Mechanosensitivity of Jagged-Notch signaling can induce a switch-type behavior in vascular homeostasis

Hemodynamic forces and Notch signaling are both known as key regulators of arterial remodeling and homeostasis. However, how these two factors integrate in vascular morphogenesis and homeostasis is unclear. Here, we combined experiments and modeling to evaluate the impact of the integration of mechanics and Notch signaling on vascular homeostasis. Vascular smooth muscle cells (VSMCs) were cyclically stretched on flexible membranes, as quantified via video tracking, demonstrating that the expression of Jagged1, Notch3, and target genes was down-regulated with strain. The data were incorporated in a computational framework of Notch signaling in the vascular wall, where the mechanical load was defined by the vascular geometry and blood pressure. Upon increasing wall thickness, the model predicted a switch-type behavior of the Notch signaling state with a steep transition of synthetic toward contractile VSMCs at a certain transition thickness. These thicknesses varied per investigated arterial location and were in good agreement with human anatomical data, thereby suggesting that the Notch response to hemodynamics plays an important role in the establishment of vascular homeostasis

Temporal and sequential transcriptional dynamics define lineage shifts in corticogenesis

The cerebral cortex contains billions of neurons, and their disorganization or misspecification leads to neurodevelopmental disorders. Understanding how the plethora of projection neuron subtypes are generated by cortical neural stem cells (NSCs) is a major challenge. Here, we focused on elucidating the transcriptional landscape of murine embryonic NSCs, basal progenitors (BPs), and newborn neurons (NBNs) throughout cortical development. We uncover dynamic shifts in transcriptional space over time and heterogeneity within each progenitor population. We identified signature hallmarks of NSC, BP, and NBN clusters and predict active transcriptional nodes and networks that contribute to neural fate specification. We find that the expression of receptors, ligands, and downstream pathway components is highly dynamic over time and throughout the lineage implying differential responsiveness to signals. Thus, we provide an expansive compendium of gene expression during cortical development that will be an invaluable resource for studying neural developmental processes and neurodevelopmental disorders

t-Test at the Probe Level: An Alternative Method to Identify Statistically Significant Genes for Microarray Data

Microarray data analysis typically consists in identifying a list of differentially expressed genes (DEG), i.e., the genes that are differentially expressed between two experimental conditions. Variance shrinkage methods have been considered a better choice than the standard t-test for selecting the DEG because they correct the dependence of the error with the expression level. This dependence is mainly caused by errors in background correction, which more severely affects genes with low expression values. Here, we propose a new method for identifying the DEG that overcomes this issue and does not require background correction or variance shrinkage. Unlike current methods, our methodology is easy to understand and implement. It consists of applying the standard t-test directly on the normalized intensity data, which is possible because the probe intensity is proportional to the gene expression level and because the t-test is scale- and location-invariant. This methodology considerably improves the sensitivity and robustness of the list of DEG when compared with the t-test applied to preprocessed data and to the most widely used shrinkage methods, Significance Analysis of Microarrays (SAM) and Linear Models for Microarray Data (LIMMA). Our approach is useful especially when the genes of interest have small differences in expression and therefore get ignored by standard variance shrinkage methods

t-Test at the Probe Level: An Alternative Method to Identify Statistically Significant Genes for Microarray Data

Microarray data analysis typically consists in identifying a list of differentially expressed genes (DEG), i.e., the genes that are differentially expressed between two experimental conditions. Variance shrinkage methods have been considered a better choice than the standard t-test for selecting the DEG because they correct the dependence of the error with the expression level. This dependence is mainly caused by errors in background correction, which more severely affects genes with low expression values. Here, we propose a new method for identifying the DEG that overcomes this issue and does not require background correction or variance shrinkage. Unlike current methods, our methodology is easy to understand and implement. It consists of applying the standard t-test directly on the normalized intensity data, which is possible because the probe intensity is proportional to the gene expression level and because the t-test is scale- and location-invariant. This methodology considerably improves the sensitivity and robustness of the list of DEG when compared with the t-test applied to preprocessed data and to the most widely used shrinkage methods, Significance Analysis of Microarrays (SAM) and Linear Models for Microarray Data (LIMMA). Our approach is useful especially when the genes of interest have small differences in expression and therefore get ignored by standard variance shrinkage methods

