Biology subjects

Lopes, F. M.

Publications and source records attributed to Lopes, F. M..

3 recordsLinked to original sources

bioRxiv2020

Evidence-based gene expression modulation correlates with transposable element knock-down

BackgroundTransposable elements (TEs) are major components of plant genomes. Despite being regarded as "junk DNA" at first, TEs play important roles for the organisms they are found in. The most obvious and easily recognizable effects caused by TEs result from their mobility, which can disrupt coding sequences or promoter regions. However, with the recent advances in transcriptomics, it is becoming increasingly evident that TEs can act as an additional layer of gene expression regulation through a number of processes, which can involve production of non-coding RNAs. Here, we describe how Tnt1, a stress-responsive LTR-retrotransposon, interferes with gene expression and modulate a number of developmental aspects in tobacco. ResultsThrough an RNAi approach, we generated tobacco (HP) lines knocked-down for Tnt1 expression. Quantitative RT-PCR experiments confirm that Tnt1 is downregulated in HP lines after ethylene exposure. A RNA-seq experiment was performed and through two independent bioinformatic approaches (with different stringencies) we found 932 and 97 differentially expressed genes in HP lines. A number of phenotypes were observed in such lines, namely lesion mimicry in leaves, underdevelopment of the root system, overproduction of root hairs and early loss of seed viability. Folding prediction of part of the Tnt1 mRNA reveals putative stem-loop secondary structures containing transcriptional regulation sequences, suggesting it could be a source of small RNAs. We also propose a model to explain the Tnt1 expression in both homeostatic and stress conditions, and how it could interact with stress-responsive genes. ConclusionsOur results are consistent that interferences with Tnt1 transcript levels correlate with transcriptomic and phenotypic changes, suggesting a functional role for this element during plant development and stress response.

molecular biology↗

bioRxiv2020

Feature Extraction Approaches for Biological Sequences: A Comparative Study of Mathematical Models

The number of available biological sequences has increased significantly in recent years due to various genomic sequencing projects, creating a huge volume of data. Consequently, new computational methods are needed to analyze and extract information from these sequences. Machine learning methods have shown broad applicability in computational biology and bioinformatics. The utilization of machine learning methods has helped to extract relevant information from various biological datasets. However, there are still several obstacles that motivate new algorithms and pipeline proposals, mainly involving feature extraction problems, in which extracting significant discriminatory information from a biological set is challenging. Considering this, our work proposes to study and analyze a feature extraction pipeline based on mathematical models (Numerical Mapping, Fourier, Entropy, and Complex Networks). As a case study, we analyze Long Non-Coding RNA sequences. Moreover, we divided this work into two studies, e.g., (I) we assessed our proposal with the most addressed problem in our review, e.g., lncRNA vs. mRNA; (II) we tested its generalization on different classification problems, e.g., circRNA vs. lncRNA. The experimental results demonstrated three main contributions: (1) An in-depth study of several mathematical models; (2) a new feature extraction pipeline and (3) its generalization and robustness for distinct biological sequence classification.

bioinformatics↗

bioRxiv2020

Oxidative switch drives mitophagy defects in dopaminergic parkin mutant patient neurons

Background Mutations in parkin are the most common cause of early onset Parkinsons disease. Parkin is an E3 ubiquitin ligase, functioning in mitophagy. Mitochondrial abnormalities are present in parkin mutant models. Patient derived neurons are a promising model in which to study pathogenic mechanisms and therapeutic targets. Here we generate induced neuronal progenitor cells from parkin mutant patient fibroblasts with a high dopaminergic neuron yield. We reveal changing mitochondrial phenotypes as neurons undergo a metabolic switch during differentiation. Methods Fibroblasts from 4 controls and 4 parkin mutant patients were transformed into induced neuronal progenitor cells and subsequently differentiated into dopaminergic neurons. Mitochondrial morphology, function and mitophagy were evaluated using live cell fluorescent imaging, cellular ATP and reactive oxygen species production quantification. Results Direct conversion of control and parkin mutant patient fibroblasts results in induced neuronal progenitor and their differentiation yields high percentage of dopaminergic neurons. We were able to observe changing mitochondrial phenotypes as neurons undergo a metabolic switch during differentiation. Our results show that when pre-neurons are glycolytic early in differentiation mitophagy is unimpaired by PRKN deficiency. However as neurons become oxidative phosphorylation dependent, mitophagy is severely impaired in the PRKN mutant patient neurons. These changes correlate with changes in mitochondrial function and morphology; resulting in lower neuron yield and altered neuronal morphology. Conclusions Induced neuronal progenitor cell conversion can produce a high yield of dopaminergic neurons. The mitochondrial phenotype, including mitophagy status, is highly dependent on the metabolic status of the cell. Only when neurons are oxidative phosphorylation reliant the extent of mitochondrial abnormalities are identified. These data provide insight into cell specific effects of PRKN mutations, in particular in relation to mitophagy dependent disease phenotypes and provide avenues for alternative therapeutic approaches.

neuroscience↗