Biology subjects

Dalmolin, R. J.

Publications and source records attributed to Dalmolin, R. J..

4 recordsLinked to original sources

bioRxiv2026

Analysis of Transcriptograms in Epithelial-Mesenchymal Transition (EMT)

Single-cell RNA sequencing (single-cell RNA-seq) has represented a revolution in gene expression analysis. However, high dropout rates and stochastic noise often reduce the amount of information captured in these experiments. The epithelial-mesenchymal transition (EMT), which is fundamental to tumor progression and organismal development, is particularly difficult to fully characterize due to the existence of intermediate states. In this work, we demonstrate that projecting transcriptomic data onto gene lists ordered using protein-protein interaction (PPI) information acts as a "biological low-pass filter", attenuating technical noise and increasing the statistical power of the analyses. We propose and validate an innovative pipeline that integrates the Transcriptogram method with Principal Component Analysis (PCA). By applying a moving average over functionally ordered genes, we drastically increase the signal-to-noise ratio, enabling the inference of cellular trajectories. The method was applied to a public dataset of TGF-{beta}1-induced MCF10A cells, with rigorous batch-effect correction based on biological controls. The results reveal that EMT is not merely a morphological change, but a coordinated, systemic reprogramming. This approach enabled the identification of critical modules that would remain hidden in conventional analyses: (i) a massive "Metabolic Switch" (Cluster 2), indicating a transition toward oxidative phosphorylation to sustain invasion; (ii) a strategic blockade of the cell cycle (Cluster 4); and (iii) a "Detoxification Shield" and chemoresistance program (Cluster 5), characterized by endogenous activation of metallothioneins. We conclude that the combination of PPI network topology and dimensionality reduction offers superior resolution for dissecting cellular plasticity. The method not only validates classical markers, but also reveals the hidden functional architecture of the transition, showing that EMT is not a single, uniform process, but rather one in which cells can follow distinct trajectories, halting at different stages of differentiation.

bioinformatics↗

bioRxiv2025

OrthoGuide: A database for rooting inference of orthologous genes

Orthology has proven to be a valuable proxy for the study of the evolution of biological systems, such as metabolic pathways and gene regulatory networks. Genes within the same orthologous group typically share the same evolutionary history, reflecting their common ancestry. To leverage this property, several tools and databases, most notably the COG database, have been developed to support evolutionary analyses based on orthology information. Building on these resources, we previously developed the Bridge algorithm to infer the evolutionary root of genes by analyzing the distribution of orthologous groups in a phylogenetic tree. Here, we introduce OrthoGuide, a database and web application that provides rooting information for all COGs across eight model species, as inferred by the Bridge algorithm. The web application and the database are hosted at https://dalmolingroup.imd.ufrn.br/orthoguide/. Significance StatementUnderstanding the evolutionary origin of genes is fundamental for systems biology but is often obstructed by computationally complex bioinformatic workflows. This creates a bottleneck for many researchers. OrthoGuide directly addresses this challenge by providing a public, pre-computed database of evolutionary rooting data for all genes across eight eukaryotic model organisms. Our web application eliminates the need for user-side computation. OrthoGuide delivers instant results as well as interactive visualizations. This resource democratizes access to evolutionary analyses based on orthology information, enabling a broader scientific community to rapidly translate simple gene lists into insights about the assembly of biological systems.

evolutionary biology↗

bioRxiv2025

Evolutionary analysis reveals repeated diversification events in immune metabolic pathways

The human immune system is a complex, multifunctional network essential for host defense, tumor surveillance, and tissue repair. While conventionally divided into rapid-response innate immunity and antigen-specific adaptive immunity with memory, both modules operate synergistically through dynamic metabolic interactions that fuel immune responses. Although host-pathogen coevolution is recognized as a major evolutionary driver, the establishment scenario of immune metabolic pathways remains poorly characterized. Crucially, a systematic understanding of how the emergence of vertebrate malignancy can influence immune adaptations is needed. We analyzed 1,063 genes from 21 KEGG Pathway immune metabolic pathways and 1,124 cancer-associated genes from OncoKB/COSMIC. Evolutionary rooting was performed using the R package GeneBridge, inferring the most probable origins for each Cluster of Orthologous Genes (COG) across a 476-species eukaryotic phylogeny. Four clades showed significant immune orthologous groups (OGs) emergence: Metamonada, SAR, Choanoflagellata, and Actinopterygii. While cancer OGs diversified primarily during multicellular organism origins, immune OGs exhibited multiple diversification peaks, most prominently during jawed vertebrate emergence. Our findings demonstrate that human immune metabolic pathways underwent recurrent adaptive events during evolution, with marked complexity escalation in jawed vertebrates. We propose that malignant neoplasm emergence, coupled with epithelial-immune coevolution, served as complementary selective pressure driving progressive refinement of vertebrate immune mechanisms.

evolutionary biology↗

bioRxiv2022

Metagenomic analyses reveal the influence of depth layers on marine biodiversity in tropical and subtropical regions.

The emergence of open ocean global-scale studies provided important information about the genomics of oceanic microbial communities. Metagenomic analyses shed a light on the structure of marine habitats, unraveling the biodiversity of different water masses. Many biological and environmental factors can contribute to marine organism composition, such as depth. However, much remains unknown about the taxonomic and functional features of microbial communities in different water layer depths. Here, we performed a metagenomic analysis of 76 samples from the Tara Ocean Project, distributed in 8 collection stations located in tropical or subtropical regions, and sampled from three layers of depth (surface water layer - SRF, deep chlorophyll maximum layer - DCM, and mesopelagic zone - MES). In total, we assigned genomic sequences to 669.713.333 organisms. The SRF and DCM depth layers are similar in abundance and diversity, while the MES layer presents greater diversity than the other layers. Diversity clustering analysis shows differences regarding the taxonomic content of samples. At the domain level, bacteria prevail in the majority of samples, and the MES layer presents the highest proportion of archaea among all samples. A core of essential biological functions was identified between the depth layers, such as DNA replication, translation, transmembrane transport, and DNA repair. However, some biological functions were found exclusively in each depth layer, suggesting different functional profiles for each of them. Taken together, our results indicate that the depth layer influences microbial sample composition and diversity.

microbiology↗