TrackSOM: Mapping immune response dynamics through clustering of time-course cytometry data.

Mapping the dynamics of immune cell populations over time or disease-course is key to understanding immunopathogenesis and devising putative interventions. We present TrackSOM, a novel method for delineating cellular populations and tracking their development over a time- or disease-course cytometry datasets. We demonstrate TrackSOM-enabled elucidation of the immune response to West Nile Virus infection in mice, uncovering heterogeneous subpopulations of immune cells and relating their functional evolution to disease severity. TrackSOM is easy to use, encompasses few parameters, is quick to execute, and enables an integrative and dynamic overview of the immune system kinetics that underlie disease progression and/or resolution.

Conceptual frameworks and key questions for assessing the contribution of marine protected areas to shark and ray conservation.

Marine protected areas (MPAs) are key tools in addressing the global decline of sharks and rays, and marine parks and shark sanctuaries of various configurations have been established to conserve shark populations. However, assessments of their efficacy are compromised by inconsistent terminology, lack of standardized approaches to assess how MPAs contribute to shark and ray conservation, and ambiguity about how to integrate movement data in assessment processes. We devised a conceptual framework to standardize key terms (e.g., protection, contribution, potential impact, risk, threat) and used the concept of portfolio risk to identify key attributes of sharks and rays (assets), the threats they face (portfolio risk), and the specific role of MPAs in risk mitigation (insurance). Movement data can be integrated into the process by informing risk exposure and mitigation through MPAs. The framework is operationalized by posing 8 key questions that prompt practitioners to consider the assessment scope, MPA type and purpose, range of existing and potential threats, species biology and ecology, and management and operational contexts. Ultimately, MPA contributions to shark and ray conservation differ according to a complex set of human and natural factors and interactions that should be carefully considered in MPA design, implementation, and evaluation.

Marcos conceptuales y preguntas clave para evaluar la contribución de las áreas marinas protegidas a la conservación de tiburones y rayas Resumen Las áreas marinas protegidas (AMP) son herramientas importantes para manejar la declinación mundial de tiburones y rayas, por lo que se han establecido parques marinos y santuarios de diversas configuraciones para conservar las poblaciones de tiburones. Sin embargo, el análisis de su eficiencia está compuesto por una terminología inconstante, la falta de estrategias estandarizadas para evaluar cómo las AMP contribuyen a la conservación de tiburones y rayas, y una ambigüedad sobre cómo integrar la información sobre movimientos en los procesos de evaluación. Diseñamos un marco conceptual para estandarizar los términos más importantes (p. ej.: protección, contribución, impacto potencial, amenaza, riesgo) y usamos el concepto de riesgo de portafolio para identificar los atributos clave de los tiburones y las rayas (activos), las amenazas que enfrentan (riesgo de portafolio) y el papel específico que juegan las AMP en la mitigación del riesgo (seguro). La información sobre los movimientos puede integrarse al proceso al guiar la exposición y mitigación del riesgo con las AMP. El marco conceptual es operado con el planteamiento de ocho preguntas clave que invitan a los practicantes a considerar el enfoque de la evaluación, el tipo de AMP y su propósito, gama de amenazas existentes y potenciales, la biología y ecología de las especies, y los contextos operativos y de manejo. Finalmente, las contribuciones que tienen las AMP a la conservación de los tiburones y las rayas difieren de acuerdo con un conjunto complejo de factores naturales y humanos e interacciones que deberían considerarse cuidadosamente en el diseño, implementación y evaluación de la AMP.

High throughput genome scale modeling predicts microbial vitamin requirements contribute to gut microbiome community structure.

