ec. Functional Annotation of WGCNA Modules by Gene Set\u0026ndash;Based Composition\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eIntegration of WGCNA modules with GSVA-derived pathway gene sets linked co-expression network architecture to hepatic functional pathways, thereby defining how coordinated transcriptional programs contribute to fetal liver maturation (Figure 5A; see Supplementary Table S2 online).\u003c/p\u003e\n\u003cp\u003eThe turquoise module emerged as the dominant maturation-associated module in fetal liver, capturing the largest proportion of genes across diverse liver-related functional gene sets. This module exhibited consistently elevated ME values across gestation, indicating a sustained and coordinated transcriptional program underlying core hepatic functions. Functional gene sets enriched within this module included cholesterol and bile acid metabolism, xenobiotic detoxification, urea-cycle activity, serum protein synthesis, vitamin and cofactor metabolism, immune signaling, stress response, developmental pathways, and morphogenesis. Together, these findings indicate that the turquoise module represents a central metabolic and biosynthetic axis of fetal liver maturation.\u003c/p\u003e\n\u003cp\u003eThe yellow module displayed a more stage-restricted profile, with increased representation in late gestation. It was enriched for gene sets related to gluconeogenesis and oxidative phosphorylation, consistent with establishment of endogenous glucose-producing capacity. In addition, it captured specialized lipid metabolic pathways including peroxisomal and very-long-chain fatty acid oxidation, reflecting preparation for neonatal lipid-dependent energy utilization. These patterns indicate that the yellow module supports metabolic refinement and energetic adaptation during late gestation.\u003c/p\u003e\n\u003cp\u003eIn contrast, the brown module was strongly enriched for cell cycle\u0026ndash;related pathways and hematopoietic heme synthesis programs, consistent with the early fetal liver\u0026rsquo;s dual role as a proliferative and hematopoietic organ. The declining representation of these pathways across gestation reflects progressive resolution of hepatic hematopoiesis and proliferative expansion.\u003c/p\u003e\n\u003cp\u003eThe blue module shared early-gestation characteristics, with enrichment of pathways related to cellular growth, genome maintenance, glycolysis, and the pentose phosphate pathway, consistent with a biosynthetic metabolic state supporting rapid tissue expansion.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ed. Transcription Factor \u0026ndash; Hub gene \u0026ndash; Pathway networks\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe turquoise and yellow modules were prioritized as key regulatory modules in fetal liver maturation based on their eigengene trajectories, strong concordance with GSVA-derived pathway activities, and consistent GO and KEGG enrichment profiles. These modules were closely associated with major hepatic functions including energy metabolism, bile acid synthesis and transport, detoxification, plasma protein synthesis, and nutrient handling, indicating that they represent core transcriptional programs underlying gestational liver development. To further delineate the regulatory architecture and identify key regulatory drivers underlying fetal hepatic functional maturation, integrated transcription factor\u0026ndash;hub gene\u0026ndash;pathway networks were constructed.\u003c/p\u003e\n\u003cp\u003eThe turquoise module represented a highly integrated metabolic program. Hub genes were enriched for oxidative phosphorylation (ALDH6A1, HAO1), mitochondrial and peroxisomal lipid \u0026beta;-oxidation (ACOX1, ACAT1, ECHDC1), glycogen and glucose metabolism (SLC2A2, UGP2, FBP1), cholesterol homeostasis (INSIG2, LRP6, APOH), bile acid synthesis and transport (PLG, ITIH4, APOH), and vitamin and iron\u0026ndash;heme metabolism (MMADHC, HSDL2, BHMT) (Figure 5B). Five transcription factors, NR3C1, HNF4G, NFKB1, ATF6, and SNAI2, were identified as putative central regulators, integrating metabolic, stress-responsive, and developmental signals. Pathway-level network mapping demonstrated convergence of these transcription factors (TFs) on hub genes, centered functional axes governing mitochondrial energy production, lipid utilization, xenobiotic detoxification, complement and coagulation factor synthesis, and micronutrient handling, indicating that the turquoise module coordinates the establishment of metabolic and biosynthetic capacity required for fetal liver maturation.