ABSTRACT
Oncogenesis is accompanied by chromatin organization alterations and reactivation of unicellular phenotypes at the metabolic and transcriptional level. The mechanisms connecting these two observations are unexplored, despite its relevance in cancer biology. Assigning evolutionary ages to genes in the context of 3D chromatin structure, we characterize the epigenomic landscape, expression regulation and spatial organization of genes according to their evolutionary ages. We describe topological changes across differentiation and find some of the patterns, involving Polycomb repression and RNA Pol II pausing, being reversed during oncogenesis. Going beyond the evidence of non-random organization of genes and chromatin features in the 3D epigenome, we suggest that these patterns lead to preferential interactions of old, intermediate and young genes, mediated by respectively RNA Polymerase II, Polycomb and the lamina. Our results are in line and expand recent findings implicating loss of Polycomb repression and activation of embryonal and early evolutionary programs in cancer.
ABSTRACT
Gilles de la Tourette syndrome (GTS) is a neurodevelopmental psychiatric disorder with complex and elusive etiology with a significant role of genetic factors. The aim of this study was to identify structural variants that could be associated with familial GTS. The study group comprised 17 multiplex families with 80 patients. Structural variants were identified from whole-genome sequencing data and followed by co-segregation and bioinformatic analyses. The localization of these variants was used to select candidate genes and create gene sets, which were subsequently processed in gene ontology and pathway enrichment analysis. Seventy putative pathogenic variants shared among affected individuals within one family but not present in the control group were identified. Only four private or rare deletions were exonic in LDLRAD4, B2M, USH2A, and ZNF765 genes. Notably, the USH2A gene is involved in cochlear development and sensory perception of sound, a process that was associated previously with familial GTS. In addition, two rare variants and three not present in the control group were co-segregating with the disease in two families, and uncommon insertions in GOLM1 and DISC1 were co-segregating in three families each. Enrichment analysis showed that identified structural variants affected synaptic vesicle endocytosis, cell leading-edge organization, and signaling for neurite outgrowth. The results further support the involvement of the regulation of neurotransmission, neuronal migration, and sound-sensing in GTS.
Subject(s)
Pedigree , Tourette Syndrome , Humans , Tourette Syndrome/genetics , Male , Female , Genetic Predisposition to Disease , Extracellular Matrix Proteins/genetics , Extracellular Matrix Proteins/metabolism , Adult , Whole Genome SequencingABSTRACT
The challenge of modelling the spatial conformation of chromatin remains an open problem. While multiple data-driven approaches have been proposed, each has limitations. This work introduces two image-driven modelling methods based on the Molecular Dynamics Flexible Fitting (MDFF) approach: the force method and the correlational method. Both methods have already been used successfully in protein modelling. We propose a novel way to employ them for building chromatin models directly from 3D images. This approach is termed image-driven modelling. Additionally, we introduce the initial structure generator, a tool designed to generate optimal starting structures for the proposed algorithms. The methods are versatile and can be applied to various data types, with minor modifications to accommodate new generation imaging techniques.
Subject(s)
Algorithms , Chromatin , Molecular Dynamics Simulation , Chromatin/chemistry , Chromatin/metabolism , Imaging, Three-Dimensional/methods , HumansABSTRACT
Intermolecular interactions of protein-protein complexes play a principal role in the process of discovering new substances used in the diagnosis and treatment of many diseases. Among such complexes of proteins, we have to mention antibodies; they interact with specific antigens of two genera of single-stranded RNA viruses belonging to the family Filoviridae-Ebolavirus and Marburgvirus; both cause rare but fatal viral hemorrhagic fever in Africa, with pandemic potential. In this research, we conduct studies aimed at the design and evaluation of antibodies targeting the filovirus glycoprotein precursor GP-1,2 to develop potential targets for the pan-filovirus easy-to-use rapid diagnostic tests. The in silico research using the available 3D structure of the natural antibody-antigen complex was carried out to determine the stability of individual protein segments in the process of its formation and maintenance. The computed free binding energy of the complex and its decomposition for all amino acids allowed us to define the residues that play an essential role in the structure and indicated the spots where potential antibodies can be improved. Following that, the study involved targeting six epitopes of the filovirus GP1,2 with two polyclonal antibodies (pABs) and 14 monoclonal antibodies (mABs). The evaluation conducted using Enzyme Immunoassays tested 62 different sandwich combinations of monoclonal antibodies (mAbs), identifying 10 combinations that successfully captured the recombinant GP1,2 (rGP). Among these combinations, the sandwich option (3G2G12* - (rGP) - 2D8F11) exhibited the highest propensity for capturing the rGP antigen.
