# Decoding Microbial Community Assembly: Insights on Vectors of Infectious Diseases

Dasiel Obregon, Apolline Maitre, Elianne Piloto-Sardiñas, Alejandra

Wu-Chuang, Lianet Abuin-Denis, Ana Laura Cano-Argüelles, Justė Aželytė, Ivan Corona-Guerrero, Lourdes Mateos-Hernández, Myriam Kratou, et al.

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## To cite this version:

Dasiel Obregon, Apolline Maitre, Elianne Piloto-Sardiñas, Alejandra Wu-Chuang, Lianet Abuin-Denis, et al. Decoding Microbial Community Assembly: Insights on Vectors of Infectious Diseases. Annual Review of Microbiology, 2025, 79 (1), pp.547-572. 10.1146/annurev-micro-082024-094943. hal-05401290

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*Annual Review of Microbiology*

Decoding Microbial
Community Assembly:
Insights on Vectors of
Infectious Diseases

Dasiel Obregon,<fnref n="1" />,* Apolline Maitre,<fnref n="2" />,<fnref n="3" />,<fnref n="4" />

Elianne Piloto-Sardiñas,<fnref n="2" />,<fnref n="5" /> Alejandra Wu-Chuang,<fnref n="2" />

Lianet Abuin-Denis,<fnref n="2" />,<fnref n="6" /> Ana Laura Cano-Argüelles,<fnref n="7" />

Justè Aželytè,<fnref n="8" /> Ivan Corona-Guerrero,<fnref n="9" />,<fnref n="10" />

Lourdes Mateos-Hernández,<fnref n="2" /> Myriam Kratou,<fnref n="11" />

Štefánia Skičková,<fnref n="12" /> Karolína Svobodová,<fnref n="13" />
and Alejandro Cabezas-Cruz<fnref n="2" />,※

ANNUAL
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Annu. Rev. Microbiol. 2025. 79:547-72

The *Annual Review of Microbiology* is online at
micro.annualreviews.org

https://doi.org/10.1146/annurev-micro-082024-094943

Copyright © 2025 by the author(s). This work is
licensed under a Creative Commons Attribution 4.0
International License, which permits unrestricted
use, distribution, and reproduction in any medium,
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See credit lines of images or other third-party
material in this article for license information.

<footnote n="1">¹School of Environmental Sciences, University of Guelph, Guelph, Ontario, Canada;
email: dasieloa@uoguelph.ca</footnote>

<footnote n="2">²ANSES, INRAE, École Nationale Vétérinaire d'Alfort, UMR BIPAR, Laboratoire de Santé Animale, Maisons-Alfort, France; email: alejandro.cabezas@vet-alfort.fr</footnote>

<footnote n="3">³EA 7310, Laboratoire de Virologie, Université de Corse, Corte, France</footnote>

<footnote n="4">⁴INRAE, UR 0045 Laboratoire de Recherches sur le Développement de L'Elevage (SELMET-LRDE), Corte, France</footnote>

<footnote n="5">⁵Direction of Animal Health, National Center for Animal and Plant Health, San José de las Lajas, Mayabeque, Cuba</footnote>

<footnote n="6">⁶Animal Biotechnology Department, Center for Genetic Engineering and Biotechnology, Havana, Cuba</footnote>

<footnote n="7">⁷Parasitology Laboratory, Institute of Natural Resources and Agrobiology (IRNASA, CSIC), Salamanca, Spain</footnote>

<footnote n="8">⁸State Scientific Research Institute Nature Research Centre, Vilnius, Lithuania</footnote>

<footnote n="9">⁹Immunology and Vaccines Laboratory, C.A. Facultad de Ciencias Naturales, Universidad Autónoma de Querétaro, Querétaro, Mexico</footnote>

<footnote n="10">¹⁰C.A. Salud Animal y Microbiología Ambiental, Facultad de Ciencias Naturales, Universidad Autónoma de Querétaro, Querétaro, Mexico</footnote>

<footnote n="11">¹¹Laboratory of Microbiology, National School of Veterinary Medicine of Sidi Thabet, University of Manouba, Manouba, Tunisia</footnote>

<footnote n="12">¹²Pavol Jozef Šafárik University in Košice, Faculty of Science, Institute of Biology and Ecology, Institute of Animal Physiology, Košice, Slovakia</footnote>

<footnote n="13">¹³University of South Bohemia, Faculty of Science, České Budějovice, Czech Republic</footnote>

<footnote>*These authors contributed equally to this article</footnote>
Keywords

vector microbiota, microbial interactions, network analysis, keystone taxa, synthetic consortia, pathogen control

Abstract

Vector-borne diseases (VBDs), which are caused by pathogens transmitted by vectors such as mosquitoes and ticks, account for more than 17% of infectious diseases and more than 700,000 deaths annually. The complexity of VBDs arises from ecological interactions among hosts, vectors, pathogens, and the environment, with vector microbiota playing a pivotal role in the modulation of vector competence. Advances in sequencing and in microbiome analysis have deepened our understanding of microbial community assembly within vectors and revealed opportunities for novel control strategies. Network analysis has become essential for uncovering microbial interactions and identifying keystone species that affect community stability and pathogen transmission. Despite progress, key challenges remain in deciphering the drivers of vector microbiota assembly. This review highlights factors shaping microbiota assembly, the potential of network analysis, and promising interventions such as antimicrobiota vaccines and paratransgenesis to reduce pathogen transmission. Future research should focus on standardizing methodologies and leveraging emerging technologies for effective and sustainable VBD control.

Contents

<table><tr><td>1. INTRODUCTION</td><td>549</td></tr><tr><td>2. THEORETICAL AND EMPIRICAL CONSIDERATIONS OF MICROBIAL COMMUNITY ASSEMBLY</td><td>550</td></tr><tr><td>2.1. Principles of Microbial Community Assembly</td><td>550</td></tr><tr><td>2.2. Understanding Community Assembly from Biotechnological Applications</td><td>551</td></tr><tr><td>3. NETWORK ANALYSIS IN MICROBIAL ECOLOGY</td><td>552</td></tr><tr><td>3.1. Methodology of Network Inference, Visualization, and Analysis</td><td>552</td></tr><tr><td>3.2. Identification of Keystone Species</td><td>555</td></tr><tr><td>3.3. Inferring Community Assembly Stability and Robustness Through Network-Based Analyses</td><td>556</td></tr><tr><td>4. FACTORS AFFECTING VECTOR MICROBIOTA ASSEMBLY</td><td>556</td></tr><tr><td>4.1. Methods for Vector Microbiota Data Collection and Sequencing</td><td>557</td></tr><tr><td>4.2. Insights on Vector Microbiome Assembly Described as Co-Occurrence Networks</td><td>559</td></tr><tr><td>5. RELEVANCE OF MICROBIOTA ASSEMBLY TO VECTOR COMPETENCE AND DISEASE DYNAMICS</td><td>559</td></tr><tr><td>5.1. Paratransgenesis</td><td>560</td></tr><tr><td>5.2. Antimicrobiota Vaccines</td><td>562</td></tr><tr><td>5.3. Microbial Interference</td><td>562</td></tr><tr><td>5.4. Phage Therapy</td><td>563</td></tr><tr><td>6. CHALLENGES, LIMITATIONS, AND FUTURE DIRECTIONS</td><td>563</td></tr><tr><td>7. CONCLUSION</td><td>564</td></tr></table>
# 1. INTRODUCTION

