Global pattern and determinant for interaction of seasonal influenza viruses.

BACKGROUND: The prevalence of different types/subtypes varies across seasons and countries for seasonal influenza viruses, indicating underlying interactions between types/subtypes. The global interaction patterns and determinants for seasonal influenza types/subtypes need to be explored. METHODS: Influenza epidemiological surveillance data, as well as multidimensional data that include population-related, environment-related, and virus-related factors from 55 countries worldwide were used to explore type/subtype interactions based on Spearman correlation coefficient. The machine learning method Extreme Gradient Boosting (XGBoost) and interpretable framework SHapley Additive exPlanation (SHAP) were utilized to quantify contributing factors and their effects on interactions among influenza types/subtypes. Additionally, causal relationships between types/subtypes were also explored based on Convergent Cross-mapping (CCM). RESULTS: A consistent globally negative correlation exists between influenza A/H3N2 and A/H1N1. Meanwhile, interactions between influenza A (A/H3N2, A/H1N1) and B show significant differences across countries, primarily influenced by population-related factors. Influenza A has a stronger driving force than influenza B, and A/H3N2 has a stronger driving force than A/H1N1. CONCLUSION: The research elucidated the globally complex and heterogeneous interaction patterns among influenza type/subtypes, identifying key factors shaping their interactions. This sheds light on better seasonal influenza prediction and model construction, informing targeted prevention strategies and ultimately reducing the global burden of seasonal influenza.

DiffDomain enables identification of structurally reorganized topologically associating domains.

Topologically associating domains (TADs) are critical structural units in three-dimensional genome organization of mammalian genome. Dynamic reorganizations of TADs between health and disease states are associated with essential genome functions. However, computational methods for identifying reorganized TADs are still in the early stages of development. Here, we present DiffDomain, an algorithm leveraging high-dimensional random matrix theory to identify structurally reorganized TADs using high-throughput chromosome conformation capture (Hi-C) contact maps. Method comparison using multiple real Hi-C datasets reveals that DiffDomain outperforms alternative methods for false positive rates, true positive rates, and identifying a new subtype of reorganized TADs. Applying DiffDomain to Hi-C data from different cell types and disease states demonstrates its biological relevance. Identified reorganized TADs are associated with structural variations and epigenomic changes such as changes in CTCF binding sites. By applying to a single-cell Hi-C data from mouse neuronal development, DiffDomain can identify reorganized TADs between cell types with reasonable reproducibility using pseudo-bulk Hi-C data from as few as 100 cells per condition. Moreover, DiffDomain reveals differential cell-to-population variability and heterogeneous cell-to-cell variability in TADs. Therefore, DiffDomain is a statistically sound method for better comparative analysis of TADs using both Hi-C and single-cell Hi-C data.

Resurgence of influenza during COVID-19 in Chongqing, China: A retrospective analysis.

To better understand the trends of influenza and the impact of public health and social measures (PHSMs) implemented during the coronavirus disease 2019 (COVID-19) period in Chongqing, China. Data from the China Influenza Surveillance Information System from January 2017 to June 2022 were extracted. Epidemiological characteristics (influenza-like illness [ILI] and ILI%) and virological characteristics (influenza positive rate and circulating (sub)types) of influenza were described and compared between the pre-COVID-19 period and the COVID-19 period. Our survey showed that the implementation of PHSMs during the COVID-19 period had a positive impact on reducing influenza transmission. However, influenza activity resurged in 2021-2022 as the PHSMs were eased. Children under 5 years old constituted the highest proportion of ILI cases. The overall influenza positive rate was 23.70%, with a higher rate observed during the pre-COVID-19 period (31.55%) compared to the COVID-19 period (13.68%). Influenza virus subtypes co-circulated and the predominant subtype varied each year, with influenza A subtypes predominated in 2018/2019, while influenza B/Victoria lineage dominated in 2020/2021. PHSMs are effective measures to mitigate the spread of influenza. The findings underscore the need for bolstering monitoring systems, advocating influenza vaccination, and implementing practical PHSMs to strengthen prevention and control measures against influenza.

