Aberrant complex formation by recurrent interaction modules, such as BTB domains, leucine zippers, or coiled coils, can disrupt signal transduction, yet whether cells detect and eliminate complexes of irregular composition is unknown. By searching for regulators of the BTB family, we discovered a quality control pathway that ensures functional dimerization [dimerization quality control (DQC)]. Key to this network is the E3 ligase SCF, which selectively binds and ubiquitylates BTB dimers of aberrant composition to trigger their clearance by proteasomal degradation. Underscoring the physiological importance of DQC, SCF is required for the differentiation, function, and survival of neural crest and neuronal cells. We conclude that metazoan organisms actively monitor BTB dimerization, and we predict that distinct E3 ligases similarly control complex formation by other recurrent domains.
The canonical Wnt/β-catenin signaling pathway plays multiple roles during Xenopus gastrulation, including posteriorization of the neural plate, patterning of the mesoderm, and induction of the neural crest. Wnt signaling stabilizes β-catenin, which then activates target genes. However, few targets of this signaling pathway that mediate early developmental processes are known. Here we sought to identify transcriptional targets of the Wnt/β-catenin signaling pathway using a genome-wide approach. We selected putative targets using the criteria of reduced expression upon zygotic Wnt knockdown, β-catenin binding within 50kb of the gene, and expression in tissues that receive Wnt signaling. Using these criteria, we found 21 novel direct transcriptional targets of Wnt/β-catenin signaling during gastrulation and in addition have identified putative regulatory elements for further characterization in future studies.
Deciphering the structure of gene regulatory networks across the tree of life remains one of the major challenges in postgenomic biology. We present a novel ChIP-seq workflow for the archaea using the model organism Halobacterium salinarum sp. NRC-1 and demonstrate its application for mapping the genome-wide binding sites of natively expressed transcription factors. This end-to-end pipeline is the first protocol for ChIP-seq in archaea, with methods and tools for each stage from gene tagging to data analysis and biological discovery. Genome-wide binding sites for transcription factors with many binding sites (TfbD) are identified with sensitivity, while retaining specificity in the identification the smaller regulons (bacteriorhodopsin-activator protein). Chromosomal tagging of target proteins with a compact epitope facilitates a standardized and cost-effective workflow that is compatible with high-throughput immunoprecipitation of natively expressed transcription factors. The Pique package, an open-source bioinformatics method, is presented for identification of binding events. Relative to ChIP-Chip and qPCR, this workflow offers a robust catalog of protein–DNA binding events with improved spatial resolution and significantly decreased cost. While this study focuses on the application of ChIP-seq in H. salinarum sp. NRC-1, our workflow can also be adapted for use in other archaea and bacteria with basic genetic tools.
Amphibian neural development occurs as a two-step process:(1) induction specifies a neural fate in undifferentiated ectoderm; and (2) transformation induces posterior spinal cord and hindbrain. Signaling through the Fgf, retinoic acid (RA) and Wnt/β-catenin pathways is necessary and sufficient to induce posterior fates in the neural plate, yet a mechanistic understanding of the process is lacking. Here, we screened for factors enriched in posterior neural tissue and identify spalt-like 4 (sall4), which is induced by Fgf. Knockdown of Sall4 results in loss of spinal cord marker expression and increased expression of pou5f3.2 (oct25), pou5f3.3 (oct60) and pou5f3.1 (oct91) (collectively, pou5f3 genes), the closest Xenopus homologs of mammalian stem cell factor Pou5f1 (Oct4). Overexpression of the pou5f3 genes results in the loss of spinal cord identity and knockdown of pou5f3 function restores spinal cord marker expression in Sall4 morphants. Finally, knockdown of Sall4 blocks the posteriorizing effects of Fgf and RA signaling in the neurectoderm. These results suggest that Sall4, activated by posteriorizing signals, represses the pou5f3 genes to provide a permissive environment allowing for additional Wnt/Fgf/RA signals to posteriorize the neural plate.
