The efficiency of multi-target drugs: the network approach might help drug design

Despite considerable progress in genome- and proteome-based high-throughput screening methods and rational drug design, the number of successful single target drugs did not increase appreciably during the past decade. Network models suggest that partial inhibition of a surprisingly small number of targets can be more efficient than the complete inhibition of a single target. This and the success stories of multi-target drugs and combinatorial therapies led us to suggest that systematic drug design strategies should be directed against multiple targets. We propose that the final effect of partial, but multiple drug actions might often surpass that of complete drug action at a single target. The future success of this novel drug design paradigm will depend not only on a new generation of computer models to identify the correct multiple hits and their multi-fitting, low-affinity drug candidates but also on more efficient in vivo testing.Comment: 6 pages, 2 figures, 1 box, 38 reference

TPMCalculator: one-step software to quantify mRNA abundance of genomic features.

The quantification of RNA sequencing (RNA-seq) abundance using a normalization method that calculates transcripts per million (TPM) is a key step to compare multiple samples from different experiments. TPMCalculator is a one-step software to process RNA-seq alignments in BAM format and reports TPM values, raw read counts and feature lengths for genes, transcripts, exons and introns. The program describes the genomic features through a model generated from the gene transfer format file used during alignments reporting of the TPM values and the raw read counts for each feature. In this paper, we show the correlation for 1256 samples from the TCGA-BRCA project between TPM and FPKM reported by TPMCalculator and RSeQC. We also show the correlation for raw read counts reported by TPMCalculator, HTSeq and featureCounts.TPMCalculator is freely available at https://github.com/ncbi/TPMCalculator. It is implemented in C++14 and supported on Mac OS X, Linux and MS Windows.Supplementary data are available at Bioinformatics online

DNA and RNA Cleavage Complexes and Repair Pathway for TOP3B RNA- and DNA-Protein Crosslinks

The present study demonstrates that topoisomerase 3B (TOP3B) forms both RNA and DNA cleavage complexes (TOP3Bccs) in vivo and reveals a pathway for repairing TOP3Bccs. For inducing and detecting cellular TOP3Bccs, we engineer a self-trapping mutant of TOP3B (R338W-TOP3B). Transfection with R338W-TOP3B induces R-loops, genomic damage, and growth defect, which highlights the importance of TOP3Bcc repair mechanisms. To determine how cells repair TOP3Bccs, we deplete tyrosyl-DNA phosphodiesterases (TDP1 and TDP2). TDP2-deficient cells show elevated TOP3Bccs both in DNA and RNA. Conversely, overexpression of TDP2 lowers cellular TOP3Bccs. Using recombinant human TDP2, we demonstrate that TDP2 can process both denatured and proteolyzed TOP3Bccs. We also show that cellular TOP3Bccs are ubiquitinated by the E3 ligase TRIM41 before undergoing proteasomal processing and excision by TDP2

The DNA secondary structure of the<i>Bacillus subtilis</i>genome

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The virtue of temperance: built-in negative regulators of quorum sensing in Pseudomonas.

Many bacteria are now believed to produce small signal molecules in order to communicate in a process called quorum sensing (QS), which mediates cooperative traits and a co-ordinated behaviour. Pseudomonads have been extensively studied for their QS response highlighting that it plays a major role in determining their lifestyle. The main QS signal molecules produced by Pseudomonas belong to the family of N-acyl-homoserine lactones (AHLs); these are synthesized by a LuxI-family synthase and sensed by a LuxR-family regulator. Most often in Pseudomonas, repressor genes intergenically located between luxI and luxR form an integral part of QS system. Recent studies have highlighted an important role of these repressors (called RsaL and RsaM) in containing the QS response within cost-effective levels; this is central for pseudomonads as they have very versatile genomes allowing them to live in constantly changing and highly dynamic environments. This review focuses on the role played by RsaL and RsaM repressors and discusses the important implications of this control of the QS response