Functional study of MAGs from Hanseniaspora spp. obtained from the spontaneous fermentation of cocoa seeds in the region of Ilhéus/Ba.

Since cocoa seed fermentation is a spontaneous and uncontrolled process, it is mediated by a complex microbial consortium that significantly affects the quality of the final product. Given that Hanseniaspora spp. has been identified as a dominant genus during the fermentation process and that efficient microorganisms are required for use in starter cultures, this study aimed to analyze the genomes of Hanseniaspora spp. obtained from metagenomic shotgun sequencing of cocoa bean fermentation samples from the Ilhéus/BA region. The genomes of Hanseniaspora spp. were assembled using SPAdes, and gene ontologies (GO) were determined using OmicsBox, EggNOG, and REVIGO. The main biological activities identified in the genes encoded by Hanseniaspora spp. revealed mechanisms associated with biogenesis, as well as the metabolism of carbohydrates, aromatic compounds, glycoproteins, and organic acids, among others. These metabolic activities directly influence the production of volatile organic compounds, which contribute to the development of a diverse range of aromatic flavor molecules. The functional annotation performed indicates that Hanseniaspora spp. possesses a highly compatible metabolic profile suited for the fermentation process, suggesting its potential use as a starter inoculum. 1. INTRODUCTION Cocoa bean fermentation is a spontaneous biochemical process that plays a crucial role in the development of chocolate flavor (Gutiérrez-Ríos et al., 2022). This process is mediated by a microbial consortium composed of yeasts, lactic acid bacteria (LAB), and acetic acid bacteria (AAB), which are naturally introduced into the cocoa beans from environmental sources such as fruit peels, banana leaves, insects, agricultural equipment, and human handling (Dulce et al., 2021; Ouattara & Niamke, 2021). During fermentation, these microorganisms utilize sugars and the pectin-rich pulp as substrates for their metabolic activity (Díaz-Muñoz et al., 2023). In the initial hours of fermentation, anaerobic yeasts dominate, leading to pulp liquefaction and ethanol production (De Vuyst & Leroy, 2020). After approximately 48 hours of fermentation, lactic acid bacteria and acetic acid bacteria convert glucose and fructose into lactic acid, acetic acid, and other metabolites. In the final stages of the process, these bacteria oxidize ethanol into acetic acid, which increases the fermentation temperature and subsequently reduces the microbial population (Barišić et al., 2019). The structure of microbial communities and their spatial and temporal variations can differ depending on seed processing and handling practices at individual farms. This variability influences the appearance and succession of LAB and AAB. For instance, a study by Lima et al. (2021) observed that when cocoa seeds were processed under good manufacturing practices, differences in cultivation and handling significantly affected the microbial profile. As a result, AAB appeared before LAB without compromising the overall fermentation quality. Since cocoa fermentation is a spontaneous and uncontrolled process, it often leads to final products with inconsistent quality, posing challenges for producers aiming to deliver products that meet desirable sensory and market standards (González et al., 2022). Given that fermentation is microbially driven, the use of selected microbial cultures with desirable traits has been explored as a means to standardize and control the process (Junior et al., 2021). Starter cultures have already been successfully applied in the production of alcoholic beverages, dairy products, sausages, and baked goods, where they accelerate and enhance fermentation efficiency (Batista et al., 2015). Advances in sequencing technologies, such as next-generation sequencing (NGS), have enabled detailed analyses of microbial communities involved in cocoa fermentation, overcoming the limitations of traditional culturing techniques (Chaitanya, 2019; Illeghems et al., 2012). Metagenomics and bioinformatics have become essential tools for taxonomic and functional analyses of these microbiomes, allowing the identification and characterization of specific microbial genomes, known as metagenome-assembled genomes (MAGs) (Li, Wang & Liu, 2017). This study aimed to analyze the genomes of Hanseniaspora spp. obtained from cocoa fermentation samples from the Ilhéus region, Brazil. The goal was to gain deeper insights into its metabolic potential and evaluate its suitability as a candidate for starter culture inoculation in cocoa fermentation processes. 