Modularity in Biological Networks.

Network modeling, from the ecological to the molecular scale has become an essential tool for studying the structure, dynamics and complex behavior of living systems. Graph representations of the relationships between biological components open up a wide variety of methods for discovering the mechanistic and functional properties of biological systems. Many biological networks are organized into a modular structure, so methods to discover such modules are essential if we are to understand the biological system as a whole. However, most of the methods used in biology to this end, have a limited applicability, as they are very specific to the system they were developed for. Conversely, from the statistical physics and network science perspective, graph modularity has been theoretically studied and several methods of a very general nature have been developed. It is our perspective that in particular for the modularity detection problem, biology and theoretical physics/network science are less connected than they should. The central goal of this review is to provide the necessary background and present the most applicable and pertinent methods for community detection in a way that motivates their further usage in biological research.

Incidence of Arterial Hypertension in People With Periodontitis and Characterization of the Oral and Subgingival Microbiome: A Study Protocol.

Cardiovascular diseases are the leading cause of morbidity and mortality worldwide. High blood pressure in particular, continues to increase throughout the global population at an increasingly fast pace. The relationship between arterial hypertension and periodontitis has been recently discussed in the context of its origins and implications. Particularly relevant is the role of the periodontal microbiome linked to persistent local and systemic inflammation, along with other risk factors and social determinants of health. The present protocol will investigate/assess the association between periodontal disease and its microbiome on the onset of hypertension, within a cohort from Mexico City. One thousand two hundred twelve participants will be studied during a 60-month period. Studies will include analysis of periodontal conditions, sampling and sequencing of the salivary and subgingival microbiome, interviews on nutritional and lifestyle habits, social determinants of health, blood pressure and anthropometric measurements. Statistical associations and several classic epidemiology and machine learning approaches will be performed to analyze the data. Implications for the generation of public policy-by early public health interventions or epidemiological surveillance approaches-and for the population empowerment-via the establishment of primary prevention recommendations, highlighting the relationship between oral and cardiovascular health-will be considered. This latter set of interventions will be supported by a carefully planned science communication and health promotion strategy. This study has been registered and approved by the Research and Ethics Committee of the School of Dentistry, Universidad Nacional Autónoma de México (CIE/0308/05/2019) and the National Institute of Genomic Medicine (CEI/2020/12). The umbrella cohort was approved by the Institutional Bioethics Committee of the National Institute of Cardiology-Ignacio Chavez (INC-ICh) under code 13-802.

A 500-year tale of co-evolution, adaptation, and virulence: Helicobacter pylori in the Americas.

Helicobacter pylori is a common component of the human stomach microbiota, possibly dating back to the speciation of Homo sapiens. A history of pathogen evolution in allopatry has led to the development of genetically distinct H. pylori subpopulations, associated with different human populations, and more recent admixture among H. pylori subpopulations can provide information about human migrations. However, little is known about the degree to which some H. pylori genes are conserved in the face of admixture, potentially indicating host adaptation, or how virulence genes spread among different populations. We analyzed H. pylori genomes from 14 countries in the Americas, strains from the Iberian Peninsula, and public genomes from Europe, Africa, and Asia, to investigate how admixture varies across different regions and gene families. Whole-genome analyses of 723 H. pylori strains from around the world showed evidence of frequent admixture in the American strains with a complex mosaic of contributions from H. pylori populations originating in the Americas as well as other continents. Despite the complex admixture, distinctive genomic fingerprints were identified for each region, revealing novel American H. pylori subpopulations. A pan-genome Fst analysis showed that variation in virulence genes had the strongest fixation in America, compared with non-American populations, and that much of the variation constituted non-synonymous substitutions in functional domains. Network analyses suggest that these virulence genes have followed unique evolutionary paths in the American populations, spreading into different genetic backgrounds, potentially contributing to the high risk of gastric cancer in the region.

Phenotype Heritability in Holobionts: An Evolutionary Model.

Many complex diseases are expressed with high incidence only in certain populations. Genealogy studies determine that these diseases are inherited with a high probability. However, genetic studies have been unable to identify the genomic signatures responsible for such heritability, as identifying the genetic variants that make a population prone to a given disease is not enough to explain its high occurrence within the population. This gap is known as the missing heritability problem. We know that the microbiota plays a very important role in determining many important phenotypic characteristics of its host, in particular the complex diseases for which the missing heritability occurs. Therefore, when computing the heritability of a phenotype, it is important to consider not only the genetic variation in the host but also in its microbiota. Here we test this hypothesis by studying an evolutionary model based on gene regulatory networks. Our results show that the holobiont (the host plus its microbiota) is capable of generating a much larger variability than the host alone, greatly reducing the missing heritability of the phenotype. This result strongly suggests that a considerably large part of the missing heritability can be attributed to the microbiome.

Modeling the Role of the Microbiome in Evolution.

There is undeniable evidence showing that bacteria have strongly influenced the evolution and biological functions of multicellular organisms. It has been hypothesized that many host-microbial interactions have emerged so as to increase the adaptive fitness of the holobiont (the host plus its microbiota). Although this association has been corroborated for many specific cases, general mechanisms explaining the role of the microbiota in the evolution of the host are yet to be understood. Here we present an evolutionary model in which a network representing the host adapts in order to perform a predefined function. During its adaptation, the host network (HN) can interact with other networks representing its microbiota. We show that this interaction greatly accelerates and improves the adaptability of the HN without decreasing the adaptation of the microbial networks. Furthermore, the adaptation of the HN to perform several functions is possible only when it interacts with many different bacterial networks in a specialized way (each bacterial network participating in the adaptation of one function). Disrupting these interactions often leads to non-adaptive states, reminiscent of dysbiosis, where none of the networks the holobiont consists of can perform their respective functions. By considering the holobiont as a unit of selection and focusing on the adaptation of the host to predefined but arbitrary functions, our model predicts the need for specialized diversity in the microbiota. This structural and dynamical complexity in the holobiont facilitates its adaptation, whereas a homogeneous (non-specialized) microbiota is inconsequential or even detrimental to the holobiont's evolution. To our knowledge, this is the first model in which symbiotic interactions, diversity, specialization and dysbiosis in an ecosystem emerge as a result of coevolution. It also helps us understand the emergence of complex organisms, as they adapt more easily to perform multiple tasks than non-complex ones.

The Human Microbiome and the Missing Heritability Problem.

The "missing heritability" problem states that genetic variants in Genome-Wide Association Studies (GWAS) cannot completely explain the heritability of complex traits. Traditionally, the heritability of a phenotype is measured through familial studies using twins, siblings and other close relatives, making assumptions on the genetic similarities between them. When this heritability is compared to the one obtained through GWAS for the same traits, a substantial gap between both measurements arise with genome wide studies reporting significantly smaller values. Several mechanisms for this "missing heritability" have been proposed, such as epigenetics, epistasis, and sequencing depth. However, none of them are able to fully account for this gap in heritability. In this paper we provide evidence that suggests that in order for the phenotypic heritability of human traits to be broadly understood and accounted for, the compositional and functional diversity of the human microbiome must be taken into account. This hypothesis is based on several observations: (A) The composition of the human microbiome is associated with many important traits, including obesity, cancer, and neurological disorders. (B) Our microbiome encodes a second genome with nearly a 100 times more genes than the human genome, and this second genome may act as a rich source of genetic variation and phenotypic plasticity. (C) Human genotypes interact with the composition and structure of our microbiome, but cannot by themselves explain microbial variation. (D) Microbial genetic composition can be strongly influenced by the host's behavior, its environment or by vertical and horizontal transmissions from other hosts. Therefore, genetic similarities assumed in familial studies may cause overestimations of heritability values. We also propose a method that allows the compositional and functional diversity of our microbiome to be incorporated to genome wide association studies.

