Received: 8 October 2020 Revised: 22November 2020

DOI: 10.1002/bies.202000272

P ROB L EM S & PA RAD I GM S

Prospects & Overviews

Fightingmicrobial pathogens by integrating host ecosysteminteractions and evolution

Alita R. Burmeister1,2 Elsa Hansen3 Jessica J. Cunningham4

E. Hesper Rego5 Paul E. Turner1,2,6 Joshua S.Weitz7,8

Michael E. Hochberg9,10

1 Department of Ecology and Evolutionary Biology, Yale University, NewHaven, Connecticut, USA

2 BEACONCenter for the Study of Evolution in Action, Michigan State University, East Lansing, Michigan, USA

3 Department of Biology, Center for Infectious Disease Dynamics, Pennsylvania State University, University Park, Pennsylvania, USA

4 Department of IntegratedMathematical Oncology, Moffitt Cancer Center & Research Institute, Tampa, Florida, USA

5 Department ofMicrobial Pathogenesis, Yale University School ofMedicine, NewHaven, Connecticut, USA

6 Program inMicrobiology, Yale School ofMedicine, NewHaven, Connecticut, USA

7 School of Biological Sciences, Georgia Institute of Technology, Atlanta, Georgia, USA

8 School of Physics, Georgia Institute of Technology, Atlanta, Georgia, USA

9 Institute of Evolutionary Sciences, University ofMontpellier, Montpellier, France

10 Santa Fe Institute, Santa Fe, NewMexico, USA

Correspondence

AlitaR.Burmeister,DepartmentofEcology

andEvolutionaryBiology, YaleUniversity,New

Haven,Connecticut,USA.

Email: alita.burmeister@yale.edu

Michael E.Hochberg, InstituteofEvolutionary

Sciences,University ofMontpellier,Montpel-

lier, France.

Email:michael.hochberg@umontpellier.fr

Funding information

PewBiomedicalResearchProgram; ITMO

CancerAVIESAN;Searle ScholarsPro-

gram;Yale Institute forBiospheric Studies;

National ScienceFoundation,Grant/Award

Number: 1806606;EberlyFamilyTrust;

National InstitutesofHealth,Grant/Award

Numbers: 1R01AI46592-01,R01CA17059,

R21AI144345,U54CA143970-05; JamesS.

McDonnell Foundation,Grant/AwardNum-

ber: 220020294; InstitutNationalDuCancer,

Grant/AwardNumber: 2014-1-PL-BIO-12-

IGR-1

Abstract

Successful therapies to combat microbial diseases and cancers require incorpo-

rating ecological and evolutionary principles. Drawing upon the fields of ecology

and evolutionary biology, we present a systems-based approach in which host and

disease-causing factors are considered as part of a complex network of interactions,

analogous to studies of “classical” ecosystems. Centering this approach around empir-

ical examples of disease treatment, we present evidence that successful therapies

invariably engage multiple interactions with other components of the host ecosystem.

Many of these factors interact nonlinearly to yield synergistic benefits and curative

outcomes. We argue that these synergies and nonlinear feedbacks must be leveraged

to improve the study of pathogenesis in situ and to develop more effective therapies.

An eco-evolutionary systems perspective has surprising and important consequences,

and we use it to articulate areas of high research priority for improving treatment

strategies.

KEYWORDS

cancer, infectious disease, nonlinear dynamics, resistance, systems approach, therapies

This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided

the original work is properly cited.

© 2020 The Authors. BioEssays published byWiley Periodicals LLC

BioEssays. 2021;43:2000272. wileyonlinelibrary.com/journal/bies 1 of 13

https://doi.org/10.1002/bies.202000272

2 of 13 BURMEISTER ET AL.

INTRODUCTION

Research efforts to combat cancer, microbial pathogens and viruses

tend to operate independently of each other, in part because their

underlying genetics and molecular mechanisms are largely distinct.

However, when considered together, cancers and infectious agents

share key ecological and evolutionary features that may help reveal

common principles to improve treatment. Here, we introduce the

term—“micros”—to describe this polyphyletic conceptual grouping of

microscopic disease agents including viruses, bacteria, protozoans, and

cancer.We use themicros concept to discuss how treatment of host ill-

ness may be improved through new approaches, in particular an eco-

evolutionary systems-based approach (see Glossary) in which host and

micro factors are considered as part of a complex, interacting system

analogous to classical ecosystems.

A systems-based approach involving micros, human disease,

therapeutic strategies, and the host ecosystem is predicated on the

following underappreciated observation: essentially all successful

therapies engage with multiple factors interacting within a system

(Figure 1). Even in traditional “monotherapy,” the target disease agent

is ostensibly cleared via synergywith other factors, commonly the host

immune system. Synergy may also happen between disease-causing

agents themselves, for instance through ecological competition that

can suppress resistant or more virulent lineages of bacteria or cancer.

This observation also draws attention to the fact that apparently

successful therapies may not necessarily contribute directly to a suc-

cessful recovery, and in these cases a systems-based approach can help

reveal the key processes that underlie a therapeutic outcome (Table 1).

Together, these examples highlight the utility of eco-evolutionary sys-

tems thinking when understanding and combating diseases caused by

micros.

We discuss the rationale for a systems approach integrating both

ecological and evolutionary processes, and stress the generality of

therapeutic synergies and the fundamental ecosystem principles that

limit or promote the growth of micros. The resulting framework—

which combines conceptual, empirical, and clinical perspectives—helps

articulate needed areas of research that will be required to develop a

robust systems-based scientific discipline of micro therapy.

THE BODY AS AN ECOSYSTEM

As is well known, complex systems have emergent properties unchar-

acteristic of their individual components. In this way, a systems

approach to medicine is directly analogous to the study of classic

ecosystem ecology and evolution: both approaches characterize how

a system’s individual units interact, including their positive, negative

or neutral effects on one another. These effects lead to changes in the

individual components as well as the system as awhole. These changes

are often the result of nonlinear interactions, meaning that predicting

how a system will react cannot be simply extrapolated from data col-

lected at one disease state or condition (Box 1). Complex biological sys-

tems also are shaped by evolutionary dynamics, including those from

the coevolutionary history of the host and micro, as well as the evolu-

tionary changes occurring within a host during the course of disease.

These historical and in situ evolutionary dynamics aid in our under-

standing of—and can be readily leveraged towards—a systems-based

medicine.

The within-host environment as an “ecosystem”

The within-host environment should be viewed as an ecosystem

in order to better understand how the introduction of a new

F IGURE 1 The four main components of the disease ecosystem (gray) and how various therapies intervene to impact amicro infection(yellow). Example therapies either directly (blue) or indirectly (red) impact themicro. For example, phage therapy and cytotoxic drugs act directlyon themicro, while fecal transplants alter themicrobiota thereby indirectly impacting the disease-causingmicro. More complex interactionsamong the four components and therapies can have indirect effects on themicro (not shown)

BURMEISTER ET AL. 3 of 13

TABLE 1 Clinical approaches that target micros and/or components of the disease ecosystem, associated therapeutic strategies from asystems perspective, and considerations for personalizedmedicine and associated risks. Example references are given for general groupings(cancer, bacteria) and two specific micro types (HIV and Plasmodium). Seemain text for more detailed descriptions

Clinical approach

Therapeutic strategy from a systems

perspective

Considerations for personalizedmedicine and

risks

Example

references

Leveraging defenses Harness or complement weakened

immune systems

Vaccinate vulnerable individuals

Deliver immunotherapies or combination drug

therapies to immunocompromised patients

with or without immunotherapy

Risks: Autoimmunity, immunoediting

Bacteria[1,2]

Cancer[3,4]

Plasmodium[5]

HIV[6,7]

Prevention and early

treatment

Relative ease at containing or

extinguishing (a smaller) micro

population

Lower the risk of resistance

Capitalize onmore effective immune

response early in disease

Vaccinate vulnerable individuals

Screen early and regularly

Determine presence of resistant or tolerant

variants in treatment decision

Risk: Overtreatment

Bacteria[8]

Cancer[9]

Plasmodium[10]

HIV[11,12]

Drug dose and/or

treatment schedule

Prevent emergence of resistant

mutants

Reduce toxicity

Reduce antagonisms in combination

therapies

Determine doses and administration schedule

based onmicro type, and presence of resistant

variants

Account for patient quality of life (to reduce risks

of toxicity) in dosing, and in administration

schedule

Risks: Selecting for variants that resist or tolerate

low drug concentrations or intermittent drug

administration

Bacteria[13]

Cancer[14,15]

Plasmodium[16]

HIV[17]

Broad spectrum

monotherapies

Target multiple genes, multiple micro

variants or types (species)

Target common phenotypes across all

species for traits such as cell division

Usewhenmicro is not knownwith certainty, or

when a knownmicro is likely to havemultiple

vulnerabilities exploitable by a treatment

Risks: Can select for resistant strains and disrupt

(e.g., select for resistance in) themicrobiome

Bacteria[18,19]

Cancer[20]

Combination therapy

directly targeting

micro

Increase impact onmicro

Increase likelihood of impacting an

unidentifiedmicro

Impact multiple micro variants or types

(species)

Prevent or treat resistance emergence

Use drug combinations or viruses (as cocktails)

with or without drugs to target a wider

spectrum ofmicros.

