Biology subjects

Lobo, F. P.

Publications and source records attributed to Lobo, F. P..

6 recordsLinked to original sources

bioRxiv2025

Transcription factors SP5 and SP8 drive primary cilia formation

While specific transcription factors are known to regulate cell fate decisions, the degree to which they can stimulate formation of specific cell organelles is less clear. We used a multi-omics comparison of the transcriptomes of ciliated and non-ciliated embryonic cells to identify transcription factors upregulated in ciliated cells, and conditional genetics in mouse embryos and stem cells to demonstrate that SP5/8 regulate cilia formation and gene expression. In Sp5/8 mutant embryos primary and motile cilia are shorter than normal and reduced in number across cell types, contributing to situs inversus and hydrocephalus. Moreover, expression of SP8 is sufficient to induce primary cilia in unciliated cells. This work opens new avenues for studying cilia assembly using stem cell models and offers new insights into human ciliopathies.

developmental biology↗

bioRxiv2025

EpitopeTransfer: a Phylogeny-aware transfer learning framework for taxon-specific linear B-cell epitope prediction

Generalist linear B-cell epitope predictors are typically trained on large, heterogeneous datasets, which can lead to biased representations and degraded performance for under-represented or emerging pathogens. We present a transfer learning framework leveraging the ESM family of protein language models (PLMs) as sequence feature embedders, adapting them to specific evolutionary contexts through phylogeny-informed fine-tuning. By coupling pretrained PLM representations with hierarchical, taxon-aware adaptation, our approach enables efficient knowledge transfer from sets of related pathogens to data-scarce targets while preserving lineage-specific signals. This strategy consistently improves predictive performance over state-of-the-art epitope prediction methods across a diverse set of targets. We further demonstrate that these gains arise from the targeted adaptation and downstream models guided by evolutionary relatedness, indicating the value of this structured transfer learning for epitope prediction and highlighting its potential usability for further predictive applications with evolutionarily-structured data.

bioinformatics↗

bioRxiv2022

Using ARCADE (ARChaeplastida Annotation DatabasE) to understand the evolution of genome size in land plants

The abundance of plant genomic information caused by the decrease of sequencing costs contrasts with the lack of databases that integrate genome annotation, taxonomy and phenotypes to produce statistically sound, biologically meaningful knowledge. Here we present ARCADE (ARChaeplastida Annotation DatabasE), a database of 171 high-quality archaeplastidian non-redundant proteomes gathered from six primary genomic databases, together with proteome quality metrics anda growing number of associated metadata. As a case study to demonstrate the usefulness of ARCADE, we used it to investigate the expansion and contraction of protein domains associated with the evolution of genome size (hereafter GS). GS varies greatly among land plants and the synthesis of large genomes can be costly to cells. Although GS has been studied extensively for decades, the molecular mechanisms involved in the adaptations of plants to the increase in GS are still poorly understood. We used the annotation and phylogenetic information available in ARCADE, together with estimated GS values available for 83 land plant species, to search for associations between the abundance of protein domain families in these species and GS variation through phylogenetic-aware methods. Additionally, we estimated the GS for the ancestral nodes of the extant land plant species. GS seems to be decreasing along the course of evolution, except for a few branches that might have undergone independent GS increases. We found 7 Pfam correlated with the variation in GS in land plants, mainly related to nucleotide metabolism, DNA repair and genome organization. We found larger genomes to have a greater frequency of the Histone 2A superfamily, responsible for diverse functions, including the nucleosome formation and silencing of transposable elements. These molecular functions we found correlated to GS variation suggests they may be associated with preserving genome stability in larger genomes, and might indicate the evolution of mechanisms to cope with the variation in GS in land plants. ARCADE is available at https://bit.ly/ARCADE_OSF.

plant biology↗

bioRxiv2021

Cross-species prediction of essential genes in insects through machine learning and sequence-based attributes

