Biology subjects

Ho, W. W. H.

Publications and source records attributed to Ho, W. W. H..

3 recordsLinked to original sources

bioRxiv2020

Arabidopsis REI-LIKE proteins activate ribosome biogenesis during cold acclimation

Arabidopsis REIL proteins are cytosolic ribosomal 60S-biogenesis factors. After shift to 10{degrees}C, reil mutants deplete and slowly replenish non-translating eukaryotic ribosome complexes of root tissue, while tightly controlling the balance of non-translating 40S- and 60S-subunits. Reil mutations compensate by hyper-accumulation of non-translating subunits at steady-state temperature; after cold-shift, a KCl-sensitive 80S sub-fraction remains depleted. We infer that Arabidopsis buffers fluctuating translation by pre-existing non-translating ribosomes before de novo synthesis meets temperature-induced demands. Reil1 reil2 double mutants accumulate 43S-preinitiation and pre-60S-maturation complexes and have altered paralog composition of ribosomal proteins in non-translating complexes. With few exceptions, e.g. RPL3B and RPL24C, these changes are not under transcriptional control. Our study suggests requirement of de novo synthesis of eukaryotic ribosomes for long-term cold acclimation, feedback control of NUC2 and eIF3C2 transcription and links new proteins, AT1G03250, AT5G60530, to plant ribosome biogenesis. We propose that Arabidopsis requires biosynthesis of specialized ribosomes for cold acclimation. Highlight of this studyREIL proteins affect paralog composition of eukaryotic ribosomes and suppress accumulation of 43S-preinitiation and pre-60S-maturation complexes, suggesting functions of ribosome heterogeneity and biogenesis in plant cold acclimation.

plant biology↗

bioRxiv2019

RPW8/HR Repeats Predict NLR-dependent Hybrid Performance

Hybrid offspring can look very different from their parents, including having greatly increased or decreased fitness. In many plant species, conflicts between divergent elements of the immune system can cause hybrids to express autoimmunity, a generally deleterious syndrome known as hybrid necrosis. We are investigating multiple hybrid necrosis cases in Arabidopsis thaliana that are caused by allele-specific interactions between different variants at two unlinked resistance (R) gene clusters. One is the RESISTANCE TO PERONOSPORA PARASITICA 7 (RPP7) cluster, which encodes an intracellular nucleotide binding site-leucine rich repeat (NLR) immune receptors that confer strain-specific resistance to oomycetes. The other is the RESISTANCE TO POWDERY MILDEW 8 (RPW8)/HOMOLOG OF RPW8 (HR) locus, which encodes atypical resistance proteins that can confer broad-spectrum resistance to filamentous pathogens. There is extensive structural variation in the RPW8/HR cluster, both at the level of gene copy number and at the level of C-terminal protein repeats of unknown function. We demonstrate that the number of RPW8/HR repeats correlate, albeit in a complex manner, with the severity of hybrid necrosis when these alleles are combined with specific RPP7 variants. This observation suggests that gross structural differences, rather than individual amino acid polymorphisms, guide the genetic interaction between RPW8/HR and RPP7 alleles. We discuss these findings in light of the similarity of RPW8/HR proteins with pore-forming toxins, MLKL and HET-S, from mammals and fungi.

genetics↗

bioRxiv2017

Best Practice Data Life Cycle Approaches for the Life Sciences

Throughout history, the life sciences have been revolutionised by technological advances; in our era this is manifested by advances in instrumentation for data generation, and consequently researchers now routinely handle large amounts of heterogeneous data in digital formats. The simultaneous transitions towards biology as a data science and towards a life cycle view of research data pose new challenges. Researchers face a bewildering landscape of data management requirements, recommendations and regulations, without necessarily being able to access data management training or possessing a clear understanding of practical approaches that can assist in data management in their particular research domain.\n\nHere we provide an overview of best practice data life cycle approaches for researchers in the life sciences/bioinformatics space with a particular focus on omics datasets and computer-based data processing and analysis. We discuss the different stages of the data life cycle and provide practical suggestions for useful tools and resources to improve data management practices.

bioinformatics↗