Subtractive proteomics assisted therapeutic targets mining and designing ensemble vaccine against Candida auris for immune response induction

The emergence of variants and the reports of co-infection caused by Candida auris in COVID-19 patients adds a further complication to the global pandemic situation. To date, no effective therapy is available for C. auris infections. Thus, characterization of therapeutic targets and designing effective vaccine candidates using subtractive proteomics and immune-informatics approaches is useful tool in controlling the emerging infections associated with SARS-CoV-2. In the current study, subtractive proteomics-assisted annotation of the vaccine targets was performed, which revealed seven vaccine targets. An immunoinformatic-driven approach was then employed to map protein-specific and proteome-wide immunogenic peptides (CTL, B cell, and HTL) for the design of multi-epitope vaccine candidates (MEVCs). The results demonstrated that the vaccine candidates possess strong antigenic features (>0.4 threshold score) and are classified as non-allergenic. Validation of the designed MEVCs through molecular docking, in-silico cloning, and immune simulation further demonstrated the efficacy of the vaccines by producing immune factor titers (ranging from 2500 to 16000 au/mL) i.e., IgM, IgG, IL-6, and Interferon-α. In conclusion, the current study provides a strong impetus in designing anti-fungal strategies against Candida auris.

Introduction
Candida auris infections, and outbreaks caused by diverse fungal groups, pose a significant threat to human health, and have affected almost 1.7 billion people worldwide. These infections are associated with the immune invasion properties of these pathogens, causing skin and mucosa diseases [1,2]. Candida species are the major source of nosocomial infections, and 0.4 million people are infected by it annually, with more than 40% of patients succumbing to it [1,2]. Candida auris was first isolated in 2009 from a female patient's ear discharge in Japan. This species is nested within the Clavispora clade of the family Metschnikowiaceae (order Saccharomycetales), and four major lineages/clades have been reported: South Asian, South African, South American, and East Asian [2]. The members of these lineages are associated with the spread of C. auris infections and outbreaks [3][4][5]. With the discovery of C. auris, the rapid expansion of its lineages across the globe has been evident from the infection cases reported from 6 continents [6]. These nosocomial outbreaks can be linked to the prolific spread of C. auris across health facilities, with its persistence in the human host and on surfaces [3,7,8]. C. auris linked nosocomial outbreaks were first observed in South Korea, followed by reports from India, South Africa, Kuwait, Venezuela, the USA, and many European countries [9][10][11][12][13][14][15].
C. auris has been isolated from patients admitted to the hospitals, and it has been reported that this pathogen favors colonizing the skin [16]. According to one study, 5% of nosocomial infections reported in intensive care units (ICUs) in India are linked with C. auris [17]. This pathogen has caused multiple infection outbreaks in hospitals [16] and is considered one of the most prolific pathogens to colonize ICUs, and therefore plays a vital role in the transmission of nosocomial infections [18].
After December 2019, due to the fast expansion of SARS-CoV-2 (COVID 19), hospitals have made additional efforts to accommodate the huge influx of patients. Of these patients, 5-30% were critically ill and were provided mechanical ventilators in ICUs [19,20]. Due to the influx of patients, proper compliance of guidelines was not followed, leading to substantial contamination of ICUs and the development of co-infections. C. auris co-infection has been associated with 50% of deaths caused by COVID 19, making it a major contributor to the death rate of critically ill COVID patients [21,22]. Moreover, as C. auris is one of the most capable colonizers of the ICU environment, it has an increased chance of co-infection in critically ill COVID patients. Fungal co-infection was reported in SARS patients treated with heavy doses of corticosteroids resulting in immunosuppression and the establishment of mycosis. The ability of C. auris to develop multidrug resistance and to grow at high temperature (42 • C), with rapid expansion and high mortality rate, makes it an emerging and deadly pathogen.
C. auris genome size ranges from 12.1 Mb to 12.7 Mb and consists of over 5500 genes [23,24]. Subtractive proteomics approaches aided by computational algorithms are used to select the most suitable proteins, among this large pool of data, to design suitable vaccines and to combat nosocomial infections caused by C. auris. Computational vaccine design is an effective approach to check all required parameters, such as antigenicity, allergenicity, physicochemical properties, and thermostability, making this approach cost-effective, thermodynamically stable, and safe [25]. In this study, outer membrane proteins which are considered highly virulent, antigenic, and essential for host-pathogen interactions were investigated [26]. The selected proteins were then used to predict cytotoxic T lymphocyte (CTL), helper T lymphocyte (HTL), and B-cell epitopes, and we have designed protein-specific and whole proteome-wide multi-epitope subunit vaccines against C. auris. This study may help to combat nosocomial infections and reduce the chances of co-infection with COVID. The designed candidate vaccines were also evaluated using molecular docking, and immune simulation strategies. However, further experimental validations are required to confirm the efficacy and safety of the proposed vaccine designs.

Retrieval of Candida auris proteome and subtractive proteomics
Candida auris standard reference proteome (UniProt ID: UP000230249) containing 5409 proteins was retrieved from UniProtKB (https://www.uniprot.org/) [27] and analyzed by using the subtractive proteomics to identify proteins that might be used in vaccine development against Candida auris infection. Firstly, the BLASTp tool using non-redundant protein sequences (nr) database with default parameters (e-value: 10 − 5 ) was used to identify the human host (Homo sapiens, ID: 9606) and pathogen homologous proteins. To avoid the autoimmune response of the host, the non-homologous proteins were utilized while the homologous proteins were eliminated [28]. To prevent overlapping's in the epitope selection owing to sequence similarity, the Cluster Data Base with High Tolerance (CD-HIT) suite (http://weizhon gli-lab.org/cd-hit/) was used to remove the duplicated proteins with an 80% percent identity (cut-off value: 0.8) [29]. This cut-off number has already been utilized by many researchers and has been submitted to the database to report the extremely high-quality matches [30]. The methodological workflow adopted in this study is shown in Fig. 1.

Prioritization of the essential proteins
The DEG (database of essential genes) (http://tubic.tju.edu.cn/deg/) contains information on key proteins and genes that pathogens need to survive. These essential proteins are promising candidates for vaccine development. Therefore, the important protein of Candida auris was found by using the DEG online tools [31]. However, the CELLO online server (http://cello.life.nctu.edu.tw/cello.html) having 88% accuracy and based on SVM was used for the identification of subcellular localization of important proteins [32]. According to scientific logic, the extracellular proteins are measured as highly appropriate for vaccine development because of their critical role in the attachments of the pathogen to the host cell surface and induction of pathogenicity [33].
Selected pathogenic proteins were also tested for antigenicity using the webserver VaxiJen (http://www.ddg-pharmfac.net/vaxijen/VaxiJen/V axiJen.html) by selecting fungi as the target organism at a threshold of 0.4 and used for further investigations [34]. The ProtParam weight calculator program was used to compute the molecular weight of chosen proteins [35].

Mapping immunogenic peptides in the target proteins
An online web server NetCTL 1.2 (http://www.cbs.dtu.dk/service s/NetCTL/) [36] was used to predict Cytotoxic T-lymphocytes. The key values used by the server for the prediction of CTL are transportation efficiency, TAP (Transport Associated with Antigen Processing), MHC I binding peptides prediction, and proteasomal C terminal cleavage. The threshold value was set to 0.75 for the prediction of CTL epitopes. The IEDB online server (http://www.iedb.org/) was used to predict HTL epitopes using the seven reference alleles set of human HLAs [37,38]. The IC50 value determines the affinity score of each peptide. Binding affinity is strong for peptides with an IC50 value less than 50 nM. Peptides with an IC50 of less than 500 nM are intermediates, whereas peptides with an IC50 of fewer than 5000 nM indicate poor epitope binding affinity. The peptides' affinity scores and percentile rankings are inversely linked, which indicates that the higher the binding affinity, the lower the percentile rank, and vice versa. The Algpred server (http:// www.imtech.res.in/raghava/algpred2/) [39,40] was used to evaluate the Allergenicity of each MHC-II predicted epitopes. Similarly, the VexiJen server (http://www.ddg-pharmfac.net/vaxijen/VaxiJen/Vax iJen.html) was used to assess the antigenicity of each epitope [34]. As a result, non-allergenic epitopes with high antigenicity were chosen for putative vaccine development. Next, we used the ABCPred (htt ps://webs.iiitd.edu.in/raghava/abcpred/) online server to predict the linear B-cell epitopes. The mentioned server has >65% accuracy, 0.49 sensitivity and 0.75 specificity [41]. For linear B-cell epitope prediction, the user employs the Kernel Method by analyzing SVM (Support Vector Machine) and uses AAP (amino acid pair) antigenicity. The Ellipro service (http://tools.iedb.org/ellipro/) was also used to predict discontinuous B-cell epitopes, with findings based on Thornton's method and a residue clustering algorithm [42]. Additionally, IFN-epitope (http:// crdd.osdd.net/raghava/ifnepitope/) server was used to investigate interferon-gamma triggering MHC-II epitopes. This online tool uses the SVM score and motif for each input epitope to predict interferon-inducing properties [43].

