Biology subjects

Espinal-Enriquez, J.

Publications and source records attributed to Espinal-Enriquez, J..

10 recordsLinked to original sources

bioRxiv2025

Exploring Single-Cell Gene Regulatory Dynamics in Rett Syndrome

Rett syndrome is a monogenic disorder with an incidence of 95% in women, characterized by the complexity of studying the associated phenotype due to the heterogeneity in patient tissues from the stochastic silencing of the affected X chromosome. Furthermore, we are largely unaware of the cascade of alterations that occur in neurons due to transcriptional changes induced by the affected MECP2 gene. To address these challenges, an in-depth network analysis was implemented using organoid single-cell transcriptomic data derived from human patients. We performed a Weighted correlation network analysis and trajectory analysis to understand the differences in the developmental processes between samples, we followed by the generation of gene regulatory networks for each relevant cell developmental pathways to assess the master regulator that are involved in this process, with the differential expressed genes with potential therapeutic implications being identified by integration with SFARI and Genes4Epi. The results were adapted into dynamic Boolean models fitted with the transcriptomic data for validation in which we evaluated the attractor field from each reachable state. These approaches allowed us to explore differences in regulatory behavior in the developmental pathways. Our study provides an insight that pinpoints the cellular stages on which the regulation and compensatory mechanism activate and regulate Rett syndrome. We identified 19 Master regulators for the Dopaminergic developmental trajectory, as well as 34 Master regulator genes for the Gabaergic developmental trajectory. Dynamic Boolean modeling of these systems showcased a comprehensive understanding of the disrupted developmental pathways of Rett syndrome, highlighting the transitional states of potential within maturation trajectories as the key point of divergence in regulation for Rett syndrome. After complementing with enrichment and clinical relevant variant analysis, we identify the key actors in this system as NR2F1 and TCF4, with TCF4 suggesting a symmetrical compensatory relationship with MeCP2, and NR2F1 as possible link with wider developmental conditions, this concluded with highlighting the possibility of regulation in this condition being affected by the MAPK-ERK pathway of transcriptional regulation, offering a novel angle for targeted research.

neuroscience↗

bioRxiv2025

Individualized pseudogenes networks for survival prognosis in B-cell acute lymphoblastic leukemia

Risk classification in B-cell acute lymphoblastic leukemia (B-ALL) remains challenging, even in the era of genomic precision medicine. Current molecular classifiers fail to fully explain the heterogeneity in patient outcomes, suggesting that key regulatory layers remain hidden. Here, we uncover a previously unexplored dimension of B-ALL biology by analyzing co-expression patterns between pseudogenes using single-sample co-expression networks (n = 1,416). Principal component analysis showed that these interactions explain a major component of variability among patients and contribute to patient stratification into clusters with distinct overall survival. After identifying interactions associated with these clusters, we used a LASSO-based feature selection pipeline to derive a three-interaction signature that predicted patient survival, with RPL7P10 -RPS3AP36 emerging as the most robust biomarker. Our study shows that co-expression between pseudogenes represents a previously unrecognized layer of molecular heterogeneity in B-ALL, harboring promising molecular markers for future studies.

cancer biology↗

bioRxiv2024

Patient-specific gene networks reveal novel subtypes and predictive biomarkers in lung cancer

Lung adenocarcinoma (LUAD) is a highly heterogenous and aggressive form of non-small cell lung cancer (NSCLC). The use of genome-wide gene co-expression networks (GCNs) has been paramount to describe changes in the transcriptional regulatory programs found between diseased and healthy states of LUAD. Recently, studies have shown that multiple cancerous phenotypes share a distinct GCN architecture, suggesting that network topology holds promise for understanding disease pathology. However, conventional GCN inference methods struggle to capture the inherent context-specificity within a patient population, thus flattening its heterogeneity. To address this issue, the use of single-sample network (SSN) modelling has emerged as a promising solution into studying heterogeneous traits of cancer through network-based approaches. Here, we reconstructed patient-specific GCNs (n=334) using the LIONESS equation and mutual information as the network inference method. Unsupervised analysis revealed six novel LUAD subtypes based on inter-patient network similarity, each with distinct network motifs reflecting unique biological programs. Supervised analysis, employing regularized Cox regression, identified 12 genes (CHRDL2, SPP2, VAC14, IRF5, GUCY1B1, NCS1, RRM2B, EIF5A2, CCDC62, CTCFL, XG, andTP53INP2) whose weighted degree in SSNs is predictive of patient survival in LUAD. These findings suggest that topological features of SSNs offer valuable insights into the context-specific nature of LUAD malignancy, highlighting the potential of SSN-based approaches for further research.

systems biology↗

bioRxiv2024

Transcriptomic signatures and network-based methods uncover new Senescent Cell Anti-Apoptotic Pathways and Senolytics

