Research
Research Themes
I am developping bio informatic and statistical methods to analyse genomic data to study health related questions. In the recent years, I have focus on two main research questions:
Evolutionary analysis of genomic data
Horizontal gene transfer in Bacteria
Recent advances of sequencing technologies have allowed the analysis of microbial communities via sequencing of the metagenomes of a variety of different environments, further highlighting the crucial importance of microorganisms on human health. The huge amount of data generated this way constitutes a tremendous opportunity to improve our understanding of microorganisms’ biology, but also requires the development of efficient dedicated methods.
The ability of bacteria belonging to different species to exchange genetic material via Horizontal Gene Transfer (HGT) is a key mechanism of microorganisms evolution and constitutes an important source of genetic novelty for microbial species. On short timescales it is the primary reason for the spread of antibiotic resistance in bacteria (1) and plays an important role in the evolution of virulence (2), and as such is of crucial importance for human health. However, HGT events occurring between distantly related organisms remain poorly studied. Notably, the biological conditions that make long distance HGT possible, and influence the rates at which species exchange material are not understood. Finally, the routes that genes take to spread via HGT over the tree of life are mostly unknown.
My work aims at developing method to study those questions. To do so, I focus on the analysis statistical properties of long Maximal Exact Matches (sheinman et al.). The rationale is that long sequences of DNA (>300bp) exactly identical between pairs of bacteria of different species are extremely unlikely to occur via classical vertical inheritance. Hence, such long exact matches must result from a recent HGT event. Using efficient algorithms detecting exact matches at low computation cost thus allow to identify HGT on very large datasets.
Bacterial phylogeny
Pursuing this avenue of research, we recently demonstrated that studying the length distribution of exact sequence matches observed in whole genome alignments of two bacterial species allow to efficiently separate sequence similarities inherited from their common ancestors from those acquired via horizontal gene transfer. Based on this result, we propose a novel concept, the “mosaic molecular clock”, and developped a mathematical framework to accurately calibrate bacterial phylogeny using solely genomic data. The full story can be found here. We are now applying the method to a large range of bacterial species to study if it can help better resolve difficult phylogenies.
Infering Human demographic history
The study of the length distribution of sequence similarities can also inform on the genome evolution in eukaryote species. The first models we developped on the topic allowed us to study the rate and mechanisms of gene duplication (Massip et al.). Applying similar modeling arguments to the comparison of the two haplotypes of a single individual, we now show that one can reconstruct the demographic history of its ancester. Compared to state of the art method, this strategy has the advantage to be computationally efficient and provides better statistical guarantees. In particular, we are able to derive confidence intervals for our estimate of the demographic history. The theoretical principle of the method are described here while we are finalizing the full pipeline.
Biomarker discovery for cancer early detection
Cancer is an aggressive disease that develop from a few abnormal cells. In the majority of cases, it can be efficiently treated and cured when discovered early enough. Hence, a second aixs of my research aims at developping methods for early diagnosis
Early Detection of Lung Cancer with transcriptomic data
Lung cancer is one of the major causes of cancer-related deaths in the western world. A direct connection to lifestyle risk in the form of cigarette smoke has long been established, and ex smokers remain at risk since >50% of all lung cancers occur in former smokers. On the other hand, only 10-20% of heavy smokers ever develop lung cancer in the course of their lifetime, raising questions about the factors that can explain such variability. It has been postulated that individual differences in smoke injury response might modulate personal cancer risk and that cancer onset results from the combination of environmental exposure and disadvantageous genetic background. Early studies into the gene expression dynamics induced by smoke exposure provided a broad characterisation of the genes involved (3). However, the lack of statistical power of these studies have prevented the discovery of individual differences among smokers. As a consequence, the role of subject specific genetic background in response to smoke injury remains poorly understood.
We have recently developped a classifier to improve early detection of lung cancer (de biase et al.). To do so, we collected nasal swab from 500 subjects to conduct transcriptomic analysis. We also genotyped the subjects in order to understand whether patients’ genetic background could influence lung cancer risk. Our study demonstrate that transcriptomic analysis of nasal swab can serve as an efficient early detection tool for lung cancer diagnosis. We further provide evidence from systems biology approaches, that demonstrates systematic dysregulation of immune regulatory networks in smokers, and its link to increased lung cancer risk. These results are also supported by the analysis of germline variants thereby providing evidence for a risk-related connection of germline, environmental and transcriptional response.
Despite these promising results, the results described above are difficult to reproduce when applied to different cohorts. This difficulty is not specific to our lung cancer cohort and similar challenges have been described in on other cohort and diseases (see for instance Haury et al.). To improve feature selection in this context, we applied a recent statistical method called knocoffs [(barber and Candes)][KO] to this problem and showed it had the power to improve biomarker discovery. This study is available as a prerpint here.
Evolution of ovarian cancer
Together with scientist from the Berlin Institute of Health (BIH), we are starting a novel project that aims at studying the early molecular alterations in healthy fallopian tube samples (which is known to be the tissue of origin of ovarian cancers). Using evolutionary approaches, we aim at identifying morphological and genetic features in cells that could be linked to the development of the disease, with the goal to better understand the mechanisms leading to the development of the disease and, on the long run, to improve early diagnosis.
The project is just starting and we are looking for students and postdoc to work on the topic. Don’t hesitate to contact me if interested!
References:
(1) : https://doi.org/10.2147/IDR.S48820
(2) : https://doi.org/10.1177/0300985813511131
(3) : https://doi.org/10.1186/gb-2007-8-9-r201