Anti-HCV antibodies: A battle for breadth and potency

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McKay, and 3 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-5876231/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Potent and broadly neutralizing antibodies (NAbs) are important for clearance of RNA virus infections. However, NAbs generated in response to RNA viruses exhibit diverse profiles of potency and breadth. In this study, we conducted a comparative analysis of NAbs targeting HIV, hepatitis C virus (HCV), SARS-CoV-2, and influenza based on their potency, measured by IC50 values, and breadth, assessed through the genetic diversity of the viruses neutralized by the NAbs. Our results reveal that while anti-HCV NAbs show a high breadth of neutralization, they are less potent and demonstrate inconsistent potency across different virus variants. These findings highlight the challenges in eliciting broad and potent antibody responses, which are essential for the development of effective vaccine strategies against HCV. Biological sciences/Immunology Biological sciences/Immunology/Infection Biological sciences/Immunology/Infectious diseases Biological sciences/Immunology/Vaccines Biological sciences/Microbiology/Virology/Hepatitis c virus Biological sciences/Microbiology/Virology/Viral host response Figures Figure 1 Figure 2 MAIN TEXT Early induction of potent and broad neutralizing antibodies (bNAbs) against RNA viruses has been shown to correlate with superior protection against infection and disease progression in HIV, hepatitis C virus (HCV), SARS-CoV-2, and influenza ( 1 , 2 ). Influenza and SARS-CoV-2 infections generate NAb responses within weeks ( 3 , 4 ) while NAb development in HCV and HIV infections are significantly delayed, often taking months to years ( 1 , 4 ). Beyond these known differences in response kinetics, a trend in both the literature and findings from our group indicate that HCV neutralising monoclonal antibodies (NmAbs) tend to have lower neutralisation potency compared to SARS-CoV-2 NmAbs. Specifically, the IC50 values for HCV NmAbs are often reported in the µM range, whereas those for SARS-CoV-2 NmAbs are generally in the nM range. To investigate this observation, we conducted a comprehensive review of the reported potency (in terms of IC50 values) of NmAbs in the literature across the four genetically diverse RNA viruses (Fig. 1 ). This examination aimed to provide a nuanced understanding of the relative potency of HCV NmAbs within the broader context of NmAb responses to different RNA viral infections. IC50 is the primary indicator of the potency or efficacy of NmAbs and is defined as the antibody concentration required to inhibit 50% of infectivity of a fixed virus inoculum, with lower IC50 values representing greater neutralizing potency. The IC50 values were extracted for 24 HCV-specific NmAbs, 40 HIV-specific NmAbs, 130 SARS-CoV-2-specific NmAbs, and 22 influenza-specific NmAbs ( Supplementary Tables 1–4 ) from 97 peer reviewed publications. One confounding factor in the analysis was that the neutralisation potency of each NmAb was often tested against a diverse panel of viral variants. For HIV, a standard multi-clade panel comprising 118 different HIV variants, representing the major genetic clades, was utilised to measure both potency and breadth ( 5 ). For SARS-CoV-2, multiple different variants were used depending on the time of testing, and for influenza NmAbs, potency was determined using both influenza A and B variants ( 3 , 4 ). For HCV, IC50 values were measured against a range of HCV genotypes and subtypes ( 6 ). To address this heterogeneity issue, we calculated the geometric mean IC50 for each NmAb across the tested variants in different studies, yielding a single representative IC50 value for each NmAb. We then compared these geometric means using a one-way ANOVA. Our analysis showed that the geometric mean IC50 was markedly higher (indicating lower potency) for anti-HCV NmAbs, with a value of 3.21µg/mL (± 2.10), followed by anti-influenza NmAbs at 2.97 µg/mL (± 4.16). In contrast, anti-SARS-CoV-2 and anti-HIV NmAbs showed lower IC50 values of 0.89 µg/mL (± 2.00) and 0.79 µg/mL (± 1.06), respectively (P < 0.0001, Fig. 2 A). We included only IC50 values below 50 µg/mL, as reporting of values higher than this value was inconsistent across studies. As some viral variants may demonstrate different sensitivity to neutralization, we aimed to limit the bias introduced by the panel of viral variants by including the lowest reported IC50 (indicating the highest potency) for a given NmAb from each study. In cases where NmAbs were tested in multiple studies, we averaged the minimum IC50 values and included these in our analysis (Fig. 2 B). The average minimum IC50 value was 0.43µg/mL (± 0.61) for HCV, 0.28µg/mL (± 0.64) for SARS-CoV-2, and 0.88 µg/mL (± 1.84) for influenza NmAbs. For anti-HIV NmAbs, only geometric mean IC50 values were reported in the literature. Thus, these were not included in our analysis. No significant differences were observed between the minimum IC50 values of HCV NmAbs and those of SARS-CoV-2 NmAbs (Fig. 2 B), suggesting that while HCV NmAbs can achieve a potency comparable to that of SARS-CoV-2 NmAbs, they fail to maintain this potency across a range of HCV variants. It is well recognised that the high genetic diversity of HCV (> 30% nucleotide divergence) poses a significant challenge for the development of effective vaccines that require both broad and potent NAb induction for protection (Fig. 1 ) ( 7 ). To explore the breadth and potency of HCV NmAbs in comparison to other viruses, we calculated the neutralizing breadth of different NmAbs using a %coverage metric, defined as the proportion of viral variants neutralized to the total variants tested. This metric was plotted against the geometric mean IC50 values to visualize the relationship between neutralization potency and breadth ( Supplementary Fig. 1 ). As there are considerable differences in genetic diversity among the tested viral panels reflecting the fact that: influenza A and B exhibit ~ 50% genetic diversity across subtypes (and less than 5% within subtypes) ( 7 ), HCV shows a median genetic diversity of ~ 30% between genotypes (and ~ 23% within genotypes)( 6 ), cross-group M HIV-1 has a genetic diversity of 10–25% ( 8 ), and SARS-CoV-2 has less than 1% divergence across strains (Fig. 1 ) ( 9 ), we scaled the breadth metric to account for this bias, termed %coverage scaled . This metric scaled the calculated breadth of each NmAb by the mean genetic distance among the tested variants or subtypes (see Materials and methods for details). All coverage values were then normalized to the maximum coverage observed across the four viruses (Fig. 2 D-H). This approach enabled a more relative comparison of the breadth of NmAb responses within the evolutionary context of each virus. The most potent and broad NmAbs were observed against HIV (Fig. 2 H, upper right quadrant—Q4). Notably, only one HCV NmAb (HEPC74) was positioned within this high breadth/potency quadrant, while none of the SARS-CoV-2 NmAbs were present in this quadrant due to their limited breadth. Influenza NmAbs were distributed across all four quadrants. Following up on the interesting results shown above, we further investigated whether HCV NmAbs might demonstrate increased potency when tested against their autologous virus (the virus against which these NmAbs were induced). We calculated the IC50 values of natively paired NmAbs against their autologous transmitted founder virus (T/F)—single or very few viruses that successfully initiate a productive infection in a new host ( 1 )—and the prototype strain