Monosome Stalls the Translation Process Mediated by IGF2BP in Arcuate Nucleus for Puberty Onset Delay.

Puberty onset through hypothalamic-pituitary-gonad (HPG) axis as an important reproductive event in postnatal development is initiated from hypothalamic arcuate nucleus (ARC). The growing evidence indicates that translational control also plays an essential role in the final expression of gonadotropin genes. To investigate the role of protein translation and behavior of ribosomes in pubertal onset, the global profiles of transcriptome, single ribosome (monosome), polysome, and tandem mass tag proteome were comprehensively investigated in rat hypothalamic ARCs of different pubertal stages using RNA sequencing, polyribo sequencing, and mass spectrum. Transcriptome-wide enrichments of N6-methyladenosine and IGF2BP2 were investigated using meRIP and RIP sequencing. Monosome was robustly enriched on a large proportion of mRNA in early puberty rats (postnatal day (PND)-25) compared to late puberty (PND-35 and PND-45). Monosome-enriched mRNAs, including HPG axis-related genes, had a large number of upstream ORFs (uORF, < 100 nt) and displayed translational repression in early puberty. Furthermore, insulin-like growth factor 2 mRNA binding protein 2 (IGF2BP2) could particularly interact with and facilitate monosome to bind with mRNA in early puberty. Finally, ectopic over-expression of IGF2BP2 in hypothalamic ARC via lateral ventricle injection in vivo could recruit monosome to aggregate on mRNA and delay puberty onset. We uncovered a novel regulatory mechanism of IGF2BP2 and monosome for translational control in puberty onset, which shed light on the neuroendocrine regulatory network involved in HPG axis activation.

Overexpression of RPOTmp Being Targeted to Either Mitochondria or Chloroplasts in Arabidopsis Leads to Overall Transcriptome Changes and Faster Growth.

The transcription of Arabidopsis organellar genes is performed by three nuclear-encoded RNA polymerases: RPOTm, RPOTmp, and RPOTp. The RPOTmp protein possesses ambiguous transit peptides, allowing participation in gene expression control in both mitochondria and chloroplasts, although its function in plastids is still under discussion. Here, we show that the overexpression of RPOTmp in Arabidopsis, targeted either to mitochondria or chloroplasts, disturbs the dormant seed state, and it causes the following effects: earlier germination, decreased ABA sensitivity, faster seedling growth, and earlier flowering. The germination of RPOTmp overexpressors is less sensitive to NaCl, while rpotmp knockout is highly vulnerable to salt stress. We found that mitochondrial dysfunction in the rpotmp mutant induces an unknown retrograde response pathway that bypasses AOX and ANAC017. Here, we show that RPOTmp transcribes the accD, clpP, and rpoB genes in plastids and up to 22 genes in mitochondria.

A Principal Components Analysis and Functional Annotation of Differentially Expressed Genes in Brain Regions of Gray Rats Selected for Tame or Aggressive Behavior.

The process of domestication, despite its short duration as it compared with the time scale of the natural evolutionary process, has caused rapid and substantial changes in the phenotype of domestic animal species. Nonetheless, the genetic mechanisms underlying these changes remain poorly understood. The present study deals with an analysis of the transcriptomes from four brain regions of gray rats (Rattus norvegicus), serving as an experimental model object of domestication. We compared gene expression profiles in the hypothalamus, hippocampus, periaqueductal gray matter, and the midbrain tegmental region between tame domesticated and aggressive gray rats and revealed subdivisions of differentially expressed genes by principal components analysis that explain the main part of differentially gene expression variance. Functional analysis (in the DAVID (Database for Annotation, Visualization and Integrated Discovery) Bioinformatics Resources database) of the differentially expressed genes allowed us to identify and describe the key biological processes that can participate in the formation of the different behavioral patterns seen in the two groups of gray rats. Using the STRING- DB (search tool for recurring instances of neighboring genes) web service, we built a gene association network. The genes engaged in broad network interactions have been identified. Our study offers data on the genes whose expression levels change in response to artificial selection for behavior during animal domestication.

Unveiling the methionine cycle: a key metabolic signature and NR4A2 as a methionine-responsive oncogene in esophageal squamous cell carcinoma.

Esophageal squamous cell carcinoma (ESCC) is a deadly malignancy with notable metabolic reprogramming, yet the pivotal metabolic feature driving ESCC progression remains elusive. Here, we show that methionine cycle exhibits robust activation in ESCC and is reversely associated with patient survival. ESCC cells readily harness exogenous methionine to generate S-adenosyl-methionine (SAM), thus promoting cell proliferation. Mechanistically, methionine augments METTL3-mediated RNA m6A methylation through SAM and revises gene expression. Integrative omics analysis highlights the potent influence of methionine/SAM on NR4A2 expression in a tumor-specific manner, mediated by the IGF2BP2-dependent stabilization of methylated NR4A2 mRNA. We demonstrate that NR4A2 facilitates ESCC growth and negatively impacts patient survival. We further identify celecoxib as an effective inhibitor of NR4A2, offering promise as a new anti-ESCC agent. In summary, our findings underscore the active methionine cycle as a critical metabolic characteristic in ESCC, and pinpoint NR4A2 as a novel methionine-responsive oncogene, thereby presenting a compelling target potentially superior to methionine restriction.

Deciphering the Molecular Mechanism of the Intermediate Secondary Growth and Internode Elongation of the Castor Bean (Ricinus communis L.) by the Combined Analysis of the Transcriptome and Metabolome.

The length of internodes plays a crucial role in determining the height of the castor plant (Ricinus communis L.). However, the specific mechanisms underlying internode elongation, particularly in the main stem of the castor plant, remain uncertain. To further investigate this, we conducted a study focusing on the internode tissue of the dwarf castor variety 071113, comparing it with the control high-stalk Zhuansihao. Our study included a cytological observation, physiological measurement, transcriptome sequencing, and metabolic determination. Our integrated findings reveal that the dwarf variety 071113 undergoes an earlier lignification development in the main stem and has a more active lignin synthesis pathway during internode intermediate development. In addition, the dwarf variety exhibited lower levels of the plant hormone indole-3-acetic acid (IAA), which had an impact on the development process. Furthermore, we identified specific enzymes and regulators that were enriched in the pathways of the cell cycle, auxin signal transduction, and secondary cell wall synthesis. Using these findings, we developed a model that explained the intermediate secondary growth observed in castor internode elongation and enhanced our comprehension of the dwarfing mechanism of the 071113 variety. This research provides a theoretical groundwork for the future breeding of dwarf castor varieties.

Application of Artificial Intelligence or machine learning in risk sharing agreements for pharmacotherapy risk management.

Applications of Artificial Intelligence in medical informatics solutions risk sharing have social value. At a time of ever-increasing cost for the provision of medicines to citizens, there is a need to restrain the growth of health care costs. The search for computer technologies to stop or slow down the growth of costs acquires a new very important and significant meaning. We discussed the two information technologies in pharmacotherapy and the possibility of combining and sharing them, namely the combination of risk-sharing agreements and Machine Learning, which was made possible by the development of Artificial Intelligence (AI). Neural networks could be used to predict the outcome to reduce the risk factors for treatment. AI-based data processing automation technologies could be also used for risk-sharing agreements automation.

Special Issue on "Plant Biology and Biotechnology: Focus on Genomics and Bioinformatics 2.0".

The analysis of molecular mechanisms underlying plant adaptation to environmental changes and stress response is crucial for plant biotechnology [...].

Biophysics education section and computational training discussion at VII Congress of Russian Biophysicists.

We present commentaries on the section "Biophysical education" of the VII Congress of Russian Biophysicists. Presentations are briefly introduced along with the current problems of biophysical education and the educational approach development. We discuss educational course on bioinformatics based on the integration of online databases and the usage of internet platforms for functional annotation of genes and proteins.

Bioinformatics tools for the sequence complexity estimates.

We review current methods and bioinformatics tools for the text complexity estimates (information and entropy measures). The search DNA regions with extreme statistical characteristics such as low complexity regions are important for biophysical models of chromosome function and gene transcription regulation in genome scale. We discuss the complexity profiling for segmentation and delineation of genome sequences, search for genome repeats and transposable elements, and applications to next-generation sequencing reads. We review the complexity methods and new applications fields: analysis of mutation hotspots loci, analysis of short sequencing reads with quality control, and alignment-free genome comparisons. The algorithms implementing various numerical measures of text complexity estimates including combinatorial and linguistic measures have been developed before genome sequencing era. The series of tools to estimate sequence complexity use compression approaches, mainly by modification of Lempel-Ziv compression. Most of the tools are available online providing large-scale service for whole genome analysis. Novel machine learning applications for classification of complete genome sequences also include sequence compression and complexity algorithms. We present comparison of the complexity methods on the different sequence sets, the applications for gene transcription regulatory regions analysis. Furthermore, we discuss approaches and application of sequence complexity for proteins. The complexity measures for amino acid sequences could be calculated by the same entropy and compression-based algorithms. But the functional and evolutionary roles of low complexity regions in protein have specific features differing from DNA. The tools for protein sequence complexity aimed for protein structural constraints. It was shown that low complexity regions in protein sequences are conservative in evolution and have important biological and structural functions. Finally, we summarize recent findings in large scale genome complexity comparison and applications for coronavirus genome analysis.

VII Congress of Russian Biophysicists-2023, Krasnodar, Russia.

This special issue of Biophysical Reviews contains the materials presented at the VII Congress of Biophysicists of Russia, held from 17 to 23 April in Krasnodar. We believe that we have managed to prepare a selection of articles that well reflects the current state of biophysical science in Russia and its place in the world science. The VII Russian Congress on Biophysics was held in Krasnodar in April 2023, continuing the tradition of the series of biophysics conferences held every 4 years. The congress discussed physical principles and mechanisms of biological processes occurring at different life levels-from molecular to cellular and population levels. The results of fundamental and applied research in molecular biophysics, cell biophysics, and biophysics of complex systems were presented at plenary, sectional, and poster sessions. The works in the field of medical biophysics and neurobiology were especially widely presented. The structure and dynamics of biopolymers and fundamental mechanisms underlying the effects of physicochemical factors on biological systems, membrane, and transport processes were actively discussed. Much attention was paid to new experimental methods of biophysical research, methods of bioinformatics, computer, and mathematical modeling as necessary tools of the research at all levels of living systems. Along with fundamental problems of studying biophysical mechanisms of regulation of processes at the molecular, subcellular, and cellular levels, much attention was paid to applied research in the field of biotechnology and environmental monitoring. The Congress has formed the National Committee of Russian biophysicists.

Analysis of Functional Single-Nucleotide Polymorphisms (SNPs) and Leaf Quality in Tea Collection under Nitrogen-Deficient Conditions.

This study discusses the genetic mutations that have a significant association with economically important traits that would benefit tea breeders. The purpose of this study was to analyze the leaf quality and SNPs in quality-related genes in the tea plant collection of 20 mutant genotypes growing without nitrogen fertilizers. Leaf N-content, catechins, L-theanine, and caffeine contents were analyzed in dry leaves via HPLC. Additionally, the photochemical yield, electron transport efficiency, and non-photochemical quenching were analyzed using PAM-fluorimetry. The next generation pooled amplicon-sequencing approach was used for SNPs-calling in 30 key genes related to N metabolism and leaf quality. The leaf N content varied significantly among genotypes (p ≤ 0.05) from 2.3 to 3.7% of dry mass. The caffeine content varied from 0.7 to 11.7 mg g-1, and the L-theanine content varied from 0.2 to 5.8 mg g-1 dry leaf mass. Significant positive correlations were detected between the nitrogen content and biochemical parameters such as theanine, caffeine, and most of the catechins. However, significant negative correlations were observed between the photosynthetic parameters (Y, ETR, Fv/Fm) and several biochemical compounds, including rutin, Quercetin-3-O-glucoside, Kaempferol-3-O-rutinoside, Kaempferol-3-O-glucoside, Theaflavin-3'-gallate, gallic acid. From our SNP-analysis, three SNPs in WRKY57 were detected in all genotypes with a low N content. Moreover, 29 SNPs with a high or moderate effect were specific for #316 (high N-content, high quality) or #507 (low N-content, low quality). The use of a linear regression model revealed 16 significant associations; theaflavin, L-theanine, and ECG were associated with several SNPs of the following genes: ANSa, DFRa, GDH2, 4CL, AlaAT1, MYB4, LHT1, F3'5'Hb, UFGTa. Among them, seven SNPs of moderate effect led to changes in the amino acid contents in the final proteins of the following genes: ANSa, GDH2, 4Cl, F3'5'Hb, UFGTa. These results will be useful for further evaluations of the important SNPs and will help to provide a better understanding of the mechanisms of nitrogen uptake efficiency in tree crops.

Short Interrupted Repeat Cassette (SIRC)-Novel Type of Repetitive DNA Element Found in Arabidopsis thaliana.

Short interrupted repeat cassette (SIRC)-a novel DNA element found throughout the A. thaliana nuclear genome. SIRCs are represented by short direct repeats interrupted by diverse DNA sequences. The maxima of SIRC's distribution are located within pericentromeric regions. We suggest that originally SIRC was a special case of the complex internal structure of the miniature inverted repeat transposable element (MITE), and further MITE amplification, transposition, and loss of terminal inverted repeats gave rise to SIRC as an independent DNA element. SIRC sites were significantly enriched with several histone modifications associated with constitutive heterochromatin and mobile genetic elements. The majority of DNA-binding proteins, strongly associated with SIRC, are related to histone modifications for transcription repression. A part of SIRC was found to overlap highly inducible protein-coding genes, suggesting a possible regulatory role for these elements, yet their definitive functions need further investigation.

Integrating omics databases for enhanced crop breeding.

Crop plant breeding involves selecting and developing new plant varieties with desirable traits such as increased yield, improved disease resistance, and enhanced nutritional value. With the development of high-throughput technologies, such as genomics, transcriptomics, and metabolomics, crop breeding has entered a new era. However, to effectively use these technologies, integration of multi-omics data from different databases is required. Integration of omics data provides a comprehensive understanding of the biological processes underlying plant traits and their interactions. This review highlights the importance of integrating omics databases in crop plant breeding, discusses available omics data and databases, describes integration challenges, and highlights recent developments and potential benefits. Taken together, the integration of omics databases is a critical step towards enhancing crop plant breeding and improving global food security.

Research Topics of the Bioinformatics of Gene Regulation.

The study of gene expression regulation raises the challenge of developing bioinformatics tools and algorithms, demanding data integration [...].

Medical Genetics, Genomics and Bioinformatics-2022.

The analysis of molecular mechanisms of disease progression challenges the development of bioinformatics tools and omics data integration [...].

BioGraph: Data Model for Linking and Querying Diverse Biological Metadata.

Studying the association of gene function, diseases, and regulatory gene network reconstruction demands data compatibility. Data from different databases follow distinct schemas and are accessible in heterogenic ways. Although the experiments differ, data may still be related to the same biological entities. Some entities may not be strictly biological, such as geolocations of habitats or paper references, but they provide a broader context for other entities. The same entities from different datasets can share similar properties, which may or may not be found within other datasets. Joint, simultaneous data fetching from multiple data sources is complicated for the end-user or, in many cases, unsupported and inefficient due to differences in data structures and ways of accessing the data. We propose BioGraph-a new model that enables connecting and retrieving information from the linked biological data that originated from diverse datasets. We have tested the model on metadata collected from five diverse public datasets and successfully constructed a knowledge graph containing more than 17 million model objects, of which 2.5 million are individual biological entity objects. The model enables the selection of complex patterns and retrieval of matched results that can be discovered only by joining the data from multiple sources.