Prevalence of mutualism in a simple model of microbial coevolution.

Evolutionary transitions among ecological interactions are widely known, although their detailed dynamics remain absent for most population models. Adaptive dynamics has been used to illustrate how the parameters of population models might shift through evolution, but within an ecological regime. Here we use adaptive dynamics combined with a generalized logistic model of population dynamics to show that transitions of ecological interactions might appear as a consequence of evolution. To this purpose, we introduce a two-microbial toy model in which population parameters are determined by a bookkeeping of resources taken from (and excreted to) the environment, as well as from the byproducts of the other species. Despite its simplicity, this model exhibits all kinds of potential ecological transitions, some of which resemble those found in nature. Overall, the model shows a clear trend toward the emergence of mutualism.

Implication of Different Tumor Biomarkers in Drug Resistance and Invasiveness in Primary and Metastatic Colorectal Cancer Cell Lines.

Protein expression profiles are directly related to the different properties of cells and are conditioned by the cellular niche. As an example, they are the cause of the characteristic cell plasticity, epithelium-mesenchymal transition (EMT), and drug resistance of cancer cells. This article characterizes ten biomarkers related to these features in three human colorectal cancer cell lines: SW-480, SW-620, and DLD-1, evaluated by flow cytometry; and in turn, resistance to oxaliplatin is studied through dose-response trials. The main biomarkers present in the three studied lines correspond to EpCAM, CD-133, and AC-133, with the latter two in low proportions in the DLD-1 line. The biomarker CD166 is present in greater amounts in SW-620 and DLD-1 compared to SW-480. Finally, DLD-1 shows high values of Trop2, which may explain the aggressiveness and resistance of these cells to oxaliplatin treatments, as EpCAM is also highly expressed. Exposure to oxaliplatin slows cell growth but also helps generate resistance to the treatment. In conclusion, the response of the cell lines is variable, due to their genetic variability, which will condition protein expression and cell growth. Further analyses in this area will provide important information for better understanding of patients' cellular response and how to prevent resistance.

Core Microbiota in Central Lung Cancer With Streptococcal Enrichment as a Possible Diagnostic Marker / Núcleo de microbiota en el cáncer de pulmón central con enriquecimiento estreptocócico como posible marcador de diagnóstico

Background: Dysbiosis in lung cancer has been underexplored. The aim of this study was to define the bacterial and fungal microbiota of the bronchi in central lung cancer and to compare it with that of the oral and intestinal compartments. Methods: Twenty-five patients with central lung cancer and sixteen controls without antimicrobial intake during the previous month were recruited. Bacterial and fungal distribution was determined by massive sequencing of bronchial biopsies and saliva and faecal samples. Complex computational analysis was performed to define the core lung microbiota. Results: Affected and contralateral bronchi of patients have almost identical microbiota dominated by Streptococcus, whereas Pseudomonas was the dominant genera in controls. Oral and pulmonary ecosystems were significantly more similar in patients, probably due to microaspirations. Streptococcal abundance in the bronchi differentiated patients from controls according to a ROC curve analysis (90.9% sensitivity, 83.3% specificity, AUC=0.897). The saliva of patients characteristically showed a greater abundance of Streptococcus, Rothia, Gemella and Lactobacillus. The mycobiome of controls (Candida) was significantly different from that of patients (Malassezia). Cancer patients’ bronchial mycobiome was similar to their saliva, but different from their contralateral bronchi. Conclusions: The central lung cancer microbiome shows high levels of Streptococcus, and differs significantly in its composition from that of control subjects. Changes are not restricted to tumour tissue, and seem to be the consequence of microaspirations from the oral cavity. These findings could be useful in the screening and even diagnosis of this disease. (AU)