H77 (see Materials and methods for details). No significant difference in IC50 values was observed between NmAbs against the autologous T/F virus and the prototype H77 virus (data not shown). Furthermore, the autologous IC50 values were comparable to other reported HCV IC50 values yet remained higher when compared to the geometric mean IC50 values of NmAbs against HIV, influenza and SARS-CoV-2 (Fig. 2 C). In conclusion, our data indicates that HCV, influenza, and HIV NmAbs generally exhibit broad neutralization capacities, enabling them to target a wide range of viral variants. However, both HCV and influenza NmAbs showed poor potency, as evidenced by higher average IC50 values. This observation suggests a potential trade-off between breadth and potency in HCV NmAbs, a phenomenon also hypothesised for HIV, SARS-CoV-2 and influenza ( 10 – 12 ). Neutralizing a wide array of viral variants is key for NmAbs to overcome the vast genetic diversity present within each circulating virus population. Nonetheless, it should be noted that insufficient potency could limit the clinical effectiveness of these antibodies. Encouragingly, anti-HIV antibodies have shown that it is possible to generate both broad and potent NAbs. The lack of significant differences in the minimum (best) IC50 values between HCV and SARS-CoV-2 NmAbs is a promising finding, suggesting that highly potent anti-HCV NAbs can be developed. Consequently, this study highlights the importance for HCV vaccine strategies to focus on inducing both high potency and broad NAbs, similar to HEPC74, which uniquely demonstrated high breadth and potency among all considered HCV NmAbs ( Supplementary Fig. 1 ). HEPC74 targets the AR3/domain B region on the neutralizing face of the HCV E2 protein, a region already identified as a suitable vaccine target ( 6 ). While several studies on anti-SARS-CoV-2 and HIV NmAbs ( 13 – 15 ) report a direct correlation between in vitro potency and in vivo protective efficacy, some studies of HIV ( 16 , 17 ), influenza ( 18 ), and SARS-CoV-2 ( 19 ) suggest that this relationship is not always straightforward. Other Fc-mediated antibody functions may increase an antibody’s protective efficacy. Currently, there is limited understanding of the role of Fc-mediated functions in protection against HCV ( 20 ). One limitation of this study was the lack of unified neutralization assay protocols and viral variant panels across the reviewed studies. As a result, the breadth of a specific antibody may appear limited if it was not tested against a sufficiently diverse range of variants (e.g., this may be the case for NmAbs against SARS-CoV-2). Additionally, although assay differences are inevitable, the consistent low potency observed for HCV NmAbs, even when evaluated against autologous viruses, indicates an inherent limitation in their quality. Addressing the factors underlying this phenomenon will be important for the development of effective HCV vaccines. (A) HIV group M gp120 Phylogenetic Tree: 32 reference sequences relating to HIV group M were downloaded from LANL HIV database ( https://www.hiv.lanl.gov ) and are labelled according to HIV subtypes. (B) SARS-CoV-2 spike phylogenetic tree: 22 SARS-COV-2 sequences of the spike of Wuhan, Alpha, Beta, Gamma, Delta and Omicron subvariants were all collected from the GISAID ( https://gisaid.org ). (C) HCV E1E2 phylogenetic tree: The amino acid sequences of E1E2 from 82 reference sequences of 7 distinct HCV genotypes (labelled with numbers and shown in different colours) were included. (D) Influenza Hemagglutinin (HA) phylogenetic tree: 34 HA amino acid sequences from influenza group A (H1-H18) were obtained from Deng et al( 21 ) and Influenza Research Database. Sequences are categorized into influenza groups (group 1 in pink, group 2 in purple). Group B (B Yamagata and B Victoria) sequences were also sourced from Hibino et al( 22 ). All trees in the analysis have a scale bar of 0.1 indicating the amount of genetic divergence represented on the trees. However, the SARS-CoV-2 has 20 times lower scale bar of 0.005, indicating its distinctly lower genetic diversity. Overall, this figure depicts a comparative view of the diversity across HIV, HCV, influenza and SARS-CoV-2, highlighting the greater diversity of influenza and HCV. The maximum genetic diversity is 37.8% for HIV, 41.2% for HCV, 63.6 for influenza and 6.1% for SARS-CoV-2. MATERIALS AND METHODS Literature review and meta-analysis We conducted an extensive literature review and meta-analysis focusing on neutralizing monoclonal antibodies (NmAbs) specific for HCV, HIV, SARS-CoV-2 and influenza. We searched PubMed for studies on NmAb potency for these four viral infections using key search terms and concepts in varying combinations including “neutralizing antibody”, “IC50”, “HIV”, “HCV”, “SARS-CoV-2”, “influenza”, “potency”, and/or “breadth”. IC50 of NmAbs, the primary indicator of the potency or efficacy defined as the NmAb concentration required to inhibit 50% of a fixed virus inoculum, following natural infection were included in this study, while vaccine-induced NmAbs were excluded ( 23 ). IC50 can be measured by either pseudo-particle (pp) or live virus neutralization assay. In this study, results from both assays were included. Analysis of NmAbs potency across four viruses In neutralization assays, the potency of each NmAb is often tested against a diverse pool of viral variants or strains. For HIV, a standard multi-clade panel consisting of 118 different HIV variants, representing the major genetic clades, is commonly used ( 5 ). For SARS-CoV-2, key viral variants such as Alpha, Beta, Gamma, Delta, Omicron, and its sub-lineages are tested. Influenza NmAbs are assessed using both influenza A and B variants ( 5 , 21 ). In the case of HCV, IC50 values of NmAbs are measured against a panel of HCV genotypes and subtypes ( 6 ). Consequently, each NmAb has distinct IC50 values for each variant tested. Furthermore, IC50 values for some NmAbs have been evaluated in multiple studies. To address variability, we calculated the geometric mean IC50 value for each NmAb across the tested variants and different studies, providing a single representative IC50 value. Moreover, as some variants can have different sensitivity to neutralization, we tried to remove panel bias by taking from each study the lowest reported IC50 for a given NmAb. For NmAbs tested in multiple studies, we averaged the minimum IC50 values and included this in the analysis, referring to it as the average "minimum" IC50. Consistency was ensured by including only IC50 values below 50 µg/mL across all viruses and studies. Assessment of NmAbs breadth across four viruses To assess the breadth of neutralization for each NmAb, we defined percentage coverage as the fraction of viral strains neutralized by a given NmAb at a standard inhibitory concentration cut-off of 50 µg/mL, based on previous research in the field (data shown in Supplementary Tables 1–4 ). In this initial metric, a NmAb tested against genetically similar viral strains might show inflated coverage. To address this disparity, we adjusted the percentage coverage by scaling it with the mean genetic difference of the strains or variants tested, determined through the Hamming distance. For HCV and influenza, these differences were computed based on reference strains of subtypes and variants (see Supplementary Tables 5 and 6 for accession numbers). For SARS-CoV-2, sequences of different variants were generated using variant-defining mutations reported on the CoVariants database ( https://covariants.org ). For HIV, sequence data for 118 variants was sourced from the CATNAP database ( 24 ). The adjusted coverage was then