Biology subjects

Yamada, Y.

Publications and source records attributed to Yamada, Y..

5 recordsLinked to original sources

bioRxiv2018

Suppressor analysis uncovers that MAPs and microtubule dynamics balance with the Cut7/Kinesin-5 motor for mitotic spindle assembly in Schizosaccharomyces pombe

The Kinesin-5 motor Cut7 in Schizosaccharomyces pombe plays essential roles in spindle pole separation, leading to the assembly of bipolar spindle. In many organisms, simultaneous inactivation of Kinesin-14s neutralizes Kinesin-5 deficiency. To uncover the molecular network that counteracts Kinesin-5, we have conducted a genetic screening for suppressors that rescue the cut7-22 temperature sensitive mutation, and identified 10 loci. Next generation sequencing analysis reveals that causative mutations are mapped in genes encoding -, {beta}-tubulins and the microtubule plus-end tracking protein Mal3/EB1, in addition to the components of the Pkl1/Kinesin-14 complex. Moreover, the deletion of various genes required for microtubule nucleation/polymerization also suppresses the cut7 mutant. Intriguingly, Klp2/Kinesin-14 levels on the spindles are significantly increased in cut7 mutants, whereas these increases are negated by suppressors, which may explain the suppression by these mutations/deletions. Consistent with this notion, mild overproduction of Klp2 confers temperature sensitivity. Surprisingly, treatment with a microtubule-destabilizing drug not only suppresses cut7 temperature sensitivity but also rescues the lethality resulting from the deletion of cut7, though a single klp2 deletion per se cannot compensate for the loss of Cut7. We propose that microtubule assembly and/or dynamics antagonize Cut7 functions, and that the orchestration between these two factors is crucial for bipolar spindle assembly.

genetics↗

bioRxiv2017

Two spatially distinct Kinesin-14 Pkl1 and Klp2 generate collaborative inward forces against Kinesin-5 Cut7 in S. pombe

Kinesin motors play central roles in bipolar spindle assembly. In many eukaryotes, spindle pole separation is driven by Kinesin-5 that generates outward force. This outward force is balanced by antagonistic inward force elicited by Kinesin-14 and/or Dynein. In fission yeast, two Kinesin-14s, Pkl1 and Klp2, play an opposing role against Kinesin-5/Cut7. However, how these two Kinesin-14s coordinate individual activities remains elusive. Here we show that while deletion of either pkl1 or klp2 rescues temperature sensitive cut7 mutants, only pkl1 deletion can bypass the lethality caused by cut7 deletion. Pkl1 is tethered to the spindle pole body, while Klp2 is localized along the spindle microtubule. Forced targeting of Klp2 to the spindle pole body, however, compensates for Pkl1 functions, indicating that cellular localizations, rather than individual motor specificities, differentiate between the two Kinesin-14s. Interestingly, human Kinesin-14/HSET can replace either Pkl1 or Klp2. Moreover, overproducing HSET induces monopolar spindles, reminiscent of the phenotype of Cut7 inactivation. Taken together, this study has uncovered the biological mechanism of how two different Kinesin-14s exert their antagonistic roles against Kinesin-5 in a spatially distinct manner.\n\nSUMMARY STATEMENTProper force-balance generated by Kinesin-5 and Kinesin-14 is crucial for spindle bipolarity. Two fission yeast Kinesin-14s localize to different structures, thereby collaboratively producing inward forces against Kinesin-5-mediated outward force.\n\nAbbreviations used

cell biology↗

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↗

bioRxiv2017

Sequential interactions with Mre11-Rad50-Nbs1 activate ATM/Tel1 at DNA double-strand breaks and telomeres

The Mre11-Rad50-Nbs1 (MRN) protein complex, CtIP/Ctp1/Sae2 and ATM/Tel1 kinase protect genome integrity through their functions in DNA double-strand break (DSB) repair, checkpoint signaling, and telomere maintenance. Nbs1 has a conserved C-terminal motif that binds ATM, but the full extent of ATM interactions with MRN are unknown. Here, we show that Tel1 overexpression in Schizosaccharomyces pombe restores Tel1 activity at DSBs and telomeres in the absence of Nbs1. This activity requires Mre11, indicating that Tel1 overexpression drives low affinity binding to the Mre11-Rad50 subcomplex. Mre11-Rad50 binds DSBs in nbs1{Delta} cells, and fusing the Tel1-binding motif of Nbs1 to Mre11 fully restores Tel1 signaling in these cells. Tel1 overexpression does not restore Tel1 signaling in cells carrying the rad50-I1192W mutation, which impairs the ability of Mre11-Rad50 to form the ATP-bound closed conformation. From these findings, we propose that Tel1 activation at DNA ends proceeds by a sequential mechanism initiated by high affinity binding to Nbs1 which recruits Tel1, followed by a low affinity interaction with Mre11-Rad50 in the closed conformation to activate Tel1.

cell biology↗

bioRxiv2017

Serial Crystallography with Multi-stage Merging of 1000’s of Images

KAMO and Blend provide particularly effective tools to manage automatically the merging of large numbers of datasets from serial crystallography. The requirement for manual intervention in the process can be reduced by extending Blend to support additional clustering options such as use of more accurate cell distance metrics and use of reflection-intensity correlation coefficients to infer "distances" among sets of reflec- tions. This increases the sensitivity to differences in unit cell parameters and allows for clustering to assemble nearly complete datasets on the basis of intensity or ampli- tude differences. If datasets are already sufficiently complete to permit it, one applies KAMO once and clusters the data using intensities only. If starting from incomplete datasets, one applies KAMO twice, first using cell parameters. In this step we use either the simple cell vector distance of the original Blend, or we use the more sensi- tive NCDist. This step tends to find clusters of sufficient size so that, when merged, each cluster is sufficiently complete to allow reflection intensities or amplitudes to be compared. One then uses KAMO again using the correlation between the reflections having a common hkl to merge clusters in a way sensitive to structural differences that may not have perturbed the cell parameters sufficiently to make meaningful clusters. Many groups have developed effective clustering algorithms that use a measurable physical parameter from each diffraction still or wedge to cluster the data into cate- gories which then can be merged, one hopes, to yield the electron density from a single protein form. Since these physical parameters are often largely independent from one another, it should be possible to greatly improve the efficacy of data clustering software by using a multi-stage partitioning strategy. Here, we have demonstrated one possible approach to multi-stage data clustering. Our strategy is to use unit-cell clustering until merged data is sufficiently complete then to use intensity-based clustering. We have demonstrated that, using this strategy, we are able to accurately cluster datasets from crystals that have subtle differences.

bioinformatics↗