Construction of multi epitopes subunit vaccine
The final putative vaccine constructs were designed using B cell, CTL, and HTL epitopes with high binding affinity, non-allergenicity, and antigenicity. Based on published data [37,39], the selected epitopes were linked together using different linkers. This included the use of epitopes specific AAY, GPGPG, and KK linkers to join the putative CTL, HTL, and B-cell epitopes, respectively. The strategy was used in the target proteins specific vaccine design. Additionally, a proteome-wide vaccine was also constructed in this study. These linkers are very important in epitope presentation as well as prevention of neoepitopes and enhance immunogenicity by stabilizing the protein structure. The EAAK linker was used to link the adjuvant to the N-terminal of the final candidate vaccine constructs [44].

Prediction of physicochemical properties
The ProtParam online tool (http://web.expasy.org/protparam/) was used to estimate the physicochemical features of constructed vaccines such as instability index, theoretical PI, in vivo and invitro half-life, amino acid composition, and GRAVY [35].

Prediction of 3D structure of the designed vaccines
The Robetta online server (http://robetta.bakerlab.org) was used to predict the tertiary structure of the putative vaccine [45]. Robetta server uses a comparative modeling approach based on BLAST, FFAS03, or 3d-jury to identify templates for the particular amino acid sequence. The Robetta server has been rated as one of the most correct and stable servers since 2014. It makes use of Continuous Automated Model Evaluation (CAMEO) to compute up to 20 pre-released PDB targets per week. This server's average LDDT (Local Distance Difference Test) score is around 69, indicating a highly independent score [45].

TLR2-vaccine docking
Using the HawkDock server (http://cadd.zju.edu.cn/hawkdock/), the designed vaccine was docked with the TLR2 (Human Toll-like receptor-2). The server employs a hybrid docking method and requires an amino acid sequence as input for analysis, setting it apart from other similar servers. The HawkDock server provides experimental information on the protein-protein binding site and small-angle x-ray drip quickly and authentically [46]. The putative vaccine design was modeled using Robetta before protein-protein docking and the TLR-2 structure was acquired from the ALPHAFOLD-2 using ID: AF-O60603-F1 [47]. The structure was prepared before the docking by removing water molecules, heteroatom, and other atoms.

Immune simulation
To create an immunogenic profile of the designed vaccine, we performed the immune simulation by using the online server called C-ImmSim (http://150.146.2.1/C-IMMSIM/index.php) [48]. C-ImmSim is a dynamic agent-based simulator for assessing the immunological responses of the body to antigens. C-ImmSim uses an agent-based approach and the specific scoring matrix PSSM to predict immune interactions and epitopes. Following the submission of the designed vaccine to the online server with the default simulation settings, the production of antibodies, interferon, and cytokines were measured; however, the same server was also used to check the reactions of both Th1 and Th2 [49].

In-silico cloning and codon optimization
We used the Java codon adaptation tool (JCat tool) for codon optimization and reversed translation to produce the multi-epitope subunit vaccine in an appropriate vector [50]. The Jcat program is also used to guarantee that the vaccination sequence is expressed at a high level in an appropriate vector. Three choices, such as bacterial ribosome binding sites, Rho-independent transcription termination, and restriction enzyme cleavage sites, were selected in this tool. Jcat determines the GC content and CAI score of the constructed vaccine to optimized the reversely transcribed vaccine construct to a bacterial expression system [51]. The XhoI and EcoRI, restriction enzymes were manually inserted into the putative vaccine sequence, and the sequence was subsequently cloned onto the pET-28a (+) plasmid using Snapgene software [26,52].

Proteome subtraction
Annotation of pathogen proteomes substantially assist mining for therapeutic targets and the development of therapeutic strategies. Identification and characterization of target proteins are important to aid vaccine target prioritization and for designing strategies for the treatment of different diseases. As such, proteome-and genome-wide, and functional genomics-based identification and validation of therapeutic biomarkers are widely used for different diseases [38,53]. In this study, we used a subtractive proteomics pipeline to identify the putative vaccine targets for the multiple drug-resistant fungi, Candida auris, which has been reported in SARS-CoV-2 hospitalized patients in India, USA, and other parts of the world. For this purpose, the whole proteome, containing 5409 proteins, of Candida auris species was screened through CELLO (version 2.5), a cellular localization prediction tool, to screen out extracellular proteins. As previously reported, extracellular proteins are prioritized as targets for vaccine design as they perform a key role in viral pathogenesis [26]. A total of 135 proteins were found to be extracellular. Furthermore, identifying non-homologous sequences and removing homologous proteins using BLASTp determined that 38 proteins were homologous to human proteins while 97 proteins were identified as non-homologous. The non-homologous sequences were further screened for paralogs using CD-HIT and a single sequence (OA2HIA3C7) was removed as it shared 91.6% identity with its paralog (A0A2H0ZN80). The remaining 96 proteins were subjected to functional characterization using the DEG database to find genes essential to the pathogenesis and survival of C. auris. This determined that only 13 of these proteins are essential and have a critical role in pathogenesis. Next, we evaluated the antigenic and allergenic properties of these shortlisted targets, which showed that only 7 proteins were determined as highly antigenic and non-allergenic, shown in Table 1. These target proteins include a mitochondrial import receptor subunit, two putative beta-glucanase/Beta-glucan synthetases, two 1,3-beta-glucanosyltransferases, a uricase, and a putative SUN family protein.
These identified proteins are known to be critical for C. auris pathogenesis. The putative mitochondrial import receptor subunit protein, with an antigenicity score 0.665 and classified as non-allergenic, is a key mediator for regulating ion and protein transport. Similarly, the two beta-glucanase/Beta-glucan synthetase proteins, with antigenicity scores of 0.4504 and 0.9645 and also classified as non-allergenic, are involved in carbohydrate metabolism and cell wall synthesis. The two 1,3-beta-glucanosyltransferase proteins, with high antigenicity scores of 0.7396 and 0.6666, play a significant role in transferase activity and the uricase protein, with an antigenicity score of 0.5435, is important in the regulation of urate oxidase activity. Finally, the SUN family protein, with an antigenicity score of 0.7738, is also essential for regulating mitochondrial and cell wall functions. All seven target proteins sequences retrieved from Uniprot [54] were then subjected to epitope screening to find CTL, HTL, and B Cell epitopes.

