An Agent-Based Metapopulation Model Simulating Virus-Based Biocontrol of Heterodera Glycines.

With recently discovered soybean cyst nematode (SCN) viruses, biological control of the nematodes is a theoretical possibility. This study explores the question of what kinds of viruses would make useful biocontrol agents, taking into account evolutionary and population dynamics. An agent-based model, Soybean Cyst Nematode Simulation (SCNSim), was developed to simulate within-host virulence evolution in a virus-nematode-soybean ecosystem. SCNSim was used to predict nematode suppression under a range of viral mutation rates, initial virulences, and release strategies. The simulation model suggested that virus-based biocontrol worked best when the nematodes were inundated with the viruses. Under lower infection prevalence, the viral burden thinned out rapidly due to the limited mobility and high reproductive rate of the SCN. In accordance with the generally accepted trade-off theory, SCNSim predicted the optimal initial virulence for the maximum nematode suppression. Higher initial virulence resulted in shorter lifetime transmission, whereas viruses with lower initial virulence values evolved toward avirulence. SCNSim also indicated that a greater viral mutation rate reinforced the virulence pathotype, suggesting the presence of a virulence threshold necessary to achieve biocontrol against SCN.

Machine learning analysis of microbial flow cytometry data from nanoparticles, antibiotics and carbon sources perturbed anaerobic microbiomes.

BACKGROUND: Flow cytometry, with its high throughput nature, combined with the ability to measure an increasing number of cell parameters at once can surpass the throughput of prevalent genomic and metagenomic approaches in the study of microbiomes. Novel computational approaches to analyze flow cytometry data will result in greater insights and actionability as compared to traditional tools used in the analysis of microbiomes. This paper is a demonstration of the fruitfulness of machine learning in analyzing microbial flow cytometry data generated in anaerobic microbiome perturbation experiments. RESULTS: Autoencoders were found to be powerful in detecting anomalies in flow cytometry data from nanoparticles and carbon sources perturbed anaerobic microbiomes but was marginal in predicting perturbations due to antibiotics. A comparison between different algorithms based on predictive capabilities suggested that gradient boosting (GB) and deep learning, i.e. feed forward artificial neural network with three hidden layers (DL) were marginally better under tested conditions at predicting overall community structure while distributed random forests (DRF) worked better for predicting the most important putative microbial group(s) in the anaerobic digesters viz. methanogens, and it can be optimized with better parameter tuning. Predictive classification patterns with DL (feed forward artificial neural network with three hidden layers) were found to be comparable to previously demonstrated multivariate analysis. The potential applications of this approach have been demonstrated for monitoring the syntrophic resilience of the anaerobic microbiomes perturbed by synthetic nanoparticles as well as antibiotics. CONCLUSION: Machine learning can benefit the microbial flow cytometry research community by providing rapid screening and characterization tools to discover patterns in the dynamic response of microbiomes to several stimuli.

A novel high-throughput multi-parameter flow cytometry based method for monitoring and rapid characterization of microbiome dynamics in anaerobic systems.

A novel multidimensional flow cytometry based method has been demonstrated to monitor and rapidly characterize the dynamics of the complex anaerobic microbiome associated with perturbations in external environmental factors. While community fingerprinting provides an estimate of the meta genomic structure, flow cytometry provides a fingerprint of the community morphology including its autofluorescence spectrum in a high-throughput manner. Using anaerobic microbial consortia perturbed with the controlled addition of various carbon sources, it is possible to quantitatively discriminate between divergent microbiome analogous to community fingerprinting techniques using automated ribosomal intergenic spacer analysis (ARISA). The utility of flow cytometry based method has also been demonstrated in a fully functional industry scale anaerobic digester to distinguish between microbiome composition caused by varying hydraulic retention time (HRT). This approach exploits the rich multidimensional information from flow cytometry for rapid characterization of the dynamics of microbial communities.

A novel flavivirus in the soybean cyst nematode.

Heterodera glycines, the soybean cyst nematode (SCN), is a subterranean root pathogen that causes the most damaging disease of soybean in the USA. A novel nematode virus genome, soybean cyst nematode virus 5 (SbCNV-5), was identified in RNA sequencing data from SCN eggs and second-stage juveniles. The SbCNV-5 RNA-dependent RNA polymerase and RNA helicase domains had homology to pestiviruses in the family Flaviviridae, suggesting that SbCNV-5 is a positive-polarity ssRNA virus. SbCNV-5 RNA was present in all nematode developmental stages, indicating a transovarial mode of transmission, but is also potentially sexually transmitted via the male. SbCNV-5 was common in SCN laboratory cultures and in nematode populations isolated from the field. Transmission electron microscopy of sections from a female SCN showed virus particles budding from the endoplasmic reticulum and in endosomes. The size of the viral genome was 19 191 nt, which makes it much larger than other known pestiviruses. Additionally, the presence of a methyltransferase in the SbCNV-5 genome is atypical for a pestivirus. When cDNA sequences were mapped to the genome of SbCNV-5, a disproportionate number aligned to the 3' NTR, suggesting that SbCNV-5 produces a subgenomic RNA, which was confirmed by RNA blot analysis. As subgenomic RNAs and methyltransferases do not occur in pestiviruses, we conclude that SbCNV-5 is a new flavivirus infecting SCNs.

Hidden hysteresis - population dynamics can obscure gene network dynamics.

