6. Life Science and Medical Science

概要

CO6-1

Radioresistance Mechanisms Acquired by Adaptive Evolution and their Evolutionary
Mechanisms III

T. Saito
Institute for Integrated Radiation and Nuclear Science,
Kyoto University
INTRODUCTION: Organisms have evolved diverse
forms by adapting to various environmental conditions,
and some can survive in hostile environments. Studying
these adaptive mechanisms can provide meaningful information regarding the evolution and biological diversity of
organisms. Some radioresistant bacteria are highly resistant to ionizing radiation [1]. The mechanism through
which bacteria resist ionizing radiation is an interesting research subject considering the adaptive mechanisms employed by organisms in nature. To elucidate the mechanisms of radioresistance in these organisms, the biological
defense mechanisms against external stress must be investigated at the molecular level. However, studies on naturally occurring radioresistant organisms are particularly
challenging, owing to limited knowledge of their genetic
and biochemical properties. Therefore, in this study, radioresistant Escherichia coli, whose wild-type genetic and
biochemical characteristics have been elucidated in detail,
were generated via an adaptive evolution experiment using
gamma rays as the selective pressure, and the characteristics of the evolved radioresistant E. coli were compared
with those of the wild type. Previously, radioresistant E.
coli have been generated with a 7.9-fold resistance compared with wild-type E. coli [2, 3, 4]. This report describes
the differences in gene expression status between wildtype and radioresistant E. coli.
EXPERIMENTS: Extraction of total RNA and RNA
sequencing： Total RNA was extracted from E. coli cells
using RNAiso Plus (Takara) and further purified using NucleoSpin RNA Clean-up XS (Macherey-Nagel). The quality of total RNA was evaluated and confirmed using an
Agilent 2100 Bioanalyzer (Agilent Technologies). rRNA
was removed from the total RNA using a Ribo-Zero Magnetic Kit (Gram-Negative Bacteria) (Illumina). A sequence
library was prepared from the resulting RNA using a
TruSeq Stranded mRNA Sample Prep Kit (Illumina). The
quality of the sequence library was evaluated and confirmed using an Agilent 2100 Bioanalyzer. Sequence analysis was performed using NovaSeq 6000 (Illumina), NovaSeq 6000 S4 Reagent Kit (Illumina), and NovaSeq Xp
4-Lane Kit (Illumina). Gene expression levels were

analyzed using Genedata Profiler Genome (Genedata) and
STAR [5]. All procedures were performed according to the
manufacturer's instructions.
Analysis of gene expression status: In the analysis, gene
expression data with “fragments per kilobase of transcript
per million mapped fragments” values less than 1 for all
samples from the two groups compared were filtered to
eliminate noise data. Differentially expressed genes
(DEGs) in radioresistant E. coli compared with wild-type
E. coli were identified using Welch's t-test and correction
for multiple testing using the Benjamini and Hochberg
method (BH method) [6]. Furthermore, Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis of
DEGs in radioresistant E. coli relative to wild-type E. coli
was performed using the Database for Annotation, Visualization and Integrated Discovery bioinformatics resources
ver.6.8.
Statistical analysis: Welch's t-test and the BH method
were used to identify DEGs among many genes, and a qvalue of less than 0.05 was considered significant. The Expression Analysis Systematic Explorer score was used to
test for significance in KEGG pathway analysis, and a Pvalue of less than 0.05 was considered significant [7].
RESULTS:
KEGG pathway analysis revealed that
genes involved in metabolism-related pathways are enriched in genes up-regulated in radioresistant E. coli compared with wild-type E. coli. Previously, the high expression of genes involved in survival, cell recovery, DNA repair, and response after stress exposure was found to be
involved in the radioresistance of radioresistant E. coli [3,
4]. Alterations in the expression of metabolism-related
genes described in this report are likely to optimize the intracellular environment for survival, cell recovery, DNA
repair, and response after irradiation by regulating the state
of intracellular molecules.
REFERENCES:
[1] T. Saito, Viva Origino, 30 (2007) 85-92.
[2] T. Saito, KURNS ProgressReport 2019, (2020) 211.
[3] T. Saito, KURNS ProgressReport 2020, (2021) 162.
[4] T. Saito, KURNS ProgressReport 2021, (2022) 158.
[5] A. Dobin et al., Bioinfomatics, 29 (2013) 15-21.
[6] Y. Benjamini and Y. Hochberg, J. R. Statist. Soc. B, 57
(1995) 289-300.
[7] D. W. Huang et al., Nat. Protoc. 4 (2009) 44-57.

