It has recently been reported that a common side effect of translation-blocking morpholino antisense oligonucleotides is the induction of a set of innate immune response genes in Xenopus embryos and that splicing-blocking morpholinos lead to unexpected off-target mis-splicing events. Here, we present an analysis of all publicly available Xenopus RNA sequencing (RNA-seq) data in a reexamination of the effects of translation-blocking morpholinos on the innate immune response. Our analysis does not support the authors' general conclusion, which was based on a limited number of RNA-seq datasets. Moreover, the strong induction of an immune response appears to be specific to the tbxt/tbxt2 morpholinos. The more comprehensive study presented here indicates that using morpholinos for targeted gene knockdowns remains of considerable value for the rapid identification of gene function.

Mesendodermal specification is one of the earliest events in embryogenesis, where cells first acquire distinct identities. Cell differentiation is a highly regulated process that involves the function of numerous transcription factors (TFs) and signaling molecules, which can be described with gene regulatory networks (GRNs). Cell differentiation GRNs are difficult to build because existing mechanistic methods are low throughput, and high-throughput methods tend to be non-mechanistic. Additionally, integrating highly dimensional data composed of more than two data types is challenging. Here, we use linked self-organizing maps to combine chromatin immunoprecipitation sequencing (ChIP-seq)/ATAC-seq with temporal, spatial, and perturbation RNA sequencing (RNA-seq) data from Xenopus tropicalis mesendoderm development to build a high-resolution genome scale mechanistic GRN. We recover both known and previously unsuspected TF-DNA/TF-TF interactions validated through reporter assays. Our analysis provides insights into transcriptional regulation of early cell fate decisions and provides a general approach to building GRNs using highly dimensional multi-omic datasets.