Designer high-density lipoprotein particles enhance endothelial barrier function and suppress inflammation.

High-density lipoprotein (HDL) nanoparticles promote endothelial cell (EC) function and suppress inflammation, but their utility in treating EC dysfunction has not been fully explored. Here, we describe a fusion protein named ApoA1-ApoM (A1M) consisting of apolipoprotein A1 (ApoA1), the principal structural protein of HDL that forms lipid nanoparticles, and ApoM, a chaperone for the bioactive lipid sphingosine 1-phosphate (S1P). A1M forms HDL-like particles, binds to S1P, and is signaling competent. Molecular dynamics simulations showed that the S1P-bound ApoM moiety in A1M efficiently activated EC surface receptors. Treatment of human umbilical vein ECs with A1M-S1P stimulated barrier function either alone or cooperatively with other barrier-enhancing molecules, including the stable prostacyclin analog iloprost, and suppressed cytokine-induced inflammation. A1M-S1P injection into mice during sterile inflammation suppressed neutrophil influx and inflammatory mediator secretion. Moreover, systemic A1M administration led to a sustained increase in circulating HDL-bound S1P and suppressed inflammation in a murine model of LPS-induced endotoxemia. We propose that A1M administration may enhance vascular endothelial barrier function, suppress cytokine storm, and promote resilience of the vascular endothelium.

POSEIDON: Peptidic Objects SEquence-based Interaction with cellular DOmaiNs: a new database and predictor.

Cell-penetrating peptides (CPPs) are short chains of amino acids that have shown remarkable potential to cross the cell membrane and deliver coupled therapeutic cargoes into cells. Designing and testing different CPPs to target specific cells or tissues is crucial to ensure high delivery efficiency and reduced toxicity. However, in vivo/in vitro testing of various CPPs can be both time-consuming and costly, which has led to interest in computational methodologies, such as Machine Learning (ML) approaches, as faster and cheaper methods for CPP design and uptake prediction. However, most ML models developed to date focus on classification rather than regression techniques, because of the lack of informative quantitative uptake values. To address these challenges, we developed POSEIDON, an open-access and up-to-date curated database that provides experimental quantitative uptake values for over 2,300 entries and physicochemical properties of 1,315 peptides. POSEIDON also offers physicochemical properties, such as cell line, cargo, and sequence, among others. By leveraging this database along with cell line genomic features, we processed a dataset of over 1,200 entries to develop an ML regression CPP uptake predictor. Our results demonstrated that POSEIDON accurately predicted peptide cell line uptake, achieving a Pearson correlation of 0.87, Spearman correlation of 0.88, and r2 score of 0.76, on an independent test set. With its comprehensive and novel dataset, along with its potent predictive capabilities, the POSEIDON database and its associated ML predictor signify a significant leap forward in CPP research and development. The POSEIDON database and ML Predictor are available for free and with a user-friendly interface at https://moreiralab.com/resources/poseidon/ , making them valuable resources for advancing research on CPP-related topics. Scientific Contribution Statement: Our research addresses the critical need for more efficient and cost-effective methodologies in Cell-Penetrating Peptide (CPP) research. We introduced POSEIDON, a comprehensive and freely accessible database that delivers quantitative uptake values for over 2,300 entries, along with detailed physicochemical profiles for 1,315 peptides. Recognizing the limitations of current Machine Learning (ML) models for CPP design, our work leveraged the rich dataset provided by POSEIDON to develop a highly accurate ML regression model for predicting CPP uptake.

Microglial Rac1 is essential for experience-dependent brain plasticity and cognitive performance.

Microglia, the largest population of brain immune cells, continuously interact with synapses to maintain brain homeostasis. In this study, we use conditional cell-specific gene targeting in mice with multi-omics approaches and demonstrate that the RhoGTPase Rac1 is an essential requirement for microglia to sense and interpret the brain microenvironment. This is crucial for microglia-synapse crosstalk that drives experience-dependent plasticity, a fundamental brain property impaired in several neuropsychiatric disorders. Phosphoproteomics profiling detects a large modulation of RhoGTPase signaling, predominantly of Rac1, in microglia of mice exposed to an environmental enrichment protocol known to induce experience-dependent brain plasticity and cognitive performance. Ablation of microglial Rac1 affects pathways involved in microglia-synapse communication, disrupts experience-dependent synaptic remodeling, and blocks the gains in learning, memory, and sociability induced by environmental enrichment. Our results reveal microglial Rac1 as a central regulator of pathways involved in the microglia-synapse crosstalk required for experience-dependent synaptic plasticity and cognitive performance.

