@article{igamberdiev_toward_2023,
	title = {Toward understanding the emergence of life: {A} dual function of the system of nucleotides in the metabolically closed autopoietic organization},
	volume = {224},
	issn = {0303-2647},
	shorttitle = {Toward understanding the emergence of life},
	doi = {10.1016/j.biosystems.2023.104837},
	abstract = {General structure of metabolism includes the reproduction of catalysts that govern metabolism. In this structure, the system becomes autopoietic in the sense of Maturana and Varela, and it is closed to efficient causation as defined by Robert Rosen. The autopoietic maintenance and operation of the catalysts takes place via the set of free nucleotides while the synthesis of catalysts occurs via the information encoded by the set of nucleotides arranged in polymers of RNA and DNA. Both energy charge and genetic information use the components of the same pool of nucleoside triphosphates, which is equilibrated by thermodynamic buffering enzymes such as nucleoside diphosphate kinase and adenylate kinase. This occurs in a way that the system becomes internally stable and metabolically closed, which initially could be realized at the level of ribozymes catalyzing basic metabolic reactions as well as own reproduction. The function of ATP, GTP, UTP, and CTP is dual, as these species participate both in the general metabolism as free nucleotides and in the transfer of genetic information via covalent polymerization to nucleic acids. The changes in their pools directly impact both bioenergetic pathways and nucleic acid turnover. Here we outline the concept of metabolic closure of biosystems grounded in the dual function of nucleotide coenzymes that serve both as energetic and informational molecules and through this duality generate the autopoietic performance and the ability for codepoietic evolutionary transformations of living systems starting from the emergence of prebiotic systems. © 2023},
	language = {English},
	journal = {BioSystems},
	author = {Igamberdiev, A.U. and Kleczkowski, L.A.},
	year = {2023},
	keywords = {Autopoiesis, Codepoiesis, Coenzyme, Metabolic closure, Nucleoside triphosphates, Ribozymes, Thermodynamic buffering},
}

General structure of metabolism includes the reproduction of catalysts that govern metabolism. In this structure, the system becomes autopoietic in the sense of Maturana and Varela, and it is closed to efficient causation as defined by Robert Rosen. The autopoietic maintenance and operation of the catalysts takes place via the set of free nucleotides while the synthesis of catalysts occurs via the information encoded by the set of nucleotides arranged in polymers of RNA and DNA. Both energy charge and genetic information use the components of the same pool of nucleoside triphosphates, which is equilibrated by thermodynamic buffering enzymes such as nucleoside diphosphate kinase and adenylate kinase. This occurs in a way that the system becomes internally stable and metabolically closed, which initially could be realized at the level of ribozymes catalyzing basic metabolic reactions as well as own reproduction. The function of ATP, GTP, UTP, and CTP is dual, as these species participate both in the general metabolism as free nucleotides and in the transfer of genetic information via covalent polymerization to nucleic acids. The changes in their pools directly impact both bioenergetic pathways and nucleic acid turnover. Here we outline the concept of metabolic closure of biosystems grounded in the dual function of nucleotide coenzymes that serve both as energetic and informational molecules and through this duality generate the autopoietic performance and the ability for codepoietic evolutionary transformations of living systems starting from the emergence of prebiotic systems. © 2023

@article{kidwai_species-specific_2023,
	title = {Species-specific transcriptional reprogramming during adventitious root initiation},
	volume = {28},
	issn = {1360-1385},
	url = {https://www.sciencedirect.com/science/article/pii/S1360138522003028},
	doi = {10.1016/j.tplants.2022.11.003},
	abstract = {Adventitious roots or shoot-borne roots transdifferentiate from cells close to vascular tissues after cell reprogramming, which is associated with increased transcriptional activity. Recently, Garg et al. provided a genome-wide landscape of transcriptional signatures during the early stages of adventitious root initiation in rice and showed that conserved transcription factors acquire species-specific function.},
	language = {en},
	number = {2},
	urldate = {2023-01-26},
	journal = {Trends in Plant Science},
	author = {Kidwai, Maria and Mishra, Priyanka and Bellini, Catherine},
	month = feb,
	year = {2023},
	keywords = {adventitious root, dicotyledons, epigenetic regulation, monocotyledons, transcription factors},
	pages = {128--130},
}

Adventitious roots or shoot-borne roots transdifferentiate from cells close to vascular tissues after cell reprogramming, which is associated with increased transcriptional activity. Recently, Garg et al. provided a genome-wide landscape of transcriptional signatures during the early stages of adventitious root initiation in rice and showed that conserved transcription factors acquire species-specific function.

