PVT1 interacts with polycomb repressive complex 2 to suppress genomic regions with pro-apoptotic and tumour suppressor functions in multiple myeloma.

Multiple myeloma is a heterogeneous hematological disease that originates from the bone marrow and is characterized by the monoclonal expansion of malignant plasma cells. Despite novel therapies, multiple myeloma remains clinically challenging. A common feature among patients with poor prognosis is the increased activity of the epigenetic silencer EZH2, which is the catalytic subunit of the PRC2. Interestingly, the recruitment of PRC2 lacks sequence specificity and, to date, the molecular mechanisms that define which genomic locations are destined for PRC2-mediated silencing remain unknown. The presence of a long non-coding RNA (lncRNA)-binding pocket on EZH2 suggests that lncRNA could potentially mediate PRC2 recruitment to specific genomic regions. Here, we coupled RNA immunoprecipitation sequencing, RNA-sequencing and chromatin immunoprecipitation-sequencing analysis of human multiple myeloma primary cells and cell lines to identify potential lncRNA partners to EZH2. We found that the lncRNA plasmacytoma variant translocation 1 (PVT1) directly interacts with EZH2 and is overexpressed in patients with a poor prognosis. Moreover, genes predicted to be targets of PVT1 exhibited H3K27me3 enrichment and were associated with pro-apoptotic and tumor suppressor functions. In fact, PVT1 inhibition independently promotes the expression of the PRC2 target genes ZBTB7C, RNF144A and CCDC136. Altogether, our work suggests that PVT1 is an interacting partner in PRC2-mediated silencing of tumor suppressor and pro-apoptotic genes in multiple myeloma, making it a highly interesting potential therapeutic target.

Proteogenomics decodes the evolution of human ipsilateral breast cancer.

Ipsilateral breast tumor recurrence (IBTR) is a clinically important event, where an isolated in-breast recurrence is a potentially curable event but associated with an increased risk of distant metastasis and breast cancer death. It remains unclear if IBTRs are associated with molecular changes that can be explored as a resource for precision medicine strategies. Here, we employed proteogenomics to analyze a cohort of 27 primary breast cancers and their matched IBTRs to define proteogenomic determinants of molecular tumor evolution. Our analyses revealed a relationship between hormonal receptors status and proliferation levels resulting in the gain of somatic mutations and copy number. This in turn re-programmed the transcriptome and proteome towards a highly replicating and genomically unstable IBTRs, possibly enhanced by APOBEC3B. In order to investigate the origins of IBTRs, a second analysis that included primaries with no recurrence pinpointed proliferation and immune infiltration as predictive of IBTR. In conclusion, our study shows that breast tumors evolve into different IBTRs depending on hormonal status and proliferation and that immune cell infiltration and Ki-67 are significantly elevated in primary tumors that develop IBTR. These results can serve as a starting point to explore markers to predict IBTR formation and stratify patients for adjuvant therapy.

Analysing high-throughput sequencing data in Python with HTSeq 2.0.

SUMMARY: HTSeq 2.0 provides a more extensive application programming interface including a new representation for sparse genomic data, enhancements for htseq-count to suit single-cell omics, a new script for data using cell and molecular barcodes, improved documentation, testing and deployment, bug fixes and Python 3 support. AVAILABILITY AND IMPLEMENTATION: HTSeq 2.0 is released as an open-source software under the GNU General Public License and is available from the Python Package Index at https://pypi.python.org/pypi/HTSeq. The source code is available on Github at https://github.com/htseq/htseq. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Cerebrospinal fluid proteome maps detect pathogen-specific host response patterns in meningitis.

Meningitis is a potentially life-threatening infection characterized by the inflammation of the leptomeningeal membranes. Many different viral and bacterial pathogens can cause meningitis, with differences in mortality rates, risk of developing neurological sequelae, and treatment options. Here, we constructed a compendium of digital cerebrospinal fluid (CSF) proteome maps to define pathogen-specific host response patterns in meningitis. The results revealed a drastic and pathogen-type specific influx of tissue-, cell-, and plasma proteins in the CSF, where, in particular, a large increase of neutrophil-derived proteins in the CSF correlated with acute bacterial meningitis. Additionally, both acute bacterial and viral meningitis result in marked reduction of brain-enriched proteins. Generation of a multiprotein LASSO regression model resulted in an 18-protein panel of cell- and tissue-associated proteins capable of classifying acute bacterial meningitis and viral meningitis. The same protein panel also enabled classification of tick-borne encephalitis, a subgroup of viral meningitis, with high sensitivity and specificity. The work provides insights into pathogen-specific host response patterns in CSF from different disease etiologies to support future classification of pathogen type based on host response patterns in meningitis.

