Biology subjects

Flores, K.

Publications and source records attributed to Flores, K..

4 recordsLinked to original sources

bioRxiv2023

Adaptation to volumetric compression drives hepatoblastoma cells to an apoptosis-resistant and invasive phenotype

Liver cancer involves tumor cells rapidly growing within a packed tissue environment. Patient tumor tissues reveal densely packed and deformed cells, especially at tumor boundaries, indicative of physical crowding and compression. It is not well understood how these physical signals modulate tumor evolution and therapeutic susceptibility. Here we investigate the impact of volumetric compression on liver cancer (HepG2) behavior. We find that conditioning cells under a highly compressed state leads to major transcriptional reprogramming, notably the loss of hepatic markers, the epithelial-to-mesenchymal transition (EMT)-like changes, and altered calcium signaling-related gene expression, over the course of several days. Biophysically, compressed cells exhibit increased Rac1-mediated cell spreading and cell-extracellular matrix interactions, cytoskeletal reorganization, increased YAP and {beta}-catenin nuclear translocation, and dysfunction in cytoplasmic and mitochondrial calcium signaling. Furthermore, compressed cells are resistant to chemotherapeutics and desensitized to apoptosis signaling. Apoptosis sensitivity can be rescued by stimulated calcium signaling. Our study demonstrates that volumetric compression is a key microenvironmental factor that drives tumor evolution in multiple pathological directions and highlights potential countermeasures to re-sensitize therapy-resistant cells. Significance statementCompression can arise as cancer cells grow and navigate within the dense solid tumor microenvironment. It is unclear how compression mediates critical programs that drive tumor progression and therapeutic complications. Here, we take an integrative approach in investigating the impact of compression on liver cancer. We identify and characterize compressed subdomains within patient tumor tissues. Furthermore, using in vitro systems, we induce volumetric compression (primarily via osmotic pressure but also via mechanical force) on liver cancer cells and demonstrate significant molecular and biophysical changes in cell states, including in function, cytoskeletal signaling, proliferation, invasion, and chemoresistance. Importantly, our results show that compressed cells have impaired calcium signaling and acquire resistance to apoptosis, which can be countered via calcium mobilization.

cancer biology↗

bioRxiv2023

A ubiquitous mobile genetic element disarms a bacterial antagonist of the gut microbiota

DNA transfer is ubiquitous in the gut microbiota, especially among species of Bacteroidales. In silico analyses have revealed hundreds of mobile genetic elements shared between these species, yet little is known about the phenotypes they encode, their effects on fitness, or pleiotropic consequences for the recipients genome. Here, we show that acquisition of a ubiquitous integrative and conjugative element encoding an antagonistic system shuts down the native contact-dependent antagonistic system of Bacteroides fragilis. Despite inactivating the native antagonism system, mobile element acquisition increases fitness of the B. fragilis transconjugant over its progenitor by arming it with a new weapon. This DNA transfer causes the strain to change allegiances so that it no longer targets ecosystem members containing the same element yet is armed for communal defense.

microbiology↗

bioRxiv2022

Parameter estimation and identifiability analysis for a bivalent analyte model of monoclonal antibody-antigen binding

1Discovery research for therapeutic antibodies and vaccine development requires an in-depth understanding of antibody-antigen interactions. Label-free techniques such as Surface Plasmon Resonance (SPR) enable the characterization of biomolecular interactions through kinetics measurements, typically by binding antigens in solution to monoclonal antibodies immobilized on a SPR chip. 1:1 Langmuir binding model is commonly used to fit the kinetics data and derive rate constants. However, in certain contexts it is necessary to immobilize the antigen to the chip and flow the antibodies in solution. One such scenario is the screening of monoclonal antibodies (mAbs) for breadth against a range of antigens, where a bivalent analyte binding model is required to adequately describe the kinetics data unless antigen immobilizaion density is optimized to eliminate avidity effects. A bivalent analyte model is offered in several existing software packages intended for standard throughput SPR instruments, but lacking for high throughput SPR instruments. Existing methods also do not explore multiple local minima and parameter identifiability, issues common in non-linear optimization. Here, we have developed a method for analyzing bivalent analyte binding kinetics directly applicable to high throughput SPR data collected in a non-regenerative fashion, and have included a grid search on initial parameter values and a profile likelihood method to determine parameter identifiability. We fit the data of a broadly neutralizing HIV-1 mAb binding to HIV-1 envelope glycoprotein gp120 to a system of ordinary differential equations modeling bivalent binding. Our identifiability analysis discovered a non-identifiable parameter when data is collected under the standard experimental design for monitoring the association and dissociation phases. We used simulations to determine an improved experimental design, which when executed, resulted in the reliable estimation of all rate constants. These methods will be valuable tools in analyzing the binding of mAbs to an array of antigens to expedite therapeutic antibody discovery research. 2 Author summaryWhile commercial software programs for the analysis of bivalent analyte binding kinetics are available for low-throughput instruments, they cannot be easily applied to data generated by high-throughput instruments, particularly when the chip surface is not regenerated between titration cycles. Further, existing software does not address common issues in fitting non-linear systems of ordinary differential equations (ODEs) such as optimizations getting trapped in local minima or parameters that are not identifiable. In this work, we introduce a pipeline for analysis of bivalent analyte binding kinetics that 1) allows for the use of high-throughput, non-regenerative experimental designs, 2) optimizes using several sets of initial parameter values to ensure that the algorithm is able to reach the lowest minimum error and 3) applies a profile likelihood method to explore parameter identifiability. In our experimental application of the method, we found that one of the kinetics parameters (kd2) cannot be reliably estimated with the standard length of the dissociation phase. Using simulation and identifiability analysis we determined the optimal length of dissociation so that the parameter can be reliably estimated, saving time and reagents. These methodologies offer robust determination of the kinetics parameters for high-throughput bivalent analyte SPR experiments.

biophysics↗

bioRxiv2021

The Core Human Fecal Metabolome

Summary ParagraphAmong the biomolecules at the center of human health and molecular biology is a system of molecules that defines the human phenotype known as the metabolome. Through an untargeted metabolomic analysis of fecal samples from human individuals from Africa and the Americas--the birthplace and the last continental expansion of our species, respectively--we present the characterization of the core human fecal metabolome. The majority of detected metabolite features were ubiquitous across populations, despite any geographic, dietary, or behavioral differences. Such shared metabolite features included hyocholic acid and cholesterol. However, any characterization of the core human fecal metabolome is insufficient without exploring the influence of industrialization. Here, we show chemical differences along an industrialization gradient, where the degree of industrialization correlates with metabolomic changes. We identified differential metabolite features like leucyl-leucine dipeptides and urobilin as major metabolic correlates of these behavioral shifts. Our results indicate that industrialization significantly influences the human fecal metabolome, but diverse human lifestyles and behavior still maintain a core human fecal metabolome. This study represents the first characterization of the core human fecal metabolome through untargeted analyses of populations along an industrialization gradient.

biochemistry↗