Exploring the skin microbiome in atopic dermatitis pathogenesis and disease modification.

Inflammatory skin diseases such as atopic eczema (atopic dermatitis [AD]) affect children and adults globally. In AD, the skin barrier is impaired on multiple levels. Underlying factors include genetic, chemical, immunologic, and microbial components. Increased skin pH in AD is part of the altered microbial microenvironment that promotes overgrowth of the skin microbiome with Staphylococcus aureus. The secretion of virulence factors, such as toxins and proteases, by S aureus further aggravates the skin barrier deficiency and additionally disrupts the balance of an already skewed immune response. Skin commensal bacteria, however, can inhibit the growth and pathogenicity of S aureus through quorum sensing. Therefore, restoring a healthy skin microbiome could contribute to remission induction in AD. This review discusses direct and indirect approaches to targeting the skin microbiome through modulation of the skin pH; UV treatment; and use of prebiotics, probiotics, and postbiotics. Furthermore, exploratory techniques such as skin microbiome transplantation, ozone therapy, and phage therapy are discussed. Finally, we summarize the latest findings on disease and microbiome modification through targeted immunomodulatory systemic treatments and biologics. We believe that targeting the skin microbiome should be considered a crucial component of successful AD treatment in the future.

Benchmarking MicrobIEM - a user-friendly tool for decontamination of microbiome sequencing data.

BACKGROUND: Microbiome analysis is becoming a standard component in many scientific studies, but also requires extensive quality control of the 16S rRNA gene sequencing data prior to analysis. In particular, when investigating low-biomass microbial environments such as human skin, contaminants distort the true microbiome sample composition and need to be removed bioinformatically. We introduce MicrobIEM, a novel tool to bioinformatically remove contaminants using negative controls. RESULTS: We benchmarked MicrobIEM against five established decontamination approaches in four 16S rRNA amplicon sequencing datasets: three serially diluted mock communities (108-103 cells, 0.4-80% contamination) with even or staggered taxon compositions and a skin microbiome dataset. Results depended strongly on user-selected algorithm parameters. Overall, sample-based algorithms separated mock and contaminant sequences best in the even mock, whereas control-based algorithms performed better in the two staggered mocks, particularly in low-biomass samples (≤ 106 cells). We show that a correct decontamination benchmarking requires realistic staggered mock communities and unbiased evaluation measures such as Youden's index. In the skin dataset, the Decontam prevalence filter and MicrobIEM's ratio filter effectively reduced common contaminants while keeping skin-associated genera. CONCLUSIONS: MicrobIEM's ratio filter for decontamination performs better or as good as established bioinformatic decontamination tools. In contrast to established tools, MicrobIEM additionally provides interactive plots and supports selecting appropriate filtering parameters via a user-friendly graphical user interface. Therefore, MicrobIEM is the first quality control tool for microbiome experts without coding experience.

Atopic dermatitis: Correlation of distinct risk factors with age of onset in adulthood compared to childhood.

BACKGROUND: Atopic dermatitis (AD) has long been regarded as a primarily pediatric disease. However, there is growing evidence for a high rate of adult-onset AD. We aimed to characterize factors associated with adult-onset versus childhood-onset AD and controls. METHODS: We analyzed cross-sectional data of the CK-CARE-ProRaD cohorts Bonn, Augsburg, Davos, Zürich of 736 adult patients stratified by age of AD onset (childhood-onset <18 years: 76.4% (subsets: 0 to 2; ≥2 to 6; ≥7 to 11; ≥12 to 18); adult-onset ≥18 years: 23.6% (subsets: ≥18 to 40; ≥41 to 60; ≥61) and 167 controls (91 atopic, 76 non-atopic)). RESULTS: We identified active smoking to be associated with adult-onset AD versus controls (adjusted Odds Ratio (aOR) = 5.54 [95% Confidence Interval: 1.06-29.01] vs. controlsnon-atopic , aOR = 4.03 [1.20-13.45] vs. controlsatopic ). Conjunctivitis showed a negative association versus controlsatopic (aOR = 0.36 [0.14-0.91]). Food allergy (aOR = 2.93 [1.44-5.96]), maternal food allergy (aOR = 9.43 [1.10-80.95]), palmar hyperlinearity (aOR = 2.11 [1.05-4.25]), and academic background (aOR = 2.14 [1.00-4.54]) increased the odds of childhood-onset AD versus controlsatopic . Shared AD-associated factors were maternal AD (4-34x), increased IgE (2-20x), atopic stigmata (2-3x) with varying effect sizes depending on AD onset and control group. Patients with adult-compared to childhood-onset had doubled odds of allergic rhinitis (aOR = 2.15 [1.12-4.13]), but reduced odds to feature multiple (3-4) atopic comorbidities (aOR = 0.34 [0.14-0.84]). Adult-onset AD, particularly onset ≥61 years, grouped mainly in clusters with low contributions of personal and familial atopy and high frequencies of physical inactivity, childhood-onset AD, particularly infant-onset, mainly in "high-atopic"-clusters. CONCLUSIONS: The identified associated factors suggest partly varying endo- and exogeneous mechanisms underlying adult-onset versus childhood-onset AD. Our findings might contribute to better assessment of the individual risk to develop AD throughout life and encourage prevention by non-smoking and physical activity as modifiable lifestyle factors.

