ABSTRACT

   Oenococcus oeni, the only species of lactic acid bacteria capable of
   fully completing malolactic fermentation under challenging wine
   conditions, continues to intrigue researchers owing to its remarkable
   adaptability, particularly in combating acid stress. However, the
   mechanism underlying its superior adaptation to wine stresses still
   remains elusive due to the lack of viable genetic manipulation tools
   for this species. In this study, we conducted genomic sequencing and
   acid resistance phenotype analysis of 255 O. oeni isolates derived from
   diverse wine regions across China, aiming to elucidate their strain
   diversity and genotype-phenotype associations of acid resistance
   through comparative genomics. A significant correlation between
   phenotypes and evolutionary relationships was observed. Notably,
   phylogroup B predominantly consisted of acid-resistant isolates,
   primarily originating from Shandong and Shaanxi wine regions.
   Furthermore, we uncovered a noteworthy linkage between prophage genomic
   islands and acid resistance phenotype. Using genome-wide association
   studies, we identified key genes correlated with acid resistance,
   primarily involved in carbohydrates and amino acid metabolism
   processes. This study offers profound insights into the genetic
   diversity and genetic basis underlying adaptation mechanisms to acid
   stress in O. oeni.

IMPORTANCE

   This study provides valuable insights into the genetic basis of acid
   resistance in Oenococcus oeni, a key lactic acid bacterium in
   winemaking. By analyzing 255 isolates from diverse wine regions in
   China, we identified significant correlations between strain diversity,
   genomic islands, and acid resistance phenotypes. Our findings reveal
   that certain prophage-related genomic islands and specific genes are
   closely linked to acid resistance, offering a deeper understanding of
   how O. oeni adapts to acidic environments. These discoveries not only
   advance our knowledge of microbial stress responses but also pave the
   way for selecting and engineering acid-resistant strains, enhancing
   malolactic fermentation efficiency and wine quality. This research
   underscores the importance of genomics in improving winemaking
   practices and addressing challenges posed by high-acidity wines.

   KEYWORDS: lactic acid bacteria, pan-genome, genomic island, horizontal
   gene transfer, genome-wide association study

INTRODUCTION

   Malolactic fermentation (MLF) is a critical process in winemaking,
   particularly red wine. During this process, lactic acid bacteria (LAB)
   convert the sharp-tasting malic acid into the milder lactic acid, which
   reduces the wine’s acidity, enhances its flavor and aroma, and improves
   microbial stability ([40]1). However, winemakers often encounter
   several challenges in the MLF process, such as prolonged fermentation,
   incomplete fermentation, and contamination by undesirable microbes
   ([41]2[42]–[43]4). These challenges mainly arise from the difficulty of
   LAB to adapt to the harsh conditions of winemaking, including high
   acidity, high ethanol content, low temperatures, high levels of sulfur
   dioxide (SO[2]), and nutrient scarcity ([44]5). The growth of LAB is
   notably inhibited under acidic stress, which can significantly hinder
   the fermentation process. Consequently, the selection of LAB strains
   that exhibit strong acid resistance is imperative to ensure the
   stability and efficiency of MLF. Oenococcus oeni is the most commonly
   employed strain in MLF due to its remarkable adaptability to the stress
   conditions prevalent in wine. In winemaking, MLF is typically carried
   out under monoculture conditions by inoculating a selected strain of O.
   oeni ([45]6). Although other LAB species, such as Lactobacillus,
   Pediococcus, and Leuconostoc, are also capable of performing MLF, they
   often struggle to survive the extreme conditions in wine, leading to
   premature fermentation cessation or the production of harmful
   metabolites, which have a detrimental effect on wine quality ([46]3).
   Despite O. oeni’s ability to tolerate some of these stress factors, its
   growth can still be inhibited if the wine’s acidity is excessively high
   ([47]7). It is therefore vital to select O. oeni strains with enhanced
   acid resistance in order to ensure the successful progression of MLF.
   Such strains can enhance fermentation efficiency, reduce fermentation
   costs, mitigate the risk of microbial contamination, and ultimately
   elevate the overall quality of the wine ([48]4, [49]8).

   In contrast to other LAB, there is a paucity of suitable molecular
   biology techniques to genetically modify O. oeni, rendering it
   challenging to investigate gene function and regulatory mechanisms in
   O. oeni ([50]5). Researchers have devised methods such as
   electroporation and antisense RNA to research O. oeni at the molecular
   level ([51]9). Nevertheless, these methodologies encounter limited
   utility due to issues such as poor reproducibility. While heterologous
   expression in other LAB has partially addressed the difficulty of
   genetic modification in O. oeni ([52]9). Despite these advances,
   challenges remain, resulting in low gene expression levels and
   potentially inaccurate outcomes due to differences in cellular
   environments and metabolic pathways among LAB. In contrast, genomics
   offers a means of effectively studying the stress response mechanism of
   O. oeni at the molecular level, significantly enhancing research
   efficiency. By analyzing the genome, we can find potential
   genotype-phenotype associations, which serve as a solid foundation for
   future genetic confirmation.

