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Francesco Gatto

Publications and source records attributed to Francesco Gatto.

2 recordsLinked to original sources

Deciphering the tissue specificity of the protein secretory pathway in humans

Proteins that are components of the secretory machinery form a cellular pathway of paramount importance for physiological regulation, development and function of human tissues. Consistently, most secretory pathway components are ubiquitously expressed in all tissues. At the same time, recent studies identified that the largest fraction of tissue-specific proteins consists of secreted and membrane proteins and not intracellular proteins. This suggests that the secretory pathway is distinctively regulated in a tissue-specific fashion. However, a systematic analysis on how the protein secretory pathway is tuned in different tissues is lacking, and it is even largely unexplored if the secretome and membrane proteome differs in, for example, posttranslation modifications across tissues. Here, analyzing publically available transcriptome data across 30 human tissues, we discovered the expression level of key components previously categorized as housekeeping proteins were specifically over-expressed in a certain tissue compared with the average expression of their corresponding secretory pathway subsystem (e.g. protein folding). These extreme genes define an exceptional fine-tuning in specific subnetworks, which neatly differentiated for example the pancreas and liver from 30 other tissues. Moreover, the subnetwork expression tuning correlated with the nature and number of post translational modification sites in the pancreas or liver-specific secretome and membrane proteome. These patterns were recurrently observed also in other tissues, like the blood, the brain and the skeletal muscle. These findings conciliate both the housekeeping and tissue-specific nature of the protein secretory pathway, which we attribute to a fine-tuned regulation of defined subnetworks in order to support the diversity of secreted proteins and their modifications.

Systems Biology

Pan-cancer analysis of the metabolic reaction network

Metabolic reprogramming is considered a hallmark of malignant transformation. However, it is not clear whether the network of metabolic reactions expressed by cancers of different origin differ from each other nor from normal human tissues. In this study, we reconstructed functional and connected genome-scale metabolic models for 917 primary tumors based on the probability of expression for 3,765 reference metabolic genes in the sample. This network-centric approach revealed that tumor metabolic networks are largely similar in terms of accounted reactions, despite diversity in the expression of the associated genes. On average, each network contained 4,721 reactions, of which 74% were core reactions (present in >95% of all models). Whilst 99.3% of the core reactions were classified as housekeeping also in normal tissues, we identified reactions catalyzed by ARG2, RHAG, SLC6 and SLC16 family gene members, and PTGS1 and PTGS2 as core exclusively in cancer. The remaining 26% of the reactions were contextual reactions. Their inclusion was dependent in one case (GLS2) on the absence of TP53 mutations and in 94.6% of cases on differences in cancer types. This dependency largely resembled differences in expression patterns in the corresponding normal tissues, with some exceptions like the presence of the NANP-encoded reaction in tumors not from the female reproductive system or of the SLC5A9-encoded reaction in kidney-pancreatic-colorectal tumors. In conclusion, tumors expressed a metabolic network virtually overlapping the matched normal tissues, raising the possibility that metabolic reprogramming simply reflects cancer cell plasticity to adapt to varying conditions thanks to redundancy and complexity of the underlying metabolic networks. At the same time, the here uncovered exceptions represent a resource to identify selective liabilities of tumor metabolism.

Cancer Biology