Identification of immune correlates of protection in Shigella infection by application of machine learning

Leveraging artificial intelligence in vaccine development: A narrative review

Vaccine development stands as a cornerstone of public health efforts, pivotal in curbing infectious diseases and reducing global morbidity and mortality. However, traditional vaccine development methods are often time-consuming, costly, and inefficient. The advent of artificial intelligence (AI) has ushered in a new era in vaccine design, offering unprecedented opportunities to expedite the process. This narrative review explores the role of AI in vaccine development, focusing on antigen selection, epitope prediction, adjuvant identification, and optimization strategies. AI algorithms, including machine learning and deep learning, leverage genomic data, protein structures, and immune system interactions to predict antigenic epitopes, assess immunogenicity, and prioritize antigens for experimentation. Furthermore, AI-driven approaches facilitate the rational design of immunogens and the identification of novel adjuvant candidates with optimal safety and efficacy profiles. Challenges such as data heterogeneity, model interpretability, and regulatory considerations must be addressed to realize the full potential of AI in vaccine development. Integrating emerging technologies, such as single-cell omics and synthetic biology, promises to enhance vaccine design precision and scalability. This review underscores the transformative impact of AI on vaccine development and highlights the need for interdisciplinary collaborations and regulatory harmonization to accelerate the delivery of safe and effective vaccines against infectious diseases.

GMP manufacture of Shigella flexneri 2a Artificial Invaplex (InvaplexAR) and evaluation in a Phase 1 Open-label, dose escalating study administered intranasally to healthy, adult volunteers

Shigella species cause severe disease among travelers to, and children living in, endemic countries. Although significant efforts have been made to improve sanitation, increased antibiotic resistance and other factors suggest an effective vaccine is a critical need. Artificial Invaplex (InvaplexAR) is a subunit vaccine approach complexing Shigella LPS with invasion plasmid antigens. In pre-clinical studies, the InvaplexAR vaccine demonstrated increased immunogenicity as compared to the first generation product and was subsequently manufactured under cGMP for clinical testing in a first-in-human Phase 1 study. The primary objective of this study was the safety of S. flexneri 2a InvaplexAR given by intranasal (IN) immunization (without adjuvant) in a single-center, open-label, dose-escalating Phase 1 trial and secondarily to assess immunogenicity to identify a dose of InvaplexAR for subsequent clinical evaluations. Subjects received three IN immunizations of InvaplexAR, two weeks apart, in increasing dose cohorts (10 µg, 50 µg, 250 µg, and 500 μg). Adverse events were monitored using symptom surveillance, memory aids, and targeted physical exams. Samples were collected throughout the study to investigate vaccine-induced systemic and mucosal immune responses. There were no adverse events that met vaccination-stopping criteria. The majority (96%) of vaccine-related adverse events were mild in severity (most commonly nasal congestion, rhinorrhea, and post-nasal drip). Vaccination with InvaplexAR induced anti-LPS serum IgG responses and anti-Invaplex IgA and IgG antibody secreting cell (ASC) responses at vaccine doses ≥250 µg. Additionally, mucosal immune responses and functional antibody responses were seen from the serum bactericidal assay measurements. Notably, the responder rates and the kinetics of ASCs and antibody lymphocyte secretion (ALS) were similar, suggesting that either assay may be employed to identify IgG and IgA secreting cells. Further studies with InvaplexAR will evaluate alternative immunization routes, vaccination schedules and formulations to further optimize immunogenicity. (Clinical Trial Registry Number NCT02445963).

Foundations for improved vaccine correlate of risk analysis using positive-unlabeled learning

Insights into mechanisms of protection afforded by vaccine efficacy field trials can be complicated by both low rates of exposure and protection. However, these barriers do not preclude the discovery of correlates of reduced risk (CoR) of infection, which are a critical first step in defining correlates of protection (CoP). Given the significant investment in large-scale human vaccine efficacy trials and immunogenicity data collected to support CoR discovery, novel approaches for analyzing efficacy trials to optimally support discovery of CoP are critically needed. By simulating immunological data and evaluating several machine learning approaches, this study lays the groundwork for deploying Positive/Unlabeled (P/U) learning methods, which are designed to differentiate between two groups in cases where only one group has a definitive label and the other remains ambiguous. This description applies to case-control analysis designs for field trials of vaccine efficacy: infected subjects, or cases, are by definition unprotected, whereas uninfected subjects, or controls, may have been either protected or unprotected but simply never exposed. Here, we investigate the value of applying P/U learning to classify study subjects using model immunogenicity data based on predicted protection status in order to support new insights into mechanisms of vaccine-mediated protection from infection. We demonstrate that P/U learning methods can reliably infer protection status, supporting the discovery of simulated CoP that are not observed in conventional comparisons of infection status cases and controls, and we propose next steps necessary for the practical deployment of this novel approach to correlate discovery.