\u003c/p\u003e\n\u003cp\u003eThe yellow module captured a complementary metabolic and stress-adaptive program. Hub genes were involved in mitochondrial energy production (SLC25A4, ETFDH, PDHB), fatty-acid and lipid catabolism (ACOX2, ACSL1, CPT1A), glucose regulation and glycogen mobilization (PDK4, PPP1R3B, PYGL), bile acid and cholesterol handling (ABCG8, ABCA1), amino-acid and nitrogen metabolism (ASS1, GLS2, GNMT), oxidative and inflammatory stress control (XDH, DUSP1, NFKBIA, IL1RN), and cell-survival pathways (MCL1, GADD45B) (Figure 5C). Five transcription factors, STAT3, RORA, CEBPB, FOSL2, and KLF6, were identified as key regulators linking cytokine/JAK\u0026ndash;STAT activity, inflammatory signaling, lipid metabolism, and developmental transcriptional remodeling. Pathway mapping showed that these TFs converge on major functional routes, including fatty-acid \u0026beta;-oxidation, peroxisome/mitochondrial lipid metabolism, gluconeogenesis, Wnt/\u0026beta;-catenin signaling, cytokine/JAK\u0026ndash;STAT pathways, apoptosis regulation, and immune\u0026ndash;metabolic stress responses, indicating that the yellow module governs the metabolic flexibility and stress resilience required for late-gestation hepatic maturation.\u003c/p\u003e"},{"header":"[Discussion]","content":"\u003cp\u003eThis study establishes a gestational transcriptomic reference of normal fetal liver maturation in sheep spanning mid-to-late gestation, corresponding to the clinically relevant human preterm window. By integrating differential gene expression analysis with GSVA-based pathway activity profiling,[\u003cspan additionalcitationids=\"CR29\" citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e] and WGCNA network analysis,[\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e, \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e] we show that fetal liver development is organized into coordinated, time-resolved transcriptional programs rather than isolated gene-level changes (Figs.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e\u0026ndash;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). Two dominant co-expression modules defined the developmental trajectory (Figs.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e\u0026ndash;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e). The turquoise module progressively increased across gestation and encompassed core hepatic metabolic, synthetic, and detoxification functions, whereas the yellow module showed preferential enrichment in late gestation and captured metabolic refinement and stress-adaptive programs. In contrast, the brown and blue modules declined with advancing gestation and were enriched for proliferative and genome-maintenance pathways, consistent with the progressive resolution of early proliferative and hematopoietic functions.\u003c/p\u003e \u003cp\u003eEnergy metabolism followed a structured developmental trajectory characterized by progressive activation of amino-acid catabolism, mitochondrial oxidative phosphorylation, and urea-cycle pathways, followed by late-gestation induction of gluconeogenesis and fatty-acid β-oxidation accompanied by reciprocal suppression of glycolysis. This pattern closely parallels human fetal liver maturation and reflects preparation for postnatal metabolic autonomy following loss of placental nutrient supply.[\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e, \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e] In parallel, bile acid synthesis and xenobiotic metabolism matured progressively across gestation, consistent with developmental induction of cytochrome P450 pathways, whereas bile transport systems exhibited more limited gestational activation, suggesting continued reliance on maternal\u0026ndash;placental clearance mechanisms during fetal life.[\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e, \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e, \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e] Complement, coagulation, and plasma protein synthesis pathways were coordinately upregulated, aligning with developmental acquisition of hepatic synthetic capacity and IL-6/STAT3-mediated acute-phase regulation.[\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e, \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e]\u003c/p\u003e \u003cp\u003eNetwork analysis further identified conserved transcriptional regulators including STAT3,[\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e] HNF4G,[\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e] CEBPB,[\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e] KLF6,[\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e] NR3C1,[\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e] RORA,[\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e] NFKB1,[\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e] and ATF6[\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e], which are established drivers of hepatocyte maturation, metabolic reprogramming, and stress adaptation in mammalian systems. The convergence of these regulators within