Immune-based epitopes screening
T-lymphocytes and B-cells play an important role in the recognition of the antigenic components of pathogens. T-cells are equipped with cell surface T-cell receptors (TCRs), which help to recognise antigens. The Tcell epitope presentation is performed by MHC molecules and is vital to their recognition of antigens. Contrastingly, B-cell differentiation results in the soluble secretion of immunoglobulins which work as antibodies to mediate adaptive immunity. These B-cell-produced antibodies perform several functions when triggered by antigen-specific binding. Other vital functions, including neutralization and targeted destruction of pathogens, are also performed by B-Cells. T-and B-cell epitope prediction aims for the specific inclusion of highly antigenic protein components for enhanced antibody production and vaccine design. Identification of antigenic immune epitopes is a prerequisite to designing peptide-based ensemble vaccines.
Recent advancements in the field of computational modeling, and the evolution of structural vaccinology-specific algorithms for epitope predictions, are a great alternative to rational vaccine design [37,38].  These methods are cost-effective and less time-consuming as compared to experimental methods. Specifically, CTLs and B-cells are key to the regulation of cell-mediated and humoral immunity. Similarly, HTL epitopes are essential for inducing and maintaining effective antibody response. Here, we screened antigenicity of the seven shortlisted proteins in C. auris for potential CTL, B-Cell, and HTL epitopes. Among the screen of CTL epitopes, a total of 6, 32, 29, 17, 9, 10, and 9 MHC binders were identified for each protein, respectively. Similarly, the number of B-Cell epitopes identified for each protein were 36, 65, 59, 46, 29, 33, and 30, and there were 2583, 4634, 3752, 3094,1806, 2002, and 1883 HTL epitopes. These epitopes were further analyzed by subjecting them to antigenicity and allergenicity checks. The epitopes with high antigenic potential and non-allergenic status then proceeded to the next step of vaccine design. During the CTL epitope prediction for protein A0A2H0ZE1, epitopes "LSVSGKINY" and "SAISYFARY", with high antigenicity scores (0.85, 0.84), and a combined score >1 (showing higher binding affinity), were selected. For protein A0A2H0ZXY4, two epitopes, "ETDTNTMKL and SSSIRTSLV" at corresponding positions of 468-476 and 263-271 were selected. Similarly, the remaining five proteins, with accession IDs A0A2H1A5Q4, A0A2H1A279, A0A2H1A7U2, A0A2H0ZDW4 and A0A2H1A5R4, having corresponding positions of 422-430, 142-150, 187-195, 377-385, 224-232, 243-251, 281-289, 102-110, 241-249, 197-205, had high combined scores ranging from 0.9 to 3.3. High antigenicity scores above 2.9 were shortlisted. All T-Cell epitopes selected for each protein were also screened for allergenicity status before inclusion in the candidate vaccine designs, as shown in Table 2.
For protein A0A2H0ZE14, specific B cell epitopes were also predicted and two of these epitopes, "CQWEQDYNANDFSIN" and "AFSIGGDLPPYAFSAL", were selected based on high binding scores (0.9, 0.8) and antigenicity scores (1.9, 0.8). Similarly, for the protein A0A2H0ZXY4, two B cell epitopes, "NGHLTWYVGSDPTLTV" and "IEAETDTNTMKLGLAS", having start positions (167, 106) and high antigenicity scores (1.8, 2), were selected. Furthermore, for the remaining five proteins, A0A2H1A5Q4, A0A2H1A279, A0A2H1A7U2, A0A2H0ZDW4, and A0A2H1A5R4), high binding scores in the range 0.5-1.4 and high antigenicity scores up to 1.5 were selected. Further analysis was performed for all the studied proteins and corresponding Bcell epitopes were examined for allergenicity status as shown in Table 3.
For potential HTL epitopes selection, SVM-based analysis was performed for the protein A0A2H0ZE1 and two Interferon γ (IFN) inducing HTL epitopes, "AHGQPTMCQWEQDYN" and "KQSSKVGVGLQVETA", were selected. Similarly, the protein A0A2H0ZXY4 returned two HTL epitopes, "CYVGYLQPRTFLL" and "YYVSYLQPRTFLL", with high antigenicity scores (0.8-0.5) and potential IFN induction. The analysis for the remaining five studied proteins (A0A2H1A5Q4, A0A2H1A279, A0A2H1A7U2, A0A2H0ZDW4, A0A2H1A5R4) possessing high antigenic potential (ranging 0.5-3.1) helped in the identification of putative IFN inducing HTL epitopes and were classified against each protein as shown in Table 4. The final shortlisted epitopes (CTL, B cell, and HTL) from Tables 2-4 were included in further MEVC design against each target protein.

Putative vaccine designing
Identification of antigenic epitopes through proteome-wide screening is vital in designing multi-epitope-based vaccines during immunoinformatic approaches. The mapped CTL, B-cell, and HTL epitopes for the selected seven essential proteins of C. auris were further subjected to in silico vaccine design. The shortlisted epitopes (Tables 2-4) were used in the full-length MEVC designs and joined together using different linkers (i.e., EAAK, AAY, GPGPG, and KK) against each target protein as shown in Table 5. This also included MEVC design based on the whole proteome, including epitopes from all the seven target proteins.
These linkers are vital to enabling epitope display, maintaining epitope separation, and preventing folding, to ensure an effective immune response. Moreover, the addition of an adjuvant enhances the immunogenic potential of MEVC peptide vaccine designs [39]. All the designed MEVC constructs were therefore subjected to the addition of a non-toxic adjuvant called human beta defensin-2 (hBD-2) following an EAAAK linker. The hBD-2 possesses the property of self-production with expression levels that enable it to induce a robust immune response against the attached antigen [55,56].

Table 5
Showing the target protein-specific and proteome-wide designed MEVCs, their length, antigenicity and allergenicity profiling.

Whole proteome (WP) based vaccine design
The whole proteome-based final MEVC (MEVC-WP) was constructed with 14 CTL, 14 HTL, and 14 B-Cell epitopes linked together. The length of the final construct was 475 amino acids with a high antigenicity score of >1, demonstrating high experimental feasibility. The general schematic diagram of the putative vaccine construct with included epitopes from different proteins and suitable added linkers is presented in Fig. 2, where each mapped vaccine and its components are presented in different colors.

Physiochemical properties
The putative vaccine constructs were then analyzed through the ProtParam server to verify several physiochemical parameters vital to confirming the stability of the vaccine designs. These parameters included calculation of theoretical pI (7.4-9.9), molecular weight (17-50 kd), and other parameters determining the feasibility and structural stability of the vaccine for further experimental designs. Moreover, the demonstrated half-life, aliphatic index (thermos-stability), and GRAVY (Grand average of hydropathicity) were also explored. These results reflected the hydrophilic and stable nature of the designed MEVCs. The different physiochemical properties for protein-specific and proteome-wide putative vaccines are given in Table 6.

Structural modelling of the designed MEVC
The widely used Robetta server generates highly efficacious structural models through parsing of potent domains and by utilizing different strategies including comparative modeling and de novo sequence-based structural prediction. Based on the confidence levels of available matching protein structures, templates are used to produce a comparative model. In contrast, the de novo Robetta fragment insertion method is used for proteins with no available matching structures. 3D structural modeling for each of the constructed MEVCs was performed with the utilities of the Robetta server. All generated models were visually evaluated using PyMOL software and the refined best models, as shown in Fig. 3, were included for further analysis.

Interaction of MEVCs with the immune receptor (TLR-2)
The HAWKDOCK server was employed to evaluate vaccine-TLR-2 interaction. The top ten docking models of vaccine-TLR-2 were generated by the server, ranked based on docking complex score. The docking scores acquired for each of the docking complexes are given in Table 7. For MEVC1 the docking score was − 5832.94 kcal/mol, with three hydrogen bonds and 1 salt bridge reported. Similarly, the MEVC4 also formed three hydrogen bonds and 1 salt bridge with a docking score − 4261.51 kcal/mol. Unlike MEVC1 and MEVC4 the total number of hydrogen bonds were higher in MEVC2, with six hydrogen bonds and 1 salt bridge, and a docking score of − 5260.80 kcal/mol. MEVC5 and MEVC7 formed 5 hydrogen bonds, with MEVC5 also having 2 salt bridges. The docking score for MEVC5 and MEVC7 were reported to be − 5263.43 and − 4600.89 kcal/mol. On the other hand, MEVC6 formed a single hydrogen bond and two salt bridges with a docking score of − 3449.97 kcal/mol, however, the non-bonded contacts were higher in this complex. For MEVCPW the docking score was − 4935.33 kcal/mol with four hydrogen bonds and 1 salt bridge reported. The results decisively suggest that the designed vaccine candidates robustly interact with the immune receptor. The best complex (lowest energy score) for each of the designed vaccine candidates were subjected to interaction analysis by utilizing PDBsum, a web tool for the graphical representation of macromolecular complexes. The docking complexes, hydrogen bonds, salt bridges, and non-bonding contacts between putative vaccines and TLR-2 are shown in Fig. 4 and Fig. S1 (A-G).

Codon optimization and in-silico cloning
All constructed vaccine sequences were improved by utilizing the Jcat server for optimal expression in E. coli strain K-12. For each of the vaccine constructs, the codons were optimized, and reverse translated to obtain the corresponding nucleotides. The codon adaptation index (CAI) values were also calculated through the Jcat server for each of the vaccine constructs. The higher calculated CAI scores, in the range 0.98-1, and optimal percentage of GC contents (51-53%) ( Table 6) indicated that the vaccine protein is expressed at a high level in E. coli. The 5 ′ and 3' ends restriction enzymes selected for cloning of the gene sequence of each vaccine into pET28a (+) were XhoI and EcoRI. The software package Snapgene was utilized for the insertion of putative vaccine sequences into the pET28a (+) plasmids to obtain the cloning designs (Fig. 5).