@article{donev_field_2023,
	title = {Field testing of transgenic aspen from large greenhouse screening identifies unexpected winners},
	volume = {n/a},
	issn = {1467-7652},
	url = {https://onlinelibrary.wiley.com/doi/abs/10.1111/pbi.14012},
	doi = {10.1111/pbi.14012},
	abstract = {Trees constitute promising renewable feedstocks for biorefinery using biochemical conversion, but their recalcitrance restricts their attractiveness for the industry. To obtain trees with reduced recalcitrance, large-scale genetic engineering experiments were performed in hybrid aspen blindly targeting genes expressed during wood formation and 32 lines representing seven constructs were selected for characterization in the field. Here we report phenotypes of five-year old trees considering 49 traits related to growth and wood properties. The best performing construct considering growth and glucose yield in saccharification with acid pretreatment had suppressed expression of the gene encoding an uncharacterized 2-oxoglutarate-dependent dioxygenase (2OGD). It showed minor changes in wood chemistry but increased nanoporosity and glucose conversion. Suppressed levels of SUCROSE SYNTHASE, (SuSy), CINNAMATE 4-HYDROXYLASE (C4H), and increased levels of GTPase activating protein for ADP-ribosylation factor ZAC led to significant growth reductions and anatomical abnormalities. However, C4H and SuSy constructs greatly improved glucose yields in saccharification without and with pretreatment, respectively. Traits associated with high glucose yields were different for saccharification with and without pretreatment. While carbohydrates, phenolics and tension wood contents positively impacted the yields without pretreatment and growth, lignin content and S/G ratio were negative factors, the yields with pretreatment positively correlated with S lignin, and negatively with carbohydrate contents. The genotypes with high glucose yields had increased nanoporosity and mGlcA/Xyl ratio, and some had shorter polymers extractable with subcritical water compared to wild-type. The pilot-scale industrial-like pretreatment of best performing 2OGD construct confirmed its superior sugar yields, supporting our strategy.},
	language = {en},
	number = {n/a},
	urldate = {2023-01-26},
	journal = {Plant Biotechnology Journal},
	author = {Donev, Evgeniy N. and Derba-Maceluch, Marta and Yassin, Zakiya and Gandla, Madhavi Latha and Sivan, Pramod and Heinonen, Saara E. and Kumar, Vikash and Scheepers, Gerhard and Vilaplana, Francisco and Johansson, Ulf and Hertzberg, Magnus and Sundberg, Björn and Winestrand, Sandra and Hörnberg, Andreas and Alriksson, Björn and Jönsson, Leif J. and Mellerowicz, Ewa J.},
	month = jan,
	year = {2023},
	note = {\_eprint: https://onlinelibrary.wiley.com/doi/pdf/10.1111/pbi.14012},
	keywords = {BET analysis, Populus, SilviScan, enzymatic saccharification, field trial, secondary cell wall, subcritical water extraction, transgenic trees, wood quality},
}

Trees constitute promising renewable feedstocks for biorefinery using biochemical conversion, but their recalcitrance restricts their attractiveness for the industry. To obtain trees with reduced recalcitrance, large-scale genetic engineering experiments were performed in hybrid aspen blindly targeting genes expressed during wood formation and 32 lines representing seven constructs were selected for characterization in the field. Here we report phenotypes of five-year old trees considering 49 traits related to growth and wood properties. The best performing construct considering growth and glucose yield in saccharification with acid pretreatment had suppressed expression of the gene encoding an uncharacterized 2-oxoglutarate-dependent dioxygenase (2OGD). It showed minor changes in wood chemistry but increased nanoporosity and glucose conversion. Suppressed levels of SUCROSE SYNTHASE, (SuSy), CINNAMATE 4-HYDROXYLASE (C4H), and increased levels of GTPase activating protein for ADP-ribosylation factor ZAC led to significant growth reductions and anatomical abnormalities. However, C4H and SuSy constructs greatly improved glucose yields in saccharification without and with pretreatment, respectively. Traits associated with high glucose yields were different for saccharification with and without pretreatment. While carbohydrates, phenolics and tension wood contents positively impacted the yields without pretreatment and growth, lignin content and S/G ratio were negative factors, the yields with pretreatment positively correlated with S lignin, and negatively with carbohydrate contents. The genotypes with high glucose yields had increased nanoporosity and mGlcA/Xyl ratio, and some had shorter polymers extractable with subcritical water compared to wild-type. The pilot-scale industrial-like pretreatment of best performing 2OGD construct confirmed its superior sugar yields, supporting our strategy.

