IET Systems Biology最新文献

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.

Complex network is an effective approach to studying complex diseases, and provides another perspective for understanding their pathological mechanisms by illustrating the interactions between various factors of diseases. Type 2 diabetes mellitus (T2DM) is a complex polygenic metabolic disease involving genetic and environmental factors. By combining the complex network approach with biological data, this study constructs a pathway-based weighted network model of T2DM-related genes to explore the interrelationships between genes, here a weight is assigned to each edge in terms of the number of the same pathways in which the two nodes (genes) connected to the edge are involved. The edge weights can reflect differences in the strength of connections (interactions) between nodes (genes), which intuitively reflect the extent of biological correlations between genes and contribute to the importance of the nodes. Analysis of statistical and topological characteristics shows that the edge weights are correlated to the network topology, and the edge weight distribution decays as a power-law. The disparity of the weights indicates that the edge weight distribution for the nodes with the same degree is of approximately equal weights; and most edges with the higher weights tend to connect with the higher degree nodes. To determine the key hub genes of the weighted network, an integrated ranking index is used to comprehensively reflect the contribution of the three indices (strength, degree and number of pathways) of nodes; by taking the threshold of integrated ranking index greater than 0.56, 12 key hub genes are identified: MAPK1, PIK3CD, PIK3CA, PIK3R1, AKT2, AKT1, KRAS, TNF, MAPK8, PRKCA, IL6 and MTOR. These genes should play an important role in the occurrence and development of T2DM, and can be regarded as potential therapeutic targets for further biological and medical research on their functions in T2DM. It can be expected that combining complex network approach with other data analysis techniques can provide more clues for exploring the pathogenesis and treatment of T2DM and other complex diseases in the future.

Abdominal aortic aneurysm (AAA) is a severe vascular condition, marked by the progressive dilation of the abdominal aorta, leading to rupture if untreated. The objective of this study was to identify key biomarkers and decipher the immune mechanisms underlying AAA utilising multi-omics data analysis and machine learning techniques. Single-cell RNA sequencing disclosed a heightened presence of macrophages and CD8-positive alpha-beta T cells in AAA, highlighting their critical role in disease pathogenesis. Analysis of cell–cell communication highlighted augmented interactions between macrophages and dendritic cells derived from monocytes. Enrichment analysis of differential expression gene indicated substantial involvement of immune and metabolic pathways in AAA pathogenesis. Machine learning techniques identified CCR7 and CBX6 as key candidate biomarkers. In AAA, CCR7 expression is upregulated, whereas CBX6 expression is downregulated, both showing significant correlations with immune cell infiltration. These findings provide valuable insights into the molecular mechanisms underlying AAA and suggest potential biomarkers for diagnosis and therapeutic intervention.

The interaction of transcription factors (TFs) with DNA precisely regulates gene transcription. In mammalian cells, thousands of TFs often interact with DNA cis-regulatory elements in a combinatorial manner rather than act alone. The identification of cooperativity between TFs can help to explore the mechanism of transcriptional regulation. However, little is known about the cooperative patterns of TFs in the genome. To identify which TFs prefer co-localisation, the authors conducted a paired motif analysis in the accessible regions of the human genome based on the Poisson background model. Especially, the authors distinguish the cooperative binding TFs and the competitive binding TFs according to the distance between TF motifs. In the K562 cell line, the authors find that TFs from a same family are always competing the same binding sites, such as FOS_JUN family, whereas KLF family TFs show significant cooperative binding in the adjacency region. Furthermore, the comparative analysis across 16 human cell lines indicates that most TF combination patterns are conserved, but there are still some cell-line-specific patterns. Finally, in human prostate cancer cells (PC-3) and human prostate normal cells (RWPE-2), the authors investigate the specific TF combination patterns in the disease cell and normal cell. The results show that the cooperative binding TF pairs shared by PC-3 and RWPE-2 account for over 90%. Simultaneously, the authors also identify 26 specific TF combination pairs in PC-3 cancer cells.

Intracranial haemorrhage (ICH) is an urgent and potentially fatal medical condition caused by brain blood vessel rupture, leading to blood accumulation in the brain tissue. Due to the pressure and damage it causes to brain tissue, ICH results in severe neurological impairment or even death. Recently, deep neural networks have been widely applied to enhance the speed and precision of ICH detection yet they are still challenged by small or subtle hemorrhages. The authors introduce DDANet, a novel haematoma segmentation model for brain CT images. Specifically, a dilated convolution pooling block is introduced in the intermediate layers of the encoder to enhance feature extraction capabilities of middle layers. Additionally, the authors incorporate a self-attention mechanism to capture global semantic information of high-level features to guide the extraction and processing of low-level features, thereby enhancing the model's understanding of the overall structure while maintaining details. DDANet also integrates residual networks, channel attention, and spatial attention mechanisms for joint optimisation, effectively mitigating the severe class imbalance problem and aiding the training process. Experiments show that DDANet outperforms current methods, achieving the Dice coefficient, Jaccard index, sensitivity, accuracy, and a specificity of 0.712, 0.601, 0.73, 0.997, and 0.998, respectively. The code is available at https://github.com/hpguo1982/DDANet.

Essential genes are necessary to sustain the life of a species under adequate nutritional conditions. These genes have attracted significant attention for their potential as drug targets, especially in developing broad-spectrum antibacterial drugs. However, studying essential genes remains challenging due to their variability in specific environmental conditions. In this study, the authors aim to develop a powerful prediction model for identifying essential genes in humans. The authors first obtained the essential gene data from human cancer cell lines and characterised gene sequences using 7 feature encoding methods such as Kmer, the Composition of K-spaced Nucleic Acid Pairs, and Z-curve. Subsequently, feature fusion and feature optimisation strategies were employed to select the impactful features. Finally, machine learning algorithms were applied to construct the prediction models and evaluate their performance. The single-feature-based model achieved the highest area under the Receiver Operating Characteristic curve (AUC) of 0.830. After fusing and filtering these features, the classical machine learning models achieved the highest AUC at 0.823 while the deep learning model reached 0.860. Results obtained by the authors show that compared to using individual features, feature fusion and feature optimisation strategies significantly improved model performance. Moreover, the study provided an advantageous method for essential gene identification compared to other methods.

Sepsis is a severe systemic inflammatory syndrome triggered by infection and is a leading cause of morbidity and mortality in intensive care units (ICUs). Immune dysfunction is a hallmark of sepsis. In this study, the authors investigated cell-cell communication among lymphoid-derived leucocytes using single-cell RNA sequencing (scRNA-seq) to gain a deeper understanding of the underlying mechanisms in late-stage sepsis. The authors’ findings revealed that both the number and strength of cellular interactions were elevated in septic patients compared to healthy individuals, with several pathways showing significant alterations, particularly in conventional dendritic cells (cDCs) and plasmacytoid dendritic cells (pDCs). Notably, pathways such as CD6-ALCAM were more activated in sepsis, potentially due to T cell suppression. This study offers new insights into the mechanisms of immunosuppression and provides potential avenues for clinical intervention in sepsis.