Human gut microbiome structure and emergent metabolic outputs impact health outcomes. However, what drives such community characteristics remains underexplored. Here, we rely on high throughput genomic reconstruction modeling, to infer the metabolic attributes and nutritional requirements of 816 gut strains, via a framework termed GEMNAST. This has been performed in terms of a group of human vitamins to examine the role vitamin exchanges have at different levels of community organization. We find that only 91 strains can satisfy their vitamin requirements (prototrophs) while the rest show various degrees of auxotrophy/specialization, highlighting their dependence on external sources, such as other members of the microbial community. Further, 79% of the strains in our sample were mapped to 11 distinct vitamin requirement profiles with low phylogenetic consistency. Yet, we find that human gut microbial community enterotype indicators display marked metabolic differences. Prevotella strains display a metabolic profile that can be complemented by strains from other genera often associated with the Prevotella enterotype and agrarian diets, while Bacteroides strains occupy a prototrophic profile. Finally, we identify pre-defined interaction modules (IMs) of gut species from human and mice predicted to be driven by, or highly independent of vitamin exchanges. Our analysis provides mechanistic grounding to gut microbiome stability and to co-abundance-based observations, a fundamental step toward understanding emergent processes that influence health outcomes. Further, our work opens a path to future explorations in the field through applications of GEMNAST to additional nutritional dimensions.

Diet-driven microbial ecology underpins associations between cancer immunotherapy outcomes and the gut microbiome.

The gut microbiota shapes the response to immune checkpoint inhibitors (ICIs) in cancer, however dietary and geographic influences have not been well-studied in prospective trials. To address this, we prospectively profiled baseline gut (fecal) microbiota signatures and dietary patterns of 103 trial patients from Australia and the Netherlands treated with neoadjuvant ICIs for high risk resectable metastatic melanoma and performed an integrated analysis with data from 115 patients with melanoma treated with ICIs in the United States. We observed geographically distinct microbial signatures of response and immune-related adverse events (irAEs). Overall, response rates were higher in Ruminococcaceae-dominated microbiomes than in Bacteroidaceae-dominated microbiomes. Poor response was associated with lower fiber and omega 3 fatty acid consumption and elevated levels of C-reactive protein in the peripheral circulation at baseline. Together, these data provide insight into the relevance of native gut microbiota signatures, dietary intake and systemic inflammation in shaping the response to and toxicity from ICIs, prompting the need for further studies in this area.

Dietary protein increases T-cell-independent sIgA production through changes in gut microbiota-derived extracellular vesicles.

Secretory IgA is a key mucosal component ensuring host-microbiota mutualism. Here we use nutritional geometry modelling in mice fed 10 different macronutrient-defined, isocaloric diets, and identify dietary protein as the major driver of secretory IgA production. Protein-driven secretory IgA induction is not mediated by T-cell-dependent pathways or changes in gut microbiota composition. Instead, the microbiota of high protein fed mice produces significantly higher quantities of extracellular vesicles, compared to those of mice fed high-carbohydrate or high-fat diets. These extracellular vesicles activate Toll-like receptor 4 to increase the epithelial expression of IgA-inducing cytokine, APRIL, B cell chemokine, CCL28, and the IgA transporter, PIGR. We show that succinate, produced in high concentrations by microbiota of high protein fed animals, increases generation of reactive oxygen species by bacteria, which in turn promotes extracellular vesicles production. Here we establish a link between dietary macronutrient composition, gut microbial extracellular vesicles release and host secretory IgA response.

Peripheral B-cell dysregulation is associated with relapse after long-term quiescence in patients with multiple sclerosis.

B cells play a major role in multiple sclerosis (MS), with many successful therapeutics capable of removing them from circulation. One such therapy, alemtuzumab, is thought to reset the immune system without the need for ongoing therapy in a proportion of patients. The exact cells contributing to disease pathogenesis and quiescence remain to be identified. We utilized mass cytometry to analyze B cells from the blood of patients with relapse-remitting MS (RRMS) before and after alemtuzumab treatment, and during relapse. A complementary RRMS cohort was analyzed by single-cell RNA sequencing. The R package "Spectre" was used to analyze these data, incorporating FlowSOM clustering, sparse partial least squares-discriminant analysis and permutational multivariate analysis of variance. Immunoglobulin (Ig)A+ and IgG1 + B-cell numbers were altered, including higher IgG1 + B cells during relapse. B-cell linker protein (BLNK), CD40 and CD210 expression by B cells was lower in patients with RRMS compared with non-MS controls, with similar results at the transcriptomic level. Finally, alemtuzumab restored BLNK, CD40 and CD210 expression by IgA+ and IgG1 + B cells, which was altered again during relapse. These data suggest that impairment of IgA+ and IgG1 + B cells may contribute to MS pathogenesis, which can be restored by alemtuzumab.

