Publications by authors named "Juan Botia"

Ageing underlies functional decline of the brain and is the primary risk factor for several neurodegenerative conditions, including Alzheimer's disease (AD). However, the molecular mechanisms that cause functional decline of the brain during ageing, and how these contribute to AD pathogenesis, are not well understood. The objective of this study was to identify biological processes that are altered during ageing in the hippocampus and that modify Ad risk and lifespan, and then to identify putative gene drivers of these programmes.

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  • Researchers have advanced understanding of Parkinson's disease genetics through genome-wide association studies (GWAS) but have found that many genetic factors still contribute to its heritability, potentially due to interactions between variants (epistasis).
  • A new screening method, VARI3, was developed to investigate these interactions using data from numerous cohorts, successfully identifying notable variant interactions in genes like SNCA, MAPT, and WNT3.
  • The study demonstrated that these epistatic signals were present across different ethnic backgrounds, including European and Native American ancestries, and linked to important biological functions related to Parkinson's disease risk.
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To identify circRNAs associated with Parkinson's disease (PD) we leveraged two of the largest publicly available studies with longitudinal clinical and blood transcriptomic data. We performed a cross-sectional study utilizing the last visit of each participant (N = 1848), and a longitudinal analysis that included 1166 participants with at least two time points. We identified 192 differentially expressed circRNAs, with effects that were sustained during disease, in mutation carriers, and diverse ancestry.

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We aimed to identify plasma cell-free transcripts (cfRNA) associated with Parkinson's disease (PD) that also have a high predictive value to differentiate PD from healthy controls. Leveraging two independent populations from two different movement disorder centers we identified 2,188 differentially expressed cfRNAs after meta-analysis. The identified transcripts were enriched in PD relevant pathways, such as PD (p=9.

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  • The study investigates how specific immune cell types in blood samples from patients with metastatic castration-resistant prostate cancer (mCRPC) can affect survival rates.
  • Researchers analyzed pre-treatment blood samples from 152 patients and found that lower CD8 T-cell counts and higher monocyte levels were linked to shorter survival.
  • Their results suggest that these immune cell types could act as important biomarkers for mCRPC management, supporting the need for further research in clinical trials.
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Background: Alzheimer's disease (AD) is a neurodegenerative condition for which there is currently no available medication that can stop its progression. Previous studies suggest that mild cognitive impairment (MCI) is a phase that precedes the disease. Therefore, a better understanding of the molecular mechanisms behind MCI conversion to AD is needed.

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Primary familial brain calcification (PFBC) is characterized by calcium deposition in the brain, causing progressive movement disorders, psychiatric symptoms, and cognitive decline. PFBC is a heterogeneous disorder currently linked to variants in six different genes, but most patients remain genetically undiagnosed. Here, we identify biallelic NAA60 variants in ten individuals from seven families with autosomal recessive PFBC.

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We aimed to identify circRNAs associated with Parkinson's disease (PD) by leveraging 1,848 participants and 1,789 circRNA from two of the largest publicly available studies with longitudinal clinical and blood transcriptomic data. To comprehensively understand changes in circRNAs we performed a cross-sectional study utilizing the last visit of each participant, and a longitudinal (mix model) analysis that included 1,166 participants with at least two time points. We identified 192 circRNAs differentially expressed in PD participants compared to healthy controls, with effects that were consistent in the mixed models, mutation carriers, and diverse ancestry.

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Advancements in genome sequencing have facilitated whole-genome characterization of numerous plant species, providing an abundance of genotypic data for genomic analysis. Genomic selection and neural networks (NNs), particularly deep learning, have been developed to predict complex traits from dense genotypic data. Autoencoders, an NN model to extract features from images in an unsupervised manner, has proven to be useful for plant phenotyping.

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There is a need for affordable, scalable, and specific blood-based biomarkers for Alzheimer's disease that can be applied to a population level. We have developed and validated disease-specific cell-free transcriptomic blood-based biomarkers composed by a scalable number of transcripts that capture AD pathobiology even in the presymptomatic stages of the disease. Accuracies are in the range of the current CSF and plasma biomarkers, and specificities are high against other neurodegenerative diseases.

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  • Scientists are studying how genes are turned on and off in the human brain to understand brain diseases better.
  • They used special techniques to look at where and how genes work in different parts of brain cells.
  • Their research found that many important gene changes happen in the brain's cytoplasm (the part of the cell outside the nucleus) and that these changes are often connected to how brain cells communicate with each other.
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Genetics and omics studies of Alzheimer's disease and other dementia subtypes enhance our understanding of underlying mechanisms and pathways that can be targeted. We identified key remaining challenges: First, can we enhance genetic studies to address missing heritability? Can we identify reproducible omics signatures that differentiate between dementia subtypes? Can high-dimensional omics data identify improved biomarkers? How can genetics inform our understanding of causal status of dementia risk factors? And which biological processes are altered by dementia-related genetic variation? Artificial intelligence (AI) and machine learning approaches give us powerful new tools in helping us to tackle these challenges, and we review possible solutions and examples of best practice. However, their limitations also need to be considered, as well as the need for coordinated multidisciplinary research and diverse deeply phenotyped cohorts.

