Human Hepatic Cell line 5 : In-Vitro Model for Hepatic Immunobiology

Human Hepatic Cell line 5 : In-Vitro Model for Hepatic Immunobiology | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Human Hepatic Cell line 5 : In-Vitro Model for Hepatic Immunobiology Smeeta Shrestha, Ming Yue Yeong, Chen Xin Yi, Wei Wang, Nidhi Bhayana, and 3 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7058022/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 03 Feb, 2026 Read the published version in Molecular Biology Reports → Version 1 posted 10 You are reading this latest preprint version Abstract Background Hepatocellular carcinoma is a major global health challenge, partly due to the lack of suitable in vitro models that mimic early host–virus interactions. Human Hepatic Cell line 5 (HHL-5), an immortalized hepatocyte cell line, retains key liver functions, lacks tumour markers, binds virus-like particles, and responds to immune stimuli. This study aimed to characterize the genetic and metabolic profile of HHL-5 to evaluate its suitability as a physiologically relevant model for studying viral infection and host immune responses. Method HHL-5 and HepG2 cell lines were analysed for morphology, growth, genetic variants, metabolite profiles, and protein expression. Immunofluorescence and MTS assays assessed cell morphology and growth, while whole exome sequencing and NMR evaluated genetic and metabolic profiles. Protein markers related to proliferation, oxidative stress, and detoxification were examined via Western blot, with significance tested using a T-test. Results Functional analysis of germ line variants in HHL-5 illustrated a highly immunocompetent genomic profile, including antigen processing and presentation, interferon signalling, dendritic cell differentiation, and leukocyte adhesion. Conversely, HepG2 exhibited enrichment in DNA replication pathways. Metabolite analysis in HHL-5 exhibited elevated levels of 1-methylnicotinamide, ADP, and UDP-GalNAc, suggesting enhanced redox function, mitochondrial respiration, and glycosylation—key features of active oxidative metabolism characteristic of primary hepatocytes. In contrast, HepG2 showed increased levels of lactate, glutathione disulfide, creatine, glycerophosphocholine, and branched-chain amino acids, indicative of a glycolytic, redox-adaptive metabolic profile typical of hepatocellular carcinoma. Conclusion HHL-5’s non-cancerous, immunocompetent profile makes it a valuable model for investigating liver disease progression and hepatocarcinogenesis. Hepatocyte HHL-5 Virus Variants Metabolite Interferon UDP-GalNAc Glycosylation Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Introduction Hepatocellular carcinoma (HCC) is an aggressive liver malignancy with poor prognosis and rank as the third most frequent cause of cancer related death in the word 1 .Hepatocarcinogenesis is a complex process driven by both genetic and epigenetic alterations that collectively contribute to the initiation, promotion, and progression of liver cancer 2 . Extensive information is available on the altered gene and metabolite profiles observed in tumours 3 , but early changes leading to initiation of cellular transformation and which liver cells are most susceptible to this process are poorly investigated 4 . HCC exhibits pleiotropic molecular profiles with diverse clinical outcomes necessitating an urgent need to innovate in vitro experimental models to understand HCC pathophysiology and perform high throughput screening for therapeutic agents. Most existing in-vitro models rely on hepatocytes derived from hepatic tumours, which inherently exhibit altered germline variants 5 , as well as distinct metabolic 6 and gene expression 7 signatures that support strong cell adhesion and proliferation. Some of the cell lines are capable of expressing viral proteins upon integration of Hepatitis B virus (HBV) or Hepatitis C virus (HCV) DNA into their genomes 8 . Such in-vitro models are suitable to examine molecular changes in the gene expression, and cell signalling that are important in tumorigenesis 9 . However, hepatic tumour-derived cell lines have poor predictive ability about carcinogenic transformation and early stages of metabolic and biochemical changes leading to tumour progression 10 . Presently, primary human hepatocytes (PHH) are highly permissive to infection and efficiently support all the steps of HBV replication. However, during in-vitro culture PHHs de-differentiate, lose hepatic function and HBV infection ability 11 . Researchers have also created liver cell lines resembling primary hepatocytes by immortalizing them using viral oncogenes that target the human telomerase reverse transcriptase (hTERT) subunit, enabling studies of viral infection and hepatocyte function 12 . Human hepatocyte lines 5 (HHL-5) are immortalized human primary hepatocyte cell line, transduced with hTERT and human papillomavirus E6E7 (HPV/E6E7), which is phenotypically like primary hepatocyte 13 . The HHL-5 cell line was established as a model to investigate hepatitis virus-mediated liver infections. It exhibits phenotypic markers characteristic of both hepatocytes and biliary epithelial cells, with minimal expression of tumour-associated proteins such as p53 and alpha-fetoprotein, supporting its non-tumorigenic profile. HHL-5 cells form adherent monolayers and show enhanced binding to recombinant hepatitis C virus-like particles, making them suitable for studying viral entry mechanisms. Importantly, stimulation with interferon-alpha (IFN-α) induces upregulation of major histocompatibility complex (MHC) molecules 14 , indicating preserved immune signalling capabilities. Furthermore, their high sensitivity to anticancer agents 15 and lack of cytotoxicity in response to silicon nanoparticle 16 treatment highlight the potential of HHL-5 for applications in immunological studies and targeted drug delivery. The present study aimed to comprehensively characterize HHL-5 in terms of its morphology, growth characteristics, germline variants, metabolite profile, and cell marker expression. This characterization is intended to evaluate the suitability of HHL-5 as a hepatocyte model for investigating viral infection and the development of hepatocellular carcinoma (HCC). In this study, we utilized HHL-5 and HepG2 liver cell lines to investigate their suitability as models for hepatic biology. Whole exome sequencing and metabolomics data were generated from both cell lines, while additional liver exome data were retrieved from the European Nucleotide Archive and analysed alongside the HHL-5 and HepG2 datasets. To assess cell phenotype, immunofluorescent staining and high-content analysis were performed. Cell proliferation was measured using the MTS assay, and protein markers related to cell proliferation, oxidative stress, and xenobiotic metabolism were evaluated to assess hepatocyte functionality. Variant analysis showed enriched immune markers in HHL-5 but not in HepG2 cells. Metabolomic profiling revealed that HHL-5 retained oxidative, mitochondrial-based energy metabolism in contrast to HepG2’s reliance on glycolysis metabolism.These findings highlight the non-tumorigenic, immune-competent nature of HHL-5 and support its utility as a physiologically relevant model for studying hepatic immune responses, virus interactions, and inflammation-driven mechanisms underlying hepatocellular carcinoma (HCC). Material and Methods Cell line and reagents The human hepatocyte line HHL-5 was kindly supplied by Professor Arvind Patel, Centre for Virus Research, School of Infection and Immunity, University of Glasgow (UK). It was maintained in low glucose Dulbecco’s Modified Eagle Medium (DMEM) and 10% fetal bovine serum (FBS) (Gibco, US). Human hepatocyte carcinoma lines (HepG2) and HepG2.2.15 were purchased from the American Type Culture Collection (ATCC) and maintained in high glucose DMEM (Gibco, US) with 10% FBS (Gibco, US). Cells were cultured at 37°C and 5% carbon dioxide in a cell culture incubator (Nuaire, US). High content analysis HHL-5 and HepG2 cells were seeded at 0.2 × 10 4 cells/well in 96-well plates, cultured for 24 h, and fixed with 4% paraformaldehyde (in pre warmed PBS) for 15 min, followed by 0.03% Triton-X permeabilization for 15 min. Cells were stained for filamentous actin (F-actin), nucleus, microtubules and plasma membrane using Phalloidin-TRITC, Hoechst (Sigma Aldrich, US), anti-α-tubulin conjugated with AlexaFluor® 488 (Sigma Aldrich, U.S), and CellMask Deep Red (Invitrogen, U.S). Plates were scanned (4 randomly selected fields per well at 10× and 20× magnification) using an automated microscope IN Cell Analyzer 2200 Imaging System (GE Healthcare, US). Acquired images were analyzed by IN Cell Investigator software (Version 1.6) using multitarget analysis bio-application module (GE Healthcare, US). MTS Cell Viability Assay Cell proliferation rates were determined using the CellTiter 96® Aq ueous Non-radioactive Cell Proliferation Assay (Promega, US) according to the manufacturer’s protocols. A total of 0.2 × 10 4 cells/well were seeded in 96-well culture plate (Nunc, US) followed by incubation with MTS solution for 0, 8, 24 and 48-hours. Further absorbance was measured at 490 nm with the Gen5™ Microplate Reader (BioTek, US). Real time monitoring of cell proliferation HHL-5 and HepG2 were seeded at 0.2 × 10 4 cells per well, respectively, into an E-plate 16 (ACEA Biosciences, San Diego, CA) containing 100 µL medium per well and monitored in real time using the xCELLigence instrument (Agilent). The cells were pre-treated with nocodazole as toxicity control and were incubated at 37°C in a 5% CO 2 incubator. Cell growth was quantified as “Baseline Cell Index”. To calculate the doubling time of both cell lines, RTCA software v. 1.2.1 was used. All the experiments were performed in triplicates and repeated at least 3 times. GSH/GSSG- Glo assay HHL-5 and HepG2 cells were seeded into collagen coated white opaque 96 well microtiter plates at density of 1x10 4 cells/well. The total amounts of combined glutathione (GSH) and oxidized GSH (GSSG) were measured following the manufacturer's instruction. Luminescence is measured using Cytation 3 image reader instrument with Gen5 microplate reader imager software. GSH/GSSG ratio was calculated as [(net total glutathione RLU – net GSSG RLU)/(net GSSG RLU)] × 2, where RLU is relative light units and students t-test was performed for significance. All the experiments were performed in triplicates and repeated at least 3 times. Whole -exome capture and sequencing Exome capture was performed using Agilent’s SureSelect Human All Exon V6 (58) Kit according to the manufacturer’s instructions (Agilent, Santa Clara, CA). Products were purified with AMPure XP system (Beckman Coulter, Beverly, USA) and quantified using the Agilent high sensitivity DNA assay on the Agilent Bioanalyzer 2100 system. Sequencing was performed on HiSeq 2500 (Illumina, San Diego, CA), in 150 bp paired-end sequencing (PE150). NovaSeq 6000 software were used for raw data processing and fastq file generation. Whole - exome sequencing data analysis Whole-exome sequencing (WES) paired-end reads(.fastq) were passed through quality control using FastQC 17 . Reads with TruSeq adaptor sequences, uncertain nucleotides (> 10%) and paired reads when single reads have more than 50% low-quality (< 5) nucleotides are removed using Trimmomatic 18 . Paired end reads were mapped to NCBI human reference genome GRCh38 using Burrows–Wheeler Aligner (BWA) software 19 . Picard tool in Genome Analysis Toolkit (GATK) suite 20 marked duplicates and GATK best practice variants called using HaplotypeCaller. The output variant call files (vcf) was Hard filtered using the VariantFilteration tool. The bcftools was used to filter vcf files based on FILTER="PASS" && %QUAL > 50 && GQ > 20. Where