microbiome | Nsa Dada, PhD

Western Kenyan Anopheles gambiae showing intense permethrin resistance harbour distinct microbiota

Mon, 08 Feb 2021 00:00:00 +0000

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Mosquito Microbiome Consortium

Tue, 17 Nov 2020 00:00:00 +0000

Mosquito-derived 16S rRNA seq data analysis using QIIME2

Thu, 28 May 2020 00:00:00 +0000

Online course (MSc/PhD level): Analysis of mosquito-derived 16S rRNA seq data using QIIME2; May 28, 2020 – July 9, 2020

Overview:

Driven by a passion for strengthening the capacity for mosquito genomics research in Africa, I have developed this 6-week course to help researchers interested in mosquito microbiome research analyze their data. While there is increasing interest in this research area, along with increasing access to molecular technologies, analysis of the resulting data remains a challenge, especially for scientists based in Africa. My hope with this is to help equip these scientists with the bioinformatics skills required to analyze their data, although anyone anywhere in the world can participate.

We will explore the QIIME 2™ platform for analysing mosquito microbiota (16S seq) data. We will go over basic concepts in microbial ecology/analysis of microbial ecology data, as well as walk through the QIIME2 ‘Moving Pictures’ tutorial together. There will be homework after each session, where you will apply what you have learned to your own data. We will review each week’s homework together in the subsequent session. Appended below is the course content. Please email me if you would like to participate or have any questions.

Goal: at the end of the course, you should be able to perform fundamental analysis of 16S rRNA seq data, and visualize outputs using QIIME2.

What you will need:

A functioning computer with internet access and QIIME2 installed (we will use Version: 2020.2, please see installation guide)
Basic linux skills (this may help)
Note taking materials

Course content and timetable:

Sessions will occur on Thursdays, starting from May 28, 2020 to July 9, 2020; from 11.00-13.00 CET

Week/Date	Content	Homework
Week 1; May 28, 2020	Steps for analysing 16S rRNA seq data; Overview of QIIME2 & Moving pictures tutorial; 10 min break; Installation of QIIME2; Loading data into QIIME 2	Install QIIME2 and load your data into QIIME2
Week 2; June 4, 2020	Homework review/troubleshooting; Overview of reads quality control, denoising and visualization of reads stats	Complete QC and denoising on your data; and visualize and explore your reads stats pre and post QC
Week 3; June 11, 2020	CANCELLED; Nsa’s sick	Check out this tutorial in the meantime
Week 4; June 18, 2020	Homework review/troubleshooting: How to identify factors that may be influencing data composition: 10 min break; Hands-on guided class exercise (linear regression)	If applicable, perform linear regression on your data
Week 5; June 25, 2020	Homework review/troubleshooting; Overview of taxonomic annotation and visualizations; 10 min break; Hands-on guided class exercise; Visualization tips and tricks if there is time	Complete taxonomic annotation on your data; and create visualizations of your outputs
Week 6; July 2, 2020	Homework review/troubleshooting; Overview of alpha and beta diversity; 10 min break; Hands-on guided class exercise	Complete alpha and beta diversity analysis on your data; and create alpha and beta diversity visualizations of your outputs
Week 7; July 9, 2020	Homework review/troubleshooting; Overview of differential abundance testing; 10 min break; Hands-on guided exercise; Classwork on your data; Review/troubleshooting	I would highly encourage exploring other QIIME 2 tutorials and integrating QIIME2 and R

Resources:

Qiime2 info: https://qiime2.org/
Qiime2 installation: https://docs.qiime2.org/2020.2/install/ we will use Version: 2020.2
Qiime2 moving pictures tutorial: https://docs.qiime2.org/2020.2/tutorials/moving-pictures/
Other Qiime2 tutorials: https://docs.qiime2.org/2020.2/tutorials/
Qiime2 forum: https://forum.qiime2.org/
Integrating Qiime2 and R: https://forum.qiime2.org/t/tutorial-integrating-qiime2-and-r-for-data-visualization-and-analysis-using-qiime2r/4121/23
Basic Linux commands for beginners: https://maker.pro/linux/tutorial/basic-linux-commands-for-beginners
What is metadata: https://www.opendatasoft.com/blog/2016/08/25/what-is-metadata-and-why-is-it-important-data
Example mosquito microbiome metadata template: https://github.com/nsadada/Mosquito-microbiome-perspective/blob/master/Metadata_template.csv
Description of metadata fields in the above template: https://github.com/nsadada/Mosquito-microbiome-perspective/blob/master/Table_1.csv

Links between microbes and insecticide resistance in mosquito populations

Mon, 27 Apr 2020 00:00:00 +0000

Overview

It is no coincidence that the stall in malaria control progress over the past half decade overlaps with the scale up of insecticide-based malaria vector control tools, and the increasing prevalence and intensity of insecticide resistance in malaria mosquito populations. A better understanding of the mechanisms underlying insecticide resistance is thus needed to mitigate its threat to malaria control. So far, insecticide resistance research has focused on mosquito biology, behavior, and genetics. But mosquitoes, like all other living organisms, harbor microbes that influence their biology, behavior, and genetics.

