MedUni PhD Recruitment

Medical University of Vienna invites applications for all currently open Ph.D. positions within their 18 Ph.D. programs. We encourage ambitious and creative young scientists to develop their original research project in the field of Behavioural Biology, Biochemistry, Biophysics, Bioinformatics & Machine Learning, Cancer, Cardiovascular Systems, Drug Targets & Drug Development, Endocrinology & Metabolism, Biomedical Engineering, Mathematics & Statistics, Immunology, Medical Physics, Mental Health, Molecular and Cellular Biology, Neuroscience and Public Health with the assistance of our renowned and international scientists . Benefit from a well-established and connected network within the science community and built important relations with your peers at our university. On top of it, become an expert in your field! All project information can be found online under https://www.meduniwien.ac.at/web/en/studies-further-education/phd-doctoral-programmes/phd-programme-un094/phd-opportunities/ Apply online till 20.11.2022

Dr Nathan Skene

Using machine learning to predict cell-type specific effects of genetic variants which influence genome regulation. This PhD project is focused on using machine learning techniques to develop novel classifiers for predicting how changes in DNA sequences alter genomic regulatory features. Many regulatory proteins recognise particular DNA sequences known as motifs, for instance, EcoRI only binds to GAATTC. DNA sequences can be converted into a machine interpretable format, using one-hot encoding. The candidate will use publicly available and inhouse datasets of genomic regulatory features to train models. Machine learning techniques will be used to predict the cell-type specific regulatory effects of genetic variants. We will provide several true-positive datasets, wherein the effect of genetic mutations on particular regulatory features has been measured. These will form validation datasets to evaluate how well the trained classifier works. We are interested in how improvements in the machine learning approach (e.g. use of transfer learning, recurrent attentional networks or graph convolution networks) can be used to improve upon existing methods. The candidate will use these techniques to identify causal pathways and candidate drug targets for neurodegenerative diseases.

In vivo scalable investigation of gene functions in the brain

Genomic investigation of sex-differential neurodevelopment and risk for autism

Precision Genomics in Neurodevelopmental Disorders

Integration of 3D human stem cell models derived from post-mortem tissue and statistical genomics to guide schizophrenia therapeutic development

Schizophrenia is a neuropsychiatric disorder characterized by positive symptoms (such as hallucinations and delusions), negative symptoms (such as avolition and withdrawal) and cognitive dysfunction1. Schizophrenia is highly heritable, and genetic studies are playing a pivotal role in identifying potential biomarkers and causal disease mechanisms with the hope of informing new treatments. Genome-wide association studies (GWAS) identified nearly 270 loci with a high statistical association with schizophrenia risk; however each locus confers only a small increase in risk therefore it is difficult to translate these findings into understanding disease biology that can lead to treatments. Induced pluripotent stem cell (iPSC) models are a tractable system to translate genetic findings and interrogate mechanisms of pathogenesis. Mounting research with patient-derived iPSCs has proposed several neurodevelopmental pathways altered in SCZ, such as neural progenitor cell (NPC) proliferation, imbalanced differentiation of excitatory and inhibitory cortical neurons. However, it is unclear what exactly these iPS models recapitulate, how potential perturbations of early brain development translates into illness in adults and how iPS models that represent fetal stages can be utilized to further drug development efforts to treat adult illness. I will present the largest transcriptome analysis of post-mortem caudate nucleus in schizophrenia where we discovered that decreased presynaptic DRD2 autoregulation is the causal dopamine risk factor for schizophrenia (Benjamin et al, Nature Neuroscience 2022 https://doi.org/10.1038/s41593-022-01182-7). We developed stem cell models from a subset of the postmortem cohort to better understand the molecular underpinnings of human psychiatric disorders (Sawada et al, Stem Cell Research 2020). We established a method for the differentiation of iPS cells into ventral forebrain organoids and performed single cell RNAseq and cellular phenotyping. To our knowledge, this is the first study to evaluate iPSC models of SZ from the same individuals with postmortem tissue. Our study establishes that striatal neurons in the patients with SCZ carry abnormalities that originated during early brain development. Differentiation of inhibitory neurons is accelerated whereas excitatory neuronal development is delayed, implicating an excitation and inhibition (E-I) imbalance during early brain development in SCZ. We found a significant overlap of genes upregulated in the inhibitory neurons in SCZ organoids with upregulated genes in postmortem caudate tissues from patients with SCZ compared with control individuals, including the donors of our iPS cell cohort. Altogether, we demonstrate that ventral forebrain organoids derived from postmortem tissue of individuals with schizophrenia recapitulate perturbed striatal gene expression dynamics of the donors’ brains (Sawada et al, biorxiv 2022 https://doi.org/10.1101/2022.05.26.493589).

Molecular recording using precision genome editing

A framework for detecting noncoding rare variant associations of large-scale whole-genome sequencing studies

The impact of emerging technologies and methods on the interpretation of genetic variation in autism and fetal genomics

At the nexus of genes, aging and environment: Understanding transcriptomic and epigenomic regulation in Parkinson's disease

Parkinson’s Disease (PD), the most common neurodegenerative movement disorder, is based on a complex interplay between genetic predispositions, aging processes, and environmental influences. In order to better understand the gene-environment axis in PD, we pursue a multi-omics approach to comprehensively interrogate genome-wide changes in histone modifications, DNA methylation, and hydroxymethylation, accompanied by transcriptomic profiling in cell and animal models of PD as well as large patient cohorts. Furthermore, we assess the plasticity of epigenomic modifications under influence of environmental factors using longitudinal cohorts of sporadic PD cases as well as mouse models exposed to specific environmental factors. Here, we present gene expression changes in PD mouse models in context of aging as well as environmental enrichment and high-fat diet.

Don't forget the gametes: Neurodevelopmental pathogenesis starts in the sperm and egg

Proper development of the nervous system depends not only on the inherited DNA sequence, but also on proper regulation of gene expression, as controlled in part by epigenetic mechanisms present in the parental gametes. In this presentation an internationally recognized research advocate explains why researchers concerned about the origins of increasingly prevalent neurodevelopmental disorders such as autism and attention deficit hyperactivity disorder should look beyond genetics in probing the origins of dysregulated transcription of brain-related genes. The culprit for a subset of cases, she contends, may lie in the exposure history of the parents, and thus their germ cells. To illustrate how environmentally informed, nongenetic dysfunction may occur, she focuses on the example of parents' histories of exposure to common agents of modern inhalational anesthesia, a highly toxic exposure that in mammalian models has been seen to induce heritable neurodevelopmental abnormality in offspring born of exposed germline.

Cell-type specific genomics and transcriptomics of HIV in the brain

Exploration of genome organization and function in the HIV infected brain is critical to aid in the understanding and development of treatments for HIV-associated neurocognitive disorder (HAND). Here, we applied a multiomic approach, including single nuclei transcriptomics, cell-type specific Hi-C 3D genome mapping, and viral integration site sequencing (IS-seq) to frontal lobe tissue from HIV-infected individuals with encephalitis (HIVE) and without encephalitis (HIV+). We observed reorganization of open/repressive (A/B) compartment structures in HIVE microglia encompassing 6.4% of the genome with enrichment for regions containing interferon (IFN) pathway genes. 3D genome remodeling was associated with transcriptomic reprogramming, including down-regulation of cell adhesion and synapse-related functions and robust activation of IFN signaling and cell migratory pathways, and was recapitulated by IFN-g stimulation of cultured microglial cells. Microglia from HIV+ brains showed, to a lesser extent, similar transcriptional alterations. IS-seq recovered 1,221 integration sites in the brain that were enriched for chromosomal domains newly mobilized into a permissive chromatin environment in HIVE microglia. Viral transcription, which was detected in 0.003% of all nuclei in HIVE brain, occurred in a subset of highly activated microglia that drove differential expression in HIVE. Thus, we observed a dynamic interrelationship of interferon-associated 3D genome and transcriptome remodeling with HIV integration and transcription in the brain.

Mapping the Dynamics of the Linear and 3D Genome of Single Cells in the Developing Brain

Three intimately related dimensions of the mammalian genome—linear DNA sequence, gene transcription, and 3D genome architecture—are crucial for the development of nervous systems. Changes in the linear genome (e.g., de novo mutations), transcriptome, and 3D genome structure lead to debilitating neurodevelopmental disorders, such as autism and schizophrenia. However, current technologies and data are severely limited: (1) 3D genome structures of single brain cells have not been solved; (2) little is known about the dynamics of single-cell transcriptome and 3D genome after birth; (3) true de novo mutations are extremely difficult to distinguish from false positives (DNA damage and/or amplification errors). Here, I filled in this longstanding technological and knowledge gap. I recently developed a high-resolution method—diploid chromatin conformation capture (Dip-C)—which resolved the first 3D structure of the human genome, tackling a longstanding problem dating back to the 1880s. Using Dip-C, I obtained the first 3D genome structure of a single brain cell, and created the first transcriptome and 3D genome atlas of the mouse brain during postnatal development. I found that in adults, 3D genome “structure types” delineate all major cell types, with high correlation between chromatin A/B compartments and gene expression. During development, both transcriptome and 3D genome are extensively transformed in the first month of life. In neurons, 3D genome is rewired across scales, correlated with gene expression modules, and independent of sensory experience. Finally, I examined allele-specific structure of imprinted genes, revealing local and chromosome-wide differences. More recently, I expanded my 3D genome atlas to the human and mouse cerebellum—the most consistently affected brain region in autism. I uncovered unique 3D genome rewiring throughout life, providing a structural basis for the cerebellum’s unique mode of development and aging. In addition, to accurately measure de novo mutations in a single cell, I developed a new method—multiplex end-tagging amplification of complementary strands (META-CS), which eliminates nearly all false positives by virtue of DNA complementarity. Using META-CS, I determined the true mutation spectrum of single human brain cells, free from chemical artifacts. Together, my findings uncovered an unknown dimension of neurodevelopment, and open up opportunities for new treatments for autism and other developmental disorders.

