Multi-modal, Label-free, Optical Mapping of Cellular Metabolic Function and Oxidative Stress in 3D Engineered Brain Tissue Models.

Brain metabolism is essential for the function of organisms. While established imaging methods provide valuable insights into brain metabolic function, they lack the resolution to capture important metabolic interactions and heterogeneity at the cellular level. Label-free, two-photon excited fluorescence imaging addresses this issue by enabling dynamic metabolic assessments at the single-cell level without manipulations. In this study, we demonstrate the impact of spectral imaging on the development of rigorous intensity and lifetime label-free imaging protocols to assess dynamically over time metabolic function in 3D engineered brain tissue models comprising human induced neural stem cells, astrocytes, and microglia. Specifically, we rely on multi-wavelength spectral imaging to identify the excitation/emission profiles of key cellular fluorophores within human brain cells, including NAD(P)H, LipDH, FAD, and lipofuscin. These enable development of methods to mitigate lipofuscin's overlap with NAD(P)H and flavin autofluorescence to extract reliable optical metabolic function metrics from images acquired at two excitation wavelengths over two emission bands. We present fluorescence intensity and lifetime metrics reporting on redox state, mitochondrial fragmentation, and NAD(P)H binding status in neuronal monoculture and triculture systems, to highlight the functional impact of metabolic interactions between different cell types. Our findings reveal significant metabolic differences between neurons and glial cells, shedding light on metabolic pathway utilization, including the glutathione pathway, OXPHOS, glycolysis, and fatty acid oxidation. Collectively, our studies establish a label-free, non-destructive approach to assess the metabolic function and interactions among different brain cell types relying on endogenous fluorescence and illustrate the complementary nature of information that is gained by combining intensity and lifetime-based images. Such methods can improve understanding of physiological brain function and dysfunction that occurs at the onset of cancers, traumatic injuries and neurodegenerative diseases.

On the reliable estimation of sequential Monod kinetic parameters.

While dozens of studies have attempted to estimate the Monod kinetic parameters of microbial reductive dechlorination, published values in the literature vary by 2-6 orders of magnitude. This lack of consensus can be attributed in part to limitations of both experimental design and parameter estimation techniques. To address these issues, Hamiltonian Monte Carlo was used to produce more than one million sets of realistic simulated microcosm data under a variety of experimental conditions. These data were then employed in model fitting experiments using a number of parameter estimation algorithms for determining Monod kinetic parameters. Analysis of data from conventional triplicate microcosms yielded parameter estimates characterized by high collinearity, resulting in poor estimation accuracy and precision. Additionally, confidence intervals computed by commonly used classical regression analysis techniques contained true parameter values much less frequently than their nominal confidence levels. Use of an alternative experimental design, requiring the same number of analyses as conventional experiments but comprised of microcosms with varying initial chlorinated ethene concentrations, is shown to result in order-of-magnitude decreases in parameter uncertainty. A Metropolis algorithm which can be run on a typical personal computer is demonstrated to return more reliable parameter interval estimates.

The emergence and diversification of a zoonotic pathogen from within the microbiota of intensively farmed pigs.

The expansion and intensification of livestock production is predicted to promote the emergence of pathogens. As pathogens sometimes jump between species, this can affect the health of humans as well as livestock. Here, we investigate how livestock microbiota can act as a source of these emerging pathogens through analysis of Streptococcus suis, a ubiquitous component of the respiratory microbiota of pigs that is also a major cause of disease on pig farms and an important zoonotic pathogen. Combining molecular dating, phylogeography, and comparative genomic analyses of a large collection of isolates, we find that several pathogenic lineages of S. suis emerged in the 19th and 20th centuries, during an early period of growth in pig farming. These lineages have since spread between countries and continents, mirroring trade in live pigs. They are distinguished by the presence of three genomic islands with putative roles in metabolism and cell adhesion, and an ongoing reduction in genome size, which may reflect their recent shift to a more pathogenic ecology. Reconstructions of the evolutionary histories of these islands reveal constraints on pathogen emergence that could inform control strategies, with pathogenic lineages consistently emerging from one subpopulation of S. suis and acquiring genes through horizontal transfer from other pathogenic lineages. These results shed light on the capacity of the microbiota to rapidly evolve to exploit changes in their host population and suggest that the impact of changes in farming on the pathogenicity and zoonotic potential of S. suis is yet to be fully realized.

