Biology subjects

Pinho, M.

Publications and source records attributed to Pinho, M..

6 recordsLinked to original sources

bioRxiv2024

Neurogenetic evidence for oscillatory behavior of Lgr3-positive growth control interneurons in Drosophila

The ability to achieve a species-specific size and proportion despite developmental or environmental perturbations is termed developmental stability. The molecular and cellular processes behind this are best understood in insects. In Drosophila, a peripheral-tissue stress signal, the relaxin/insulin-like peptide Dilp8, promotes developmental stability during larval development via its neuronal receptor, Lgr3, an ortholog of vertebrate relaxin receptors. Lgr3 signaling is widely accepted to occur in-and to activate (depolarize)-the central brain growth-coordinating interneurons (PIL/GCL neurons). Here, using neurogenetic approaches, we confirm the requirement of Lgr3 in PIL/GCL neurons, but unexpectedly find that they require both silenced (hyperpolarized) and active (depolarized) states for an appropriate response to Dilp8. These results are most simply explained if Lgr3 activation by Dilp8 triggers PIL/GCL-neuron oscillatory activity, and such oscillations promote developmental stability. PIL/GCL neurons express and require Cyclin A-which can form cell-cycle oscillator complexes with cyclin-dependent kinases-for their response to Dilp8, independently of Rca1 (regulator of CycA)/Emi1 (early mitotic inhibitor). This opens the possibility that cell-cycle machinery can be co-opted for postmitotic neuron oscillations, adding to an increasing list of postmitotic roles for cyclins. Neuroanatomically, we show that PIL/GCL neurons form reciprocally-innervating loops, which are common architectures in oscillating circuits and central pattern generators. The role of PIL/GCL neurons in developmental stability mirrors other homeostasis-regulating, peptide-driven oscillatory circuits found in the vertebrate hypothalamus, a developmentally-homologous region to the one occupied by PIL/GCL neurons in the fly brain.

neuroscience↗

bioRxiv2022

String/Cdc25 phosphatase is a suppressor of Tau-associated neurodegeneration

Tau pathology is defined by the intracellular accumulation of abnormally phosphorylated Tau and is prevalent in several neurodegenerative disorders. The identification of modulators of Tau abnormal phosphorylation and aggregation is key to understand disease progression and develop targeted therapeutic approaches. In this study we identify String/Cdc25 phosphatase as a suppressor of Tau abnormal phosphorylation and associated toxicity. Using a Drosophila model of tauopathy we show that Tau dephosphorylation by Stg/Cdc25 correlates with reduced Tau oligomerization, brain vacuolization and locomotor deficits in flies. Moreover, using a disease mimetic model, we provide evidence that Stg/Cdc25 reduces Tau phosphorylation levels independently of Tau aggregation status and delays neurodegeneration progression in the fly. These findings uncover a role for Stg/Cdc25 phosphatases as regulators of Tau biology, that extends beyond their well-characterized function as cell-cycle regulators during cell proliferation, and point-out Stg/Cdc25 based approaches as promising entry points to target abnormal Tau phosphorylation.

neuroscience↗

bioRxiv2021

Pitfalls and recommended strategies and metrics for suppressing motion artifacts in functional MRI

In resting-state functional magnetic resonance imaging (rs-fMRI), artefactual signals arising from subject motion can dwarf and obfuscate the neuronal activity signal. Typical motion correction approaches involve the generation of nuisance regressors, which are timeseries of non-brain signals regressed out of the fMRI timeseries to yield putatively artifact-free data. Recent work suggests that concatenating all regressors into a single regression model is more effective than the sequential application of individual regressors, which may reintroduce previously removed artifacts. This work compares 18 motion correction pipelines consisting of head motion, independent components analysis, and non-neuronal physiological signal regressors in sequential or concatenated combinations. The pipelines are evaluated on a dataset of cognitively normal individuals with repeat imaging and on datasets of studies of Autism Spectrum Disorder, Major Depressive Disorder, and Parkinsons Disease. Extensive metrics of motion artifact removal are measured, including resting state network recovery, Quality Control-Functional Connectivity (QC-FC) correlation, distance-dependent artifact, network modularity, and test-retest reliability of multiple rs-fMRI analyses. The results reveal limitations in previously proposed metrics, including the QC-FC correlation and modularity quality, and identify more robust motion correction metrics. The results also reveal limitations in the concatenated regression approach, which is outperformed by the sequential regression approach in the test-retest reliability metrics. Finally, pipelines are recommended that perform well based on quantitative and qualitative comparisons across multiple datasets and robust metrics. These new insights and recommendations help address the need for effective motion artifact correction to reduce noise and confounds in rs-fMRI.