normalized by dividing by the maximum value obtained across all viruses, ensuring the metric ranged from 0 to 100. We refer to this refined metric as %coverage scaled . Ethics statement For the autologous HCV analysis, human research ethics approvals were obtained from Human Research Ethics Committees of Justice Health (reference number GEN 31/05), New South Wales Department of Corrective Services (05/0884), and the University of New South Wales (05094, 08081), all located in Sydney, Australia. Written informed consent was obtained from the participants. All methods were performed in accordance with the relevant guidelines and regulations. Subjects and Samples Previously, samples were obtained from 14 incident cases of hepatitis C virus (HCV) infection identified through the Hepatitis C Incidence and Transmission Studies (HITS) in prisons (HITS-p) and the general community (HITS-c). Longitudinal plasma and PBMC samples were collected frequently over 12 weeks before antiviral treatment was offered at 24 weeks, allowing for the observation of natural clearance or progression to chronic infection ( 1 ). Viral Sequencing Briefly, as previously described by us, near full-length HCV genome amplification was carried out using an nRT-PCR method, followed by next-generation sequencing (Roche 454 FLX and Illumina MiSeq) of longitudinal samples ( 1 , 25 ). A bioinformatics pipeline was employed for read cleaning, alignment, and single nucleotide polymorphism (SNP) calling using ShoRAH, LoFreq, and Geneious software. Haplotype reconstruction of the E1E2 region was performed with ShoRAH, and Shannon entropy was determined. The transmitted/founder (T/F) virus was identified using the PoissonFitter statistical model and phylogenetic analysis and sequences have been previously published ( 1 ). Cell culture, antibodies and reagents Expi293-Freestyle cells (Applied Biosystems, Tullamarine, VIC, Australia) were cultured at 37°C and 8% CO 2 in a growth medium containing Expi293 Expression Medium (Applied Biosystems, Tullamarine, VIC, Australia) and Huh7.5 cells (Apath, New York, NY, USA) were maintained at 37°C and 5% atmospheric CO 2 in growth medium containing High Glucose Dulbecco's Modified Eagle Medium (HG-DMEM, Gibco, Thermo Fisher Scientific, Waltham, MA, USA) and 10% v/v heat-inactivated fetal bovine serum (FBS) (Gibco). H77 derived E1/E2 proteins were provided by Prof. Jonathon Ball and A/Prof. Alexander Tarr. MLV gag/pol and luciferase plasmids which are used to produce pseudo-typed lentiviral particles were provided by Prof. Francois-Loic Cosset (University of Lyon, France) ( 26 ). Generation of monoclonal antibodies (mAbs) Sequences of the variable regions of the natively paired heavy (VH) and light (VL) chains were identified through single-cell RNA sequencing. These sequences were then utilized to design specific primers, enabling the amplification and subsequent recombinant antibody expression of the natively paired VH and VL genes as described by us previously ( 27 ). Briefly, single HCV E2-specific B cells (CD19 + CD20 + CD10 − IgD − tetramer+) from HITS subjects were sorted and collected in 96 well plates containing in a final volume of 2 µl per well: 0.5 µl of dNTP (10 mM) (ThermoFisher Scientific, Waltham, MA, USA), 0.5 µl of 5 µM oligo-dT primer and 1 µl of lysis buffer. Briefly, using the SmartSeq2 approach, each single B cell was RT-PCR amplified and sequenced using Illumina 2 × 150 PE Nextera XT Library Preparation Kit and VDJpuzzle 2.0 bioinformatics tool was used to reconstruct BCR sequences ( 27 ). Both heavy (VH) and light (VL) chains were ordered as plasmid from Biointron (company deets). Monoclonal Abs we expressed as described briefly by us. Monoclonal Abs were expressed using Expi293 Expression System (ThermoFisher) using manufactures’ protocol, unless stated otherwise. Briefly, Expi293-FreeStyle cells were prepared 3 days in advance to reach the required density (3x106 cells/mL) and viability (> 95%) for transfection. The cells were then diluted in Expi293 expression medium (Thermo Fisher Scientific). Plasmid DNA encoding the mAb was also diluted in OptiMEM, mixed with ExpiFectamine 293 transfection reagent (Thermo Fisher Scientific), and incubated. The DNA-lipid complexes were added to the cells and incubated at 37°C, 8% CO2 with shaking. Following that, ExpiFectamine enhancers 1 and 2 (Thermo Fisher Scientific) were added. cells were harvested 72 hours post transfection and the supernatant containing secreted protein was collected. Cell supernatant was filtered through 0.22µm pore membrane filters (Millipore) and the His-tagged mAbs were then affinity purified using a HisTrap HP Column (GE Healthcare, Rydalmere, NSW, Australia) as per manufacturer’s instructions ( 27 , 28 ). As previously described by our group, E2 binding specificity of mAbs was validated by a rE2 binding ELISA using HCV Gt1a (H77) and Gt3a (UKN3A13.6) ( 1 ). HCVpp production, infection and neutralization As previously described, E1E2 glycoproteins were cloned and co-transfected with MLV gag/pol and luciferase vectors in 293T cells to produce HCVpp. Briefly, neutralization assays were performed by incubating HCVpp with mAbs for one hour at 37°C( 1 ). MAbs were used at concentrations of 100 µg/mL, 33.33 µg/mL, 11.11 µg/mL, 3.70 µg/mL, 1.23 µg/mL, 0.41 µg/mL, and 0.13 µg/mL. This mixture was applied directly to Huh-7.5 hepatoma cells (Apath, L.L.C, New York, NY, USA), and incubated for 72 hours after which luciferase activity was measured. Neutralization of HCVpp was calculated using the formula: % inhibition = 1 − (inhibited activity)/(normal activity)] × 100, after subtraction of negative control (pp generated without glycoproteins) RLU, where normal activity is HCVpp incubated with PBS. The 50% IC50 titre was calculated as the mAb concentration that caused a 50% reduction in RLU for each mAb/HCVpp combination tested in neutralization. All data were fitted using non-linear regression plots (GraphPad, Prism). Statistical Analysis All data analysis was performed, and graphs created using GraphPad Prism Software (version 10.0, GraphPad) for windows using repeated measures ANOVA and non-parametric student T-test. P-values below 0.05 were considered as statistically significant. Phylogenetic tree To visualize the level of genetic diversity between the HIV, influenza, SARS-CoV-2 and HCV a phylogenetic tree was plotted. For this purpose, 32 reference sequences relating to HIV group M were downloaded from LANL HIV database ( https://www.hiv.lanl.gov ). To plot the HCV E1E2 phylogenetic tree amino acid sequences of E1E2 from 82 reference sequences of 7 distinct HCV genotypes. Influenza Hemagglutinin (HA) phylogenetic tree was also plotted using 34 HA amino acid sequences from influenza group A (H1-H18) which were obtained from Deng et al ( 21 ) and Influenza Research Database. Group B (B Yamagata and B Victoria) sequences were also sourced from Hibino et al ( 22 ). To depict the SARS-CoV-2 spike phylogenetic tree 22 SARS-COV-2 sequences of the spike of Wuhan, Alpha, Beta, Gamma, Delta and Omicron subvariants were collected from the GISAID ( https://gisaid.org ). Following these multiple sequence alignments were performed using MAFFT software to align the amino acid sequences. Phylogenetic trees were constructed using the neighbour joining method. Figtree v1.4.4 and Adobe Illustrator were used to visualize the data. All plotted trees have a scale bar of 0.1 except for the SARS-CoV-2 that has 20 times lower scale bar of 0.005, indicating its distinctly lower genetic diversity. Declarations DATA AVAILABILITY Sequences used in this study are listed in the supplementary section. COMPETING INTERESTS The authors have declared that no competing interests exists. Author Contribution E.J., A.A.Q., N.T., M.R.M., A.L., M.W. and R.A.B. contributed to conception and design. A.L. provided clinical samples. E.J., A.A.Q., M.W and R.A.B. contributed to investigation. E.J. and A.A.Q. performed data analysis and interpretation. M.W. and R.A.B. are co-senior authors. All authors contributed to manuscript writing and to the final approval of the manuscript. All authors reviewed the manuscript. ACKNOWLEDGEMENTS The Kirby Institute is funded by the Australian Government of Health and Ageing. 