Chromatin Image-driven modelling

The challenge of modeling 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 modeling 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 modeling. We propose a novel way to employ them for building chromatin models directly from 3D images. This approach is termed image-driven modeling. 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.

A systematic analyses of different bioinformatics pipelines for genomic data and its impact on deep learning models for chromatin loop prediction

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.

Unveiling the Molecular Mechanism of Trastuzumab Resistance in SKBR3 and BT474 Cell Lines for HER2 Positive Breast Cancer

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.

LoopSage: An energy-based Monte Carlo approach for the loop extrusion modeling of chromatin

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.

In vitro immune evaluation of adenoviral vector-based platform for infectious diseases

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.

Hematopoietic stem cells undergo a lymphoid to myeloid switch in early stages of emergency granulopoiesis

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/EBPβ 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.

Novel combinatorial therapy of oncolytic adenovirus AdV5/3-D24-ICOSL-CD40L with anti PD-1 exhibits enhanced anti-cancer efficacy through promotion of intratumoral T-cell infiltration and modulation of tumour microenvironment in mesothelioma mouse model

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.

cudaMMC: GPU-enhanced multiscale Monte Carlo chromatin 3D modelling

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.

The dynamic role of cohesin in maintaining human genome architecture

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.

Inferring CTCF-binding patterns and anchored loops across human tissues and cell types

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.

BERTrand-peptide:TCR binding prediction using Bidirectional Encoder Representations from Transformers augmented with random TCR pairing

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.

Enhanced performance of gene expression predictive models with protein-mediated spatial chromatin interactions

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.

3D-GNOME 3.0: a three-dimensional genome modelling engine for analysing changes of promoter-enhancer contacts in the human genome

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/.

Enhanced performance of gene expression predictive models with protein-mediated spatial chromatin interactions

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.

Promoter-pervasive transcription causes RNA polymerase II pausing to boost DOG1 expression in response to salt

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.

Identification of Human miRNA Biomarkers Targeting the SARS-CoV-2 Genome

SARS-CoV-2 poses a great challenge toward mankind, majorly due to its evolution and frequently occurring variants. On the other hand, in human hosts, microRNA (miRNA) plays a vital role in replication and propagation during a viral infection and can control the biological processes. This may be essential for the progression of viral infection. Moreover, human miRNAs can play a therapeutic role in treatment of different viral diseases by binding to the target sites of the virus genome, thereby hindering the essential functioning of the virus. Motivated by this fact, we have hypothesized a new approach in order to identify human miRNAs that can target the mRNA (genome) of SARS-CoV-2 to degrade their protein synthesis. In this regard, the multiple sequence alignment technique Clustal Omega is used to align a complement of 2656 human miRNAs with the SARS-CoV-2 reference genome (mRNA). Thereafter, ranking of these aligned human miRNAs is performed with the help of a new scoring function that takes into account the (a) total number of nucleotide matches between the human miRNA and the SARS-CoV-2 genome, (b) number of consecutive nucleotide matches between the human miRNA and the SARS-CoV-2 genome, (c) number of nucleotide mismatches between the human miRNA and the SARS-CoV-2 genome, and (d) the difference in length before and after alignment of the human miRNA. As a result, from the 2656 ranked miRNAs, the top 20 human miRNAs are reported, which are targeting different coding and non-coding regions of the SARS-CoV-2 genome. Moreover, molecular docking of such human miRNAs with virus mRNA is performed to verify the efficacy of the interactions. Furthermore, 4 miRNAs out of the top 20 miRNAs are identified to have the seed region. In order to inhibit the virus, the key human targets of the seed regions may be targeted. Repurposable drugs like carfilzomib, bortezomib, hydralazine, and paclitaxel are identified for such purpose.

Unveiling the Biomarkers of Cancer and COVID-19 and Their Regulations in Different Organs by Integrating RNA-Seq Expression and Protein-Protein Interactions

Cancer and COVID-19 have killed millions of people worldwide. COVID-19 is even more dangerous to people with comorbidities such as cancer. Thus, it is imperative to identify the key human genes or biomarkers that can be targeted to develop novel prognosis and therapeutic strategies. The transcriptomic data provided by the next-generation sequencing technique makes this identification very convenient. Hence, mRNA (messenger ribonucleic acid) expression data of 2265 cancer and 282 normal patients were considered, while for COVID-19 assessment, 784 and 425 COVID-19 and normal patients were taken, respectively. Initially, volcano plots were used to identify the up- and down-regulated genes for both cancer and COVID-19. Thereafter, protein-protein interaction (PPI) networks were prepared by combining all the up- and down-regulated genes for each of cancer and COVID-19. Subsequently, such networks were analyzed to identify the top 10 genes with the highest degree of connection to provide the biomarkers. Interestingly, these genes were all up-regulated for cancer, while they were down-regulated for COVID-19. This study had also identified common genes between cancer and COVID-19, all of which were up-regulated in both the diseases. This analysis revealed that FN1 was highly up-regulated in different organs for cancer, while EEF2 was dysregulated in most organs affected by COVID-19. Then, functional enrichment analysis was performed to identify significant biological processes. Finally, the drugs for cancer and COVID-19 biomarkers and the common genes between them were identified using the Enrichr online web tool. These drugs include lucanthone, etoposide, and methotrexate, targeting the biomarkers for cancer, while paclitaxel is an important drug for COVID-19.

