Open thesis topics

Exploring the Role of Nasal Microbiota in Neurological Diseases (M.Sc.)

Background
Microorganisms, including those in the human nasal cavity, maintain stability and
functionality. Recent research suggests a potential link between the nasal microbiota and
neurological diseases such as Parkinson’s disease (PD), Alzheimer’s disease (AD), and
multiple sclerosis (MS)(1). However, the nature of this relationship remains unclear due to
a limited number of studies.
While much focus has been on the gut-brain axis, the influence of the nose-brain axis on
the immune system and respiratory homeostasis requires further investigation (2). Some
studies have indicated that altering the nasal microbiota could potentially prevent or treat
neurological diseases, highlighting the need to understand the complex interactions
between the nasal microbiota and the brain. Evidence suggests that the nasal microbiome
may travel through the olfactory pathway to the brain (2, 3). The diversity of bacteria in the
nasal cavity is highly dynamic and can vary depending on age, physiology, and lifestyle.
This project will investigate how nasal microbiota stability impacts the blood-brain barrier
(BBB) and its potential role in the development and progression of neurological diseases.
Our goal is to gain a comprehensive understanding of the nasal microbial community, the
conditions under which it remains stable, and how disruptions in nasal homeostasis might
contribute to neurodegeneration.

Objective
The primary objective of this project is to explore the conditions under which nasal
microbiota stability or instability is associated with neurological diseases, focusing on
potential diagnostic and therapeutic applications.

Methodology
1. Literature Review: Conduct a thorough review of existing studies on the nasal
microbiota and its potential impact on neurological diseases.
2. Data Comparison and Analysis: Compare data gathered from literature on the nasal
microbiota, analyzing differences in composition and diversity, and identifying potential
patterns.
3. Mechanistic Studies: Explore how alterations in the nasal microbiota might influence
the BBB and contribute to the pathology of neurological diseases.
4. Model Creation and Analysis: Develop a model based on literature data to analyze
the stability of the nasal microbiota and its potential role in modulating the risk of
neurological diseases.

Expected Outcome
This project aims to shed light on the role of the nasal microbiota in neurological diseases,
potentially leading to novel diagnostic and therapeutic strategies. By understanding the
dynamics of nasal microbiota stability, we hope to uncover new insights into preventing
and treating neurodegenerative conditions.

Reference
1.García-Jiménez, Beatriz, et al., Computational and Structural Biotechnology Journal 19
(2021): 226-246.
2. Xie, Jin, et al. Pharmacological Research 179 (2022): 106189.
3. Thangaleela, Subramanian, et al., Microorganisms 10.7 (2022): 1405

Contact: Dr. Reihaneh Mostolizadeh

The Landscape of the Oral Microbiome and Its Relationship with Other Body Site
Microbiomes in Humans

Background
This project aims to investigate the complex microbial ecosystem of the human
oral cavity and its interactions with microbiomes at other body sites (6). The oral cavity is a
key interface between the human body and the external environment. As the second
largest microbial community in the human body, the oral microbiota plays a crucial role in
maintaining host health locally and systemically. Recent research suggests significant
interactions between the oral microbiome and microbial communities at other body sites
(1, 2, 3, 4, 5), with evidence of microbial migration contributing to infections and disease.
Under circumcision, microbes migrate from the oral cavity to other body sites, and the link
to infections is still unclear. Understanding these dynamics is critical for advancing human
health research and developing targeted therapies.

Objective
The main objective of this project is to explore how the composition and diversity of the
oral microbiome vary between healthy and diseased states. Additionally, the potential
interactions and correlations between the oral microbiome and microbiomes at other body
sites will be investigated, which will be important for understanding microbial migration and
its role in disease development.

Methods
1. Literature Review: Conduct a comprehensive review of existing research on the oral
microbiome and its interactions with other body site microbiomes in health and disease
contexts.
2. Data Analysis: Analyze data to identify significant correlations between the oral
microbiome and microbiomes at other body sites, focusing on how these relationships
impact health.
3. Categorization: Group findings by intrinsic correlations and microbial migration
patterns to identify links between microbiome shifts and disease.
4. Visualization: Create visual maps using appropriate tools to present key results and
findings in a standardized format.

Expected Outcome
This project aims to enhance understanding of the oral microbiome’s role in disease by
identifying potential oral microbial biomarkers. By characterizing the variations in the oral
microbiome under different health conditions, these biomarkers could be valuable for
disease risk assessment and the development of targeted therapies.

References
1. Jameie, Melika, et al. "The hidden link: how oral and respiratory microbiomes affect multiple
sclerosis." Multiple Sclerosis and Related Disorders (2024): 105742.
2. Liao, Ying, et al. "Microbes translocation from oral cavity to nasopharyngeal carcinoma in
patients." Nature Communications 15.1 (2024): 1645.
3. Xu, Tiansong, et al. "The relationship of oral and other body sites microbiome in human
diseases." Frontiers in Cellular and Infection Microbiology 13 (2023): 1276473.
4. Thangaleela, Subramanian, et al. "Nasal microbiota, olfactory health, neurological disorders and
aging—a review." Microorganisms 10.7 (2022): 1405.
5. Peng, Xian, et al. "Oral microbiota in human systematic diseases." International journal of oral
science 14.1 (2022): 14.
6. Lamont, Richard J., Hyun Koo, and George Hajishengallis. "The oral microbiota: dynamic
communities and host interactions." Nature reviews microbiology (2018).

Contact: Dr. Reihaneh Mostolizadeh

Reconstruction and visualization of KEGG metabolic pathways in the EDGAR platform (M.Sc.)

