PhD Proposal Exam
Tuesday, June 12, 2018 at 10:10am, MW 229- University of Toronto at Scarborough
Colleen Gillon (Richards Lab)
“How does the brain learn about the statistical structure of the environment?”
Over the past decade, artificial intelligence has progressed at great speed, with impressive breakthroughs in fields like computer vision and speech processing using neural network algorithms. These can be broadly divided into two classes: (1) discriminative models, like feedforward, convolutional and recurrent neural nets, which learn to map inputs, like images, to specific outputs, like categories or classes and (2) generative models, like bidirectional Helmholtz machines, generative-adversarial networks and expectation maximization models, which are learn the underlying joint structure of the data. Studies of visual processing in the cortex strongly suggest that in learning to process environmental stimuli, our brains behave like generative models, developing internal models of the joint distribution over sensory stimuli in the environment. Thus, these algorithms could shed light on our brain’s remarkable ability to represent and process sensory information efficiently and accurately. We propose to investigate this by comparing how the brain and different algorithms trained on visual tasks process and adapt to major changes in the relationship between incoming visual stimuli and somatosensory or motor inputs. Specifically, we will record and analyse changes in the activity of layer 2/3 pyramidal neurons in primary visual cortex (V1) in response to a shift in the relationship between visual stimuli and sensory stimuli or motor commands. We predict that this shift will transiently increase activity in the apical dendrites, and alter the rate of apical trunk calcium spikes of these neurons while the system adapts. In parallel, we will train different generative algorithms on this same task, and analyze changes in network activity in order to identify those algorithms that show the greatest potential for explaining how our brains process sensory information.
PhD Proposal Exam
Tuesday, May 22nd, 2018 at 1:10 pm – CCT-4034, University of Toronto at Mississauga
Klotilda Narkaj (Zovkic lab)
“Histone Variant MacroH2A in Memory Formation”
Epigenetic modifications are widely recognized for their role in memory formation. Although existing research has focused almost exclusively on DNA methylation and histone post-translational modifications (PTMs), we recently discovered that histone variant exchange, in which canonical histones are replaced by distinct variants, is a novel branch of epigenetics for regulating memory. Our initial work showed that binding of the histone variant H2A.Z is modified by learning, suggesting that the composition of histones that make up nucleosomes is subject to learning- and memory-related modification. Though H2A variants can replace one another in chromatin, which histones replace one another and how distinct variants influence memory is largely unknown. H2A.Z is one of several functionally diverse H2A variants that functions as a memory suppressor. For my thesis I will investigate another potential candidate for memory regulation, histone variant macroH2A (mH2A), its relationship with H2A.Z, and their interaction in memory formation. MacroH2A has a widely reported role in regulating gene expression, it is encoded by 2 genes, H2afy (encodes mH2A1) and H2afy2 (encodes mH2A2), both of which are expressed throughout the mouse brain, including the hippocampus, a brain region that is vital for memory formation. To explore the role of mH2A in memory, we use adeno-associated virus (AAV) to knock down either H2afy or H2afy2 in area CA1 and tested mice on an array of hippocampus-dependent memory tasks at the 24-hour and 7-day time points. We found that mice with depleted levels of both mH2A1 and mH2A2 had impaired fear memory 24 hours and 7 days after training, suggesting that both mH2A-encoding genes promote hippocampus-dependent memory formation. To identify the mechanism by which mH2A regulates memory, area CA1 was extracted 30 min after fear conditioning, exposed to mH2A chromatin-immunoprecipitation combined with next-generation sequencing, and compared to genome-wide gene-expression 1h after training, based on time points at which our lab previously found an association between H2A.Z dynamics and gene expression. To elucidate the relationship between H2A.Z and mH2A in memory, I will investigate binding of mH2A in chromatin in response to H2A.Z depletion, after learning. These data will explore involvement of histone variant exchange as a novel epigenetic regulator of behaviour and they are the first to show mH2A as a regulator of memory.
PhD Proposal Exam
Tuesday, January 25th, 2018 at 10:10 am – Ramsay Wright Building, Room 432
Luís Eduardo Abatti (Mitchell lab)
“Investigating the SOX2 transcriptional network in estrogen-responsive and estrogen-resistant breast cancer cells”
Breast cancer is a multifactorial disease characterized by aberrant gene expression. The sex-determining region Y box2 (SOX2), a key transcription factor associated with pluripotency, is often overexpressed in breast cancer cells, where it has been linked to epithelial-mesenchymal transition (EMT) and hormone resistance. In mouse embryonic stem cells, Sox2 is regulated by a wide transcription factor network that interacts with its distal enhancer. However, the SOX2 transcriptional network in breast cancer cells remains unknown. Mammary epithelial cells rely on the estrogen receptor alpha (ESR1) and its cofactors – FOXA1 and GATA3 – to properly respond to estrogen stimulation, while breast cancer cells frequently display a dysfunctional estrogen response. My hypothesis is that SOX2 is normally downregulated by the repressive action of ESR1, FOXA1 and GATA3 at a distal enhancer. Once the estrogen pathway is disrupted in hormone-resistant cells, the repressive effect of estrogen over SOX2 expression is abolished, and SOX2 recruits the RNA Polymerase II transcriptional complex at multiple genomic targets. To better understand the role and regulation of SOX2 in this scenario, I propose three objectives: first, to identify the SOX2 transcriptional network in breast cancer cells; second, to investigate SOX2 cis- and trans-regulatory elements in MCF-7 cells; and third, to understand SOX2 upregulation in hormone-resistant MCF-7 cells. This SOX2 functional investigation will elucidate how breast cancer cells rely on this transcription factor to maintain their tumourigenesis and how its upregulation is linked to hormone resistance.