Integration, exploration, and analysis of high-dimensional single-cell cytometry data using Spectre.

As the size and complexity of high-dimensional (HD) cytometry data continue to expand, comprehensive, scalable, and methodical computational analysis approaches are essential. Yet, contemporary clustering and dimensionality reduction tools alone are insufficient to analyze or reproduce analyses across large numbers of samples, batches, or experiments. Moreover, approaches that allow for the integration of data across batches or experiments are not well incorporated into computational toolkits to allow for streamlined workflows. Here we present Spectre, an R package that enables comprehensive end-to-end integration and analysis of HD cytometry data from different batches or experiments. Spectre streamlines the analytical stages of raw data pre-processing, batch alignment, data integration, clustering, dimensionality reduction, visualization, and population labelling, as well as quantitative and statistical analysis. Critically, the fundamental data structures used within Spectre, along with the implementation of machine learning classifiers, allow for the scalable analysis of very large HD datasets, generated by flow cytometry, mass cytometry, or spectral cytometry. Using open and flexible data structures, Spectre can also be used to analyze data generated by single-cell RNA sequencing or HD imaging technologies, such as Imaging Mass Cytometry. The simple, clear, and modular design of analysis workflows allow these tools to be used by bioinformaticians and laboratory scientists alike. Spectre is available as an R package or Docker container. R code is available on Github (https://github.com/immunedynamics/spectre).

Enabling rational gut microbiome manipulations by understanding gut ecology through experimentally-evidenced in silico models.

The gut microbiome has emerged as a contributing factor in non-communicable disease, rendering it a target of health-promoting interventions. Yet current understanding of the host-microbiome dynamic is insufficient to predict the variation in intervention outcomes across individuals. We explore the mechanisms that underpin the gut bacterial ecosystem and highlight how a more complete understanding of this ecology will enable improved intervention outcomes. This ecology varies within the gut over space and time. Interventions disrupt these processes, with cascading consequences throughout the ecosystem. In vivo studies cannot isolate and probe these processes at the required spatiotemporal resolutions, and in vitro studies lack the representative complexity required. However, we highlight that, together, both approaches can inform in silico models that integrate cellular-level dynamics, can extrapolate to explain bacterial community outcomes, permit experimentation and observation over ecological processes at high spatiotemporal resolution, and can serve as predictive platforms on which to prototype interventions. Thus, it is a concerted integration of these techniques that will enable rational targeted manipulations of the gut ecosystem.

Dietary carbohydrate, particularly glucose, drives B cell lymphopoiesis and function.

While diet modulates immunity, its impact on B cell ontogeny remains unclear. Using mixture modeling, a large-scale isocaloric dietary cohort mouse study identified carbohydrate as a major driver of B cell development and function. Increasing dietary carbohydrate increased B cell proportions in spleen, mesenteric lymph node and Peyer's patches, and increased antigen-specific immunoglobulin G production after immunization. This was linked to increased B lymphopoiesis in the bone marrow. Glucose promoted early B lymphopoiesis and higher total B lymphocyte numbers than fructose. It drove B cell development through glycolysis and oxidative phosphorylation, independently of fatty acid oxidation in vitro and reduced B cell apoptosis in early development via mTOR activation, independently of interleukin-7. Ours is the first comprehensive study showing the impact of macronutrients on B cell development and function. It shows the quantitative and qualitative interplay between dietary carbohydrate and B cells and argues for dietary modulation in B cell-targeting strategies.

