Frontiers in systems biology最新文献

Introduction: Antimicrobial resistance (AMR) poses a severe global health threat, with Acinetobacter baumannii among the critical AMR priorities highlighted by World Health Organization (WHO). This Gram-negative pathogen exhibits intrinsic resistance traits, exceptional environmental persistence, and high genomic plasticity, harboring resistance islands.

Methods: To combat AMR through synthetic biology, this study characterizes a library of BioBrick parts to be adopted in A. baumannii engineering and develops a modular CRISPR interference (CRISPRi) platform.

Results: Key components were characterized, including two plasmid vectors, a library of inducible and constitutive promoters, and a CRISPRi-mediated repression system; for the latter, a testbed for biofilm-related genes implicated in the downregulation of antibiotic resistance is also provided.

Discussion: By enabling tunable transcriptional control through the characterized promoters and ensuring the ability to downregulate gene expression via CRISPRi, this synthetic biology toolkit lays the foundation for the rational design of genetic circuits to study and counteract AMR in A. baumannii. The modular platform here characterized provides a valuable resource for the iGEM community to advance functional genomic approaches against this alarming global health challenge.

Introduction: Bats play important ecological roles but can also harbor a wide diversity of pathogens, including trypanosomatids. Knowledge about the circulation of Trypanosoma spp. in bat ectoparasites remains limited, particularly in peri-urban environments.

Methods: In this study, we used shotgun metagenomic sequencing to investigate the presence of Trypanosoma spp. in streblid flies parasitizing Carollia perspicillata bats collected in a peri-urban fragment of the Atlantic Forest in São Paulo, Brazil. A small, preliminary set of pooled samples was analyzed, followed by phylogenetic reconstruction.

Results: Trypanosoma sequences were detected in flies from the family Streblidae. Phylogenetic analysis showed that these sequences cluster within the Neobat 4 clade, which has previously been reported in Carollia spp. bats. This represents the first detection of Trypanosoma sp. in streblid flies parasitizing bats in São Paulo.

Discussion: Although the vector competence of streblid flies for Trypanosoma transmission is still unknown, their close ecological association with bats suggests that they may serve as a non-invasive tool for pathogen surveillance when direct bat sampling is limited. This study expands the known geographic distribution of the Neobat 4 clade and contributes to understanding parasite circulation among bats and their ectoparasites.

Electroencephalography (EEG) and functional Magnetic Resonance Imaging (fMRI) are two widely used neuroimaging techniques, with complementary strengths and weaknesses. Predicting fMRI activity from EEG activity could give us the best of both worlds, and open new horizons for neuroscience research and neurotechnology applications. Here, we formulate this prediction objective both as a classification task (predicting whether the fMRI signal increases or decreases) and a regression task (predicting the value of this signal). We follow two distinct strategies: training classical machine learning and deep learning models (including MLP, CNN, RNN, and transformer) on an EEG-fMRI dataset, or leveraging the capabilities of pre-trained large language models (LLMs) and large multimodal models. We show that predicting fMRI activity from EEG activity is possible for the brain regions defined by the Harvard-Oxford cortical atlas, in the context of subjects performing a neurofeedback task. Interestingly, both strategies yield promising results, possibly highlighting two complementary paths for our prediction objective. Furthermore, a Chain-of-Thought approach demonstrates that LLMs can infer the cognitive functions associated with EEG data, and subsequently predict the fMRI data from these cognitive functions. The natural combination of the two strategies, i.e., fine-tuning an LLM on an EEG-fMRI dataset, is not straightforward and would certainly require further study. These findings could provide important insights for enhancing neural interfaces and advancing toward a multimodal foundation model for neuroscience, integrating EEG, fMRI, and possibly other neuroimaging modalities.

Biomedical Knowledge Graphs (BKGs) integrate diverse datasets to elucidate complex relationships within the biomedical field. Effective link prediction on these graphs can uncover valuable connections, such as potential new drug-disease relations. We introduce a novel multimodal approach that unifies embeddings from specialized Language Models (LMs) with Graph Contrastive Learning (GCL) to enhance intra-entity relationships while employing a Knowledge Graph Embedding (KGE) model to capture inter-entity relationships for effective link prediction. To address limitations in existing BKGs, we present PrimeKG++, an enriched knowledge graph incorporating multimodal data, including biological sequences and textual descriptions for each entity type. By combining semantic and relational information in a unified representation, our approach demonstrates strong generalizability, enabling accurate link predictions even for unseen nodes. Experimental results in PrimeKG++ and the DrugBank drug-target interaction dataset demonstrate the effectiveness and robustness of our method in diverse biomedical datasets. Our source code, pre-trained models, and data are publicly available at https://github.com/HySonLab/BioMedKG.

Cystic fibrosis (CF) is the most prevalent inherited disease. Inactivating mutations in the Cystic Fibrosis Transmembrane Conductance Regulator (CFTR) gene lead to the accumulation of viscous mucus and subsequent respiratory complications. This study optimized a prime editing (PE) approach to correct CFTR mutations focusing on the F508del mutation. Prime editing allowed to introduce missing bases without double-strand breaks using a Cas9-nickase fused with a reverse transcriptase in combination with a prime editing guide RNA (pegRNA). Various self-designed pegRNAs were compared. For delivery, various lipid nanoparticles (LNP) were tested, which were optimized for stability and lung cells targeting based on lipid selection or chitosan complexion. A fluorescence reporter system, pPEAR_CFTR, was developed mimicking F508del for validation. The five pegRNAs yielding the highest efficiency were used for genomic CFTR correction in a CF bronchial cell line. Nanopore sequencing of genomic DNA revealed approximate 5% edited reads. These results highlight the promise of prime editing-LNP systems for precise and lung-specific gene correction, paving the way for novel therapies in cystic fibrosis and other pulmonary genetic disorders.

