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Microorganisms
Special Issues
Insight into Bacterial Pathogens: Pathogenesis and Host Response
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Insight into Bacterial Pathogens: Pathogenesis and Host Response

A special issue of Microorganisms (ISSN 2076-2607). This special issue belongs to the section "Molecular Microbiology and Immunology".

Deadline for manuscript submissions: 15 June 2025 | Viewed by 3108

Special Issue Editors

Prof. Dr. Karen Angeliki Krogfelt

E-Mail Website
Guest Editor
Statens Serum Institut, Copenhagen, Denmark
Interests: bacterial pathogenesis and host response to the infections; testing drugs and assessing the pathogenicity; bacterial community interactions , microbiota analyses and co-infections; serology
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Luis Cláudio Nascimento Da Silva

E-Mail Website
Guest Editor
Microbial Pathogenicity Laboratory, Universidade Ceuma, São Luís 65075-120, Brazil
Interests: antimicrobial agents; antivirulence agents; immunomodulators; polymers; lectins; polysaccharides; infection models; wound healing; action mechanisms
Special Issues, Collections and Topics in MDPI journals
Dr. Afonso Gomes-Abreu

E-Mail Website
Guest Editor
Programa de Pós-Graduação em Biologia Microbiana, Universidade Ceuma, São Luís 65075-120, Brazil
Interests: virulence factors of E. coli.; interaction of bacterial proteases with the host; bacterial resistance to the antimicrobials

Special Issue Information

Dear Colleagues,

Bacterial pathogens remain a significant threat to human and animal health. Understanding the intricate mechanisms underlying their pathogenesis and the host's response is crucial for developing effective therapeutic strategies. This Special Issue of Microorganisms aims to present cutting-edge research exploring the multifaceted interplay between bacterial pathogens and their hosts.

We welcome submissions addressing all aspects of bacterial pathogenesis, including the following:

  • Novel virulence factors and their modes of action;
  • Adhesion and invasion mechanisms;
  • Bacterial immune evasion strategies;
  • Host inflammatory responses and immune signaling pathways;
  • Antibiotic resistance and its impact on virulence;
  • New approaches to target bacterial virulence factors.

This Special Issue seeks to provide a comprehensive platform for researchers to showcase their latest findings and foster discussions on critical areas in bacterial pathogenesis research. We encourage submissions employing diverse methodologies from in vitro and in vivo models to genomics and systems biology approaches.

We look forward to receiving your valuable contributions!

Prof. Dr. Karen Angeliki Krogfelt
Prof. Dr. Luis Cláudio Nascimento Da Silva
Dr. Afonso Gomes-Abreu
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Microorganisms is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2700 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • bacterial pathogenesis
  • virulence factors
  • host–pathogen interaction
  • immune response
  • antibiotic resistance
  • virulence regulation

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  • External promotion: Articles in Special Issues are often promoted through the journal's social media, increasing their visibility.
  • e-Book format: Special Issues with more than 10 articles can be published as dedicated e-books, ensuring wide and rapid dissemination.

Further information on MDPI's Special Issue polices can be found here.

Published Papers (3 papers)

