RESUMO
Glioblastoma Multiforme is a brain tumor distinguished by its aggressiveness. We suggested that this aggressiveness leads single-cell RNA-sequence data (scRNA-seq) to span a representative portion of the cancer attractors domain. This conjecture allowed us to interpret the scRNA-seq heterogeneity as reflecting a representative trajectory within the attractor's domain. We considered factors such as genomic instability to characterize the cancer dynamics through stochastic fixed points. The fixed points were derived from centroids obtained through various clustering methods to verify our method sensitivity. This methodological foundation is based upon sample and time average equivalence, assigning an interpretative value to the data cluster centroids and supporting parameters estimation. We used stochastic simulations to reproduce the dynamics, and our results showed an alignment between experimental and simulated dataset centroids. We also computed the Waddington landscape, which provided a visual framework for validating the centroids and standard deviations as characterizations of cancer attractors. Additionally, we examined the stability and transitions between attractors and revealed a potential interplay between subtypes. These transitions might be related to cancer recurrence and progression, connecting the molecular mechanisms of cancer heterogeneity with statistical properties of gene expression dynamics. Our work advances the modeling of gene expression dynamics and paves the way for personalized therapeutic interventions.
Assuntos
Neoplasias Encefálicas , Glioblastoma , Análise de Célula Única , Glioblastoma/genética , Glioblastoma/patologia , Glioblastoma/metabolismo , Humanos , Análise de Célula Única/métodos , Neoplasias Encefálicas/genética , Neoplasias Encefálicas/patologia , Neoplasias Encefálicas/metabolismo , Regulação Neoplásica da Expressão Gênica , Heterogeneidade Genética , Perfilação da Expressão Gênica/métodos , Instabilidade Genômica , Análise de Sequência de RNA/métodos , Análise por ConglomeradosRESUMO
Network dynamics are crucial for action and sensation. Changes in synaptic physiology lead to the reorganization of local microcircuits. Consequently, the functional state of the network impacts the output signal depending on the firing patterns of its units. Networks exhibit steady states in which neurons show various activities, producing many networks with diverse properties. Transitions between network states determine the output signal generated and its functional results. The temporal dynamics of excitation/inhibition allow a shift between states in an operational network. Therefore, a process capable of modulating the dynamics of excitation/inhibition may be functionally important. This process is known as disinhibition. In this review, we describe the effect of GABA levels and GABAB receptors on tonic inhibition, which causes changes (due to disinhibition) in network dynamics, leading to synchronous functional oscillations.
Assuntos
Fenômenos Fisiológicos do Sistema Nervoso , Receptores de GABA-B , Receptores de GABA-B/metabolismo , Neurônios/metabolismo , Inibição Neural/fisiologia , Ácido gama-Aminobutírico , Receptores de GABA-A , Antagonistas GABAérgicosRESUMO
Ecological interactions are highly dynamic in time and space. Previous studies of plant-animal mutualistic networks have shown that the occurrence of interactions varies substantially across years. We analyzed interannual variation of a quantitative mutualistic network, in which links are weighted by interaction frequency. The network was sampled over six consecutive years, representing one of the longest time series for a community-wide mutualistic network. We estimated the interannual similarity in interactions and assessed the determinants of their persistence. The occurrence of interactions varied greatly among years, with most interactions seen in only one year (64%) and few (20%) in more than two years. This variation was associated with the frequency and position of interactions relative to the network core, so that the network consisted of a persistent core of frequent interactions and many peripheral, infrequent interactions. Null model analyses suggest that species abundances play a substantial role in generating these patterns. Our study represents an important step in the study of ecological networks, furthering our mechanistic understanding of the ecological processes driving the temporal persistence of interactions.