ABSTRACT
HER2-positive breast cancer is one of the most prevalent forms of cancer among women worldwide. Generally, the molecular characteristics of this breast cancer include activation of human epidermal growth factor receptor-2 (HER2) and hormone receptor activation. HER2-positive is associated with a higher death rate, which led to the development of a monoclonal antibody called trastuzumab, specifically targeting HER2. The success rate of HER2-positive breast cancer treatment has been increased; however, drug resistance remains a challenge. This fact motivated us to explore the underlying molecular mechanisms of trastuzumab resistance. For this purpose, a two-fold approach was taken by considering well-known breast cancer cell lines SKBR3 and BT474. In the first fold, trastuzumab treatment doses were optimized separately for both cell lines. This was done based on the proliferation rate of cells in response to a wide variety of medication dosages. Thereafter, each cell line was cultivated with a steady dosage of herceptin for several months. During this period, six time points were selected for further in vitro analysis, ranging from the untreated cell line at the beginning to a fully resistant cell line at the end of the experiment. In the second fold, nucleic acids were extracted for further high throughput-based microarray experiments of gene and microRNA expression. Such expression data were further analyzed in order to infer the molecular mechanisms involved in the underlying development of trastuzumab resistance. In the list of differentially expressed genes and miRNAs, multiple genes (e.g., BIRC5, E2F1, TFRC, and USP1) and miRNAs (e.g., hsa miR 574 3p, hsa miR 4530, and hsa miR 197 3p) responsible for trastuzumab resistance were found. Downstream analysis showed that TFRC, E2F1, and USP1 were also targeted by hsa-miR-8485. Moreover, it indicated that miR-4701-5p was highly expressed as compared to TFRC in the SKBR3 cell line. These results unveil key genes and miRNAs as molecular regulators for trastuzumab resistance.
ABSTRACT
Genomic data analysis has witnessed a surge in complexity and volume, primarily driven by the advent of high-throughput technologies. In particular, studying chromatin loops and structures has become pivotal in understanding gene regulation and genome organization. This systematic investigation explores the realm of specialized bioinformatics pipelines designed specifically for the analysis of chromatin loops and structures. Our investigation incorporates two protein (CTCF and Cohesin) factor-specific loop interaction datasets from six distinct pipelines, amassing a comprehensive collection of 36 diverse datasets. Through a meticulous review of existing literature, we offer a holistic perspective on the methodologies, tools and algorithms underpinning the analysis of this multifaceted genomic feature. We illuminate the vast array of approaches deployed, encompassing pivotal aspects such as data preparation pipeline, preprocessing, statistical features and modelling techniques. Beyond this, we rigorously assess the strengths and limitations inherent in these bioinformatics pipelines, shedding light on the interplay between data quality and the performance of deep learning models, ultimately advancing our comprehension of genomic intricacies.
ABSTRACT
The connection between the patterns observed in 3C-type experiments and the modeling of polymers remains unresolved. This paper presents a simulation pipeline that generates thermodynamic ensembles of 3D structures for topologically associated domain (TAD) regions by loop extrusion model (LEM). The simulations consist of two main components: a stochastic simulation phase, employing a Monte Carlo approach to simulate the binding positions of cohesins, and a dynamical simulation phase, utilizing these cohesins' positions to create 3D structures. In this approach, the system's total energy is the combined result of the Monte Carlo energy and the molecular simulation energy, which are iteratively updated. The structural maintenance of chromosomes (SMC) protein complexes are represented as loop extruders, while the CCCTC-binding factor (CTCF) locations on DNA sequence are modeled as energy minima on the Monte Carlo energy landscape. Finally, the spatial distances between DNA segments from ChIA-PET experiments are compared with the computer simulations, and we observe significant Pearson correlations between predictions and the real data. LoopSage model offers a fresh perspective on chromatin loop dynamics, allowing us to observe phase transition between sparse and condensed states in chromatin.