Vector-borne diseases (VBDs), including malaria, dengue, Lyme disease, West Nile fever, and Zika virus fever, are responsible for substantial morbidity and mortality worldwide: They account for more than 17% of all infectious diseases and more than 700,000 deaths annually (165). The complexity of VBDs is amplified by the intricate ecological interactions among hosts, vectors (e.g., mosquitoes, ticks, and flies), pathogens, and the environment, which collectively govern pathogen transmission cycles (38). At the core of this complex interplay is the vector microbiota, which represents a diverse community of microorganisms (i.e., bacteria, archaea, viruses, fungi, and pro-tozoa) residing within vector organs such as the gut and salivary glands. This microbiota plays a pivotal role in modulating vector physiology and competence, thereby directly influencing the dynamics of disease transmission (160).

Recent studies have highlighted the intricate relationship between vector microbiota com-
position and VBDs (12, 53). The microbiota can enhance or inhibit pathogen transmission either
directly through interactions with the pathogen or indirectly through interactions with the vector,
such as by modulating the vector's immune system and/or by providing beneficial nutrients (109,
152). Consequently, the manipulation of the vector microbiome emerges as a promising strategy
for developing effective control measures against vectors and VBDs (39, 178). However, despite
the acknowledged significance of vector microbiota, substantial gaps remain in our understanding
of the factors driving their assembly and functionality within vectors (26, 171).

Community assembly encompasses the processes by which species from a regional species pool colonize and interact to form stable local communities (77). A myriad of deterministic and stochastic factors govern this assembly (126, 179). Among these factors, microbe-microbe and host-microbe interactions are paramount in influencing the assembly of microbial communities (23, 163). A paradox in microbial ecology arises from the observation that despite the potential for an astronomical number of community configurations (e.g., from a pool of 100 species, more than 17 trillion 10-species combinations are possible), natural communities tend to exhibit a limited range of observed configurations (33). This phenomenon suggests that the vast array of possible combinations is constrained by the interplay of stochastic and deterministic elements inherent in ecological processes (33, 179). The core challenge of vector microbial ecology lies in unraveling how these elements collectively influence the assembly of microbial communities, thereby affecting crucial ecological functions such as vector competence and pathogen transmission (103, 171).

Advances in high-throughput sequencing technologies and bioinformatics have significantly enhanced the study of microbial communities and offered unprecedented resolution and depth. These developments have opened new avenues for investigating microbial community assembly (42, 48). However, the interpretation of the extensive datasets produced by these technologies presents challenges, particularly when the objective is to quantitatively infer the mechanisms underlying community assembly (128, 147). Network analysis has emerged as a powerful approach to deciphering the complex interactions within microbial communities (34, 51, 82). By focusing on network analysis, researchers can uncover the patterns of microbial co-occurrence and interactions and can identify species with pivotal roles in community assembly and function (15, 20). Particularly in vector microbiome research, this approach enables researchers to relate microbiota assembly to vector biology and pathogen transmission (111, 170).

In this review, we explore the current state of knowledge concerning microbial community as-
sembly within vectors. Through the synthesis of theoretical frameworks and empirical research,
we highlight the potential of network analysis to revolutionize our comprehension of vector mi-
crobiota and its implications for vector ecology and competence. As we unravel the complexities

**Vector-borne diseases (VBDs):**
diseases transmitted by vectors such as mosquitoes and ticks; these diseases include malaria and Lyme disease

**Vector microbiota:**
microorganisms that reside in vector organs and that influence physiology and pathogen transmission

**Community assembly:** process by which microbial species colonize and form local communities in an environment

**Vector competence:**
vector's ability to acquire, maintain, and transmit a pathogen

**Network analysis:**
tool for examining microbial co-occurrence and interactions through mathematical graph structures
# INTERPLAY OF STOCHASTIC AND DETERMINISTIC THEORIES IN MICROBIAL COMMUNITY ASSEMBLY

Stochastic theory posits that random processes, including drift and colonization order, influence microbial community assembly (179). These processes introduce unpredictability, as communities can form differently depending on random factors such as species dispersal. Key elements include ecological drift, which contributes to distinct compositions of communities across host species (89), and historical contingency, wherein past events shape present community composition, functions, and resilience (83).

Deterministic theory, by contrast, emphasizes the influence of environmental factors and species interactions (e.g., competition, cooperation) to guide community assembly. Grounded in niche theory, deterministic processes such as environmental filtering select for species with traits suited to specific conditions and thereby shape community assembly in a predictable manner (129).

The interplay of both theories highlights the complexity of microbial communities. Stochastic and deterministic processes intertwine, working in tandem to mold community structure (42, 126). Dispersal is an assembly-driving factor and serves as a prime example of the interplay between randomness and predictability by combining random movement of organisms with species-specific traits that determine successful establishment (48, 147).

inherent in microbial community interactions, we pave the way for innovative interventions targeting vector microbiota. Such strategies promise a leap forward in the development of more effective and sustainable control measures for VBDs in the coming years.

## 2. THEORETICAL AND EMPIRICAL CONSIDERATIONS OF MICROBIAL COMMUNITY ASSEMBLY

### 2.1. Principles of Microbial Community Assembly

Comprehension of the assembly of microbial communities within ecosystems is foundational to microbial ecology. This assembly is fundamentally guided by stochastic and deterministic theories (42). Stochastic processes, which are characterized by random events, introduce elements of unpredictability into community assembly. In contrast, deterministic processes are underscored by niche differentiation and species-specific interactions, which dictate a more predictable assembly pattern (179) (see the sidebar titled Interplay of Stochastic and Deterministic Theories in Microbial Community Assembly). Although stochastic and deterministic forces both play indispensable roles in shaping microbial community structures, our review places a stronger emphasis on deterministic processes. This focus enables deeper insights into how microbial interactions within vector microbiota influence assembly outcomes, and it sheds light on mechanisms that can be manipulated for ecological enhancements.

Within a microbiome, microorganisms establish either cooperative or competitive relationships that significantly affect community composition and resilience (107). Cooperative interactions occur when microbes support the growth of other microbes through mechanisms such as enzyme secretion, waste elimination, or quorum sensing (148, 162). In contrast, competition arises from the need for shared resources, such as nutrients, and acts as a key selective force in these communities (107, 177). The strength and nature of these interactions are crucial for shaping community structure, as they directly affect how microbial communities assemble and function. For instance, communities with stronger interactions often struggle to form viable subcommunities and thus have more limited assembly outcomes (7, 34). Moreover, while cooperative interactions may enhance community feedback, they require precise balance; as a result, the formation of diverse cooperative communities is difficult in practice (33).

