Multi-organ single-cell transcriptomics of immune cells uncovered organ-specific gene expression and functions.

Despite the wealth of publicly available single-cell datasets, our understanding of distinct resident immune cells and their unique features in diverse human organs remains limited. To address this, we compiled a meta-analysis dataset of 114,275 CD45+ immune cells sourced from 14 organs in healthy donors. While the transcriptome of immune cells remains relatively consistent across organs, our analysis has unveiled organ-specific gene expression differences (GTPX3 in kidney, DNTT and ACVR2B in thymus). These alterations are linked to different transcriptional factor activities and pathways including metabolism. TNF-α signaling through the NFkB pathway was found in several organs and immune compartments. The presence of distinct expression profiles for NFkB family genes and their target genes, including cytokines, underscores their pivotal role in cell positioning. Taken together, immune cells serve a dual role: safeguarding the organs and dynamically adjusting to the intricacies of the host organ environment, thereby actively contributing to its functionality and overall homeostasis.

Convert-Pheno: A software toolkit for the interconversion of standard data models for phenotypic data.

Efficient sharing and integration of phenotypic data is crucial for advancing biomedical research and enhancing patient outcomes in precision medicine and public health. To achieve this, the health data community has developed standards to promote the harmonization of variable names and values. However, the use of diverse standards across different research centers can hinder progress. Here we present Convert-Pheno, an open-source software toolkit that enables the interconversion of common data models for phenotypic data such as Beacon v2 Models, CDISC-ODM, OMOP-CDM, Phenopackets v2, and REDCap. Along with the software, we have created a detailed documentation that includes information on deployment and installation.

Development of a novel streamlined workflow (AACRE) and database (inCREDBle) for genomic analysis of carbapenem-resistant Enterobacterales.

In response to the threat of increasing antimicrobial resistance, we must increase the amount of available high-quality genomic data gathered on antibiotic-resistant bacteria. To this end, we developed an integrated pipeline for high-throughput long-read sequencing, assembly, annotation and analysis of bacterial isolates and used it to generate a large genomic data set of carbapenemase-producing Enterobacterales (CPE) isolates collected in Spain. The set of 461 isolates were sequenced with a combination of both Illumina and Oxford Nanopore Technologies (ONT) DNA sequencing technologies in order to provide genomic context for chromosomal loci and, most importantly, structural resolution of plasmids, important determinants for transmission of antimicrobial resistance. We developed an informatics pipeline called Assembly and Annotation of Carbapenem-Resistant Enterobacteriaceae (AACRE) for the full assembly and annotation of the bacterial genomes and their complement of plasmids. To explore the resulting genomic data set, we developed a new database called inCREDBle that not only stores the genomic data, but provides unique ways to filter and compare data, enabling comparative genomic analyses at the level of chromosomes, plasmids and individual genes. We identified a new sequence type, ST5000, and discovered a genomic locus unique to ST15 that may be linked to its increased spread in the population. In addition to our major objective of generating a large regional data set, we took the opportunity to compare the effects of sample quality and sequencing methods, including R9 versus R10 nanopore chemistry, on genome assembly and annotation quality. We conclude that converting short-read and hybrid microbial sequencing and assembly workflows to the latest nanopore chemistry will further reduce processing time and cost, truly enabling the routine monitoring of resistance transmission patterns at the resolution of complete chromosomes and plasmids.

Dissecting the Origin of Heterogeneity in Uterine and Ovarian Carcinosarcomas.

Gynecologic carcinosarcomas (CS) are biphasic neoplasms composed of carcinomatous (C) and sarcomatous (S) malignant components. Because of their rarity and histologic complexity, genetic and functional studies on CS are scarce and the mechanisms of initiation and development remain largely unknown. Whole-genome analysis of the C and S components reveals shared genomic alterations, thus emphasizing the clonal evolution of CS. Reconstructions of the evolutionary history of each tumor further reveal that C and S samples are composed of both ancestral cell populations and component-specific subclones, supporting a common origin followed by distinct evolutionary trajectories. However, while we do not find any recurrent genomic features associated with phenotypic divergence, transcriptomic and methylome analyses identify a common mechanism across the cohort, the epithelial-to-mesenchymal transition (EMT), suggesting a role for nongenetic factors in inflicting changes to cellular fate. Altogether, these data accredit the hypothesis that CS tumors are driven by both clonal evolution and transcriptomic reprogramming, essential for susceptibility to transdifferentiation upon encountering environmental cues, thus linking CS heterogeneity to genetic, transcriptomic, and epigenetic influences. Significance: We have provided a detailed characterization of the genomic landscape of CS and identified EMT as a common mechanism associated with phenotypic divergence, linking CS heterogeneity to genetic, transcriptomic, and epigenetic influences.

