Hospital-treated infectious diseases, infection burden and risk of lung cancer: an observational and Mendelian randomisation study.

BACKGROUND: Although infections play a role in the development of lung cancer, the longitudinal association between infection and the risk of lung cancer is disputed and data relating to pathogen types and infection sites is sparse. RESEARCH QUESTION: How do infections impact subsequent lung cancer risk and whether the impact is limited to specific microbes rather than infection burden? METHODS: We ascertained 900+ infectious diseases from the UK Biobank study. Short- and long-term effect of infections was assessed using time-varying Cox proportional hazard models. The analysis was repeated, excluding patients with concurrent multi-pathogen infections or outcomes within the ten years after initial hospitalization for the index infection. Life table was used to estimate years of life lost from lung cancer. Infection burden was defined as the sum of the number of infection episodes over time and co-occurring infections. The genome-wide association studies (GWAS) used in two-sample Mendelian randomization (MR) were obtained from mostly European ancestry. RESULTS: Hospital-treated infectious disease was associated with a greater risk of lung cancer (adjusted HR [aHR] 1.79 [95% CI 1.74-1.83]). aHRs for lung cancer ranged from 1.39 to 2.82 across pathogen types. The impact of lower respiratory tract infections (LRTIs) on lung cancer was the strongest, with an aHR of 3.22 [95% CI 2.64-3.92], while the aHR for extra-LRTIs was 1.29 [1.16-1.44]). A dose-response association was observed between infection burden and lung cancer risk across different FEV1% predicted (p-trend <0.001). Multiple infections led to a significant life lost from lung cancer at the age of 50. MR analysis reaffirmed the causal association. INTERPRETATION: Both observational and genetic analyses suggested that infectious diseases could increase the risk of lung cancer. The dual perspective on the LRTIs and extra-LRTIs impacts may inform lung cancer preventive strategies.

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.

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.

Deciphering the pathogenic risks of microplastics as emerging particulate organic matter in aquatic ecosystem.

Microplastics are accumulating rapidly in aquatic ecosystems, providing habitats for pathogens and vectors for antibiotic resistance genes (ARGs), potentially increasing pathogenic risks. However, few studies have considered microplastics as particulate organic matter (POM) to elucidate their pathogenic risks and underlying mechanisms. Here, we performed microcosm experiments with microplastics and natural POM (leaves, algae, soil), thoroughly investigating their distinct effects on the community compositions, functional profiles, opportunistic pathogens, and ARGs in Particle-Associated (PA) and Free-Living (FL) bacterial communities. We found that both microplastics and leaves have comparable impacts on microbial community structures and functions, enriching opportunistic pathogens and ARGs, which may pose potential environmental risks. These effects are likely driven by their influences on water properties, including dissolved organic carbon, nitrate, DO, and pH. However, microplastics uniquely promoted pathogens as keystone species and further amplified their capacity as hosts for ARGs, potentially posing a higher pathogenic risk than natural POM. Our research also emphasized the importance of considering both PA and FL bacteria when assessing microplastic impacts, as they exhibited different responses. Overall, our study elucidates the role and underlying mechanism of microplastics as an emerging POM in intensifying pathogenic risks of aquatic ecosystems in comparison with conventional natural POM.

Identifications of novel host cell factors that interact with the receptor-binding domain of the SARS-CoV-2 spike protein.

SARS-CoV-2 entry into host cells is facilitated by the interaction between the receptor-binding domain of its spike protein (CoV2-RBD) and host cell receptor, ACE2, promoting viral membrane fusion. The virus also uses endocytic pathways for entry, but the mediating host factors remain largely unknown. It is also unknown whether mutations in the RBD of SARS-CoV-2 variants promote interactions with additional host factors to promote viral entry. Here, we used the GST pull-down approach to identify novel surface-located host factors that bind to CoV2-RBD. One of these factors, SH3BP4, regulates internalization of CoV2-RBD in an ACE2-independent but integrin- and clathrin-dependent manner and mediates SARS-CoV-2 pseudovirus entry, suggesting that SH3BP4 promotes viral entry via the endocytic route. Many of the identified factors, including SH3BP4, ADAM9, and TMEM2, show stronger affinity to CoV2-RBD than to RBD of the less infective SARS-CoV, suggesting SARS-CoV-2-specific utilization. We also found factors preferentially binding to the RBD of the SARS-CoV-2 Delta variant, potentially enhancing its entry. These data identify the repertoire of host cell surface factors that function in the events leading to the entry of SARS-CoV-2.