Investigating linear and nonlinear associations of LDL cholesterol with incident chronic kidney disease, atherosclerotic cardiovascular disease and all-cause mortality: A prospective and Mendelian randomization study.

BACKGROUND AND AIMS: Observational studies suggest potential nonlinear associations of low-density lipoprotein cholesterol (LDL-C) with cardio-renal diseases and mortality, but the causal nature of these associations is unclear. We aimed to determine the shape of causal relationships of LDL-C with incident chronic kidney disease (CKD), atherosclerotic cardiovascular disease (ASCVD) and all-cause mortality, and to evaluate the absolute risk of adverse outcomes contributed by LDL-C itself. METHODS: Observational analysis and one-sample Mendelian randomization (MR) with linear and nonlinear assumptions were performed using the UK Biobank of >0.3 million participants with no reported prescription of lipid-lowering drugs. Two-sample MR on summary-level data from the Global Lipid Genetics Consortium (N = 296,680) and the CKDGen (N = 625,219) was employed to replicate the relationship for kidney traits. The 10-year probabilities of the outcomes was estimated by integrating the MR and Cox models. RESULTS: Observationally, participants with low LDL-C were significantly associated with a decreased risk of ASCVD, but an increased risk of CKD and all-cause mortality. Univariable MR showed an inverse total effect of LDL-C on incident CKD (HR [95% CI]:0.84 [0.73-0.96]; p = 0.011), a positive effect on ASCVD (1.41 [1.29-1.53]; p<0.001), and no significant causal effect on all-cause mortality. Multivariable MR, controlling for high-density lipoprotein cholesterol (HDL-C) and triglycerides, identified a positive direct effect on ASCVD (1.32 [1.18-1.47]; p<0.001), but not on CKD and all-cause mortality. These results indicated that genetically predicted low LDL-C had an inverse indirect effect on CKD mediated by HDL-C and triglycerides, which was validated by a two-sample MR analysis using summary-level data from the Global Lipid Genetics Consortium (N = 296,680) and the CKDGen consortium (N = 625,219). Suggestive evidence of a nonlinear causal association between LDL-C and CKD was found. The 10-year probability curve showed that LDL-C concentrations below 3.5 mmol/L were associated with an increased risk of CKD. CONCLUSIONS: In the general population, lower LDL-C was causally associated with lower risk of ASCVD, but appeared to have a trade-off for an increased risk of CKD, with not much effect on all-cause mortality. LDL-C concentration below 3.5 mmol/L may increase the risk of CKD.

Non-linear association between daily mean temperature and children's hand foot and mouth disease in Chongqing, China.

Chongqing was seriously affected by hand, foot, and mouth disease (HFMD), but the relationships between daily mean temperature and the incidence of HFMD remain unclear. This study used distributed lag nonlinear model to evaluate the effect of daily mean temperature on the incidence of HFMD in children aged < 5 years in Chongqing. Daily HFMD data from 2012 to 2019 in Chongqing were retrieved from the notifiable infectious disease surveillance system. A total of 413,476 HFMD cases aged < 5 years were reported in Chongqing from 2012 to 2019. The exposure-response curve of daily mean temperature and daily HFMD cases was wavy-shaped. The relative risks (RRs) increased as daily mean temperature below 5.66 °C or above 9.43 °C, with two peaks at 16.10 °C and 26.68 °C. The RRs reached the highest when the daily mean temperature at 26.68 °C on the current day (RR = 1.20, 95% CI 1.09-1.32), followed by the daily mean temperature at 16.10 °C at lag 5 days (RR = 1.07, 95% CI 1.05-1.08). The RRs for girls and daycare children were much higher than those for boys and scattered children, respectively. Taken together, daily mean temperature has strong effect on HFMD in children aged < 5 years old in Chongqing, particularly for girls and daycare children.

