I am a postdoctoral researcher in the Imaging Genetics section at the Radiology Department of Leiden University Medical Center . I am also a member of the Delft Bioinformatics Lab at TU Delft . I am interested in developing computational methods to analyze brain transcriptome atlases to better understand molecular mechanisms underlying biological functions and diseases of the brain. I am also also interested in understanding the molecular mechanisms behind imaging phenotypes.
In 2014, I spent three months as a visiting scientist at the Department of Genome Sciences at the University of Washington (Seattle, USA) where I worked on identifying autism risk genes at the lab of Evan Eichler. This research visit was supported by a fellowship from the European Molecular Biology Organization (EMBO). I completed my undergraduate studies on Systems and Biomedical Engineering at Cairo University, Egypt in 2008 and I received my M.Sc. in Communication and Information Technology from Nile University, Egypt in 2010.
Genome-wide Coexpression of Steroid Receptors in the Mouse Brain
Steroid hormones coordinate the activity of many brain regions by binding to nuclear receptors that act as transcription factors. This study uses genome wide correlation of gene expression in the mouse brain (from the Allen Mouse Brain Atlas) to discover 1) brain regions that respond in a similar manner to particular steroids, 2) signaling pathways that are used in a steroid receptor and brain region-specific manner, and 3) potential target genes and relationships between groups of target genes. The data constitute a rich repository for the research community to support new insights in neuroendocrine relationships, and to develop novel ways to manipulate brain activity in research of clinical settings.
Mahfouz A, Lelieveldt BPF, Grefhorst A, van Weert LTCM, Mol IM, Sips HCM, van den Heuvel JK, Datson NA, Visser JA, Reinders MJT, Meijer OC. Genome-wide co-expression of steroid receptors in the mouse brain: identifying signaling pathways and functionally coordinated regions.. PNAS (2016). Early Edition.
Co-expression Networks of Autism Genes
Hundred of genes have been implicated in autism spectrumdisorder (ASD). However, understanding how these functionally diverse genes can all be associated to ASD has proved challenging. We used the BrainSpan atlas of gene expression in the developing human brain to identify convergent biological processes between a heterogeneous set of autism-related genes. Using differential co-expression networks of autism-related genes and genome-wide co-expression network analysis, we found that autism-related genes are associated to synaptogenesis, apoptosis, GABA-ergic neurons, mitochondrial function, protein translation, and ubiquitination.
Mahfouz A, Ziats MN, Rennert OM, Lelieveldt BPF, Reinders MJT. Shared Pathways Among Autism Candidate Genes Determined by Co-expression Network Analysis of the Developing Human Brain Transcriptome. J. Mol. Neurosci. (2015). doi:10.1007/s12031-015-0641-3.
Hi-C Chromatin Interaction Networks Predict Co-expression in the Mouse Cortex
We studied the functional role of three dimensional conformation of the genome in the cell nucleus on gene expression regulation. Long-range chromatin interactions arise as a result of the three-dimensional (3D) conformation of chromosomes in the cell nucleus and can result in the co-localization of co-regulated genes. To assess the influence of 3D conformation on gene co-expression, we used chromatin conformation capture (Hi-C) data from the mouse cortex to build a chromatin interaction network (CIN)
of genes. We show that by characterizing the topology of the CIN at different scales it is possible to accurately predict spatial co-expression between genes in the mouse cortex.
Babaei S, Mahfouz A, Hulsman M, Lelieveldt BPF, de Ridder J, Reinders MJT. Hi-C Chromatin Interaction Networks Predict Co-expression in the Mouse Cortex. PLOS Comput. Biol. 11, e1004221 (2015).
Visualizing Brain Gene Expression Organization
Spatially-mapped, genome-wide gene expression atlases of the brain are very valuable to study the genetic contribution to the anatomical organization
of the brain. However, given the high-dimensionality of these atlases there is need for dimensionality reduction methods that can summarize both local and global relationships in the data to allow informative visualizations. We quantitatively assessed the performance of different dimensionality reduction techniques in separating neuroanatomical regions in low-dimensional (2D) embeddings of the mouse and human brains. We show that t-distributed Stochastic Neighbor Embedding (t-SNE) produces consistent embedding across 6 human brains from the Allen Human Brain Atlas as well as between the sagittal and coronal sections of the Allen Mouse Brain Atlas. We used low-dimensional embeddings to analyze the contribution of different cell-type markers in determining the structural organization of the mammalian brain.
Mahfouz A, van de Giessen M, van der Maaten L, Huisman S, Reinders M, Hawrylycz MJ, Lelieveldt BPF. Visualizing the spatial gene expression organization in the brain through non-linear similarity embeddings . Methods 73 (2015), 79–89. doi:10.1016/j.ymeth.2014.10.004.
Genomic Connectivity Networks in the Developing Human Brain
In this work, we construct connectivity networks between brain regions based on the similarity of their gene expression signature, termed "Genomic Connectivity Networks" (GCNs). Genomic connectivity networks were constructed using data from the BrainSpan transcriptional atlas of the developing human brain with the aim to understand how the genetic signatures of anatomically distinct brain regions relate to each other across development. We assessed the neurodevelopmental changes in connectivity patterns of brain regions when networks were constructed with genes implicated in the neurodevelopmental disorder autism (autism spectrum disorder; ASD). Using graph theory metrics to characterize the GCNs, we show that ASD-GCNs are relatively less connected later in development with the cerebellum showing a very distinct expression of ASD-associated genes compared to other brain regions.
Mahfouz A, Ziats MN, Rennert OM, Lelieveldt BPF, Reinders MJT. Genomic connectivity networks based on the BrainSpan atlas of the developing human brain . SPIE Medical Imaging 90344G–90344G (2014).
Bioinformatics NB2161 (TU Delft, 2014 – 2015)
Imaging Project TI2710-D (TU Delft, 2011 – 2012)
Hormones and the Nervous System (LUMC, 2013)