QUAL is probability that the site has no variant and GQ, probability that the call is incorrect. Finally, variants were annotated using annovar 21 and analysed using vcfshiny 22 tool. Variants were prioritised by filtering based on pathogenicity scores, SIFT 23 and POLYPHEN 24 . Variants located in exons were extracted and genes were used for enrichment analysis using EnrichR 25 . Normal human liver exome data was downloaded from European nucleotide archive from project PRJNA207681. A total of 3 normal human liver exome fastq files (SRR893106, SRR894448, SRR894453) were processed and variants detected as mentioned above. The liver tissue whole exome data was retrieved from the European nucleotide Archive 26 . Metabolic profiling using NMR Cells collected were subjected to methanol and water extraction (2:1) using tissue lyser. The supernatant collected was dried using Spin-Vac and dried extracts were reconstituted in 600 µL 0.1 M phosphate buffer (pH = 7.4, K 2 HPO 4 /NaH 2 PO 4 = 4:1, 0.005% TSP-d 4, 100% D 2 O) and then centrifuged 10 min at 16000×g and 4°C; a total of 550 µL of supernatant was transferred into a 5 mm NMR tube for further NMR analysis. Proton ( 1 H) NMR spectra of cell extracts were recorded by III HD 600 MHz Ascend NMR spectrometer (Bruker), equipped with 5mm BBI 600 MHz Z-Gradient high-resolution probe. The one-dimensional (1D) NMR spectra were acquired at 298 K with the first increment of NOESY pulse sequence. A pre-saturation method was used to suppress the water signal during recycle delay (2s) and mixing time (100ms). For each sample, the spectral width was 20 ppm and 32 transients were collected into 32 k data points. NMR spectral peak assignment was performed based on previous publication 27 . NMR spectral processing and multivariate data analysis The free induction decay (FID) of NMR spectra were Fourier transformation (FT) and the derived NMR spectra were phased, and baseline corrected manually on Topspin 3.6.2 (Bruker Biospin, Karlsruhe, Germany). The calibration of spectra was referenced to the TSP peak with chemical shift of δ 0.00 by Topspin 3.0. NMR spectral region of δ 0.5–9.5 was integrated into 0.002 ppm wide buckets by AMIX package (Bruker Biospin, Karlsruhe, Germany). The region δ 4.55–4.75 was excluded to avoid the disturbance of the remaining water signal. Total intensity normalization was applied prior to multivariate data analysis. Multivariate data analysis was performed by SIMCA 16.0 software (Umetrics, Sweden). Normalized data sets were analyzed by Orthogonal Projection to Latent Structure Discriminant analysis (O-PLS-DA) with unit variance (UV) scaling. The model was cross-validated by CV-ANOVA method ( p = 0.0015 < 0.05) and permutation test. The back-transformed and plotted with color-coded correlation coefficients (|r|) using an in-house developed script written in MATLAB 7.1 (the MathWorks, USA) with the red color indicating statistical significance, while the blue color no significance. The cutoff value derived from a 95% confidence limit for each model changes according to the number of samples (n) in the groups, here the cutoff value |r| is 0.523. Protein Isolation and Detection Whole cell lysates were prepared in cell lysis buffer and quantitated using Bio-Rad Protein Assay Dye Reagent Concentrate (Bio-Rad, US). A total of 30 ug protein was separated on sodium dodecyl sulphate (SDS) polyacrylamide gel electrophoresis and transferred to nitrocellulose membranes (Bio-Rad, US). The membranes were blocked with 5% BSA and then incubated overnight at 4°C with primary antibodies (Supplementary Table 1). Post incubation, membrane was washed in 1x TBS-T washing buffer, then incubated in appropriate secondary antibody for 1 hour at room temperature. The blots were developed using the WesternBright™ ECL detection kit (Advansta, US) and imaged by ChemiDoc™ MP Imaging System using Image Lab™ Software (Bio-Rad, US). Densitometry analysis of the Western blots was performed using ImageJ software. For Nrf2 and antioxidant enzymes, Odyssey system was applied according to the manufacturer's instructions. Statistical analysis Each experiment was performed three times. Statistical analysis was performed with GraphPad Prism 9 software. Data was evaluated for normality and Grubbs outlier analysis. The results are presented as means ± standard deviations (SD), and the student’s T-test was used to compare the means of independent samples. For cell morphometry analysis nonparametric Mann Whitney U test was performed. p values of < 0.05 were considered statistically significant. Results HHL-5 Cells Exhibit Slower Cell Growth Than HepG2. Cell morphology and function are closely related characteristics and monitoring of cell morphology helps to distinguish between normal and transformed (e.g., cancer-like) cells. To evaluate the difference in HHL-5 and HepG2 cell morphology immunofluorescence staining of cytoskeleton (F actin and alpha tubulin) and nucleus (DAPI) was performed. Figure 1 illustrates the staining of nuclei (Hoechst), α-tubulin, and F-actin in HHL-5 and HepG2 cells. Hoechst staining reveals that HHL-5 cells have smaller nuclei compared to HepG2. The α-tubulin staining patterns differ between the two cell types, indicating variations in cytoskeletal organization, while F-actin distribution appears comparable in both. High-content analysis further confirms that HHL-5 cells have a significantly smaller overall cell area (Fig. 1 B) and nuclear area (Fig. 1 C) compared to HepG2 cells. The trendline graph shows the MTS assay results over 48 hours, comparing viability between HHL-5 and HepG2 cell lines. HHL-5 (blue) shows moderate growth with absorbance rising from ~ 0.5 to 1.0, while HepG2 (red) shows a steeper increase to ~ 1.8, indicating significantly higher proliferation (Fig. 1 D). Real time cell analysed the impedance-based cell index over 96 hours, comparing adhesion and growth of two cell lines with and without nocodazole (noc, anti-mitotic drug). HepG2 displays stronger adhesion and proliferation (~ 1.5), while HHL-5 shows limited growth (~ 0.5). Nocodazole slows down the growth and attachment of both cell types. However, HHL-5 is affected more quickly and severely, while HepG2 is affected more slowly, meaning HHL-5 is more sensitive to the drug (Fig. 1 E). Lastly, cell doubling time, a key indicator of cell growth rate was calculated for HHL-5 and HepG2. HHL-5 cells showed a doubling time of 53.68 ± 14.43 (hr ± SD), significantly longer than the 18.75 ± 2.07 observed in HepG2 cells, indicating slower proliferation (Fig. 1 G). Together, these findings suggest that HHL-5 cells differ markedly from HepG2 in both morphology and growth characteristics. Exonic Single Nucleotide Variant (SNV) profile Analysis of exonic single nucleotide variants revealed comparable variant counts in HepG2 and HHL5 cell lines, whereas primary liver tissues exhibited slightly lower counts (Fig. 2 A). Analysis of the proportion of pathogenic SNVs in liver samples showed a modestly higher percentage (~ 14.5%) relative to HepG2 and HHL5 (~ 13.5%), suggesting a distinct pathogenic profile in the tissue samples (Fig. 2 B). Pathogenic variants were compared across three groups and 813 pathogenic SNVs was common in the three groups. HepG2 and HHL5 had unique variants 1854 and 1217, respectively, while primary liver tissue contained the largest number of unique pathogenic SNVs (3649), indicating group-specific vairant landscapes (Fig. 2 C). Enrichment analysis of GO biological processes identified distinct pathways across groups. In HepG2, terms like “protein homooligomerization” and “DNA-template replication fidelity” were enriched. HHL5 showed enrichment in immune-related processes such as “antigen processing via MHC class Ib” and “leukocyte adhesion.” Liver tissues displayed enrichment in developmental processes including “cilium organization,” “intercellular transport,” and “tongue development,” highlighting functional specificity in variant (Fig. 2 D). “KEGG pathway enrichment revealed that SNVs in HepG2 cells were associated with genes involved in olfactory transduction, HIV-1 infection, butanoate metabolism, the Fanconi anemia pathway, and cell cycle regulation. In HHL-5, enriched pathways included viral myocarditis, antigen processing and presentation, allograft rejection, type 1 diabetes mellitus, and ABC transporters. Liver tissue variants were linked to folate and retinol metabolism, N-glycan biosynthesis, African trypanosomiasis, and antifolate resistance (Fig. 2 E).” Insertion Deletion (INDEL) Variant profile Analysis of insertion and deletion variants show that HepG2 (mean ≈ 520) and HHL5 (mean ≈ 500) contain a greater number of INDELs compared to liver tissue (mean ≈ 300) (Fig. 3 A). Functional categorisation of indels show higher non -frame shift deletions across all groups. HepG2 shows highest variants count across all indel categories. Cell lines, HepG2 and HHL-5 showed higher frameshift variants compared to liver tissue (Fig. 3 B). Frameshift variants were compared across 3 groups. A total of 63 variants were shared among all three groups. However, each group also showed a substantial number of unique variants: 133 in HepG2, 105 in HHL5, and 169 in liver, indicating context-specific variant profiles (Fig. 3 C). GO enrichment analysis revealed distinct functional signatures across groups: ‘heterotypic cell-cell adhesion’ in HepG2, ‘antigen processing and presentation’ in HHL-5, and both ‘negative regulation of phagocytosis’ and ‘mismatch repair’ in liver tissue. (Fig. 3 D). KEGG terms enriched in HepG2 and HHL-5 report immune related pathways, “Graft versus host disease”, “Autoimmune thyroid disease”, “Allograft rejection” and autoimmune term, “Type 1 diabetes mellitus”. Liver and HepG2 show enriched KEGG term, “Olfactory transduction”. Liver only showed enrichment for “RNA degradation” (Fig. 3 E). Collectively, these results show distinct mutational and functional landscape of INDELs across liver-derived cell lines and primary liver tissue, reflecting both shared and divergent genetic landscape. Hepatocyte function markers in HepG2 and HHL-5 cell line. Cell proliferation, xenobiotic and oxidation processes are important for hepatocyte function and cancer development. We measured protein expression of cancer proliferative markers c-myc, signal transducer and activator of transcription 3 (STAT3) and phospho-STAT3 (pSTAT3) levels in HHL-5 and HepG2 to understand HHL-5 function in contrast to hepatoma. There was lower c-myc (Fig. 4 A, 4 E) and pSTAT3/tSTAT3 (Fig. 4 B, 4 F) protein expression in HHL-5 against HepG2 indicating that cell proliferation is lower in HHL-5. We later evaluated the protein expression of enzymes involved in xenobiotic detoxification in HHL-5. HHL-5 showed significantly reduced expression of cytochrome P450, family 2, subfamily A, polypeptide 7 (CYP2A7) (Fig. 4 C, 4 G) and no expression in Cytochrome P450 Family 1 Subfamily A Member 2 (CYP1A2) (Fig. 4 C). Oxidative stress is the pathological feature of poor xenobiotic detoxification in liver diseases and hepatocytes contain cytoprotective genes to prevent oxidative stress. We measured the protein expression of nuclear erythroid 2-related factor 2 (Nrf2), key regulator of cytoprotective genes, catalase (CAT), UDP glucuronosyltransferase family 1 member A1 (UGT1A1), quinone oxidoreductase 1 (NQO1), glutamyl cysteine synthetase (GCS), glutathione S-transferase alpha 1 (GSTA1), heme oxygenase-1 (HO-1) to evaluate oxidative stress in HHL-5. HHL-5 cells express low levels of activated Nrf2 (pNrf2/Nrf2) and its panel of cytoprotective genes (Supplementary Fig. 1) in contrast to HepG2. Additionally, the ratio of reduced glutathione (GSH) to oxidised glutathione (GSSG) is used as a marker of oxidative stress. HHL-5 cells showed significantly low GSH/GSSG ratio compared to HepG2 (Fig. 4 I) Distinct Metabolic Signatures in HHL-5 and HepG2 Cells Multivariate statistical analysis of the 1H-NMR spectra revealed distinct metabolic profiles between HepG2 and HHL5 cell pellets. In the PLS-DA score plot (Fig. 5 A), clear separation of the two groups was observed—HepG2 (green) and HHL5 (blue)—indicating marked differences in metabolite composition. The robustness and reliability of the model were confirmed by permutation testing (Fig. 5 B), yielding a high predictive value (Q² = 0.895), excellent model fit (R² = 0.988), and strong statistical significance (p = 0.0015), ruling out overfitting. Spectral decomposition of the aromatic and ribose region (Fig. 5 C) in HHL-5 illustrate elevated levels of key metabolites involved in redox regulation, energy metabolism, and biosynthesis. Increased NAD and its derivative 1-methylnicotinamide (1-meNAM) indicate enhanced mitochondrial respiration and NAD⁺ turnover. Higher levels of ADP reflect elevated ATP utilization and energy demand. Additionally, enrichment of UDP-GalNAc and uridine suggests active glycosylation and RNA metabolic processes, supporting a biosynthetically active phenotype.HepG2 cells showed increased levels of uracil, indicating enhanced nucleotide degradation and RNA turnover. Elevated aromatic amino acids such as tyrosine and phenylalanine suggest higher protein turnover or altered amino acid metabolism, reflecting a more catabolic or stress-associated metabolic state. (Fig. 5 C). The aliphatic region of the 1H-NMR difference spectrum (Fig. 5 D) revealed distinct metabolic profiles between HHL-5 and HepG2 cells. Only glutamate exhibited a positive peak (+ 0.02), indicating it is elevated in HHL-5 and suggests greater engagement in TCA cycle activity and amino acid metabolism. In contrast, a series of metabolites showed negative peaks, indicating they are elevated in HepG2. These included lactate, creatine, oxidized glutathione (GSSG), glycerophosphocholine (GPC), aspartate, citrate, and the branched-chain amino acids (valine, leucine, isoleucine). This pattern reflects a metabolic state in HepG2 characterized by increased aerobic glycolysis, ATP buffering via creatine, membrane turnover (GPC), and amino acid catabolism. Together, these results indicate that HepG2 cells rely more heavily on glycolytic energy production, redox stress adaptation, and amino acid turnover, whereas HHL-5 exhibits relatively higher glutamate-linked mitochondrial activity. (Fig. 5 D). Discussion In this study we characterize human hepatic cell line, HHL-5 to support its utility as a primary immortalized hepatic cell line to study liver pathology. We evaluated HHL-5 growth characteristics, variant and metabolite profiles, along with the expression of markers related to proliferation, xenobiotic biotransformation, and oxidative stress. Genetic variants serve as critical indicators of a cell’s molecular function, offering valuable insights into its signalling and immune pathways, and thereby enhancing its utility as an effective in vitro model for studying liver-specific viral infections. Germ line variants in HepG2, HHL-5, and primary human liver tissue revealed distinct landscapes reflective of their biological contexts. While both hepatic cell lines exhibited a higher overall variant burden than primary liver tissue, consistent with genomic instability in immortalized models 28 . HHL-5 uniquely demonstrated enrichment in immune-related pathways, including antigen processing and presentation, interferon signalling, dendritic cell differentiation, and leukocyte adhesion. These immune-relevant variant signatures are absent in HepG2, which was instead enriched for pathways associated with DNA replication and the Fanconi anemia pathway, aligning with its tumorigenic and highly proliferative nature. Primary liver tissue showed a comparatively lower burden of variants but was enriched in developmental and xenobiotic metabolism pathways 29 , highlighting its intact physiological complexity and in vivo exposure to environmental stressors. Notably, the immune-enriched variant profile in HHL-5 corresponds with its expression of antiviral components such as MHC molecules and toll-like receptors 13 , supporting a competent innate immune environment. In contrast HepG2 lacks robust intrinsic immune functionality due to downregulation of innate immune sensors and impaired interferon signalling making HepG2 less capable of mounting effective intrinsic antiviral responses compared to primary hepatocytes 30 . These distinctions reinforce HHL-5’s non-tumorigenic, immune-competent phenotype and its value as a physiologically relevant model for investigating hepatic immune responses, HBV-host interactions, and inflammation-driven mechanisms in HCC. Metabolites reflect the dynamic biochemical state of a cell, providing key insights into its metabolic and immune functions, and are essential for evaluating the suitability of a model system to study liver-specific viral infections. HHL-5 displays metabolite profiles more similar to primary hepatocytes than HepG2, marked by elevated 1-methylnicotinamide 31 , ADP, and UDP-GalNAc—metabolites linked to mitochondrial respiration 32 , redox balance, and glycosylation 33 , which are hallmarks of functional oxidative metabolism in hepatocytes. Elevated glutamate in HHL-5 indicates enhanced TCA cycle activity and oxidative phosphorylation, further highlighting its bioenergetic profile as more reflective of primary liver tissue 34 . In contrast, HepG2 cells display cancer-associated metabolic reprogramming, with increased levels of lactate, glutathione disulphide (GSSG), creatine, glycerophosphocholine (GPC), and branched-chain amino acids (BCAAs). Elevated lactate reflects a shift toward glycolysis 35 , typical of the Warburg effect in cancer cells and is linked to immune suppression and tumour progression 36 . High GSSG levels indicate oxidative stress and a heightened antioxidant response 37 , while increased creatine supports elevated energy demands 38 . Elevated GPC is associated with membrane turnover and phospholipid remodelling, a hallmark of malignancy 39 , and increased BCAAs have been implicated in cancer metabolism 40 and are consistently observed in HCC 41 including HepG2 42 . Together, these findings reinforce that HHL-5 retains oxidative, mitochondrial-based energy metabolism with minimal signs of metabolic transformation, thereby preserving key aspects of normal hepatocyte function. Importantly, given HHL-5’s immunocompetent genomic profile and bioenergetic similarity to primary hepatocytes, it provides a robust platform for studying the immunometabolism of liver diseases, including viral infection and inflammation-associated hepatocarcinogenesis, under physiologically relevant, non-tumorigenic conditions. The protein expression profile of HHL-5 cells supports their utility as a physiologically relevant, non-tumorigenic hepatocyte model suitable for studying hepatocellular carcinoma (HCC) development. Protein markers for proliferation, cytochrome enzymes, and oxidation are essential for evaluating hepatocyte cell line suitability as models for liver function and disease research. Compared to the transformed HepG2 line, HHL-5 exhibits markedly lower levels of proliferative markers such as c-Myc 43 and phosphorylated STAT3 44 , consistent with a quiescent phenotype typical of primary hepatocytes and indicative of reduced oncogenic signalling. Additionally, HHL-5 shows low or absent expression of xenobiotic metabolism enzymes like CYP2A7 and CYP1A2, indicating a preserved inducibility of these pathways and tighter regulatory control 45 , which contrasts with the dysregulated enzyme expression often seen in HCC. Importantly, HHL-5 also demonstrates diminished oxidative stress response signalling, as evidenced by lower phosphorylated Nrf2 and reduced expression of antioxidant genes, CAT, NQO1, HO-1, and GSTA1, along with a lower GSH/GSSG ratio, hallmarks of primary hepatocytes that are more susceptible to oxidative insults than cancer cells 46 . These collective features highlight the functional alignment of HHL-5 with non-malignant liver tissue. Conclusion In conclusion, the HHL-5 cell line has been characterized as immunocompetent and non-tumorigenic, with a distinct variant and metabolic profile that preserves key hepatocyte functions and immunological responsiveness. Unlike transformed hepatoma lines such as HepG2, HHL-5 serves as a physiologically relevant model, making it a valuable resource for studying HBV-host immune interactions and advancing therapeutic strategies against viral-mediated hepatocellular carcinoma (HCC) development. Declarations Author Contribution Smeeta Shrestha: Investigation, formal analysis, Data curation, writing-original draft preparation. Min Yue Yeong: Investigation. Chen Xin Yi: Investigation. Wei Wang: Validation, Investigation. Nidhi Bhayana: Investigation. Navin Kumar Verma: Conceptualisation, resources. Yongping Bao: Visualization, resources, writing-reviewing and editing. Yulan Wang: Funding acquisition Project administration. Data availability statement Data is deposited at NCBI – Bio Project ID PRJNA1069993 Funding statement Lee Kong Chian School of Medicine, Singapore Cancer Prevention Research Trust UK Conflict of interest disclosure There is no conflict of interest. Ethics approval statement Not applicable Patient consent statement Not applicable Permission to reproduce material from other sources. Not applicable Clinical trial registration Not applicable Acknowledgements We would like to acknowledge Dr. Jingtao Zhang and Miss Abigail Thomson for their help in NMR profiling, the Lee Kong Chian School of Medicine for providing the start-up grant and the Cancer Prevention Research Trust, UK. References Ferlay J et al (2019) Estimating the global cancer incidence and mortality in 2018: GLOBOCAN sources and methods. Int J Cancer 144:1941–1953. https://doi.org:10.1002/ijc.31937 Liu M, Jiang L, Guan XY (2014) The genetic and epigenetic alterations in human hepatocellular carcinoma: a recent update. Protein Cell 5:673–691. https://doi.org:10.1007/s13238-014-0065-9 Ramesh V, Ganesan K (2016) Integrative functional genomic analysis unveils the differing dysregulated metabolic processes across hepatocellular carcinoma stages. 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Nature 483:603–607. https://doi.org:10.1038/nature11003 Wilkening S, Stahl F, Bader A, COMPARISON OF PRIMARY HUMAN HEPATOCYTES, AND HEPATOMA CELL LINE HEPG2 WITH REGARD TO THEIR BIOTRANSFORMATION PROPERTIES (2003) Drug Metab Dispos 31:1035–1042. https://doi.org:10.1124/dmd.31.8.1035 Arzumanian VA, Kiseleva OI, Poverennaya EV (2021) The Curious Case of the HepG2 Cell Line: 40 Years of Expertise. Int J Mol Sci 22. https://doi.org:10.3390/ijms222313135 Deng X, Li Y, Jiang L, Xie X, Wang X (2025) 1-methylnicotinamide modulates IL-10 secretion and voriconazole metabolism. Front Immunol 16:1529660. https://doi.org:10.3389/fimmu.2025.1529660 Yusri K, Jose S, Vermeulen KS, Tan TCM, Sorrentino V (2025) The role of NAD + metabolism and its modulation of mitochondria in aging and disease. npj Metabolic Health Disease 3:26. https://doi.org:10.1038/s44324-025-00067-0 Hopp AK, Grüter P, Hottiger MO (2019) Regulation of Glucose Metabolism by NAD(+) and ADP-Ribosylation. 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Nat Reviews Endocrinol 13:624–624. https://doi.org:10.1038/nrendo.2017.120 Glunde K, Bhujwalla ZM, Ronen SM (2011) Choline metabolism in malignant transformation. Nat Rev Cancer 11:835–848. https://doi.org:10.1038/nrc3162 Ericksen RE et al (2019) Loss of BCAA Catabolism during Carcinogenesis Enhances mTORC1 Activity and Promotes Tumor Development and Progression. Cell Metabol 29:1151–1165e1156. https://doi.org/10.1016/j.cmet.2018.12.020 . https://doi.org: Mansoori S, Ho MY-m, Ng KK-w, Cheng K (2025) K.