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Unravelling the key factors for the dominance of Leuconostoc starters during kimchi fermentation

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• Published: 25 April 2025

Unravelling the key factors for the dominance of Leuconostoc starters during kimchi fermentation

• Jisu Lee, 
• Min Ji Lee, 
• Mi-Ja Jung, 
• Yeon Bee Kim, 
• Seong Woon Roh, 
• Byung Hee Ryu, 
• Che Ok Jeon, 
• Hak-Jong Choi, 
• Tae Woong Whon & 
• Se Hee Lee 

npj Science of Food volume 9, Article number: 61 (2025) Cite this article

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Abstract

Recent studies aim to prevent kimchi spoilage and enhance the sensory and nutritional qualities using lactic acid bacteria, particularly Leuconostoc species, as kimchi starters. However, the factors enabling the successful adaptation and predominance of Leuconostoc species remain unclear. This study investigates the factors that contribute to the successful adaptation of Leuconostoc starter strains WiKim32, WiKim33, WiKim0121 and CBA3628 during kimchi fermentation using a comprehensive multi-omics approach. Our findings reveal that ATP-dependent molecular chaperones, which respond to cold and acidic kimchi environments, play crucial roles in successfully adapting Leuconostoc starter strains. Moreover, genes involved in carbohydrate metabolic pathways enhance ATP production, thereby supporting chaperone activity and bacterial growth. This study highlights the practical use of Leuconostoc starter strains WiKim32, WiKim33 and WiKim0121 and identifies essential factors for their successful adaptation and predominance during kimchi fermentation.

최근 연구들은 김치 부패를 방지하고 유산균,

특히 Leuconostoc 종을 김치 스타터로 사용하여 감

각적 및 영양적 품질을 향상시키는 데 초점을 맞추고 있습니다.

그러나

Leuconostoc 종이 성공적으로 적응하고 우세해지는 데 기여하는 요인은

아직 명확하지 않습니다.

본 연구는 다중 오믹스 접근법을 사용하여

김치 발효 중 Leuconostoc 스타터 균주 WiKim32, WiKim33, WiKim0121 및 CBA3628의

성공적인 적응에 기여하는 요인을 조사합니다.

연구 결과,

차갑고 산성인 김치 환경에 반응하는 ATP 의존성 분자 샤페론이

Leuconostoc 스타터 균주의 성공적인 적응에 중요한 역할을 한다는 것을 밝혔습니다.

또한 탄수화물 대사 경로와 관련된 유전자가 ATP 생산을 증진시켜

샤페론 활성과 세균 성장을 지원합니다.

이 연구는

Leuconostoc 스타터 균주 WiKim32, WiKim33, WiKim0121의 실용적인 사용을 강조하고,

김치 발효 중 그들의 성공적인 적응과 우세에 필수적인 요인을 식별합니다.

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Introduction

Kimchi, a popular traditional Korean food, is consumed worldwide for its health benefits, unique flavours, and tastes1,2,3,4. These benefits can be primarily attributed to spontaneous kimchi fermentation facilitated by lactic acid bacteria (LAB) originating from kimchi ingredients, including kimchi cabbage, radish, garlic, red pepper powder, ginger, and salted seafood5,6. However, spontaneous fermentation can lead to microbial contamination and inconsistent quality, prompting the development of kimchi starter cultures to ensure safety and consistency7. Researchers and commercial food companies have developed various kimchi starter cultures to prevent spoilage by undesirable microorganisms and enhance the nutritional and sensory properties of kimchi8.

Studies have identified numerous LAB, including Leuconostoc (Leu.) mesenteroides, Leuconostoc (Leu.) citreum, Leuconostoc gelidum, Latilactobacillus sakei, and Lactiplantibacillus plantarum, as predominant microbes in the kimchi fermentation environment9,10,11,12. Consequently, the use of LAB-based starters has garnered significant attention13,14,15,16,17,18. Weissella (Wei.) cibaria and Weissella (Wei.) koreensis are also preval‎ent in kimchi; however, they are rarely used as starter strains as they are not classified as 'Generally recognised as safe' (GRAS) by the Food and Drug Administration (FDA) (https://www.fda.gov/food/generally-recognized-safe-gras/microorganisms-microbial-derived-ingredients-used-food-partial-list) or included in the 'Qualified Presumption of Safety' (QPS) list by the European Food Safety Authority (EFSA) (https://www.efsa.europa.eu/en/efsajournal/pub/7747)19. Unlike homofermentative Lactobacillus species, heterofermentative Leuconostoc species produce lactate, acetate, ethanol, carbon dioxide, and mannitol, which enhance both the taste and nutritional properties of kimchi20,21,22,23.

Among Leuconostoc species, Leu. mesenteroides is predominant during the early to middle stages of kimchi fermentation24,25,26. Specific strains, such as Leu. mesenteroides strain B1 (KACC 16355)27, LK9328, and ATCC 8293T29, have been developed as starter strains to accelerate the fermentation, enhance metabolite production, and prevent spoilage. These strains also improve sensory qualities by increasing sourness and reducing bitterness. Notably, while several studies have highlighted the predominance of Leu. mesenteroides during spontaneous fermentation24,25,26 and its positive effects on sensory quality27,30 and prevention of spoilage28 when used as a starter, research is yet to identify what factors enable the predominance of Leu. mesenteroides in starters during kimchi fermentation.

This study aimed to investigate factors contributing to Leuconostoc predominance by inoculating kimchi samples with Leuconostoc starter strains and conducting multi-omics analyses, including metataxonomics, metagenomics, metatranscriptomics, and metabolomics4. Our goal is to assess bacterial diversity, metabolite production, and significant genes influencing the adaptation and predominance of starter strains during fermentation. This is the first study to elucidate the crucial factors contributing to the differential dominance of inoculated starter strains in a kimchi fermentation environment.

김치, 인기 있는 전통 한국 음식,은

전 세계적으로 건강상의 이점, 독특한 풍미, 맛으로 소비되고 있습니다¹,²,³,⁴.

이러한 이점은 주로

김치 재료(김치 배추, 무, 마늘, 고춧가루, 생강, 염장 해산물 등)에서 기원한

유산균(LAB)에 의한 자연 발효에 주로 기인합니다⁵,⁶.

그러나

자연 발효는 미생물 오염과 품질 불일치를 초래할 수 있어

안전성과 일관성을 보장하기 위해 김치 스타터 배양의 개발이 촉진되었습니다⁷.

연구자와 상업용 식품 회사는 바람직하지 않은 미생물에 의한 부패를 방지하고 김

치의 영양 및 감각적 특성을 향상시키기 위해 다양한 김치 스타터 배양을 개발했습니다⁸.

연구들은 김치 발효 환경에서

Leuconostoc (Leu.) mesenteroides,

Leuconostoc (Leu.) citreum,

Leuconostoc gelidum,

Latilactobacillus sakei, 및 Lactiplantibacillus plantarum을 포함한

수많은 LAB를 주요 미생물로 식별했습니다⁹,¹⁰,¹¹,¹².

이에 따라 LAB 기반 스타터의 사용이

큰 주목을 받았습니다¹³,¹⁴,¹⁵,¹⁶,¹⁷,¹⁸.

Weissella (Wei.) cibaria와 Weissella (Wei.) koreensis도 김치에서 흔하지만,

이들은 미국 식품의약국(FDA)에서

'일반적으로 안전하다(GRAS)'로 분류되지 않았거나( https://www.fda.gov/food/generally-recognized-safe-gras/microorganisms-microbial-derived-ingredients-used-food-partial-list),

유럽식품안전청(EFSA)의 '자격 있는 안전 추정(QPS)' 목록에 포함되지 않았기 때문에

스타터 균주로 거의 사용되지 않습니다( https://www.efsa.europa.eu/en/efsajournal/pub/7747)¹⁹.

호모발효성 Lactobacillus 종과 달리,

헤테로발효성 Leuconostoc 종은

젖산, 아세테이트, 에탄올, 이산화탄소, 만니톨을 생성하여 김치의 맛과 영양 특성을 향상시킵니다²⁰,²¹,²²,²³.

Leuconostoc 종 중 Leu. mesenteroides는

김치 발효의 초기에서 중간 단계에서 우세합니다²⁴,²⁵,²⁶.

Leu. mesenteroides 균주 B1(KACC 16355)²⁷, LK9328, 및 ATCC 8293T²⁹와 같은 특정 균주는

발효를 가속화하고 대사 산물 생성을 증진하며 부패를 방지하기 위해 스타터 균주로 개발되었습니다.

이러한 균주는

신맛을 증가시키고 쓴맛을 줄여 감각적 품질을 개선하기도 합니다.

