The Cancer Microbiome: Distinguishing Direct and Indirect Effects Requires a Systemic View

The collection of microbes that live in and on the human body - the human microbiome - can impact on cancer initiation, progression, and response to therapy, including cancer immunotherapy. The mechanisms by which microbiomes impact on cancers can yield new diagnostics and treatments, but much remains unknown. The interactions between microbes, diet, host factors, drugs, and cell-cell interactions within the cancer itself likely involve intricate feedbacks, and no single component can explain all the behavior of the system. Understanding the role of host-associated microbial communities in cancer systems will require a multidisciplinary approach combining microbial ecology, immunology, cancer cell biology, and computational biology - a systems biology approach

Evolution at the edge of expanding populations

Predicting evolution of expanding populations is critical to control biological threats such as invasive species and cancer metastasis. Expansion is primarily driven by reproduction and dispersal, but nature abounds with examples of evolution where organisms pay a reproductive cost to disperse faster. When does selection favor this 'survival of the fastest?' We searched for a simple rule, motivated by evolution experiments where swarming bacteria evolved into an hyperswarmer mutant which disperses $\sim 100\%$ faster but pays a growth cost of $\sim 10 \%$ to make many copies of its flagellum. We analyzed a two-species model based on the Fisher equation to explain this observation: the population expansion rate ($v$) results from an interplay of growth ($r$) and dispersal ($D$) and is independent of the carrying capacity: $v=2\sqrt{rD}$. A mutant can take over the edge only if its expansion rate ($v_2$) exceeds the expansion rate of the established species ($v_1$); this simple condition ($v_2 > v_1$) determines the maximum cost in slower growth that a faster mutant can pay and still be able to take over. Numerical simulations and time-course experiments where we tracked evolution by imaging bacteria suggest that our findings are general: less favorable conditions delay but do not entirely prevent the success of the fastest. Thus, the expansion rate defines a traveling wave fitness, which could be combined with trade-offs to predict evolution of expanding populations

A simple rule for the evolution of fast dispersal at the edge of expanding populations

Evolution by natural selection is commonly perceived as a process that favors those that replicate faster to leave more offspring; nature, however, seem to abound with examples where organisms forgo some replicative potential to disperse faster. When does selection favor invasion of the fastest? Motivated by evolution experiments with swarming bacteria we searched for a simple rule. In experiments, a fast hyperswarmer mutant that pays a reproductive cost to make many copies of its flagellum invades a population of mono-flagellated bacteria by reaching the expanding population edge; a two-species mathematical model explains that invasion of the edge occurs only if the invasive species' expansion rate, v₂, which results from the combination of the species growth rate and its dispersal speed (but not its carrying capacity), exceeds the established species', v₁. The simple rule that we derive, v₂ > v₁, appears to be general: less favorable initial conditions, such as smaller initial sizes and longer distances to the population edge, delay but do not entirely prevent invasion. Despite intricacies of the swarming system, experimental tests agree well with model predictions suggesting that the general theory should apply to other expanding populations with trade-offs between growth and dispersal, including non-native invasive species and cancer metastases.First author draf

Modeling microbial cross-feeding at intermediate scale portrays community dynamics and species coexistence

Social interaction between microbes can be described at many levels of details, ranging from the biochemistry of cell-cell interactions to the ecological dynamics of populations. Choosing the best level to model microbial communities without losing generality remains a challenge. Here we propose to model cross-feeding interactions at an intermediate level between genome-scale metabolic models of individual species and consumer-resource models of ecosystems, which is suitable to empirical data. We applied our method to three published examples of multi-strain Escherichia coli communities with increasing complexity consisting of uni-, bi-, and multi-directional cross-feeding of either substitutable metabolic byproducts or essential nutrients. The intermediate-scale model accurately described empirical data and could quantify exchange rates elusive by other means, such as the byproduct secretions, even for a complex community of 14 amino acid auxotrophs. We used the three models to study each community's limits of robustness to perturbations such as variations in resource supply, antibiotic treatments and invasion by other "cheaters" species. Our analysis provides a foundation to quantify cross-feeding interactions from experimental data, and highlights the importance of metabolic exchanges in the dynamics and stability of microbial communities.Comment: 6 figure

