Stiff extracellular matrix drives the differentiation of mesenchymal stem cells toward osteogenesis by the multiscale 3D genome reorganization.

Extracellular matrix (ECM) stiffness is a major driver of stem cell fate. However, the involvement of the three-dimensional (3D) genomic reorganization in response to ECM stiffness remains unclear. Here, we generated comprehensive 3D chromatin landscapes of mesenchymal stem cells (MSCs) exposed to various ECM stiffness. We found that there were more long-range chromatin interactions, but less compartment A in MSCs cultured on stiff ECM than those cultured on soft ECM. However, the switch from compartment B in MSCs cultured on soft ECM to compartment A in MSCs cultured on stiff ECM included genes encoding proteins primarily enriched in cytoskeleton organization. At the topologically associating domains (TADs) level, stiff ECM tends to have merged TADs on soft ECM. These merged TADs on stiff ECM include upregulated genes encoding proteins enriched in osteogenesis, such as SP1, ETS1, and DCHS1, which were validated by quantitative real-time polymerase chain reaction and found to be consistent with the increase of alkaline phosphatase staining. Knockdown of SP1 or ETS1 led to the downregulation of osteogenic marker genes, including COL1A1, RUNX2, ALP, and OCN in MSCs cultured on stiff ECM. Our study provides an important insight into the stiff ECM-mediated promotion of MSC differentiation towards osteogenesis, emphasizing the influence of mechanical cues on the reorganization of 3D genome architecture and stem cell fate.

Using GWAS and Machine Learning to Identify and Predict Genetic Variants Associated with Foodborne Bacteria Phenotypic Traits.

One of the main challenges in food microbiology is to prevent the risk of outbreaks by avoiding the distribution of food contaminated by bacteria. This requires constant monitoring of the circulating strains throughout the food production chain. Bacterial genomes contain signatures of natural evolution and adaptive markers that can be exploited to better understand the behavior of pathogen in the food industry. The monitoring of foodborne strains can therefore be facilitated by the use of these genomic markers capable of rapidly providing essential information on isolated strains, such as the source of contamination, risk of illness, potential for biofilm formation, and tolerance or resistance to biocides. The increasing availability of large genome datasets is enhancing the understanding of the genetic basis of complex traits such as host adaptation, virulence, and persistence. Genome-wide association studies have shown very promising results in the discovery of genomic markers that can be integrated into rapid detection tools. In addition, machine learning has successfully predicted phenotypes and classified important traits. Genome-wide association and machine learning tools have therefore the potential to support decision-making circuits intending at reducing the burden of foodborne diseases. The aim of this chapter review is to provide knowledge on the use of these two methods in food microbiology and to recommend their use in the field.

Structural Maintenance of Chromosomes Complexes.

The Structural Maintenance of Chromosomes (SMC) protein complexes are DNA-binding molecular machines required to shape chromosomes into functional units and to safeguard the genome through cell division. These ring-shaped multi-subunit protein complexes, which are present in all kingdoms of life, achieve this by organizing chromosomes in three-dimensional space. Mechanistically, the SMC complexes hydrolyze ATP to either stably entrap DNA molecules within their lumen, or rapidly reel DNA into large loops, which allow them to link two stretches of DNA in cis or trans. In this chapter, the canonical structure of the SMC complexes is first introduced, followed by a description of the composition and general functions of the main types of eukaryotic and prokaryotic SMC complexes. Thereafter, the current model for how SMC complexes perform in vitro DNA loop extrusion is presented. Lastly, chromosome loop formation by SMC complexes is introduced, and how the DNA loop extrusion mechanism contributes to chromosome looping by SMC complexes in cells is discussed.

Micro-C Analysis Workflow Using Pairtools and Juicer.

Three-dimensional (3D) chromosome structures are closely related to various chromosomal functions, and deep analysis of the structures is crucial for the elucidation of the functions. In recent years, chromosome conformation capture (3C) techniques combined with next-generation sequencing analysis have been developed to comprehensively reveal 3D chromosome structures. Micro-C is one such method that can detect the structures at nucleosome resolution. In this chapter, I provide a basic method for Micro-C analysis. I present and discuss a series of data analyses ranging from mapping to basic downstream analyses, including loop detection.

Exploring Contact Distance Distributions with Google Colaboratory.

