IET Systems Biology最新文献

Parkinson's disease (PD), a degenerative disorder affecting the nervous system, manifests as unbalanced movements, stiffness, tremors, and coordination difficulties. Its cause, believed to involve genetic and environmental factors, underscores the critical need for prompt diagnosis and intervention to enhance treatment effectiveness. Despite the array of available diagnostics, their reliability remains a challenge. In this study, an innovative predictor PADG-Pred is proposed for the identification of Parkinson's associated biomarkers, utilising a genomic profile. In this study, a novel predictor, PADG-Pred, which not only identifies Parkinson's associated biomarkers through genomic profiling but also uniquely integrates multiple statistical feature extraction techniques with ensemble-based classification frameworks, thereby providing a more robust and interpretable decision-making process than existing tools. The processed dataset was utilised for feature extraction through multiple statistical moments and it is further involved in extensive training of the model using diverse classification techniques, encompassing Ensemble methods; XGBoost, Random Forest, Light Gradient Boosting Machine, Bagging, ExtraTrees, and Stacking. State-of-the-art validation procedures are applied, assessing key metrics such as specificity, accuracy, sensitivity/recall, and Mathew's correlation coefficient. The outcomes demonstrate the outstanding performance of PADG-RF, showcasing accuracy metrics consistently achieving ∼91% for the independent set, ∼94% for 5-fold, and ∼96% for 10-fold in cross-validation.

As an economically important tree species, mulberry (Morus spp.) has exhibited a remarkable tolerance for salinity, drought and heavy metals. However, the precise mechanism of metabolome-mediated drought adaptation is unclear. In this study, two new mulberry varieties—‘drought-sensitive guisangyou62 (GSY62) and highly drought-tolerant guiyou2024 (GY2024)’—after three days (62F or 2024F) and six days (62B or 2024B) of drought–stress conditions were subjected to transcriptome and metabolome analyses. The enrichment analysis demonstrated that the differentially expressed genes (DEGs) were mainly enriched in carbohydrate metabolism, amino acid metabolism, energy metabolism and secondary metabolite biosynthesis under drought–stress conditions. Notably, compared with the CK group (without drought treatment), 60 and 70 DEGs in GY2024 and GSY62 were involved in sucrose and starch biosynthesis, respectively. The genes encoding sucrose phosphate synthase 2 and 4 were downregulated in GY2024, with a lower expression. The genes encoding key enzymes in starch biosynthesis were upregulated in GY2024 and the transcriptional abundance was significantly higher than in GSY62. These results indicated that drought stress reduced sucrose synthesis but accelerated starch synthesis in mulberry.

With the development of high-throughput sequencing technology, the analysis of single-cell RNA sequencing data has become the focus of current research. Matrix analysis and processing of downstream gene expression after preprocessing is a hot topic for researchers. This paper proposed an iterative block matrix completion algorithm, called SeqBMC, based on matrix factorisation. The algorithm is used to complete the missing value of the gene expression matrix caused by the defect of sequencing technology. The gene frequency of the matrix is used to block the matrix, and then the matrix factorisation algorithm is used to complete the small matrix after the block, and then the biological zeros that may exist in the block matrix are retained. Experimental results show that the matrix completion algorithm can significantly improve the classification performance of the gene expression matrix after completion with 86.81% F1 score, which is conducive to the recognition of cell types in sequencing data. Moreover, this completion method can be completed only by the machine learning method without too much prior knowledge related to biology and has good effects. Compared with ALRA, SeqBMC increased 5.47% accuracy and 5.03% F1 score. It indicates that SeqBMC has significant advantages in the matrix completion of single-cell RNA sequencing data.