Advances in computational frameworks in the fight against TB: The way forward

Around 1.6 million people lost their life to Tuberculosis in 2021 according to WHO estimates. Although an intensive treatment plan exists against the causal agent, Mycobacterium Tuberculosis, evolution of multi-drug resistant strains of the pathogen puts a large number of global populations at risk. Vaccine which can induce long-term protection is still in the making with many candidates currently in different phases of clinical trials. The COVID-19 pandemic has further aggravated the adversities by affecting early TB diagnosis and treatment. Yet, WHO remains adamant on its "End TB" strategy and aims to substantially reduce TB incidence and deaths by the year 2035. Such an ambitious goal would require a multi-sectoral approach which would greatly benefit from the latest computational advancements. To highlight the progress of these tools against TB, through this review, we summarize recent studies which have used advanced computational tools and algorithms for-early TB diagnosis, anti-mycobacterium drug discovery and in the designing of the next-generation of TB vaccines. At the end, we give an insight on other computational tools and Machine Learning approaches which have successfully been applied in biomedical research and discuss their prospects and applications against TB.

Dissecting microbial communities and resistomes for interconnected humans, soil, and livestock

A debate is currently ongoing as to whether intensive livestock farms may constitute reservoirs of clinically relevant antimicrobial resistance (AMR), thus posing a threat to surrounding communities. Here, combining shotgun metagenome sequencing, machine learning (ML), and culture-based methods, we focused on a poultry farm and connected slaughterhouse in China, investigating the gut microbiome of livestock, workers and their households, and microbial communities in carcasses and soil. For both the microbiome and resistomes in this study, differences are observed across environments and hosts. However, at a finer scale, several similar clinically relevant antimicrobial resistance genes (ARGs) and similar associated mobile genetic elements were found in both human and broiler chicken samples. Next, we focused on Escherichia coli, an important indicator for the surveillance of AMR on the farm. Strains of E. coli were found intermixed between humans and chickens. We observed that several ARGs present in the chicken faecal resistome showed correlation to resistance/susceptibility profiles of E. coli isolates cultured from the same samples. Finally, by using environmental sensing these ARGs were found to be correlated to variations in environmental temperature and humidity. Our results show the importance of adopting a multi-domain and multi-scale approach when studying microbial communities and AMR in complex, interconnected environments.

Systems approach to define humoral correlates of immunity to Shigella

Shigella infection is the second leading cause of death due to diarrheal disease in young children worldwide. With the rise of antibiotic resistance, initiatives to design and deploy a safe and effective Shigella vaccine are urgently needed. However, efforts to date have been hindered by the limited understanding of immunological correlates of protection against shigellosis. We applied systems serology to perform a comprehensive analysis of Shigella-specific antibody responses in sera obtained from volunteers before and after experimental infection with S. flexneri 2a in a series of controlled human challenge studies. Polysaccharide-specific antibody responses are infrequent prior to infection and evolve concomitantly with disease severity. In contrast, pre-existing antibody responses to type 3 secretion system proteins, particularly IpaB, consistently associate with clinical protection from disease. Linked to particular Fc-receptor binding patterns, IpaB-specific antibodies leverage neutrophils and monocytes, and complement and strongly associate with protective immunity. IpaB antibody-mediated functions improve with a subsequent rechallenge resulting in complete clinical protection. Collectively, our systems serological analyses indicate protein-specific functional correlates of immunity against Shigella in humans.