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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-5876231","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Short Report","associatedPublications":[],"authors":[{"id":406150577,"identity":"eaafabfc-64e5-44aa-bc29-026c3d8bfe11","order_by":0,"name":"Elham Jamali","email":"","orcid":"","institution":"University of New South Wales","correspondingAuthor":false,"prefix":"","firstName":"Elham","middleName":"","lastName":"Jamali","suffix":""},{"id":406150578,"identity":"d8bf9551-7998-4671-a4fe-d0ec11ffe400","order_by":1,"name":"Ahmed Abdul Quadeer","email":"","orcid":"","institution":"University of Melbourne","correspondingAuthor":false,"prefix":"","firstName":"Ahmed","middleName":"Abdul","lastName":"Quadeer","suffix":""},{"id":406150579,"identity":"6b84f5cb-541a-4aca-8903-8a05626afb79","order_by":2,"name":"Nicodemus Tedla","email":"","orcid":"","institution":"University of New South Wales","correspondingAuthor":false,"prefix":"","firstName":"Nicodemus","middleName":"","lastName":"Tedla","suffix":""},{"id":406150580,"identity":"d740b617-5250-4f60-a463-f9b864b36834","order_by":3,"name":"Matthew R. McKay","email":"","orcid":"","institution":"University of Melbourne","correspondingAuthor":false,"prefix":"","firstName":"Matthew","middleName":"R.","lastName":"McKay","suffix":""},{"id":406150581,"identity":"5266f0b5-fa48-439d-bf16-265fa69acb89","order_by":4,"name":"Andrew Lloyd","email":"","orcid":"","institution":"University of New South Wales","correspondingAuthor":false,"prefix":"","firstName":"Andrew","middleName":"","lastName":"Lloyd","suffix":""},{"id":406150582,"identity":"8dce88c0-618b-4154-8790-71b8b7a3f0ec","order_by":5,"name":"Melanie Walker","email":"","orcid":"","institution":"University of New South Wales","correspondingAuthor":false,"prefix":"","firstName":"Melanie","middleName":"","lastName":"Walker","suffix":""},{"id":406150583,"identity":"33f299d6-2821-4874-8214-fc4b0036443c","order_by":6,"name":"Rowena Bull","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAwElEQVRIiWNgGAWjYFACHgaGBwU2UA4bsVoSDNJI13KYBC3mDbzHJBIMzidu5z9jwPCh7DAD/4wE/FpkDvClAbXcTtw5I8eAcca5wwwSNwhokWDgMQNr2XCDdwMzbxvQhURqOZe44fzZDcx/gVrkidRyIHHDgdwNzIxALQYEtTDzJVskGCQbb7iR/+Fgz7l0HsMzDwhoYe89eONDhZ3shvPHEh/8KLOWkztOwBYGZiT2AQZwNI2CUTAKRsEooBgAAGmlQOa/+9qJAAAAAElFTkSuQmCC","orcid":"","institution":"University of New South Wales","correspondingAuthor":true,"prefix":"","firstName":"Rowena","middleName":"","lastName":"Bull","suffix":""}],"badges":[],"createdAt":"2025-01-21 23:08:07","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-5876231/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-5876231/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":74957743,"identity":"7b5bc91e-9ba1-4473-a5a8-93c91c2375b3","added_by":"auto","created_at":"2025-01-28 18:03:21","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":183511,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eComparison of genetic diversity of HIV, HCV, influenza and SARS-CoV-2. \u003cbr\u003e\n \u003c/strong\u003e(A) HIV group M gp120 Phylogenetic Tree: 32 reference sequences relating to HIV group M were downloaded from LANL HIV database (https://www.hiv.lanl.gov) and are labelled according to HIV subtypes. (B) SARS-CoV-2 spike phylogenetic tree: 22 SARS-COV-2 sequences of the spike of Wuhan, Alpha, Beta, Gamma, Delta and Omicron subvariants were all collected from the GISAID (https://gisaid.org). (C) HCV E1E2 phylogenetic tree: The amino acid sequences of E1E2 from 82 reference sequences of 7 distinct HCV genotypes (labelled with numbers and shown in different colours) were included. (D) Influenza Hemagglutinin (HA) phylogenetic tree: 34 HA amino acid sequences from influenza group A (H1-H18) were obtained from Deng et al(21)and Influenza Research Database. Sequences are categorized into influenza groups (group 1 in pink, group 2 in purple). Group B (B Yamagata and B Victoria) sequences were also sourced from Hibino et al(22). All trees in the analysis have a scale bar of 0.1 indicating the amount of genetic divergence represented on the trees. However, the SARS-CoV-2 has 20 times lower scale bar of 0.005, indicating its distinctly lower genetic diversity. Overall, this figure depicts a comparative view of the diversity across HIV, HCV, influenza and SARS-CoV-2, highlighting the greater diversity of influenza and HCV. The maximum genetic diversity is 37.8% for HIV, 41.2% for HCV, 63.6 for influenza and 6.1% for SARS-CoV-2.\u003c/p\u003e","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-5876231/v1/6fb98f2337275988f81391fc.png"},{"id":74958841,"identity":"e7cb172e-3a5b-46fe-a9fd-1f90307a2def","added_by":"auto","created_at":"2025-01-28 18:19:21","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":417368,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eComparisons of IC50 and %coverage for NmAbs against HIV, influenza, SARS-CoV-2, and HCV.\u003c/strong\u003e (A) Average NmAb IC50 values across the four RNA viruses. (B) Minimum NmAb IC50 values across the influenza, SARS-CoV-2, and HCV. (C) IC50 values of NmAbs for HIV, influenza, SARS-CoV-2, and autologous, natively paired NmAbs against HCV T/F viruses. For panels A, B, and C, a one-way ANOVA with multiple comparisons was performed (* p = 0.05, ** p = 0.01, *** p = 0.001 and **** p = 0.0001). Statistical significance was defined as a p-value of less than 0.05.\u0026nbsp; (D–G) present the geometric mean of IC50 values plotted against the %coverage\u003csub\u003escaled\u003c/sub\u003e for HIV, influenza, SARS-CoV-2 and HCV NmAbs, respectively. NmAbs are colored according to their target sites. (H) The geometric mean of IC50 values versus %coverage\u003csub\u003escaled\u003c/sub\u003e for NmAbs against the four viruses altogether (HIV, purple; influenza, green; SARS-CoV-2, red; and HCV, blue). The pie chart represents frequency of NmAbs across different viruses within each quadrant.\u003c/p\u003e","description":"","filename":"floatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-5876231/v1/cc518aa32e7cf79954990598.png"},{"id":74997698,"identity":"a2332d9d-7cd8-4d0b-9193-2835b40028e6","added_by":"auto","created_at":"2025-01-29 09:16:58","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1186285,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5876231/v1/674c38be-b710-4c15-9a9c-ecdca797e7db.pdf"},{"id":74958567,"identity":"2460220f-658c-487d-9ea3-4a7f520fa2ce","added_by":"auto","created_at":"2025-01-28 18:11:21","extension":"docx","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":548776,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryfilenpjV1.docx","url":"https://assets-eu.researchsquare.com/files/rs-5876231/v1/588b7a1c9517b5eea0d3773d.