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bioRxiv2018

Using Google Trends to Examine the Spatio-Temporal Incidence and Behavioral Patterns of Dengue Disease: A Case Study in Metropolitan Manila, Philippines

Dengue is a major public health concern and an economic burden in the Philippines. Despite the countrys improved dengue surveillance, it still suffers from various setbacks and therefore needs to be complemented with alternative approaches. Previous studies have demonstrated the potential of internet-based surveillance such as Google Dengue Trends (GDT) in supplementing current epidemiological methods for predicting future dengue outbreaks and patterns. With this, our study aims to assess the temporal relationship of GDT and dengue incidence in Metropolitan Manila from previous years and examine web search behavior of the population towards the disease. The study collated and organized the population statistics and reported dengue cases in Metropolitan Manila from respective government agencies to calculate the spatial and temporal dengue incidence. The relative search volume of the term dengue and top dengue-related search queries in Metropolitan Manila were obtained and organized from the Google trends platform. Data processing of GDT and dengue incidence was performed by conducting an adjustment procedure and subsequently used for correlation and cross-correlation analyses. Moreover, a thematic analysis was employed on the top dengue-related search queries. Results revealed a high temporal relationship between GDT and dengue incidence when either one of the variables is adjusted. Cross-correlation showed that there is delayed effect (1-2 weeks) of GDT to dengue incidence, demonstrating its potential in predicting future dengue outbreaks and patterns in Metropolitan Manila. Thematic analysis of dengue-related search queries indicated 5 categories namely; (a) dengue, (b) sign and symptoms of dengue, (c) treatment and prevention, (d) mosquito and (e) other diseases where the majority of the search queries was signs and symptoms which indicate the health-seeking behavior of the population towards the disease.

epidemiology↗

bioRxiv2018

Genome-scale metabolic network reconstruction of the chloroform-respiring Dehalobacter restrictus strain CF

BackgroundOrganohalide-respiring bacteria (OHRB) play an important role in the global halogen cycle and bioremediation of industrial sites contaminated with chlorinated organics. One notable OHRB is Dehalobacter restrictus strain CF, which is capable of respiring chloroform to dichloromethane. Improved bioremediation strategies could be employed with a greater understanding of D. restrictus metabolism in isolate and community cultures. To this end, we reconstructed the genome-scale metabolic network of D. restrictus to study its metabolism in future studies using flux balance analysis.\n\nMethodThe RAST annotation server and Model SEED framework were used to obtain a draft metabolic network reconstruction. Additional curation was required for its acetyl-CoA sources, the Wood-Ljungdahl pathway, TCA cycle, electron transport chain, hydrogenase complexes, and formate dehydrogenase complexes.\n\nResultsiHH623 is the first curated genome-scale metabolic model in the Peptococcaceae family. It spans 1087 reactions and 983 metabolites, covering 623 genes (21% of all ORFs). Its potential sources of acetyl-CoA are pyruvate ferredoxin oxidoreductase, pyruvate formate lyase, acetyl-CoA synthetase, phosphate acetyltransferase, and CO-methylating acetyl-CoA synthase. NADPH may be regenerated by isocitrate dehydrogenase, malic enzyme, NADP-reducing hydrogenase, cytosolic formate dehydrogenase, ferredoxin-dependent bifurcating transhydrogenase, 5-methyltetrahydrofolate dehydrogenase, and 5-10-methylenetetrahydrofolate. Additional reactions that were added or removed to the D. restrictus reconstruction are discussed.\n\nConclusionsWe reconstructed the genome-scale metabolic network of D. restricus by obtaining an initial draft with the RAST server and Model SEED framework. Curation was required for D. restricus acetyl-CoA sources, TCA cycle, electron transport chain, hydrogenase complexes, and formate dehydrogenase complexes. This metabolic model can be used to decipher D. restrictus metabolism in isolate and community cultures in future studies, or as a template to reconstruct the metabolic network of other Peptococcaceae species. The extensive curation of the draft metabolic network reconstruction highlights the need to be cautious of automated metabolic network reconstruction.

systems biology↗