MCIBox: a toolkit for single-molecule multi-way chromatin interaction visualization and micro-domains identification

The emerging ligation-free three-dimensional (3D) genome mapping technologies can identify multiplex chromatin interactions with single-molecule precision. These technologies not only offer new insight into high-dimensional chromatin organization and gene regulation, but also introduce new challenges in data visualization and analysis. To overcome these challenges, we developed MCIBox, a toolkit for multi-way chromatin interaction (MCI) analysis, including a visualization tool and a platform for identifying micro-domains with clustered single-molecule chromatin complexes. MCIBox is based on various clustering algorithms integrated with dimensionality reduction methods that can display multiplex chromatin interactions at single-molecule level, allowing users to explore chromatin extrusion patterns and super-enhancers regulation modes in transcription, and to identify single-molecule chromatin complexes that are clustered into micro-domains. Furthermore, MCIBox incorporates a two-dimensional kernel density estimation algorithm to identify micro-domains boundaries automatically. These micro-domains were stratified with distinctive signatures of transcription activity and contained different cell-cycle-associated genes. Taken together, MCIBox represents an invaluable tool for the study of multiple chromatin interactions and inaugurates a previously unappreciated view of 3D genome structure.

The 3D enhancer network of the developing T cell genome is shaped by SATB1

Mechanisms of tissue-specific gene expression regulation via 3D genome organization are poorly understood. Here we uncover the regulatory chromatin network of developing T cells and identify SATB1, a tissue-specific genome organizer, enriched at the anchors of promoter-enhancer loops. We have generated a T-cell specific Satb1 conditional knockout mouse which allows us to infer the molecular mechanisms responsible for the deregulation of its immune system. H3K27ac HiChIP and Hi-C experiments indicate that SATB1-dependent promoter-enhancer loops regulate expression of master regulator genes (such as Bcl6), the T cell receptor locus and adhesion molecule genes, collectively being critical for cell lineage specification and immune system homeostasis. SATB1-dependent regulatory chromatin loops represent a more refined layer of genome organization built upon a high-order scaffold provided by CTCF and other factors. Overall, our findings unravel the function of a tissue-specific factor that controls transcription programs, via spatial chromatin arrangements complementary to the chromatin structure imposed by ubiquitously expressed genome organizers.

ConsensuSV-from the whole-genome sequencing data to the complete variant list

SUMMARY

The detection of the structural variants (SVs) using Illumina sequencing of human DNA is not an easy task. Multiple approaches have been proposed; however, all the methods have their limitations. In this article, we present ConsensuSV pipeline that aids the research in complex variant detection. By using consensus meta-approach, eight independent SV callers are being used to identify a uniform set of high-quality SVs. The pipeline works using raw sequencing data and performs all the necessary steps automatically, significantly reducing the researchers' time required for processing the data. The output files contain SVs, single nucleotide polymorphisms and Indels. The pipeline uses luigi framework, allowing the software to be run efficiently and parallelly using the high-performance computing infrastructure. We strongly believe that the software is useful to the scientific community interested in the germline variant detection.

AVAILABILITY AND IMPLEMENTATION

https://github.com/SFGLab/ConsensuSV-pipeline.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

PFP-GO: Integrating protein sequence, domain and protein-protein interaction information for protein function prediction using ranked GO terms

Protein function prediction is gradually emerging as an essential field in biological and computational studies. Though the latter has clinched a significant footprint, it has been observed that the application of computational information gathered from multiple sources has more significant influence than the one derived from a single source. Considering this fact, a methodology, PFP-GO, is proposed where heterogeneous sources like Protein Sequence, Protein Domain, and Protein-Protein Interaction Network have been processed separately for ranking each individual functional GO term. Based on this ranking, GO terms are propagated to the target proteins. While Protein sequence enriches the sequence-based information, Protein Domain and Protein-Protein Interaction Networks embed structural/functional and topological based information, respectively, during the phase of GO ranking. Performance analysis of PFP-GO is also based on Precision, Recall, and F-Score. The same was found to perform reasonably better when compared to the other existing state-of-art. PFP-GO has achieved an overall Precision, Recall, and F-Score of 0.67, 0.58, and 0.62, respectively. Furthermore, we check some of the top-ranked GO terms predicted by PFP-GO through multilayer network propagation that affect the 3D structure of the genome. The complete source code of PFP-GO is freely available at https://sites.google.com/view/pfp-go/.