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Serological profiling of Shigella human challenge studies indicates protective markers

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Pre-existing IpaB-specific functional antibodies associate with less severe disease

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OPS immune responses post challenge are linked to less severe disease

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Shigella rechallenge boosts IpaB but not OPS functional antibody responses

Shigella-Controlled Human Infection Models: Current and Future Perspectives

Shigella-controlled human infection models (CHIMs) are an invaluable tool utilized by the vaccine community to combat one of the leading global causes of infectious diarrhea, which affects infants, children and adults regardless of socioeconomic status. The impact of shigellosis disproportionately affects children in low- and middle-income countries (LMICs) resulting in cognitive and physical stunting, perpetuating a cycle that must be halted. Shigella-CHIMs not only facilitate the early evaluation of enteric countermeasures and up-selection of the most promising products but also provide insight into mechanisms of infection and immunity that are not possible utilizing animal models or in vitro systems. The greater understanding of shigellosis obtained in CHIMs builds and empowers the development of new generation solutions to global health issues which are unattainable in the conventional laboratory and clinical settings. Therefore, refining, mining and expansion of safe and reproducible infection models hold the potential to create effective means to end diarrheal disease and associated co-morbidities associated with Shigella infection.

A Study of the Recent Trends of Immunology: Key Challenges, Domains, Applications, Datasets, and Future Directions

The human immune system is very complex. Understanding it traditionally required specialized knowledge and expertise along with years of study. However, in recent times, the introduction of technologies such as AIoMT (Artificial Intelligence of Medical Things), genetic intelligence algorithms, smart immunological methodologies, etc., has made this process easier. These technologies can observe relations and patterns that humans do and recognize patterns that are unobservable by humans. Furthermore, these technologies have also enabled us to understand better the different types of cells in the immune system, their structures, their importance, and their impact on our immunity, particularly in the case of debilitating diseases such as cancer. The undertaken study explores the AI methodologies currently in the field of immunology. The initial part of this study explains the integration of AI in healthcare and how it has changed the face of the medical industry. It also details the current applications of AI in the different healthcare domains and the key challenges faced when trying to integrate AI with healthcare, along with the recent developments and contributions in this field by other researchers. The core part of this study is focused on exploring the most common classifications of health diseases, immunology, and its key subdomains. The later part of the study presents a statistical analysis of the contributions in AI in the different domains of immunology and an in-depth review of the machine learning and deep learning methodologies and algorithms that can and have been applied in the field of immunology. We have also analyzed a list of machine learning and deep learning datasets about the different subdomains of immunology. Finally, in the end, the presented study discusses the future research directions in the field of AI in immunology and provides some possible solutions for the same.

Shigella-Specific Immune Profiles Induced after Parenteral Immunization or Oral Challenge with Either Shigella flexneri 2a or Shigella sonnei

Shigella spp. are a leading cause of diarrhea-associated global morbidity and mortality. Development and widespread implementation of an efficacious vaccine remain the best option to reduce Shigella-specific morbidity. Unfortunately, the lack of a well-defined correlate of protection for shigellosis continues to hinder vaccine development efforts. Shigella controlled human infection models (CHIM) are often used in the early stages of vaccine development to provide preliminary estimates of vaccine efficacy; however, CHIMs also provide the opportunity to conduct in-depth immune response characterizations pre- and postvaccination or pre- and postinfection. In the current study, principal-component analyses were used to examine immune response data from two recent Shigella CHIMs in order to characterize immune response profiles associated with parenteral immunization, oral challenge with Shigella flexneri 2a, or oral challenge with Shigella sonnei. Although parenteral immunization induced an immune profile characterized by robust systemic antibody responses, it also included mucosal responses. Interestingly, oral challenge with S. flexneri 2a induced a distinctively different profile compared to S. sonnei, characterized by a relatively balanced systemic and mucosal response. In contrast, S. sonnei induced robust increases in mucosal antibodies with no differences in systemic responses across shigellosis outcomes postchallenge. Furthermore, S. flexneri 2a challenge induced significantly higher levels of intestinal inflammation compared to S. sonnei, suggesting that both serotypes may also differ in how they trigger induction and activation of innate immunity. These findings could have important implications for Shigella vaccine development as protective immune mechanisms may differ across Shigella serotypes. IMPORTANCE Although immune correlates of protection have yet to be defined for shigellosis, prior studies have demonstrated that Shigella infection provides protection against reinfection in a serotype-specific manner. Therefore, it is likely that subjects with moderate to severe disease post-oral challenge would be protected from a homologous rechallenge, and investigating immune responses in these subjects may help identify immune markers associated with the development of protective immunity. This is the first study to describe distinct innate and adaptive immune profiles post-oral challenge with two different Shigella serotypes. Analyses conducted here provide essential insights into the potential of different immune mechanisms required to elicit protective immunity, depending on the Shigella serotype. Such differences could have significant impacts on vaccine design and development within the Shigella field and should be further investigated across multiple Shigella serotypes.

