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28We used publicly available ChIP-seq data on EIN3 binding, RNA-seq data on ethylene-induced trans-
criptomes in Arabidopsis seedlings [1] and DAP-seq data [2]. We used Homer [3] for de novo motif search in
the peaks, and MCOT [4] for enrichment analysis of EBS repeats. Associations of EBS configurations with peaks
and genes features were estimated with Fisher�s exact test. We discovered a previously unknown EBS architec-
ture that is enriched in EIN3 bound sequences to a much greater extent than a single EBS motif. This new con-
figuration is a tail-to-tail inverted repeat of EBS-like sequences with 1 bp overlap referred to as 2EBS(-1). We
also demonstrated that the inverted repeat of the coreEBS with the overlap of the motifs but not with a spacer
is enriched in the Arabidopsis genome. We further showed that of all EBS configurations under study only
2EBS(-1) was significantly associated with transcriptional response of EIN3 targets to ethylene treatment.
Moreover, it tended to cause a more pronounced transcriptional response than other EBS configurations.
Based on these findings we consider that 2EBS(-1) is a preferred EIN3 binding site in A. thailana genome. We
applied these findings to design a new genetic sensor for highly sensitive detection of ethylene signaling. Tak-
en together, this work provides new insight on the molecular mechanisms utilized for regulation of transcrip-
tional response to ethylene in plants and offers a workflow for the inference of the cis-regulatory code.
This work was supported by the Russian Science Foundation (grant no. 20-14-00140).
References
1. Chang K.N. et al. Temporal transcriptional response to ethylene gas drives growth hormone cross-regulation in
Arabidopsis // Elife. 2013. V. 2, P. e00675.
2. O'Malley R.C. et al. Cistrome and Epicistrome Features Shape the Regulatory DNA Landscape // Cell. 2016. V. 165,
N. 5, P. 1280-1292.
3. Heinz S. et al. Simple combinations of lineage-determining transcription factors prime cis-regulatory elements
required for macrophage and b cell identities // Mol. Cell. 2010. V. 38, N. 4, P. 576-589.
4. Levitsky V. et al. A single ChIP-seq dataset is sufficient for comprehensive analysis of motifs co-occurrence with
MCOT package // Nucleic Acids Res. 2019. V. 47, N. 21, P. e139.
Changes in transcriptome profiles during enzymatic degradation of cell walls in fig (Ficus carica L.) leaves
A. V. Doroshkov1,2, U. S. Zubairova1,2, O. V. Kryvenko3, O. M. Kuleshova3, I. V. Mitrofanova4
1Institute of Cytology and Genetics SB RAS
2Novosibirsk State University
3A.O. Kovalevsky Institute of Biology of the Southern Seas, Sevastopol
4Nikita Botanical Gardens - National Scientific Center, Yalta
Email: ad@bionet.nsc.ru
DOI 10.24412/cl-35065-2021-1-02-38
Isolated protoplasts provide an opportunity to study a wide variety of cellular processes. As usual, the iso-
lation of protoplasts from plant tissues occurs due to the cell wall enzymatic degradation. While this process,
the pattern of gene expression changes from that characteristic to a mature cell towards undifferentiated
cells. In this work, we analyzed the leaves of a typical variety, �Sabrucia Rosea�, obtained from microshoots
grown in vitro were incubated in an enzymatic mixture. Several successive steps in the protoplast isolation
protocol served as sampling points. For control, RNA was obtained from the original tissue. For all mRNA
isoforms, expression levels were normalized relative to expression levels at the point corresponding to the
transcriptome of untouched leaves. Based on the principal component analysis and k-means clustering, we
identified groups with similar behavior. Among them, several mRNA isoforms showed significant changes in
the expression level throughout the experiment. These data could be used to search for genes that act as
"time markers" while enzymatic degradation of leaf tissue in order to optimize and test the protoplast isola-
tion protocols.
This work was supported by Genomic Research Center �Kurchatov Genomic Center � NBG-NSC�.
New possibilities of the PCA-Seq method in the analysis of time series
V. M. Efimov1,2,3,4, K. V. Efimov5, D. A. Polunin2, and V. Y. Kovaleva3
1Institute of Cytology and Genetics SB RAS
2Novosibirsk State University
3Institute of Systematics and Ecology SB RAS
4Tomsk State University
5Higher School of Economics
E-mail: efimov@bionet.nsc.ru
DOI 10.24412/cl-35065-2021-1-02-97
When decomposing a one-dimensional time series using SSA, two PCs correspond to one frequency. But
they can be modulated by a third PC without breaking orthogonality [1, 2].
In the PCA-Seq method [3], PCs are calculated through a matrix of distances between fragments of a time
series. The original and modulating signals can be separated by fragments pre-normalizing. A reliable zero cor-
relation with the time axis may testify in favor of the hypothesis that the found regularity really exists and can
be continued in both directions from the interval under study. We use traditional solar activity data as an ex-
ample.
This work was supported by the RFBR grant #19-07-00658-a and the Budget Project of the ICG SB RAS #0259-2021-
0009.
References
1. Efimov V. M., Galaktionov Y. K., Shushpanova N. F. Analiz i prognoz vremennykh ryadov metodom glavnykh
komponent. Novosibirsk: Nauka. Sib. otd-nie, 1988.
2. Courtillot V., Lopes F., Le Mouel J. L. //Solar Physics. 2021. V. 296, N. 1. P. 1-23.
3. Efimov V. M., Efimov K. V., and Kovaleva V. // Vavilov J. Gen. and Breeding. 2019. V. 23. P. 1032-1036.
Automatic abdominal aneurysm segmentation using deep learning
R. UI. Epifanov1, R. I. Mullyadzhanov1,2, A. A. Karpenko1,3
1Novosibirsk State University
2Institute of Thermophysics SB RAS
3Meshalkin National Medical Research Center
Email: rostepifanov@gmail.com
DOI 10.24412/cl-35065-2021-1-02-39
Abdominal aortic aneurysm is a serious disease, delayed diagnosis and surgical treatment of which is often
fatal. Early detection and subsequent observation of the dynamics of the development of aneurysmal trans-
formations of the aorta allows for advance planning of surgical intervention, which significantly reduces the
risk of postoperative complications and mortality in patients. The main method for diagnosing this disease at
the moment is laborious visual analysis of three-dimensional computed tomography images of the patient.
Creation of a tool for automatic segmentation of an abdominal aortic aneurysm will simplify the diagnosis of
this disease. The paper considers the creation of an abdominal aortic aneurysm segmentation tool based on
deep learning methods. The algorithm is built by adapting the U-net architecture, which has proven itself for