Online Predictor Using Machine Learning to Predict Novel Coronavirus and Other Pathogenic Viruses

The problem of virus classification is always a subject of concern for virology or epidemiology over the decades. In this regard, a machine learning technique can be used to predict the novel coronavirus by considering its sequence. Thus, we are proposing a machine learning-based novel coronavirus prediction technique, called COVID-Predictor, where 1000 sequences of SARS-CoV-1, MERS-CoV, SARS-CoV-2, and other viruses are used to train a Naive Bayes classifier so that it can predict any unknown sequences of these viruses. The model has been validated using 10-fold cross-validation in comparison with other machine learning techniques. The results show the superiority of our predictor by achieving an average 99.7% accuracy on an unseen validation set of viruses. The same pre-trained model has been used to design a web-based application where sequences of unknown viruses can be uploaded to predict the novel coronavirus.

Multi-scale phase separation by explosive percolation with single-chromatin loop resolution

The 2 m-long human DNA is tightly intertwined into the cell nucleus of the size of 10 μm. The DNA packing is explained by folding of chromatin fiber. This folding leads to the formation of such hierarchical structures as: chromosomal territories, compartments; densely-packed genomic regions known as Topologically Associating Domains (TADs), or Chromatin Contact Domains (CCDs), and loops. We propose models of dynamical human genome folding into hierarchical components in human lymphoblastoid, stem cell, and fibroblast cell lines. Our models are based on explosive percolation theory. The chromosomes are modeled as graphs where CTCF chromatin loops are represented as edges. The folding trajectory is simulated by gradually introducing loops to the graph following various edge addition strategies that are based on topological network properties, chromatin loop frequencies, compartmentalization, or epigenomic features. Finally, we propose the genome folding model - a biophysical pseudo-time process guided by a single scalar order parameter. The parameter is calculated by Linear Discriminant Analysis of chromatin features. We also include dynamics of loop formation by using Loop Extrusion Model (LEM) while adding them to the system. The chromatin phase separation, where fiber folds in 3D space into topological domains and compartments, is observed when the critical number of contacts is reached. We also observe that at least 80% of the loops are needed for chromatin fiber to condense in 3D space, and this is constant through various cell lines. Overall, our in-silico model integrates the high-throughput 3D genome interaction experimental data with the novel theoretical concept of phase separation, which allows us to model event-based time dynamics of chromatin loop formation and folding trajectories.

Drug repurposing for identification of potential spike inhibitors for SARS-CoV-2 using molecular docking and molecular dynamics simulations

For the last two years, the COVID-19 pandemic has continued to bring consternation on most of the world. According to recent WHO estimates, there have been more than 5.6 million deaths worldwide. The virus continues to evolve all over the world, thus requiring both vigilance and the necessity to find and develop a variety of therapeutic treatments, including the identification of specific antiviral drugs. Multiple studies have confirmed that SARS-CoV-2 utilizes its membrane-bound spike protein to recognize human angiotensin-converting enzyme 2 (ACE2). Thus, preventing spike-ACE2 interactions is a potentially viable strategy for COVID-19 treatment as it would block the virus from binding and entering into a host cell. This work aims to identify potential drugs using an in silico approach. Molecular docking was carried out on both approved drugs and substances previously tested in vivo. This step was followed by a more detailed analysis of selected ligands by molecular dynamics simulations to identify the best molecules that thwart the ability of the virus to interact with the ACE2 receptor. Because the SARS-CoV-2 virus evolves rapidly due to a plethora of immunocompromised hosts, the compounds were tested against five different known lineages. As a result, we could identify substances that work well on individual lineages and those showing broader efficacy. The most promising candidates among the currently used drugs were zafirlukast and simeprevir with an average binding affinity of -22 kcal/mol for spike proteins originating from various lineages. The first compound is a leukotriene receptor antagonist that is used to treat asthma, while the latter is a protease inhibitor used for hepatitis C treatment. From among the in vivo tested substances that concurrently exhibit promising free energy of binding and ADME parameters (indicating a possible oral administration) we selected the compound BDBM50136234. In conclusion, these molecules are worth exploring further by in vitro and in vivo studies against SARS-CoV-2.

Single-Hit Inactivation Drove Tumor Suppressor Genes Out of the X Chromosome during Evolution

Cancer-related genes are under intense evolutionary pressure. In this study, we conjecture that X-linked tumor suppressor genes (TSG) are not protected by the Knudson's two-hit mechanism and are therefore subject to negative selection. Accordingly, nearly all mammalian species exhibited lower TSG-to-noncancer gene ratios on their X chromosomes compared with nonmammalian species. Synteny analysis revealed that mammalian X-linked TSGs were depleted shortly after the emergence of the XY sex-determination system. A phylogeny-based model unveiled a higher X chromosome-to-autosome relocation flux for human TSGs. This was verified in other mammals by assessing the concordance/discordance of chromosomal locations of mammalian TSGs and their orthologs in Xenopus tropicalis. In humans, X-linked TSGs are younger or larger in size. Consistently, pan-cancer analysis revealed more frequent nonsynonymous somatic mutations of X-linked TSGs. These findings suggest that relocation of TSGs out of the X chromosome could confer a survival advantage by facilitating evasion of single-hit inactivation.

SIGNIFICANCE

This work unveils extensive trafficking of TSGs from the X chromosome to autosomes during evolution, thus identifying X-linked TSGs as a genetic Achilles' heel in tumor suppression.

