Simulation of phage dynamics in multi-reactor models of complex wastewater treatment systems

Bacteriophage-based biocontrol technology to enhance the efficiency of wastewater treatment and reduce targeted bacterial biofilms

Wastewater treatment is an essential process for public health and a sustainable ecosystem. Inadequate wastewater treatment can lead to the release of organic and inorganic pollutants and pathogenic bacteria into the receiving waters which could be further utilized for recreation purposes. The interaction between bacteriophage and bacteria in a wastewater treatment plant plays a major role in maintaining the treatment process. Phage therapy has been proposed as an alternative to conventional treatment methods as bacteriophages can be used on specific targets and leave useful bacteria unharmed. The bacterial species, which are responsible for bulking, foaming, and biofilm formation in a wastewater treatment plant (WWTP) have been identified and their respective phages are isolated to control their growth. Phages with lytic life cycles are preferred to lysogenic. Lytic phages can kill the specific target as they lyse the cell, infect most of the hosts, and have an immediate effect on controlling problems caused by bacteria in a WWTP. The bacteriophages such as T7, SPI1, GTE7, PhaxI, MAG1, MAG2, ϕPh_Se01, ϕPh_Se02, and Bxb1 have been investigated for the removal of bacterial biofilms from wastewater. Novel experimental setups have improved the efficiency of phage therapy in small-scale and pilot-scale experiments. Much more in-depth knowledge of the microbial community and their interaction would help promote the usage of phage therapy in large-scale wastewater treatments. This paper has covered the recent advancements in phage therapy as an effective biocontrol of pathogenic bacteria in the wastewater treatment process and has looked at certain shortcomings that have to be improved.

Phage-host interactions: The neglected part of biological wastewater treatment

In wastewater treatment plants (WWTPs), the stable operation of biological wastewater treatment is strongly dependent on the stability of associated microbiota. Bacteriophages (phages), viruses that specifically infect bacteria and archaea, are highly abundant and diverse in WWTPs. Although phages do not have known metabolic functions for themselves, they can shape functional microbiota via various phage-host interactions to impact biological wastewater treatment. However, the developments of phage-host interaction in WWTPs and their impact on biological wastewater treatment are overlooked. Here, we review the current knowledge regarding the phage-host interactions in biological wastewater treatment, mainly focusing on the characteristics of different phage populations, the phage-driven changes in functional microbiota, and the potential driving factors of phage-host interactions. We also discuss the efforts required further to understand and manipulate the phage-host interactions in biological wastewater treatment. Overall, this review advocates more attention to the phage dynamics in WWTPs.

Minimizing Foaming and Bulking in Activated Sludge with Bacteriophage Treatment: A Review of Mathematical Modeling

The interest in the ability of phages to control bacterial populations has extended from medical applications into the fields of agriculture, aquaculture, and the food industry. In particular, several authors have proposed using bacteriophages as an alternative method to control foaming and bulking in wastewater treatment. This strategy has shown successful results at the laboratory scale. However, this technology is still in development, and there are several challenges to overcome before bacteriophages can be widely used to control foaming and bulking in pilot or larger-scale treatment plants. Several models of the infection mechanisms in individual bacteria–phage pairs have been reported, i.e., for controlled systems with only one bacterium species in the presence of one phage species. However, activated sludge treatment systems largely differ from this situation, which opens a large horizon for future research. Mathematical models will play a key role in this development process, and this review offers an overview of the proposed models: their applications, potential, and challenges. A particular focus is placed on the model properties, such as parameter identifiability and states’ observability, which are essential for process prediction, monitoring, or dynamic optimization.

Evaluating the effect of silver nanoparticles on bacteriophage lytic infection cycle-a mechanistic understanding

Bacteriophages and engineered nano-material (AgNPS) interactions is a relatively unexplored area of research. To answer the fundamental question whether bacteriophage lytic growth cycle is affected by the presence of AgNPs, laboratory experiments were performed with phages of Klebsiella pneumoniae, Delftia tsuruhatensis, Salmonella typhimurium, and Shigella flexneri using silver nanoparticles (AgNPs) with coating materials. One-step growth curves of bacteriophages indicated that the presence of these nanoparticles, and the associated ions of silver, produced pronounced effects on the lytic infection of certain bacteriophages. Effects included 96% reductions in post-infection phage yield in terms of plaque forming units (PFUs) after phages were incubated with silver nanoparticles and 28%-43% reductions from the presence of Ag⁺ alone. However, when Klebsiella pneumonia phage KL and Salmonella typhimurium phage Det7 were exposed to silver nanoparticles coated with poly-N-vinyl-2 pyrrolidone (PVP), an increase in final phage yield by as much as 250% was observed compared with the same phage not incubated with nanoparticles. A proposed mechanism, observed by transmission electron microscopy and verified using synthetic biology by which the nanoparticle binding phenotype can be produced, is that the binding of metal nanomaterial to phage virions results in potentially inhibitory effects. This binding was found to be dependent on the presence of exposed positively charged C-terminal amino-acid residues on the phage capsid surface, implied at first by amino-acid sequence comparisons between capsid proteins of the different phages used in this study. This was then proven experimentally using targeted DNA editing methods to fuse positive charged amino-acid residues to the coat protein C-terminus of non-binding phage. This induced the AgNP binding phenotype, as observed by TEM, DLS size measurements, and growth curve data that show the mutant constructs to be functionally inhibited after exposure to AgNPs. This research sets up a first platform for further research in the unexplored area of phage and AgNP interactions and provides useful findings.

Computational Modelling of Large Scale Phage Production Using a Two-Stage Batch Process

Cost effective and scalable methods for phage production are required to meet an increasing demand for phage, as an alternative to antibiotics. Computational models can assist the optimization of such production processes. A model is developed here that can simulate the dynamics of phage population growth and production in a two-stage, self-cycling process. The model incorporates variable infection parameters as a function of bacterial growth rate and employs ordinary differential equations, allowing application to a setup with multiple reactors. The model provides simple cost estimates as a function of key operational parameters including substrate concentration, feed volume and cycling times. For the phage and bacteria pairing examined, costs and productivity varied by three orders of magnitude, with the lowest cost found to be most sensitive to the influent substrate concentration and low level setting in the first vessel. An example case study of phage production is also presented, showing how parameter values affect the production costs and estimating production times. The approach presented is flexible and can be used to optimize phage production at laboratory or factory scale by minimizing costs or maximizing productivity.

Computational Modeling of Bacteriophage Production for Process Optimization

Computational models can be used to optimize the production of bacteriophages. Here a model is described for production in a two-stage self-cycling process. Theoretical and practical considerations for modeling bacteriophage production are first introduced. The key experimental protocols required to estimate key kinetic parameters for the model, including determining variable infection rates as a function of substrate concentration, are described. ppSim is an open-source R-script that can simulate bacteriophage production to optimize productivity or minimize costs. The steps included to run the simulation using the experimentally determined infection parameters are described. An example is also presented, where a level sensor and cycle time are optimized to maximize bacteriophage productivity in two sequential 1-L bioreactors, resulting in a production rate of 4.46 × 10¹⁰ bacteriophage particles/hour. The protocols and programs described here will allow users to potentially optimize production of their own bacteriophage-bacteria pairing by effectively applying bacteriophage modeling.