특히, 여러 연구에서 자연 발효 중

Leu. mesenteroides의 우세²⁴,²⁵,²⁶와 스타터로서의 부패 방지 효과²⁸,

감각적 품질 개선²⁷,³⁰가 강조되었으나,

김치 발효 중 스타터로서 Leu. mesenteroides가 우세하게 되는 요인을 밝히는 연구는

아직 이루어지지 않았습니다.

본 연구는

Leuconostoc 스타터 균주로 김치 샘플을 접종하고

메타택소노믹스, 메타게노믹스, 메타트랜스크립토믹스, 메타볼로믹스를 포함한

다중 오믹스 분석을 수행하여

Leuconostoc의 우세에 기여하는 요인을 조사하는 것을 목표로 합니다⁴.

목표는

발효 중 스타터 균주의 적응과 우세에 영향을 미치는

세균 다양성, 대사 산물 생산, 중요한 유전자를 평가하는 것입니다.

이는 김치 발효 환경에서 접종된 스타터 균주의 차별적 우세에 기여하는

중요한 요인을 최초로 해명하는 연구입니다.

Results

Changes in pH, LAB colony-forming units (CFUs), and metabolite concentrations

During kimchi fermentation, significant changes in pH and CFUs of LAB were observed (Fig. 1a, b). From days 3 to 33, pH decreased in all samples (W32, W33, W0121, C3628, and CTR—the samples inoculated with starter strains Leu. mesenteroides WiKim32, Leu. mesenteroides WiKim33, Leu. mesenteroides WiKim0121, Leuconostoc sp. CBA3628, and saline solution instead of starter strain, respectively) owing to organic acids produced by LAB31. A slight increase in pH was observed from days 0 to 3 in most samples, except in W33. This phenomenon could be attributed to osmotic pressure caused by salt, which drew water from cabbage and diluted acidic compounds. Initial CFUs were similar across samples; however, after day 11, LAB counts in the starter samples increased over 70 times compared to those in the CTR, indicating that inoculated starter cultures either multiplied or stimulated the growth of other LABs. Higher LAB amounts in the starter samples caused greater acidification from day 0 to 22, resulting in a faster decrease in pH than in CTR.

pH, LAB 집락 형성 단위(CFUs), 및 대사 산물 농도의 변화

김치 발효 과정에서

pH와 유산균(LAB)의 집락 형성 단위(CFUs)에 뚜렷한 변화가 관찰되었습니다(그림 1a, b).

3일부터 33일까지 모든 샘플(W32, W33, W0121, C3628,

그리고 CTR—각각 Leu. mesenteroides WiKim32,

Leu. mesenteroides WiKim33,

Leu. mesenteroides WiKim0121,

Leuconostoc sp. CBA3628으로 접종된 샘플 및 스타터 균주 대신 식염수를 사용한 샘플)에서

유산균에 의해 생성된 유기산으로 인해 pH가 감소했습니다³¹.

W33을 제외한 대부분의 샘플에서 0일부터 3일까지 pH가 약간 증가하는 현상이 관찰되었으며,

이는 소금에 의한 삼투압이 배추에서 물을 끌어내 산성 화합물을 희석시켰기 때문일 수 있습니다.

초기 CFUs는 샘플 간 유사했으나,

11일 이후 스타터 샘플의 LAB 수는 CTR에 비해 70배 이상 증가했으며,

이는 접종된 스타터 배양이 증식하거나 다른 LAB의 성장을 촉진했음을 시사합니다.

스타터 샘플의 높은 LAB 양은 0일부터 22일까지 더 큰 산성화 반응을 일으켜

CTR보다 pH가 더 빠르게 감소했습니다.

Fig. 1: Fermentation profiles of kimchi samples inoculated with different Leuconostoc starter strains.

Changes in a pH levels, b colony-forming units of lactic acid bacteria, and the concentrations of c fructose, d glucose, e acetate, f lactate, g mannitol, and h ethanol in the CTR, W32, W33, W0121, and C3628 samples during kimchi fermentation. The kimchi samples inoculated with strains WiKim32, WiKim33, WiKim0121, and CBA3628, and saline solution were labelled as 'W32', 'W33', 'W0121', 'C3628', and 'CTR', respectively. Data were presented as mean ± standard deviation. Experiments were performed in triplicate.

pH 수준, 유산균 집락 형성 단위(CFUs), 및 주요 대사 산물 농도의 변화

김치 발효 중 CTR, W32, W33, W0121, C3628 샘플에서 pH 수준(a),

유산균의 집락 형성 단위(CFUs)(b),

및 프럭토스(c),

포도당(d),

아세테이트(e),

젖산(f),

만니톨(g),

에탄올(h) 농도의 변화를 측정했습니다.

WiKim32, WiKim33, WiKim0121, CBA3628 균주 및 식염수로 접종된 김치 샘플은 각각 'W32', 'W33', 'W0121', 'C3628', 'CTR'로 표시되었습니다. 데이터는 평균 ± 표준편차로 제시되었으며, 실험은 3회 반복되었습니다.

Full size image

Concentrations of main kimchi metabolites during fermentation were measured (Fig. 1c–h). Fructose content rapidly decreased in starter samples, whereas it decreased slowly in CTR (Fig. 1c). Acetate, lactate, mannitol, and ethanol levels increased in all samples (Fig. 1e–h); acetate and lactate lowered the pH, while mannitol enhanced the taste and nutritional value of kimchi. Starter samples showed a faster increase in acetate and mannitol levels compared with CTR. The non-metric multidimensional scaling (NMDS) plots revealed dynamic changes in metabolite profiles, with CTR showing slower fermentation than starter samples (Supplementary Fig. 1). By the end stage, metabolite profiles were similar across all samples. Although the profiles of pH, CFUs of LAB, and fermentation speed varied depending on whether the sample was inoculated with a starter strain, all samples followed the typical kimchi fermentation pattern2,5.

발효 중 주요 김치 대사 산물 농도를 측정했습니다(그림 1c–h).

스타터 샘플의 프럭토스 함량은

급격히 감소한 반면, CTR에서는 천천히 감소했습니다(그림 1c).

아세테이트, 젖산, 만니톨, 에탄올 수준은

모든 샘플에서 증가했습니다(그림 1e–h);

아세테이트와 젖산은 pH를 낮추었고,

만니톨은 김치의 맛과 영양 가치를 향상시켰습니다.

스타터 샘플은 CTR에 비해 아세테이트와 만니톨 수준이 더 빠르게 증가했습니다.

비측량 다차원 스케일링(NMDS) 플롯은 대사 산물 프로파일의 역동적인 변화를 보여주었으며, CTR은 스타터 샘플보다 발효가 느렸습니다(부가 도표 1). 발효 말기에는 모든 샘플의 대사 산물 프로파일이 유사했습니다. pH, LAB CFUs, 발효 속도의 프로파일은 스타터 균주 접종 여부에 따라 달라졌지만, 모든 샘플은 전형적인 김치 발효 패턴을 따랐습니다²,⁵.

Changes in bacterial community succession and alpha diversity

The study investigated the impact of starter strains on bacterial succession during kimchi fermentation (Fig. 2). At the amplicon sequence variant (ASV) level, ASV001 was abundant in the WiKim samples (W32, W33, and W0121), but low in C3628 and CTR (Fig. 2a). ASV002 was only observed in C3628, while ASV003 was preval‎ent in C3628 and CTR. Phylogenetic tree analysis identified ASV001, ASV002, and ASV003 as Leu. mesenteroides, Leuconostoc sp. CBA3628, and Leu. citreum, respectively (Fig. 2b). The high abundance of ASV001 in WiKim samples represents the inoculated Leu. mesenteroides starter strains, whereas ASV001 in C3628 and CTR originated from kimchi ingredients. ASV002 represented the inoculated CBA3628 strain, showing low relative abundance. ASV003 (Leu. citreum) was preval‎ent in the bacterial community profile of CTR between days 0 and 6. The WiKim starters adapted and predominated better than the spontaneously derived Leu. citreum, indicating their practicality as kimchi starter cultures, unlike the less effective CBA3628 strain.