**Stochastic processes:** random ecological events affecting microbial community composition and dynamics

**Deterministic processes:** predictable ecological forces, such as species interactions, that shape microbial community structure
The prediction of assembly probabilities of different communities reveals that, even if capable of assembling, diverse communities are unlikely to reach a fully assembled state unless species arrival significantly outpaces species loss (67). Communities with strong positive interactions struggle to assemble from scratch because interdependent species can fail to establish when each relies on the other to colonize first (33). Abundant positive interactions also restrict the spectrum of viable subcommunities, inherently constraining the routes to assembly. Remarkably, highly positive interactions increase predictability, highlighting their critical role in shaping assembly patterns (67).

However, the challenges positive interactions pose to the assembly of diverse communities can be mitigated under certain conditions. Simultaneous colonization, whether occurring naturally (e.g., during birth) or occurring through interventions (e.g., fecal microbiota transplants), can facilitate community assembly (56, 84). These processes enable a coordinated introduction of microbial species and thus allow them to establish a balanced ecosystem more rapidly and effectively. While simultaneous colonization broadens the possible assembly pathways, it also reduces predictability and maintains a clear distinction between pioneer and secondary colonizers (33). Additionally, the identity and characteristics of the initial colonizing species play a crucial role in shaping the future structure and function of microbial communities (31, 179).

## 2.2. Understanding Community Assembly from Biotechnological Applications

The identification of optimal microbial assemblies is a key step in developing microbial consortia for biotechnological applications (119). This process also provides valuable insights into the ecological principles that govern microbial communities in natural environments (27, 67). Mutualistic interactions, such as metabolic cross-feeding, lead to ecological coselection and are essential for the cohesiveness and productivity of these communities (40). These interactions, viewed through the lens of evolutionary and ecological dynamics, reveal the potential for the design of microbial communities that harness cooperative behaviors for biotechnological use. Experimental evidence supports these principles and shows that both the functionality and the composition of microbial communities can be highly predictable and largely influenced by nutrient availability, despite inherent species variability (63, 67).

In the construction of synthetic microbial communities, two primary approaches are used: bottom-up and top-down (158). In the bottom-up approach, individual strains are first cultured in isolation and then combined to form consortia. This approach allows for precise control over composition and molecular-level functions, which are critical for technological applications (30, 68). However, the predictability of interactions, including syntrophy, can lead to challenges such as incompatibility or instability when deployed in real-world environments (63). The top-down approach, by contrast, starts with natural microbial communities that are simplified or streamlined through selective pressures to isolate functional subsets. This approach leverages the inherent resilience and established interactions within natural communities (45). However, the maintenance of desired functions can be challenging, as simplification often disrupts complex ecological interdependencies that underpin community stability and performance (133). For instance, Chen et al. (29) demonstrated that even taxonomically stable communities may experience substantial loss of functionality when reduced. Moreover, simplification may overlook context-dependent interactions—particularly host-mediated processes—that are fundamental to microbiome function but are difficult to replicate in controlled environments (106).

The innovative framework proposed by George & Korolev (62) advances our understanding of these methodologies by addressing the complexities of microbial community assembly. Their model highlights the selection of high-performing microbial consortia via heuristic search

**Synthetic microbial community:**
artificially assembled
microbial consortia
designed for functional
or experimental
purposes
**Co-occurrence**

**network:** network
wherein nodes (taxa)
are linked based on
statistically significant
copresence patterns

**Keystone species:**

microbial taxa that
disproportionately
influence community
structure and function

**SparCC:**

a correlation-based
method for inferring
microbial associations
from compositional
sequencing data

**SPIEC-EASI:** robust

algorithm inferring
microbial ecological
networks via sparse
inverse covariance
estimation

strategies that consider factors such as resource distribution, niche overlap, cross-feeding, and the ruggedness of ecological landscapes. This model provides valuable insights into microbial community assembly that are relevant for bioengineering applications across diverse ecosystems. Building on this foundation, Ho et al. (79) introduced a coarse-grained consumer-resource model that accurately predicts community assembly and quantifies the landscape of resource competition among microbial species. By integrating metabolomics data with growth measurements in spent media, this study illustrates the potential of combining theoretical and experimental approaches to better understand microbial community assembly dynamics in vitro (79).

# 3. NETWORK ANALYSIS IN MICROBIAL ECOLOGY

Network analysis has emerged as a critical tool in applied microbiome research and has enabled deep insights into the intricate interactions among microbial taxa within communities (140). This analytical method enables the identification of synergistic and antagonistic relationships. For instance, co-occurrence networks reveal taxa that appear together consistently, more often than expected if by chance, and suggest potential cooperative relationships (Figure 1). Conversely, network analysis also adeptly detects significant patterns of coexclusion, wherein certain taxa consistently avoid each other, that likely indicate competition (34, 51).

## 3.1. Methodology of Network Inference, Visualization, and Analysis

Network analysis primarily involves three key steps: association matrix calculation; network visualization; and the analysis of network properties, including the identification of highly connected taxa—referred to as hub nodes—which may indicate species that are keystone species because of their structural and functional roles within the microbiome (4, 20). Network comparison has gained prominence recently and has provided valuable insights into variations in microbiome assembly across contexts (114). Co-occurrence networks remain the most frequently used approach in microbiome research, though alternative methodologies such as Bayesian networks are emerging as viable tools (82). During the past decade, the methods for constructing co-occurrence networks have evolved considerably, especially in addressing challenges related to the compositional and sparse nature of microbiome sequencing data (114, 140). These methods fall into three general categories (114): (a) correlation-based approaches, which rely on conventional coefficients such as Pearson and Spearman, as well as on more advanced methods such as SparCC; (b) regularized linear regression, which enhances model stability in high-dimensional settings by shrinking regression coefficients; and (c) conditional dependence or graphical methods, which infer more complex network structures from data.

Among correlation-based methods, SparCC (59) stands out for its ability to compute correlations between log-transformed components by approximating the true covariance matrix (i.e., the unbiased estimate of covariation across components). This approach reduces biases introduced by data sparsity and compositionality. However, SparCC has some limitations, such as high computational demand and an inability to account for zeros in the data matrix, which can result from true absences or low detection thresholds. To address these issues, regularized linear regression methods such as CCLasso (49) and REBACCA (13) have been developed. These techniques use latent variable models—statistical frameworks that infer hidden factors influencing observed data—to directly estimate positive definite correlation matrices and to thereby improve the accuracy of inferred associations. In the third category, SPIEC-EASI (94) distinguishes itself by combining log-ratio transformations with sparse neighborhood selection to estimate inverse covariance matrices and enhance the robustness of network inference. This method is widely regarded as one of the most robust techniques for microbiome network analysis (13, 114).
<figure><img src="image_4.png" /><figcaption>Figure 1</figcaption></figure>

The network approach is a method based on statistics that is used to study the assembly of microbiomes. (a) The process requires a consistent dataset obtained from vector samples, from which DNA is sequenced to determine the taxonomic composition of the microbiome of the gut or of other organs. (b) Microbial co-occurrence networks are constructed from these taxonomic profiles. The networks are analyzed for topological features such as the number of nodes, number of edges, and average degree (Table 1). Additionally, network robustness can be assessed via a simulation of the impacts of node removal on connectivity loss. Network stability is evaluated through metrics such as network cohesion and modularity. Robustness and stability can be used to compare networks between different conditions (e.g., between control and experimental groups). (c) Network analysis enables the identification of potential keystone taxa (hub nodes). This identification is achieved via the evaluation of degree and centrality metrics such as closeness, eigenvector, and betweenness centrality, each of which may highlight different hub nodes. The combination of these metrics and the validation of findings through laboratory experiments (in the wet lab) are recommended for the accurate identification of keystone taxa. Figure adapted from images created in BioRender; Dunfield K. 2025. https://BioRender.com/ig4b20b.