Proteogenomic Workflow Reveals Molecular Phenotypes Related to Breast Cancer Mammographic Appearance.

Proteogenomic approaches have enabled the generat̲ion of novel information levels when compared to single omics studies although burdened by extensive experimental efforts. Here, we improved a data-independent acquisition mass spectrometry proteogenomic workflow to reveal distinct molecular features related to mammographic appearances in breast cancer. Our results reveal splicing processes detectable at the protein level and highlight quantitation and pathway complementarity between RNA and protein data. Furthermore, we confirm previously detected enrichments of molecular pathways associated with estrogen receptor-dependent activity and provide novel evidence of epithelial-to-mesenchymal activity in mammography-detected spiculated tumors. Several transcript-protein pairs displayed radically different abundances depending on the overall clinical properties of the tumor. These results demonstrate that there are differentially regulated protein networks in clinically relevant tumor subgroups, which in turn alter both cancer biology and the abundance of biomarker candidates and drug targets.

Analysis of nonleukemic cellular subcompartments reconstructs clonal evolution of acute myeloid leukemia and identifies therapy-resistant preleukemic clones.

To acquire a better understanding of clonal evolution of acute myeloid leukemia (AML) and to identify the clone(s) responsible for disease recurrence, we have comparatively studied leukemia-specific mutations by whole-exome-sequencing (WES) of both the leukemia and the nonleukemia compartments derived from the bone marrow of AML patients. The T-lymphocytes, B-lymphocytes and the functionally normal hematopoietic stem cells (HSC), that is, CD34+ /CD38- /ALDH+ cells for AML with rare-ALDH+ blasts (<1.9% ALDH+ cells) were defined as the nonleukemia compartments. WES identified 62 point-mutations in the leukemia compartment derived from 12 AML-patients at the time of diagnosis and 73 mutations in 3 matched relapse cases. Most patients (8/12) showed 4 to 6 point-mutations per sample at diagnosis. Other than the mutations in the recurrently mutated genes such as DNMT3A, NRAS and KIT, we were able to identify novel point-mutations that have not yet been described in AML. Some leukemia-specific mutations and cytogenetic abnormalities including DNMT3A(R882H), EZH2(I146T) and inversion(16) were also detectable in the respective T-lymphocytes, B-lymphocytes and HSC in 5/12 patients, suggesting that preleukemia HSC might represent the source of leukemogenesis for these cases. The leukemic evolution was reconstructed for five cases with detectable preleukemia clones, which were tracked in follow-up and relapse samples. Four of the five patients with detectable preleukemic mutations developed relapse. The presence of leukemia-specific mutations in these nonleukemia compartments, especially after chemotherapy or after allogeneic stem cell transplantation, is highly relevant, as these could be responsible for relapse. This discovery may facilitate the identification of novel targets for long-term cure.

Meta-analysis of fecal metagenomes reveals global microbial signatures that are specific for colorectal cancer.

Association studies have linked microbiome alterations with many human diseases. However, they have not always reported consistent results, thereby necessitating cross-study comparisons. Here, a meta-analysis of eight geographically and technically diverse fecal shotgun metagenomic studies of colorectal cancer (CRC, n = 768), which was controlled for several confounders, identified a core set of 29 species significantly enriched in CRC metagenomes (false discovery rate (FDR) < 1 × 10-5). CRC signatures derived from single studies maintained their accuracy in other studies. By training on multiple studies, we improved detection accuracy and disease specificity for CRC. Functional analysis of CRC metagenomes revealed enriched protein and mucin catabolism genes and depleted carbohydrate degradation genes. Moreover, we inferred elevated production of secondary bile acids from CRC metagenomes, suggesting a metabolic link between cancer-associated gut microbes and a fat- and meat-rich diet. Through extensive validations, this meta-analysis firmly establishes globally generalizable, predictive taxonomic and functional microbiome CRC signatures as a basis for future diagnostics.

Recovery of gut microbiota of healthy adults following antibiotic exposure.