Immune simulation of the proposed vaccine and immune response
To evaluate the potential antigen-based induction of the human immune response, each of the designed vaccines was examined as an injected antigen with in silico immune simulation. As shown in (Fig. 6A) the injected antigen MEVC1 (A0A2H0ZE14), after achieving the highest antigen counts at day 2, was slowly neutralized until day 5. Afterward, a strong antibody (IgM + IgG) response was observed with the highest achieved titers of >700,000 au/ml between days 10-15. Similar high antigen counts (day 2) were found for the designed vaccines MEVC2, MEVC3, MEVC4, MEVC5, MEVC6, MEVC7, and MEVCWP, following the    same pattern of neutralization (day 5) for all the designed vaccines (Fig. 6). However, variation in the antibody titers was significant between 10 and 15 days post-injection for each of the designed MEVCs. The highest titers of antibodies (IgM + IgG) for MEVC2 were observed at about 600,000 au/ml (Fig. 6B). For MEVC3, a similar higher ratio 700,000 au/ml of antibody (IgM + IgG) titers was observed (Fig. 6C). For MEVC4, the antibody titers were about 600,000 au/ml (Fig. 6D). Similarly, for MEVC5, MEVC6 and MEVC7, the observed antibody titers were ≥700,000 au/ml (Fig. 6E-G). Additionally, the MEVC designed against the whole proteome of C. auris, MEVCWP, also showed about 700,000 au/ml for combined (IgM + UgG) antibody titers (Fig. 6H). This was followed by IgM-specific antibody titers of about 500,000 au/ml. These results showed great immunogenic potential for each designed vaccine, all of which triggered a robust immune response. Consequently, the designed vaccine candidates may trigger the production of protective immunity against the human pathogen C. auris in further in vivo and in vitro models.

Limitations of the study
This study was based on in silico analysis to shortlist putative epitopes which need validation in experimental vaccine designs. Similarly, the physiochemical properties of each vaccine candidate were explored but also need further verification in the wet lab. The important step involving the expression and purification of the protein needs advanced testing using recombinant DNA technologies. Further evaluations are needed to justify the clinical manifestations associated with the vaccine designs against Candida auris.

Conclusion
In conclusion, a subtractive proteomics approach has been utilized to shortlist seven protein targets for putative vaccine design against Candida auris. Different epitopes for CTL, B-cell, and HTL have been explored for each of the shortlisted target proteins. Additionally, a whole proteome-based vaccine has been designed which includes each target protein-specific vaccine design. The constructed MEVCs have been modeled and evaluated for potential immunization against C. auris. The present study provides new and valuable epitope candidates and prompts future vaccine development against this pathogen. The suggested highly antigenic MEVCs in this study have paved a way for experimental processing of vaccines against Candida auris, however, despite showing immune reinforcement potential, the candidate vaccines need further validation through experimental processing to confirm their efficacy and safety.

Declaration of competing interest
Authors declare there is no declaration of interest.


s2fieldsofstudy: Biology Medicine

text:
89Zr-PET imaging to predict tumor uptake of 177Lu-NNV003 anti-CD37 radioimmunotherapy in mouse models of B cell lymphoma

[177Lu]Lu-DOTA-NNV003, a radioimmunoconjugate targeting CD37, is developed as novel radioimmunotherapy (RIT) treatment for patients with B cell non-Hodgkin’s lymphoma (NHL). Since patients are at risk for developing hematological toxicities due to CD37 expression on normal B cells, we aimed to develop 89Zr-labeled NNV003 for positron emission tomography (PET) imaging as a surrogate tool to predict [177Lu]Lu-DOTA-NNV003 RIT whole-body distribution and tumor uptake. NNV003 antibody was first radiolabeled with 89Zr. [89Zr]Zr-N-sucDf-NNV003 tumor uptake was evaluated by PET imaging of mice bearing human CD37-expressing REC1 B cell NHL or RAMOS Burkitt’s lymphoma xenograft tumors followed by ex vivo analysis. Finally, CD37-targeting of [89Zr]Zr-N-sucDf-NNV003 and [177Lu]Lu-DOTA-NNV003 RIT were compared. [89Zr]Zr-N-sucDf-NNV003 accumulated in REC1 tumors over time, which was not observed for non-specific, 111In-labeled IgG control molecule. In RAMOS tumor-bearing mice, [89Zr]Zr-N-sucDf-NNV003 tumor uptake was higher than [111In]In-DTPA-IgG at all tested tracer protein doses (10 µg, 25 µg and 100 µg; P < 0.01), further confirming [89Zr]Zr-N-sucDf-NNV003 tumor uptake is CD37-mediated. [89Zr]Zr-N-sucDf-NNV003 and [177Lu]Lu-DOTA-NNV003 RIT showed similar ex vivo biodistribution and tumor uptake in the RAMOS tumor model. In conclusion, [89Zr]Zr-N-sucDf-NNV003 PET imaging can serve to accurately predict CD37-targeting of [177Lu]Lu-DOTA-NNV003. To enable clinical implementation, we established a good manufacturing practice (GMP)-compliant production process for [89Zr]Zr-N-sucDf-NNV003.

Higher spleen uptake was observed for [ 177 Lu]Lu-DOTA-NNV003 compared to [ 89 Zr]Zr-N-sucDf-NNV003 (5.6 vs. 2.1%ID/g; P < 0.05). Murine CD37 is expected to be expressed on B cells present in the spleen, however NNV003 is not cross-reactive with murine CD37. Furthermore, [ 89 Zr]Zr-N-sucDf-NNV003 spleen uptake was similar to [ 111 In]In-DTPA-IgG, indicating this uptake is not CD37-mediated. In mice, the spleen plays an important role in antibody pharmacokinetics due to its high blood flow and loose capillaries, but also due to the    , is currently in phase II for patients with anti-CD20 refractory follicular lymphoma and has previously been studied in phase I/IIa clinical trials in patients with relapsed, CD37-positive, indolent and aggressive NHL (ClinicalTrials.gov Identifier: NCT01796171, NCT02658968) 3 . These studies showed high RIT uptake in tumors, but also in red bone marrow, liver, spleen, and kidneys 9,10 . This may be explained by CD37 expression on mature, normal B cells in these tissues. Predosing with unlabeled lilotomab significantly reduced the absorbed radiation dose in healthy CD37expressing tissues due to 177 Lu-lilotomab satetraxetan 9 . Also, 177 Lu-lilotomab satetraxetan dosimetry, biodistribution, and tumor targeting were improved by lilotomab predosing compared with rituximab predosing or no predosing 3 . Informing clinicians on whether tumors are effectively targeted by [ 177 Lu]Lu-DOTA-NNV003 and gaining insight into the amount of cold antibody dose required to saturate CD37 expression in healthy tissues are essential to optimize future [ 177 Lu]Lu-DOTA-NNV003 RIT dose-regimens. We showed that [ 89 Zr]Zr-N-sucDf-NNV003 PET imaging can serve as a surrogate for [ 177 Lu]Lu-DOTA-NNV003 RIT whole-body distribution and represents a potentially attractive tool to assess distribution to tumors and healthy tissues.
The combined approach of RIT and diagnostics such as molecular imaging may support precise cancer therapy in both palliative and curative settings 19 . SPECT/CT imaging was routinely used for assessing biodistribution  www.nature.com/scientificreports/ and dosimetry of 90 Y-and 131 I-based RIT antibodies in the early 2000s 20,21 , but low quantities of γ-photons emitted by 177 Lu complicate quantification. As a therapeutic agent, even low dose pre-treatment imaging of 177 Lu may result in local toxicity, while this risk is limited for the low energy β + -rays of PET radioisotopes. In this respect, gallium-68 ( 68 Ga)/ 177 Lu is a commonly used theranostic pair for studying receptor expression or drug distribution. However, 68 Ga, given its relatively short physical half-life of 67.6 min, provides no insight in internalizing properties of an antibody. As internalization is an essential factor for both efficacy and toxicity of 177 Lu-based RIT agents, longer-lived PET radioisotopes such as 89 Zr may better reflect 177 Lu RIT in vivo behavior. In a recent study, response at the tumor lesion level after treatment with 177 Lu-lilotomab satetraxetan was evaluated by FDG PET/CT and did not correlate with tumor-absorbed dose 11 . They hypothesized that the combination regimen of radiolabeled and cold antibodies might preclude such a correlation. In our study, we were able to quantitatively visualize tumor uptake in the presence of unlabeled antibody using [ 89 Zr]Zr-N-sucDf-NNV003 PET imaging. In patients with relapsed B cell NHL, a pre-therapy scan with 89 Zr-ibritumomab tiuxetan was used to predict radiation dosimetry during 90 Y-ibritumomab tiuxetan therapy 14 . Importantly, 89 Zr-ibritumomab tiuxetan whole-body distribution was not affected by simultaneous 90 Y-ibritumomab tiuxetan therapy. These findings emphasize the potential of an [ 89 Zr]Zr-N-sucDf-NNV003 pre-therapy scan to predict CD37-targeting by  www.nature.com/scientificreports/

