The PENGUIN approach to reconstruct protein interactions at enhancer-promoter regions and its application to prostate cancer.

We introduce Promoter-Enhancer-Guided Interaction Networks (PENGUIN), a method for studying protein-protein interaction (PPI) networks within enhancer-promoter interactions. PENGUIN integrates H3K27ac-HiChIP data with tissue-specific PPIs to define enhancer-promoter PPI networks (EPINs). We validated PENGUIN using cancer (LNCaP) and benign (LHSAR) prostate cell lines. Our analysis detected EPIN clusters enriched with the architectural protein CTCF, a regulator of enhancer-promoter interactions. CTCF presence was coupled with the prevalence of prostate cancer (PrCa) single nucleotide polymorphisms (SNPs) within the same EPIN clusters, suggesting functional implications in PrCa. Within the EPINs displaying enrichments in both CTCF and PrCa SNPs, we also show enrichment in oncogenes. We substantiated our identified SNPs through CRISPR/Cas9 knockout and RNAi screens experiments. Here we show that PENGUIN provides insights into the intricate interplay between enhancer-promoter interactions and PPI networks, which are crucial for identifying key genes and potential intervention targets. A dedicated server is available at https://penguin.life.bsc.es/ .

The relapsed acute lymphoblastic leukemia network (ReALLNet): a multidisciplinary project from the spanish society of pediatric hematology and oncology (SEHOP).

Acute lymphoblastic leukemia (ALL) is the most common pediatric cancer, with survival rates exceeding 85%. However, 15% of patients will relapse; consequently, their survival rates decrease to below 50%. Therefore, several research and innovation studies are focusing on pediatric relapsed or refractory ALL (R/R ALL). Driven by this context and following the European strategic plan to implement precision medicine equitably, the Relapsed ALL Network (ReALLNet) was launched under the umbrella of SEHOP in 2021, aiming to connect bedside patient care with expert groups in R/R ALL in an interdisciplinary and multicentric network. To achieve this objective, a board consisting of experts in diagnosis, management, preclinical research, and clinical trials has been established. The requirements of treatment centers have been evaluated, and the available oncogenomic and functional study resources have been assessed and organized. A shipping platform has been developed to process samples requiring study derivation, and an integrated diagnostic committee has been established to report results. These biological data, as well as patient outcomes, are collected in a national registry. Additionally, samples from all patients are stored in a biobank. This comprehensive repository of data and samples is expected to foster an environment where preclinical researchers and data scientists can seek to meet the complex needs of this challenging population. This proof of concept aims to demonstrate that a network-based organization, such as that embodied by ReALLNet, provides the ideal niche for the equitable and efficient implementation of "what's next" in the management of children with R/R ALL.

Structural Basis for Highly Selective Class II Alpha Phosphoinositide-3-Kinase Inhibition.

Class II phosphoinositide-3-kinases (PI3Ks) play central roles in cell signaling, division, migration, and survival. Despite evidence that all PI3K class II isoforms serve unique cellular functions, the lack of isoform-selective inhibitors severely hampers the systematic investigation of their potential relevance as pharmacological targets. Here, we report the structural evaluation and molecular determinants for selective PI3K-C2α inhibition by a structure-activity relationship study based on a pteridinone scaffold, leading to the discovery of selective PI3K-C2α inhibitors called PITCOINs. Cocrystal structures and docking experiments supported the rationalization of the structural determinants essential for inhibitor activity and high selectivity. Profiling of PITCOINs in a panel of more than 118 diverse kinases showed no off-target kinase inhibition. Notably, by addressing a selectivity pocket, PITCOIN4 showed nanomolar inhibition of PI3K-C2α and >100-fold selectivity in a general kinase panel. Our study paves the way for the development of novel therapies for diseases related to PI3K-C2α function.

Isoform-selective targeting of PI3K: time to consider new opportunities?

