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Three new aspochracin-type cyclic tripeptides, sclerotiotides M–O (1–3), together with three known analogues, sclerotiotide L (4), sclerotiotide F (5), and sclerotiotide B (6), were obtained from the ethyl acetate extract of the fungus Aspergillus insulicola HDN151418, which was isolated from an unidentified Antarctica sponge. Spectroscopic and chemical approaches were used to elucidate their structures. The absolute configuration of the side chain in compound 4 was elucidated for the first time. Compounds 1 and 2 showed broad antimicrobial activity against a panel of pathogenic strains, including Bacillus cereus, Proteus species, Mycobacterium phlei, Bacillus subtilis, Vibrio parahemolyticus, Edwardsiella tarda, MRCNS, and MRSA, with MIC values ranging from 1.56 to 25.0 µM.

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Several knowledgebases are manually curated to support clinical interpretations of thousands of hotspot somatic mutations in cancer. However, discrepancies or even conflicting interpretations are observed among these databases. Furthermore, many previously undocumented mutations may have clinical or functional impacts on cancer but are not systematically interpreted by existing knowledgebases. To address these challenges, we developed CancerVar to facilitate automated and standardized interpretations for 13 million somatic mutations based on the AMP/ASCO/CAP 2017 guidelines. We further introduced a deep learning framework to predict oncogenicity for these variants using both functional and clinical features. CancerVar achieved satisfactory performance when compared to several independent knowledgebases and, using clinically curated datasets, demonstrated practical utility in classifying somatic variants. In summary, by integrating clinical guidelines with a deep learning framework, CancerVar facilitates clinical interpretation of somatic variants, reduces manual work, improves consistency in variant classification, and promotes implementation of the guidelines.

High-throughput sequencing-based viromics highly depends on reference databases, but foreign contamination is widespread in public databases and often leads to confusing and even wrong conclusions in genomic analysis and viromic profiling. To address this issue, we systematically detected and identified the contamination in the largest viral sequence collections of GenBank and UniProt based on a stringent scrutiny pipeline.

A metabolic illness known as non-alcoholic fatty liver disease (NAFLD), affects more than one-quarter of the world’s population. Bile acids (BAs), as detergents involved in lipid digestion, show an abnormal metabolism in patients with NAFLD. However, BAs can affect other organs as well, such as the brain, where it has a neuroprotective effect. According to a series of studies, brain disorders may be extrahepatic manifestations of NAFLD, such as depression, changes to the cerebrovascular system, and worsening cognitive ability. Consequently, we propose that NAFLD affects the development of brain disease, through the bile acid signaling pathway. Through direct or indirect channels, BAs can send messages to the brain. Some BAs may operate directly on the central Farnesoid X receptor (FXR) and the G protein bile acid-activated receptor 1 (GPBAR1) by overcoming the blood–brain barrier (BBB). Furthermore, glucagon-like peptide-1 (GLP-1) and the fibroblast growth factor (FGF) 19 are released from the intestine FXR and GPBAR1 receptors, upon activation, both of which send signals to the brain. Inflammatory, systemic metabolic disorders in the liver and brain are regulated by the bile acid-activated receptors FXR and GPBAR1, which are potential therapeutic targets. From a bile acid viewpoint, we examine the bile acid signaling changes in NAFLD and brain disease. We also recommend the development of dual GPBAR1/FXR ligands to reduce side effects and manage NAFLD and brain disease efficiently.

Background It remains an important challenge to predict the functional consequences or clinical impacts of genetic variants in human diseases, such as cancer. An increasing number of genetic variants in cancer have been discovered and documented in public databases such as COSMIC, but the vast majority of them have no functional or clinical annotations. Some databases, such as CiVIC are available with manual annotation of functional mutations, but the size of the database is small due to the use of human annotation. Since the unlabeled data (millions of variants) typically outnumber labeled data (thousands of variants), computational tools that take advantage of unlabeled data may improve prediction accuracy. Result To leverage unlabeled data to predict functional importance of genetic variants, we introduced a method using semi-supervised generative adversarial networks (SGAN), incorporating features from both labeled and unlabeled data. Our SGAN model incorporated features from clinical guidelines and predictive scores from other computational tools. We also performed comparative analysis to study factors that influence prediction accuracy, such as using different algorithms, types of features, and training sample size, to provide more insights into variant prioritization. We found that SGAN can achieve competitive performances with small labeled training samples by incorporating unlabeled samples, which is a unique advantage compared to traditional machine learning methods. We also found that manually curated samples can achieve a more stable predictive performance than publicly available datasets. Conclusions By incorporating much larger samples of unlabeled data, the SGAN method can improve the ability to detect novel oncogenic variants, compared to other machine-learning algorithms that use only labeled datasets. SGAN can be potentially used to predict the pathogenicity of more complex variants such as structural variants or non-coding variants, with the availability of more training samples and informative features.

Widespread in public databases, the notorious contamination in virus reference databases often leads to confusing even wrong conclusions in applications like viral disease diagnosis and viromic analysis, highlighting the need of a high-quality database. Here, we report the comprehensive scrutiny and the purification of the largest viral sequence collections of GenBank and UniProt by detection and characterization of heterogeneous sequences (HGSs). A total of 766 nucleotide- and 276 amino acid-HGSs were determined with length up to 6,605 bp, which were widely distributed in 39 families, with many involving highly public health-related viruses, such as hepatitis C virus, Crimea-Congo hemorrhagic fever virus and filovirus. Majority of these HGSs are sequences of a wide range of hosts including humans, with the rest resulting from vectors, misclassification and laboratory components. However, these HGSs cannot be simply considered as exotic contaminants, since part of which are resultants of natural occurrence or artificial engineering of the viruses. Nevertheless, they significantly disturb the genomic analysis, and hence were deleted from the database. A further augmentation was implemented with addition of the risk and vaccine sequences, which finally results in a high-quality eukaryotic virus reference database (EVRD). EVRD showed higher accuracy and less time-consuming without coverage compromise by reducing false positives than other integrated databases in viromic analysis. EVRD is freely accessible with favorable application in viral disease diagnosis, taxonomic clustering, viromic analysis and novel virus detection.