S. pneumoniae remains one of the world’s most consequential bacterial pathogens, causing pneumonia and meningitis with the highest disease burden falling on young children and the elderly. When PCVs were first rolled out in the United States in 2000, they delivered striking reductions in disease and in carriage of the serotypes they target, which are defined by the bacterium’s capsular polysaccharide. But because the vaccines cover only a subset of the more than ninety known serotypes, they reshape competition within the species. Strains carrying non-targeted serotypes experience relaxed competition and can expand, replacing the vaccine types and eroding some of the public health gains.

Traditionally, pneumococcal epidemiology has been organised around serotypes, yet the capsular locus that defines them represents only a small slice of the species’ genetic diversity. Whole genome sequencing has revealed a far richer picture, including lineages called global pneumococcal sequence clusters (GPSCs), which are defined by variation across the entire genome. Because many GPSCs carry multiple serotypes, and individual serotypes appear in multiple GPSCs, the two classifications provide complementary information. Serotype replacement can therefore arise either from closely related strains switching capsule within a lineage or from genetically distant lineages expanding into the niche left open by vaccination.

A leading explanation for these dynamics is negative frequency-dependent selection, or NFDS, a form of selection in which a trait confers greater benefit when it is rare than when it is common. In bacteria, NFDS is thought to act on accessory genes such as antimicrobial resistance genes and bacteriocins, helping to maintain a diverse pangenome in which no single gene combination sweeps to fixation. Earlier modelling work, notably by Corander and colleagues in 2017, showed that NFDS could explain much of the post-vaccine reshuffling of pneumococcal populations, but those models were tightly coupled to specific datasets and difficult for non-specialists to redeploy.

The new study, led by Leonie Lorenz and John Lees of the European Molecular Biology Laboratory’s European Bioinformatics Institute together with collaborators across Nepal, the United Kingdom and Canada, addresses three gaps. First, the team rebuilt the population dynamics model in the odin modelling framework, separating model code, genomic inputs and fitting procedures so that public health bodies can adapt it to their own settings. Second, they tested whether cheaper data types, such as serotyping alone or targeted sequencing of a fixed gene set, could substitute for whole genome sequencing. Third, they used simulation to ask how sample size and sampling frequency affect the reliability of parameter estimates and forecasts.

The model itself is a compartmental extension of the Wright-Fisher framework, arranged on a two-dimensional grid in which one axis represents GPSC lineages and the other represents serotypes. Each generation, which corresponds roughly to a month of transmission, the population is replenished by offspring from the current generation plus immigrants drawn from an external reservoir of observed strain-serotype combinations. Four parameters are fitted to data by Markov chain Monte Carlo: vaccination effectiveness, the strength of NFDS, the proportion of intermediate-frequency genes subject to NFDS, and the immigration rate. Genes are summarised at the lineage level, and those changing least in frequency over time, ranked by a delta statistic, are flagged as candidates under balancing selection.

When fitted to carriage data from Kathmandu, where 1,881 samples were collected between 2009 and 2019 around the introduction of PCV10, and to previously published datasets from Massachusetts and Southampton, the model reproduced the observed serotype frequency changes closely, with model confidence intervals overlapping the data in nearly every case. Vaccination effectiveness estimates were consistent across all three locations, ranging from roughly 0.08 to 0.12 per generation, while immigration rates were similarly stable. Notably, the genes inferred to be under NFDS differed substantially between countries, and model comparison using likelihood-ratio tests and the Bayesian Information Criterion showed that only a subset of intermediate-frequency genes, not all of them, appear to be under NFDS. This suggests that other forces, such as balanced rates of gene gain and loss or ecological niche partitioning, also help maintain the accessory genome.

The search for a universal set of NFDS genes proved disappointing in an instructive way. A genetic algorithm applied to the same data found far more overlap between countries than the delta statistic did, yet even that shared set of 155 genes was no larger than expected by chance. Serotype-only model versions fit the data poorly. Together, these results indicate that neither serotyping nor targeted sequencing of a fixed gene panel can substitute for whole genome surveillance, underscoring that each country needs its own genomic monitoring programme to understand local replacement dynamics rather than importing conclusions from elsewhere.