Use drug combinations and cocktails to reduce

the spectrum ofmicro escape variants (e.g.,

resistance, biofilms, persisters, spatial refuges)

Use whenmicro target is not knownwith

certainty

Risks: Select for resistance tomultiple

therapeutic agents; Increased toxicity

Bacteria[21–24]

Cancer[25–27]

Plasmodium[28]

Combinations including

components indirectly

impactingmicro (e.g.,

adjuvant therapy)

Increase impact onmicro

Impact multiple micro variants

Prevent or treat resistance emergence

Rendermicro habitat less hospitable

Determinewhether harnessing other system

components, such as the immune system or

modulating vascularization (angiogenesis),

contributes to controlling or eliminating an

indolent micro

Risk: Increased toxicity

Bacteria[29]

Cancer[30–36]

Plasmodium[37]

HIV[38]

Modify or interrupt

signaling or

communication

Reduce growth or virulence factors,

increase death signals

Use although largely untested and unlikely as

stand-alone therapy

Quench cell-cell signaling, such as quorum

sensingmolecules in “social” bacteria

Use tumor exosomes to increase angiogenesis,

apoptosis, and immune function

Risk: Select for resistant variants

Bacteria[39]

Cancer[40,41]

Evolutionary trade-offs Directly impact micro and select for

residual micros, the latter of which

are either less virulent, or more

vulnerable to other therapeutic

agents

Preemptively treat known resistance

mechanisms

Use for drug resistant micros

Leverage known trade-off mechanisms

Risks: Chronic disease if selection for less

virulence;Micros that evade the tradeoff

Bacteria[42–44]

Cancer[45–47]

Plasmodium[48]

4 of 13 BURMEISTER ET AL.

BOX1. Immunophage synergy

Bacteriophages are obligate intracellular parasites; that is, they can only replicate inside of bacterial cells. However, the replication and

lysis (death) of individual cells by lytic phages need not translate into population-level elimination. Instead, bacteriophages coexist and

coevolve with their bacterial hosts in natural systems, raising questions on when and how phage therapy works. In a disease context,

microbial pathogens are targeted both by a therapeutic (e.g., phages, antibiotics, and/or a combination of factors) and by components of

the human disease ecosystem including immune cells. In systemswith phages and bacteria alone, theory, experiments, and environmental

field work suggests that phages and bacteria should coexist. To investigate how the immune systemwould impact coexistence, a model of

“immunophage synergy” was developed that examined tripartite interactions between phages, bacteria, and immune effector cells.[49] In

this model, phages infect and lyse bacteria, immune responses increase in response to bacterial proliferation, and immune cells directly

target and eliminate bacteria (at least at low densities). Analysis of this model revealed key outcomes arising from systems thinking. First,

phage therapy is not expected to eliminate bacteria in the absence of a sufficient immune response. Second, bacterial infections may

not be controllable by an immune system alone if the pathogen density exceeds a critical “tipping point.” However, together phages and

the immune response can eliminate a microbial pathogen. In doing so, phage lysis reduces bacterial densities to levels controllable by an

activated immune response. This control is possible even if phage-resistant bacterial mutants are present. This model of immunophage

synergy was also verified experimentally in a murine model of acute respiratory infections, such that phage therapy was effective in

immunocompetent animals but not in those animals that were neutropenic or had immune signaling deficiencies.[50] Hence, the system

(and not just the therapeutic) is critical to the elimination of pathogens.

component (e.g., a therapy) will impact overall system health. Ecolog-

ical interactions such as predation, competition and mutualism are of

central importance to population dynamics, species coexistence and

evolutionary change in terrestrial and aquatic (classic) ecosystems.

The ecosystem concept has proved a general way to classify different

species in a given habitat by their interrelationships (e.g., predators

and prey, hosts and parasites), as well as to define the processes that

generate resources such as decomposition and nutrient recycling. Like

the body during disease, ecosystems are dynamic entities that are ulti-

mately regulated by the direct and indirect ecological interactions and

evolution of the component species.

Individual host organisms have many parallels with classic ecosys-

tems. These similarities include fluctuations in glucose and oxygen

that echo ecosystem resource flows; they involve interactions among

cells, tissues and organ systems that are reminiscent of population

and community interactions; and they include production of metabolic

wastes analogous to the byproducts produced by ecosystemmembers.

A healthy host ecosystem can be disrupted by invasion by a micro (e.g.,

an infecting bacterial pathogen) or emergence of a micro from within

the system (e.g., an evolved tumor). These disruptions create a diseased

host ecosystem, somewhat akin to the spread of an invasive species in

a classical ecosystem[51] or the spread of native invading species.[52]

The disease ecosystem contains many of the same components of the

healthy host ecosystem, but it is centered around how the growth,

spread and evolution of the micro is influenced by and affects interac-

tions with the host. Considering disease as a system and more specifi-

cally an ecosystem, lends itself to a more general and integrated view

on treatment. However, there are also some differences between nat-

ural ecosystems and the host ecosystem.[51] For instance, organisms

have evolved homeostasis mechanisms as a means of habitat mainte-

nance, unlike classical ecosystems (but see.[53])

Within the host ecosystem, the immune response—analogous in

some ways to predation—is of central importance in disease control.

For example, host-cell mimics are analogous to prey camouflaging in

natural ecosystems; micros invading certain tissues that are inacces-

sible to the host immune response is analogous to hiding behavior in

prey; and bacterial biofilms are analogous to herd protection. One of

the immune system’s major roles is preventing a micro from becom-

ing established in the first place.[54] For example, in HIV patients

immunosuppression increases the risk of infection by oncogenic

viruses and the emergence of associated cancers.[55] In immuno-

competent systems, micros that do persist are often either host-cell

mimics undetectable by the immune system, occupants of spaces not

reachable by appropriate immune cells, or capable of outgrowing

the innate immune response.[56] In other cases, the invading micro

may successfully establish if the host immune system is too slow to

respond, in particular due to the time-lags required for full activation

of the adaptive immune response. However, given sufficient time, the

immune response may control or eliminate the micro either on its

own[57,58] or together with treatments.[49,50,59]

Treating the ecosystem, not just the target micro

Asystems-based approach to therapeutic interventionsmust integrate

both knowledge of system dynamics as well as current conventional

approaches. One common conventional approach is empiric therapy,

which typically aims to directly eliminate themicro and alleviate symp-

toms. This strategy is then often credited to any successful outcome,

for example when an antibiotic is given during the early stages of

sepsis before the underlying bacteria can be screened for antibiotic

sensitivity, or when cancer chemotherapy successfully brings a patient

BURMEISTER ET AL. 5 of 13

F IGURE 2 Major routes of micro escape and systems-based therapeutic approaches to address each. (A) Evolution of resistance: Throughpopulation-level genetic changes, the evolution of resistance provides ameans of therapeutic escape in all micros. Therapeutic approaches involveleveraging in situ evolution and the ongoing development of new therapies. (B) Phenotypic Shielding: Through changes due to phenotypic plasticityor environmental conditions, phenotypic shielding provides time, space, or both for amicro to evade treatment. Therapeutic approaches involvedevelopment of drugs that can permeate themicro’s phenotypic barriers. (C) Dormancy: Through transient changes to themicro’s phenotype, themicro is temporarily resistant to treatment. Therapeutic approaches include a variety of methods to keep the dormant population size low orinactive

into remission. Empiric therapy may also fail, either because the micro

and its associated disease are untreatable (e.g., multi-resistant bacte-

rial pathogens, inoperable cancers, and weakened patient conditions),

or because the therapy did not sufficiently consider elements and

processes in the disease ecosystem. Systems-based thinking aids in

understanding these outcomes, and it forms the basis of treating

multiple parts of the host ecosystem and not just the micro itself.

For example, a systems perspective is a way to mitigate the risk of

therapeutic failure when resistance (Figure 2) is a real concern and

complete elimination of the micro is unlikely.[60] Systems thinking

could also facilitate the development of drug dosing regimens that

leverage host immunity to clear the infection.[61]

The immune system is oft-neglected and little understood in

explaining actual therapeutic outcomes, but it plays a central role in

the disease ecosystemof a host undergoingmedical treatment. For tar-

geted therapies to be effective, theymust not only expose themicro to

sufficient treatment concentrations for long enough periods, but also

act in concertwith the body’s immunocompetence. In the total absence

of immune responses in vitro, single agents aremost likely to eliminate

bacteria when the latter are at low numbers and/or low diversity.[62,63]

6 of 13 BURMEISTER ET AL.

