Topology of nucleosomes in chromatin structure

DNA u eukariotskih kromosoma organizirana je u nukleosome, strukture građene od proteina i DNA, koje predstavljaju osnovnu građevnu i funkcionalnu jedinicu kromosomskog materijala, kromatina. Smatanjem niza nukleosoma nastaje viša razina strukture, kromatinska nit debljine 30 nm. I dok se o strukturi nukleosoma danas praktički sve zna, o 30 nm-niti ali i o višim razinama strukture kromatina još se uvijek malo zna. U ovom radu izloženi su modeli koji pokušavaju objasniti strukturu kromatinske niti: solenoidni model, model zavijene vrpce, dvopočetni ukriženi model te nesekvencijski jednopočetni model, sa razlikom u putu koji prelazi DNA između dva nukleosoma. Razumijevanje strukture 30 nm-niti od izuzetne je važnosti za shvaćanje regulacije procesa transkripcije, rekombinacije, DNA popravka te replikacije.DNA in eukaryotic chromosomes is organized in the nucleosomes, structures which contain proteins and DNA, and which represent elementary structural and functional unit of chromosomal material, chromatin. Arrays of nucleosomes are folded into higher order structure, 30 nm chromatin fiber. Although the structure of nucleosome is known in smallest detail, only a few facts are known about 30 nm fibre and higher chromatin structures. In this work, I exposed models for structure of 30 nm chromatin fibers: solenoid, twisted-ribbon, two-started crossed-linker and one-started nonsequential model, with difference in the path of DNA between two nucleosomes. Uncovering of 30 nm fibre structure is crucial for understanding the regulation of processes such as transcription, recombination, DNA repair and replication

Topology of nucleosomes in chromatin structure

DNA u eukariotskih kromosoma organizirana je u nukleosome, strukture građene od proteina i DNA, koje predstavljaju osnovnu građevnu i funkcionalnu jedinicu kromosomskog materijala, kromatina. Smatanjem niza nukleosoma nastaje viša razina strukture, kromatinska nit debljine 30 nm. I dok se o strukturi nukleosoma danas praktički sve zna, o 30 nm-niti ali i o višim razinama strukture kromatina još se uvijek malo zna. U ovom radu izloženi su modeli koji pokušavaju objasniti strukturu kromatinske niti: solenoidni model, model zavijene vrpce, dvopočetni ukriženi model te nesekvencijski jednopočetni model, sa razlikom u putu koji prelazi DNA između dva nukleosoma. Razumijevanje strukture 30 nm-niti od izuzetne je važnosti za shvaćanje regulacije procesa transkripcije, rekombinacije, DNA popravka te replikacije.DNA in eukaryotic chromosomes is organized in the nucleosomes, structures which contain proteins and DNA, and which represent elementary structural and functional unit of chromosomal material, chromatin. Arrays of nucleosomes are folded into higher order structure, 30 nm chromatin fiber. Although the structure of nucleosome is known in smallest detail, only a few facts are known about 30 nm fibre and higher chromatin structures. In this work, I exposed models for structure of 30 nm chromatin fibers: solenoid, twisted-ribbon, two-started crossed-linker and one-started nonsequential model, with difference in the path of DNA between two nucleosomes. Uncovering of 30 nm fibre structure is crucial for understanding the regulation of processes such as transcription, recombination, DNA repair and replication

Machine learning framework to extract the biomarker potential of plasma IgG N-glycans towards disease risk stratification

Effective management of chronic diseases and cancer can greatly benefit from disease-specific biomarkers that enable informative screening and timely diagnosis. IgG N-glycans found in human plasma have the potential to be minimally invasive disease-specific biomarkers for all stages of disease development due to their plasticity in response to various genetic and environmental stimuli. Data analysis and machine learning (ML) approaches can assist in harnessing the potential of IgG glycomics towards biomarker discovery and the development of reliable predictive tools for disease screening. This study proposes an ML-based N-glycomic analysis framework that can be employed to build, optimise, and evaluate multiple ML pipelines to stratify patients based on disease risk in an interpretable manner. To design and test this framework, a published colorectal cancer (CRC) dataset from the Study of Colorectal Cancer in Scotland (SOCCS) cohort (1999-2006) was used. In particular, among the different pipelines tested, an XGBoost-based ML pipeline, which was tuned using multi-objective optimisation, calibrated using an inductive Venn-Abers predictor (IVAP), and evaluated via a nested cross-validation (NCV) scheme, achieved a mean area under the Receiver Operating Characteristic Curve (AUC-ROC) of 0.771 when classifying between age-, and sex-matched healthy controls and CRC patients. This performance suggests the potential of using the relative abundance of IgG N-glycans to define populations at elevated CRC risk who merit investigation or surveillance. Finally, the IgG N-glycans that highly impact CRC classification decisions were identified using a global model-agnostic interpretability technique, namely Accumulated Local Effects (ALE). We envision that open-source computational frameworks, such as the one presented herein, will be useful in supporting the translation of glycan-based biomarkers into clinical applications

Anti-TNF Biologicals Enhance the Anti-Inflammatory Properties of IgG N-Glycome in Crohn's Disease

