Genomics & informatics最新文献

Background: To what degree a string of symbols can be compressed reveals important details about its complexity. For instance, strings that are not compressible are random and carry a low information potential while the opposite is true for highly compressible strings. We explore to what extent microbial genomes are amenable to compression as they vary considerably both with respect to size and base composition. For instance, microbial genome sizes vary from less than 100,000 base pairs in symbionts to more than 10 million in soil-dwellers. Genomic base composition, often summarized as genomic AT or GC content due to the similar frequencies of adenine and thymine on one hand and cytosine and guanine on the other, also vary substantially; the most extreme microbes can have genomes with AT content below 25% or above 85% AT. Base composition determines the frequency of DNA words, consisting of multiple nucleotides or oligonucleotides, and may therefore also influence compressibility. Using 4,713 RefSeq genomes, we examined the association between compressibility, using both a DNA based- (MBGC) and a general purpose (ZPAQ) compression algorithm, and genome size, AT content as well as genomic oligonucleotide usage variance (OUV) using generalized additive models.

Results: We find that genome size (p < 0.001) and OUV (p < 0.001) are both strongly associated with genome redundancy for both type of file compressors. The DNA-based MBGC compressor managed to improve compression with approximately 3% on average with respect to ZPAQ. Moreover, MBGC detected a significant (p < 0.001) compression ratio difference between AT poor and AT rich genomes which was not detected with ZPAQ.

Conclusion: As lack of compressibility is equivalent to randomness, our findings suggest that smaller and AT rich genomes may have accumulated more random mutations on average than larger and AT poor genomes which, in turn, were significantly more redundant. Moreover, we find that OUV is a strong proxy for genome compressibility in microbial genomes. The ZPAQ compressor was found to agree with the MBGC compressor, albeit with a poorer performance, except for the compressibility of AT-rich and AT-poor/GC-rich genomes.

Many subtypes of SARS-CoV-2 have emerged since its early stages, with mutations showing regional and racial differences. These mutations significantly affected the infectivity and severity of the virus. This study aimed to predict the mutations that occur during the evolution of SARS-CoV-2 and identify the key characteristics for making these predictions. We collected and organized data on the lineage, date, clade, and mutations of SARS-CoV-2 from publicly available databases and processed them to predict the mutations. In addition, we utilized various artificial intelligence models to predict newly emerging mutations and created various training sets based on clade information. Using only mutation information resulted in low performance of the learning models, whereas incorporating clade differentiation resulted in high performance in machine learning models, including XGBoost (accuracy: 0.999). However, mutations fixed in the receptor-binding motif (RBM) region of Omicron resulted in decreased predictive performance. Using these models, we predicted potential mutation positions for 24C, following the recently emerged 24A and 24B clades. We identified a mutation at position Q493 in the RBM region. Our study developed effective artificial intelligence models and characteristics for predicting new mutations in continuously evolving infectious viruses.

The molecular characterization of genetically modified organisms (GMOs) is essential for ensuring safety and gaining regulatory approval for commercialization. According to CODEX standards, this characterization involves evaluating the presence of introduced genes, insertion sites, copy number, and nucleotide sequence structure. Advances in technology have led to the increased use of next-generation sequencing (NGS) over traditional methods such as Southern blotting. While both methods provide high reproducibility and accuracy, Southern blotting is labor-intensive and time-consuming due to the need for repetitive probe design and analyses for each target, resulting in low throughput. Conversely, NGS facilitates rapid and comprehensive analysis by mapping whole-genome sequencing (WGS) data to plasmid sequences, accurately identifying T-DNA insertion sites and flanking regions. This advantage allows for efficient detection of T-DNA presence, copy number, and unintended gene insertions without additional probe work. This paper reviews the current status of GMO genome characterization using NGS and proposes more efficient strategies for this purpose.

Objective: Antibiotic resistance poses a pressing and crucial global public health challenge, leading to significant clinical and health-related consequences. Substantial evidence highlights the pivotal involvement of rifampicin monooxygenase (RIFMO) in the context of antibiotic resistance. Hence, inhibiting RIFMO could offer potential in the treatment of various infections. Anthraquinones, a group of organic compounds, have shown promise in addressing tuberculosis. This study employed integrated bioinformatics approaches to evaluate the potential inhibitory effects of a selection of anthraquinones on RIFMO. The findings were subsequently compared with those of rifampicin (RIF), serving as a positive control inhibitor.