Dysbiosis of oropharyngeal microbiome and antibiotic resistance in hospitalized COVID-19 patients.

The severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) pandemic is ongoing and multiple studies have elucidated its pathogenesis, however, the related- microbiome imbalance caused by SARS-CoV-2 is still not clear. In this study, we have comprehensively compared the microbiome composition and associated function alterations in the oropharyngeal swabs of healthy controls and coronavirus disease 2019 (COVID-19) patients with moderate or severe symptoms by metatranscriptomic sequencing. We did observe a reduced microbiome alpha-diversity but significant enrichment of opportunistic microorganisms in patients with COVID-19 compared with healthy controls, and the microbial homeostasis was rebuilt following the recovery of COVID-19 patients. Correspondingly, less functional genes in multiple biological processes and weakened metabolic pathways such as carbohydrate metabolism, energy metabolism were also observed in COVID-19 patients. We only found higher relative abundance of limited genera such as Lachnoanaerobaculum between severe patients and moderate patients while no worthy-noting microbiome diversity and function alteration were observed. Finally, we noticed that the co-occurrence of antibiotic resistance and virulence was closely related to the microbiome alteration caused by SRAS-CoV-2. Overall, our findings demonstrate that microbial dysbiosis may enhance the pathogenesis of SARS-CoV-2 and the antibiotics treatment should be critically considered.

Novel Network Method Major Minor Variation Clustering Enables Identification of Poliovirus Clusters with High-Resolution Linkages.

The Global Polio Eradication Initiative uses an outbreak response protocol that defines type 2 Sabin or Sabin-like virus as those with 0-5 nucleotides diverging from their parental strain in the complete VP1 genomic region. Sabin or Sabin-like viruses share highly similar genome sequences, regardless of their origin. Thus, it is challenging to distinguish viruses at a higher resolution to detect polio clusters or trace sources for local transmissions of viruses at an early stage. To identify type 2 Sabin or Sabin-like sources and improve our ability to map viral sources to campaigns during the polio endgame, we investigated the feasibility of a new method for genetic sequence analysis. We named the method Major Minor Variation Clustering (MMVC), which uses a network model to simultaneously incorporate sequence similarity in major and minor variants in addition to onset dates to detect fine-scale polio clusters. Each identified cluster represents a collection of sequences that are highly similar in both major and minor variants, enabling the discovery of new links between viruses. By applying the method to a published data set collected in Nigeria during 2009-2012, we found that clusters identified using this method have several improvements over clusters derived from a phylogenetic tree approach. Integrative data analysis reveals that sequences in the same cluster have greater genomic similarities and better agreement with onset dates. As a complement to current phylogenetic tree approaches, MMVC has the potential to improve epidemiological surveillance and investigation precision to guide polio eradication.

Vaccination strategy for preventing the spread of SARS-CoV-2 in the limited supply condition: A mathematical modeling study.

To mitigate SARS-CoV-2 transmission, vaccines have been urgently approved. With their limited availability, it is critical to distribute the vaccines reasonably. We simulated the SARS-CoV-2 transmission for 365 days over four intervention periods: free transmission, structural mitigation, personal mitigation, and vaccination. Sensitivity analyses were performed to obtain robust results. We further evaluated two proposed vaccination allocations, including one-dose-high-coverage and two-doses-low-coverage, when the supply was low. 33.35% (infection rate, 2.68 in 10 million people) and 40.54% (2.36) of confirmed cases could be avoided as the nonpharmaceutical interventions (NPIs) adherence rate rose from 50% to 70%. As the vaccination coverage reached 60% and 80%, the total infections could be reduced by 32.72% and 41.19%, compared to the number without vaccination. When the durations of immunity were 90 and 120 days, the infection rates were 2.67 and 2.38. As the asymptomatic infection rate rose from 30% to 50%, the infection rate increased 0.92 (SD, 0.16) times. Conditioned on 70% adherence rate, with the same amount of limited available vaccines, the 20% and 40% vaccination coverage of one-dose-high-coverage, the infection rates were 2.70 and 2.35; corresponding to the two-doses-low-coverage with 10% and 20% vaccination coverage, the infection rates were 3.22 and 2.92. Our results indicated as the duration of immunity prolonged, the second wave of SARS-CoV-2 would be delayed and the scale would be declined. On average, the total infections in two-doses-low-coverage was 1.48 times (SD, 0.24) as high as that in one-dose-high-coverage. It is crucial to encourage people in order to improve vaccination coverage and establish immune barriers. Particularly when the supply is limited, a wiser strategy to prevent SARS-CoV-2 is equally distributing doses to the same number of individuals. Besides vaccination, NPIs are equally critical to the prevention of widespread of SARS-CoV-2.

