Compiling Multicopy Single-Stranded DNA Sequences from Bacterial Genome Sequences

A retron is a bacterial retroelement that encodes an RNA gene and a reverse transcriptase (RT). The former, once transcribed, works as a template primer for reverse transcription by the latter. The resulting DNA is covalently linked to the upstream part of the RNA; this chimera is called multicopy single-stranded DNA (msDNA), which is extrachromosomal DNA found in many bacterial species. Based on the conserved features in the eight known msDNA sequences, we developed a detection method and applied it to scan National Center for Biotechnology Information (NCBI) RefSeq bacterial genome sequences. Among 16,844 bacterial sequences possessing a retron-type RT domain, we identified 48 unique types of msDNA. Currently, the biological role of msDNA is not well understood. Our work will be a useful tool in studying the distribution, evolution, and physiological role of msDNA

Intravenous lipid emulsion therapy for cardiac arrest and refractory ventricular tachycardia due to multiple herb intoxication

Herbal products have been used for therapeutic purposes for a long time. However, many herbs can be toxic and even life-threatening. If refractory ventricular tachycardia (VT) is caused by herbal products and shows no response to conventional therapy, intravenous lipid emulsion (ILE) therapy can be considered. We report a case of herbal intoxication leading to refractory VT, which was successfully treated with ILE therapy. A 36-year-old woman with aplastic anemia presented with mental changes. She had taken an unknown herbal decoction three days before visiting the hospital. Soon after coming to the hospital, she went into cardiac arrest. Cardiopulmonary resuscitation was performed, and return of spontaneous circulation with VT was achieved. Synchronized cardioversion was then performed and amiodarone was administered. However, VT with pulse continued, so ILE therapy was attempted, which led to the resolution of VT

Remodeling Pattern of Spinal Canal after Full Endoscopic Uniportal Lumbar Endoscopic Unilateral Laminotomy for Bilateral Decompression: One Year Repetitive MRI and Clinical Follow-Up Evaluation

Objective: There is limited literature on repetitive postoperative MRI and clinical evaluation after Uniportal Lumbar Endoscopic Unilateral Laminotomy for Bilateral Decompression. Methods: Clinical visual analog scale, Oswestry Disability Index, McNab's criteria evaluation and MRI evaluation of the axial cut spinal canal area of the upper end plate, mid disc and lower end plate were performed for patients who underwent single-level Uniportal Lumbar Endoscopic Unilateral Laminotomy for Bilateral Decompression. From the evaluation of the axial cut MRI, four types of patterns of remodeling were identified: type A: continuous expanded spinal canal, type B: restenosis with delayed expansion, type C: progressive expansion and type D: restenosis. Result: A total of 126 patients with single-level Uniportal Lumbar Endoscopic Unilateral Laminotomy for Bilateral Decompression were recruited with a minimum follow-up of 26 months. Thirty-six type A, fifty type B, thirty type C and ten type D patterns of spinal canal remodeling were observed. All four types of patterns of remodeling had statistically significant improvement in VAS at final follow-up compared to the preoperative state with type A (5.59 +/- 1.58), B (5.58 +/- 1.71), C (5.58 +/- 1.71) and D (5.27 +/- 1.68), p < 0.05. ODI was significantly improved at final follow-up with type A (49.19 +/- 10.51), B (50.00 +/- 11.29), C (45.60 +/- 10.58) and D (45.60 +/- 10.58), p < 0.05. A significant MRI axial cut increment of the spinal canal area was found at the upper endplate at postoperative day one and one year with type A (39.16 +/- 22.73; 28.00 +/- 42.57) mm(2), B (47.42 +/- 18.77; 42.38 +/- 19.29) mm(2), C (51.45 +/- 18.16; 49.49 +/- 18.41) mm(2) and D (49.10 +/- 23.05; 38.18 +/- 18.94) mm(2), respectively, p < 0.05. Similar significant increment was found at the mid-disc at postoperative day one, 6 months and one year with type A (55.16 +/- 27.51; 37.23 +/- 25.88; 44.86 +/- 25.73) mm(2), B (72.83 +/- 23.87; 49.79 +/- 21.93; 62.94 +/- 24.43) mm(2), C (66.85 +/- 34.48; 54.92 +/- 30.70; 64.33 +/- 31.82) mm(2) and D (71.65 +/- 16.87; 41.55 +/- 12.92; 49.83 +/- 13.31) mm(2) and the lower endplate at postoperative day one and one year with type A (49.89 +/- 34.50; 41.04 +/- 28.56) mm(2), B (63.63 +/- 23.70; 54.72 +/- 24.29) mm(2), C (58.50 +/- 24.27; 55.32 +/- 22.49) mm(2) and D (81.43 +/- 16.81; 58.40 +/- 18.05) mm(2) at postoperative day one and one year, respectively, p < 0.05. Conclusions: After full endoscopic lumbar decompression, despite achieving sufficient decompression immediately postoperatively, varying severity of asymptomatic restenosis was found in postoperative six months MRI without clinical significance. Further remodeling with a varying degree of increment of the spinal canal area occurs at postoperative one year with overall good clinical outcomes

SpliceHetero: An information theoretic approach for measuring spliceomic intratumor heterogeneity from bulk tumor RNA-seq.

