Characterization of a Schistosoma mansoni homologue of the gene encoding the breast basic conserved protein 1/L13 ribosomal protein

Preclinical-to-clinical Anti-cancer Drug Response Prediction and Biomarker Identification Using TINDL

Prediction of the response of cancer patients to different treatments and identification of biomarkers of drug response are two major goals of individualized medicine. Here, we developed a deep learning framework called TINDL, completely trained on preclinical cancer cell lines (CCLs), to predict the response of cancer patients to different treatments. TINDL utilizes a tissue-informed normalization to account for the tissue type and cancer type of the tumors and to reduce the statistical discrepancies between CCLs and patient tumors. Moreover, by making the deep learning black box interpretable, this model identifies a small set of genes whose expression levels are predictive of drug response in the trained model, enabling identification of biomarkers of drug response. Using data from two large databases of CCLs and cancer tumors, we showed that this model can distinguish between sensitive and resistant tumors for 10 (out of 14) drugs, outperforming various other machine learning models. In addition, our small interfering RNA (siRNA) knockdown experiments on 10 genes identified by this model for one of the drugs (tamoxifen) confirmed that tamoxifen sensitivity is substantially influenced by all of these genes in MCF7 cells, and seven of these genes in T47D cells. Furthermore, genes implicated for multiple drugs pointed to shared mechanism of action among drugs and suggested several important signaling pathways. In summary, this study provides a powerful deep learning framework for prediction of drug response and identification of biomarkers of drug response in cancer. The code can be accessed at https://github.com/ddhostallero/tindl.

Molecular characterization of a light-responsive gene, breast basic conserved protein 1 (OsiBBC1), encoding nuclear-localized protein homologous to ribosomal protein L13 from Oryza sativa indica

Rice (Oryza sativa L. subsp. indica) cDNA for the gene OsiBBC1, encoding homologue of the breast basic conserved protein 1 (BBC1), similar to ribosomal protein L13, has been identified and characterized. OsiBBC1 codes for a 24 kDa highly basic protein with two potential bipartite nuclear localization signals (NLS) and a transcriptional activation domain (TAD). The structural part of the gene is interrupted by four introns. The OsiBBC1 gene is represented by two copies in the rice genome and both of them are expressed. Northern analysis showed that OsiBBC1 is expressed more in the young root, post-fertilized influorescence, leaf base and callus tissue, which are comprised of actively dividing cells, indicating its role in cell division. The OsiBBC1 transcript accumulated more in the root of light-grown seedlings as compared to the shoot while its levels were higher in the shoot as compared to root of the etiolated seedlings, indicating its down-regulation by light. The western analysis, carried out using antibodies raised against a recombinant fusion protein, 6xHis-OsiBBC1, corroborated its tissue-specific expression profile observed by northern analysis. In addition, OsiBBC1/RPL13 protein could be targetted to the nucleus by particle bombardment of OsiBBC1::GUS fusion construct in the onion epidermal cells.

The Schistosoma gene discovery program: state of the art

Schistosoma are dioecious digenetic trematodes carrying a large (270 Mb) genome. Gaining knowledge about the genome of these parasites is of importance for the understanding of their biology, mechanisms of drug resistance and antigenic variation that determine escape from the host's immune system. This review will provide an update on the Schistosoma Gene Discovery Program, which is part of the Schistosoma Genome Project created in 1992. One of the main objectives of this program is the discovery and characterisation of new genes of Schistosoma mansoni and Schistosoma japonicum in an attempt to search for new targets for drugs and vaccine development. The success of the Schistosoma Gene Discovery Program is demonstrated by the number of catalogued genes, that now reaches 15 to 20% of the full gene complement of its genome.