Phosphoinositide-3-kinases (PI3Ks) are central to several cellular signaling pathways in human physiology and are potential pharmacological targets for many pathologies including cancer, thrombosis, and pulmonary diseases. Tremendous efforts to develop isoform-selective inhibitors have culminated in the approval of several drugs, validating PI3K as a tractable and therapeutically relevant target. Although successful therapeutic validation has focused on isoform-selective class I orthosteric inhibitors, recent clinical findings have indicated challenges regarding poor drug tolerance owing to sustained on-target inhibition. Hence, additional approaches are warranted to increase the clinical benefits of specific clinical treatment options, which may involve the employment of so far underexploited targeting modalities or the development of inhibitors for currently underexplored PI3K class II isoforms. We review recent key discoveries in the development of isoform-selective inhibitors, focusing particularly on PI3K class II isoforms, and highlight the emerging importance of developing a broader arsenal of pharmacological tools.

Development of selective inhibitors of phosphatidylinositol 3-kinase C2α.

Phosphatidylinositol 3-kinase type 2α (PI3KC2α) and related class II PI3K isoforms are of increasing biomedical interest because of their crucial roles in endocytic membrane dynamics, cell division and signaling, angiogenesis, and platelet morphology and function. Herein we report the development and characterization of PhosphatidylInositol Three-kinase Class twO INhibitors (PITCOINs), potent and highly selective small-molecule inhibitors of PI3KC2α catalytic activity. PITCOIN compounds exhibit strong selectivity toward PI3KC2α due to their unique mode of interaction with the ATP-binding site of the enzyme. We demonstrate that acute inhibition of PI3KC2α-mediated synthesis of phosphatidylinositol 3-phosphates by PITCOINs impairs endocytic membrane dynamics and membrane remodeling during platelet-dependent thrombus formation. PITCOINs are potent and selective cell-permeable inhibitors of PI3KC2α function with potential biomedical applications ranging from thrombosis to diabetes and cancer.

The Styryl Benzoic Acid Derivative DC10 Potentiates Radiotherapy by Targeting the xCT-Glutathione Axis.

Metastatic tumors with moderate radiosensitivity account for most cancer-related deaths, highlighting the limitations of current radiotherapy regimens. The xCT-inhibitor sulfasalazine (SAS) sensitizes cancer cells to radiotherapy by blocking cystine uptake via the xCT membrane antiporter, and thereby glutathione (GSH) synthesis protecting against radiation-induced oxidative stress. The expression of xCT in multiple tumor types implies it as a target generic to cancer rather than confined to few subtypes. However, SAS has limited clinical potential as a radiosensitizer due to side effects and low bioavailability. Using SAS as a starting point, we previously developed synthetic xCT-inhibitors through scaffold hopping and structure optimization aided by structure-activity relationship analysis (SAR). Notably, the compound DC10 exhibited inhibition of GSH synthesis. In this study, we validated DC10 as a radiosensitizer in the xCT-expressing cancer cell lines A172, A375 and MCF7, and mice harboring melanoma xenografts. After DC10 treatment, we measured 14C-cystine uptake in the cancer cells using liquid scintillation counting, and intracellular GSH levels and reactive oxygen species (ROS) using luminescence assays. We performed immunoblotting of H2AX and ATM to assess DNA damage after treatment with DC10 and radiotherapy. We then assessed the effect of adding DC10 to radiation upon cancer cell colony formation. Blood samples from mice treated with DC10 underwent biochemical analysis to assess toxicity. Finally, mice with A375 melanomas in the flank, received DC10 and radiotherapy in combination, as monotherapies or no treatment. Notably, DC10 reduced cystine uptake and GSH synthesis and increased ROS levels in a dose-dependent manner. Furthermore, DC10 interacted synergistically with radiation to increase DNA damage and reduce tumor cell colony formation. Mice receiving DC10 were clinically unaffected, whereas blood samples analysis to assess bone marrow suppression, liver or kidney toxicity revealed no significant differences between treated mice and untreated controls. Importantly, DC10 potentiated the anti-tumor efficacy of radiation in mice with melanoma xenografts. We conclude that DC10 is well tolerated and acts as a radiosensitizer by inhibiting cystine uptake, leading to GSH depletion and increased oxidative stress. Our findings demonstrate the feasibility of using synthetic xCT-inhibitors to overcome radioresistance.

Structure-Activity-Relationship-Aided Design and Synthesis of xCT Antiporter Inhibitors.