Hi-C and Micro-C are the three-dimensional (3D) genome assays that use high-throughput sequencing. In the analysis, the sequenced paired-end reads are mapped to a reference genome to generate a two-dimensional contact matrix for identifying topologically associating domains (TADs), chromatin loops, and chromosomal compartments. On the other hand, the distance distribution of the paired-end mapped reads also provides insight into the 3D genome structure by highlighting global contact frequency patterns at distances indicative of loops, TADs, and compartments. This chapter presents a basic workflow for visualizing and analyzing contact distance distributions from Hi-C data. The workflow can be run on Google Colaboratory, which provides a ready-to-use Python environment accessible through a web browser. The notebook that demonstrates the workflow is available in the GitHub repository at https://github.com/rnakato/Springer_contact_distance_plot.

CWL-Based Analysis Pipeline for Hi-C Data: From FASTQ Files to Matrices.

Over a decade has passed since the development of the Hi-C method for genome-wide analysis of 3D genome organization. Hi-C utilizes next-generation sequencing (NGS) technology to generate large-scale chromatin interaction data, which has accumulated across a diverse range of species and cell types, particularly in eukaryotes. There is thus a growing need to streamline the process of Hi-C data analysis to utilize these data sets effectively. Hi-C generates data that are much larger compared to other NGS techniques such as chromatin immunoprecipitation sequencing (ChIP-seq) or RNA-seq, making the data reanalysis process computationally expensive. In an effort to bridge this resource gap, the 4D Nucleome (4DN) Data Portal has reanalyzed approximately 600 Hi-C data sets, allowing users to access and utilize the analyzed data. In this chapter, we provide detailed instructions for the implementation of the common workflow language (CWL)-based Hi-C analysis pipeline adopted by the 4DN Data Portal ecosystem. This reproducible and portable pipeline generates standard Hi-C contact matrices in formats such as .hic or .mcool from FASTQ files. It enables users to output their own Hi-C data in the same format as those registered in the 4DN Data portal, facilitating comparative analysis using data registered in the portal. Our custom-made scripts are available on GitHub at https://github.com/kuzobuta/4dn_cwl_pipeline .

Step-by-Step Protocol to Generate Hi-C Contact Maps Using the rfy_hic2 Pipeline.

The Hi-C method has emerged as an indispensable tool for analyzing the 3D organization of the genome, becoming increasingly accessible and frequently utilized in chromatin research. To effectively leverage 3D genomics data obtained through advanced technologies, it is crucial to understand what processes are undertaken and what aspects require special attention within the bioinformatics pipeline. This protocol aims to demystify the Hi-C data analysis process for field newcomers. In a step-by-step manner, we describe how to process Hi-C data, from the initial sequencing of the Hi-C library to the final visualization of Hi-C contact data as heatmaps. Each step of the analysis is clearly explained to ensure an understanding of the procedures and their objectives. By the end of this chapter, readers will be equipped with the knowledge to transform raw Hi-C reads into informative visual representations, facilitating a deeper comprehension of the spatial genomic structures critical to cellular functions.

Analysis and Visualization of Multiple Hi-C and Micro-C Data with CustardPy.

Three-dimensional (3D) genome structure plays crucial roles in biological processes and disease pathogenesis. Hi-C and Micro-C, well-established methods for 3D genome analysis, can identify a variety of 3D genome structures. However, selecting appropriate pipelines and tools for the analysis and setting up the required computing environment can sometimes pose challenges. To address this, we have introduced CustardPy, a Docker-based pipeline specifically designed for 3D genome analysis. CustardPy is designed to compare and evaluate multiple samples and wraps several existing tools to cover the entire workflow from FASTQ mapping to visualization. In this chapter, we demonstrate how to analyze and visualize Hi-C data using CustardPy and introduce several 3D genome features observed in Hi-C data.

4D Genome Analysis Using PHi-C2.

Hi-C methods reveal 3D genome features but lack correspondence to dynamic chromatin behavior. PHi-C2, Python software, addresses this gap by transforming Hi-C data into polymer models. After the optimization algorithm, it enables us to calculate 3D conformations and conduct dynamic simulations, providing insights into chromatin dynamics, including the mean-squared displacement and rheological properties. This chapter introduces PHi-C2 usage, offering a tutorial for comprehensive 4D genome analysis.