RV144 HIV-1 vaccination impacts post-infection antibody responses

The RV144 vaccine efficacy clinical trial showed a reduction in HIV-1 infections by 31%. Vaccine efficacy was associated with stronger binding antibody responses to the HIV Envelope (Env) V1V2 region, with decreased efficacy as responses wane. High levels of Ab-dependent cellular cytotoxicity (ADCC) together with low plasma levels of Env-specific IgA also correlated with decreased infection risk. We investigated whether B cell priming from RV144 vaccination impacted functional antibody responses to HIV-1 following infection. Antibody responses were assessed in 37 vaccine and 63 placebo recipients at 6, 12, and 36 months following HIV diagnosis. The magnitude, specificity, dynamics, subclass recognition and distribution of the binding antibody response following infection were different in RV144 vaccine recipients compared to placebo recipients. Vaccine recipients demonstrated increased IgG1 binding specifically to V1V2, as well as increased IgG2 and IgG4 but decreased IgG3 to HIV-1 Env. No difference in IgA binding to HIV-1 Env was detected between the vaccine and placebo recipients following infection. RV144 vaccination limited the development of broadly neutralizing antibodies post-infection, but enhanced Fc-mediated effector functions indicating B cell priming by RV144 vaccination impacted downstream antibody function. However, these functional responses were not associated with clinical markers of disease progression. These data reveal that RV144 vaccination primed B cells towards specific binding and functional antibody responses following HIV-1 infection.

Leveraging Computational Modeling to Understand Infectious Diseases

Purpose of Review

Computational and mathematical modeling have become a critical part of understanding in-host infectious disease dynamics and predicting effective treatments. In this review, we discuss recent findings pertaining to the biological mechanisms underlying infectious diseases, including etiology, pathogenesis, and the cellular interactions with infectious agents. We present advances in modeling techniques that have led to fundamental disease discoveries and impacted clinical translation.

Recent Findings

Combining mechanistic models and machine learning algorithms has led to improvements in the treatment of Shigella and tuberculosis through the development of novel compounds. Modeling of the epidemic dynamics of malaria at the within-host and between-host level has afforded the development of more effective vaccination and antimalarial therapies. Similarly, in-host and host-host models have supported the development of new HIV treatment modalities and an improved understanding of the immune involvement in influenza. In addition, large-scale transmission models of SARS-CoV-2 have furthered the understanding of coronavirus disease and allowed for rapid policy implementations on travel restrictions and contract tracing apps.

Summary

Computational modeling is now more than ever at the forefront of infectious disease research due to the COVID-19 pandemic. This review highlights how infectious diseases can be better understood by connecting scientists from medicine and molecular biology with those in computer science and applied mathematics.

Immune Response Characterization after Controlled Infection with Lyophilized Shigella sonnei 53G

Shigella is a major cause of moderate to severe diarrhea largely affecting children (<5 years old) living in low- and middle-income countries. Several vaccine candidates are in development, and controlled human infection models (CHIMs) can be useful tools to provide an early assessment of vaccine efficacy and potentially support licensure. A lyophilized strain of S. sonnei 53G was manufactured and evaluated to establish a dose that safely and reproducibly induced a ≥60% attack rate. Samples were collected pre- and postchallenge to assess intestinal inflammatory responses, antigen-specific serum and mucosal antibody responses, functional antibody responses, and memory B cell responses. Infection with S. sonnei 53G induced a robust intestinal inflammatory response as well as antigen-specific antibodies in serum and mucosal secretions and antigen-specific IgA- and IgG-secreting B cells positive for the α4β7 gut-homing marker. There was no association between clinical disease outcomes and systemic or functional antibody responses postchallenge; however, higher lipopolysaccharide (LPS)-specific serum IgA- and IgA-secreting memory B cell responses were associated with a reduced risk of disease postchallenge. This study provides unique insights into the immune responses pre- and postinfection with S. sonnei 53G in a CHIM, which could help guide the rational design of future vaccines to induce protective immune responses more analogous to those triggered by infection.IMPORTANCE Correlate(s) of immunity have yet to be defined for shigellosis. As previous disease protects against subsequent infection in a serotype-specific manner, investigating immune response profiles pre- and postinfection provides an opportunity to identify immune markers potentially associated with the development of protective immunity and/or with a reduced risk of developing shigellosis postchallenge. This study is the first to report such an extensive characterization of the immune response after challenge with S. sonnei 53G. Results demonstrate an association of progression to shigellosis with robust intestinal inflammatory and mucosal gut-homing responses. An important finding in this study was the association of elevated Shigella LPS-specific serum IgA and memory B cell IgA responses at baseline with reduced risk of disease. The increased baseline IgA responses may contribute to the lack of dose response observed in the study and suggests that IgA responses should be further investigated as potential correlates of immunity.