The xCT antiporter is a cell membrane protein involved in active counter-transportation of glutamate (outflux) with cystine (influx) over the human cell membrane. This feature makes the xCT antiporter a crucial element of the biosynthesis of the vital free radical scavenger glutathione. The prodrug sulfasalazine, a medication for the treatment of ulcerative colitis, was previously proven to inhibit the xCT antiporter. Starting from sulfasalazine, a molecular scaffold jumping followed by SAR-assisted design and synthesis provided a series of styryl hydroxy-benzoic acid analogues that were biologically tested in vitro for their ability to decrease intracellular glutathione levels using four different cancer cell lines: A172 (glioma), A375 (melanoma), U87 (glioma) and MCF7 (breast carcinoma). Depletion of glutathione levels varied among the compounds as well as among the cell lines. Flow cytometry using propidium iodide and the annexin V marker demonstrated minimal toxicity in normal human astrocytes for a promising candidate molecule (E)-5-(2-([1,1'-biphenyl]-4-yl)vinyl)-2-hydroxybenzoic acid.

The multilayer community structure of medulloblastoma.

Multilayer networks allow interpreting the molecular basis of diseases, which is particularly challenging in rare diseases where the number of cases is small compared with the size of the associated multi-omics datasets. In this work, we develop a dimensionality reduction methodology to identify the minimal set of genes that characterize disease subgroups based on their persistent association in multilayer network communities. We use this approach to the study of medulloblastoma, a childhood brain tumor, using proteogenomic data. Our approach is able to recapitulate known medulloblastoma subgroups (accuracy >94%) and provide a clear characterization of gene associations, with the downstream implications for diagnosis and therapeutic interventions. We verified the general applicability of our method on an independent medulloblastoma dataset (accuracy >98%). This approach opens the door to a new generation of multilayer network-based methods able to overcome the specific dimensionality limitations of rare disease datasets.

Artificial intelligence in cancer research: learning at different levels of data granularity.

From genome-scale experimental studies to imaging data, behavioral footprints, and longitudinal healthcare records, the convergence of big data in cancer research and the advances in Artificial Intelligence (AI) is paving the way to develop a systems view of cancer. Nevertheless, this biomedical area is largely characterized by the co-existence of big data and small data resources, highlighting the need for a deeper investigation about the crosstalk between different levels of data granularity, including varied sample sizes, labels, data types, and other data descriptors. This review introduces the current challenges, limitations, and solutions of AI in the heterogeneous landscape of data granularity in cancer research. Such a variety of cancer molecular and clinical data calls for advancing the interoperability among AI approaches, with particular emphasis on the synergy between discriminative and generative models that we discuss in this work with several examples of techniques and applications.

Discovering the 3' UTR-mediated regulation of alpha-synuclein.

Recent evidence indicates a link between Parkinson's Disease (PD) and the expression of a-synuclein (SNCA) isoforms with different 3' untranslated regions (3'UTRs). Yet, the post-transcriptional mechanisms regulating SNCA expression are unknown. Using a large-scale in vitro /in silico screening we identified RNA-binding proteins (RBPs) that interact with SNCA 3' UTRs. We identified two RBPs, ELAVL1 and TIAR, that bind with high affinity to the most abundant and translationally active 3' UTR isoform (575 nt). Knockdown and overexpression experiments indicate that both ELAVL1 and TIAR positively regulate endogenous SNCA in vivo. The mechanism of regulation implies mRNA stabilization as well as enhancement of translation in the case of TIAR. We observed significant alteration of both TIAR and ELAVL1 expression in motor cortex of post-mortem brain donors and primary cultured fibroblast from patients affected by PD and Multiple System Atrophy (MSA). Moreover, trans expression quantitative trait loci (trans-eQTLs) analysis revealed that a group of single nucleotide polymorphisms (SNPs) in TIAR genomic locus influences SNCA expression in two different brain areas, nucleus accumbens and hippocampus. Our study sheds light on the 3' UTR-mediated regulation of SNCA and its link with PD pathogenesis, thus opening up new avenues for investigation of post-transcriptional mechanisms in neurodegeneration.

Neurodegeneration and Cancer: Where the Disorder Prevails.