DELFOS-drug efficacy leveraging forked and specialized networks-benchmarking scRNA-seq data in multi-omics-based prediction of cancer sensitivity.

MOTIVATION: Cancer is currently one of the most notorious diseases, with over 1 million deaths in the European Union alone in 2022. As each tumor can be composed of diverse cell types with distinct genotypes, cancer cells can acquire resistance to different compounds. Moreover, anticancer drugs can display severe side effects, compromising patient well-being. Therefore, novel strategies for identifying the optimal set of compounds to treat each tumor have become an important research topic in recent decades. RESULTS: To address this challenge, we developed a novel drug response prediction algorithm called Drug Efficacy Leveraging Forked and Specialized networks (DELFOS). Our model learns from multi-omics data from over 65 cancer cell lines, as well as structural data from over 200 compounds, for the prediction of drug sensitivity. We also evaluated the benefits of incorporating single-cell expression data to predict drug response. DELFOS was validated using datasets with unseen cell lines or drugs and compared with other state-of-the-art algorithms, achieving a high prediction performance on several correlation and error metrics. Overall, DELFOS can effectively leverage multi-omics data for the prediction of drug responses in thousands of drug-cell line pairs. AVAILABILITY AND IMPLEMENTATION: The DELFOS pipeline and associated data are available at github.com/MoreiraLAB/delfos.

The World of GPCR dimers - Mapping dopamine receptor D2 homodimers in different activation states and configuration arrangements.

G protein-coupled receptors (GPCRs) are known to dimerize, but the molecular and structural basis of GPCR dimers is not well understood. In this study, we developed a computational framework to generate models of symmetric and asymmetric GPCR dimers using different monomer activation states and identified their most likely interfaces with molecular details. We chose the dopamine receptor D2 (D2R) homodimer as a case study because of its biological relevance and the availability of structural information. Our results showed that transmembrane domains 4 and 5 (TM4 and TM5) are mostly found at the dimer interface of the D2R dimer and that these interfaces have a subset of key residues that are mostly nonpolar from TM4 and TM5, which was in line with experimental studies. In addition, TM2 and TM3 appear to be relevant for D2R dimers. In some cases, the inactive configuration is unaffected by the partnered protomer, whereas in others, the active protomer adopts the properties of an inactive receptor. Additionally, the ß-arrestin configuration displayed the properties of an active receptor in the absence of an agonist, suggesting that a switch to another meta-state during dimerization occurred. Our findings are consistent with the experimental data, and this method can be adapted to study heterodimers and potentially extended to include additional proteins such as G proteins or ß-arrestins. In summary, this approach provides insight into the impact of the conformational status of partnered protomers on the overall quaternary GPCR macromolecular structure and dynamics.

Biodegradation of Pollutants in the Environment: Omics Approaches.

This special edition intends to highlight how omics approaches have been used in biodegradation studies to understand the mechanisms involved and improve biodegradation processes [...].

MUG: A mutation overview of GPCR subfamily A17 receptors.

G protein-coupled receptors (GPCRs) mediate several signaling pathways through a general mechanism that involves their activation, upholding a chain of events that lead to the release of molecules responsible for cytoplasmic action and further regulation. These physiological functions can be severely altered by mutations in GPCR genes. GPCRs subfamily A17 (dopamine, serotonin, adrenergic and trace amine receptors) are directly related with neurodegenerative diseases, and as such it is crucial to explore known mutations on these systems and their impact in structure and function. A comprehensive and detailed computational framework - MUG (Mutations Understanding GPCRs) - was constructed, illustrating key reported mutations and their effect on receptors of the subfamily A17 of GPCRs. We explored the type of mutations occurring overall and in the different families of subfamily A17, as well their localization within the receptor and potential effects on receptor functionality. The mutated residues were further analyzed considering their pathogenicity. The results reveal a high diversity of mutations in the GPCR subfamily A17 structures, drawing attention to the considerable number of mutations in conserved residues and domains. Mutated residues were typically hydrophobic residues enriched at the ligand binding pocket and known activating microdomains, which may lead to disruption of receptor function. MUG as an interactive web application is available for the management and visualization of this dataset. We expect that this interactive database helps the exploration of GPCR mutations, their influence, and their familywise and receptor-specific effects, constituting the first step in elucidating their structures and molecules at the atomic level.

