Latest Publications

Developmental gene regulatory network connections predicted by machine learning from gene expression data alone

Tue, 12/28/2021 - 03:00

PLoS One. 2021 Dec 28;16(12):e0261926. doi: 10.1371/journal.pone.0261926. eCollection 2021.


Gene regulatory network (GRN) inference can now take advantage of powerful machine learning algorithms to complement traditional experimental methods in building gene networks. However, the dynamical nature of embryonic development-representing the time-dependent interactions between thousands of transcription factors, signaling molecules, and effector genes-is one of the most challenging arenas for GRN prediction. In this work, we show that successful GRN predictions for a developmental network from gene expression data alone can be obtained with the Priors Enriched Absent Knowledge (PEAK) network inference algorithm. PEAK is a noise-robust method that models gene expression dynamics via ordinary differential equations and selects the best network based on information-theoretic criteria coupled with the machine learning algorithm Elastic Net. We test our GRN prediction methodology using two gene expression datasets for the purple sea urchin, Stronglyocentrotus purpuratus, and cross-check our results against existing GRN models that have been constructed and validated by over 30 years of experimental results. Our results find a remarkably high degree of sensitivity in identifying known gene interactions in the network (maximum 81.58%). We also generate novel predictions for interactions that have not yet been described, which provide a resource for researchers to use to further complete the sea urchin GRN. Published ChIPseq data and spatial co-expression analysis further support a subset of the top novel predictions. We conclude that GRN predictions that match known gene interactions can be produced using gene expression data alone from developmental time series experiments.

PMID:34962963 | PMC:PMC8714117 | DOI:10.1371/journal.pone.0261926

Categories: pubmed

Sequence Diversity, Locus Structure, and Evolutionary History of the <em>SpTransformer</em> Genes in the Sea Urchin Genome

Mon, 12/06/2021 - 03:00

Front Immunol. 2021 Nov 15;12:744783. doi: 10.3389/fimmu.2021.744783. eCollection 2021.


The generation of large immune gene families is often driven by evolutionary pressure exerted on host genomes by their pathogens, which has been described as the immunological arms race. The SpTransformer (SpTrf) gene family from the California purple sea urchin, Strongylocentrotus purpuratus, is upregulated upon immune challenge and encodes the SpTrf proteins that interact with pathogens during an immune response. Native SpTrf proteins bind both bacteria and yeast, and augment phagocytosis of a marine Vibrio, while a recombinant SpTrf protein (rSpTrf-E1) binds a subset of pathogens and a range of pathogen associated molecular patterns. In the sequenced sea urchin genome, there are four SpTrf gene clusters for a total of 17 genes. Here, we report an in-depth analysis of these genes to understand the sequence complexities of this family, its genomic structure, and to derive a putative evolutionary history for the formation of the gene clusters. We report a detailed characterization of gene structure including the intron type and UTRs with conserved transcriptional start sites, the start codon and multiple stop codons, and locations of polyadenylation signals. Phylogenetic and percent mismatch analyses of the genes and the intergenic regions allowed us to predict the last common ancestral SpTrf gene and a theoretical evolutionary history of the gene family. The appearance of the gene clusters from the theoretical ancestral gene may have been driven by multiple duplication and deletion events of regions containing SpTrf genes. Duplications and ectopic insertion events, indels, and point mutations in the exons likely resulted in the extant genes and family structure. This theoretical evolutionary history is consistent with the involvement of these genes in the arms race in responses to pathogens and suggests that the diversification of these genes and their encoded proteins have been selected for based on the survival benefits of pathogen binding and host protection.

PMID:34867968 | PMC:PMC8634487 | DOI:10.3389/fimmu.2021.744783

Categories: pubmed