R4004
- 146 -

CO6-2

Integrated approach for structural analysis of a biomacromolecule in a polydispersed
solution using analytical ultracentrifugation and small-angle X-ray scattering

K. Morishima1, Y. Yunoki1, A. Okuda1, M. Shimizu1, N.
Sato1, R. Inoue1, M. Sugiyama1
Institute for Integrated Radiation and Nuclear Science,
Kyoto University

1

INTRODUCTION:
Small-angle X-ray and neutron scatterings (SAXS and
SANS; collectively called SAS) offer overwhelming
opportunities
for
structural
analysis
of
a
biomacromolecule in solution. Especially, state-of-the-art
computational analyzing methods for SAS offer a high
resolution three-dimensional structural model and/or its
dynamics in the solution. To build a reliable structural
model through the advanced analyses, it is essential to
obtain the precise SAS profile from only a target
biomacromolecule. Hence, a sample should be purified to
be a monodisperse solution prior to a SAS measurement.
Nevertheless, non-specific oligomers, namely aggregates,
often remain in the solution even after purification. Even
if the weight fraction of aggregates is a few %, the
experimental SAS profile is deteriorated by them. As the
result, an incorrect structural model is built as the target
biomacromolecule: This is the fatal problem on the
structural analysis with SAS.
To overcome this problem, a new data-reduction method,
“AUC-SAS”[1], has been developed with integration of
analytical ultracentrifugation (AUC) and SAS. AUC
provides the weight fractions and molecular weights of
all components in solution without destruction of
aggregates and complexes. AUC-SAS derives the
scattering profile of a target biomacromolecule from the
deteriorated experimental profile using the information
provided by AUC. However, the first designed AUC-SAS
(first-AUC-SAS)[1] is constrained by the weight fraction
of the aggregates (less than approximately 10 %). In this
study, we improved AUC-SAS, which is applicable to
samples with relatively large weight fraction of
aggregates (> 10 %).
EXPERIMENTS:
As the demonstration of AUC-SAS, bovine serum
albumin, apoferritin, catalase, lysozyme, ovalbumin, and
ribonuclease A were subjected to the SAXS and AUC
measurements.
SAXS measurements were carried out with NANOPIX
(Rigaku). AUC measurements were conducted with a
ProteomeLab
XL-I
(Beckman
Coulter).
All
measurements were conduceted at 25 ºC.
RESULTS:
AUC-SAS derives the scattering profile of a target
molecule through following steps;
Step 1. Derivation of forward scattering intensity.
Step 2. Derivation of scattering profile in high q region.
Step 3. Connection of forward scattering and scattering
profile in high q region with Guinier approximation.

On the Step 2 in the first-AUC-SAS, the scattering
profile in high q region for the target molecules is
approximated to be identical with that for aggregates
because it is not different in the inner local structure
between the monomer and the aggregates in the sample
for a general SAS measurement. However, we found that
this approximation leads to the error in the target
scattering profile for the samples with relatively large
weight fraction of aggregates (> 10 %).
We improved the procedure of the Step 2 with carefully
reconsidering the scattering profile of an aggregate. To
express the scattering profile of an aggregate, we applied
the random flight model. Consequently, the
improved-AUC-SAS offered the correct scattering profile
of the target molecule even for the sample with ~20% of
weight fraction of aggregates: The improved-AUC-SAS
was demonstrated to offer the consistent result with size
exclusion chromatography (SEC)-SAXS for various
proteins (bovine serum albumin, apoferritin, catalase,
lysozyme, ovalbumin, and ribonuclease A).[2]
Because AUC-SAS does not require a large amount of
sample or a very highly intensive instrument, such as
synchrotron-light SAXS, it has a potential to be applied
to laboratory-based SAXS. Additionally, AUC-SAS is
applicable to also SANS which faces the same
aggregation problem as well as SAXS. Indeed, we
confirmed that the improved-AUC-SANS successfully
works with the SANS data measured with SANS-U at
JRR-3. Finally, we developed the software of the
improved-AUC-SAS for broad SAS users (available at
[http://www.rri.kyoto-u.ac.jp/NSBNG/activity.html]) [2].

Fig. 1. Interface of AUC-SAS software on the Igor Pro.
REFERENCES:
[1] K. Morishima et al., Commun. Biol., 3 (2020) 294.
[2] K. Morishima et al., J. Appl. Crystallogr., (2023)
in press.

R4010
- 147 -

CO6-3 Mutational and environmental effects on the dynamic conformational distributions of
Lys48- linked di-ubiquitin chains
K. Kato, M. Yagi-Utsumi, S. Yanaka, H. Yagi, K. Morishima1, R. Inoue1, N. Sato1 and M. Sugiyama1
Graduate School of Pharmaceutical Sciences, Nagoya
City University
Institute for Integrated Radiation and Nuclear Science,
Kyoto University

1

INTRODUCTION: In multidomain proteins, individual
domains connected by flexible linkers are dynamically rearranged upon ligand binding and sensing changes in environmental factors, such as pH and temperature. ...