NanoForms: an integrated server for processing, analysis and assembly of raw sequencing data of microbial genomes, from Oxford Nanopore technology

Background

Next Generation Sequencing (NGS) techniques dominate today's landscape of genetics and genomics research. Though Illumina still dominates worldwide sequencing, Oxford Nanopore is one of the leading technologies currently being used by biologists, medics and geneticists across various applications. Oxford Nanopore is automated and relatively simple for conducting experiments, but generates gigabytes of raw data, to be processed by often ambiguous set of alternative bioinformatics command-line tools, and genomics frameworks which require a knowledge of bioinformatics to run.

Results

We established an inter-collegiate collaboration across experimentalists and bioinformaticians in order to provide a novel bioinformatics tool, free for academics. This tool allows people without extensive bioinformatics knowledge to simply process their raw genome sequencing data. Currently, due to ICT resources' maintenance reasons, our server is only capable of handling small genomes (up to 15 Mb). In this paper, we introduce our tool, NanoForms: an intuitive and integrated web server for the processing and analysis of raw prokaryotic genome data, coming from Oxford Nanopore. NanoForms is freely available for academics at the following locations: http://nanoforms.tech (webserver) and https://github.com/czmilanna/nanoforms (GitHub source repository).

Activation-induced chromatin reorganization in neurons depends on HDAC1 activity

Spatial chromatin organization is crucial for transcriptional regulation and might be particularly important in neurons since they dramatically change their transcriptome in response to external stimuli. We show that stimulation of neurons causes condensation of large chromatin domains. This phenomenon can be observed in vitro in cultured rat hippocampal neurons as well as in vivo in the amygdala and hippocampal neurons. Activity-induced chromatin condensation is an active, rapid, energy-dependent, and reversible process. It involves calcium-dependent pathways but is independent of active transcription. It is accompanied by the redistribution of posttranslational histone modifications and rearrangements in the spatial organization of chromosome territories. Moreover, it leads to the reorganization of nuclear speckles and active domains located in their proximity. Finally, we find that the histone deacetylase HDAC1 is the key regulator of this process. Our results suggest that HDAC1-dependent chromatin reorganization constitutes an important level of transcriptional regulation in neurons.

PRDX-1 Supports the Survival and Antitumor Activity of Primary and CAR-Modified NK Cells under Oxidative Stress

Oxidative stress, caused by the imbalance between reactive species generation and the dysfunctional capacity of antioxidant defenses, is one of the characteristic features of cancer. Here, we quantified hydrogen peroxide in the tumor microenvironment (TME) and demonstrated that hydrogen peroxide concentrations are elevated in tumor interstitial fluid isolated from murine breast cancers in vivo, when compared with blood or normal subcutaneous fluid. Therefore, we investigated the effects of increased hydrogen peroxide concentration on immune cell functions. NK cells were more susceptible to hydrogen peroxide than T cells or B cells, and by comparing T, B, and NK cells' sensitivities to redox stress and their antioxidant capacities, we identified peroxiredoxin-1 (PRDX1) as a lacking element of NK cells' antioxidative defense. We observed that priming with IL15 protected NK cells' functions in the presence of high hydrogen peroxide and simultaneously upregulated PRDX1 expression. However, the effect of IL15 on PRDX1 expression was transient and strictly dependent on the presence of the cytokine. Therefore, we genetically modified NK cells to stably overexpress PRDX1, which led to increased survival and NK cell activity in redox stress conditions. Finally, we generated PD-L1-CAR NK cells overexpressing PRDX1 that displayed potent antitumor activity against breast cancer cells under oxidative stress. These results demonstrate that hydrogen peroxide, at concentrations detected in the TME, suppresses NK cell function and that genetic modification strategies can improve CAR NK cells' resistance and potency against solid tumors.

JUPPI: A Multi-Level Feature Based Method for PPI Prediction and a Refined Strategy for Performance Assessment

Over the years, several methods have been proposed for the computational PPI prediction with different performance evaluation strategies. While attempting to benchmark performance scores, most of these methods often suffer with ill-treated cross-validation strategies, adhoc selection of positive/negative samples etc. To address these issues, in our proposed multi-level feature based PPI prediction approach (JUPPI), using sequence, domain and GO information as features, a refined evaluation strategy has been introduced. During the evaluation process, we first extract high quality negative data using three-stage filtering, and then introduce a pair-input based cross validation strategy with three difficulty levels for test-set predictions. Our proposed evaluation strategy reduces the component-level overlapping issue in test sets. Performance of JUPPI is compared with those of the state-of-the-art approaches in this domain and tested on six independent PPI datasets. In almost all the datasets, JUPPI outperforms the state-of-the-art not only at human proteome level for PPI prediction, but also for prediction of interactors for intrinsic disordered human proteins. https://figshare.com/projects/JUPPI_A_Multi-level_Feature_Based_Method_for_PPI_Prediction_and_a_Refined_Strategy_for_Performance_Assessment/81656 JUPPI tool and the developed datasets (JUPPId) are available in public domain for academic use along with supplementary materials, which can be found on the Computer Society Digital Library at http://doi.ieeecomputersociety.org/10.1109/TCBB.2020.3004970.