Enhanced neuronal differentiation by dynamic piezoelectric stimulation.

Electroactive smart materials play an important role for tissue regenerative applications. Poly(vinylidene fluoride) (PVDF) is a specific subtype of piezoelectric electroactive material that generates electrical potential upon mechanical stimulation. This work focuses on the application of piezoelectric PVDF films for neural differentiation. Human neural precursor cells (hNPCs) are cultured on piezoelectric poled and non-poled ß-PVDF films with or without a pre-coating step of poly-d-lysine and laminin (PDL/L). Subsequently, hNPCs differentiation into the neuronal lineage is assessed (MAP2+ and DCX+ ) under static or dynamic (piezoelectric stimulation) culture conditions. The results demonstrate that poled and coated ß-PVDF films induce neuronal differentiation under static culture conditions which is further enhanced with mechanical stimulation. In silico calculations of the electrostatic potential of different domains of laminin, highlight the high polarity of those domains, which shows a clear preference to interact with the varying surface electric field of the piezoelectric material under mechanical stimulation. These interactions might explain the higher neuronal differentiation induced by poled ß-PVDF films pre-coated with PDL/L under dynamic conditions. Our results suggest that electromechanical stimuli, such as the ones induced by piezoelectric ß-PVDF films, are suitable to promote neuronal differentiation and hold great promise for the development of neuroregenerative therapies.

Biodegradation and Metabolic Pathway of the Neonicotinoid Insecticide Thiamethoxam by Labrys portucalensis F11.

Thiamethoxam (TMX) is an effective neonicotinoid insecticide. However, its widespread use is detrimental to non-targeted organisms and water systems. This study investigates the biodegradation of this insecticide by Labrys portucalensis F11. After 30 days of incubation in mineral salt medium, L. portucalensis F11 was able to remove 41%, 35% and 100% of a supplied amount of TMX (10.8 mg L-1) provided as the sole carbon and nitrogen source, the sole carbon and sulfur source and as the sole carbon source, respectively. Periodic feeding with sodium acetate as the supplementary carbon source resulted in faster degradation of TMX (10.8 mg L-1); more than 90% was removed in 3 days. The detection and identification of biodegradation intermediates was performed by UPLC-QTOF/MS/MS. The chemical structure of 12 metabolites is proposed. Nitro reduction, oxadiazine ring cleavage and dechlorination are the main degradation pathways proposed. After biodegradation, toxicity was removed as indicated using Aliivibrio fischeri and by assessing the synthesis of an inducible ß-galactosidase by an E. coli mutant (Toxi-Chromo test). L. portucalensis F11 was able to degrade TMX under different conditions and could be effective in bioremediation strategies.

DrugTax: package for drug taxonomy identification and explainable feature extraction.

DrugTax is an easy-to-use Python package for small molecule detailed characterization. It extends a previously explored chemical taxonomy making it ready-to-use in any Artificial Intelligence approach. DrugTax leverages small molecule representations as input in one of their most accessible and simple forms (SMILES) and allows the simultaneously extraction of taxonomy information and key features for big data algorithm deployment. In addition, it delivers a set of tools for bulk analysis and visualization that can also be used for chemical space representation and molecule similarity assessment. DrugTax is a valuable tool for chemoinformatic processing and can be easily integrated in drug discovery pipelines. DrugTax can be effortlessly installed via PyPI ( https://pypi.org/project/DrugTax/ ) or GitHub ( https://github.com/MoreiraLAB/DrugTax ).

SYNPRED: prediction of drug combination effects in cancer using different synergy metrics and ensemble learning.

