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MotivationCells are complex systems composed of hundreds of genes whose products interact to produce elaborated behaviors. To control such behaviors, cells rely on transcription factors to regulate gene expression, and gene regulatory networks (GRNs) are employed to describe and understand such behavior. However, GRNs are static models, and dynamic models are difficult to obtain due to their size, complexity, stochastic dynamics and interactions with other cell processes.ResultsWe developed Atlas, a Python software that converts genome graphs and gene regulatory, interaction and metabolic networks into dynamic models. The software employs these biological networks to write rule-based models for the PySB framework. The underlying method is a divide-and-conquer strategy to obtain sub-models and combine them later into an ensemble model. To exemplify the utility of Atlas, we used networks of varying size and complexity of Escherichia coli and evaluated in silico modifications, such as gene knockouts and the insertion of promoters and terminators. Moreover, the methodology could be applied to the dynamic modeling of natural and synthetic networks of any bacteria.Availability and implementationCode, models and tutorials are available online (https://github.com/networkbiolab/atlas).Supplementary informationSupplementary data are available at Bioinformatics online.

The close contact between people and their pets has generated the exchange of skin microbiota, accompanied by bacteria that present resistance to antibiotics.

The oral microbiome in dogs is a complex community. Under some circumstances, it contributes to periodontal disease, a prevalent inflammatory disease characterized by a complex interaction between oral microbes and the immune system. Porphyromonas and Tannerella spp. are usually dominant in this disease. How the oral microbiome community is altered in periodontal disease, especially sub-dominant microbial populations is unclear. Moreover, how microbiome functions are altered in this disease has not been studied. In this study, we compared the composition and the predicted functions of the microbiome of the cavity of healthy dogs to those with from periodontal disease. The microbiome of both groups clustered separately, indicating important differences. Periodontal disease resulted in a significant increase in Bacteroidetes and reductions in Actinobacteria and Proteobacteria. Porphyromonas abundance increased 2.7 times in periodontal disease, accompanied by increases in Bacteroides and Fusobacterium. It was predicted that aerobic respiratory processes are decreased in periodontal disease. Enrichment in fermentative processes and anaerobic glycolysis were suggestive of an anaerobic environment, also characterized by higher lipopolysaccharide biosynthesis. This study contributes to a better understanding of how periodontal disease modifies the oral microbiome and makes a prediction of the metabolic pathways that contribute to the inflammatory process observed in periodontal disease.

The regulation of gene expression is a key factor in the development and maintenance of life in all organisms. Even so, little is known at whole genome scale for most genes and contexts. We propose a method, Tool for Weighted Epigenomic Networks in Drosophila melanogaster (Fly T-WEoN), to generate context-specific gene regulatory networks starting from a reference network that contains all known gene regulations in the fly. Unlikely regulations are removed by applying a series of knowledge-based filters. Each of these filters is implemented as an independent module that considers a type of experimental evidence, including DNA methylation, chromatin accessibility, histone modifications and gene expression. Fly T-WEoN is based on heuristic rules that reflect current knowledge on gene regulation in D. melanogaster obtained from the literature. Experimental data files can be generated with several standard procedures and used solely when and if available. Fly T-WEoN is available as a Cytoscape application that permits integration with other tools and facilitates downstream network analysis. In this work, we first demonstrate the reliability of our method to then provide a relevant application case of our tool: early development of D. melanogaster. Fly T-WEoN together with its step-by-step guide is available at https://weon.readthedocs.io.

MotivationThe regulation of gene expression is a key factor in the development and maintenance of life in all organisms. This process is carried out mainly through the action of transcription factors (TFs), although other actors such as ncRNAs are involved. In this work, we propose a new method to construct Gene Regulatory Networks (GRNs) depicting regulatory events in a certain context for Drosophila melanogaster. Our approach is based on known relationships between epigenetics and the activity of transcription factors.ResultsWe developed method, Tool for Weighted Epigenomic Networks in D. melanogaster (Fly T-WEoN), which generates GRNs starting from a reference network that contains all known gene regulations in the fly. Regulations that are unlikely taking place are removed by applying a series of knowledge-based filters. Each of these filters is implemented as an independent module that considers a type of experimental evidence, including DNA methylation, chromatin accessibility, histone modifications, and gene expression. Fly T-WEoN is based on heuristic rules that reflect current knowledge on gene regulation in D. melanogaster obtained from literature. Experimental data files can be generated with several standard procedures and used solely when and if available.Fly T-WEoN is available as a Cytoscape application that permits integration with other tools, and facilitates downstream network analysis. In this work, we first demonstrate the reliability of our method to then provide a relevant application case of our tool: early development of D. melanogaster.AvailabilityFly T-WEoN, together with its step-by-step guide is available at https://weon.readthedocs.ioContactalberto.martin@umayor.cl