세균 군집 연속성 및 알파 다양성의 변화

본 연구는 김치 발효 중 스타터 균주가 세균 연속성에 미치는 영향을 조사했습니다(그림 2). 증폭 서열 변이체(ASV) 수준에서 ASV001은 WiKim 샘플(W32, W33, W0121)에 풍부했으나 C3628과 CTR에서는 낮았습니다(그림 2a). ASV002는 C3628에서만 관찰되었고, ASV003은 C3628과 CTR에서 우세했습니다. 계통 발생 나무 분석은 ASV001, ASV002, ASV003을 각각 Leu. mesenteroides, Leuconostoc sp. CBA3628, Leu. citreum으로 식별했습니다(그림 2b). WiKim 샘플에서 ASV001의 높은 풍부도는 접종된 Leu. mesenteroides 스타터 균주를 나타내며, C3628과 CTR의 ASV001은 김치 재료에서 유래했습니다. ASV002는 접종된 CBA3628 균주를 나타내며 상대적으로 낮은 풍부도를 보였습니다. ASV003(Leu. citreum)은 0일에서 6일 사이 CTR의 세균 군집 프로파일에서 우세했습니다.

WiKim 스타터는

자연 유래 Leu. citreum보다 더 잘 적응하고 우세했으며,

이는 김치 스타터 배양으로서의 실용성을 보여주었고,

덜 효과적인 CBA3628 균주와 대조를 이룹니다.

Fig. 2: Microbial composition and phylogeny of kimchi samples.

a Bacterial community profiles at amplicon sequence variant (ASV) level in the CTR, W32, W33, W0121, and C3628 samples during kimchi fermentation. ‘F’ indicates family level. b A phylogenetic tree of Leuconostoc species, including Leuconostoc ASV001, Leuconostoc ASV002, Leuconostoc ASV003, and four starter strains (WiKim32, WiKim33, WiKim0121, and CBA3628). The tree was inferred using the neighbour-joining method and bootstrap values were calculated from 1000 replicates. Weissella cibaria CBA3612 and Weissella koreensis KACC 15510 were used as the outgroup. Bar, 0.01 substitution per nucleotide position. The kimchi samples inoculated with strains WiKim32, WiKim33, WiKim0121, and CBA3628, and saline solution were labelled as 'W32', 'W33', 'W0121', 'C3628', and 'CTR', respectively.

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Additionally, ASV008 and ASV009, identified as Wei. cibaria and Wei. koreensis, respectively (Supplementary Fig. 2), were markedly present in the CTR and C3628 samples. Microbial alpha diversity analysis32 showed that WiKim samples initially exhibited higher diversity indices than C3628 and CTR (Supplementary Table 1). CTR and C3628 samples, dominated by ASV003 and ASV008, exhibited relatively stable diversity indices throughout the fermentation process. In contrast, inoculated WiKim strains appeared to be in competition with ASV003 and ASV008, resulting in higher diversity and lower evenness during the early fermentation. However, ASV001 (starter strains in WiKim samples) became predominant during the later stages of fermentation, leading to a stabilising trend in diversity indices during the late fermentation period. This is explained by the effect of inoculated WiKim strains (WiKim32, WiKim33, and WiKim0121) instead of the dominance of spontaneously derived LAB, including ASV003, ASV008, and ASV009 (Fig. 2a). Strain CBA3628 could not significantly alter natural microbial succession, showing a diversity similar to that seen in CTR.

Genetic differences between starter strains

Comparative genomic analyses between starter strains were conducted to identify the factors influencing the differences in adaptation and predominance in kimchi fermentation environments. Despite high 16S rRNA gene sequence similarity, Leuconostoc sp. CBA3628 showed distinct genomic differences with lower average nucleotide identity (ANI) and in silico DNA–DNA hybridisation (DDH) values compared with other Leu. mesenteroides strains (Supplementary Fig. 3a–c).

The general genomic features of starter strains indicated that Leuconostoc sp. CBA3628 had fewer total genes and coding sequences (CDSs) than WiKim strains (Supplementary Table 2). Functional features of the four starter strains were compared using clusters of orthologous groups (COG), carbohydrate-active enzymes (CAZymes), and Kyoto Encyclopaedia of Genes and Genomes (KEGG) analyses. COG analysis showed that strain CBA3628 had the lowest functional gene counts in key categories for fermentative metabolism (C, E, G, J, and K; Fig. 3a)33, making its fermentative capacity weaker. CAZyme comparison revealed that strain CBA3628 contained fewer genes encoding glycoside hydrolases (GH) and glycosyl transferases (GT); this reduced its carbohydrate metabolism efficiency (Fig. 3b).

스타터 균주 간 유전적 차이

스타터 균주 간 비교 게놈 분석은 김치 발효 환경에서 적응과 우세 차이에 영향을 미치는 요인을 식별하기 위해 수행되었습니다. 16S rRNA 유전자 서열 유사도가 높음에도 불구하고, Leuconostoc sp. CBA3628는 다른 Leu. mesenteroides 균주와 비교하여 평균 뉴클레오타이드 동일성(ANI) 및 인 실리코 DNA-DNA 하이브리다이제이션(DDH) 값이 낮아 뚜렷한 게놈 차이를 보였습니다(부가 도표 3a–c).

스타터 균주의 일반적인 게놈 특징에 따르면, Leuconostoc sp. CBA3628은 WiKim 균주보다 총 유전자 수와 코딩 서열(CDS)이 적었습니다(부가 표 2). 네 가지 스타터 균주의 기능적 특징은 정형 유전자 그룹(COG), 탄수화물 활성 효소(CAZymes), 그리고 교토 유전자 및 게놈 백과사전(KEGG) 분석을 사용하여 비교되었습니다. COG 분석은 CBA3628 균주가 발효 대사에 중요한 주요 범주(C, E, G, J, K)(그림 3a)³³에서 기능적 유전자 수가 가장 낮았음을 보여주었으며, 이는 발효 능력이 약함을 나타냅니다. CAZyme 비교는 CBA3628 균주가 글리코시드 가수분해효소(GH)와 글리코실 전이효소(GT)를 코딩하는 유전자가 더 적어 탄수화물 대사 효율이 낮음을 밝혔습니다(그림 3b).

Fig. 3: Functional classification of genes and CAZyme profiles in Leuconostoc starter strains.

a Clusters of orthologous groups (COG) functional categories display the number of genes in each starter strain, classified as follows: C, energy production and conversion; D, cell cycle control, cell division, chromosome partitioning; E, amino acid transport and metabolism; F, nucleotide transport and metabolism; G, carbohydrate transport and metabolism; H, coenzyme transport and metabolism; I, lipid transport and metabolism; J, translation, ribosomal structure and biogenesis; K, transcription; L, replication, recombination and repair; M, cell wall/membrane/envelope biogenesis; N, cell motility; O, post-translational modification, protein turnover, and chaperones; P, inorganic ion transport and metabolism; Q, secondary metabolites biosynthesis, transport, and catabolism; S, function unknown; T, signal transduction mechanisms; U, intracellular trafficking, secretion, and vesicular transport; and V, defence mechanisms. b The number of CAZyme genes in each starter strain is classified as follows: GH glycoside hydrolases, GT glycosyl transferases, AA auxiliary activities, CE carbohydrate esterases, PL polysaccharide lyases, and CBM carbohydrate-binding molecules.

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Metabolic pathway analysis displayed using iPath based on KEGG Orthology (KO) numbers showed that all strains shared genes for phosphoketolase and incomplete glycolysis pathways (Fig. 4), which are key pathways in Leu. mesenteroides33. WiKim strains also harboured genes related to energy metabolism, including galactose and pyruvate metabolism, which CBA3628 lacked (red lines in Fig. 4). The absence of these genes might result in lower ATP production in Leuconostoc sp. CBA3628 than WiKim strains.

Fig. 4: Comparative metabolic pathways reconstructed from Leuconostoc starter strains.

Metabolic pathways of Leuconostoc mesenteroides WiKim32, Leuconostoc mesenteroides WiKim33, Leuconostoc mesenteroides WiKim0121, and Leuconostoc sp. CBA3628 were generated using the iPath v2 module based on Kyoto Encyclopaedia of Genes and Genomes (KEGG) Orthology (KO) numbers. The pathways are displayed in different colours depending on the presence and/or absence of genes in each starter strain, as depicted in the table inside the figure.