2. METHODOLOGY 2.1 Metagenome-Assembled Genomes (MAGs) The genome data of Hanseniaspora spp. used in this study were obtained from a previous study by Lima et al. (2021), in which cocoa bean fermentation samples were collected from a Brazilian agroindustry: Riachuelo Farm and the Mendoá Chocolate Factory. This facility follows good manufacturing practices and produces high-quality chocolate (www.mendoachocolates.com.br) in Uruçuca, Bahia, Brazil (Latitude: -14.7719058; Longitude: -39.0492701). The researchers performed shotgun metagenomics sequencing on samples from two different cocoa seed varieties: Forastero (FOR) and a hybrid mixture (MIX) of PS1319 and CCN51. These samples were collected at seven different time points during fermentation, totaling 14 biosamples, which were deposited in the National Center for Biotechnology Information (NCBI) under the Sequence Read Archive (SRA) project code PRJNA552479. For data processing, a co-assembly of quality-filtered reads was performed using methodologies previously described in the literature (Babraham Bioinformatics, 2010; Martin, 2011; Langmead & Salzberg, 2012; Magoc & Salzberg, 2011). Quality control of raw sequencing reads was performed using FASTQC (v0.11.4), and adapters were removed using CUTADAPT (v1.18). Reads were aligned using BOWTIE2 (v2.3.4.3) to filter out unmapped reads from Theobroma cacao (reference genome: Creole cocoa genome V2, NCBI). Filtered reads were then merged using FLASH (v1.2.11). To assemble contigs, quality-filtered reads from all samples within each cocoa variety (FOR and MIX) were combined across all seven fermentation time points. Genome assembly was performed using SPAdes (v3.13.1) with the –meta flag, which is recommended for metagenomic datasets. The scripts containing all parameters used for assembly are provided in Annex I. 2.2 Identification of MAGs of Hanseniaspora spp. To confirm the presence of the yeast genus Hanseniaspora in the FOR and MIX samples, the taxonomic classification program Kaiju (v1.6.2) (Menzel, Lee Ng & Krogh, 2016) was used. This tool performs high-sensitivity classification of metagenomic sequencing reads and also identifies the specific Hanseniaspora species present in each sample, along with their relative abundance in terms of contig percentage. Kaiju was run via its online web server using the following parameters: • Input format: FASTA (sequential read files) •

database: NCBI BLAST nr+euk (non-redundant protein database for bacteria, archaea, viruses, fungi, and microbial eukaryotes) • Sequence filtering: SEG filter to remove low-complexity protein query sequences • Search mode: MEM (Maximum Exact Matches) After taxonomic confirmation, MAGs were identified through sequence similarity analyses using BLAST, comparing them against the NCBI nucleotide collection database (nt). The BLASTn search mode was selected due to its high accuracy in species comparison, reducing false positives. Through this approach, we identified the contigs in the total sample that exhibited similarity to Hanseniaspora species. Additionally, we determined which contigs were derived from other genera and matched them to the species previously highlighted by Kaiju. The contigs corresponding to Hanseniaspora species were then selected and isolated for further analysis. 2.3 Genome Quality Assessment To evaluate the quality of the Hanseniaspora spp. genomes recovered from the metagenomes of cocoa fermentation samples (FOR and MIX), the QUAST (v4.4) program (Gurevich et al., 2013) was used. QUAST allows genome quality assessment with or without a reference genome and enables comparisons between multiple genome assemblies using various metrics. The MIX and FOR sequence data were loaded into QUAST in FASTA format without using a reference genome. The minimum contig length threshold was set at 500 base pairs (bp) for the analysis. 