Subject(s)
Chromatin , Chromosomal Proteins, Non-Histone , Chromatin/genetics , Chromosomal Proteins, Non-Histone/genetics , Chromosomal Proteins, Non-Histone/metabolism , Cell Cycle Proteins/genetics , Cell Cycle Proteins/metabolism , Chromosomes/metabolism , CohesinsABSTRACT
Production of morphologically and physiologically variable seeds is an important strategy that helps plants to survive in unpredictable natural conditions. However, the model plant Arabidopsis thaliana and most agronomically essential crops produce visually homogenous seeds. Using automated phenotype analysis, we observed that small seeds in Arabidopsis tend to have higher primary and secondary dormancy levels than large seeds. Transcriptomic analysis revealed distinct gene expression profiles between large and small seeds. Large seeds have higher expression of translation-related genes implicated in germination competence. By contrast, small seeds have elevated expression of many positive regulators of dormancy, including a key regulator of this process, the DOG1 gene. Differences in DOG1 expression are associated with differential production of its alternative cleavage and polyadenylation isoforms; in small seeds, the proximal poly(A) site is selected, resulting in a short mRNA isoform. Furthermore, single-seed RNA sequencing analysis demonstrated that large seeds resemble DOG1 knockout mutant seeds. Finally, on the single-seed level, expression of genes affected by seed size is correlated with expression of genes that position seeds on the path toward germination. Our results demonstrate an unexpected link between seed size and dormancy phenotypes in a species that produces highly homogenous seed pools, suggesting that the correlation between seed morphology and physiology is more widespread than initially assumed.
Subject(s)
Arabidopsis Proteins , Arabidopsis , Arabidopsis/metabolism , Arabidopsis Proteins/genetics , Arabidopsis Proteins/metabolism , Plant Dormancy/genetics , Germination/genetics , Seeds/geneticsABSTRACT
Introduction: Malignant mesothelioma is a rare and aggressive form of cancer. Despite improvements in cancer treatment, there are still no curative treatment modalities for advanced stage of the malignancy. The aim of this study was to evaluate the anti-tumor efficacy of a novel combinatorial therapy combining AdV5/3-D24-ICOSL-CD40L, an oncolytic vector, with an anti-PD-1 monoclonal antibody. Methods: The efficacy of the vector was confirmed in vitro in three mesothelioma cell lines - H226, Mero-82, and MSTO-211H, and subsequently the antineoplastic properties in combination with anti-PD-1 was evaluated in xenograft H226 mesothelioma BALB/c and humanized NSG mouse models. Results and discussion: Anticancer efficacy was attributed to reduced tumour volume and increased infiltration of tumour infiltrating lymphocytes, including activated cytotoxic T-cells (GrB+CD8+). Additionally, a correlation between tumour volume and activated CD8+ tumour infiltrating lymphocytes was observed. These findings were confirmed by transcriptomic analysis carried out on resected human tumour tissue, which also revealed upregulation of CD83 and CRTAM, as well as several chemokines (CXCL3, CXCL9, CXCL11) in the tumour microenvironment. Furthermore, according to observations, the combinatorial therapy had the strongest effect on reducing mesothelin and MUC16 levels. Gene set enrichment analysis suggested that the combinatorial therapy induced changes to the expression of genes belonging to the "adaptive immune response" gene ontology category. Combinatorial therapy with oncolytic adenovirus with checkpoint inhibitors may improve anticancer efficacy and survival by targeted cancer cell destruction and triggering of immunogenic cell death. Obtained results support further assessment of the AdV5/3-D24-ICOSL-CD40L in combination with checkpoint inhibitors as a novel therapeutic perspective for mesothelioma treatment.