A critical spotlight on the paradigms of FFPE-DNA sequencing.

In the late 19th century, formalin fixation with paraffin-embedding (FFPE) of tissues was developed as a fixation and conservation method and is still used to this day in routine clinical and pathological practice. The implementation of state-of-the-art nucleic acid sequencing technologies has sparked much interest for using historical FFPE samples stored in biobanks as they hold promise in extracting new information from these valuable samples. However, formalin fixation chemically modifies DNA, which potentially leads to incorrect sequences or misinterpretations in downstream processing and data analysis. Many publications have concentrated on one type of DNA damage, but few have addressed the complete spectrum of FFPE-DNA damage. Here, we review mitigation strategies in (I) pre-analytical sample quality control, (II) DNA repair treatments, (III) analytical sample preparation and (IV) bioinformatic analysis of FFPE-DNA. We then provide recommendations that are tested and illustrated with DNA from 13-year-old liver specimens, one FFPE preserved and one fresh frozen, applying target-enriched sequencing. Thus, we show how DNA damage can be compensated, even when using low quantities (50 ng) of fragmented FFPE-DNA (DNA integrity number 2.0) that cannot be amplified well (Q129 bp/Q41 bp = 5%). Finally, we provide a checklist called 'ERROR-FFPE-DNA' that summarises recommendations for the minimal information in publications required for assessing fitness-for-purpose and inter-study comparison when using FFPE samples.

Machine learning and multi-omics data in chronic lymphocytic leukemia: the future of precision medicine?

Chronic lymphocytic leukemia is a complex and heterogeneous hematological malignancy. The advance of high-throughput multi-omics technologies has significantly influenced chronic lymphocytic leukemia research and paved the way for precision medicine approaches. In this review, we explore the role of machine learning in the analysis of multi-omics data in this hematological malignancy. We discuss recent literature on different machine learning models applied to single omic studies in chronic lymphocytic leukemia, with a special focus on the potential contributions to precision medicine. Finally, we highlight the recently published machine learning applications in multi-omics data in this area of research as well as their potential and limitations.

The RD-Connect Genome-Phenome Analysis Platform: Accelerating diagnosis, research, and gene discovery for rare diseases.

Rare disease patients are more likely to receive a rapid molecular diagnosis nowadays thanks to the wide adoption of next-generation sequencing. However, many cases remain undiagnosed even after exome or genome analysis, because the methods used missed the molecular cause in a known gene, or a novel causative gene could not be identified and/or confirmed. To address these challenges, the RD-Connect Genome-Phenome Analysis Platform (GPAP) facilitates the collation, discovery, sharing, and analysis of standardized genome-phenome data within a collaborative environment. Authorized clinicians and researchers submit pseudonymised phenotypic profiles encoded using the Human Phenotype Ontology, and raw genomic data which is processed through a standardized pipeline. After an optional embargo period, the data are shared with other platform users, with the objective that similar cases in the system and queries from peers may help diagnose the case. Additionally, the platform enables bidirectional discovery of similar cases in other databases from the Matchmaker Exchange network. To facilitate genome-phenome analysis and interpretation by clinical researchers, the RD-Connect GPAP provides a powerful user-friendly interface and leverages tens of information sources. As a result, the resource has already helped diagnose hundreds of rare disease patients and discover new disease causing genes.

Impact of DNA methylation on 3D genome structure.

Determining the effect of DNA methylation on chromatin structure and function in higher organisms is challenging due to the extreme complexity of epigenetic regulation. We studied a simpler model system, budding yeast, that lacks DNA methylation machinery making it a perfect model system to study the intrinsic role of DNA methylation in chromatin structure and function. We expressed the murine DNA methyltransferases in Saccharomyces cerevisiae and analyzed the correlation between DNA methylation, nucleosome positioning, gene expression and 3D genome organization. Despite lacking the machinery for positioning and reading methylation marks, induced DNA methylation follows a conserved pattern with low methylation levels at the 5' end of the gene increasing gradually toward the 3' end, with concentration of methylated DNA in linkers and nucleosome free regions, and with actively expressed genes showing low and high levels of methylation at transcription start and terminating sites respectively, mimicking the patterns seen in mammals. We also see that DNA methylation increases chromatin condensation in peri-centromeric regions, decreases overall DNA flexibility, and favors the heterochromatin state. Taken together, these results demonstrate that methylation intrinsically modulates chromatin structure and function even in the absence of cellular machinery evolved to recognize and process the methylation signal.

