Uncovering the secrets of resistance: An introduction to computational methods in infectious disease research.

Antimicrobial resistance (AMR) is a growing global concern with significant implications for infectious disease control and therapeutics development. This chapter presents a comprehensive overview of computational methods in the study of AMR. We explore the prevalence and statistics of AMR, underscoring its alarming impact on public health. The role of AMR in infectious disease outbreaks and its impact on therapeutics development are discussed, emphasizing the need for novel strategies. Resistance mutations are pivotal in AMR, enabling pathogens to evade antimicrobial treatments. We delve into their importance and contribution to the spread of AMR. Experimental methods for quantitatively evaluating resistance mutations are described, along with their limitations. To address these challenges, computational methods provide promising solutions. We highlight the advantages of computational approaches, including rapid analysis of large datasets and prediction of resistance profiles. A comprehensive overview of computational methods for studying AMR is presented, encompassing genomics, proteomics, structural bioinformatics, network analysis, and machine learning algorithms. The strengths and limitations of each method are briefly outlined. Additionally, we introduce ResScan-design, our own computational method, which employs a protein (re)design protocol to identify potential resistance mutations and adaptation signatures in pathogens. Case studies are discussed to showcase the application of ResScan in elucidating hotspot residues, understanding underlying mechanisms, and guiding the design of effective therapies. In conclusion, we emphasize the value of computational methods in understanding and combating AMR. Integration of experimental and computational approaches can expedite the discovery of innovative antimicrobial treatments and mitigate the threat posed by AMR.

RNA therapeutics for infectious diseases.

Ribonucleic acids (RNAs), including the messenger RNA (mRNA), transfer RNA (tRNA), and ribosomal RNA (rRNA), play important roles in living organisms and viruses. In recent years, the RNA-based technologies including the RNAs inhibiting other RNA activities, the RNAs targeting proteins, the RNAs reprograming genetic information, and the RNAs encoding therapeutical proteins, are useful methods to apply in prophylactic and therapeutic vaccines. In this review, we summarize and highlight the current application of the RNA therapeutics, especially on mRNA vaccines which have potential for prevention and treatment against human and animal infectious diseases.

Inference of Infectious Disease Transmission through a Relaxed Bottleneck Using Multiple Genomes Per Host.

In recent times, pathogen genome sequencing has become increasingly used to investigate infectious disease outbreaks. When genomic data is sampled densely enough amongst infected individuals, it can help resolve who infected whom. However, transmission analysis cannot rely solely on a phylogeny of the genomes but must account for the within-host evolution of the pathogen, which blurs the relationship between phylogenetic and transmission trees. When only a single genome is sampled for each host, the uncertainty about who infected whom can be quite high. Consequently, transmission analysis based on multiple genomes of the same pathogen per host has a clear potential for delivering more precise results, even though it is more laborious to achieve. Here, we present a new methodology that can use any number of genomes sampled from a set of individuals to reconstruct their transmission network. Furthermore, we remove the need for the assumption of a complete transmission bottleneck. We use simulated data to show that our method becomes more accurate as more genomes per host are provided, and that it can infer key infectious disease parameters such as the size of the transmission bottleneck, within-host growth rate, basic reproduction number, and sampling fraction. We demonstrate the usefulness of our method in applications to real datasets from an outbreak of Pseudomonas aeruginosa amongst cystic fibrosis patients and a nosocomial outbreak of Klebsiella pneumoniae.

Elucidating the Clinical Interpretation and Impact of a Positive Plasma Cell-Free DNA Metagenomics Test Result-A Single Center Retrospective Study.

BACKGROUND: The Karius Test (KT), a cell-free DNA metagenomic next-generation sequencing assay, has potential to improve diagnostic evaluation of infectious diseases. Published data describing clinical impact of positive KT results are limited. We attempt to elucidate the clinical interpretation and impact of positive KT results based on types and patterns of detected pathogens and patient characteristics. METHODS: All positive KT results from a single institution in 2022 were screened. Patients with results that met predefined categories were included for review by a panel of 3 infectious diseases physicians and one clinical microbiologist. Predefined categories included reports with fungal, parasitic, notable bacterial, notable viral pathogens, or polybacterial results (≥3 bacteria). Polybacterial results were further classified into patterns of microbiome detected. Clinical impact and its correlation with result or patient characteristics were explored. RESULTS: Ninety-two patients met the inclusion criteria, most were immunocompromised (73%). Positive KT results that met predefined categories had the following clinical impact: positive in 30.4%, negative in 2.2%, and none in 65.2%. Polybacterial results, especially interpreted as oral flora had lowest clinical impact (7.1% and 0.0%, respectively), while detection of parasites or notable bacterial pathogens had the highest clinical impact (100% and 77.8%, respectively). There was no correlation between patient characteristics and clinical impact. CONCLUSIONS: Among a cohort of largely immunocompromised patients, we were able to demonstrate clinical impact of specific KT result types and patterns but did not find correlation between patient characteristics and clinical impact. Our results should be confirmed in future larger cohorts.

