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.

Lung Tissue Multi-Layer Network Analysis Uncovers the Molecular Heterogeneity of COPD.

BACKGROUND: Chronic Obstructive Pulmonary Disease (COPD) is a heterogeneous condition. We hypothesized that the unbiased integration of different COPD lung omics using a novel multi-layer approach may unravel mechanisms associated with clinical characteristics. METHODS: We profiled mRNA, miRNA and methylome in lung tissue samples from 135 former smokers with COPD. For each omic (layer) we built a patient network based on molecular similarity. The three networks were used to build a multi-layer network, and optimization of multiplex-modularity was employed to identify patient communities across the three distinct layers. Uncovered communities were related to clinical features. RESULTS: We identified five patient communities in the multi-layer network which were molecularly distinct and related to clinical characteristics, such as FEV1 and blood eosinophils. Two communities (C#3 and C#4) had both similarly low FEV1 values and emphysema, but were molecularly different: C#3, but not C#4, presented B and T cell signatures and a downregulation of secretory (SCGB1A1/SCGB3A1) and ciliated cells. A machine learning model was set up to discriminate C#3 and C#4 in our cohort, and to validate them in an independent cohort. Finally, using spatial transcriptomics we characterized the small airway differences between C#3 and C#4, identifying an upregulation of T/B cell homing chemokines, and bacterial response genes in C#3. CONCLUSIONS: A novel multi-layer network analysis is able to identify clinically relevant COPD patient communities. Patients with similarly low FEV1 and emphysema can have molecularly distinct small airways and immune response patterns, indicating that different endotypes can lead to similar clinical presentation.

Novel genotype-phenotype correlations, differential cerebellar allele-specific methylation, and a common origin of the (ATTTC)n insertion in spinocerebellar ataxia type 37.

Spinocerebellar ataxia subtype 37 (SCA37) is a rare disease originally identified in ataxia patients from the Iberian Peninsula with a pure cerebellar syndrome. SCA37 patients carry a pathogenic intronic (ATTTC)n repeat insertion flanked by two polymorphic (ATTTT)n repeats in the Disabled-1 (DAB1) gene leading to cerebellar dysregulation. Herein, we determine the precise configuration of the pathogenic 5'(ATTTT)n-(ATTTC)n-3'(ATTTT)n SCA37 alleles by CRISPR-Cas9 and long-read nanopore sequencing, reveal their epigenomic signatures in SCA37 lymphocytes, fibroblasts, and cerebellar samples, and establish new molecular and clinical correlations. The 5'(ATTTT)n-(ATTTC)n-3'(ATTTT)n pathogenic allele configurations revealed repeat instability and differential methylation signatures. Disease age of onset negatively correlated with the (ATTTC)n, and positively correlated with the 3'(ATTTT)n. Geographic origin and gender significantly correlated with age of onset. Furthermore, significant predictive regression models were obtained by machine learning for age of onset and disease evolution by considering gender, the (ATTTC)n, the 3'(ATTTT)n, and seven CpG positions differentially methylated in SCA37 cerebellum. A common 964-kb genomic region spanning the (ATTTC)n insertion was identified in all SCA37 patients analysed from Portugal and Spain, evidencing a common origin of the SCA37 mutation in the Iberian Peninsula originating 859 years ago (95% CI 647-1378). In conclusion, we demonstrate an accurate determination of the size and configuration of the regulatory 5'(ATTTT)n-(ATTTC)n-3'(ATTTT)n repeat tract, avoiding PCR bias amplification using CRISPR/Cas9-enrichment and nanopore long-read sequencing, resulting relevant for accurate genetic diagnosis of SCA37. Moreover, we determine novel significant genotype-phenotype correlations in SCA37 and identify differential cerebellar allele-specific methylation signatures that may underlie DAB1 pathogenic dysregulation.

Novel Xanomeline-Containing Bitopic Ligands of Muscarinic Acetylcholine Receptors: Design, Synthesis and FRET Investigation.