Theoretical models suggest that immune systems can be central in

reducing the emergence of drug resistance during, for example, antibi-

otic treatment.[64] With the possible exception of mitigated successes

of combination therapies on immunosuppressed patients (Table 1), we

are unaware of in vivo assessments of whether introduced therapies

alone explain therapeutic success.

Therapies and immune systemsmayhave complementary effects on

the micro, the former eliminating the most exposed and least resis-

tant micros, and the latter clearing remaining segments of the micro

population.[50,51,65] Moreover, their effects may be synergistic, as for

example “immune storms” induced by certain oncolytic viruses.[66]

Evidence also suggests that drugs and immune responses may inter-

act in complex ways, potentially either augmenting or weakening

their independent actions. For example, prior work[67] showed how

antibiotics and pathogenic bacteria may metabolically alter the dis-

ease ecosystem, resulting in lowered antibiotic activity and either the

enhancement or impairment of immune responses. Although immune

responses can contribute to successful therapeutic outcomes, they can

also result in failure or unintended side effects, such as immunoediting,

autoimmunity, and cytokine release syndromes associatedwith certain

immunotherapies.[68–70]

Despite the immune system’s central importance in the disease

ecosystem, other underappreciated host features can influence thera-

peutic impacts. These include tissue heterogeneities that result in spa-

tial refuges for micros,[71–73] differences in organ system habitats,[74]

the commensal microbiome,[75] and resource replenishment.[51] For

example, the disorganized and often leaky vasculature within the

tumormicroenvironment not only compromises drug delivery tomany

areas of the tumor but also results in stress, such as hypoxia, acidity,

and limited nutrient supply, all of which mediate metabolic changes

that influence drug sensitivity.[76] Moreover, when treated with

chemotherapeutics, the tumor microenvironment itself can engage

in tissue repair responses that indirectly promote tumor growth and

invasion.[77]

Not only do these host features have the capacity to influence

the effectiveness of traditional micro-targeted therapies, they also

present opportunities to modify the host ecosystem in beneficial

ways (Figure 1). Indeed, there is already precedent for treating micro

disease bymodulating other features of the host ecosystem. For exam-

ple, bacteriotherapy (i.e., fecal transplantation) is a recommended

treatment for recurrent Clostridium difficile infections.[78,79] There is

also evidence that iron-chelators used in combination with antibiotics

could be an effective treatment for biofilms, although further develop-

ment of this approach is required before it will be regularly used in the

clinic.[80–82]

Integrating nonlinearity and networks is essential toa systems approach

Ecosystems are often thought of as having characteristic features that

persist well beyond the typical lifetime of constituent organisms. Yet

counterintuitively, ecosystems can suddenly shift from one seemingly

stable state to another, for example when nutrient influx shifts a clear

lake with abundant fish populations to a turbid lake dominated by

algae. These types of sudden shifts are hallmarks of nonlinear dynam-

ical systems.[83] A nonlinear system is one in which changes in inputs

result in disproportionate changes in themagnitude of an output. Non-

linear dynamics arise because of the interactions between components

in a system. These interactions can change the populations and/or phe-

notypes of interacting components, for example, predators and prey

in a natural ecosystem, or immune cells and micro in a host ecosys-

tem. In turn, nonlinear changes in populations and/or phenotypes can

drive further feedback that impacts process rates (e.g., cell death, cell

division, or change in cell state) and the trajectory of the system as a

whole. These nonlinearities in process rates are themselves embedded

in a network context.

Ecosystems are often composed of networks of interacting organ-

isms, and these networks have important implications on the stability

of ecosystems and their nonlinear dynamics. A network is a simplifi-

cation of the heterogeneous (and often sparse) interactions between

system components. For example, predators consume a subset of

potential prey, parasites infect a subset of potential hosts, and cell

types interact with a subset of each other. Defining the structure of

networked interactions—whether for predator-prey, host-parasite, or

cell-cell systems—is a critical first step to understanding hownetworks

shape dynamics, often in surprising ways. For example, despite early

claims that large complex systems are inherently unstable,[84] we now

understand that inmany instances networked interactions can actually

stabilize the system as a whole.[85–87] Hence, large complex systems

may be “resilient” to small perturbations, such that the characteristic

features of a system (e.g., nutrient uptake rate and population diver-

sity) are not fundamentally altered by small changes in environmental

drivers or endogenous fluctuations.

The resilience to small perturbations can also underlie a different

phenomenon in which complex, networked ecosystems exhibit thresh-

old effects or tipping points given suitably large perturbations.[88] A

tipping point represents the critical system state that, when perturbed,

undergoes a cascade of events that shifts the system to another stable

state. In addition to understanding the tipping points themselves, it is

also important to understand the processes that tip a system from one

state to another. For example, a shift thatmoves a disease ecosystem to

a healthy ecosystemmay be caused by a drug’s specific mechanism, its

dosing, and/or its scheduling. In terms of disease onset, tipping points

are often revealed by a minimal infectious dose (the lowest number of

micro units required to establish disease). For example, ∼105 cells of

Staphylococcus aureus are estimated to robustly establish infection.[89]

Below this point, the infection tends to fail, while above this point, the

infection is expected to proliferate. Similarly, cancer emergence may

also depend on immune system dynamics during tumor growth.[90] For

example, in multiple myeloma, patients that never progress from the

preneoplastic gammopathyphase possess a significant number of rapid

effector T cells that react to antigens of the preneoplastic cells. This T

cell response is not found in patients that have progressed tomalignant

multiplemyeloma, suggesting that the immune system can successfully

prevent the progression of some early cancers.[91] The quantitative

BURMEISTER ET AL. 7 of 13

aspects of this apparent tipping point in multiple myeloma are not yet

well understood andwill likely vary between cancer types andpatients.

The development of novel therapeutics that are not focused on cell

death, but instead on shifting the disease ecosystem to promote equi-

librium or elimination of cancerous cells could prove extremely useful

in systems-based oncology. Furthermore, manipulating these tipping

points could increase the effectiveness of currently available drugs due

to the altered disease state.

Host-micro interactions have been molded by speciesevolution

Multicellular host organisms are constantly subject to challenges

from micros. These challenges come from diverse sources, includ-

ing transmissible infections, pathogens emerging from within the

microbiome, and host cells carrying mutations leading to malignancy.

Unchecked, these threats can result in damage or destruction at

different levels, ranging from subcellular modification, to cellular

lysis, tissue damage, organ failure and death. The pervasiveness and

diversity of such dangers throughout the course of evolution have

constituted important selection pressures for a range of diverse

defense systems—most notably the immune system—to limit micro

establishment and growth[92,93]

As mentioned in the previous section, hosts have a considerable

repertoire of evolved systems that contribute to preventing, control-

ling and eliminating micros. The immune system is central, deploying a

range of tactics, including a rapid, general response (innate immunity

and inflammation), delayed responses to uncontrolled and evolving

threats (adaptive immunity), intra-cellular (humoral) and extra-cellular

(antibody) responses, and diverse immune cell types associated with

each of these responses. But a wide array of systems either redun-

dant or complementary to the immune system also contribute to

micro control. Some of these constitute barriers to micro invasion

and emergence. Examples include the host cell membrane, cell behav-

iors such as apoptosis and senescence, and larger-scale protection

through tissue architecture.[94,95] Some provide general protection

against many micro types, such as enhanced stem cell protection

in tissues exposed to the outer environment. Others are more spe-

cific, including tumor suppressor genes that initiate cellular apop-

tosis in reaction to danger signals, and in particular those indicat-

ing the threat of malignant transformation.[96] Finally, rather than

only resist micros, hosts may also have co-evolved with their micro-

biota to tolerate them.[75] For example, commensal microbiota can

contribute to remediating certain pathologies due to micros, includ-

ing tissue repair, toxin neutralization, inflammation and metabolic

homeostasis.[75]

Natural selection shapes host-micro interactions, providing an evo-

lutionary basis for systems thinking and new therapeutic strategies.

These strategiesmay replace limited or defective defenses, or add new,

complementary defenses. The complementary and adaptive nature

of the immune response is the basis for why combination therapies,

immunotherapy and viral therapies, which might not succeed on their

own, are so promising (Table 1). It also makes the immune response a

model for howexisting defenses could be augmented or supplemented.

In particular, the use of self-amplifying viruses as therapeutics offers

the potential to seed the host ecosystem with a small founding pop-

ulation that then grows as it targets the pathogenic micro.[97,98] For

bacterial infections, these include lytic bacteriophages (Boxes 1 and 2)

that target specific bacterial strains. Similarly, oncolytic viruses target

specific tumor-associated cells. Using natural micro-defense systems

leverages deep host-micro evolutionary histories to create new syner-

gies in therapeutic design.

