Biology subjects

Abe, Y.

Publications and source records attributed to Abe, Y..

4 recordsLinked to original sources

bioRxiv2018

BIN1 genetic risk factor for Alzheimer is sufficient to induce early structural tract alterations in entorhinal cortex-dentate gyrus pathway and related hippocampal multi-scale impairments

Genetic factors are known to contribute to Late Onset Alzheimers disease (LOAD) but their contribution to pathophysiology, specially to prodomic phases accessible to therapeutic approaches are far to be understood. To translate genetic risk of Alzheimers disease (AD) into mechanistic insight, we generated transgenic mouse lines that express a [~]195 kbp human BAC that includes only BIN1, a gene associated to LOAD. This model gives a modest BIN1 overexpression, dependent of the number of BAC copies. At 6 months of age, we detected impaired entorhinal cortex (EC)-hippocampal pathways with specific impairments in EC-dentate gyrus synaptic long-term potentiation, dendritic spines of granular cells and recognition episodic memory. Structural changes were quantified using MRI. Their whole-brain functional impact were analyzed using resting state fMRI with a hypoconnectivity centered on entorhinal cortex. These early phenotype defects independent of any changes in A-beta can be instrumental in the search for new AD drug targets.

neuroscience↗

bioRxiv2017

An unconventional NOI/RIN4 domain of a rice NLR protein binds host EXO70 protein to confer fungal immunity

As much as 10% of plant immune receptors from the nucleotide-binding domain leucine-rich repeat (NLR) family carry integrated domains (IDs) that can directly bind pathogen effectors. However, it remains unclear whether direct binding to effectors is a universal feature of ID-containing NLRs given that only a few NLR-IDs have been functionally characterized. Here we show that the rice (Oryza sativa) sensor NLR-ID Pii2 confers resistance to strains of the rice blast fungus Magnaporthe oryzae that carry the effector AVR-Pii without directly binding this protein. First, we show that AVR-Pii binds the exocyst subunit OsExo70F2 in rice (Oryza sativa) to dissociate preformed complexes of OsExo70F2 with host RPM1 INTERACTING PROTEIN4 (RIN4) at the conserved NOI motif, facilitating a possible virulence function. Second, we show that in its resting state, Pii2 binds OsExo70F2 and OsExo70F3, essential components of Pii-mediated resistance, through its integrated NOI domain. Remarkably, AVR-Pii binding to OsExo70F2/F3 leads to dissociation of the Pii2-OsExo70F2 and Pii2-OsExo70F3 complexes, destabilization of Pii2, and activation of immunity. These findings support a novel conceptual model in which an NLR-ID monitors alterations of tethered host proteins targeted by pathogen effectors, providing insight into pathogen recognition mechanisms. Significance statementPlant diseases diminish crop yields by over 20% each year, and deploying resistant crops is the most effective way to combat them. Nucleotide-binding domain leucine-rich repeat (NLR)-type receptors are the major player in plant resistance against pathogens, with a subset of NLRs containing unconventional domains called integrated domains (ID) derived from host proteins. Previous studies suggest that pathogen avirulence (AVR) effectors directly bind or modify NLR-IDs before they are recognized by the host. Here, we reveal that the rice NLR-ID receptor Pii2 indirectly recognizes AVR-Pii when the effector dissociates Pii2 from the host Exo70 proteins tethered to Pii2. We propose a new model of how NLRs can recognize pathogens, expanding our understanding of plant immunity.

plant biology↗

bioRxiv2017

Rice blast resistance gene Pii is controlled by a pair of NBS-LRR genes Pii-1 and Pii-2

Nucleotide-binding, leucine-rich repeat receptors (NLRs) are conserved cytosolic receptors that recognize pathogen effectors and trigger immunity in plants. Recent studies indicate that NLRs function in pairs. Rice resistance gene Pii has been known to confer resistance against rice blast pathogen Magnaporthe oryzae carrying AVR-Pii. Previously we reported isolation of Pii gene from the rice cultivar Hitomebore (Takagi et al. 2013). To further understand rice components required for Pii-mediated resistance, we screened 5,600 mutant lines of Hitomebore cultivar and identified two mutants that lost Pii resistance without any changes in Pii gene sequence. Application of MutMap-Gap, the whole genome sequencing-based method of mutation identification, to the two mutants revealed that they have mutations in another NLR gene located close to Pii. The F1 plants derived from a cross of the two mutants showed pii phenotype, demonstrating that the newly identified NLR gene is indeed a component of Pii resistance. We thus designate the previously isolated Pii gene as Pii-1 and the newly isolated NLR gene as Pii-2.

genetics↗

bioRxiv2017

A common neural network among state, trait, and pathological anxiety from whole-brain functional connectivity

Anxiety is one of the most common mental states of humans. Although it drives us to avoid frightening situations and to achieve our goals, it may also impose significant suffering and burden if it becomes extreme. Because we experience anxiety in a variety of forms, previous studies investigated neural substrates of anxiety in a variety of ways. These studies revealed that individuals with high state, trait, or pathological anxiety showed altered neural substrates. However, no studies have directly investigated whether the different dimensions of anxiety share a common neural substrate, despite its theoretical and practical importance. Here, we investigated a neural network of anxiety shared by different dimensions of anxiety in a unified analytical framework using functional magnetic resonance imaging (fMRI). We analyzed different datasets in a single scale, which was defined by an anxiety-related neural network derived from whole brain. Through the fMRI task for provoking anxiety, we found a common neural network of state anxiety across participants (1,638 trials obtained from 10 participants). Then, using the resting-state fMRI in combination with the participants trait anxiety scale (879 participants from the Human Connectome Project), we demonstrated that trait anxiety also shared the same neural network as state anxiety. Furthermore, the common neural network between state and trait anxiety could detect patients with obsessive-compulsive disorder, which is characterized by pathological anxiety-driven behaviors (174 participants from multi-site datasets). Our findings provide direct evidence that different dimensions of anxiety are not completely independent but have a substantial biological inter-relationship. Our results also provide a biologically defined dimension of anxiety, which may promote further investigation of various human characteristics, including psychiatric disorders, from the perspective of anxiety.

neuroscience↗