gestationally dynamic gene modules provides mechanistic support for translational alignment between ovine and human fetal liver development. Beyond these mechanistic insights, the findings may also help contextualize clinical vulnerability associated with preterm birth and guide future translational studies. For example, the staged shift toward gluconeogenesis and lipid oxidation provides a molecular framework for the vulnerability of preterm infants, especially those born at very early gestations, to hypoglycemia, impaired lipid utilization, and nitrogen imbalance,[\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e] because preterm birth may interrupt coordinated \u0026ldquo;fuel switching\u0026rdquo; before full metabolic autonomy is achieved. Similarly, progressive maturation of bile acid metabolism and detoxification pathways supports clinical observations that hyperbilirubinemia and altered drug metabolism in preterm infants reflect incomplete development of both metabolic enzymes and transport systems.[\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e, \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e] Developmental activation of complement, coagulation, and immune-metabolic signaling further suggests that hepatic acute-phase competence is physiologically acquired across gestation. Immaturity of IL-6/STAT3-, NF-κB-, and ATF6-dependent programs may therefore contribute to reduced inflammatory responsiveness and immune resilience in preterm neonates.[\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e, \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e, \u003cspan additionalcitationids=\"CR46 CR47\" citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e] Collectively, these trajectories provide a reference framework for interpreting hepatic vulnerability in prematurity and contextualizing molecular effects of antenatal steroids,[\u003cspan additionalcitationids=\"CR22 CR23 CR24 CR25 CR26\" citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e] intrauterine inflammation,[\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e] growth restriction,[\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e] and artificial placenta support.[\u003cspan additionalcitationids=\"CR14 CR15 CR16 CR17 CR18 CR19\" citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]\u003c/p\u003e \u003cp\u003eThis systems-level reference framework also enables mechanistic comparison of pathological states and clinical interventions against a defined baseline of normal maturation, where deviations in module-level trajectories may help distinguish delayed maturation from maladaptive reprogramming.\u003c/p\u003e \u003cp\u003eSeveral limitations should be considered. First, bulk RNA sequencing precluded cell-type\u0026ndash;specific resolution of hepatocytes, endothelial cells, Kupffer cells, and hematopoietic populations. Second, although sheep closely model human perinatal physiology,[\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e] species differences in gestational timing and endocrine regulation may limit direct extrapolation. Third, transcriptomic activation does not necessarily equate to functional protein activity; therefore, complementary proteomic, metabolomic, and functional studies will be required to determine how these transcriptional programs translate into metabolic capacity and pathway flux. Fourth, the analysis was restricted to three gestational time points, and finer temporal sampling may reveal additional transitional inflection points during liver maturation. Finally, network-based inference identifies putative regulatory drivers but does not establish direct causal relationships. An additional limitation regarding clinical translation is that although the ovine model closely approximates human gestational physiology and developmental timing, species-specific differences in placentation, endocrine regulation, immune ontogeny, and hepatic gene expression may influence the extent to which these findings can be directly extrapolated to humans. Transcriptomic patterns may not fully reflect post-transcriptional regulation or functional enzymatic activity, and environmental exposures in controlled experimental settings differ from the complexity of human pregnancies. Consequently, validation in human tissues and complementary experimental systems will be necessary before definitive clinical application can be established. Nevertheless, the concordance among module eigengene trajectories, pathway enrichment patterns, and conserved transcriptional regulators supports the robustness and biological coherence of the developmental framework described.