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Expression‎ levels of genes involved in carbohydrate metabolism

Based on the genetic differences between WiKim strains and Leuconostoc sp. CBA3628, we constructed a pathway map focusing on energy metabolism (Fig. 5a). As shown in red, yellow, purple, and green pathways in the map, strain CBA3628 could not use various carbon sources, such as raffinose, lactose, maltose, xylose, and arabinose, owing to the absence of related genes, unlike WiKim strains. The expression‎ levels of these genes were investigated and visualised using a heatmap (Fig. 5b). Compared with strain CBA3628, no additional genes related to energy metabolism (including Arabic and KO numbers) were identified in the WiKim strains: pyruvate oxidase (14, K00158); alpha-galactosidase (30, K07407); beta-galactosidase (36, K01190); galactokinase (37, K00849); maltose phosphorylase (38, K00691); UDP-glucose-hexose-1-phosphate uridylyltransferase (39, K00965); xylose isomerase (51, K01805); xylulokinase (52, K00854); l-ribulose-5-phosphate 4-epimerase (53, K03077); l-arabinose isomerase (54, K01804); l-ribulokinase (55, K00853); ascorbate PTS system EIIA component (56, K02821); ascorbate PTS system EIIB component (57, K02822); l-ascorbate 6-phosphate lactonase (58, K03476); 3-dehydro-l-gulonate-6-phosphate decarboxylase (59, K03078); and l-ribulose-5-phosphate 3-epimerase (60, K03079). These genes are involved in ATP production from raffinose, galactose, lactose, maltose, pyruvate, ribose, xylose, arabinose, and ascorbate. The differential expression‎ levels of these genes may result in distinct ATP yields, contributing to the growth and predominance of the WiKim strains during fermentation12. Indeed, these carbon sources did exist in the kimchi samples, although present in small amounts, and were consumed during the fermentation (Supplementary Fig. 4).

탄수화물 대사에 관여하는 유전자의 발현 수준

WiKim 균주와 Leuconostoc sp. CBA3628 간 유전적 차이를 바탕으로 에너지 대사에 초점을 맞춘 경로 맵을 구성했습니다(그림 5a). 지도에서 빨간색, 노란색, 보라색, 초록색 경로로 표시된 바와 같이, CBA3628 균주는 라피노스, 락토스, 말토스, 자일로스, 아라비노스와 같은 다양한 탄소원을 사용할 수 없었으며, 이는 관련 유전자가 없는 반면 WiKim 균주는 그렇지 않았습니다. 이러한 유전자의 발현 수준은 히트맵을 사용하여 조사 및 시각화되었습니다(그림 5b). CBA3628 균주와 비교했을 때, WiKim 균주에서 에너지 대사(아랍어 및 KO 번호 포함)와 관련된 추가 유전자는 식별되지 않았습니다: 피루브산 옥시다제(14, K00158); 알파-갈락토시다제(30, K07407); 베타-갈락토시다제(36, K01190); 갈락토키나제(37, K00849); 말토스 포스포릴라제(38, K00691); UDP-포도당-헥소스-1-포스페이트 우리딜릴트랜스퍼라제(39, K00965); 자일로스 이성질화효소(51, K01805); 자일루로키나제(52, K00854); L-리불로스-5-포스페이트 4-에피메라제(53, K03077); L-아라비노스 이성질화효소(54, K01804); L-리불로키나제(55, K00853); 아스코베이트 PTS 시스템 EIIA 성분(56, K02821); 아스코베이트 PTS 시스템 EIIB 성분(57, K02822); L-아스코베이트 6-포스페이트 락톤라제(58, K03476); 3-데히드로-L-굴루네이트-6-포스페이트 데카르복실라제(59, K03078); L-리불로스-5-포스페이트 3-에피메라제(60, K03079). 이러한 유전자는 라피노스, 갈락토스, 락토스, 말토스, 피루브산, 리보스, 자일로스, 아라비노스, 아스코베이트로부터 ATP 생산에 관여합니다. 이러한 유전자의 차별적 발현 수준은 서로 다른 ATP 수율을 초래할 수 있으며, 이는 발효 중 WiKim 균주의 성장과 우세에 기여할 수 있습니다¹². 실제로 이러한 탄소원은 김치 샘플에 소량 존재했으며, 발효 중 소모되었습니다(부가 도표 4).

Fig. 5: Carbohydrate metabolic pathways and gene expression‎ profiles of Leuconostoc starter strains during kimchi fermentation.

a Reconstructed carbohydrate metabolic pathways of Leuconostoc mesenteroides, Leuconostoc citreum, Weissella cibaria, and Weissella koreensis. The pathways are represented by different colours depending on the presence (P) and/or absence (A) of genes in each strain, as depicted in the table inside the figure. b Expression‎ levels of genes in Leuconostoc mesenteroides, Leuconostoc citreum, Weissella cibaria, and Weissella koreensis in response to carbohydrate availability. Arabic numbers and KO numbers are positioned close to the lines in a reconstructed pathways and to b the heatmap, showing the expression‎ levels of each gene across the strains. The kimchi samples inoculated with strains WiKim32, WiKim33, WiKim0121, and CBA3628, and saline solution were labelled as 'W32', 'W33', 'W0121', 'C3628', and 'CTR', respectively.

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Additionally, some of these genes—such as pyruvate oxidase, alpha-galactosidase, beta-galactosidase, galactokinase, maltose phosphorylase, UDP-glucose-hexose-1-phosphate uridylyltransferase, xylose isomerase, xylulokinase, l-ribulose-5-phosphate 4-epimerase, L-arabinose isomerase, l-ribulokinase, ascorbate PTS system EIIA or EIIB component, l-ascorbate 6-phosphate lactonase, 3-dehydro-l-gulonate-6-phosphate decarboxylase, and l-ribulose-5-phosphate 3-epimerase—were also expressed in LAB derived from raw ingredients, including Leu. citreum, Wei. cibaria, and/or Wei. koreensis, which compete with strain CBA3628 for carbon sources during fermentation (Fig. 5b). This suggests that strain CBA3628 is unable to utilise various carbon sources for ATP production as efficiently as the WiKim strains and the LAB from raw ingredients, leading to the non-dominance of strain CBA3628.

Selection of putative genes associated with predominance of starter strains

Gene expression‎ from each starter strain was categorised based on KEGG BRITE hierarchical classifications to identify putative genes affecting the adaptation and predominance of starter strains in the kimchi fermentation environment. The 1238 genes annotated within the WiKim32, WiKim33, WiKim0121, CBA3628, and ATCC 8293T strains across 33 categories were analysed to calculate the average transcripts per million (TPM) values. Among them, eight categories—oxidative phosphorylation, messenger RNA biogenesis, ribosomes, translation factors, chaperones and folding catalysts, membrane trafficking, mitochondrial biogenesis, and exosomes—exceeded the average TPM values of 2000 (Supplementary Fig. 5). In these categories, 83 genes had expression‎ levels above 2000 TPM, and subsequent network analysis revealed 12 functional genes—encoding pyruvate kinase (K00873); glucose-6-phosphate isomerase (GPI; K01810); F-type H+-transporting ATPase subunit delta (K02113); molecular chaperones DnaJ (K03686), GprE (K03687), DnaK (K04043), ClpL (K04086), and Hsp20 (K13993); translation initiation factor IF-2 (K02520), small subunit ribosomal protein S2 (K02967) and S9 (K02996); and electron transfer flavoprotein alpha subunit (K03522)—closely correlated with bacterial adaptation and predominance (Fig. 6a). Most of them (8 of 12) encode stress-response proteins, which may help maintain protein homoeostasis under the cold and acidic environments of kimchi; therefore, we selected these genes as putative genes associated with the predominance of starter strains.

Fig. 6: Correlation network analysis and gene expression‎ profiles of stress-related functions in Leuconostoc starter strains during kimchi fermentation.

The average transcripts per million (TPM) values of 1238 genes in 33 categories based on KEGG BRITE hierarchical classifications were calculated, and 83 genes that exhibited transcript levels above 2000 TPM were selected. Spearman correlation coefficients were calculated between the relative abundance of Leuconostoc ASV001 and the TPM values mapped to Leuconostoc mesenteroides ATCC 8293T, Leuconostoc mesenteroides WiKim32, Leuconostoc mesenteroides WiKim33, and Leuconostoc mesenteroides WiKim0121 in the kimchi samples CTR, W32, W33, and W0121 samples, respectively, and between the relative abundance of Leuconostoc ASV002 and the TPM values mapped to Leuconostoc sp. CBA3628 in the C3628 sample. Then, a a network analysis was performed. The colours of nodes represent the following: red, bacterial relative abundances; yellow-green, KO number within oxidative phosphorylation category; grey, KO number within ribosome category; white-yellow, KO number within translation factors category; pink, KO number within chaperones and folding catalysts category; green, KO number within membrane trafficking category; blue, KO number within mitochondrial biogenesis category; apricot, KO number within exosome category; and sky-blue, KO number within the categories more than two. The edges represent Spearman correlations, with thicker edges indicating a stronger positive correlation and thinner edges displaying a weaker correlation or a negative correlation, as per the correlation scale ranging from −1.0 to 1.0. The highly ‘strong’ positive correlation of over 0.6 is visualised with red edges. b The TPM values of genes encoding F-type ATPase and chaperone proteins from five strains in each kimchi sample during fermentation were visualised using the heatmap. Gene expression‎ levels are indicated by colour intensity, with red representing higher TPM values (0 to 50,000 or greater). The pH values at each time point are provided above the heatmap, with the intensity of colour (yellow–black) indicating the pH, as shown on the scale bar (4.06‒5.14). The boxes of gene names are filled with colours corresponding to each category described in the network analysis. The kimchi samples inoculated with strains WiKim32, WiKim33, WiKim0121, and CBA3628, and saline solution were labelled as 'W32', 'W33', 'W0121', 'C3628', and 'CTR', respectively.

