The capsule and genetic background, rather than specific individual loci, strongly influence in vitro pneumococcal growth kinetics

Once termed ‘the captain of the men of death’ by Sir William Osler in the 20th century, Streptococcus pneumoniae, or the pneumococcus, remains a significant cause of life-threatening invasive diseases, such as sepsis, pneumonia, and meningitis (Ikuta et al., 2022), despite the widespread implementation of life-saving serotype-specific polysaccharide pneumococcal conjugate vaccines (PCVs) (von Gottberg et al., 2014). The high capsular diversity, comprising ~100 serotypes with varying potential for colonising the nasopharynx and causing invasive diseases (Ganaie et al., 2020; Brueggemann et al., 2003) and mortality (Grabenstein and Musey, 2014; Müller et al., 2022; Cohen et al., 2015), demonstrates the remarkable genetic diversity of the pneumococcus. Such high genetic diversity reflects the plasticity of the pneumococcal genome, which readily exchanges genetic material with other strains through recombination and horizontal gene transfer (Donati et al., 2010). Capsule switching, resulting from the swapping of the capsule biosynthesis (cps) loci, which encode the extracellular polysaccharide capsule that determines the capsular serotype, exemplifies genetic exchange between pneumococcal strains (Mostowy et al., 2017). While serotypes typically exist in specific lineages (Croucher et al., 2013), such as those defined by the global pneumococcal sequence cluster (GPSC) nomenclature (Gladstone et al., 2019), some serotypes are expressed across distinct lineages, while some lineages express multiple serotypes, reflecting capsule switching (Croucher et al., 2015a). However, the impact of individual capsular serotypes, genetic background, and their combinations on the bacterial phenotypes, especially those associated with disease pathogenesis and clinical manifestations, remains less understood.

The pneumococcus possesses an arsenal of virulence factors. The extracellular polysaccharide capsule is a primary virulence factor crucial for pneumococcal transmissibility, survival, and pathogenicity in the human host (Kadioglu et al., 2008; Hathaway et al., 2016; Hamaguchi et al., 2018). Recent studies on the pneumococcus and other bacterial pathogens suggest that variability in growth kinetics affects disease pathogenesis, clinical manifestation, and antibiotic therapy. For example, differential pneumococcal growth features appear to correlate with fitness for colonisation and invasive disease (Im et al., 2022), clinical manifestation (Im et al., 2022; Arends et al., 2022; Hathaway et al., 2012), and response to environmental conditions (Hathaway et al., 2012; Tóthpál et al., 2019; Small et al., 1986). Studies of other bacterial pathogens suggest that growth rate influences gene and protein expression (Klumpp et al., 2009; Nilsson et al., 1984), adhesion (Rogers et al., 1984), bacterial competition (Russell et al., 1979), plasmid replication (Lin-Chao and Bremer, 1986), bacteriophage dynamics (Nabergoj et al., 2018), and antibiotic accumulation and efficacy (Łapińska et al., 2022; Smirnova and Oktyabrsky, 2018; Tuomanen et al., 1986). Similarly, experimental and mathematical modelling studies have demonstrated that bacterial growth correlates with bacterial load and host-mediated cell lysis, impacting dispersal to different host cells and tissues during systemic infection (Grant et al., 2009). Previous studies of pneumococcus and other bacteria have suggested the influence of environmental factors (Tóthpál et al., 2019; Small et al., 1986) and bacterial-specific factors, including capsular serotype (Hathaway et al., 2012; Müller et al., 2020) and the presence of plasmids (Gulig and Doyle, 1993), on bacterial growth phenotypes. These growth phenotypes may consequently impact the pathogenesis of invasive pneumococcal disease (Im et al., 2022; Arends et al., 2022; Hathaway et al., 2012). However, the specific pneumococcal genomic variations affecting growth kinetics remain less understood.

Studying bacterial growth kinetics sheds light on the influence of genes, genomic variation, and growth media conditions on bacterial fitness (Shlla et al., 2021; Zhang et al., 2021). However, studying bacterial growth kinetics in vivo remains intrinsically challenging due to the complexity of the conditions in the hosts, although innovative approaches are being developed (Gao and Li, 2018; Korem et al., 2015). On the other hand, in vitro methods provide a valuable alternative for assessing the impact of specific bacterial and environmental factors on bacterial fitness. Although these in vitro methods have yielded remarkable insights into bacterial fitness, they have primarily provided strain-specific rather than population-level insights due to the analysis of a small number of isolates from a limited number of genetic backgrounds.