Detection and characterization of chemotaxis without cell tracking.

Swarming has been observed in various biological systems from collective animal movements to immune cells. In the cellular context, swarming is driven by the secretion of chemotactic factors. Despite the critical role of chemotactic swarming, few methods to robustly identify and quantify this phenomenon exist. Here, we present a novel method for the analysis of time series of positional data generated from realizations of agent-based processes. We convert the positional data for each individual time point to a function measuring agent aggregation around a given area of interest, hence generating a functional time series. The functional time series, and a more easily visualized swarming metric of agent aggregation derived from these functions, provide useful information regarding the evolution of the underlying process over time. We extend our method to build upon the modelling of collective motility using drift-diffusion partial differential equations (PDEs). Using a functional linear model, we are able to use the functional time series to estimate the drift and diffusivity terms associated with the underlying PDE. By producing an accurate estimate for the drift coefficient, we can infer the strength and range of attraction or repulsion exerted on agents, as in chemotaxis. Our approach relies solely on using agent positional data. The spatial distribution of diffusing chemokines is not required, nor do individual agents need to be tracked over time. We demonstrate our approach using random walk simulations of chemotaxis and experiments investigating cytotoxic T cells interacting with tumouroids.

Integrated immune dynamics define correlates of COVID-19 severity and antibody responses.

SARS-CoV-2 causes a spectrum of COVID-19 disease, the immunological basis of which remains ill defined. We analyzed 85 SARS-CoV-2-infected individuals at acute and/or convalescent time points, up to 102 days after symptom onset, quantifying 184 immunological parameters. Acute COVID-19 presented with high levels of IL-6, IL-18, and IL-10 and broad activation marked by the upregulation of CD38 on innate and adaptive lymphocytes and myeloid cells. Importantly, activated CXCR3+cTFH1 cells in acute COVID-19 significantly correlate with and predict antibody levels and their avidity at convalescence as well as acute neutralization activity. Strikingly, intensive care unit (ICU) patients with severe COVID-19 display higher levels of soluble IL-6, IL-6R, and IL-18, and hyperactivation of innate, adaptive, and myeloid compartments than patients with moderate disease. Our analyses provide a comprehensive map of longitudinal immunological responses in COVID-19 patients and integrate key cellular pathways of complex immune networks underpinning severe COVID-19, providing important insights into potential biomarkers and immunotherapies.

Using single-cell cytometry to illustrate integrated multi-perspective evaluation of clustering algorithms using Pareto fronts.

MOTIVATION: Many 'automated gating' algorithms now exist to cluster cytometry and single cell sequencing data into discrete populations. Comparative algorithm evaluations on benchmark datasets rely either on a single performance metric, or a few metrics considered independently of one another. However, single metrics emphasise different aspects of clustering performance and do not rank clustering solutions in the same order. This underlies the lack of consensus between comparative studies regarding optimal clustering algorithms and undermines the translatability of results onto other non-benchmark datasets. RESULTS: We propose the Pareto fronts framework as an integrative evaluation protocol, wherein individual metrics are instead leveraged as complementary perspectives. Judged superior are algorithms that provide the best trade-off between the multiple metrics considered simultaneously. This yields a more comprehensive and complete view of clustering performance. Moreover, by broadly and systematically sampling algorithm parameter values using the Latin Hypercube sampling method, our evaluation protocol minimises (un)fortunate parameter value selections as confounding factors. Furthermore, it reveals how meticulously each algorithm must be tuned in order to obtain good results, vital knowledge for users with novel data. We exemplify the protocol by conducting a comparative study between three clustering algorithms (ChronoClust, FlowSOM and Phenograph) using four common performance metrics applied across four cytometry benchmark datasets. To our knowledge, this is the first time Pareto fronts have been used to evaluate the performance of clustering algorithms in any application domain. AVAILABILITY: Implementation of our Pareto front methodology and all scripts to reproduce this article are available at https://github.com/ghar1821/ParetoBench.