NEAT1 in inflammatory infectious diseases: An integrated perspective on molecular modulation.

The long non-coding RNA (lncRNA), NEAT1, has emerged as a central figure in the intricate network of molecular regulators in inflammatory infectious diseases (IIDs). The review initiates a comprehensive exploration of NEAT1's multifaceted roles and molecular interactions in the context of these complex diseases. The study begins by acknowledging the global health burden of IIDs, underscoring the urgency for innovative insights into their pathogenesis and therapeutic avenues. NEAT1 is introduced as a pivotal lncRNA with growing relevance in immune responses and inflammatory processes. The core of this review unravels the NEAT1 landscape, elucidating its involvement in the modulation of immune signalling pathways, regulation of inflammatory cytokines, and interactions with various immune cells during infection. It explores NEAT1's role in orchestrating immune responses and balancing host defence mechanisms with the risk of immunopathology. Furthermore, the review underscores the clinical significance of NEAT1 in infectious diseases, discussing its associations with disease severity, prognosis, and potential as a diagnostic and therapeutic target. It provides insights into ongoing research endeavours aimed at harnessing NEAT1 for innovative disease management strategies, including developing RNA-based therapeutics. Concluding on a forward-looking note, the review highlights the broader implications of NEAT1 in the context of emerging infectious diseases and the possibility for precision medicine approaches that leverage NEAT1's regulatory capacities. In summary, this review illuminates the pivotal role of NEAT1 in IIDs by navigating its complex landscape, offering profound insights into its implications for disease pathogenesis and the development of targeted therapies.

The microbiome, resistome, and their co-evolution in sewage at a hospital for infectious diseases in Shanghai, China.

The emergence of antibiotic-resistant bacteria (ARB) caused by the overuse of antibiotics severely threatens human health. Hospital sewage may be a key transmission hub for ARB. However, the complex link between the microbiome and resistomeresistance in hospital sewage remains unclear. In this study, metagenomic assembly and binning methods were used to investigate the microbial community, resistome, and association of antibiotic resistance genes (ARGs) with ARB in sewage from 10 representative sites (outpatient building, surgery building, internal medicine buildings [IMB1-4], staff dormitory, laboratory animal building, tuberculosis building [TBB], and hospital wastewater treatment plant) of a hospital in Shanghai from June 2021 to February 2022. A total of 252 ARG subtypes, belonging to 17 antibiotic classes, were identified. The relative abundance of KPC-2 was higher at IMBs and TBB than at other sites. Of the ARG-carrying contigs, 47.3%-62.6% were associated with mobile genetic elements, and the proportion of plasmid-associated ARGs was significantly higher than that of chromosome-associated ARGs. Although a similar microbiome composition was shared, certain bacteria were enriched at different sites. Potential pathogens Enterococcus B faecium and Klebsiella pneumoniae were primarily enriched in IMB2 and IMB4, respectively. The same ARGs were identified in diverse bacterial hosts (especially pathogenic bacteria), and accordingly, the latter possessed multiple ARGs. Furthermore, gene flow was frequently observed in the sewage of different buildings. The results provide crucial information on the characterization profiles of resistomes in hospital sewage in Shanghai.IMPORTANCEEnvironmental antibiotic resistance genes (ARGs) play a critical role in the emergence and spread of antimicrobial resistance, which poses a global health threat. Wastewater from healthcare facilities serves as a significant reservoir for ARGs. Here, we characterized the microbial community along with the resistome (comprising all antibiotic resistance genes) in wastewater from a specialized hospital for infectious diseases in Shanghai. Potential pathogenic bacteria (e.g., Escherichia coli, Pseudomonas aeruginosa, Klebsiella pneumoniae, Enterococcus B faecium) were frequently detected in hospital wastewater and carried multiple ARGs. A complex link between microbiome and resistome was observed in the wastewater of this hospital. The monitoring of ARGs and antibiotic-resistant bacteria (ARB) in hospital wastewater might be of great significance for preventing the spread of ARB.