@article{rohricht_mitochondrial_2023,
	title = {Mitochondrial ferredoxin-like is essential for forming complex {I}-containing supercomplexes in {Arabidopsis}},
	issn = {0032-0889},
	url = {https://doi.org/10.1093/plphys/kiad040},
	doi = {10.1093/plphys/kiad040},
	abstract = {In eukaryotes, mitochondrial ATP is mainly produced by the oxidative phosphorylation (OXPHOS) system, which is composed of five multiprotein complexes (complexes I to V). Analyses of the OXPHOS system by native gel electrophoresis have revealed an organization of OXPHOS complexes into supercomplexes, but their roles and assembly pathways remain unclear. In this study, we characterized an atypical mitochondrial ferredoxin (mitochondrial ferredoxin-like, mFDX-like). This protein was previously found to be part of the bridge domain linking the matrix and membrane arms of the complex I. Phylogenetic analysis suggested that the Arabidopsis (Arabidopsis thaliana) mFDX-like evolved from classical mitochondrial ferredoxin but lost one of the cysteines required for the coordination of the iron-sulfur (Fe-S) cluster, supposedly essential for the electron transfer function of ferredoxins. Accordingly, our biochemical study showed that AtmFDX-like does not bind an Fe-S cluster and is therefore unlikely to be involved in electron transfer reactions. To study the function of mFDX-like, we created deletion lines in Arabidopsis using a CRISPR/Cas9-based strategy. These lines did not show any abnormal phenotype under standard growth conditions. However, the characterization of the OXPHOS system demonstrated that mFDX-like is important for the assembly of complex I and essential for the formation of complex I-containing supercomplexes. We propose that mFDX-like and the bridge domain are required for the correct conformation of the membrane arm of complex I that is essential for the association of complex I with complex III to form supercomplexes.},
	urldate = {2023-01-26},
	journal = {Plant Physiology},
	author = {Röhricht, Helene and Przybyla-Toscano, Jonathan and Forner, Joachim and Boussardon, Clément and Keech, Olivier and Rouhier, Nicolas and Meyer, Etienne H},
	month = jan,
	year = {2023},
	pages = {kiad040},
}

In eukaryotes, mitochondrial ATP is mainly produced by the oxidative phosphorylation (OXPHOS) system, which is composed of five multiprotein complexes (complexes I to V). Analyses of the OXPHOS system by native gel electrophoresis have revealed an organization of OXPHOS complexes into supercomplexes, but their roles and assembly pathways remain unclear. In this study, we characterized an atypical mitochondrial ferredoxin (mitochondrial ferredoxin-like, mFDX-like). This protein was previously found to be part of the bridge domain linking the matrix and membrane arms of the complex I. Phylogenetic analysis suggested that the Arabidopsis (Arabidopsis thaliana) mFDX-like evolved from classical mitochondrial ferredoxin but lost one of the cysteines required for the coordination of the iron-sulfur (Fe-S) cluster, supposedly essential for the electron transfer function of ferredoxins. Accordingly, our biochemical study showed that AtmFDX-like does not bind an Fe-S cluster and is therefore unlikely to be involved in electron transfer reactions. To study the function of mFDX-like, we created deletion lines in Arabidopsis using a CRISPR/Cas9-based strategy. These lines did not show any abnormal phenotype under standard growth conditions. However, the characterization of the OXPHOS system demonstrated that mFDX-like is important for the assembly of complex I and essential for the formation of complex I-containing supercomplexes. We propose that mFDX-like and the bridge domain are required for the correct conformation of the membrane arm of complex I that is essential for the association of complex I with complex III to form supercomplexes.

@article{chandra_transformer-based_2023,
	title = {Transformer-based deep learning for predicting protein properties in the life sciences},
	volume = {12},
	issn = {2050-084X},
	url = {https://doi.org/10.7554/eLife.82819},
	doi = {10.7554/eLife.82819},
	abstract = {Recent developments in deep learning, coupled with an increasing number of sequenced proteins, have led to a breakthrough in life science applications, in particular in protein property prediction. There is hope that deep learning can close the gap between the number of sequenced proteins and proteins with known properties based on lab experiments. Language models from the field of natural language processing have gained popularity for protein property predictions and have led to a new computational revolution in biology, where old prediction results are being improved regularly. Such models can learn useful multipurpose representations of proteins from large open repositories of protein sequences and can be used, for instance, to predict protein properties. The field of natural language processing is growing quickly because of developments in a class of models based on a particular model—the Transformer model. We review recent developments and the use of large-scale Transformer models in applications for predicting protein characteristics and how such models can be used to predict, for example, post-translational modifications. We review shortcomings of other deep learning models and explain how the Transformer models have quickly proven to be a very promising way to unravel information hidden in the sequences of amino acids.},
	urldate = {2023-01-20},
	journal = {eLife},
	author = {Chandra, Abel and Tünnermann, Laura and Löfstedt, Tommy and Gratz, Regina},
	editor = {Dötsch, Volker},
	month = jan,
	year = {2023},
	keywords = {deep learning, life sciences, machine learning, protein property prediction, transformers},
	pages = {e82819},
}

Recent developments in deep learning, coupled with an increasing number of sequenced proteins, have led to a breakthrough in life science applications, in particular in protein property prediction. There is hope that deep learning can close the gap between the number of sequenced proteins and proteins with known properties based on lab experiments. Language models from the field of natural language processing have gained popularity for protein property predictions and have led to a new computational revolution in biology, where old prediction results are being improved regularly. Such models can learn useful multipurpose representations of proteins from large open repositories of protein sequences and can be used, for instance, to predict protein properties. The field of natural language processing is growing quickly because of developments in a class of models based on a particular model—the Transformer model. We review recent developments and the use of large-scale Transformer models in applications for predicting protein characteristics and how such models can be used to predict, for example, post-translational modifications. We review shortcomings of other deep learning models and explain how the Transformer models have quickly proven to be a very promising way to unravel information hidden in the sequences of amino acids.