A marine-derived fatty acid targets the cell membrane of Gram-positive bacteria.

IMPORTANCE: With the lack of new antibiotics in the drug discovery pipeline, coupled with accelerated evolution of antibiotic resistance, new sources of antibiotics that target pathogens of clinical importance are paramount. Here, we use bacterial cytological profiling to identify the mechanism of action of the monounsaturated fatty acid (Z)-13-methyltetra-4-decenoic acid isolated from the marine bacterium Olleya marilimosa with antibacterial effects against Gram-positive bacteria. The fatty acid antibiotic was found to rapidly destabilize the cell membrane by pore formation and membrane aggregation in Bacillus subtilis, suggesting that this fatty acid may be a promising adjuvant used in combination to enhance antibiotic sensitivity.

An updated catalogue of diverse type II polyketide synthase biosynthetic gene clusters captured from large-scale nucleotide databases.

Nature serves as a rich source of molecules with immense chemical diversity. Aptly named, these 'natural products' boast a wide variety of environmental, medicinal and industrial applications. Type II polyketides, in particular, confer substantial medicinal benefits, including antibacterial, antifungal, anticancer and anti-inflammatory properties. These molecules are produced by enzyme assemblies known as type II polyketide synthases (PKSs), which use domains such as the ketosynthase chain-length factor and acyl carrier protein to produce polyketides with varying lengths, cyclization patterns and oxidation states. In this work, we use a novel bioinformatic workflow to identify biosynthetic gene clusters (BGCs) that code for the core type II PKS enzymes. This method does not rely on annotation and thus was able to unearth previously 'hidden' type II PKS BGCs. This work led us to identify over 6000 putative type II PKS BGCs spanning a diverse set of microbial phyla, nearly double those found in most recent studies. Notably, many of these newly identified BGCs were found in non-actinobacteria, which are relatively underexplored as sources of type II polyketides. Results from this work lay an important foundation for future bioprospecting and engineering efforts that will enable sustainable access to diverse and structurally complex molecules with medicinally relevant properties.

Toward Predicting Human Performance Outcomes From Wearable Technologies: A Computational Modeling Approach.

Wearable technologies for measuring digital and chemical physiology are pervading the consumer market and hold potential to reliably classify states of relevance to human performance including stress, sleep deprivation, and physical exertion. The ability to efficiently and accurately classify physiological states based on wearable devices is improving. However, the inherent variability of human behavior within and across individuals makes it challenging to predict how identified states influence human performance outcomes of relevance to military operations and other high-stakes domains. We describe a computational modeling approach to address this challenge, seeking to translate user states obtained from a variety of sources including wearable devices into relevant and actionable insights across the cognitive and physical domains. Three status predictors were considered: stress level, sleep status, and extent of physical exertion; these independent variables were used to predict three human performance outcomes: reaction time, executive function, and perceptuo-motor control. The approach provides a complete, conditional probabilistic model of the performance variables given the status predictors. Construction of the model leverages diverse raw data sources to estimate marginal probability density functions for each of six independent and dependent variables of interest using parametric modeling and maximum likelihood estimation. The joint distributions among variables were optimized using an adaptive LASSO approach based on the strength and directionality of conditional relationships (effect sizes) derived from meta-analyses of extant research. The model optimization process converged on solutions that maintain the integrity of the original marginal distributions and the directionality and robustness of conditional relationships. The modeling framework described provides a flexible and extensible solution for human performance prediction, affording efficient expansion with additional independent and dependent variables of interest, ingestion of new raw data, and extension to two- and three-way interactions among independent variables. Continuing work includes model expansion to multiple independent and dependent variables, real-time model stimulation by wearable devices, individualized and small-group prediction, and laboratory and field validation.

