L. monocytogenes is a significant foodborne pathogen, causing listeriosis, a substantial public health problem globally (Cossart 2011). While many studies have delved into the genomic sequences of L. monocytogenes, the relationships between the strain's lineage, typing, isolation source, and geographic region of isolation remained ambiguous. In this research, a thorough analysis of the genomes of 316 L. monocytogenes strains was conducted, shedding light on the correlations and variations between the strain's stress response genes, lineage, strain typing, isolation source, and geographic area of isolation. Moreover, it examines the potential effects of environmental noise on the expression of stress resistance genes, thereby aiding in the comprehension of the adaptability and transmission routes of L. monocytogenes.
Extensive phylogenetic and subtyping research revealed that L. monocytogenes exhibited a distinct population structure, encompassing at least four different evolutionary lineages: I, II, III, and IV (Liu et al. 2006). Within these lineages, Lineages I and II boasted the most typical strains and had been associated with the majority of human clinical cases (den Bakker et al. 2008; Orsi et al. 2011). In our study, we classified 316 strains of L. monocytogenes into these lineages and found a broad representation of lineages I and II in all the samples. Gray et al. (2004) analyzed 502 L. monocytogenes isolates from food and 492 from clinical, respectively, and found that Lineage I and III were mainly derived from clinical samples, while Lineage II was mainly derived from food samples. However, they detected only a small proportion of lineage III strains in both food and clinical samples, thus hypothesizing that lineage III strains struggled to survive in food processing or storage environments. Notably, our research also demonstrated a lower prevalence of lineage III strains in our samples, which aligned with the findings of Gray et al. (2004). As for lineage IV, Orsi et al. (2011) argued that while it was most frequently detected in animal isolates and can also be isolated from human cases, its limited number of isolates means that it is often not extensively discussed in some studies.
In addition to phylogenetic distribution, we further developed into the variations of L. monocytogenes in terms of isolation sources and geographic locations. The L. monocytogenes isolates obtained from the database in our study were divided into four major categories, mainly including clinical, food, animal, and environmental sources, among which isolates from clinical and food sources accounted for the largest proportion. As L. monocytogenes is a foodborne pathogen, diseases are usually triggered after human consumption of food, which has been validated in multiple studies (Maury et al. 2016). Chen et al. (2016) summarized outbreaks of listeriosis on a global scale, discovering that outbreaks were mostly food-related, and corresponding L. monocytogenes was detected in the clinic, further validating our findings. Moreover, a large number of L. monocytogenes were also isolated from animal and environmental sources, which are equally significant causes of disease outbreaks.
In the analysis of geographical distribution differences, it was reasonably posited that dietary habits might influence the transmission patterns of L. monocytogenes consequently altering its distribution across various regions. Some studies have shown that dietary habits indeed could influence the incidence of listeriosis. For instance, consumption of undercooked meats, preparing raw foods, or making soft cheese at home have been identified as risk factors for listeriosis (Silk et al. 2014). Additionally, regional outbreaks of listeriosis, such as the Boston cheese incident (Pightling et al. 2014) and packaged salad outbreaks in the United States (Centers for Disease Control and Prevention, 2016), suggest that regional dietary preferences could contribute to the occurrence of listeriosis. However, the acquisition and analysis of research data are often influenced by geographical and temporal factors, and a substantial portion of the data, due to its confidential nature, is not uploaded to public databases. Therefore, these findings necessitate further validation. Despite these limitations, a deep understanding of the geographic distribution of L. monocytogenes and dietary exposure can undoubtedly aid in better prevention and control of listeriosis.
Further analysis of the association between strain typing and isolation information revealed that 56.65% of the 316 L. monocytogenes strains in this study belong to seven predominant CC types. The pronounced clustering suggests these primary CC types might possess greater adaptability or transmission capability in certain environments or conditions. Specifically, CC8, accounting for 11.08%, is closely linked to clinical sources, a finding corroborated by Maury et al. (2016) who also observed a significant correlation between CC8 and clinical origins, especially bacteremia. CC183's strong association with food sources indicates this CC type may possess specialized adaptive mechanisms during food processing, storage, or transmission. The potent link between CC14 and animal origins implies it has unique survival capabilities within animal hosts. Geographically, the associations of CC9 with Europe, CC1 and CC5 with North America, and CC14 and CC224 with Asia, suggest strain distribution is influenced by local culture, economic activities, food consumption habits, and public health initiatives, underscoring the significance of regional factors. However, data acquisition and analysis are often influenced by geographic and temporal factors. A substantial portion of the data, considered confidential, isn't uploaded to public databases, necessitating further validation of these findings. Despite these limitations, analyzing the origins and regions of L. monocytogenes can assist in better controlling the outbreak of listeriosis.