   Microorganisms can adapt to stressful environments through horizontal
   gene transfer (HGT), which results in the generation of a pan-genome
   comprising multiple accessory genes ([53]10). Comparative genomics
   analysis of the O. oeni pan-genome can reveal evolutionary
   relationships among strains and elucidate strain diversity associated
   with phenotypes. Currently, comparative genomics is frequently
   integrated with genome-wide association studies (GWASs) to more
   comprehensively elucidate correlations between genotype and phenotype
   ([54]11). GWAS has been employed to investigate a multitude of
   statistical associations related to bacterial pathogenicity, antibiotic
   resistance, and other traits ([55]12).

   In this study, we sequenced, assembled, and annotated 255 O. oeni
   isolates for large-scale comparative genomic analysis. Subsequently,
   the isolates were subjected to an assessment of their resistance to
   acid stress environments. By integrating comparative genomics with
   GWAS, we elucidated the evolutionary relationships among O. oeni
   isolates and investigated genotype-phenotype associations of acid
   resistance. The primary objectives of this study were to examine the
   relationship between isolation sources, strain diversity, and genomic
   islands, as well as to identify genes associated with acid resistance.
   Our comprehensive analysis of population structural characteristics
   provides novel insights into the genetic traits of O. oeni and offers a
   foundation for further investigation into the potential application of
   acid-resistant O. oeni in the winemaking industry.

RESULTS

Genomic and pan-genomic characterization

   In order to gain a comprehensive understanding of the strain diversity
   of O. oeni comprehensively, we utilized 255 O. oeni isolates from
   various regions of China between 1999 and 2016 ([56]Table S1). Among
   these, seven isolates were obtained by downloading from the NCBI
   database. The genomes of the remaining 248 isolates were sequenced and
   assembled in this study. The average genome size of 255 isolates was
   2.1 Mb, with an average GC content of 37.88%, an average N50 of 1.12
   Mb, and an average depth of 912× ([57]Table S1). Based on the genomic
   data of 243 O.oeni uploaded to the NCBI database, the genome size
   ranges from 1.7 to 2.5 Mb, with an N50 ranging from 4.7 kb to 2.0 Mb.
   The GC content is between 37.5% and 38.5%. The sequencing coverage of
   the bacterial genomes typically ranges from 30× to 150×, with coverage
   exceeding 150× can enhance the accuracy of genome data ([58]13). Our
   genomic data fall within these reasonable ranges. The GC content of O.
   oeni exhibited significant regional differences (P = 4.27e−12,
   Kruskal-Wallis test) ([59]Fig. 1a).

Fig 1.

   [60]Box plot depicts GC content differences across Hebei, Inner
   Mongolia, Ningxia, Shaanxi, and Shandong. Pie chart depicts most genes
   in cloud. Bar graph depicts gene functions by highest count in unknown
   category.
   [61]Open in a new tab

   Genomic characterization and functional annotation of O. oeni. (a)
   Boxplot illustrating the GC content (%) of O. oeni genomes across
   various provinces of China. Significant differences in GC content were
   observed between isolates from different provinces (P < 0.05,
   Kruskal-Wallis test). Pairwise comparisons indicated the following
   significance levels: *P < 0.05; **P < 0.01; ***P < 0.001 (Wilcoxon
   tests). Only significant results are presented in the figure. (b) Fan
   chart depicting the distribution of pan-genomic genes among 255 O. oeni
   isolates. (c) Functional annotation of the pan-genome utilizing the COG
   database. Pan-genome genes are categorized into four groups:
   information processing and storage, cellular processing and signaling,
   metabolism, and poorly characterized.

   After pan-genomic analysis of 255 O. oeni isolates, a total of 16,416
   pan-genes were identified. Among these, the core genome comprised 1,028
   genes, including 840 core genes and 188 soft-core genes ([62]Fig. 1b).
   The soft-core genes are those present in 95–99% of the isolates, while
   the core genes are those found in more than 99% of the isolates. The
   pan-genome fit curve exhibited an increasing trend. The fit of Heaps'
   law with an exponent of γ = 0.37 indicated that the pan-genome of O.
   oeni was open ([63]Fig. S1). The exponent γ of the cumulative curve of
   the core genome was close to 0, indicating that the core genome was in
   a stable state. Among the 10,906 pan-genes annotated by the COG
   database, except for genes of unknown function, the majority are
   involved in metabolism, cellular processes, and signaling, as well as
   information processing and storage ([64]Fig. 1c).

Analysis of the strain diversity

   Pairwise average nucleotide identity (ANI) is widely used in genomic
   studies as a reliable metric for determining species boundaries. An ANI
   value exceeding 95% generally suggests that the organisms belong to the
   same species ([65]14). The PSU-1 strain (assembly accession
   [66]NC_008528.1) was the first O. oeni strain to have its whole-genome
   sequenced. Over the years, it has been extensively studied, with
   high-quality data and comprehensive annotations, making it a widely
   used reference genome in O. oeni research. In this study, we used PSU-1
   as the reference strain and compared it with all the isolates. The ANI
   values ranged from 98.88% to 99.85%, all exceeding 95%, thereby
   confirming that the isolates all belong to O. oeni.

   Lorentzen et al. [67]1 classified O. oeni strains from wine, cider, and
   kombucha into four phylogroups (A, B, C, and D) based on the core
   genome phylogenetic tree. To assign the 255 O. oeni isolates used in
   this study to these phylogroups, we randomly selected 45 strains from
   the data set of Lorentzen et al. ([68]Table S2), which represent all
   four phylogroups. These strains served as references to construct a