Uncovering the Transcription Factor Network Underlying Mammalian Sex Determination

<p>Understanding transcriptional regulation in development and disease is one of the central questions in modern biology. The current working model is that Transcription Factors (TFs) combinatorially bind to specific regions of the genome and drive the expression of groups of genes in a cell-type specific fashion. In organisms with large genomes, particularly mammals, TFs bind to enhancer regions that are often several kilobases away from the genes they regulate, which makes identifying the regulators of gene expression difficult. In order to overcome these obstacles and uncover transcriptional regulatory networks, we used an approach combining expression profiling and genome-wide identification of enhancers followed by motif analysis. Further, we applied these approaches to uncover the TFs important in mammalian sex determination.</p><p>Using expression data from a panel of 19 human cell lines we identified genes showing patterns of cell-type specific up-regulation, down-regulation and constitutive expression. We then utilized matched DNase-seq data to assign DNase Hypersensitivity Sites (DHSs) to each gene based on proximity. These DHSs were scanned for matches to motifs and compiled to generate scores reflecting the presence of TF binding sites (TFBSs) in each gene's putative regulatory regions. We used a sparse logistic regression classifier to classify differentially regulated groups of genes. Comparing our approach to proximal promoter regions, we discovered that using sequence features in regions of open chromatin provided significant performance improvement. Crucially, we discovered both known and novel regulators of gene expression in different cell types. For some of these TFs, we found cell-type specific footprints indicating direct binding to their cognate motifs.</p><p>The mammalian gonad is an excellent system to study cell fate determination processes and the dynamic regulation orchestrated by TFs in development. At embryonic day (E) 10.5, the bipotential gonad initiates either testis development in XY embryos, or ovarian development in XX embryos. Genetic studies over the last 3 decades have revealed about 30 genes important in this process, but there are still significant gaps in our understanding. Specifically, we do not know the network of TFs and their specific combinations that cause the rapid changes in gene expression observed during gonadal fate commitment. Further, more than half the cases of human sex reversal are as yet unexplained. </p><p>To apply the methods we developed to identify regulators of gene expression to the gonad, we took two approaches. First, we carried out a careful dissection of the transcriptional dynamics during gonad differentiation in the critical window between E11.0 and E12.0. We profiled the transcriptome at 6 equally spaced time points and developed a Hidden Markov Model to reveal the cascades of transcription that drive the differentiation of the gonad. Further, we discovered that while the ovary maintains its transcriptional state at this early stage, concurrent up- and down-regulation of hundreds of genes are orchestrated by the testis pathway. Further, we compared two different strains of mice with differential susceptibility to XY male-to-female sex reversal. This analysis revealed that in the C57BL/6J strain, the male pathway is delayed by ~5 hours, likely explaining the increased susceptibility to sex reversal in this strain. Finally, we validated the function of Lmo4, a transcriptional co-factor up-regulated in XY gonads at E11.6 in both strains. RNAi mediated knockdown of Lmo4 in primary gonadal cells led to the down-regulation of male pathway genes including key regulators such as Sox9 and Fgf9. </p><p>To find the enhancers in the XY gonad, we conducted DNase-seq in E13.5 XY supporting cells. In addition, we conducted ChIP-seq for H3K27ac, a mark correlated with active enhancer activity. Further, we conducted motif analysis to reveal novel regulators of sex determination. Our work is an important step towards combining expression and chromatin profiling data to assemble transcriptional networks and is applicable to several systems.</p>Dissertatio

Linking electronic structure calculations to generalized stacking fault energies in multicomponent alloys

The generalized stacking fault energy is a key ingredient to mesoscale models of dislocations. Here we develop an approach to quantify the dependence of generalized stacking fault energies on the degree of chemical disorder in multicomponent alloys. We introduce the notion of a "configurationally-resolved planar fault" (CRPF) energy and extend the cluster expansion method from alloy theory to express the CRPF as a function of chemical occupation variables of sites surrounding the fault. We apply the approach to explore the composition and temperature dependence of the unstable stacking fault energy (USF) in binary Mo-Nb alloys. First-principles calculations are used to parameterize a formation energy and CRPF cluster expansion. Monte Carlo simulations show that the distribution of USF energies is significantly affected by chemical composition and temperature. The formalism can be applied to any multicomponent alloy and will enable the development of rigorous models for deformation mechanisms in high-entropy alloys

Scale bridging materials physics: Active learning workflows and integrable deep neural networks for free energy function representations in alloys

The free energy plays a fundamental role in descriptions of many systems in continuum physics. Notably, in multiphysics applications, it encodes thermodynamic coupling between different fields. It thereby gives rise to driving forces on the dynamics of interaction between the constituent phenomena. In mechano-chemically interacting materials systems, even consideration of only compositions, order parameters and strains can render the free energy to be reasonably high-dimensional. In proposing the free energy as a paradigm for scale bridging, we have previously exploited neural networks for their representation of such high-dimensional functions. Specifically, we have developed an integrable deep neural network (IDNN) that can be trained to free energy derivative data obtained from atomic scale models and statistical mechanics, then analytically integrated to recover a free energy density function. The motivation comes from the statistical mechanics formalism, in which certain free energy derivatives are accessible for control of the system, rather than the free energy itself. Our current work combines the IDNN with an active learning workflow to improve sampling of the free energy derivative data in a high-dimensional input space. Treated as input-output maps, machine learning accommodates role reversals between independent and dependent quantities as the mathematical descriptions change with scale bridging. As a prototypical system we focus on Ni-Al. Phase field simulations using the resulting IDNN representation for the free energy density of Ni-Al demonstrate that the appropriate physics of the material have been learned. To the best of our knowledge, this represents the most complete treatment of scale bridging, using the free energy for a practical materials system, that starts with electronic structure calculations and proceeds through statistical mechanics to continuum physics

Genome-wide identification of regulatory elements in Sertoli cells

A current goal of molecular biology is to identify transcriptional networks that regulate cell differentiation. However, identifying functional gene regulatory elements has been challenging in the context of developing tissues where material is limited and cell types are mixed. To identify regulatory sites during sex determination, we subjected Sertoli cells from mouse fetal testes to DNaseI-seq and ChIP-seq for H3K27ac. DNaseI-seq identified putative regulatory sites around genes enriched in Sertoli and pregranulosa cells; however, active enhancers marked by H3K27ac were enriched proximal to only Sertoli-enriched genes. Sequence analysis identified putative binding sites of known and novel transcription factors likely controlling Sertoli cell differentiation. As a validation of this approach, we identified a novel Sertoli cell enhancer upstream of Wt1, and used it to drive expression of a transgenic reporter in Sertoli cells. This work furthers our understanding of the complex genetic network that underlies sex determination and identifies regions that potentially harbor non-coding mutations underlying disorders of sexual development

Efficient CRISPR-Cas9 mediated gene disruption in primary erythroid progenitor cells

The study of isolated primary progenitor cells offers great insight into developmental biology and human disease. In particular, ex vivo culture of isolated primary erythroid progenitor cells replicates the differentiation events that occur during in vivo erythropoiesis. Herein we report a high-efficiency method for CRISPR-Cas9 mediated gene disruption in isolated primary erythroid progenitor cells. We use this method to generate the novel result that Lmna is required in terminal erythroid differentiation.Frederick Lovejoy (Research Grant)National Institutes of Health (U.S.) (grant NIH/NHLBI 2 P01 HL032262-25

Efficient emotion recognition using hyperdimensional computing with combinatorial channel encoding and cellular automata

In this paper, a hardware-optimized approach to emotion recognition based on the efficient brain-inspired hyperdimensional computing (HDC) paradigm is proposed. Emotion recognition provides valuable information for human-computer interactions, however the large number of input channels (>200) and modalities (>3) involved in emotion recognition are significantly expensive from a memory perspective. To address this, methods for memory reduction and optimization are proposed, including a novel approach that takes advantage of the combinatorial nature of the encoding process, and an elementary cellular automaton. HDC with early sensor fusion is implemented alongside the proposed techniques achieving two-class multi-modal classification accuracies of >76% for valence and >73% for arousal on the multi-modal AMIGOS and DEAP datasets, almost always better than state of the art. The required vector storage is seamlessly reduced by 98% and the frequency of vector requests by at least 1/5. The results demonstrate the potential of efficient hyperdimensional computing for low-power, multi-channeled emotion recognition tasks

Temporal Transcriptional Profiling of Somatic and Germ Cells Reveals Biased Lineage Priming of Sexual Fate in the Fetal Mouse Gonad

The divergence of distinct cell populations from multipotent progenitors is poorly understood, particularly in vivo. The gonad is an ideal place to study this process, because it originates as a bipotential primordium where multiple distinct lineages acquire sex-specific fates as the organ differentiates as a testis or an ovary. To gain a more detailed understanding of the process of gonadal differentiation at the level of the individual cell populations, we conducted microarrays on sorted cells from XX and XY mouse gonads at three time points spanning the period when the gonadal cells transition from sexually undifferentiated progenitors to their respective sex-specific fates. We analyzed supporting cells, interstitial/stromal cells, germ cells, and endothelial cells. This work identified genes specifically depleted and enriched in each lineage as it underwent sex-specific differentiation. We determined that the sexually undifferentiated germ cell and supporting cell progenitors showed lineage priming. We found that germ cell progenitors were primed with a bias toward the male fate. In contrast, supporting cells were primed with a female bias, indicative of the robust repression program involved in the commitment to XY supporting cell fate. This study provides a molecular explanation reconciling the female default and balanced models of sex determination and represents a rich resource for the field. More importantly, it yields new insights into the mechanisms by which different cell types in a single organ adopt their respective fates

Machine-learning the configurational energy of multicomponent crystalline solids

Machine learning tools such as neural networks and Gaussian process regression are increasingly being implemented in the development of atomistic potentials. Here, we develop a formalism to leverage such non-linear interpolation tools in describing properties dependent on occupation degrees of freedom in multicomponent solids. Symmetry-adapted cluster functions are used to differentiate distinct local orderings. These local features are used as input to neural networks that reproduce local properties such as the site energy. We apply the technique to reproduce a synthetic cluster expansion Hamiltonian with multi-body interactions, as well as the formation energies calculated from first-principles for the intercalation of lithium into TiS2. The formalism and results presented here show that complex multi-body interactions may be approximated by non-linear models involving smaller clusters

Linking electronic structure calculations to generalized stacking fault energies in multicomponent alloys

The generalized stacking fault energy is a key ingredient to mesoscale models of dislocations. Here we develop an approach to quantify the dependence of generalized stacking fault energies on the degree of chemical disorder in multicomponent alloys. We introduce the notion of a “configurationally-resolved planar fault” (CRPF) energy and extend the cluster expansion method from alloy theory to express the CRPF as a function of chemical occupation variables of sites surrounding the fault. We apply the approach to explore the composition and temperature dependence of the unstable stacking fault energy (USF) in binary Mo–Nb alloys. First-principles calculations are used to parameterize a formation energy and CRPF cluster expansion. Monte Carlo simulations show that the distribution of USF energies is significantly affected by chemical composition and temperature. The formalism is broadly applicable to arbitrary crystal structures and alloy chemistries and will enable the development of rigorous models for deformation mechanisms in high-entropy alloys.MADE