Frontiers in systems biology最新文献

Introduction: Preterm hypoxic-ischemic encephalopathy (pHIE) is a complex brain injury that contributes to chronic neural inflammation and neurological disorders. The signs and symptoms of in utero pHIE can often be overlooked, untreated or lumped into more generic conditions such as encephalopathy of prematurity (EOP). Clinical interventions like hypothermia and erythropoietin do not improve pHIE. We characterized a murine model for pHIE, which includes hypoxia and maternal factors as a cost-effective alternative to large animal models of HIE.

Methods: We injected pregnant mouse dams with LPS to stimulate an inflammatory response on embryonic days 15-16 (E15-E16), and whole cage hypoxia exposures occurred from postnatal days 3 to 9. To quantify the development of inflammation in the pHIE model, we used immunohistochemistry to stain for Caspase-9 in the cortex (20 μm per slice) and then counted Caspase-9 positive cells using unbiased stereology. We stained brain tissue with MAP2 to quantify neuronal intermediate filament expression and staining using a machine-learning based image analysis approach. We quantified cytokines (IL-1β, IL-6, IL-10, IL-18 and TNF-α) using RT-qPCR and (IL-18) ELISA to characterize differential expression in all treatment groups. The pHIE animals were compared with controls (LPS-Normoxia, Saline-Hypoxia, Saline-Normoxia, and Naïve) and with a model of only hypoxia (10% O₂) exposure in mouse pups.

Results: The pHIE pups showed significantly higher expression of Caspase-9 throughout the cortex compared to Naïve pup brains (p < 0.05). MAP2 expression was significantly decreased (p < 0.05) between 1.5-6.0 mm of the brain compared to Saline-Hypoxia and Naïve animals. Both IL-1β and IL-10 expression in LPS-Hypoxia animals was significantly higher (p < 0.05) than in Saline-Hypoxia and Naive animals. TNF-α expression was not significantly different between LPS-Hypoxia and Saline-Hypoxia animals. However, both showed significantly different transcription, compared to Naive animals.

Discussion: The model we describe here shows cortical damage similar to that seen in human HIE.

Global textile manufacturing practices are responsible for an increasing amount of textile waste that pollutes our planet. Mixed fiber blends pose a recycling challenge due to their heterogeneous structure. Current mechanical, chemical, thermochemical and enzymatic strategies suffer from several limitations such as high energy costs, extensive pre-treatment requirements and enzyme instability. This mini-review aims to present recent developments in the research field and to introduce Spore Surface Display (SSD) technology as a new biological approach for mixed textile degradation. SSD allows enzymes to be anchored on the robust bacterial spore surface, immobilizing multiple enzymes required for simultaneous cotton-polyester degradation into their respective monomers. The mini-review also includes an initial proposal for a process design suitable for a full mixed textile degradation process using this synthetic biology approach.

Current evidence suggests sodium-glucose cotransporter 2 inhibitors (SGLT2i) do not consistently improve patient physical function, despite improvements in clinical symptoms and reductions in both adiposity and body weight. We highlight heterogenous methodologies in SGLT2i physical function trials. We then provide context to these findings by collating new data which describes how reduced glycaemia with SGLT2i alters numerous physiological processes and discuss how these alterations may diminish or prevent expected functional improvements. Alterations include changes to energy homeostasis, pancreatic hormones, muscle metabolism, physical activity, and appetite regulation. Current evidence in humans is limited and the mechanistic interaction between SGLT2i, skeletal muscle, and physical function remains incompletely understood. Future investigations must embed comprehensive molecular techniques within suitably designed clinical trials to determine how skeletal muscle health and patient mobility is influenced by acute and long term SGLT2i prescription.

Losartan is a selective angiotensin II AT1-receptor antagonist for the treatment of arterial hypertension and heart failure. It is converted to a pharmacologically active metabolite carboxylosartan (E-3174) in the liver mainly by CYP2C9 enzyme, a member of the cytochrome P450 superfamily. The gene encoding this protein is highly polymorphic: numerous single nucleotide polymorphisms that alter the enzyme function have been described in the literature. The most widespread CYP2C9 alleles are CYP2C9*1 (wild-type), CYP2C9*2, and CYP2C9*3. Here we performed mathematical modeling of the metabolism of orally administered losartan to E-3174 taking into account combinations of the most common CYP2C9 alleles. Next, using the previously created model of the human cardiovascular and renal systems, we demonstrated that the blood pressure response to losartan therapy in a cohort of virtual hypertensive patients depended on CYP2C9 allelic variants. Individuals with the CYP2C9*1/CYP2C9*1 genotype responded better to treatment than patients carrying CYP2C9*2 or CYP2C9*3 alleles. The results of the modeling can potentially be used for personalization of drug therapy for arterial hypertension.