Cellular senescence is an irreversible cell cycle arrest caused by various stressors that damage cells. Over time, senescent cells accumulate and contribute to the progression of multiple age-related degenerative diseases. It is believed that these cells accumulate partly due to their ability to evade programmed cell death through the development and activation of survival and anti-apoptotic resistance mechanisms; however, many aspects of how these survival mechanisms develop and activate are still unknown. By analyzing transcriptomic signature profiles generated by the LINCS L1000 project and using network-based methods, we identified various genes that could represent new senescence-related survival mechanisms. Additionally, employing the same methodology, we identified over 600 molecules with potential senolytic activity. Experimental validation of our computational findings confirmed the senolytic activity of Fluorouracil, whose activity would be mediated by a multi-target mechanism, revealing that its targets AURKA, EGFR, IRS1, SMAD4, and KRAS are new senescence-associated survival and anti-apoptotic resistance pathways. The development of these pathways could depend on the stimulus that induces cellular senescence. The SCAPs development and activation mechanisms proposed in this work offer new insights into how senescent cells survive. Identifying new anti-apoptotic resistance targets and drugs with potential senolytic activity paves the way for developing new pharmacological therapies to eliminate senescent cells selectively. Graphical Abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=125 SRC="FIGDIR/small/596326v1_ufig1.gif" ALT="Figure 1"> View larger version (26K): org.highwire.dtl.DTLVardef@137c0d4org.highwire.dtl.DTLVardef@154af55org.highwire.dtl.DTLVardef@c32030org.highwire.dtl.DTLVardef@765ee4_HPS_FORMAT_FIGEXP M_FIG C_FIG

cell biology↗

bioRxiv2023

Differential effects of follicle-stimulating hormone glycoforms on the transcriptome profile of cultured rat granulosa cells as disclosed by RNA-seq

It has been documented that variations in glycosylation on glycoprotein hormones, confer distinctly different biological features to the corresponding glycoforms when multiple in vitro biochemical readings are analyzed. We here applied next generation RNA sequencing to explore changes in the transcriptome of rat granulosa cells exposed for 0, 6, and 12 h to 100 ng/ml of four highly purified follicle-stimulating hormone (FSH) glycoforms, each exhibiting different glycosylation patterns: human pituitary FSH18/21 and equine FSH (eqFSH) (hypo-glycosylated), and human FSH24 and chinese-hamster ovary cell-derived human recombinant FSH (recFSH) (fully-glycosylated). Total RNA from triplicate incubations was prepared from FSH glycoform-exposed cultured granulosa cells obtained from DES-pretreated immature female rats, and RNA libraries were sequenced in a HighSeq 2500 sequencer (2 x 125 bp paired-end format, 10-15 x 106 reads/sample). The computational workflow focused on investigating differences among the four FSH glycoforms at three levels: gene expression, enriched biological processes, and perturbed pathways. Among the top 200 differentially expressed genes, only 4 (0.6%) were shared by all 4 glycoforms at 6 h, whereas 118 genes (40%) were shared at 12 h. Follicle-stimulating hormone glycocoforms stimulated different patterns of exclusive and associated up regulated biological processes in a glycoform and time-dependent fashion with more shared biological processes after 12 h of exposure and fewer treatment-specific ones, except for recFSH, which exhibited stronger responses with more specifically associated processes at this time. Similar results were found for down-regulated processes, with a greater number of processes at 6 h or 12 h, depending on the particular glycoform. In general, there were fewer downregulated than upregulated processes at both 6 h and 12 h, with FSH18/21 exhibiting the largest number of down-regulated associated processes at 6 h while eqFSH exhibited the greatest number at 12 h. Signaling cascades, largely linked to cAMP-PKA, MAPK, and PI3/AKT pathways were detected as differentially activated by the glycoforms, with each glycoform exhibiting its own molecular signature. These data extend previous observations demonstrating glycosylation-dependent differential regulation of gene expression and intracellular signaling pathways triggered by FSH in granulosa cells. The results also suggest the importance of individual FSH glycoform glycosylation for the conformation of the ligand-receptor complex and induced signalling pathways.

genomics↗

bioRxiv2022

The network structure of hematopoietic cancers

Hematopoietic cancers (HCs) are a heterogeneous group of malignancies that affect blood, bone marrow and lymphatic system. Here, by analyzing 1,960 RNA-Seq samples from three independent datasets, we explored the co-expression landscape in HCs, by inferring gene co-expression networks (GCNs) with four cancer phenotypes (B and T-cell acute leukemia -BALL, TALL-, acute myeloid leukemia -AML-, and multiple myeloma -MM-) as well as non-cancer bone marrow. We characterized their structure (topological features) and function (enrichment analyses). We found that, as in other types of cancer, the highest co-expression interactions are intra-chromosomal, which is not the case for control GCNs. We also detected a highly co-expressed group of overexpressed pseudogenes in HC networks. The four GCNs present only a small fraction of common interactions, related to canonical functions, like immune response or erythrocyte differentiation. With this approach, we were able to reveal cancer-specific features useful for detection of disease manifestations. SignificanceWe demonstrate that gene co-expression is deregulated in four HC, observed by an elevated proportion of intrachromosome interactions in their GCNs with respect to their normal counterparts, and increased interactions between pseudogenes (more evident in AML). This deregulation might be associated with the age of the patients.