Antecedentes: La disbiosis en cáncer pulmonar no ha sido suficientemente estudiada. Los objetivos de este estudio fueron definir la microbiota bacteriana y fúngica de bronquios con cáncer central de pulmón, y compararla con la del compartimento intestinal en heces y saliva. Métodos: Se reclutaron 25 pacientes con cáncer central de pulmón y 16 controles sin exposición antibiótica durante el mes anterior. Se determinó la composición de bacterias y hongos en biopsias de bronquio, saliva y heces. Se realizó un análisis computacional para definir el núcleo de microbiota del pulmón. Resultados: Los bronquios afectados y contralaterales de pacientes presentaron una microbiota similar dominada por Streptococcus, mientras que Pseudomonas destacó en los controles. Los ecosistemas orales y pulmonares fueron significativamente más parecidos en pacientes, probablemente debido a microaspiraciones. La abundancia bronquial de estreptococos permitió diferenciar a los pacientes de los controles mediante una curva ROC (90,9% de sensibilidad, 83,3% de especificidad, AUC=0,897). La saliva de los pacientes presentó mayor abundancia de Streptococcus, Rothia, Gemella y Lactobacillus. El micobioma de los controles (Candida) fue significativamente diferente al de los pacientes (Malassezia), con los bronquios afectados por el cáncer similares a su saliva, pero diferentes de sus bronquios contralaterales. Conclusiones: En el cáncer de pulmón central hay enriquecimiento de Streptococcus, y su composición es significativamente diferente de sujetos control. Las alteraciones no se limitan al tejido tumoral, y parecen ser consecuencia de microaspiraciones desde la cavidad oral. Estos hallazgos podrían ser útiles para la detección e incluso el diagnóstico de esta patología. (AU)

Flow field-based data analysis in interfacial shear rheometry.

Developments in interfacial shear rheometers have considerably improved the quality of experimental data. However, data analysis in interfacial shear rheometry is still an active field of research and development due to the intrinsic complexity introduced by the unavoidable contact of the interface with, at least, one supporting bulk subphase. Nonlinear velocity profiles, both at the interface and the bulk phases, pervade the system dynamical behavior in the most usual experimental geometries, particularly in the case of soft interfaces. Such flow configurations demand data analysis schemes based on the explicit calculation of the flow field in both the interface and the bulk phases. Such procedures are progressively becoming popular in this context. In this review, we discuss the most recent advances in interfacial shear rheology data analysis techniques. We extensively review some recently proposed flow field-based data analysis schemes for the three most common interfacial shear rheometer geometries (magnetic needle, double wall-ring, and bicone), showing under what circumstances the calculation of the flow field is mandatory for a proper analysis of the experimental data. All cases are discussed starting at the appropriate hydrodynamical models and using the equation of motion of the probe to set up an iterative procedure to compute the value of the complex Boussinesq number and, from it, the complex interfacial viscosity or, equivalently, the complex interfacial modulus. Moreover, two examples of further extensions of such techniques are proposed, concerning the micro-button interfacial shear rheometer and the potential application of interfacial rheometry instruments, together with adapted flow field-based data analysis techniques, for bulk rheometry, particularly in the case of soft samples.

Core Microbiota in Central Lung Cancer With Streptococcal Enrichment as a Possible Diagnostic Marker.

BACKGROUND: Dysbiosis in lung cancer has been underexplored. The aim of this study was to define the bacterial and fungal microbiota of the bronchi in central lung cancer and to compare it with that of the oral and intestinal compartments. METHODS: Twenty-five patients with central lung cancer and sixteen controls without antimicrobial intake during the previous month were recruited. Bacterial and fungal distribution was determined by massive sequencing of bronchial biopsies and saliva and faecal samples. Complex computational analysis was performed to define the core lung microbiota. RESULTS: Affected and contralateral bronchi of patients have almost identical microbiota dominated by Streptococcus, whereas Pseudomonas was the dominant genera in controls. Oral and pulmonary ecosystems were significantly more similar in patients, probably due to microaspirations. Streptococcal abundance in the bronchi differentiated patients from controls according to a ROC curve analysis (90.9% sensitivity, 83.3% specificity, AUC=0.897). The saliva of patients characteristically showed a greater abundance of Streptococcus, Rothia, Gemella and Lactobacillus. The mycobiome of controls (Candida) was significantly different from that of patients (Malassezia). Cancer patients' bronchial mycobiome was similar to their saliva, but different from their contralateral bronchi. CONCLUSIONS: The central lung cancer microbiome shows high levels of Streptococcus, and differs significantly in its composition from that of control subjects. Changes are not restricted to tumour tissue, and seem to be the consequence of microaspirations from the oral cavity. These findings could be useful in the screening and even diagnosis of this disease.

