Noriko Hiroi

Mammalian Rcd1 is a novel transcriptional cofactor that mediates retinoic acid-induced cell differentiation

Rcd1, initially identified as a factor essential for the commitment to nitrogen starvation‐invoked differentiation in fission yeast, is one of the most conserved proteins found across eukaryotes, and its mammalian homolog is expressed in a variety of differentiating tissues. Here we show that mammalian Rcd1 is a novel transcriptional cofactor and is critically involved in the commitment step in the retinoic acid‐induced differentiation of F9 mouse teratocarcinoma cells, at least in part, via forming complexes with retinoic acid receptor and activation transcription factor‐2 (ATF‐2). In addition, antisense oligonucleotide treatment of embryonic mouse lung explants suggests that Rcd1 also plays a role in retinoic acid‐controlled lung development.

Detection of Temperature Difference in Neuronal Cells

For a better understanding of the mechanisms behind cellular functions, quantification of the heterogeneity in an organism or cells is essential. Recently, the importance of quantifying temperature has been highlighted, as it correlates with biochemical reaction rates. Several methods for detecting intracellular temperature have recently been established. Here we develop a novel method for sensing temperature in living cells based on the imaging technique of fluorescence of quantum dots. We apply the method to quantify the temperature difference in a human derived neuronal cell line, SH-SY5Y. Our results show that temperatures in the cell body and neurites are different and thus suggest that inhomogeneous heat production and dissipation happen in a cell. We estimate that heterogeneous heat dissipation results from the characteristic shape of neuronal cells, which consist of several compartments formed with different surface-volume ratios. Inhomogeneous heat production is attributable to the localization of specific organelles as the heat source.

FPGA Implementation of a Data-Driven Stochastic Biochemical Simulator with the Next Reaction Method

This paper introduces a scalable FPGA implementation of a stochastic simulation algorithm (SSA) called the next reaction method. There are some hardware approaches of SSAs that obtained high-throughput on reconfigurable devices such as FPGAs, but these works lacked in scalability. The design of this work can accommodate to the increasing size of target biochemical models, or to make use of increasing capacity of FPGAs. Interconnection network between arithmetic circuits and multiple simulation circuits aims to perform a data-driven multi-threading simulation. Approximately 8 times speedup was obtained compared to an execution on Xeon 2.80 GHz.

Comparative studies of suppression of malignant cancer cell phenotype by antisense oligo DNA and small interfering RNA

One of the distinguishing features of malignant tumor cells is the ability to proliferate in an anchorage-independent manner; methods that effectively suppress this phenotype may be applicable to the therapeutic inhibition of the malignancy of cancers. Interfering RNA is a potentially powerful tool for cancer therapy because of its specificity of target selection and remarkably high efficiency in target mRNA suppression. We studied the use of two knockdown strategies, antisense oligo DNA (AS-ODN) and small interfering RNA (siRNA), and showed how the anchorage-independent proliferation of malignant cells could be blocked efficiently. Anchorage-independent proliferation of rat fibroblasts transformed with v-src was suppressed with only a single 1-μM dose of AS-ODN; similar suppression using siRNA required treatment with 1 nM siRNA every 12 h. With our experimental system, the molecular stability of AS-ODN allowed the use of a simple treatment regimen to control the amount of the target molecule, providing that the treatment dose was sufficiently high. In comparison, siRNA treatment was effective at lower doses, but more frequent treatment was necessary to achieve the same suppression of proliferation.

High-speed microscopy with an electrically tunable lens to image the dynamics of in vivo molecular complexes

We provide an evaluation for an electrically tunable lens (ETL), combined with a microscope system, from the viewpoint of tracking intracellular protein complexes. We measured the correlation between the quantitative axial focus shift and the control current for ETL, and determined the stabilization time for refocusing to evaluate the electrical focusing behaviour of our system. We also confirmed that the change of relative magnification by the lens and associated resolution does not influence the ability to find intracellular targets. By applying the ETL system to observe intracellular structures and protein complexes, we confirmed that this system can obtain 10 nm order z-stacks, within video rate, while maintaining the quality of images and that this system has sufficient optical performance to detect the molecules.