-y. Branched-chain amino acid metabolism: Pathophysiological mechanism and therapeutic intervention in metabolic diseases. Obes Rev 26:e13856. https://doi.org:https://doi.org/10.1111/obr.13856 Ananieva EA, Wilkinson AC (2018) Branched-chain amino acid metabolism in cancer. Curr Opin Clin Nutr Metab Care 21:64–70. https://doi.org:10.1097/mco.0000000000000430 Zheng K, Cubero FJ, Nevzorova YA (2017) c-MYC-Making Liver Sick: Role of c-MYC in Hepatic Cell Function, Homeostasis and Disease. Genes (Basel) 8. https://doi.org:10.3390/genes8040123 Li W, Liang X, Kellendonk C, Poli V, Taub R (2002) STAT3 Contributes to the Mitogenic Response of Hepatocytes during Liver Regeneration*. J Biol Chem 277:28411–28417. https://doi.org:https://doi.org/10.1074/jbc.M202807200 George J, Goodwin B, Liddle C, Tapner M, Farrell GC (1997) Time-dependent expression of cytochrome p450 genes in primary cultures of well-differentiated human hepatocytes. J Lab Clin Med 129:638–648. https://doi.org:https://doi.org/10.1016/S0022-2143(97)90199-2 Cassim S, Raymond V-A, Lapierre P, Bilodeau M (2017) From in vivo to in vitro: Major metabolic alterations take place in hepatocytes during and following isolation. PLoS ONE 12:e0190366. https://doi.org:10.1371/journal.pone.0190366 Additional Declarations No competing interests reported. Supplementary Files SupplementaryInformation.docx Cite Share Download PDF Status: Published Journal Publication published 03 Feb, 2026 Read the published version in Molecular Biology Reports → Version 1 posted Editorial decision: Revision requested 29 Sep, 2025 Reviews received at journal 31 Aug, 2025 Reviews received at journal 20 Aug, 2025 Reviewers agreed at journal 05 Aug, 2025 Reviewers agreed at journal 03 Aug, 2025 Reviewers agreed at journal 24 Jul, 2025 Reviewers invited by journal 09 Jul, 2025 Editor assigned by journal 09 Jul, 2025 Submission checks completed at journal 08 Jul, 2025 First submitted to journal 06 Jul, 2025 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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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-7058022","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":482832164,"identity":"eec797c1-3efc-426c-86fa-ef4dc297caa2","order_by":0,"name":"Smeeta Shrestha","email":"","orcid":"","institution":"Lee Kong Chian School of Medicine, Nanyang Technological University","correspondingAuthor":false,"prefix":"","firstName":"Smeeta","middleName":"","lastName":"Shrestha","suffix":""},{"id":482832165,"identity":"22ca03da-faa8-41b3-be93-1517a35db24c","order_by":1,"name":"Ming Yue Yeong","email":"","orcid":"","institution":"Lee Kong Chian School of Medicine, Nanyang Technological University","correspondingAuthor":false,"prefix":"","firstName":"Ming","middleName":"Yue","lastName":"Yeong","suffix":""},{"id":482832166,"identity":"a66bab3a-d891-4a82-8b62-958551d6c9c0","order_by":2,"name":"Chen Xin Yi","email":"","orcid":"","institution":"Lee Kong Chian School of Medicine, Nanyang Technological University","correspondingAuthor":false,"prefix":"","firstName":"Chen","middleName":"Xin","lastName":"Yi","suffix":""},{"id":482832167,"identity":"65e6eac0-af1b-45d1-9ece-719a6829da71","order_by":3,"name":"Wei Wang","email":"","orcid":"","institution":"University of East Anglia","correspondingAuthor":false,"prefix":"","firstName":"Wei","middleName":"","lastName":"Wang","suffix":""},{"id":482832168,"identity":"39278c62-badf-474b-adc2-286a9d3b5dcf","order_by":4,"name":"Nidhi Bhayana","email":"","orcid":"","institution":"Lee Kong Chian School of Medicine, Nanyang Technological University","correspondingAuthor":false,"prefix":"","firstName":"Nidhi","middleName":"","lastName":"Bhayana","suffix":""},{"id":482832170,"identity":"fe947a93-0cdf-4e1b-9541-25cb5d48e726","order_by":5,"name":"Navin Kumar Verma","email":"","orcid":"","institution":"Lee Kong Chian School of Medicine, Nanyang Technological University","correspondingAuthor":false,"prefix":"","firstName":"Navin","middleName":"Kumar","lastName":"Verma","suffix":""},{"id":482832174,"identity":"fbcd4674-c2c8-4854-a3d1-1f1e274db19e","order_by":6,"name":"Yongping Bao","email":"","orcid":"","institution":"University of East Anglia","correspondingAuthor":false,"prefix":"","firstName":"Yongping","middleName":"","lastName":"Bao","suffix":""},{"id":482832176,"identity":"15a543ac-c0f8-4fdb-ac5f-041d8bea113b","order_by":7,"name":"Yulan Wang","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA8ElEQVRIiWNgGAWjYFAC5gYY4wCEPkBQCyNMC1sCRDUJWngMiNNicCOx8XMBw2F5c/413x5/bGOQ47uRwPzxC34tzdIzGA4b7pzxdrvBwTYGY8kbCWzSMvi1NEjzMNxm3HDj7DYJoJbEDUAtzBIEbPkN1GK/4caZZyAt9UAtzJ8JaGkD2ZK44XwPG0hLgsGNBAbJD3i0SJ552GbNY/A/ecMNNjOJM+ckDGcCRaTx6GDgO558+DZPRZrthvOHn0lUlNnIg0Q+/sCjReEA2HlALJEAYoE8wdjAzINHi3wDjMV/ACHKiM+WUTAKRsEoGHEAAOGKV2LLQ6XEAAAAAElFTkSuQmCC","orcid":"","institution":"Lee Kong Chian School of Medicine, Nanyang Technological University","correspondingAuthor":true,"prefix":"","firstName":"Yulan","middleName":"","lastName":"Wang","suffix":""}],"badges":[],"createdAt":"2025-07-06 13:23:06","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-7058022/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-7058022/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1007/s11033-026-11502-w","type":"published","date":"2026-02-03T15:58:00+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":86531818,"identity":"877219a2-18b0-4809-aca9-0fd27e93f387","added_by":"auto","created_at":"2025-07-11 17:10:42","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":896959,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eHHL-5 Cells Exhibit Slower Cell Growth Than HepG2.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e(A) Immunofluorescence 20X image of HHL-5 and HepG2 cells stained with for nucleus (blue), F-actin (orange), alpha-tubulin (green) and merged. The 4 images randomly selected fields/well were acquired using an INCell Analyzer™ 2200 and cell morphology quantified using IN Cell Analyzer™ 1000 software v 1.6 for (B) cell area and (C), nuclear area. (D) MTS Cell viability trendline plot at 0, 8,24,48 hr time for HHL-5 (blue) and HepG2 (red) cells. The MTS reagent was added and absorbance at 490nm measured, data plotted using exponential (Malthusian) growth with GraphPad Prism 9. (E) Real-Time Cell Analyzer (RTCA) profiles generated for HHL-5 and HepG2. Cell growth was continuously monitored for 96 hours using the xCELLigence Real-Time Cell Analyzer Dual Plate, which measures electrical impedance to quantify growth as the “Baseline Cell Index.” (F) Bar plot illustrating doubling time /hr in HHL-5 (53.68 ± 14.43) and HepG2 cell (18.75 ± 2.07). Data represent at least three biological replicates. Data represent the mean ± SD. Significance of data was evaluated using unpaired t-test automated with GraphPad Prism 9; **p\u0026lt;0.01, ****p\u0026lt;0.0001.\u003c/p\u003e","description":"","filename":"image1.png","url":"https://assets-eu.researchsquare.com/files/rs-7058022/v1/408e086f8282df38f955603f.png"},{"id":86531181,"identity":"1680fc88-f124-4c08-a522-309c4e8fdcea","added_by":"auto","created_at":"2025-07-11 17:02:42","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":443850,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eCharacterization of Exonic and Pathogenic SNVs Across HepG2, HHL5, and Liver Tissue.\u003c/strong\u003e Bar plot showing (A) mean exonic variant count (± SD) for HepG2, HHL5, Liver. (B) Mean percentage (± SD) of pathogenic variants per sample group.(C) Venn diagram showing the overlap of pathogenic SNVs across HepG2, HHL5, and Liver groups.(D) Top 5 significantly enriched Gene Ontology (GO) biological processes (p \u0026lt; 0.05) in each group based on pathogenic variant genes. Gene ratio is indicated by dot size, and p-values are represented by color gradient (red = more significant).(E) Top 5 significantly enriched KEGG pathways (p \u0026lt; 0.05) per group. Dot size indicates gene ratio; color reflects significance (red = lower p-value).\u003c/p\u003e","description":"","filename":"image2.png","url":"https://assets-eu.researchsquare.com/files/rs-7058022/v1/6638a75ce0cf606c122d905d.png"},{"id":86531820,"identity":"49f85601-9c27-4dff-b5e8-2eac2b0807b2","added_by":"auto","created_at":"2025-07-11 17:10:43","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":387351,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eComparative analysis of exonic INDEL variants across HepG2, HHL5, and liver tissue.(\u003c/strong\u003eA) Bar graph showing the mean count (± SD) of exonic INDELs detected in HepG2, HHL5, and liver tissue samples. B) Functional categorization of INDELs into frameshift and non-frameshift insertions and deletions across the three sample groups.(C) Venn diagram depicting the overlap of frameshift INDEL variants among HepG2, HHL5, and liver samples. A core set of 63 shared variants were detected across all groups, while each group also harbored a large number of unique frameshift INDELs.(D) Bubble plot showing the top 5 enriched Gene Ontology (GO) biological processes (p \u0026lt; 0.05) for each sample group. (E) Bubble plot showing the top 5 significantly enriched KEGG pathways (p \u0026lt; 0.05) for each sample. Dot size indicates gene ratio; color reflects significance (red = lower p-value).\u003c/p\u003e","description":"","filename":"image3.png","url":"https://assets-eu.researchsquare.com/files/rs-7058022/v1/9e812065e2e6819503e2c09e.png"},{"id":86531819,"identity":"bbd63a58-fd0f-4307-a6c8-9eac46059113","added_by":"auto","created_at":"2025-07-11 17:10:42","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":621206,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eExpression of hepatocyte protein markers in HHL-5\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWestern blot images illustrating cell proliferation (A) c-Myc (B) pStat3, tSTAT3, xenobiotic metabolism (C) CYP2A7, CYP1A2 and oxidative stress (D) pNRF2, NRF2 markers in HepG2 and HHL-5 cell lines. ImageJ was used to quantify bands and bar plot show significant reduction in (E) c-Myc (F) pSTAT3/STAT3 ratio (G) CYP2A7 and (H) pNRrf2/Nrf2 ratio in HHL-5 cells compared to HepG2. Ratio of reduced GSH to oxidized GSSG an indicator of cellular oxidative stress and GSH/GSSG ratio was determined using GSH luminescence assay.\u003c/p\u003e\n\u003cp\u003eBar plot showing (I) GSH/GSSG ratio, (J) GSH and (K) GSSG is significantly lower in HHL-5. Data were compared using unpaired t-test; *p\u0026lt;0.05, n=3). Red bar is HepG2 and blue bar is HHL-5.\u003c/p\u003e","description":"","filename":"image4.png","url":"https://assets-eu.researchsquare.com/files/rs-7058022/v1/a947aa28e9b04a408b86f393.png"},{"id":86531190,"identity":"83eaf7c2-6675-45ae-ae95-77d3185f5d84","added_by":"auto","created_at":"2025-07-11 17:02:43","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":363034,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003ePartial least squares discriminant analysis (PLS-DA) and 1H-NMR spectral comparison between HepG2 and HHL5 cells.\u003c/strong\u003e (A) Score plot showing clear separation of HepG2 (green) and HHL5 (blue) metabolite profiles, indicating distinct metabolic states. (B) Validation plot from permutation testing (n=200), demonstrating model robustness (Q² = 0.895, R² = 0.988, p = 0.00153645). R2 is the explained variance, and Q2 is the predictive ability of the model. Loading plots showing differential metabolite contributions in (C) aromatic/ribose region (D) and aliphatic region. Peaks in the positive direction indicate the increased metabolites in the HHL-5 group. Peaks in the negative direction represent the decreased metabolites. Notable metabolites include NAD, ADP, UDP-GalNAc, uridine, glutamate, GSSG, and creatine, with color indicating variable importance projection (VIP) scores.