Here, we studied the impact of pneumococcal genetics on in vitro growth kinetics to understand the influence of bacterial genetic variation on pneumococcal pathogenesis. Specifically, we performed high-throughput quantification of the in vitro growth kinetics and whole-genome sequencing of 348 invasive pneumococcal isolates from the Netherlands to determine the impact of genomic variation on growth kinetics. We hypothesised that the capsular serotype, genetic background, and specific individual genomic loci independent of the genetic background influence in vitro pneumococcal growth kinetics features or parameters derived from the growth curves. We tested this hypothesis through a series of statistical and comparative genomics analyses, including a bacterial genome-wide association study (GWAS) approach. Our study provides insights into the influence of capsular serotype, genetic background or lineage, and individual genomic loci on pneumococcal growth kinetics – a fundamental trait that potentially influences nasopharyngeal colonisation, invasive disease pathogenesis, and clinical manifestations.

Bacterial growth kinetics impact several traits, including gene and protein expression (Klumpp et al., 2009; Nilsson et al., 1984), antibiotic accumulation and efficacy (Łapińska et al., 2022; Smirnova and Oktyabrsky, 2018; Tuomanen et al., 1986), cell surface adhesion (Rogers et al., 1984), intra- and inter-specific competition (Russell et al., 1979), response to environmental conditions (Hathaway et al., 2012; Tóthpál et al., 2019; Small et al., 1986), plasmid replication (Lin-Chao and Bremer, 1986), bacteriophage dynamics (Nabergoj et al., 2018), colonisation and disease pathogenesis (Im et al., 2022), including clinical manifestation (Im et al., 2022; Arends et al., 2022; Hathaway et al., 2012). Whilst previous studies have attempted to understand bacterial, host, and environmental factors influencing bacterial growth, a major drawback has been a narrow focus on a small subset of strains (Schaffner et al., 2014). Therefore, the insights gained from these studies have been mostly limited to specific strains, as they ignored potential variability across different genetic backgrounds. Such population-level studies are critical for genetically diverse bacterial pathogens with highly structured populations (Gladstone et al., 2019), substantial antigenic diversity (Bentley et al., 2006), and recombination rates, such as the pneumococcus (Donati et al., 2010; Croucher et al., 2013; Croucher et al., 2011). Here, we investigate the association between pneumococcal genetics and growth kinetics using a combination of high-throughput quantification of in vitro pneumococcal growth, whole-genome sequencing, and population genomic analyses employing a bacterial GWAS approach. Notably, our findings reveal a strong influence of the capsular serotype compared to the genetic background, and the absence of specific individual genomic loci modulating pneumococcal growth kinetics, particularly the in vitro growth rate. These findings may partially explain why some serotypes and lineages colonise and cause invasive diseases more effectively than others and may have implications for developing measures to prevent and control pneumococcal infections.

Our findings suggest that the capsular serotype and genetic background influence the growth kinetics of pneumococci. The findings are generally consistent with results from experimental studies by others (Arends et al., 2022; Hathaway et al., 2012; Tóthpál et al., 2019), which used capsule-switched strains to exclude the impact of the genetic background and demonstrated an alteration in growth dynamics due to the capsule. However, there may be potential differences in the growth media and conditions. Specifically, encapsulated strains exhibited higher growth rates than unencapsulated strains in nutrient-rich conditions (Hathaway et al., 2012). These findings suggest that the capsule is crucial for pneumococcal growth, but its effect depends on nutrient availability – promoting growth when nutrients are abundant and interfering with growth when nutrients are limited, respectively (Hathaway et al., 2012; Müller et al., 2020). A recent study by Tóthpál et al., 2019 suggested that the serotype partially explains pneumococcal growth and suggested the genetic backgrounds, which vary in their ability to colonise the nasopharynx and cause invasive diseases (Brueggemann et al., 2003; Hanage et al., 2005; Colijn et al., 2020; Massora et al., 2019), may also play a role. However, the authors did not fully quantify the impact of the genetic background as the isolates were not sequenced (Tóthpál et al., 2019). Our study confirms the strong influence of both the pneumococcal capsular serotype and genetic background or lineage on the growth kinetics of the isolates. The influence of the serotype appeared to be greater than that of the genetic background on the average growth rate, while the other growth features seemed to show an opposite trend. These findings suggest that the respective influence of the capsular serotype and lineage varies by specific derived pneumococcal growth features.

