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: [email protected]
Michael E.Hochberg, InstituteofEvolutionary
Sciences,University ofMontpellier,Montpel-
lier, France.
Email:[email protected]
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
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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
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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