Core Microbiota in Central Lung Cancer With Streptococcal Enrichment as a Possible Diagnostic Marker.

BACKGROUND: Dysbiosis in lung cancer has been underexplored. The aim of this study was to define the bacterial and fungal microbiota of the bronchi in central lung cancer and to compare it with that of the oral and intestinal compartments. METHODS: Twenty-five patients with central lung cancer and sixteen controls without antimicrobial intake during the previous month were recruited. Bacterial and fungal distribution was determined by massive sequencing of bronchial biopsies and saliva and faecal samples. Complex computational analysis was performed to define the core lung microbiota. RESULTS: Affected and contralateral bronchi of patients have almost identical microbiota dominated by Streptococcus, whereas Pseudomonas was the dominant genera in controls. Oral and pulmonary ecosystems were significantly more similar in patients, probably due to microaspirations. Streptococcal abundance in the bronchi differentiated patients from controls according to a ROC curve analysis (90.9% sensitivity, 83.3% specificity, AUC=0.897). The saliva of patients characteristically showed a greater abundance of Streptococcus, Rothia, Gemella and Lactobacillus. The mycobiome of controls (Candida) was significantly different from that of patients (Malassezia). Cancer patients' bronchial mycobiome was similar to their saliva, but different from their contralateral bronchi. CONCLUSIONS: The central lung cancer microbiome shows high levels of Streptococcus, and differs significantly in its composition from that of control subjects. Changes are not restricted to tumour tissue, and seem to be the consequence of microaspirations from the oral cavity. These findings could be useful in the screening and even diagnosis of this disease.

Mindfulness-based social cognition training (SocialMIND) versus psychoeducational multicomponent intervention for people with a first episode of psychosis: a study protocol for a randomised controlled trial.

BACKGROUND: People who suffer a first episode of psychosis experience higher levels of distress and suffering. Early intervention programs combine pharmacological and psychosocial strategies that include different components, such as cognitive-behavioural therapy, psychosocial interventions, medication adherence, family psychoeducation, counselling, etc. Among the complementary approaches, mindfulness-based interventions help participants to cultivate a radical acceptance of their psychotic experiences within a person-centered framework. They show promising results for people with longer duration of psychosis, but there is still no evidence for people who have recently experienced their first episode of psychosis. METHODS: The present parallel-group, single-blind (evaluator), randomised (1:1 ratio), controlled (versus active comparator), superiority, clinical trial will compare the effectiveness of SocialMIND on social functioning as measured by the Personal and Social Performance (PSP) scale. The active comparator will be a psychoeducational multicomponent intervention (PMI) that incorporates elements of early intervention programs that are effective for people who have suffered a first episode of psychosis. Both SocialMIND and PMI encompass eight weekly sessions, four bi-weekly sessions, and five monthly sessions. Changes in primary and secondary outcomes will be measured after weekly (8th week), bi-weekly (16th week) and monthly sessions (56th week), and 3 months after completing the intervention (68th week). Secondary outcomes include symptoms of psychosis, anxiety and depression, as well as indicators of general functioning. Tertiary outcomes are measures of social cognition, neurocognition, mindfulness, and indicators of inflammation and oxidative stress. A final sample of 80 participants is proposed to detect clinically significant differences in social functioning. DISCUSSION: This is the first mindfulness-based social cognition training for people with psychosis. SocialMIND aims to generate changes in the real-life functioning of people who have experienced a first episode of psychosis, and to be at least as effective as a psychoeducational multicomponent program. Adherence to the interventions is a common problem among young people with psychosis, so several difficulties are anticipated, and some methodological issues are discussed. TRIAL REGISTRATION: The trial was registered in ClinicalTrials.gov in October 2018 (NCT03309475).

Ranking of critical species to preserve the functionality of mutualistic networks using the k-core decomposition.

