Cooperation and Competition Shape Ecological Resistance During Periodic Spatial Disturbance of Engineered Bacteria

Cooperation is fundamental to the survival of many bacterial species. Previous studies have shown that spatial structure can both promote and suppress cooperation. Most environments where bacteria are found are periodically disturbed, which can affect the spatial structure of the population. Despite the important role that spatial disturbances play in maintaining ecological relationships, it remains unclear as to how periodic spatial disturbances affect bacteria dependent on cooperation for survival. Here, we use bacteria engineered with a strong Allee effect to investigate how the frequency of periodic spatial disturbances affects cooperation. We show that at intermediate frequencies of spatial disturbance, the ability of the bacterial population to cooperate is perturbed. A mathematical model demonstrates that periodic spatial disturbance leads to a tradeoff between accessing an autoinducer and accessing nutrients, which determines the ability of the bacteria to cooperate. Based on this relationship, we alter the ability of the bacteria to access an autoinducer. We show that increased access to an autoinducer can enhance cooperation, but can also reduce ecological resistance, defined as the ability of a population to resist changes due to disturbance. Our results may have implications in maintaining stability of microbial communities and in the treatment of infectious diseases

Predictive biology: modelling, understanding and harnessing microbial complexity

Predictive biology is the next great chapter in synthetic and systems biology, particularly for microorganisms. Tasks that once seemed infeasible are increasingly being realized such as designing and implementing intricate synthetic gene circuits that perform complex sensing and actuation functions, and assembling multi-species bacterial communities with specific, predefined compositions. These achievements have been made possible by the integration of diverse expertise across biology, physics and engineering, resulting in an emerging, quantitative understanding of biological design. As ever-expanding multi-omic data sets become available, their potential utility in transforming theory into practice remains firmly rooted in the underlying quantitative principles that govern biological systems. In this Review, we discuss key areas of predictive biology that are of growing interest to microbiology, the challenges associated with the innate complexity of microorganisms and the value of quantitative methods in making microbiology more predictable.Defence Threat Reduction Agency (Grant HDTRA1-15-1-0051

Conjugation dynamics depend on both the plasmid acquisition cost and the fitness cost

Abstract Plasmid conjugation is a major mechanism responsible for the spread of antibiotic resistance. Plasmid fitness costs are known to impact long‐term growth dynamics of microbial populations by providing plasmid‐carrying cells a relative (dis)advantage compared to plasmid‐free counterparts. Separately, plasmid acquisition introduces an immediate, but transient, metabolic perturbation. However, the impact of these short‐term effects on subsequent growth dynamics has not previously been established. Here, we observed that de novo transconjugants grew significantly slower and/or with overall prolonged lag times, compared to lineages that had been replicating for several generations, indicating the presence of a plasmid acquisition cost. These effects were general to diverse incompatibility groups, well‐characterized and clinically captured plasmids, Gram‐negative recipient strains and species, and experimental conditions. Modeling revealed that both fitness and acquisition costs modulate overall conjugation dynamics, validated with previously published data. These results suggest that the hours immediately following conjugation may play a critical role in both short‐ and long‐term plasmid prevalence. This time frame is particularly relevant to microbiomes with high plasmid/strain diversity considered to be hot spots for conjugation

Bacterial Temporal Dynamics Enable Optimal Design of Antibiotic Treatment

<div><p>There is a critical need to better use existing antibiotics due to the urgent threat of antibiotic resistant bacteria coupled with the reduced effort in developing new antibiotics. β-lactam antibiotics represent one of the most commonly used classes of antibiotics to treat a broad spectrum of Gram-positive and -negative bacterial pathogens. However, the rise of extended spectrum β-lactamase (ESBL) producing bacteria has limited the use of β-lactams. Due to the concern of complex drug responses, many β-lactams are typically ruled out if ESBL-producing pathogens are detected, even if these pathogens test as susceptible to some β-lactams. Using quantitative modeling, we show that β-lactams could still effectively treat pathogens producing low or moderate levels of ESBLs when administered properly. We further develop a metric to guide the design of a dosing protocol to optimize treatment efficiency for any antibiotic-pathogen combination. Ultimately, optimized dosing protocols could allow reintroduction of a repertoire of first-line antibiotics with improved treatment outcomes and preserve last-resort antibiotics.</p></div

Recovery time guides design of effective injection based regimen.