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Research

Jump to: Review

20 pages, 5975 KiB  
Article
Novel Populations of Mycobacterium smegmatis Under Hypoxia and Starvation: Some Insights on Cell Viability and Morphological Changes
by Ruben Zaragoza-Contreras, Diana A. Aguilar-Ayala, Lázaro García-Morales, Miguel A. Ares, Addy Cecilia Helguera-Repetto, Jorge Francisco Cerna-Cortés, Lizbel León-Solis, Fernando Suárez-Sánchez, Jorge A. González-Y-Merchand and Sandra Rivera-Gutiérrez
Microorganisms 2024, 12(11), 2280; https://doi.org/10.3390/microorganisms12112280 - 10 Nov 2024
Viewed by 698
Abstract
The general features of the shift to a dormant state in mycobacterial species include several phenotypic changes, reduced metabolic activities, and increased resistance to host and environmental stress conditions. In this study, we aimed to provide novel insights into the viability state and [...] Read more.
The general features of the shift to a dormant state in mycobacterial species include several phenotypic changes, reduced metabolic activities, and increased resistance to host and environmental stress conditions. In this study, we aimed to provide novel insights into the viability state and morphological changes in dormant M. smegmatis that contribute to its long-term survival under starvation or hypoxia. To this end, we conducted assays to evaluate cell viability, morphological changes and gene expression. During starvation, M. smegmatis exhibited a reduction in cell length, the presence of viable but non-culturable (VBNC) cells and the formation of anucleated small cells, potentially due to a phenomenon known as reductive cell division. Under hypoxia, a novel population of pleomorphic mycobacteria with a rough surface before the cells reached nonreplicating persistence 1 (NRP1) was identified. This population exhibited VBNC-like behaviour, with a loss of cell wall rigidity and the presence of lipid-body-like structures. Based on dosR and hspX expression, we suggest that M. smegmatis encounters reductive stress conditions during starvation, while lipid storage may induce oxidative stress during hypoxia. These insights into the heterogeneous populations presented here could offer valuable opportunities for developing new therapeutic strategies to control dormant mycobacterial populations. Full article
(This article belongs to the Special Issue Insight into Bacterial Pathogens: Pathogenesis and Host Response)
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Figure 1

