A Web Resource to Understand Craniofacial Development through Network Biology

Main Supervisor
Prof. Giuseppe Testa (giuseppe.testa@fht.org)

Computational Supervisors
Daniele Capocefalo (Daniele.capocefalo@ieo.it)
Alessandro Vitriolo (alessandro.vitriolo@ieo.it)

Center of Neurogenomics, Human Technopole, Milan, IT

Starting Date:
February 2022

Project Description

Human Craniofacial development (CFD) – the process in which the skull and the bones encompassing the face occur-is a complex and tightly regulated processthat startsimmediatelyafter conception and proceeds through fetal and postnatal development (Murillo-Rincón and Kaucka, 2020).

Understanding how the timely and precise gene regulation occurs during CFD at transcriptional and epigenetic level is necessary, both from a developmental and from an evolutionary perspective. In fact, in neurodevelopmental disorders (NDD), haploinsufficiency of chromatin regulators and derangements of gene regulatory networks frequently cause facial dysmorphisms (Gabriele et al., 2018). Moreover, we have already demonstrated how differences between modern and archaic human face formation could be explained by relatively few chromatin regulatory changes (Theofanopoulou et al., 2017, https://www.biorxiv.org/content/10.1101/2021.01.22.427608v1, Zanella, Vitriolo et al., 2019.). While evolutionary and clinical studies of CFD may seem two separate research fields, the two are indeed deeply intertwined. We recently demonstrated that chromatin alterations occurring in two NDDs (the Williams-Beuren and the 7DUP syndrome, can be informative of recent evolutionary changes, and provided the first empirical validation of self-domestication hypothesis: which states that modern humans acquired milder facial features by self-selecting pro-social behaviors (Zanella, Vitriolo et al. 2019). The integration of omics data (RNA-SeqandChIP-Seq) has been already useful to understand some of the major features of CFD (Wilderman et al., 2018). However, the combined lack of public resources and the scarce availability of a proper methodology to treat and represent the regulatory underpinnings of CFD remains a major issue. To this end, Network Biology, abranch of Systems Biology that exploitsGraphTheory to understand biological processes (Barabási et al., 2011), can be a driving force and lead the field towards new and exciting frontiers, using mathematical properties of networks to pinpoint key-regulators of CFD.

In this project, the candidate will be involved in the construction of a public repository that, similarly to the database of Circadian clock Genes, CircaDB (Pizarro et al., 2013) (http://circadb.hogeneschlab.org/), will use available public data to provide a comprehensive view on the behavior of gene and gene regulatory networks during CFD. During the first part of the project, the candidate will use state-of-the-art methods in Python Programming and Network Biology to i) rebuild, form the High-Resolution Epigenomic Atlas of Human Embryonic Craniofacial Development (Wilderman et al., 2018), a set of biological networks for each time point in craniofacial development that represent a specific layer of biological interaction (protein-protein interaction, gene regulation, gene-to-phenotype relationships, and so on), ii) identify methods to integrate developmental stages and layers, and iii) collaborate in the identification of simple biological questions to interrogate and query the generated networks. In the second part of the project, the candidate will use Flask or other comparable web frameworks to build a website that allows for the dynamic exploration of the data and make our findings accessible for the scientific community.

Techniques the student will learn during the thesis
• Next generation Sequencing Data Analysis (RNA-seq, ChIP-Seq, and standard omics integration)
• Systems and Network Biology: from Network Inference to Graph-based methods for the identification
of key biological molecules in networks. Both from a theoretical and a practical standpoint
• Python Programming and the main libraries for Data Analysis, libraries development, and web design

Developed by the CNBA-team C: prof. Andrea Cabibbo
N: Andrea Ninni
B: Francesca Brunetti
A: Romina Appierdo