<p><b>(A) Dependence of the recovery time on the initial antibiotic concentration</b>. If the initial antibiotic concentration is too low, then the population will not be affected and its recovery time will be zero. However, after the initial antibiotic concentration is high enough, increasing the concentration results in an increase in the time it takes for a population to recover from a single dose. <b>(B) Predictive power of recovery time for the outcome of long-term periodic antibiotic dosing</b>. For each antibiotic concentration-period combination, we calculate the final population density after 100 antibiotic doses. Subplots demonstrate the outcomes for the first couple of doses of regimens using periods less than one recovery time (bacteria final density is below the defined threshold of 10^-10) versus regimens using periods greater than one recovery time (bacteria final density returns to carrying capacity). <b>(C) Dependence of treatment efficiency on the antibiotic concentration and the dosing period</b>. Each combination using an antibiotic concentration with a recovery time > 0 (<i>a</i>₀ > 0.5) and any period less than 1 recovery time can eventually eliminate the population. Different combinations will reduce the population density to a pre-defined threshold (10^-10) with varying efficiency: the combination is more efficient if fewer doses are needed to reach the threshold. <i>a</i>₀ < 0.5 could not clear the infection in 100 doses. <b>(D) Dependence of total antibiotic usage on the antibiotic concentration and dosing period</b>. The total usage is calculated as the antibiotic concentration multiplied by number of doses needed to reduce population density to a predefined threshold.</p

Recovery time guides design of effective intravenous drip based regimen.

<p><b>(A) Dependence of the recovery time on the antibiotic concentration during IV</b>. We maintain each concentration for a fixed duration (time = 3) and then calculate the corresponding recovery time. <b>(B) Time course of an IV-drip regimen</b>. The antibiotic was delivered for a fixed duration until the bacteria density dropped below a pre-defined threshold (10^-10). <b>(C) Predictive power of recovery time for the outcome of long-term periodic antibiotic dosing</b>. For each antibiotic concentration-period combination, we calculate the final population density after applying 100 antibiotic doses. <b>(D) Dependence of treatment efficiency on the antibiotic concentration and the dosing period</b>. The efficacy is determined in the same manner as in <a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1004201#pcbi.1004201.g003" target="_blank">Fig. 3</a>. <b>(E) Dependence of total antibiotic usage on the antibiotic concentration and the dosing period</b>. The total usage is calculated as in <a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1004201#pcbi.1004201.g003" target="_blank">Fig. 3</a>.</p

Potential use of recovery time to guide clinical practice.

<p>A critical step entails the construction of a comprehensive database of the recovery time curves of various pathogens under different antibiotics. Based on the recovery time curve, the optimal antibiotic concentration (X), dose number (Y), and period length (Z) can be calculated for each pathogen-antibiotic combination and entered into a database. Given this database and a proper diagnosis of a pathogen, one can readily identify the most effective treatment protocol.</p

Mechanism and dynamics of antibiotic-mediated death.

<p><b>(A) Antibiotic-mediated death</b>. Black represents bacterial actions, blue represents Bla actions, and red represents antibiotic actions. Arrows denote induction or activation; T-lines indicate inhibition; the dashed arrow represents the ability for the model to simulate inducible or constitutive Bla production. <b>(B) Typical time courses of bacterial density, antibiotic, and Bla after one dose of antibiotic treatment</b>. The antibiotic can cause cell lysis, which triggers the release of Bla into the environment. Sufficient degradation of the antibiotic by the Bla allows the surviving bacteria to recover. <b>(C) Collective tolerance</b>. A bacterial population can only recover from an antibiotic dose if enough bacteria are present for sufficient Bla to be produced.</p