Biology subjects

Trippe, B. L.

Publications and source records attributed to Trippe, B. L..

2 recordsLinked to original sources

bioRxiv2018

ZAG-1/ZEB and EGL-44/TEAD form a negative feedback loop to safeguard the choice of cell fate

Terminal differentiation generates the specialized features and functions that allow postmitotic cells to acquire their distinguishing characteristics. This process is thought to be controlled by transcription factors called \"terminal selectors\" that directly activate a set of downstream effector genes. In Caenorhabditis elegans the differentiation of both the mechanosensory touch receptor neurons (TRNs) and the multidendritic nociceptor FLP neurons utilize the terminal selectors UNC-86 and MEC-3. The FLP neurons fail to activate TRN genes, however, because a complex of two transcriptional repressors (EGL-44/EGL-46) prevents their expression. Here we show that the ZEB family transcriptional factor ZAG-1 promotes TRN differentiation not by activating TRN genes but by preventing the expression of EGL-44/EGL-46. Since EGL-44/EGL-46 also inhibits the production of ZAG-1, these proteins form a bistable, negative feedback loop that regulates the choice between the two neuronal fates.\n\nSummary statementTranscriptional repressors regulate binary fate choices through reciprocal inhibition during terminal neuronal differentiation. Specifically, ZEB family transcription factor safeguards fate specification of touch receptor neuron by inhibiting TEA domain-containing repressor.

developmental biology↗

bioRxiv2016

K-mer Motif Multinomial Mixtures

1MotivationThe advent of inexpensive high-throughput sequencing (HTS) places new demands on motif discovery algorithms. To confront the challenges and embrace the opportunities presented by the growing wealth of information tied up in HTS datasets, we developed K-mer motif multinomial mixtures (KMMMs), a flexible class of Bayesian models for identifying multiple motifs in sequence sets using K-mer tables. Advantages of this framework are inference with time and space complexities that only scale with K, and the ability to be incorporated into larger Bayesian models.\n\nResultsWe derived a class of probabilistic models of K-mer tables generated from sequence containing multiple motifs. KMMMs model the K-mer table as a multinomial mixture, with motif and background components, which are distributions over K-mers overlapping with each of the latent motifs and over K-mers that do not overlap with any motif, respectively. The framework casts motif discovery as a posterior inference problem, and we present several approximate inference methods that provide accurate reconstructions of motifs in synthetic data. Finally we apply the method to discover motifs in DNAse hypersensitive sites and ChIP-seq peaks obtained from the ENCODE project.

bioinformatics↗