In the last few years, fluorescence resonance energy transfer (FRET) receptor sensors have contributed to the understanding of GPCR ligand binding and functional activation. FRET sensors based on muscarinic acetylcholine receptors (mAChRs) have been employed to study dual-steric ligands, allowing for the detection of different kinetics and distinguishing between partial, full, and super agonism. Herein, we report the synthesis of the two series of bitopic ligands, 12-Cn and 13-Cn, and their pharmacological investigation at the M1, M2, M4, and M5 FRET-based receptor sensors. The hybrids were prepared by merging the pharmacophoric moieties of the M1/M4-preferring orthosteric agonist Xanomeline 10 and the M1-selective positive allosteric modulator 77-LH-28-1 (1-[3-(4-butyl-1-piperidinyl)propyl]-3,4-dihydro-2(1H)-quinolinone) 11. The two pharmacophores were connected through alkylene chains of different lengths (C3, C5, C7, and C9). Analyzing the FRET responses, the tertiary amine compounds 12-C5, 12-C7, and 12-C9 evidenced a selective activation of M1 mAChRs, while the methyl tetrahydropyridinium salts 13-C5, 13-C7, and 13-C9 showed a degree of selectivity for M1 and M4 mAChRs. Moreover, whereas hybrids 12-Cn showed an almost linear response at the M1 subtype, hybrids 13-Cn evidenced a bell-shaped activation response. This different activation pattern suggests that the positive charge anchoring the compound 13-Cn to the orthosteric site ensues a degree of receptor activation depending on the linker length, which induces a graded conformational interference with the binding pocket closure. These bitopic derivatives represent novel pharmacological tools for a better understanding of ligand-receptor interactions at a molecular level.

Artificial Intelligence-Aided Precision Medicine for COVID-19: Strategic Areas of Research and Development.

Artificial intelligence (AI) technologies can play a key role in preventing, detecting, and monitoring epidemics. In this paper, we provide an overview of the recently published literature on the COVID-19 pandemic in four strategic areas: (1) triage, diagnosis, and risk prediction; (2) drug repurposing and development; (3) pharmacogenomics and vaccines; and (4) mining of the medical literature. We highlight how AI-powered health care can enable public health systems to efficiently handle future outbreaks and improve patient outcomes.

Tacrine-xanomeline and tacrine-iperoxo hybrid ligands: Synthesis and biological evaluation at acetylcholinesterase and M1 muscarinic acetylcholine receptors.

We synthesized a set of new hybrid derivatives (7-C8, 7-C10, 7-C12 and 8-C8, 8-C10, 8-C12), in which a polymethylene spacer chain of variable length connected the pharmacophoric moiety of xanomeline, an M1/M4-preferring orthosteric muscarinic agonist, with that of tacrine, a well-known acetylcholinesterase (AChE) inhibitor able to allosterically modulate muscarinic acetylcholine receptors (mAChRs). When tested in vitro in a colorimetric assay for their ability to inhibit AChE, the new compounds showed higher or similar potency compared to that of tacrine. Docking analyses were performed on the most potent inhibitors in the series (8-C8, 8-C10, 8-C12) to rationalize their experimental inhibitory power against AChE. Next, we evaluated the signaling cascade at M1 mAChRs by exploring the interaction of Gαq-PLC-ß3 proteins through split luciferase assays and the myo-Inositol 1 phosphate (IP1) accumulation in cells. The results were compared with those obtained on the known derivatives 6-C7 and 6-C10, two quite potent AChE inhibitors in which tacrine is linked to iperoxo, an exceptionally potent muscarinic orthosteric activator. Interestingly, we found that 6-C7 and 6-C10 behaved as partial agonists of the M1 mAChR, at variance with hybrids 7-Cn and 8-Cn containing xanomeline as the orthosteric molecular fragment, which were all unable to activate the receptor subtype response.

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.

omiXcore: a web server for prediction of protein interactions with large RNA.

SUMMARY: Here we introduce omiXcore, a server for calculations of protein binding to large RNAs (> 500 nucleotides). Our webserver allows (i) use of both protein and RNA sequences without size restriction, (ii) pre-compiled library for exploration of human long intergenic RNAs interactions and (iii) prediction of binding sites. RESULTS: omiXcore was trained and tested on enhanced UV Cross-Linking and ImmunoPrecipitation data. The method discriminates interacting and non-interacting protein-RNA pairs and identifies RNA binding sites with Areas under the ROC curve > 0.80, which suggests that the tool is particularly useful to prioritize candidates for further experimental validation. AVAILABILITY AND IMPLEMENTATION: omiXcore is freely accessed on the web at http://service.tartaglialab.com/grant_submission/omixcore. CONTACT: gian.tartaglia@crg.es. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

By the company they keep: interaction networks define the binding ability of transcription factors.

