Biology subjects

Apodaca, S.

Publications and source records attributed to Apodaca, S..

3 recordsLinked to original sources

bioRxiv2025

Placental transcriptome profiling in congenital Chagas disease: gene networks associated with transmission

Chagas disease, caused by Trypanosoma cruzi, affects over seven million people globally. Vertical transmission during pregnancy significantly contributes to the urban spread of the disease, even in non-endemic areas. The placental barrier plays a key role in preventing fetal infection, although the molecular mechanisms underlying congenital transmission remain unclear. To identify placental factors associated with transmission, we conducted a transcriptomic study comparing placental tissues from deliveries of congenitally infected (M+B+), exposed but uninfected (M+B-), and unexposed/uninfected (M-B-) newborns. Differential gene expression analysis of transmitting placentas revealed that ENSG00000304767, a novel lncRNA sense intronic to CEMIP was overexpressed as well as CGB5, while CEMIP, CADM3, CADH11 and PRXX1 were underexpressed. In non-transmitting placentas, the long non-coding RNA MIR4300 was overexpressed while CGB5 was underexpressed. These results suggest that cell adhesion and the extracellular matrix integrity are altered in transmitting placentas. Additionally, gene set enrichment analysis using the GO library revealed immune-related terms underrepresented in both infected mother groups, and confirmed that extracellular matrix processes, particularly collagen organization and metabolism, constitute important factors in transmission events. Analysis using the Cell Type library showed that extravillous trophoblasts were overrepresented in M+B+, but the opposite in M+B-. In contrast, syncytiotrophoblasts and villous cytotrophoblasts were overrepresented in non-transmitting vs. control cases. Immune-related placental cell types were consistently reduced in both M+ groups when compared to controls. The co-expression network analysis confirmed that the placental signaling and structural integrity were compromised in transmitting cases. ENPP1 and SLC16A10 emerged as hub genes with pivotal roles in the pathways altered during congenital infection. These findings highlight key placental transcriptional alterations linked to congenital T. cruzi transmission and provide insight into potential molecular mechanisms of fetal protection or susceptibility. Author summaryChagas disease, caused by the parasite Trypanosoma cruzi, can be passed from mother to baby during pregnancy, constituting the main transmission way in urban and non-endemic areas. The placenta normally acts as a barrier to protect the fetus, but how this barrier fails in congenitally transmitted cases is still not well understood. Thus, we analyzed the gene expression in placental tissues from three groups: infected mothers that transmitted the parasite, infected mothers that did not transmit it, and uninfected mothers. We found that certain genes involved in immune response, hormone regulation, and the structure of the placenta showed clear differences among these groups. In particular, CGB5, involved in the production of pregnancy hormones, showed opposite activity patterns depending on whether the mother transmitted the parasite or not. We also found that specific placental cell types that help anchor the placenta or form its protective outer layer changed their expression pattern in transmitting cases. These shifts suggest that both the physical structure and immune system of the placenta may be altered in a way that allows the parasite to reach the fetus. Our findings reveal important molecular clues that could help predict congenital transmission of Chagas disease.

molecular biology↗

bioRxiv2023

Distinguishing mutants that resist drugs via different mechanisms by examining fitness tradeoffs across hundreds of fluconazole-resistant yeast strains

There is growing interest in designing multidrug therapies that leverage tradeoffs to combat resistance. Tradeoffs are common in evolution and occur when, for example, resistance to one drug results in sensitivity to another. Major questions remain about the extent to which tradeoffs are reliable, specifically, whether the mutants that provide resistance to a given drug all suffer similar tradeoffs. This question is difficult because the drug-resistant mutants observed in the clinic, and even those evolved in controlled laboratory settings, are often biased towards those that provide large fitness benefits. Thus, the mutations (and mechanisms) that provide drug resistance may be more diverse than current data suggests. Here, we perform evolution experiments utilizing lineage-tracking to capture a fuller spectrum of mutations that give yeast cells a fitness advantage in fluconazole, a common antifungal drug. We then quantify fitness tradeoffs for each of 774 evolved mutants across 12 environments, finding these mutants group into 6 classes with characteristically different tradeoffs. Their unique tradeoffs may imply that each group of mutants affects fitness through different underlying mechanisms. Some of the groupings we find are surprising. For example, we find some mutants that resist single drugs do not resist their combination, while others do. And some mutants to the same gene have different tradeoffs than others. These findings, on one hand, demonstrate the difficulty in relying on consistent or intuitive tradeoffs when designing multidrug treatments. On the other hand, by demonstrating that hundreds of adaptive mutations can be reduced to a few groups with characteristic tradeoffs, our findings may yet empower multidrug strategies that leverage tradeoffs to combat resistance. More generally speaking, by grouping mutants that likely affect fitness through similar underlying mechanisms, our work guides efforts to map the phenotypic effects of mutation.

evolutionary biology↗

bioRxiv2022

Extreme sensitivity of fitness to environmental conditions; lessons from #1BigBatch

The phrase "survival of the fittest" has become an iconic descriptor of how natural selection works. And yet, precisely measuring fitness, even for single-celled microbial populations growing in controlled laboratory conditions, remains a challenge. While numerous methods exist to perform these measurements, including recently developed methods utilizing DNA barcoding, all methods seem limited in their precision to differentiate strains with small fitness differences. This limit on precision is relevant in many fields, including the field of experimental evolution. In this study, we hone in on the factors that contribute to noisy fitness measurements and suggest solutions to avoid certain sources of noise. Surprisingly, even when common sources of technical noise are controlled for, we find that fitness measurements are still very noisy. Our data suggest that subtle environmental differences among replicates create substantial variation across fitness measurements. We conclude by providing best practices for obtaining precise fitness measurements and by discussing how these measurements should be interpreted given their extreme context dependence. This work was inspired by the scientific community who followed us and gave us tips as we live-tweeted a high-replicate fitness measurement experiment at #1BigBatch.

evolutionary biology↗