Figure 1
<p>Correlation between growth measurement and cell viability methods of <span class="html-italic">M. smegmatis</span>. (<b>A</b>) Growth and cell viability measurements during starvation. (<b>B</b>) Growth and cell viability measurements during hypoxia. White circles represent the optical density (OD), blue squares represent the number of CFU/mL, red diamonds represent total bacteria/mL (t-bact/mL) detected by FCM, and green triangles represent viable bacteria/mL (v-bact/mL) detected by FCM. The time when NRP1 (initial fading of the methylene blue, ▽f) and NRP2 phases (total discolouration of the methylene blue occurred, ▽d) began are indicated. Values are displayed on a logarithmic scale and mean ± SD are plotted. All experiments were carried out in triplicate.</p> Full article ">Figure 2
<p>Multiparametric FCM analysis of <span class="html-italic">M. smegmatis</span> grown under starvation conditions. (<b>A</b>) Dot plots of dead (†), viable (§) and the combination of dead and viable (*) cell populations are displayed; green fluorescence (FL1-H, SYTO 9) vs. red fluorescence (FL2-H, PI) is charted through time (0 h to 120 h). (<b>B</b>) Overlap of frequency histograms of the fluorescence profile of SYTO 9 (FL1-H) at different starvation times. (<b>C</b>) Histograms of cell size. The forward scatter depicts size (FSC-H), and the ordinate (cell counts) denotes single events. (<b>D</b>) Frequency histograms of cell granularity/complexity (SSC-H); the ordinate (cell counts) denotes single events.</p> Full article ">Figure 3
<p>Multiparametric analysis of the results obtained by FCM of M. smegmatis in hypoxia. (<b>A</b>) Dot plots of dead cells defined as SYTO 9lowPIhigh (†), viable cells defined as SYTO 9<sup>high</sup>PI<sup>low</sup> (§) and cells defined as SYTO 9<sup>high</sup>PI<sup>high</sup> (‡) are displayed; fluorescence microspheres were used for calibration (p), red fluorescence (FL2-H, PI) vs. green fluorescence (FL1-H, SYTO 9) is charted through time (0 h to 120 h). (<b>B</b>) Measurements of mycobacterial populations detected by FCM and their correlation with measurements in solid media. The blue squares represent CFU/mL; brown diamonds represent total bacteria per mL (t-bact/mL); black circles represent SYTO 9<sup>high</sup>PI<sup>high</sup> bacteria/mL (SYTO 9<sup>high</sup>PI<sup>high</sup>-bact/mL); green triangles represent viable bacteria per mL (v-bact/mL); red circles represent dead bacteria per mL (d-bact/mL). The times when a noticeable fading (NRP1, ▽f) and the total discolouration (NRP2, ▽d) of the methylene blue occurred are indicated. Values are displayed on a logarithmic scale and mean ± SD are charted. All experiments were carried out in triplicate. (<b>C</b>,<b>D</b>) Frequency histograms of cell size (FSC-H) and cell complexity (SSC-H) of <span class="html-italic">M. smegmatis</span>, respectively. In both cases, the ordinate (cell counts) denotes single events.</p> Full article ">Figure 4
<p>Gene expression of <span class="html-italic">M. smegmatis</span> cultures under in vitro latency conditions over time. Absolute gene expression normalised to 16S rRNA was measured in <span class="html-italic">M. smegmatis</span> either under starvation (<b>A</b>) or hypoxia (<b>C</b>); standard deviations are charted and <span class="html-italic">p</span> &lt; 0.05 was considered significantly different (represented by a star) between expression in time points of in vitro latency and the time 0 h (exponential phase). The relative gene quantification in starvation (at 4, 12, 24 and 120 h) is expressed as the ratio of transcription overtime/transcription at 0 h (<b>B</b>). The relative gene quantification in hypoxia (at 12, 36, 72 and 120 h) is expressed as the ratio of transcription overtime/transcription at 0 h (<b>D</b>).</p> Full article ">Figure 5
<p>TEM images of <span class="html-italic">M. smegmatis</span> under starvation. (<b>A</b>) Representative electron micrographs at 0 h, 12 h, 24 h, and 120 h of incubation; magnification was ×10 K; scale bars represent 2 µm; arrows point to electron-transparent cells without nucleoid and debris are indicated with an asterisk. (<b>B</b>) Ultrastructure of <span class="html-italic">M. smegmatis</span> at 0 h, 24 h, and 120 h of incubation; magnification was ×150 K and scale bars represent 100 nm. Arrow heads point to different cells structure such as a a: cytoplasmatic material, b: cell wall, c: chromatin, d: cellular debris, and e: electron-transparent zone surrounding the nucleoid. The acceleration voltage was 70 kV. These images represent three independent experiments and were chosen from five random fields.</p> Full article ">Figure 6
<p>SEM images of <span class="html-italic">M. smegmatis</span> under starvation. (<b>A</b>) Cells at 0, 24 and 120 h of starvation, at ×10 K magnification. (<b>B</b>) Cells at ×25 K, ×30 K and ×40 K magnification at 24 and 120 h of starvation. Rod cell surfaces with a rough appearance are indicated with an asterisk, and septa are indicated by an arrow. These images represent three independent experiments and were chosen from five random fields. Scale bars represent 500 nm, and the acceleration voltage was 15 kV in all cases.</p> Full article ">Figure 7
<p>SEM images of <span class="html-italic">M. smegmatis</span> cells grown under hypoxic conditions. (<b>A</b>) Representative electron micrographs taken at various times; magnification was ×10 K, and scale bars represent 1 µm. (<b>B</b>) Morphological changes at a higher magnification; times 0 to 36 h were ×20 K, and the scale bar represents 1 µm; times 72 to 120 were ×30 K, and scale bars represent 500 nm. Regular rods (RR), pleomorphic cells (PC), and rough surface cells (RC); the star indicates cellular debris. The acceleration voltage was 15 kV. These images represent three independent experiments and were chosen from five random fields.</p> Full article ">Figure 8
<p>TEM images of <span class="html-italic">M. smegmatis</span> under hypoxia. (<b>A</b>) <span class="html-italic">M. smegmatis</span> cells over time, at ×10K magnification. Scale bars represent 2 µm. (<b>B</b>) <span class="html-italic">M. smegmatis</span> cells at 0 h and 72 h under hypoxia, magnification was ×45 K or ×150 K, respectively. Scale bars represent 100 nm. (<b>C</b>) <span class="html-italic">M. smegmatis</span> cells at 120 h under hypoxia, magnification was ×150 K. Scale bars represent 100 nm. Black arrows point to cells with a low density of electrons. Numbers in panels point to ① Lipid-body-like structures, ② compacted DNA with highly dense electrons, ③ low-density electron zone, ④ loss of rigidity of cell wall, ⑤ cytoplasm, ⑥ star-shaped nucleoid and ⑦ vesicular structures with internal membranes. An acceleration voltage of 70 kV was used. Smaller frames within each panel were magnified ×120 K, and the acceleration voltage was 60 kV; scale bars represent 500 nm. These images represent three independent experiments and were chosen from five random fields.</p> Full article ">
22 pages, 1645 KiB  
Article
Differential Host Gene Expression in Response to Infection by Different Mycobacterium tuberculosis Strains—A Pilot Study
by Dewi Megawati, Lisa Y. Armitige and Loubna Tazi
Microorganisms 2024, 12(11), 2146; https://doi.org/10.3390/microorganisms12112146 - 25 Oct 2024
Viewed by 961
Abstract
Tuberculosis (TB) represents a global public health threat and is a leading cause of morbidity and mortality worldwide. Effective control of TB is complicated with the emergence of multidrug resistance. Yet, there is a fundamental gap in understanding the complex and dynamic interactions [...] Read more.
Tuberculosis (TB) represents a global public health threat and is a leading cause of morbidity and mortality worldwide. Effective control of TB is complicated with the emergence of multidrug resistance. Yet, there is a fundamental gap in understanding the complex and dynamic interactions between different Mycobacterium tuberculosis strains and the host. In this pilot study, we investigated the host immune response to different M. tuberculosis strains, including drug-sensitive avirulent or virulent, and rifampin-resistant or isoniazid-resistant virulent strains in human THP-1 cells. We identified major differences in the gene expression profiles in response to infection with these strains. The expression of IDO1 and IL-1β in the infected cells was stronger in all virulent M. tuberculosis strains. The most striking result was the overexpression of many interferon-stimulated genes (ISGs) in cells infected with the isoniazid-resistant strain, compared to the rifampin-resistant and the drug-sensitive strains. Our data indicate that infection with the isoniazid-resistant M. tuberculosis strain preferentially resulted in cGAS-STING/STAT1 activation, which induced a characteristic host immune response. These findings reveal complex gene signatures and a dynamic variation in the immune response to infection by different M. tuberculosis strains. Full article
(This article belongs to the Special Issue Insight into Bacterial Pathogens: Pathogenesis and Host Response)
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Figure 1