\u003cstrong\u003e \u003c/strong\u003eAbbreviation: meNAM, N-methyl nicotinamide; NAD, nicotinamide adenine dinucleotide; ADP, adenosine diphosphate; UDP-GalNAc, uridine diphosphate-N-acetylgalactosamine; Phe, phenylalanine; Tyr, tyrosine; Lac, lactate; Gly, glycine; GSSG, glutathione oxidized; GPC, glycerophosphocholine; Asp, aspartate; Val, valine; Leu, leucine; isoLeu, iso-leucine.\u003c/p\u003e","description":"","filename":"image5.png","url":"https://assets-eu.researchsquare.com/files/rs-7058022/v1/e87afc7d256707657d558bdc.png"},{"id":102234255,"identity":"4dcbaab0-9dd2-45f0-ad43-2b7981a544e8","added_by":"auto","created_at":"2026-02-09 16:08:34","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":3734622,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7058022/v1/b351b316-43df-467e-acfa-8dff86ef0172.pdf"},{"id":86531178,"identity":"e5f43264-9917-4186-bf7f-b608dc092e14","added_by":"auto","created_at":"2025-07-11 17:02:42","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":381613,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryInformation.docx","url":"https://assets-eu.researchsquare.com/files/rs-7058022/v1/93fc2babb53bd9ff01197059.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Human Hepatic Cell line 5 : In-Vitro Model for Hepatic Immunobiology","fulltext":[{"header":"Introduction","content":"\u003cp\u003eHepatocellular carcinoma (HCC) is an aggressive liver malignancy with poor prognosis and rank as the third most frequent cause of cancer related death in the word \u003csup\u003e\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u003c/sup\u003e.Hepatocarcinogenesis is a complex process driven by both genetic and epigenetic alterations that collectively contribute to the initiation, promotion, and progression of liver cancer \u003csup\u003e\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eExtensive information is available on the altered gene and metabolite profiles observed in tumours \u003csup\u003e\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u003c/sup\u003e, but early changes leading to initiation of cellular transformation and which liver cells are most susceptible to this process are poorly investigated \u003csup\u003e\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u003c/sup\u003e. HCC exhibits pleiotropic molecular profiles with diverse clinical outcomes necessitating an urgent need to innovate \u003cem\u003ein vitro\u003c/em\u003e experimental models to understand HCC pathophysiology and perform high throughput screening for therapeutic agents. Most existing \u003cem\u003ein-vitro\u003c/em\u003e models rely on hepatocytes derived from hepatic tumours, which inherently exhibit altered germline variants \u003csup\u003e\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u003c/sup\u003e, as well as distinct metabolic \u003csup\u003e\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e\u003c/sup\u003e and gene expression \u003csup\u003e\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u003c/sup\u003e signatures that support strong cell adhesion and proliferation.\u003c/p\u003e\u003cp\u003eSome of the cell lines are capable of expressing viral proteins upon integration of Hepatitis B virus (HBV) or Hepatitis C virus (HCV) DNA into their genomes \u003csup\u003e\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e\u003c/sup\u003e. Such \u003cem\u003ein-vitro\u003c/em\u003e models are suitable to examine molecular changes in the gene expression, and cell signalling that are important in tumorigenesis \u003csup\u003e\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u003c/sup\u003e. However, hepatic tumour-derived cell lines have poor predictive ability about carcinogenic transformation and early stages of metabolic and biochemical changes leading to tumour progression \u003csup\u003e\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003ePresently, primary human hepatocytes (PHH) are highly permissive to infection and efficiently support all the steps of HBV replication. However, during \u003cem\u003ein-vitro\u003c/em\u003e culture PHHs de-differentiate, lose hepatic function and HBV infection ability \u003csup\u003e\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u003c/sup\u003e. Researchers have also created liver cell lines resembling primary hepatocytes by immortalizing them using viral oncogenes that target the human telomerase reverse transcriptase (hTERT) subunit, enabling studies of viral infection and hepatocyte function \u003csup\u003e\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e\u003c/sup\u003e. Human hepatocyte lines 5 (HHL-5) are immortalized human primary hepatocyte cell line, transduced with hTERT and human papillomavirus E6E7 (HPV/E6E7), which is phenotypically like primary hepatocyte \u003csup\u003e\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eThe HHL-5 cell line was established as a model to investigate hepatitis virus-mediated liver infections. It exhibits phenotypic markers characteristic of both hepatocytes and biliary epithelial cells, with minimal expression of tumour-associated proteins such as p53 and alpha-fetoprotein, supporting its non-tumorigenic profile. HHL-5 cells form adherent monolayers and show enhanced binding to recombinant hepatitis C virus-like particles, making them suitable for studying viral entry mechanisms. Importantly, stimulation with interferon-alpha (IFN-α) induces upregulation of major histocompatibility complex (MHC) molecules \u003csup\u003e\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e\u003c/sup\u003e, indicating preserved immune signalling capabilities. Furthermore, their high sensitivity to anticancer agents \u003csup\u003e\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e\u003c/sup\u003e and lack of cytotoxicity in response to silicon nanoparticle \u003csup\u003e\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e\u003c/sup\u003e treatment highlight the potential of HHL-5 for applications in immunological studies and targeted drug delivery.\u003c/p\u003e\u003cp\u003eThe present study aimed to comprehensively characterize HHL-5 in terms of its morphology, growth characteristics, germline variants, metabolite profile, and cell marker expression. This characterization is intended to evaluate the suitability of HHL-5 as a hepatocyte model for investigating viral infection and the development of hepatocellular carcinoma (HCC). In this study, we utilized HHL-5 and HepG2 liver cell lines to investigate their suitability as models for hepatic biology. Whole exome sequencing and metabolomics data were generated from both cell lines, while additional liver exome data were retrieved from the European Nucleotide Archive and analysed alongside the HHL-5 and HepG2 datasets. To assess cell phenotype, immunofluorescent staining and high-content analysis were performed. Cell proliferation was measured using the MTS assay, and protein markers related to cell proliferation, oxidative stress, and xenobiotic metabolism were evaluated to assess hepatocyte functionality. Variant analysis showed enriched immune markers in HHL-5 but not in HepG2 cells. Metabolomic profiling revealed that HHL-5 retained oxidative, mitochondrial-based energy metabolism in contrast to HepG2\u0026rsquo;s reliance on glycolysis metabolism.These findings highlight the non-tumorigenic, immune-competent nature of HHL-5 and support its utility as a physiologically relevant model for studying hepatic immune responses, virus interactions, and inflammation-driven mechanisms underlying hepatocellular carcinoma (HCC).\u003c/p\u003e"},{"header":"Material and Methods","content":"\u003cp\u003e\u003cb\u003eCell line and reagents\u003c/b\u003e\u003c/p\u003e\u003cp\u003eThe human hepatocyte line HHL-5 was kindly supplied by Professor Arvind Patel, Centre for Virus Research, School of Infection and Immunity, University of Glasgow (UK). It was maintained in low glucose Dulbecco\u0026rsquo;s Modified Eagle Medium (DMEM) and 10% fetal bovine serum (FBS) (Gibco, US). Human hepatocyte carcinoma lines (HepG2) and HepG2.2.15 were purchased from the American Type Culture Collection (ATCC) and maintained in high glucose DMEM (Gibco, US) with 10% FBS (Gibco, US). Cells were cultured at 37\u0026deg;C and 5% carbon dioxide in a cell culture incubator (Nuaire, US).\u003c/p\u003e\u003cp\u003e\u003cb\u003eHigh content analysis\u003c/b\u003e\u003c/p\u003e\u003cp\u003eHHL-5 and HepG2 cells were seeded at 0.2 \u0026times; 10\u003csup\u003e4\u003c/sup\u003e cells/well in 96-well plates, cultured for 24 h, and fixed with 4% paraformaldehyde (in pre warmed PBS) for 15 min, followed by 0.03% Triton-X permeabilization for 15 min. Cells were stained for filamentous actin (F-actin), nucleus, microtubules and plasma membrane using Phalloidin-TRITC, Hoechst (Sigma Aldrich, US), anti-α-tubulin conjugated with AlexaFluor\u0026reg; 488 (Sigma Aldrich, U.S), and CellMask Deep Red (Invitrogen, U.S). Plates were scanned (4 randomly selected fields per well at 10\u0026times; and 20\u0026times; magnification) using an automated microscope IN Cell Analyzer 2200 Imaging System (GE Healthcare, US). Acquired images were analyzed by IN Cell Investigator software (Version 1.6) using multitarget analysis bio-application module (GE Healthcare, US).\u003c/p\u003e\u003cp\u003e\u003cb\u003eMTS Cell Viability Assay\u003c/b\u003e\u003c/p\u003e\u003cp\u003eCell proliferation rates were determined using the CellTiter 96\u0026reg; Aq\u003csub\u003eueous\u003c/sub\u003e Non-radioactive Cell Proliferation Assay (Promega, US) according to the manufacturer\u0026rsquo;s protocols. A total of 0.2 \u0026times; 10\u003csup\u003e4\u003c/sup\u003e cells/well were seeded in 96-well culture plate (Nunc, US) followed by incubation with MTS solution for 0, 8, 24 and 48-hours. Further absorbance was measured at 490 nm with the Gen5\u0026trade; Microplate Reader (BioTek, US).\u003c/p\u003e\u003cp\u003e\u003cb\u003eReal time monitoring of cell proliferation\u003c/b\u003e\u003c/p\u003e\u003cp\u003eHHL-5 and HepG2 were seeded at 0.2 \u0026times; 10\u003csup\u003e4\u003c/sup\u003e cells per well, respectively, into an E-plate 16 (ACEA Biosciences, San Diego, CA) containing 100 \u0026micro;L medium per well and monitored in real time using the xCELLigence instrument (Agilent). The cells were pre-treated with nocodazole as toxicity control and were incubated at 37\u0026deg;C in a 5% CO\u003csub\u003e2\u003c/sub\u003e incubator. Cell growth was quantified as \u0026ldquo;Baseline Cell Index\u0026rdquo;. To calculate the doubling time of both cell lines, RTCA software v. 1.2.1 was used. All the experiments were performed in triplicates and repeated at least 3 times.\u003c/p\u003e\u003cp\u003e\u003cb\u003eGSH/GSSG- Glo assay\u003c/b\u003e\u003c/p\u003e\u003cp\u003eHHL-5 and HepG2 cells were seeded into collagen coated white opaque 96 well microtiter plates at density of 1x10\u003csup\u003e4\u003c/sup\u003e cells/well. The total amounts of combined glutathione (GSH) and oxidized GSH (GSSG) were measured following the manufacturer's instruction. Luminescence is measured using Cytation 3 image reader instrument with Gen5 microplate reader imager software. GSH/GSSG ratio was calculated as [(net total glutathione RLU \u0026ndash; net GSSG RLU)/(net GSSG RLU)] \u0026times; 2, where RLU is relative light units and students t-test was performed for significance. All the experiments were performed in triplicates and repeated at least 3 times.\u003c/p\u003e\u003cp\u003e\u003cb\u003eWhole -exome capture and sequencing\u003c/b\u003e\u003c/p\u003e\u003cp\u003eExome capture was performed using Agilent\u0026rsquo;s SureSelect Human All Exon V6 (58) Kit according to the manufacturer\u0026rsquo;s instructions (Agilent, Santa Clara, CA). Products were purified with AMPure XP system (Beckman Coulter, Beverly, USA) and quantified using the Agilent high sensitivity DNA assay on the Agilent Bioanalyzer 2100 system. Sequencing was performed on HiSeq 2500 (Illumina, San Diego, CA), in 150 bp paired-end sequencing (PE150). NovaSeq 6000 software were used for raw data processing and fastq file generation.