Evolution also occurs within the host

While evolutionary responses are often thought of as long-term

changes within populations, micros can evolve rapidly during the

course of disease in an individual patient. These evolutionary changes

influence disease ecosystem dynamics and therapeutic outcomes,

including ways that within-host evolution can both hinder and help

micros during infection. During within-host evolution, micro popu-

lation dynamics and therapeutic outcomes are determined by the

within-host population size, growth rate, and diversity of the micro. A

very large micro population can have considerable adaptive potential,

including the emergence of resistance mutations (Figure 2). This adap-

tive potential suggests that monotherapies themselves are unlikely

to fully explain therapeutic success in sufficiently advanced disease,

as monotherapies can act in synergy with other factors, such as the

immune system. However, it is important to note that proliferation

of resistance within an individual host is not inevitable and is prob-

abilistic based on the micro’s mutation rate, population size, and the

dose and time period of treatment. Controlling or eliminating resis-

tant variants will either mean strategically employing combinations,

such asmulti-drug combinations[100] or phage-antibiotic combinations

for bacterial infections (Box 1),[21] or engaging mono- or combination

therapies that act in conjunction with host-based processes, such as

the immune response.[101] Moreover,within-host evolutionalsooccurs

within components of the host ecosystem. For example, B cell popula-

tions of the host immune system are both amplifying as well as adap-

tive, changing genetically in a within-host evolutionary process that

improves immune recognition of micro antigens.[102]

Two particularly promising therapeutic strategies that capitalize on

within-host evolution are to contain or eliminate micros using evo-

lutionary trade-offs and/or competitive effects (Table 1).[42,103–105]

For instance, “phage steering” leverages trade-offs through within-

host evolution to restore antibiotic sensitive genotypes (Box 2). Lever-

aging competitive effects has also shown promising results both

in vitro[14,106] and in vivo.[15] For example, drug-dosing protocols

designed to maximize competition between sensitive and resistant

strains can (under certain circumstances) more effectively manage

in vitro populations of Escherichia coli than attempting to eliminate

them.[14,106] It is also possible to simultaneously leverage both evolu-

tionary trade-offs and competition to contain (and possibly eliminate)

resistance by changing resource levels available to resistant variants

8 of 13 BURMEISTER ET AL.

BOX2. Phage Steering

Phage therapy typically utilizes strictly-lytic (bactericidal) phages to kill target pathogenic bacteria within a host organism, and this

approach predates the discovery and subsequent therapeutic use of chemical antibiotics.[97] However, a long-recognized limitation is

that phages impose strong selection for evolution of resistance in the bacteria, a seemingly inevitable outcome that permits treatment

failure. Onemodern approach to phage therapy is to both acknowledge and capitalize on this certainty, leveraging the evolution of phage

resistance as a strength rather than a weakness. In particular, if the proximate binding of a lytic phage is known to associate with a

mechanism for antibiotic resistance in the target bacteria, this should exert strong selection for the bacteria to mutate or down regu-

late the phage-binding target(s), likely compromising their ability to maintain resistance to chemical antibiotics. This phenotypic conces-

sion of gaining greater phage resistance at the expense of drug re-sensitivity would exemplify an evolutionary trade-off in the bacterial

pathogen. Thus, this “steering” approach to phage therapy should be doubly effective; success is achieved when phages lyse the target

bacteria, but also when evolution of phage resistance causes the bacteria to suffer greater antibiotic sensitivity. Mechanistically, this pre-

diction was demonstrated via interactions between lytic phages and bacterial efflux pumps: protein complexes whose functions include

active removal of chemical antibiotics that enter the cell, thus contributing to the widespread ability for some bacterial pathogens to

resist antibiotics of various drug classes. In particular, the use of a lytic phage to bind the outermost protein of the TolC efflux-pumps

in E. coli selects for evolution of phage resistance that can coincide with antibiotic re-sensitivity.[42,43] Based on this logic of predictable

phage/antibiotic synergy observed in vitro,[42] a lytic phage plus ceftazidime antibiotic was successfully administered to treat a chronic P.

aeruginosa-biofilm infected aortic-arch graft.[99] Although highly encouraging, it is important to note that somemutations for phage resis-

tance would not necessarily cause this useful trade-off; rather, the possibility exists for bacteria to evolve phage escape while remaining

unchanged in their antibiotic resistance, or to even increase growth in presence of the drug (i.e., an evolutionary “trade-up”.[43]) Further

research will show whether useful trade-off-generating mutations occur reliably within the “black box” of the human disease ecosystem.

If so, phage/antibiotic synergies would constitute a reliable approach to phage therapy. This approach would re-invigorate our waning

antibacterial arsenal and address the widespread failure of standalone chemical antibiotics.

growing in competitionwith sensitive strains. For example, limitationof

the nutrient para-aminobenzoic acid (pABA) in vivowas recently found

to prevent emergence of pyrimethamine-resistant strains of themalar-

ial parasite Plasmodium chabaudi.[48]

Translating systems thinking into new therapies

Conventional therapeutic interventions aim to remedy disease by

direct elimination of targeted micros. A systems approach expands

conventional therapies by emphasizing that there are (i) many ways to

effect change (e.g., interventions may directly or indirectly target the

micro) and (ii) many types of desirable change (e.g., interventions do

not need to eliminate the micro to be successful) (Table 1). For exam-

ple, rather than trying to avoid resistance (Figure 2), a novel approach

leverages trade-offs to steer bacterial evolution towards resensitiza-

tion of antibiotics (Box 2). Below, we present three general approaches

that will help integrate eco-evolutionary systems thinking into existing

paradigms, ultimately leading to new therapies.

Understanding how therapeutic strategies relate to“natural” processes

Cellularmaintenance, tissuearchitecture, and the immunesystemhave

been subjected to selection by micros throughout the course of meta-

zoanevolution. The immune systememploys amulti-prongedapproach

that kills or immobilizes micros, continually adapts to novel micro vari-

ants and cordons-off and remediates the diseased area. Conventional

micro-targeted monotherapies are analogous to single components of

the innate immune response that kill or immobilize a micro. Drug com-

binations are then analogous to the immune system’s diverse collection

of cell types.[107] Some therapies are evenmore similar to the adaptive

immune system in that they self-amplify (e.g., lytic bacteriophages and

oncolytic viruses), analogous to how B- and T- cells specific to micro

variants replicate during infection.

Redesigning in-vitro platforms to screen for synergy

Anti-micro drugs are generally evaluated in terms of their ability to

eliminate micros in vitro and then in animal models before being

applied in a clinical context. Elimination is often predicated on the abil-

ity of the therapeutic to kill a micro or halt micro growth in the in vitro

environment. However, if therapeutic efficacy is driven by synergistic

interactions with other factors, including but not limited to the host

immune system, then efforts to develop a translation pipelinewill need

to revisit those synergies during initial screening for new therapeu-

tics. Moving from in vitro to in vivo work within the next decade will

require addressing the “lack of reality” usually inherent to lab studies.

Improved assessments would need to involve the factors not typically

replicated in the lab: large population sizes, environmental complexity,

immune-cell populations, diversity of the microbiome, and horizontal

gene transfer.

BURMEISTER ET AL. 9 of 13

From a system perspective, we advocate for considering ther-

apeutics through the lens of synergistic properties, that is, the

extent to which they stimulate the ecosystem to respond to a micro,

thereby enabling disease remediation. Ecosystem effects may include

activating immune cells, leveraging resident members of the com-

mensal microbiome to outcompete the micro, or other combinations

of effects that elicit a host response. For instance, if a therapeutic

agent works by tipping the system to clear a bacterial pathogen,

virus, or cancer, then pre-clinical development should emphasize

synergistic interactions, combination use, or other properties rather

than strictly through direct killing assays. Such an approach requires

development of diseasemodel systems that replicate interactions with

host features.[108–110] As an example, a study in small-cell lung cancer

showed that while the use of the a p53 vaccine for ovarian cancer pro-

vided little benefit as amonotherapy, subsequent use of chemotherapy

resulted in a significantly increased rate of clinical response. If this

connection had not been found, the p53 vaccine likely would have

been discontinued due to lack of efficacy.[30] In-vitro screening and

in-vivo trials that explicitly test for these kinds of synergies will

greatly increase the number of drugs in the cancer therapy arsenal.[25]

Screening for synergymight also yield “ecophillic” therapeutics, that is,

those that are selected for their interactions with components of the

host ecosystem.[111]

Considering management over elimination

A systems approach emphasizes that micro elimination is not the only

acceptable therapeutic outcome. Focusing on elimination artificially

limits therapeutic options, may hasten treatment failure by promot-

ing resistance and increasing toxic side effects for the patient. The

motivating principle behind micro management is that there are situ-

ations where treatment should be used to manipulate the size of the

micro population in beneficial ways as opposed to attempting to elim-

inate the micro population entirely. In practice, micro management

can take many different forms (e.g., adaptive[15] and bipolar androgen

therapies[112] for prostate cancer). One of its key benefits is reduc-

ing toxic and other side effects of some therapies on the host. Man-

agement may also better address the risk of resistance than elim-

ination (e.g., competition between drug-sensitive and drug-resistant

micros[113] and drug-addiction of resistant micros.[114]) Successfully

employingmanagement will require identifying situations where it will

be beneficial and then personalizing the treatment approach to capital-

ize on feedbackmechanisms.