Drivers and forecasts of multiple waves of the coronavirus disease 2019 pandemic: A systematic analysis based on an interpretable machine learning framework.

Coronavirus disease 2019 (COVID-19) has become a global pandemic and continues to prevail with multiple rebound waves in many countries. The driving factors for the spread of COVID-19 and their quantitative contributions, especially to rebound waves, are not well studied. Multidimensional time-series data, including policy, travel, medical, socioeconomic, environmental, mutant and vaccine-related data, were collected from 39 countries up to 30 June 2021, and an interpretable machine learning framework (XGBoost model with Shapley Additive explanation interpretation) was used to systematically analyze the effect of multiple factors on the spread of COVID-19, using the daily effective reproduction number as an indicator. Based on a model of the pre-vaccine era, policy-related factors were shown to be the main drivers of the spread of COVID-19, with a contribution of 60.81%. In the post-vaccine era, the contribution of policy-related factors decreased to 28.34%, accompanied by an increase in the contribution of travel-related factors, such as domestic flights, and contributions emerged for mutant-related (16.49%) and vaccine-related (7.06%) factors. For single-peak countries, the dominant ones were policy-related factors during both the rising and fading stages, with overall contributions of 33.7% and 37.7%, respectively. For double-peak countries, factors from the rebound stage contributed 45.8% and policy-related factors showed the greatest contribution in both the rebound (32.6%) and fading (25.0%) stages. For multiple-peak countries, the Delta variant, domestic flights (current month) and the daily vaccination population are the three greatest contributors (8.12%, 7.59% and 7.26%, respectively). Forecasting models to predict the rebound risk were built based on these findings, with accuracies of 0.78 and 0.81 for the pre- and post-vaccine eras, respectively. These findings quantitatively demonstrate the systematic drivers of the spread of COVID-19, and the framework proposed in this study will facilitate the targeted prevention and control of the ongoing COVID-19 pandemic.

Effect of absolute humidity on influenza activity across different climate regions in China.

Until now, we have no thorough understanding the role of absolute humidity on influenza activity, especially in tropical and subtropical areas. In this study, we investigated the relationship between absolute humidity and influenza activity in seven municipalities/provinces covering different climatic zones in China. Weekly meteorological data and influenza surveillance data in seven provinces/municipalities in China were collected from January 2012 to December 2019. A distributed lag nonlinear model was adopted to investigate the association between absolute humidity (AH) and influenza activity in each study site. Then, seven study sites were grouped into three regions: northern, intermediate, and southernmost regions. A multivariate meta-analysis was applied to estimate the exposure-lag-response associations in three regions. The province-specific or municipality-specific curves appeared to be nonlinear, and the association between influenza activity and AH varied across regions. In Beijing and Tianjin, located in northern China, the cumulative relative risks (RRs) increased as weekly average AHmean fell below 3.41 g/m3 and 6.62 g/m3. In Guangdong and Hainan, located in southernmost China, the risk of influenza activity increased with rising average AHmean with 16.74 g/m3 and 20.18 g/m3 as the break points. In Shanghai, Zhejiang, and Chongqing, the relationship between weekly average AHmean and influenza could be described as U-shaped curves, with the lowest RRs when weekly average AHmean was 11.95 g/m3, 11.94 g/m3, and 15.96 g/m3, respectively. Meta-analysis results showed the cumulative RRs significantly increased as weekly average AHmean fell below 3.86 g/m3 in the northern region, whereas significantly increased as weekly average AHmean rose above 18.46 g/m3 and 15.22 g/m3 in intermediate and southernmost regions, respectively. Both low and high AH might increase influenza risk in China, and the relationship varies geographically. Our findings suggest that public health policies for climate change adaptation should be tailored to the local climate conditions.