Figure 1
<p>Multidimensional scaling (MDS) plot based on the expression of all microarray genes between <span class="html-italic">M. tuberculosis</span>-infected and mock-infected THP-1 cells at two time points of infection. Circles represent data based on 4 h post-infection, and triangles depict data based on 24 h post-infection. Each specifically colored circle or triangle represents a replicate of each sample.</p> Full article ">Figure 2
<p>Number of differentially expressed genes (DEGs) in THP-1 cells at 4 h and 24 h post-infection (<span class="html-italic">p</span> &lt; 0.05). (<b>A</b>) <span class="html-italic">M. tuberculosis</span>-infected cells relative to mock-infected cells. (<b>B</b>) Pairwise comparisons of <span class="html-italic">M. tuberculosis</span>-infected cells.</p> Full article ">Figure 3
<p>Venn diagrams of DEGs in THP-1 cells at 24 h post-infection (<span class="html-italic">p</span> &lt; 0.05). The overlapping genes represent the overall number of DEGs between the different comparison groups, while the non-overlapping numbers designate the unique genes to each group. (<b>A</b>) Pairwise comparisons between <span class="html-italic">M. tuberculosis</span>-infected and mock-infected cells. (<b>B</b>) Pairwise comparisons between <span class="html-italic">M. tuberculosis</span>-infected cells.</p> Full article ">Figure 4
<p>Fold change in the expression of DEGs in THP-1 cell lines infected with reference <span class="html-italic">M. tuberculosis</span> strains relative to mock-infected cells (24 h post-infection; <span class="html-italic">p</span> &lt; 0.05). Representatives of different degrees of variation in gene expression are depicted in this figure. (<b>A</b>) Relative fold changes in gene expression above 10 are shown. (<b>B</b>) Relative fold changes in gene expression between 5 and 10 are shown. (<b>C</b>) Relative fold changes in gene expression between 1 and 5 are shown.</p> Full article ">Figure 5
<p>Volcano plots displaying differentially expressed genes in THP-1 cells in three different comparison groups (24 h post-infection; <span class="html-italic">p</span> &lt; 0.05). The 20 most highly significant DEGs in each plot are indicated in the insets. These genes all indicate higher expression in H37Rv-INH-R-infected cells, relative to H37Rv- and H37Rv-RIF-R-infected cells. (<b>A</b>) H37Rv vs. H37Rv-INH-R. (<b>B</b>) H37Rv-RIF-R vs. H37Rv-INH-R. (<b>C</b>) H37Rv-INH-R vs. H37Ra.</p> Full article ">Figure 6
<p>Heatmap and clustering across <span class="html-italic">M. tuberculosis</span>-infected THP-1 cells relative to mock-infected cells at 4 h and 24 h post-infection, using the top 100 most significant DEGs in cells infected with isoniazid-resistant H37Rv strain at 24 h post-infection. Samples with a relatively high expression of a given gene are shown in red, and samples with a relatively low expression are shown in blue. Lighter color shades and white indicate genes with intermediate expression levels. Some of the interferon-stimulated genes (ISGs) are marked in green, and their overexpression in cells infected with the isoniazid-resistant H37Rv strain is indicated with a green box in the plot.</p> Full article ">Figure 7
<p>Bubble plot of GO enrichment analysis of up-regulated DEGs in cells infected with the isoniazid-resistant H37Rv strain (24 h post-infection; <span class="html-italic">p</span> &lt; 0.05). All GO terms are grouped into three categories: biological process (BP), cellular component (CC), and molecular function (MF). (<b>A</b>) Comparison group: drug-sensitive H37Rv vs. isoniazid-resistant H37Rv. (<b>B</b>) Comparison group: rifampin-resistant H37Rv vs. isoniazid-resistant H37Rv.</p> Full article ">Figure 8
<p>KEGG analysis of up-regulated DEGs in cells infected with the isoniazid-resistant H37Rv strain. The first ten enriched pathways at 24 h post-infection are shown (<span class="html-italic">p</span> &lt; 0.05). The KEGG pathways were subsequently divided into three or four categories: environmental information processing, organismal systems, genetic information processing and human diseases. (<b>A</b>) Comparison group drug-sensitive H37Rv vs. isoniazid-resistant H37Rv. (<b>B</b>) Comparison group rifampin-resistant H37Rv vs. isoniazid-resistant H37Rv.</p> Full article ">Figure 9
<p>Model for host signaling response to reference <span class="html-italic">M. tuberculosis</span> strains. Different font size is used to illustrate the changes in host response to the different reference <span class="html-italic">M. tuberculosis</span> strains. Higher gene expression is shown in a big font size, and lower gene expression is shown in small font size. H37Rv-INH-R induced the highest expression of ISGs and PKR compared to the other strains, as indicated by the circles and the big font size.</p> Full article ">

