Biology subjects

Raittz, R. T.

Publications and source records attributed to Raittz, R. T..

8 recordsLinked to original sources

bioRxiv2026

Altered microglial communication in brains with Alzheimers Disease pathology

Microglia are the resident immune cells of the central nervous system and are highly versatile, continuously monitoring the brain microenvironment with their motile processes and responding to perturbations in diverse ways. Recent studies have revealed substantial functional heterogeneity among microglial states, suggesting that analyzing individual subpopulations is essential to capture specialized signaling programs and disease-associated interactions that would otherwise be obscured in bulk analyses. To better understand how these cells interact, we performed a ligand-receptor communication analysis using single-nucleus RNA sequencing data from the dorsolateral prefrontal cortex, comprising 16 microglial subpopulations together with other myeloid cells identified in brain tissue, including macrophages and monocytes. We characterized the signaling roles of these cell populations, compared individuals with and without neuropathologically defined Alzheimers disease, and associated cell-pair interactions with AD-related clinical and neuropathological traits. We found that AD was characterized by altered microglial composition and rewiring of intercellular communication, including disease-specific signaling pathways and distinct interaction hubs. Lipid-associated microglia were associated with tau pathology, whereas nuclear receptor signaling microglia were linked to hippocampal sclerosis. These findings were replicated in three independent datasets. Overall, this work provides an initial but comprehensive map of microglial communication in the aging human brain and identifies candidate signaling interactions for future functional validation.

neuroscience↗

bioRxiv2025

Integration of aged brain multi-omics reveals cross-system mechanisms underlying Alzheimer's disease heterogeneity

The molecular correlates of Alzheimers disease (AD) are increasingly being defined by omics. Yet, the findings from different data types or cohorts are often difficult to reconcile. Collecting multiple omics from the same individuals allows a comprehensive view of disease-related molecular mechanisms, while addressing conflicting findings derived from single omics. Such same-sample multi-omics can reveal, for instance, when changes observed in the transcriptome share distinct but coordinated signals in epigenetics and proteomics, relationships otherwise unclear. Here, we apply a data-driven multi-omic framework to integrate epigenomic, transcriptomic, proteomic, metabolomic, and cell-type-specific population data from up to 1,358 aged human brain samples from the Religious Orders Study (ROS) and Rush Memory and Aging Project (MAP). We demonstrate the existence of sprawling cross-omics cross-system biological factors that also relate to AD phenotypes. The strongest AD-associated factor (factor 8) involved elevated immune activity at the epigenetic level, decreased expression of heat shock genes in the transcriptome, and disrupted energy metabolism and cytoskeletal dynamics in the proteome. We also showed immune-related factors (factors 2 and 3) with discordant enrichments, reflecting reactive-like glial subpopulations and protective contributions from surveillance microglia. Both were negatively associated with AD pathology, suggesting potential immune resilience mechanisms. Finally, unsupervised clustering of participants revealed eleven molecular subtypes of the aging brain, including three clusters strongly associated with AD but displaying distinct molecular signatures and phenotypic characteristics. Our findings provide a comprehensive map of molecular mechanisms underlying AD heterogeneity, highlighting the complex role of neuroinflammatory processes, and yielding potential novel biomarkers and therapeutic targets for precision medicine approaches to AD treatment.

molecular biology↗

bioRxiv2025

Cytokine expression profile in the human brain of older adults

Alzheimers disease (AD) is a complex neurodegenerative condition linked to chronic neuroinflammation. This study investigates the cytokine gene expression profile in cortical tissue samples from elderly individuals with and without AD to identify potential biomarkers and enhance our understanding of disease pathogenesis. Utilizing high-depth RNA sequencing data, we identified a set of cytokines whose expression significantly associated with different aspects of the AD phenotype, including measures of neurofibrillary tangles, amyloid-{beta} deposition, and a person-specific rate of cognitive decline. Single-nucleus transcriptomics data facilitated the identification of specific cell types, such as microglia and oligodendrocytes, that significantly contribute to the inflammatory response in AD. Additionally, we observed a strong correlation between the expression of certain cytokines and genetic risk for the disease. Our findings indicate that cytokine-mediated neuroinflammation plays a vital role in AD progression and that modulating the immune response may offer a promising strategy for developing new therapies.

molecular biology↗

bioRxiv2024

Generative Approaches for Nucleotide Sequences to Enhance Non-coding RNA Classification