For network visualization and analysis, several R packages and software tools are commonly used, including Igraph (35), Cytoscape (145), and Gephi (17). These platforms provide robust statistical tools that help quantify crucial network metrics (Figure 1b) and offer insights into microbiota assembly. Common metrics include general topology indicators, such as number of nodes,
<table><caption>Table 1 Key global and local (taxon-specific) network metrics and their ecological interpretation</caption><thead><tr><th>Metric</th><th>Description</th><th>Ecological meaning</th></tr></thead><tbody><tr><td colspan="3"><strong>Global topological features</strong></td></tr><tr><td>Average degree</td><td>Mean number of edges per node</td><td>A low average degree indicates less interdependence between taxa.</td></tr><tr><td>Diameter</td><td>Longest shortest path distance between any two nodes</td><td>A short diameter indicates that the network is more compact, with stronger overall microbial interactions.</td></tr><tr><td>Modularity</td><td>Extent to which the network partitions into densely connected modules (communities)</td><td>A high modularity indicates a clear community structure, with distinct modules that each represent a potential functional group.</td></tr><tr><td>Average clustering coefficient</td><td>Mean probability that two neighbors of a node are also connected</td><td>A high clustering coefficient indicates that taxa tend to form tightly knit groups, suggesting localized cooperation or interaction.</td></tr><tr><td>Cohesion</td><td>Net sum of positive and negative edge weights across the network</td><td>Negative cohesion suggests competitive but stable communities; positive cohesion reflects cooperation, but greater vulnerability to disturbance.</td></tr><tr><td>Network density</td><td>Proportion of possible edges present in the network</td><td>High density indicates that taxa are extensively interconnected, suggesting a tightly linked and potentially robust community structure.</td></tr><tr><td>Largest connected component (LCC)</td><td>Number of nodes in the largest subnetwork where every node is reachable from every other</td><td>A large LCC indicates a highly interconnected community and suggests greater resilience to disturbances.</td></tr><tr><td>Average path length (APL)</td><td>Mean length of shortest paths between all node pairs</td><td>A lower APL suggests that taxa are more closely connected, enabling efficient interaction and rapid response throughout the community.</td></tr><tr><td colspan="3"><strong>Local features</strong></td></tr><tr><td>Degree</td><td>Number of edges connected to a node</td><td>A high degree means that the taxon interacts with many others and has a potential hub role.</td></tr><tr><td>Betweenness centrality</td><td>Proportion of shortest paths that pass through a node</td><td>A high betweenness centrality score means that the taxon is important for the connection of different modules or communities.</td></tr><tr><td>Closeness centrality</td><td>Reciprocal of the sum of a node's shortest path distances to all other nodes</td><td>High closeness centrality indicates that a taxon can quickly influence or respond to changes within the community.</td></tr><tr><td>Eigenvector centrality</td><td>Node importance weighted by the centralities of its neighbors</td><td>Taxa with high eigenvector centrality are highly influential in the microbiota, are connected to other important taxa, and likely serve as keystone species within the community.</td></tr><tr><td>Node modularity</td><td>Identifier of the module or community to which a node (taxon) belongs.</td><td>Indicates the group of taxa with which the taxon most closely interacts, potentially reflecting ecological association, functional complementarity, or shared habitat.</td></tr><tr><td>Clustering coefficient</td><td>Proportion of a node's neighbors that are also connected (mutually) to each other</td><td>A taxon with a high clustering coefficient is part of a tightly knit community, supporting strong localized interactions.</td></tr></tbody></table>

**Modularity:**

measure of network compartmentalization into distinct subcommunities

average degree, and modularity, and taxon-specific metrics (i.e., local features) such as degree and centrality measures (Figure 1b; Table 1). Furthermore, several methods have been proposed for cross-sectional network comparisons between two conditions or treatments. Examples include NetShift (93), MDiNE (120), and NetCoMi (134). Recently, the meconetcomp R package was developed to address the challenges of comparing more than two networks simultaneously (104).
## 3.2. Identification of Keystone Species

Network analysis is also instrumental in identifying hub nodes, often referred to as keystone species (15, 20). These taxa play pivotal roles in maintaining microbiome composition, assembly, and functionality (156, 169). The significance of keystone species can vary across different hosts or even within individual hosts and is influenced by factors such as diet, microbiome specialization, and environmental conditions (15, 57, 76). Removing these taxa can dramatically disrupt the microbial network (20) and can potentially impair microbiome assembly and function (173). The absence of dominant species creates ecological voids that other bacteria may fill, reducing alpha diversity and affecting community composition (4, 15). Such dynamics underscore the critical role of keystone taxa in shaping the overall structure of the microbiome.

Keystone species exhibit specific traits: (a) They maintain a stable presence despite spatiotemporal shifts, (b) they are not necessarily the most abundant taxa, and (c) they are crucial for community structure and function (15, 150). These species may influence microbiome dynamics through mechanisms such as the fostering of colonization or production of metabolites that enhance resistance to disturbances (4, 28). The identification of keystone species has been leveraged in microbiome analysis to develop effective VBD control strategies through rational manipulation of microbial compositions (109, 173), as discussed in Section 5.

Despite strong evidence supporting the role of keystone taxa, methods for their identification are still debated. Traditional network-based local metrics, such as closeness centrality, betweenness centrality, eigenvector centrality, and degree, are commonly used (15, 20, 134). However, there is no consensus on which metric is most effective, with each metric offering a different interpretation of which taxa are most central (Figure 1c). Newer approaches, including data-driven statistical inference (9, 99) and metabolic modeling (19), aim to complement these metrics by offering deeper insights into microbial interactions and dynamics (see the sidebar titled Statistical Approaches for Identifying Keystone Species). However, further research is still needed to refine these methods and increase their reliability. A key challenge is the lack of empirical validation (66, 141), especially in environmental microbiology, where many organisms cannot currently be cultured—a limitation that hinders direct verification (16, 66). As a result, network inference often serves as a preliminary screening tool and requires further validation, depending on the study's objectives (15, 76).

**Hub node:** highly connected node in a network; often indicates a functionally significant taxon

**Eigenvector centrality:** network metric identifying influential nodes based on their connections to other well-connected nodes

## STATISTICAL APPROACHES FOR IDENTIFYING KEYSTONE SPECIES

The identification of keystone species in microbial communities remains a key challenge in microbial ecology due to the complexity of microbial interactions and limitations in statistical methods (15, 141). Network metrics, which assess species' importance based on their position in the network (Figure 2; Table 1), are the most widely used approach. Metrics such as betweenness centrality and degree are often used, and studies show that combinations of these metrics improve keystone identification (14, 78). A seminal study by Berry & Widder (20) suggested that species with low betweenness centrality but high degree and closeness centrality often serve as keystone species, and this method has become popular. Other studies have relied on eigenvector centrality, which considers connection quality (173).