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The impact of the genes encoding stress-response proteins on the adaptation and predominance of WiKim strains compared to CBA3628 during kimchi fermentation was eval‎uated (Fig. 6b). We first assessed the expression‎ of genes encoding F-type ATPase and Chaperones DnaJ, GrpE, and DnaK, which were correlated at day 6 of fermentation (Fig. 6a). F-type ATPase is likely a stress-response protein that maintains bacterial intracellular pH homoeostasis under acidic conditions34,35. Chaperones DnaJ, GrpE, and DnaK coordinate a crucial protein folding and repair cycle, ensuring cellular resilience and resistance under stress conditions like cold environments36,37,38,39,40,41. The expression‎ levels of F-type ATPase complex genes were similar in all samples; however, the expression‎ pattern of chaperone genes (dnaJ, grpE, and dnaK) was significantly different only in WiKim kimchi samples. WiKim strains showed higher expression‎ of these genes encoding chaperones at day 6 than strains CBA3628 and ATCC 8293T, indicating greater resistance to low temperatures in early fermentation (Fig. 6b).

Higher expression‎ of clpL in WiKim strains than in strains CBA3628 and ATCC 8293T during late fermentation indicates their significant impact on consistent predominance. The findings suggest that acidic and cold-stress-response proteins significantly contribute to bacterial adaptation and predominance during kimchi fermentation. In early fermentation, the F-type ATPase complex and chaperones DnaJ, DnaK, and GrpE play a crucial role, with cold-stress resistance by chaperones being more critical for initial adaptation. Chaperone ClpL plays a pivotal role both in cold- and acidic-stress resistance during late fermentation, impacting consistent predominance as pH decreases.

These stress-response proteins require ATP42,43, emphasising the importance of ATP production by starter strains in the kimchi fermentation environment, as mentioned earlier. In addition, the genes encoding pyruvate kinase and GPI, which showed a close correlation with bacterial relative abundance (Fig. 6a), are also key metabolic genes involved in ATP production. These points are further elaborated in the discussion section.

Dominance of Wei. koreensis in a kimchi environment without starter strains

Weissella ASV009 (Wei. koreensis) was predominant in CTR from day 11 to 33 (Fig. 2a). As transcript levels of Wei. koreensis in carbohydrate fermentative pathways were unremarkable, the arginine deiminase (ADI) pathway in Wei. koreensis was explored44. During kimchi fermentation, TPM values of genes involved in the ADI pathway were highest in CTR (Fig. 7a), corresponding to the high relative abundances of ASV009 (Fig. 2a). Simultaneously, arginine levels decreased, and ornithine levels increased at day 11 in CTR (Fig. 7b, c). Weissella ASV008 (Wei. cibaria) did not affect arginine and ornithine concentrations. Wei. koreensis can produce one molecule of ATP while metabolising arginine to produce ornithine, which can be a metabolic energy source advantageous for survival. In addition, NH3 is produced during this process, which can contribute to buffering the lowered pH around the cell.

Fig. 7: Arginine deiminase pathway activity in Weissella strains and associated metabolite changes during kimchi fermentation.

a Putative schematic diagram of arginine deiminase pathway of Weissella cibaria and Weissella koreensis with their TPM values observed in each pathway and visualised by the heatmap during kimchi fermentation. Changes in the concentrations of b arginine and c ornithine in the CTR, W32, W33, W0121, and C3628 kimchi samples. The kimchi samples inoculated with strains WiKim32, WiKim33, WiKim0121, and CBA3628, and saline were labelled as 'W32', 'W33', 'W0121', 'C3628', and 'CTR', respectively. Data were presented as mean ± standard deviation. Experiments were performed in triplicate.

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Discussion

In this study, we aimed to identify the key factors enabling the dominance of Leuconostoc strains in the kimchi fermentation environment. While it is well-established that inoculated Leuconostoc starter cultures dominate during kimchi fermentation16, this study uniquely reveals the molecular and metabolic mechanisms underlying the successful predominance of Leu. mesenteroides WiKim32, WiKim33, and WiKim0121, compared with Leuconostoc sp. CBA3628, which failed to establish dominance. Comparison of the genomes of WiKim and CBA3628 strains revealed that CBA3628 harboured fewer genes involved in crucial metabolic pathways, especially carbohydrate metabolism (Figs. 3, 4), which likely contributed to its reduced adaptability and lower dominance in the kimchi environment (Fig. 2a)33. The transcript-level expression‎ of these genes, present in WiKim strains but absent in strain CBA3628, demonstrated that WiKim strains can use a higher number of carbon sources such as raffinose, lactose, maltose, xylose, and arabinose than strain CBA3628, likely contributing to their higher ATP production (Fig. 5).

The network analysis further highlighted the importance of genes encoding pyruvate kinase and GPI, both of which are involved in ATP production (Fig. 6a). Considering that fructose is the main carbon source for starter strains in this study (Fig. 1c), GPI plays a critical role in ATP production as a key component of fructose metabolism. Pyruvate kinase is a crucial rate-limiting enzyme in glycolysis45. As rate-limiting enzymes determine the overall speed of a metabolic pathway, the expression‎ levels of pyruvate kinase directly affect both the quantities and rates of ATP production in the starter strains. Based on the transcript-level expression‎ and network analysis results, ATP production by the starter cultures is essential for their adaptation and predominance during kimchi fermentation. Large amounts of ATP not only promote bacterial growth but are also utilised by stress-response proteins to ensure the survival and consistent predominance of these bacteria in the fermentation environment, as further discussed below.

Considering that kimchi fermentation is typically conducted at low temperatures (0–10 °C) with pH decreasing to around 4, stress-response proteins might be crucial for adapting to kimchi’s cold and acidic environments. Based on network analysis (Fig. 6a), ATPase and molecular chaperones play crucial roles in helping bacteria adapt to various environmental stresses46,47,48. ATPase, typically associated with oxidative phosphorylation in aerobic conditions49,50, is unlikely to function in this capacity in Leuconostoc owing to the anaerobic nature of the kimchi environment. Instead, it likely serves as a proton pump, maintaining bacterial intracellular pH homoeostasis in acidic environments34,35. Analysis of genes encoding the F-type ATPase complex revealed that the overall expression‎ levels decreased from day 6 to 33 in WiKim32, WiKim33, WiKim0121, and CBA3628, whereas ATCC 8293T showed an increase from day 6 to 11, followed by a decrease (Fig. 6b). These patterns can be explained by pH changes in each kimchi sample during fermentation. Prior research indicates that bacterial ATPase activity is pH-dependent, with certain Bifidobacterium strains showing increased ATPase expression‎ at pH 4 than in more acidic (pH 3) or more alkaline (pH 5) conditions51. Similarly, the TPM values of the genes encoding ATPase complex from strains WiKim32, WiKim0121, and CBA3628 on day 6, and those from strain ATCC 8293T on day 11 were similar, reflecting similar pH conditions (Fig. 6b). This suggests that the variations in ATPase activity among strains could be attributed to different pH values of each kimchi sample.

Chaperones, including DnaJ, DnaK, GrpE, and ClpL, play vital roles in helping bacteria adapt to cold and acidic conditions37,40,41,52,53 typical of the kimchi environment. This study highlights that the expression‎ levels of these chaperone genes were significantly higher in WiKim strains than in strain CBA3628 during kimchi fermentation (Fig. 6b). This enhanced expression‎ might be advantageous in maintaining protein homoeostasis and cellular functions under stress conditions, thereby promoting the adaptation and predominance of starter strains in kimchi environments. Particularly, DnaJ, DnaK, and GrpE may contribute to the cold resistance of WiKim strains for their initial adaptation in an early kimchi environment, while ClpL plays a crucial role in their predominance by mediating both cold and acidic stress responses as pH decreases. These stress-response proteins require ATP42,43,54, indicating that ATP production is important for bacterial adaptation and predominance not only for their growth but also for their resistance in the kimchi fermentation environment.