Nuclear F-actin counteracts nuclear deformation and promotes fork repair during replication stress.

Filamentous actin (F-actin) provides cells with mechanical support and promotes the mobility of intracellular structures. Although F-actin is traditionally considered to be cytoplasmic, here we reveal that nuclear F-actin participates in the replication stress response. Using live and super-resolution imaging, we find that nuclear F-actin is polymerized in response to replication stress through a pathway regulated by ATR-dependent activation of mTORC1, and nucleation through IQGAP1, WASP and ARP2/3. During replication stress, nuclear F-actin increases the nuclear volume and sphericity to counteract nuclear deformation. Furthermore, F-actin and myosin II promote the mobility of stressed-replication foci to the nuclear periphery through increasingly diffusive motion and directed movements along the nuclear actin filaments. These actin functions promote replication stress repair and suppress chromosome and mitotic abnormalities. Moreover, we find that nuclear F-actin is polymerized in vivo in xenograft tumours after treatment with replication-stress-inducing chemotherapeutic agents, indicating that this pathway has a role in human disease.

Disease risk analysis in sea turtles: A baseline study to inform conservation efforts.

The impact of a range of different threats has resulted in the listing of six out of seven sea turtle species on the IUCN Red List of endangered species. Disease risk analysis (DRA) tools are designed to provide objective, repeatable and documented assessment of the disease risks for a population and measures to reduce these risks through management options. To the best of our knowledge, DRAs have not previously been published for sea turtles, although disease is reported to contribute to sea turtle population decline. Here, a comprehensive list of health hazards is provided for all seven species of sea turtles. The possible risk these hazards pose to the health of sea turtles were assessed and "One Health" aspects of interacting with sea turtles were also investigated. The risk assessment was undertaken in collaboration with more than 30 experts in the field including veterinarians, microbiologists, social scientists, epidemiologists and stakeholders, in the form of two international workshops and one local workshop. The general finding of the DRA was the distinct lack of knowledge regarding a link between the presence of pathogens and diseases manifestation in sea turtles. A higher rate of disease in immunocompromised individuals was repeatedly reported and a possible link between immunosuppression and environmental contaminants as a result of anthropogenic influences was suggested. Society based conservation initiatives and as a result the cultural and social aspect of interacting with sea turtles appeared to need more attention and research. A risk management workshop was carried out to acquire the insights of local policy makers about management options for the risks relevant to Queensland and the options were evaluated considering their feasibility and effectiveness. The sea turtle DRA presented here, is a structured guide for future risk assessments to be used in specific scenarios such as translocation and head-starting programs.

Cytotoxic T cells swarm by homotypic chemokine signalling.

Cytotoxic T lymphocytes (CTLs) are thought to arrive at target sites either via random search or following signals by other leukocytes. Here, we reveal independent emergent behaviour in CTL populations attacking tumour masses. Primary murine CTLs coordinate their migration in a process reminiscent of the swarming observed in neutrophils. CTLs engaging cognate targets accelerate the recruitment of distant T cells through long-range homotypic signalling, in part mediated via the diffusion of chemokines CCL3 and CCL4. Newly arriving CTLs augment the chemotactic signal, further accelerating mass recruitment in a positive feedback loop. Activated effector human T cells and chimeric antigen receptor (CAR) T cells similarly employ intra-population signalling to drive rapid convergence. Thus, CTLs recognising a cognate target can induce a localised mass response by amplifying the direct recruitment of additional T cells independently of other leukocytes.

