Biology subjects

Maekawa, T.

Publications and source records attributed to Maekawa, T..

3 recordsLinked to original sources

bioRxiv2018

Subfamily-specific functionalization of diversified immune receptors in wild barley

Gene-for-gene immunity between plants and host-adapted pathogens is often linked to population-level diversification of immune receptors encoded by disease resistance (R) genes. The complex barley (Hordeum vulgare L.) R gene locus Mildew Locus A (Mla) provides isolate-specific resistance against the powdery mildew fungus Blumeria graminis f. sp. hordei (Bgh) and has been introgressed into modern barley cultivars from diverse germplasms, including the wild relative H. spontaneum. Known Mla disease resistance specificities to Bgh appear to encode allelic variants of the R Gene Homolog 1 (RGH1) family of nucleotide-binding domain and leucine-rich repeat (NLR) proteins. To gain insights into Mla diversity in wild barley populations, we here sequenced and assembled the transcriptomes of 50 accessions of H. spontaneum representing nine populations distributed throughout the Fertile Crescent. The assembled Mla transcripts exhibited rich sequence diversity, which is linked neither to geographic origin nor population structure. Mla transcripts in the tested H. spontaneum accessions could be grouped into two similar-sized subfamilies based on two major N-terminal coiled-coil signaling domains that are both capable of eliciting cell death. The presence of positively selected sites, located mainly in the C-terminal leucine-rich repeats of both MLA subfamilies, together with the fact that both coiled-coil signaling domains mediate cell death, implies that the two subfamilies are actively maintained in the host population. Unexpectedly, known MLA receptor variants that confer Bgh resistance belong exclusively to one subfamily. Thus, signaling domain divergence, potentially to distinct pathogen populations, is an evolutionary signature of functional diversification of an immune receptor.

evolutionary biology↗

bioRxiv2018

Signatures of host specialization and a recent transposable element burst in the dynamic one-speed genome of the fungal barley powdery mildew pathogen

Powdery mildews are biotrophic pathogenic fungi infecting a number of economically important plants. The grass powdery mildew, Blumeria graminis, has become a model organism to study host specialization of obligate biotrophic fungal pathogens. We resolved the large-scale genomic architecture of B. graminis forma specialis hordei (Bgh) to explore the potential influence of its genome organization on the co-evolutionary process with its host plant, barley (Hordeum vulgare). The near-chromosome level assemblies of the Bgh reference isolate DH14 and one of the most diversified isolates, RACE1, enabled a comparative analysis of these haploid genomes, which are highly enriched with transposable elements (TEs). We found largely retained genome synteny and gene repertoires, yet detected copy number variation (CNV) of secretion signal peptide-containing protein-coding genes (SPs) and locally disrupted synteny blocks. Genes coding for sequence-related SPs are often locally clustered, but neither the SP clusters nor TEs are enriched in specific genomic regions. Extended comparative analysis with different host-specific B. graminis formae speciales revealed the existence of a core suite of SPs, but also isolate-specific SP sets as well as congruence of SP CNV and phylogenetic relationship. We further detected evidence for a recent, lineage-specific expansion of TEs in the Bgh genome. The characteristics of the Bgh genome (largely retained synteny, CNV of SP genes, recently proliferated TEs and a lack of compartmentalization) are consistent with a \"one-speed\" genome that differs in its architecture and (co-)evolutionary pattern from the \"two-speed\" genomes reported for several other filamentous phytopathogens.

genomics↗

bioRxiv2017

Experience-dependent modulation of behavioral features in sensory navigation of nematodes and bats revealed by machine learning

Animal behavior is the final and integrated output of the brain activity. Thus, recording and analyzing behavior is critical to understand the underlying brain function. While recording animal behavior has become easier than ever with the development of compact and inexpensive devices, detailed behavioral data analysis requires sufficient previous knowledge and/or high content data such as video images of animal postures, which makes it difficult for most of the animal behavioral data to be efficiently analyzed to understand brain function. Here, we report a versatile method using a hybrid supervised/unsupervised machine learning approach to efficiently estimate behavioral states and to extract important behavioral features only from low-content animal trajectory data. As proof of principle experiments, we analyzed trajectory data of worms, fruit flies, rats, and bats in the laboratories, and penguins and flying seabirds in the wild, which were recorded with various methods and span a wide range of spatiotemporal scales--from mm to 1000 km in space and from sub-seconds to days in time. We estimated several states during behavior and comprehensively extracted characteristic features from a behavioral state and/or a specific experimental condition. Physiological and genetic experiments in worms revealed that the extracted behavioral features reflected specific neural or gene activities. Thus, our method provides a versatile and unbiased way to extract behavioral features from simple trajectory data to understand brain function.

neuroscience↗