A proteomic study of mitotic phase-specific interactors of EB1 reveals a role for SXIP-mediated protein interactions in anaphase onset

ABSTRACT Microtubules execute diverse mitotic events that are spatially and temporally separated; the underlying regulation is poorly understood. By combining drug treatments, large-scale immunoprecipitation and mass spectrometry, we report the first comprehensive map of mitotic phase-specific protein interactions of the microtubule-end binding protein, EB1. EB1 interacts with some, but not all, of its partners throughout mitosis. We show that the interaction of EB1 with Astrin-SKAP complex, a key regulator of chromosome segregation, is enhanced during prometaphase, compared to anaphase. We find that EB1 and EB3, another EB family member, can interact directly with SKAP, in an SXIP-motif dependent manner. Using an SXIP defective mutant that cannot interact with EB, we uncover two distinct pools of SKAP at spindle microtubules and kinetochores. We demonstrate the importance of SKAPs SXIP-motif in controlling microtubule growth rates and anaphase onset, without grossly disrupting spindle function. Thus, we provide the first comprehensive map of temporal changes in EB1 interactors during mitosis and highlight the importance of EB protein interactions in ensuring normal mitosis.

Automated tracking of mitotic spindle pole positions shows that LGN is required for spindle rotation but not orientation maintenance.

Spindle orientation defines the plane of cell division and, thereby, the spatial position of all daughter cells. Here, we develop a live cell microscopy-based methodology to extract spindle movements in human epithelial cell lines and study how spindles are brought to a pre-defined orientation. We show that spindles undergo two distinct regimes of movements. Spindles are first actively rotated toward the cells’ long-axis and then maintained along this pre-defined axis. By quantifying spindle movements in cells depleted of LGN, we show that the first regime of rotational movements requires LGN that recruits cortical dynein. In contrast, the second regime of movements that maintains spindle orientation does not require LGN, but is sensitive to 2ME2 that suppresses microtubule dynamics. Our study sheds first insight into spatially defined spindle movement regimes in human cells, and supports the presence of LGN and dynein independent cortical anchors for astral microtubules.

The systems biology simulation core algorithm

BackgroundWith the increasing availability of high dimensional time course data for metabolites, genes, and fluxes, the mathematical description of dynamical systems has become an essential aspect of research in systems biology. Models are often encoded in formats such as SBML, whose structure is very complex and difficult to evaluate due to many special cases.ResultsThis article describes an efficient algorithm to solve SBML models that are interpreted in terms of ordinary differential equations. We begin our consideration with a formal representation of the mathematical form of the models and explain all parts of the algorithm in detail, including several preprocessing steps. We provide a flexible reference implementation as part of the Systems Biology Simulation Core Library, a community-driven project providing a large collection of numerical solvers and a sophisticated interface hierarchy for the definition of custom differential equation systems. To demonstrate the capabilities of the new algorithm, it has been tested with the entire SBML Test Suite and all models of BioModels Database.ConclusionsThe formal description of the mathematics behind the SBML format facilitates the implementation of the algorithm within specifically tailored programs. The reference implementation can be used as a simulation backend for Java™-based programs. Source code, binaries, and documentation can be freely obtained under the terms of the LGPL version 3 from http://simulation-core.sourceforge.net. Feature requests, bug reports, contributions, or any further discussion can be directed to the mailing list simulation-core-development@lists.sourceforge.net.

The design of scalable stochastic biochemical simulator on FPGA

Biochemical simulations including whole-cell models require high performance computing systems. Reconfigurable systems are expected to be an alternative solution for conventional methods by PC clusters or vector computers. This paper shows the implementation of a stochastic biochemical simulation algorithm called Next Reaction Method for Virtex-II PRO. As the result of benchmarking with a small reaction system, the FPGA-based simulator outperforms the software implementation on Xeon 2.40 GHz by 17.1 times

Efficient scheduling of rate law functions for ODE-based multimodel biochemical simulation on an FPGA

A reconfigurable biochemical simulator by solving ordinary differential equations has received attention as a personal high speed environment for biochemical researchers. For efficient use of the reconfigurable hardware, static scheduling of high-throughput arithmetic pipeline structures is essential. This paper shows and compares some scheduling alternatives, and analyzes the tradeoffs between performance and hardware amount. Through the evaluation, it is shown that the sharing first scheduling reduces the hardware cost by 33.8% in average, with the up to 11.5% throughput degradation. Effects of sharing of rate law functions are also analyzed.