Methods: The AutoDock 4.0 tool assessed the binding free energy between 21 anthraquinones and the RIFMO catalytic cleft. The ligands were ranked based on the most favorable scores derived from ΔG_binding. The docking analyses for the highest-ranked anthraquinone and RIF underwent a cross-validation process. This validation procedure utilized the SwissDock server and the Schrödinger Maestro docking software. Molecular dynamics simulations were conducted to scrutinize the stability of the backbone atoms in free RIFMO, RIFMO-RIF, and RIFMO complexed with the top-ranked anthraquinone throughout a 100-ns computer simulation. The Discovery Studio Visualizer tool visualized interactions between RIFMO residues and ligands. An evaluation of the pharmacokinetics and toxicity profiles of the tested compounds was also conducted.

Results: Five anthraquinones were indicated with ΔG_binding scores less than - 10 kcal/mol. Hypericin emerged as the most potent RIFMO inhibitor, boasting a ΔG_binding score and inhibition constant value of - 12.11 kcal/mol and 798.99 pM, respectively. The agreement across AutoDock 4.0, SwissDock, and Schrödinger Maestro results highlighted hypericin's notable binding affinity to the RIFMO catalytic cleft. The RIFMO-hypericin complex achieved stability after a 70-ns computer simulation, exhibiting a root-mean-square deviation of 0.55 nm. Oral bioavailability analysis revealed that all anthraquinones except hypericin, sennidin A, and sennidin B may be suitable for oral administration. Furthermore, the carcinogenicity prediction analysis indicated a favorable safety profile for all examined anthraquinones.

Conclusion: Inhibiting RIFMO, particularly with anthraquinones such as hypericin, holds promise as a potential therapeutic strategy for infectious diseases.

Over hundreds of years, cats have been domesticated and selectively bred, resulting in numerous pedigreed breeds expedited by recent cat shows and breeding associations. Concerns have been raised about the limited breeding options and the genetic implications of inbreeding, indicating challenges in maintaining genetic diversity and accurate identification in purebred cats. In this study, genetic variability and structure were examined in 5 Thai domestic cat breeds using 15 microsatellite markers and mitochondrial DNA (mtDNA) D-loop sequencing. In total, 184 samples representing the Wichien Maat (WCM), Suphalak (SL), Khao-Manee (KM), Korat (KR), and Konja (KJ) breeds were analyzed. High genetic diversity (H_o and H_e > 0.5) was observed in all breeds, and mtDNA analysis revealed two primary haplogroups (A and B) that were shared among all domestic cat breeds in Thailand and globally. However, minor differences were observed between Thai domestic cat breeds based on clustering analyses, in which a distinct genetic structure was observed in the WCM breed. This suggests that allele fixation for distinctive morphological traits has occurred in Thai domestic cat breeds that emerged in isolated regions with shared racial origins. Analysis of relationships among individuals within the breed revealed high identification efficiency in Thai domestic cat breeds (P_(ID)sibs < 10^-4). Additionally, diverse and effective individual identification can be ensured by optimizing marker efficiency by using only nine loci. This comprehensive genetic characterization provides valuable insights into conservation strategies and breeding practices for Thai domestic cat breeds.

Variable surface antigens (VSAs) encoded by var and vir genes in Plasmodium falciparum and Plasmodium vivax, respectively, are known to be involved in malaria pathogenesis and host immune escape through antigenic variations. Knowledge of the genetic diversity of these antigens is essential for malaria control and effective vaccine development. In this study, we analysed the genetic diversity and evolutionary patterns of two fragments (DBL2X and DBL3X) of VAR2CSA gene and four vir genes (vir 4, vir 12, vir 21 and vir 27) from different endemic regions, including Southeast Asia and sub-Saharan Africa. High levels of segregating sites (S) and haplotype diversity (Hd) were observed in both var and vir genes. Among vir genes, vir 12 (S = 131, Hd = 0.996) and vir 21 (S = 171, Hd = 892) were found to be more diverse as compared to vir 4 (S = 11, Hd = 0.748) and vir 27 (S = 23, Hd = 0.814). DBL2X (S = 99, Hd = 0.996) and DBL3X (S = 307, Hd = 0.999) fragments showed higher genetic diversity. Our analysis indicates that var and vir genes are highly diverse and follow the similar evolutionary pattern globally. Some codons showed signatures of positive or negative selection pressure, but vir and var genes are likely to be under balancing selection. This study highlights the high variability of var and vir genes and underlines the need of functional experimental studies to determine the most relevant allelic forms for effective progress towards vaccine formulation and testing.

