The expanded genome of Hexamita inflata, a free-living diplomonad by Zeynep Akdeniz, Michal Havelka, Michal Stoklasa, Alejandro Jiménez-González, Vojtěch Žárský, Feifei Xu, Courtney W. Stairs, Jon Jerlström-Hultqvist, Martin Kolísko, Jan Provazník, Staffan Svärd, Jan O. Andersson, Jan Tachezy

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Exploiting fourth-generation synchrotron radiation for enzyme and photoreceptor characterization by Tek Narsingh Malla, Srinivasan Muniyappan, David Menendez, Favour Ogukwe, Aleksandar N. Dale, Joseph D. Clayton, Dominique D. Weatherall, Prabin Karki, Shishir Dangi, Victoria Mandella, A. Andrew Pacheco, Emina A. Stojković, Samuel L. Rose, Julien Orlans, Shibom Basu, Daniele de Sanctis, Marius Schmidt

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Fecal microbiota transplantation to prevent acute graft-versus-host disease: pre-planned interim analysis of donor effect by Swetha Reddi, Liliia Senyshyn, Maryam Ebadi, Daniel Podlesny, Samuel S. Minot, Ted Gooley, Amanda J. Kabage, Geoffrey R. Hill, Stephanie J. Lee, Alexander Khoruts, Armin Rashidi

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Machine learning and molecular dynamics simulations predict potential TGR5 agonists for type 2 diabetes treatment by Ojochenemi A. Enejoh, Chinelo H. Okonkwo, Hector Nortey, Olalekan A. Kemiki, Ainembabazi Moses, Ainembabazi Moses, Florence N. Mbaoji, Abdulrazak S. Yusuf, Olaitan I. Awe

“…This study used machine learning-based models (ML), molecular docking (MD), and molecular dynamics simulations (MDS) to explore novel small molecules as potential TGR5 agonists.MethodsBioactivity data for known TGR5 agonists were obtained from the ChEMBL database. The dataset was cleaned and molecular descriptors based on Lipinski’s rule of five were selected as input features for the ML model, which was built using the Random Forest algorithm. …”
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Effect of Acetyl tributyl citrate on bone metabolism based on network toxicology and molecular docking technology by Xuan Lin, Kun Lin, Yue Lai, Qingping Peng, Miao Xu, Yiting Xu, Jialin Yang, Huan Liu, Jianlin Shen

“…Leveraging the exhaustive exploration of databases such as ChEMBL, STITCH, GeneCards, and OMIM, we have identified a comprehensive list of 164 potential targets intimately associated with both ATBC and bone metabolism. …”
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Nicotinic α7 receptors on cholinergic neurons in the striatum mediate cocaine-reinforcement, but not food reward by Michael Fritz, Michael Fritz, Michael Fritz, Priscila Batista Rosa, Daniel Wilhelms, Daniel Wilhelms, Maarit Jaarola, Johan Ruud, David Engblom, Anna M. Klawonn, Anna M. Klawonn, Anna M. Klawonn, Anna M. Klawonn

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Better understanding the phenotypic effects of drugs through shared targets in genetic disease networks by Elena Díaz-Santiago, Aurelio A. Moya-García, Jesús Pérez-García, Raquel Yahyaoui, Raquel Yahyaoui, Christine Orengo, Florencio Pazos, James R. Perkins, James R. Perkins, James R. Perkins, Juan A. G. Ranea, Juan A. G. Ranea, Juan A. G. Ranea, Juan A. G. Ranea

“…Gaining traction in this field is the use of disease data linking genes with pathological phenotypes and combining this with drugtarget interaction data.MethodsWe introduce methodology to associate drugs with effects, both intended and adverse, using a tripartite network approach that combines drug-target and target-phenotype data, in which targets can be represented as proteins and protein domains.ResultsWe were able to detect associations for over 140,000 ChEMBL drugs and 3,800 phenotypes, represented as Human Phenotype Ontology (HPO) terms. …”
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Inflammatory bowel disease increases the risk of pancreatitis: a two-sample bidirectional Mendelian randomization analysis by Li-Hui Fang, Jia-Qi Zhang, Jin-Ke Huang, Xu-Dong Tang

“…Four independent summary statistics of pancreatitis from the the European Bioinformatics Institute (EMBL-EBI, 10,630 AP cases and 844,679 controls, 1,424 CP cases and 476,104 controls) and FinnGen Consortium (8,446 AP cases, 4,820 CP cases and 437,418 controls) were used for bidirectional MR analyses and sensitivity analysis. …”
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Integrating pharmacogenomics and cheminformatics with diverse disease phenotypes for cell type-guided drug discovery by Arda Halu, Sarvesh Chelvanambi, Julius L. Decano, Joan T. Matamalas, Mary Whelan, Takaharu Asano, Namitra Kalicharran, Sasha A. Singh, Joseph Loscalzo, Masanori Aikawa

“…Using these networks and a large-scale disease-gene network consisting of 569 disease signatures from the Enrichr database, we calculate Pathophenotypic Congruity Scores (PACOS) between input gene signatures and drug perturbation signatures and combine these scores with cheminformatic data from ChEMBL to prioritize drugs. We benchmark our approach by calculating area under the receiver operating characteristic curves (AUROC) for 73 gene sets from the Molecular Signatures Database (MSigDB) using target gene expression profiles from the Comparative Toxicogenomics Database (CTD). …”
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A conserved switch to less catalytically active Polycomb repressive complexes in non-dividing cells by Rachel McCole, James Nolan, David M. Reck, Craig Monger, Samantha Rustichelli, Eric Conway, Gerard L. Brien, Cheng Wang, Orla Deevy, Hannah K. Neikes, Frances M. Bashore, Aoibhinn Mooney, Richard Flavin, Elisabeth Vandenberghe, Sarena F. Flanigan, Diego Pasini, Chen Davidovich, Michiel Vermeulen, Lindsey I. James, Evan Healy, Adrian P. Bracken

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