To minimize the impact of antibiotics, gut microorganisms harbour and exchange antibiotics resistance genes, collectively called their resistome. Using shotgun sequencing-based metagenomics, we analysed the partial eradication and subsequent regrowth of the gut microbiota in 12 healthy men over a 6-month period following a 4-day intervention with a cocktail of 3 last-resort antibiotics: meropenem, gentamicin and vancomycin. Initial changes included blooms of enterobacteria and other pathobionts, such as Enterococcus faecalis and Fusobacterium nucleatum, and the depletion of Bifidobacterium species and butyrate producers. The gut microbiota of the subjects recovered to near-baseline composition within 1.5 months, although 9 common species, which were present in all subjects before the treatment, remained undetectable in most of the subjects after 180 days. Species that harbour ß-lactam resistance genes were positively selected for during and after the intervention. Harbouring glycopeptide or aminoglycoside resistance genes increased the odds of de novo colonization, however, the former also decreased the odds of survival. Compositional changes under antibiotic intervention in vivo matched results from in vitro susceptibility tests. Despite a mild yet long-lasting imprint following antibiotics exposure, the gut microbiota of healthy young adults are resilient to a short-term broad-spectrum antibiotics intervention and their antibiotics resistance gene carriage modulates their recovery processes.

HTSeq--a Python framework to work with high-throughput sequencing data.

MOTIVATION: A large choice of tools exists for many standard tasks in the analysis of high-throughput sequencing (HTS) data. However, once a project deviates from standard workflows, custom scripts are needed. RESULTS: We present HTSeq, a Python library to facilitate the rapid development of such scripts. HTSeq offers parsers for many common data formats in HTS projects, as well as classes to represent data, such as genomic coordinates, sequences, sequencing reads, alignments, gene model information and variant calls, and provides data structures that allow for querying via genomic coordinates. We also present htseq-count, a tool developed with HTSeq that preprocesses RNA-Seq data for differential expression analysis by counting the overlap of reads with genes. AVAILABILITY AND IMPLEMENTATION: HTSeq is released as an open-source software under the GNU General Public Licence and available from http://www-huber.embl.de/HTSeq or from the Python Package Index at https://pypi.python.org/pypi/HTSeq.

h5vc: scalable nucleotide tallies with HDF5.

SUMMARY: As applications of genome sequencing, including exomes and whole genomes, are expanding, there is a need for analysis tools that are scalable to large sets of samples and/or ultra-deep coverage. Many current tool chains are based on the widely used file formats BAM and VCF or VCF-derivatives. However, for some desirable analyses, data management with these formats creates substantial implementation overhead, and much time is spent parsing files and collating data. We observe that a tally data structure, i.e. the table of counts of nucleotides × samples × strands × genomic positions, provides a reasonable intermediate level of abstraction for many genomics analyses, including single nucleotide variant (SNV) and InDel calling, copy-number estimation and mutation spectrum analysis. Here we present h5vc, a data structure and associated software for managing tallies. The software contains functionality for creating tallies from BAM files, flexible and scalable data visualization, data quality assessment, computing statistics relevant to variant calling and other applications. Through the simplicity of its API, we envision making low-level analysis of large sets of genome sequencing data accessible to a wider range of researchers. AVAILABILITY AND IMPLEMENTATION: The package H5VC for the statistical environment R is available through the Bioconductor project. The HDF5 system is used as the core of our implementation. CONTACT: pyl@embl.de or whuber@embl.de SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

The genomic and transcriptomic landscape of a HeLa cell line.

HeLa is the most widely used model cell line for studying human cellular and molecular biology. To date, no genomic reference for this cell line has been released, and experiments have relied on the human reference genome. Effective design and interpretation of molecular genetic studies performed using HeLa cells require accurate genomic information. Here we present a detailed genomic and transcriptomic characterization of a HeLa cell line. We performed DNA and RNA sequencing of a HeLa Kyoto cell line and analyzed its mutational portfolio and gene expression profile. Segmentation of the genome according to copy number revealed a remarkably high level of aneuploidy and numerous large structural variants at unprecedented resolution. Some of the extensive genomic rearrangements are indicative of catastrophic chromosome shattering, known as chromothripsis. Our analysis of the HeLa gene expression profile revealed that several pathways, including cell cycle and DNA repair, exhibit significantly different expression patterns from those in normal human tissues. Our results provide the first detailed account of genomic variants in the HeLa genome, yielding insight into their impact on gene expression and cellular function as well as their origins. This study underscores the importance of accounting for the strikingly aberrant characteristics of HeLa cells when designing and interpreting experiments, and has implications for the use of HeLa as a model of human biology.