BACKGROUND: In cancer research, high-throughput screening technologies produce large amounts of multiomics data from different populations and cell types. However, analysis of such data encounters difficulties due to disease heterogeneity, further exacerbated by human biological complexity and genomic variability. The specific profile of cancer as a disease (or, more realistically, a set of diseases) urges the development of approaches that maximize the effect while minimizing the dosage of drugs. Now is the time to redefine the approach to drug discovery, bringing an artificial intelligence (AI)-powered informational view that integrates the relevant scientific fields and explores new territories. RESULTS: Here, we show SYNPRED, an interdisciplinary approach that leverages specifically designed ensembles of AI algorithms, as well as links omics and biophysical traits to predict anticancer drug synergy. It uses 5 reference models (Bliss, Highest Single Agent, Loewe, Zero Interaction Potency, and Combination Sensitivity Score), which, coupled with AI algorithms, allowed us to attain the ones with the best predictive performance and pinpoint the most appropriate reference model for synergy prediction, often overlooked in similar studies. By using an independent test set, SYNPRED exhibits state-of-the-art performance metrics either in the classification (accuracy, 0.85; precision, 0.91; recall, 0.90; area under the receiver operating characteristic, 0.80; and F1-score, 0.91) or in the regression models, mainly when using the Combination Sensitivity Score synergy reference model (root mean square error, 11.07; mean squared error, 122.61; Pearson, 0.86; mean absolute error, 7.43; Spearman, 0.87). Moreover, data interpretability was achieved by deploying the most current and robust feature importance approaches. A simple web-based application was constructed, allowing easy access by nonexpert researchers. CONCLUSIONS: The performance of SYNPRED rivals that of the existing methods that tackle the same problem, yielding unbiased results trained with one of the most comprehensive datasets available (NCI ALMANAC). The leveraging of different reference models allowed deeper insights into which of them can be more appropriately used for synergy prediction. The Combination Sensitivity Score clearly stood out with improved performance among the full scope of surveyed approaches and synergy reference models. Furthermore, SYNPRED takes a particular focus on data interpretability, which has been in the spotlight lately when using the most advanced AI techniques.

Biodegradation and Metabolic Pathway of 17ß-Estradiol by Rhodococcus sp. ED55.

Endocrine disrupting compounds (EDCs) in the environment are considered a motif of concern, due to the widespread occurrence and potential adverse ecological and human health effects. The natural estrogen, 17ß-estradiol (E2), is frequently detected in receiving water bodies after not being efficiently removed in conventional wastewater treatment plants (WWTPs), promoting a negative impact for both the aquatic ecosystem and human health. In this study, the biodegradation of E2 by Rhodococcus sp. ED55, a bacterial strain isolated from sediments of a discharge point of WWTP in Coloane, Macau, was investigated. Rhodococcus sp. ED55 was able to completely degrade 5 mg/L of E2 in 4 h in a synthetic medium. A similar degradation pattern was observed when the bacterial strain was used in wastewater collected from a WWTP, where a significant improvement in the degradation of the compound occurred. The detection and identification of 17 metabolites was achieved by means of UPLC/ESI/HRMS, which proposed a degradation pathway of E2. The acute test with luminescent marine bacterium Aliivibrio fischeri revealed the elimination of the toxicity of the treated effluent and the standardized yeast estrogenic (S-YES) assay with the recombinant strain of Saccharomyces cerevisiae revealed a decrease in the estrogenic activity of wastewater samples after biodegradation.

Actinobacteria isolated from wastewater treatment plants located in the east-north of Algeria able to degrade pesticides.

The pollution of water resources by pesticides poses serious problems for public health and the environment. In this study, Actinobacteria strains were isolated from three wastewater treatment plants (WWTPs) and were screened for their ability to degrade 17 pesticide compounds. Preliminary screening of 13 of the isolates of Actinobacteria allowed the selection of 12 strains with potential for the degradation of nine different pesticides as sole carbon source, including aliette, for which there are no previous reports of biodegradation. Evaluation of the bacterial growth and degradation kinetics of the pesticides 2,4-dichlorophenol (2,4-DCP) and thiamethoxam (tiam) by selected Actinobacteria strains was performed in liquid media. Strains Streptomyces sp. ML and Streptomyces sp. OV were able to degrade 45% of 2,4-DCP (50 mg/l) as the sole carbon source in 30 days and 84% of thiamethoxam (35 mg/l) in the presence of 10 mM of glucose in 18 days. The biodegradation of thiamethoxam by Actinobacteria strains was reported for the first time in this study. These strains are promising for use in bioremediation of ecosystems polluted by this type of pesticides.

