Horizontal gene transfer dynamics and distribution of fitness effects during microbial in silico evolution

Storm promotes the dissemination of antibiotic resistome in an urban lagoon through enhancing bio-interactions

Antibiotic-resistance genes (ARGs) and resistant bacteria (ARB) are abundant in stormwater that could cause serious infections, posing a potential threat to public health. However, there is no inference about how stormwater contributes to ARG profiles as well as the dynamic interplay between ARGs and bacteria via vertical gene transfer (VGT) or horizontal gene transfer (HGT) in urban water ecosystems. In this study, the distribution of ARGs, their host communities, and the source and community assembly process of ARGs were investigated in Yundang Lagoon (China) via high-throughput quantitative PCR, 16S rRNA gene amplicon sequencing, and application of SourceTracker before, after and recovering from an extreme precipitation event (132.1 mm). The abundance of ARGs and mobile genetic elements (MGEs) was the highest one day after precipitation and then decreased 2 days after precipitation and so on. Based on SourceTracker and NMDS analysis, the ARG and bacterial communities in lagoon surface water from one day after precipitation were mainly contributed by the wastewater treatment plant (WWTP) influent and effluent. However, the contribution of WWTP to ARG communities was minor 11 days after the precipitation, suggesting that the storm promoted the ARG levels by introducing the input of ARGs, MGEs, and ARB from point and non-point sources, such as sewer overflow and land-applied manure. Based on a novel microbial network analysis framework, the contribution of positive biological interactions between ARGs and MGEs or bacteria was the highest one day after precipitation, indicating a promoted VGT and HGT for ARG dissemination. The microbial networks deconstructed 11 days after precipitation, suggesting the stormwater practices (e.g., tide gate opening, diversion channels, and pumping) alleviated the spread of ARGs. These results advanced our understanding of the distribution and transport of ARGs associated with their source in urban stormwater runoff.

Structural based screening of potential inhibitors of SMAD4: a step towards personalized medicine for gall bladder and other associated cancers

Gall bladder cancer (GBC) is an aggressive and most common malignancy of biliary tract lacking effective treatment due to unavailability of suitable biomarkers and therapeutics. SMAD4 is an essential mediator of transforming growth factor-β pathway involved in various cellular processes like growth, differentiation and apoptosis and also recognized as therapeutic target for GBC and other gastrointestinal tract cancers. In the present study, 3D structure of SMAD4 mutants was optimized through molecular dynamics simulation (MDS) along with wildtype. Furthermore, binding site of protein was predicted through hybrid approach and structural based virtual screening against two drug libraries was performed followed by docking. MDS of top docking score protein-ligand complexes were carried, and binding free energy was rescored. Two potential inhibitors, namely ZINC2098840 and ZINC8789167, were screened that displayed higher binding affinity towards mutant proteins compared with wildtype and both hydrophilic as well as hydrophobic interactions play a crucial role during protein-ligand binding. Current study identified novel and potent inhibitors of SMAD4 mutant that could be used as a drug candidate for the development of personalized medicine for gall bladder and other associated cancers.

Genomic and molecular evolutionary dynamics of transcriptional response regulator genes in bacterial species of the Harveyi clade of Vibrio

Transcriptional response regulators (TRR) are the most abundant signal transducers in prokaryotic systems that mediate intracellular changes in response to environmental signals. They are involved in a wide range of biological processes that allow bacteria to persist in particular habitats. There is strong evidence that the bacterial habitat and their lifestyle influence the size of their TRR genetic repertoire. Therefore, it would be expected that the evolution of bacterial genomes could be linked to natural selection processes. To test this hypothesis, we explored the evolutionary dynamics of TRR genes of the widely studied Harveyi clade of the genus Vibrio at the molecular and genomic levels. Our results suggest that the TRR genetic repertoire of the species belonging to the Harveyi clade is a product of genomic reduction and expansion. The gene loss and gains that drive their genomic reduction and expansion could be attributed to natural selection and random genetic drift. It seems that natural selection acts to maintain the ancestral state of core TRR genes (shared by all species) by purifying processes and could be driving the loss of some accessory (found in certain species) genes through the diversification of sequences. The neutrality observed in gene gain could be attributed to spontaneous events as horizontal gene transfer driven by stochastic events as occurs in random genetic drift.

Extreme Deviations from Expected Evolutionary Rates in Archaeal Protein Families

Origin of new biological functions is a complex phenomenon ranging from single-nucleotide substitutions to the gain of new genes via horizontal gene transfer or duplication. Neofunctionalization and subfunctionalization of proteins is often attributed to the emergence of paralogs that are subject to relaxed purifying selection or positive selection and thus evolve at accelerated rates. Such phenomena potentially could be detected as anomalies in the phylogenies of the respective gene families. We developed a computational pipeline to search for such anomalies in 1,834 orthologous clusters of archaeal genes, focusing on lineage-specific subfamilies that significantly deviate from the expected rate of evolution. Multiple potential cases of neofunctionalization and subfunctionalization were identified, including some ancient, house-keeping gene families, such as ribosomal protein S10, general transcription factor TFIIB and chaperone Hsp20. As expected, many cases of apparent acceleration of evolution are associated with lineage-specific gene duplication. On other occasions, long branches in phylogenetic trees correspond to horizontal gene transfer across long evolutionary distances. Significant deceleration of evolution is less common than acceleration, and the underlying causes are not well understood; functional shifts accompanied by increased constraints could be involved. Many gene families appear to be “highly evolvable,” that is, include both long and short branches. Even in the absence of precise functional predictions, this approach allows one to select targets for experimentation in search of new biology.