Materials and methods
Cell lines and flow cytometry experiments. Human CD37-expressing cell lines RAMOS (Burkitt's lymphoma) and REC1 (Mantle cell lymphoma) were obtained from the American Type Culture Collection. RAMOS and REC1 cell lines were tested and authenticated in July and October 2019 respectively using short tandem repeat profiling. Cells were cultured in Roswell Park Memorial Institute (RPMI) medium, supplemented with 10% fetal calf serum (FCS) and incubated at 37 °C in a humidified atmosphere with 5% CO 2 . CD37 expression by RAMOS and REC1 cells was determined by flow cytometry. Cells were harvested in 2% FCS in phosphate-buffered saline (PBS) and kept on ice prior to use. NNV003 and non-specific human IgG control molecule (Nanogam ® , Sanquin) were diluted with 2% FCS in PBS to 20 µg/mL and incubated with 2 × 10 5 cells/mL for 1 h at 4 °C. Bound NNV003 and control antibodies were detected using a phycoerythrin-conjugated goat anti-human IgG secondary antibody (SouthernBiotech; 2040-09) diluted 1:50 with 2% FCS in PBS and analyzed on a BD Accuri C6 flow cytometer (BD Biosciences). Data analysis was performed using FlowJo v10 (Tree Star) and surface receptor expression was expressed as mean fluorescent intensity (MFI).
Radiolabeling of NNV003 and IgG control for animal studies. NNV003 antibody (IgG 1 , mouse variable regions, κ, and human constant region, κ; Nordic Nanovector) was conjugated to TFP-N-sucDf (ABX GmbH) and subsequently radiolabeled with 89 Zr as described previously 22 . To date, several 89 Zr-labeled antibodies are produced according to this methodology and were evaluated in animals and patients without any sign of toxicity 16,17,23,24 . In short, NNV003 was incubated with a twofold molar excess of TFP-N-sucDf at pH 9.0-9.5. After incubation for 1 h at room temperature (RT), pH was set to 4.0-4.5. Ethylenediaminetetraacetic acid (EDTA; Hospital Pharmacy UMCG) 25 mg/mL was added and incubated for 30 min at 35 °C to transchelate Fe(III) from the TFP-N-sucDf hydroxamate groups. NNV003-N-sucDf was subsequently purified using a Vivaspin-2 concentrator (Sartorius GmbH), aliquoted and stored at − 80 °C until use. On the day of tracer injection, NNV003-N-sucDf was radiolabeled using GMP-grade 89 Zr oxalate (Perkin Elmer). RCP of [ 89 Zr]Zr-N-sucDf-NNV003 was determined by trichloroacetic acid precipitation test 17 . Furthermore, NNV003 antibody was conjugated to p-SCN-Bn-DOTA (Macrocyclics) and subsequently radiolabeled using 177 Lu chloride (Perkin Elmer) as described previously 4,5 .
Non-specific IgG control molecule was conjugated with a 50-fold molar excess of p-SCN-Bn-DTPA (Macrocyclics) as described previously 25 . Radiolabeling of IgG-DTPA was performed using 111 In chloride (Mallinckrodt) by incubation during 1-2 h in ammonium acetate pH 5.5. Radiochemical purity of [ 111 In]In-DTPA-IgG was assessed by instant thin-layer chromatography using 0.1 M citrate buffer pH 6.0 as eluent.
[ 89 Zr]Zr-N-sucDf-NNV003 quality control. [ 89 Zr]Zr-N-sucDf-NNV003 purity and concentration were determined by size-exclusion high-performance liquid chromatography (SE-HPLC). A Waters SE-HPLC system was equipped with a dual-wavelength absorbance detector, in-line radioactivity detector and TSK-Gel SW column G3000SWXL 5 µm, 7.8 mm (Joint Analytical Systems GmbH). PBS (9.0 mM sodium phosphate, 1.3 mM potassium phosphate, 140 mM sodium chloride, pH 7.2; Hospital Pharmacy UMCG) was used as mobile phase at a flow of 0.7 mL/min. NNV003-N-sucDf IRF was determined on human CD37-expressing Burkitt's lymphoma RAMOS cells. Cells were harvested in PBS with 0.5% bovine serum albumin (BSA), diluted to 75 × 10 6 cells and 0.2 mL added per tube to a total of 5 tubes. CD37-specific binding sites were blocked in 2 tubes by incubation with 20 µg NNV003-N-sucDf for 15 min at RT. Subsequently, 8 ng [ 89 Zr]Zr-N-sucDf-NNV003 (~ 9000 counts per minute) was added to each tube and incubated for 1 h at RT. Tubes were counted in a calibrated well-type gamma counter (LKB instruments), subsequently spun down and washed with 0.5% BSA in PBS for three times, after which tubes were counted again. IRF was expressed as the average percentage of CD37-bound [ 89 Zr]Zr-N-sucDf-NNV003 as a fraction of the percentage of total activity added in non-blocked tubes corrected for non-specific binding in the blocked tubes. Acceptance criteria were set at ≤ 5% non-specific binding in blocked tubes and NNV003-N-sucDf IRF at ≥ 0.8. can be compared within the same animal, thereby providing valid results on target-specific uptake. Also, the number of animals required for these studies can be reduced using this strategy. Mice underwent microPET scanning at 1, 3 and 5 days post injection (pi), followed by ex vivo biodistribution.
MicroPET scans were performed using a Focus 220 rodent scanner (CTI Siemens). Scans were reconstructed using a 2-dimensional ordered-subset expectation maximization reconstruction algorithm with Fourier rebinning, 4 iterations, and 16 subsets. Data sets were corrected for decay, random coincidences, scatter, and attenuation. For in vivo quantification, regions of interest were drawn for tumor based upon ex vivo weight, assuming 1 g/cm 3 tissue density, and heart using AMIDE medical image data examiner software v1.0.4. Tracer uptake was www.nature.com/scientificreports/ quantified as SUV mean and SUV max , calculated from the mean or maximum activity in the region of interest and divided by the injected dose per gram body weight. For ex vivo biodistribution studies, relevant organs were collected, weighed and counted using a calibrated well-type gamma counter. Standards of injected tracer were included to correlate measured counts to the percentage of injected tracer activity. After correction for decay, ex vivo tissue uptake was expressed as the percentage of injected radioactivity dose per gram tissue (%ID/g) and standardized uptake value (SUV) by correcting for injected dose and mouse body weight. Animal experiments involving [ 177 Lu]Lu-DOTA-NNV003 were approved by the Norwegian Animal Research Authority. Biodistribution of [ 177 Lu]Lu-DOTA-NNV003 was studied in the RAMOS tumor model. Female Hsd:Athymic Nude-Foxn1 nu mice (Envigo) 7-11 weeks of age were subcutaneously injected with 100 µL RAMOS cell suspension from a donor mouse xenograft to enhance tumor take-rate. Mice received intravenous injections of 4-10 µg (0.5-0.9 MBq) [ 177 Lu]Lu-DOTA-NNV003 (IRF 74.4-81.7%), followed by tissue collection and ex vivo biodistribution analysis at 1 h, 6 h, 1 day and 3 days pi (n = 4 mice per group).
Ex vivo tissue preparation and immunohistochemistry. For ex vivo tissue analysis, formalin-fixed paraffin-embedded (FFPE) tumor tissue blocks were prepared. FFPE blocks were sliced into 4 µm tumor tissue sections, fixated on microscope slides and dried overnight at 60 °C. For CD37 immunohistochemistry, tumor tissue sections were deparaffinized in xylene and rehydrated. Heat-induced antigen retrieval was performed in 10 mM citrate (pH 6.0) for 15 min at 95-100 °C. Endogenous peroxidase was blocked by 10-min incubation with 10% hydrogen peroxide in PBS. Slides were incubated with rabbit anti-human CD37 antibody (Proteintech; 21044-1) or rabbit IgG antibody control (Abcam; ab172730) diluted to 0.8 µg/mL in 1% BSA in PBS for 1 h at RT. Thereafter, slides were incubated with Dako EnVision horseradish peroxidase system (Agilent Technologies) for 30 min at RT, followed by 10-min incubation with diaminobenzidine chromogen. Hematoxylin counterstaining was applied routinely. For histological analysis of tumors, hematoxylin/eosin staining was performed on subsequent tissue sections. Digital scans of slides were acquired by a Hamamatsu NanoZoomer 2.0-HT multi-slide scanner and analyzed with NanoZoomer Digital Pathology viewer software. Statistical analysis. Data were analyzed for statistical significance in GraphPad Prism v7.0 using the Mann-Whitney U test for non-parametric data followed by Bonferroni post-test correction for comparison of more than two groups. Ex vivo biodistribution of [ 89 Zr]Zr-N-sucDf-NNV003 and [ 177 Lu]Lu-DOTA-NNV003 were compared with Welch's t-test for unequal variances. Correlation was assessed by Spearman's rank-order correlation test. In vitro experiments were repeated at least 3 times. p values < 0.05 were considered significant.


