Epigenetic activation of the FLT3 gene by ZNF384 fusion confers a therapeutic susceptibility in acute lymphoblastic leukemia.

FLT3 is an attractive therapeutic target in acute lymphoblastic leukemia (ALL) but the mechanism for its activation in this cancer is incompletely understood. Profiling global gene expression in large ALL cohorts, we identify over-expression of FLT3 in ZNF384-rearranged ALL, consistently across cases harboring different fusion partners with ZNF384. Mechanistically, we discover an intergenic enhancer element at the FLT3 locus that is exclusively activated in ZNF384-rearranged ALL, with the enhancer-promoter looping directly mediated by the fusion protein. There is also a global enrichment of active enhancers within ZNF384 binding sites across the genome in ZNF384-rearranged ALL cells. Downregulation of ZNF384 blunts FLT3 activation and decreases ALL cell sensitivity to FLT3 inhibitor gilteritinib in vitro. In patient-derived xenograft models of ZNF384-rearranged ALL, gilteritinib exhibits significant anti-leukemia efficacy as a monotherapy in vivo. Collectively, our results provide insights into FLT3 regulation in ALL and point to potential genomics-guided targeted therapy for this patient population

Deregulation of DUX4 and ERG in acute lymphoblastic leukemia

Chromosomal rearrangements deregulating hematopoietic transcription factors are common in acute lymphoblastic leukemia (ALL).1,2 Here, we show that deregulation of the homeobox transcription factor gene DUX4 and the ETS transcription factor gene ERG are hallmarks of a subtype of B-progenitor ALL that comprises up to 7% of B-ALL. DUX4 rearrangement and overexpression was present in all cases, and was accompanied by transcriptional deregulation of ERG, expression of a novel ERG isoform, ERGalt, and frequent ERG deletion. ERGalt utilizes a non-canonical first exon whose transcription was initiated by DUX4 binding. ERGalt retains the DNA-binding and transactivating domains of ERG, but inhibits wild-type ERG transcriptional activity and is transforming. These results illustrate a unique paradigm of transcription factor deregulation in leukemia, in which DUX4 deregulation results in loss-of-function of ERG, either by deletion or induction of expression of an isoform that is a dominant negative inhibitor of wild type ERG function

Large expert-curated database for benchmarking document similarity detection in biomedical literature search

Document recommendation systems for locating relevant literature have mostly relied on methods developed a decade ago. This is largely due to the lack of a large offline gold-standard benchmark of relevant documents that cover a variety of research fields such that newly developed literature search techniques can be compared, improved and translated into practice. To overcome this bottleneck, we have established the RElevant LIterature SearcH consortium consisting of more than 1500 scientists from 84 countries, who have collectively annotated the relevance of over 180 000 PubMed-listed articles with regard to their respective seed (input) article/s. The majority of annotations were contributed by highly experienced, original authors of the seed articles. The collected data cover 76% of all unique PubMed Medical Subject Headings descriptors. No systematic biases were observed across different experience levels, research fields or time spent on annotations. More importantly, annotations of the same document pairs contributed by different scientists were highly concordant. We further show that the three representative baseline methods used to generate recommended articles for evaluation (Okapi Best Matching 25, Term Frequency-Inverse Document Frequency and PubMed Related Articles) had similar overall performances. Additionally, we found that these methods each tend to produce distinct collections of recommended articles, suggesting that a hybrid method may be required to completely capture all relevant articles. The established database server located at https://relishdb.ict.griffith.edu.au is freely available for the downloading of annotation data and the blind testing of new methods. We expect that this benchmark will be useful for stimulating the development of new powerful techniques for title and title/abstract-based search engines for relevant articles in biomedical research.Peer reviewe

Fast and Accurate Computation Schemes for Evaluating Vibrational Entropy of Proteins

Standard normal mode analysis (NMA) method is able to calculate vibrational entropy of proteins, but it is computationally intensive, especially for large proteins. To evaluate vibrational entropy efficiently and accurately, we, here, propose computation schemes based on coarse-grained NMA methods. This can be achieved by rescaling coarse-grained results with a specific factor that is derived on the basis of the linear correlation of protein vibrational entropy between standard NMA and coarse-grained NMA. Our coarse-grained NMA computation schemes can repeat correctly and efficiently the results of standard NMA for large proteins. (C) 2011 Wiley Periodicals, Inc. J Comput Chem 32: 3188-3193, 201

An all-atom knowledge-based energy function for protein-DNA threading, docking decoy discrimination, and prediction of transcription-factor binding profiles

How to make an accurate representation of protein-DNA interaction by an energy function is a long-standing unsolved problem in structural biology. Here, we modified a statistical potential based on the distancescaled, finite ideal-gas reference state so that it is optimized for protein-DNA interactions. The changes include a volume-fraction correction to account for unmixable atom types in proteins and DNA in addition to the usage of a low-count correction, residue/base-specific atom types, and a shorter cutoff distance for protein-DNA interactions. The new statistical energy functions are tested in threading and docking decoy discriminations and prediction of protein-DNA binding affinities and transcriptionfactor binding profiles. The results indicate that new proposed energy functions are among the best in existing energy functions for protein-DNA interactions. The new energy functions are available as a web-server called DDNA 2.0 at http://sparks. informatics.iupui.edu. The server version was trained by the entire 212 protein-DNA complexes

Structural based strategy for predicting transcription factor binding sites.