Crohn’s disease (CD) is a chronic inflammation of the digestive tract that significantly impairs patients’ quality of life and well-being. Anti-TNF biologicals revolutionised the treatment of CD,yet many patients do not adequately respond to such therapy. Previous studies have demonstrated apro-inflammatory pattern in the composition of CD patients’ immunoglobulin G (IgG) N-glycomecompared to healthy individuals. Here, we utilised the high-throughput UHPLC method for N-glycan analysis to explore the longitudinal effect of the anti-TNF drugs infliximab and adalimumabon N-glycome composition of total serum IgG in 198 patients, as well as the predictive potential ofIgG N-glycans at baseline to detect primary non-responders to anti-TNF therapy in 1315 patients. Wediscovered a significant decrease in IgG agalactosylation and an increase in monogalactosylation,digalactosylation and sialylation during the 14 weeks of anti-TNF treatment, regardless of therapyresponse, all of which suggested a diminished inflammatory environment in CD patients treated withanti-TNF therapy. Furthermore, we observed that IgG N-glycome might contain certain informationregarding the anti-TNF therapy outcome before initiating the treatment. However, it is impossible to predict future primary non-responders to anti-TNF therapy based solely on IgG N-glycomecomposition at baseline

Periodic changes in the N-glycosylation of immunoglobulin G during the menstrual cycle

Immunoglobulin G (IgG) is the most abundant plasma glycoprotein and a prominent humoral immune mediator. Glycan composition affects the affinity of IgG to ligands and consequent immune responses. The modification of IgG N-glycosylation is considered to be one of the various mechanisms by which sex hormones modulate the immune system. Although the menstrual cycle is the central sex hormone-related physiological process in most women of reproductive age, IgG N-glycosylation dynamics during the menstrual cycle have not yet been investigated. To fill this gap, we profiled the plasma IgG N-glycans of 70 healthy premenopausal women at 12 time points during their menstrual cycles (every 7 days for 3 months) using hydrophilic interaction ultra-performance liquid chromatography (HILIC-UPLC). We observed cyclic periodic changes in the N-glycosylation of IgG in association with the menstrual cycle phase and sex hormone concentration in plasma. On the integrated cohort level, the modeled average menstrual cycle effect on the abundance of IgG N-glycosylation traits was low for each trait, with the highest being 1.1% for agalactosylated N-glycans. However, intrapersonal changes were relatively high in some cases; for example, the largest difference between the minimum and maximum values during the menstrual cycle was up to 21% for sialylated N-glycans. Across all measurements, the menstrual cycle phase could explain up to 0.72% of the variation in the abundance of a single IgG glycosylation trait of monogalactosylation. In contrast, up to 99% of the variation in the abundance of digalactosylation could be attributed to interpersonal differences in IgG N-glycosylation. In conclusion, the average extent of changes in the IgG N-glycopattern that occur during the menstrual cycle is small; thus, the IgG N-glycoprofiling of women in large sample-size studies can be performed regardless of menstrual cycle phase

Changes in IgA-targeted microbiota following fecal transplantation for recurrent Clostridioides difficile infection

Secretory immunoglobulin A (IgA) interacts with intestinal microbiota and promotes mucosal homeostasis. IgA-bacteria interactions are altered during inflammatory diseases, but how these interactions are shaped by bacterial, host, and environmental factors remains unclear. In this study, we utilized IgA-SEQ to profile IgA-bound fecal bacteria in 48 recurrent Clostridioides difficile patients before and after successful fecal microbiota transplantation (FMT) to gain further insight. Prior to FMT, Escherichia coli was the most highly IgA-targeted taxon; following restoration of the microbiota by FMT, highly IgA-targeted taxa included multiple Firmicutes species. Post-FMT IgA-targeting was unaffected by the route of FMT delivery (colonoscopy versus capsule), suggesting that both methods lead to the establishment of healthy immune–bacterial interactions in the gut. Interestingly, IgA-targeting in FMT recipients closely resembled the IgA-targeting patterns of the donors, and fecal donor identity was significantly associated with IgA-targeting of the recipient microbiota. These data support the concept that intrinsic bacterial properties drive IgA recognition across genetically distinct human hosts. Together, this study suggests that IgA-bacterial interactions are reestablished in human FMT recipients to resemble that of the healthy fecal donor

A Multi-Factorial Observational Study on Sequential Fecal Microbiota Transplant in Patients with Medically Refractory Clostridioides difficile Infection

Fecal microbiota transplantation (FMT) is highly effective in recurrent Clostridioides difficile infection (CDI); increasing evidence supports FMT in severe or fulminant Clostridioides difficile infection (SFCDI). However, the multifactorial mechanisms that underpin the efficacy of FMT are not fully understood. Systems biology approaches using high-throughput technologies may help with mechanistic dissection of host-microbial interactions. Here, we have undertaken a deep phenomics study on four adults receiving sequential FMT for SFCDI, in which we performed a longitudinal, integrative analysis of multiple host factors and intestinal microbiome changes. Stool samples were profiled for changes in gut microbiota and metabolites and blood samples for alterations in targeted epigenomic, metabonomic, glycomic, immune proteomic, immunophenotyping, immune functional assays, and T-cell receptor (TCR) repertoires, respectively. We characterised temporal trajectories in gut microbial and host immunometabolic data sets in three responders and one non-responder to sequential FMT. A total of 562 features were used for analysis, of which 78 features were identified, which differed between the responders and the non-responder. The observed dynamic phenotypic changes may potentially suggest immunosenescent signals in the non-responder and may help to underpin the mechanisms accompanying successful FMT, although our study is limited by a small sample size and significant heterogeneity in patient baseline characteristics. Our multi-omics integrative longitudinal analytical approach extends the knowledge regarding mechanisms of efficacy of FMT and highlights preliminary novel signatures, which should be validated in larger studies