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Anti-HCV antibodies: A battle for breadth and potency","fulltext":[{"header":"MAIN TEXT","content":"\u003cp\u003eEarly induction of potent and broad neutralizing antibodies (bNAbs) against RNA viruses has been shown to correlate with superior protection against infection and disease progression in HIV, hepatitis C virus (HCV), SARS-CoV-2, and influenza (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e). Influenza and SARS-CoV-2 infections generate NAb responses within weeks (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e) while NAb development in HCV and HIV infections are significantly delayed, often taking months to years (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e). Beyond these known differences in response kinetics, a trend in both the literature and findings from our group indicate that HCV neutralising monoclonal antibodies (NmAbs) tend to have lower neutralisation potency compared to SARS-CoV-2 NmAbs. Specifically, the IC50 values for HCV NmAbs are often reported in the \u0026micro;M range, whereas those for SARS-CoV-2 NmAbs are generally in the nM range.\u003c/p\u003e \u003cp\u003eTo investigate this observation, we conducted a comprehensive review of the reported potency (in terms of IC50 values) of NmAbs in the literature across the four genetically diverse RNA viruses (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). This examination aimed to provide a nuanced understanding of the relative potency of HCV NmAbs within the broader context of NmAb responses to different RNA viral infections. IC50 is the primary indicator of the potency or efficacy of NmAbs and is defined as the antibody concentration required to inhibit 50% of infectivity of a fixed virus inoculum, with lower IC50 values representing greater neutralizing potency. The IC50 values were extracted for 24 HCV-specific NmAbs, 40 HIV-specific NmAbs, 130 SARS-CoV-2-specific NmAbs, and 22 influenza-specific NmAbs (\u003cb\u003eSupplementary Tables\u0026nbsp;1\u0026ndash;4\u003c/b\u003e) from 97 peer reviewed publications.\u003c/p\u003e \u003cp\u003eOne confounding factor in the analysis was that the neutralisation potency of each NmAb was often tested against a diverse panel of viral variants. For HIV, a standard multi-clade panel comprising 118 different HIV variants, representing the major genetic clades, was utilised to measure both potency and breadth (\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e). For SARS-CoV-2, multiple different variants were used depending on the time of testing, and for influenza NmAbs, potency was determined using both influenza A and B variants (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e). For HCV, IC50 values were measured against a range of HCV genotypes and subtypes (\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e). To address this heterogeneity issue, we calculated the geometric mean IC50 for each NmAb across the tested variants in different studies, yielding a single representative IC50 value for each NmAb. We then compared these geometric means using a one-way ANOVA.\u003c/p\u003e \u003cp\u003eOur analysis showed that the geometric mean IC50 was markedly higher (indicating lower potency) for anti-HCV NmAbs, with a value of 3.21\u0026micro;g/mL (\u0026plusmn;\u0026thinsp;2.10), followed by anti-influenza NmAbs at 2.97 \u0026micro;g/mL (\u0026plusmn;\u0026thinsp;4.16). In contrast, anti-SARS-CoV-2 and anti-HIV NmAbs showed lower IC50 values of 0.89 \u0026micro;g/mL (\u0026plusmn;\u0026thinsp;2.00) and 0.79 \u0026micro;g/mL (\u0026plusmn;\u0026thinsp;1.06), respectively (P\u0026thinsp;\u0026lt;\u0026thinsp;0.0001, Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eA). We included only IC50 values below 50 \u0026micro;g/mL, as reporting of values higher than this value was inconsistent across studies.\u003c/p\u003e \u003cp\u003eAs some viral variants may demonstrate different sensitivity to neutralization, we aimed to limit the bias introduced by the panel of viral variants by including the lowest reported IC50 (indicating the highest potency) for a given NmAb from each study. In cases where NmAbs were tested in multiple studies, we averaged the minimum IC50 values and included these in our analysis (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eB). The average minimum IC50 value was 0.43\u0026micro;g/mL (\u0026plusmn;\u0026thinsp;0.61) for HCV, 0.28\u0026micro;g/mL (\u0026plusmn;\u0026thinsp;0.64) for SARS-CoV-2, and 0.88 \u0026micro;g/mL (\u0026plusmn;\u0026thinsp;1.84) for influenza NmAbs. For anti-HIV NmAbs, only geometric mean IC50 values were reported in the literature. Thus, these were not included in our analysis. No significant differences were observed between the minimum IC50 values of HCV NmAbs and those of SARS-CoV-2 NmAbs (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eB), suggesting that while HCV NmAbs can achieve a potency comparable to that of SARS-CoV-2 NmAbs, they fail to maintain this potency across a range of HCV variants.\u003c/p\u003e \u003cp\u003eIt is well recognised that the high genetic diversity of HCV (\u0026gt;\u0026thinsp;30% nucleotide divergence) poses a significant challenge for the development of effective vaccines that require both broad and potent NAb induction for protection (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e) (\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e). To explore the breadth and potency of HCV NmAbs in comparison to other viruses, we calculated the neutralizing breadth of different NmAbs using a %coverage metric, defined as the proportion of viral variants neutralized to the total variants tested. This metric was plotted against the geometric mean IC50 values to visualize the relationship between neutralization potency and breadth (\u003cb\u003eSupplementary Fig.\u0026nbsp;1\u003c/b\u003e).\u003c/p\u003e \u003cp\u003eAs there are considerable differences in genetic diversity among the tested viral panels reflecting the fact that: influenza A and B exhibit\u0026thinsp;~\u0026thinsp;50% genetic diversity across subtypes (and less than 5% within subtypes) (\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e), HCV shows a median genetic diversity of ~\u0026thinsp;30% between genotypes (and ~\u0026thinsp;23% within genotypes)(\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e), cross-group M HIV-1 has a genetic diversity of 10\u0026ndash;25% (\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e), and SARS-CoV-2 has less than 1% divergence across strains (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e) (\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e), we scaled the breadth metric to account for this bias, termed %coverage\u003csub\u003escaled\u003c/sub\u003e. This metric scaled the calculated breadth of each NmAb by the mean genetic distance among the tested variants or subtypes (see \u003cb\u003eMaterials and methods\u003c/b\u003e for details). All coverage values were then normalized to the maximum coverage observed across the four viruses (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eD-H). This approach enabled a more relative comparison of the breadth of NmAb responses within the evolutionary context of each virus. The most potent and broad NmAbs were observed against HIV (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eH, upper right quadrant\u0026mdash;Q4). Notably, only one HCV NmAb (HEPC74) was positioned within this high breadth/potency quadrant, while none of the SARS-CoV-2 NmAbs were present in this quadrant due to their limited breadth. Influenza NmAbs were distributed across all four quadrants.