Potent but transient immunosuppression of T-cells is a general feature of CD71+ erythroid cells

CD71+ erythroid cells (CECs) have been recently recognized in both neonates and cancer patients as potent immunoregulatory cells. Here, we show that in mice early-stage CECs expand in anemia, have high levels of arginase 2 (ARG2) and reactive oxygen species (ROS). In the spleens of anemic mice, CECs expansion-induced L-arginine depletion suppresses T-cell responses. In humans with anemia, CECs expand and express ARG1 and ARG2 that suppress T-cells IFN-γ production. Moreover, bone marrow CECs from healthy human donors suppress T-cells proliferation. CECs differentiated from peripheral blood mononuclear cells potently suppress T-cell activation, proliferation, and IFN-γ production in an ARG- and ROS-dependent manner. These effects are the most prominent for early-stage CECs (CD71highCD235adim cells). The suppressive properties disappear during erythroid differentiation as more differentiated CECs and mature erythrocytes lack significant immunoregulatory properties. Our studies provide a novel insight into the role of CECs in the immune response regulation.

Genomic Marks Associated with Chromatin Compartments in the CTCF, RNAPII Loop and Genomic Windows

The nature of genome organization into two basic structural compartments is as yet undiscovered. However, it has been indicated to be a mechanism of gene expression regulation. Using the classification approach, we ranked genomic marks that hint at compartmentalization. We considered a broad range of marks, including GC content, histone modifications, DNA binding proteins, open chromatin, transcription and genome regulatory segmentation in GM12878 cells. Genomic marks were defined over CTCF or RNAPII loops, which are basic elements of genome 3D structure, and over 100 kb genomic windows. Experiments were carried out to empirically assess the whole set of features, as well as the individual features in classification of loops/windows, into compartment A or B. Using Monte Carlo Feature Selection and Analysis of Variance, we constructed a ranking of feature importance for classification. The best simple indicator of compartmentalization is DNase-seq open chromatin measurement for CTCF loops, H3K4me1 for RNAPII loops and H3K79me2 for genomic windows. Among DNA binding proteins, this is RUNX3 transcription factor for loops and RNAPII for genomic windows. Chromatin state prediction methods that indicate active elements like promoters, enhancers or heterochromatin enhance the prediction of loop segregation into compartments. However, H3K9me3, H4K20me1, H3K27me3 histone modifications and GC content poorly indicate compartments.

From DNA human sequence to the chromatin higher order organisation and its biological meaning: Using biomolecular interaction networks to understand the influence of structural variation on spatial genome organisation and its functional effect

The three-dimensional structure of the human genome has been proven to have a significant functional impact on gene expression. The high-order spatial chromatin is organised first by looping mediated by multiple protein factors, and then it is further formed into larger structures of topologically associated domains (TADs) or chromatin contact domains (CCDs), followed by A/B compartments and finally the chromosomal territories (CTs). The genetic variation observed in human population influences the multi-scale structures, posing a question regarding the functional impact of structural variants reflected by the variability of the genes expression patterns. The current methods of evaluating the functional effect include eQTLs analysis which uses statistical testing of influence of variants on spatially close genes. Rarely, non-coding DNA sequence changes are evaluated by their impact on the biomolecular interaction network (BIN) reflecting the cellular interactome that can be analysed by the classical graph-theoretic algorithms. Therefore, in the second part of the review, we introduce the concept of BIN, i.e. a meta-network model of the complete molecular interactome developed by integrating various biological networks. The BIN meta-network model includes DNA-protein binding by the plethora of protein factors as well as chromatin interactions, therefore allowing connection of genomics with the downstream biomolecular processes present in a cell. As an illustration, we scrutinise the chromatin interactions mediated by the CTCF protein detected in a ChIA-PET experiment in the human lymphoblastoid cell line GM12878. In the corresponding BIN meta-network the DNA spatial proximity is represented as a graph model, combined with the Proteins-Interaction Network (PIN) of human proteome using the Gene Association Network (GAN). Furthermore, we enriched the BIN with the signalling and metabolic pathways and Gene Ontology (GO) terms to assert its functional context. Finally, we mapped the Single Nucleotide Polymorphisms (SNPs) from the GWAS studies and identified the chromatin mutational hot-spots associated with a significant enrichment of SNPs related to autoimmune diseases. Afterwards, we mapped Structural Variants (SVs) from healthy individuals of 1000 Genomes Project and identified an interesting example of the missing protein complex associated with protein Q6GYQ0 due to a deletion on chromosome 14. Such an analysis using the meta-network BIN model is therefore helpful in evaluating the influence of genetic variation on spatial organisation of the genome and its functional effect in a cell.

Disentangling the complexity of low complexity proteins

There are multiple definitions for low complexity regions (LCRs) in protein sequences, with all of them broadly considering LCRs as regions with fewer amino acid types compared to an average composition. Following this view, LCRs can also be defined as regions showing composition bias. In this critical review, we focus on the definition of sequence complexity of LCRs and their connection with structure. We present statistics and methodological approaches that measure low complexity (LC) and related sequence properties. Composition bias is often associated with LC and disorder, but repeats, while compositionally biased, might also induce ordered structures. We illustrate this dichotomy, and more generally the overlaps between different properties related to LCRs, using examples. We argue that statistical measures alone cannot capture all structural aspects of LCRs and recommend the combined usage of a variety of predictive tools and measurements. While the methodologies available to study LCRs are already very advanced, we foresee that a more comprehensive annotation of sequences in the databases will enable the improvement of predictions and a better understanding of the evolution and the connection between structure and function of LCRs. This will require the use of standards for the generation and exchange of data describing all aspects of LCRs.

Short abstract

There are multiple definitions for low complexity regions (LCRs) in protein sequences. In this critical review, we focus on the definition of sequence complexity of LCRs and their connection with structure. We present statistics and methodological approaches that measure low complexity (LC) and related sequence properties. Composition bias is often associated with LC and disorder, but repeats, while compositionally biased, might also induce ordered structures. We illustrate this dichotomy, plus overlaps between different properties related to LCRs, using examples.

Genome-wide analysis of 10664 SARS-CoV-2 genomes to identify virus strains in 73 countries based on single nucleotide polymorphism

Since the onslaught of SARS-CoV-2, the research community has been searching for a vaccine to fight against this virus. However, during this period, the virus has mutated to adapt to the different environmental conditions in the world and made the task of vaccine design more challenging. In this situation, the identification of virus strains is very much timely and important task. We have performed genome-wide analysis of 10664 SARS-CoV-2 genomes of 73 countries to identify and prepare a Single Nucleotide Polymorphism (SNP) dataset of SARS-CoV-2. Thereafter, with the use of this SNP data, the advantage of hierarchical clustering is taken care of in such a way so that Average Linkage and Complete Linkage with Jaccard and Hamming distance functions are applied separately in order to identify the virus strains as clusters present in the SNP data. In this regard, the consensus of both the clustering results are also considered while Silhouette index is used as a cluster validity index to measure the goodness of the clusters as well to determine the number of clusters or virus strains. As a result, we have identified five major clusters or virus strains present worldwide. Apart from quantitative measures, these clusters are also visualized using Visual Assessment of Tendency (VAT) plot. The evolution of these clusters are also shown. Furthermore, top 10 signature SNPs are identified in each cluster and the non-synonymous signature SNPs are visualised in the respective protein structures. Also, the sequence and structural homology-based prediction along with the protein structural stability of these non-synonymous signature SNPs are reported in order to judge the characteristics of the identified clusters. As a consequence, T85I, Q57H and R203M in NSP2, ORF3a and Nucleocapsid respectively are found to be responsible for Cluster 1 as they are damaging and unstable non-synonymous signature SNPs. Similarly, F506L and S507C in Exon are responsible for both Clusters 3 and 4 while Clusters 2 and 5 do not exhibit such behaviour due to the absence of any non-synonymous signature SNPs. In addition to all these, the code, SNP dataset, 10664 labelled SARS-CoV-2 strains and additional results as supplementary are provided through our website for further use.

COVID-DeepPredictor: Recurrent Neural Network to Predict SARS-CoV-2 and Other Pathogenic Viruses

The COVID-19 disease for Novel coronavirus (SARS-CoV-2) has turned out to be a global pandemic. The high transmission rate of this pathogenic virus demands an early prediction and proper identification for the subsequent treatment. However, polymorphic nature of this virus allows it to adapt and sustain in different kinds of environment which makes it difficult to predict. On the other hand, there are other pathogens like SARS-CoV-1, MERS-CoV, Ebola, Dengue, and Influenza as well, so that a predictor is highly required to distinguish them with the use of their genomic information. To mitigate this problem, in this work COVID-DeepPredictor is proposed on the framework of deep learning to identify an unknown sequence of these pathogens. COVID-DeepPredictor uses Long Short Term Memory as Recurrent Neural Network for the underlying prediction with an alignment-free technique. In this regard, k-mer technique is applied to create Bag-of-Descriptors (BoDs) in order to generate Bag-of-Unique-Descriptors (BoUDs) as vocabulary and subsequently embedded representation is prepared for the given virus sequences. This predictor is not only validated for the dataset using K -fold cross-validation but also for unseen test datasets of SARS-CoV-2 sequences and sequences from other viruses as well. To verify the efficacy of COVID-DeepPredictor, it has been compared with other state-of-the-art prediction techniques based on Linear Discriminant Analysis, Random Forests, and Gradient Boosting Method. COVID-DeepPredictor achieves 100% prediction accuracy on validation dataset while on test datasets, the accuracy ranges from 99.51 to 99.94%. It shows superior results over other prediction techniques as well. In addition to this, accuracy and runtime of COVID-DeepPredictor are considered simultaneously to determine the value of k in k-mer, a comparative study among k values in k-mer, Bag-of-Descriptors (BoDs), and Bag-of-Unique-Descriptors (BoUDs) and a comparison between COVID-DeepPredictor and Nucleotide BLAST have also been performed. The code, training, and test datasets used for COVID-DeepPredictor are available at http://www.nitttrkol.ac.in/indrajit/projects/COVID-DeepPredictor/.

