Biology subjects

Sosa, D.

Publications and source records attributed to Sosa, D..

5 recordsLinked to original sources

bioRxiv2024

Prediction of myeloid malignant cells in Fanconi anemia using machine learning

Fanconi anemia (FA) is an inherited bone marrow failure syndrome with cancer predisposition. Most FA patients develop aplastic anemia during childhood and have an extremely high cumulative risk to develop cancer during their lifespan. Myeloid malignancy is one of the main tumor risks for patients with FA, including high-risk myelodysplastic syndrome (MDS) and acute myeloid leukemia (AML). Although bone marrow transplantation is the treatment of choice for FA patients that develop aplastic anemia, patients with a more stable bone marrow remain at a high risk of presenting MDS/AML and should be monitored for appearance of myeloid malignant clones. Markers for an as-early-as-possible identification of emerging myeloid malignant cells are needed for the monitoring of patients with FA, since quick medical action after detection of neoplastic transformation is needed. In this work we have leveraged publicly available single cell RNA seq (scRNAseq) datasets of patients with MDS and AML for training deep neural networks (DNN). We have generated two machine learning models aimed to identify myeloid malignant transcriptional profiles in scRNAseq datasets from the bone marrow of patients with FA, one for detection of MDS and a second one for AML. Both predictors displayed high sensitivity, specificity, and accuracy for detection of single cell resolution myeloid malignant transcriptional profiles. Multiple tools for analysis of single cell transcriptional data were implemented to characterize the predicted MDS and AML cells. Our analysis suggests that the predicted MDS and AML cells from FA patients are enriched in the lympho-myeloid-primed progenitor (LMPP) and the granulocyte-monocyte progenitor (GMP) populations. The predicted MDS and AML cells have gene expression and master transcriptional factor profiles that suggest malignant transformation and that differ from the rest of FA cells. Also cues of immune evasion were detected using single cell pathway analysis (SCPA) and cell-cell communication profiles. Next work will be aimed to find potential cell surface markers on the predicted MDS and AML cells as well as to assess our predictions in primary samples from FA patients.

cancer biology↗

bioRxiv2023

One million years of solitude: the rapid evolution of de novo protein structure and complex

Recent studies have established that de novo genes, evolving from non-coding sequences, enhance protein diversity through a stepwise process. However, the pattern and rate of their structural evolution over time remain unclear. Here, we addressed these issues within a short evolutionary timeframe ([~]1 million years for 97% of rice de novo genes). We found that de novo genes evolve faster than gene duplicates in the intrinsic disordered regions (IDRs, such as random coils), secondary structural elements (such as -helix and {beta}-strand), hydrophobicity, and molecular recognition features (MoRFs). Specifically, we observed an 8-14% decay in random coils and IDR lengths per million years per protein, and a 2.3-6.5% increase in structured elements, hydrophobicity, and MoRFs. These patterns of structural evolution align with changes in amino acid composition over time. We also revealed significantly higher positive charges but smaller molecular weights for de novo proteins than duplicates. Tertiary structure predictions demonstrated that most de novo proteins, though not typically well-folded on their own, readily form low-energy and compact complexes with extensive residue contacts and conformational flexibility, suggesting "a faster-binding" scenario in de novo proteins to promote interaction. Our findings illuminate the rapid evolution of protein structure in the early life of de novo proteins in rice genome, originating from noncoding sequences, highlighting their quick transformation into active, complex-forming components within a remarkably short evolutionary timeframe.

evolutionary biology↗

bioRxiv2023

Evolutionarily new genes in humans with disease phenotypes reveal functional enrichment patterns shaped by adaptive innovation and sexual selection.