Large-scale genomic analysis of antimicrobial resistance in the zoonotic pathogen Streptococcus suis.

BACKGROUND: Antimicrobial resistance (AMR) is among the gravest threats to human health and food security worldwide. The use of antimicrobials in livestock production can lead to emergence of AMR, which can have direct effects on humans through spread of zoonotic disease. Pigs pose a particular risk as they are a source of zoonotic diseases and receive more antimicrobials than most other livestock. Here we use a large-scale genomic approach to characterise AMR in Streptococcus suis, a commensal found in most pigs, but which can also cause serious disease in both pigs and humans. RESULTS: We obtained replicated measures of Minimum Inhibitory Concentration (MIC) for 16 antibiotics, across a panel of 678 isolates, from the major pig-producing regions of the world. For several drugs, there was no natural separation into 'resistant' and 'susceptible', highlighting the need to treat MIC as a quantitative trait. We found differences in MICs between countries, consistent with their patterns of antimicrobial usage. AMR levels were high even for drugs not used to treat S. suis, with many multidrug-resistant isolates. Similar levels of resistance were found in pigs and humans from regions associated with zoonotic transmission. We next used whole genome sequences for each isolate to identify 43 candidate resistance determinants, 22 of which were novel in S. suis. The presence of these determinants explained most of the variation in MIC. But there were also interesting complications, including epistatic interactions, where known resistance alleles had no effect in some genetic backgrounds. Beta-lactam resistance involved many core genome variants of small effect, appearing in a characteristic order. CONCLUSIONS: We present a large dataset allowing the analysis of the multiple contributing factors to AMR in S. suis. The high levels of AMR in S. suis that we observe are reflected by antibiotic usage patterns but our results confirm the potential for genomic data to aid in the fight against AMR.

Compressed sensing two-dimensional Bragg scatter imaging.

Here we introduce a new reconstruction technique for two-dimensional Bragg scattering tomography (BST), based on the Radon transform models of Webber and Miller [Inverse Probl. Imaging15, 683 (2021).10.3934/ipi.2021010]. Our method uses a combination of ideas from multibang control and microlocal analysis to construct an objective function which can regularize the BST artifacts; specifically the boundary artifacts due to sharp cutoff in sinogram space (as observed in [arXiv preprint, arXiv:2007.00208 (2020)]), and artifacts arising from approximations made in constructing the model used for inversion. We then test our algorithm in a variety of Monte Carlo (MC) simulated examples of practical interest in airport baggage screening and threat detection. The data used in our studies is generated with a novel Monte-Carlo code presented here. The model, which is available from the authors upon request, captures both the Bragg scatter effects described by BST as well as beam attenuation and Compton scatter.

Head motion classification using thread-based sensor and machine learning algorithm.

Human machine interfaces that can track head motion will result in advances in physical rehabilitation, improved augmented reality/virtual reality systems, and aid in the study of human behavior. This paper presents a head position monitoring and classification system using thin flexible strain sensing threads placed on the neck of an individual. A wireless circuit module consisting of impedance readout circuitry and a Bluetooth module records and transmits strain information to a computer. A data processing algorithm for motion recognition provides near real-time quantification of head position. Incoming data is filtered, normalized and divided into data segments. A set of features is extracted from each data segment and employed as input to nine classifiers including Support Vector Machine, Naive Bayes and KNN for position prediction. A testing accuracy of around 92% was achieved for a set of nine head orientations. Results indicate that this human machine interface platform is accurate, flexible, easy to use, and cost effective.