Interestingly, although our findings revealed that the capsular serotype and genetic background influence pneumococcal growth kinetics, statistical analysis showed that the phenotypic variability attributed to the capsular serotype was greater than that attributed to the genetic background. Specifically, the capsular serotype exhibits a more pronounced growth rate, maximum growth change, and maximum growth density than the genetic background. In contrast, the genetic background had a greater effect on the lag phase duration than the serotype, a growth phase suggested to be responsible for bacterial adaptation to new environments (Rolfe et al., 2012). We acknowledge that our comparison of growth features in the same genetic background may also indicate a significant amount of genetic variation for some of the lineages, some of which could be responsible for changes in growth kinetics. These findings illustrate the combined, albeit variable, importance of the capsular serotype and genetic background on pneumococcal growth kinetics, which may partially explain the pathogenesis and prevalence of the pneumococcus in nasopharyngeal carriage and invasive disease.

Previous studies have revealed individual bacterial genomic loci that impact other phenotypes, including pathogenicity (Lees et al., 2019a; Li et al., 2019b; Young et al., 2019; Chaguza et al., 2022; Davies et al., 2019; Chewapreecha et al., 2019; Peters et al., 2021; Biggel et al., 2020; Wee et al., 2021), virulence (Laabei et al., 2014; Chaguza et al., 2020; Boeck et al., 2022; Galardini et al., 2020), host, niche, and environmental adaptation (Chaguza et al., 2022; Chewapreecha et al., 2019; Ma et al., 2020), conjugation (Clark et al., 2022), and antimicrobial resistance (Chewapreecha et al., 2014; Coll et al., 2018; Creasy-Marrazzo et al., 2022; Farhat et al., 2013; Hicks et al., 2019; Farhat et al., 2019; Alam et al., 2014; Mortimer et al., 2022), independent of the genetic background. Although our study focused primarily on the pneumococcal growth rate parameter, the other growth parameters are also biologically relevant. For example, the lag phase duration potentially reflects the ability to adapt to environmental conditions, such as nutrient availability or stress, and may be more influenced by regulatory genes involved in sensing and responding to environmental cues (Rolfe et al., 2012; Bertrand, 2019). On the other hand, the average growth rate may be impacted by genes controlling the rate of cell division under optimal conditions (Li et al., 2019a) while the maximum growth density, which reflects the final cell density, might be shaped by factors related to nutrient utilisation efficiency, waste tolerance, or quorum sensing. The duration of the stationary phase may reflect the initiation of the lytic growth phase, possibly due to the effects of an autolysin inhibitor (Höltje and Tomasz, 1975). Our GWAS analysis, undertaken to gain a more nuanced understanding of how genetic variants contribute to different physiological aspects of microbial growth, revealed no elevated signals in specific genomic loci associated with pneumococcal growth features. This suggests that individual variants may not significantly influence pneumococcal growth kinetics independently of the genetic background and serotype. Previous studies have shown that mutations in other capsule biosynthesis genes (wchA or cpsE) encoding a protein that links activated glucose phosphates to lipid carriers (Bentley et al., 2006) also influence the growth dynamics of the pneumococcus (Schaffner et al., 2014). However, our results contradict our initial speculation that genetic variation in the capsule polysaccharide synthesis genes would be strongly associated with pneumococcal growth features. These findings suggest that the contribution of specific individual pneumococcal genomic loci to growth kinetics does not stand out, hinting at the potential polygenic nature of growth kinetics and an overall impact of the genetic background, which exerts a cumulative effect, diluting the influence of individual loci. Uncovering such effects will require follow-up studies with larger sample sizes.

Compared to the findings of a previous study by Hathaway et al., 2012, we found no robust evidence for an association between the in vitro growth rates and the population-level prevalence of the serotypes in nasopharyngeal carriage. However, this may have occurred as a result of a limitation of our study, since we studied a collection of randomly sampled invasive isolates, and the growth experiments were conducted under conditions similar to those of blood. In contrast, Hathaway et al., 2012 used a non-random selection of both carriage and disease isolates. Isolates likely have adaptations to the environmental conditions in which they were isolated; therefore, in vitro growth characteristics for any given isolate may be different under experimental conditions simulating different niches. In addition, the in vitro growth environment cannot fully represent in vivo conditions and fails to consider the impact of selective pressures arising from serotype-specific responses (antibodies) or non-specific responses (phagocytosis) as well as potential interactions with other pathogens. The lack of association between the invasiveness and prevalence of serotypes in invasive disease and carriage, as well as the growth rates, could reflect that the conditions in the in vitro growth environment do not fully represent in vivo conditions. Collectively, these findings suggest that these and other factors may substantially influence the complex pathogenesis of pneumococcal disease in vivo.