Access to genome-wide data provides the opportunity to address questions concerning the ability of transcription factors (TFs) to assemble in distinct macromolecular complexes. Here, we introduce the PAnDA (Protein And DNA Associations) approach to characterize DNA associations with human TFs using expression profiles, protein-protein interactions and recognition motifs. Our method predicts TF binding events with >0.80 accuracy revealing cell-specific regulatory patterns that can be exploited for future investigations. Even when the precise DNA-binding motifs of a specific TF are not available, the information derived from protein-protein networks is sufficient to perform high-confidence predictions (area under the ROC curve of 0.89). PAnDA is freely available at http://service.tartaglialab.com/new_submission/panda.

Neurodegenerative diseases: quantitative predictions of protein-RNA interactions.

Increasing evidence indicates that RNA plays an active role in a number of neurodegenerative diseases. We recently introduced a theoretical framework, catRAPID, to predict the binding ability of protein and RNA molecules. Here, we use catRAPID to investigate ribonucleoprotein interactions linked to inherited intellectual disability, amyotrophic lateral sclerosis, Creutzfeuld-Jakob, Alzheimer's, and Parkinson's diseases. We specifically focus on (1) RNA interactions with fragile X mental retardation protein FMRP; (2) protein sequestration caused by CGG repeats; (3) noncoding transcripts regulated by TAR DNA-binding protein 43 TDP-43; (4) autogenous regulation of TDP-43 and FMRP; (5) iron-mediated expression of amyloid precursor protein APP and α-synuclein; (6) interactions between prions and RNA aptamers. Our results are in striking agreement with experimental evidence and provide new insights in processes associated with neuronal function and misfunction.

ccSOL omics: a webserver for solubility prediction of endogenous and heterologous expression in Escherichia coli.

SUMMARY: Here we introduce ccSOL omics, a webserver for large-scale calculations of protein solubility. Our method allows (i) proteome-wide predictions; (ii) identification of soluble fragments within each sequences; (iii) exhaustive single-point mutation analysis. RESULTS: Using coil/disorder, hydrophobicity, hydrophilicity, ß-sheet and α-helix propensities, we built a predictor of protein solubility. Our approach shows an accuracy of 79% on the training set (36 990 Target Track entries). Validation on three independent sets indicates that ccSOL omics discriminates soluble and insoluble proteins with an accuracy of 74% on 31 760 proteins sharing <30% sequence similarity. AVAILABILITY AND IMPLEMENTATION: ccSOL omics can be freely accessed on the web at http://s.tartaglialab.com/page/ccsol_group. Documentation and tutorial are available at http://s.tartaglialab.com/static_files/shared/tutorial_ccsol_omics.html. CONTACT: gian.tartaglia@crg.es SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

X-inactivation: quantitative predictions of protein interactions in the Xist network.

The transcriptional silencing of one of the female X-chromosomes is a finely regulated process that requires accumulation in cis of the long non-coding RNA X-inactive-specific transcript (Xist) followed by a series of epigenetic modifications. Little is known about the molecular machinery regulating initiation and maintenance of chromosomal silencing. Here, we introduce a new version of our algorithm catRAPID to investigate Xist associations with a number of proteins involved in epigenetic regulation, nuclear scaffolding, transcription and splicing processes. Our method correctly identifies binding regions and affinities of protein interactions, providing a powerful theoretical framework for the study of X-chromosome inactivation and other events mediated by ribonucleoprotein associations.

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.

SeAMotE: a method for high-throughput motif discovery in nucleic acid sequences.

BACKGROUND: The large amount of data produced by high-throughput sequencing poses new computational challenges. In the last decade, several tools have been developed for the identification of transcription and splicing factor binding sites. RESULTS: Here, we introduce the SeAMotE (Sequence Analysis of Motifs Enrichment) algorithm for discovery of regulatory regions in nucleic acid sequences. SeAMotE provides (i) a robust analysis of high-throughput sequence sets, (ii) a motif search based on pattern occurrences and (iii) an easy-to-use web-server interface. We applied our method to recently published data including 351 chromatin immunoprecipitation (ChIP) and 13 crosslinking immunoprecipitation (CLIP) experiments and compared our results with those of other well-established motif discovery tools. SeAMotE shows an average accuracy of 80% in finding discriminative motifs and outperforms other methods available in literature. CONCLUSIONS: SeAMotE is a fast, accurate and flexible algorithm for the identification of sequence patterns involved in protein-DNA and protein-RNA recognition. The server can be freely accessed at http://s.tartaglialab.com/new_submission/seamote.

catRAPID omics: a web server for large-scale prediction of protein-RNA interactions.