\u003c/p\u003e \u003cp\u003eFollowing up on the interesting results shown above, we further investigated whether HCV NmAbs might demonstrate increased potency when tested against their autologous virus (the virus against which these NmAbs were induced). We calculated the IC50 values of natively paired NmAbs against their autologous transmitted founder virus (T/F)\u0026mdash;single or very few viruses that successfully initiate a productive infection in a new host (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e)\u0026mdash;and the prototype strain H77 (see \u003cb\u003eMaterials and methods\u003c/b\u003e for details). No significant difference in IC50 values was observed between NmAbs against the autologous T/F virus and the prototype H77 virus (data not shown). Furthermore, the autologous IC50 values were comparable to other reported HCV IC50 values yet remained higher when compared to the geometric mean IC50 values of NmAbs against HIV, influenza and SARS-CoV-2 (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eC).\u003c/p\u003e \u003cp\u003eIn conclusion, our data indicates that HCV, influenza, and HIV NmAbs generally exhibit broad neutralization capacities, enabling them to target a wide range of viral variants. However, both HCV and influenza NmAbs showed poor potency, as evidenced by higher average IC50 values. This observation suggests a potential trade-off between breadth and potency in HCV NmAbs, a phenomenon also hypothesised for HIV, SARS-CoV-2 and influenza (\u003cspan additionalcitationids=\"CR11\" citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e). Neutralizing a wide array of viral variants is key for NmAbs to overcome the vast genetic diversity present within each circulating virus population. Nonetheless, it should be noted that insufficient potency could limit the clinical effectiveness of these antibodies. Encouragingly, anti-HIV antibodies have shown that it is possible to generate both broad and potent NAbs.\u003c/p\u003e \u003cp\u003eThe lack of significant differences in the minimum (best) IC50 values between HCV and SARS-CoV-2 NmAbs is a promising finding, suggesting that highly potent anti-HCV NAbs can be developed. Consequently, this study highlights the importance for HCV vaccine strategies to focus on inducing both high potency and broad NAbs, similar to HEPC74, which uniquely demonstrated high breadth and potency among all considered HCV NmAbs (\u003cb\u003eSupplementary Fig.\u0026nbsp;1\u003c/b\u003e). HEPC74 targets the AR3/domain B region on the neutralizing face of the HCV E2 protein, a region already identified as a suitable vaccine target (\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eWhile several studies on anti-SARS-CoV-2 and HIV NmAbs (\u003cspan additionalcitationids=\"CR14\" citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e) report a direct correlation between \u003cem\u003ein vitro\u003c/em\u003e potency and \u003cem\u003ein vivo\u003c/em\u003e protective efficacy, some studies of HIV (\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e), influenza (\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e), and SARS-CoV-2 (\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e) suggest that this relationship is not always straightforward. Other Fc-mediated antibody functions may increase an antibody\u0026rsquo;s protective efficacy. Currently, there is limited understanding of the role of Fc-mediated functions in protection against HCV (\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eOne limitation of this study was the lack of unified neutralization assay protocols and viral variant panels across the reviewed studies. As a result, the breadth of a specific antibody may appear limited if it was not tested against a sufficiently diverse range of variants (e.g., this may be the case for NmAbs against SARS-CoV-2). Additionally, although assay differences are inevitable, the consistent low potency observed for HCV NmAbs, even when evaluated against autologous viruses, indicates an inherent limitation in their quality. Addressing the factors underlying this phenomenon will be important for the development of effective HCV vaccines.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e(A) HIV group M gp120 Phylogenetic Tree: 32 reference sequences relating to HIV group M were downloaded from LANL HIV database (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.hiv.lanl.gov\u003c/span\u003e\u003cspan address=\"https://www.hiv.lanl.gov\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) and are labelled according to HIV subtypes. (B) SARS-CoV-2 spike phylogenetic tree: 22 SARS-COV-2 sequences of the spike of Wuhan, Alpha, Beta, Gamma, Delta and Omicron subvariants were all collected from the GISAID (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://gisaid.org\u003c/span\u003e\u003cspan address=\"https://gisaid.org\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). (C) HCV E1E2 phylogenetic tree: The amino acid sequences of E1E2 from 82 reference sequences of 7 distinct HCV genotypes (labelled with numbers and shown in different colours) were included. (D) Influenza Hemagglutinin (HA) phylogenetic tree: 34 HA amino acid sequences from influenza group A (H1-H18) were obtained from Deng et al(\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e) and Influenza Research Database. Sequences are categorized into influenza groups (group 1 in pink, group 2 in purple). Group B (B Yamagata and B Victoria) sequences were also sourced from Hibino et al(\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e). All trees in the analysis have a scale bar of 0.1 indicating the amount of genetic divergence represented on the trees. However, the SARS-CoV-2 has 20 times lower scale bar of 0.005, indicating its distinctly lower genetic diversity. Overall, this figure depicts a comparative view of the diversity across HIV, HCV, influenza and SARS-CoV-2, highlighting the greater diversity of influenza and HCV. The maximum genetic diversity is 37.8% for HIV, 41.2% for HCV, 63.6 for influenza and 6.1% for SARS-CoV-2.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e"},{"header":"MATERIALS AND METHODS","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eLiterature review and meta-analysis\u003c/h2\u003e \u003cp\u003eWe conducted an extensive literature review and meta-analysis focusing on neutralizing monoclonal antibodies (NmAbs) specific for HCV, HIV, SARS-CoV-2 and influenza. We searched PubMed for studies on NmAb potency for these four viral infections using key search terms and concepts in varying combinations including \u0026ldquo;neutralizing antibody\u0026rdquo;, \u0026ldquo;IC50\u0026rdquo;, \u0026ldquo;HIV\u0026rdquo;, \u0026ldquo;HCV\u0026rdquo;, \u0026ldquo;SARS-CoV-2\u0026rdquo;, \u0026ldquo;influenza\u0026rdquo;, \u0026ldquo;potency\u0026rdquo;, and/or \u0026ldquo;breadth\u0026rdquo;.\u003c/p\u003e \u003cp\u003eIC50 of NmAbs, the primary indicator of the potency or efficacy defined as the NmAb concentration required to inhibit 50% of a fixed virus inoculum, following natural infection were included in this study, while vaccine-induced NmAbs were excluded (\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e). IC50 can be measured by either pseudo-particle (pp) or live virus neutralization assay. In this study, results from both assays were included.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eAnalysis of NmAbs potency across four viruses\u003c/h3\u003e\n\u003cp\u003eIn neutralization assays, the potency of each NmAb is often tested against a diverse pool of viral variants or strains. For HIV, a standard multi-clade panel consisting of 118 different HIV variants, representing the major genetic clades, is commonly used (\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e). For SARS-CoV-2, key viral variants such as Alpha, Beta, Gamma, Delta, Omicron, and its sub-lineages are tested. Influenza NmAbs are assessed using both influenza A and B variants (\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e). In the case of HCV, IC50 values of NmAbs are measured against a panel of HCV genotypes and subtypes (\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e). Consequently, each NmAb has distinct IC50 values for each variant tested. Furthermore, IC50 values for some NmAbs have been evaluated in multiple studies. To address variability, we calculated the geometric mean IC50 value for each NmAb across the tested variants and different studies, providing a single representative IC50 value. Moreover, as some variants can have different sensitivity to neutralization, we tried to remove panel bias by taking from each study the lowest reported IC50 for a given NmAb. For NmAbs tested in multiple studies, we averaged the minimum IC50 values and included this in the analysis, referring to it as the average \"minimum\" IC50. Consistency was ensured by including only IC50 values below 50 \u0026micro;g/mL across all viruses and studies.