Gut-microbiome to control Agricultural Insect Pests


Gut microbiota are mainly microorganisms, especially bacteria and fungus, lives in the gut of a host organism, and form a symbiotic relationship with the host. The symbiotic relationship between host and gut microbiota can be beneficial or pathogenic. It is well reported that, these microbial symbionts play key role in host biology, including nutrition uptake, immunity, reproduction, and ecology [1].

Numerous gut-microbiome works undergoing to profile microbiome diversity in numerous insect pests. Recently, microbiome profiling among larvae of Tephritid Fruitfly species showed high variability across and within species [2]. There is a scarcity of literatures focusing usage of such profiling/abundance resources in insect control measures. Gut-microbiome has great potential to be used as “bio-pesticide” to reduce agricultural pests, also as “probiotics” to enhance fitness of biological control agents, (e.g.- SIT males) [3,4].

The aim of the study is to characterise microbiome diversities of different insect pests and compare inter- and intra-species variations, and finally utilisation of gut-microbiota in insect control measures. Different state-of-art bioinformatics pipelines and machine learning algorithms will be used to validate the findings.



  1. Literature review to create a list of candidate gut-microbiota for agricultural pest control.
  2. Validate a bioinformatics pipeline (e.g. – GHAP, Usearch, QIIME, DADA2).
  3. Measure alpha and beta diversity, species diversity, relative abundance.
  4. Functional annotation and host metabolic pathways modulated by different microorganisms.
  5. Conducting statistical analysis and developing machine learning models.
  6. Propose a list of candidate gut-microbiota to manipulate pest behaviuor and potential utilisation in integrated pest management programmes.
  7. Modulate insect gut-microbiota and test different QC-parameters to functionally characterise gut-microbiota effects on host physiology.



  1. Knowledge in Python or R and statistics.
  2. Working knowledge with Linux/Unix, Bash Scripts.
  3. Basic knowledge in NCBI blast, SRA tool kits, FastQC is an advantage.


Background Literature

  1. Broderick, Nichole A., Nicolas Buchon, and Bruno Lemaitre. "Microbiota-induced changes in Drosophila melanogaster host gene expression and gut morphology." MBio 5.3 (2014).
  2. De Cock, Maarten, et al. "Comparative Microbiomics of Tephritid Frugivorous Pests (Diptera: Tephritidae) From the Field: A Tale of High Variability Across and Within Species." Frontiers in microbiology 11 (2020): 1890.
  3. Deutscher, Ania T., et al. "Tephritid-microbial interactions to enhance fruit fly performance in sterile insect technique programs." BMC microbiology 19.1 (2019): 1-14.
  4. Raza, Muhammad Fahim, et al. "Tephritidae fruit fly gut microbiome diversity, function and potential for applications." Bull. Entomol. Res 110 (2020): 423-347.