\u003c/p\u003e \u003cp\u003eOverall, fetal liver maturation proceeds through coordinated, network-level transcriptional programs that evolve systematically across gestation, transitioning from proliferative/hematopoietic dominance to progressive establishment of metabolic, synthetic, detoxification, immune, and stress-adaptive capacities. These trajectories closely recapitulate patterns described in human fetal liver development and appear to be governed by conserved transcriptional regulators. By defining gestational module dynamics and pathway architecture in a translational large-animal model, this study provides a systems-level developmental reference for interpreting hepatic immaturity in preterm infants and for evaluating how perinatal conditions and therapeutic interventions influence hepatic developmental trajectories.\u003c/p\u003e"},{"header":"[Methods]","content":"\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eAnimal Work\u003c/h2\u003e \u003cp\u003e All procedures were conducted in accordance with the ARRIVE guidelines and approved by the Animal Ethics Committee of the University of Western Australia (RA/3/100/1378). Merino ewes carrying singleton fetuses were studied in Perth, Western Australia.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003eDelivery and Tissue Collection\u003c/h2\u003e \u003cp\u003eFetuses were delivered at GD100, GD124, and GD144 (term\u0026thinsp;~\u0026thinsp;GD150), corresponding approximately to 24, 32, and 37 human weeks\u0026rsquo; gestation (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e, Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eA). Ewes were anesthetized with an intravenous bolus of midazolam (0.5 mg/kg) and ketamine (10 mg/kg), followed by immediate laparotomy and fetal delivery to minimize anesthetic exposure. While under anesthesia, ewes were euthanized using pentobarbital (160 mg/kg). Immediately after delivery, fetuses were euthanized using pentobarbital. Fetal livers were promptly excised, sectioned, snap-frozen in liquid nitrogen, and stored at \u0026minus;\u0026thinsp;80\u0026deg;C until RNA extraction.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003eRNA extraction and Bulk RNA sequencing\u003c/h2\u003e \u003cp\u003eA total of 18 fetal liver samples (n\u0026thinsp;=\u0026thinsp;6 per gestational age group) were initially collected for bulk RNA sequencing. Total RNA was extracted using the RNeasy Plus Mini Kit (Qiagen), and RNA integrity was assessed with the Agilent RNA 6000 Nano assay. Following quality control (RNA integrity number [RIN]\u0026thinsp;\u0026gt;\u0026thinsp;7) and exclusion of one major outlier identified by PCA, 15 samples were retained for downstream analysis (GD100, n\u0026thinsp;=\u0026thinsp;4; GD124, n\u0026thinsp;=\u0026thinsp;5; GD144, n\u0026thinsp;=\u0026thinsp;6) (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). Library preparation and 3\u0026prime; directional bulk RNA sequencing (~\u0026thinsp;30\u0026nbsp;million reads/sample) were performed by Novogene Singapore. Reads were aligned using the DRAGEN RNA Pipeline v3.8.4 (Illumina) against the Rambouillet sheep reference genome (ARS-UI_Ramb_v3.0). To reduce rRNA contamination, the 18S\u0026ndash;5.8S\u0026ndash;28S rRNA locus (Chr2: 250,088,714\u0026ndash;250,098,250) was designated as a decoy region. The mean alignment rate was 97.35%, with 1.95% of reads mapping to rRNA and 0.69% unmapped reads. Ortholog mapping was performed using biomaRt (v2.62.0) with Ensembl annotations to map \u003cem\u003eOvis aries\u003c/em\u003e genes to their Homo sapiens orthologs, where necessary for downstream pathway analyses.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003eDifferentially Expressed Gene (DEG) Analysis\u003c/h2\u003e \u003cp\u003eDEG analysis was performed using edgeR (v3.42.4) and limma-voom (v3.56.2). Low-expression genes were filtered, normalized with TMM, and variance-stabilized with voom. DEGs across the three gestational contrasts were evaluated, and genes with an adjusted p value (FDR)\u0026thinsp;\u0026lt;\u0026thinsp;0.05 and |log₂FC| \u0026ge; 1 were considered significant.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003eGene Set Variation Analysis (GSVA)\u003c/h2\u003e \u003cp\u003eGene Set Variation Analysis (GSVA; v1.50.1) was performed on gene expression values using the Poisson kernel without gene set size restrictions to compute GSVA scores.[\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e] Gene sets of hepatic functions, including pathways related to glucose and lipid metabolism, bile acids, detoxification, the urea cycle, serum proteins, vitamins, iron\u0026ndash;heme metabolism, steroid hormones, immune and stress responses, developmental signaling, morphogenesis, and the cell cycle, were compiled from MSigDB Hallmark and Reactome databases.[\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e, \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e] These GSVA scores were then integrated with the corresponding WGCNA module eigengenes to characterize pathway\u0026ndash;module relationships.