Mibianto: ultra-efficient online microbiome analysis through k-mer based metagenomics.

Quantifying microbiome species and composition from metagenomic assays is often challenging due to its time-consuming nature and computational complexity. In Bioinformatics, k-mer-based approaches were long established to expedite the analysis of large sequencing data and are now widely used to annotate metagenomic data. We make use of k-mer counting techniques for efficient and accurate compositional analysis of microbiota from whole metagenome sequencing. Mibianto solves this problem by operating directly on read files, without manual preprocessing or complete data exchange. It handles diverse sequencing platforms, including short single-end, paired-end, and long read technologies. Our sketch-based workflow significantly reduces the data volume transferred from the user to the server (up to 99.59% size reduction) to subsequently perform taxonomic profiling with enhanced efficiency and privacy. Mibianto offers functionality beyond k-mer quantification; it supports advanced community composition estimation, including diversity, ordination, and differential abundance analysis. Our tool aids in the standardization of computational workflows, thus supporting reproducibility of scientific sequencing studies. It is adaptable to small- and large-scale experimental designs and offers a user-friendly interface, thus making it an invaluable tool for both clinical and research-oriented metagenomic studies. Mibianto is freely available without the need for a login at: https://www.ccb.uni-saarland.de/mibianto.

EpiSegMix: a flexible distribution hidden Markov model with duration modeling for chromatin state discovery.

MOTIVATION: Automated chromatin segmentation based on ChIP-seq (chromatin immunoprecipitation followed by sequencing) data reveals insights into the epigenetic regulation of chromatin accessibility. Existing segmentation methods are constrained by simplifying modeling assumptions, which may have a negative impact on the segmentation quality. RESULTS: We introduce EpiSegMix, a novel segmentation method based on a hidden Markov model with flexible read count distribution types and state duration modeling, allowing for a more flexible modeling of both histone signals and segment lengths. In a comparison with existing tools, ChromHMM, Segway, and EpiCSeg, we show that EpiSegMix is more predictive of cell biology, such as gene expression. Its flexible framework enables it to fit an accurate probabilistic model, which has the potential to increase the biological interpretability of chromatin states. AVAILABILITY AND IMPLEMENTATION: Source code: https://gitlab.com/rahmannlab/episegmix.

Three-Year Mortality of Older Hospitalized Patients with Osteosarcopenia: Data from the OsteoSys Study.

Osteosarcopenia, the concurrent presence of sarcopenia and osteopenia/osteoporosis, poses a significant health risk to older adults, yet its impact on clinical outcomes is not fully understood. The aim of this prospective, longitudinal multicentre study was to examine the impact of osteosarcopenia on 3-year mortality and unplanned hospitalizations among 572 older hospitalized patients (mean age 75.1 ± 10.8 years, 78% female). Sarcopenia and low bone mineral density (BMD) were evaluated using Dual Energy X-ray Absorptiometry and the European Working Group on Sarcopenia in Older People (EWGSOP2) and WHO criteria, respectively. Among participants, 76% had low BMD, 9% were sarcopenic, and 8% had osteosarcopenia. Individuals with osteosarcopenia experienced a significantly higher rate of mortality (46%, p < 001) and unplanned hospitalization (86%, p < 001) compared to those without this condition. Moreover, "healthy" subjects-those without sarcopenia or low BMD-showed markedly lower 3-year mortality (9%, p < 001) and less unplanned hospitalization (53%, p < 001). The presence of osteosarcopenia (p = 0.009) increased the 3-year mortality risk by 30% over sarcopenia alone and by 8% over low BMD alone, underscoring the severe health implications of concurrent muscle and bone deterioration. This study highlights the substantial impact of osteosarcopenia on mortality among older adults, emphasizing the need for targeted diagnostic and therapeutic strategies.

Pancreatic cancer acquires resistance to MAPK pathway inhibition by clonal expansion and adaptive DNA hypermethylation.

BACKGROUND: Pancreatic ductal adenocarcinoma (PDAC) is an aggressive cancer with poor prognosis. It is marked by extraordinary resistance to conventional therapies including chemotherapy and radiation, as well as to essentially all targeted therapies evaluated so far. More than 90% of PDAC cases harbor an activating KRAS mutation. As the most common KRAS variants in PDAC remain undruggable so far, it seemed promising to inhibit a downstream target in the MAPK pathway such as MEK1/2, but up to now preclinical and clinical evaluation of MEK inhibitors (MEKi) failed due to inherent and acquired resistance mechanisms. To gain insights into molecular changes during the formation of resistance to oncogenic MAPK pathway inhibition, we utilized short-term passaged primary tumor cells from ten PDACs of genetically engineered mice. We followed gain and loss of resistance upon MEKi exposure and withdrawal by longitudinal integrative analysis of whole genome sequencing, whole genome bisulfite sequencing, RNA-sequencing and mass spectrometry data. RESULTS: We found that resistant cell populations under increasing MEKi treatment evolved by the expansion of a single clone but were not a direct consequence of known resistance-conferring mutations. Rather, resistant cells showed adaptive DNA hypermethylation of 209 and hypomethylation of 8 genomic sites, most of which overlap with regulatory elements known to be active in murine PDAC cells. Both DNA methylation changes and MEKi resistance were transient and reversible upon drug withdrawal. Furthermore, MEKi resistance could be reversed by DNA methyltransferase inhibition with remarkable sensitivity exclusively in the resistant cells. CONCLUSION: Overall, the concept of acquired therapy resistance as a result of the expansion of a single cell clone with epigenetic plasticity sheds light on genetic, epigenetic and phenotypic patterns during evolvement of treatment resistance in a tumor with high adaptive capabilities and provides potential for reversion through epigenetic targeting.

A MYCN-driven de-differentiation profile identifies a subgroup of aggressive retinoblastoma.

Retinoblastoma are childhood eye tumors arising from retinal precursor cells. Two distinct retinoblastoma subtypes with different clinical behavior have been described based on gene expression and methylation profiling. Using consensus clustering of DNA methylation analysis from 61 retinoblastomas, we identify a MYCN-driven cluster of subtype 2 retinoblastomas characterized by DNA hypomethylation and high expression of genes involved in protein synthesis. Subtype 2 retinoblastomas outside the MYCN-driven cluster are characterized by high expression of genes from mesodermal development, including NKX2-5. Knockdown of MYCN expression in retinoblastoma cell models causes growth arrest and reactivates a subtype 1-specific photoreceptor signature. These molecular changes suggest that removing the driving force of MYCN oncogenic activity rescues molecular circuitry driving subtype 1 biology. The MYCN-RB gene signature generated from the cell models better identifies MYCN-driven retinoblastoma than MYCN amplification and can identify cases that may benefit from MYCN-targeted therapy. MYCN drives tumor progression in a molecularly defined retinoblastoma subgroup, and inhibiting MYCN activity could restore a more differentiated and less aggressive tumor biology.