2.4 Functional Annotation To characterize the functional potential of genes within the metagenome-assembled genomes ( Hanseniaspora spp.) from the MIX and FOR samples, ontology-based annotation of contigs was performed. For this analysis, the OmicsBox platform was used (Gonçalves et al., 2021; More et al., 2022). In the software’s interface, the FASTA file containing Hanseniaspora spp. contigs was uploaded, and InterProScan was applied for functional annotation. This tool predicts important protein domains and sequence motifs, which were subsequently analyzed using EggNOG (Evolutionary Genealogy of Genes: Non-supervised Orthologous Groups). EggNOG allows for the classification of orthologous gene groups based on the COG/KOG database (Clusters of Orthologous Groups of Proteins for Eukaryotes) and identifies Gene Ontology (GO) IDs associated with these domains (Götz et al., 2008; Gonçalves et al., 2021). The parameters used for annotation were: • E-value threshold: 1.0E-6 (default) • Annotation cutoff threshold: 55 • Weight-GO default value: 5 • Coverage cut-off (HSP-Hit): 80 • Taxonomic filtering: Eukarya The list of identified genes was further processed using REVIGO, a web-based tool designed to summarize and cluster long lists of GO terms based on semantic similarity measures (Bonnot & Nagel, 2019). This tool facilitated the identification of key biological processes associated with the predicted genes. The GO IDs obtained from the OmicsBox annotation of MAGs (MIX and FOR samples) were directly uploaded to the REVIGO web server, using the following parameters: • Obsolete GO terms removed •

database: UniProt (default) • Semantic similarity measure: Standard 3. RESULTS 3.1 Identification of MAGs of Hanseniaspora spp. For the MIX samples, a total of 193,296 sequencing reads were uploaded to the program server. Of these, 156,226 (80.8%) were successfully classified, allowing for the determination of taxonomic abundance, while 37,070 (19.2%) remained unclassified. The classification process determined taxonomic levels from kingdom to species, assigning gene sequences accordingly. Regarding Hanseniaspora spp., a comparison of sequencing reads with taxa from the NCBI reference database (which contains microbial protein sequences) confirmed the presence of this genus in 5,842 reads, accounting for 3% of the total sample. Among the sequences classified as Hanseniaspora, the species distribution was as follows: • 40.9% exhibited homology with Hanseniaspora uvarum, • 28.7% with Hanseniaspora opuntiae, • 8.3% with Hanseniaspora guilliermondii, • 1.4% with Hanseniaspora valbyensis, • 0.9% with Hanseniaspora osmophila, • 19.9% of the sequences could not be assigned to a specific species. For the FOR samples, a total of 185,287 sequencing reads were generated and uploaded to the program server. Of these, 149,537 (80.7%) were successfully classified, while 35,750 (19.3%) remained unclassified. The bioinformatics analysis identified Hanseniaspora as a genus in 9,019 sequencing reads, representing 4.9% of the total sample. Within this genus, the species distribution was as follows: • 48.6% exhibited similarity to H. uvarum, • 22.4% to H. opuntiae, • 8.8% to H. guilliermondii, • 1.2% to H. valbyensis, • 1.4% to H. osmophila, • 17.6% of the reads remained unclassified at the species level. To identify the MAGs of the Hanseniaspora genus, the MIX and FOR sequencing files were analyzed using BLAST, which located regions of local similarity by comparing the nucleotide sequences with reference databases. The following parameters were assessed: • Percentage identity – reflects the similarity between the query sequence and the aligned sequence, • Sequence coverage – indicates the extent of alignment between the query and the database sequence, • Alignment start and end values, • E-value – represents the probability of obtaining the alignment by chance (closer to zero implies higher reliability), • Score – represents the statistical significance of the alignment (Annex II). In the MIX dataset, 50 sequences were aligned with similarity to Hanseniaspora species. Similarly, in the FOR dataset, 50 sequences also aligned with species of the genus. Some alignments corresponded to different parts of the same genome. 3.2 Genome Quality Assessment Only the genomes from MIX and FOR samples that showed similarity to Hanseniaspora species (confirmed through BLAST) were included in the quality assessment. Genome quality analysis was conducted using QUAST, which determined key metrics, including: • Number of contigs, • N50/N75 values, • L50/L75 values, • GC content, • Number of uncalled bases (N’s), • Predicted genes. All quality statistics were based on contigs ≥500 bp (Table 1). Table 1: Results of the quality analysis performed by the Quast software on the genome sequences of Hanseniaspora spp. isolated from cocoa fermentation samples from FOR and MIX samples. | # contigs | 30 | 23 | | # contigs (>= 0 bp) | 30 | 23 | | # contigs (>= 1000 bp) | 19 | 15 | | # contigs (>= 10000 bp) | 7 | 8 | | # contigs (>= 100000 bp) | 3 | 2 | | # contigs (>= 1000000 bp) | 0 | 0 | | Largest contig | 153294 | 129943 | | Total length | 572259 | 468730 | | Total length (>= 0 bp) | 572259 | 468730 | | Total length (>= 1000 bp) | 564540 | 463015 | | Total length (>= 10000 bp) | 532486 | 444148 | | Total length (>= 100000 bp) | 373987 | 230991 | | Total length (>= 1000000 bp) | 0 | 0 | | N50 | 101048 | 58213 | | N75 | 56149 | 40196 | | L50 | 3 | 3 | | L75 | 4 | 5 | | GC (%) | 38.74 | 35.9 | | MISMATCHES | || | # N’s | 1900 | 1800 | | # N’s per 100 kbp | 332.02 | 384.02 | | PREDICTED GENES | || | # predicted genes (unique) | 221 | 163 | | # predicted genes (>= 0 bp) | 217 + 4 part | 163 + 0 part | | # predicted genes (>= 300 bp) | 205 + 3 part | 159 + 0 part | | # predicted genes (>= 1500 bp) | 89 + 0 part | 81 + 0 part | | # predicted genes (>= 3000 bp) | 31 + 0 part | 0 part | 3.3 Functional Annotation The contigs corresponding to Hanseniaspora spp. from the MIX and FOR samples were uploaded into OmicsBox to search for Gene Ontology (GO) IDs, which represent the functional properties of gene products and their potential biological activities during spontaneous cocoa fermentation. 3.3.1 MIX Sample Analysis Analysis of the MIX sample generated significant results for 17 out of 23 contigs, yielding a GO list with 78 IDs. These results are presented in Figure 1A, with the species distribution as follows: • 10 contigs from H. uvarum, • 4 contigs from H. valbyensis, • 2 contigs from H. guilliermondii, • 1 contig from H. opuntiae . 3.3.2 FOR Sample Analysis Analysis of the FOR sample produced significant results for 18 out of 30 contigs, generating a GO list with 126 IDs. These results are shown in Figure 1B, with the following distribution: • 8 contigs from H. uvarum, • 3 contigs from H. valbyensis, • 5 contigs from H. guilliermondii, • 2 contigs from H. opuntiae . Figure 1: A. B. 3.3.3 Gene Ontology Analysis by Species GO analysis was performed separately for each Hanseniaspora species in the MIX and FOR samples to determine their potential functional roles in the fermentation process. H. guilliermondii : MIX samples: 16 GO IDs were uploaded to REVIGO, of which 5 were associated with biological processes. FOR samples: 27 GO IDs were uploaded, with 18 linked to biological processes (Figure 2). Figure 2. H. opuntiae, H. uvarum, and H. valbyensis : • MIX samples: • H. opuntiae : 8 GO IDs uploaded, 2 linked to biological processes, • H. uvarum : 40 GO IDs uploaded, 13 linked to biological processes, • H. valbyensis : 14 GO IDs uploaded, 6 linked to biological processes. • FOR samples: • H. opuntiae : 14 GO IDs uploaded, 6 linked to biological processes, • H. uvarum : 43 GO IDs uploaded, 19 linked to biological processes, • H. valbyensis : 42 GO IDs uploaded, 19 linked to biological processes (Figures 3, 4, and 5). Figure 3. Figure 4. Figure 5. The complete list of GO IDs for the MAGs analyzed in Section 3.3 is available in Annex III, with those related to biological processes highlighted. 4. DISCUSSION To validate the taxonomic classification of MAGs presented by Lima et al. (2021), the MIX and FOR sequence files were analyzed using Kaiju. Kaiju, which is not constrained by input sequence length, is a highly suitable tool for classifying assembled contigs (Menzel, Lee, & Krogh, 2016). The genus of interest, Hanseniaspora spp., was identified in 3% of the MIX sample sequences and 4.9% of the FOR samples. One of the major challenges in metagenomics is accurately inferring the composition of microbial communities, given the phylogenetic distribution of available reference genomes (Segata et al., 2013). Many reference databases overrepresent model organisms and pathogens, such as Saccharomyces cerevisiae, a yeast extensively used in industrial fermentation. In contrast, species that are difficult to cultivate in laboratory settings remain underrepresented, complicating taxonomic classification—particularly in environmental samples (Menzel et al., 2015; Zhao, Tang, & Ye, 2012). Moreover, microorganisms, especially viruses, exhibit higher evolutionary rates than eukaryotes due to rapid replication cycles. A viable strategy to enhance classification accuracy in these samples is protein-level classification, which offers improved precision as proteins tend to be more conserved than underlying DNA sequences (Li, 2013; Menzel, Lee NG, & Krogh, 2016; Menzel et al., 2015). Beyond taxonomic confirmation, Hanseniaspora spp. contigs in the MIX and FOR samples