Genome Reduction Is Associated with Bacterial Pathogenicity across Different Scales of Temporal and Ecological Divergence.

Emerging bacterial pathogens threaten global health and food security, and so it is important to ask whether these transitions to pathogenicity have any common features. We present a systematic study of the claim that pathogenicity is associated with genome reduction and gene loss. We compare broad-scale patterns across all bacteria, with detailed analyses of Streptococcus suis, an emerging zoonotic pathogen of pigs, which has undergone multiple transitions between disease and carriage forms. We find that pathogenicity is consistently associated with reduced genome size across three scales of divergence (between species within genera, and between and within genetic clusters of S. suis). Although genome reduction is also found in mutualist and commensal bacterial endosymbionts, genome reduction in pathogens cannot be solely attributed to the features of their ecology that they share with these species, that is, host restriction or intracellularity. Moreover, other typical correlates of genome reduction in endosymbionts (reduced metabolic capacity, reduced GC content, and the transient expansion of nonfunctional elements) are not consistently observed in pathogens. Together, our results indicate that genome reduction is a consistent correlate of pathogenicity in bacteria.

Limited-View X-Ray Tomography Combining Attenuation and Compton Scatter Data: Approach and Experimental Results.

X-ray inspection systems are critical in medical, non-destructive testing, and security applications, with systems typically measuring attenuation along straight-line paths connecting sources and detectors. Computed tomography (CT) systems can provide higher-quality images than single- or dual-view systems, but the need to measure many projections leads to greater system cost and complexity. Typically, off-angle Compton scattered photons are treated as noise during tomographic inversion. We seek to maximize the image quality of limited-view systems by combining attenuation data with measurements of Compton-scattered photons, exploiting the fact that the broken-ray paths followed by scattered photons provide additional geometric sampling of the scene. We describe a single-scatter forward model for Compton-scatter data measured with energy-resolving detectors, and demonstrate a reconstruction algorithm for density that combines both attenuation and scatter measurements. The experimental results suggest that including Compton-scattered data in the reconstruction process can improve image quality for density reconstruction using limited-view systems.

Eavesdropping and crosstalk between secreted quorum sensing peptide signals that regulate bacteriocin production in Streptococcus pneumoniae.

Quorum sensing (QS), where bacteria secrete and respond to chemical signals to coordinate population-wide behaviors, has revealed that bacteria are highly social. Here, we investigate how diversity in QS signals and receptors can modify social interactions controlled by the QS system regulating bacteriocin secretion in Streptococcus pneumoniae, encoded by the blp operon (bacteriocin-like peptide). Analysis of 4096 pneumococcal genomes detected nine blp QS signals (BlpC) and five QS receptor groups (BlpH). Imperfect concordance between signals and receptors suggested widespread social interactions between cells, specifically eavesdropping (where cells respond to signals that they do not produce) and crosstalk (where cells produce signals that non-clones detect). This was confirmed in vitro by measuring the response of reporter strains containing six different blp QS receptors to cognate and non-cognate peptides. Assays between pneumococcal colonies grown adjacent to one another provided further evidence that crosstalk and eavesdropping occur at endogenous levels of signal secretion. Finally, simulations of QS strains producing bacteriocins revealed that eavesdropping can be evolutionarily beneficial even when the affinity for non-cognate signals is very weak. Our results highlight that social interactions can mediate intraspecific competition among bacteria and reveal that competitive interactions can be modified by polymorphic QS systems.

Microbiological quality and antimicrobial resistance characterization of Salmonella spp. in fresh milk value chains in Ghana.

