En els darrers anys hem orientat la nostra recerca al desenvolupament d'eïnes bioinformàtiques aplicades a la predicció de mutacions patològiques en proteïnes (PMID: 11812146, 15390262, 15879453, 16208716, 17059831). Per assolir aquest objectiu hem seguit un protocol de dues passes. En primer lloc, hem caracteritzat les mutaciones patològiques en termes de propietats de seqüència, estructura i evolució. En segon lloc, hem fet servir aquests resultats per identificar aquells paràmetres amb un poder predictiu més gran. Posteriorment, hem utilitzat aquests paràmetres per "entrenar" una xarxa neuronal que ens ha proporcionat un model empíric que permet l'identificació de mutacions patològiques, amb una tasa d'encert moderadament elevada (prop del 70%; PMID: 15390262, 15879453, 16208716).

En aquests moments estem aplicant aquesta aproximació al cas de patologies concretes. Pensem que aquest és un pas natural vers el desenvolupament d'una medicina personalitzada. Dins d'aquest context, estem caracteritzant l'impacte de les mutacions causants de la malaltia de Fabry sobre l'estructura i la funció de l'alfa-galactosidasa. Seguim l'aproximació descrita més amunt, però en aquest cas ens beneficiem de la col.laboració estreta amb el grup del Dr. Joan Montaner.

Single-cell techniques can characterize cells in terms of different molecular properties and, usually, at a genome-wide scale, e.g., the gene-expression pattern of the entire genome, epigenetic marks, etc. A distinguishing characteristic of these techniques is their intrinsically large-scale nature, which ties their clinical application in biomedicine to the development of data analysis and AI techniques. In this project, we are interested in scRNAseq for two main reasons. First, it allows a simple approach to close the gap between molecular and cell-level understanding of the hereditary disease. Additionally, since scRNAseq was one of the first single-cell techniques to be developed, many datasets are publicly available (Moreno et al., 2022). This opens the door to addressing a biomedically relevant problem (Duan et al., 2020; Lähneman et al., 2020): the understanding of cell identity in molecular terms, using machine learning techniques. The resulting conceptual framework can be applied to relevant challenges in the clinical analysis of scRNAseq experiments, e.g., the assignment of cell identities in breast tumor samples. In this project, we focus on this problem, devising an original approach to bridge the gap between interactome and cellular identity.

Various studies show how the loss of specific interactions/network disruptions, contribute to the origin of hereditary disease (Luck et al., 2020; Sahni et al., 2015; Cheng et al., 2021). That is, it is relatively straightforward to relate the pathogenic impact of variants with changes in protein interaction patterns. In parallel, descriptions of the human interactome are becoming available (Luck et al., 2020) together with results confirming the intimate relationship between interactome and cell identity (Ye et al., 2018; Mohammady et al., 2019). We propose that this wealth of data enables the development of an innovative, data-driven model. This model would relate interactome and cell identity and could be applied to single-cell studies of breast tissue in both healthy individuals and cancer patients.

BIBLIOGRAPHY

.- Cheng, F., Zhao, J., Wang, Y., Lu, W., Liu, Z., Zhou, Y., Martin, W.R., Wang, R., Huang, J., Hao, T., et al. (2021). Comprehensive characterization of protein–protein interactions perturbed by disease mutations. Nat. Genet. 53, 342–353.

.- Duan, B., Zhu, C., Chuai, G., Tang, C., Chen, X., Chen, S., Fu, S., and Liu, Q. (2020). Learning for single-cell assignment. Sci. Adv. 6, eabd0855.

.- Lähnemann, D., Köster, J., Szczurek, E., McCarthy, D.J., Hicks, S.C., Robinson, M.D., Vallejos, C.A., Campbell, K.R., Beerenwinkel, N., Mahfouz, A., et al. (2020). Eleven grand challenges in single-cell data science (Genome Biology).

.- Luck, K., Kim, D.K., Lambourne, L., Spirohn, K., Begg, B.E., Bian, W., Brignall, R., Cafarelli, T., Campos-Laborie, F.J., Charloteaux, B., et al. (2020). A reference map of the human binary protein interactome. Nature 580, 402–408.

.-  Mohammadi, S., Davila-Velderrain, J., and Kellis, M. (2019). Reconstruction of cell-type specific interactomes at single-cell resolution. Cell Syst. 9, 559–568.

.- Moreno, P., Fexova, S., George, N., Manning, J.R., Miao, Z., Mohammed, S., MuñozPomer, A., Fullgrabe, A., Bi, Y., Bush, N., et al. (2022). Expression Atlas update: Gene and protein expression in multiple species. Nucleic Acids Res. 50, D129–D140.

.- Sahni, N., Yi, S., Taipale, M., Fuxman Bass, J.I., Coulombe-Huntington, J., Yang, F., Peng, J., Weile, J., Karras, G.I., Wang, Y., et al. (2015). Widespread macromolecular interaction perturbations in human genetic disorders. Cell 161, 647–660.

.- Ye, Z., and Sarkar, C.A. (2018). Towards a quantitative understanding of cell identity. Trends Cell Biol. 28, 1030–1048.