Immune cells known as cytotoxic T lymphocytes, or CTLs for short, move around the body searching for infected or damaged cells that may cause harm. Once these specialised killer cells identify a target, they launch an attack, removing the harmful cell from the body. CTLs can also recognise and eliminate cancer cells, and can be infused into cancer patients as a form of treatment called adoptive cell transfer immunotherapy. Unfortunately, this kind of treatment does not yet work well on solid tumours because the immune cells often do not infiltrate them sufficiently. It is thought that CTLs arrive at their targets either by randomly searching or by following chemicals secreted by other immune cells. However, the methods used to map the movement of these killer cells have made it difficult to determine how populations of CTLs coordinate their behaviour independently of other cells in the immune system. To overcome this barrier, Galeano Niño, Pageon, Tay et al. employed a three-dimensional model known as a tumouroid embedded in a matrix of proteins, which mimics the tissue environment of a real tumour in the laboratory. These models were used to track the movement of CTLs extracted from mice and humans, as well as human T cells engineered to recognise cancer cells. The experiments showed that when a CTL identifies a tumour cell, it releases chemical signals known as chemokines, which attract other CTLs and recruit them to the target site. Further experiments and computer simulations revealed that as the number of CTLs arriving at the target site increases, this amplifies the chemokine signal being secreted, resulting in more and more CTLs being attracted to the tumour. Other human T cells that had been engineered to recognize cancer cells were also found to employ this method of mass recruitment, and collectively 'swarm' towards targeted tumours. These findings shed new light on how CTLs work together to attack a target. It is possible that exploiting the mechanism used by CTLs could help improve the efficiency of tumour-targeting immunotherapies. However, further studies are needed to determine whether these findings can be applied to solid tumours in cancer patients.

Fecal microbiota transplantation from high caloric-fed donors alters glucose metabolism in recipient mice, independently of adiposity or exercise status.

Studies suggest the gut microbiota contributes to the development of obesity and metabolic syndrome. Exercise alters microbiota composition and diversity and is protective of these maladies. We tested whether the protective metabolic effects of exercise are mediated through fecal components through assessment of body composition and metabolism in recipients of fecal microbiota transplantation (FMT) from exercise-trained (ET) mice fed normal or high-energy diets. Donor C57BL/6J mice were fed a chow or high-fat, high-sucrose diet (HFHS) for 4 wk to induce obesity and glucose intolerance. Mice were divided into sedentary (Sed) or ET groups (6 wk treadmill-based ET) while maintaining their diets, resulting in four donor groups: chow sedentary (NC-Sed) or ET (NC-ET) and HFHS sedentary (HFHS-Sed) or ET (HFHS-ET). Chow-fed recipient mice were gavaged with feces from the respective donor groups weekly, creating four groups (NC-Sed-R, NC-ET-R, HFHS-Sed-R, HFHS-ET-R), and body composition and metabolism were assessed. The HFHS diet led to glucose intolerance and obesity in the donors, whereas exercise training (ET) restrained adiposity and improved glucose tolerance. No donor group FMT altered recipient body composition. Despite unaltered adiposity, glucose levels were disrupted when challenged in mice receiving feces from HFHS-fed donors, irrespective of donor-ET status, with a decrease in insulin-stimulated glucose clearance into white adipose tissue and large intestine and specific changes in the recipient's microbiota composition observed. FMT can transmit HFHS-induced disrupted glucose metabolism to recipient mice independently of any change in adiposity. However, the protective metabolic effect of ET on glucose metabolism is not mediated through fecal factors.

Strategies for calibrating models of biology.

