MEIS1 in Hematopoiesis and Cancer. How MEIS1-PBX Interaction Can Be Used in Therapy

Recently MEIS1 emerged as a major determinant of the MLL-r leukemic phenotype. The latest and most efficient drugs effectively decrease the levels of MEIS1 in cancer cells. Together with an overview of the latest drugs developed to target MEIS1 in MLL-r leukemia, we review, in detail, the role of MEIS1 in embryonic and adult hematopoiesis and suggest how a more profound knowledge of MEIS1 biochemistry can be used to design potent and effective drugs against MLL-r leukemia. In addition, we present data showing that the interaction between MEIS1 and PBX1 can be blocked efficiently and might represent a new avenue in anti-MLL-r and anti-leukemic therapy

Purification and characterization of a DNA-binding recombinant PREP1:PBX1 complex.

Human PREP1 and PBX1 are homeodomain transcriptional factors, whose biochemical and structural characterization has not yet been fully described. Expression of full-length recombinant PREP1 (47.6 kDa) and PBX1 (46.6 kDa) in E. coli is difficult because of poor yield, high instability and insufficient purity, in particular for structural studies. We cloned the cDNA of both proteins into a dicistronic vector containing an N-terminal glutathione S-transferase (GST) tag and co-expressed and co-purified a stable PBX1:PREP1 complex. For structural studies, we produced two C-terminally truncated complexes that retain their ability to bind DNA and are more stable than the full-length proteins through various purification steps. Here we report the production of large amounts of soluble and pure recombinant human PBX1:PREP1 complex in an active form capable of binding DNA

Setting up of a machine learning algorithm for the identification of severe liver fibrosis profile in the general US population cohort

Background The progress of digital transformation in clinical practice opens the door to transforming the current clinical line for liver disease diagnosis from a late-stage diagnosis approach to an early-stage based one. Early diagnosis of liver fibrosis can prevent the progression of the disease and decrease liver-related morbidity and mortality. We developed here a machine learning (ML) algorithm containing standard parameters that can identify liver fibrosis in the general US population. Materials and methods Starting from a public database (National Health and Nutrition Examination Survey, NHANES), representative of the American population with 7265 eligible subjects (control population n = 6828, with Fibroscan values E < 9.7 KPa; target population n = 437 with Fibroscan values E ≥ 9.7 KPa), we set up an SVM algorithm able to discriminate for individuals with liver fibrosis among the general US population. The algorithm set up involved the removal of missing data and a sampling optimization step to managing the data imbalance (only ∼ 5 % of the dataset is the target population). Results For the feature selection, we performed an unbiased analysis, starting from 33 clinical, anthropometric, and biochemical parameters regardless of their previous application as biomarkers of liver diseases. Through PCA analysis, we identified the 26 more significant features and then used them to set up a sampling method on an SVM algorithm. The best sampling technique to manage the data imbalance was found to be oversampling through the SMOTE-NC. For final model validation, we utilized a subset of 300 individuals (150 with liver fibrosis and 150 controls), subtracted from the main dataset prior to sampling. Performances were evaluated on multiple independent runs. Conclusions We provide proof of concept of an ML clinical decision support tool for liver fibrosis diagnosis in the general US population. Though the presented ML model represents at this stage only a prototype, in the future, it might be implemented and potentially applied to program broad screenings for liver fibrosis

Electrophoretic migration of protein samples after three chromatographic steps.

<p><b>A, B, and C.</b> 2 μl of concentrated samples (at 10 mg/ml) of PBX1_1–430:PREP1_1–436, PBX1_1–308:PREP1_1–344 and PBX1_1–317:PREP1_1–344 were loaded onto SDS PAGEs. The full-length proteins are shown in panel A and some degradation products are present. The C-terminal deletion mutants of high purity are shown on the panels B and C. <b>D and E.</b> Immunoblots of purified PBX1_1–430:PREP1_1–436 complex, where the N-terminal degradation of both PBX1 and PREP1 is evident.</p

O1 oligos.

<p>O1 oligos.</p

Limited proteolysis analysis of recombinant PREP1 and PBX1.

<p>Full length PREP1 and PBX1 were subjected to limited proteolysis with trypsin. The reactions (total volume 100 μl) were performed at room temperature, 10 μl volumes were taken out at the indicated time points, supplemented with sample buffer and boiled prior to loading onto SDS PAGE. The gels were Coomassie stained. <b>A. Limited proteolysis of PREP1.</b> Lane M, Bio-Rad size standard; two bands of ~40 kDa (1) and ~28 kDa (2) were chosen for subsequent N-terminal sequencing. <b>B. Limited proteolysis of PBX1 with trypsin.</b> Lane M, Bio-Rad size standard; band 3 is the proteolysis fragment chosen for mass spectrometry analysis. <b>C and D. Identification of PREP1 and PBX1 fragments by MALDI-TOF mass spectrometry analysis.</b> Peptides of PREP1 and PBX1 were identified by MALDI-TOF analysis after digestion of fragments 1–3 with trypsin. Fragment 1 contained PREP1 and the matching peptides (red) covered 52.5% starting from the N-terminus and ending at residue 344. Fragment 2 contains the N-terminal part of PREP1 excluding the homeodomain. Fragment 3 contains PBX1, and the peptides (blue) covered 40.2% of the sequence, from residue 7 to 308.</p

Primers used for cloning of PREP1 and PBX1 constructs into PGEX-6p-2rbs.

<p>Primers used for cloning of PREP1 and PBX1 constructs into PGEX-6p-2rbs.</p