Review

Jump to: Research

18 pages, 1981 KiB  
Review
Unravelling the Roles of Bacterial Nanomachines Bistability in Pathogens’ Life Cycle
by Romain Gory, Nicolas Personnic and Didier Blaha
Microorganisms 2024, 12(9), 1930; https://doi.org/10.3390/microorganisms12091930 - 23 Sep 2024
Viewed by 1063
Abstract
Bacterial nanomachines represent remarkable feats of evolutionary engineering, showcasing intricate molecular mechanisms that enable bacteria to perform a diverse array of functions essential to persist, thrive, and evolve within ecological and pathological niches. Injectosomes and bacterial flagella represent two categories of bacterial nanomachines [...] Read more.
Bacterial nanomachines represent remarkable feats of evolutionary engineering, showcasing intricate molecular mechanisms that enable bacteria to perform a diverse array of functions essential to persist, thrive, and evolve within ecological and pathological niches. Injectosomes and bacterial flagella represent two categories of bacterial nanomachines that have been particularly well studied both at the molecular and functional levels. Among the diverse functionalities of these nanomachines, bistability emerges as a fascinating phenomenon, underscoring their dynamic and complex regulation as well as their contribution to shaping the bacterial community behavior during the infection process. In this review, we examine two closely related bacterial nanomachines, the type 3 secretion system, and the flagellum, to explore how the bistability of molecular-scale devices shapes the bacterial eco-pathological life cycle. Full article
(This article belongs to the Special Issue Insight into Bacterial Pathogens: Pathogenesis and Host Response)
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Figure 1

