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Accurate Prediction of Kinase-Substrate Networks Using Knowledge Graphs

ABSTRACT: Phosphorylation of specific substrates by protein kinases is a key control mechanism for vital cell-fate decisions and other cellular processes. However, discovering specific kinase-substrate relationships is time-consuming and often rather serendipitous. Computational predictions alleviate these challenges, but the current approaches suffer from limitations like restricted kinome coverage and inaccuracy. They also typically utilise only local features without reflecting broader interaction context. To address these limitations, we have developed an alternative predictive model. It uses statistical relational learning on top of phosphorylation networks interpreted as knowledge graphs, a simple yet robust model for representing networked knowledge. Compared to a representative selection of six existing systems, our model has the highest kinome coverage and produces biologically valid high-confidence predictions not possible with the other tools. Specifically, we have experimentally validated predictions of previously unknown phosphorylations by the LATS1, AKT1, PKA and MST2 kinases in human. Thus, our tool is useful for focusing phosphoproteomic experiments, and facilitates the discovery of new phosphorylation reactions. Our model can be accessed publicly via an easy-to-use web interface (LinkPhinder).

INSTRUMENT(S): Q Exactive

ORGANISM(S): Homo Sapiens (human)

TISSUE(S): Kidney Epithelium, Kidney

SUBMITTER: David Matallanas

LAB HEAD: David Gomez

PROVIDER: PXD018905 | Pride | 2022-02-20

REPOSITORIES: Pride

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Project description:Cilia assembly, maintenance and the localization of signal-transduction proteins necessitate a functioning intraflagellar transport (IFT). The IFT machinery moves cargo along a microtubule scaffold from the proximal to the distal end of the cilium. This anterograde transport is facilitated by a large multiprotein complex IFT-B, which consists of 16 proteins in total and recruits motor protein Kinesin-2 for active movement. TTC30A and TTC30B are integral components of this complex and were shown before to have redundant function in context of IFT. One paralogue could compensate the loss of the other, preventing the disruption of IFT-B and thus a severe ciliogenesis defect with no formation of the cilium. Affinity-based protein complex analysis of endogenously tagged cells, generated via CRISPR/Cas9 system as described before, revealed paralogue specific protein interactors proposing the involvement of TTC30A or TTC30B in other ciliary functions, particularly Sonic hedgehog signaling (Shh). Defects in this ciliary signaling pathway are often correlated to synpolydactyly, which intriguingly is also linked to a rare TTC30 variant. In this study we focus on the so far unknown interaction of TTC30A with protein kinase A catalytic subunit α PRKACA, which is a negative regulator of Shh pathway. For an in-depth analysis of this unique interaction and the influence on Shh, we used previously, via the CRISPR/Cas9 system, generated TTC30A or B single- and double-knockout hTERT-RPE1 cells as well as rescue cells harboring the TTC30 mutation. Our systematic approach revealed the paralogue specific influence of TTC30A KO and mutated TTC30A on the activity of PRKACA as well as the uptake of Smoothened into the cilium resulting in a downregulation of Sonic hedgehog signaling. Affinity-based protein complex analysis of wildtype versus mutated TTC30A or TTC30B uncovered differences in interaction pattern. These interactome alterations combined with Shh downregulation suggest a possible mechanism of how mutant TTC30A and respectively TTC30A KO are linked to synpolydactyly.

Project description:Liposomal amphotericin B is an important frontline drug for the treatment of visceral leishmaniasis, a neglected disease of poverty. The mechanism of action of amphotericin B (AmB) is thought to involve interaction with ergosterol and other ergostane sterols, resulting in disruption of the integrity and key functions of the plasma membrane. Emergence of clinically refractory isolates of L. donovani and L. infantum is an ongoing issue and knowledge of potential resistance mechanisms can help to alleviate this problem. Here we report the characterisation of four independently selected L. donovani clones that are resistant to AmB. Whole genome sequencing revealed that in three of the moderately resistant clones, resistance was due solely to the deletion of a gene encoding C24-sterol methyltransferase (SMT1). The fourth, hyper-resistant resistant clone (>60-fold) was found to have a 24 bp deletion in both alleles of a gene encoding a putative cytochrome P450 reductase (P450R1). Metabolic profiling indicated these parasites were virtually devoid of ergosterol (0.2% versus 18% of total sterols in wild-type) and had a marked accumulation of 14-methylfecosterol (75% versus 0.1% of total sterols in wild-type) and other 14-alpha methylcholestanes. These are substrates for sterol 14-alpha demethylase (CYP51) suggesting that this enzyme is a bona fide P450R specifically involved in electron transfer from NADPH to CYP51 during catalysis. Deletion of P450R1 in wild-type cells phenocopied the metabolic changes observed in our AmB hyper-resistant clone as well as in CYP51 nulls. Likewise, addition of a wild type P450R1 gene restored sterol profiles to wild type. Our studies indicate that P450R1 is essential for L. donovani amastigote viability, thus loss of this gene is unlikely to be a driver of clinical resistance. Nevertheless, investigating the mechanisms underpinning AmB resistance in these cells provided insights that refine our understanding of the L. donovani sterol biosynthetic pathway.

Project description:The high incidence of pre-eclampsia, affecting 5-10% of pregnancies, makes it a major health problem. Early detection of pre-eclampsia, before the appearance of the first clinical symptoms, is therefore an urgent need, since it would allow the early intervention. Identification of plasma/serum biomarkers would pave the way to develop new strategies for the diagnosis of pre-eclampsia. Liquid biopsy emerges as a promising source of protein biomarkers that circumvent some of the inherent challenges of proteome wide analysis of plasma and serum. Besides, purified exosomes have the added interest of being communication vehicles between cells and tissues both in physiological and pathological processes. Methods We compared the protein abundance in purified exosomes from three different cohorts of control and preeclamptic serum samples, obtained around the sixth month and at the end of pregnancy. To this end, a shotgun label-free proteomics analysis was conducted and the relevant differential proteins were then validated by targeted MRM. Results An exosome purification method was developed that yielded highly enriched preparations, as indicated by the detection of a large number of exosomal markers. Likewise, the presence of specific pregnancy protein markers suggested that a very significant percentage of purified exosomes come from tissues related to pregnancy. Shotgun quantitative proteomic analysis allowed us to identify 10, 114 and 98 differentially regulated proteins in the three studied cohorts, with a high degree of concordance among them. Functional analysis suggests that these proteins participate in biological processes closely related to pre-eclampsia, such as angiogenesis, inflammation or cell migration. Differential abundance of 66 proteins were validated by MRM. Finally, we show the impact of the pre-eclampsia associated exosomes in the proteome of a cellular model, compared to normal exosomes. Conclusions We identified and validated differential proteins in liquid biopsy of patients that open new possibilities for the early diagnostic of pre-eclampsia. Additionally, the functional impact of the different proteome composition of purified pre-eclamptic exosomes in target cells provide new information to further understand changes on embryo-maternal interactions and therefore the pathogenesis of this disease.

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	3_DMSO_MST2_FSBA_a.raw	Raw

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Accurate Prediction of Kinase-Substrate Networks Using Knowledge Graphs

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