Disentangling archaic introgression and genomic signatures of selection at human immunity genes.

Pathogens and infectious diseases have imposed exceptionally strong selective pressure on ancient and modern human genomes and contributed to the current variation in many genes. There is evidence that modern humans acquired immune variants through interbreeding with ancient hominins, but the impact of such variants on human traits is not fully understood. The main objectives of this research were to infer the genetic signatures of positive selection that may be involved in adaptation to infectious diseases and to investigate the function of Neanderthal alleles identified within a set of 50 Lithuanian genomes. Introgressed regions were identified using the machine learning tool ArchIE. Recent positive selection signatures were analysed using iHS. We detected high-scoring signals of positive selection at innate immunity genes (EMB, PARP8, HLAC, and CDSN) and evaluated their interactions with the structural proteins of pathogens. Interactions with human immunodeficiency virus (HIV) 1 and severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) were identified. Overall, genomic regions introgressed from Neanderthals were shown to be enriched in genes related to immunity, keratinocyte differentiation, and sensory perception.

The common H232 STING allele shows impaired activities in DNA sensing, susceptibility to viral infection, and in monocyte cell function, while the HAQ variant possesses wild-type properties.

Different innate immune pathways converge to Stimulator of interferon genes (STING) and trigger type I interferon responses after recognition of abnormal nucleic acids in the cells. This non-redundant function renders STING a major player in immunosurveillance, and an emerging target for cancer and infectious diseases therapeutics. Beyond somatic mutations that often occur in cancer, the human gene encoding STING protein, TMEM173 (STING1), holds great genetic heterogeneity; R232, HAQ (R71H-G230A-R293Q) and H232 are the most common alleles. Although some of these alleles are likely to be hypomorphic, their function is still debated, due to the available functional assessments, which have been performed in biased biological systems. Here, by using genetic background-matched models, we report on the functional evaluation of R232, HAQ and H232 variants on STING function, and on how these genotypes affect the susceptibility to clinically relevant viruses, thus supporting a potential contributing cause to differences in inter-individual responses to infections. Our findings also demonstrate a novel toll-like receptor-independent role of STING in modulating monocytic cell function and differentiation into macrophages. We further supported the interplay of STING1 variants and human biology by demonstrating how monocytes bearing the H232 allele were impaired in M1/M2 differentiation, interferon response and antigen presentation. Finally, we assessed the response to PD-1 inhibitor in a small cohort of melanoma patients stratified according to STING genotype. Given the contribution of the STING protein in sensing DNA viruses, bacterial pathogens and misplaced cancer DNA, these data may support the development of novel therapeutic options for infectious diseases and cancer.

MicroRNAs in infectious diseases: potential diagnostic biomarkers and therapeutic targets.

MicroRNAs (miRNAs) are conserved, short, non-coding RNAs that play a crucial role in the post-transcriptional regulation of gene expression. They have been implicated in the pathogenesis of cancer and neurological, cardiovascular, and autoimmune diseases. Several recent studies have suggested that miRNAs are key players in regulating the differentiation, maturation, and activation of immune cells, thereby influencing the host immune response to infection. The resultant upregulation or downregulation of miRNAs from infection influences the protein expression of genes responsible for the immune response and can determine the risk of disease progression. Recently, miRNAs have been explored as diagnostic biomarkers and therapeutic targets in various infectious diseases. This review summarizes our current understanding of the role of miRNAs during viral, fungal, bacterial, and parasitic infections from a clinical perspective, including critical functional mechanisms and implications for their potential use as biomarkers and therapeutic targets.

Recent Advances Using Genetic Therapies Against Infectious Diseases and for Vaccination.

The development of prophylatic or therapeutic medicines for infectious diseases is one of the priorities for health organizations worldwide. Innovative solutions are required to achieve effective, safe, and accessible treatments for most if not all infectious diseases, particularly those that are chronic in nature or that emerge unexpectedly over time. Genetic technologies offer versatile possibilities to design therapies against pathogens. Recent developments such as mRNA vaccines, CRISPR gene editing, and immunotherapies provide unprecedented hope to achieve significant results in the field of infectious diseases. This review will focus on advances in this domain, showcasing the cross-fertilization with other fields (e.g., oncology), and addressing some of the logistical and economic concerns important to consider when making these advances accessible to diverse populations around the world.

The Relationship between TLR3 rs3775291 Polymorphism and Infectious Diseases: A Meta-Analysis of Case-Control Studies.