Three-Dimensional Simulation of Whole-Genome Structuring Through the Transition from Anaphase to Interphase.

In order to analyze the three-dimensional genome architecture, it is important to simulate how the genome is structured through the cell cycle progression. In this chapter, we present the usage of our computation codes for simulating how the human genome is formed as the cell transforms from anaphase to interphase. We do not use the global Hi-C data as an input into the genome simulation but represent all chromosomes as linear polymers annotated by the neighboring region contact index (NCI), which classifies the A/B type of each local chromatin region. The simulated mitotic chromosomes heterogeneously expand upon entry to the G1 phase, which induces phase separation of A and B chromatin regions, establishing chromosome territories, compartments, and lamina and nucleolus associations in the interphase nucleus. When the appropriate one-dimensional chromosomal annotation is possible, using the protocol of this chapter, one can quantitatively simulate the three-dimensional genome structure and dynamics of human cells of interest.

Genomic Pipeline for Analysis of Mutational Events in Bacteria.

Unveiling the strategies of bacterial adaptation to stress constitute a challenging area of research. The understanding of mechanisms governing emergence of resistance to antimicrobials is of particular importance regarding the increasing threat of antibiotic resistance on public health worldwide. In the last decades, the fast democratization of sequencing technologies along with the development of dedicated bioinformatical tools to process data offered new opportunities to characterize genomic variations underlying bacterial adaptation. Thereby, research teams have now the possibility to dive deeper in the deciphering of bacterial adaptive mechanisms through the identification of specific genetic targets mediating survival to stress. In this chapter, we proposed a step-by-step bioinformatical pipeline enabling the identification of mutational events underlying biocidal stress adaptation associated with antimicrobial resistance development using Escherichia marmotae as an illustrative model.

The genome sequence of the Dracula fish, Danionella dracula (Britz, Conway & Rüber, 2009).

We present a genome assembly from an individual Danionella dracula (the Dracula fish; Chordata; Actinopterygii; Cypriniformes; Danionidae; Danioninae). The genome sequence is 665.21 megabases in span. This is a scaffold-level assembly, with a scaffold N50 of 10.29 Mb.

Approaches to diagnosis for individuals with a suspected inherited white matter disorder.

Leukodystrophies are heritable disorders with white matter abnormalities observed on central nervous system magnetic resonance imaging. Pediatric leukodystrophies have long been known for their classically high, "unsolved" rate. Indeed, these disorders provide a diagnostic dilemma for many clinicians as over 100 genetic disorders alone may present with white matter abnormalities, with this figure not taking into account the substantial number of infectious agents, toxicities, and acquired disorders that may affect the white matter of the brain. Achieving a diagnosis may be the single most important step in the clinical course of a leukodystrophy-affected individual, with important implications for care and quality of life. For certain disorders, prompt recognition can direct therapeutic intervention with significant implications and requires urgent recognition. In this review, we cover newborn screening efforts, standard-of-care testing methodologies, and next generation sequencing approaches that continue to change the landscape of leukodystrophy diagnosis. Early studies have shown that next generation sequencing approaches, particularly exome and now genome sequencing have proven to be powerful in helping resolve many cases that were refractory to a single gene or linkage analysis approach. In addition, other methods are required for cases that remain persistently unsolved after next generation sequencing methods have been used. In the past more than half of affected individuals never achieved an etiologic diagnosis, and when they did, the reported times to diagnosis were >5 years although molecular testing has allowed this to be reduced to closer to 16 months. For affected families, next generation sequencing technologies have finally provided a way to fill gaps in diagnosis.

Comparative assessment of genotyping-by-sequencing and whole-exome sequencing for estimating genetic diversity and geographic structure in small sample sizes: insights from wild jaguar populations.