Framework for quality assessment of whole genome cancer sequences.

Bringing together cancer genomes from different projects increases power and allows the investigation of pan-cancer, molecular mechanisms. However, working with whole genomes sequenced over several years in different sequencing centres requires a framework to compare the quality of these sequences. We used the Pan-Cancer Analysis of Whole Genomes cohort as a test case to construct such a framework. This cohort contains whole cancer genomes of 2832 donors from 18 sequencing centres. We developed a non-redundant set of five quality control (QC) measurements to establish a star rating system. These QC measures reflect known differences in sequencing protocol and provide a guide to downstream analyses and allow for exclusion of samples of poor quality. We have found that this is an effective framework of quality measures. The implementation of the framework is available at: https://dockstore.org/containers/quay.io/jwerner_dkfz/pancanqc:1.2.2 .

Genomic evidence for recurrent genetic admixture during the domestication of Mediterranean olive trees (Olea europaea L.).

BACKGROUND: Olive tree (Olea europaea L. subsp. europaea, Oleaceae) has been the most emblematic perennial crop for Mediterranean countries since its domestication around 6000 years ago in the Levant. Two taxonomic varieties are currently recognized: cultivated (var. europaea) and wild (var. sylvestris) trees. However, it remains unclear whether olive cultivars derive from a single initial domestication event followed by secondary diversification, or whether cultivated lineages are the result of more than a single, independent primary domestication event. To shed light into the recent evolution and domestication of the olive tree, here we analyze a group of newly sequenced and available genomes using a phylogenomics and population genomics framework. RESULTS: We improved the assembly and annotation of the reference genome, newly sequenced the genomes of twelve individuals: ten var. europaea, one var. sylvestris, and one outgroup taxon (subsp. cuspidata)-and assembled a dataset comprising whole genome data from 46 var. europaea and 10 var. sylvestris. Phylogenomic and population structure analyses support a continuous process of olive tree domestication, involving a major domestication event, followed by recurrent independent genetic admixture events with wild populations across the Mediterranean Basin. Cultivated olives exhibit only slightly lower levels of genetic diversity than wild forms, which can be partially explained by the occurrence of a mild population bottleneck 3000-14,000 years ago during the primary domestication period, followed by recurrent introgression from wild populations. Genes associated with stress response and developmental processes were positively selected in cultivars, but we did not find evidence that genes involved in fruit size or oil content were under positive selection. This suggests that complex selective processes other than directional selection of a few genes are in place. CONCLUSIONS: Altogether, our results suggest that a primary domestication area in the eastern Mediterranean basin was followed by numerous secondary events across most countries of southern Europe and northern Africa, often involving genetic admixture with genetically rich wild populations, particularly from the western Mediterranean Basin.

High degree of polyclonality hinders somatic mutation calling in lung brush samples of COPD cases and controls.

Chronic obstructive pulmonary disease (COPD) is induced by cigarette smoking and characterized by inflammation of airway tissue. Since smokers with COPD have a higher risk of developing lung cancer than those without, we hypothesized that they carry more mutations in affected tissue. We called somatic mutations in airway brush samples from medium-coverage whole genome sequencing data from healthy never and ex-smokers (n = 8), as well as from ex-smokers with variable degrees of COPD (n = 4). Owing to the limited concordance of resulting calls between the applied tools we built a consensus, a strategy that was validated with high accuracy for cancer data. However, consensus calls showed little promise of representing true positives due to low mappability of corresponding sequence reads and high overlap with positions harbouring known genetic polymorphisms. A targeted re-sequencing approach suggested that only few mutations would survive stringent verification testing and that our data did not allow the inference of any difference in the mutational load of bronchial brush samples between former smoking COPD cases and controls. High polyclonality in airway brush samples renders medium-depth sequencing insufficient to provide the resolution to detect somatic mutations. Deep sequencing data of airway biopsies are needed to tackle the question.

Recurrent somatic mutations reveal new insights into consequences of mutagenic processes in cancer.