Identification of miRNA Biomarkers for Diverse Cancer Types Using Statistical Learning Methods at the Whole-Genome Scale

Genome-wide analysis of miRNA molecules can reveal important information for understanding the biology of cancer. Typically, miRNAs are used as features in statistical learning methods in order to train learning models to predict cancer. This motivates us to propose a method that integrates clustering and classification techniques for diverse cancer types with survival analysis via regression to identify miRNAs that can potentially play a crucial role in the prediction of different types of tumors. Our method has two parts. The first part is a feature selection procedure, called the stochastic covariance evolutionary strategy with forward selection (SCES-FS), which is developed by integrating stochastic neighbor embedding (SNE), the covariance matrix adaptation evolutionary strategy (CMA-ES), and classifiers, with the primary objective of selecting biomarkers. SNE is used to reorder the features by performing an implicit clustering with highly correlated neighboring features. A subset of features is selected heuristically to perform multi-class classification for diverse cancer types. In the second part of our method, the most important features identified in the first part are used to perform survival analysis via Cox regression, primarily to examine the effectiveness of the selected features. For this purpose, we have analyzed next generation sequencing data from The Cancer Genome Atlas in form of miRNA expression of 1,707 samples of 10 different cancer types and 333 normal samples. The SCES-FS method is compared with well-known feature selection methods and it is found to perform better in multi-class classification for the 17 selected miRNAs, achieving an accuracy of 96%. Moreover, the biological significance of the selected miRNAs is demonstrated with the help of network analysis, expression analysis using hierarchical clustering, KEGG pathway analysis, GO enrichment analysis, and protein-protein interaction analysis. Overall, the results indicate that the 17 selected miRNAs are associated with many key cancer regulators, such as MYC, VEGFA, AKT1, CDKN1A, RHOA, and PTEN, through their targets. Therefore the selected miRNAs can be regarded as putative biomarkers for 10 types of cancer.

Aggregated network centrality shows non-random structure of genomic and proteomic networks

Network analysis is a powerful tool for modelling biological systems. We propose a new approach that integrates the genomic interaction data at population level with the proteomic interaction data. In our approach we use chromatin interaction analysis by paired-end tag sequencing (ChIA-PET) data from human genome to construct a set of genomic interaction networks, considering the natural partitioning of chromatin into chromatin contact domains (CCD). The genomic networks are then mapped onto proteomic interactions, to create protein-protein interaction (PPI) subnetworks. Furthermore, the network-based topological properties of these proteomic subnetworks are investigated, namely closeness centrality, betweenness centrality and clustering coefficient. We statistically confirm, that networks identified by our method significantly differ from random networks in these network properties. Additionally, we identify one of the regions, namely chr6:32014923-33217929, as having an above-random concentration of the single nucleotide polymorphisms (SNPs) related to autoimmune diseases. Then we present it in the form of a meta-network, which includes multi-omic data: genomic contact sites (anchors), genes, proteins and SNPs. Using this example we demonstrate, that the created networks provide a valid mapping of genes to SNPs, expanding on the raw SNP dataset used.

Spring Model - Chromatin Modeling Tool Based on OpenMM

Chromatin structure modeling is a rapidly developing field. Parallel to the enormous growth of available experimental data, there is a growing need of building and visualizing 3D structures of nuclei, chromosomes, chromatin domains, and single loops associated with particular gene loci. Here, we present a tool for chromatin domain modeling; it is available as a webservice and standalone python script. Our tool is based on molecular mechanics and utilizes the OpenMM engine for model generation. In this method the user provides contacts between chromatin regions and obtains a 3D structure that satisfies them. Additional parameters allow for the control of fibre stiffness, initial structure adjustments and simulation resolution, there are also options for structure refinement and modeling in a spherical container. The user may provide contacts in the form of bead indices, or insert interactions in genome coordinates sourced from BEDPE files. After the simulation is complete, the user is able to download the structure in the Protein Data Bank (PDB) format for further analysis. We dedicate this tool to all who are interested in chromatin structures. It is suitable for quick visualization of datasets, studying the impact of structural variants (SVs), inspecting the effects of adding and removing particular contacts, and measuring features such as maximum distances between sites (e.g.promoter-enhancer), or local chromatin density.

3D-GNOME 2.0: a three-dimensional genome modeling engine for predicting structural variation-driven alterations of chromatin spatial structure in the human genome

Structural variants (SVs) that alter DNA sequence emerge as a driving force involved in the reorganisation of DNA spatial folding, thus affecting gene transcription. In this work, we describe an improved version of our integrated web service for structural modeling of three-dimensional genome (3D-GNOME), which now incorporates all types of SVs to model changes to the reference 3D conformation of chromatin. In 3D-GNOME 2.0, the default reference 3D genome structure is generated using ChIA-PET data from the GM12878 cell line and SVs data are sourced from the population-scale catalogue of SVs identified by the 1000 Genomes Consortium. However, users may also submit their own structural data to set a customized reference genome structure, and/or a custom input list of SVs. 3D-GNOME 2.0 provides novel tools to inspect, visualize and compare 3D models for regions that differ in terms of their linear genomic sequence. Contact diagrams are displayed to compare the reference 3D structure with the one altered by SVs. In our opinion, 3D-GNOME 2.0 is a unique online tool for modeling and analyzing conformational changes to the human genome induced by SVs across populations. It can be freely accessed at https://3dgnome.cent.uw.edu.pl/.