Heavy chain sequence-based classifier for the specificity of human antibodies.

Antibodies specifically bind to antigens and are an essential part of the immune system. Hence, antibodies are powerful tools in research and diagnostics. High-throughput sequencing technologies have promoted comprehensive profiling of the immune repertoire, which has resulted in large amounts of antibody sequences that remain to be further analyzed. In this study, antibodies were downloaded from IMGT/LIGM-DB and Sequence Read Archive databases. Contributing features from antibody heavy chains were formulated as numerical inputs and fed into an ensemble machine learning classifier to classify the antigen specificity of six classes of antibodies, namely anti-HIV-1, anti-influenza virus, anti-pneumococcal polysaccharide, anti-citrullinated protein, anti-tetanus toxoid and anti-hepatitis B virus. The classifier was validated using cross-validation and a testing dataset. The ensemble classifier achieved a macro-average area under the receiver operating characteristic curve (AUC) of 0.9246 from the 10-fold cross-validation, and 0.9264 for the testing dataset. Among the contributing features, the contribution of the complementarity-determining regions was 53.1% and that of framework regions was 46.9%, and the amino acid mutation rates occupied the first and second ranks among the top five contributing features. The classifier and insights provided in this study could promote the mechanistic study, isolation and utilization of potential therapeutic antibodies.

Susceptibility identification for seasonal influenza A/H3N2 based on baseline blood transcriptome.

Introduction: Influenza susceptibility difference is a widely existing trait that has great practical significance for the accurate prevention and control of influenza. Methods: Here, we focused on the human susceptibility to the seasonal influenza A/H3N2 of healthy adults at baseline level. Whole blood expression data for influenza A/H3N2 susceptibility from GEO were collected firstly (30 symptomatic and 19 asymptomatic). Then to explore the differences at baseline, a suite of systems biology approaches - the differential expression analysis, co-expression network analysis, and immune cell frequencies analysis were utilized. Results: We found the baseline condition, especially immune condition between symptomatic and asymptomatic, was different. Co-expression module that is positively related to asymptomatic is also related to immune cell type of naïve B cell. Function enrichment analysis showed significantly correlation with "B cell receptor signaling pathway", "immune response-activating cell surface receptor signaling pathway" and so on. Also, modules that are positively related to symptomatic are also correlated to immune cell type of neutrophils, with function enrichment analysis showing significantly correlations with "response to bacterium", "inflammatory response", "cAMP-dependent protein kinase complex" and so on. Responses of symptomatic and asymptomatic hosts after virus exposure show differences on resisting the virus, with more effective frontline defense for asymptomatic hosts. A prediction model was also built based on only baseline transcription information to differentiate symptomatic and asymptomatic population with accuracy of 0.79. Discussion: The results not only improve our understanding of the immune system and influenza susceptibility, but also provide a new direction for precise and targeted prevention and therapy of influenza.

Effect of meteorological factors on the activity of influenza in Chongqing, China, 2012-2019.