Neural Circuitry Polarization in the Spinal Dorsal Horn (SDH): A Novel Form of Dysregulated Circuitry Plasticity during Pain Pathogenesis.

Pathological pain emerges from nociceptive system dysfunction, resulting in heightened pain circuit activity. Various forms of circuitry plasticity, such as central sensitization, synaptic plasticity, homeostatic plasticity, and excitation/inhibition balance, contribute to the malfunction of neural circuits during pain pathogenesis. Recently, a new form of plasticity in the spinal dorsal horn (SDH), named neural circuit polarization (NCP), was discovered in pain models induced by HIV-1 gp120 and chronic morphine administration. NCP manifests as an increase in excitatory postsynaptic currents (EPSCs) in excitatory neurons and a decrease in EPSCs in inhibitory neurons, presumably facilitating hyperactivation of pain circuits. The expression of NCP is associated with astrogliosis. Ablation of reactive astrocytes or suppression of astrogliosis blocks NCP and, concomitantly, the development of gp120- or morphine-induced pain. In this review, we aim to compare and integrate NCP with other forms of plasticity in pain circuits to improve the understanding of the pathogenic contribution of NCP and its cooperation with other forms of circuitry plasticity during the development of pathological pain.

Association of hospital-treated infectious diseases and infection burden with cardiovascular diseases and life expectancy.

BACKGROUND: The association of a broad spectrum of infectious diseases with cardiovascular outcomes remains unclear. OBJECTIVES: We aim to provide the cardiovascular risk profiles associated with a wide range of infectious diseases and explore the extent to which infections reduce life expectancy. METHODS: We ascertained exposure to 900+ infectious diseases before cardiovascular disease (CVD) onset in 453,102 participants from the UK Biobank study. Time-varying Cox proportional hazard models were used. Life table was used to estimate the life expectancy of individuals aged ≥50 with different levels of infection burden (defined as the number of infection episodes over time and the number of co-occurring infections). RESULTS: Infectious diseases were associated with a greater risk of CVD events (adjusted HR [aHR] 1.79 [95% confidence interval {CI} 1.74-1.83]). For type-specific analysis, bacterial infection with sepsis had the strongest risk of CVD events [aHR 4.76 (4.35-5.20)]. For site-specific analysis, heart and circulation infections posed the greatest risk of CVD events [aHR 4.95 (95% CI 3.77-6.50)], whereas noncardiac infections also showed excess risk [1.77 (1.72-1.81)]. Synergistic interactions were observed between infections and genetic risk score. A dose-response relationship was found between infection burden and CVD risks (p-trend <0.001). Infection burden >1 led to a CVD-related life loss at age 50 by 9.3 years [95% CI 8.6-10.3]) for men and 6.6 years [5.5-7.8] for women. CONCLUSIONS: The magnitude of the infection-CVD association showed specificity in sex, pathogen type, infection burden, and infection site. High genetic risk and infection synergistically increased the CVD risk.

Employing bimodal representations to predict DNA bendability within a self-supervised pre-trained framework.

The bendability of genomic DNA, which measures the DNA looping rate, is crucial for numerous biological processes of DNA. Recently, an advanced high-throughput technique known as 'loop-seq' has made it possible to measure the inherent cyclizability of DNA fragments. However, quantifying the bendability of large-scale DNA is costly, laborious, and time-consuming. To close the gap between rapidly evolving large language models and expanding genomic sequence information, and to elucidate the DNA bendability's impact on critical regulatory sequence motifs such as super-enhancers in the human genome, we introduce an innovative computational model, named MIXBend, to forecast the DNA bendability utilizing both nucleotide sequences and physicochemical properties. In MIXBend, a pre-trained language model DNABERT and convolutional neural network with attention mechanism are utilized to construct both sequence- and physicochemical-based extractors for the sophisticated refinement of DNA sequence representations. These bimodal DNA representations are then fed to a k-mer sequence-physicochemistry matching module to minimize the semantic gap between each modality. Lastly, a self-attention fusion layer is employed for the prediction of DNA bendability. In conclusion, the experimental results validate MIXBend's superior performance relative to other state-of-the-art methods. Additionally, MIXBend reveals both novel and known motifs from the yeast. Moreover, MIXBend discovers significant bendability fluctuations within super-enhancer regions and transcription factors binding sites in the human genome.