The Triprenylated Anthranoid Ferruginin A, a Promising Scaffold for the Development of Novel Antibiotics against Gram-Positive Bacteria

In today’s post-antibiotic era, the search for new antimicrobial compounds is of major importance and nature represents one of the primary sources of bioactive molecules. In this work, through a cheminformatics approach, we clustered an in-house library of natural products and their derivatives based on a combination of fingerprints and substructure search. We identified the prenylated emodine-type anthranoid ferruginin A as a novel antimicrobial compound. We tested its ability to inhibit and kill a panel of Gram-positive and Gram-negative bacteria, and compared its activity with that of two analogues, vismione B and ferruanthrone. Furthermore, the capability of these three anthranoids to disrupt staphylococcal biofilm was investigated, as well as their effect on the viability of human keratinocytes. Ferruginin A showed a potent activity against both the planktonic and biofilm forms of Gram-positive bacteria (i.e., Staphylococcus aureus and S. epidermidis) and had the best therapeutic index compared to vismione B and ferruanthrone. In conclusion, ferruginin A represents a promising scaffold for the further development of valuable antimicrobial agents.

Introduction
The discovery of penicillin and the subsequent use of antibiotics in the treatment of bacterial infections has drastically changed human lifespan, increasing it by about 23 years [1]. However, the golden age of antibiotics is at the end; fewer and fewer compounds are being developed, while the onset of multidrug-resistant strains is increasing, making classic therapies mostly useless [2]. There is therefore an urgent need to discover new compounds capable of overcoming antimicrobial resistance (AMR). Although it is difficult to quantify and to predict the number of deaths per year due to AMR, it is estimated that in 2050 it will be 10 million [3]. These numbers are alarming, considering that many microorganisms can transition from a planktonic lifestyle to a sessile form, called biofilm. In biofilm communities, bacterial cells are surrounded by extra-cellular polymeric substances and DNA, which protect them from the antibiotic action, making their eradication even more difficult [4]. As the history of penicillin has taught us, nature is one of the primary sources of bioactive compounds [5,6]. A unique high-diversity library composed of around 1000 individual natural products, isolated mainly from indigenous plants collected in biodiversity-rich countries especially in the tropics and subtropics, is stored at the Organic Chemistry Laboratory of the Department of Chemistry and Technology of Drugs (Sapienza University of Rome, Rome, Italy) [7]. Complementarity between biogenetic enzymes and eukaryotic protein domains is the general molecular basis for the prominence of natural products in biomedical research and drug discovery [8]. Indeed, the structural and chemical diversity of natural products cannot be matched by any synthetically-based corporate screening library, and they remain the single most productive source of leads in modern drug discovery projects, often providing chemical structures as useful platforms for the development of drugs, or for the understanding of biological processes [9]. In recent years, the library has been enlarged with semi-synthetic and synthetic derivatives of several natural compounds, including up to 2000 components. Computer-aided methods have been efficiently exploited to screen this in-house library towards specific biological targets, and several hits and leads have been identified thanks to the wide range of pharmacophores and the high scaffold diversity of the library, which is continuously enlarged with compounds not available from other commercial or literature sources [10][11][12][13]. In the present study, compounds of the in-house library were clustered based on a combination of fingerprints and substructure search through a cheminformatics approach previously established [14,15], with the aim of grouping the library members into homogeneous chemical clusters. The representative compounds of the most populated clusters (1-25) have been screened for their strength in inhibiting and killing bacterial cells, as well as for their antibiofilm and cytotoxic activities. Interestingly, compound 7 showed a potent anti-Gram-positive activity, especially against the biofilm forms of Staphylococcus aureus and S. epidermidis, along with the better biological profile. Overall, our findings suggest molecule 7 as a novel antimicrobial candidate for further investigation and development.

Screeening of Compounds: Structural Characterization and Preliminary Antibacterial Profile
Our in-house library is a valid source of chemotypes for the modulation of biomolecular targets, and it has been successfully screened in silico and in vitro for the identification of hit and lead compounds in previous early-stage drug discovery projects [7].
To focus experimental efforts on a relatively low number of molecules, and to explore the chemical and scaffold space of the library as much as possible, a cheminformatics approach was employed to identify new potential chemotypes of antibacterial agents. To this end, a diversity-oriented random selection (DORS) of compounds was performed by means of a clustering algorithm, which relies on a combination of fingerprints and common substructure search to group compounds endowed with chemical similarity index within a given threshold [14]. Thanks to the algorithm, compounds of the library were grouped, and the representative molecules of the most abundant clusters (1-25) were selected for biological assays (Table 1).            Compounds 1-25 were screened in vitro against reference Gram-negative (i.e., Escherichia coli) and Gram-positive (i.e., S. aureus and S. epidermidis) bacterial strains by evaluating their capability of inhibiting microbial growth through the inhibition zone assay. All compounds were not active against E. coli (data not shown), while only few of them were active against the Gram-positive bacterial strains (Table 2). According to the literature, gallic acid (6) showed a significant antimicrobial activity against S. aureus, while usnic acid (17) and 2 ,4-hydroxy-4 -methoxy-chalcone (18) showed activity against both S. aureus and S. epidermidis [52][53][54].
In addition, compound 7, ferruginin A, showed a good activity against both strains, with an inhibition zone ranging from 0.55 to 0.62 cm. Considering the limited number of studies performed on its biological activity to date, we decided to characterize its antimicrobial and cytotoxic activity. Ferruginin A is a prenylated emodine-type anthranoid, a polyphenols subclass, isolated from Vismia baccifera var. ferruginea and Vismia decipiens [22]. To thoroughly investigate the antibacterial activity of the emodine-type anthranoid scaffold, we also tested two other structurally-related compounds, ferruanthrone (26) and vismione B (27), reported in Table 3.
These anthranoids share the same biogenetic pathway and were isolated from the same plant. Compound 26 is classified as one of the unique compounds of the library, i.e., not available from other commercial or literature sources. It differs from compound 7 for the anthrone scaffold, a tricyclic aromatic ketone, and the position of the prenyl groups [22]. Compound 27 is a pre-anthraquinone non substituted with a γ, γ-dimethylallyl chain cyclized to form a chromane ring in pace of the C-prenyl group [55]. Compounds 1-25 were screened in vitro against reference Gram-negative (i.e., Escherichia coli) and Gram-positive (i.e., S. aureus and S. epidermidis) bacterial strains by evaluating their capability of inhibiting microbial growth through the inhibition zone assay. All compounds were not active against E. coli (data not shown), while only few of them were active against the Gram-positive bacterial strains ( Table 2). According to the literature, gallic acid (6) showed a significant antimicrobial activity against S. aureus, while usnic acid (17) and 2′,4-hydroxy-4′-methoxy-chalcone (18) showed activity against both S. aureus and S. epidermidis [52][53][54].
In addition, compound 7, ferruginin A, showed a good activity against both strains, with an inhibition zone ranging from 0.55 to 0.62 cm. Considering the limited number of studies performed on its biological activity to date, we decided to characterize its antimicrobial and cytotoxic activity. Ferruginin A is a prenylated emodine-type anthranoid, a polyphenols subclass, isolated from Vismia baccifera var. ferruginea and Vismia decipiens [22]. To thoroughly investigate the antibacterial activity of the emodinetype anthranoid scaffold, we also tested two other structurally-related compounds, ferruanthrone (26) and vismione B (27), reported in Table 3.
These anthranoids share the same biogenetic pathway and were isolated from the same plant. Compound 26 is classified as one of the unique compounds of the library, i.e., not available from other commercial or literature sources. It differs from compound 7 for the anthrone scaffold, a tricyclic aromatic ketone, and the position of the prenyl groups [22]. Compound 27 is a pre-anthraquinone non substituted with a γ, γ-dimethylallyl chain cyclized to form a chromane ring in pace of the C-prenyl group [55]. The antimicrobial activity of 7, 26, and 27 was evaluated against different Gramnegative and Gram-positive bacterial strains and a yeast by determining their minimum inhibitory concentrations (MIC) by the microdilution assay in broth. After an incubation of about 18 h, the concentration of compounds able to totally inhibit microbial growth was detected and is reported in Table 4.