Consumer perception of poor hygiene of fresh milk products is a major barrier to promotion of milk consumption as an intervention to alleviate the burden of malnutrition in Ghana. Fresh milk is retailed raw, boiled, or processed into unfermented cheese and spontaneously fermented products in unlicensed outlets. In this study, we have determined microbiological quality of informally retailed fresh milk products and characterized the genomic diversity and antimicrobial resistance (AMR) patterns of non-typhoidal Salmonella (NTS) in implicated products. A total of 159 common dairy products were purchased from five traditional milk markets in Accra. Samples were analysed for concentrations of aerobic bacteria, total and fecal coliforms, Escherichia coli, staphylococci, lactic acid bacteria and yeast and moulds. The presence of Salmonella, E. coli O157:H7, Listeria monocytogenes and Staphylococcus aureus were determined. AMR of Salmonella against 18 antibiotics was experimentally determined. Genome sequencing of 19 Salmonella isolates allowed determination of serovars, antigenic profiles, prediction of AMR genes in silico and inference of phylogenetic relatedness between strains. Raw and heat-treated milk did not differ significantly in overall bacterial quality (P = 0.851). E. coli O157:H7 and Staphylococcus aureus were present in 34.3% and 12.9% of dairy products respectively. Multidrug resistant (MDR) Salmonella enterica serovars Muenster and Legon were identified in 11.8% and 5.9% of unfermented cheese samples respectively. Pan genome analysis revealed a total of 3712 core genes. All Salmonella strains were resistant to Trimethoprim/Sulfamethoxazole, Cefoxitin, Cefuroxime Axetil and Cefuroxime. Resistance to Chloramphenicol (18%) and Ciprofloxacin (100%), which are first line antibiotics used in treatment of NTS bacteremia in Ghana, was evident. AMR was attributed to presence and/or mutations in the following genes: golS, sdiA for cephalosporins, aac(6')-Iy, ant(9) for aminoglycosides, mdtK, gyrA, gyrB, parC, parE for quinolones and cat1, cat4 for phenicols. Phylogenetic analysis based on accessory genes clustered S. Legon strains separately from the S. Muenster strains. These strains were from different markets suggesting local circulation of related strains. Our study justifies consumer resistance to consumption of unripened soft cheese without further lethal heat treatment, and provides evidence that supports the Ghana Health Service recommendation for use of 3rd generation cephalosporins for the treatment of MDR NTS infections.

Cerebrovascular network registration via an efficient attributed graph matching technique.

Registration of vascular networks is an indispensable element of prognostic and diagnostic studies that require structural analysis and comparison over time, among different samples, and to a gold standard. However, vascular networks manifest low spatial texture and sparse structural content so that even small variations in their location can make the intensity-based registration inefficient and prone to errors. Motivated by geometrical graph-based models developed in our prior work, we use the shape information in the graph topology sense to enhance the registration performance. An efficient feature-based registration is presented that seeks correspondence of the bifurcations and branches in a graph matching scheme. Since the graph matching is originally posed a NP-hard quadratic assignment problem (QAP) in the literature, we have designed a node signature that incorporates edge correspondences indirectly. This allows removing the quadratic term in the QAP to recast the problem as a linear assignment problem (LAP) to relieve the computational burden. The LAP is efficiently solvable and is scalable to data with graph representation of larger size. The performance is tested and validated using clinical 3-D angiography images of the human cerebrovasculature as well as synthetic datasets. This method proves to be robust in the face of different structural and algorithm's parameters. Quality of inter-subject and multimodal matching of clinical data has also been confirmed.

Closed-loop control of targeted ultrasound drug delivery across the blood-brain/tumor barriers in a rat glioma model.

Cavitation-facilitated microbubble-mediated focused ultrasound therapy is a promising method of drug delivery across the blood-brain barrier (BBB) for treating many neurological disorders. Unlike ultrasound thermal therapies, during which magnetic resonance thermometry can serve as a reliable treatment control modality, real-time control of modulated BBB disruption with undetectable vascular damage remains a challenge. Here a closed-loop cavitation controlling paradigm that sustains stable cavitation while suppressing inertial cavitation behavior was designed and validated using a dual-transducer system operating at the clinically relevant ultrasound frequency of 274.3 kHz. Tests in the normal brain and in the F98 glioma model in vivo demonstrated that this controller enables reliable and damage-free delivery of a predetermined amount of the chemotherapeutic drug (liposomal doxorubicin) into the brain. The maximum concentration level of delivered doxorubicin exceeded levels previously shown (using uncontrolled sonication) to induce tumor regression and improve survival in rat glioma. These results confirmed the ability of the controller to modulate the drug delivery dosage within a therapeutically effective range, while improving safety control. It can be readily implemented clinically and potentially applied to other cavitation-enhanced ultrasound therapies.