CONCLUSIONS AND OUTLOOK

The scientific disciplines of ecology and evolutionary biology have

much to contribute to our understanding of disease development

and treatment. There is considerable potential in developing an eco-

evolutionary systems-based framework by evaluating analogies with

better understood terrestrial and aquatic ecosystems. Identifying the

system components and quantifying their interactions in both healthy

host ecosystems and disease ecosystems can contribute to design-

ing improved therapies, given the realistic constraints likely to occur

in actual treatment situations. To do this will require combining the

knowledge and technical expertise of theoreticians who work at the

most abstract level, basic empirical researchers who work in vitro and

in vivo, and clinical researchers and clinicians who can translate this

knowledge in situ to patients (Box 3).

We believe that an eco-evolutionary systems-based approach will

lead to new options and better patient outcomes in the form of per-

sonalized medicine. This approach will integrate tested systems mod-

els with patient data to produce treatment options that account for

risks such as the evolution of resistant variants. Systems approaches

to personalized medicine will be well suited to confront specificities

and idiosyncrasies of the disease (e.g., bacterial strain and tissue or

organ affected) and the patient (e.g., condition, age, genetics), given

the knowledge of the ecological interactions between a treatment and

the systems-level feedbacks of the host. Similar to pest management

systems,[121,122] given the complexity of even the simplest treatment

scenarios, a future personalized medicine will require focusing on a

small number of salient interactions, while being attentive to other fac-

tors that could intervene as well. Nevertheless, we cannot ignore the

fact that the use of traditional empiric therapy and targetedmonother-

apy often works.[123] Insights from empiric therapy should be inte-

grated into the systems approach for risk assessment and therapeutic

objectives.

Glossary

Combination therapy: Any therapeutic approach that involves

more than one introduced agent or pre-existing component of

the disease ecosystem.

Disease ecosystem: The host ecosystem when disrupted by

a micro and characterized by interactions involving micro

growth, evolution and spread, and host ecosystem responses

to limit themicro and remediate associated damage.

Empiric therapy: Also referred to as “heuristics," involves the use

of experience to select a treatment for presented symptoms,

especially when data are not readily available.

Host ecosystem: A conceptual model that views the host as

harboring processes and ecological interactions analogous to

those found in classical ecosystems.

Nonlinear dynamics/interactions: A change in one part of a sys-

tem that results in a disproportionate change in another part.

Synergy: When two or more processes− be they part of the host

ecosystem (e.g., immune response), therapies, or both − con-

tribute to improved outcomes compared to fewer acting pro-

cesses.

Systems-based approach: The view that a system’s behavior can-

not be represented as the sum of identifiable, discrete parts;

instead identifying direct and indirect interactions between

components are required to explain emergent phenomena.

Targeted therapy: A therapy aimed at a single element in the dis-

ease ecosystem, usually themicro itself.

10 of 13 BURMEISTER ET AL.

BOX3. Towards an in situ science

A future science of micro therapy will identify and measure disease ecosystem dynamics, and then relate these to the efficacy of vari-

ous therapies. Achieving this andmoving beyond single-action target-drug paradigmswill require that we address several conceptual and

logistic challenges. First, the complexity of disease ecosystems needs to be categorized in an actionableway. Factors that determine these

categories must not only includemicro and host properties (micro type, host age and condition), but also the current state of progression

of the disease.[115] For example, a recent proposal for an “eco-evo” classification of cancers[116] partitioned ecological (“eco”) parameters

into hazards (e.g., immune system, toxins, waste), and resource levels (e.g., oxygen, glucose). Parameterizing the host ecosystem in such

a way may foster prediction of how system components and processes will interact. Despite the promise of this and simpler (e.g.,[51])

frameworks, significant challenges remain in measuring parameters directly or via indicators, and evaluating their significance in improv-

ing treatment strategies. Second, improved therapies will require consideration of the possible roles played by the innate and adaptive

components of the immune system. Such considerations will need to include accurate assessments of immunocompetence relative to the

disease in question, as well as options for increasing competence.[117] For example, immunity-boosting immunotherapies combined with

conventional chemotherapy are most effective early in tumor progression, as for patients with BRAF-mutant metastatic melanoma.[118]

Likewise, targeted immunotherapy is also an opportunity to treat bacterial sepsis in combination with antibiotics.[119] Third, we need

a deeper understanding of the synergies among drugs and existing components of the host ecosystem. This will notably help address

the major and growing problem of multi-drug resistance in infectious diseases[120] and cancer. Conventional high-dose, monotherapeu-

tic drug-based strategies are unlikely to succeed as standalone options to treat multidrug resistant bacteria, and indeed, such strategies

may even select for their more rapid emergence. Rather, drug combinations and/or combinations of other ecosystem components such

as the immune system, microbiome, nutrients and introduced control agents such as viruses will need to be seriously considered (Table 1,

Figure 1).

Tumor microenvironment: The disease ecosystem associated

with cancer that focuses on the activity of healthy and dis-

eased cells, tissues andvasculature in theproximity of a tumor.

ACKNOWLEDGMENTS

Special thanks to Michael Donoghue and the Yale Institute for Bio-

spheric Studies for hosting the workshop “Novel Approaches to

Combating Therapeutic Resistance.”We thank Jeffrey Townsend, Troy

Day and Jessica Metcalf for discussions, and Mike Blazanin and Silvie

Huijben and an anonymous reviewer for useful comments on this

manuscript. BioRender.comwas used to generate Figures 1 and 2. ARB

and PET: Our work was supported by an NIH Grant #R21AI144345

from the National Institute of Allergy and Infectious Diseases. PET:

Generous support from the Yale Institute for Biospheric Studies. EH:

Eberly Family Trust. EHR is supported by the PewBiomedical Research

Program and the Searle Scholars Program. JJC: James S. McDonnell

Foundation grant, Cancer therapy: Perturbing a complex adaptive

system, a V Foundation grant, NIH/National Cancer Institute (NCI)

R01CA170595, Application of Evolutionary Principles to Maintain

Cancer Control (PQ21), and NIH/NCI U54CA143970-05 [Physical

Science Oncology Network (PSON)] Cancer as a complex adaptive

system. JSW acknowledges support from NIH 1R01AI46592-01, NSF

1806606, and ARO W911NF1910384. MEH thanks ITMO Cancer

AVIESAN (HetColi 226132), the McDonnell Foundation (Studying

Complex Systems Research Award No. 220020294), and the Institut

National du Cancer (2014-1-PL-BIO-12-IGR-1) for funding.

CONFLICT OF INTEREST

P.E.T. is a co-founder of Felix Biotechnology Inc., and declares a finan-

cial interest in this company that seeks to commercially developphages

for use as therapeutics. A.R.B. and P.E.T. disclose two provisional

patent applications involving phage therapy. JSW discloses a provi-

sional patent application related to bacteriophage therapy.

ORCID

AlitaR. Burmeister https://orcid.org/0000-0003-4304-5551

ElsaHansen https://orcid.org/0000-0003-1874-529X

Jessica J. Cunningham https://orcid.org/0000-0001-8704-8848

E.HesperRego https://orcid.org/0000-0002-2973-8354

Paul E. Turner https://orcid.org/0000-0003-3490-7498

Joshua S.Weitz https://orcid.org/0000-0002-3433-8312

Michael E.Hochberg https://orcid.org/0000-0002-6774-5213

REFERENCES

1. Timsit, J.-F., Bassetti, M., Cremer, O., Daikos, G., deWaele, J., Kallil, A.,

. . . Barbier, F. (2019). Rationalizing antimicrobial therapy in the ICU: a

narrative review. Intensive Care Med., 45, 172–189.2. Kennedy, D. A., & Read, A. F. (2017). Why does drug resistance read-

ily evolve but vaccine resistance does not? Proc Biol Sci, 284, 2016–2562.

3. Binnewies, M., Roberts, E. W., Kersten, K., Chan, V., Fearon, D. F.,

Merad, M., . . . Krummel, M. F. (2018). Understanding the tumor

immune microenvironment (TIME) for effective therapy. Nat. Med.,24, 541–550.

4. Fridman,W.H., Zitvogel, L., Sautès-Fridman,C., &Kroemer,G. (2017).

The immune contexture in cancer prognosis and treatment. Nat. Rev.Clin. Oncol., 14, 717–734.

5. Artavanis-Tsakonas, K., Tongren, J. E., & Riley, E. M. (2003). The war

between the malaria parasite and the immune system: immunity,

immunoregulation and immunopathology. Clin. Exp. Immunol., 133,145–152.

6. Yarchoan, R., & Uldrick, T. S. (2018). HIV-associated cancers and

related diseases.N. Engl. J. Med., 378, 1029–1041.