were also identified through local sequence alignment using the Basic Local Alignment Search Tool (BLAST) from the National Center for Biotechnology Information (NCBI). BLAST results showed alignment gaps but confirmed similarity with H. uvarum, H. opuntiae, H. guilliermondii, and H. valbyensis . The sequence alignments were of high quality, as indicated by identity percentages exceeding 75%, reaching 100% in certain sequences, and exhibiting strong similarity indices. Genome Quality Analysis Genome quality analysis using QUAST allowed for the evaluation and comparison of MIX and FOR sample assemblies. The FOR samples exhibited superior assembly metrics, including: • Higher total contig count • Longer contigs • Higher N50 values, which indicate greater contiguity (where contigs of this length or longer account for at least 50% of the total base content) • Higher N75 values, reflecting the same metric at the 75% coverage threshold • Higher L75 values, which represent the minimum number of contigs containing 75% of the total genome base content (Gurevich et al., 2013). In contrast, no significant differences were observed between MIX and FOR MAGs for L50 values (number of contigs equal to or greater than N50 length) or GC content (%), which measures the proportion of guanine (G) and cytosine (C) nucleotides in the genome (Mikheenko, Saveliev, & Gurevich, 2016). Across both samples, most assembled contigs were short (<10,000 bp), and the GC content was below 40%, which may negatively impact genome stability. GC-rich regions are often gene-dense, making them critical for functional annotation (Suzuki et al., 2018). The higher N50 values in the FOR samples suggest reduced fragmentation and improved assembly contiguity. However, N50 alone does not confirm assembly correctness, as some software may generate longer contigs by erroneously joining unrelated regions (Angel et al., 2018). Functional Annotation Despite differences in N50 metrics between MIX and FOR, all contigs underwent genomic annotation to identify structural elements (genes) and assign functions. However, not all contigs produced functional annotation results. External annotation software, such as BLAST, relies on similarity searches between query sequences and reference protein sequences. Consequently, genes lacking close homologs in available databases may remain unidentified (Wang et al., 2017; BioBam Bioinformatics, 2019). Gene Ontology (GO) terms were assigned to three main categories: 1. Molecular function 2. Cellular component 3. Biological process (The Gene Ontology Consortium, 2019). For the purpose of this study, only biological process-related GO terms were considered, with separate analyses conducted for each Hanseniaspora species identified in the contigs. Biological Processes in Cocoa Fermentation In spontaneous cocoa fermentation, the most significant biological processes associated with H. guilliermondii involved: Ribosomal biogenesis, which is essential for protein synthesis, Metabolic pathways related to carbohydrates, organic acids, and aromatic compounds. Ribosomal biogenesis correlates directly with cellular protein production capacity. Mota-Gutiérrez et al. (2018) reviewed microbial communities involved in spontaneous fermentations and found that H. guilliermondii is a key contributor to the production of volatile organic compounds (VOCs), such as 2-phenylethanol, phenylacetaldehyde, 2-methylbutanal, benzaldehyde, limonene, and 2-phenylethyl acetate. These compounds originate from natural precursor transformations (e.g., sugars, organic acids, amino acids, and fatty acids) and contribute to the flavor and aroma profiles of cocoa. For H. opuntiae, metabolic activities were strongly associated with carbohydrate degradation and biosynthesis, as observed in studies by Ooi et al. (2020) and Díaz-Muñoz et al. (2023). Both studies demonstrated that H. opuntiae accelerates carbohydrate metabolism in yeast starter cultures, effectively shortening fermentation time. H. uvarum and H. valbyensis exhibited functional activities related to: • Proteolysis, • Aromatic compound metabolism, • Macromolecule and glycoprotein processing, • Organic acid metabolism, • Cellular nitrogen utilization. A significant biological process observed in these species was autophagy, a mechanism that maintains cellular homeostasis under high sugar, alcohol, and acid stress a common condition in cocoa fermentation (De Vuyst & Weckx, 2016). Studies have consistently identified H. uvarum as a dominant yeast