Pherotype Polymorphism in Streptococcus pneumoniae Has No Obvious Effects on Population Structure and Recombination.

Natural transformation in the Gram-positive pathogen Streptococcus pneumoniae occurs when cells become "competent," a state that is induced in response to high extracellular concentrations of a secreted peptide signal called competence stimulating peptide (CSP) encoded by the comC locus. Two main CSP signal types (pherotypes) are known to dominate the pherotype diversity across strains. Using 4,089 fully sequenced pneumococcal genomes, we confirm that pneumococcal populations are highly genetically structured and that there is significant variation among diverged populations in pherotype frequencies; most carry only a single pherotype. Moreover, we find that the relative frequencies of the two dominant pherotypes significantly vary within a small range across geographical sites. It has been variously proposed that pherotypes either promote genetic exchange among cells expressing the same pherotype, or conversely that they promote recombination between strains bearing different pherotypes. We attempt to distinguish these hypotheses using a bioinformatics approach by estimating recombination frequencies within and between pherotypes across 4,089 full genomes. Despite underlying population structure, we observe extensive recombination between populations; additionally, we found significantly higher (although marginal) rates of genetic exchange between strains expressing different pherotypes than among isolates carrying the same pherotype. Our results indicate that pherotypes do not restrict, and may even slightly facilitate, recombination between strains; however, these marginal effects suggest the more likely possibility that the cause of CSP polymorphism lies outside of its effects on transformation. Our results suggest that the CSP balanced polymorphism does not causally underlie population differentiation. Therefore, when strains carrying different pherotypes encounter one another during cocolonization, genetic exchange can occur without restriction.

Diminishing Returns on Intragenic Repeat Number Expansion in the Production of Signaling Peptides.

Signaling peptides enable communication between cells, both within and between individuals, and are therefore key to the control of complex physiological and behavioral responses. Since their small sizes prevent direct transmission to secretory pathways, these peptides are often produced as part of a larger polyprotein comprising precursors for multiple related or identical peptides; the physiological and behavioral consequences of this unusual gene structure are not understood. Here, we show that the number of mature-pheromone-encoding repeats in the yeast α-mating-factor gene MFα1 varies considerably between closely related isolates of both Saccharomyces cerevisiae and its sister species Saccharomyces paradoxus. Variation in repeat number has important phenotypic consequences: Increasing repeat number caused higher pheromone production and greater competitive mating success. However, the magnitude of the improvement decreased with increasing repeat number such that repeat amplification beyond that observed in natural isolates failed to generate more pheromone, and could actually reduce sexual fitness. We investigate multiple explanations for this pattern of diminishing returns and find that our results are most consistent with a translational trade-off: Increasing the number of encoded repeats results in more mature pheromone per translation event, but also generates longer transcripts thereby reducing the rate of translation-a phenomenon known as length-dependent translation. Length-dependent translation may be a powerful constraint on the evolution of genes encoding repetitive or modular proteins, with important physiological and behavioral consequences across eukaryotes.

Joint volumetric extraction and enhancement of vasculature from low-SNR 3-D fluorescence microscopy images.