Experience-Dependent Transcription: From Genomic Mechanisms to Neural Circuit Function

Experience-dependent transcription is a key molecular mechanisms for regulating the development and plasticity of synapses and neural circuits and is thought to underlie cognitive functions such as perception, learning and memory. After two years of COVID-pandemic, the goal of this online conference is to allow investigators in the field to reconnect and to discuss their recent scientific findings.

Brain Basics: A peak into the Brain!

My talk will be a ’Neuro 101’ - also called ‘Basics of Neuroscience’. I hope to introduce the field of Neuroscience and give a brief glimpse into the function, history and evolution of the brain. I will guide you through questions such as - What is a brain? What are its basic building blocks and functions?

Towards a More Authentic Vision of the (multi)Coding Potential of RNA

Ten of thousands of open reading frames (ORFs) are hidden within transcripts. They have eluded annotations because they are either small or within unsuspected locations. These are named alternative ORFs (altORFs) or small ORFs and have recently been highlighted by innovative proteogenomic approaches, such as our OpenProt resource, revealing their existence and implications in biological functions. Due to the absence of altORFs from annotations, pathogenic mutations within these are being ignored. I will discuss our latest progress on the re-analysis of large-scale proteomics datasets to improve our knowledge of proteomic diversity, and the functional characterization of a second protein coded by the FUS gene. Finally, I will explain the need to map the coding potential of the transcriptome using artificial intelligence rather than with conventional annotations that do not capture the full translational activity of ribosomes.

Assembly of the neocortex

The symposium will start with Prof Song-Hai Shi who will present “Assembly of the neocortex”. Then, Dr Lynette Lim will talk about “Shared and Unique Developmental Trajectories of Cortical Inhibitory Neurons”. Dr Alfredo Molina will deal with the “Tuneable progenitor cells to build the cerebral cortex”, and Prof Tomasz Nowakowski will present “Charting the molecular 'protomap' of the human cerebral cortex using single cell genomic”.

Genome architecture and disease: the 16p11.2 paradigm

Integrative genomics of paper wasp behavior: Molecular underpinnings of complex traits and insights into social evolution

Developmental trajectories of autism-spectrum disorder associated genes

Bridging computational neuroscience and genomics in the era of big data

Role of mechanical morphogenesis in the development and evolution of the cerebral cortex

CRISPR-based functional genomics in iPSC-based models of brain disease

Human genes associated with brain-related diseases are being discovered at an accelerating pace. A major challenge is an identification of the mechanisms through which these genes act, and of potential therapeutic strategies. To elucidate such mechanisms in human cells, we established a CRISPR-based platform for genetic screening in human iPSC-derived neurons, astrocytes and microglia. Our approach relies on CRISPR interference (CRISPRi) and CRISPR activation (CRISPRa), in which a catalytically dead version of the bacterial Cas9 protein recruits transcriptional repressors or activators, respectively, to endogenous genes to control their expression, as directed by a small guide RNA (sgRNA). Complex libraries of sgRNAs enable us to conduct genome-wide or focused loss-of-function and gain-of-function screens. Such screens uncover molecular players for phenotypes based on survival, stress resistance, fluorescent phenotypes, high-content imaging and single-cell RNA-Seq. To uncover disease mechanisms and therapeutic targets, we are conducting genetic modifier screens for disease-relevant cellular phenotypes in patient-derived neurons and glia with familial mutations and isogenic controls. In a genome-wide screen, we have uncovered genes that modulate the formation of disease-associated aggregates of tau in neurons with a tauopathy-linked mutation (MAPT V337M). CRISPRi/a can also be used to model and functionally evaluate disease-associated changes in gene expression, such as those caused by eQTLs, haploinsufficiency, or disease states of brain cells. We will discuss an application to Alzheimer’s Disease-associated genes in microglia.