Insects are organisms with a vast phenotypic diversity and key ecological roles. Several insect species also have medical, agricultural and veterinary importance as parasites and vectors of diseases. Therefore, strategies to identify potential essential genes in insects may reduce the resources needed to find molecular players in central processes of insect biology. Furthermore, the detection of essential genes that occur only in certain groups within insects, such as lineages containing insect pests and vectors, may provide a more rational approach to select essential genes for the development of insecticides with fewer off-target effects. However, most predictors of essential genes in multicellular eukaryotes using machine learning rely on expensive and laborious experimental data to be used as gene features, such as gene expression profiles or protein-protein interactions. This information is not available for the vast majority of insect species, which prevents this strategy to be effectively used to survey genomic data from non-model insect species for candidate essential genes. Here we present a general machine learning strategy to predict essential genes in insects using only sequence-based attributes (statistical and physicochemical data). We validate our strategy using genomic data for the two insect species where large-scale gene essentiality data is available: Drosophila melanogaster (fruit fly, Diptera) and Tribolium castaneum (red flour beetle, Coleoptera). We used publicly available databases plus a thorough literature review to obtain databases of essential and non-essential genes for D. melanogaster and T. castaneum, and proceeded by computing sequence-based attributes that were used to train statistical models (Random Forest and Gradient Boosting Trees) to predict essential genes for each species. Both models are capable of distinguishing essential from non-essential genes significantly better than zero-rule classifiers. Furthermore, models trained in one insect species are also capable of predicting essential genes in the other species significantly better than expected by chance. The Random Forest D. melanogaster model can also distinguish between essential and non-essential T. castaneum genes with no known homologs in the fly significantly better than a zero-rule model, demonstrating that it is possible to use our models to predict lineage-specific essential genes in a phylogenetically distant insect order. Here we report, to the best of our knowledge, the development and validation of the first general predictor of essential genes in insects using sequence-based attributes that can, in principle, be computed for any insect species where genomic information is available. The code and data used to predict essential genes in insects are freely available at https://github.com/g1o/GeneEssentiality/.

genomics↗

bioRxiv2019

Admixture/fine-mapping in Brazilians reveals a West African associated potential regulatory variant (rs114066381) with a strong female-specific effect on body mass- and fat mass-indexes

Admixed populations are a resource to study the global genetic architecture of complex phenotypes, which is critical, considering that non-European populations are severely under-represented in genomic studies. Leveraging admixture in Brazilians, whose chromosomes are mosaics of fragments of Native American, European and African origins, we used genome-wide data to perform admixture mapping/fine-mapping of Body Mass Index (BMI) in three population-based cohorts from Northeast (Salvador), Southeast (Bambui) and South (Pelotas) of the country. We found significant associations with African-associated alleles in children from Salvador (PALD1 and ZMIZ1 genes), and in young adults from Pelotas (NOD2 and MTUS2 genes). More importantly, in Pelotas, rs114066381, mapped in a potential regulatory region, is significantly associated only in females (p= 2.76 e-06). This variant is very rare in Europeans but with frequencies of ~3% in West Africa, and has a strong female-specific effect (95%CI: 2.32-5.65 kg/m2 per each A allele). We confirmed this sex-specific association and replicated its strong effect for an adjusted fat-mass index in the same Pelotas cohort, and for BMI in another Brazilian cohort from Sao Paulo (Southeast Brazil). A meta-analysis confirmed the significant association. Remarkably, we observed that while the frequency of rs114066381-A allele ranges from 0.8 to 2.1% in the studied populations, it attains ~9% among morbidly obese women from Pelotas, Sao Paulo, and Bambui. The effect size of rs114066381 is at least five-times the effect size of the FTO SNPs rs9939609 and rs1558902, already emblematic for their high effects, and for which we replicated associations in Pelotas. We demonstrate how, after a decade of GWAS mostly performed in European-ancestry populations, non-European and admixed populations are a source of new relevant phenotype-associated genetic variants.

genetics↗

bioRxiv2019

Recounting the FANTOM Cage Associated Transcriptome

Long non-coding RNAs (lncRNAs) have emerged as key coordinators of biological and cellular processes. Characterizing lncRNA expression across cells and tissues is key to understanding their role in determining phenotypes including human diseases. We present here FC-R2, a comprehensive expression atlas across a broadly-defined human transcriptome, inclusive of over 109,000 coding and non-coding genes, as described in the FANTOM CAGE-Associated Transcriptome (FANTOM-CAT) study. This atlas greatly extends the gene annotation used in the original recount2 resource. We demonstrate the utility of the FC-R2 atlas by reproducing key findings from published large studies and by generating new results across normal and diseased human samples. In particular, we (a) identify tissue specific transcription profiles for distinct classes of coding and non-coding genes, (b) perform differential expression analyses across thirteen cancer types, providing new insights linking promoter and enhancer lncRNAs expression to tumor pathogenesis, and (c) confirm the prognostic value of several enhancers in cancer. Comprised of over 70,000 samples, the FC-R2 atlas will empower other researchers to investigate functions and biological roles of both known coding genes and novel lncRNAs. Most importantly, access to the FC-R2 atlas is available from https://jhubiostatistics.shinyapps.io/recount/, the recount Bioconductor package, and http://marchionnilab.org/fcr2.html.

genomics↗