As the host's first line of defense against pathogens, Toll-like receptors (TLRs), such as the TLR3, are genes encoding transmembrane receptors of the same name. Depending on their expression, TLRs cause a pro- or anti-inflammatory response. The purpose of the article was to determine whether there is an association between the Toll-like receptor 3 (TLR3) rs3775291 Single Nucleotide Polymorphism-SNP and susceptibility to infections. This review was conducted according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) 2020 guidelines and was registered in PROSPERO under the code CRD42023429533. A systematic search for relevant studies was performed using PubMed, Scopus, SciELO, Google Scholar, and Science Direct by the MeSH descriptors and the Boolean Operator "AND": "Infections"; "TLR3"; "SNP", between January 2005 and July 2022. Summary odds ratios (ORs) and corresponding 95% confidence intervals (CIs) were calculated for genotypic comparison assuming a dominant genetic model (CT + TT vs. CC). A meta-analysis of 18 studies consisting of 3118 cases and 4368 controls found a significant association for risk between the presence of the TLR3 SNP rs3775291 and infections as part of the general analysis (OR = 1.16, 95% CI = 1.04-1.28, p = 0.004). In the subgroups of continents, the SNP had a protective role in Europe for 1044 cases and 1471 controls (OR = 0.83, 95% CI = 0.70-0.99, p = 0.04); however, the Asian (for 1588 patients and 2306 controls) and American (for 486 patients and 591 controls) continents had an increase in infectious risk (OR = 1.37, 95% CI = 1.19-1.58, p < 0.001; OR = 1.42, 95% CI = 1.08-1.86, and p = 0.01, respectively). Heterogeneity between studies was detected (I2 = 58%) but was explained in meta-regression by the subgroup of continents itself and publication bias was not evident. The results of the meta-analysis suggest a significant association between the TLR3 rs3775291 polymorphism and susceptibility to infections. Thus, when analyzing subgroups, the Asian and American continents showed that this SNP confers a higher risk against infections in a dominant genotypic model. Therefore, more studies are necessary to fully elucidate the role of TLR3 rs3775291 in infections.

Evolution of pathogens with cross-immunity in response to healthcare interventions.

Cross-immunity, as an evolutionary driver, can contribute to pathogen evolution, particularly pathogen diversity. Healthcare interventions aimed at reducing disease severity or transmission are commonly used to control diseases and can also induce pathogen evolution. Understanding pathogen evolution in the context of cross-immunity and healthcare interventions is crucial for infection control. This study starts by modelling cross-immunity, the extent of which is determined by strain traits and host characteristics. Given that all hosts have the same characteristics, full cross-immunity between residents and mutants occurs when mutation step sizes are small enough. Cross-immunity can be partial when the step size is large. The presence of partial cross-immunity reduces pathogen load and shortens the infectious period inside hosts, reducing transmission between hosts and improving host population survival and recovery. This study focuses on how pathogens evolve through small and large mutational steps and how healthcare interventions affect pathogen evolution. Using the theory of adaptive dynamics, we found that when mutational steps are small (only full cross-immunity is present), pathogen diversity cannot occur because it maximises the basic reproduction number. This results in intermediate values for both pathogen growth and clearance rates. However, when large mutational steps are allowed (with full and partial cross-immunity present), pathogens can evolve into multiple strains and induce pathogen diversity. The study also shows that different healthcare interventions can have varying effects on pathogen evolution. Generally, low levels of intervention are more likely to induce strain diversity, while high levels are more likely to result in strain reduction.

Genome-Wide Meta-Analysis Identifies Multiple Novel Rare Variants to Predict Common Human Infectious Diseases Risk.

Infectious diseases still threaten global human health, and host genetic factors have been indicated as determining risk factors for observed variations in disease susceptibility, severity, and outcome. We performed a genome-wide meta-analysis on 4624 subjects from the 10,001 Dalmatians cohort, with 14 infection-related traits. Despite a rather small number of cases in some instances, we detected 29 infection-related genetic associations, mostly belonging to rare variants. Notably, the list included the genes CD28, INPP5D, ITPKB, MACROD2, and RSF1, all of which have known roles in the immune response. Expanding our knowledge on rare variants could contribute to the development of genetic panels that could assist in predicting an individual's life-long susceptibility to major infectious diseases. In addition, longitudinal biobanks are an interesting source of information for identifying the host genetic variants involved in infectious disease susceptibility and severity. Since infectious diseases continue to act as a selective pressure on our genomes, there is a constant need for a large consortium of biobanks with access to genetic and environmental data to further elucidate the complex mechanisms behind host-pathogen interactions and infectious disease susceptibility.

