RNAseq-Based Working Model for Transcriptional Regulation of Crosstalk between Simultaneous Abiotic UV-B and Biotic Stresses in Plants

Plants adjust their secondary metabolism by altering the expression of corresponding genes to cope with both abiotic and biotic stresses. In the case of UV-B radiation, plants produce protective flavonoids; however, this reaction is impeded during pattern-triggered immunity (PTI) induced by pathogens. Pathogen attack can be mimicked by the application of microbial associated molecular patterns (e.g., flg22) to study crosstalk between PTI and UV-B-induced signaling pathways. Switching from Arabidopsis cell cultures to in planta studies, we analyzed whole transcriptome changes to gain a deeper insight into crosstalk regulation. We performed a comparative transcriptomic analysis by RNAseq with four distinct mRNA libraries and identified 10778, 13620, and 11294 genes, which were differentially expressed after flg22, UV-B, and stress co-treatment, respectively. Focusing on genes being either co-regulated with the UV-B inducible marker gene chalcone synthase CHS or the flg22 inducible marker gene FRK1 identified a large set of transcription factors from diverse families, such as MYB, WRKY, or NAC. These data provide a global view of transcriptomic reprogramming during this crosstalk and constitute a valuable dataset for further deciphering the underlying regulatory mechanism(s), which appear to be much more complex than previously anticipated. The possible involvement of MBW complexes in this context is discussed

End-to-End Adversarial Shape Learning for Abdomen Organ Deep Segmentation

Automatic segmentation of abdomen organs using medical imaging has many potential applications in clinical workflows. Recently, the state-of-the-art performance for organ segmentation has been achieved by deep learning models, i.e., convolutional neural network (CNN). However, it is challenging to train the conventional CNN-based segmentation models that aware of the shape and topology of organs. In this work, we tackle this problem by introducing a novel end-to-end shape learning architecture -- organ point-network. It takes deep learning features as inputs and generates organ shape representations as points that located on organ surface. We later present a novel adversarial shape learning objective function to optimize the point-network to capture shape information better. We train the point-network together with a CNN-based segmentation model in a multi-task fashion so that the shared network parameters can benefit from both shape learning and segmentation tasks. We demonstrate our method with three challenging abdomen organs including liver, spleen, and pancreas. The point-network generates surface points with fine-grained details and it is found critical for improving organ segmentation. Consequently, the deep segmentation model is improved by the introduced shape learning as significantly better Dice scores are observed for spleen and pancreas segmentation.Comment: Accepted to International Workshop on Machine Learning in Medical Imaging (MLMI2019

3D Anisotropic Hybrid Network: Transferring Convolutional Features from 2D Images to 3D Anisotropic Volumes

While deep convolutional neural networks (CNN) have been successfully applied for 2D image analysis, it is still challenging to apply them to 3D anisotropic volumes, especially when the within-slice resolution is much higher than the between-slice resolution and when the amount of 3D volumes is relatively small. On one hand, direct learning of CNN with 3D convolution kernels suffers from the lack of data and likely ends up with poor generalization; insufficient GPU memory limits the model size or representational power. On the other hand, applying 2D CNN with generalizable features to 2D slices ignores between-slice information. Coupling 2D network with LSTM to further handle the between-slice information is not optimal due to the difficulty in LSTM learning. To overcome the above challenges, we propose a 3D Anisotropic Hybrid Network (AH-Net) that transfers convolutional features learned from 2D images to 3D anisotropic volumes. Such a transfer inherits the desired strong generalization capability for within-slice information while naturally exploiting between-slice information for more effective modelling. The focal loss is further utilized for more effective end-to-end learning. We experiment with the proposed 3D AH-Net on two different medical image analysis tasks, namely lesion detection from a Digital Breast Tomosynthesis volume, and liver and liver tumor segmentation from a Computed Tomography volume and obtain the state-of-the-art results

Sequence variation and selection of small RNAs in domesticated rice

<p>Abstract</p> <p>Background</p> <p>Endogenous non-coding small RNAs (21-24 nt) play an important role in post-transcriptional gene regulation in plants. Domestication selection is the most important evolutionary force in shaping crop genomes. The extent of polymorphism at small RNA loci in domesticated rice and whether small RNA loci are targets of domestication selection have not yet been determined.</p> <p>Results</p> <p>A polymorphism survey of 94 small RNA loci (88 <it>MIRNAs</it>, four <it>TAS3 </it>loci and two miRNA-like long hairpins) was conducted in domesticated rice, generating 2 Mb of sequence data. Many mutations (substitution or insertion/deletion) were observed at small RNA loci in domesticated rice, e.g. 12 mutation sites were observed in the mature miRNA sequences of 11 <it>MIRNAs </it>(12.5% of the investigated <it>MIRNAs</it>). Several small RNA loci showed significant signals for positive selection and/or potential domestication selection.</p> <p>Conclusions</p> <p>Sequence variation at miRNAs and other small RNAs is higher than expected in domesticated rice. Like protein-coding genes, non-coding small RNA loci could be targets of domestication selection and play an important role in rice domestication and improvement.</p

RNAseq-Based Working Model for Transcriptional Regulation of Crosstalk between Simultaneous Abiotic UV-B and Biotic Stresses in Plants

Plants adjust their secondary metabolism by altering the expression of corresponding genes to cope with both abiotic and biotic stresses. In the case of UV-B radiation, plants produce protective flavonoids; however, this reaction is impeded during pattern-triggered immunity (PTI) induced by pathogens. Pathogen attack can be mimicked by the application of microbial associated molecular patterns (e.g., flg22) to study crosstalk between PTI and UV-B-induced signaling pathways. Switching from Arabidopsis cell cultures to in planta studies, we analyzed whole transcriptome changes to gain a deeper insight into crosstalk regulation. We performed a comparative transcriptomic analysis by RNAseq with four distinct mRNA libraries and identified 10778, 13620, and 11294 genes, which were differentially expressed after flg22, UV-B, and stress co-treatment, respectively. Focusing on genes being either co-regulated with the UV-B inducible marker gene chalcone synthase CHS or the flg22 inducible marker gene FRK1 identified a large set of transcription factors from diverse families, such as MYB, WRKY, or NAC. These data provide a global view of transcriptomic reprogramming during this crosstalk and constitute a valuable dataset for further deciphering the underlying regulatory mechanism(s), which appear to be much more complex than previously anticipated. The possible involvement of MBW complexes in this context is discussed

BioIndustrie2021 - Biokatalyse2021. Verbundprojekt: Isolierung, Charakterisierung und Produktion antimikrobieller Peptide (AMPs) aus marinen Mikroorganismen : Veröffentlichung der Ergebnisse von Forschungsvorhaben im BMBF-Programm "Biologie" ; Laufzeit: 01.07.2008 bis 30.06.2011

Isolierung, Charakterisierung und Produktion antimikrobieller Peptide (AMPs) aus marinen Mikroorganisme