Free energy-based model of CTCF-mediated chromatin looping in the human genome

In recent years, high-throughput techniques have revealed considerable structural organization of the human genome with diverse regions of the chromatin interacting with each other in the form of loops. Some of these loops are quite complex and may encompass regions comprised of many interacting chain segments around a central locus. Popular techniques for extracting this information are chromatin interaction analysis by paired-end tag sequencing (ChIA-PET) and high-throughput chromosome conformation capture (Hi-C). Here, we introduce a physics-based method to predict the three-dimensional structure of chromatin from population-averaged ChIA-PET data. The approach uses experimentally-validated data from human B-lymphoblastoid cells to generate 2D meta-structures of chromatin using a dynamic programming algorithm that explores the chromatin free energy landscape. By generating both optimal and suboptimal meta-structures we can calculate both the free energy and additionally the relative thermodynamic probability. A 3D structure prediction program with applied restraints then can be used to generate the tertiary structures. The main advantage of this approach for population-averaged experimental data is that it provides a way to distinguish between the principal and the spurious contacts. This study also finds that euchromatin appear to have rather precisely regulated 2D meta-structures compared to heterochromatin. The program source-code is available at https://github.com/plewczynski/looper.

Chromatin topology reorganization and transcription repression by PML-RARα in acute promyeloid leukemia

BACKGROUND

Acute promyeloid leukemia (APL) is characterized by the oncogenic fusion protein PML-RARα, a major etiological agent in APL. However, the molecular mechanisms underlying the role of PML-RARα in leukemogenesis remain largely unknown.

RESULTS

Using an inducible system, we comprehensively analyze the 3D genome organization in myeloid cells and its reorganization after PML-RARα induction and perform additional analyses in patient-derived APL cells with native PML-RARα. We discover that PML-RARα mediates extensive chromatin interactions genome-wide. Globally, it redefines the chromatin topology of the myeloid genome toward a more condensed configuration in APL cells; locally, it intrudes RNAPII-associated interaction domains, interrupts myeloid-specific transcription factors binding at enhancers and super-enhancers, and leads to transcriptional repression of genes critical for myeloid differentiation and maturation.

CONCLUSIONS

Our results not only provide novel topological insights for the roles of PML-RARα in transforming myeloid cells into leukemia cells, but further uncover a topological framework of a molecular mechanism for oncogenic fusion proteins in cancers.

The Mixture of Autoregressive Hidden Markov Models of Morphology for Dentritic Spines During Activation Process

The dendritic spines play a crucial role in learning and memory processes, epileptogenesis, drug addiction, and postinjury recovery. The shape of the dendritic spine is a morphological key to understand learning and memory process. The classification of the dendritic spines is based on their shapes but the major questions are how the shapes changes in time, how the synaptic strength changes, and is there a correlation between shapes and synaptic strength? Because the changes of the classes by dendritic spines during activation are time dependent, the forward-directed autoregressive hidden Markov model (ARHMM) can be used to model these changes. It is also more appropriate to use an ARHMM directed backward in time. Thus, the mixture of forward-directed ARHMM and backward-directed ARHMM (MARHMM) is used to model time-dependent data related to the dendritic spines. In this article, we discuss (1) how to choose the initial probability vector and transition and dependence matrices in ARHMM and MARHMM for modeling the dendritic spines changes and (2) how to estimate these matrices. Many descriptors to classify dendritic spines in two-dimensional or/and three-dimensional (3D) are available. Our results from sensitivity analysis show that the classification that comes from 3D descriptors is closer to the truth, and estimated transition and dependence probability matrices are connected with the molecular mechanism of the dendritic spines activation.

Corrigendum: Identification of Breast Cancer Subtype Specific MicroRNAs Using Survival Analysis to Find Their Role in Transcriptomic Regulation

[This corrects the article DOI: 10.3389/fgene.2019.01047.].

Exploring 3D chromatin contacts in gene regulation: The evolution of approaches for the identification of functional enhancer-promoter interaction

Mechanisms underlying gene regulation are key to understand how multicellular organisms with various cell types develop from the same genetic blueprint. Dynamic interactions between enhancers and genes are revealed to play central roles in controlling gene transcription, but the determinants to link functional enhancer-promoter pairs remain elusive. A major challenge is the lack of reliable approach to detect and verify functional enhancer-promoter interactions (EPIs). In this review, we summarized the current methods for detecting EPIs and described how developing techniques facilitate the identification of EPI through assessing the merits and drawbacks of these methods. We also reviewed recent state-of-art EPI prediction methods in terms of their rationale, data usage and characterization. Furthermore, we briefly discussed the evolved strategies for validating functional EPIs.