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Biology subjects

Martin, B. E.

Publications and source records attributed to Martin, B. E..

3 recordsLinked to original sources

Direct cell reprogramming by a designed agonist inducing HER2-FGFR proximity

Growth factor induced receptor dimerization and activation of downstream pathways can modulate cell fate decisions. Here, we investigate the potential of de novo designed synthetic ligands, termed Novokines, to reprogram cell identity by inducing proximity of novel pairs of receptor subunits. We find that a design, H2F, that brings together HER2 (which has no known natural ligand) and the FGF receptor has potent signaling activity. H2F induces robust signaling and reprograms fibroblasts into myogenic cells. Unlike native FGF ligands, H2F selectively activates the MAPK pathway without engaging PLC{gamma}-mediated Ca{superscript 2} signaling. FRET assays confirm H2F-mediated HER2-FGFR proximity, and phosphoproteomic analysis reveals activation of MAPK effectors. H2F-induced ERK phosphorylation is abolished in cells expressing a kinase-dead FGFR1 (K514M) mutant, confirming the requirement for FGFR catalytic activity. H2F treatment significantly increases myofiber formation from adult patient-derived primary myoblasts, demonstrating its capacity to promote myogenic regeneration. Our findings demonstrate that synthetic receptor pairings can rewire signaling outputs to drive regeneration, providing a programmable platform for cell fate engineering.

biochemistry↗

Cellular co-infection increases the speed and efficiency of influenza A virus production and shapes the interferon response to infection

During viral infection, the numbers of virions infecting individual cells can vary significantly over time and space. The functional consequences of this variation in cellular multiplicity of infection (MOI) remain poorly understood. Here, we rigorously quantify the phenotypic consequences of cellular MOI during influenza A virus (IAV) infection over a single round of replication in terms of cell death rates, viral output kinetics, interferon and antiviral effector gene transcription, and superinfection potential. By statistically fitting mathematical models to our data, we precisely define specific functional forms that quantitatively describe the modulation of these phenotypes by MOI at the single cell level. To determine the generality of these functional forms, we compare two distinct cell lines (MDCK cells and A549 cells), both infected with the H1N1 strain A/Puerto Rico/8/1934 (PR8). We find that a model assuming that infected cell death rates are independent of cellular MOI best fits the experimental data in both cell lines. We further observe that a model in which the rate and efficiency of virus production increase with cellular co-infection best fits our observations in MDCK cells, but not in A549 cells. In A549 cells, we also find that induction of type III interferon, but not type I interferon, is highly dependent on cellular MOI, especially at early timepoints. This finding identifies a role for cellular co-infection in shaping the innate immune response to IAV infection. Finally, we show that higher cellular MOI is associated with more potent superinfection exclusion, thus limiting the total number of virions capable of infecting a cell. Overall, this study suggests that the extent of cellular co-infection by influenza viruses may be a critical determinant of both viral production kinetics and cellular infection outcomes in a host cell type-dependent manner. AUTHOR SUMMARYDuring influenza A virus (IAV) infection, the number of virions to enter individual cells can be highly variable. Cellular co-infection appears to be common and plays an essential role in facilitating reassortment for IAV, yet little is known about how cellular co-infection influences infection outcomes at the cellular level. Here, we combine quantitative in vitro infection experiments with statistical model fitting to precisely define the phenotypic consequences of cellular co-infection in two cell lines. We reveal that cellular co-infection can increase and accelerate the efficiency of IAV production in a cell line-dependent fashion, identifying it as a potential determinant of viral replication kinetics. We also show that induction of type III, but not type I, interferon is highly dependent upon the number of virions that infect a given cell, implicating cellular co-infection as an important determinant of the host innate immune response to infection. Altogether, our findings show that cellular co-infection plays a crucial role in determining infection outcome. The integration of experimental and statistical modeling approaches detailed here represents a significant advance in the quantitative study of influenza virus infection and should aid ongoing efforts focused on the construction of mathematical models of IAV infection.

microbiology↗

Illumina-based sequencing framework for accurate detection and mapping of influenza virus defective interfering particle-associated RNAs

The mechanisms and consequences of defective interfering particle (DIP) formation during influenza virus infection remain poorly understood. The development of next generation sequencing (NGS) technologies has made it possible to identify large numbers of DIP-associated sequences, providing a powerful tool to better understand their biological relevance. However, NGS approaches pose numerous technical challenges including the precise identification and mapping of deletion junctions in the presence of frequent mutation and base-calling errors, and the potential for numerous experimental and computational artifacts. Here we detail an Illumina-based sequencing framework and bioinformatics pipeline capable of generating highly accurate and reproducible profiles of DIP-associated junction sequences. We use a combination of simulated and experimental control datasets to optimize pipeline performance and demonstrate the absence of significant artifacts. Finally, we use this optimized pipeline to generate a high-resolution profile of DIP-associated junctions produced during influenza virus infection and demonstrate how this data can provide insight into mechanisms of DIP formation. This work highlights the specific challenges associated with NGS-based detection of DIP-associated sequences, and details the computational and experimental controls required for such studies.

microbiology↗