Topics in cell signaling and bioinformatics: operating principles of Notch signaling pathway and supervised variational relevance learning

Na primeira parte desta tese, estudamos os princípios operacionais das decisões celulares mediadas pelo sistema de sinalização Notch. Este sistema tem papel importante nas decisões celulares que ocorrem durante o desenvolvimento embrionário, cicatrização de feridas e na formação de tumores. O circuito de sinalização é ativado quando o receptor Notch de uma célula interage com um dos ligantes - Delta ou Jagged - de uma célula vizinha. O circuito Notch-Delta forma um comutador intercelular, e duas células vizinhas tendem a adotar estados diferentes - Emissor (muito ligante e pouco receptor) e Recebedor (pouco ligante e muito receptor). Neste manuscrito, apresentamos uma nova abordagem teórica que integra ambos Delta e Jagged no circuito Notch. Mostramos que o circuito Notch-Delta-Jagged permite um novo estado - um híbrido Emissor/Recebedor (E/R) com concentrações intermediárias de receptores e ligantes, e portanto o circuito é age como uma chave de três vias. Em seguida, observamos que a taxa de produção de ambos os ligantes, assim como a modulação assimétrica da afinidade de ligação do Notch com seus ligantes mediada pela glicosiltransferase Fringe, afeta severamente o intervalo de existência dos estados e sua relativa estabilidade - altos níveis de Jagged, mas não de Fringe ou Delta, promovem o estado híbrido E/R e o processo de indução lateral. Nós elucidamos o papel de Jagged na determinação dos estados celulares e discutimos sua possível implicação no entendimento da comunicação entre tumor e estroma, que frequentemente envolve comunicação via interações Notch-Jagged. Posteriormente, avaliamos a interação entre Notch, inflamação e a população de Células Cancerígenas Estaminais (CCE). Mostramos que inflamação pode expandir a população de CCE por meio do aumento dos níveis de produção de Jagged que posteriormente ativa o sistema de sinalização Notch em células vizinhas não-CCE. Nossos resultados sugerem que a inibição da produção de Jagged atenua o efeito da expansão de CCE devido a inflamação, indicando que inflamação cresce a população de CCE via interações Notch-Jagged. Nossos resultados são consistentes com observações em câncer de mama do subtipo basal (triplo negativo), onde a perda de Fringe e a ativação constitutiva do eixo NF-kB - Jag1 promove a expansão da população de CCE. Nossa abordagem computacional pode ser adaptada para incluir circuitos adicionais tais como p53 e hipóxia, que afetam a plasticidade celular, proporcionando assim uma plataforma útil para a projeção de novas terapias. Na segunda parte desta tese, introduzimos um novo método para seleção de características: Suvrel. Este é um método variacional, inspirado em aprendizado de relevância, para determinar tensores métricos para definição de distâncias baseadas em similaridades, para utilização em métodos de classificação. Nós introduzimos uma nova metodologia na qual o tensor métrico pode ser calculado analiticamente. O preprocessamento das características por uma transformação linear utilizando o tensor métrico calculado via Suvrel melhora a eficiência dos classificadores. Testamos nosso método para conjuntos de dados públicos, utilizando os classificadores mais comumente utilizados. Nós também aplicamos esta metodologia no estudo da relação entre parâmetros estruturais globais e o sistema de classificação de função enzimática. Por último, introduzimos uma nova metodologia para a identificação de genes diferencialmente expressos utilizando a tecnologia de microarranjos