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Biomedical subjects

Andrew F Neuwald

Publications and source records attributed to Andrew F Neuwald.

2 recordsLinked to original sources

Evolutionary and Functional Analysis of Caspase-8 and ASC Interactions to Drive Lytic Cell Death, PANoptosis.

Caspases are evolutionarily conserved proteins essential for driving cell death in development and host defense. Caspase-8, a key member of the caspase family, is implicated in nonlytic apoptosis, as well as lytic forms of cell death. Recently, caspase-8 has been identified as an integral component of PANoptosomes, multiprotein complexes formed in response to innate immune sensor activation. Several innate immune sensors can nucleate caspase-8-containing PANoptosome complexes to drive inflammatory lytic cell death, PANoptosis. However, how the evolutionarily conserved and diverse functions of caspase-8 drive PANoptosis remains unclear. To address this, we performed evolutionary, sequence, structural, and functional analyses to decode caspase-8's complex-forming abilities and its interaction with the PANoptosome adaptor ASC. Our study distinguished distinct subgroups within the death domain superfamily based on their evolutionary and functional relationships, identified homotypic traits among subfamily members, and captured key events in caspase evolution. We also identified critical residues defining the heterotypic interaction between caspase-8's death effector domain and ASC's pyrin domain, validated through cross-species analyses, dynamic simulations, and in vitro experiments. Overall, our study elucidated recent evolutionary adaptations of caspase-8 that allowed it to interact with ASC, improving our understanding of critical molecular associations in PANoptosome complex formation and the underlying PANoptotic responses in host defense and inflammation. These findings have implications for understanding mammalian immune responses and developing new therapeutic strategies for inflammatory diseases.

Caspase 8

Bayesian identification of differentially expressed isoforms using a novel joint model of RNA-seq data.

We develop a Bayesian approach, BayesIso, to identify differentially expressed isoforms from RNA-seq data. The approach features a novel joint model of the sample variability and the deferential state of isoforms. Specifically, the within-sample variability and the between-sample variability of each isoform are modeled by a Poisson-Lognormal model and a Gamma-Gamma model, respectively. Using a Bayesian framework, the differential state of each isoform and the model parameters are jointly estimated by a Markov Chain Monte Carlo (MCMC) method. Extensive studies using simulation and real data demonstrate that BayesIso can effectively detect isoforms of less differentially expressed and differential transcripts for genes with multiple isoforms. We applied the approach to breast cancer RNA-seq data and uncovered a unique set of isoforms that form key pathways associated with breast cancer recurrence. First, PI3K/AKT/mTOR signaling and PTEN signaling pathways are identified as being involved in breast cancer development. Further integrated with protein-protein interaction data, pathways of Jak-STAT, mTOR, MAPK and Wnt signaling are revealed in association with breast cancer recurrence. Finally, several pathways are activated in the early recurrence of breast cancer. In tumors that occur early, members of pathways of cellular metabolism and cell cycle (such as CD36 and TOP2A) are upregulated, while immune response genes such as NFATC1 are downregulated.

Humans