The simulation experiments delivered the study’s most actionable finding. By generating a synthetic twenty-year dataset and then subsampling it under a fixed budget, the researchers showed that when resources are scarce, it is better to sample less often with larger samples per round. At the lowest budgets, annual and quadrennial sampling produced biased estimates, whereas biennial and triennial sampling performed best, balancing statistical power against the risk of missing critical change points. With larger budgets, sampling frequency mattered little. The team has released the model as an open-source R package called Stubentiger, giving surveillance agencies a practical tool for anticipating how pneumococcal populations will respond as vaccine formulations evolve.

Subject of Research: Mathematical modelling of negative frequency-dependent selection in the Streptococcus pneumoniae pangenome to inform genomic surveillance strategies during pneumococcal conjugate vaccine introduction

Article Title: A reusable model of pangenome selection informs optimal surveillance strategies over vaccine introductions

Article References: Lorenz, L. J., Hellewell, J., Horsfield, S. T., Russell, M. J., Shrestha, S., Pollard, A. J., Bentley, S. D., Lo, S. W., Colijn, C., Croucher, N. J., & Lees, J. A. (2026). A reusable model of pangenome selection informs optimal surveillance strategies over vaccine introductions. Genome Medicine, 18(1), Article 131. https://doi.org/10.1186/s13073-026-01672-4

Image Credits: AI Generated

DOI: 10.1186/s13073-026-01672-4

Keywords: Streptococcus pneumoniae, pangenome, negative frequency-dependent selection, pneumococcal conjugate vaccine, serotype replacement, genomic surveillance, Wright-Fisher model, mathematical modelling, GPSC lineages, vaccine effectiveness, public health, Nepal

The research team, led by scientists at Northwest A&F University in Yangling, China, sequenced the whole genomes of 65 wine-related H. uvarum strains isolated from major wine-producing regions of northwestern China, including Shaanxi, Ningxia, Gansu, and Xinjiang. These newly generated sequences were combined with 86 publicly available genome datasets, yielding a total of 151 strains for population genomic analysis, with samples drawn from China, Australia, Europe, the United States, and other locations. The newly sequenced strains showed exceptionally deep sequencing coverage, ranging from roughly 726-fold to more than 1,500-fold, ensuring high confidence in the variants detected.

From the combined dataset, the researchers identified 575,222 high-quality genetic variants, including more than half a million single nucleotide polymorphisms, after masking repetitive and low-complexity regions of the reference genome. Downstream analyses focused on 483,564 biallelic SNPs. Three independent approaches—phylogenetic tree construction, ancestry coefficient estimation, and principal component analysis—told a consistent story: strains sampled in China are broadly distinguishable from those sampled on other continents, confirming that geographic isolation has played a major role in shaping intercontinental population divergence in this species.

Yet the picture is far more tangled than a simple tale of isolated populations drifting apart. Using the TreeMix software to model migration between clades, the team detected substantial gene flow between geographically paired clades after their divergence. In the optimal model, gene flow was inferred from a Chinese clade to an Australian clade, from another Chinese clade to a European clade, and from the European clade back to a third Chinese clade. The f-branch statistic, a method that detects excess allele sharing between paired lineages, corroborated these signals: the highest intercontinental value, 49.84 percent, linked a Chinese clade with an Australian clade, with additional elevated values connecting Chinese, European, and Australian lineages. Together, these results point to ongoing intercontinental dispersal of H. uvarum, likely facilitated by human activity or insect vectors, a pattern reminiscent of what has been documented in the better-known wine yeast Saccharomyces cerevisiae.

Beyond connectivity, the study revealed that local environments have left distinct evolutionary fingerprints on different populations. Scanning for selective sweeps with the RAiSD tool, the researchers identified 117 candidate genes under positive selection in strains from Ningxia, China, and 141 in Australian strains, with only 23 genes shared between the two sets. These positively selected regions showed significantly elevated genetic differentiation and reduced diversity compared to the genomic background—classic hallmarks of recent adaptation. The Ningxia genes were significantly enriched for Gene Ontology terms related to the positive regulation of filamentous growth in response to external stimuli, such as starvation, pH stress, and biotic stimulus. The authors suggest these adaptations may help Ningxia strains colonize fruit surfaces, echoing earlier proposals that the yeast’s ability to form pseudohyphae in rich media confers an advantage for fruit colonization.