Figure 1
<p>Molecular mechanisms governing phenotypic heterogeneity. Schematic representation of molecular pathways involved in different phenotypic heterogeneity mechanisms. (<b>a</b>) The expression of two genes may become uncorrelated in individual cells due to intrinsic expression noise, resulting in a population where some cells express higher levels of one fluorescent protein (green) compared to the other (red). (<b>b</b>) Epigenetics can prevent gene expression by DNA methylation patterns or histone-like nucleoid-structuring proteins (H-NS) in the promoter region. It modifies chromosome conformation and accessibility for the activator proteins, resulting in the switching off of downstream genes. (<b>c</b>) Asymmetrical division in bacteria can contribute to phenotypic heterogeneity through the stochastic segregation of cytoplasmic molecules. Different regulators and enzymes, for instance, can affect the gene expression profile of each cell after division. (<b>d</b>) Periodic oscillations in <span class="html-italic">Synechococcus elongatus</span> form a circadian clock that is influenced by light. This process revolves around the phosphorylation of KaiC by KaiA, which can be either inhibited or enhanced depending on light conditions. KaiB acts to counteract both of these effects. (<b>e</b>) Quorum sensing is a cell–cell interaction via diffusible molecules: autoinducers (AI). In <span class="html-italic">Vibrio fisheri</span>, LuxI is N-acyl homoserine lactone synthetase, which generates and secretes AI. Other cells bind AI on LuxR, influencing gene expression and promoting the production of more AI. (<b>f</b>) Microenvironmental heterogeneity of nitric oxide (NO) levels at infection sites leads to varying gene activations within the Yersinia pseudotuberculosis population in response to NO molecules.</p> Full article ">Figure 2
<p>Structure and regulation of the injectisome. Schematic comparison of structures and regulations of the injectisome and the flagellum. (<b>a</b>) Schematic overview of the injectisome structure. The outer membrane (OM) ring, the inner membrane (IM) ring and the cytosolic C ring are principal components stabilized by the membranes and the peptidoglycan (PG). (<b>b</b>) Simplified genetic organization and regulation of the <span class="html-italic">Pseudomonas aeruginosa</span> injectisome (T3SS). ExsA-binding sites (square) are located upstream of all T3SS genes. In high Ca<sup>2+</sup> conditions, ExsE binds ExsC, and ExsD binds ExsA, preventing the regulon transcription. In low Ca<sup>2+</sup> and high secretion activity, ExsE is secreted, freeing ExsC that binds ExsD and ExsA that can bind to the gene’s promoter region and allow the injectisome gene to be expressed.</p> Full article ">Figure 3
<p>Structure and regulation of the flagellum. Schematic comparison of structures and regulations of the injectisome and the flagellum. (<b>a</b>) Schematic overview of the flagellum structure. (<b>b</b>) Simplified regulation of <span class="html-italic">Salmonella</span> and <span class="html-italic">Escherichia coli</span> flagellum. Global signals induced the expression of class I genes encoding the FlhDC protein complex. It promotes the expression of class II genes encoding basal body structures and regulators FlgM and FliA. The latter is an activator of the class III genes encoding for the flagellar filament. FlgM binds FliA and prevents the class III transcription. Once the hook and basal body are assembled, FlgM is secreted, freeing FliA.</p> Full article ">Figure 4
<p>Bistability in pathogens’ life cycle: bet-hedging and division of labour. (<b>a</b>) Bet-hedging in <span class="html-italic">Salmonella enterica</span> infections. Expression of the injectisome (T3SS; type III secretion system) leads to slow growth of bacterial cells. This slower metabolism makes bacteria less sensitive to antibiotics and can result in the formation of persisters; the T3SS-expressing subpopulation is more likely to survive the stress. These resistant, slow-growing bacteria can then switch back or divide into fast-growth cells and cause chronic infections. (<b>b</b>) Cooperative colonization in <span class="html-italic">Pseudomonas aeruginosa</span> infections. When a flagellated cell encounters host cells, the cyclic-di-GMP level increases, allowing for the expression of pili. This is followed by an asymmetric division, with the c-di-GMP being polarly located and having a lower concentration at the flagellated pole. The daughter cell with a lower c-di-GMP level (light grey) becomes a spreader and is involved in dispersing the infection, while the high c-di-GMP (dark grey) cell remains anchored to the host and provides a local attack. (<b>c</b>) Cooperative virulence in <span class="html-italic">Salmonella enterica</span> gut infection.</p> Full article ">
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