\u003c/p\u003e\n\u003ch3\u003eAssessment of NmAbs breadth across four viruses\u003c/h3\u003e\n\u003cp\u003eTo assess the breadth of neutralization for each NmAb, we defined percentage coverage as the fraction of viral strains neutralized by a given NmAb at a standard inhibitory concentration cut-off of 50 \u0026micro;g/mL, based on previous research in the field (data shown in \u003cb\u003eSupplementary Tables\u0026nbsp;1\u0026ndash;4\u003c/b\u003e). In this initial metric, a NmAb tested against genetically similar viral strains might show inflated coverage.\u003c/p\u003e \u003cp\u003eTo address this disparity, we adjusted the percentage coverage by scaling it with the mean genetic difference of the strains or variants tested, determined through the Hamming distance. For HCV and influenza, these differences were computed based on reference strains of subtypes and variants (see \u003cb\u003eSupplementary Tables\u0026nbsp;5 and 6\u003c/b\u003e for accession numbers). For SARS-CoV-2, sequences of different variants were generated using variant-defining mutations reported on the CoVariants database (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://covariants.org\u003c/span\u003e\u003cspan address=\"https://covariants.org\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e).\u003c/span\u003e For HIV, sequence data for 118 variants was sourced from the CATNAP database (\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe adjusted coverage was then normalized by dividing by the maximum value obtained across all viruses, ensuring the metric ranged from 0 to 100. We refer to this refined metric as %coverage\u003csub\u003escaled\u003c/sub\u003e.\u003c/p\u003e\n\u003ch3\u003eEthics statement\u003c/h3\u003e\n\u003cp\u003eFor the autologous HCV analysis, human research ethics approvals were obtained from Human Research Ethics Committees of Justice Health (reference number GEN 31/05), New South Wales Department of Corrective Services (05/0884), and the University of New South Wales (05094, 08081), all located in Sydney, Australia. Written informed consent was obtained from the participants. All methods were performed in accordance with the relevant guidelines and regulations.\u003c/p\u003e\n\u003ch3\u003eSubjects and Samples\u003c/h3\u003e\n\u003cp\u003ePreviously, samples were obtained from 14 incident cases of hepatitis C virus (HCV) infection identified through the Hepatitis C Incidence and Transmission Studies (HITS) in prisons (HITS-p) and the general community (HITS-c). Longitudinal plasma and PBMC samples were collected frequently over 12 weeks before antiviral treatment was offered at 24 weeks, allowing for the observation of natural clearance or progression to chronic infection (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eViral Sequencing\u003c/h2\u003e \u003cp\u003eBriefly, as previously described by us, near full-length HCV genome amplification was carried out using an nRT-PCR method, followed by next-generation sequencing (Roche 454 FLX and Illumina MiSeq) of longitudinal samples (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e). A bioinformatics pipeline was employed for read cleaning, alignment, and single nucleotide polymorphism (SNP) calling using ShoRAH, LoFreq, and Geneious software. Haplotype reconstruction of the E1E2 region was performed with ShoRAH, and Shannon entropy was determined. The transmitted/founder (T/F) virus was identified using the PoissonFitter statistical model and phylogenetic analysis and sequences have been previously published (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eCell culture, antibodies and reagents\u003c/h3\u003e\n\u003cp\u003eExpi293-Freestyle cells (Applied Biosystems, Tullamarine, VIC, Australia) were cultured at 37\u0026deg;C and 8% CO\u003csub\u003e2\u003c/sub\u003e in a growth medium containing Expi293 Expression Medium (Applied Biosystems, Tullamarine, VIC, Australia) and Huh7.5 cells (Apath, New York, NY, USA) were maintained at 37\u0026deg;C and 5% atmospheric CO\u003csub\u003e2\u003c/sub\u003e in growth medium containing High Glucose Dulbecco's Modified Eagle Medium (HG-DMEM, Gibco, Thermo Fisher Scientific, Waltham, MA, USA) and 10% v/v heat-inactivated fetal bovine serum (FBS) (Gibco). H77 derived E1/E2 proteins were provided by Prof. Jonathon Ball and A/Prof. Alexander Tarr. MLV gag/pol and luciferase plasmids which are used to produce pseudo-typed lentiviral particles were provided by Prof. Francois-Loic Cosset (University of Lyon, France) (\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e).\u003c/p\u003e\n\u003ch3\u003eGeneration of monoclonal antibodies (mAbs)\u003c/h3\u003e\n\u003cp\u003eSequences of the variable regions of the natively paired heavy (VH) and light (VL) chains were identified through single-cell RNA sequencing. These sequences were then utilized to design specific primers, enabling the amplification and subsequent recombinant antibody expression of the natively paired VH and VL genes as described by us previously (\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e). Briefly, single HCV E2-specific B cells (CD19\u0026thinsp;+\u0026thinsp;CD20\u0026thinsp;+\u0026thinsp;CD10\u0026thinsp;\u0026minus;\u0026thinsp;IgD\u0026thinsp;\u0026minus;\u0026thinsp;tetramer+) from HITS subjects were sorted and collected in 96 well plates containing in a final volume of 2 \u0026micro;l per well: 0.5 \u0026micro;l of dNTP (10 mM) (ThermoFisher Scientific, Waltham, MA, USA), 0.5 \u0026micro;l of 5 \u0026micro;M oligo-dT primer and 1 \u0026micro;l of lysis buffer. Briefly, using the SmartSeq2 approach, each single B cell was RT-PCR amplified and sequenced using Illumina 2 \u0026times; 150 PE Nextera XT Library Preparation Kit and VDJpuzzle 2.0 bioinformatics tool was used to reconstruct BCR sequences (\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eBoth heavy (VH) and light (VL) chains were ordered as plasmid from Biointron (company deets). Monoclonal Abs we expressed as described briefly by us. Monoclonal Abs were expressed using Expi293 Expression System (ThermoFisher) using manufactures\u0026rsquo; protocol, unless stated otherwise. Briefly, Expi293-FreeStyle cells were prepared 3 days in advance to reach the required density (3x106 cells/mL) and viability (\u0026gt;\u0026thinsp;95%) for transfection. The cells were then diluted in Expi293 expression medium (Thermo Fisher Scientific). Plasmid DNA encoding the mAb was also diluted in OptiMEM, mixed with ExpiFectamine 293 transfection reagent (Thermo Fisher Scientific), and incubated. The DNA-lipid complexes were added to the cells and incubated at 37\u0026deg;C, 8% CO2 with shaking. Following that, ExpiFectamine enhancers 1 and 2 (Thermo Fisher Scientific) were added. cells were harvested 72 hours post transfection and the supernatant containing secreted protein was collected. Cell supernatant was filtered through 0.22\u0026micro;m pore membrane filters (Millipore) and the His-tagged mAbs were then affinity