BURMEISTER ET AL. 11 of 13

7. Caskey, M., Klein, F., & Nussenzweig, M. C. (2019). Broadly neutraliz-

ing anti-HIV-1monoclonal antibodies in the clinic.Nat.Med.,25, 547–553.

8. Bratzler, D. W., Dellinger, E. P., Olsen, K. M., Perl, T. M., Auwaerter,

P. G., Bolon, M. K., . . . Weinstein, R. A., American Society of Health-

System Pharmacists (ASHP), Infectious Diseases Society of America

(IDSA), Surgical Infection Society (SIS), Society for Healthcare Epi-

demiology of America (SHEA) (2013). Clinical practice guidelines for

antimicrobial prophylaxis in surgery. Surg Infect, 14, 73–156.9. Vasan, N., Baselga, J., & Hyman, D. M. (2019). A view on drug resis-

tance in cancer.Nature, 575, 299–309.10. Schlagenhauf, P., Wilson, M. E., Petersen, E., McCarthy, A., & Chen, L.

H. (2019). Malaria chemoprophylaxis. In: Travel Medicine. Elsevier, pp145–167.

11. INSIGHT START Study Group, Lundgren, J. D., Babiker, A. G., Gordin,

F., Emery, S., Grund, B., . . . Neaton, J. D. (2015). Initiation of antiretro-

viral therapy in early asymptomatic HIV infection. N. Engl. J. Med.,373, 795–807.

12. US Preventive Services Task Force, Owens, D. K., Davidson, K. W.,

Krist, A. H., Barry, M. J., Cabana, M., . . . Wong, J. B. (2019). Preexpo-

sure prophylaxis for the prevention of HIV infection: US Preventive

Services Task Force Recommendation Statement. JAMA, J. Am. Med.Assoc., 321, 2203–2213.

13. Yoshida, M., Reyes, S. G., Tsuda, S., Horinouchi, T., Furusawa, C., &

Cronin, L. (2017). Time-programmable drug dosing allows themanip-

ulation, suppression and reversal of antibiotic drug resistance in

vitro.Nat. Commun., 8, 15589.14. Bacevic, K., Noble, R., Soffar, A., Wael Ammar, O., Boszonyik, B., Pri-

eto, S., . . . Fisher, D. (2017). Spatial competition constrains resistance

to targeted cancer therapy.Nat. Commun., 8, 1995.15. Zhang, J., Cunningham, J. J., Brown, J. S., &Gatenby, R. A. (2017). Inte-

grating evolutionary dynamics into treatment ofmetastatic castrate-

resistant prostate cancer.Nat. Commun., 8, 1816.16. Desai, M., Hill, J., Fernandes, S., Walker, P., Pell, C., Gutman, J., . . . ter

Kuile, F. O. (2018). Prevention of malaria in pregnancy. Lancet Infect.Dis., 18: e119–e132.

17. Sethi, A. K., Celentano, D. D., Gange, S. J., Moore, R. D., & Gallant, J. E.

(2003). Association between adherence to antiretroviral therapy and

human immunodeficiency virus drug resistance. Clin. Infect. Dis., 37,1112–1118.

18. Silver, L. L. (2007). Multi-targeting by monotherapeutic antibacteri-

als.Nat. Rev. Drug Discov., 6, 41–55.19. Paharik, A. E., Schreiber, H. L. 4th, Spaulding, C. N., Dodson, K. W., &

Hultgren, S. J. (2017). Narrowing the spectrum: The new frontier of

precision antimicrobials.GenomeMed, 9, 110.20. Ingham,M., & Schwartz, G. K. (2017). Cell-cycle therapeutics come of

age. J. Clin. Oncol., 35, 2949–2959.21. Torres-Barceló, C., & Hochberg, M. E. (2016). Evolutionary rationale

for phages as complements of antibiotics. Trends Microbiol., 24, 249–256.

22. Waterer, G. W., Somes, G. W., &Wunderink, R. G. (2001). Monother-

apy may be suboptimal for severe bacteremic pneumococcal pneu-

monia. Arch. Intern. Med., 161, 1837–1842.23. Chan, B. K., Abedon, S. T., & Loc-Carrillo, C. (2013). Phage cocktails

and the future of phage therapy. Future Microbiol., 8, 769–783.24. Mouton, J.W. (1999). Combination therapy as a tool to prevent emer-

gence of bacterial resistance. Infection 27 Suppl, 2: S24–S28.25. Palmer, A. C., & Sorger, P. K. (2017). Combination cancer therapy can

confer benefit via patient-to-patient variability without drug additiv-

ity or synergy. Cell, 171, 1678–1691.e13.e13.26. Einhorn, L. H. (2002). Curing metastatic testicular cancer. Proc. Natl.

Acad. Sci. U. S. A., 99, 4592–4595.27. Wennier, S. T., Liu, J., & McFadden, G. (2012). Bugs and drugs:

Oncolytic virotherapy in combination with chemotherapy. Curr.Pharm. Biotechnol., 13, 1817–1833.

28. Allman, E. L., Painter, H. J., Samra, J., Carrasquilla, M., & Llinás, M.

(2016). Metabolomic profiling of the malaria box reveals antimalar-

ial target pathways. Antimicrob. Agents Chemother., 60, 6635–6649.29. Wright, G. D. (2016). Antibiotic Adjuvants: rescuing Antibiotics from

Resistance. TrendsMicrobiol., 24, 862–871.30. Antonia, S. J., Mirza, N., Fricke, I., Chiappori, A., Thompson, P.,

Williams, N., . . . Gabrilovich, D. I. (2006). Combination of p53 cancer

vaccinewith chemotherapy in patientswith extensive stage small cell

lung cancer. Clin. Cancer Res., 12, 878–887.31. Jacobson, C. A., & Abramson, J. S. (2012). HIV-associated Hodgkin’s

lymphoma: Prognosis and Therapy in the Era of cART. Adv Hematol,2012, 1.

32. Bayat Mokhtari, R., Homayouni, T. S., Baluch, N., Morgatskaya, E.,

Kumar, S., Das, B., & Yeger, H. (2017). Combination therapy in com-

bating cancer.Oncotarget, 8, 38022–38043.33. Sharma, P., Hu-Lieskovan, S., Wargo, J. A., & Ribas, A. (2017). Pri-

mary, adaptive, and acquired resistance to cancer immunotherapy.

Cell, 168, 707–723.34. Kennedy, L. B., & Salama, A. K. S. (2020). A review of cancer

immunotherapy toxicity. CA Cancer J. Clin., 70, 86–104.35. Bommareddy, P. K., Shettigar, M., & Kaufman, H. L. (2018). Integrat-

ing oncolytic viruses in combination cancer immunotherapy.Nat. Rev.Immunol., 18, 498–513.

36. Weichselbaum, R. R., Liang, H., Deng, L., & Fu, Y.-X. (2017). Radiother-

apy and immunotherapy: A beneficial liaison?Nat. Rev. Clin. Oncol.,14,365–379.

37. Bijker, E. M., Bastiaens, G. J. H., Teirlinck, A. C., van Gemert, G.-J.,

Graumans, W., van de Vegte-Bolmer, M., . . . Sauerwein, R. W. (2013).

Protection against malaria after immunization by chloroquine pro-

phylaxis and sporozoites is mediated by preerythrocytic immunity.

Proc. Natl. Acad. Sci. U. S. A., 110, 7862–7867.38. Jilek, B. L., Zarr, M., Sampah, M. E., Rabi, S. A., Bullen, C. K., Lai, J., . . .

Siliciano, R. F. (2012). A quantitative basis for antiretroviral therapy

for HIV-1 infection.Nat. Med., 18, 446–451.39. Whiteley, M., Diggle, S. P., & Greenberg, E. P. (2017). Progress in and

promise of bacterial quorum sensing research.Nature,551, 313–320.40. Marleau, A. M., Chen, C.-S., Joyce, J. A., & Tullis, R. H. (2012). Exo-

some removal as a therapeutic adjuvant in cancer. J. Transl. Med., 10,1–12.

41. Deng, D., & Shah, K. (2020). TRAIL of hopemeeting resistance in can-

cer. Trends Cancer, 989–1001.42. Chan, B. K., Sistrom, M., Wertz, J. E., Kortright, K. E., Narayan, D., &

Turner, P. E. (2016). Phage selection restores antibiotic sensitivity in

MDR Pseudomonas aeruginosa. Sci. Rep., 6, 26717.43. Burmeister, A. R., Fortier, A., Roush, C., Lessing, A. J., Bender, R. G.,

Barahman, R., . . . Turner, P. E. (2020). Pleiotropy complicates a trade-

off between phage resistance and antibiotic resistance. Proc. Natl.Acad. Sci. U. S. A., 117, 11207–11216.