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec17\" class=\"Section2\"\u003e \u003ch2\u003eWeighted Gene Co-expression Network Analysis\u003c/h2\u003e \u003cp\u003eGene co-expression networks were constructed using the top 10,000 most variable genes using WGCNA (v1.72.1),[\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e] after removing low-abundance transcripts and applying variance stabilizing transformation. Modules were identified by dynamic tree cutting, and module eigengenes were used to define five biologically relevant modules (turquoise, yellow, brown, blue, and green) that captured coherent temporal expression patterns; the grey module contained unassigned genes. For each module, associations with GSVA pathway scores were evaluated, and functional enrichment was performed using GO Biological Process and KEGG pathways. We generated module-specific TFs\u0026ndash;gene\u0026ndash;pathway networks to integrate TFs, hub genes, and functional pathway networks. Intramodular connectivity was calculated for all genes, and those with module eigengene\u0026ndash;based connectivity (kME)\u0026thinsp;\u0026ge;\u0026thinsp;0.90 were designated as hub gene candidates. To refine hub gene selection using protein-level evidence, genes were mapped to \u003cem\u003eOvis aries\u003c/em\u003e STRING (v12) identifiers, and module-restricted protein\u0026ndash;protein interaction (PPI) networks were constructed.[\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e] TFs were identified by intersecting module genes with a curated TF reference derived from the human DoRothEA (v1.8.0) and the human TF annotations from AnimalTFDB (v3.0). Module-specific TFs that also passed the double-filtering criteria (kME\u0026thinsp;\u0026ge;\u0026thinsp;0.90 and high STRING-PPI degree) were included in the final network models.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec18\" class=\"Section2\"\u003e \u003ch2\u003eFunctional Enrichment Analysis\u003c/h2\u003e \u003cp\u003eGenes identified from the DEG analysis and WGCNA modules were subjected to Gene Ontology (GO) Biological Process (BP) enrichment analysis using the enrichGO function in clusterProfiler (v4.10.0) with the human annotation databases org.Hs.eg.db (v3.19.1) and GO.db (v3.19.1). GO terms with an FDR\u0026thinsp;\u0026lt;\u0026thinsp;0.05 were considered statistically significant. KEGG pathway enrichment analysis was performed on the corresponding human Entrez gene IDs using the enrichKEGG function in clusterProfiler, retaining pathways with FDR\u0026thinsp;\u0026lt;\u0026thinsp;0.05.[\u003cspan additionalcitationids=\"CR50\" citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e]\u003c/p\u003e \u003c/div\u003e"},{"header":"Abbreviations","content":"\u003cp\u003e\u003cstrong\u003e\u003cem\u003eDEG:\u003c/em\u003e\u003c/strong\u003e Differentially Expressed Gene\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eGD:\u003c/em\u003e\u003c/strong\u003e Gestational Day\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eGO:\u003c/em\u003e\u003c/strong\u003e Gene Ontology\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eGSVA:\u003c/em\u003e\u003c/strong\u003e Gene Set Variation Analysis\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eKEGG:\u003c/em\u003e\u003c/strong\u003e Kyoto Encyclopedia of Genes and Genomes\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003ekME:\u003c/em\u003e\u003c/strong\u003e Module Eigengene\u0026ndash;based connectivity\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003ePCA:\u003c/em\u003e\u003c/strong\u003e Principal Component Analysis\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eTF:\u003c/em\u003e\u003c/strong\u003e Transcription Factor\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eWGCNA:\u0026nbsp;\u003c/em\u003e\u003c/strong\u003eWeighted Gene Co-expression Network Analysis\u003c/p\u003e"},{"header":"Declarations","content":"\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eHI, MK, and HU conceived and designed the study. HI, SW, EJ, SC, YK, YS, TS, MS, NM, KS, HW, SK, SI, MS, MC, MK, and HU performed the animal studies. HI, SC, and HU conducted the laboratory experiments. HI and HU analyzed the data. HI wrote the original draft of the manuscript and figures. All authors reviewed the manuscript.\u003c/p\u003e\u003ch2\u003eData Availability\u003c/h2\u003e\u003cp\u003eThe RNA sequence datasets generated in this study have been deposited in the Gene Expression Omnibus (GEO) under accession number GSE325213. Raw sequencing data are available in the Sequence Read Archive (SRA) under BioProject accession number PRJNA1438060.\u003c/p\u003e\u003ch2\u003eFunding statement\u003c/h2\u003e\n\u003cp\u003eThis study was supported by grants to MK from the Channel 7 Telethon Trust, the Department of Health, Government of Western Australia, the Stan Perron Charitable Foundation, the National University of Singapore (NUHSRO/2021/075), and the Ministry of Education Government of Singapore (NUHSRO/2021/109/T1/Seed-Sep/02). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eGordillo, M., Evans, T. \u0026amp; Gouon-Evans, V. Orchestrating liver development. \u003cem\u003eDevelopment\u003c/em\u003e \u003cstrong\u003e142\u003c/strong\u003e, 2094\u0026ndash;108 (2015).\u003c/li\u003e\n\u003cli\u003eZorn, A. 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Maturation of the Acute Hepatic TLR4/NF-\u0026kappa;B Mediated Innate Immune Response Is p65 Dependent in Mice. \u003cem\u003eFront. Immunol.\u003c/em\u003e \u003cstrong\u003e11\u003c/strong\u003e, (2020).\u003c/li\u003e\n\u003cli\u003eLee, E.-J. \u003cem\u003eet al.\u003c/em\u003e Mutations in unfolded protein response regulator ATF6 cause hearing and vision loss syndrome. \u003cem\u003eJournal of Clinical Investigation\u003c/em\u003e \u003cstrong\u003e135\u003c/strong\u003e, (2025).\u003c/li\u003e\n\u003cli\u003eSuo, C. \u003cem\u003eet al.\u003c/em\u003e Mapping the developing human immune system across organs. \u003cem\u003eScience (1979).\u003c/em\u003e \u003cstrong\u003e376\u003c/strong\u003e, (2022).\u003c/li\u003e\n\u003cli\u003eWu, J. \u0026amp; Kaufman, R. J. From acute ER stress to physiological roles of the Unfolded Protein Response. \u003cem\u003eCell Death Differ.\u003c/em\u003e \u003cstrong\u003e13\u003c/strong\u003e, 374\u0026ndash;384 (2006).\u003c/li\u003e\n\u003cli\u003eKanehisa, M. KEGG: Kyoto Encyclopedia of Genes and Genomes. \u003cem\u003eNucleic Acids Res.\u003c/em\u003e \u003cstrong\u003e28\u003c/strong\u003e, 27\u0026ndash;30 (2000).\u003c/li\u003e\n\u003cli\u003eKanehisa, M. Toward understanding the origin and evolution of cellular organisms. \u003cem\u003eProtein Science\u003c/em\u003e \u003cstrong\u003e28\u003c/strong\u003e, 1947\u0026ndash;1951 (2019).\u003c/li\u003e\n\u003cli\u003eKanehisa, M., Furumichi, M., Sato, Y., Matsuura, Y. \u0026amp; Ishiguro-Watanabe, M. KEGG: biological systems database as a model of the real world. \u003cem\u003eNucleic Acids Res.\u003c/em\u003e \u003cstrong\u003e53\u003c/strong\u003e, D672\u0026ndash;D677 (2025).\u003c/li\u003e\n\u003c/ol\u003e"},{"header":"Table","content":"\u003cp\u003e\u003cstrong\u003eTable 1. General characteristics of fetal lambs at delivery\u003c/strong\u003e\u003c/p\u003e\n\u003cdiv align=\"\"\u003e\n \u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"631\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" style=\"width: 75px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eGroup\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 38px;\"\u003e\n \u003cp\u003e\u003cstrong\u003en\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 151px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSex (Male / Female)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 236px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eGestational day at delivery (days)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 131px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eBirth weight (kg)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" style=\"width: 75px;\"\u003e\n \u003cp\u003eGestational day 100 (67%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 38px;\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 151px;\"\u003e\n \u003cp\u003e2 / 2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 236px;\"\u003e\n \u003cp\u003e99.5 \u0026plusmn; 0.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 131px;\"\u003e\n \u003cp\u003e1.07 \u0026plusmn; 0.06\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" style=\"width: 75px;\"\u003e\n \u003cp\u003eGestational day 124 (83%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 38px;\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 151px;\"\u003e\n \u003cp\u003e3 / 2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 236px;\"\u003e\n \u003cp\u003e123.4 \u0026plusmn; 0.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 131px;\"\u003e\n \u003cp\u003e2.99 \u0026plusmn; 0.47\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" style=\"width: 75px;\"\u003e\n \u003cp\u003eGestational day 144 (96%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 38px;\"\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 151px;\"\u003e\n \u003cp\u003e3 / 3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 236px;\"\u003e\n \u003cp\u003e144.0 \u0026plusmn; 0.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 131px;\"\u003e\n \u003cp\u003e5.37 \u0026plusmn; 0.42\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003eData are presented as mean \u0026plusmn; standard deviation (SD). Percentages indicate gestational age relative to full term (150 days).