The relationship between mental health problems and risk of infectious diseases: A Mendelian randomization analysis.

The causal effects of mental health problems on the risk of infectious diseases remain vague. Investigating them via observational study is challenging as it presents possible confounding factors. Therefore, the objective of this study was to utilize Mendelian randomization (MR) techniques to evaluate the causal relationship between mental health problems and the risk of infectious diseases. Multivariable MR analyses were performed using genome-wide association data for sleep disorders (N = 216,700), depression (N = 500,199), anxiety (N = 290,361), nervous feelings (N = 450,700), unspecified mental disorder (N = 218,792), pneumonia (N = 486,484), skin and subcutaneous tissue infection (SSTI; N = 218,792), intestinal infectious diseases (IIDs; N = 218,792), urinary tract infection (N = 463,010), and central nervous system (CNS) infections (N = 218,792) among individuals of European ancestry. Independent genetic variants significantly (P < 10-8) associated with each exposure were considered instruments. The primary analysis used an inverse variance-weighted method, followed by a series of sensitivity analyses. Genetically predicted sleep disorders were associated with an increased risk of SSTI (odds ratio [OR], 1.29 [95% confidence interval (CI), 1.05-1.59]; P = .017). Genetically predicted depression was linked with an increased risk of CNS infections (OR, 1.59 [95% CI, 1.00-2.53]; P = .049) and SSTI (1.24 [95% CI, 1.03-1.49]; P = .024). Genetically predicted anxiety was associated with IIDs (OR, 1.19 [95% CI, 1.03-1.37]; P = .017) and SSTI (OR, 1.21 [95% CI, 1.02-1.43]; P = .029). There was no significant causal evidence for genetic prediction of nervous feelings and unspecified mental disorders in IIDs, CNS infections, SSTI, pneumonia, or urinary tract infection. Sensitivity analyses showed that the above causal association estimates were robust. In this MR study, we demonstrated a causal relationship between sleep disorders, depression, anxiety, and the risk of infectious diseases. However, no evidence was found to support causality between nervous feelings, unspecified mental disorders, and the risk of infectious diseases.

Insights into the genetic theory of infectious diseases.

Over the past century, classical approaches from microbiology and immunology have produced spectacular results in the control of infectious diseases. However, the recent SARS-COV-2 pandemic has highlighted our continued failure to control some infections. Other microorganisms still pose a threat to humanity such as HIV, Ebola, and influenza viruses. It seems that conventional approaches are not able to solve all the current problems caused by infectious diseases. Human genetics has shown that infections have a strong genetic determinism that can lead to a predisposition or resistance to infections. This explains much of the clinical variability observed in individuals infected with the same pathogen. The identification of the genetic etiology allows a better understanding of the pathogenesis of infectious diseases and, consequently, the consideration of appropriate preventive and therapeutic strategies. This review provides insights into the genetic theory and the concrete evidence to support it. We highlight the role of primary immunodeficiencies in the discovery of Mendelian and monogenic susceptibility to infections, then we show how genetic and phenotypic heterogeneity, redundancy, and resistance to infection manifest in the context of this genetic determinism. To effectively combat the constant threat of microbes, it is essential to integrate human genetics with microbiology to examine the interactions between pathogens and our immune system.

Clinical diagnostic value of targeted next­generation sequencing for infectious diseases (Review).

As sequencing technology transitions from research to clinical settings, due to technological maturity and cost reductions, metagenomic next­generation sequencing (mNGS) is increasingly used. This shift underscores the growing need for more cost­effective and universally accessible sequencing assays to improve patient care and public health. Therefore, targeted NGS (tNGS) is gaining prominence. tNGS involves enrichment of target pathogens in patient samples based on multiplex PCR amplification or probe capture with excellent sensitivity. It is increasingly used in clinical diagnostics due to its practicality and efficiency. The present review compares the principles of different enrichment methods. The high positivity rate of tNGS in the detection of pathogens was found in respiratory samples with specific instances. tNGS maintains high sensitivity (70.8­95.0%) in samples with low pathogen loads, including blood and cerebrospinal fluid. Furthermore, tNGS is effective in detecting drug­resistant strains of Mycobacterium tuberculosis, allowing identification of resistance genes and guiding clinical treatment decisions, which is difficult to achieve with mNGS. In the present review, the application of tNGS in clinical settings and its current limitations are assessed. The continued development of tNGS has the potential to refine diagnostic accuracy and treatment efficacy and improving infectious disease management. However, further research to overcome technical challenges such as workflow time and cost is required.

Hospital-treated infectious diseases, genetic susceptibility and risk of type 2 diabetes: A population-based longitudinal study.

