Anticancer peptides (ACPs) are promising therapeutic agents for targeting and killing cancer cells. The accurate prediction of ACPs from given peptide sequences remains as an open problem in the field of immunoinformatics. Recently, machine learning algorithms have emerged as a promising tool for helping experimental scientists predict ACPs. However, the performance of existing methods still needs to be improved. In this study, we present a novel approach for the accurate prediction of ACPs, which involves the following two steps: (i) We applied a two-step feature selection protocol on seven feature encodings that cover various aspects of sequence information (composition-based, physicochemical properties and profiles) and obtained their corresponding optimal feature-based models. The resultant predicted probabilities of ACPs were further utilized as feature vectors. (ii) The predicted probability feature vectors were in turn used as an input to support vector machine to develop the final prediction model called mACPpred. Cross-validation analysis showed that the proposed predictor performs significantly better than individual feature encodings. Furthermore, mACPpred significantly outperformed the existing methods compared in this study when objectively evaluated on an independent dataset.

Summer mortality, caused by thermal conditions, is the biggest threat to abalone aquaculture production industries. Various measures have been taken to mitigate this issue by adjusting the environment; however, the cellular processes of Pacific abalone (Haliotis discus hannai) have been overlooked due to the paucity of genetic information. The draft genome of H. discus hannai has recently been reported, prompting exploration of the genes responsible for thermal regulation in Pacific abalone. In this study, 413 proteins were systematically annotated as members of the heat shock protein (HSP) super families, and among them 26 HSP genes from four Pacific abalone tissues (hemocytes, gill, mantle, and muscle) were differentially expressed under cold and heat stress conditions. The co-expression network revealed that HSP expression patterns were tissue-specific and similar to those of other shellfish inhabiting intertidal zones. Finally, representative HSPs were selected at random and their expression patterns were identified by RNA sequencing and validated by qRT-PCR to assess expression significance. The HSPs expressed in hemocytes were highly similar in both analyses, suggesting that hemocytes could be more reliable samples for validating thermal condition markers compared to other tissues.

Approximately 33 types of commonly consumed fruits and vegetables are members of the family Cucurbitaceae, making it an important crop family worldwide. However, pathogen resistance to pesticides and fungicides has become a growing problem in cultivation practices. The identification of the effector proteins in each unique fungus–host pair would help toward the development of strategies for preventing the infection of important crops. In this study, we characterized the genome of Podosphaera xanthii, the fungal pathogen that causes powdery mildew disease in cucurbitaceous plants. A first-draft genome of 209.08 MB was assembled and compared with those of 25 other fungal pathogens, particularly for identifying candidate secreted effector proteins. This draft genome can serve as a valuable resource for future genomic and proteomic studies of P. xanthii and its host-specific pathogenesis. [ ] Copyright © 2021 The Author(s). This is an open access article distributed under the CC BY-NC-ND 4.0 International license .

Poppies are well-known plants in the family Papaveraceae that are rich in alkaloids. This family contains 61 species, and in this study we sequenced the transcriptomes of four species’ (Papaver rhoeas, Papaver nudicaule, Papaver fauriei, and Papaver somniferum) leaves. These transcripts were systematically assessed for the expression of secondary metabolite biosynthesis (SMB) genes and cytochromes, and their expression profiles were assessed for use in bioinformatics analyses. This study contributed 265 Gb (13 libraries with three biological replicates) of leaf transcriptome data from three Papaver plant developmental stages. Sequenced transcripts were assembled into 815 Mb of contigs, including 226 Mb of full-length transcripts. The transcripts for 53 KEGG pathways, 55 cytochrome superfamilies, and benzylisoquinoline alkaloid biosynthesis (BIA) were identified and compared to four other alkaloid-rich genomes. Additionally, 22 different alkaloids and their relative expression profiles in three developmental stages of Papaver species were assessed by targeted metabolomics using LC-QTOF-MS/MS. Collectively, the results are given in co-occurrence heat-maps to help researchers obtain an overview of the transcripts and their differential expression in the Papaver development life cycle, particularly in leaves. Moreover, this dataset will be a valuable resource to derive hypotheses to mitigate an array of Papaver developmental and secondary metabolite biosynthesis issues in the future.

Extracts from the plants Phlomis umbrosa and Dipsacus asperoides—which are widely used in Korean and Chinese traditional medicine to treat osteoarthritis and other bone diseases—were used to treat experimental osteoarthritis (OA) rats. Genome-wide differential methylation regions (DMRs) of these medicinal-plant-treated rats were profiled as therapeutic evidence associated with traditional medicine, and they need to be investigated further using detailed molecular research to extrapolate traditional practices to modern medicine. In total, 49 protein-encoding genes whose expression is differentially regulated during disease progression and recovery have been discovered via systematic bioinformatic analysis and have been approved/proposed as druggable targets for various bone diseases by the US food and drug administration. Genes encoding proteins involved in the PI3K/AKT pathway were found to be enriched, likely as this pathway plays a crucial role during OA progression as well as during the recovery process after treatment with the aforementioned plant extracts. The four sub-networks of PI3K/AKT were highly regulated by these plant extracts. Overall, 29 genes were seen in level 2 (51–75%) DMRs and were correlated highly with OA pathogenesis. Here, we propose that these genes could serve as targets to study OA; moreover, the iridoid and triterpenoid phytochemicals obtained from these two plants may serve as potential therapeutic agents.