Data-driven statistical inference techniques such as the spread intensity (99) and top-down identification (9) leverage large datasets to identify keystone species. These methods assess species' influence based on interspecies correlations, though they require high-quality data. Deep learning-based approaches, such as a DKI framework (161), simulate species removal to identify keystone species but also demand well-annotated data. Metabolic modeling approaches, such as the M2M framework, reconstruct genome-scale metabolic networks to identify key species based on their metabolic roles (19). Yet this framework requires comprehensive metabolic data.
### 3.3. Inferring Community Assembly Stability and Robustness Through Network-Based Analyses

As complex systems, microbial and ecological communities often face disturbances that may disrupt their structural integrity and functionality. Despite such challenges, these systems tend to demonstrate remarkable robustness and resilience: They maintain core functions and often recover from disruptions. Given the difficulties in replicating entire natural microbial communities in laboratory conditions, network analyses offer a valuable alternative for understanding the stability and dynamics of these communities (130, 140). These network models enable researchers to generate hypotheses and make preliminary inferences about microbiome assembly and behavior under various conditions (116, 132, 176). Two main features used to assess these systems are stability and robustness (Figure 1b).

Stability in microbial communities refers to their capacity to return to equilibrium after a disturbance (7). Stability is typically assessed by determining whether the community can revert to its equilibrium (local stability) and whether it can withstand larger disturbances (global stability) (34). Stability is influenced by factors such as species interactions (competition and cooperation), diversity, connectivity, and feedback mechanisms. Key mechanisms promoting stability include host immune regulation, spatial structuring, and nutrient availability (34). Network metrics associated with stability include cohesion and modularity (Table 1). Cohesion, both positive and negative, measures the strength and number of associations. A balance among these interactions is crucial for stability (74). Positive cohesion suggests the presence of cooperation but could be a signal of instability in stressed environments, where cross-feeding and niche overlap lead to positive connections but the community relies on each member. Conversely, communities with higher competition (negative cohesion) tend to be more stable and resist disturbances (75). Modularity, which measures network compartmentalization, also contributes to stability by isolating perturbations within specific modules and by thus preventing disruptions from spreading throughout the network (74).

Robustness refers to the ability of a system to maintain certain attributes despite external perturbations. This ability involves mechanisms such as buffering, canalization (stabilization of phenotypes), homeorhesis (return to a developmental trajectory after disturbance), and resilience (recovery from perturbations) (88). Microbial robustness can be analyzed from both structural and functional perspectives, and it provides insights into a microbiome's adaptability and persistence under stress. Structural robustness pertains to the community's ability to retain an intact structure despite disturbances, while functional robustness relates to the community's ability to sustain core functions even when structural changes occur (95, 155). To test robustness, researchers evaluate how microbial networks respond to various perturbations while maintaining their structure and functionality (6).

Different tools are used to model network robustness, including node removal simulations, which mimic the loss of species or functional groups owing to environmental disturbances (Figure 1b), and node additions, which assess how the introduction of new species affects the community. The NetSwan R package (98) tests network robustness by applying different removal strategies, such as random or directed attacks based on betweenness centrality, degree, and cascading failure. Similarly, the response to node addition can be modeled via the Igraph R package (35), wherein new nodes are randomly connected to the existing network, and the response is measured by the size of the largest connected component and average path length (Table 1).

### 4. FACTORS AFFECTING VECTOR MICROBIOTA ASSEMBLY

Arthropod vectors, integral to complex ecological communities, influence trophic cascades at macroecological levels (96, 151). These vectors harbor diverse microbial communities, which
include both pathogenic and nonpathogenic microorganisms, that form dynamic microecosys-
tems, evolving in close interdependence with the vector host (5, 69). Similar ecological forces
shape the assembly of these microbial communities and affect the physiology of the vector and its
capacity to transmit pathogens (144, 171). This colonization process within vectors is driven by
deterministic and stochastic processes that operate across spatial and temporal scales (Figure 2).
Arthropod vectors also serve as vehicles for passive microbial dispersal, whereby they acquire and
replicate microorganisms that are crucial for pathogen transmission to susceptible hosts (26, 38).
The success of this microbial cycle within vectors depends on several factors, such as microbial
interactions, the ability of the vector to withstand environmental fluctuations, and its capacity
to evade immune responses (Figure 2). All these factors have a pivotal influence on pathogen
transmission (60, 178).

The tick microbiome has been a significant focus of research during the past decade, with early
studies suggesting high diversity that is comparable to the diversity of other arthropods (171).
More recent findings, however, suggest that while ticks harbor diverse microbiota, they are often
dominated by a few core species, particularly those that have symbiotic relationships with the host
tick (41, 71, 125). These core species are essential for the tick's survival and reproduction in that
they provide crucial functions such as nutritional supplementation and physiological regulation
(22, 125). On the other hand, mosquito microbiomes exhibit a similarly complex pattern, with
diversity influenced by factors such as diet, habitat, and developmental stage. This diversity affects
the vector's interactions with pathogens and its capacity for disease transmission (2, 112). The
extensive diversity of mosquito microbiota, alongside the dominance of a few species, highlights
the complex assembly of mosquito microbiomes and has significant implications for vector control
strategies (26, 121).

## 4.1. Methods for Vector Microbiota Data Collection and Sequencing

Because arthropod vectors harbor distinct microbiomes across different body organs, the sam-
pling strategy is a critical step in the study of vector microbiota. For mosquitoes, microbiota
sampling frequently involves the dissection of specific organs, such as the midgut, salivary glands,
and Malpighian tubules. These organs are critical for the study of vector competence and pathogen
interactions (178). Conversely, in ticks, the focus is primarily on the gut microbiome, which di-
rectly interacts with transmitted pathogens. Techniques such as whole-body maceration, selective
culturing, and direct DNA extraction are employed to study these interactions (Figure 1a). The
vector's developmental stage also can profoundly affect microbiome assembly (24, 175). Addition-
ally, contamination in samples may initially suggest higher diversity than is truly present and, as
a result, may affect the interpretation of microbiome studies (171). External decontamination,
particularly in ticks, influences microbiome study outcomes. For instance, bleach is more effec-
tive than is 70% ethanol for decontamination, although further refinement of bleach protocols is
necessary (21, 54).

Two primary methods are used for high-throughput DNA sequencing in vector microbiota
studies (69). The most common is target amplicon sequencing, which focuses on marker genes
with strong taxonomic resolution, such as 16S rRNA for prokaryotes and 18S rRNA or inter-
nal transcribed spacers for eukaryotes (105). These markers have hypervariable regions that act
as molecular fingerprints for the identification of microbial members. Advantages of amplicon
sequencing include rapid analysis, low biomass requirements, and suitability for samples contam-
inated with host DNA. However, limitations include PCR and primer biases, as well as, typically,
the limitation of taxonomic resolution to the genus level (105).