Distinct dietary habits shape the selection, processing, and storage of food, leading to variations in its physical and chemical properties, e.g., pH, salinity, and water activity. These changes may apply evolutionary pressure to L. monocytogenes, driving the emergence of corresponding resilience (Gandhi and Chikindas 2007b). Stress resistance genes play pivotal roles in L. monocytogenes, endowing the bacteria with abilities to thrive and reproduce under various environmental pressures (Chaturongakul and Boor 2004). As evident from Fig. 4, different L. monocytogenes types display variations in the carriage of resistance genes, with acid resistance genes showing the most significant differences. Furthermore, strains of the same CC type consistently harbor similar stress resistance genes, reflecting the gene's stability and the evolutionary pressures unique to that CC type. This consistency aids predictions in clinical or public health contexts, as knowledge of a strain's CC type can give insights into its resilience attributes. Concurrently, analyzing stress resistance genes in L. monocytogenes across different isolation sources provides insights into their adaptability. For instance, strains from animal origins tend to carry genes for heat and cold resistance, likely correlated with animal body temperatures and external environmental fluctuations.
Further examination of gene differences among the collected strains revealed that the carriage rate for genes in the Stress Survival Island (SSI)-1 is approximately 50%, with no observed gene mutations. Typically, SSI-1 enhances the tolerance of L. monocytogenes in food processing environments and plays a significant role under stress conditions such as low pH and high salinity (Ryan et al. 2010). Moreover, SSI-1 is closely associated with the biofilm formation of L. monocytogenes under environmental stress conditions (Keeney et al. 2018). Ryan et al. (2010) analyzed the stress resistance characteristics of SSI-1 genes in the EGD-e strain, indicating that SSI-1 might enhance the adaptability to food environments. However, in Hingston's phenotype validation of other strains (Hingston et al. 2017), no significant stress tolerance differences were observed in strains possessing SSI-1. Hence, further in-depth studies are warranted, employing methods such as gene knockout.
Other genes, including heat, cold, acid, osmotic, desiccation, and regulatory genes, are commonly present in the 316 strains of L. monocytogenes and the ATCC standard strain. In heat resistance gene-related research, Omori et al. (2017) powerfully validated the participation of heat resistance genes in L. monocytogenes expression. In cold resistance genes, the lisK gene encodes arginine kinase, enabling L. monocytogenes to grow at low temperatures (Pöntinen et al. 2015). The functions of acid resistance (Hingston et al. 2017)), osmotic resistance (Kang et al. 2019), desiccation resistance (Hingston et al., 2017), and regulatory genes (Chaturongakul and Boor 2004) were also confirmed in the study, further illustrating their significant impact on enhancing strain tolerance. However, in the gene variation results of the ATCC standard strain, variations in acid-resistance genes were common. Therefore, this part still requires more in-depth genetic information analysis (such as SNPs) to better understand the influence of gene variation on the growth and propagation of L. monocytogenes.
From the discussions above, it's evident that the variance of resistance genes in L. monocytogenes across different strain types and isolation data can illuminate the bacterium's evolutionary adaptation capabilities under varying conditions. Existing research has already demonstrated that L. monocytogenes in foods typically stored in high-salt or low-temperature environments may possess heightened salt or cold resistance compared to those in other conditions (Elena and Lenski. 2003; Nightingale et al. 2004). The strain type is somewhat linked with both the source of isolation and the geographical area, and resistance genes within a strain type exhibit consistency. This suggests that prolonged environmental adaptation can lead to specific genotypic evolution in L. monocytogenes (Durack et al. 2013), hinting at a potential causative chain: "consumption habits-food-food environment-evolution of L. monocytogenes." These genes are also prevalent in standard strains, with variations in acid resistance genes aligning with sample strains, making standard strains highly typical for studying stress resistance attributes. However, it's noteworthy that of the 53,186 L. monocytogenes strains uploaded to the NCBI database, only 0.58% possess complete genomic sequences, indicating ample scope for further research.
While significant variability and divergence have been noted in the resistance genes across different strains, identical genes within strains exhibit notable effects on their resilience due to random fluctuations in gene expression—termed 'noise'. Studies have suggested that intrinsic noise, such as stochastic events during transcription and translation, and extrinsic noise, induced by environmental changes affecting gene expression, can variably impact a strain's resistance traits (Raser and O'Shea 2005). This is particularly pronounced in foodborne pathogens, where extrinsic noise may play a larger role in determining cellular phenotypes (Rosenfeld et al. 2005). Research by Suzuki et al. (2014) using expression profiles to predict antibiotic resistance has indicated that even minor variations in gene expression levels can lead to significant changes in resistance, underscoring the impact of gene expression noise on microbial resistance. Erickson et al. (2017) analyzed the variability of gene expression in bacteria undergoing adaptive evolution, finding that expression variability correlates with a population's tolerance to stress, reflecting the bacteria's capacity to adapt to pressures, including the influence of gene expression noise. Thus, a deeper investigation into the relationship between gene expression noise and strain resistance is not only crucial for understanding how genes maintain functionality in diverse environments but also for developing strategies to prevent and control the transmission of foodborne diseases.