New genes (or young genes) are genetic novelties pivotal in mammalian evolution. However, their phenotypic impacts and evolutionary patterns over time remain elusive in humans due to the technical and ethical complexities of functional studies. Integrating gene age dating with Mendelian disease phenotyping, our research shows a gradual rise in disease gene proportion as gene age increases. Logistic regression modeling indicates that this increase in older genes may be related to their longer sequence lengths and higher burdens of deleterious de novo germline variants (DNVs). We also find a steady integration of new genes with biomedical phenotypes into the human genome over macroevolutionary timescales ([~]0.07% per million years). Despite this stable pace, we observe distinct patterns in phenotypic enrichment, pleiotropy, and selective pressures across gene ages. Notably, young genes show significant enrichment in diseases related to the male reproductive system, indicating strong sexual selection. Young genes also exhibit disease-related functions in tissues and systems potentially linked to human phenotypic innovations, such as increased brain size, musculoskeletal phenotypes, and color vision. We further reveal a logistic growth pattern of pleiotropy over evolutionary time, indicating a diminishing marginal growth of new functions for older genes due to intensifying selective constraints over time. We propose a "pleiotropy-barrier" model that delineates higher potentials for phenotypic innovation in young genes compared to older genes, a process that is subject to natural selection. Our study demonstrates that evolutionarily new genes are critical in influencing human reproductive evolution and adaptive phenotypic innovations driven by sexual and natural selection, with low pleiotropy as a selective advantage.

evolutionary biology↗

bioRxiv2022

New gene evolution with subcellular expression patterns detected in PacBio-sequenced genomes of Drosophila genus

Previous studies described gene age distributions in the focal species of Drosophila melanogaster. Using third-generation PacBio technology to sequence Drosophila species we investigated gene age distribution in the two subgenera of Drosophila. Our work resulted in several discoveries. First, our data detected abundant new genes in entire Drosophila genus. Second, in analysis of subcellular expression, we found that new genes tend to secret into extracellular matrix and are involved in regulation, environmental adaption, and reproductive functions. We also found that extracellular localization for new genes provides a possible environment to promote their fast evolution. Third, old genes tend to be enriched in mitochondrion and the plasma membrane compared with young genes which may support the endosymbiotic theory that mitochondria originate from bacteria that once lived in primitive eukaryotic cells. Fourth, as gene age becomes older the subcellular compartments in which their products reside broadens suggesting that the evolution of new genes in subcellular location drives functional evolution and diversity in Drosophila species. Additionally, based on the analysis of RNA-Seq of two D. melanogaster populations, we determined a universal paradigm of "from specific to constitutive" expression pattern during the evolutionary process of new genes.

evolutionary biology↗

bioRxiv2021

Species-specific gene duplication in Arabidopsis thaliana evolved novel phenotypic effects on morphological traits under strong positive selection

Gene duplication is increasingly recognized as an important mechanism for the origination of new genes, as revealed by comparative genomic analysis. However, the ways in which new duplicate genes contribute to phenotypic evolution remain largely unknown, especially in plants, owing to a lack of experimental and phenotypic data. In this study, we identified the new gene Exov, derived from a partial gene region duplication of its parental gene Exov-L, which is a member of an exonuclease family, into a different chromosome in Arabidopsis thaliana. We experimentally investigated the phenotypic effects of Exov and Exov-L in an attempt to understand how the new gene diverged from the parental copy and contributes to phenotypic evolution. Evolutionary analysis demonstrated that Exov is a species-specific gene that originated within the last 3.5 million years and shows strong signals of positive selection. Unexpectedly, RNAseq analyses reveal that the new gene, despite its young age, has acquired a large number of novel direct and indirect interactions in which the parental gene does not engage. This is consistent with a high, selection-driven substitution rate in the protein sequence encoded by Exov in contrast to the slowly evolving Exov-L, suggesting an important role for Exov in phenotypic evolution. We analyzed phenotypic effects of exov and exov-l single T-DNA-insertion mutants;double exov, exov-l T-DNA insertion mutants; and CRISPR/Cas9-mediated exovcrp and exov-lcrp knockouts on seven morphological traits in both the new and parental genes. We detected significant segregation of morphological changes for all seven traits when assessed in terms of single mutants, as well as morphological changes for seven traits associated with segregation of double exov, exov-l mutants. Substantial divergence of phenotypic effects between new and parental genes was revealed by principal component analyses, suggesting neofunctionalization in the new gene. These results reveal a young gene that plays critical roles in biological processes that underlie morphological and developmental evolution in Arabidopsis thaliana.

evolutionary biology↗