Biologists currently have an assortment of high-throughput sequencing techniques allowing the study of population dynamics in increasing detail. The utility of genetic estimates depends on their ability to recover meaningful approximations while filtering out noise produced by artifacts. In this study, we empirically compared the congruence of two reduced representation approaches (genotyping-by-sequencing, GBS, and whole-exome sequencing, WES) in estimating genetic diversity and population structure using SNP markers typed in a small number of wild jaguar (Panthera onca) samples from South America. Due to its targeted nature, WES allowed for a more straightforward reconstruction of loci compared to GBS, facilitating the identification of true polymorphisms across individuals. We therefore used WES-derived metrics as a benchmark against which GBS-derived indicators were compared, adjusting parameters for locus assembly and SNP filtering in the latter. We observed significant variation in SNP call rates across samples in GBS datasets, leading to a recurrent miscalling of heterozygous sites. This issue was further amplified by small sample sizes, ultimately impacting the consistency of summary statistics between genotyping methods. Recognizing that the genetic markers obtained from GBS and WES are intrinsically different due to varying evolutionary pressures, particularly selection, we consider that our empirical comparison offers valuable insights and highlights critical considerations for estimating population genetic attributes using reduced representation datasets. Our results emphasize the critical need for careful evaluation of missing data and stringent filtering to achieve reliable estimates of genetic diversity and differentiation in elusive wildlife species.

Prediction of the 3D cancer genome from whole-genome sequencing using InfoHiC.

The 3D genome prediction in cancer is crucial for uncovering the impact of structural variations (SVs) on tumorigenesis, especially when they are present in noncoding regions. We present InfoHiC, a systemic framework for predicting the 3D cancer genome directly from whole-genome sequencing (WGS). InfoHiC utilizes contig-specific copy number encoding on the SV contig assembly, and performs a contig-to-total Hi-C conversion for the cancer Hi-C prediction from multiple SV contigs. We showed that InfoHiC can predict 3D genome folding from all types of SVs using breast cancer cell line data. We applied it to WGS data of patients with breast cancer and pediatric patients with medulloblastoma, and identified neo topologically associating domains. For breast cancer, we discovered super-enhancer hijacking events associated with oncogenic overexpression and poor survival outcomes. For medulloblastoma, we found SVs in noncoding regions that caused super-enhancer hijacking events of medulloblastoma driver genes (GFI1, GFI1B, and PRDM6). In addition, we provide trained models for cancer Hi-C prediction from WGS at https://github.com/dmcb-gist/InfoHiC , uncovering the impacts of SVs in cancer patients and revealing novel therapeutic targets.

Large-scale genome-wide interaction analyses on multiple cardiometabolic risk factors to identify age-specific genetic risk factors.

The genetic landscape of cardiometabolic risk factors has been explored extensively. However, insight in the effects of genetic variation on these risk factors over the life course is sparse. Here, we performed genome-wide interaction studies (GWIS) on different cardiometabolic risk factors to identify age-specific genetic risks. This study included 270,276 unrelated European-ancestry participants from the UK Biobank (54.2% women, a median age of 58 [interquartile range (IQR): 50, 63] years). GWIS models with interaction terms between genetic variants and age were performed on apolipoprotein B (ApoB), low-density lipoprotein-cholesterol (LDL-C), log-transformed triglycerides (TG), body mass index (BMI) and systolic blood pressure (SBP). Replication was subsequently performed in the Copenhagen General Population Study (CGPS) and the Estonian Biobank (EstBB). Multiple lead variants were identified to have genome-wide significant interactions with age (Pinteraction < 1e - 08). In detail, rs429358 (tagging APOE4) was identified for ApoB (Pinteraction = 9.0e - 14) and TG (Pinteraction = 5.4e - 16). Three additional lead variants were identified for ApoB: rs11591147 (R46L in PCSK9, Pinteraction = 3.9e - 09), rs34601365 (near APOB, Pinteraction = 8.4e - 09) and rs17248720 (near LDLR, Pinteraction = 2.0e - 09). Effect sizes of the identified lead variants were generally closer to the null with increasing age. No variant-age interactions were identified for LDL-C, SBP and BMI. The significant interactions of rs429358 with age on ApoB and TG were replicated in both CGPS and EstBB. The majority of genetic effects on cardiometabolic risk factors remain relatively constant over age, with the noted exceptions of specific genetic effects on ApoB and TG.

Detection of circulating tumor DNA by tumor-informed whole-genome sequencing enables prediction of recurrence in stage III colorectal cancer patients.