The SARS-CoV-2 global pandemic will disproportionately impact countries with weak economies and vulnerable populations including people with dementia. Latin American and Caribbean countries (LACs) are burdened with unstable economic development, fragile health systems, massive economic disparities, and a high prevalence of dementia. Here, we underscore the selective impact of SARS-CoV-2 on dementia among LACs, the specific strain on health systems devoted to dementia, and the subsequent effect of increasing inequalities among those with dementia in the region. Implementation of best practices for mitigation and containment faces particularly steep challenges in LACs. Based upon our consideration of these issues, we urgently call for a coordinated action plan, including the development of inexpensive mass testing and multilevel regional coordination for dementia care and related actions. Brain health diplomacy should lead to a shared and escalated response across the region, coordinating leadership, and triangulation between governments and international multilateral networks. Fil: Ibañez, Agustin Mariano. Universidad Adolfo Ibáñez; Chile. Universidad de San Andrés; Argentina. Universidad Autónoma del Caribe; Colombia. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina Fil: Santamaria Garcia, Hernando. Pontificia Universidad Javeriana; Colombia. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina Fil: Guerrero Barragan, Alejandra. University of California; Estados Unidos Fil: Kornhuber, Alexander. University of California; Estados Unidos Fil: Mendonca Marques Ton, Alyne. Universidade Vila Velha; Brasil Fil: Slachevsky, Andrea. Universidad de Chile; Chile Fil: Teixeira, Antonio Lucio. No especifíca; Fil: Mar Meza, Beatriz Marcela. University of California; Estados Unidos Fil: Serrano, Cecilia Mariela. Unidad Asistencial "Dr. César Milstein"; Argentina Fil: Cano, Carlos. Pontificia Universidad Javeriana; Colombia Fil: Arias Gonzalez, Carolina. University of California; Estados Unidos Fil: González Billault, Christian. Universidad de Chile; Chile Fil: Butler, Christopher. University of Oxford; Reino Unido. Pontificia Universidad Católica de Chile; Chile Fil: Bustin, Julian. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Houssay. Instituto de Neurociencia Cognitiva y Traslacional. Fundación Ineco Rosario Sede del Incyt | Instituto de Neurología Cognitiva. Instituto de Neurociencia Cognitiva y Traslacional. Fundación Ineco Rosario Sede del Incyt | Fundación Favaloro. Instituto de Neurociencia Cognitiva y Traslacional. Fundación Ineco Rosario Sede del Incyt; Argentina Fil: Duran Aniotz, Claudia. Universidad Adolfo Ibañez; Chile Fil: Acosta, Daisy. Universidad Nacional Pedro Henríquez Ureña; República Dominicana Fil: Matallana, Diana L.. Pontificia Universidad Javeriana; Colombia Fil: Acosta Alvear, Diego. University of California; Estados Unidos Fil: Trépel, Dominic. No especifíca; Fil: De Paula França Resende, Elisa. University of California; Estados Unidos. Universidade Federal do Rio de Janeiro; Brasil Fil: Ferreira de Oliveira, Fabricio. Universidade Federal de Sao Paulo; Brasil Fil: Ibanez, Francisco. Universidad Nacional de San Juan; Argentina Fil: De Felice, Fernanda G.. Universidade Federal do Rio de Janeiro; Brasil Fil: Navarrete, Gorka. Universidad Adolfo Ibañez; Chile Fil: Tarnanas, Ioannis. No especifíca; Fil: Meier, Irene B.. No especifíca; Fil: Smid, Jerusa. No especifíca; Fil: Llibre-Guerra, Jorge. University of California; Estados Unidos Fil: Llibre Rodriguez, Juan J.. Universidad de Ciencias Médicas; Cuba Fil: Fajersztajn, Laís. University of California; Estados Unidos. Universidade de Sao Paulo; Brasil Fil: Tadao Takada, Leonel. Universidade de Sao Paulo; Brasil Fil: Duque, Lissette. National Commission In Bioethics; Ecuador Fil: Okada de Oliveira, Maira. Universidade de Sao Paulo; Brasil Fil: Aparecida Camargos Bicalho, Maria. Universidade Federal de Minas Gerais; Brasil Fil: Behrens, María Isabel. Universidad Nacional de San Juan; Argentina. Universidad de Chile; Chile Fil: Pintado Caipa, Maritza. University of California; Estados Unidos Fil: Parra, Mario. University of Strathclyde; Reino Unido Fil: Wilson, Maxwell Z.. University of California; Estados Unidos Fil: De La Cruz Puebla, Myriam. Universidad de Barcelona; España. University of California; Estados Unidos Fil: Custodio, Nilton. Peruvian National Institute Of Health; Perú Fil: Santibanez, Rodrigo. Pontificia Universidad Católica de Chile; Chile. Universidad Católica de Chile; Chile Fil: Serafim, Rodrigo Bernardo. No especifíca; Fil: Melo Tavares, Ronnielly. Universidade Federal do Rio de Janeiro; Brasil Fil: Piña Escudero, Stefanie Danielle. University of California; Estados Unidos Fil: Rodriguez, Tomas Leon. Universidad de Chile; Chile Fil: Dawson, Walter. University of California; Estados Unidos Fil: Miller, Bruce L.. University of California; Estados Unidos Fil: Kosik, Kenneth. University of California; Estados Unidos

Computational simulation is a widely employed methodology to study the dynamic behavior of complex systems. Although common approaches are based either on ordinary differential equations or stochastic differential equations, these techniques make several assumptions which, when it comes to biological processes, could often lead to unrealistic models. Among others, model approaches based on differential equations entangle kinetics and causality, failing when complexity increases, separating knowledge from models, and assuming that the average behavior of the population encompasses any individual deviation. To overcome these limitations, simulations based on the Stochastic Simulation Algorithm (SSA) appear as a suitable approach to model complex biological systems. In this work, we review three different models executed in PISKaS: a rule-based framework to produce multiscale stochastic simulations of complex systems. These models span multiple time and spatial scales ranging from gene regulation up to Game Theory. In the first example, we describe a model of the core regulatory network of gene expression in Escherichia coli highlighting the continuous model improvement capacities of PISKaS. The second example describes a hypothetical outbreak of the Ebola virus occurring in a compartmentalized environment resembling cities and highways. Finally, in the last example, we illustrate a stochastic model for the prisoner's dilemma; a common approach from social sciences describing complex interactions involving trust within human populations. As whole, these models demonstrate the capabilities of PISKaS providing fertile scenarios where to explore the dynamics of complex systems.

The regulation of gene expression is a key factor in the development and maintenance of life in all organisms. Even so, little is known at whole genome scale for most genes and contexts. We propose a method, Tool for Weighted Epigenomic Networks in Drosophila melanogaster (Fly T-WEoN), to generate context-specific gene regulatory networks starting from a reference network that contains all known gene regulations in the fly. Unlikely regulations are removed by applying a series of knowledge-based filters. Each of these filters is implemented as an independent module that considers a type of experimental evidence, including DNA methylation, chromatin accessibility, histone modifications and gene expression. Fly T-WEoN is based on heuristic rules that reflect current knowledge on gene regulation in D. melanogaster obtained from the literature. Experimental data files can be generated with several standard procedures and used solely when and if available. Fly T-WEoN is available as a Cytoscape application that permits integration with other tools and facilitates downstream network analysis. In this work, we first demonstrate the reliability of our method to then provide a relevant application case of our tool: early development of D. melanogaster . Fly T-WEoN together with its step-by-step guide is available at https://weon.readthedocs.io .

Sucralose is an artificial non-nutritive sweetener used in foods aimed to reduce sugar and energy intake. While thought to be inert, the impact of sucralose on metabolic control has shown to be the opposite. The gut microbiome has emerged as a factor shaping metabolic responses after sweetener consumption. We examined the short-term effect of sucralose consumption on glucose homeostasis and gut microbiome of healthy male volunteers. We performed a randomised, double-blind study in thirty-four subjects divided into two groups, one that was administered sucralose capsules (780 mg/d for 7 d; n 17) and a control group receiving placebo (n 17). Before and after the intervention, glycaemic and insulinaemic responses were assessed with a standard oral glucose load (75 g). Insulin resistance was determined using homeostasis model assessment of insulin resistance and Matsuda indexes. The gut microbiome was evaluated before and after the intervention by 16S rRNA sequencing. During the study, body weight remained constant in both groups. Glycaemic control and insulin resistance were not affected during the 7-d period. At the phylum level, gut microbiome was not modified in any group. We classified subjects according to their change in insulinaemia after the intervention, to compare the microbiome of responders and non-responders. Independent of consuming sucralose or placebo, individuals with a higher insulinaemic response after the intervention had lower Bacteroidetes and higher Firmicutes abundances. In conclusion, consumption of high doses of sucralose for 7 d does not alter glycaemic control, insulin resistance, or gut microbiome in healthy individuals. However, it highlights the need to address individual responses to sucralose.

Complex systems are governed by dynamic processes whose underlying causal rules are difficult to unravel. However, chemical reactions, molecular interactions, and many other complex systems can be usually represented as concentrations or quantities that vary over time, which provides a framework to study these dynamic relationships. An increasing number of tools use these quantifications to simulate dynamically complex systems to better understand their underlying processes. The application of such methods covers several research areas from biology and chemistry to ecology and even social sciences.In the following chapter, we introduce the concept of rule-based simulations based on the Stochastic Simulation Algorithm (SSA) as well as other mathematical methods such as Ordinary Differential Equations (ODE) models to describe agent-based systems. Besides, we describe the mathematical framework behind Kappa (κ), a rule-based language for the modeling of complex systems, and some extensions for spaßtial models implemented in PISKaS (Parallel Implementation of a Spatial Kappa Simulator). To facilitate the understanding of these methods, we include examples of how these models can be used to describe population dynamics in a simple predator-prey ecosystem or to simulate circadian rhythm changes.

Complex systems are governed by dynamic processes whose underlying causal rules are difficult to unravel. However, chemical reactions, molecular interactions, and many other complex systems can be usually represented as concentrations or quantities that vary over time, which provides a framework to study these dynamic relationships. An increasing number of tools use these quantifications to simulate dynamically complex systems to better understand their underlying processes. The application of such methods covers several research areas from biology and chemistry to ecology and even social sciences.In the following chapter, we introduce the concept of rule-based simulations based on the Stochastic Simulation Algorithm (SSA) as well as other mathematical methods such as Ordinary Differential Equations (ODE) models to describe agent-based systems. Besides, we describe the mathematical framework behind Kappa (κ), a rule-based language for the modeling of complex systems, and some extensions for spastial models implemented in PISKaS (Parallel Implementation of a Spatial Kappa Simulator). To facilitate the understanding of these methods, we include examples of how these models can be used to describe population dynamics in a simple predator-prey ecosystem or to simulate circadian rhythm changes.

The marginal mandibular branch of the facial nerve (MMB) has a significant variation in relation to the lower border of the mandible (LBM). That is why it is important to know its topographical description to prevent damage in submandibular surgical procedures. The objective of this study was to determine the distance between the MMB and LBM based on descriptive studies carried out on human corpses and surgical patients. A systematic review of literature in MEDLINE, "Science Citation Index Expanded" of Web of Science (ISI) databases and manual search was performed. The articles with number of samples greater than or equal to 10 facial samples and recording of measurements between the MMB and the LBM or Gonion were selected. From the selected articles, the authors registered year of publication, country of origin, number of samples, sample type (fresh, embalmed body/fixed cadavers or surgical patient), distribution percentage of the MMB in relation to LBM, average and maximum distance between MMB and LBM or Gonion. The results were presented in tables with descriptive statistic. Seventeen articles describing measurements of the distance between MMB and the lower border of the mandible carried out in cadavers and patients were selected. In these articles a total of 1,408 samples were dissected. The maximum distance observed was 4.01 cm (mean±SD 1.64±0.92 cm, Min= 0.69; Max= 4.01). Several reports suggest that an incision 2 cm below the lower border of the mandible would be enough to avoid damage of the MMB. However, according to the maximal distances registered, such an incision might involve risk for the MMB. For this reason, we propose that the MMB of facial nerve should be at least 4 cm below the lower border of the mandible.

The vomeronasal system is crucial for social and sexual communication in mammals. Two populations of vomeronasal sensory neurons, each expressing Gαi2 or Gαo proteins, send projections to glomeruli of the rostral or caudal accessory olfactory bulb, rAOB and cAOB, respectively. In rodents, the Gαi2- and Gαo-expressing vomeronasal pathways have shown differential responses to small/volatile vs. large/non-volatile semiochemicals, respectively. Moreover, early gene expression suggests predominant activation of rAOB and cAOB neurons in sexual vs. aggressive contexts, respectively. We recently described the AOB of Octodon degus, a semiarid-inhabiting diurnal caviomorph. Their AOB has a cell indentation between subdomains and the rAOB is twice the size of the cAOB. Moreover, their AOB receives innervation from the lateral aspect, contrasting with the medial innervation of all other mammals examined to date. Aiming to relate AOB anatomy with lifestyle, we performed a morphometric study on the AOB of the capybara, a semiaquatic caviomorph whose lifestyle differs remarkably from that of O. degus. Capybaras mate in water and scent-mark their surroundings with oily deposits, mostly for male-male communication. We found that, similar to O. degus, the AOB of capybaras shows a lateral innervation of the vomeronasal nerve, a cell indentation between subdomains and heterogeneous subdomains, but in contrast to O. degus the caudal portion is larger than the rostral one. We also observed that four other caviomorph species present a lateral AOB innervation and a cell indentation between AOB subdomains, suggesting that those traits could represent apomorphies of the group. We propose that although some AOB traits may be phylogenetically conserved in caviomorphs, ecological specializations may play an important role in shaping the AOB.