The sheer size of the human genome makes it improbable that identical somatic mutations at the exact same position are observed in multiple tumours solely by chance. The scarcity of cancer driver mutations also precludes positive selection as the sole explanation. Therefore, recurrent mutations may be highly informative of characteristics of mutational processes. To explore the potential, we use recurrence as a starting point to cluster >2,500 whole genomes of a pan-cancer cohort. We describe each genome with 13 recurrence-based and 29 general mutational features. Using principal component analysis we reduce the dimensionality and create independent features. We apply hierarchical clustering to the first 18 principal components followed by k-means clustering. We show that the resulting 16 clusters capture clinically relevant cancer phenotypes. High levels of recurrent substitutions separate the clusters that we link to UV-light exposure and deregulated activity of POLE from the one representing defective mismatch repair, which shows high levels of recurrent insertions/deletions. Recurrence of both mutation types characterizes cancer genomes with somatic hypermutation of immunoglobulin genes and the cluster of genomes exposed to gastric acid. Low levels of recurrence are observed for the cluster where tobacco-smoke exposure induces mutagenesis and the one linked to increased activity of cytidine deaminases. Notably, the majority of substitutions are recurrent in a single tumour type, while recurrent insertions/deletions point to shared processes between tumour types. Recurrence also reveals susceptible sequence motifs, including TT[C>A]TTT and AAC[T>G]T for the POLE and 'gastric-acid exposure' clusters, respectively. Moreover, we refine knowledge of mutagenesis, including increased C/G deletion levels in general for lung tumours and specifically in midsize homopolymer sequence contexts for microsatellite instable tumours. Our findings are an important step towards the development of a generic cancer diagnostic test for clinical practice based on whole-genome sequencing that could replace multiple diagnostics currently in use.

The Genome Sequence of the Eastern Woodchuck (Marmota monax) - A Preclinical Animal Model for Chronic Hepatitis B.

The Eastern woodchuck (Marmota monax) has been extensively used in research of chronic hepatitis B and liver cancer because its infection with the woodchuck hepatitis virus closely resembles a human hepatitis B virus infection. Development of novel immunotherapeutic approaches requires genetic information on immune pathway genes in this animal model. The woodchuck genome was assembled with a combination of high-coverage whole-genome shotgun sequencing of Illumina paired-end, mate-pair libraries and fosmid pool sequencing. The result is a 2.63 Gigabase (Gb) assembly with a contig N50 of 74.5 kilobases (kb), scaffold N50 of 892 kb, and genome completeness of 99.2%. RNA sequencing (RNA-seq) from seven different tissues aided in the annotation of 30,873 protein-coding genes, which in turn encode 41,826 unique protein products. More than 90% of the genes have been functionally annotated, with 82% of them containing open reading frames. This genome sequence and its annotation will enable further research in chronic hepatitis B and hepatocellular carcinoma and contribute to the understanding of immunological responses in the woodchuck.

Pheno-seq - linking visual features and gene expression in 3D cell culture systems.

Patient-derived 3D cell culture systems are currently advancing cancer research since they potentiate the molecular analysis of tissue-like properties and drug response under well-defined conditions. However, our understanding of the relationship between the heterogeneity of morphological phenotypes and the underlying transcriptome is still limited. To address this issue, we here introduce "pheno-seq" to directly link visual features of 3D cell culture systems with profiling their transcriptome. As prototypic applications breast and colorectal cancer (CRC) spheroids were analyzed by pheno-seq. We identified characteristic gene expression signatures of epithelial-to-mesenchymal transition that are associated with invasive growth behavior of clonal breast cancer spheroids. Furthermore, we linked long-term proliferative capacity in a patient-derived model of CRC to a lowly abundant PROX1-positive cancer stem cell subtype. We anticipate that the ability to integrate transcriptome analysis and morphological patho-phenotypes of cancer cells will provide novel insight on the molecular origins of intratumor heterogeneity.

Partially methylated domains are hypervariable in breast cancer and fuel widespread CpG island hypermethylation.

Global loss of DNA methylation and CpG island (CGI) hypermethylation are key epigenomic aberrations in cancer. Global loss manifests itself in partially methylated domains (PMDs) which extend up to megabases. However, the distribution of PMDs within and between tumor types, and their effects on key functional genomic elements including CGIs are poorly defined. We comprehensively show that loss of methylation in PMDs occurs in a large fraction of the genome and represents the prime source of DNA methylation variation. PMDs are hypervariable in methylation level, size and distribution, and display elevated mutation rates. They impose intermediate DNA methylation levels incognizant of functional genomic elements including CGIs, underpinning a CGI methylator phenotype (CIMP). Repression effects on tumor suppressor genes are negligible as they are generally excluded from PMDs. The genomic distribution of PMDs reports tissue-of-origin and may represent tissue-specific silent regions which tolerate instability at the epigenetic, transcriptomic and genetic level.

Single cell RNA-seq identifies the origins of heterogeneity in efficient cell transdifferentiation and reprogramming.