Additionally, the dominance of Wei. koreensis was confirmed in the CTR kimchi environment (Fig. 2a). The arginine deamination pathway of Wei. koreensis uses arginine to produce ornithine, ATP, and NH3 together (Fig. 7)44. In a highly acidic environment such as kimchi fermentation, NH3 can help protect cells from acid stress55. Wei. koreensis secures metabolic energy and overcomes stressful conditions in a competitive fermentation environment, using strategies similar to those used by the WiKim strains, although via different genes, to gain an advantage in competition with other microorganisms. This could potentially be the reason Weissella ASV009 (Wei. koreensis) became dominant in the late kimchi fermentation environment, where there was no particularly dominant starter strain.

This study provides valuable insights into the molecular and metabolic mechanisms underpinning the adaptation and predominance of Leuconostoc starter strains in kimchi fermentation. The results of this study emphasise that the ability to cope with stress conditions, such as low temperature and pH, and efficient carbohydrate metabolism are key factors determining the growth and dominance of kimchi LAB in fermentation environments. The importance of ATP production in maintaining stress-response activities in kimchi starter cultures further underscores the complex interplay between metabolism and adaptation in fermentation. These insights could inform the development of more robust and effective starter cultures for kimchi production, potentially contributing to quicker acidification, reducing safety risks, and minimising flavour defects caused by spoilage microorganisms. Starter cultures may also improve the sensory and nutritional properties of the final product, but this remains to be investigated.

Despite these insights, certain limitations remain. While our findings identify key contributors that confer competitive advantages to Leu. mesenteroides WiKim32, WiKim33, and WiKim0121 as kimchi starter strains, it remains unclear whether these key metabolic pathways are strain-specific or conserved at the species level among Leuconostoc spp. and Weissella spp. Future studies should investigate whether these pathways and stress-response mechanisms are widespread among Leu. mesenteroides strains or unique to strains WiKim32, WiKim33, and WiKim0121, as well as how they compare to those in Leu. citreum, Wei. cibaria, and Wei. koreensis. Understanding the species- and strain-specific contributions of these factors could further refine microbial selection criteria for optimal starter cultures, ultimately enhancing the stability and functional properties of kimchi fermentation.

토론

본 연구에서는 김치 발효 환경에서

Leuconostoc 균주의 우세를 가능하게 하는 핵심 요인을 식별하는 것을

목표로 하였습니다.

접종된 Leuconostoc 스타터 배양이

김치 발효 중 우세하다는 것은 이미 잘 알려져 있지만¹⁶,

본 연구는 Leu. mesenteroides WiKim32, WiKim33, WiKim0121이

Leuconostoc sp. CBA3628과 비교하여

성공적으로 우세한 분자 및 대사 메커니즘을 독창적으로 밝혔습니다.

CBA3628은 우세를 확립하지 못했습니다.

WiKim 균주와 CBA3628의 게놈 비교 결과,

CBA3628은 특히 탄수화물 대사(그림 3, 4)와 같은 중요한 대사 경로에 관여하는 유전자가 더 적게 포함되어 있었으며,

이는 김치 환경에서 적응성과 낮은 우세에 기여했을 가능성이 높습니다(그림 2a)³³.

WiKim 균주에 존재하지만 CBA3628 균주에 없는 이러한 유전자의 전사 수준 발현은 WiKim 균주가 라피노스, 락토스, 말토스, 자일로스, 아라비노스와 같은 더 많은 탄소원을 사용할 수 있음을 보여주었으며, 이는 CBA3628 균주보다 높은 ATP 생산에 기여했을 가능성이 있습니다(그림 5).

네트워크 분석은

추가로 피루브산 키나제 및 GPI를 코딩하는 유전자의 중요성을 강조했으며,

이 둘 모두 ATP 생산에 관여합니다(그림 6a).

본 연구에서 스타터 균주의 주요 탄소원인 프럭토스를 고려할 때(그림 1c),

GPI는 프럭토스 대사의 핵심 구성 요소로서 ATP 생산에서 중요한 역할을 합니다.

피루브산 키나제는

해당 과정에서 중요한 속도 제한 효소로⁴⁵,

속도 제한 효소는 대사 경로의 전체 속도를 결정하므로,

피루브산 키나제의 발현 수준은

스타터 균주에서 ATP 생산의 양과 속도에 직접적인 영향을 미칩니다.

전사 수준 발현 및 네트워크 분석 결과를 바탕으로,

스타터 배양의 ATP 생산은 김치 발효 중 적응과 우세에 필수적입니다.

다량의 ATP는 세균 성장을 촉진할 뿐만 아니라

스트레스 반응 단백질이 발효 환경에서 생존과 일관된 우세를 보장하는 데 사용되며,

이는 아래에서 더 논의됩니다.

김치 발효는

일반적으로 저온(0~10°C)에서 pH가 약 4로 감소하는 조건에서 이루어지므로,

스트레스 반응 단백질이 김치의 차갑고 산성 환경에 적응하는 데 중요할 수 있습니다.

네트워크 분석(그림 6a)에 따르면, A

TPase와 분자 샤페론은 다양한 환경적 스트레스에 적응하는 데 중요한 역할을 합니다⁴⁶,⁴⁷,⁴⁸.

ATPase는 호기성 조건에서 산화적 인산화와 일반적으로 연관되지만⁴⁹,⁵⁰,

김치 환경의 혐기성 특성으로 인해 Leuconostoc에서 이 기능은 작동하지 않을 가능성이 높습니다.

대신, 이는 산성 환경에서 세균 내 pH 항상성을 유지하는

양성자 펌프 역할을 할 가능성이 높습니다³⁴,³⁵.

F형 ATPase 복합체를 코딩하는 유전자 분석 결과, WiKim32, WiKim33, WiKim0121, CBA3628은

6일부터 33일까지 전체 발현 수준이 감소했으며,

ATCC 8293T는 6일부터 11일까지 증가한 후 감소했습니다(그림 6b).

이러한 패턴은 발효 중 각 김치 샘플의 pH 변화로 설명할 수 있습니다. 기존 연구에 따르면 세균 ATPase 활성은 pH 의존적이며, 일부 Bifidobacterium 균주는 pH 4에서 pH 3(더 산성)이나 pH 5(더 알칼리성) 조건에서 ATPase 발현이 증가한다고 합니다⁵¹. 마찬가지로, WiKim32, WiKim0121, CBA3628 균주의 6일째 ATPase 복합체를 코딩하는 유전자의 TPM 값과 ATCC 8293T 균주의 11일째 값은 유사한 pH 조건을 반영하며(그림 6b), 이는 각 김치 샘플의 pH 값 차이에 따른 ATPase 활성 변이가 있을 수 있음을 시사합니다.

DnaJ, DnaK, GrpE, ClpL을 포함한 샤페론은 김치 환경의 전형적인 차갑고 산성 조건에 적응하는 데 중요한 역할을 합니다³⁷,⁴⁰,⁴¹,⁵²,⁵³. 본 연구는 이러한 샤페론 유전자의 발현 수준이 김치 발효 중 WiKim 균주에서 CBA3628 균주보다 유의미하게 높았음을 보여줍니다(그림 6b). 이러한 발현 증가는 스트레스 조건에서 단백질 항상성과 세포 기능을 유지하는 데 유리할 수 있으며, 이에 따라 김치 환경에서 스타터 균주의 적응과 우세를 촉진할 수 있습니다. 특히, DnaJ, DnaK, GrpE는 초기 김치 환경에서 WiKim 균주의 차가운 저항성을 높여 초기 적응에 기여할 수 있으며, ClpL은 pH가 감소함에 따라 차갑고 산성 스트레스 반응을 매개하여 우세에 중요한 역할을 합니다. 이러한 스트레스 반응 단백질은 ATP를 필요로 합니다⁴²,⁴³,⁵⁴, 이는 ATP 생산이 세균 성장뿐만 아니라 김치 발효 환경에서 저항성을 위한 적응과 우세에 중요함을 나타냅니다.

또한, CTR 김치 환경에서 Wei. koreensis의 우세가 확인되었습니다(그림 2a). Wei. koreensis의 아르기닌 탈아미노산 경로는 아르기닌을 이용하여 오르니틴, ATP, NH₃를 함께 생성합니다(그림 7)⁴⁴. 김치 발효와 같은 고도로 산성 환경에서 NH₃는 산성 스트레스에서 세포를 보호하는 데 도움이 될 수 있습니다⁵⁵. Wei. koreensis는 WiKim 균주와 유사한 전략을 사용하더라도 다른 유전자를 통해 대사 에너지를 확보하고 경쟁적인 발효 환경에서 스트레스 조건을 극복하여 다른 미생물과의 경쟁에서 우위를 점할 수 있습니다. 이는 특히 우세한 스타터 균주가 없는 후기 김치 발효 환경에서 Weissella ASV009(Wei. koreensis)가 지배적이 되었을 가능성의 이유일 수 있습니다.