Discovery of Virus-Host interactions using bioinformatic tools.

Viruses are a diverse biological group capable of infecting several hosts such as bacteria, plants, and animals, including humans. Viral infections constitute a threat to the human population as they may cause high mortality rates, decrease food production, and generate large economical losses. Viruses co-evolve with their hosts and this constant evolution must be clarified to better predict possible viral outbreaks, and to develop improved diagnostic methods and therapeutical approaches. In this review, we summarize several viral databases that store key information retrieved from a variety of omics approaches. Furthermore, we explore the use of such databases to predict Virus-Host interactions through artificial intelligence algorithms, focusing on the latest methodologies to characterize biological networks.

SARS-CoV-2 Membrane Protein: From Genomic Data to Structural New Insights.

Severe Acute Respiratory Syndrome CoronaVirus-2 (SARS-CoV-2) is composed of four structural proteins and several accessory non-structural proteins. SARS-CoV-2's most abundant structural protein, Membrane (M) protein, has a pivotal role both during viral infection cycle and host interferon antagonism. This is a highly conserved viral protein, thus an interesting and suitable target for drug discovery. In this paper, we explain the structural nature of M protein homodimer. To do so, we developed and applied a detailed and robust in silico workflow to predict M protein dimeric structure, membrane orientation, and interface characterization. Single Nucleotide Polymorphisms (SNPs) in M protein were retrieved from over 1.2 M SARS-CoV-2 genomes and proteins from the Global Initiative on Sharing All Influenza Data (GISAID) database, 91 of which were located at the predicted dimer interface. Among those, we identified SNPs in Variants of Concern (VOC) and Variants of Interest (VOI). Binding free energy differences were evaluated for dimer interfacial SNPs to infer mutant protein stabilities. A few high-prevalent mutated residues were found to be especially relevant in VOC and VOI. This realization may be a game-changer to structure-driven formulation of new therapeutics for SARS-CoV-2.

Class A and C GPCR Dimers in Neurodegenerative Diseases.

Neurodegenerative diseases affect over 30 million people worldwide with an ascending trend. Most individuals suffering from these irreversible brain damages belong to the elderly population, with onset between 50 and 60 years. Although the pathophysiology of such diseases is partially known, it remains unclear upon which point a disease turns degenerative. Moreover, current therapeutics can treat some of the symptoms but often have severe side effects and become less effective in long-term treatment. For many neurodegenerative diseases, the involvement of G proteincoupled receptors (GPCRs), which are key players of neuronal transmission and plasticity, has become clearer and holds great promise in elucidating their biological mechanism. With this review, we introduce and summarize class A and class C GPCRs, known to form heterodimers or oligomers to increase their signalling repertoire. Additionally, the examples discussed here were shown to display relevant alterations in brain signalling and had already been associated with the pathophysiology of certain neurodegenerative diseases. Lastly, we classified the heterodimers into two categories of crosstalk, positive or negative, for which there is known evidence.

Network biology and artificial intelligence drive the understanding of the multidrug resistance phenotype in cancer.

Globally with over 10 million deaths per year, cancer is the most transversal disease across countries, cultures, and ethnicities, affecting both developed and developing regions. Tumorigenesis is dynamically altered by distinct events and can be lethal when untreated. Despite the innovative therapeutics available, multidrug resistance (MDR) to chemotherapy remains the major hindrance to the success of cancer therapy. The multiple mechanisms by which cancer cells evade cell death are diverse, indicating that MDR involves complex interconnected biological networks. Molecular profiling is currently able to stratify cancer into its distinct subtypes and help identify the best therapeutics, leading to "translational systems medicine". Highly specialized methodologies are generating a large amount of "omics" data - including epigenetics, genomics, transcriptomics, proteomics, metabolomics, as well as pharmacogenomics. Many of the resulting databases store data in non-standard formats, which need to be converted, interpreted, and merged into readable formats. The latest development of artificial intelligence (AI) methodologies and tools, coupled with advancements in large-scale data management and powerful graphic processing computing units, potentiate the integration of these large data sources into relevant biological networks, which will enhance our understanding of cancer MDR. In this review, we revisit common MDR mechanisms and compile a list of the most relevant "omics" public databases. We highlight examples of AI methods that are now decisively contributing to clear advances in cancer research, such as identification of new drugs from large databases and prediction of relevant drug, target, and system properties. An overview of several freely available "ready-to-use" algorithms is also provided. The described molecular scale AI algorithms and tools will undoubtedly guide important improvements in efficiency and efficacy of traditional methods of cancer diagnostics and treatment.