Spatial transcriptomics combined with single-cell RNA-sequencing unravels the complex inflammatory cell network in atopic dermatitis.

BACKGROUND: Atopic dermatitis (AD) is the most common chronic inflammatory skin disease with complex pathogenesis for which the cellular and molecular crosstalk in AD skin has not been fully understood. METHODS: Skin tissues examined for spatial gene expression were derived from the upper arm of 6 healthy control (HC) donors and 7 AD patients (lesion and nonlesion). We performed spatial transcriptomics sequencing to characterize the cellular infiltrate in lesional skin. For single-cell analysis, we analyzed the single-cell data from suction blister material from AD lesions and HC skin at the antecubital fossa skin (4 ADs and 5 HCs) and full-thickness skin biopsies (4 ADs and 2 HCs). The multiple proximity extension assays were performed in the serum samples from 36 AD patients and 28 HCs. RESULTS: The single-cell analysis identified unique clusters of fibroblasts, dendritic cells, and macrophages in the lesional AD skin. Spatial transcriptomics analysis showed the upregulation of COL6A5, COL4A1, TNC, and CCL19 in COL18A1-expressing fibroblasts in the leukocyte-infiltrated areas in AD skin. CCR7-expressing dendritic cells (DCs) showed a similar distribution in the lesions. Additionally, M2 macrophages expressed CCL13 and CCL18 in this area. Ligand-receptor interaction analysis of the spatial transcriptome identified neighboring infiltration and interaction between activated COL18A1-expressing fibroblasts, CCL13- and CCL18-expressing M2 macrophages, CCR7- and LAMP3-expressing DCs, and T cells. As observed in skin lesions, serum levels of TNC and CCL18 were significantly elevated in AD, and correlated with clinical disease severity. CONCLUSION: In this study, we show the unknown cellular crosstalk in leukocyte-infiltrated area in lesional skin. Our findings provide a comprehensive in-depth knowledge of the nature of AD skin lesions to guide the development of better treatments.

Skin microbiome and its association with host cofactors in determining atopic dermatitis severity.

BACKGROUND: Atopic dermatitis (AD) is a heterogeneous, chronic inflammatory skin disease linked to skin microbiome dysbiosis with reduced bacterial diversity and elevated relative abundance of Staphylococcus aureus (S. aureus). OBJECTIVES: We aimed to characterize the yet incompletely understood association between the skin microbiome and patients' demographic and clinical cofactors in relation to AD severity. METHODS: The skin microbiome in 48 adult moderate-to-severe AD patients was investigated using next-generation deep sequencing (16S rRNA gene, V1-V3 region) followed by denoising (DADA2) to obtain amplicon sequence variant (ASV) composition. RESULTS: In lesional skin, AD severity was associated with S. aureus relative abundance (rS  = 0.53, p < 0.001) and slightly better with the microbiome diversity measure Evenness (rS  = -0.58, p < 0.001), but not with Richness. Multiple regression confirmed the association of AD severity with microbiome diversity, including Shannon (in lesional skin, p < 0.001), Evenness (in non-lesional skin, p = 0.015) or S. aureus relative abundance (p < 0.012), and with patient's IgE levels (p < 0.001), race (p < 0.032), age (p < 0.034) and sex (p = 0.012). The lesional model explained 62% of the variation in AD severity, and the non-lesional model 50% of the variation. CONCLUSIONS: Our results specify the frequently reported "reduced diversity" of the AD-related skin microbiome to reduced Evenness, which was in turn mainly driven by S. aureus relative abundance, rather than to a reduced microbiome Richness. Finding associations between AD severity, the skin microbiome and patient's cofactors is a key aspect in developing new personalized AD treatments, particularly those targeting the AD microbiome.