Antimicrobial Activity of Compounds 7, 26, and 27
The antimicrobial activity of 7, 26, and 27 was evaluated against different Gramnegative and Gram-positive bacterial strains and a yeast by determining their minimum inhibitory concentrations (MIC) by the microdilution assay in broth. After an incubation of about 18 h, the concentration of compounds able to totally inhibit microbial growth was detected and is reported in Table 4. Compounds 26 and 27 were inactive against all the strains tested, with the only exception being B. megaterium Bm11 (MIC = 256 µM). Compound 7 was inactive against C. albicans and Gram-negative strains. In comparison, according to the results of the inhibition zone assay (Table 2), it showed a potent activity against Gram-positive microorganisms with MIC values of 8 µM against B. megaterium, 16 µM against S. epidermidis, and 64 µM against S. aureus.
As 7 was the only active compound, we investigated whether its antimicrobial activity was due to a bacteriostatic or bactericidal effect. To this aim, aliquots from the wells corresponding to MIC, 2 × MIC, and 4 × MIC were plated on agar plates for the counting of colony-forming units (CFU). As reported in Figure 1, compound 7 provoked about a 2-log reduction of viable S. aureus cells within 18 h treatment at its MIC and 2 × MIC and a total killing at its 4 × MIC. Differently, against S. epidermidis, 7 showed a bactericidal effect (about 2-log reduction of viable cells) at its 2 × MIC and 4 × MIC. Compound 7 also displayed the same antimicrobial efficacy against B. megaterium at all the concentrations tested.
As reported in Figure 2, 27 was inactive (100% biofilm viability) against preformed biofilms of both strains at all concentrations. Differently, 26 showed only a weak activity at higher concentrations ranging from 64 to 256 µM against the S. aureus biofilm, while a more pronounced activity (about 60% of killing) was recorded against the S. epidermidis biofilm at the same concentrations. Considering its MIC values against both strains (i.e., >256 µM), these results have indicated a different activity of compound 26; despite it was not able to completely inhibit microbial growth after 18 h, it caused a significant reduction in the amount of viable biofilm cells within a short time (i.e., 2 h of treatment). Compound 7 revealed to be the most efficacious molecule causing more than 90% biofilm killing at the concentration range between 32 and 256 µM, and about 60% killing at lower concentrations of 16 and 8 µM, against both strains.
As reported in Figure 2, 27 was inactive (100% biofilm viability) against preformed biofilms of both strains at all concentrations. Differently, 26 showed only a weak activity at higher concentrations ranging from 64 to 256 μM against the S. aureus biofilm, while a more pronounced activity (about 60% of killing) was recorded against the S. epidermidis biofilm at the same concentrations. Considering its MIC values against both strains (i.e., >256 μM), these results have indicated a different activity of compound 26; despite it was not able to completely inhibit microbial growth after 18 h, it caused a significant reduction in the amount of viable biofilm cells within a short time (i.e., 2 h of treatment). Compound 7 revealed to be the most efficacious molecule causing more than 90% biofilm killing at the concentration range between 32 and 256 μM, and about 60% killing at lower concentrations of 16 and 8 μM, against both strains.  To better estimate the extent of the anti-biofilm activity of compound 7, we then compared our findings with the activity of a conventional antibiotic (i.e., ciprofloxacin) against the preformed biofilm of S. aureus ATCC 25923 and S. epidermidis ATCC 12228 at MIC, 2 × MIC, and 4 × MIC. As reported in Figure 3, ciprofloxacin was inactive against the S. epidermidis biofilm, while only 20% killing of biofilm cells (~80% of biofilm viability) was detected at all concentrations tested against S. aureus.
To better estimate the extent of the anti-biofilm activity of compound 7, we then compared our findings with the activity of a conventional antibiotic (i.e., ciprofloxacin) against the preformed biofilm of S. aureus ATCC 25923 and S. epidermidis ATCC 12228 at MIC, 2 × MIC, and 4 × MIC. As reported in Figure 3, ciprofloxacin was inactive against the S. epidermidis biofilm, while only 20% killing of biofilm cells (~80% of biofilm viability) was detected at all concentrations tested against S. aureus.

Cytotoxicity
To investigate any potential harmful action of compounds 7, 26, and 27 on host tissues (a crucial aspect for clinical antimicrobial applications of drugs), we evaluated their effect on the viability of human immortalized HaCaT cells, the major cell type of human skin [57,58], using the MTT assay. After 24 h of treatment, compounds 26 and 27 did not induce any toxicity up to a concentration of 64 μM; at 128 μM, cell viability was approximately 60% and 72% for 26 and 27, respectively (Figure 4). A different trend was shown for compound 7, which was not toxic at 8 μM and 16 μM; however, incubation of HaCaT cells with increasing concentrations of this compound led to about 25% cell viability at 32 μM and less than 10% at 64 μM and 128 μM ( Figure 4). All three compounds were strongly toxic at 256 μM (data not shown).

Cytotoxicity
To investigate any potential harmful action of compounds 7, 26, and 27 on host tissues (a crucial aspect for clinical antimicrobial applications of drugs), we evaluated their effect on the viability of human immortalized HaCaT cells, the major cell type of human skin [57,58], using the MTT assay. After 24 h of treatment, compounds 26 and 27 did not induce any toxicity up to a concentration of 64 µM; at 128 µM, cell viability was approximately 60% and 72% for 26 and 27, respectively (Figure 4). A different trend was shown for compound 7, which was not toxic at 8 µM and 16 µM; however, incubation of HaCaT cells with increasing concentrations of this compound led to about 25% cell viability at 32 µM and less than 10% at 64 µM and 128 µM (Figure 4). All three compounds were strongly toxic at 256 µM (data not shown).
the preformed biofilm of S. aureus ATCC 25923 and S. epidermidis ATCC 12228 at MIC, 2 × MIC, and 4 × MIC. As reported in Figure 3, ciprofloxacin was inactive against the S. epidermidis biofilm, while only 20% killing of biofilm cells (~80% of biofilm viability) was detected at all concentrations tested against S. aureus.

Cytotoxicity
To investigate any potential harmful action of compounds 7, 26, and 27 on host tissues (a crucial aspect for clinical antimicrobial applications of drugs), we evaluated their effect on the viability of human immortalized HaCaT cells, the major cell type of human skin [57,58], using the MTT assay. After 24 h of treatment, compounds 26 and 27 did not induce any toxicity up to a concentration of 64 μM; at 128 μM, cell viability was approximately 60% and 72% for 26 and 27, respectively (Figure 4). A different trend was shown for compound 7, which was not toxic at 8 μM and 16 μM; however, incubation of HaCaT cells with increasing concentrations of this compound led to about 25% cell viability at 32 μM and less than 10% at 64 μM and 128 μM ( Figure 4). All three compounds were strongly toxic at 256 μM (data not shown).  Based on the in vitro results of the antibiofilm and cytotoxic activities, we calculated the lethal concentration causing 50% cell death (LC 50 ) and the minimum effective dose for 50% biofilm killing (ABC 50 ) to evaluate the potential therapeutic index (TI) of the most active compounds (i.e., 7 and 26; Table 5). As the TI value was calculated as the ratio between the toxic concentrations and the effective concentrations, the higher the value, the better the biological profile. Although compound 7 showed cytotoxicity compared to 26, its TI values (i.e., 25.81-6.19) were higher than those of 26, underlining compound 7 as the most promising new antibiofilm molecule.

Discussion
The demand for new antibiotic compounds is of major importance in the postantibiotic era we are currently living. Investments by pharmaceutical companies are often directed to projects aimed at producing drugs for the treatment of chronic diseases that can bring greater revenues than antimicrobials [59]. However, microbial infections remain a challenge to keep under constant control. Indeed, the European Centre for Disease Prevention and Control has conservatively estimated that, in Europe alone, AMR can cause additional annual cost to health care systems of at least €1.5 billion [60].
Considering that biofilm formation is involved in 65-80% of bacterial infections in humans, new antibiotic compounds must have the ability to kill not only the planktonic but also the sessile form of microorganisms [56]. Conventional antibiotics are usually not efficacious against biofilms. Therefore, to prevent the onset of resistant and persistent bacterial cells, it would ideal to identify a molecule able to disrupt sessile bacterial communities within a short time. Here, through a clustering algorithm, we obtained a set of molecules that were firstly screened for their antimicrobial activity. Ferruginin A (7) was found to be the most interesting molecule, due to its anti-Gram-positive activity and the poor information available in the literature. The molecule was isolated in the 1979 from Vismia baccifera var. ferruginea [22], as well as from other Vismia species [61]. Over the years, ferruginin A (7) was also isolated from the leaves of Harungana madagascariensis; Tankeo and coworkers tested it, together with betulinic acid, madagascin, and Kaempferol-3-O-β-D-gluco-pyranoside, for its antimicrobial activity using ciprofloxacin as an antibiotic control, but also against a panel of Gram-negative bacteria (e.g., Enterobacter cloacae, Providencia stuartii, and Klebsiella pneunomaniae) [62,63]. Considering that Staphylococci, especially S. aureus and S. epidermidis, are common inhabitants of the human skin microbiota, as well as being the major microorganisms responsible for hospital-acquired infections, we characterized the capability of ferruginin A (7) to kill the biofilm cells of these pathogens [64]. Compound 7 showed a potent antimicrobial activity against Gram-positive bacteria (MIC values ranging from 8 to 64 µM) and a bactericidal rather than a bacteriostatic effect, as indicated by its ability to provoke a significant CFU reduction. In addition, compound 7 was found to possess a potent antibiofilm activity against both S. aureus and S. epidermidis, with an ABC 50 of 1.08 and 4.5 µM, respectively. In comparison to the two related compounds ferruanthrone (26) and vismione B (27), ferruginin A (7) had the higher antibiofilm activity, but also the higher cytotoxicity against HaCaT cells. Despite this, its biological profile (evaluated as TI; Table 5) was better than compound 26 (25.81-6.19 vs. 0.26-2.65, respectively). It is worth noting that the antibiofilm activity was obtained only after 2 h of treatment, while ciprofloxacin, used as a control, was totally inactive against both staphylococcal biofilms within such short time. This fast kinetics is of high importance also for destroying biofilms associated with medical devices. The use of medical devices is rapidly increasing and, despite the multiple precautionary sterilization measures that are being followed in hospitals, staphylococcal colonization on these devices still happens quite frequently [65,66]. Although compound 7 exhibits cytotoxicity at concentrations greater than 32 µM, advances in nanotechnologies would likely help to overcome its noxious ef-fects upon incorporation into appropriate nanoparticulate systems. In addition, it could be developed as an antibiofilm agent, only for disinfection of medical equipment and surgical tools. Furthermore, as even small changes in the chemical structure of ferruginin analogs (26 and 27) are sufficient to lead to a substantial modification of their activity /toxicity, this confirms ferruginin A as a novel promising scaffold for further development of valuable antimicrobial agents.

In-House Library Clustering
The algorithm has been described previously [14], and it is inspired by the work published by Stahl and Mauser [15]. Briefly, the custom Python script first performs a preliminary clustering based on maccs166 fingerprints, grouping compounds with a Tanimoto similarity equal or higher than 0.8. Then, isolated compounds are assigned to preformed clusters based on substructure comparison, if they have a substructure matching equal or higher than 0.85 according to the Raymond cutoff. Compounds not matching these criteria are classified as singletons and are not included in any of the existing clusters.

Chemistry
All of the tested compounds (namely, 1-27) are known structures belonging to our in-house library of natural products. The chemical identity of the compounds was assessed by re-running nuclear magnetic resonance spectroscopy (NMR) experiments and were proven to agree with the literature data reported below for each compound. The purity of all compounds, checked by reversed-phase high performance liquid chromatography (HPLC), was always higher than 95%.

Antibacterial Screening: Inhibition Zone Assay
To screen the library for any antimicrobial activity, we tested all compounds against a reference Gram-negative (i.e., E. coli) and two Gram-positive bacterial strains (i.e., S. aureus and S. epidermidis) through the inhibition zone assay. Bacteria were grown at 37 • C in Luria−Bertani broth (LB) with gentle shaking until reaching an optical density (O.D.) of 0.8 at 590 nm. Then, the bacterial culture was diluted 1:2000 and plated in LB-agarose plates, and aliquots of 3 µL of each compound (5 mM) were loaded into holes previously made in the agarose plates. The plates were incubated overnight, and the diameters of the inhibition zone were measured and are reported in Table 2.

Antimicrobial Assays
The minimum inhibitory concentrations were determined by the microdilution assay in a 96-well plate. Aliquots of 50 µL of bacterial suspension in Mueller-Hinton broth (MH) in mid-log phase (at a concentration of 2 × 10 6 cells/mL) were added to 50 µL MH containing serial dilutions of the compounds (in a concentration range of 2-256 µM). The controls were vehicle-treated cells. After an incubation time of 16 h at 37 • C, the MIC was defined as the lowest concentration causing 100% visible inhibition of microbial growth. To determine the bactericidal activity of the tested compounds, aliquots from the wells corresponding to the MIC, 2 × MIC, and 4 × MIC were withdrawn and plated onto agar plates for colony forming unit (CFU) counting.
For the antibiofilm activity, S. aureus and S. epidermidis were grown as reported above; then aliquots of 100 µL of bacteria in LB (at a concentration of 1 × 10 6 CFU/mL) were dispensed into the wells of a 96-multiwell plate, which was incubated for 20 h at 37 • C to allow for biofilm formation. Subsequently, the medium containing planktonic cells was aspirated from the wells and the latter were rinsed twice with 150 µL of phosphate buffered saline (PBS) to remove any non-adherent cells. After washing, each well was filled with PBS supplemented with different two-fold serial dilutions of compounds 7, 26, and 27 (from 256 to 8 µM). For comparison, a conventional antibiotic (i.e., ciprofloxacin) was tested. The plate was then incubated for 2 h at 37 • C and, after treatment, the wells were rinsed twice with PBS, as indicated above. Aliquots of 150 µL of MTT (0.5 mg/mL) were dispensed in each well to evaluate biofilm cell viability after 4 h incubation at 37 • C. This colorimetric assay consisted in the conversion of the water-soluble yellow dye MTT to the insoluble purple formazan crystals by dehydrogenases. The higher intensity of purple color corresponds to a higher percentage of metabolically active cells and consequently to a higher cell viability. The reaction was stopped by adding sodium dodecyl sulfate (SDS) (at a final concentration of 5% v/v) and the absorbance of each well was recorded at 570 nm using a microplate reader (Infinite M200; Tecan, Salzburg, Austria). The percentage of biofilm viability was calculated with respect to the untreated samples.

Cytotoxicity Assays
Compounds 7, 26, and 27 were assayed for their potential in vitro toxicity against human keratinocytes (HaCaT cells), as already described in [67]. Cells resuspended in DMEMg supplemented with 2% FBS were seeded at a density of 4 × 10 4 per well in a 96-well plate and were incubated at 37 • C in a 5% CO 2 atmosphere. After overnight incubation, the cells were treated with compounds at the indicated concentrations for 24 h. Controls were HaCaT cells treated with the vehicle. Afterwards, the medium of each well was replaced by 0.5 mg/mL of MTT in Hank's buffer and the plate was incubated for further 4 h at 37 • C and 5% CO 2 . Acidified isopropanol was added to each well and absorbance was measured at 570 nm by the same microplate reader employed for the antimicrobial assays. The percentage of cell viability was calculated with respect to the control cells. The values of LC 50 and ABC 50 were calculated using the "Quest Graph™ LC 50 Calculator (AAT Bioquest, Inc., Sunnyvale, CA, USA, Access date: 19 October 2021, https://www.aatbio.com/tools/lc50-calculator).