To simultaneously overcome the challenges imposed by the nature of optical imaging characterized by a range of artifacts including space-varying signal to noise ratio (SNR), scattered light, and non-uniform illumination, we developed a novel method that segments the 3-D vasculature directly from original fluorescence microscopy images eliminating the need for employing pre- and post-processing steps such as noise removal and segmentation refinement as used with the majority of segmentation techniques. Our method comprises two initialization and constrained recovery and enhancement stages. The initialization approach is fully automated using features derived from bi-scale statistical measures and produces seed points robust to non-uniform illumination, low SNR, and local structural variations. This algorithm achieves the goal of segmentation via design of an iterative approach that extracts the structure through voting of feature vectors formed by distance, local intensity gradient, and median measures. Qualitative and quantitative analysis of the experimental results obtained from synthetic and real data prove the effcacy of this method in comparison to the state-of-the-art enhancing-segmenting methods. The algorithmic simplicity, freedom from having a priori probabilistic information about the noise, and structural definition gives this algorithm a wide potential range of applications where i.e. structural complexity significantly complicates the segmentation problem.

Steady at the wheel: conservative sex and the benefits of bacterial transformation.

Many bacteria are highly sexual, but the reasons for their promiscuity remain obscure. Did bacterial sex evolve to maximize diversity and facilitate adaptation in a changing world, or does it instead help to retain the bacterial functions that work right now? In other words, is bacterial sex innovative or conservative? Our aim in this review is to integrate experimental, bioinformatic and theoretical studies to critically evaluate these alternatives, with a main focus on natural genetic transformation, the bacterial equivalent of eukaryotic sexual reproduction. First, we provide a general overview of several hypotheses that have been put forward to explain the evolution of transformation. Next, we synthesize a large body of evidence highlighting the numerous passive and active barriers to transformation that have evolved to protect bacteria from foreign DNA, thereby increasing the likelihood that transformation takes place among clonemates. Our critical review of the existing literature provides support for the view that bacterial transformation is maintained as a means of genomic conservation that provides direct benefits to both individual bacterial cells and to transformable bacterial populations. We examine the generality of this view across bacteria and contrast this explanation with the different evolutionary roles proposed to maintain sex in eukaryotes. This article is part of the themed issue 'Weird sex: the underappreciated diversity of sexual reproduction'.

Diverse Ecological Strategies Are Encoded by Streptococcus pneumoniae Bacteriocin-Like Peptides.

The opportunistic pathogen Streptococcus pneumoniae is commonly carried asymptomatically in the human nasopharynx. Due to high rates of cocolonization with other pneumococcus strains, intraspecific competitive interactions partly determine the carriage duration of strains and thereby their potential to cause disease. These interactions may be mediated by bacteriocins, such as the type IIb bacteriocins encoded by the blp (bacteriocin-like peptide) locus. To understand blp diversity and evolution, we undertook a bioinformatic analysis of 4,418 pneumococcal genomes, including 168 newly sequenced genomes. We describe immense variation at all levels of genomic organization: Gene presence/absence, gene order, and allelic diversity. If we make the extreme and naive hypothesis that assumes all genes in this operon can assort randomly, this variation could lead to 10(15) distinct bacteriocin-related phenotypes, each potentially representing a unique ecological strategy; however, we provide several explanations for why this extreme is not realized. Although rarefaction analysis indicates that the number of unique strategies is not saturated, even after sampling thousands of genomes, we show that the variation is neither unbounded nor random. We delimit three bacteriocin groups, which contain group-specific bacteriocins, immunity genes, and blp operon gene order, and argue that this organization places a constraint on realized ecological strategies. We additionally show that ecological strategy diversity is significantly constrained by pneumococcal phylogeny and clonal structure. By examining patterns of association between alleles within the blp operon, we show that bacteriocin genes, which were believed to function in pairs, can be found with a broad diversity of partner alleles and immunity genes; this overall lack of allelic fidelity likely contributes to the fluid structure of this operon. Our results clarify the diversity of antagonistic ecological strategies in the global pneumococcal population and highlight the potential role of blp bacteriocins in competition within the nasopharynx.