Th2/Th17 cell associated cytokines found in seroma fluids after breast cancer surgery.

PURPOSE: The development of a seroma after breast cancer surgery is a common postoperative complication seen after simple mastectomy and axillary surgery. We could recently demonstrate that breast cancer patients undergoing a simple mastectomy with subsequent seroma formation developed a T-helper cell increase within the aspirated fluid measured by flow cytometry. The same study revealed a Th2 and/or a Th17 immune response in peripheral blood and seroma fluid of the same patient. Based on these results and within the same study population, we now analyzed the Th2/Th17 cell associated cytokine content as well as the best known clinical important cytokine IL-6. METHODS: Multiplex cytokine measurements (IL-4, IL-5, IL-13, IL-10, IL-17, and IL-22) were done on 34 seroma fluids (Sf) after fine needle aspiration of patients who developed a seroma after a simple mastectomy. Serum of the same patient (Sp) and that of healthy volunteers (Sc) were used as controls. RESULTS: We found the Sf to be highly cytokine rich. Almost all analyzed cytokines were significantly higher in abundance in the Sf compared to Sp and Sc, especially IL-6, which promotes Th17 differentiation as well as suppresses Th1 differentiation in favor of Th2 development. CONCLUSION: Our Sf cytokine measurements reflect a local immune event. In contrast, former study results on T-helper cell populations in both Sf and Sp tend to demonstrate a systemic immune process.

Molecular Assessment of Staphylococcus Aureus Strains in STAT3 Hyper-IgE Syndrome Patients.

Hyper-IgE syndromes (HIES) are a group of inborn errors of immunity (IEI) caused by monogenic defects such as in the gene STAT3 (STAT3-HIES). Patients suffering from HIES show an increased susceptibility to Staphylococcus aureus (S. aureus) including skin abscesses and pulmonary infections. To assess if the underlying immune defect of STAT3-HIES patients influences the resistance patterns, pathogenicity factors or strain types of S. aureus. We characterized eleven S. aureus strains isolated from STAT3-HIES patients (n = 4) by whole genome sequencing (WGS) to determine presence of resistance and virulence genes. Additionally, we used multi-locus sequence typing (MLST) and protein A (spa) typing to classify these isolates. Bacterial isolates collected from this cohort of STAT3-HIES patients were identified as common spa types in Germany. Only one of the isolates was classified as methicillin-resistant S. aureus (MRSA). For one STAT3 patient WGS illustrated that infection and colonization occurred with different S. aureus isolates rather than one particular clone. The identified S. aureus carriage profile on a molecular level suggests that S. aureus strain type in STAT3-HIES patients is determined by local epidemiology rather than the underlying immune defect highlighting the importance of microbiological assessment prior to antibiotic treatment.

Microbiome of Barrier Organs in Allergy: Who Runs the World? Germs!

Over the last few decades, allergic diseases have been steadily increasing worldwide, a phenomenon that is not yet completely understood. Recent evidence, however, suggests that alterations in the microbiome may be a contributing factor. The microbiome refers to all microorganisms in a habitat including bacteria, fungi, and viruses. Using modern sequencing technologies, we are now capable of detecting and analyzing the human microbiome in more detail than ever before. Epidemiological and experimental studies have indicated that a complex intestinal microbiome supports the development of the immune system during childhood, thus providing protection from allergic diseases, including food allergy. The microbiome becomes an important part of human physiology and forms dynamic relationships with our various barrier systems. For example, bacterial dysbiosis is a hallmark of atopic eczema and correlates with disease progression. Similarly, the lung and nasopharyngeal microbiome is altered in patients with asthma and allergic rhinitis. While these results are interesting, the underlying mechanisms are still unclear and need to be investigated with functional studies. This review gives a short overview of the terminology and methods used in microbiome research before highlighting results concerning the lung, skin, and intestinal microbiome in allergic diseases.

An Overview of the Latest Metabolomics Studies on Atopic Eczema with New Directions for Study.

Atopic eczema (AE) is an inflammatory skin disorder affecting approximately 20% of children worldwide and early onset can lead to asthma and allergies. Currently, the mechanisms of the disease are not fully understood. Metabolomics, the analysis of small molecules in the skin produced by the host and microbes, opens a window to observe the mechanisms of the disease which then may lead to new drug targets for AE treatment. Here, we review the latest advances in AE metabolomics, highlighting both the lipid and non-lipid molecules, along with reviewing the metabolites currently known to reside in the skin.