Autoimmune disorders (ADs) are chronic conditions resulting from failure or breakdown of immunological tolerance, resulting in the host immune system attacking its cells or tissues. Recent studies report shared effects, mechanisms, and evolutionary origins among ADs; however, the possible factors connecting them are unknown. This study attempts to identify gene signatures commonly shared between different autoimmune disorders and elucidate their molecular pathways linking the pathogenesis of these ADs using an integrated gene expression approach. We employed differential gene expression analysis across 19 datasets of whole blood/peripheral blood cell samples with five different autoimmune disorders (rheumatoid arthritis, multiple sclerosis, systemic lupus erythematosus, Crohn's disease, and type 1 diabetes) to get nine key genes-EGR1, RUNX3, SMAD7, NAMPT, S100A9, S100A8, CYBB, GATA2, and MCEMP1 that were primarily involved in cell and leukocyte activation, leukocyte mediated immunity, IL-17, AGE-RAGE signaling in diabetic complications, prion disease, and NOD-like receptor signaling confirming its role in immune-related pathways. Combined with biological interpretations such as gene ontology (GO), pathway enrichment, and protein-protein interaction (PPI) network, our current study sheds light on the in-depth research on early detection, diagnosis, and prognosis of different ADs.

Objective: Cervical cancer (CxCa) stands as a significant global health challenge, ranking fourth in cancer-related mortality among the female population. While chemotherapy regimens have demonstrated incremental progress in extending overall survival, the outlook for recurrent CxCa patients remains disheartening. An imperative necessity arises to delve into innovative therapeutic avenues, with molecular targeted therapy emerging as a promising candidate. Previous investigations have shed light on the therapeutic effectiveness of five distinct herbal compounds, epicatechin, curcumin, myricetin, jatrorrhizine, and arborinine, within the context of CxCa.

Methods: A systems biology approach was employed to discern differentially expressed genes (DEGs) in CxCa tissues relative to healthy cervical epithelial tissues. A protein-protein interaction network (PPIN) was constructed, anchored in the genes related to CxCa. The central genes were discerned within the PPIN, and Kaplan-Meier survival curves explored their prognostic significance. An assessment of the binding affinity of the selected herbal compounds to the master regulator of prognostic markers in CxCa was conducted.

Results: A significant correlation between the overexpression of MYC, IL6, JUN, RRM2, and VEGFA and an adverse prognosis in CxCa was indicated. The regulation of these markers is notably influenced by the transcription factor CEBPD. Molecular docking analysis indicated that the binding affinity between myricetin and the CEBPD DNA binding site was robust.

Conclusion: The findings presented herein have unveiled pivotal genes and pathways that play a central role in the malignant transformation of CxCa. CEBPD has emerged as a potential target for harnessing the therapeutic potential of myricetin in this context.

Though genes are already known to be responsible for type 1 diabetes mellitus (T1DM), the knowledge of missense mutation of that disease gene has still to be under covered. A genomic database and a bioinformatics-based approach are integrated in the present study in order to address this issue. Initially, nine variants associated with T1DM were retrieved from the GWAS catalogue. Different genomic algorithms such as PolyPhen2.0, SNPs and GTEx analyser programs were used to study the structural and functional effects of these mutations. Subsequently, SNPnexus was also employed to understand the effect of these mutations on the function of the expressed protein. Nine missense variants of T1DM were identified using the GWAS catalogue database. Among these nine SNPs, three were predicted to be related to the progression of T1DM disease by affecting the protein level. TYK2 gene variants with SNP rs34536443 were thought to have a probably damaging effect. Meanwhile, both COL4A3 and IFIH1 genes with SNPs rs55703767 and rs35667974, respectively, might alter protein function through a possibly damaging prediction. Among the variants of the three genes, the TYK2 gene with SNP rs34536443 had the strongest contribution in affecting the development of T1DM, with a score of 0.999. We sincerely hope that the results could be of immense importance in understanding the genetic basis of T1DM.

This study evaluated large language models (LLMs), particularly the GPT-4 with vision (GPT-4 V) and GPT-4 Turbo, for annotating biomedical figures, focusing on cellular senescence. We assessed the ability of LLMs to categorize and annotate complex biomedical images to enhance their accuracy and efficiency. Our experiments employed prompt engineering with figures from review articles, achieving more than 70% accuracy for label extraction and approximately 80% accuracy for node-type classification. Challenges were noted in the correct annotation of the relationship between directionality and inhibitory processes, which were exacerbated as the number of nodes increased. Using figure legends was a more precise identification of sources and targets than using captions, but sometimes lacked pathway details. This study underscores the potential of LLMs in decoding biological mechanisms from text and outlines avenues for improving inhibitory relationship representations in biomedical informatics.