\u003c/p\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"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":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Liver development, Fetal liver maturation, Preterm liver physiology, Gestational transcriptomics, Gene Set Variation Analysis (GSVA), Gene co-expression network (WGCNA)","lastPublishedDoi":"10.21203/rs.3.rs-9094102/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-9094102/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eBackground:\u003c/strong\u003e Fetal liver maturation is essential for postnatal metabolic, synthetic, detoxification, and immune function. Disruption of this process by preterm birth contributes to hypoglycemia, impaired detoxification, and immune immaturity. However, the transcriptional programs governing hepatic maturation during mid-to-late gestation remain incompletely defined. We addressed this gap using a translationally relevant ovine model.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods:\u003c/strong\u003e Bulk RNA sequencing was performed on ovine fetal livers collected at gestational days 100, 124, and 144 (term = 150). Differential expression analysis, gene set variation analysis (GSVA), and weighted gene co-expression network analysis (WGCNA) were integrated to characterize temporal pathway dynamics, transcriptional modules, and regulatory architecture.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults:\u003c/strong\u003e Global transcriptomic profiles segregated clearly by gestational age. Advancing gestation was associated with a coordinated transition from proliferative and hematopoietic programs toward metabolic, biosynthetic, detoxification, and immune competence pathways. Late gestation showed increased activity of gluconeogenesis, fatty acid β-oxidation, bile acid metabolism, xenobiotic detoxification, the urea cycle, and complement/coagulation pathways, together with suppression of cell-cycle programs. WGCNA identified two major maturation-associated modules and highlighted conserved candidate regulators, including STAT3, HNF4G, CEBPB, KLF6, NR3C1, and RORA, linking metabolic reprogramming with stress-adaptive signaling.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusions:\u003c/strong\u003e These findings define a systems-level transcriptomic reference of fetal liver maturation across gestation and provide a framework for investigating how perinatal conditions or therapeutic interventions may alter hepatic developmental trajectories, questions that are difficult to address directly in human fetuses due to limited access to fetal liver tissue.\u003c/p\u003e","manuscriptTitle":"Transcriptomic analysis defines gestation-specific programs of fetal liver maturation in premature sheep","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-03-25 16:14:06","doi":"10.21203/rs.3.rs-9094102/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2026-05-06T12:44:22+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-05-06T03:04:37+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"236495659241229169743849065878927418618","date":"2026-05-05T07:25:22+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-04-15T05:17:10+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"335461003864675705923311293985664985670","date":"2026-03-28T17:46:23+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"232983968802615434823513363770061699436","date":"2026-03-23T17:02:35+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2026-03-20T11:13:45+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-03-20T10:24:37+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2026-03-20T10:07:00+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2026-03-19T05:54:59+00:00","index":"","fulltext":""},{"type":"submitted","content":"Scientific Reports","date":"2026-03-19T05:08:30+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"23315cfe-708b-4474-8d5e-a445af5604fd","owner":[],"postedDate":"March 25th, 2026","published":true,"recentEditorialEvents":[{"type":"decision","content":"Revision requested","date":"2026-05-06T12:44:22+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-05-06T03:04:37+00:00","index":46,"fulltext":""},{"type":"reviewerAgreed","content":"236495659241229169743849065878927418618","date":"2026-05-05T07:25:22+00:00","index":44,"fulltext":""}],"rejectedJournal":[],"revision":"","amendment":"","status":"in-revision","subjectAreas":[{"id":64997456,"name":"Biological sciences/Developmental biology"},{"id":64997457,"name":"Biological sciences/Genetics"},{"id":64997458,"name":"Biological sciences/Molecular biology"}],"tags":[],"updatedAt":"2026-05-06T12:56:32+00:00","versionOfRecord":[],"versionCreatedAt":"2026-03-25 16:14:06","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-9094102","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-9094102","identity":"rs-9094102","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}