BACKGROUND: The effects of multiple meteorological factors on influenza activity remain unclear in Chongqing, the largest municipality in China. We aimed to fix this gap in this study. METHODS: Weekly meteorological data and influenza surveillance data in Chongqing were collected from 2012 to 2019. Distributed lag nonlinear models (DLNMs) were conducted to estimate the effects of multiple meteorological factors on influenza activity. RESULTS: Inverted J-shaped nonlinear associations between mean temperature, absolute humidity, wind speed, sunshine and influenza activity were found. The relative risks (RRs) of influenza activity increased as weekly average mean temperature fell below 18.18°C, average absolute humidity fell below 12.66 g/m3, average wind speed fell below 1.55 m/s and average sunshine fell below 2.36 hours. Taking the median values as the references, lower temperature, lower absolute humidity and windless could significantly increase the risks of influenza activity and last for 4 weeks. A J-shaped nonlinear association was observed between relative humidity and influenza activity; the risk of influenza activity increased with rising relative humidity with 78.26% as the break point. Taking the median value as the reference, high relative humidity could increase the risk of influenza activity and last for 3 weeks. In addition, we found the relationship between aggregate rainfall and influenza activity could be described with a U-shaped curve. Rainfall effect has significantly higher RR than rainless effect. CONCLUSIONS: Our study shows that multiple meteorological factors have strong associations with influenza activity in Chongqing, providing evidence for developing a meteorology-based early warning system for influenza to facilitate timely response to upsurge of influenza activity.

MOCHI enables discovery of heterogeneous interactome modules in 3D nucleome.

The composition of the cell nucleus is highly heterogeneous, with different constituents forming complex interactomes. However, the global patterns of these interwoven heterogeneous interactomes remain poorly understood. Here we focus on two different interactomes, chromatin interaction network and gene regulatory network, as a proof of principle to identify heterogeneous interactome modules (HIMs), each of which represents a cluster of gene loci that is in spatial contact more frequently than expected and that is regulated by the same group of transcription factors. HIM integrates transcription factor binding and 3D genome structure to reflect "transcriptional niche" in the nucleus. We develop a new algorithm, MOCHI, to facilitate the discovery of HIMs based on network motif clustering in heterogeneous interactomes. By applying MOCHI to five different cell types, we found that HIMs have strong spatial preference within the nucleus and show distinct functional properties. Through integrative analysis, this work shows the utility of MOCHI to identify HIMs, which may provide new perspectives on the interplay between transcriptional regulation and 3D genome organization.

Identifying gene regulatory network rewiring using latent differential graphical models.

Gene regulatory networks (GRNs) are highly dynamic among different tissue types. Identifying tissue-specific gene regulation is critically important to understand gene function in a particular cellular context. Graphical models have been used to estimate GRN from gene expression data to distinguish direct interactions from indirect associations. However, most existing methods estimate GRN for a specific cell/tissue type or in a tissue-naive way, or do not specifically focus on network rewiring between different tissues. Here, we describe a new method called Latent Differential Graphical Model (LDGM). The motivation of our method is to estimate the differential network between two tissue types directly without inferring the network for individual tissues, which has the advantage of utilizing much smaller sample size to achieve reliable differential network estimation. Our simulation results demonstrated that LDGM consistently outperforms other Gaussian graphical model based methods. We further evaluated LDGM by applying to the brain and blood gene expression data from the GTEx consortium. We also applied LDGM to identify network rewiring between cancer subtypes using the TCGA breast cancer samples. Our results suggest that LDGM is an effective method to infer differential network using high-throughput gene expression data to identify GRN dynamics among different cellular conditions.

Plasma Metabonomic Profiling of Diabetic Retinopathy.

