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.

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.

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.

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.

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.

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.

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.

Digital biomarkers and sex impacts in Alzheimer's disease management - potential utility for innovative 3P medicine approach.

Digital biomarkers are defined as objective, quantifiable physiological and behavioral data that are collected and measured by means of digital devices. Their use has revolutionized clinical research by enabling high-frequency, longitudinal, and sensitive measurements. In the field of neurodegenerative diseases, an example of a digital biomarker-based technology is instrumental activities of daily living (iADL) digital medical application, a predictive biomarker of conversion from mild cognitive impairment (MCI) due to Alzheimer's disease (AD) to dementia due to AD in individuals aged 55 + . Digital biomarkers show promise to transform clinical practice. Nevertheless, their use may be affected by variables such as demographics, genetics, and phenotype. Among these factors, sex is particularly important in Alzheimer's, where men and women present with different symptoms and progression patterns that impact diagnosis. In this study, we explore sex differences in Altoida's digital medical application in a sample of 568 subjects consisting of a clinical dataset (MCI and dementia due to AD) and a healthy population. We found that a biological sex-classifier, built on digital biomarker features captured using Altoida's application, achieved a 75% ROC-AUC (receiver operating characteristic - area under curve) performance in predicting biological sex in healthy individuals, indicating significant differences in neurocognitive performance signatures between males and females. The performance dropped when we applied this classifier to more advanced stages on the AD continuum, including MCI and dementia, suggesting that sex differences might be disease-stage dependent. Our results indicate that neurocognitive performance signatures built on data from digital biomarker features are different between men and women. These results stress the need to integrate traditional approaches to dementia research with digital biomarker technologies and personalized medicine perspectives to achieve more precise predictive diagnostics, targeted prevention, and customized treatment of cognitive decline. Supplementary Information: The online version contains supplementary material available at 10.1007/s13167-022-00284-3.

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.

COVID-19 Flow-Maps an open geographic information system on COVID-19 and human mobility for Spain.

COVID-19 is an infectious disease caused by the SARS-CoV-2 virus, which has spread all over the world leading to a global pandemic. The fast progression of COVID-19 has been mainly related to the high contagion rate of the virus and the worldwide mobility of humans. In the absence of pharmacological therapies, governments from different countries have introduced several non-pharmaceutical interventions to reduce human mobility and social contact. Several studies based on Anonymized Mobile Phone Data have been published analysing the relationship between human mobility and the spread of coronavirus. However, to our knowledge, none of these data-sets integrates cross-referenced geo-localised data on human mobility and COVID-19 cases into one all-inclusive open resource. Herein we present COVID-19 Flow-Maps, a cross-referenced Geographic Information System that integrates regularly updated time-series accounting for population mobility and daily reports of COVID-19 cases in Spain at different scales of time spatial resolution. This integrated and up-to-date data-set can be used to analyse the human dynamics to guide and support the design of more effective non-pharmaceutical interventions.

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.

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.

How to Design AI-Driven Clinical Trials in Nuclear Medicine.

Artificial intelligence (AI) is an overarching term for a multitude of technologies which are currently being discussed and introduced in several areas of medicine and in medical imaging specifically. There is, however, limited literature and information about how AI techniques can be integrated into the design of clinical imaging trials. This article will present several aspects of AI being used in trials today and how imaging departments and especially nuclear medicine departments can prepare themselves to be at the forefront of AI-driven clinical trials. Beginning with some basic explanation on AI techniques currently being used and existing challenges of its implementation, it will also cover the logistical prerequisites which have to be in place in nuclear medicine departments to participate successfully in AI-driven clinical trials.

Simulating SARS-CoV-2 epidemics by region-specific variables and modeling contact tracing app containment.

Targeted contact-tracing through mobile phone apps has been proposed as an instrument to help contain the spread of COVID-19 and manage the lifting of nation-wide lock-downs currently in place in USA and Europe. However, there is an ongoing debate on its potential efficacy, especially in light of region-specific demographics. We built an expanded SIR model of COVID-19 epidemics that accounts for region-specific population densities, and we used it to test the impact of a contact-tracing app in a number of scenarios. Using demographic and mobility data from Italy and Spain, we used the model to simulate scenarios that vary in baseline contact rates, population densities, and fraction of app users in the population. Our results show that, in support of efficient isolation of symptomatic cases, app-mediated contact-tracing can successfully mitigate the epidemic even with a relatively small fraction of users, and even suppress altogether with a larger fraction of users. However, when regional differences in population density are taken into consideration, the epidemic can be significantly harder to contain in higher density areas, highlighting potential limitations of this intervention in specific contexts. This work corroborates previous results in favor of app-mediated contact-tracing as mitigation measure for COVID-19, and draws attention on the importance of region-specific demographic and mobility factors to achieve maximum efficacy in containment policies.

Sex and gender differences and biases in artificial intelligence for biomedicine and healthcare.

Precision Medicine implies a deep understanding of inter-individual differences in health and disease that are due to genetic and environmental factors. To acquire such understanding there is a need for the implementation of different types of technologies based on artificial intelligence (AI) that enable the identification of biomedically relevant patterns, facilitating progress towards individually tailored preventative and therapeutic interventions. Despite the significant scientific advances achieved so far, most of the currently used biomedical AI technologies do not account for bias detection. Furthermore, the design of the majority of algorithms ignore the sex and gender dimension and its contribution to health and disease differences among individuals. Failure in accounting for these differences will generate sub-optimal results and produce mistakes as well as discriminatory outcomes. In this review we examine the current sex and gender gaps in a subset of biomedical technologies used in relation to Precision Medicine. In addition, we provide recommendations to optimize their utilization to improve the global health and disease landscape and decrease inequalities.