A Quantitative Test of Population Genetics Using Spatio-Genetic Patterns in Bacterial Colonies

It is widely accepted that population genetics theory is the cornerstone of evolutionary analyses. Empirical tests of the theory, however, are challenging because of the complex relationships between space, dispersal, and evolution. Critically, we lack quantitative validation of the spatial models of population genetics. Here we combine analytics, on and off-lattice simulations, and experiments with bacteria to perform quantitative tests of the theory. We study two bacterial species, the gut microbe Escherichia coli and the opportunistic pathogen Pseudomonas aeruginosa, and show that spatio-genetic patterns in colony biofilms of both species are accurately described by an extension of the one-dimensional stepping-stone model. We use one empirical measure, genetic diversity at the colony periphery, to parameterize our models and show that we can then accurately predict another key variable: the degree of short-range cell migration along an edge. Moreover, the model allows us to estimate other key parameters including effective population size (density) at the expansion frontier. While our experimental system is a simplification of natural microbial community, we argue it is a proof of principle that the spatial models of population genetics can quantitatively capture organismal evolution

Pathogenicity locus, core genome, and accessory gene contributions to Clostridium difficile virulence

Clostridium difficile is a spore-forming anaerobic bacterium that causes colitis in patients with disrupted colonic microbiota. While some individuals are asymptomatic C. difficile carriers, symptomatic disease ranges from mild diarrhea to potentially lethal toxic megacolon. The wide disease spectrum has been attributed to the infected host’s age, underlying diseases, immune status, and microbiome composition. However, strain-specific differences in C. difficile virulence have also been implicated in determining colitis severity. Because patients infected with C. difficile are unique in terms of medical history, microbiome composition, and immune competence, determining the relative contribution of C. difficile virulence to disease severity has been challenging, and conclusions regarding the virulence of specific strains have been inconsistent. To address this, we used a mouse model to test 33 clinical C. difficile strains isolated from patients with disease severities ranging from asymptomatic carriage to severe colitis, and we determined their relative in vivo virulence in genetically identical, antibiotic-pretreated mice. We found that murine infections with C. difficile clade 2 strains (including multilocus sequence type 1/ribotype 027) were associated with higher lethality and that C. difficile strains associated with greater human disease severity caused more severe disease in mice. While toxin production was not strongly correlated with in vivo colonic pathology, the ability of C. difficile strains to grow in the presence of secondary bile acids was associated with greater disease severity. Whole-genome sequencing and identification of core and accessory genes identified a subset of accessory genes that distinguish high-virulence from lower-virulence C. difficile strains

Bow-tie signaling in c-di-GMP: Machine learning in a simple biochemical network

Bacteria of many species rely on a simple molecule, the intracellular secondary messenger c-di-GMP (Bis-(3'-5')-cyclic dimeric guanosine monophosphate), to make a vital choice: whether to stay in one place and form a biofilm, or to leave it in search of better conditions. The c-di-GMP network has a bow-tie shaped architecture that integrates many signals from the outside world—the input stimuli—into intracellular c-di-GMP levels that then regulate genes for biofilm formation or for swarming motility—the output phenotypes. How does the ‘uninformed’ process of evolution produce a network with the right input/output association and enable bacteria to make the right choice? Inspired by new data from 28 clinical isolates of Pseudomonas aeruginosa and strains evolved in laboratory experiments we propose a mathematical model where the c-di-GMP network is analogous to a machine learning classifier. The analogy immediately suggests a mechanism for learning through evolution: adaptation though incremental changes in c-di-GMP network proteins acquires knowledge from past experiences and enables bacteria to use it to direct future behaviors. Our model clarifies the elusive function of the ubiquitous c-di-GMP network, a key regulator of bacterial social traits associated with virulence. More broadly, the link between evolution and machine learning can help explain how natural selection across fluctuating environments produces networks that enable living organisms to make sophisticated decisions