Objective: To investigate the association of neutrophil-to-lymphocyte ratio (NLR) with the cardiovascular disease (CVD) and all-cause mortality in patients with chronic kidney disease (CKD).

Methods: Using date from NHANES survey 2009-2018, 2,635 patients with CKD were eventually included in this study. The population was stratified into two groups based on the median NLR. Kaplan-Meier method with log-rank tests for significance was used for survival analysis. Weighted Cox proportional hazards regression models were employed to estimate the hazard ratio (HR) and corresponding 95% confidence interval (CI) for all-cause and CVD mortality. The potential nonlinear relationship between NLR and CVD and all-cause mortality was assessed using restricted cubic spline (RCS) models. The time-dependent receiver operating characteristic (ROC) curve was utilized to assess the precision of NLR in predicting survival outcomes.

Results: The Kaplan-Meier curve indicated a significant difference in overall survival between the two groups (log-rank test, p < 0.0001). Compared to lower NLR group, participants in the higher NLR group had HR of 1.56 (1.30, 1.87) for all-cause mortality and 2.07 (1.51, 2.84) for CVD mortality, respectively. We observed a significant nonlinear relationship between NLR and CVD and all-cause mortality (p < 0.0001). The time-dependent ROC curve demonstrated that the areas under the curve for 1-, 3-, 5-, and 10-year survival rates were 0.69, 0.65, 0.63, and 0.62 for all-cause mortality, and 0.71, 0.67, 0.66, and 0.64 for CVD mortality, respectively.

Conclusion: A higher NLR is linked to an elevated risk of CVD and all-cause mortality in patients with CKD. Additionally, NLR can serve as a potential prognostic indicator for CKD patients.

[This corrects the article DOI: 10.3389/fsysb.2025.1544432.].

[This corrects the article DOI: 10.3389/fsysb.2025.1631901.].

Bacteria rely on two-component signaling systems (TCSs) to detect environmental cues and orchestrate adaptive responses. Despite their apparent simplicity, TCSs exhibit a rich spectrum of dynamic behaviors arising from network architectures, such as bifunctional enzymes, multi-step phosphorelays, transcriptional feedback loops, and auxiliary interactions. This study develops a generalized mathematical model of a TCS that integrates these various elements. Using systems-level analysis, we elucidate how network architecture and biochemical parameters shape key properties such as stability, monotonicity, and signal amplification. Analytical conditions are derived for when the steady-state levels of phosphorylated proteins exhibit robustness to variations in protein abundance. The model characterizes how equilibrium phosphorylation levels depend on the absolute and relative abundances of the two components. Specific scenarios are explored, including the MprAB system from Mycobacterium tuberculosis and the EnvZ/OmpR system from textit Escherichia coli, to describe the potential role of reverse phosphotransfer reactions. By combining mechanistic modeling with system-level techniques, such as nullcline analysis, this study offers a unified perspective on the design principles underlying the versatility of bacterial signal transduction. The generalized modeling framework lays a theoretical foundation for interpreting experimental dynamics and rationally engineering synthetic TCS circuits with prescribed response dynamics.

Purpose: Fasting is known to have beneficial effects on human physiology and health due to changes in gut microbiota and its associated metabolites. We investigated the effects of intermittent and Ramadan fasting on the gut microbial composition, diversity, and short-chain fatty acid (SCFA) profile in a Pakistani population.

Methods: Paired fecal samples-a total of 29 for Ramadan fasting (divided into three groups, before and after completion and after 3 months) and 22 for intermittent fasting (divided into two groups, day 1 and day 10)-were collected for both 16S rRNA microbiome profiling and SCFA analysis. Study volunteers also provided a detailed questionnaire about the dietary regimen before and during the fasting period. Descriptive statistics were applied to ascertain variations in the gut microbiome and SCFAs attributable to changes in food consumption during fasting.

Results: Ramadan fasting increased the bacterial taxonomic and functional diversity and decreased the abundance of certain harmful microbes such as Blautia, Haemophilus, Desulfovibrio, Lachnoclostridium, and Porphyromonas. Intermittent fasting showed increased abundance of Prevotella, Lactobacillus, and Anaerostipes. Ramadan fasting also led to a significant increase in SCFAs including C7, iC4, and iC6, accounting for variability in microbial composition and phylogeny, respectively. In intermittent fasting, C5, iC5, and iC6 contributed to variability in microbial composition, phylogeny, and function, respectively.

Conclusion: Both fasting regimens impacted gut microbiome and metabolic signatures, but Ramadan fasting showed a more drastic effect due to the 30 days compliance period and water restriction than intermittent fasting. Ramadan fasting also improved metabolic health by increasing the abundance of SCFA-producing microbes. With Ramadan fasting, most microbial taxa reverted to their prefasting state after resumption of normal feeding patterns with few exceptions, indicating impact on microbial niche creation with prolonged fasting regimens that benefit Enterococcus, Turibacter, and Klebsiella colonization. The dietary regimen adopted during fasting, especially the consumption of high-fat-content food items, accounted for persistent gut microbial changes.