bioengineering↗

bioRxiv2019

Bacterial DNA promotes Tau aggregation

A hallmark feature of Alzheimers disease (AD) and other tauopathies is the misfolding, aggregation and cerebral accumulation of tau deposits. Compelling evidence indicates that misfolded tau aggregates are neurotoxic, producing synaptic loss and neuronal damage. Misfolded tau aggregates are able to spread the pathology from cell-to-cell by a prion like seeding mechanism. The factors implicated in the initiation and progression of tau misfolding and aggregation are largely unclear. In this study, we evaluated the effect of DNA extracted from diverse prokaryotic and eukaryotic cells in tau misfolding and aggregation. Our results show that DNA from various, unrelated gram-positive and gram-negative bacteria results in a more pronounced tau misfolding compared to eukaryotic DNA. Interestingly, a higher effect in promoting tau aggregation was observed for DNA extracted from certain bacterial species previously detected in the brain, CSF or oral cavity of patients with AD. Our findings indicate that microbial DNA may play a previously overlooked role in the propagation of tau protein misfolding and AD pathogenesis, providing a new conceptual framework that positions the compromised blood-brain and intestinal barriers as important sources of microbial DNA in the CNS, opening novel opportunities for therapeutic interventions.

microbiology↗

bioRxiv2019

Fully Automated Brain Tumor Segmentation and Survival Prediction of Gliomas using Deep Learning and MRI

Tumor segmentation of magnetic resonance (MR) images is a critical step in providing objective measures of predicting aggressiveness and response to therapy in gliomas. It has valuable applications in diagnosis, monitoring, and treatment planning of brain tumors. The purpose of this work was to develop a fully automated deep learning method for brain tumor segmentation and survival prediction. Well curated brain tumor cases with multi-parametric MR Images from the BraTS2019 dataset were used. A three-group framework was implemented, with each group consisting of three 3D-Dense-UNets to segment whole tumor (WT), tumor core (TC) and enhancing tumor (ET). This method was implemented to decompose the complex multi-class segmentation problem into individual binary segmentation problems for each sub-component. Each group was trained using different approaches and loss functions. The output segmentations of a particular label from their respective networks from the 3 groups were ensembled and post-processed. For survival analysis, a linear regression model based on imaging texture features and wavelet texture features extracted from each of the segmented components was implemented. The networks were tested on the BraTS2019 validation dataset including 125 cases for the brain tumor segmentation task and 29 cases for the survival prediction task. The segmentation networks achieved average dice scores of 0.901, 0.844 and 0.801 for WT, TC and ET respectively. The survival prediction network achieved an accuracy score of 0.55 and mean squared error (MSE) of 119244. This method could be implemented as a robust tool to assist clinicians in primary brain tumor management and follow-up.

neuroscience↗

bioRxiv2019

Classification of Brain Tumor IDH Status using MRI and Deep Learning

Isocitrate dehydrogenase (IDH) mutation status is an important marker in glioma diagnosis and therapy. We propose a novel automated pipeline for predicting IDH status noninvasively using deep learning and T2-weighted (T2w) MR images with minimal preprocessing (N4 bias correction and normalization to zero mean and unit variance). T2w MRI and genomic data were obtained from The Cancer Imaging Archive dataset (TCIA) for 260 subjects (120 High grade and 140 Low grade gliomas). A fully automated 2D densely connected model was trained to classify IDH mutation status on 208 subjects and tested on another held-out set of 52 subjects, using 5-fold cross validation. Data leakage was avoided by ensuring subject separation during the slice-wise randomization. Mean classification accuracy of 90.5% was achieved for each axial slice in predicting the three classes of no tumor, IDH mutated and IDH wild-type. Test accuracy of 83.8% was achieved in predicting IDH mutation status for individual subjects on the test dataset of 52 subjects. We demonstrate a deep learning method to predict IDH mutation status using T2w MRI alone. Radiologic imaging studies using deep learning methods must address data leakage (subject duplication) in the randomization process to avoid upward bias in the reported classification accuracy.

neuroscience↗