de DNA. Diferentemente das abordagens tradicionais, nossa metodologia evita passos intermediários de preprocessamento que são desnecessários e devido a isto não acumula erros destas análises, o que resulta em um método mais sensível e robusto.In the first part of this thesis, we studied the operating principles of cell fate decisions mediated by Notch signaling pathway. This pathway have important role in cell fate determination during embryonic development, wound healing and tumorigenesis. Notch signaling is activated by binding of Notch receptor of one cell to either of its ligand- Delta or Jagged- of another cell. Notch-Delta circuit forms an intercellular toggle switch, and two neighboring cells tend to adopt different fates - Sender (high ligand, low receptor) and Receiver (low ligand, high receptor). Here, we present a new tractable theoretical framework that incorporates both Delta and Jagged in Notch signaling, and show that Notch-Delta-Jagged circuit enables an additional fate - hybrid Sender/Receiver (S/R) (medium ligand, medium receptor) and behaves as a three-way switch. Further, we found that production rates of both the ligands and the asymmetric modulation of binding affinity of Notch to its ligands by glycosyltransferase Fringe severely affects the parameter range of the existence of these states and their relative stability - high levels of Jagged, but not that of Fringe or Delta, promote hybrid S/R state and lateral induction. We elucidate the role of Jagged in cell fate determination and discuss its possible implications in understanding tumor-stroma crosstalk, which frequently entails Notch-Jagged communication. We further evaluate the interplay among Notch signaling, inflamation and Cancer Stem Cell population. We show that inflammation can expand the population of Cancer Stem Cells (CSCs) by increasing the levels of Jagged in cells that can further activate Notch signaling pathway in neighboring non-CSCs. Our results suggest that, inhibiting the production of Jagged dampens the effect of inflammation in expanding the CSC population, indicating that inflammatory signal function through Notch-Jagged signaling to increase CSCs. Our results are consistent with observations in basal-like breast cancer, where loss of Fringe and constitutive activation of NF-kB-Jag1 axis promotes CSC population. Our computational framework can be tailored to include additional signals such as p53 and hypoxia that affect this plasticity to gain stemness, thus providing a platform that can be useful in designing novel therapies. In the second part of the thesis, we introduce a new method for feature selection: Supervised Variational Relevance Learning (Suvrel). This is a variational method to determine metric tensors to define distance based similarity in pattern classification, inspired in relevance learning. We propose a new methodology where the metric tensor can be calculated analytically. Preprocessing the patterns by doing linear transformations using the metric tensor yields a dataset which can be more efficiently classified. We test our methods using publicly available datasets, for some standard classifiers. We also applied this methodology to study the relationship between global structural parameters and the Enzyme Commission hierarchy. Lastly, we propose a new methodology for identifying the differentially expressed genes using DNA microarray technology. Unlike traditional approaches, our methodology skips intermediate unnecessary preprocessing steps and therefore does not accumulate errors due to these analysis, resulting in a more sensitive and robust method