Seroma after Simple Mastectomy in Breast Cancer-The Role of CD4+ T Helper Cells and the Evidence as a Possible Specific Immune Process.

Seroma development after breast cancer surgery is the most common postoperative complication seen after mastectomy but neither its origin nor its cellular composition is known. To investigate the assumption of immunological significance, one of the first aims of this pilot study is to describe the cellular content of collected seroma fluids and its corresponding serum in patients with simple mastectomy after needle aspiration, as well as the serum of healthy controls. The content of red blood cells (RBC) was measured by haemato-counter analyses, and the lymphocyte identification/quantification was conducted by flow cytometry analyses in seroma fluid (SFl) and the sera of patients (PBp) as well as controls (PBc). Significantly lower numbers of RBCs were measured in SFl. Cytotoxic T cells are significantly reduced in SFl, whereas T helper (Th) cells are significantly enriched compared to PBp. Significantly higher numbers of Th2 cells were found in SFl and PBp compared to PBc. The exact same pattern is seen when analyzing the Th17 subgroup. In conclusion, in contrast to healthy controls, significantly higher Th2 and Th17 cell subgroup-mediated immune responses were measured in seroma formations and were further confirmed in the peripheral blood of breast cancer (including DCIS) patients after simple mastectomy. This could lead to the assumption of a possible immunological cause for the origin of a seroma.

The power and potential of BIOMAP to elucidate host-microbiome interplay in skin inflammatory diseases.

The two most common chronic inflammatory skin diseases are atopic dermatitis (AD) and psoriasis. The underpinnings of the remarkable degree of clinical heterogeneity of AD and psoriasis are poorly understood and, as a consequence, disease onset and progression are unpredictable and the optimal type and time point for intervention are as yet unknown. The BIOMAP project is the first IMI (Innovative Medicines Initiative) project dedicated to investigating the causes and mechanisms of AD and psoriasis and to identify potential biomarkers responsible for the variation in disease outcome. The consortium includes 7 large pharmaceutical companies and 25 non-industry partners including academia. Since there is mounting evidence supporting an important role for microbial exposures and our microbiota as factors mediating immune polarization and AD and psoriasis pathogenesis, an entire work package is dedicated to the investigation of skin and gut microbiome linked to AD or psoriasis. The large collaborative BIOMAP project will enable the integration of patient cohorts, data and knowledge in unprecedented proportions. The project has a unique opportunity with a potential to bridge and fill the gaps between current problems and solutions. This review highlights the power and potential of the BIOMAP project in the investigation of microbe-host interplay in AD and psoriasis.

A new era of atopic eczema research: Advances and highlights.

Atopic eczema (AE) is an inflammatory skin disease with involvement of genetic, immunological and environmental factors. One hallmark of AE is a skin barrier disruption on multiple, highly interconnected levels: filaggrin mutations, increased skin pH and a microbiome dysbiosis towards Staphylococcus aureus overgrowth are observed in addition to an abnormal type 2 immune response. Extrinsic factors seem to play a major role in the development of AE. As AE is a first step in the atopic march, its prevention and appropriate treatment are essential. Although standard therapy remains topical treatment, powerful systemic treatment options emerged in the last years. However, thorough endotyping of the individual patients is still required for ideal precision medicine approaches in future. Therefore, novel microbial and immunological biomarkers were described recently for the prediction of disease development and treatment response. This review summarizes the current state of the art in AE research.

[The skin microbiome-useful for diagnosis and therapy?] / Hautmikrobiom ­ Wertvoll für Diagnostik und Therapie?

BACKGROUND: Our skin is a very important and complex organ of the body. The microorganisms of the skin, the so-called microbiome, represent an important part of the healthy skin barrier and are influenced by various external and internal factors. AIM: The question to what extent the skin microbiome represents a diagnostic or even therapeutic target in the context of skin diseases is discussed. MATERIALS AND METHODS: A literature search was performed. RESULTS: Several diseases are associated with negative alterations of the skin microbiome. In atopic dermatitis, a correlation between severity and increased availability of Staphylococcus aureus is known, with a loss of bacterial diversity on the skin. In the future, S. aureus will not only be used as a diagnostic marker in atopic dermatitis, but also represents a promising target as a predictive marker for therapeutic success. The role of the skin microbiome in psoriasis has not yet been researched in depth. However, there is evidence that dysbiosis of the skin microbiome contributes to the course of psoriasis and that there is a disturbance in immune tolerance in patients. In the case of acne, the involvement of Cutibacterium acnes in the clinical picture is well known; however, recent findings show that it is not sufficient to identify the species, but certain characteristics of C. acnes strains are associated. CONCLUSION: Microbial biomarkers are currently only established in atopic dermatitis. For other diseases, this might be the case in the future; however combinations of microorganisms, single species and also strains with specific characteristics must be considered.

Skin pH-dependent Staphylococcus aureus abundance as predictor for increasing atopic dermatitis severity.

BACKGROUND: Atopic eczema (atopic dermatitis, AD) is characterized by disrupted skin barrier associated with elevated skin pH and skin microbiome dysbiosis, due to high Staphylococcus aureus loads, especially during flares. Since S aureus shows optimal growth at neutral pH, we investigated the longitudinal interplay between these factors and AD severity in a pilot study. METHOD: Emollient (with either basic pH 8.5 or pH 5.5) was applied double-blinded twice daily to 6 AD patients and 6 healthy (HE) controls for 8 weeks. Weekly, skin swabs for microbiome analysis (deep sequencing) were taken, AD severity was assessed, and skin physiology (pH, hydration, transepidermal water loss) was measured. RESULTS: Physiological, microbiome, and clinical results were not robustly related to the pH of applied emollient. In contrast to longitudinally stable microbiome in HE, S aureus frequency significantly increased in AD over 8 weeks. High S aureus abundance was associated with skin pH 5.7-6.2. High baseline S aureus frequency predicted both increase in S aureus and in AD severity (EASI and local SCORAD) after 8 weeks. CONCLUSION: Skin pH is tightly regulated by intrinsic factors and limits the abundance of S aureus. High baseline S aureus abundance in turn predicts an increase in AD severity over the study period. This underlines the importance and potential of sustained intervention regarding the skin pH and urges for larger studies linking skin pH and skin S aureus abundance to understand driving factors of disease progression.

Distribution of ACE2, CD147, CD26, and other SARS-CoV-2 associated molecules in tissues and immune cells in health and in asthma, COPD, obesity, hypertension, and COVID-19 risk factors.

BACKGROUND: Morbidity and mortality from COVID-19 caused by novel coronavirus SARS-CoV-2 is accelerating worldwide, and novel clinical presentations of COVID-19 are often reported. The range of human cells and tissues targeted by SARS-CoV-2, its potential receptors and associated regulating factors are still largely unknown. The aim of our study was to analyze the expression of known and potential SARS-CoV-2 receptors and related molecules in the extensive collection of primary human cells and tissues from healthy subjects of different age and from patients with risk factors and known comorbidities of COVID-19. METHODS: We performed RNA sequencing and explored available RNA-Seq databases to study gene expression and co-expression of ACE2, CD147 (BSG), and CD26 (DPP4) and their direct and indirect molecular partners in primary human bronchial epithelial cells, bronchial and skin biopsies, bronchoalveolar lavage fluid, whole blood, peripheral blood mononuclear cells (PBMCs), monocytes, neutrophils, DCs, NK cells, ILC1, ILC2, ILC3, CD4+ and CD8+ T cells, B cells, and plasmablasts. We analyzed the material from healthy children and adults, and from adults in relation to their disease or COVID-19 risk factor status. RESULTS: ACE2 and TMPRSS2 were coexpressed at the epithelial sites of the lung and skin, whereas CD147 (BSG), cyclophilins (PPIA andPPIB), CD26 (DPP4), and related molecules were expressed in both epithelium and in immune cells. We also observed a distinct age-related expression profile of these genes in the PBMCs and T cells from healthy children and adults. Asthma, COPD, hypertension, smoking, obesity, and male gender status generally led to the higher expression of ACE2- and CD147-related genes in the bronchial biopsy, BAL, or blood. Additionally, CD147-related genes correlated positively with age and BMI. Interestingly, we also observed higher expression of CD147-related genes in the lesional skin of patients with atopic dermatitis. CONCLUSIONS: Our data suggest different receptor repertoire potentially involved in the SARS-CoV-2 infection at the epithelial barriers and in the immune cells. Altered expression of these receptors related to age, gender, obesity and smoking, as well as with the disease status, might contribute to COVID-19 morbidity and severity patterns.

Phage-mediated dispersal of biofilm and distribution of bacterial virulence genes is induced by quorum sensing.

The microbiome and the phage meta-genome within the human gut are influenced by antibiotic treatments. Identifying a novel mechanism, here we demonstrate that bacteria use the universal communication molecule AI-2 to induce virulence genes and transfer them via phage release. High concentrations (i.e. 100 µM) of AI-2 promote dispersal of bacteria from already established biofilms, and is associated with release of phages capable of infecting other bacteria. Enterococcus faecalis V583ΔABC harbours 7 prophages in its genome, and a mutant deficient in one of these prophages (i.e. prophage 5) showed a greatly reduced dispersal of biofilm. Infection of a probiotic E. faecalis strain without lytic prophages with prophage 5 resulted in increased biofilm formation and also in biofilm dispersal upon induction with AI-2. Infection of the probiotic E. faecalis strain with phage-containing supernatants released through AI-2 from E. faecalis V583ΔABC resulted in a strong increase in pathogenicity of this strain. The polylysogenic probiotic strain was also more virulent in a mouse sepsis model and a rat endocarditis model. Both AI-2 and ciprofloxacin lead to phage release, indicating that conditions in the gastrointestinal tract of hospitalized patients treated with antibiotics might lead to distribution of virulence genes to apathogenic enterococci and possibly also to other commensals or even to beneficial probiotic strains.

The phosphorylation flow of the Vibrio harveyi quorum-sensing cascade determines levels of phenotypic heterogeneity in the population.

UNLABELLED: Quorum sensing (QS) is a communication process that enables a bacterial population to coordinate and synchronize specific behaviors. The bioluminescent marine bacterium Vibrio harveyi integrates three autoinducer (AI) signals into one quorum-sensing cascade comprising a phosphorelay involving three hybrid sensor kinases: LuxU; LuxO, an Hfq/small RNA (sRNA) switch; and the transcriptional regulator LuxR. Using a new set of V. harveyi mutants lacking genes for the AI synthases and/or sensors, we assayed the activity of the quorum-sensing cascade at the population and single-cell levels, with a specific focus on signal integration and noise levels. We found that the ratios of kinase activities to phosphatase activities of the three sensors and, hence, the extent of phosphorylation of LuxU/LuxO are important not only for the signaling output but also for the degree of noise in the system. The pools of phosphorylated LuxU/LuxO per cell directly determine the amounts of sRNAs produced and, consequently, the copy number of LuxR, generating heterogeneous quorum-sensing activation at the single-cell level. We conclude that the ability to drive the heterogeneous expression of QS-regulated genes in V. harveyi is an inherent feature of the architecture of the QS cascade. IMPORTANCE: V. harveyi possesses one of the most complex quorum-sensing (QS) cascades known, using three different autoinducers (AIs) to control the induction of, e.g., bioluminescence, virulence factors, and biofilm and exoprotease production. We constructed various V. harveyi mutants to study the impact of each component and subsystem of the QS signaling cascade on QS activation at the population and single-cell levels. We found that the output was homogeneous only in the presence of all AIs. In the absence of any one AI, QS activation varied from cell to cell, resulting in phenotypic heterogeneity. This study elucidates a molecular design principle which enables a tightly integrated signaling cascade to control the expression of diverse phenotypes within a genetically homogeneous population.

A modular view of the diversity of cell-density-encoding schemes in bacterial quorum-sensing systems.

Certain environmental parameters are accessible to cells only indirectly and require an encoding step for cells to retrieve the relevant information. A prominent example is the phenomenon of quorum sensing by microorganisms, where information about cell density is encoded by means of secreted signaling molecules. The mapping of cell density to signal molecule concentration and the corresponding network modules involved have been at least partially characterized in many bacteria, and vary markedly between different systems. In this study, we investigate theoretically how differences in signal transport, signal modification, and site of signal detection shape the encoding function and affect the sensitivity and the noise characteristics of the cell-density-encoding process. We find that different modules are capable of implementing both fairly basic as well as more complex encoding schemes, whose qualitative characteristics vary with cell density and are linked to network architecture, providing the basis for a hierarchical classification scheme. We exploit the tight relationship between encoding behavior and network architecture to constrain the network topology of partially characterized natural systems, and verify one such prediction by showing experimentally that Vibrio harveyi is capable of importing Autoinducer 2. The framework developed in this research can serve not only to guide reverse engineering of natural systems but also to stimulate the design of synthetic systems and generally facilitate a better understanding of the complexities arising in the quorum-sensing process because of variations in the physical organization of the encoder network module.