Classifying non-coding RNA (ncRNA) sequences, particularly mirtrons, is essential for elucidating gene regulation mechanisms. However, the prevalent class imbalance in ncRNA datasets presents significant challenges, often resulting in overfitting and diminished generalization in machine learning models. In this study, GENerative Approaches for NUcleotide Sequences (GENNUS) is proposed, introducing novel data augmentation strategies using Generative Adversarial Networks (GANs) and Synthetic Minority Over-sampling Technique (SMOTE) to enhance ncRNA classification performance. Our GAN-based methods effectively generate high-quality synthetic data that capture the intricate patterns and diversity of real mirtron sequences, eliminating the need for extensive feature engineering. Through four experiments, it is demonstrated that models trained on a combination of real and GAN-generated data improve classification accuracy compared to traditional SMOTE techniques or only with real data. Our findings reveal that GANs enhance model performance and provide a richer representation of minority classes, thus improving generalization capabilities across various machine learning frameworks. This work highlights the transformative potential of synthetic data generation in addressing data limitations in genomics, offering a pathway for more effective and scalable ncRNA classification methodologies.

bioinformatics↗

bioRxiv2021

Biotext: Exploiting Biological-like format for text mining

The large amount of existing textual data justifies the development of new text mining tools. Bioinformatics tools can be brought to Text Mining, increasing the arsenal of resources. Here, we present BIOTEXT, a package of strategies for converting natural language text into biological-like information data, providing a general protocol with standardized functions, allowing to share, encode and decode textual data for amino acid and DNA. The package was used to encode the arbitrary information present in the headings of the biological sequences found in a BLAST survey. The protocol implemented in this study consists of 12 steps, which can be easily executed and/ or changed by the user, depending on the study area. BIOTEXT empowers users to perform text mining using bioinformatics tools. BIOTEXT is freely available at https://pypi.org/project/BIOTEXT/ (Python package) and https://sourceforge.net/projects/BIOTEXTtools/files/AMINOcode_GUI/ (Standalone tool).

bioinformatics↗

bioRxiv2020

rSWeeP: A R/Bioconductor package deal with SWeeP sequences representation

The rSWeeP package is an R implementation of the SWeeP model, designed to handle Big Data. rSweeP meets to the growing demand for efficient methods of heuristic representation in the field of Bioinformatics, on platforms accessible to the entire scientific community. We explored the implementation of rSWeeP using a dataset containing 31,386 viral proteomes, performing phylogenetic and principal component analysis. As a case study we analyze the viral strains closest to the SARS-CoV, responsible for the current pandemic of COVID-19, confirming that rSWeeP can accurately classify organisms taxonomically. rSWeeP package is freely available at https://bioconductor.org/packages/release/bioc/html/rSWeeP.html.

bioinformatics↗

bioRxiv2018

RAFTS3G - An efficient and versatile clustering software to analyses in large protein datasets

The need to develop computational tools and techniques that can predict efficiently consistent groups of family proteins in large volume of biological information is still a great perspective in Bioinformatic studies. Besides that, it is difficult to increase speed demanding low computational processing to minimize the information complexity. Tools already consolidated as the CD-HIT and UCLUST generates very compact data that makes the Data Mining difficult and have low efficiency when used for detect homology among proteins requiring manual intervention, therefore it is necessary a tool that is also efficient in low similarity. Here we present a new approach for the Data Mining and analysis of homology in large dataset of protein sequences, the RAFTS3G. We used the UniProtKB/Swiss-Prot database with the most popular clustering tools and RAFTS3G proved to be more than 10 times faster than CD-HIT and its strategy increases the performance in low similarity to detect protein families.\n\nContact: raittz@ufpr.br

bioinformatics↗

bioRxiv2018

The identification of DNA binding regions of the σ54 factor using artificial neural network

Transcription of many bacterial genes is regulated by alternative RNA polymerase sigma factors as the sigma 54 ({sigma}54). A single essential {sigma} promotes transcription of thousands of genes and many alternative {sigma} factors promote transcription of multiple specialized genes required for coping with stress or development. Bacterial genomes have two families of sigma factors, sigma 70 ({sigma}70) and sigma 54 ({sigma}54). {sigma}54 uses a more complex mechanism with specialized enhancers-binding proteins and DNA melting and is well known for its role in regulation of nitrogen metabolism in proteobacteria. The identification of these regulatory elements is the main step to understand the metabolic networks. In this study, we propose a supervised pattern recognition model with neural network to identify Transcription Factor Binding Sites (TFBSs) for {sigma}54. This approach is capable of detecting {sigma}54 TFBSs with sensitivity higher than 98% in recent published data. False positives are reduced with the addition of ANN and feature extraction, which increase the specificity of the program. We also propose a free, fast and friendly tool for {sigma}54 recognition and a {sigma}54 related genes database, available for consult. S54Finder can analyze from short DNA sequences to complete genomes and is available online. The software was used to determine {sigma}54 TFBSs on the complete bacterial genomes database from NCBI and the result is available for comparison. S54Finder does the identification of {sigma}54 regulated genes for a large set of genomes allowing evolutionary and conservation studies of the regulation system between the organisms.

bioinformatics↗