INTRODUCTION: Circulating tumor (ctDNA) can be used to detect residual disease after cancer treatment. Detecting low-level ctDNA is challenging, due to the limited number of recoverable ctDNA fragments at any target loci. In response, we applied tumor-informed whole-genome sequencing (WGS), leveraging thousands of mutations for ctDNA detection. METHODS: Performance was evaluated in serial plasma samples (n = 1283) from 144 stage III colorectal cancer patients. Tumor/normal WGS was used to establish a patient-specific mutational fingerprint, which was searched for in 20x WGS plasma profiles. For reproducibility, paired aliquots of 172 plasma samples were analyzed in two independent laboratories. De novo variant calling was performed for serial plasma samples with a ctDNA level > 10 % (n = 17) to explore genomic evolution. RESULTS: WGS-based ctDNA detection was prognostic of recurrence: post-operation (Hazard ratio [HR] 6.75, 95 %CI 3.18-14.3, p < 0.001), post-adjuvant chemotherapy (HR 28.9, 95 %CI 10.1-82.8; p < 0.001), and during surveillance (HR 22.8, 95 %CI 13.7-37.9, p < 0.0001). The 3-year cumulative incidence of ctDNA detection in recurrence patients was 95 %. ctDNA was detected a median of 8.7 months before radiological recurrence. The independently analyzed plasma aliquots showed excellent agreement (Cohens Kappa=0.9, r = 0.99). Genomic characterization of serial plasma revealed significant evolution in mutations and copy number alterations, and the timing of mutational processes, such as 5-fluorouracil-induced mutations. CONCLUSION: Our study supports the use of WGS for sensitive ctDNA detection and demonstrates that post-treatment ctDNA detection is highly prognostic of recurrence. Furthermore, plasma WGS can identify genomic differences distinguishing the primary tumor and relapsing metastasis, and monitor treatment-induced genomic changes.

The genome sequence of the Blue-bordered Carpet moth Plemyria rubiginata (Denis & Schiffermüller) 1775.

We present a genome assembly from an individual female Plemyria rubiginata (the Blue-bordered Carpet moth; Arthropoda; Insecta; Lepidoptera; Geometridae). The genome sequence is 356.2 megabases in span. Most of the assembly is scaffolded into 30 chromosomal pseudomolecules, including the Z and W sex chromosomes. The mitochondrial genome has also been assembled and is 17.64 kilobases in length.

The genome sequence of the Purple Clay moth, Diarsia brunnea (Denis & Schiffermüller) 1775.

We present a genome assembly from an individual male Diarsia brunnea (the Purple Clay moth; Arthropoda; Insecta; Lepidoptera; Noctuidae). The genome sequence has a total length of 586.80 megabases. Most of the assembly is scaffolded into 31 chromosomal pseudomolecules, including the Z sex chromosome. The mitochondrial genome has also been assembled and is 15.29 kilobases in length. Gene annotation of this assembly on Ensembl identified 18,730 protein-coding genes.

Whole genome sequence data of ZEN-degrading strain Levilactobacillus brevis PYN10_6_2 isolated from Tenebrio molitor larval feces.

Levilactobacillus brevis PYN10_6_2, a lactic acid bacterial strain previously isolated from Tenebrio molitor larval feces, possesses the ability to convert zearalenone (ZEN) to α-/ß-Zearalenol (α-/ß-ZEL). However, the genes involved in the ZEN reduction reaction and the biosafety of this strain remain unknown. In this study, we sequenced, assembled, and annotated the whole genome of L. brevis PYN10_6_2. Genomic sequencing was conducted using short-read sequencing on the Illumina HiSeq X Ten platform and long-read sequencing on the PacBio RS II Single Molecule Real-Time (SMRT) platform. The assembled genome consisted of one circular chromosome, four circular plasmids, with a total size of 2,745,725 bp and a G + C content of 45.52 %. Annotation identified 2,660 coding sequences, 5 rRNAs, 66 tRNAs, and a single CRISPR locus. Average nucleotide identity (ANI) between L. brevis PYN10_6_2 and L. brevis DSM 20054T yielded a value of 98.94 %. Further in-depth analysis revealed 182 antibiotic resistance genes, 237 putative virulence genes, 2 prophages, and 10 genomic islands. Additionally, functional annotation through COG and KEGG databases revealed the presence of three genes encoding 3α- and 3ß-hydroxysteroid dehydrogenase (3α-/3ß-HSD) within the bacterial chromosome. This comprehensive genomic characterization provides valuable insights into the genetic basis of L. brevis PYN10_6_2's ZEN-reducing ability and its biosafety profile.