The second method, shotgun or metagenomic sequencing, takes a more inclusive approach
by sequencing all DNA and RNA molecules in a sample. This method provides species-level
<figure><img src="image_5.png" /><figcaption>**Figure 2**</figcaption></figure>

Factors affecting vector microbiome assembly in ticks are shown, as is the microbiome's impacts on vector competence. The tick microbiome is present across all four life stages (eggs, larvae, nymph, and adult). Throughout the tick life cycle, microbiome assembly is influenced by a dynamic interplay between stochastic factors, such as ecological drift and historical contingency, and deterministic factors, including environment, climate, geographical location, and the specific host type encountered at each stage (host 1, host 2, and host 3 in the case of three host ticks). These factors collectively shape the structure of the microbial community in each specific context, which in turn plays a crucial role in determining vector competence. The microbiome affects vector competence by facilitating or attenuating pathogen acquisition, colonization, and transmission, as well as by influencing overall vector fitness. For instance, certain microbial assemblies can inhibit or promote pathogen survival and replication within the vector and thereby can directly affect the vector's ability to transmit diseases. Figure adapted from images created in BioRender; Dunfield K. 2025. https://BioRender.com/v76q803.
precision, functional insights, and access to genomes of uncultured microbes (105). However, it comes with higher costs, greater time requirements, and a need for rigorous computational analysis because of the risk of host-derived contamination (138). Metagenomic sequencing is particularly valuable for the comprehensive analysis of vector microbiota, as it includes viral sequences and can identify both RNA and DNA viruses through virome analysis (18, 153).

## 4.2. Insights on Vector Microbiome Assembly Described as Co-Occurrence Networks

Bacterial co-occurrence networks have become valuable tools for understanding the structure and dynamics of arthropod vector microbiomes across various species and environmental conditions. For example, Estrada-Peña et al. (47) employed a network-based framework to analyze the complexity of the tick microbiome in *Ixodes ricinus* and its primary host, the vole *Myodes glareolus*, and demonstrated how biotopes influence microbiome assembly in both vectors and hosts. Further research explored the microbiome of *Ixodes scapularis* and revealed its resistance to factors such as antimicrobial peptide IAFGP and host-specific antitick immunity (46). Even though taxonomic and structural changes occurred in response to disturbances, Estrada-Peña and colleagues showed that the functional potential of the microbiome remained intact, likely because of functional redundancy. Via a network modularity-based approach, the researchers found that different bacterial subcommunities within *I. scapularis* could maintain a broad range of functional pathways (45).

Network analysis has also been pivotal in the assessment of how pathogen infections affect vector microbiome assembly (11, 45, 70). For example, infections with *Anaplasma phagocytophilum* in *I. scapularis* significantly altered the co-occurrence network in the tick's gut and reduced node connectivity and clustering coefficients (46). Similar patterns were observed in *I. ricinus* nymphs infected with pathogens from the genera *Rickettsia*, *Borrelia*, and *Anaplasma*, with a decrease in taxa concurrence compared with that of noninfected ticks (97). A comparative analysis of co-occurrence networks in *I. ricinus* revealed that *Rickettsia helvetica*-infected ticks had networks that were 40 times less connected than were those in noninfected ticks, and this finding correlates with reduced bacterial diversity and significant alterations in the microbiome's topological structure (110).

Recent studies have extended the use of co-occurrence network analysis to infer the robustness and stability of vector microbiota assemblies under ecological and pathological pressures. For instance, perturbation modeling in tick microbiomes has revealed how infection and antibiotic treatment reduce connectivity and disrupt community architecture; this finding highlights a loss of network resilience and potential keystone taxa displacement (91, 92, 108). In *Rhipicephalus microplus* and *Haemaphysalis longicornis*, robustness indices and node hub dynamics differed across infection status and developmental stages, and these differences revealed ontogeny-dependent vulnerabilities (1, 91). Similar approaches in mosquito vectors demonstrated how pathogen exposure reshapes the microbiome's modular structure and keystone configuration, with implications for transmission potential (10). These examples underscore how the integration of network-derived metrics into microbiome studies not only reveals taxonomic shifts but also quantifies ecological resilience, and thus, they support the development of predictive models for pathogen-vector-microbiome interactions.

## 5. RELEVANCE OF MICROBIOTA ASSEMBLY TO VECTOR COMPETENCE AND DISEASE DYNAMICS

The composition of the vector microbiota plays a critical role in vector competence by influencing the vector's ability to transmit pathogens (55, 123). Once a pathogen is ingested during a

**Functional redundancy:** presence of different taxa performing similar ecological roles and thus stabilizing community function
**Paratransgenesis:**
strategy using
genetically modified
symbiotic microbes to
disrupt pathogen
transmission in vectors

**Antimicrobiota vaccine:** host vaccine targeting vector microbes to disrupt vector competence and pathogen transmission

**Microbial interference:** use of competitive microbes to exclude or inhibit pathogens in vector microbiota

blood meal, it immediately encounters the gut-associated microbiota, and this encounter creates
a critical bottleneck for pathogen survival (Figure 2). This interaction significantly affects the
vector's susceptibility to infection and, consequently, its overall competence (26, 124). Mechanis-
tically, vector microbiota can interfere with pathogens either directly through mechanisms such
as lysis or biofilm formation or indirectly through activation of immune pathways in the vector
(44, 124). While numerous studies have examined the roles of specific bacterial strains in influ-
encing pathogen loads within vectors, less attention has been given to how the microbiota, as a
community, affects vector competence (38).

Pathogen infections often modify the structure of bacterial community assemblies within vec-
tors. For example, infection with *Plasmodium relictum* (avian malaria) in mosquitoes and *Borrelia
afzelii* (Lyme disease) in ticks can induce significant shifts in the microbiome’s structure (11, 172).
These changes are pathogen-specific: *P. relictum* decreases the number of bacterial interactions
within vector microbiota (11), and *B. afzelii* increases them (172). Important interactions be-
tween pathogens and symbionts are also revealed through co-occurrence network analysis. For
instance, *Rickettsia parkeri* correlates positively with the endosymbiont “*Candidatus* Midichloria
mitochondrii” and negatively with *Francisella* (3). Additionally, networks have been used to study
the robustness and assembly of experimentally perturbed tick microbiota and have identified prop-
erties associated with infection-resistant states (172). This finding provides further insights into
the molecular mechanisms through which the vector microbiota modulates pathogen survival
(55, 160).

Network analysis has the potential to deepen our understanding of disease dynamics (136, 146). For example, dynamic protein-protein interaction networks can identify dynamic network biomarkers that serve as early warning signals for disease deterioration and thus can offer a systems-level perspective on pathogen dynamics (100). Network-based approaches such as Network Impulsive Dynamics are employed on multiplex biological networks to predict disease-related genes by analyzing the dynamic responses of nodes to perturbations. This framework can also be applied to vector-pathogen interactions via its identification of key microbial taxa involved in these processes (174). Furthermore, multilayer network analysis has emerged as a powerful tool to study VBD dynamics, whereby each layer can represent different time points; this approach reflects the progression of vector microbiota in the presence of pathogens (135).