Diabetic retinopathy (DR) is the most common microvascular complication of diabetes and the leading cause of visual impairment in working-age adults. Patients with diabetes often develop DR despite appropriate control of systemic risk factors, suggesting the involvement of other pathogenic factors. We hypothesize that the plasma metabolic signature of DR is distinct and resolvable from that of diabetes alone. A nested population-based case-control metabonomic study was first performed on 40 DR cases and 40 control subjects with diabetes using gas chromatography-mass spectrometry. Eleven metabolites were found to be correlated with DR, and the majority were robust when adjusted for metabolic risk factors and confounding kidney disease. The metabolite markers 2-deoxyribonic acid; 3,4-dihydroxybutyric acid; erythritol; gluconic acid; and ribose were validated in an independent sample set with 40 DR cases, 40 control subjects with diabetes, and 40 individuals without diabetes. DR cases and control subjects with diabetes were matched by HbA1c in the validation set. Activation of the pentose phosphate pathway was identified from the list of DR metabolite markers. The identification of novel metabolite markers for DR provides insights into potential new pathogenic pathways for this microvascular complication and holds translational value in DR risk stratification and the development of new therapeutic measures.

Retinal Vein Occlusion in a Multi-Ethnic Asian Population: The Singapore Epidemiology of Eye Disease Study.

PURPOSE: To describe the prevalence of retinal vein occlusion (RVO) and its risk factors in a multi-ethnic Asian population. METHODS: This population-based study of 10,033 participants (75.7% response rate) included Chinese, Indian and Malay persons aged 40 years and older. A comprehensive ophthalmic examination, standardized interviews and laboratory blood tests were performed. Digital fundus photographs were assessed for presence of RVO following the definitions used in the Blue Mountains Eye Study. Regression analysis models were constructed to study the relationship between ocular and systemic factors and RVO. Age-specific prevalence rates of RVO were applied to project the number of people affected in Asia from 2013 to 2040. RESULTS: The overall crude prevalence of RVO was 0.72% (n = 71; 95% confidence interval, CI, 0.54-0.87%). The crude prevalence of RVO was similar in Chinese, Indian and Malay participants (p = 0.865). In multivariable regression models, significant risk factors of RVO included increased age (odds ratio, OR, 1.03, 95% CI 1.01-1.06), hypertension (OR 3.65, 95% CI 1.61-8.31), increased serum creatinine (OR 1.04, 95% CI 1.01-1.06, per 10 mmol/L increase), history of heart attack (OR 2.25, 95% CI 1.11-4.54) and increased total cholesterol (OR 1.31, 95% CI 1.07-1.59, per 1 mmol/L increase). None of the ocular parameters were associated with RVO. RVO is estimated to affect up to 16 and 21 million people in Asia by 2020 and 2040, respectively. CONCLUSION: RVO was detected in 0.72% of a multi-ethnic Asian population aged 40-80 years in Singapore. The significant systemic risk factors of RVO are consistent with studies in white populations.

Global Metabonomic and Proteomic Analysis of Human Conjunctival Epithelial Cells (IOBA-NHC) in Response to Hyperosmotic Stress.

"Dry eye" is a multifactorial inflammatory disease affecting the ocular surface. Tear hyperosmolarity in dry eye contributes to inflammation and cell damage. Recent research efforts on dry eye have been directed toward biomarker discovery for diagnosis, response to treatment, and disease mechanisms. This study employed a spontaneously immortalized normal human conjunctival cell line, IOBA-NHC, as a model to investigate hyperosmotic stress-induced changes of metabolites and proteins. Global and targeted metabonomic analyses as well as proteomic analysis were performed on IOBA-NHC cells incubated in serum-free media at 280 (control), 380, and 480 mOsm for 24 h. Twenty-one metabolites and seventy-six iTRAQ-identified proteins showed significant changes under at least one hyperosmotic stress treatment as compared with controls. SWATH-based proteomic analysis further confirmed the involvement of inflammatory pathways such as prostaglandin 2 synthesis in IOBA-NHC cells under hyperosmotic stress. This study is the first to identify glycerophosphocholine synthesis and O-linked ß-N-acetylglucosamine glycosylation as key activated pathways in ocular surface cells under hyperosmotic stress. These findings extend the current knowledge in metabolite markers of dry eye and provide potential therapeutic targets for its treatment.