purified using a HisTrap HP Column (GE Healthcare, Rydalmere, NSW, Australia) as per manufacturer\u0026rsquo;s instructions (\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e, \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e). As previously described by our group, E2 binding specificity of mAbs was validated by a rE2 binding ELISA using HCV Gt1a (H77) and Gt3a (UKN3A13.6) (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eHCVpp production, infection and neutralization\u003c/h2\u003e \u003cp\u003eAs previously described, E1E2 glycoproteins were cloned and co-transfected with MLV gag/pol and luciferase vectors in 293T cells to produce HCVpp. Briefly, neutralization assays were performed by incubating HCVpp with mAbs for one hour at 37\u0026deg;C(\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e). MAbs were used at concentrations of 100 \u0026micro;g/mL, 33.33 \u0026micro;g/mL, 11.11 \u0026micro;g/mL, 3.70 \u0026micro;g/mL, 1.23 \u0026micro;g/mL, 0.41 \u0026micro;g/mL, and 0.13 \u0026micro;g/mL. This mixture was applied directly to Huh-7.5 hepatoma cells (Apath, L.L.C, New York, NY, USA), and incubated for 72 hours after which luciferase activity was measured. Neutralization of HCVpp was calculated using the formula: % inhibition\u0026thinsp;=\u0026thinsp;1 \u0026minus; (inhibited activity)/(normal activity)] \u0026times; 100, after subtraction of negative control (pp generated without glycoproteins) RLU, where normal activity is HCVpp incubated with PBS. The 50% IC50 titre was calculated as the mAb concentration that caused a 50% reduction in RLU for each mAb/HCVpp combination tested in neutralization. All data were fitted using non-linear regression plots (GraphPad, Prism).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eStatistical Analysis\u003c/h2\u003e \u003cp\u003eAll data analysis was performed, and graphs created using GraphPad Prism Software (version 10.0, GraphPad) for windows using repeated measures ANOVA and non-parametric student T-test. P-values below 0.05 were considered as statistically significant.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003ePhylogenetic tree\u003c/h2\u003e \u003cp\u003eTo visualize the level of genetic diversity between the HIV, influenza, SARS-CoV-2 and HCV a phylogenetic tree was plotted. For this purpose, 32 reference sequences relating to HIV group M were downloaded from LANL HIV database (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.hiv.lanl.gov\u003c/span\u003e\u003cspan address=\"https://www.hiv.lanl.gov\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). To plot the HCV E1E2 phylogenetic tree amino acid sequences of E1E2 from 82 reference sequences of 7 distinct HCV genotypes. Influenza Hemagglutinin (HA) phylogenetic tree was also plotted using 34 HA amino acid sequences from influenza group A (H1-H18) which were obtained from Deng et al (\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e) and Influenza Research Database. Group B (B Yamagata and B Victoria) sequences were also sourced from Hibino et al (\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e). To depict the SARS-CoV-2 spike phylogenetic tree 22 SARS-COV-2 sequences of the spike of Wuhan, Alpha, Beta, Gamma, Delta and Omicron subvariants were collected from the GISAID (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://gisaid.org\u003c/span\u003e\u003cspan address=\"https://gisaid.org\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). Following these multiple sequence alignments were performed using MAFFT software to align the amino acid sequences. Phylogenetic trees were constructed using the neighbour joining method. Figtree v1.4.4 and Adobe Illustrator were used to visualize the data. All plotted trees have a scale bar of 0.1 except for the SARS-CoV-2 that has 20 times lower scale bar of 0.005, indicating its distinctly lower genetic diversity.\u003c/p\u003e \u003c/div\u003e"},{"header":"Declarations","content":" \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003eDATA AVAILABILITY\u003c/h2\u003e \u003cp\u003eSequences used in this study are listed in the supplementary section.\u003c/p\u003e \u003c/div\u003e\u003cp\u003e \u003ch2\u003eCOMPETING INTERESTS\u003c/h2\u003e \u003cp\u003eThe authors have declared that no competing interests exists.\u003c/p\u003e \u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eE.J., A.A.Q., N.T., M.R.M., A.L., M.W. and R.A.B. contributed to conception and design. A.L. provided clinical samples. E.J., A.A.Q., M.W and R.A.B. contributed to investigation. E.J. and A.A.Q. performed data analysis and interpretation. M.W. and R.A.B. are co-senior authors. All authors contributed to manuscript writing and to the final approval of the manuscript. All authors reviewed the manuscript.\u003c/p\u003e\u003ch2\u003eACKNOWLEDGEMENTS\u003c/h2\u003e \u003cp\u003eThe Kirby Institute is funded by the Australian Government of Health and Ageing. R.A.B. is supported by NHMRC Research Fellowship (Numbers 1080001).\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eWalker MR, Leung P, Eltahla AA, Underwood A, Abayasingam A, Brasher NA, et al. Clearance of hepatitis C virus is associated with early and potent but narrowly-directed, Envelope-specific antibodies. Scientific reports. 2019;9(1):13300.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAbebe EC, Dejenie TA. Protective roles and protective mechanisms of neutralizing antibodies against SARS-CoV-2 infection and their potential clinical implications. 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Cell reports. 2022;38(6).\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"","lastPublishedDoi":"10.21203/rs.3.rs-5876231/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-5876231/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003ePotent and broadly neutralizing antibodies (NAbs) are important for clearance of RNA virus infections. However, NAbs generated in response to RNA viruses exhibit diverse profiles of potency and breadth. In this study, we conducted a comparative analysis of NAbs targeting HIV, hepatitis C virus (HCV), SARS-CoV-2, and influenza based on their potency, measured by IC50 values, and breadth, assessed through the genetic diversity of the viruses neutralized by the NAbs. Our results reveal that while anti-HCV NAbs show a high breadth of neutralization, they are less potent and demonstrate inconsistent potency across different virus variants. These findings highlight the challenges in eliciting broad and potent antibody responses, which are essential for the development of effective vaccine strategies against HCV.\u003c/p\u003e","manuscriptTitle":"Anti-HCV antibodies: A battle for breadth and potency","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-01-28 18:03:16","doi":"10.21203/rs.3.rs-5876231/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"83db9444-bcad-48b8-af44-6ebf17609a1e","owner":[],"postedDate":"January 28th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":43310055,"name":"Biological sciences/Immunology"},{"id":43310056,"name":"Biological sciences/Immunology/Infection"},{"id":43310057,"name":"Biological sciences/Immunology/Infectious diseases"},{"id":43310058,"name":"Biological sciences/Immunology/Vaccines"},{"id":43310059,"name":"Biological sciences/Microbiology/Virology/Hepatitis c virus"},{"id":43310060,"name":"Biological sciences/Microbiology/Virology/Viral host response"}],"tags":[],"updatedAt":"2025-01-29T09:08:51+00:00","versionOfRecord":[],"versionCreatedAt":"2025-01-28 18:03:16","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-5876231","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-5876231","identity":"rs-5876231","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}