Differential interactions between Notch and ID factors control neurogenesis by modulating Hes factor autoregulation

During embryonic and adult neurogenesis, neural stem cells (NSCs) generate the correct number and types of neurons in a temporospatial fashion. Control of NSC activity and fate is crucial for brain formation and homeostasis. Neurogenesis in the embryonic and adult brain differ considerably, but Notch signaling and inhibitor of DNA-binding (ID) factors are pivotal in both. Notch and ID factors regulate NSC maintenance; however, it has been difficult to evaluate how these pathways potentially interact. Here, we combined mathematical modeling with analysis of single-cell transcriptomic data to elucidate unforeseen interactions between the Notch and ID factor pathways. During brain development, Notch signaling dominates and directly regulates Id4 expression, preventing other ID factors from inducing NSC quiescence. Conversely, during adult neurogenesis, Notch signaling and Id2/3 regulate neurogenesis in a complementary manner and ID factors can induce NSC maintenance and quiescence in the absence of Notch. Our analyses unveil key molecular interactions underlying NSC maintenance and mechanistic differences between embryonic and adult neurogenesis. Similar Notch and ID factor interactions may be crucial in other stem cell systems

Cell-based simulations of Notch-dependent cell differentiation on growing domains

Notch signalling controls cell differentiation and proliferation in many tissues. The Notch signal is generated by the interaction between the Notch receptor of one cell with the Notch ligand (Delta or Jagged) of a neighbouring cell. Therefore, the pathway requires cell-cell contact in order to be active. During organ development, cell differentiation occurs concurrently with tissue growth and changes in cell morphology. How growth impacts on Notch signalling and cell differentiation remains poorly understood. Here, we developed a modelling environment to simulate Notch signalling in a growing tissue. We use our model to simulate the differentiation process of pancreatic progenitor cells. Our results suggest that Notch-mediated differentiation in the developing pancreas is first mediated by geometric effects that result in loss of Notch signalling on the tissue boundary, leading to the differentiation of tip versus trunk cells. A second wave of differentiation further happens in the trunk cells due to a reduction in the expression of the ligand Jagged, which has been shown to be controlled by signalling factors secreted from the surrounding mesenchyme. Our results bring new insights into how cells coordinate tissue growth with cell fate specification during organ development

Positional information encoded in the dynamic differences between neighboring oscillators during vertebrate segmentation

A central problem in developmental biology is to understand how cells interpret their positional information to give rise to spatial patterns, such as the process of periodic segmentation of the vertebrate embryo into somites. For decades, somite formation has been interpreted according to the clock-and-wavefront model. In this conceptual framework, molecular oscillators set the frequency of somite formation while the positional information is encoded in signaling gradients. Recent experiments using ex vivo explants have challenged this interpretation, suggesting that positional information is encoded in the properties of the oscillators, independent of long-range modulations such as signaling gradients. Here, we propose that positional information is encoded in the difference in the levels of neighboring oscillators. The differences gradually increase because both the amplitude and the period of the oscillators increase with time. When this difference exceeds a certain threshold, the segmentation program starts. Using this framework, we quantitatively fit experimental data from in vivo and ex vivo mouse segmentation, and propose mechanisms of somite scaling. Our results suggest a novel mechanism of spatial pattern formation based on the local interactions between dynamic molecular oscillators

Supervised variational relevance learning, an analytic geometric feature selection with applications to omic datasets

We introduce Supervised Variational Relevance Learning (Suvrel), a variational method to determine metric tensors to define distance based similarity in pattern classification, inspired in relevance learning. The variational method is applied to a cost function that penalizes large intraclass distances and favors small interclass distances. We find analytically the metric tensor that minimizes the cost function. Preprocessing the patterns by doing linear transformations using the metric tensor yields a dataset which can be more efficiently classified. We test our methods using publicly available datasets, for some standard classifiers. Among these datasets, two were tested by the MAQC-II project and, even without the use of further preprocessing, our results improve on their performance.Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq

Relationship between global structural parameters and Enzyme Commission hierarchy: Implications for function prediction

In protein databases there is a substantial number of proteins structurally determined but without function annotation. Understanding the relationship between function and structure can be useful to predict function on a large scale. We have analyzed the similarities in global physicochemical parameters for a set of enzymes which were classified according to the four Enzyme Commission (EC) hierarchical levels. Using relevance theory we introduced a distance between proteins in the space of physicochemical characteristics. This was done by minimizing a cost function of the metric tensor built to reflect the EC classification system. Using an unsupervised clustering method on a set of 1025 enzymes, we obtained no relevant clustering formation compatible with EC classification. The distance distributions between enzymes from the same EC group and from different EC groups were compared by histograms. Such analysis was also performed using sequence alignment similarity as a distance. Our results suggest that global structure parameters are not sufficient to segregate enzymes according to EC hierarchy. This indicates that features essential for function are rather local than global. Consequently, methods for predicting function based on global attributes should not obtain high accuracy in main EC classes prediction without relying on similarities between enzymes from training and validation datasets. Furthermore, these results are consistent with a substantial number of studies suggesting that function evolves fundamentally by recruitment, i.e., a same protein motif or fold can be used to perform different enzymatic functions and a few specific amino acids (AAs) are actually responsible for enzyme activity. These essential amino acids should belong to active sites and an effective method for predicting function should be able to recognize them. (C) 2012 Elsevier Ltd. All rights reserved.Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq