BACKGROUND: Decoding human genomic sequences requires comprehensive analysis of DNA sequence functionality. Through computational and experimental approaches, researchers have studied the genotype-phenotype relationship and generate important datasets that help unravel complicated genetic blueprints. Thus, the recently developed artificial intelligence methods can be used to interpret the functions of those DNA sequences.
METHODS: This study explores the use of deep learning, particularly pre-trained genomic models like DNA_bert_6 and human_gpt2-v1, in interpreting and representing human genome sequences. Initially, we meticulously constructed multiple datasets linking genotypes and phenotypes to fine-tune those models for precise DNA sequence classification. Additionally, we evaluate the influence of sequence length on classification results and analyze the impact of feature extraction in the hidden layers of our model using the HERV dataset. To enhance our understanding of phenotype-specific patterns recognized by the model, we perform enrichment, pathogenicity and conservation analyzes of specific motifs in the human endogenous retrovirus (HERV) sequence with high average local representation weight (ALRW) scores.
RESULTS: We have constructed multiple genotype-phenotype datasets displaying commendable classification performance in comparison with random genomic sequences, particularly in the HERV dataset, which achieved binary and multi-classification accuracies and F1 values exceeding 0.935 and 0.888, respectively. Notably, the fine-tuning of the HERV dataset not only improved our ability to identify and distinguish diverse information types within DNA sequences but also successfully identified specific motifs associated with neurological disorders and cancers in regions with high ALRW scores. Subsequent analysis of these motifs shed light on the adaptive responses of species to environmental pressures and their co-evolution with pathogens.
CONCLUSIONS: These findings highlight the potential of pre-trained genomic models in learning DNA sequence representations, particularly when utilizing the HERV dataset, and provide valuable insights for future research endeavors. This study represents an innovative strategy that combines pre-trained genomic model representations with classical methods for analyzing the functionality of genome sequences, thereby promoting cross-fertilization between genomics and artificial intelligence.

UNASSIGNED: Computational studies were performed to investigate the unknown status of endosomal and cell surface receptors in SARS-CoV-2 infection. The interactions between Toll-like receptors (TLRs)- 4/7/8/9 or ACE2 receptor and different SARS-CoV-2 variants were investigated.
UNASSIGNED: The RNA motifs for TLR7, TLR8 and a CpG motif for TLR9 were analyzed in different variants. Molecular docking and molecular dynamics (MD) simulations were performed to investigate receptor-ligand interactions.
UNASSIGNED: The number of motifs recognized by TLR7/8/9 in the Alpha, Delta and Iranian variants was lower than in the wild type (WT). Docking analysis revealed that the Alpha, Delta and some Iranian spike variants had a higher affinity for ACE2 and TLR4 than the WT, which may account for their higher transmission rate. The MD simulation also showed differences in stability and structure size between the variants and the WT, indicating potential variations in viral load.
UNASSIGNED: It appears that Alpha and some Iranian isolates are the variants of concern due to their higher transmissibility and rapid spread. The Delta mutant is also a variant of concern, not only because of its closer interaction with ACE2, but also with TLR4. Our results emphasize the importance of ACE2 and TLR4, rather than endosomal TLRs, in mediating the effects of different viral mutations and suggest their potential therapeutic applications.

The FK506 Binding Protein (FKBP), ubiquitously present across diverse species, is characterized by its evolutionarily conserved FK506 binding domain (FKBd). In plants, evidence suggests that this gene family plays integral roles in regulating growth, development, and responses to environmental stresses. Notably, research on the identification and functionality of FKBP genes in rice remains limited. Therefore, this study utilized bioinformatic tools to identify 30 FKBP-encoding genes in rice. It provides a detailed analysis of their chromosomal locations, evolutionary relationships with the Arabidopsis thaliana FKBP family, and gene structures. Further analysis of the promoter elements of these rice FKBP genes revealed a high presence of stress-responsive elements. Quantitative PCR assays under drought and heat stress conditions demonstrated that genes OsFKBP15-2, OsFKBP15-3, OsFKBP16-3, OsFKBP18, and OsFKBP42b are inducible by these adverse conditions. These findings suggest a significant role for the rice FKBP gene family in stress adaptation. This research establishes a critical foundation for deeper explorations of the functional roles of the OsFKBP genes in rice.

Bos taurus is known for its tolerance of coarse grains, adaptability, high temperature, humidity, and disease resistance. Primarily, cattle are raised for their meat and milk, and pinpointing genes associated with traits relevant to meat production can enhance their overall productivity. The aim of this study was to identify the genome, analyze the evolution, and explore the function of the Pax gene family in B. taurus to provide a new molecular target for breeding in meat-quality-trait cattle. In this study, 44 Pax genes were identified from the genome database of five species using bioinformatics technology, indicating that the genetic relationships of bovids were similar. The Pax3 and Pax7 protein sequences of the five animals were highly consistent. In general, the Pax gene of the buffalo corresponds to the domestic cattle. In summary, there are differences in affinity between the Pax family genes of buffalo and domestic cattle in the Pax1/9, Pax2/5/8, Pax3/7, and Pax4/6 subfamilies. We believe that Pax1/9 has an effect on the growth traits of buffalo and domestic cattle. The Pax3/7 gene is conserved in the evolution of buffalo and domestic animals and may be a key gene regulating the growth of B. taurus. The Pax2/5/8 subfamily affects coat color, reproductive performance, and milk production performance in cattle. The Pax4/6 subfamily had an effect on the milk fat percentage of B. taurus. The results provide a theoretical basis for understanding the evolutionary, structural, and functional characteristics of the Pax family members of B. taurus and for molecular genetics and the breeding of meat-production B. taurus species.

Magnetoreceptive biology as a field remains relatively obscure; compared with the breadth of species believed to sense magnetic fields, it remains under-studied. Here, we present grounds for the expansion of magnetoreception studies among teleosts. We begin with the electromagnetic perceptive gene (EPG) from Kryptopterus vitreolus and expand to identify 72 teleosts with homologous proteins containing a conserved three-phenylalanine (3F) motif. Phylogenetic analysis provides insight as to how EPG may have evolved over time and indicates that certain clades may have experienced a loss of function driven by different fitness pressures. One potential factor is water type with freshwater fish significantly more likely to possess the functional motif version (FFF), and saltwater fish to have the non-functional variant (FXF). It was also revealed that when the 3F motif from the homologue of Brachyhypopomus gauderio (B.g.) is inserted into EPG-EPG(B.g.)-the response (as indicated by increased intracellular calcium) is faster. This indicates that EPG has the potential to be engineered to improve upon its response and increase its utility to be used as a controller for specific outcomes.

The current investigation endeavors to identify differentially expressed alternatively spliced (DAS) genes that exhibit concordant expression with splicing factors (SFs) under diverse multifactorial abiotic stress combinations in Arabidopsis seedlings. SFs serve as the post-transcriptional mechanism governing the spatiotemporal dynamics of gene expression. The different stresses encompass variations in salt concentration, heat, intensive light, and their combinations. Clusters demonstrating consistent expression profiles were surveyed to pinpoint DAS/SF gene pairs exhibiting concordant expression. Through rigorous selection criteria, which incorporate alignment with documented gene functionalities and expression patterns observed in this study, four members of the serine/arginine-rich (SR) gene family were delineated as SFs concordantly expressed with six DAS genes. These regulated SF genes encompass cactin, SR1-like, SR30, and SC35-like. The identified concordantly expressed DAS genes encode diverse proteins such as the 26.5 kDa heat shock protein, chaperone protein DnaJ, potassium channel GORK, calcium-binding EF hand family protein, DEAD-box RNA helicase, and 1-aminocyclopropane-1-carboxylate synthase 6. Among the concordantly expressed DAS/SF gene pairs, SR30/DEAD-box RNA helicase, and SC35-like/1-aminocyclopropane-1-carboxylate synthase 6 emerge as promising candidates, necessitating further examinations to ascertain whether these SFs orchestrate splicing of the respective DAS genes. This study contributes to a deeper comprehension of the varied responses of the splicing machinery to abiotic stresses. Leveraging these DAS/SF associations shows promise for elucidating avenues for augmenting breeding programs aimed at fortifying cultivated plants against heat and intensive light stresses.

This work aimed to design a synthetic salt-inducible promoter using a cis-engineering approach. The designed promoter (PS) comprises a minimal promoter sequence for basal-level expression and upstream cis-regulatory elements (CREs) from promoters of salinity-stress-induced genes. The copy number, spacer lengths, and locations of CREs were manually determined based on their occurrence within native promoters. The initial activity profile of the synthesized PS promoter in transiently transformed N. tabacum leaves shows a seven-fold, five-fold, and four-fold increase in reporter GUS activity under salt, drought, and abscisic acid stress, respectively, at the 24-h interval, compared to the constitutive CaMV35S promoter. Analysis of gus expression in stable Arabidopsis transformants showed that the PS promoter induces over a two-fold increase in expression under drought or abscisic acid stress and a five-fold increase under salt stress at 24- and 48-h intervals, compared to the CaMV35S promoter. The promoter PS exhibits higher and more sustained activity under salt, drought, and abscisic acid stress compared to the constitutive CaMV35S.

The 3D structures of many ribonucleic acid (RNA) loops are characterized by highly organized networks of non-canonical interactions. Multiple computational methods have been developed to annotate structures with those interactions or automatically identify recurrent interaction networks. By contrast, the reverse problem that aims to retrieve the geometry of a look from its sequence or ensemble of interactions remains much less explored. In this chapter, we will describe how to retrieve and build families of conserved structural motifs using their underlying network of non-canonical interactions. Then, we will show how to assign sequence alignments to those families and use the software BayesPairing to build statistical models of structural motifs with their associated sequence alignments. From this model, we will apply BayesPairing to identify in new sequences regions where those loop geometries can occur.

Cellular microRNAs (miRNAs) can be selectively secreted or retained, adding another layer to their critical role in regulating human health and disease. To date, select RNA-binding proteins (RBPs) have been proposed to be a mechanism underlying miRNA localization, but the overall relevance of RBPs in systematic miRNA sorting remains unclear. This study profiles intracellular and small extracellular vesicles\' (sEVs) miRNAs in NPY-expressing hypothalamic neurons. These findings were corroborated by the publicly available sEV and intracellular miRNA profiles of white and brown adipocytes, endothelium, liver, and muscle from various databases. Using experimentally determined binding motifs of 93 RBPs, our enrichment analysis revealed that sEV-originating miRNAs contained significantly different RBP motifs than those of intracellularly retained miRNAs. Multiple RBP motifs were shared across cell types; for instance, RBM4 and SAMD4 are significantly enriched in neurons, hepatocytes, skeletal muscle, and endothelial cells. Homologs of both proteins physically interact with Argonaute1/2 proteins, suggesting that they play a role in miRNA sorting. Machine learning modelling also demonstrates that significantly enriched RBP motifs could predict cell-specific preferential miRNA sorting. Non-optimized machine learning modeling of the motifs using Random Forest and Naive Bayes in all cell types except WAT achieved an area under the receiver operating characteristic (ROC) curve of 0.77-0.84, indicating a high predictive accuracy. Given that the RBP motifs have a significant predictive power, these results underscore the critical role that RBPs play in miRNA sorting within mammalian cells and reinforce the importance of miRNA sequencing in preferential localization. For the future development of small RNA therapeutics, considering these RBP-RNA interactions could be crucial to maximize delivery effectiveness and minimize off-target effects.

There is a growing interest in inferring context specific gene regulatory networks from single-cell RNA sequencing (scRNA-seq) data. This involves identifying the regulatory relationships between transcription factors (TFs) and genes in individual cells, and then characterizing these relationships at the level of specific cell types or cell states. In this study, we introduce scGATE (single-cell gene regulatory gate) as a novel computational tool for inferring TF-gene interaction networks and reconstructing Boolean logic gates involving regulatory TFs using scRNA-seq data. In contrast to current Boolean models, scGATE eliminates the need for individual formulations and likelihood calculations for each Boolean rule (e.g. AND, OR, XOR). By employing a Bayesian framework, scGATE infers the Boolean rule after fitting the model to the data, resulting in significant reductions in time-complexities for logic-based studies. We have applied assay for transposase-accessible chromatin with sequencing (scATAC-seq) data and TF DNA binding motifs to filter out non-relevant TFs in gene regulations. By integrating single-cell clustering with these external cues, scGATE is able to infer context specific networks. The performance of scGATE is evaluated using synthetic and real single-cell multi-omics data from mouse tissues and human blood, demonstrating its superiority over existing tools for reconstructing TF-gene networks. Additionally, scGATE provides a flexible framework for understanding the complex combinatorial and cooperative relationships among TFs regulating target genes by inferring Boolean logic gates among them.