SUMMARY: Here we introduce catRAPID omics, a server for large-scale calculations of protein-RNA interactions. Our web server allows (i) predictions at proteomic and transcriptomic level; (ii) use of protein and RNA sequences without size restriction; (iii) analysis of nucleic acid binding regions in proteins; and (iv) detection of RNA motifs involved in protein recognition. RESULTS: We developed a web server to allow fast calculation of ribonucleoprotein associations in Caenorhabditis elegans, Danio rerio, Drosophila melanogaster, Homo sapiens, Mus musculus, Rattus norvegicus, Saccharomyces cerevisiae and Xenopus tropicalis (custom libraries can be also generated). The catRAPID omics was benchmarked on the recently published RNA interactomes of Serine/arginine-rich splicing factor 1 (SRSF1), Histone-lysine N-methyltransferase EZH2 (EZH2), TAR DNA-binding protein 43 (TDP43) and RNA-binding protein FUS (FUS) as well as on the protein interactomes of U1/U2 small nucleolar RNAs, X inactive specific transcript (Xist) repeat A region (RepA) and Crumbs homolog 3 (CRB3) 3'-untranslated region RNAs. Our predictions are highly significant (P < 0.05) and will help the experimentalist to identify candidates for further validation. AVAILABILITY: catRAPID omics can be freely accessed on the Web at http://s.tartaglialab.com/catrapid/omics. Documentation, tutorial and FAQs are available at http://s.tartaglialab.com/page/catrapid_group.

Rare disease research workflow using multilayer networks elucidates the molecular determinants of severity in Congenital Myasthenic Syndromes.

Exploring the molecular basis of disease severity in rare disease scenarios is a challenging task provided the limitations on data availability. Causative genes have been described for Congenital Myasthenic Syndromes (CMS), a group of diverse minority neuromuscular junction (NMJ) disorders; yet a molecular explanation for the phenotypic severity differences remains unclear. Here, we present a workflow to explore the functional relationships between CMS causal genes and altered genes from each patient, based on multilayer network community detection analysis of complementary biomedical information provided by relevant data sources, namely protein-protein interactions, pathways and metabolomics. Our results show that CMS severity can be ascribed to the personalized impairment of extracellular matrix components and postsynaptic modulators of acetylcholine receptor (AChR) clustering. This work showcases how coupling multilayer network analysis with personalized -omics information provides molecular explanations to the varying severity of rare diseases; paving the way for sorting out similar cases in other rare diseases.

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.

Raising awareness of sex and gender bias in artificial intelligence and health.

Historically, biomedical research has been led by and focused on men. The recent introduction of Artificial Intelligence (AI) in this area has further proven this practice to be discriminatory for other sexes and genders, more noticeably for women. To move towards a fair AI development, it is essential to include sex and gender diversity both in research practices and in the workplace. In this context, the Bioinfo4women (B4W) program of the Barcelona Supercomputing Center (i) promotes the participation of women scientists by improving their visibility, (ii) fosters international collaborations between institutions and programs and (iii) advances research on sex and gender bias in AI and health. In this article, we discuss methodology and results of a series of conferences, titled â€Å“Sex and Gender Bias in Artificial Intelligence and Health, organized by B4W and La Caixa Foundation from March to June 2021 in Barcelona, Spain. The series consisted of nine hybrid events, composed of keynote sessions and seminars open to the general audience, and two working groups with invited experts from different professional backgrounds (academic fields such as biology, engineering, and sociology, as well as NGOs, journalists, lawyers, policymakers, industry). Based on this awareness-raising action, we distilled key recommendations to facilitate the inclusion of sex and gender perspective into public policies, educational programs, industry, and biomedical research, among other sectors, and help overcome sex and gender biases in AI and health.

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/ .