Computational and mathematical modelling has become a valuable tool for investigating biological systems. Modelling enables prediction of how biological components interact to deliver system-level properties and extrapolation of biological system performance to contexts and experimental conditions where this is unknown. A model's value hinges on knowing that it faithfully represents the biology under the contexts of use, or clearly ascertaining otherwise and thus motivating further model refinement. These qualities are evaluated through calibration, typically formulated as identifying model parameter values that align model and biological behaviours as measured through a metric applied to both. Calibration is critical to modelling but is often underappreciated. A failure to appropriately calibrate risks unrepresentative models that generate erroneous insights. Here, we review a suite of strategies to more rigorously challenge a model's representation of a biological system. All are motivated by features of biological systems, and illustrative examples are drawn from the modelling literature. We examine the calibration of a model against distributions of biological behaviours or outcomes, not only average values. We argue for calibration even where model parameter values are experimentally ascertained. We explore how single metrics can be non-distinguishing for complex systems, with multiple-component dynamic and interaction configurations giving rise to the same metric output. Under these conditions, calibration is insufficiently constraining and the model non-identifiable: multiple solutions to the calibration problem exist. We draw an analogy to curve fitting and argue that calibrating a biological model against a single experiment or context is akin to curve fitting against a single data point. Though useful for communicating model results, we explore how metrics that quantify heavily emergent properties may not be suitable for use in calibration. Lastly, we consider the role of sensitivity and uncertainty analysis in calibration and the interpretation of model results. Our goal in this manuscript is to encourage a deeper consideration of calibration, and how to increase its capacity to either deliver faithful models or demonstrate them otherwise.

Models of the Gut for Analyzing the Impact of Food and Drugs.

Models of the human gastrointestinal tract (GIT) can be powerful tools for examining the biological interactions of food products and pharmaceuticals. This can be done under normal healthy conditions or using models of disease-many of which have no curative therapy. This report outlines the field of gastrointestinal modeling, with a particular focus on the intestine. Traditional in vivo animal models are compared to a range of in vitro models. In vitro systems are elaborated over time, recently culminating with microfluidic intestines-on-chips (IsOC) and 3D bioengineered models. Macroscale models are also reviewed for their important contribution in the microbiota studies. Lastly, it is discussed how in silico approaches may have utility in predicting and interpreting experimental data. The various advantages and limitations of the different systems are contrasted. It is posited that only through complementary use of these models will salient research questions be able to be addressed.

Memory B cells are reactivated in subcapsular proliferative foci of lymph nodes.

Vaccine-induced immunity depends on the generation of memory B cells (MBC). However, where and how MBCs are reactivated to make neutralising antibodies remain unknown. Here we show that MBCs are prepositioned in a subcapsular niche in lymph nodes where, upon reactivation by antigen, they rapidly proliferate and differentiate into antibody-secreting plasma cells in the subcapsular proliferative foci (SPF). This novel structure is enriched for signals provided by T follicular helper cells and antigen-presenting subcapsular sinus macrophages. Compared with contemporaneous secondary germinal centres, SPF have distinct single-cell molecular signature, cell migration pattern and plasma cell output. Moreover, SPF are found both in human and mouse lymph nodes, suggesting that they are conserved throughout mammalian evolution. Our data thus reveal that SPF is a seat of immunological memory that may be exploited to rapidly mobilise secondary antibody responses and improve vaccine efficacy.

Towards an Integrative Understanding of Diet-Host-Gut Microbiome Interactions.

Over the last 20 years, a sizeable body of research has linked the microbiome and host diet to a remarkable diversity of diseases. Yet, unifying principles of microbiome assembly or function, at levels required to rationally manipulate a specific individual's microbiome to their benefit, have not emerged. A key driver of both community composition and activity is the host diet, but diet-microbiome interactions cannot be characterized without consideration of host-diet interactions such as appetite and digestion. This becomes even more complex if health outcomes are to be explored, as microbes engage in multiple interactions and feedback pathways with the host. Here, we review these interactions and set forth the need to build conceptual models of the diet-microbiome-host axes that draw out the key principles governing this system's dynamics. We highlight how "units of response," characterizations of similarly behaving microbes, do not correlate consistently with microbial sequence relatedness, raising a challenge for relating high-throughput data sets to conceptual models. Furthermore, they are question-specific; responses to resource environment may be captured at higher taxonomic levels, but capturing microbial products that depend on networks of different interacting populations, such as short-chain fatty acid production through anaerobic fermentation, can require consideration of the entire community. We posit that integrative approaches to teasing apart diet-microbe-host interactions will help bridge between experimental data sets and conceptual models and will be of value in formulating predictive models.