in spontaneous cocoa fermentations (Illeghems et al., 2012; Pereira et al., 2013; Papalexandratou et al., 2019). Given its metabolic potential, H. uvarum is of interest for starter culture development aimed at standardizing fermentation quality. Batista et al. (2015) demonstrated that fermentation inoculated with a mixed yeast culture (Hanseniaspora spp., Saccharomyces cerevisiae, and Pichia kluyveri) led to: • Faster sugar metabolism, • Higher ethanol production in early fermentation stages, • Altered flavor profiles in finished chocolates. The biological activities associated with Hanseniaspora spp. genes contribute to ethanol production, sugar metabolism, and aeration, which facilitate microbial succession (Pacheco et al., 2020). Additionally, Hanseniaspora species exhibit antioxidant activity, participate in the citric acid cycle, and contribute to organic acid and free amino acid production (Almeida et al., 2021). 5. CONCLUSION Metagenome-assembled genomes (MAGs) analysis identified four yeast species: • Hanseniaspora uvarum • Hanseniaspora opuntiae • Hanseniaspora guilliermondii • Hanseniaspora valbyensis The main biological functions detected include protein synthesis, cellular homeostasis, and metabolic pathways involving carbohydrates, organic acids, macromolecules, and aromatic compounds. These functions suggest that Hanseniaspora spp. may accelerate carbohydrate metabolism, shorten fermentation time, and enhance ethanol production, optimizing the cocoa fermentation process. Given the taxonomic identification and known metabolic functions, we recommend H. uvarum and H. opuntiae for future starter culture trials in cocoa bean fermentation in southern Bahia. ACKNOWLEDGMENTS I would like to express my deep gratitude to all the co-authors of this study, whose collaboration and expertise were fundamental to the development of this research. I am grateful for all the valuable discussions and continued support at every stage of this work. I would also like to thank the Coordination for the Improvement of Higher Education Personnel (CAPES) for the financial support grant, essential for the completion of this research; the Postgraduate Program in Biology and Biotechnology of Microorganisms (PPGBBM); and the team at the UESC Agroindustry Applied Microbiology Laboratory (LABMA), as all the support provided by you was fundamental to the advancement of this work.

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The Gene Ontology Resource: 20 years and still GOing strong . Nucleic Acids Res. Jan 8;47(D1):D330-D338, 2019. Doi: 10.1093/nar/gky1055 Wang, R. et al. Genome annotation: Determination of the coding potential of the genome . Bioinformatics in Aquaculture: Principles and Methods, John Wiley & Sons, Ltd Chichester, UK, p. 74–85, 2017. DOI: 10.1002/9781118782392.ch4 Zhao, Y; Tang, H; Ye, Y. RAPSearch2: a fast and memory-efficient protein similarity search tool for next-generation sequencing data . Bioinformatics 28, 125–126, 2012. https://doi.org/10.1093/bioinformatics/btr595 Figure legend Figure 1: A - Number of GO terms identified in the contigs of each Hanseniaspora species in the MIX samples. B - Number of GO terms identified in the contigs of each Hanseniaspora species in the FOR samples. Figure 2 : Biological processes related to the GO terms of H. guilliermondii from the MIX and FOR samples. Figure 3: Biological processes related to the GO terms of H. opuntiae from the MIX and FOR samples. Figure 4: Biological processes related to the GO terms of H. uvarum from the MIX and FOR samples. Figure 5: Biological processes related to the GO terms of H. valbyensis from the MIX and FOR samples. Information & Authors Information Version history Copyright This work is licensed under a Non Exclusive No Reuse License.

Authors Metrics & Citations Metrics Article Usage 245views 112downloads Citations Download citation Caroline Santos de Souza, Giovanni Marques de Castro, Ícaro Santos Lopes, et al. Functional study of MAGs from Hanseniaspora spp. obtained from the spontaneous fermentation of cocoa seeds in the region of Ilhéus/Ba.. Authorea. 05 March 2025. DOI: https://doi.org/10.22541/au.174113476.64158351/v1 DOI: https://doi.org/10.22541/au.174113476.64158351/v1 If you have the appropriate software installed, you can download article citation data to the citation manager of your choice. Simply select your manager software from the list below and click Download. For more information or tips please see 'Downloading to a citation manager' in the Help menu.