BACKGROUND: Network analysis has become a relevant approach to analyze cascading species extinctions resulting from perturbations on mutualistic interactions as a result of environmental change. In this context, it is essential to be able to point out key species, whose stability would prevent cascading extinctions, and the consequent loss of ecosystem function. In this study, we aim to explain how the k-core decomposition sheds light on the understanding the robustness of bipartite mutualistic networks. METHODS: We defined three k-magnitudes based on the k-core decomposition: k-radius, k-degree, and k-risk. The first one, k-radius, quantifies the distance from a node to the innermost shell of the partner guild, while k-degree provides a measure of centrality in the k-shell based decomposition. k-risk is a way to measure the vulnerability of a network to the loss of a particular species. Using these magnitudes we analyzed 89 mutualistic networks involving plant pollinators or seed dispersers. Two static extinction procedures were implemented in which k-degree and k-risk were compared against other commonly used ranking indexes, as for example MusRank, explained in detail in Material and Methods. RESULTS: When extinctions take place in both guilds, k-risk is the best ranking index if the goal is to identify the key species to preserve the giant component. When species are removed only in the primary class and cascading extinctions are measured in the secondary class, the most effective ranking index to identify the key species to preserve the giant component is k-degree. However, MusRank index was more effective when the goal is to identify the key species to preserve the greatest species richness in the second class. DISCUSSION: The k-core decomposition offers a new topological view of the structure of mutualistic networks. The new k-radius, k-degree and k-risk magnitudes take advantage of its properties and provide new insight into the structure of mutualistic networks. The k-risk and k-degree ranking indexes are especially effective approaches to identify key species to preserve when conservation practitioners focus on the preservation of ecosystem functionality over species richness.

Relaxation time of the global order parameter on multiplex networks: The role of interlayer coupling in Kuramoto oscillators.

This work considers the time scales associated with the global order parameter and the interlayer synchronization of coupled Kuramoto oscillators on multiplexes. For two-layer multiplexes with an initially high degree of synchronization in each layer, the difference between the average phases in each layer is analyzed from two different perspectives: the spectral analysis and the nonlinear Kuramoto model. Both viewpoints confirm that the prior time scales are inversely proportional to the interlayer coupling strength. Thus, increasing the interlayer coupling always shortens the transient regimes of both the global order parameter and the interlayer synchronization. Surprisingly, the analytical results show that the convergence of the global order parameter is faster than the interlayer synchronization, and the latter is generally faster than the global synchronization of the multiplex. The formalism also outlines the effects of frequencies on the difference between the average phases of each layer, and it identifies the conditions for an oscillatory behavior. Computer simulations are in fairly good agreement with the analytical findings, and they reveal that the time scale of the global order parameter is half the size of the time scale of the multiplex, if not smaller.

Inducing self-organized criticality in a network toy model by neighborhood assortativity.

Complex networks are a recent type of framework used to study complex systems with many interacting elements, such as self-organized criticality (SOC). The network nodes' tendency to link to other nodes of similar type is characterized by assortative mixing. Real networks exhibit assortative mixing by vertex degree, however, typical random network models, such as the Erdos-Rényi or the Barabási-Albert model, show no assortative arrangements. In this paper we introduce the notion of neighborhood assortativity as the tendency of a node to belong to a community (its neighborhood) showing an average property similar to its own. Imposing neighborhood assortative mixing by degree in a network toy model, SOC dynamics can be found. These dynamics are driven only by the network topology. The long-range correlations resulting from criticality have been characterized by means of fluctuation analysis and show an anticorrelation in the node's activity. The model contains only one parameter and its statistics plots for different values of the parameter can be collapsed into a single curve. The simplicity of the model allows us to perform numerical simulations and also to study analytically the statistics for a specific value of the parameter, making use of the Markov chains.

Rethinking the logistic approach for population dynamics of mutualistic interactions.

Mutualistic communities have an internal structure that makes them resilient to external perturbations. Late research has focused on their stability and the topology of the relations between the different organisms to explain the reasons of the system robustness. Much less attention has been invested in analyzing the systems dynamics. The main population models in use are modifications of the r-K formulation of logistic equation with additional terms to account for the benefits produced by the interspecific interactions. These models have shortcomings as the so-called r-K formulation diverges under some conditions. In this work, we introduce a model for population dynamics under mutualism that preserves the original logistic formulation. It is mathematically simpler than the widely used type II models, although it shows similar complexity in terms of fixed points and stability of the dynamics. We perform an analytical stability analysis and numerical simulations to study the model behavior in general interaction scenarios including tests of the resilience of its dynamics under external perturbations. Despite its simplicity, our results indicate that the model dynamics shows an important richness that can be used to gain further insights in the dynamics of mutualistic communities.