Hypertension, often known as high blood pressure, is a major concern to millions of individuals globally. Recent studies have demonstrated the significant efficacy of naturally derived peptides in reducing blood pressure. Hypertension is one of the risks associated with cardiovascular disorders and other health problems. Naturally sourced bioactive peptides possessing antihypertensive properties provide considerable potential as viable substitutes for conventional pharmaceutical medications. Currently, thorough examination of antihypertensive peptide (AHTPs), by using traditional wet-lab methods is highly expensive and labours. Therefore, in-silico approaches especially machine-learning (ML) algorithms are favourable due to saving time and cost in the discovery of AHTPs. In this study, a novel ML-based predictor, called StackAHTP was developed for predicting accurate AHTPs from sequence only. The proposed method, utilise two types of feature descriptors Pseudo-Amino Acid Composition and Dipeptide Composition to encode the local and global hidden information from peptide sequences. Furthermore, the encoded features are serially merged and ranked through SHapley Additive explanations (SHAP) algorithm. Then, the top ranked are fed into three different ensemble classifiers (Bagging, Boosting, and Stacking) for enhancing the prediction performance of the model. The StackAHTPs method achieved superior performance compare to other ML classifiers (AdaBoost, XGBoost and Light Gradient Boosting (LightGBM), Bagging and Boosting) on 10-fold cross validation and independent test. The experimental outcomes demonstrate that our proposed method outperformed the existing methods and achieved an accuracy of 92.25% and F1-score of 89.67% on independent test for predicting AHTPs and non-AHTPs. The authors believe this research will remarkably contribute in predicting large-scale characterisation of AHTPs and accelerate the drug discovery process. At https://github.com/ali-ghulam/StackAHTPs you may find datasets features used.

Metal ions are significant ligands that bind to proteins and play crucial roles in cell metabolism, material transport, and signal transduction. Predicting the protein-metal ion ligand binding residues (PMILBRs) accurately is a challenging task in theoretical calculations. In this study, the authors employed fused amino acids and their derived information as feature parameters to predict PMILBRs using three classical machine learning algorithms, yielding favourable prediction results. Subsequently, deep learning algorithm was incorporated in the prediction, resulting in improved results for the sets of Ca²⁺ and Mg²⁺ compared to previous studies. The validation matrix provided the optimal prediction model for each ionic ligand binding residue, exhibiting the capability of effectively predicting the binding sites of metal ion ligands for real protein chains.

The herbal sitz bath formula, as a complementary therapy, effectively alleviates postoperative wound pain and accelerates healing time in patients with perianal abscesses. To investigate its mechanism of action, this study conducted 16S rRNA gene sequencing and bioinformatics analysis on wound exudate samples from patients after perianal abscess surgery. Patients were randomly divided into two groups: one receiving the herbal sitz bath as an adjunctive therapy and the other without this adjunctive therapy. Samples were collected on the first and eighth days after surgery to compare the differences in microbial community composition between the two groups on the eighth day and between the first and eighth days within each group. The study revealed that the herbal sitz bath significantly altered the structure of the microbial community, increasing its diversity and abundance. By reducing Enterococcus and increasing Bifidobacterium, Faecalibacterium, and Ruminococcus, the therapy enhanced the wound's anti-infective capacity and accelerated healing. This study explored the potential mechanism of the herbal sitz bath formula as an adjunctive therapy in promoting postoperative recovery from perianal abscesses, providing valuable data for further research on the role of microorganisms in wound care. These findings contribute to optimising postoperative treatment regimens and facilitating patient recovery.

One of the challenges that beset modelling complex biological networks is to relate networks to function to dynamics. A further challenge is deciphering the cellular function and dynamics that can change drastically when the network edge is tinkered with by adding or removing it. To illustrate this, the authors took a well-studied three-variable Goodwin oscillatory motif with only a negative feedback loop. To this motif, an edge was added that results in an emergent structure consisting of new feedforward and feedback loops while retaining Goodwin's original negative feedback loop. To relate emergent structure to oscillatory dynamics, the authors took all the combinations of edge signs in the interlocked motif. Bifurcation analysis reveals that all the structural combinations can be grouped into two categories based on their unique dynamics. These two groups also exhibit unique amplitude-frequency (amp-freq) plots. These two categories are attributed to the emergence of interlocked motifs with specific edge signs. To support the ideas, a well-studied plant circadian model of Arabidopsis thaliana was taken to illustrate the importance of interlocked motifs in fine-tuning amplitude and frequency in circadian oscillators. The authors briefly discuss its implications for central oscillators' adaptation to different environmental cues.