The strength of this study lies in the analysis of an extensive collection of well-phenotyped natural isolates, combined with phenotyping and the estimation of growth features using statistical models and statistical genetics to determine the association between the growth features and genetic variation across the entire genome. As previously noted by Tóthpál et al., 2019, the culturing history of the isolates may impact the growth kinetics. In our study, we consistently cultured all isolates after isolation from patients using standardised culturing procedures (single assay), identical initial concentration, environmental conditions (temperature), and equipment to measure optical density, and repeated measurements to spot technical variability (Arends et al., 2022). Compared to the present study, future studies would benefit from larger sample sizes and may yield more results, as we only explained a portion of the heritability of the growth features. We also acknowledge that the methods used to estimate heritability may also sometimes perform poorly on certain bacterial species; therefore, more robust methods are needed to validate our findings further (Mallawaarachchi et al., 2022). We conducted our in vitro culture experiments for 15 hours, as the growth curves indicate that most isolates had reached the stationary phase by that time. Increasing the culturing time from 15 to >24 hours, as done elsewhere (Hathaway et al., 2016; Hamaguchi et al., 2018), might be considered for follow-up studies. However, Streptococcus pneumoniae undergoes autolysis upon reaching a specific cell density, which could distort growth measurements and complicate interpretation if incubation were prolonged. Furthermore, since the pneumococcus invades different niches and systemic tissues under varying conditions, including oxygen concentration and temperature (Im et al., 2022; Hathaway et al., 2012; Tóthpál et al., 2019; Small et al., 1986), future studies should investigate the effect of these environmental conditions on pneumococcal growth and identify pneumococcal genomic variants associated with growth kinetics for each condition. Considering that our dataset contained only a third of the >100 known pneumococcal serotypes globally (Ganaie et al., 2020), due to the geographical variation in the distribution of pneumococcal serotypes (Johnson et al., 2010), future studies should investigate all serotypes to provide further insights into the impact of different serotypes on pneumococcal growth kinetics.

Additionally, considering that we generated the phylogeny of the entire pneumococcal species rather than isolates belonging to a single strain or lineage, as defined by several approaches (Gladstone et al., 2019; McGee et al., 2001; Cheng et al., 2013), we did not remove recombinations using widely used methods designed for clonal bacterial populations, such as Gubbins (Croucher et al., 2015b). Therefore, there is a possibility that some branch lengths in the phylogeny used for ancestral trait reconstruction analysis may be less accurate, although the topology is usually robust to recombination (Hedge and Wilson, 2014). Furthermore, there may be other variants that potentially influence the pneumococcal growth kinetics, which are regarded as not statistically significant in this study based on our conservative genome-wide significance threshold. As more similar studies are conducted, particularly those utilising much larger datasets, potential variants associated with pneumococcal growth kinetics may be discovered.

Our analysis of population-level genomic and high-throughput in vitro pneumococcal growth data suggests that the capsular serotype and lineage influence pneumococcal growth. However, although some growth features are highly heritable, there appears to be no specific genetic variants that strongly influence these phenotypes, suggesting that a combination of genomic loci with minor effects, individually or collectively, may play a greater role in pneumococcal growth kinetics. Overall, our study provides a proof-of-concept for combining phenotyping and population genomics to understand the genetic basis for pneumococcal growth, an approach which can be applied to study other bacteria of clinical significance and those used in bioengineering, genetics, and the food industry (Tonner et al., 2020; Yang et al., 2018; Alkema et al., 2016). Ultimately, we anticipate that applying our approach will have implications for devising strategies to reduce life-threatening bacterial infections.

We have deposited the whole-genome sequencing data for the study isolates in the European Nucleotide Archive (ENA) and provided the accession numbers and other isolate metadata in Supplementary file 1. All the other data supporting the findings of this study are described in this paper or are available as part of the supplementary material. We have also provided additional raw data and code used for the analysis on GitHub: https://github.com/ChrispinChaguza/SpnGrowthKinetics (copy archived at Chaguza, 2025).