Biological removal processes in aerobic granular sludge exposed to diclofenac.

Diclofenac is a worldwide consumed drug included in the watch list of substances to be monitored according to the European Union Water Framework Directive (Directive 2013/39/EU). Aerobic granular sludge sequencing batch reactors (AGS-SBR) are increasingly used for wastewater treatment but there is scant information on the fate and effect of micropollutants to nutrient removal processes. An AGS-SBR fed with synthetic wastewater containing diclofenac was bioaugmented with a diclofenac degrading bacterial strain and performance and microbial community dynamics was analysed. Chemical oxygen demand, phosphate and ammonia removal were not affected by the micropollutant at 0.03 mM (9.54 mg L-1). The AGS was able to retain the degrading strain, which was detected in the sludge throughout after augmentation. Nevertheless, besides some adsorption to the biomass, diclofenac was not degraded by the augmented sludge given the short operating cycles and even if batch degradation assays confirmed that the bioaugmented AGS was able to biodegrade the compound. The exposure to the pharmaceutical affected the microbial community of the sludge, separating the two first phases of reactor operation (acclimatization and granulation) from subsequent phases. The AGS was able to keep the bioaugmented strain and to maintain the main functions of nutrient removal even through the long exposure to the pharmaceutical, but combined strategies are needed to reduce the spread of micropollutants in the environment.

In Silico End-to-End Protein-Ligand Interaction Characterization Pipeline: The Case of SARS-CoV-2.

SARS-CoV-2 triggered a worldwide pandemic disease, COVID-19, for which an effective treatment has not yet been settled. Among the most promising targets to fight this disease is SARS-CoV-2 main protease (Mpro), which has been extensively studied in the last few months. There is an urgency for developing effective computational protocols that can help us tackle these key viral proteins. Hence, we have put together a robust and thorough pipeline of in silico protein-ligand characterization methods to address one of the biggest biological problems currently plaguing our world. These methodologies were used to characterize the interaction of SARS-CoV-2 Mpro with an α-ketoamide inhibitor and include details on how to upload, visualize, and manage the three-dimensional structure of the complex and acquire high-quality figures for scientific publications using PyMOL (Protocol 1); perform homology modeling with MODELLER (Protocol 2); perform protein-ligand docking calculations using HADDOCK (Protocol 3); run a virtual screening protocol of a small compound database of SARS-CoV-2 candidate inhibitors with AutoDock 4 and AutoDock Vina (Protocol 4); and, finally, sample the conformational space at the atomic level between SARS-CoV-2 Mpro and the α-ketoamide inhibitor with Molecular Dynamics simulations using GROMACS (Protocol 5). Guidelines for careful data analysis and interpretation are also provided for each Protocol.

Decoding Partner Specificity of Opioid Receptor Family.

This paper describes an exciting big data analysis compiled in a freely available database, which can be applied to characterize the coupling of different G-Protein coupled receptors (GPCRs) families with their intracellular partners. Opioid receptor (OR) family was used as case study in order to gain further insights into the physiological properties of these important drug targets, known to be associated with the opioid crisis, a huge socio-economic issue directly related to drug abuse. An extensive characterization of all members of the ORs family (µ (MOR), δ (DOR), κ (KOR), nociceptin (NOP)) and their corresponding binding partners (ARRs: Arr2, Arr3; G-protein: Gi1, Gi2, Gi3, Go, Gob, Gz, Gq, G11, G14, G15, G12, Gssh, Gslo) was carried out. A multi-step approach including models' construction (multiple sequence alignment, homology modeling), complex assembling (protein complex refinement with HADDOCK and complex equilibration), and protein-protein interface characterization (including both structural and dynamics analysis) were performed. Our database can be easily applied to several GPCR sub-families, to determine the key structural and dynamical determinants involved in GPCR coupling selectivity.