Diffusive flux of CH4 and N2O from agricultural river networks: Regression tree and importance analysis

River networks in subtropical agricultural hilly region become an inconvenient greenhouse gas (GHG, methane and nitrous oxide) source because of the influence of human activities, which has caused large uncertainties for refinement of national GHG inventories and their global budget. Based on field monitoring experiments at high temporal resolution, we employed regression tree and importance analysis to identify quantitatively factors that influence the diffusive flux of GHGs to provide a scientific basis for reducing GHG emissions and controlling regional carbon and nitrogen losses. The results indicate that significant spatiotemporal variation of methane (CH₄) nitrous oxide (N₂O) diffusion occurs in all the four reaches (W1, W2, W3 and W4) of Tuojia river networks. Among them, W1 contributed lowest CH₄ (22.55 μg C m^-2 h^-1) and N₂O (5.00 μg N m^-2 h^-1) diffusive flux than the other three (P < 0.05), while W4 offered highest CH₄ (166.15 μg C m^-2 h^-1) and N₂O (30.47 μg N m^-2 h^-1) diffusive flux but with no statistically significant difference between W2 and W3 due to homogeneous extraneous nutrition loading into the two reaches. W4 also contributed largest cumulative flux of CH₄ (14.55 kg C ha^-1 yr^-1) and N₂O (2.69 kg N ha^-1 yr^-1) in Tuojia River networks (P < 0.05). Furthermore, the regression tree and importance analysis indicate that, in the anaerobic environment, dissolved oxygen saturation controlled the production and diffusion for both CH₄ and N₂O. The findings of this investigation highlighted that decision support tools provide an effective pathway to enhance the GHG mitigation technology research in agroecosystems and simultaneously shed light on the global campaign on refinement of national GHG inventories as well as regional nutrient management.

A multicenter random forest model for effective prognosis prediction in collaborative clinical research network

BACKGROUND

The accuracy of a prognostic prediction model has become an essential aspect of the quality and reliability of the health-related decisions made by clinicians in modern medicine. Unfortunately, individual institutions often lack sufficient samples, which might not provide sufficient statistical power for models. One mitigation is to expand data collection from a single institution to multiple centers to collectively increase the sample size. However, sharing sensitive biomedical data for research involves complicated issues. Machine learning models such as random forests (RF), though they are commonly used and achieve good performances for prognostic prediction, usually suffer worse performance under multicenter privacy-preserving data mining scenarios compared to a centrally trained version.

METHODS AND MATERIALS

In this study, a multicenter random forest prognosis prediction model is proposed that enables federated clinical data mining from horizontally partitioned datasets. By using a novel data enhancement approach based on a differentially private generative adversarial network customized to clinical prognosis data, the proposed model is able to provide a multicenter RF model with performances on par with-or even better than-centrally trained RF but without the need to aggregate the raw data. Moreover, our model also incorporates an importance ranking step designed for feature selection without sharing patient-level information.

RESULT

The proposed model was evaluated on colorectal cancer datasets from the US and China. Two groups of datasets with different levels of heterogeneity within the collaborative research network were selected. First, we compare the performance of the distributed random forest model under different privacy parameters with different percentages of enhancement datasets and validate the effectiveness and plausibility of our approach. Then, we compare the discrimination and calibration ability of the proposed multicenter random forest with a centrally trained random forest model and other tree-based classifiers as well as some commonly used machine learning methods. The results show that the proposed model can provide better prediction performance in terms of discrimination and calibration ability than the centrally trained RF model or the other candidate models while following the privacy-preserving rules in both groups. Additionally, good discrimination and calibration ability are shown on the simplified model based on the feature importance ranking in the proposed approach.

CONCLUSION

The proposed random forest model exhibits ideal prediction capability using multicenter clinical data and overcomes the performance limitation arising from privacy guarantees. It can also provide feature importance ranking across institutions without pooling the data at a central site. This study offers a practical solution for building a prognosis prediction model in the collaborative clinical research network and solves practical issues in real-world applications of medical artificial intelligence.