Where we’ve worked so far

We hypothesized that the mosquito microbiome could be contributing to resistance following evidence in agricultural pest insects. Focusing on malaria vectors across Latin America and Sub-Saharan Africa; including Anopheles albimanus, An. gambiae s.s. and An. coluzzii, we studied how insecticide exposure; and resistance phenotypes, intensity, and genotype (kdr mutations), affect mosquito microbiota composition. Across all locations, Anopheles species, and insecticides considered, results consistently show significant mosquito microbiota alterations due to all factors tested except kdr mutations, which was fixed in the population studied regardless of insecticide resistance phenotype. Results also show specific microbes and putative microbial functions associated with either insecticide resistance or susceptibility. Of particular note are significantly higher abundance of Serratia in susceptible mosquitoes regardless of Anopheles species, location or insecticide class; and Lysinibacillus in pyrethroid resistant mosquitoes regardless of Anopheles species or location. Serratia is a known insect pathogen, and its pathogenicity could compromise the host’s ability to withstand insecticide exposure. Similarly, the pyrethroid-degrading ability of Lysinibacillus may contribute to resistance.

The figure below provides a summary of what we know so far

Furthermore, alteration of microbial composition due to insecticide exposure is suggestive of selection pressure on the mosquito microbiome that likely favors the resistance phenotype. Conversely, the identification of pathogenic microbial taxa in susceptible mosquitoes suggests a cause of insecticide susceptibility. Together, these findings suggest an underlying microbial mechanism of insecticide resistance. The lack of association between host insecticide genotype and microbial composition suggests that this microbial mechanism is likely of a metabolic nature that may not be related to known resistance-associated host genetics. Further work is being conducted to elucidate this microbial mechanism of insecticide resistance.

Please see below for publications associated with this project.

Collaborators/Partners

Kenya

Diana Omoke (KEMRI & KU)
Ezekiel Mugendi Njeru (KU)
Mathew Kipsum (KEMRI)
Samson Otieno (KEMRI)
Edward Esalimba (KEMRI)
Eric Ochomo (KEMRI)

Mexico

Pablo Manrique-Saide (UADY)
Azael Che-Mendoza (UADY)
Sergio Dzib Florez (UADY)
Lucio Ariel Alcocer Coronado (SSY)
Leonardo Daniel Ku Caamal (SSY)
Gilberto Castillo Chi (SSY)

Peru (Instituto Nacional de Salud)

Jesus Pinto

Benin (Tropical Infectious Diseases Research Centre)

Wassiyath Agnikè Mousse
Luc Djogbénou

Cote d’Ivoire (Centre Suisse de Recherches Scientifiques)

Edi Constant

UK (London School of Hygiene and Tropical Medicine)

Bethanie Pelloquin
Louisa Messenger

Italy (Sapienza University of Rome)

Verena Pichler
Beniamino Caputo

Guatemala

Norma Padilla (UVG)
Juan Carlos Lol (UVG)
Daniela Da’Costa (UVG)
Ana Christina Benedict (UVG)
Fransisco Lopez (UVG)
Pedro Peralta (UVG)
Adel Mejia (UVG)
Alfonso Salam (UVG)
Nelson Jimenez (MSPAS)
Ricardo Valle (MSPAS)
Ricardo Santos (MSPAS)

USA (CDC)

Audrey Lenhart
Kelly Liebman
Nicole Dzuris
Mili Sheth
Core Facility
Scientific Computing team
Entomology branch
Insecticide Resistance & Vector Control Team

Funding and support

Effects of microbes on Aedes aegypti infestation in domestic water containers

Mon, 01 Feb 2010 00:00:00 +0000

As part of a larger project aimed at understanding the links between dengue and diarrheal diseases, and how control measures for both can be integrated, I developed and led work on the relationships between dengue vector infestation and fecal contamination in household water storage containers. We focused on rural and suburban villages in northeastern Thailand and the south of Lao People’s Democratic Republic (Laos). Specific questions that I was interested in answering included whether:

there was any relationship between contamination levels of fecal bacteria and numbers of Aedes pupae (as a proxy for dengue transmission risk) in household water containers, and how this relationship varied by type of container
there were any differences in microbiota composition between immature Aedes aegypti and water from their breeding containers

I utilized a combination of classical and molecular biology/microbiology tools to quantify and characterize mosquito infestation and bacterial communities (using E. coli as a proxy for fecal contamination) in household water storage containers.

Results showed a positive correlation between fecal contamination levels and infestation levels of immature Aedes aegypti, suggesting that household water storage containers in these settings could represent a common denominator of dengue and diarrheal disease risk, and thus a potential target for integrated dengue and diarrheal disease control. Furthermore, Aedes aegypti larvae harbored bacteria acquired from their breeding habitat. However, their bacterial diversity was lower than those of their breeding water, suggesting the presence of a mechanism that controls microbial colonization within mosquitoes.

For more information please see associated publications below.