CRISPR-Cas12-Based Diagnostic Applications in Infectious and Zoonotic Diseases.

Rapid detection of infectious and zoonotic diseases is very important for pathogen identification and infection control. Molecular diagnostic assays are well-known for high accuracy and sensitivity; however, conventional methods such as real-time PCR may require professional instruments and operations, preventing their wide applications in scenarios including animal quarantine. The recently developed CRISPR diagnostic (CRISPR-Dx) methods, employing the trans-cleavage activities of either Cas12 (e.g., HOLMES) or Cas13 (e.g., SHERLOCK), have shown great potential in rapid and convenient nucleic acid detection. Guided by specially designed CRISPR RNA (crRNA), Cas12 binds target DNA sequences and trans-cleaves ssDNA reporters, generating detectable signals, while Cas13 recognizes target ssRNA and trans-cleaves ssRNA reporters. To achieve high detection sensitivity, both HOLMES and SHERLOCK systems can be combined with pre-amplification procedures including both PCR and isothermal amplifications. Here, we present the employment of the HOLMESv2 method for convenient detection of the infectious and zoonotic diseases. Specifically, target nucleic acid is first amplified by LAMP or RT-LAMP, and the products are then detected by the thermophilic Cas12b. In addition, Cas12b reaction can be combined with LAMP amplification to achieve one-pot reaction systems. In this chapter, we provide a step-by-step description of the HOLMESv2-mediated rapid and sensitive detection of Japanese encephalitis virus (JEV), an RNA pathogen as an example.

Roles of alternative splicing in infectious diseases: from hosts, pathogens to their interactions.

ABSTRACT: Alternative splicing (AS) is an evolutionarily conserved mechanism that removes introns and ligates exons to generate mature messenger RNAs (mRNAs), extremely improving the richness of transcriptome and proteome. Both mammal hosts and pathogens require AS to maintain their life activities, and inherent physiological heterogeneity between mammals and pathogens makes them adopt different ways to perform AS. Mammals and fungi conduct a two-step transesterification reaction by spliceosomes to splice each individual mRNA (named cis -splicing). Parasites also use spliceosomes to splice, but this splicing can occur among different mRNAs (named trans -splicing). Bacteria and viruses directly hijack the host's splicing machinery to accomplish this process. Infection-related changes are reflected in the spliceosome behaviors and the characteristics of various splicing regulators (abundance, modification, distribution, movement speed, and conformation), which further radiate to alterations in the global splicing profiles. Genes with splicing changes are enriched in immune-, growth-, or metabolism-related pathways, highlighting approaches through which hosts crosstalk with pathogens. Based on these infection-specific regulators or AS events, several targeted agents have been developed to fight against pathogens. Here, we summarized recent findings in the field of infection-related splicing, including splicing mechanisms of pathogens and hosts, splicing regulation and aberrant AS events, as well as emerging targeted drugs. We aimed to systemically decode host-pathogen interactions from a perspective of splicing. We further discussed the current strategies of drug development, detection methods, analysis algorithms, and database construction, facilitating the annotation of infection-related splicing and the integration of AS with disease phenotype.

Increasing serum iron levels and their role in the risk of infectious diseases: a Mendelian randomization approach.

OBJECTIVES: Increased iron stores have been associated with elevated risks of different infectious diseases, suggesting that iron supplementation may increase the risk of infections. However, these associations may be biased by confounding or reverse causation. This is important, since up to 19% of the population takes iron supplementation. We used Mendelian randomization (MR) to bypass these biases and estimate the causal effect of iron on infections. METHODS: As instrumental variables, we used genetic variants associated with iron biomarkers in two genome-wide association studies (GWASs) of European ancestry participants. For outcomes, we used GWAS results from the UK Biobank, FinnGen, the COVID-19 Host Genetics Initiative or 23andMe, for seven infection phenotypes: 'any infections', combined, COVID-19 hospitalization, candidiasis, pneumonia, sepsis, skin and soft tissue infection (SSTI) and urinary tract infection (UTI). RESULTS: Most of our analyses showed increasing iron (measured by its biomarkers) was associated with only modest changes in the odds of infectious outcomes, with all 95% odds ratios confidence intervals within the 0.88 to 1.26 range. However, for the three predominantly bacterial infections (sepsis, SSTI, UTI), at least one analysis showed a nominally elevated risk with increased iron stores (P <0.05). CONCLUSION: Using MR, we did not observe an increase in risk of most infectious diseases with increases in iron stores. However for bacterial infections, higher iron stores may increase odds of infections. Hence, using genetic variation in iron pathways as a proxy for iron supplementation, iron supplements are likely safe on a population level, but we should continue the current practice of conservative iron supplementation during bacterial infections or in those at high risk of developing them.