Decoupled genomic elements and the evolution of partner quality in nitrogen-fixing rhizobia

Understanding how mutualisms evolve in response to a changing environment will be critical for predicting the long-term impacts of global changes, such as increased N (nitrogen) deposition. Bacterial mutualists in particular might evolve quickly, thanks to short generation times and the potential for independent evolution of plasmids through recombination and/or HGT (horizontal gene transfer). In a previous work using the legume/rhizobia mutualism, we demonstrated that long-term nitrogen fertilization caused the evolution of less-mutualistic rhizobia. Here, we use our 63 previously isolated rhizobium strains in comparative phylogenetic and quantitative genetic analyses to determine the degree to which variation in partner quality is attributable to phylogenetic relationships among strains versus recent genetic changes in response to N fertilization. We find evidence of distinct evolutionary relationships between chromosomal and pSym genes, and broad similarity between pSym genes. We also find that nifD has a unique evolutionary history that explains much of the variation in partner quality, and suggest MoFe subunit interaction sites in the evolution of less-mutualistic rhizobia. These results provide insight into the mechanisms behind the evolutionary response of rhizobia to long-term N fertilization, and we discuss the implications of our results for the evolution of the mutualism.

Inferring microbial interaction network from microbiome data using RMN algorithm

Background

Microbial interactions are ubiquitous in nature. Recently, many similarity-based approaches have been developed to study the interaction in microbial ecosystems. These approaches can only explain the non-directional interactions yet a more complete view on how microbes regulate each other remains elusive. In addition, the strength of microbial interactions is difficult to be quantified by only using correlation analysis.

Results

In this study, a rule-based microbial network (RMN) algorithm, which integrates regulatory OTU-triplet model with parametric weighting function, is being developed to construct microbial regulatory networks. The RMN algorithm not only can extrapolate the cooperative and competitive relationships between microbes, but also can infer the direction of such interactions. In addition, RMN algorithm can theoretically characterize the regulatory relationship composed of microbial pairs with low correlation coefficient in microbial networks. Our results suggested that Bifidobacterium, Streptococcus, Clostridium XI, and Bacteroides are essential for causing abundance changes of Veillonella in gut microbiome. Furthermore, we inferred some possible microbial interactions, including the competitive relationship between Veillonella and Bacteroides, and the cooperative relationship between Veillonella and Clostridium XI.

Conclusions

The RMN algorithm provides the reconstruction of gut microbe networks, and can shed light on the dynamical interactions of microbes in the infant intestinal tract.

Electronic supplementary material

The online version of this article (doi:10.1186/s12918-015-0199-2) contains supplementary material, which is available to authorized users.

Prospect, isolation, and characterization of microorganisms for potential use in cases of oil bioremediation along the coast of Trindade Island, Brazil

In the present study, acrylic coupons with a thin layer of oil on the surface were incubated in the coastal water of Trindade Island, Brazil, for 60 days. The microorganisms adhered to the coupons were isolated using enrichment medium with hexadecane and naphthalene as the sole carbon and energy source. A total of 15 bacterial isolates were obtained, and the ability of these isolates to use different hydrocarbons as the source of carbon and energy was investigated. None of the isolates produced biosurfactants under our experimental conditions. Subsequently, identification methods such as partial sequencing of the 16S rRNA gene and analysis of fatty acids (MIDI) profile were employed. Among the 15 isolates, representatives of Actinobacteria, Firmicutes, and Alphaproteobacteria were detected. The isolates Rhodococcus rhodochrous TRN7 and Nocardia farcinica TRH1 were able to use all the hydrocarbons added to the culture medium (toluene, octane, xylene, naphthalene, phenanthrene, pyrene, hexadecane, anthracene, eicosane, tetracosane, triacontane, and pentacontane). Polymerase chain reaction amplification of the DNA isolated by employing primers for catechol 2,3-dioxygenase, alkane dehydrogenase and the alpha subunit of hydroxylating dioxygenases polycyclic aromatic hydrocarbon rings genes demonstrated that various isolates capable of utilizing hydrocarbons do not exhibit genes of known routes of catabolism, suggesting the existence of unknown catabolic pathways in these microorganisms. Our findings suggest that the microbiota associated to the coast of tropical oceanic islands has the ability to assist in environmental regeneration in cases of accidents involving oil spills in its shore. Thus, it motivates studies to map bioremediation strategies using the autochthonous microbiota from these environments.