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  • Genome-wide association studies have identified genetic variants linked to Parkinson's disease, but the mechanisms behind these variants are still unclear.
  • The study focuses on two genes, KAT8 and KANSL1, part of a non-specific lethal complex involved in gene regulation, both in the nucleus and mitochondria.
  • Researchers found that the expression of this complex correlates with genes associated with Parkinson's in various brain regions, suggesting significant involvement in the disease's biological pathways.
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  • The study aimed to find genetic factors that might influence the development of Parkinson's disease (PD) by analyzing different genetic haplotypes.
  • Researchers utilized data from the International Parkinson's Disease Genomics Consortium and UK Biobank to conduct genome-wide association studies and burden analyses, focusing on patients with specific genetic profiles.
  • They discovered new loci linked to PD in certain genetic carriers, but results were not consistently replicated in larger samples, indicating the need for further research to confirm these findings.
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  • Improvements in functional genomic annotation have accelerated neurogenetic discoveries, particularly in hereditary ataxia, which involves over 300 genes but still leaves 75% of patients undiagnosed even with advanced sequencing techniques.
  • The study aimed to enhance understanding of hereditary ataxia's genetic architecture by employing multi-omics data to create 294 genic features related to gene characteristics and expression patterns.
  • The findings revealed a notable density of short tandem repeats (STRs) in childhood-onset genes, suggesting pathogenic repeat expansions may be overlooked and indicating a potential link between STRs and ataxia development.
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Background: Gene set enrichment analysis (detecting phenotypic terms that emerge as significant in a set of genes) plays an important role in bioinformatics focused on diseases of genetic basis. To facilitate phenotype-oriented gene set analysis, we developed PhenoExam, a freely available R package for tool developers and a web interface for users, which performs: (1) phenotype and disease enrichment analysis on a gene set; (2) measures statistically significant phenotype similarities between gene sets and (3) detects significant differential phenotypes or disease terms across different databases.

Results: PhenoExam generates sensitive and accurate phenotype enrichment analyses.

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Dysregulation of RNA splicing contributes to both rare and complex diseases. RNA-sequencing data from human tissues has shown that this process can be inaccurate, resulting in the presence of novel introns detected at low frequency across samples and within an individual. To enable the full spectrum of intron use to be explored, we have developed IntroVerse, which offers an extensive catalogue on the splicing of 332,571 annotated introns and a linked set of 4,679,474 novel junctions covering 32,669 different genes.

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  • The study focuses on creating a machine learning model to predict the mortality and hospitalization risk of COVID-19 patients using minimal data from electronic medical records.
  • The model shows high accuracy in predicting outcomes, particularly regarding death (90-93%) and a medium level of accuracy for hospitalization risk (71-73%).
  • Key factors influencing predictions include age, sex, comorbidities, and a user-friendly website has been created for clinicians to easily access the model's predictions.
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Expression quantitative trait loci (eQTLs) are associations between genetic variants, such as Single Nucleotide Polymorphisms (SNPs), and gene expression. eQTLs are an important tool to understand the genetic variance of gene expression of complex phenotypes. eQTLs analyses are common in biomedical models but are scarce in woody crop species such as fruit trees or grapes.

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There is growing evidence for the importance of 3' untranslated region (3'UTR) dependent regulatory processes. However, our current human 3'UTR catalogue is incomplete. Here, we develop a machine learning-based framework, leveraging both genomic and tissue-specific transcriptomic features to predict previously unannotated 3'UTRs.

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Personalized medicine promises individualized disease prediction and treatment. The convergence of machine learning (ML) and available multimodal data is key moving forward. We build upon previous work to deliver multimodal predictions of Parkinson's disease (PD) risk and systematically develop a model using GenoML, an automated ML package, to make improved multi-omic predictions of PD, validated in an external cohort.

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Recently, we reported oligoadenylate synthetase 1 (OAS1) contributed to the risk of Alzheimer's disease, by its enrichment in transcriptional networks expressed by microglia. However, the function of OAS1 within microglia was not known. Using genotyping from 1313 individuals with sporadic Alzheimer's disease and 1234 control individuals, we confirm the OAS1 variant, rs1131454, is associated with increased risk for Alzheimer's disease.

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Argininosuccinate lyase (ASL) is essential for the NO-dependent regulation of tyrosine hydroxylase (TH) and thus for catecholamine production. Using a conditional mouse model with loss of ASL in catecholamine neurons, we demonstrate that ASL is expressed in dopaminergic neurons in the substantia nigra pars compacta, including the ALDH1A1 subpopulation that is pivotal for the pathogenesis of Parkinson disease (PD). Neuronal loss of ASL results in catecholamine deficiency, in accumulation and formation of tyrosine aggregates, in elevation of α-synuclein, and phenotypically in motor and cognitive deficits.

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  • The study investigates the causes of reduced cortical thickness in human epilepsies using brain imaging and gene expression data to understand underlying mechanisms.* -
  • Researchers found higher levels of activated microglia and endothelial cells in areas of reduced cortical thickness, both in imaging studies and post-mortem brain tissue from epilepsy patients.* -
  • Targeted depletion of activated microglia in a mouse model prevented cortical thinning and neuronal loss, suggesting microglia play a crucial role in these changes, potentially offering new approaches for epilepsy treatment beyond seizure control.*
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Neuropathological and experimental evidence suggests that the cell-to-cell transfer of α-synuclein has an important role in the pathogenesis of Parkinson's disease (PD). However, the mechanism underlying this phenomenon is not fully understood. We undertook a small interfering RNA (siRNA), genome-wide screen to identify genes regulating the cell-to-cell transfer of α-synuclein.

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