\u003c/p\u003e\u003cp\u003e\u003cb\u003eWhole - exome sequencing data analysis\u003c/b\u003e\u003c/p\u003e\u003cp\u003eWhole-exome sequencing (WES) paired-end reads(.fastq) were passed through quality control using FastQC \u003csup\u003e\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e\u003c/sup\u003e. Reads with TruSeq adaptor sequences, uncertain nucleotides (\u0026gt;\u0026thinsp;10%) and paired reads when single reads have more than 50% low-quality (\u0026lt;\u0026thinsp;5) nucleotides are removed using Trimmomatic \u003csup\u003e\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e\u003c/sup\u003e. Paired end reads were mapped to NCBI human reference genome GRCh38 using Burrows\u0026ndash;Wheeler Aligner (BWA) software \u003csup\u003e\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e\u003c/sup\u003e. Picard tool in Genome Analysis Toolkit (GATK) suite \u003csup\u003e\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e\u003c/sup\u003e marked duplicates and GATK best practice variants called using HaplotypeCaller. The output variant call files (vcf) was Hard filtered using the VariantFilteration tool. The bcftools was used to filter vcf files based on FILTER=\"PASS\" \u0026amp;\u0026amp; %QUAL\u0026thinsp;\u0026gt;\u0026thinsp;50 \u0026amp;\u0026amp; GQ\u0026thinsp;\u0026gt;\u0026thinsp;20. Where QUAL is probability that the site has no variant and GQ, probability that the call is incorrect. Finally, variants were annotated using annovar \u003csup\u003e\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e\u003c/sup\u003e and analysed using vcfshiny \u003csup\u003e\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e\u003c/sup\u003e tool. Variants were prioritised by filtering based on pathogenicity scores, SIFT \u003csup\u003e\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e\u003c/sup\u003e and POLYPHEN \u003csup\u003e\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e\u003c/sup\u003e. Variants located in exons were extracted and genes were used for enrichment analysis using EnrichR \u003csup\u003e\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e\u003c/sup\u003e. Normal human liver exome data was downloaded from European nucleotide archive from project PRJNA207681. A total of 3 normal human liver exome fastq files (SRR893106, SRR894448, SRR894453) were processed and variants detected as mentioned above. The liver tissue whole exome data was retrieved from the European nucleotide Archive \u003csup\u003e\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003e\u003cb\u003eMetabolic profiling using NMR\u003c/b\u003e\u003c/p\u003e\u003cp\u003eCells collected were subjected to methanol and water extraction (2:1) using tissue lyser. The supernatant collected was dried using Spin-Vac and dried extracts were reconstituted in 600 \u0026micro;L 0.1 M phosphate buffer (pH\u0026thinsp;=\u0026thinsp;7.4, K\u003csub\u003e2\u003c/sub\u003eHPO\u003csub\u003e4\u003c/sub\u003e/NaH\u003csub\u003e2\u003c/sub\u003ePO\u003csub\u003e4\u003c/sub\u003e\u0026thinsp;=\u0026thinsp;4:1, 0.005% TSP-d\u003csub\u003e4,\u003c/sub\u003e 100% D\u003csub\u003e2\u003c/sub\u003eO) and then centrifuged 10 min at 16000\u0026times;g and 4\u0026deg;C; a total of 550 \u0026micro;L of supernatant was transferred into a 5 mm NMR tube for further NMR analysis. Proton (\u003csup\u003e\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u003c/sup\u003eH) NMR spectra of cell extracts were recorded by III HD 600 MHz Ascend NMR spectrometer (Bruker), equipped with 5mm BBI 600 MHz Z-Gradient high-resolution probe. The one-dimensional (1D) NMR spectra were acquired at 298 K with the first increment of NOESY pulse sequence. A pre-saturation method was used to suppress the water signal during recycle delay (2s) and mixing time (100ms). For each sample, the spectral width was 20 ppm and 32 transients were collected into 32 k data points. NMR spectral peak assignment was performed based on previous publication \u003csup\u003e\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003e\u003cb\u003eNMR spectral processing and multivariate data analysis\u003c/b\u003e\u003c/p\u003e\u003cp\u003eThe free induction decay (FID) of NMR spectra were Fourier transformation (FT) and the derived NMR spectra were phased, and baseline corrected manually on Topspin 3.6.2 (Bruker Biospin, Karlsruhe, Germany). The calibration of spectra was referenced to the TSP peak with chemical shift of δ 0.00 by Topspin 3.0. NMR spectral region of δ 0.5\u0026ndash;9.5 was integrated into 0.002 ppm wide buckets by AMIX package (Bruker Biospin, Karlsruhe, Germany). The region δ 4.55\u0026ndash;4.75 was excluded to avoid the disturbance of the remaining water signal. Total intensity normalization was applied prior to multivariate data analysis. Multivariate data analysis was performed by SIMCA 16.0 software (Umetrics, Sweden). Normalized data sets were analyzed by Orthogonal Projection to Latent Structure Discriminant analysis (O-PLS-DA) with unit variance (UV) scaling. The model was cross-validated by CV-ANOVA method (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.0015\u0026thinsp;\u0026lt;\u0026thinsp;0.05) and permutation test. The back-transformed and plotted with color-coded correlation coefficients (|r|) using an in-house developed script written in MATLAB 7.1 (the MathWorks, USA) with the red color indicating statistical significance, while the blue color no significance. The cutoff value derived from a 95% confidence limit for each model changes according to the number of samples (n) in the groups, here the cutoff value |r| is 0.523.\u003c/p\u003e\u003cp\u003e\u003cb\u003eProtein Isolation and Detection\u003c/b\u003e\u003c/p\u003e\u003cp\u003eWhole cell lysates were prepared in cell lysis buffer and quantitated using Bio-Rad Protein Assay Dye Reagent Concentrate (Bio-Rad, US). A total of 30 ug protein was separated on sodium dodecyl sulphate (SDS) polyacrylamide gel electrophoresis and transferred to nitrocellulose membranes (Bio-Rad, US). The membranes were blocked with 5% BSA and then incubated overnight at 4\u0026deg;C with primary antibodies (Supplementary Table\u0026nbsp;1). Post incubation, membrane was washed in 1x TBS-T washing buffer, then incubated in appropriate secondary antibody for 1 hour at room temperature. The blots were developed using the WesternBright\u0026trade; ECL detection kit (Advansta, US) and imaged by ChemiDoc\u0026trade; MP Imaging System using Image Lab\u0026trade; Software (Bio-Rad, US). Densitometry analysis of the Western blots was performed using ImageJ software. For Nrf2 and antioxidant enzymes, Odyssey system was applied according to the manufacturer's instructions.\u003c/p\u003e\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003eStatistical analysis\u003c/h2\u003e\u003cp\u003eEach experiment was performed three times. Statistical analysis was performed with GraphPad Prism 9 software. Data was evaluated for normality and Grubbs outlier analysis. The results are presented as means\u0026thinsp;\u0026plusmn;\u0026thinsp;standard deviations (SD), and the student\u0026rsquo;s T-test was used to compare the means of independent samples. For cell morphometry analysis nonparametric Mann Whitney U test was performed. \u003cem\u003ep\u003c/em\u003e values of \u0026lt;\u0026thinsp;0.05 were considered statistically significant.\u003c/p\u003e\u003c/div\u003e"},{"header":"Results","content":"\u003cp\u003e\u003cstrong\u003eHHL-5 Cells Exhibit Slower Cell Growth Than HepG2.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eCell morphology and function are closely related characteristics and monitoring of cell morphology helps to distinguish between normal and transformed (e.g., cancer-like) cells. To evaluate the difference in HHL-5 and HepG2 cell morphology immunofluorescence staining of cytoskeleton (F actin and alpha tubulin) and nucleus (DAPI) was performed. Figure \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e illustrates the staining of nuclei (Hoechst), \u0026alpha;-tubulin, and F-actin in HHL-5 and HepG2 cells. Hoechst staining reveals that HHL-5 cells have smaller nuclei compared to HepG2. The \u0026alpha;-tubulin staining patterns differ between the two cell types, indicating variations in cytoskeletal organization, while F-actin distribution appears comparable in both. High-content analysis further confirms that HHL-5 cells have a significantly smaller overall cell area (Fig. \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003eB) and nuclear area (Fig. \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003eC) compared to HepG2 cells. The trendline graph shows the MTS assay results over 48 hours, comparing viability between HHL-5 and HepG2 cell lines. HHL-5 (blue) shows moderate growth with absorbance rising from ~\u0026thinsp;0.5 to 1.0, while HepG2 (red) shows a steeper increase to ~\u0026thinsp;1.8, indicating significantly higher proliferation (Fig. \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003eD). Real time cell analysed the impedance-based cell index over 96 hours, comparing adhesion and growth of two cell lines with and without nocodazole (noc, anti-mitotic drug). HepG2 displays stronger adhesion and proliferation (~\u0026thinsp;1.5), while HHL-5 shows limited growth (~\u0026thinsp;0.5). Nocodazole slows down the growth and attachment of both cell types. However, HHL-5 is affected more quickly and severely, while HepG2 is affected more slowly, meaning HHL-5 is more sensitive to the drug (Fig. \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003eE). Lastly, cell doubling time, a key indicator of cell growth rate was calculated for HHL-5 and HepG2. HHL-5 cells showed a doubling time of 53.68\u0026thinsp;\u0026plusmn;\u0026thinsp;14.43 (hr\u0026thinsp;\u0026plusmn;\u0026thinsp;SD), significantly longer than the 18.75\u0026thinsp;\u0026plusmn;\u0026thinsp;2.07 observed in HepG2 cells, indicating slower proliferation (Fig. \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003eG). Together, these findings suggest that HHL-5 cells differ markedly from HepG2 in both morphology and growth characteristics.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eExonic Single Nucleotide Variant (SNV) profile\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAnalysis of exonic single nucleotide variants revealed comparable variant counts in HepG2 and HHL5 cell lines, whereas primary liver tissues exhibited slightly lower counts (Fig. \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003eA). Analysis of the proportion of pathogenic SNVs in liver samples showed a modestly higher percentage (~\u0026thinsp;14.5%) relative to HepG2 and HHL5 (~\u0026thinsp;13.5%), suggesting a distinct pathogenic profile in the tissue samples (Fig. \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003eB). Pathogenic variants were compared across three groups and 813 pathogenic SNVs was common in the three groups. HepG2 and HHL5 had unique variants 1854 and 1217, respectively, while primary liver tissue contained the largest number of unique pathogenic SNVs (3649), indicating group-specific vairant landscapes (Fig. \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003eC). Enrichment analysis of GO biological processes identified distinct pathways across groups. In HepG2, terms like \u0026ldquo;protein homooligomerization\u0026rdquo; and \u0026ldquo;DNA-template replication fidelity\u0026rdquo; were enriched. HHL5 showed enrichment in immune-related processes such as \u0026ldquo;antigen processing via MHC class Ib\u0026rdquo; and \u0026ldquo;leukocyte adhesion.