The Legacy of Infectious Disease Exposure on the Genomic Diversity of Indigenous Southern Mexicans.

To characterize host risk factors for infectious disease in Mesoamerican populations, we interrogated 857,481 SNPs assayed using the Affymetrix 6.0 genotyping array for signatures of natural selection in immune response genes. We applied three statistical tests to identify signatures of natural selection: locus-specific branch length (LSBL), the cross-population extended haplotype homozygosity (XP-EHH), and the integrated haplotype score (iHS). Each of the haplotype tests (XP-EHH and iHS) were paired with LSBL and significance was determined at the 1% level. For the paired analyses, we identified 95 statistically significant windows for XP-EHH/LSBL and 63 statistically significant windows for iHS/LSBL. Among our top immune response loci, we found evidence of recent directional selection associated with the major histocompatibility complex (MHC) and the peroxisome proliferator-activated receptor gamma (PPAR-γ) signaling pathway. These findings illustrate that Mesoamerican populations' immunity has been shaped by exposure to infectious disease. As targets of selection, these variants are likely to encode phenotypes that manifest themselves physiologically and therefore may contribute to population-level variation in immune response. Our results shed light on past selective events influencing the host response to modern diseases, both pathogenic infection as well as autoimmune disorders.

The differential expression of toll like receptors and RIG-1 correlates to the severity of infectious diseases.

The toll like receptors (TLRs) and RIG-1 are proteins involved in the initial reaction of the innate immune system to infectious diseases and, thus, can provide much information to the surgical pathologist in terms of the molecular dynamics of the infection. The TLRs (TLR1, 2, 3, 4, 7, 8) and RIG-1 distribution as determined by immunohistochemistry was examined in the following diseases: human papillomavirus (n = 30 including 15 squamous intraepithelial lesions (SIL), 5 cancers, and 10 controls); molluscum contagiosum (n = 8 including 4 controls), SARS-CoV2 (n = 52 including 20 mild, 5 fatal, and 27 controls) and reovirus infection as oncolytic therapy. Mild, regressing infection (molluscum contagiosum, mild SARS-CoV2 and low grade SIL) each showed the same pattern: marked up regulation of at least three of the TLRs/RIG-1 with decreased expression of none compared to the controls. Severe infection (fatal SARS-CoV2, and cervical cancer) each showed marked decrease expression in at least three of the TLRs/RIG-1. We recently documented an equivalent marked decrease expression of the TLRs/RIG-1 in the placenta in fatal in utero infections. The reoviral infected tissues showed an overall pattern of marked increase expression of TLRs/RIG-1, consistent with a strong anti-viral response. Thus, the in situ testing of infectious diseases by a panel of these early infectious disease recognition proteins may allow the surgical pathologist to predict the outcome of the disease which, in turn, may assist in the understanding of the role of the TLRs/RIG-1 in determining the fate of a given infectious process.

Pathogen exposure misclassification can bias association signals in GWAS of infectious diseases when using population-based common control subjects.

Genome-wide association studies (GWASs) have been performed to identify host genetic factors for a range of phenotypes, including for infectious diseases. The use of population-based common control subjects from biobanks and extensive consortia is a valuable resource to increase sample sizes in the identification of associated loci with minimal additional expense. Non-differential misclassification of the outcome has been reported when the control subjects are not well characterized, which often attenuates the true effect size. However, for infectious diseases the comparison of affected subjects to population-based common control subjects regardless of pathogen exposure can also result in selection bias. Through simulated comparisons of pathogen-exposed cases and population-based common control subjects, we demonstrate that not accounting for pathogen exposure can result in biased effect estimates and spurious genome-wide significant signals. Further, the observed association can be distorted depending upon strength of the association between a locus and pathogen exposure and the prevalence of pathogen exposure. We also used a real data example from the hepatitis C virus (HCV) genetic consortium comparing HCV spontaneous clearance to persistent infection with both well-characterized control subjects and population-based common control subjects from the UK Biobank. We find biased effect estimates for known HCV clearance-associated loci and potentially spurious HCV clearance associations. These findings suggest that the choice of control subjects is especially important for infectious diseases or outcomes that are conditional upon environmental exposures.