Atomistic Molecular Insights into Angiotensin-(1-7) Interpeptide Interactions.

Angiotensin-(1-7) is an endogenous peptide known for its vasoprotective, antioxidant, and anti-inflammatory effects, making it a promising therapeutic candidate for various clinical conditions. However, the peptide exhibits pH-dependent physical instability in aqueous solutions, and a comprehensive atomistic study elucidating this behavior and its implications is currently lacking. Therefore, we performed all-atom molecular dynamics simulations to investigate the early formation of angiotensin-(1-7) oligomeric aggregates under different conditions: acidic and neutral pH-like conditions, physiological and high ionic strength, and high and low peptide concentrations. Our results are as follows: (1) under acidic pH-like conditions, angiotensin-(1-7) showed minimal clustering, (2) under neutral pH-like conditions, the peptides aggregated into a single cluster, consistent with the reported physical instability, and (3) increasing salt concentration under acidic pH-like conditions resulted in aggregation similar to that observed under neutral pH-like conditions. These results suggest that a combination of salt concentration and pH conditions can modulate angiotensin-(1-7) aggregation. Our protocol (molecular dynamics + cluster analysis + amino acid interaction map analysis) is general and could be applied to other peptides to study interpeptide interaction mechanisms.

Topological data analysis of antibody dynamics of severe and non-severe patients with COVID-19.

The COVID-19 pandemic is a significant public health threat with unanswered questions regarding the immune system's role in the disease's severity level. Here, based on antibody kinetic data of severe and non-severe COVID-19 patients, topological data analysis (TDA) highlights that severity is not binary. However, there are differences in the shape of antibody responses that further classify COVID-19 patients into non-severe, severe, and intermediate cases of severity. Based on the results of TDA, different mathematical models were developed to represent the dynamics between the different severity groups. The best model was the one with the lowest average value of the Akaike Information Criterion for all groups of patients. Our results suggest that different immune mechanisms drive differences between the severity groups. Further inclusion of different components of the immune system will be central for a holistic way of tackling COVID-19.

A new computational approach, based on images trajectories, to identify the subjacent heterogeneity of sperm to the effects of ketanserin.

The identification of kinematic subpopulations is of paramount importance to understanding the biological nature of the sperm heterogeneity. Nowadays, the data of motility parameters obtained by a computer-assisted sperm analysis (CASA) system has been used as input to distinct algorithms to identify kinematic subpopulations. In contrast, the images of the trajectories were depicted only as examples of the patterns of motility in each subpopulation. Here, python code was written to reconstruct the images of trajectories, from their coordinates, then the images of trajectories were used as input to a machine learning clustering algorithm of classification, and the subpopulations were described statistically by the motility parameters. Finally, the images of trajectories in each subpopulation were displayed in a way we called Pollock plots. Semen samples of boar sperm were treated with distinct concentrations of ketanserin (an antagonist of the 5-HT2 receptor of serotonin) and untreated samples were used as a control. The motility of sperm in each sample was analyzed at 0 and 30 min of incubation. Six subpopulations were found. The subpopulation 2 presented the highest values of velocities at 0 or 30 min. After 30 min of incubation, the ketanserin increased the values of the curvilinear velocity at high concentrations, whereas the linearity and the straight velocity decreased. Our computational model permits better identification of the kinematic subpopulations than the traditional approach and provides insights onto the heterogeneity of the response to ketanserin; thus, it could significantly impact the research on the relationship between sperm heterogeneity-fertility.

Computational simulations to dissect the cell immune response dynamics for severe and critical cases of SARS-CoV-2 infection.

BACKGROUND: COVID-19 is a global pandemic leading to high death tolls worldwide day by day. Clinical evidence suggests that COVID-19 patients can be classified as non-severe, severe, and critical cases. In particular, studies have highlighted the relationship between lymphopenia and the severity of the illness, where CD8+ T cells have the lowest levels in critical cases. However, a quantitative understanding of the immune responses in COVID-19 patients is still missing. OBJECTIVES: In this work, we aim to elucidate the key parameters that define the course of the disease deviating from severe to critical cases. The dynamics of different immune cells are taken into account in mechanistic models to elucidate those that contribute to the worsening of the disease. METHODS: Several mathematical models based on ordinary differential equations are proposed to represent data sets of different immune response cells dynamics such as CD8+ T cells, NK cells, and also CD4+ T cells in patients with SARS-CoV-2 infection. Parameter fitting is performed using the differential evolution algorithm. Non-parametric bootstrap approach is introduced to abstract the stochastic environment of the infection. RESULTS: The mathematical model that represents the data more appropriately is considering CD8+ T cell dynamics. This model had a good fit to reported experimental data, and in accordance with values found in the literature. The NK cells and CD4+ T cells did not contribute enough to explain the dynamics of the immune responses. CONCLUSIONS: Our computational results highlight that a low viral clearance rate by CD8+ T cells could lead to the severity of the disease. This deregulated clearance suggests that it is necessary immunomodulatory strategies during the course of the infection to avoid critical states in COVID-19 patients.

Stress distribution in two-dimensional silos.

Simulations of a polydispersed two-dimensional silo were performed using molecular dynamics, with different numbers of grains reaching up to 64 000, verifying numerically the model derived by Janssen and also the main assumption that the walls carry part of the weight due to the static friction between grains with themselves and those with the silo's walls. We vary the friction coefficient, the radii dispersity, the silo width, and the size of grains. We find that the Janssen's model becomes less relevant as the the silo width increases since the behavior of the stresses becomes more hydrostatic. Likewise, we get the normal and tangential stress distribution on the walls evidencing the existence of points of maximum stress. We also obtained the stress matrix with which we observe zones of concentration of load, located always at a height around two thirds of the granular columns. Finally, we observe that the size of the grains affects the distribution of stresses, increasing the weight on the bottom and reducing the normal stress on the walls, as the grains are made smaller (for the same total mass of the granulate), giving again a more hydrostatic and therefore less Janssen-type behavior for the weight of the column.