Adaptive resistance to antibiotics in bacteria: a systems biology perspective.

Despite all the major breakthroughs in antibiotic development and treatment procedures, there is still no long-term solution to the bacterial antibiotic resistance problem. Among all the known types of resistance, adaptive resistance (AdR) is particularly inconvenient. This phenotype is known to emerge as a consequence of concentration gradients, as well as contact with subinhibitory concentrations of antibiotics, both known to occur in human patients and livestock. Moreover, AdR has been repeatedly correlated with the appearance of multidrug resistance, although the biological processes behind its emergence and evolution are not well understood. Epigenetic inheritance, population structure and heterogeneity, high mutation rates, gene amplification, efflux pumps, and biofilm formation have all been reported as possible explanations for its development. Nonetheless, these concepts taken independently have not been sufficient to prevent AdR's fast emergence or to predict its low stability. New strains of resistant pathogens continue to appear, and none of the new approaches used to kill them (mixed antibiotics, sequential treatments, and efflux inhibitors) are completely efficient. With the advent of systems biology and its toolsets, integrative models that combine experimentally known features with computational simulations have significantly improved our understanding of the emergence and evolution of the adaptive-resistant phenotype. Apart from outlining these findings, we propose that one of the main cornerstones of AdR in bacteria, is the conjunction of two types of mechanisms: one rapidly responding to transient environmental challenges but not very efficient, and another much more effective and specific, but developing on longer time scales. WIREs Syst Biol Med 2016, 8:253-267. doi: 10.1002/wsbm.1335 For further resources related to this article, please visit the WIREs website.

Complex regulation of Hsf1-Skn7 activities by the catalytic subunits of PKA in Saccharomyces cerevisiae: experimental and computational evidences.

BACKGROUND: The cAMP-dependent protein kinase regulatory network (PKA-RN) regulates metabolism, memory, learning, development, and response to stress. Previous models of this network considered the catalytic subunits (CS) as a single entity, overlooking their functional individualities. Furthermore, PKA-RN dynamics are often measured through cAMP levels in nutrient-depleted cells shortly after being fed with glucose, dismissing downstream physiological processes. RESULTS: Here we show that temperature stress, along with deletion of PKA-RN genes, significantly affected HSE-dependent gene expression and the dynamics of the PKA-RN in cells growing in exponential phase. Our genetic analysis revealed complex regulatory interactions between the CS that influenced the inhibition of Hsf1/Skn7 transcription factors. Accordingly, we found new roles in growth control and stress response for Hsf1/Skn7 when PKA activity was low (cdc25Δ cells). Experimental results were used to propose an interaction scheme for the PKA-RN and to build an extension of a classic synchronous discrete modeling framework. Our computational model reproduced the experimental data and predicted complex interactions between the CS and the existence of a repressor of Hsf1/Skn7 that is activated by the CS. Additional genetic analysis identified Ssa1 and Ssa2 chaperones as such repressors. Further modeling of the new data foresaw a third repressor of Hsf1/Skn7, active only in the absence of Tpk2. By averaging the network state over all its attractors, a good quantitative agreement between computational and experimental results was obtained, as the averages reflected more accurately the population measurements. CONCLUSIONS: The assumption of PKA being one molecular entity has hindered the study of a wide range of behaviors. Additionally, the dynamics of HSE-dependent gene expression cannot be simulated accurately by considering the activity of single PKA-RN components (i.e., cAMP, individual CS, Bcy1, etc.). We show that the differential roles of the CS are essential to understand the dynamics of the PKA-RN and its targets. Our systems level approach, which combined experimental results with theoretical modeling, unveils the relevance of the interaction scheme for the CS and offers quantitative predictions for several scenarios (WT vs. mutants in PKA-RN genes and growth at optimal temperature vs. heat shock).