ABSTRACT
Emergency granulopoiesis is the enhanced and accelerated production of granulocytes that occurs during acute infection. The contribution of hematopoietic stem cells (HSCs) to this process was reported; however, how HSCs participate in emergency granulopoiesis remains elusive. Here, using a mouse model of emergency granulopoiesis we observe transcriptional changes in HSCs as early as 4 h after lipopolysaccharide (LPS) administration. We observe that the HSC identity is changed towards a myeloid-biased HSC and show that CD201 is enriched in lymphoid-biased HSCs. While CD201 expression under steady-state conditions reveals a lymphoid bias, under emergency granulopoiesis loss of CD201 marks the lymphoid-to-myeloid transcriptional switch. Mechanistically, we determine that lymphoid-biased CD201+ HSCs act as a first response during emergency granulopoiesis due to direct sensing of LPS by TLR4 and downstream activation of NF-κΒ signaling. The myeloid-biased CD201- HSC population responds indirectly during an acute infection by sensing G-CSF, increasing STAT3 phosphorylation, and upregulating LAP/LAP* C/EBPß isoforms. In conclusion, HSC subpopulations support early phases of emergency granulopoiesis due to their transcriptional rewiring from a lymphoid-biased to myeloid-biased population and thus establishing alternative paths to supply elevated numbers of granulocytes.
Subject(s)
Hematopoietic Stem Cells , Lipopolysaccharides , Lipopolysaccharides/metabolism , Hematopoiesis , Granulocytes/metabolismABSTRACT
MOTIVATION: Investigating the 3D structure of chromatin provides new insights into transcriptional regulation. With the evolution of 3C next-generation sequencing methods like ChiA-PET and Hi-C, the surge in data volume has highlighted the need for more efficient chromatin spatial modelling algorithms. This study introduces the cudaMMC method, based on the Simulated Annealing Monte Carlo approach and enhanced by GPU-accelerated computing, to efficiently generate ensembles of chromatin 3D structures. RESULTS: The cudaMMC calculations demonstrate significantly faster performance with better stability compared to our previous method on the same workstation. cudaMMC also substantially reduces the computation time required for generating ensembles of large chromatin models, making it an invaluable tool for studying chromatin spatial conformation. AVAILABILITY AND IMPLEMENTATION: Open-source software and manual and sample data are freely available on https://github.com/SFGLab/cudaMMC.
Subject(s)
Chromatin , Software , Chromosomes , Algorithms , Molecular Conformation , Monte Carlo MethodABSTRACT
CCCTC-binding factor (CTCF) is a transcription regulator with a complex role in gene regulation. The recognition and effects of CTCF on DNA sequences, chromosome barriers, and enhancer blocking are not well understood. Existing computational tools struggle to assess the regulatory potential of CTCF-binding sites and their impact on chromatin loop formation. Here we have developed a deep-learning model, DeepAnchor, to accurately characterize CTCF binding using high-resolution genomic/epigenomic features. This has revealed distinct chromatin and sequence patterns for CTCF-mediated insulation and looping. An optimized implementation of a previous loop model based on DeepAnchor score excels in predicting CTCF-anchored loops. We have established a compendium of CTCF-anchored loops across 52 human tissue/cell types, and this suggests that genomic disruption of these loops could be a general mechanism of disease pathogenesis. These computational models and resources can help investigate how CTCF-mediated cis-regulatory elements shape context-specific gene regulation in cell development and disease progression.
ABSTRACT
Recent advances in genomic and imaging techniques have revealed the complex manner of organizing billions of base pairs of DNA necessary for maintaining their functionality and ensuring the proper expression of genetic information. The SMC proteins and cohesin complex primarily contribute to forming higher-order chromatin structures, such as chromosomal territories, compartments, topologically associating domains (TADs) and chromatin loops anchored by CCCTC-binding factor (CTCF) protein or other genome organizers. Cohesin plays a fundamental role in chromatin organization, gene expression and regulation. This review aims to describe the current understanding of the dynamic nature of the cohesin-DNA complex and its dependence on cohesin for genome maintenance. We discuss the current 3C technique and numerous bioinformatics pipelines used to comprehend structural genomics and epigenetics focusing on the analysis of Cohesin-centred interactions. We also incorporate our present comprehension of Loop Extrusion (LE) and insights from stochastic modelling.