\u0026rdquo; Liver tissues displayed enrichment in developmental processes including \u0026ldquo;cilium organization,\u0026rdquo; \u0026ldquo;intercellular transport,\u0026rdquo; and \u0026ldquo;tongue development,\u0026rdquo; highlighting functional specificity in variant (Fig. \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003eD). \u0026ldquo;KEGG pathway enrichment revealed that SNVs in HepG2 cells were associated with genes involved in olfactory transduction, HIV-1 infection, butanoate metabolism, the Fanconi anemia pathway, and cell cycle regulation. In HHL-5, enriched pathways included viral myocarditis, antigen processing and presentation, allograft rejection, type 1 diabetes mellitus, and ABC transporters. Liver tissue variants were linked to folate and retinol metabolism, N-glycan biosynthesis, African trypanosomiasis, and antifolate resistance (Fig. \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003eE).\u0026rdquo;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eInsertion Deletion (INDEL) Variant profile\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAnalysis of insertion and deletion variants show that HepG2 (mean\u0026thinsp;\u0026asymp;\u0026thinsp;520) and HHL5 (mean\u0026thinsp;\u0026asymp;\u0026thinsp;500) contain a greater number of INDELs compared to liver tissue (mean\u0026thinsp;\u0026asymp;\u0026thinsp;300) (Fig. \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003eA). Functional categorisation of indels show higher non -frame shift deletions across all groups. HepG2 shows highest variants count across all indel categories. Cell lines, HepG2 and HHL-5 showed higher frameshift variants compared to liver tissue (Fig. \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003eB). Frameshift variants were compared across 3 groups. A total of 63 variants were shared among all three groups. However, each group also showed a substantial number of unique variants: 133 in HepG2, 105 in HHL5, and 169 in liver, indicating context-specific variant profiles (Fig. \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003eC). GO enrichment analysis revealed distinct functional signatures across groups: \u0026lsquo;heterotypic cell-cell adhesion\u0026rsquo; in HepG2, \u0026lsquo;antigen processing and presentation\u0026rsquo; in HHL-5, and both \u0026lsquo;negative regulation of phagocytosis\u0026rsquo; and \u0026lsquo;mismatch repair\u0026rsquo; in liver tissue. (Fig. \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003eD). KEGG terms enriched in HepG2 and HHL-5 report immune related pathways, \u0026ldquo;Graft versus host disease\u0026rdquo;, \u0026ldquo;Autoimmune thyroid disease\u0026rdquo;, \u0026ldquo;Allograft rejection\u0026rdquo; and autoimmune term, \u0026ldquo;Type 1 diabetes mellitus\u0026rdquo;. Liver and HepG2 show enriched KEGG term, \u0026ldquo;Olfactory transduction\u0026rdquo;. Liver only showed enrichment for \u0026ldquo;RNA degradation\u0026rdquo; (Fig. \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003eE). Collectively, these results show distinct mutational and functional landscape of INDELs across liver-derived cell lines and primary liver tissue, reflecting both shared and divergent genetic landscape.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eHepatocyte function markers in HepG2 and HHL-5 cell line.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eCell proliferation, xenobiotic and oxidation processes are important for hepatocyte function and cancer development. We measured protein expression of cancer proliferative markers c-myc, signal transducer and activator of transcription 3 (STAT3) and phospho-STAT3 (pSTAT3) levels in HHL-5 and HepG2 to understand HHL-5 function in contrast to hepatoma. There was lower c-myc (Fig. \u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003eA, \u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003eE) and pSTAT3/tSTAT3 (Fig. \u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003eB,\u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003eF) protein expression in HHL-5 against HepG2 indicating that cell proliferation is lower in HHL-5.\u003c/p\u003e\n\u003cp\u003eWe later evaluated the protein expression of enzymes involved in xenobiotic detoxification in HHL-5. HHL-5 showed significantly reduced expression of cytochrome P450, family 2, subfamily A, polypeptide 7 (CYP2A7) (Fig. \u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003eC, \u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003eG) and no expression in Cytochrome P450 Family 1 Subfamily A Member 2 (CYP1A2) (Fig. \u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003eC). Oxidative stress is the pathological feature of poor xenobiotic detoxification in liver diseases and hepatocytes contain cytoprotective genes to prevent oxidative stress. We measured the protein expression of nuclear erythroid 2-related factor 2 (Nrf2), key regulator of cytoprotective genes, catalase (CAT), UDP glucuronosyltransferase family 1 member A1 (UGT1A1), quinone oxidoreductase 1 (NQO1), glutamyl cysteine synthetase (GCS), glutathione S-transferase alpha 1 (GSTA1), heme oxygenase-1 (HO-1) to evaluate oxidative stress in HHL-5. HHL-5 cells express low levels of activated Nrf2 (pNrf2/Nrf2) and its panel of cytoprotective genes (Supplementary Fig. 1) in contrast to HepG2. Additionally, the ratio of reduced glutathione (GSH) to oxidised glutathione (GSSG) is used as a marker of oxidative stress. HHL-5 cells showed significantly low GSH/GSSG ratio compared to HepG2 (Fig. \u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003eI)\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDistinct Metabolic Signatures in HHL-5 and HepG2 Cells\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eMultivariate statistical analysis of the 1H-NMR spectra revealed distinct metabolic profiles between HepG2 and HHL5 cell pellets. In the PLS-DA score plot (Fig. \u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003eA), clear separation of the two groups was observed\u0026mdash;HepG2 (green) and HHL5 (blue)\u0026mdash;indicating marked differences in metabolite composition. The robustness and reliability of the model were confirmed by permutation testing (Fig. \u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003eB), yielding a high predictive value (Q\u0026sup2; = 0.895), excellent model fit (R\u0026sup2; = 0.988), and strong statistical significance (p\u0026thinsp;=\u0026thinsp;0.0015), ruling out overfitting. Spectral decomposition of the aromatic and ribose region (Fig. \u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003eC) in HHL-5 illustrate elevated levels of key metabolites involved in redox regulation, energy metabolism, and biosynthesis. Increased NAD and its derivative 1-methylnicotinamide (1-meNAM) indicate enhanced mitochondrial respiration and NAD⁺ turnover. Higher levels of ADP reflect elevated ATP utilization and energy demand. Additionally, enrichment of UDP-GalNAc and uridine suggests active glycosylation and RNA metabolic processes, supporting a biosynthetically active phenotype.HepG2 cells showed increased levels of uracil, indicating enhanced nucleotide degradation and RNA turnover. Elevated aromatic amino acids such as tyrosine and phenylalanine suggest higher protein turnover or altered amino acid metabolism, reflecting a more catabolic or stress-associated metabolic state. (Fig. \u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003eC). The aliphatic region of the 1H-NMR difference spectrum (Fig. \u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003eD) revealed distinct metabolic profiles between HHL-5 and HepG2 cells. Only glutamate exhibited a positive peak (+\u0026thinsp;0.02), indicating it is elevated in HHL-5 and suggests greater engagement in TCA cycle activity and amino acid metabolism. In contrast, a series of metabolites showed negative peaks, indicating they are elevated in HepG2. These included lactate, creatine, oxidized glutathione (GSSG), glycerophosphocholine (GPC), aspartate, citrate, and the branched-chain amino acids (valine, leucine, isoleucine). This pattern reflects a metabolic state in HepG2 characterized by increased aerobic glycolysis, ATP buffering via creatine, membrane turnover (GPC), and amino acid catabolism. Together, these results indicate that HepG2 cells rely more heavily on glycolytic energy production, redox stress adaptation, and amino acid turnover, whereas HHL-5 exhibits relatively higher glutamate-linked mitochondrial activity. (Fig. \u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003eD).\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eIn this study we characterize human hepatic cell line, HHL-5 to support its utility as a primary immortalized hepatic cell line to study liver pathology. We evaluated HHL-5 growth characteristics, variant and metabolite profiles, along with the expression of markers related to proliferation, xenobiotic biotransformation, and oxidative stress.\u003c/p\u003e\u003cp\u003eGenetic variants serve as critical indicators of a cell\u0026rsquo;s molecular function, offering valuable insights into its signalling and immune pathways, and thereby enhancing its utility as an effective \u003cem\u003ein vitro\u003c/em\u003e model for studying liver-specific viral infections. Germ line variants in HepG2, HHL-5, and primary human liver tissue revealed distinct landscapes reflective of their biological contexts. While both hepatic cell lines exhibited a higher overall variant burden than primary liver tissue, consistent with genomic instability in immortalized models \u003csup\u003e\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e\u003c/sup\u003e. HHL-5 uniquely demonstrated enrichment in immune-related pathways, including antigen processing and presentation, interferon signalling, dendritic cell differentiation, and leukocyte adhesion. These immune-relevant variant signatures are absent in HepG2, which was instead enriched for pathways associated with DNA replication and the Fanconi anemia pathway, aligning with its tumorigenic and highly proliferative nature. Primary liver tissue showed a comparatively lower burden of variants but was enriched in developmental and xenobiotic metabolism pathways \u003csup\u003e\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e\u003c/sup\u003e, highlighting its intact physiological complexity and \u003cem\u003ein vivo\u003c/em\u003e exposure to environmental stressors. Notably, the immune-enriched variant profile in HHL-5 corresponds with its expression of antiviral components such as MHC molecules and toll-like receptors \u003csup\u003e\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e\u003c/sup\u003e, supporting a competent innate immune environment. In contrast HepG2 lacks robust intrinsic immune functionality due to downregulation of innate immune sensors and impaired interferon signalling making HepG2 less capable of mounting effective intrinsic antiviral responses compared to primary hepatocytes \u003csup\u003e\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e\u003c/sup\u003e. These distinctions reinforce HHL-5\u0026rsquo;s non-tumorigenic, immune-competent phenotype and its value as a physiologically relevant model for investigating hepatic immune responses, HBV-host interactions, and inflammation-driven mechanisms in HCC.