systems biology↗

bioRxiv2022

Loss of long-range co-expression is a common trait in cancer

Cancer cells display common features and enabling characteristics collectively known as the Hallmarks of Cancer, which occur alongside alterations in the regulatory mechanisms controlling gene transcription. Gene co-expression networks (GCNs) provide a framework to identify correlated gene sets that may share these regulatory mechanisms. Previously, we reported the loss of long-range co-expression in breast, lung, kidney, and hematopoietic cancer GCNs. Here, we expand the analysis to fifteen tissues, comprising 8,772 samples from two independent datasets. Unlike healthy phenotypes, cancer GCNs show that the strongest gene-pair interactions are intra-chromosomal, with their strength decaying as base-pair distance increases. Tumor GCN communities are strongly associated with cancerrelated processes and are enriched in gene families located on the same chromosome. In contrast, normal GCN communities are linked to metabolic and cell maintenance processes. Riboproteins remain highly co-expressed in both cancer and normal GCNs, highlighting their importance for cell viability. Notably, in other chronic diseases, such as Type-2 Diabetes and Alzheimers disease, the loss of long-range co-expression is absent, suggesting it is a distinctive feature of cancer.

cancer biology↗

bioRxiv2019

Highly-connected, non-redundant microRNAs functional control in breast cancer molecular subtypes

Transcriptional patterns are altered in breast cancer. These alterations capture the heterogeneity of breast cancer, leading to the emergence of molecular subtypes. Network biology approaches to study gene co-expression are able to capture the differences between breast cancer subtypes.\n\nNetwork biology approaches may be extended to include other co-expression patterns, like those found between genes and non-coding RNA: such as mi-croRNAs (miRs). Commodore miRs are microRNAs that, based on their connectivity and redundancy in co-expression networks, have been proposed as potential control elements of biological functions.\n\nIn this work, we reconstructed miR-gene co-expression networks for each breast cancer molecular subtype. We identified Commodore miRs in three out of four molecular subtypes. We found that in each subtype, each cdre-miR had a different set of associated genes, as well as a different set of associated biological functions. We used a systematic literature validation strategy, and identified that the associated biological functions to these cdre-miRs are hallmarks of cancer.

genomics↗

bioRxiv2018

Loss of Trans Regulation in Breast Cancer Molecular Subtypes

Breast carcinomas are characterized by anomalous gene regulatory programs. As is well known, gene expression programs are able to shape phenotypes. Hence, the understanding of gene co-expression may shed light on the underlying mechanisms behind the transcriptional regulatory programs affecting tumor development and evolution. For instance, in breast cancer, there is a clear loss of inter-chromosomal (trans-) co-expression, compared with healthy tissue. At the same time cis- (intra-chromosomal) interactions are favored in breast tumors. In order to have a deeper understanding of regulatory phenomena in cancer, here, we constructed Gene Co-expression Networks by using 848 RNA-seq whole-genome samples corresponding to the four breast cancer molecular subtypes, as well as healthy tissue. We quantify the cis-/trans- co-expression imbalance in all phenotypes. Additionally, we measured the association between co-expression and physical distance between genes, and characterized the proportion of intra/inter-cytoband interactions per phenotype. We confirmed loss of trans- co-expression in all molecular subtypes. We also observed that gene cisco-expression decays abruptly with distance in all tumors in contrast with healthy tissue. We observed co-expressed gene hotspots, that tend to be connected at cytoband regions, and coincide accurately with already known copy number altered regions, such as Chr17q12, or Chr8q24.3 for all subtypes. Our methodology recovered different alterations already reported for specific breast cancer subtypes, showing how co-expression network approaches might help to capture distinct events that modify the cell regulatory program.

systems biology↗

bioRxiv2018

Identification of Casiopeina II-gly secondary targets through a systems pharmacology approach

Casiopeinas are a group of copper-based compounds designed to be used as less toxic, more efficient chemotherapeutic agents. In this study, we analyzed the in vitro effects of Casiopeina Il-gly on the expression of canonical biological pathways. Using microarray data from HeLa cell lines treated with Casiopeina II-gly, we identified biological pathways that are perturbed after treatment. We present a novel approach integrating pathway analysis and network theory: The Pathway Crosstalk Network. We constructed a network with deregulated pathways, featuring links between those pathways that crosstalk with each other. We identified modules grouping deregulated pathways that are functionally related. Through this approach, we were able to identify three features of Casiopeina treatment: a) Perturbation of signaling pathways, related to induction of apoptosis; b) perturbation of metabolic pathways, and c) activation of immune responses. These findings can be useful to drive new experimental exploration on their role in adverse effects and efficacy of Casiopeinas.

systems biology↗