Biology subjects

Pinol-Ripoll, G.

Publications and source records attributed to Pinol-Ripoll, G..

3 recordsLinked to original sources

bioRxiv2025

Non-linear and time-domain sleep qEEG features predict CSF protein damage markers in early Alzheimers disease

Study ObjectivesIn preclinical Alzheimers disease (AD), oxidative stress plays a key role in the pathogenesis by promoting non-enzymatic protein modifications that lead to the accumulation of oxidized species, established biomarkers of oxidative damage detectable in cerebrospinal fluid (CSF). These molecular alterations contribute to impaired proteostasis and the dysfunction of sleep-regulating neural circuits, resulting in altered sleep electroencephalographic patterns. Due to the invasiveness of CSF sampling, quantitative electroencephalography (qEEG) is proposed as a non-invasive alternative for assessing oxidatively modified protein levels via Machine Learning (ML). MethodsForty-two mild-to-moderate AD patients underwent polysomnography (PSG). QEEG features were extracted. CSF protein oxidation markers levels --glutamic semialdehyde, aminoadipic semialdehyde, N{varepsilon}-carboxyethyl-lysine, N{varepsilon}-carboxymethyl-lysine, and N{varepsilon}-malondialdehyde-lysine --were assessed by gas chromatography/mass spectrometry, and ML models trained to predict CSF biomarker levels. Model generalizability was validated using EEG data from healthy controls. ResultsqEEG features from slow-wave sleep (SWS) and rapid eye movement (REM) sleep, particularly over frontal and central regions, yielded R2 > 0.625 for patients biomarker prediction. ConclusionqEEG is a non-invasive, scalable tool for detecting AD-related oxidative processes, with potential implications for early diagnosis and risk stratification. HighlightsO_LISleep EEG features predict CSF oxidative biomarkers in early Alzheimers disease. C_LIO_LIPeak-to-peak amplitude, variance, and entropy were the top predictive features. C_LIO_LINREM sleep, especially SWS, and REM sleep provided the most informative EEG signals. C_LIO_LIEEG-based models achieved accurate, non-invasive biomarker estimation. C_LIO_LISleep qEEG may enable early detection and risk stratification in Alzheimers. C_LI

neuroscience↗

bioRxiv2022

Lipidomic alterations in the cerebral cortex and white matter in sporadic Alzheimer's disease

AimsNon-targeted lipidomics analysis was conducted in post-mortem human frontal cortex area 8 (GM) and white matter of the frontal lobe centrum semi-ovale (WM) to identify lipidomes in middle-aged individuals with no neurofibrillary tangles and senile plaques, and cases at progressive stages of sporadic Alzheimers disease (sAD). MethodsLipidomic analysis using an LC-MS/MS platform was carried out in selected cases with suitable post-mortem lacking co-morbidities and concomitant brain pathologies. Complementary data were obtained using RT-qPCR and immunohistochemistry. ResultsThe WM shows an adaptive lipid phenotype resistant to lipid peroxidation, characterized by a lower fatty acid unsaturation, peroxidizability index, and higher ether lipid content than the GM. Changes in the lipidomic profile more marked in the WM than in GM occur in AD with disease progression. WM alterations are characterized by a decline in the content of branched fatty acid esters of hydroxy fatty acids (FAHFA), diacylglycerols (DG), triacylglycerols (TG), glycerophospholipids (GP) (especially ether lipids), and sphingolipids (especially sulfatides, ceramides, and glycosphingolipids). The GM acquires a fatty acid profile prone to peroxidation in sAD, while WM reinforces its peroxidation-resistant trait. Transcriptomic data point to additional defects of peroxisomal function. ConclusionsFour functional categories are associated with the different lipid classes affected in sAD: membrane structural composition, bioenergetics, antioxidant protection, and bioactive lipids, with deleterious consequences affecting both neurons and glial cells favoring disease progression.

neuroscience↗

bioRxiv2020

Autosomal Recessive Alzheimer disease (arAD): homozygosity mapping of genomic regions containing arAD loci

Long runs of homozygosity (ROH) are contiguous stretches of homozygous genotypes, which are a footprint of recent inbreeding and recessive inheritance. The presence of recessive loci is suggested for Alzheimers disease (AD). However, the search for recessive variants has been poorly assessed to date. To investigate homozygosity in AD, we performed a fine-scale ROH analysis including 21,100 individuals from 10 cohorts of European ancestry (11,919 AD cases and 9,181 controls). We detected an increase of homozygosity in AD cases compared to controls [{beta}FROH (CI95%) = 0.051 (0.023 - 0.078); P = 3.25 x 10-4]. ROHs increasing the risk of AD (OR > 1) were significantly overrepresented compared to ROHs increasing protection (p < 2.20 x 10-16). The top associated ROH with AD risk ({beta} (CI95%) = 1.09 (0.48 - 1.48), p value = 9.03 x 10-4) was detected upstream the HS3ST1 locus (chr4:11,189,482-11,305,456), previously related to AD. Next, to construct a homozygosity map of AD cases, we selected ROHs shared by inbred AD cases extracted from an outbred population. We used whole-exome sequencing data from 1,449 individuals from the Knight-ADRC-NIA-LOAD (KANL) cohort to identify potential recessive variants in candidate ROHs. We detected a candidate marker, rs117458494, mapped in the SPON1 locus, which has been previously associated with amyloid metabolism. Here, we provide a research framework to look for recessive variants in AD using outbred populations. Our results showed that AD cases have enriched homozygosity, suggesting that recessive effects may explain a proportion of AD heritability.

genetics↗