Spatially resolved transcriptomics technologies potentially provide the extra spatial position information and tissue image to better infer spatial cell–cell interactions (CCIs) in processes such as tissue homeostasis, development, and disease progression. However, methods for effectively integrating spatial multimodal data to infer CCIs are still lacking. Here, the authors propose a deep learning method for integrating features through co-convolution, called SpaGraphCCI, to effectively integrate data from different modalities of SRT by projecting gene expression and image feature into a low-dimensional space. SpaGraphCCI can achieve significant performance on datasets from multiple platforms including single-cell resolution datasets (AUC reaches 0.860–0.907) and spot resolution datasets (AUC ranges from 0.880 to 0.965). SpaGraphCCI shows better performance by comparing with the existing deep learning-based spatial cell communication inference methods. SpaGraphCCI is robust to high noise and can effectively improve the inference of CCIs. We test on a human breast cancer dataset and show that SpaGraphCCI can not only identify proximal cell communication but also infer new distal interactions. In summary, SpaGraphCCI provides a practical tool that enables researchers to decipher spatially resolved cell–cell communication based on spatial transcriptome data.

Oral squamous cell carcinoma (OSCC) is a common head and neck malignant tumour with high incidence and poor prognosis. Arsenic trioxide (ATO) has therapeutic effects on solid tumours. Microwave ablation (MWA) has unique advantages in the treatment of solid tumours. However, the therapeutic mechanism of ATO and MWA, as well as their combined effect on OSCC were largely unelucidated. Cal-27 cell-bearing nude mice were treated with ATO and/or MWA, respectively. RNA sequencing was used to obtain gene expression profiles in tumour tissues of mice treated by ATO or MWA. RNA sequencing results were verified by real-time polymerase chain reaction (PCR). The lncRNA-miRNA-mRNA co-expression network was constructed based on the competitive endogenous RNA (ceRNA) theory. Gene ontology and Kyoto Encyclopedia of Genes and Genomes analyses were performed using differentially expressed genes. The combined effect of ATO and MWA on OSCC was evaluated. Finally, CCK-8 assay, EdU assay and transwell migration assay were performed to detect the effect of HSPA6 on the proliferation and migration of OSCC cells. The reduced volume of tumour tissues was observed in both ATO- and MWA-treated groups. 37.8% decreased in the ATO group and 35.0% in the MWA group. A total of 207 and 539 differentially expressed mRNAs and lncRNAs were identified in the ATO group. And a total of 200 and 522 differentially expressed mRNAs and lncRNAs in the MWA group were identified. The expression levels of 8 genes were verified by real-time PCR. The differentially expressed genes were closely related to “chemical carcinogenesis”, “herpes simplex infection”, “porphyrin and chlorophyll metabolism”, and “MAPK signalling pathway”. The lncRNA-miRNA-mRNA co-expression networks were constructed. The combined treatment with ATO and MWA showed a better inhibitive effect on OSCC than either of them. The synergistic effect of ATO and MWA was related to the upregulation of HSPA6. The downregulation of HSPA6 could promote the proliferation and migration of OSCC cells. This study detected the long non-coding RNA and mRNA expression profiles related to the treatment of OSCC and constructed corresponding ceRNA networks. Arsenic trioxide and MWA have a synergistic effect on OSCC, which was related to the upregulation of HSPA6.

Klebsiella pneumoniae, an opportunistic pathogen, is pervasively distributed across the world. Its escalating antibiotic resistance poses a serious threat to global public health. The mechanisms behind this resistance remain largely elusive. In this study, we performed antibiotic susceptibility testing on several clinical isolates of Klebsiella pneumoniae, and a reference strain ATCC13883, and then analysed their transcriptomic profiles to identify genes and pathways associated with antibiotic resistance. Our results showed that a clinical isolate DY16KPN may counteract antibiotics by enhancing the biosynthesis of building blocks of bacterial cell, such as fatty acids, proteins, and DNA, and reducing transmembrane transport. Increased butanoate metabolism and lipopolysaccharide biosynthesis may also contribute to the drug-resistance of Klebsiella pneumoniae. Additionally, we identified resistance-promoting mutations in gene promoter regions, which regulate the activity of downstream drug-resistant genes and pathways. Our results also demonstrated that DY16KPN counterbalances the trimethoprim/sulfamethoxazole-mediated inhibitory effect on the synthesis of tetrahydrofolates and DNA by up-regulating the expression of targeted enzymes of trimethoprim/sulfamethoxazole, dihydrofolate reductase and dihydropteroate synthase.