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Author details

1. Chrispin Chaguza

   1. Department of Epidemiology of Microbial Diseases, Yale School of Public Health, Yale University, New Haven, United States
   2. Yale Institute for Global Health, Yale University, New Haven, United States
   3. Parasites and Microbes Programme, Wellcome Sanger Institute, Wellcome Genome Campus, Hinxton, United Kingdom
   4. Department of Host-Microbe Interactions, St Jude Children’s Research Hospital, Memphis, United States

   Contribution

   Conceptualization, Data curation, Formal analysis, Validation, Visualization, Methodology, Writing – original draft, Writing – review and editing

   For correspondence

   chrispin.chaguza@gmail.com

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0002-2108-1757

2. Daan W Arends

   Laboratory of Medical Immunology, Radboud Institute of Molecular Life Sciences, Radboudumc Center for Infectious Diseases, Radboudumc, Nijmegen, Netherlands

   Contribution

   Data curation, Investigation, Methodology, Writing – review and editing

   Competing interests

   No competing interests declared

3. Stephanie W Lo

   Parasites and Microbes Programme, Wellcome Sanger Institute, Wellcome Genome Campus, Hinxton, United Kingdom

   Contribution

   Data curation, Funding acquisition, Writing – review and editing

   Competing interests

   No competing interests declared

4. Indri Hapsari Putri

   Laboratory of Medical Immunology, Radboud Institute of Molecular Life Sciences, Radboudumc Center for Infectious Diseases, Radboudumc, Nijmegen, Netherlands

   Contribution

   Data curation, Investigation, Methodology, Writing – review and editing

   Competing interests

   No competing interests declared

5. Anna York

   Department of Epidemiology of Microbial Diseases, Yale School of Public Health, Yale University, New Haven, United States

   Contribution

   Writing – review and editing

   Competing interests

   No competing interests declared

6. John A Lees

   European Molecular Biology Laboratory–European Bioinformatics Institute, Cambridge, United Kingdom

   Contribution

   Software, Supervision, Methodology, Writing – review and editing

   Competing interests

   No competing interests declared

7. Anne L Wyllie

   Department of Epidemiology of Microbial Diseases, Yale School of Public Health, Yale University, New Haven, United States

   Contribution

   Writing – review and editing

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0001-6015-0279

8. Daniel M Weinberger

   Department of Epidemiology of Microbial Diseases, Yale School of Public Health, Yale University, New Haven, United States

   Contribution

   Conceptualization, Resources, Supervision, Investigation, Methodology, Writing – original draft, Project administration, Writing – review and editing

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0003-1178-8086

9. Stephen D Bentley

   Parasites and Microbes Programme, Wellcome Sanger Institute, Wellcome Genome Campus, Hinxton, United Kingdom

   Contribution

   Conceptualization, Resources, Software, Supervision, Funding acquisition, Investigation, Methodology, Writing – original draft, Project administration, Writing – review and editing

   Competing interests

   No competing interests declared

10. Marien I de Jonge

    Laboratory of Medical Immunology, Radboud Institute of Molecular Life Sciences, Radboudumc Center for Infectious Diseases, Radboudumc, Nijmegen, Netherlands

    Contribution

    Conceptualization, Resources, Data curation, Supervision, Funding acquisition, Investigation, Methodology, Writing – original draft, Project administration, Writing – review and editing

    Contributed equally with

    Amelieke JH Cremers

    Competing interests

    No competing interests declared

11. Amelieke JH Cremers

    1. Laboratory of Medical Immunology, Radboud Institute of Molecular Life Sciences, Radboudumc Center for Infectious Diseases, Radboudumc, Nijmegen, Netherlands
    2. Department of Fundamental Microbiology, University of Lausanne, Lausanne, Switzerland

    Contribution

    Conceptualization, Resources, Data curation, Supervision, Funding acquisition, Validation, Investigation, Methodology, Writing – original draft, Project administration, Writing – review and editing

    Contributed equally with

    Marien I de Jonge

    For correspondence

    Amelieke.cremers@unil.ch

    Competing interests

    No competing interests declared

    "This ORCID iD identifies the author of this article:" 0000-0003-4704-4978

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