An understanding of the assembly and interactions within vector microbiomes is fundamen-
tal to the development of targeted strategies that aim to disrupt pathogen transmission and to
control VBDs. For example, some bacterial associations in mosquitoes remain robust across vari-
ous environmental and host factors and are consistently present in different species in both field
and laboratory settings (32, 90). Research has shown that the introduction of symbionts to either
gnotobiotic mosquitoes or conventionally reared mosquitoes can inhibit the colonization of spe-
cific bacterial taxa; this inhibition is mediated by the resident microbiota (73). Approaches such as
paratransgenesis, antimicrobiota vaccines, microbial interference, and bacteriophage therapy have
demonstrated efficacy in reducing pathogen load, vector fitness, and pathogen transmission rates
(Figure 3).

**5.1. Paratransgenesis**

Paratransgenesis involves the genetic modification of bacteria that naturally reside in the vector's microbiome either to interfere with pathogen development or to make the vector inhospitable to pathogens (Figure 3a). This modification is achieved via the introduction of genes that enable bacteria to produce pathogen-inhibiting substances or that trigger the vector's immune response against pathogens (81). The selection of appropriate bacterial targets is critical to their successful
<figure><img src="image_6.png" /><figcaption>Figure 3</figcaption></figure>

Four main strategies are employed to control vector-borne pathogens via the modulation of vector microbiomes. (a) Paratransgenesis involves genetic modification of symbiotic bacteria within the vector to express antipathogenic molecules. The engineered bacteria are reintroduced into the vector population, where they propagate and inhibit pathogen colonization and transmission. (b) Antimicrobiota vaccines utilize antigens derived from the vector's keystone microbiota to create vaccines administered to the host. Such vaccination induces an immune response targeting the vector microbiota, disrupting microbiome assembly, attenuating pathogen colonization, and consequently affecting the vector's fitness and competence. (c) Microbial interference introduces competitive, nonpathogenic bacteria into the vector's microbiome. These microbes outcompete or inhibit the growth of pathogenic organisms within the vector, reduce vector competence, and potentially affect vector fitness and population. (d) Phage therapy employs bacteriophages to selectively target and lyse pathogenic bacteria within the vector, as well as uses crucial bacteria involved in microbiome assembly and the colonization of other pathogens, such as viruses. This targeted disruption of the microbiome reduces pathogen transmission, lowers pathogen load, and can influence vector population dynamics. Figure adapted from images created in BioRender; Dunfield K. 2025. https://BioRender.com/lmfnt7t.

integration into the vector microbiota and their subsequent transmission across generations. The bacteria must maintain mutualistic or commensal relationships within the microbiota, be transstadially transmitted, and localize to the same compartment as does the pathogen (131). Moreover, Wilke & Marrelli (166) proposed three conditions for suitable bacterial candidates: (a) production of an effective effector molecule, (b) a mechanism for displaying or excreting this molecule, and (c) long-term survival within the vector to produce sufficient effector quantities.

*Wolbachia*, an intracellular bacterium found in many insects, has proven effective for paratransgenesis (58). In mosquitoes, *Wolbachia* is maternally transmitted and infects the gonads. It is prevalent in wild mosquito populations, such as *Aedes albopictus* and *Culex pipiens* (131, 166). Three
strategies have been proposed for *Wolbachia*-based paratransgenesis: (a) insertion of transgenes directly into the *Wolbachia* genome to leverage cytoplasmic incompatibility for population suppression, (b) incorporation of transgenes into coinherited cytoplasmic elements, and (c) transformation of mosquito genomes to use *Wolbachia* as a gene-insertion mechanism (166).

Other bacteria, such as *Asaia*, are also potential candidates for paratransgenesis, especially in *Anopheles* mosquitoes. *Asaia* is dominant in mosquito microbiota, easily cultivated and genetically manipulated, and widely distributed in mosquito bodies (52). Studies have shown successful deployment of genetically modified *Asaia* in *Anopheles gambiae* and *Anopheles stephensi* under semifield conditions, wherein *Asaia* spread rapidly and maintained vertical and transstadial transmission (113). In ticks, the bacterium *Sphingomonas* was genetically engineered to reduce *A. phagocytophilum* infections in *I. scapularis*, lowering the risk of human granulocytic anaplasmosis (118).

## 5.2. Antimicrobiota Vaccines

Antimicrobiota vaccines aim to disrupt the vector microbiota by targeting key bacteria and thus shifting the microbial community into a state that inhibits pathogen colonization or survival (173). These vaccines immunize the vertebrate host against keystone bacteria and lead to the production of antibodies that vectors ingest during blood meals (109). These antibodies can alter vector physiology and competence by targeting specific bacterial species within the vector (Figure 3b). Identifying key bacteria in vector microbiota is essential for developing an antimicrobiota vaccine. To date, this process has primarily relied on network-based analyses, particularly those using metrics such as eigenvector centrality and hub node identification to detect bacteria that are both central and abundant within microbial networks. The targeting of these central taxa has been shown to disrupt microbiota homeostasis within vectors (116, 172).

In *I. ricinus* ticks, an anti-Escherichia coli vaccine successfully reduced Escherichia-Shigella abundance, decreased species diversity, and decreased robustness of bacterial networks. These effects were not observed when nonkeystone bacteria were targeted. The vaccine also altered metabolic activity by reducing the lysine degradation pathway in ticks fed on immunized mice (117), and the vaccine influenced tick performance, with vaccinated ticks showing higher weights than did controls (117). Furthermore, the vaccine successfully impaired pathogen colonization. For instance, *I. ricinus* ticks fed on *Borrelia*-infected and *E. coli*-immunized mice had significantly lower pathogen loads, and this finding suggests that the vaccine created an infection-refractory microbiota state (172).

Similarly, antimicrobiota vaccines have been effective in mosquitoes, wherein they altered the microbiota and affected both physiology and pathogen colonization (11, 115). In *A. albopictus*, the targeting of *Chryseobacterium* resulted in reduced fecundity and egg-hatching rates in females fed on immunized blood. These reductions correlated with a decline in *Chryseobacterium* abundance and a shift in the bacterial community (115). In *Culex quinquefasciatus*, the targeting of *Enterobacteriaceae* modified midgut microbiota and led to reduced occurrences of *P. relictum* oocysts and sporozoites in both midguts and salivary glands (11). These findings demonstrate the potential of antimicrobiota vaccines as precise tools for the manipulation of vector microbiota to control pathogen transmission. However, the full effect of these vaccines on pathogen transmission from vector to host, and thus on vector competence, remains to be fully tested.

## 5.3. Microbial Interference

Microbial interference offers another strategy for reducing pathogen transmission by modifying the microbial communities of arthropod vectors. During antagonistic microbial interactions, two types of ecological competition can occur: exploitation, wherein microorganisms compete for
nutrients (86), and interference, wherein microorganisms antagonize each other through signaling mechanisms or the production of growth-inhibiting substances such as antibiotics, bacteriocins, and toxins (61, 86, 139). These interference mechanisms influence the composition and dynamics of microbial communities. The introduction of competitive, nonpathogenic bacterial strains can alter the microbial environment to reduce pathogen transmission (Figure 3c).