본 연구는 김치 발효에서 Leuconostoc 스타터 균주의 적응과 우세를 뒷받침하는 분자 및 대사 메커니즘에 대한 귀중한 통찰을 제공합니다. 이 연구 결과는 저온 및 pH와 같은 스트레스 조건에 대처하는 능력과 효율적인 탄수화물 대사가 발효 환경에서 김치 LAB의 성장과 우세를 결정짓는 핵심 요인임을 강조합니다. 김치 스타터 배양에서 스트레스 반응 활동을 유지하는 데 ATP 생산의 중요성은 발효에서 대사와 적응 간의 복잡한 상호작용을 더욱 부각시킵니다. 이러한 통찰은 김치 생산을 위한 더 강력하고 효과적인 스타터 배양 개발에 기여할 수 있으며, 이는 산성화 속도를 높이고, 안전성 위험을 줄이며, 부패 미생물로 인한 맛 결함을 최소화할 수 있습니다. 스타터 배양은 최종 제품의 감각적 및 영양적 특성을 개선할 수 있지만, 이는 추가로 조사해야 합니다.

그럼에도 불구하고 몇 가지 한계가 남아 있습니다. 우리의 결과는 Leu. mesenteroides WiKim32, WiKim33, WiKim0121이 김치 스타터 균주로서 경쟁 우위를 제공하는 주요 기여 요인을 식별했지만, 이러한 주요 대사 경로가 균주 특이적인지, Leuconostoc spp. 및 Weissella spp.에서 종 수준에서 보존되는지 여부는 여전히 불분명합니다. 미래 연구는 이러한 경로와 스트레스 반응 메커니즘이 Leu. mesenteroides 균주들 사이에서 널리 퍼져 있는지, WiKim32, WiKim33, WiKim0121 균주에 독특한지, 그리고 Leu. citreum, Wei. cibaria, Wei. koreensis와 비교하여 어떤지에 대해 조사해야 합니다. 이러한 요인의 종 및 균주 특이적 기여를 이해하면 최적의 스타터 배양을 위한 미생물 선택 기준이 더욱 정교화될 수 있으며, 궁극적으로 김치 발효의 안정성과 기능적 특성을 향상시킬 수 있습니다.

Methods

Preparation of starter strains and kimchi samples

Four strains, Leu. mesenteroides WiKim32, Leu. mesenteroides WiKim33, Leu. mesenteroides WiKim0121, and Leuconostoc sp. CBA3628, which were previously isolated at the World Institute of Kimchi in Gwangju, Korea, were used as kimchi starter cultures in this study. The strains WiKim32, WiKim33, and WiKim0121 are well-studied for their use as kimchi starters and have been deposited as patent strains with the numbers KR101836365B1, KR101660847B1, and KR102488052B1, respectively, owing to their ability to dominate during kimchi fermentation and to enhance sensory and/or nutritional properties. These strains were cultured in de Man–Rogosa–Sharpe (MRS; BD, USA) medium at 30 °C before inoculation into the kimchi samples.

Kimchi ingredients, including kimchi cabbages, red pepper powders, garlic, ginger, green onions, and radishes, were purchased from a commercial market in Gwangju. The preparation followed a previously established method with slight modifications56. Specifically, sugar and starch were not added to kimchi samples, and the fermentation temperature was lowered to 5 °C. Briefly, kimchi cabbages were soaked in 10% (w/v) solar salt solution for 10 h, washed three times with tap water, and drained. Garlic, ginger, and radishes were ground using a homogeniser (Hanil, Seoul, Korea) and mixed with salted kimchi cabbages. The final kimchi mixture consisted of 70% salted kimchi cabbages, 4.5% red pepper powders, 3.75% ground garlic, 1.2% ground ginger, 3.75% green onions, 4.5% ground radishes, and 12.3% distilled water.

Each kimchi sample was inoculated with one of the four Leuconostoc strains at 107 CFUs per gram of kimchi. The control sample was inoculated with 0.9% (w/v) saline instead of the starter strain. The samples inoculated with strains WiKim32, WiKim33, WiKim0121, and CBA3628, and saline solution instead of starter strain were labelled as 'W32', 'W33', 'W0121', 'C3628', and 'CTR', respectively. The experiments were conducted in triplicate, resulting in a total of fifteen kimchi samples. All samples were pre-fermented at room temperature for 6 h to allow the starter strains to adapt well in the kimchi samples before being fermented at 5 °C for 33 days. In this study, sampling time at day 0 corresponded to the beginning of fermentation at 5 °C.

Sample collection

Approximately 8 mL of kimchi soup samples were collected at days 0, 3, 6, 11, 22, and 33 during fermentation and filtered through sterile stomacher filter bags (Nasco, WI, USA) to remove large kimchi particles. A portion of the filtered kimchi soup was used to determine pH and LAB CFUs using a pH metre (Thermo Scientific, MA, USA) and a 3MTM PetrifilmTM Lactic Acid Bacteria Count Plate (3M™, MN, USA), respectively. Of the remaining filtrate, 4 mL was subject to centrifugation at 8000×g for 10 min at 4 °C. The pellets were used for metataxonomic and metatranscriptomic analyses, whereas the supernatants were utilised for metabolomic analysis.

Bacterial community succession observation

Total genomic DNA (gDNA) was extracted from the pellets using the DNeasy PowerSoil Kit (Qiagen, Hilden, Germany), following the manufacturer’s instructions. The extracted gDNA was used for amplifying the V3 to V4 regions of the 16S rRNA gene through polymerase chain reaction (PCR) using the following primer sets: 341F and 805R57,58. Subsequently, PCR products were purified using the QIAquick PCR Purification Kit (Qiagen, Hilden, Germany). Library preparation was performed using the Nextera XT DNA Library Preparation Kit v2 with i5 and i7 primers (Illumina) according to the manufacturer’s instructions, with modifications in the PCR amplification step (San Diego, CA, USA) to attach barcodes. After a second PCR, products were purified and pooled in one tube for adjusting to equal concentrations. The pooled amplicons were sequenced by Macrogen (Seoul, Korea) on a MiSeq™ platform (Illumina, CA, USA).

Following the trimming of barcode and adaptor sequences from FASTQ reads, the trimmed reads were demultiplexed through the bcl2fastq2 conversion software (version 2.20.0; Illumina, CA, USA). The Illumina paired-end sequencing reads were assembled and filtered for quality scores less than q20 using VSEARCH. Subsequent analyses of the reads were performed with the Qiime2 version 2023.0259. The DADA2 plugin was used for quality filtering, denoising, and clustering of the imported paired reads60, exclusion of chimeric sequences, and singleton ASVs for further analyses. Taxonomic classification was performed with the q2-feature classifier plugin, employing the classify-sklearn method61 and the pre-trained SILVA version 138.1 database62 with 99% identity. Sequences from non-target sources, including cyanobacteria, mitochondria, and chloroplasts, were removed in silico. Sample libraries were normalised by rarefying to the lowest sequencing depth. Bacterial community composition and alpha diversity were analysed using the q2-diversity plugin in the Qiime2. The sequences of the V3–V4 region of the 16S rRNA genes of the strains and those of ASVs from the metataxonomic results were aligned and used to construct phylogenetic trees using the neighbour-joining (NJ) algorithm in the MEGA 11 software package63.

Metabolites measurement

Proton nuclear magnetic resonance (1H-NMR) spectroscopy was used to determine metabolites generated during kimchi fermentation, following a previously established method with slight modifications64. First, 0.5 mL of each kimchi sample supernatant was mixed with 0.5 mL of a solution containing 99.9% deuterium oxide (D2O; Sigma-Aldrich, MO, USA) and 2.9 mM 3-(trimethylsilyl) propionic-2,2,3,3-d4 acid (TSP; Sigma-Aldrich, MO, USA) (pH adjusted to 7.0). Samples were then transferred into 5-mm NMR tubes, and 1H-NMR spectra were obtained using the Varian Inova 600-MHz 1H-NMR spectrometer (Varian, CA, USA) with the standard PRESAT pulse sequence at 25 °C. Spectral data, collected over 32 k data points across a spectral width of 9,615 Hz, were subjected to manual phase adjustment and baseline correction using the Chenomx Processor NMR Suite Programme version 8.3 (Chenomx, Edmonton, Canada). Spectral intensities were segmented into 0.04-ppm bins spanning 0.5‒10 ppm and normalised to the TSP signal intensity at 0 ppm for comparison. For metabolite identification and quantification in kimchi samples, 1H-NMR spectra were analysed using the Chenomx Profiler NMR Suite Programme version 9.0 (Chenomx, Edmonton, Canada), with TSP serving as an internal standard against the reference library 9 for 600-MHz compounds65.

