Project description:Deciphering Soil Bacterial Community Structure of Lagos Mainland Waste Dump Sites, Nigeria: Insights on Potential Impacts of Different Pollutants from Municipal and Industrial Waste
| PRJNA540227 | ENA
Project description:Bacterial community in waste leachate sludge
Project description:Understanding the bacterial community structure, and their functional analysis for active bioremediation process is essential to design better and cost effective strategies. Microarray analysis enables us to simultaneously study the functional and phylogenetic markers of hundreds of microorganisms which are involved in active bioremediation process in an environment. We have previously described development of a hybrid 60-mer multibacterial microarray platform (BiodegPhyloChip) for profiling the bacterial communities and functional genes simultaneously in environments undergoing active bioremediation process (Pathak et al; Appl Microbiol Biotechnol,Vol. 90, 1739-1754). The present study involved profiling the status of bacterial communities and functional (biodegradation) genes using the developed 60-mer oligonucleotide microarray BiodegPhyloChip at five contaminated hotspots in the state of Gujarat, in western India. The expression pattern of functional genes (coding for key enzymes in active bioremediation process) at these sites was studied to understand the dynamics of biodegradation in the presence of diverse group of chemicals. The results indicated that the nature of pollutants and their abundance greatly influence the structure of bacterial communities and the extent of expression of genes involved in various biodegradation pathways. In addition, site specific factors also play a pivotal role to affect the microbial community structure as was evident from results of 16S rRNA gene profiling of the five contaminated sites, where the community structure varied from one site to another drastically.
Project description:Understanding the bacterial community structure, and their functional analysis for active bioremediation process is essential to design better and cost effective strategies. Microarray analysis enables us to simultaneously study the functional and phylogenetic markers of hundreds of microorganisms which are involved in active bioremediation process in an environment. We have previously described development of a hybrid 60-mer multibacterial microarray platform (BiodegPhyloChip) for profiling the bacterial communities and functional genes simultaneously in environments undergoing active bioremediation process (Pathak et al; Appl Microbiol Biotechnol,Vol. 90, 1739-1754). The present study involved profiling the status of bacterial communities and functional (biodegradation) genes using the developed 60-mer oligonucleotide microarray BiodegPhyloChip at five contaminated hotspots in the state of Gujarat, in western India. The expression pattern of functional genes (coding for key enzymes in active bioremediation process) at these sites was studied to understand the dynamics of biodegradation in the presence of diverse group of chemicals. The results indicated that the nature of pollutants and their abundance greatly influence the structure of bacterial communities and the extent of expression of genes involved in various biodegradation pathways. In addition, site specific factors also play a pivotal role to affect the microbial community structure as was evident from results of 16S rRNA gene profiling of the five contaminated sites, where the community structure varied from one site to another drastically. Agilent one-color CGH experiment and one-color Gene Expresssion expereiment,Organism: Genotypic designed Agilent-17159 Genotypic designed Agilent Multibacterial 8x15k Array , Labeling kits: Agilent Genomic DNA labeling Kit (Part Number: 5190-0453) and Agilent Quick Amp Kit PLUS (Part number: 5190-0442).
Project description:This work examines the impact of various non-pharmaceutical control measures (government and personal) on the population dynamics of the novel coronavirus disease 2019 (COVID-19) in Lagos, Nigeria, using an appropriately formulated mathematical model. Using the available data, since its first reported case on 16 March 2020, we seek to develop a predicative tool for the cumulative number of reported cases and the number of active cases in Lagos; we also estimate the basic reproduction number of the disease outbreak in the aforementioned State in Nigeria. Using numerical simulations, we show the effect of control measures, specifically the common social distancing, use of face mask and case detection (via contact tracing and subsequent testings) on the dynamics of COVID-19. We also provide forecasts for the cumulative number of reported cases and active cases for different levels of the control measures being implemented. Numerical simulations of the model show that if at least 55% of the population comply with the social distancing regulation with about 55% of the population effectively making use of face masks while in public, the disease will eventually die out in the population and that, if we can step up the case detection rate for symptomatic individuals to about 0.8 per day, with about 55% of the population complying with the social distancing regulations, it will lead to a great decrease in the incidence (and prevalence) of COVID-19.
Project description:In this study, microbial communities from triplicate leach-bed anaerobic bioreactors digesting grass were analysed. Each reactor comprised two microbial fractions, one immobilized on grass (biofilm) and the other in a planktonic state present in the leachate. Microbial communities from the two fractions were systematically investigated for community composition and function. This was carried out using DNA, RNA and protein co-extraction. The microbial structure of each fraction was examined using 16S rRNA deep sequencing, while the active members of the consortia were identified using the same approach on cDNA generated from co-extracted RNA samples. Microbial function was investigated using a metaproteomic workflow combining SDS-PAGE and LC-MS/MS analysis.