<p>An illustration of the structural based strategy for predicting transcription factor binding sites: a) A native structure of TF bound with TFBS (PDB id: 2ERE) is used as the structure template (image created by Pymol <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0052460#pone.0052460-Schrodinger1" target="_blank">[54]</a>). Each base pair in the TFBS with length L (length of TFBS is listed in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0052460#pone-0052460-t001" target="_blank">Table 1</a>) is replaced by four kinds of base pairs and only the energy of binding contributed by the substituted base pair and the TF is calculated after the replacement. A L×4 position energy matrix (PEM) is then generated for each TF. b) The sequences of ORFs (here for example YMR108W upstream −470∼−455) are threaded into a specific TF's position energy matrix to get the binding energy of a sequence with the TF. For example, the binding energy of a sequence CTGCCGGTACCGGC would be given as , meanwhile the binding energy of sequence offset by a position, TGCCGGTACCGGCT would be given as . c) The binding energies of all sequences are sorted from lowest to highest (Left), and the binding sites from TRANSFAC and SCPD database (Right) are matched by overlapped position in the same ORF. Overlapped base pairs (Lo) with more than 50% of the binding sites in database (Ld) is considered as True Positive <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0052460#pone.0052460-Chen2" target="_blank">[55]</a>. Note that some binding sites are much longer than that in native complex structure (Ln), in this case, we used Lo/Ln>50% as the criterion for classifying the site as the binding site. False Positive indicates a predicted TFBS not overlapping any TFBSs in the databases. True Negative indicates a TFBS in the databases overlapping with a predicted result not classified as a TFBS. d) Position energy matrix derived from PDB 2ere can be converted to PWM by Boltzmann formula <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0052460#pone.0052460-Berg1" target="_blank">[51]</a>. The weblogo <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0052460#pone.0052460-Crooks1" target="_blank">[56]</a> of converted PWM where β = 0.05. Position 3∼12 is identical to part (e) position 1∼10. e) MA0324.1 NAME: LEU3 from the JASPAR CORE database <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0052460#pone.0052460-Vlieghe1" target="_blank">[50]</a> as comparison. We successful predicted most probability base pair on 9 out of 10 positions.</p

Prediction accuracy.

<p>tFIRE can achieve a average AUC at 0.85±0.13 and many of the predictions are top ranked TFBS.</p>a<p>Transcription factor name, ‘_’ denotes complex by two transcription factors.</p>b<p>TP: true positive. FN: false negative. FP: false positive. SE: Sensitivity [TP/(TP+FN)]. SP: Specificity [TP/(TP+FP)].</p>c<p>Area Under Receiver operating characteristic Curve.</p>d<p>N_TSites: Number of sites ranked top, the higher the better discrimination ability in ORF.</p>e<p>N_Sites: Number of sites collected from TRANSFAC and SCPD.</p>f<p>N_TORF: Number of prediction on how many ORFs achieved top ranked TFBS.</p>g<p>N_ORF: Number of ORFs these binding sites taking place.</p

A Structural-Based Strategy for Recognition of Transcription Factor Binding Sites

<div><p>Scanning through genomes for potential transcription factor binding sites (TFBSs) is becoming increasingly important in this post-genomic era. The position weight matrix (PWM) is the standard representation of TFBSs utilized when scanning through sequences for potential binding sites. However, many transcription factor (TF) motifs are short and highly degenerate, and methods utilizing PWMs to scan for sites are plagued by false positives. Furthermore, many important TFs do not have well-characterized PWMs, making identification of potential binding sites even more difficult. One approach to the identification of sites for these TFs has been to use the 3D structure of the TF to predict the DNA structure around the TF and then to generate a PWM from the predicted 3D complex structure. However, this approach is dependent on the similarity of the predicted structure to the native structure. We introduce here a novel approach to identify TFBSs utilizing structure information that can be applied to TFs without characterized PWMs, as long as a 3D complex structure (TF/DNA) exists. This approach utilizes an energy function that is uniquely trained on each structure. Our approach leads to increased prediction accuracy and robustness compared with those using a more general energy function. The software is freely available upon request.</p> </div