Biology subjects

Januario, C.

Publications and source records attributed to Januario, C..

4 recordsLinked to original sources

bioRxiv2024

Small non-coding RNA content in plasma-derived extracellular vesicles distinguish ataxic SCA3 mutation carriers from pre-ataxic and control subjects

Spinocerebellar ataxia type 3 (SCA3), a neurodegenerative disorder caused by a CAG expansion in the ATXN3 gene, is the most common spinocerebellar ataxia subtype worldwide. Currently, there is no therapy to stop or prevent disease progression. Promising therapeutic strategies are emerging, but their translation into clinical practice requires sensitive and reliable biomarkers. Blood circulating extracellular vesicles constitute a promising source of biomarkers with potential to track alterations of the central nervous system due to their ability to cross the blood brain barrier. Here, we perform sequencing analysis of small RNAs from plasma-derived extracellular vesicles from SCA3 mutation carriers (10 pre-ataxic and 10 ataxic) and 12 control subjects to identify potential RNA biomarker candidates for this disease. Data showed that plasma-derived extracellular vesicles from ataxic SCA3 mutation carriers are enriched in mitochondrial, nuclear, and nucleolar RNA biotypes compared to pre-ataxic and control subjects. Moreover, ataxic mutation carriers could be discriminated from control and pre-ataxic subjects based on the miRNAs or piRNAs content, but not tRNA. Furthermore, we identified a subset of differentially expressed miRNAs and piRNAs that clearly differentiate ataxic mutation carriers from pre-ataxic and control subjects. These findings open new avenues for further investigation on the role of these RNAs in the pathogenesis of SCA3 and their potential as biomarkers for this disease.

neuroscience↗

bioRxiv2023

Unmasking Hidden Systemic Effects of Neurodegenerative Diseases: A Two-Pronged Approach to Biomarker Discovery

Identification of reliable blood biomarkers for neurodegenerative diseases (NDs) is crucial for translational and clinical research. However, conventional omics struggle with blood samples complexity, hindering desired outcomes. In this work the potential of High Molecular Weight (HMW) fractionation under non-denaturing conditions as a complementary approach to the conventional proteomics for identifying serum biomarkers in NDs was explored. A cohort of 58 serum samples of Alzheimers disease (AD), Parkinsons disease (PD) patients and control (CT) individuals was used to compare the two proteomics strategies: i) direct analysis of whole serum and ii) non-denaturing fractionation using 300 kDa cut-off filters (HMW serum). Although both approaches quantified a similar set of proteins, each approach captured a distinct subset of differentially altered proteins, suggesting that HMW fractionation identified additional types of alterations beyond conventional protein level changes. A discriminant model combining altered proteins from both datasets effectively distinguished between the three groups (AUC = 0.999 and median sensitivity and specificity of 97.4% and 91.7%, respectively). Importantly, this performance surpassed that of any model created using each method individually. Altogether, this work demonstrated that HMW fractionation can be a valuable complementary method to direct serum analysis and could enhance biomarker discovery. The 10 proteins included in the model (5 from each strategy), comprise clear evidence for the contribution of apolipoproteins for the diagnosis of NDs, revealing potential changes within lipid metabolism and the organization of macromolecules and their complexes, thereby uncovering effects that remain hidden from a conventional serum proteome analysis.

biochemistry↗

bioRxiv2022

Mitochondrial and redox modifications in early stages of Huntington disease

Defects in mitochondrial function and mitochondrial-related redox deregulation have been attributed to Huntingtons disease (HD), a genetic neurodegenerative disorder largely affecting the striatum. However, whether these changes occur in early stages of the disease and can be detected in vivo is still unclear. Thus, in the present study, we analyzed changes in mitochondrial function and overreduced states associated with production of reactive oxygen species (ROS) at early stages and along disease progression. Studies were performed in vivo in human brain using positron emission tomography (PET) using [64Cu]-ATSM and ex vivo in human skin fibroblasts of premanifest and prodromal (Pre-M) and manifest HD patients; in vivo brain [64Cu]-ATSM PET and isolated mitochondria derived from striatum and cortex were also analyzed in YAC128 transgenic mouse at pre-symptomatic (3 month-old, mo) and symptomatic (6 to 12 mo) stages. Oxygen consumption rates were assessed by Seahorse analysis, hydrogen peroxide levels were determined using fluorescent probes and mitochondrial morphology by transmission electron microscopy in human skin fibroblasts and mouse striatal and cortical isolated mitochondria. Pre-M HD carriers exhibited enhanced whole-brain (with exception of caudate) [64Cu]-ATSM labelling, correlating with CAG repeat number. Fibroblasts from Pre-M showed enhanced basal and maximal respiration, proton (H+) leak and increased hydrogen peroxide levels, the later progressing to manifest HD; mitochondria from fibroblasts of Pre-M HD carriers also showed reduced roundness, while higher number of mitochondrial DNA copies correlated with maximal respiratory capacity. In vivo animal PET analysis showed increased accumulation of [64Cu]-ATSM in YAC128 mouse striatum. Pre-symptomatic YAC128 mouse striatal isolated mitochondria exhibited a rise in basal and maximal mitochondrial respiration and in ATP production, along with increased complex II and III activities; mouse HD mitochondria also showed enhanced mitochondrial hydrogen peroxide levels and roundness, as revealed by brain ultrastructure analysis, and defects in Ca2+ handling, supporting increased striatal susceptibility in YAC128 mouse brain. Data demonstrate both human and mouse mitochondrial overactivity and altered morphology at early HD stages, facilitating redox unbalance, the latter extending over manifest disease stages.

neuroscience↗

bioRxiv2017

Optimal homotopy analysis of a chaotic HIV-1 model incorporating AIDS-related cancer cells

The studies of nonlinear models in epidemiology have generated a deep interest in gaining insight into the mechanisms that underlie AIDS-related cancers, providing us with a better understanding of cancer immunity and viral oncogenesis. In this article, we analyse an HIV-1 model incorporating the relations between three dynamical variables: cancer cells, healthy CD4+ T lymphocytes and infected CD4+ T lymphocytes. Recent theoretical investigations indicate that these cells interactions lead to different dynamical outcomes, for instance to periodic or chaotic behavior. Firstly, we analytically prove the boundedness of the trajectories in the systems attractor. The complexity of the coupling between the dynamical variables is quantified using observability indices. Our calculations reveal that the highest observable variable is the population of cancer cells, thus indicating that these cells could be monitored in future experiments in order to obtain time series for attractors reconstruction. We identify different dynamical behaviors of the system varying two biologically meaningful parameters: r1, representing the uncontrolled proliferation rate of cancer cells, and k1, denoting the immune systems killing rate of cancer cells. The maximum Lyapunov exponent is computed to identify the chaotic regimes. Considering very recent developments in the literature related to the homotopy analysis method (HAM), we construct the explicit series solution of the cancer model and focus our analysis on the dynamical variable with the highest observability index. An optimal homotopy analysis approach is used to improve the computational efficiency of HAM by means of appropriate values for the convergence control parameter, which greatly accelerate the convergence of the series solution.

systems biology↗