Background: The increasing incidence of inflammatory bowel diseases (IBD) over the last two decades has prompted the need to create new types of therapeutic interventions. The gut microbiome has emerged as a key component in the prognosis and pathophysiology of IBDs. The alteration or dysbiosis of the gut microbiome has been shown to exacerbate IBDs. The bacterial composition of the gut microbiome can be modulated through the usage of probiotics, prebiotics, and synbiotics. These interventions induce the growth of beneficial bacteria. Additionally, these interventions could be used to maintain gut homeostasis, reduce the inflammation seen in these morbidities, and strengthen the gut epithelial barrier.

Methods: The literature review was conducted in October 2024 using PubMed, Scopus, and Google Scholar screening for recent clinical trials in addition to reviews relevant to the topic.

Aims: This review aims to summarize the recent clinical trials of probiotics, prebiotics, and synbiotics in IBD patients highlighting their potential benefits in alleviating symptoms and enhancing the quality of life.

Conclusion: Certain probiotic formulations such as single strain ones consisting of Lactobacillus, or mixed-strain combinations of Lactobacillus and Bifidobacterium, prebiotic compounds such as fructooligosaccharides, and synbiotic combinations of both have proven effective in improving the clinical, immunological, and symptomatic aspects of the disease course. While promising, these findings remain inconclusive due to inconsistent study designs, small sample sizes, and varying patient responses. This emphasizes the need for larger, well-controlled trials to determine their clinical efficacy.

Post-Acute Sequelae of SARS-CoV-2 infection (PASC or "Long COVID"), includes numerous chronic conditions associated with widespread morbidity and rising healthcare costs. PASC has highly variable clinical presentations, and likely includes multiple molecular subtypes, but it remains poorly understood from a molecular and mechanistic standpoint. This hampers the development of rationally targeted therapeutic strategies. The NIH-sponsored "Researching COVID to Enhance Recovery" (RECOVER) initiative includes several retrospective/prospective observational cohort studies enrolling adult, pregnant adult and pediatric patients respectively. RECOVER formed an "OMICS" multidisciplinary task force, including clinicians, pathologists, laboratory scientists and data scientists, charged with developing recommendations to apply cutting-edge system biology technologies to achieve the goals of RECOVER. The task force met biweekly over 14 months, to evaluate published evidence, examine the possible contribution of each "omics" technique to the study of PASC and develop study design recommendations. The OMICS task force recommended an integrated, longitudinal, simultaneous systems biology study of participant biospecimens on the entire RECOVER cohorts through centralized laboratories, as opposed to multiple smaller studies using one or few analytical techniques. The resulting multi-dimensional molecular dataset should be correlated with the deep clinical phenotyping performed through RECOVER, as well as with information on demographics, comorbidities, social determinants of health, the exposome and lifestyle factors that may contribute to the clinical presentations of PASC. This approach will minimize lab-to-lab technical variability, maximize sample size for class discovery, and enable the incorporation of as many relevant variables as possible into statistical models. Many of our recommendations have already been considered by the NIH through the peer-review process, resulting in the creation of a systems biology panel that is currently designing the studies we proposed. This system biology strategy, coupled with modern data science approaches, will dramatically improve our prospects for accurate disease subtype identification, biomarker discovery and therapeutic target identification for precision treatment. The resulting dataset should be made available to the scientific community for secondary analyses. Analogous system biology approaches should be built into the study designs of large observational studies whenever possible.

Introduction: The availability of large-scale multi-omic data has revolution-ized the study of cellular machinery, enabling a systematic understanding of biological processes. However, the integration of these datasets into Genome-Scale Models of Metabolism (GEMs) re-mains underexplored. Existing methods often link transcriptome and proteome data independently to reaction boundaries, providing models with estimated maximum reaction rates based on individual datasets. This independent approach, however, introduces uncertainties and inaccuracies.

Methods: To address these challenges, we applied a principal component analysis (PCA)-based approach to integrate transcriptome and proteome data. This method facilitates the reconstruction of context-specific models grounded in multi-omics data, enhancing their biological relevance and predictive capacity.

Results: Using this approach, we successfully reconstructed an astrocyte GEM with improved prediction capabilities compared to state-of-the-art models available in the literature.

Discussion: These advancements underscore the potential of multi-omic inte-gration to refine metabolic modeling and its critical role in studying neurodegeneration and developing effective therapies.

Gaussian Graphical Models (GGMs) are a type of network modeling that uses partial correlation rather than correlation for representing complex relationships among multiple variables. The advantage of using partial correlation is to show the relation between two variables after "adjusting" for the effects of other variables and leads to more parsimonious and interpretable models. There are well established procedures to build GGMs from a sample of independent and identical distributed observations. However, many studies include clustered and longitudinal data that result in correlated observations and ignoring this correlation among observations can lead to inflated Type I error. In this paper, we propose a cluster-based bootstrap algorithm to infer GGMs from correlated data. We use extensive simulations of correlated data from family-based studies to show that the proposed bootstrap method does not inflate the Type I error while retaining statistical power compared to alternative solutions when there are sufficient number of clusters. We apply our method to learn the GGM that represents complex relations between 47 Polygenic Risk Scores generated using genome-wide genotype data from the Long Life Family Study. By comparing it to the conventional methods that ignore within-cluster correlation, we show that our method controls the Type I error well without power loss.