A Natural Vibrio parahaemolyticus ΔpirA Vp pirB Vp+ Mutant Kills Shrimp but Produces neither Pir Vp Toxins nor Acute Hepatopancreatic Necrosis Disease Lesions.

Acute hepatopancreatic necrosis disease (AHPND) of shrimp is caused by Vibrio parahaemolyticus isolates (VPAHPND isolates) that harbor a pVA plasmid encoding toxins PirA Vp and PirB Vp These are released from VPAHPND isolates that colonize the shrimp stomach and produce pathognomonic AHPND lesions (massive sloughing of hepatopancreatic tubule epithelial cells). PCR results indicated that V. parahaemolyticus isolate XN87 lacked pirA Vp but carried pirB Vp Unexpectedly, Western blot analysis of proteins from the culture broth of XN87 revealed the absence of both toxins, and the lack of PirB Vp was further confirmed by enzyme-linked immunosorbent assay. However, shrimp immersion challenge with XN87 resulted in 47% mortality without AHPND lesions. Instead, lesions consisted of collapsed hepatopancreatic tubule epithelia. In contrast, control shrimp challenged with typical VPAHPND isolate 5HP gave 90% mortality, accompanied by AHPND lesions. Sequence analysis revealed that the pVA plasmid of XN87 contained a mutated pirA Vp gene interrupted by the out-of-frame insertion of a transposon gene fragment. The upstream region and the beginning of the original pirA Vp gene remained intact, but the insertion caused a 2-base reading frameshift in the remainder of the pirA Vp gene sequence and in the downstream pirB Vp gene sequence. Reverse transcription-PCR and sequencing of 5HP revealed a bicistronic pirAB Vp mRNA transcript that was not produced by XN87, explaining the absence of both toxins in its culture broth. However, the virulence of XN87 revealed that some V. parahaemolyticus isolates carrying mutant pVA plasmids that produce no Pir Vp toxins can cause mortality in shrimp in ponds experiencing an outbreak of early mortality syndrome (EMS) but may not have been previously recognized to be AHPND related because they did not cause pathognomonic AHPND lesions.IMPORTANCE Shrimp acute hepatopancreatic necrosis disease (AHPND) is caused by Vibrio parahaemolyticus isolates (VPAHPND isolates) that harbor the pVA1 plasmid encoding toxins PirA Vp and PirB Vp The toxins are produced in the shrimp stomach but cause death by massive sloughing of hepatopancreatic tubule epithelial cells (pathognomonic AHPND lesions). V. parahaemolyticus isolate XN87 harbors a mutant pVA plasmid that produces no Pir toxins and does not cause AHPND lesions but still causes ∼50% shrimp mortality. Such isolates may cause a portion of the mortality in ponds experiencing an outbreak of EMS that is not ascribed to VPAHPND Thus, they pose to shrimp farmers an additional threat that would be missed by current testing for VPAHPND Moribund shrimp from ponds experiencing an outbreak of EMS that exhibit collapsed hepatopancreatic tubule epithelial cells can serve as indicators for the possible presence of such isolates, which can then be confirmed by additional PCR tests for the presence of a pVA plasmid.

Chemical probing of the human sirtuin 5 active site reveals its substrate acyl specificity and peptide-based inhibitors.

Sirtuins are NAD(+)-dependent deacetylases acting as sensors in metabolic pathways and stress response. In mammals there are seven isoforms. The mitochondrial sirtuin 5 is a weak deacetylase but a very efficient demalonylase and desuccinylase; however, its substrate acyl specificity has not been systematically analyzed. Herein, we investigated a carbamoyl phosphate synthetase 1 derived peptide substrate and modified the lysine side chain systematically to determine the acyl specificity of Sirt5. From that point we designed six potent peptide-based inhibitors that interact with the NAD(+) binding pocket. To characterize the interaction details causing the different substrate and inhibition properties we report several X-ray crystal structures of Sirt5 complexed with these peptides. Our results reveal the Sirt5 acyl selectivity and its molecular basis and enable the design of inhibitors for Sirt5.

Graph Convolutional Networks for Drug Response Prediction.

BACKGROUND: Drug response prediction is an important problem in computational personalized medicine. Many machine-learning-based methods, especially deep learning-based ones, have been proposed for this task. However, these methods often represent the drugs as strings, which are not a natural way to depict molecules. Also, interpretation (e.g., what are the mutation or copy number aberration contributing to the drug response) has not been considered thoroughly. METHODS: In this study, we propose a novel method, GraphDRP, based on graph convolutional network for the problem. In GraphDRP, drugs were represented in molecular graphs directly capturing the bonds among atoms, meanwhile cell lines were depicted as binary vectors of genomic aberrations. Representative features of drugs and cell lines were learned by convolution layers, then combined to represent for each drug-cell line pair. Finally, the response value of each drug-cell line pair was predicted by a fully-connected neural network. Four variants of graph convolutional networks were used for learning the features of drugs. RESULTS: We found that GraphDRP outperforms tCNNS in all performance measures for all experiments. Also, through saliency maps of the resulting GraphDRP models, we discovered the contribution of the genomic aberrations to the responses. CONCLUSION: Representing drugs as graphs can improve the performance of drug response prediction. Availability of data and materials: Data and source code can be downloaded athttps://github.com/hauldhut/GraphDRP.

Integrating Molecular Graph Data of Drugs and Multiple -Omic Data of Cell Lines for Drug Response Prediction.

Previous studies have either learned drug's features from their string or numeric representations, which are not natural forms of drugs, or only used genomic data of cell lines for the drug response prediction problem. Here, we proposed a deep learning model, GraOmicDRP, to learn drug's features from their graph representation and integrate multiple -omic data of cell lines. In GraOmicDRP, drugs are represented as graphs of bindings among atoms; meanwhile, cell lines are depicted by not only genomic but also transcriptomic and epigenomic data. Graph convolutional and convolutional neural networks were used to learn the representation of drugs and cell lines, respectively. A combination of the two representations was then used to be representative of each pair of drug-cell line. Finally, the response value of each pair was predicted by a fully connected network. Experimental results indicate that transcriptomic data shows the best among single -omic data; meanwhile, the combinations of transcriptomic and other -omic data achieved the best performance overall in terms of both Root Mean Square Error and Pearson correlation coefficient. In addition, we also show that GraOmicDRP outperforms some state-of-the-art methods, including ones integrating -omic data with drug information such as GraphDRP, and ones using -omic data without drug information such as DeepDR and MOLI.

Pyrazolidine-3,5-dione-based inhibitors of phosphoenolpyruvate carboxylase as a new class of potential C4 plant herbicides.

Phosphoenolpyruvate carboxylase (PEPC) is a key enzyme in the C4 photosynthetic pathway of many of the world's worst weeds and a valuable target to develop C4 plant-selective herbicides. By virtual screening, analog synthesis, and in vitro validation, we identified pyrazolidine-3,5-diones as a new class of small molecules with inhibitory potential down to the submicromolar range against C4 PEPC and a selectivity factor of up to 16 over C3 PEPC. No other biological activity has yet been reported for the best compound, (3-bromophenyl)-4-(3-hydroxybenzylidene)-pyrazolidine-3,5-dione. A systematic variation in the substituents allowed the derivation of a qualitative structure-activity relationship. These findings make this compound class highly interesting for further investigations toward generating potent, C4 plant-selective herbicides with a low potential for unwanted effects.