44. Burmeister, A. R., & Turner, P. E. (2020). Trading-off and trading-up in

the world of bacteria–phage evolution. Curr. Biol., 30: R1120–R1124.45. Basanta, D., Gatenby, R. A., & Anderson, A. R. A. (2012). Exploiting

evolution to treat drug resistance: combination therapy and the dou-

ble bind.Mol. Pharm., 9, 914–921.46. Acar, A., Nichol, D., Fernandez-Mateos, J., Cresswell, G. D., Barozzi, I.,

Hong, S. P., . . . Sottoriva, A. (2020). Exploiting evolutionary steering to

induce collateral drug sensitivity in cancer.Nat. Commun., 11, 1923.47. O’Neil, N. J., Bailey, M. L., & Hieter, P. (2017). Synthetic lethality and

cancer.Nat. Rev. Genet., 18, 613–623.48. Wale, N., Sim, D. G., Jones, M. J., Salathe, R., Day, T., & Read, A. F.

(2017). Resource limitation prevents the emergence of drug resis-

tance by intensifying within-host competition. Proc. Natl. Acad. Sci.USA, 114, 13774–13779.

49. Leung, C. Y. J., &Weitz, J. S. (2017). Modeling the synergistic elimina-

tion of bacteria byphage and the innate immune system. J. Theor. Biol.,429, 241–252.

12 of 13 BURMEISTER ET AL.

50. Roach, D. R., Leung, C. Y., Henry, M., Morello, E., Singh, D., Di

Santo, J. P., . . . Debarbieux, L. (2017). Synergy between the host

immune system and bacteriophage is essential for successful phage

therapy against an acute respiratory pathogen. Cell Host Microbe, 22,38–47.e4.e4.

51. Hochberg, M. E. (2018). An ecosystem framework for understanding

and treating disease. Evol Med Public Health, 2018, 270–286.52. Carey, M. P., Sanderson, B. L., Barnas, K. A., & Olden, J. D. (2012).

Native invaders – Challenges for science, management, policy, and

society. Front. Ecol. Environ., 10, 373–381.53. Free, A., & Barton, N. H. (2007). Do evolution and ecology need the

Gaia hypothesis? Trends Ecol. Evol., 22, 611–619.54. Glickman,M. S., & Sawyers, C. L. (2012). Converting cancer therapies

into cures: lessons from infectious diseases. Cell, 148, 1089–1098.55. de Martel, C., Shiels, M. S., Franceschi, S., Simard, E. P., Vignat, J.,

Hall, H. I., . . . Plummer, M. (2015). Cancers attributable to infections

among adults with HIV in the United States. AIDS, 29, 2173–2181.56. Antia, R., & Lipsitch, M. (1997). Mathematical models of parasite

responses to host immune defences. Parasitology, 115(Suppl), 155–167.

57. Fenton, A., & Perkins, S. E. (2010). Applying predator-prey theory

to modelling immune-mediated, within-host interspecific parasite

interactions. Parasitology, 137, 1027–1038.58. Sontag, E. D. (2017). A dynamic model of immune responses to anti-

gen presentation predicts different regions of tumor or pathogen

elimination. Cell Syst., 4, 231–241.e11.e11.59. Day, T., & Read, A. F. (2016). Does high-dose antimicrobial

chemotherapy prevent the evolution of resistance? PLoS Comput.Biol., 12: e1004689.

60. Hansen, E., & Read, A. F. (2020). Cancer therapy: Attempt cure or

manage drug resistance? Evol Appl, 13, 1660–1672.61. Gjini, E., & Brito, P. H. (2016). Integrating antimicrobial therapy with

host immunity to fight drug-resistant infections: classical vs.AdaptiveTreatment. PLoS Comput Biol, 12: e1004857.

62. Ramsayer, J., Kaltz, O., &Hochberg,M. E. (2013). Evolutionary rescue

in populations of Pseudomonas fluorescens across an antibiotic gradi-

ent. Evol Appl, 6, 608–616.63. Harcombe,W. R., & Bull, J. J. (2005). Impact of phages on two-species

bacterial communities. Appl. Environ. Microbiol., 71, 5254–5259.64. Handel, A.,Margolis, E., & Levin, B. R. (2009). Exploring the role of the

immune response in preventing antibiotic resistance. J. Theor. Biol.,256, 655–662.

65. Gordillo Altamirano, F. L., & Barr, J. J. (2019). Phage therapy in the

postantibiotic era. Clin. Microbiol. Rev., 32: e00066–18.66. Melcher, A., Parato, K., Rooney,C.M., &Bell, J. C. (2011). Thunder and

lightning: immunotherapy and oncolytic viruses collide.Mol. Ther.,19,1008–1016.

67. Yang, J. H., Bhargava, P., McCloskey, D., Mao, N., Palsson, B. O., &

Collins, J. J. (2017). Antibiotic-induced changes to the host metabolic

environment inhibit drug efficacy and alter immune function. CellHost Microbe, 22, 757–765.e3.e3.

68. Mittal, D., Gubin, M. M., Schreiber, R. D., & Smyth, M. J. (2014). New

insights into cancer immunoediting and its three component phases–

elimination, equilibrium and escape. Curr. Opin. Immunol., 27, 16–25.69. Hay, K. A., Hanafi, L.-A., Li, D., Gust, J., Liles, W. C., Wurfel, M. M.,

. . . Turtle, C. J. (2017). Kinetics and biomarkers of severe cytokine

release syndrome after CD19 chimeric antigen receptor-modified T-

cell therapy. Blood, 130, 2295–2306.70. Young, A., Quandt, Z., & Bluestone, J. A. (2018). The balancing

act between cancer immunity and autoimmunity in response to

immunotherapy. Cancer Immunol. Res., 6, 1445–1452.71. Fuentes-Hernández, A., Hernández-Koutoucheva, A., Muñoz, A. F.,

Domínguez Palestino, R., & Peña-Miller, R. (2019). Diffusion-driven

enhancement of the antibiotic resistance selection window. J. R. Soc.Interface, 16, 20190363.

72. Lourenço,M., Chaffringeon, L., Lamy-Besnier,Q., Campagne, P., Eberl,

C., Bérard,M., . . . De Sordi, L. The spatial heterogeneity of the gut lim-

its bacteriophage predation leading to the coexistence of antagonist

populations of bacteria and their viruses. Cell Host Microbe, 28, 390–401.e5

73. Lorenzo-Redondo, R., Fryer, H. R., Bedford, T., Kim, E.-Y., Archer,

J., Pond, S. L. K., . . . Wolinsky, S. M. (2016). Persistent HIV-1 repli-

cation maintains the tissue reservoir during therapy. Nature, 530,51–56.

74. Rynkiewicz, E. C., Pedersen, A. B., & Fenton, A. (2015). An ecosystem

approach to understanding and managing within-host parasite com-

munity dynamics. Trends Parasitol., 31, 212–221.75. Ayres, J. S. (2016). Cooperativemicrobial tolerancebehaviors in host-

microbiotamutualism. Cell, 165, 1323–1331.76. Trédan, O., Galmarini, C. M., Patel, K., & Tannock, I. F. (2007). Drug

resistance and the solid tumormicroenvironment. J. Natl. Cancer Inst.,99, 1441–1454.

77. Wu, T., & Dai, Y. (2017). Tumor microenvironment and therapeutic

response. Cancer Lett., 387, 61–68.78. McDonald, L. C., Gerding, D. N., Johnson, S., Bakken, J. S., Carroll, K.

C., Coffin, S. E., . . . Wilcox, M. H. (2018). Clinical practice guidelines

for Clostridium difficile Infection in adults and children, 2017 update

by the Infectious Diseases Society of America (IDSA) and Society for

Healthcare Epidemiology of America (SHEA). Clin. Infect. Dis., 66: e1–e48.

79. Mullish, B. H., Quraishi, M. N., Segal, J. P., McCune, V. L., Baxter, M.,

Marsden, G. L., . . . Williams, H. R. T. (2018). The use of faecal micro-

biota transplant as treatment for recurrent or refractory infection

and other potential indications: Joint British Society of Gastroen-

terology (BSG) andHealthcare InfectionSociety (HIS) guidelines.Gut,67, 1920–1941.

80. Reid,D.W.,O’May,C., Kirov, S.M., Roddam, L., Lamont, I. L., & Sander-

son, K. (2009). Iron chelation directed against biofilms as an adjunct

to conventional antibiotics. Am. J. Physiol. Lung Cell. Mol. Physiol., 296:L857–L858.

81. Parquet, M. D. C., Savage, K. A., Allan, D. S., Davidson, R. J., & Holbein,

B. E. (2018). Novel iron-chelator DIBI inhibits growth, suppresses

experimental MRSA infection in mice and enhances the activities of

diverse antibiotics. Front Microbiol, 9, 1811.82. Moon, J.-H., Kim, C., Lee, H.-S., Kim, S.-W., & Lee, J.-Y. (2013). Antibac-

terial and antibiofilm effects of iron chelators against Prevotella inter-media. J. Med. Microbiol., 62, 1307–1316.