MOTIVATION:Intratumor heterogeneity (ITH) represents the diversity of cell populations that make up cancer tissue. The level of ITH in a tumor is usually measured by a genomic variation profile, such as copy number variation and somatic mutation. However, a recent study has identified ITH at the transcriptome level and suggested that ITH at gene expression levels is useful for predicting prognosis. Measuring ITH levels at the spliceome level is a natural extension. There are serious technical challenges in measuring spliceomic ITH (sITH) from bulk tumor RNA sequencing (RNA-seq) due to the complex splicing patterns. RESULTS:We propose an information-theoretic method to measure the sITH of bulk tumors to overcome the above challenges. This method has been extensively tested in experiments using synthetic data, xenograft tumor data, and TCGA pan-cancer data. As a result, we showed that sITH is closely related to cancer progression and clonal heterogeneity, along with clinically significant features such as cancer stage, survival outcome and PAM50 subtype. As far as we know, it is the first study to define ITH at the spliceome level. This method can greatly improve the understanding of cancer spliceome and has great potential as a diagnostic and prognostic tool

Corrigendum: MONTI: A Multi-Omics Non-Negative Tensor Decomposition Framework for Gene-Level Integrative Analysis (Front. Genet, (2021), 12, (682841), 10.3389/fgene.2021.682841)

Copyright © 2021 Jung, Kim, Rhee, Lim and Kim.There is an error in the Funding statement. The correct number for “the Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Education” is “2020M3C9A5085604.” Corrected statement is given below:N

Embedding of FDA Approved Drugs in Chemical Space Using Cascade Autoencoder with Metric Learning

© 2022 IEEE.Deep learning methods have been successfully used to predict characteristics of small molecules such as physicochemical properties and biological properties. Prediction is typically done by embedding compounds into a low-dimensional chemical space. The goal of our study is to create embedding space that can be used to distinguish approved and withdrawn drugs using compound information only. U.S. Food and Drug Administration (FDA) approved chemical drugs are validated substances in terms of therapeutic effect, toxicity, and side effects. Some of approved drugs are withdrawn due to various reasons, including toxic and disease-causing effects. Our study aims to propose a framework that embed FDA approved chemical drugs on chemical space by integrating representation of chemical structure from various encoding methods. Because withdrawn drugs were approved drugs, distinguishing them using compound information is quite challenging. Our proposed framework consists of three stacked deep autoencoder modules and effectively integrates the information of the chemical compounds by cascade modeling that continuously use latent representation learned from previous modules. Results showed that FDA approved chemical compounds have discriminative regions in the embedding space and complex representation information to understand the embedding of FDA drugs were incorporated well. Such results showed that our framework can be used as an embedding method for determining whether or not drug candidates will be approved.N

Author Correction: Subnetwork representation learning for discovering network biomarkers in predicting lymph node metastasis in early oral cancer (Scientific Reports, (2021), 11, 1, (23992), 10.1038/s41598-021-03333-5)

© The Author(s) 2022.In the original version of this Article, Doh Young Lee was omitted as a corresponding author. Correspondence and requests for materials should also be addressed to [email protected]

Supervised chemical graph mining improves drug-induced liver injury prediction

Summary: Drug-induced liver injury (DILI) is the main cause of drug failure in clinical trials. The characterization of toxic compounds in terms of chemical structure is important because compounds can be metabolized to toxic substances in the liver. Traditional machine learning approaches have had limited success in predicting DILI, and emerging deep graph neural network (GNN) models are yet powerful enough to predict DILI. In this study, we developed a completely different approach, supervised subgraph mining (SSM), a strategy to mine explicit subgraph features by iteratively updating individual graph transitions to maximize DILI fidelity. Our method outperformed previous methods including state-of-the-art GNN tools in classifying DILI on two different datasets: DILIst and TDC-benchmark. We also combined the subgraph features by using SMARTS-based frequent structural pattern matching and associated them with drugs’ ATC code

Exploring chemical space for lead identification by propagating on chemical similarity network

Motivation: Lead identification is a fundamental step to prioritize candidate compounds for downstream drug discovery process. Machine learning (ML) and deep learning (DL) approaches are widely used to identify lead compounds using both chemical property and experimental information. However, ML or DL methods rarely consider compound similarity information directly since ML and DL models use abstract representation of molecules for model construction. Alternatively, data mining approaches are also used to explore chemical space with drug candidates by screening undesirable compounds. A major challenge for data mining approaches is to develop efficient data mining methods that search large chemical space for desirable lead compounds with low false positive rate. Results: In this work, we developed a network propagation (NP) based data mining method for lead identification that performs search on an ensemble of chemical similarity networks. We compiled 14 fingerprint-based similarity networks. Given a target protein of interest, we use a deep learning-based drug target interaction model to narrow down compound candidates and then we use network propagation to prioritize drug candidates that are highly correlated with drug activity score such as IC50. In an extensive experiment with BindingDB, we showed that our approach successfully discovered intentionally unlabeled compounds for given targets. To further demonstrate the prediction power of our approach, we identified 24 candidate leads for CLK1. Two out of five synthesizable candidates were experimentally validated in binding assays. In conclusion, our framework can be very useful for lead identification from very large compound databases such as ZINC