Intriguingly, the stress-related GO term “cytoplasmic stress granule” was significantly enriched among positively selected genes in both the Ningxia and Australian subsets, but the two regions shared only two of the six stress granule-associated genes in each set. Stress granules are cytoplasmic assemblies that sequester inactive messenger RNAs under conditions such as heat shock, oxidative stress, and nutrient deprivation, allowing cells to preferentially translate stress-response proteins. The fact that different genes underpin this same function in the two regions suggests that Ningxia and Australian populations have faced distinct environmental pressures and have evolved different molecular solutions to cope with stress.

The study also touched on one of the most curious features of the genus Hanseniaspora: its extensive loss of DNA repair genes. Previous work showed that the Faster-Evolving Lineage, to which H. uvarum belongs, lost dozens of genes involved in cell-cycle control and DNA repair, and also evolved a novel cis-regulatory mode for its core histone genes. Despite this genetic erosion, the new analysis found that DNA repair terms showed marginal enrichment among positively selected genes in the Ningxia subset, and that two classes of histone acetyltransferase complexes involved in DNA repair—the NuA4 complex and the SAGA and SLIK complexes—were significantly or marginally enriched in both regional subsets. Moreover, the pangenome analysis revealed that a subset of strains may carry DNA repair complex genes not present in the rest of the species, hinting that some populations have partially compensated for ancient gene losses through acquired or retained genetic material.

To capture the full gene repertoire of the species, the team constructed a pangenome from 159 strains, including eight additional genome assemblies. They identified 4,991 gene families, of which 3,340 were present in all strains (the core), while the remainder were divided into soft-core, shell, cloud, and private categories. Perhaps the most striking result came from Heaps’ Law analysis: with a decay parameter of 0.51—well below the threshold of 1—the H. uvarum pangenome is open, meaning new gene families keep appearing as more genomes are sampled. This is notable because the sampling focused almost entirely on anthropogenic environments such as vineyards and wineries, a relatively homogeneous niche. An earlier pangenome analysis based on just eight strains had concluded the pangenome was closed; the new, much larger dataset overturns that view and suggests the species harbors substantial adaptive potential for diverse stresses.

The practical implications reach well beyond evolutionary theory. H. uvarum is valued in winemaking for its ability to increase esters and volatile phenols, reduce volatile acidity, and improve the sensory profile of wine when used in co-fermentation with Saccharomyces cerevisiae. It also participates in fermentations of cider, cocoa, coffee, and pineapple wine, and related species serve as biocontrol agents against postharvest fruit diseases. A genomic understanding of how regional populations differ—in stress tolerance, filamentous growth, and metabolic capacity—could guide the selection of indigenous starter strains tailored to specific terroirs and fermentation goals, including recent efforts to screen Chinese Hanseniaspora strains for ethanol reduction in wine.

The authors caution that their sampling, while broad, remains uneven: wild-niche strains were absent because genomic data were not publicly available, and Chinese sampling was concentrated in Ningxia and Shaanxi. Future studies incorporating strains from more diverse ecological niches, broader geographic regions, and longer time spans will be needed to refine the evolutionary picture. Nevertheless, by combining deep whole-genome sequencing, phylogenetics, admixture modeling, selective sweep detection, and pangenomics, the study delivers high-quality genomic resources and a foundational framework for population genomics of non-Saccharomyces yeasts—a field long overshadowed by work on Saccharomyces cerevisiae. For a species once considered merely a fleeting early fermenter, Hanseniaspora uvarum is proving to be a genetically rich, globally connected, and remarkably adaptable microbe whose evolutionary story is only beginning to unfold.

Subject of Research: Population genomics, gene flow, and positive selection in the wine-related yeast Hanseniaspora uvarum

Article Title: Population genomics reveals gene flow and positive selection patterns in the wine-related yeast Hanseniaspora uvarum

Article References: Ma, R., Wang, H., Wei, Y., Sun, Y., Xue, J., Qin, Y., Tao, S., & Liu, Y. (2026). Population genomics reveals gene flow and positive selection patterns in the wine-related yeast Hanseniaspora uvarum. Stress Biology, 6(1), Article 54. https://doi.org/10.1007/s44154-026-00319-z

Image Credits: AI Generated

DOI: 10.1007/s44154-026-00319-z

Keywords: Hanseniaspora uvarum, non-Saccharomyces yeasts, population genomics, gene flow, introgression, positive selection, pangenome, wine fermentation, selective sweep, stress granules, DNA repair, geographic divergence