Updates on immunologic correlates of vaccine-induced protection

Correlates of protection (CoPs) are increasingly important in the development and licensure of vaccines. Although the study of CoPs was initially directed at identifying a single immune function that could explain vaccine efficacy, it has become increasingly clear that there are often multiple functions responsible for efficacy. This review is meant to supplement prior articles on the subject, illustrating both simple and complex CoPs.

Functional antibodies as immunological endpoints to evaluate protective immunity against Shigella

The development, clinical advancement and licensure of vaccines, and monitoring of vaccine effectiveness could be expedited and simplified by the ability to measure immunological endpoints that can predict a favorable clinical outcome. Antigen-specific and functional antibodies have been described in the context of naturally acquired immunity and vaccination against Shigella, and their presence in serum has been associated with reduced risk of disease in human subjects. The relevance of these antibodies as correlates of protective immunity, their mechanistic contribution to protection (e.g. target antigens, interference with pathogenesis, and participation in microbial clearance), and factors that influence their magnitude and makeup (e.g. host age, health condition, and environment) are important considerations that need to be explored. In addition to facilitating vaccine evaluation, immunological correlates of protection could be useful for identifying groups at risk and advancing immune therapies. Herein we discuss the precedent and value of functional antibodies as immunological endpoints to predict vaccine efficacy and the relevance of functional antibody activity to evaluate protective immunity against shigellosis.

Novel Strategies for Malaria Vaccine Design

The quest for a licensed effective vaccine against malaria remains a global priority. Even though classical vaccine design strategies have been successful for some viral and bacterial pathogens, little success has been achieved for Plasmodium falciparum, which causes the deadliest form of malaria due to its diversity and ability to evade host immune responses. Nevertheless, recent advances in vaccinology through high throughput discovery of immune correlates of protection, lymphocyte repertoire sequencing and structural design of immunogens, provide a comprehensive approach to identifying and designing a highly efficacious vaccine for malaria. In this review, we discuss novel vaccine approaches that can be employed in malaria vaccine design.

Prediction of Shigellosis outcomes in Israel using machine learning classifiers

Shigellosis causes significant morbidity and mortality in developing and developed countries, mostly among infants and young children. The World Health Organization estimates that more than one million people die from Shigellosis every year. In order to evaluate trends in Shigellosis in Israel in the years 2002-2015, we analysed national notifiable disease reporting data. Shigella sonnei was the most commonly identified Shigella species in Israel. Hospitalisation rates due to Shigella flexenri were higher in comparison with other Shigella species. Shigella morbidity was higher among infants and young children (age 0-5 years old). Incidence of Shigella species differed among various ethnic groups, with significantly high rates of S. flexenri among Muslims, in comparison with Jews, Druze and Christians. In order to improve the current Shigellosis clinical diagnosis, we developed machine learning algorithms to predict the Shigella species and whether a patient will be hospitalised or not, based on available demographic and clinical data. The algorithms' performances yielded an accuracy of 93.2% (Shigella species) and 94.9% (hospitalisation) and may consequently improve the diagnosis and treatment of the disease.

Transcriptomics in Human Challenge Models

Human challenge models, in which volunteers are experimentally infected with a pathogen of interest, provide the opportunity to directly identify both natural and vaccine-induced correlates of protection. In this review, we highlight how the application of transcriptomics to human challenge studies allows for the identification of novel correlates and gives insight into the immunological pathways required to develop functional immunity. In malaria challenge trials for example, innate immune pathways appear to play a previously underappreciated role in conferring protective immunity. Transcriptomic analyses of samples obtained in human challenge studies can also deepen our understanding of the immune responses preceding symptom onset, allowing characterization of innate immunity and early gene signatures, which may influence disease outcome. Influenza challenge studies demonstrate that these gene signatures have diagnostic potential in the context of pandemics, in which presymptomatic diagnosis of at-risk individuals could allow early initiation of antiviral treatment and help limit transmission. Furthermore, gene expression analysis facilitates the identification of host factors contributing to disease susceptibility, such as C4BPA expression in enterotoxigenic Escherichia coli infection. Overall, these studies highlight the exceptional value of transcriptional data generated in human challenge trials and illustrate the broad impact molecular data analysis may have on global health through rational vaccine design and biomarker discovery.