*Wolbachia* has demonstrated success in microbial interference by reducing the transmission of arboviruses such as dengue, chikungunya, Zika, and West Nile (43, 122), as well as of filarial nematodes (137) and malaria parasites (122). In ticks, microbial interference has been observed during coinfections of *Rickettsia* species, in which one species inhibits the replication of another and thereby affects pathogen competition and vector competence (36, 72). For instance, the non-pathogenic *Rickettsia peacockii* blocks the colonization of *Rickettsia rickettsii* in *Dermacentor andersoni* ovaries and thus reduces transmission rates (25). These findings show that microbial interference, wherein nonpathogenic species inhibit pathogens, can be used as a strategy to alter the vector competence of arthropods.

## 5.4. Phage Therapy

Phage therapy represents a promising nonantibiotic approach to controlling pathogens, particularly in light of the increasing threat of antimicrobial resistance (149). This method utilizes bacteriophages—viruses that specifically infect bacteria—to target and reduce populations of particular bacterial species within the microbiome (102). It is considered a safe and environmentally friendly method. For instance, bacteriophages have been used prophylactically in animal feed to significantly reduce colonization of pathogens such as *Salmonella* (159). Additionally, phage therapy has been tested successfully against various pathogens, including *Pectobacterium carotovorum*, *Staphylococcus aureus*, *Vibrio harveyi*, *E. coli*, and *Klebsiella pneumoniae*, and has yielded promising results in human and animal health (101, 127).

While phages are highly specific, they can also produce cascading effects that influence non-target species and thereby alter bacterial community dynamics and the gut metabolome in mice (80). Moreover, phage cocktails have demonstrated efficacy in disrupting and clearing bacterial biofilms; this efficacy makes them particularly suitable for targeting pathogen-supportive bacteria in vectors (8, 87). Preliminary studies in mosquitoes have shown that phages can modulate the microbiota of larvae and subsequently affect adult mosquitoes. For example, a combination of bacteriophages targeting *Enterobacter* and *Pseudomonas* led to an 80% reduction in larval survival and altered developmental patterns; these illustrate the potential of phage therapy as a tool for vector control through microbiome disruption (154).

## 6. CHALLENGES, LIMITATIONS, AND FUTURE DIRECTIONS

Despite advances in our understanding of vector microbiota and its role in VBDs, several challenges persist. One major hurdle in microbial ecology network analysis is the lack of standardized methodologies. The variety of tools, especially of R packages (164), leads to inconsistent interpretations and reduces reproducibility (82). Researchers may select methods tailored to specific datasets, and such selection increases the risk of nonreplicable and overly optimistic results (157). Additionally, discerning whether associations in microbial networks are driven by shared environmental conditions or by direct biotic interactions is difficult (50). Most co-occurrence networks are undirected, meaning they cannot infer interaction types such as amensalism, parasitism, mutualism, or commensalism; this limitation highlights the complexity of microbial relationships and the need for complementary research methods.

Technical issues also arise in the integration of high-throughput sequencing data with bioinformatics tools, as well as in scalability and performance optimization (143). For example, zeros in

Phage therapy: application of bacteriophages to selectively target and kill specific bacteria within microbiomes
sequencing data may represent either true absence or undetectable levels of taxa, and this am-
biguity complicates interpretation and obscures rare taxa diversity. Accurate normalization or
transformation strategies are essential to ensure reliable results (50, 65). Contamination, especially
in low-biomass samples, remains another issue and potentially skews bacterial diversity interpreta-
tions (167). This contamination can lead to misinterpretation of bacterial diversity and erroneous
data analysis (64, 85). Mitigation strategies include the use of controlled environments and of neg-
ative controls (85, 168). Statistical tools such as the decontam R package are useful for identifying
contaminants in silico (37).

Efforts to overcome these challenges will focus on several key areas, such as the development
of standardized methodologies, advanced network analysis tools, and improved algorithms to han-
dle the complexity and scale of microbial data (134, 142). Expanding research geographically is
also critical for understanding the diversity of vector microbiomes across various environments
and ecological contexts. Collaboration with industry and academia will be essential to translate
research into practical applications, such as antimicrobiota vaccines, which could have significant
impacts on VBD management. Additionally, the engagement of policymakers in the development
of industry standards and regulations will foster a supportive environment for microbiome-based
technologies. Public awareness campaigns will also be necessary to inform stakeholders about the
safety and benefits of these disease control strategies.

7. CONCLUSION

The study of VBDs and the intricate microbial communities within vectors offer both signifi-
cant challenges and promising opportunities for public health. Understanding the interactions
among hosts, vectors, pathogens, and environments is critical for advancing knowledge of mi-
crobial community assembly and its role in vector competence and disease transmission. Recent
advances in high-throughput sequencing and bioinformatics have deepened our understanding of
vector microbiomes, while network analysis has emerged as a powerful tool for unraveling micro-
bial interactions, deciphering community stability, and identifying keystone species. Despite these
advances, gaps remain in our understanding of the factors influencing vector microbiota assembly
and functionality. The interplay of stochastic processes and deterministic processes and microbial
interactions adds complexity to these systems and requires a multifaceted approach that integrates
theoretical models, empirical research, and advanced analytical techniques.

Innovative strategies, such as antimicrobiota vaccines, paratransgenesis, and microbial interfer-
ence, hold promise for reducing pathogen transmission and improving vector control. However,
these approaches must be carefully evaluated for ecological impact and real-world effectiveness.
Future research should focus on standardizing methodologies for network analysis, refining the
interpretation of sequencing data, and exploring emerging technologies such as artificial intelli-
gence and microfluidic PCR. By continuing to unravel the complexities of vector microbiomes,
we can develop more effective and sustainable interventions to combat VBDs and mitigate their
global health burden.

DISCLOSURE STATEMENT

The authors are not aware of any affiliations, memberships, funding, or financial holdings that
might be perceived as affecting the objectivity of this review.

AUTHOR CONTRIBUTIONS

D.O. and A.C.-C. conceptualized the review and supervised the writing process. D.O., A.M., and A.C.-C. led the writing of the manuscript, and they drafted the introduction and key sections on
microbial community assembly and network analysis. E.P.-S., A.W.-C., and L.A.-D. contributed to the writing of the sections on vector microbiota and pathogen interactions. A.L.C.-A. and I.C.-G. focused on biotechnological applications and interventions, including antimicrobiota vaccines and paratransgenesis. J.A. and L.M.-H. contributed to the literature review on microbial community assembly theories and visualization techniques. M.K. and Š.S. were responsible for reviewing the microbiome data collection methods and the impact of microbial interactions on vector competence. K.S. drafted the sections on keystone species and network stability metrics. All authors reviewed and edited the manuscript, contributed to the final version, and approved it for submission. Visualization contributions, including figures and diagrams, were made by D.O.

## ACKNOWLEDGMENTS

The authors thank the members of their respective laboratories for valuable discussions and insights on microbial community assembly, which greatly contributed to the development of this review. We are grateful to Dr. Kari Dunfield for support with figure preparation.

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