Background

EDGAR is a web-based platform for analyzing microbial data. It is developed by employees of the Bioinformatics and Systems Biology department at JLU Giessen and provides multifaceted methods for investigating genomes.

KEGG ( Kyoto Encyclopedia of Genes and Genomes) provides curated databases and resources for (among other things) the functional annotation and classification of genes. In previous projects, KEGG functional categories for all organisms and their corresponding genes were computed in the EDGAR platform. These are currently displayed directly in two analysis modules, in purely quantitative terms.

MinPath is a program for reconstructing biological/metabolic pathways. It attempts to infer a minimal biological metabolic network by excluding redundant metabolic pathways that can explain the genes found in a given dataset. The above-mentioned KEGG categories will be used as input for this program.

The goal of the project is to develop a comparative analysis module, based on KEGG pathway information, for the EDGAR platform.

Thesis Aims

• Parse the available KEGG data in a structured manner and compute KEGG metabolic pathways for all given genomes in EDGAR using MinPath.
• Design comparative visualizations for the EDGAR frontend using the resulting data, allowing users to interactively explore their data (see fig. 4 here as an example)
• Adjust the project scope in consultation with the student depending on the project status to accommodate shared ideas, as EDGAR incorporates a wide selection of data with potential for creative analysis methods.

Requirements 

• Programming skills in Python and JavaScript (can also be learned during the process)

• Basic SQL database knowledge

PlasmidHunter: Validation of a metagenome-based plasmid search using public plasmid sequences (M.Sc.)

Background

Plasmids play an important role in the genetic variability of organisms. They replicate independently and between organisms - within and between species. Therefore, plasmids are key drivers of horizontal gene transfer. Often, they are the effective and only difference between commensal and pathogenic bacterial strains. In recent years, it became obvious that plasmids belong to the main mechanisms for the dissemination of antimicrobial resistances and hence are of special interest in medical microbiology. Detecting plasmids and analyzing their dissemination is an important epidemiological and scientific topic that might help to detect current and prevent future outbreaks of antibiotic resistances.

One promising data source containing known and unknown plasmids are whole-metagenome datasets of samples from different sources (soil, waste water, the human gut). For many of these samples, sequencing data is freely accessible in public databases, often annotated with additional meta information such as date, source and location of each sample.

Our project processes these datasets from the MGnify database in a standardized way via modern cloud technologies and makes them accessible to users for a fast search of new plasmids within this huge amount of data.

This master thesis should validate this search via existing plasmid databases (such as PLSDB) and analyze search results including comprehensive visualizations.

Thesis Aims

• Implementation of a workflow to process PLSDB entries with our existing search workflow
• Statistical analysis of the results, and screen for potential interesting candidates for further analysis
• Visualization of the results

Prerequisites 

• Knowledge of command line tools and Python
• Interest in cloud technologies
• Prior experience with workflow systems, like Nextflow or Snakemake

Contact: Sebastian Beyvers

Ribosomal binding site prediction based on 16S-rRNA (M.Sc.)

Background

Bacterial translation is initiated by the assembly of ribosomal proteins as part of the translation initiation complex at the coding sequence (CDS) start site. For most CDS, there is a ribosomal binding site (RBS) immediately upstream of the gene, consisting of a 5-10bp spacer and a (partial or complete) Shine-Dalgarno sequence (SD) 5’-AGGAGG-3’ to which the ribosome binds. However, some genes have neither an SD nor a known RBS and are still expressed (Omotajo, D. et al., 2015). The Shine-Dalgarno sequence was first described in E. coli but is found in many bacterial genomes and is complementary to the anti-SD sequence at the 3′-end of 16S-rRNA.

The exact Shine-Dalgarno and spacer sequences vary between bacterial species. However, because the anti-Shine-Dalgarno sequence is present in the 16S-rRNA of each bacterial genome, it can be used to predict RBS in a species-independent manner.  Therefore, a deep learning approach using the 16S-rRNA sequences and the sequence upstream of the CDS is promising for accurately predicting the presence of RBS independent of species-specific variants.

Thesis Aims

• Design and implementation of a neural network for ribosomal binding site prediction in bacteria,
• evaluation of the features used by the neural network, and
• analysis of the presence of RBS in exemplary bacterial genomes

Prerequisites 

• Prior experience with deep learning frameworks such as Tensorflow/Keras, or willingness to learn them
• Prior experience in the development of documented code and dependency management or willingness to learn them

Contact: Julian Hahnfeld

Integrative Omics FAIR Workflow (M.Sc.)

Background

Processing and analysing 'omics data often requires applying predefined building blocks of code, i.e. for performing quality control, statistical analysis or machine learning. However, biologists and ecologists are often overwhelmed with the technical complexity of programmatic approaches and interfaces. Hence, scientific workflows can not just automate, but also facilitate important re-occuring processes in high-throughput 'omics analysis.

The existing modularized iESTIMATE pipeline aims at automating and facilitating the complex analysis of ecological metabolomics data and the integration with other phenomics and preparation for sequencing and (meta-)genomics data. The central aim of the pipeline is to extract so called molecular traits that explain molecular mechanisms in plants or microorganisms.

Thesis Aims

• Revision and modularisation of existing code to create the R package "iESTIMATE"
• Implementing a workflow in NextFlow or Common Workflow Language (CWL) using test data, implementing unit tests and capture provenance information
• Publish R package and the workflow following the FAIR principles

Prerequisites 

• Knowledge of R and a bit of Python
• Knowledge of Linux command line, containers, NextFlow (Groovy), YAML, or motivation to become acquainted with them
• Keen interest in analysis of integrative 'omics data and in topics in molecular ecology

Contact: Kristian Peters