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1. Unambiguous Stereochemical Assignment of Cyclo(Phe-Pro), Cyclo(Leu-Pro), and Cyclo(Val-Pro) by Electronic Circular Dichroic Spectroscopy

   https://doi.org/10.3390/molecules26195981  

   Domzalski, Alison; Margent, Liliana; Vigo, Valeria; Dewan, Faizunnahar; Pilarsetty, Naga Vara; Xu, Yujia; Kawamura, Akira (October 2021, Molecules)

   2,5-diketopiperazines (DKPs) are cyclic dipeptides ubiquitously found in nature. In particular, cyclo(Phe-Pro), cyclo(Leu-Pro), and cyclo(Val-Pro) are frequently detected in many microbial cultures. Each of these DKPs has four possible stereoisomers due to the presence of two chirality centers. However, absolute configurations of natural DKPs are often ambiguous due to the lack of a simple, sensitive, and reproducible method for stereochemical assignment. This is an important problem because stereochemistry is a key determinant of biological activity. Here, we report a synthetic DKP library containing all stereoisomers of cyclo(Phe-Pro), cyclo(Leu-Pro), and cyclo(Val-Pro). The library was subjected to spectroscopic characterization using mass spectrometry, NMR, and electronic circular dichroism (ECD). It turned out that ECD can clearly differentiate DKP stereoisomers. Thus, our ECD dataset can serve as a reference for unambiguous stereochemical assignment of cyclo(Phe-Pro), cyclo(Leu-Pro), and cyclo(Val-Pro) samples from natural sources. The DKP library was also subjected to a biological screening using assays for E. coli growth and biofilm formation, which revealed distinct biological effects of cyclo(D-Phe-L-Pro). 

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2. Pro/Con: Neural Detection of Stance in Argumentative Opinions

   Hosseinia, M. Dragut (July 2019, International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation)

   Accurate information from both sides of the contemporary issues is known to be an ‘antidote in confirmation bias’. While these types of information help the educators to improve their vital skills including critical thinking and open-mindedness, they are relatively rare and hard to find online. With the well-researched argumentative opinions (arguments) on controversial issues shared by Procon.org in a non-partisan format, detecting the stance of arguments is a crucial step to automate organizing such resources. We use a universal pretrained language model with weight-dropped LSTM neural network to leverage the context of an argument for stance detection on the proposed dataset. Experimental results show that the dataset is challenging, however, utilizing the pretrained language model fine-tuned on context information yields a general model that beats the competitive baselines. We also provide analysis to find the informative segments of an argument to our stance detection model and investigate the relationship between the sentiment of an argument with its stance. 

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3. Pro/Con: Neural Detection of Stance in Argumentative Opinion

   Hosseinia, M. (July 2019, International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation)

   Accurate information from both sides of the contemporary issues is known to be an `antidote in confirmation bias'. While these types of information help the educators to improve their vital skills including critical thinking and open-mindedness, they are relatively rare and hard to find online. With the well-researched argumentative opinions (arguments) on controversial issues shared by Procon.org in a nonpartisan format, detecting the stance of arguments is a crucial step to automate organizing such resources. We use a universal pretrained language model with weight-dropped LSTM neural network to leverage the context of an argument for stance detection on the proposed dataset. Experimental results show that the dataset is challenging, however, utilizing the pretrained language model fine-tuned on context information yields a general model that beats the competitive baselines. We also provide analysis to find the informative segments of an argument to our stance detection model and investigate the relationship between the sentiment of an argument with its stance. 

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4. Pro/Con: Neural Detection of Stance in Argumentative Opinion

   https://doi.org/10.1007/978-3-030-21741-9_3  

   Hosseinia, Marjan; Dragut, Eduard; Mukherjee, Arjun (January 2019, Social Computing, Behavioral Modeling, and Prediction (SBP))

   Accurate information from both sides of the contemporary issues is known to be an `antidote in confirmation bias'. While these types of information help the educators to improve their vital skills including critical thinking and open-mindedness, they are relatively rare and hard to find online. With the well-researched argumentative opinions (arguments) on controversial issues shared by Procon.org in a nonpartisan format, detecting the stance of arguments is a crucial step to automate organizing such resources. We use a universal pretrained language model with weight-dropped LSTM neural network to leverage the context of an argument for stance detection on the proposed dataset. Experimental results show that the dataset is challenging, however, utilizing the pretrained language model fine-tuned on context information yields a general model that beats the competitive baselines. We also provide analysis to find the informative segments of an argument to our stance detection model and investigate the relationship between the sentiment of an argument with its stance. 

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5. Characterization of the cyclic dipeptide cyclo(His-Pro) in Arabidopsis

   https://doi.org/10.1093/plphys/kiaf174  

   Minen, Romina_I; Camalle, Maria_Dolores; Schwertfeger, Tyler_J; Abdulhakim, Fatimah; Reish, Hannah; Perez_de_Souza, Leonardo; Moreno, Juan_C; Schilmiller, Anthony; Thirumalaikumar, Venkatesh_P; Agarwal, Pallavi; et al (May 2025, Plant Physiology)

   Abstract Diketopiperazines (DKPs) are chemically and functionally diverse cyclic dipeptides associated primarily with microbes. Few DKPs have been reported from plants and animals; the best characterized is cyclo(His-Pro), found in the mammalian central nervous system, where it arises from the proteolytic cleavage of a thyrotropin-releasing tripeptide hormone. Herein, we report the identification of cyclo(His-Pro) in Arabidopsis (Arabidopsis thaliana), where its levels increase upon abiotic stress conditions, including high salt, heat, and cold. To screen for potential protein targets, we used isothermal shift assays, which examine changes in protein-melting stability upon ligand binding. Among the identified proteins, we focused on the glycolytic enzyme, cytosolic glyceraldehyde-3-phosphate dehydrogenase (GAPC1). Binding between the GAPC1 protein and cyclo(His-Pro) was validated using nano-differential scanning fluorimetry and microscale thermophoresis, and we could further demonstrate that cyclo(His-Pro) inhibits GAPC1 activity with an IC50 of ∼200 μm. This inhibition was conserved in human GAPDH. Inhibition of glyceraldehyde-3-phosphate dehydrogenase activity has previously been reported to reroute carbon from glycolysis toward the pentose phosphate pathway. Accordingly, cyclo(His-Pro) supplementation augmented NADPH levels, increasing the NADPH/NADP+ ratio. Phenotypic screening revealed that plants supplemented with cyclo(His-Pro) were more tolerant to high-salt stress, as manifested by higher biomass, which we show is dependent on GAPC1/2. Our work reports the identification and functional characterization of cyclo(His-Pro) as a modulator of glyceraldehyde-3-phosphate dehydrogenase in plants. 

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