83. Pascual, M. (2005). Computational ecology: From the complex to the

simple and back. PLoS Comput. Biol., 1, 101–105.84. May, R.M. (1972).Will a large complex systembe stable?Nature,238,

413–414.

85. Roberts, A. (1974). The stability of a feasible random ecosystem.

Nature, 251, 607–608.86. Allesina, S., & Tang, S. (2012). Stability criteria for complex ecosys-

tems.Nature, 483, 205–208.87. Allesina, S., & Tang, S. (2015). The stability–complexity relationship at

age 40: A randommatrix perspective. Popul Ecol, 57, 63–75.88. Scheffer, M., Carpenter, S. R., Lenton, T. M., Bascompte, J., Brock, W.,

Dakos, V., . . . Vandermeer, J. (2012). Anticipating critical transitions.

Science, 338, 344–348.89. Schmid-Hempel, P., & Frank, S. A. (2007). Pathogenesis, virulence, and

infective dose. PLoS Pathog., 3, 1372–1373.90. Kim, R., Emi, M., & Tanabe, K. (2007). Cancer immunoediting from

immune surveillance to immune escape. Immunology, 121, 1–14.91. Dhodapkar, M. V., Krasovsky, J., Osman, K., & Geller, M. D. (2003).

Vigorous premalignancy-specific effector T cell response in the bone

marrow of patients with monoclonal gammopathy. J. Exp. Med., 198,1753–1757.

92. Cobey, S. (2014). Pathogen evolution and the immunological niche.

Ann. N. Y. Acad. Sci., 1320, 1–15.

BURMEISTER ET AL. 13 of 13

93. Mayer, A., Mora, T., Rivoire, O., & Walczak, A. M. (2016). Diversity

of immune strategies explained by adaptation to pathogen statistics.

Proc. Natl. Acad. Sci. U. S. A., 113, 8630–8635.94. Günther, J., & Seyfert, H.-M. (2018). The first line of defence: Insights

into mechanisms and relevance of phagocytosis in epithelial cells.

Semin. Immunopathol., 40, 555–565.95. Harris, V. K., Schiffman, J. D., & Boddy, A. M. (2017). Evolution of can-

cer defense mechanisms across species. Ecol. Evolution Cancer, 99–110

96. Domazet-Loso, T., & Tautz, D. (2010). Phylostratigraphic tracking of

cancer genes suggests a link to the emergence of multicellularity in

metazoa. BMC Biol., 8, 66.97. Kortright, K. E., Chan, B. K., Koff, J. L., & Turner, P. E. (2019). Phage

therapy: A renewed approach to combat antibiotic-resistant bacte-

ria. Cell Host Microbe, 25, 219–232.98. Bell, J., & McFadden, G. (2014). Viruses for tumor therapy. Cell Host

Microbe, 15, 260–265.99. Chan, B. K., Turner, P. E., Kim, S., Mojibian, H. R., Elefteriades, J. A., &

Narayan, D. (2018). Phage treatment of an aortic graft infected with.

Evol Med Public Health, 2018, 60–66.100. Singh, N., & Yeh, P. J. (2017). Suppressive drug combinations and their

potential to combat antibiotic resistance. J. Antibiot., 70, 1033–1042.101. Rodriguez-Gonzalez, R. A., Leung, C. Y., Chan, B. K., Turner, P. E., &

Weitz, J. S. (2020). Quantitativemodels of phage-antibiotic combina-

tion therapy.mSystems, 5: e00756–19.102. Cobey, S., Wilson, P., &Matsen, F. A. 4th (2015). The evolution within

us. Philos. Trans. R. Soc. Lond. B Biol. Sci., 370, 20140235.103. Gurney, J., Brown, S. P., Kaltz, O., & Hochberg, M. E. (2020). Steering

phages to combat bacterial pathogens. TrendsMicrobiol., 28, 85–94.104. Gatenby, R. A., Brown, J., & Vincent, T. (2009). Lessons from applied

ecology: cancer control using an evolutionary double bind. CancerRes., 69, 7499–7502.

105. Bull, J. J., Levin, B. R., &Molineux, I. J. (2019). Promises and pitfalls of

in vivo evolution to improve phage therapy. Viruses, 11, 1083.106. Hansen, E., Karslake, J., Woods, R. J., Read, A. F., & Wood, K. B.

(2020). Antibiotics can be used to contain drug-resistant bacteria

by maintaining sufficiently large sensitive populations. PLoS Biol., 18:e3000713.

107. Müller, V., de Boer, R. J., Bonhoeffer, S., & Szathmáry, E. (2018). An

evolutionary perspective on the systems of adaptive immunity. Biol.Rev. Camb. Philos. Soc., 93, 505–528.

108. Bar-Ephraim, Y. E., Kretzschmar, K., & Clevers, H. (2020). Organoids

in immunological research.Nat. Rev. Immunol., 20, 279–293.109. Shanti, A., Teo, J., & Stefanini, C. (2018). In vitro immune organs-on-

chip for drug development: A review. Pharmaceutics, 10, 278.110. Cornforth, D. M., Diggle, F. L., Melvin, J. A., Bomberger, J. M., &

Whiteley,M. (2020). Quantitative framework formodel evaluation in

microbiology researchusingPseudomonas aeruginosa and cystic fibro-sis infection as a test case.MBio, 11: e03042–19.

111. Stanley, S. A., Barczak, A. K., Silvis, M. R., Luo, S. S., Sogi, K., Vokes, M.,

. . . Hung, D. T. (2014). Identification of host-targeted small molecules

that restrict intracellular Mycobacterium tuberculosis growth. PLoSPathog., 10: e1003946.

112. Schweizer, M. T., Antonarakis, E. S., Wang, H., Ajiboye, A. S., Spitz,

A., Cao, H., . . . Denmeade, S. R. (2015). Effect of bipolar androgen

therapy for asymptomatic men with castration-resistant prostate

cancer: Results from a pilot clinical study. Sci. Transl. Med., 7,269ra2.

113. Hansen, E.,Woods,R. J., &Read,A. F. (2017).Howtousea chemother-

apeutic agent when resistance to it threatens the patient. PLoS Biol.,15: e2001110.

114. Das Thakur, M., Salangsang, F., Landman, A. S., Sellers, W. R., Pryer,

N. K., Levesque, M. P., . . . Stuart, D. D. (2013). Modelling vemurafenib

resistance in melanoma reveals a strategy to forestall drug resis-

tance.Nature, 494, 251–255.115. Torres, B. Y., Oliveira, J. H. M., Tate, A. T., Rath, P., Cumnock, K., &

Schneider, D. S. (2016). Tracking resilience to infections by mapping

disease space. PLoS Biol., 14: e1002436116. Maley, C. C., Aktipis, A., Graham, T. A., Sottoriva, A., Boddy, A. M.,

Janiszewska, M., . . . Shibata, D. (2017). Classifying the evolutionary

and ecological features of neoplasms.Nat. Rev. Cancer, 17, 605–619.117. Wang, A., Luan, H. H., & Medzhitov, R. (2019). An evolutionary per-

spective on immunometabolism. Science, 363: eaar3932.118. Gotwals, P., Cameron, S., Cipolletta, D., Cremasco, V., Crystal, A.,

Hewes, B., . . . Dranoff, G. (2017). Prospects for combining targeted

and conventional cancer therapy with immunotherapy.Nat. Rev. Can-cer, 17, 286–301.

119. Rubio, I., Osuchowski,M. F., Shankar-Hari,M., Skirecki, T.,Winkler,M.

S., Lachmann, G., . . . Bermejo-Martín, J. F. (2019). Current gaps in sep-

sis immunology: New opportunities for translational research. LancetInfect. Dis., 19: e422–e436.

120. Cassini, A., Högberg, L. D., Plachouras, D., Quattrocchi, A., Hoxha,

A., & Simonsen, G. S., . . . Burden of AMR Collaborative Group

(2019). Attributable deaths and disability-adjusted life-years caused

by infections with antibiotic-resistant bacteria in the EU and the

European Economic Area in 2015: a population-level modelling anal-

ysis. Lancet Infect. Dis., 19, 56–66.121. Barzman, M., Bàrberi, P., Birch, A. N. E., Boonekamp, P., Dachbrodt-

Saaydeh, S., Graf, B., . . . Sattin, M. (2015). Eight principles of

integrated pest management. Agron. Sustainable Dev., 35, 1199–1215.

122. Cunningham, J. J. (2019). A call for integrated metastatic manage-

ment.Nat. Ecol. Evol., 3, 996–998.123. Read, A. F., Day, T., & Huijben, S. (2011). The evolution of drug resis-

tance and the curious orthodoxy of aggressive chemotherapy. Proc.Natl. Acad. Sci. USA, 108, 10871–10877.

How to cite this article: Burmeister, A. R., Hansen, E.,

Cunningham, J. J., et al. (2021). Fightingmicrobial pathogens by

integrating host ecosystem interactions and evolution.

BioEssays, 43, e2000272.

https://doi.org/10.1002/bies.202000272