The relationships between bacterial taxa and all metabolites across fermentation periods were visualised using NMDS based on the Bray–Curtis dissimilarity index, employing metaMDS and envfit functions from the vegan package within the R package version 4.3.266. In the NMDS plot, the points differentiated by colour represent the metabolite profiles of kimchi samples during fermentation, with the proximity of points indicating their similarity. The distances between the points of each kimchi sample reflect the dynamic changes in metabolite profiles during fermentation.

Comparative genome analyses

For whole-genome sequence analysis of four Leuconostoc starter strains, gDNA was extracted using the Wizard Genomic DNA Purification Kit (Promega, WI, USA), following the manufacturer’s instructions. As described previously55, the genomes extracted from four starter strains were sequenced by Macrogen using a combination of Illumina HiSeq 2500 sequencing and PacBio RS single-molecule real-time sequencing based on a 20-kb library. The quality of the genomes, i.e. their completeness and contamination rates, was verified using the CheckM software version 1.0.467. Whole-genome sequences of Leu. mesenteroides WiKim32, Leu. mesenteroides WiKim33, Leu. mesenteroides WiKim0121, and Leuconostoc sp. CBA3628, have been deposited in GenBank with the accession numbers CP037750–4, CP021491–4, CP098784–9, and CP042404–7, respectively. These sequences were automatically annotated through the NCBI prokaryotic genome annotation pipeline (PGAP)68.

For comparative genomic analyses, sequences of 16S rRNA gene and whole genomes from other Leuconostoc strains were retrieved from GenBank. Genome qualities were verified using the CheckM software. 16S rRNA gene sequence similarity, ANI values, and in silico DDH values of 23 Leuconostoc strains were calculated using BLASTn (NCBI; MD, USA), the Orthologous ANI Tool (OAT; https://www.ezbiocloud.net/tools/orthoani)69, and the Genome-to-Genome Distance Calculator version 3.0 (GGDC 3.0; https://ggdc.dsmz.de/ggdc.php)70, respectively, according to previously described parameters33.

In order to compare the genomic features of the starter strains—WiKim32, WiKim33, WiKim0121, and CBA3628—COG, CAZyme, and KEGG analyses were performed using corresponding amino acid sequences. For clustering functional genes of four starter strains, the corresponding amino acid sequences were analysed using the Evolutionary Genealogy of Genes: Non-supervised Orthologous Groups (eggNOG) mapper version 2.0, hosted on the public database eggNOG version 5.071. Each starter strain’s gene numbers were assigned to COG categories, and their functional characteristics were compared. The CAZyme gene profiles of strains were analysed using DIAMOND and HMMER tools, referencing the pre-annotated CAZyme sequence database in dbCAN372.

Functional annotation of predicted proteins in four starter strains was performed using the Blast KEGG Orthology And Links Annotation (BlastKOALA)73, and the metabolic pathways of these strains were displayed using the iPath version 3.0, based on KO. Pathway visualisation was achieved by varying line thickness and colour depending on the presence and/or absence of genes in each strain.

Transcriptional expression‎ analysis

Total RNA was extracted from the kimchi pellets using Trizol reagent (Invitrogen, MA, USA). Subsequently, the clear aqueous phase was collected and precipitated with ethanol. RNA quality was assessed using an Agilent 2100 bioanalyzer with an RNA 6000 Nano Chip (Agilent Technologies, Amstelveen, The Netherlands). Total RNA extraction was followed by the removal of ribosomal RNA (rRNA) and transfer RNA (tRNA) to isolate messenger RNA (mRNA) using the NEBNext® Ultra™ II Directional RNA Library Prep Kit (NEB, USA). The isolated mRNAs were used for cDNA synthesis and shearing following the manufacturer’s instructions. Indexing was performed using the Illumina indexes 1–12. The enrichment step was carried out using PCR. After pooling the products into one tube, high-throughput sequencing was performed as paired-end 100 sequencing using NovaSeq 6000 (Illumina, CA, USA) at Ebiogen (Seoul, Korea).

The raw sequencing reads were trimmed, and those shorter than 30 nucleotides were removed using Sickle software74. The Burrows–Wheeler Aligner (BWA) software75 was used to map the putative mRNA reads from each starter kimchi sample with the whole-genome sequences of the respective strains WiKim32, WiKim33, WiKim0121, and CBA3628. For the control sample, the reads were annotated to the complete genomes of type strain Leu. mesenteroides subsp. mesenteroides ATCC 8293T. Following the added starter strains, Leu. citreum, Wei. cibaria, and Wei. koreensis were identified as the preval‎ent lactic acid bacteria during the kimchi fermentation process, and their reads were mapped to the whole-genome sequences of Leu. citreum CBA3621 (GenBank accession no. CP042410–11), Wei. cibaria CBA3612 (CP041193–6), and Wei. koreensis KACC 15510 (CP002899–900), respectively. From the BWA-aligned reads, TPM values were calculated to quantify relative gene expression‎ against the strains in the respective kimchi samples. In this study, TPM values from days 0 and 3 could not be obtained owing to insufficient mRNA sequencing reads. The metatranscriptomic analyses of kimchi fermentation were conducted from early (day 6) to late stages (day 33).

The carbohydrate fermentative pathways were reconstructed and metatranscriptome reads were mapped to the whole-genome sequences of Leu. mesenteroides ATCC 8293T (in the CTR), Leu. mesenteroides WiKim32 (in W32), Leu. mesenteroides WiKim33 (in W33), Leu. mesenteroides WiKim0121 (in W0121), Leuconostoc sp. CBA3628 (in C3628), and Leu. citreum CBA3621, Wei. cibaria CBA3612, and Wei. koreensis KACC 15510 (in all samples) with their transcriptional expression‎ levels in each pathway. The pathways were illustrated with lines of different colours to represent the presence and/or absence of genes in each strain, and their TPM values were visualised using a heatmap. Arabic numbers and KO numbers are positioned close to the lines in the pathways and the heatmap, showing the expression‎ levels of each gene across the strains.

Putative genes associated with the dominance of starter strains

Owing to the vast number of genes in each starter strain, a threshold was initially established to select putative genes potentially related to the adaptation of starter strains and their predominance during kimchi fermentation. The genes from each strain were categorized based on KEGG BRITE hierarchical classifications, and then gene expression‎ levels were quantified. Within each functional category, the average expression‎ level was calculated by summing the TPM values of the assigned genes from each starter strain and dividing by the total number of genes within that category. Gene expression‎ levels are typically categorized as ‘low’ (below 100 TPM), ‘intermediate’ (between 100 and 1000 TPM), and ‘high’ (above 1000 TPM)76. In this study, the threshold was set to 2000 TPM to identify expressions significantly exceeding the ‘high’ threshold. The categories surpassing the established average TPM values were first selected, and then individual genes above 2000 TPM within the selected categories were further analysed for network diagram construction.

Spearman correlation coefficients were calculated using the Prism software version 9.5.1 (GraphPad Software, CA, USA) to assess the relationships between the relative abundances of four starter strains and their respective TPM values at each fermentation period. Network analysis was then performed using the Cytoscape version 3.10.177 based on these correlation results and visualised by varying the colour of nodes and the width of edges to represent relative abundances and putative genes of Leuconostoc starter strains. The red edges indicate Spearman correlation coefficients above 0.6, suggesting a ‘strong’ positive correlation78. The potential effects of the genes encoding stress-response proteins F-type ATPase and molecular chaperones on the adaptation and predominance of the starter strains were visualised using a heatmap based on the Spearman correlation coefficients.

Fermentative metabolic pathways were reconstructed based on annotated KEGG pathways and associated KO numbers to analyse the carbohydrate metabolism of four starter strains during kimchi fermentation. Leu. citreum, Wei. cibaria, and Wei. koreensis were also included for a comprehensive comparison with the metabolic profiles of the starter strains. A heatmap based on TPM values was used to illustrate expression‎ levels across different fermentation periods. Arabic numbers and KO numbers corresponding to the respective genes involved in each pathway were annotated alongside the heatmap.

Data availability

The kimchi metatranscriptome sequencing and the 16S rRNA gene sequencing data are publicly available in the NCBI Short Read Archive (SRA). Accession no. of metatranscriptome sequencing data are SRR28544770–28544789, and those of 16S rRNA gene sequencing data are SRR22317905, SRR22317916–22317939, SRR22317950 and SRR22317956–22317959.

Code availability

Not applicable.

References

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