Biology subjects

Miller, M.

Publications and source records attributed to Miller, M..

93 records · Page 6Linked to original sources

bioRxiv2017

Morphogen dynamics control patterning in a stem cell model of the human embryo

During embryonic development, diffusible signaling molecules called morphogens are thought to determine cell fates in a concentration-dependent manner1-4, and protocols for directed stem cell differentiation are based on this picture5-8. However, in the mammalian embryo, morphogen concentrations change rapidly compared to the time for making cell fate decisions9-12. It is unknown how changing ligand levels are interpreted, and whether the precise timecourse of ligand exposure plays a role in cell fate decisions. Nodal and BMP4 are morphogens crucial for gastrulation in vertebrates13. Each pathway has distinct receptor complexes that phosphorylate specific signal transducers, known as receptor-Smads, which then complex with the shared cofactor Smad4 to activate target genes14. Here we show in human embryonic stem cells (hESCs) that the response to BMP4 signaling indeed is determined by the ligand concentration, but that unexpectedly, the expression of many mesodermal targets of Activin/Nodal depends on rate of concentration increase. In addition, we use live imaging of hESCs with GFP integrated at the endogenous SMAD4 locus to show that a stem cell model for the human embryo15 generates a wave of Nodal signaling. Cells experience rapidly increasing Nodal specifically in the region of mesendoderm differentiation. We also demonstrate that pulsatile stimulation with Activin induces repeated strong signaling and enhances mesoderm differentiation. Our results break with the paradigm of concentration-dependent differentiation and demonstrate an important role for morphogen dynamics in the cell fate decisions associated with mammalian gastrulation. They suggest a highly dynamic picture of embryonic patterning where some cell fates depend on rapid concentration increase rather than absolute levels, and point to ligand dynamics as a new dimension to optimize protocols for directed stem cell differentiation.

developmental biology↗

bioRxiv2017

A near complete haplotype-phased genome of the dikaryotic wheat stripe rust fungus Puccinia striiformis f. sp. tritici reveals high inter-haplome diversity

A long-standing biological question is how evolution has shaped the genomic architecture of dikaryotic fungi. To answer this, high quality genomic resources that enable haplotype comparisons are essential. Short-read genome assemblies for dikaryotic fungi are highly fragmented and lack haplotype-specific information due to the high heterozygosity and repeat content of these genomes. Here we present a diploidaware assembly of the wheat stripe rust fungus Puccinia striiformis f. sp. tritici based on long-reads using the FALCON-Unzip assembler. RNA-seq datasets were used to infer high quality gene models and identify virulence genes involved in plant infection referred to as effectors. This represents the most complete Puccinia striiformis f. sp. tritici genome assembly to date (83 Mb, 156 contigs, N50 1.5 Mb) and provides phased haplotype information for over 92% of the genome. Comparisons of the phase blocks revealed high inter-haplotype diversity of over 6%. More than 25% of all genes lack a clear allelic counterpart. When investigating genome features that potentially promote the rapid evolution of virulence, we found that candidate effector genes are spatially associated with conserved genes commonly found in basidiomycetes. Yet candidate effectors that lack an allelic counterpart are more distant from conserved genes than allelic candidate effectors, and are less likely to be evolutionarily conserved within the P. striiformis species complex and Pucciniales. In summary, this haplotype-phased assembly enabled us to discover novel genome features of a dikaryotic plant pathogenic fungus previously hidden in collapsed and fragmented genome assemblies.\n\nImportanceCurrent representations of eukaryotic microbial genomes are haploid, hiding the genomic diversity intrinsic to diploid and polyploid life forms. This hidden diversity contributes to the organisms evolutionary potential and ability to adapt to stress conditions. Yet it is challenging to provide haplotype-specific information at a whole-genome level. Here, we take advantage of long-read DNA sequencing technology and a tailored-assembly algorithm to disentangle the two haploid genomes of a dikaryotic pathogenic wheat rust fungus. The two genomes display high levels of nucleotide and structural variations, which leads to allelic variation and the presence of genes lacking allelic counterparts. Non-allelic candidate effector genes, which likely encode important pathogenicity factors, display distinct genome localization patterns and are less likely to be evolutionary conserved than those which are present as allelic pairs. This genomic diversity may promote rapid host adaptation and/or be related to the age of the sequenced isolate since last meiosis.

microbiology↗

bioRxiv2017

Raspberry Pi Powered Imaging for Plant Phenotyping

O_LIPremise of the study: Image-based phenomics is a powerful approach to capture and quantify plant diversity. However, commercial platforms that make consistent image acquisition easy are often cost-prohibitive. To make high-throughput phenotyping methods more accessible, low-cost microcomputers and cameras can be used to acquire plant image data.\nC_LIO_LIMethods and Results: We used low-cost Raspberry Pi computers and cameras to manage and capture plant image data. Detailed here are three different applications of Raspberry Pi controlled imaging platforms for seed and shoot imaging. Images obtained from each platform were suitable for extracting quantifiable plant traits (shape, area, height, color) en masse using open-source image processing software such as PlantCV.\nC_LIO_LIConclusion: This protocol describes three low-cost platforms for image acquisition that are useful for quantifying plant diversity. When coupled with open-source image processing tools, these imaging platforms provide viable low-cost solutions for incorporating high-throughput phenomics into a wide range of research programs.\nC_LI

plant biology↗