BACKGROUND: The longitudinal association between infectious diseases and the risk of type 2 diabetes (T2D) remains unclear. METHODS: Based on the UK Biobank, the prospective cohort study included a total of 396,080 participants without diabetes at baseline. We determined the types and sites of infectious diseases and incident T2D using the International Classification of Diseases 10th Revision codes (ICD-10). Time-varying Cox proportional hazard model was used to assess the association. Infection burden was defined as the number of infection episodes over time and the number of co-occurring infections. Genetic risk score (GRS) for T2D consisted of 424 single nucleotide polymorphisms. RESULTS: During a median of 9.04 [IQR, 8.3-9.7] years of follow-up, hospital-treated infectious diseases were associated with a greater risk of T2D (adjusted HR [aHR] 1.54 [95 % CI 1.46-1.61]), with risk difference per 10,000 individuals equal to 154.1 [95 % CI 140.7-168.2]. The heightened risk persisted after 5 years following the index infection. Bacterial infection with sepsis had the strongest risk of T2D (aHR 2.95 [95 % CI 2.53-3.44]) among different infection types. For site-specific analysis, bloodstream infections posed the greatest risk (3.01 [95 % CI 2.60-3.48]). A dose-response association was observed between infection burden and T2D risk within each GRS tertile (p-trend <0.001). High genetic risk and infection synergistically increased the T2D risk. CONCLUSION: Infectious diseases were associated with an increased risk of subsequent T2D. The risk showed specificity according to types, sites, severity of infection and the period since infection occurred. A potential accumulative effect of infection was revealed.

Depression and risk of infectious diseases: A mendelian randomization study.

Previous observational inquiries have revealed a correlation between depression and infectious maladies. This study seeks to elucidate the causal linkages between depression, specifically Major Depressive Disorder (MDD), and infectious diseases. Nevertheless, the causative nature of the association between MDD and infectious diseases remains elusive. Two-sample Mendelian Randomization (MR) analyses was executed utilizing single nucleotide polymorphisms (SNPs) significantly connected with MDD and infectious diseases as instrumental variables (IVs). A series of sensitivity analyses were subsequently conducted. Genetic variants linked to MDD were employed as instrumental variables sourced from a genome-wide meta-analyses comprising 500,199 individuals. Summary-level data on five infectious diseases, including candidiasis, pneumonia, skin and soft tissue infections (SSTI), upper respiratory tract infections (URTI), and urinary tract infections (UTI), were acquired from the UK Biobank and FinnGen study. Our findings evinced that genetically predicted MDD exhibited a heightened risk of candidiasis (OR = 1.52, 95% CI 1.06-2.17; P = 2.38E-02), pneumonia (OR = 1.14, 95% CI 1.01-1.29; P = 3.16E-02), URTI (OR = 1.23, 95% CI 1.12-1.36; P = 3.71E-05), and UTI (OR = 1.26, 95% CI 1.12-1.42; P = 8.90E-05). Additionally, we identified bidirectional causal relationships between UTI and MDD. The associations between MDD and the risk of URTI and UTI remained consistent in multivariable MR analyses, accounting for genetically predicted smoking and body mass index. In conclusion, this investigation ascertained a causal connection between MDD and the susceptibility to infectious diseases, particularly URTI and UTI.

Hospital-treated infectious diseases, infection burden and risk of Parkinson disease: An observational and Mendelian randomization study.

BACKGROUND: Experimental and cross-sectional evidence has suggested a potential role of infection in the ethology of Parkinson's disease (PD). We aim to examine the longitudinal association of infections with the incidence of PD and to explore whether the increased risk is limited to specific infection type rather than infection burden. METHODS: Based on the UK Biobank, hospital-treated infectious diseases and incident PD were ascertained through record linkage to national hospital inpatient registers. Infection burden was defined as the sum of the number of infection episodes over time and the number of co-occurring infections. The polygenic risk score (PRS) for PD was calculated. The genome-wide association studies (GWAS) used in two-sample Mendelian Randomization (MR) were obtained from observational cohort participants of mostly European ancestry. RESULTS: Hospital-treated infectious diseases were associated with an increased risk of PD (adjusted HR [aHR] 1.35 [95 % CI 1.20-1.52]). This relationship persisted when analyzing new PD cases occurring more than 10 years post-infection (aHR 1.22 [95 % CI 1.04-1.43]). The greatest PD risk was observed in neurological/eye infection (aHR 1.72 [95 % CI 1.32-2.34]), with lower respiratory tract infection (aHR 1.43 [95 % CI 1.02-1.99]) ranked the second. A dose-response association was observed between infection burden and PD risk within each PD-PRS tertile (p-trend < 0.001). Multivariable MR showed that bacterial and viral infections increase the PD risk. CONCLUSIONS: Both observational and genetic analysis suggested a causal association between infections and the risk of developing PD. A dose-response relationship between infection burden and incident PD was revealed.

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.

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.

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.

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.

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.

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.

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.

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.

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.