BACKGROUND: Positive feedback is a common motif in gene regulatory networks. It can be used in synthetic networks as an amplifier to increase the level of gene expression, as well as a nonlinear module to create bistable gene networks that display hysteresis in response to a given stimulus. Using a synthetic positive feedback-based tetracycline sensor in E. coli, we show that the population dynamics of a cell culture has a profound effect on the observed hysteretic response of a population of cells with this synthetic gene circuit. RESULTS: The amount of observable hysteresis in a cell culture harboring the gene circuit depended on the initial concentration of cells within the culture. The magnitude of the hysteresis observed was inversely related to the dilution procedure used to inoculate the subcultures; the higher the dilution of the cell culture, lower was the observed hysteresis of that culture at steady state. Although the behavior of the gene circuit in individual cells did not change significantly in the different subcultures, the proportion of cells exhibiting high levels of steady-state gene expression did change.Although the interrelated kinetics of gene expression and cell growth are unpredictable at first sight, we were able to resolve the surprising dilution-dependent hysteresis as a result of two interrelated phenomena - the stochastic switching between the ON and OFF phenotypes that led to the cumulative failure of the gene circuit over time, and the nonlinear, logistic growth of the cell in the batch culture. CONCLUSIONS: These findings reinforce the fact that population dynamics cannot be ignored in analyzing the dynamics of gene networks. Indeed population dynamics may play a significant role in the manifestation of bistability and hysteresis, and is an important consideration when designing synthetic gene circuits intended for long-term application.

A positive feedback-based gene circuit to increase the production of a membrane protein.

BACKGROUND: Membrane proteins are an important class of proteins, playing a key role in many biological processes, and are a promising target in pharmaceutical development. However, membrane proteins are often difficult to produce in large quantities for the purpose of crystallographic or biochemical analyses. RESULTS: In this paper, we demonstrate that synthetic gene circuits designed specifically to overexpress certain genes can be applied to manipulate the expression kinetics of a model membrane protein, cytochrome bd quinol oxidase in E. coli, resulting in increased expression rates. The synthetic circuit involved is an engineered, autoinducer-independent variant of the lux operon activator LuxR from V. fischeri in an autoregulatory, positive feedback configuration. CONCLUSIONS: Our proof-of-concept experiments indicate a statistically significant increase in the rate of production of the bd oxidase membrane protein. Synthetic gene networks provide a feasible solution for the problem of membrane protein production.

A modular positive feedback-based gene amplifier.

BACKGROUND: Positive feedback is a common mechanism used in the regulation of many gene circuits as it can amplify the response to inducers and also generate binary outputs and hysteresis. In the context of electrical circuit design, positive feedback is often considered in the design of amplifiers. Similar approaches, therefore, may be used for the design of amplifiers in synthetic gene circuits with applications, for example, in cell-based sensors. RESULTS: We developed a modular positive feedback circuit that can function as a genetic signal amplifier, heightening the sensitivity to inducer signals as well as increasing maximum expression levels without the need for an external cofactor. The design utilizes a constitutively active, autoinducer-independent variant of the quorum-sensing regulator LuxR. We experimentally tested the ability of the positive feedback module to separately amplify the output of a one-component tetracycline sensor and a two-component aspartate sensor. In each case, the positive feedback module amplified the response to the respective inducers, both with regards to the dynamic range and sensitivity. CONCLUSIONS: The advantage of our design is that the actual feedback mechanism depends only on a single gene and does not require any other modulation. Furthermore, this circuit can amplify any transcriptional signal, not just one encoded within the circuit or tuned by an external inducer. As our design is modular, it can potentially be used as a component in the design of more complex synthetic gene circuits.

Synthetic gene networks: the next wave in biotechnology?

Engineering novel, reusable gene networks to provide greater control over cellular processes is one of the goals of the emerging discipline of synthetic biology. This article reviews the landmark literature pertaining to the development of synthetic gene networks, the engineering framework used to design and characterize them and the technological developments on the horizon that could potentially advance the field in new directions. As gene network engineering enters its second decade, an attempt is also made to outline the challenges in advancing this nascent field, especially with regard to the practical limitations of component reusability and reliability and the opportunities that present themselves in the development of novel gene expression controllers and single-cell biosensors.

A folic acid-based functionalized surface for biosensor systems.

The performance of a biosensor depends largely on its interface with the biological system. This interface imparts a biologically relevant function to the device and provides a measure of specificity towards the biological analyte of interest. This paper documents the choice of folic acid as the functional component of a cantilever sensor to recognize nasopharyngeal (KB) cancer cells. A conjugation chemistry protocol has been outlined to deploy folic acid onto a titanium-coated sensor surface using a silane linker. The presence and biological activity of the sensor was verified by means of an immunospecific (ELISA) procedure. The overall performance of the folic acid-based cantilever sensor was measured using cancerous KB cell-binding experiments.

Bounds in the sensitivity of BioMEMS devices for cell detection.

This paper presents an ongoing effort to characterize performance and reliability of micro electromechanical systems used for biomedical diagnostics (BioMEMS). In order to study the interactions of human osteosarcoma (HOS) cells with BioMEMS devices, cultures were performed on silicon (Si) surfaces as well as silicon surfaces coated with 50 nm of titanium (Ti). Cell spreading on the surfaces was observed over time for up to 2 hours. It was seen that titanium coated silicon surfaces have the potential to provide a better interface for BioMEMS devices, due to enhanced adherence and spreading of the cells on these surfaces. Atomic force microscope (AFM) cantilevers were used as cell detection sensors. These cantilevers were coated with 50nm of titanium metal to provide a cell friendly surface. Theoretical models were then developed for the prediction of the vibrational responses of the AFM cantilevers before and after cell attachment. The models were used to relate the experimentally observed changes in frequency to the number of cells that are attached on the cantilever. The bounds in the possible frequency changes were determined within a theoretical framework. From experimentally calculated values for the mass of cells, random number simulations were carried out to determine the probability of cell attachment as a function of the change in resonance frequency of the cantilever sensor. The implications of the results are then discussed for the future reliability modeling of the sensor.