\u003c/p\u003e\u003cp\u003eMetabolites reflect the dynamic biochemical state of a cell, providing key insights into its metabolic and immune functions, and are essential for evaluating the suitability of a model system to study liver-specific viral infections. HHL-5 displays metabolite profiles more similar to primary hepatocytes than HepG2, marked by elevated 1-methylnicotinamide \u003csup\u003e\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e\u003c/sup\u003e, ADP, and UDP-GalNAc\u0026mdash;metabolites linked to mitochondrial respiration \u003csup\u003e\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e\u003c/sup\u003e, redox balance, and glycosylation \u003csup\u003e\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e\u003c/sup\u003e, which are hallmarks of functional oxidative metabolism in hepatocytes. Elevated glutamate in HHL-5 indicates enhanced TCA cycle activity and oxidative phosphorylation, further highlighting its bioenergetic profile as more reflective of primary liver tissue \u003csup\u003e\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e\u003c/sup\u003e. In contrast, HepG2 cells display cancer-associated metabolic reprogramming, with increased levels of lactate, glutathione disulphide (GSSG), creatine, glycerophosphocholine (GPC), and branched-chain amino acids (BCAAs). Elevated lactate reflects a shift toward glycolysis \u003csup\u003e\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e\u003c/sup\u003e, typical of the Warburg effect in cancer cells and is linked to immune suppression and tumour progression \u003csup\u003e\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e\u003c/sup\u003e. High GSSG levels indicate oxidative stress and a heightened antioxidant response \u003csup\u003e\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e\u003c/sup\u003e, while increased creatine supports elevated energy demands \u003csup\u003e\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e\u003c/sup\u003e. Elevated GPC is associated with membrane turnover and phospholipid remodelling, a hallmark of malignancy \u003csup\u003e\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e\u003c/sup\u003e, and increased BCAAs have been implicated in cancer metabolism \u003csup\u003e\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e\u003c/sup\u003e and are consistently observed in HCC \u003csup\u003e\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e\u003c/sup\u003e including HepG2 \u003csup\u003e42\u003c/sup\u003e. Together, these findings reinforce that HHL-5 retains oxidative, mitochondrial-based energy metabolism with minimal signs of metabolic transformation, thereby preserving key aspects of normal hepatocyte function. Importantly, given HHL-5\u0026rsquo;s immunocompetent genomic profile and bioenergetic similarity to primary hepatocytes, it provides a robust platform for studying the immunometabolism of liver diseases, including viral infection and inflammation-associated hepatocarcinogenesis, under physiologically relevant, non-tumorigenic conditions.\u003c/p\u003e\u003cp\u003eThe protein expression profile of HHL-5 cells supports their utility as a physiologically relevant, non-tumorigenic hepatocyte model suitable for studying hepatocellular carcinoma (HCC) development. Protein markers for proliferation, cytochrome enzymes, and oxidation are essential for evaluating hepatocyte cell line suitability as models for liver function and disease research. Compared to the transformed HepG2 line, HHL-5 exhibits markedly lower levels of proliferative markers such as c-Myc \u003csup\u003e\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e\u003c/sup\u003e and phosphorylated STAT3 \u003csup\u003e44\u003c/sup\u003e, consistent with a quiescent phenotype typical of primary hepatocytes and indicative of reduced oncogenic signalling. Additionally, HHL-5 shows low or absent expression of xenobiotic metabolism enzymes like CYP2A7 and CYP1A2, indicating a preserved inducibility of these pathways and tighter regulatory control \u003csup\u003e\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e\u003c/sup\u003e, which contrasts with the dysregulated enzyme expression often seen in HCC. Importantly, HHL-5 also demonstrates diminished oxidative stress response signalling, as evidenced by lower phosphorylated Nrf2 and reduced expression of antioxidant genes, CAT, NQO1, HO-1, and GSTA1, along with a lower GSH/GSSG ratio, hallmarks of primary hepatocytes that are more susceptible to oxidative insults than cancer cells \u003csup\u003e\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e\u003c/sup\u003e. These collective features highlight the functional alignment of HHL-5 with non-malignant liver tissue.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eIn conclusion, the HHL-5 cell line has been characterized as immunocompetent and non-tumorigenic, with a distinct variant and metabolic profile that preserves key hepatocyte functions and immunological responsiveness. Unlike transformed hepatoma lines such as HepG2, HHL-5 serves as a physiologically relevant model, making it a valuable resource for studying HBV-host immune interactions and advancing therapeutic strategies against viral-mediated hepatocellular carcinoma (HCC) development.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAuthor Contribution\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eSmeeta Shrestha: Investigation, formal analysis, Data curation, writing-original draft preparation. Min Yue Yeong: Investigation. Chen Xin Yi: Investigation. Wei Wang: Validation, Investigation. Nidhi Bhayana: Investigation. Navin Kumar Verma: Conceptualisation, resources. Yongping Bao: Visualization, resources, writing-reviewing and editing. Yulan Wang: Funding acquisition Project administration.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData availability statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eData is deposited at NCBI \u0026ndash; Bio Project ID PRJNA1069993\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eLee Kong Chian School of Medicine, Singapore\u003c/p\u003e\n\u003cp\u003eCancer Prevention Research Trust UK\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConflict of interest disclosure\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThere is no conflict of interest.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics approval statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ePatient consent statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ePermission to reproduce material from other sources.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eClinical trial registration\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe would like to acknowledge Dr. Jingtao Zhang and Miss Abigail Thomson for their help in NMR profiling, the Lee Kong Chian School of Medicine for providing the start-up grant and the Cancer Prevention Research Trust, UK.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eFerlay J et al (2019) Estimating the global cancer incidence and mortality in 2018: GLOBOCAN sources and methods. 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PLoS ONE 12:e0190366. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org:10.1371/journal.pone.0190366\u003c/span\u003e\u003cspan address=\"https://doi.org:10.1371/journal.pone.0190366\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"molecular-biology-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"mole","sideBox":"Learn more about [Molecular Biology Reports](https://www.springer.com/journal/11033)","snPcode":"11033","submissionUrl":"https://submission.nature.com/new-submission/11033/3","title":"Molecular Biology Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"Hepatocyte, HHL-5, Virus, Variants, Metabolite, Interferon, UDP-GalNAc, Glycosylation","lastPublishedDoi":"10.21203/rs.3.rs-7058022/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7058022/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eBackground\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eHepatocellular carcinoma is a major global health challenge, partly due to the lack of suitable \u003cem\u003ein vitro\u003c/em\u003e models that mimic early host–virus interactions. Human Hepatic Cell line 5 (HHL-5), an immortalized hepatocyte cell line, retains key liver functions, lacks tumour markers, binds virus-like particles, and responds to immune stimuli. This study aimed to characterize the genetic and metabolic profile of HHL-5 to evaluate its suitability as a physiologically relevant model for studying viral infection and host immune responses.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethod\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eHHL-5 and HepG2 cell lines were analysed for morphology, growth, genetic variants, metabolite profiles, and protein expression. Immunofluorescence and MTS assays assessed cell morphology and growth, while whole exome sequencing and NMR evaluated genetic and metabolic profiles. Protein markers related to proliferation, oxidative stress, and detoxification were examined via Western blot, with significance tested using a T-test.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eFunctional analysis of germ line variants in HHL-5 illustrated a highly immunocompetent genomic profile, including antigen processing and presentation, interferon signalling, dendritic cell differentiation, and leukocyte adhesion. Conversely, HepG2 exhibited enrichment in DNA replication pathways. Metabolite analysis in HHL-5 exhibited elevated levels of 1-methylnicotinamide, ADP, and UDP-GalNAc, suggesting enhanced redox function, mitochondrial respiration, and glycosylation—key features of active oxidative metabolism characteristic of primary hepatocytes. In contrast, HepG2 showed increased levels of lactate, glutathione disulfide, creatine, glycerophosphocholine, and branched-chain amino acids, indicative of a glycolytic, redox-adaptive metabolic profile typical of hepatocellular carcinoma.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusion\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eHHL-5’s non-cancerous, immunocompetent profile makes it a valuable model for investigating liver disease progression and hepatocarcinogenesis.\u003c/p\u003e","manuscriptTitle":"Human Hepatic Cell line 5 : In-Vitro Model for Hepatic Immunobiology","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-07-11 17:02:38","doi":"10.21203/rs.3.rs-7058022/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-09-29T09:50:10+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-08-31T19:33:39+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-08-20T14:45:07+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"294471192782264611557951812705055359017","date":"2025-08-05T18:06:12+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"162897755077759716475217488285230481097","date":"2025-08-03T13:42:18+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"324543271712703792231545656772713487516","date":"2025-07-24T06:42:06+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-07-09T07:59:14+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-07-09T07:53:57+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-07-08T13:23:24+00:00","index":"","fulltext":""},{"type":"submitted","content":"Molecular Biology Reports","date":"2025-07-06T13:10:00+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"molecular-biology-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"mole","sideBox":"Learn more about [Molecular Biology Reports](https://www.springer.com/journal/11033)","snPcode":"11033","submissionUrl":"https://submission.nature.com/new-submission/11033/3","title":"Molecular Biology Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"b1061c6f-df26-4d16-b780-a029b7c5546f","owner":[],"postedDate":"July 11th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2026-02-09T16:04:25+00:00","versionOfRecord":{"articleIdentity":"rs-7058022","link":"https://doi.org/10.1007/s11033-026-11502-w","journal":{"identity":"molecular-biology-reports","isVorOnly":false,"title":"Molecular Biology Reports"},"publishedOn":"2026-02-03 15:58:00","publishedOnDateReadable":"February 3rd, 2026"},"versionCreatedAt":"2025-07-11 17:02:38","video":"","vorDoi":"10.1007/s11033-026-11502-w","vorDoiUrl":"https://doi.org/10.1007/s11033-026-11502-w","workflowStages":[]},"version":"v1","identity":"rs-7058022","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7058022","identity":"rs-7058022","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}