Subject(s)
Chromosomal Proteins, Non-Histone , Genome, Human , Humans , Chromosomal Proteins, Non-Histone/genetics , Cell Cycle Proteins/genetics , Chromatin/genetics , CohesinsABSTRACT
MOTIVATION: The advent of T-cell receptor (TCR) sequencing experiments allowed for a significant increase in the amount of peptide:TCR binding data available and a number of machine-learning models appeared in recent years. High-quality prediction models for a fixed epitope sequence are feasible, provided enough known binding TCR sequences are available. However, their performance drops significantly for previously unseen peptides. RESULTS: We prepare the dataset of known peptide:TCR binders and augment it with negative decoys created using healthy donors' T-cell repertoires. We employ deep learning methods commonly applied in Natural Language Processing to train part a peptide:TCR binding model with a degree of cross-peptide generalization (0.69 AUROC). We demonstrate that BERTrand outperforms the published methods when evaluated on peptide sequences not used during model training. AVAILABILITY AND IMPLEMENTATION: The datasets and the code for model training are available at https://github.com/SFGLab/bertrand.
Subject(s)
Peptides , Receptors, Antigen, T-Cell , Peptides/metabolism , Receptors, Antigen, T-Cell/metabolism , Protein Binding , Epitopes , Machine LearningABSTRACT
There have been multiple attempts to predict the expression of the genes based on the sequence, epigenetics, and various other factors. To improve those predictions, we have decided to investigate adding protein-specific 3D interactions that play a significant role in the condensation of the chromatin structure in the cell nucleus. To achieve this, we have used the architecture of one of the state-of-the-art algorithms, ExPecto, and investigated the changes in the model metrics upon adding the spatially relevant data. We have used ChIA-PET interactions that are mediated by cohesin (24 cell lines), CTCF (4 cell lines), and RNAPOL2 (4 cell lines). As the output of the study, we have developed the Spatial Gene Expression (SpEx) algorithm that shows statistically significant improvements in most cell lines. We have compared ourselves to the baseline ExPecto model, which obtained a 0.82 Spearman's rank correlation coefficient (SCC) score, and 0.85, which is reported by newer Enformer were able to obtain the average correlation score of 0.83. However, in some cases (e.g. RNAPOL2 on GM12878), our improvement reached 0.04, and in some cases (e.g. RNAPOL2 on H1), we reached an SCC of 0.86.
Subject(s)
Chromatin , Chromosomes , Chromatin/genetics , CCCTC-Binding Factor/genetics , Chromosomes/metabolism , Cell Nucleus/metabolism , Cell Cycle Proteins/metabolism , Gene ExpressionABSTRACT
In the current update, we added a feature for analysing changes in spatial distances between promoters and enhancers in chromatin 3D model ensembles. We updated our datasets by the novel in situ CTCF and RNAPII ChIA-PET chromatin loops obtained from the GM12878 cell line mapped to the GRCh38 genome assembly and extended the 1000 Genomes SVs dataset. To handle the new datasets, we applied GPU acceleration for the modelling engine, which gives a speed-up of 30× versus the previous versions. To improve visualisation and data analysis, we embedded the IGV tool for viewing ChIA-PET arcs with additional genes and SVs annotations. For 3D model visualisation, we added a new viewer: NGL, where we provided colouring by gene and enhancer location. The models are downloadable in mmcif and xyz format. The web server is hosted and performs calculations on DGX A100 GPU servers that provide optimal performance with multitasking. 3D-GNOME 3.0 web server provides unique insights into the topological mechanism of human variations at the population scale with high speed-up and is freely available at https://3dgnome.mini.pw.edu.pl/.
