Netboost: boosting-supported network analysis improves high-dimensional omics prediction in acute myeloid leukemia and Huntington’s disease

State-of-the art selection methods fail to identify weak but cumulative effects of features found in many high-dimensional omics datasets. Nevertheless, these features play an important role in certain diseases. We present Netboost, a three-step dimension reduction technique. First, a boosting-based filter is combined with the topological overlap measure to identify the essential edges of the network. Second, sparse hierarchical clustering is applied on the selected edges to identify modules and finally module information is aggregated by the first principal components. We demonstrate the application of the newly developed Netboost in combination with CoxBoost for survival prediction of DNA methylation and gene expression data from 180 acute myeloid leukemia (AML) patients and show, based on cross-validated prediction error curve estimates, its prediction superiority over variable selection on the full dataset as well as over an alternative clustering approach. The identified signature related to chromatin modifying enzymes was replicated in an independent dataset, the phase II AMLSG 12-09 study. In a second application we combine Netboost with Random Forest classification and improve the disease classification error in RNA-sequencing data of Huntington's disease mice. Netboost is a freely available Bioconductor R package for dimension reduction and hypothesis generation in high-dimensional omics applications

A promoter DNA demethylation landscape of human hematopoietic differentiation

Global mechanisms defining the gene expression programs specific for hematopoiesis are still not fully understood. Here, we show that promoter DNA demethylation is associated with the activation of hematopoietic-specific genes. Using genome-wide promoter methylation arrays, we identified 694 hematopoietic-specific genes repressed by promoter DNA methylation in human embryonic stem cells and whose loss of methylation in hematopoietic can be associated with gene expression. The association between promoter methylation and gene expression was studied for many hematopoietic-specific genes including CD45, CD34, CD28, CD19, the T cell receptor (TCR), the MHC class II gene HLA-DR, perforin 1 and the phosphoinositide 3-kinase (PI3K) and results indicated that DNA demethylation was not always sufficient for gene activation. Promoter demethylation occurred either early during embryonic development or later on during hematopoietic differentiation. Analysis of the genome-wide promoter methylation status of induced pluripotent stem cells (iPSCs) generated from somatic CD34+ HSPCs and differentiated derivatives from CD34+ HSPCs confirmed the role of DNA methylation in regulating the expression of genes of the hemato-immune system, and indicated that promoter methylation of these genes may be associated to stemness. Together, these data suggest that promoter DNA demethylation might play a role in the tissue/cell-specific genome-wide gene regulation within the hematopoietic compartment

Microbiota-based markers predictive of development of Clostridioides difficile infection

Antibiotic-induced modulation of the intestinal microbiota can lead to Clostridioides difficile infection (CDI), which is associated with considerable morbidity, mortality, and healthcare-costs globally. Therefore, identification of markers predictive of CDI could substantially contribute to guiding therapy and decreasing the infection burden. Here, we analyze the intestinal microbiota of hospitalized patients at increased CDI risk in a prospective, 90-day cohort-study before and after antibiotic treatment and at diarrhea onset. We show that patients developing CDI already exhibit significantly lower diversity before antibiotic treatment and a distinct microbiota enriched in Enterococcus and depleted of Ruminococcus, Blautia, Prevotella and Bifidobacterium compared to non-CDI patients. We find that antibiotic treatment-induced dysbiosis is class-specific with beta-lactams further increasing enterococcal abundance. Our findings, validated in an independent prospective patient cohort developing CDI, can be exploited to enrich for high-risk patients in prospective clinical trials, and to develop predictive microbiota-based diagnostics for management of patients at risk for CDI.Clostridioides difficile infection (CDI) is the most common cause of antibiotic-associated diarrhoea (AAD); however, markers predictive of CDI or AAD development are as yet lacking. Here, to identify markers predictive of CDI, the authors profile the intestinal microbiota of 945 hospitalised patients from 34 hospitals in 6 different European countries and show distinct microbiota enriched in Enterococcus and depleted of Ruminococcus, Blautia, Prevotella and Bifidobacterium compared to non-CDI patients