It has been reported that genes up-regulated in cancer are often down-regulated in neurodegenerative disorders and vice versa. The fact that apparently unrelated diseases share functional pathways suggests a link between their etiopathogenesis and the properties of molecules involved. Are there specific features that explain the exclusive association of proteins with either cancer or neurodegeneration? We performed a large-scale analysis of physico-chemical properties to understand what characteristics differentiate classes of diseases. We found that structural disorder significantly distinguishes proteins up-regulated in neurodegenerative diseases from those linked to cancer. We also observed high correlation between structural disorder and age of onset in Frontotemporal Dementia, Parkinson's and Alzheimer's diseases, which strongly supports the role of protein unfolding in neurodegenerative processes.

Constitutive patterns of gene expression regulated by RNA-binding proteins.

BACKGROUND: RNA-binding proteins regulate a number of cellular processes, including synthesis, folding, translocation, assembly and clearance of RNAs. Recent studies have reported that an unexpectedly large number of proteins are able to interact with RNA, but the partners of many RNA-binding proteins are still uncharacterized. RESULTS: We combined prediction of ribonucleoprotein interactions, based on catRAPID calculations, with analysis of protein and RNA expression profiles from human tissues. We found strong interaction propensities for both positively and negatively correlated expression patterns. Our integration of in silico and ex vivo data unraveled two major types of protein-RNA interactions, with positively correlated patterns related to cell cycle control and negatively correlated patterns related to survival, growth and differentiation. To facilitate the investigation of protein-RNA interactions and expression networks, we developed the catRAPID express web server. CONCLUSIONS: Our analysis sheds light on the role of RNA-binding proteins in regulating proliferation and differentiation processes, and we provide a data exploration tool to aid future experimental studies.

Principles of self-organization in biological pathways: a hypothesis on the autogenous association of alpha-synuclein.

Previous evidence indicates that a number of proteins are able to interact with cognate mRNAs. These autogenous associations represent important regulatory mechanisms that control gene expression at the translational level. Using the catRAPID approach to predict the propensity of proteins to bind to RNA, we investigated the occurrence of autogenous associations in the human proteome. Our algorithm correctly identified binding sites in well-known cases such as thymidylate synthase, tumor suppressor P53, synaptotagmin-1, serine/ariginine-rich splicing factor 2, heat shock 70 kDa, ribonucleic particle-specific U1A and ribosomal protein S13. In addition, we found that several other proteins are able to bind to their own mRNAs. A large-scale analysis of biological pathways revealed that aggregation-prone and structurally disordered proteins have the highest propensity to interact with cognate RNAs. These findings are substantiated by experimental evidence on amyloidogenic proteins such as TAR DNA-binding protein 43 and fragile X mental retardation protein. Among the amyloidogenic proteins, we predicted that Parkinson's disease-related α-synuclein is highly prone to interact with cognate transcripts, which suggests the existence of RNA-dependent factors in its function and dysfunction. Indeed, as aggregation is intrinsically concentration dependent, it is possible that autogenous interactions play a crucial role in controlling protein homeostasis.

Characterization of novel paternal ncRNAs at the Plagl1 locus, including Hymai, predicted to interact with regulators of active chromatin.

Genomic imprinting is a complex epigenetic mechanism of transcriptional control that utilizes DNA methylation and histone modifications to bring about parent-of-origin specific monoallelic expression in mammals. Genes subject to imprinting are often organised in clusters associated with large non-coding RNAs (ncRNAs), some of which have cis-regulatory functions. Here we have undertaken a detailed allelic expression analysis of an imprinted domain on mouse proximal chromosome 10 comprising the paternally expressed Plagl1 gene. We identified three novel Plagl1 transcripts, only one of which contains protein-coding exons. In addition, we characterised two unspliced ncRNAs, Hymai, the mouse orthologue of HYMAI, and Plagl1it (Plagl1 intronic transcript), a transcript located in intron 5 of Plagl1. Imprinted expression of these novel ncRNAs requires DNMT3L-mediated maternal DNA methylation, which is also indispensable for establishing the correct chromatin profile at the Plagl1 DMR. Significantly, the two ncRNAs are retained in the nucleus, consistent with a potential regulatory function at the imprinted domain. Analysis with catRAPID, a protein-ncRNA association prediction algorithm, suggests that Hymai and Plagl1it RNAs both have potentially high affinity for Trithorax chromatin regulators. The two ncRNAs could therefore help to protect the paternal allele from DNA methylation by attracting Trithorax proteins that mediate H3 lysine-4 methylation.