Insights into the posttranslational structural heterogeneity of thyroglobulin and its role in the development, diagnosis, and management of benign and malignant thyroid diseases

Thyroglobulin (Tg) is the major glycoprotein produced by the thyroid gland, where it serves as a template for thyroid hormone synthesis and as an intraglandular store of iodine. Measurement of Tg levels in serum is of great practical importance in the follow-up of differentiated thyroid carcinoma (DTC), a setting in which elevated levels after total thyroidectomy are indicative of residual or recurrent disease. The most recent methods for serum Tg measurement are monoclonal antibody-based and are highly sensitive. However, major challenges remain regarding the interpretation of the results obtained with these immunometric methods, particularly in patients with endogenous antithyroglobulin antibodies or in the presence of heterophile antibodies, which may produce falsely low or high Tg values, respectively. The increased prevalence of antithyroglobulin antibodies in patients with DTC, as compared with the general population, raises the very pertinent possibility that tumor Tg may be more immunogenic. This inference makes sense, as the tumor microenvironment (tumor cells plus normal host cells) is characterized by several changes that could induce posttranslational modification of many proteins, including Tg. Attempts to understand the structure of Tg have been made for several decades, but findings have generally been incomplete due to technical hindrances to analysis of such a large protein (660 kDa). This review article will explore the complex structure of Tg and the potential role of its marked heterogeneity in our understanding of normal thyroid biology and neoplastic processes.FapespCNPqCapesUniv Fed Sao Paulo EPM Unifesp, Escola Paulista Med, Lab Endocrinol Mol & Translac, Div Endocrinol & Metab,Dept Med, Sao Paulo, SP, BrazilUniv Fed Mato Grosso do Sul UFMS, Fac Med Famed, Dept Med, Clin Integrada 5,Endocrinol & Metab, Campo Grande, MS, BrazilUniv Fed Sao Paulo, EPM, Dept Bioquim, Div Mol Biol, Sao Paulo, SP, BrazilUniv Fed Sao Paulo EPM Unifesp, Escola Paulista Med, Lab Endocrinol Mol & Translac, Div Endocrinol & Metab,Dept Med, Sao Paulo, SP, BrazilUniv Fed Sao Paulo, EPM, Dept Bioquim, Div Mol Biol, Sao Paulo, SP, BrazilWeb of Scienc

Social interaction, noise and antibiotic-mediated switches in the intestinal microbiota

The intestinal microbiota plays important roles in digestion and resistance against entero-pathogens. As with other ecosystems, its species composition is resilient against small disturbances but strong perturbations such as antibiotics can affect the consortium dramatically. Antibiotic cessation does not necessarily restore pre-treatment conditions and disturbed microbiota are often susceptible to pathogen invasion. Here we propose a mathematical model to explain how antibiotic-mediated switches in the microbiota composition can result from simple social interactions between antibiotic-tolerant and antibiotic-sensitive bacterial groups. We build a two-species (e.g. two functional-groups) model and identify regions of domination by antibiotic-sensitive or antibiotic-tolerant bacteria, as well as a region of multistability where domination by either group is possible. Using a new framework that we derived from statistical physics, we calculate the duration of each microbiota composition state. This is shown to depend on the balance between random fluctuations in the bacterial densities and the strength of microbial interactions. The singular value decomposition of recent metagenomic data confirms our assumption of grouping microbes as antibiotic-tolerant or antibiotic-sensitive in response to a single antibiotic. Our methodology can be extended to multiple bacterial groups and thus it provides an ecological formalism to help interpret the present surge in microbiome data.Comment: 20 pages, 5 figures accepted for publication in Plos Comp Bio. Supplementary video and information availabl