Subject(s)
Chromatin , Data Visualization , Genome, Human , Genomics , Humans , Chromatin/chemistry , Enhancer Elements, Genetic , Genome, Human/genetics , Promoter Regions, Genetic , Genomics/instrumentation , Genomics/methods , Molecular Conformation , Computer Simulation , InternetABSTRACT
There have been multiple attempts to predict the expression of the genes based on the sequence, epigenetics, and various other factors. To improve those predictions, we have decided to investigate adding protein-specific 3D interactions that play a major role in the compensation of the chromatin structure in the cell nucleus. To achieve this, we have used the architecture of one of the state-of-the-art algorithms, ExPecto (J. Zhou et al., 2018), and investigated the changes in the model metrics upon adding the spatially relevant data. We have used ChIA-PET interactions that are mediated by cohesin (24 cell lines), CTCF (4 cell lines), and RNAPOL2 (4 cell lines). As the output of the study, we have developed the Spatial Gene Expression (SpEx) algorithm that shows statistically significant improvements in most cell lines.
ABSTRACT
Eukaryotic genomes are pervasively transcribed by RNA polymerase II. Yet, the molecular and biological implications of such a phenomenon are still largely puzzling. Here, we describe noncoding RNA transcription upstream of the Arabidopsis thaliana DOG1 gene, which governs salt stress responses and is a key regulator of seed dormancy. We find that expression of the DOG1 gene is induced by salt stress, thereby causing a delay in seed germination. We uncover extensive transcriptional activity on the promoter of the DOG1 gene, which produces a variety of lncRNAs. These lncRNAs, named PUPPIES, are co-directionally transcribed and extend into the DOG1 coding region. We show that PUPPIES RNAs respond to salt stress and boost DOG1 expression, resulting in delayed germination. This positive role of pervasive PUPPIES transcription on DOG1 gene expression is associated with augmented pausing of RNA polymerase II, slower transcription and higher transcriptional burst size. These findings highlight the positive role of upstream co-directional transcription in controlling transcriptional dynamics of downstream genes.
Subject(s)
Arabidopsis Proteins , Arabidopsis , RNA, Long Noncoding , Animals , Arabidopsis/metabolism , Arabidopsis Proteins/metabolism , Gene Expression Regulation, Plant , Germination/genetics , Mutation , RNA Polymerase II/genetics , RNA Polymerase II/metabolism , RNA, Long Noncoding/metabolismABSTRACT
New prophylactic vaccine platforms are imperative to combat respiratory infections. The efficacy of T and B memory cell-mediated protection, generated through the adenoviral vector, was tested to assess the effectiveness of the new adenoviral-based platforms for infectious diseases. A combination of adenovirus AdV1 (adjuvant), armed with costimulatory ligands (ICOSL and CD40L), and rRBD (antigen: recombinant nonglycosylated spike protein rRBD) was used to promote the differentiation of T and B lymphocytes. Adenovirus AdV2 (adjuvant), without ligands, in combination with rRBD, served as a control. In vitro T-cell responses to the AdV1+rRBD combination revealed that CD8+ platform-specific T-cells increased (37.2 ± 0.7% vs. 23.1 ± 2.1%), and T-cells acted against SARS-CoV-2 via CD8+TEMRA (50.0 ± 1.3% vs. 36.0 ± 3.2%). Memory B cells were induced after treatment with either AdV1+rRBD (84.1 ± 0.8% vs. 82.3 ± 0.4%) or rRBD (94.6 ± 0.3% vs. 82.3 ± 0.4%). Class-switching from IgM and IgD to isotype IgG following induction with rRBD+Ab was observed. RNA-seq profiling identified gene expression patterns related to T helper cell differentiation that protect against pathogens. The analysis determined signaling pathways controlling the induction of protective immunity, including the MAPK cascade, adipocytokine, cAMP, TNF, and Toll-like receptor signaling pathway. The AdV1+rRBD formulation induced IL-6, IL-8, and TNF. RNA-seq of the VERO E6 cell line showed differences in the apoptosis gene expression stimulated with the platforms vs. mock. In conclusion, AdV1+rRBD effectively generates T and B memory cell-mediated protection, presenting promising results in producing CD8+ platform-specific T cells and isotype-switched IgG memory B cells. The platform induces protective immunity by controlling the Th1, Th2, and Th17 cell differentiation gene expression patterns. Further studies are required to confirm its effectiveness.