Forced transcription factor expression can transdifferentiate somatic cells into other specialised cell types or reprogram them into induced pluripotent stem cells (iPSCs) with variable efficiency. To better understand the heterogeneity of these processes, we used single-cell RNA sequencing to follow the transdifferentation of murine pre-B cells into macrophages as well as their reprogramming into iPSCs. Even in these highly efficient systems, there was substantial variation in the speed and path of fate conversion. We predicted and validated that these differences are inversely coupled and arise in the starting cell population, with Mychigh large pre-BII cells transdifferentiating slowly but reprogramming efficiently and Myclow small pre-BII cells transdifferentiating rapidly but failing to reprogram. Strikingly, differences in Myc activity predict the efficiency of reprogramming across a wide range of somatic cell types. These results illustrate how single cell expression and computational analyses can identify the origins of heterogeneity in cell fate conversion processes.

Systems Genetics of Hepatic Metabolome Reveals Octopamine as a Target for Non-Alcoholic Fatty Liver Disease Treatment.

Non-alcoholic fatty liver disease (NAFLD) is often associated with obesity and type 2 diabetes. To disentangle etiological relationships between these conditions and identify genetically-determined metabolites involved in NAFLD processes, we mapped 1H nuclear magnetic resonance (NMR) metabolomic and disease-related phenotypes in a mouse F2 cross derived from strains showing resistance (BALB/c) and increased susceptibility (129S6) to these diseases. Quantitative trait locus (QTL) analysis based on single nucleotide polymorphism (SNP) genotypes identified diet responsive QTLs in F2 mice fed control or high fat diet (HFD). In HFD fed F2 mice we mapped on chromosome 18 a QTL regulating liver micro- and macrovesicular steatosis and inflammation, independently from glucose intolerance and adiposity, which was linked to chromosome 4. Linkage analysis of liver metabolomic profiling data identified a QTL for octopamine, which co-localised with the QTL for liver histopathology in the cross. Functional relationship between these two QTLs was validated in vivo in mice chronically treated with octopamine, which exhibited reduction in liver histopathology and metabolic benefits, underlining its role as a mechanistic biomarker of fatty liver with potential therapeutic applications.

Labelled regulatory elements are pervasive features of the macrophage genome and are dynamically utilized by classical and alternative polarization signals.

The concept of tissue-specific gene expression posits that lineage-determining transcription factors (LDTFs) determine the open chromatin profile of a cell via collaborative binding, providing molecular beacons to signal-dependent transcription factors (SDTFs). However, the guiding principles of LDTF binding, chromatin accessibility and enhancer activity have not yet been systematically evaluated. We sought to study these features of the macrophage genome by the combination of experimental (ChIP-seq, ATAC-seq and GRO-seq) and computational approaches. We show that Random Forest and Support Vector Regression machine learning methods can accurately predict chromatin accessibility using the binding patterns of the LDTF PU.1 and four other key TFs of macrophages (IRF8, JUNB, CEBPA and RUNX1). Any of these TFs alone were not sufficient to predict open chromatin, indicating that TF binding is widespread at closed or weakly opened chromatin regions. Analysis of the PU.1 cistrome revealed that two-thirds of PU.1 binding occurs at low accessible chromatin. We termed these sites labelled regulatory elements (LREs), which may represent a dormant state of a future enhancer and contribute to macrophage cellular plasticity. Collectively, our work demonstrates the existence of LREs occupied by various key TFs, regulating specific gene expression programs triggered by divergent macrophage polarizing stimuli.

gemBS: high throughput processing for DNA methylation data from bisulfite sequencing.

MOTIVATION: DNA methylation is essential for normal embryogenesis and development in mammals and can be captured at single base pair resolution by whole genome bisulfite sequencing (WGBS). Current available analysis tools are becoming rapidly outdated as they lack sensible functionality and efficiency to handle large amounts of data now commonly created. RESULTS: We developed gemBS, a fast high-throughput bioinformatics pipeline specifically designed for large scale BS-Seq analysis that combines a high performance BS-mapper (GEM3) and a variant caller specifically for BS-Seq data (BScall). gemBS provides genotype information and methylation estimates for all genomic cytosines in different contexts (CpG and non-CpG) and a set of quality reports for comprehensive and reproducible analysis. gemBS is highly modular and can be easily automated, while producing robust and accurate results. AVAILABILITY AND IMPLEMENTATION: gemBS is released under the GNU GPLv3+ license. Source code and documentation are freely available from www.statgen.cat/gemBS. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.