An autonomous in vivo dual selection protocol for boolean genetic circuits

Success in synthetic biology depends on the efficient construction of robust genetic circuitry. However, even the direct engineering of the simplest genetic elements (switches, logic gates) is a challenge and involves intense lab work. As the complexity of biological circuits grows, it becomes more complicated and less fruitful to rely on the rational design paradigm, because it demands many time-consuming trial-and-error cycles. One of the reasons is the context-dependent behavior of small assembly parts (like BioBricks), which in a complex environment often interact in an unpredictable way. Therefore, the idea of evolutionary engineering (artificial directed in vivo evolution) based on screening and selection of randomized combinatorial genetic circuit libraries became popular. In this article we build on the so-called dual selection technique. We propose a plasmid-based framework using toxin-antitoxin pairs together with the relaxase conjugative protein, enabling an efficient autonomous in vivo evolutionary selection of simple Boolean circuits in bacteria (E. coli was chosen for demonstration). Unlike previously reported protocols, both on and off selection steps can run simultaneously in various cells in the same environment without human intervention; and good circuits not only survive the selection process but are also horizontally transferred by conjugation to the neighbor cells to accelerate the convergence rate of the selection process. Our directed evolution strategy combines a new dual selection method with fluorescence-based screening to increase the robustness of the technique against mutations. As there are more orthogonal toxin-antitoxin pairs in E. coli, the approach is likely to be scalable to more complex functions. In silico experiments based on empirical data confirm the high search and selection capability of the protocol.

Optimal part and module selection for synthetic gene circuit design automation

An integral challenge in synthetic circuit design is the selection of optimal parts to populate a given circuit topology, so that the resulting circuit behavior best approximates the desired one. In some cases, it is also possible to reuse multipart constructs or modules that have been already built and experimentally characterized. Efficient part and module selection algorithms are essential to systematically search the solution space, and their significance will only increase in the following years due to the projected explosion in part libraries and circuit complexity. Here, we address this problem by introducing a structured abstraction methodology and a dynamic programming-based algorithm that guaranties optimal part selection. In addition, we provide three extensions that are based on symmetry check, information look-ahead and branch-and-bound techniques, to reduce the running time and space requirements. We have evaluated the proposed methodology with a benchmark of 11 circuits, a database of 73 parts and 304 experimentally constructed modules with encouraging results. This work represents a fundamental departure from traditional heuristic-based methods for part and module selection and is a step toward maximizing efficiency in synthetic circuit design and construction.

Microbial evolution in vivo and in silico: methods and applications

Microbial evolution has been extensively studied in the past fifty years, which has lead to seminal discoveries that have shaped our understanding of evolutionary forces and dynamics. It is only recently however, that transformative technologies and computational advances have enabled a larger in-scale and in-depth investigation of the genetic basis and mechanistic underpinnings of evolutionary adaptation. In this review we focus on the strengths and limitations of in vivo and in silico techniques for studying microbial evolution in the laboratory, and we discuss how these complementary approaches can be integrated in a unifying framework for elucidating microbial evolution.

SBROME: a scalable optimization and module matching framework for automated biosystems design

The development of a scalable framework for biodesign automation is a formidable challenge given the expected increase in part availability and the ever-growing complexity of synthetic circuits. To allow for (a) the use of previously constructed and characterized circuits or modules and (b) the implementation of designs that can scale up to hundreds of nodes, we here propose a divide-and-conquer Synthetic Biology Reusable Optimization Methodology (SBROME). An abstract user-defined circuit is first transformed and matched against a module database that incorporates circuits that have previously been experimentally characterized. Then the resulting circuit is decomposed to subcircuits that are populated with the set of parts that best approximate the desired function. Finally, all subcircuits are subsequently characterized and deposited back to the module database for future reuse. We successfully applied SBROME toward two alternative designs of a modular 3-input multiplexer that utilize pre-existing logic gates and characterized biological parts.

A flood-based information flow analysis and network minimization method for gene regulatory networks

BACKGROUND

Biological networks tend to have high interconnectivity, complex topologies and multiple types of interactions. This renders difficult the identification of sub-networks that are involved in condition- specific responses. In addition, we generally lack scalable methods that can reveal the information flow in gene regulatory and biochemical pathways. Doing so will help us to identify key participants and paths under specific environmental and cellular context.

RESULTS

This paper introduces the theory of network flooding, which aims to address the problem of network minimization and regulatory information flow in gene regulatory networks. Given a regulatory biological network, a set of source (input) nodes and optionally a set of sink (output) nodes, our task is to find (a) the minimal sub-network that encodes the regulatory program involving all input and output nodes and (b) the information flow from the source to the sink nodes of the network. Here, we describe a novel, scalable, network traversal algorithm and we assess its potential to achieve significant network size reduction in both synthetic and E. coli networks. Scalability and sensitivity analysis show that the proposed method scales well with the size of the network, and is robust to noise and missing data.

CONCLUSIONS

The method of network flooding proves to be a useful, practical approach towards information flow analysis in gene regulatory networks. Further extension of the proposed theory has the potential to lead in a unifying framework for the simultaneous network minimization and information flow analysis across various "omics" levels.