Purpose The percentage of a maternal cell-free DNA (cfDNA) sample that is fetal-derived (the fetal fraction; FF) is a key driver of the sensitivity and specificity of noninvasive prenatal screening (NIPS). On certain NIPS platforms, >20% of women with high body mass index (and >5% overall) receive a test failure due to low FF (<4%). Methods A scalable fetal fraction amplification (FFA) technology was analytically validated on 1264 samples undergoing whole-genome sequencing (WGS)–based NIPS. All samples were tested with and without FFA. Results Zero samples had FF < 4% when screened with FFA, whereas 1 in 25 of these same patients had FF < 4% without FFA. The average increase in FF was 3.9-fold for samples with low FF (2.3-fold overall) and 99.8% had higher FF with FFA. For all abnormalities screened on NIPS, z-scores increased 2.2-fold on average in positive samples and remained unchanged in negative samples, powering an increase in NIPS sensitivity and specificity. Conclusion FFA transforms low-FF samples into high-FF samples. By combining FFA with WGS–based NIPS, a single round of NIPS can provide nearly all women with confident results about the broad range of potential fetal chromosomal abnormalities across the genome.
There are many examples of problems in pattern analysis for which it is often possible to obtain systematic characterizations, if in addition a small number of useful features or parameters of the image are known a priori or can be estimated reasonably well. Often, the relevant features of a particular pattern analysis problem are easy to enumerate, as when statistical structures of the patterns are well understood from the knowledge of the domain. We study a problem from molecular image analysis, where such a domain-dependent understanding may be lacking to some degree and the features must be inferred via machine-learning techniques. In this paper, we propose a rigorous, fully automated technique for this problem. We are motivated by an application of atomic force microscopy (AFM) image processing needed to solve a central problem in molecular biology, aimed at obtaining the complete transcription profile of a single cell, a snapshot that shows which genes are being expressed and to what degree. Reed et al. (“Single molecule transcription profiling with AFM,” Nanotechnology, vol. 18, no. 4, 2007) showed that the transcription profiling problem reduces to making high-precision measurements of biomolecule backbone lengths, correct to within 20–25 bp (6–7.5 nm). Here, we present an image processing and length estimation pipeline using AFM that comes close to achieving these measurement tolerances. In particular, we develop a biased length estimator on trained coefficients of a simple linear regression model, biweighted by a Beaton–Tukey function, whose feature universe is constrained by James–Stein shrinkage to avoid overfitting. In terms of extensibility and addressing the model selection problem, this formulation subsumes the models we studied.
We discuss a novel atomic force microscope-based method for identifying individual short DNA molecules (,5000 bp) within a complex mixture by measuring the intra-molecular spacing of a few sequence-specific topographical labels in each molecule. Using this method, we accurately determined the relative abundance of individual DNA species in a 15-species mixture, with fewer than 100 copies per species sampled. To assess the scalability of our approach, we conducted a computer simulation, with realistic parameters, of the hypothetical problem of detecting abundance changes in individual gene transcripts between two single-cell human messenger RNA samples, each containing roughly 9000 species. We found that this approach can distinguish transcript species abundance changes accurately in most cases, including transcript isoforms which would be challenging to quantitate with traditional methods. Given its sensitivity and procedural simplicity, our approach could be used to identify transcript-derived complementary DNAs, where it would have substantial technical and practical advantages versus established techniques in situations where sample material is scarce.
During Xenopus gastrulation, Wnt and FGF signaling pathways cooperate to induce posterior structures. Wnt target expression around the blastopore falls into two main categories: a horseshoe shape with a dorsal gap, as in Wnt8 expression; or a ring, as in FGF8 expression. Using ChIP-seq, we show, surprisingly, that the FGF signaling mediator Ets2 binds near all Wnt target genes. However, βcatenin preferentially binds at the promoters of genes with horseshoe patterns, but further from the promoters of genes with ring patterns. Manipulation of FGF or Wnt signaling demonstrated that 'ring' genes are responsive to FGF signaling at the dorsal midline, whereas 'horseshoe' genes are predominantly regulated by Wnt signaling. We suggest that, in the absence of active β-catenin at the dorsal midline, the DNA-binding protein TCF binds and actively represses gene activity only when close to the promoter. In contrast, genes without functional TCF sites at the promoter may be predominantly regulated by Ets at the dorsal midline and are expressed in a ring. These results suggest recruitment of only short-range repressors to potential Wnt targets in the Xenopus gastrula.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
334 Leonard St
Brooklyn, NY 11211
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.