Studying Autophagy in Microglia: Overcoming the Obstacles.

In this chapter, we provide an overview of the main techniques and experimental approaches that can be used to analyze autophagy flux in microglia, the brain-resident macrophages. For this purpose, we first briefly introduce the main peculiarities of microglial biology, describe the basic mechanisms and functions of autophagy, and summarize the evidence accumulated so far on the role of autophagy in the regulation of microglial survival and functions, mainly phagocytosis and inflammation. Then, we highlight conceptual and technical aspects of autophagic recycling and microglial physiology that need to be taken into account for the accurate evaluation of autophagy flux in microglia. Finally, we describe the main assays that can be used to analyze the complete sequence of autophagosome formation and degradation or autophagy flux, mainly in cultured microglia and in vivo. The main approaches include indirect tracking of autophagosomes by autophagic enzymes such as LC3 by western blot and fluorescence-based confocal microscopy, as well as direct analysis of autophagic vesicles by electron microscopy. We also discuss the advantages and disadvantages of using these methods in specific experimental contexts and highlight the need to complement LC3 and/or electron microscopy data with analysis of other autophagic effectors and lysosomal proteins that participate in the initiation and completion of autophagy flux, respectively. In summary, we provide an experimental guide for the analysis of autophagosome turnover in microglia, emphasizing the need to combine as many markers and complementary approaches as possible to fully characterize the status of autophagy flux in microglia.

Isolation and Characterization of Testis Macrophages Using Flow Cytometry.

Testis-resident macrophages are first responders of the innate immune system against pathogens. They also exert day-to-day functions that are poorly understood. To study testis macrophages, several techniques are used, among which we can find flow cytometry.Flow cytometry is a powerful tool that enables analysis of macrophages at a cellular as well as population level. To analyze testis macrophages using flow cytometry, a specific tissue processing is necessary to extract them. In this protocol, we explain how to extract and analyze the distinct macrophage populations.

Protein-Ligand Blind Docking Using CB-Dock2.

Protein-ligand blind docking is a widely used method for studying the binding sites and poses of ligands and receptors in pharmaceutical and biological research. Recently, our new blind docking server named CB-Dock2 has been released and is currently being utilized by researchers worldwide. CB-Dock2 outperforms state-of-the-art methods due to its accuracy in binding site identification and binding pose prediction, which are enabled by its knowledge-based docking engine. This highly automated server offers interactive and intuitive input and output web interfaces, making it an efficient and user-friendly tool for the bioinformatics and cheminformatics communities. This chapter provides a brief overview of the methods, followed by a detailed guide on using the CB-Dock2 server. Additionally, we present a case study that evaluates the performance of protein-ligand blind docking using this tool.

Techniques for Developing Reliable Machine Learning Classifiers Applied to Understanding and Predicting Protein:Protein Interaction Hot Spots.

With machine learning now transforming the sciences, successful prediction of biological structure or activity is mainly limited by the extent and quality of data available for training, the astute choice of features for prediction, and thorough assessment of the robustness of prediction on a variety of new cases. In this chapter, we address these issues while developing and sharing protocols to build a robust dataset and rigorously compare several predictive classifiers using the open-source Python machine learning library, scikit-learn. We show how to evaluate whether enough data has been used for training and whether the classifier has been overfit to training data. The most telling experiment is 500-fold repartitioning of the training and test sets, followed by prediction, which gives a good indication of whether a classifier performs consistently well on different datasets. An intuitive method is used to quantify which features are most important for correct prediction.The resulting well-trained classifier, hotspotter, can robustly predict the small subset of amino acid residues on the surface of a protein that are energetically most important for binding a protein partner: the interaction hot spots. Hotspotter has been trained and tested here on a curated dataset assembled from 1046 non-redundant alanine scanning mutation sites with experimentally measured change in binding free energy values from 97 different protein complexes; this dataset is available to download. The accessible surface area of the wild-type residue at a given site and its degree of evolutionary conservation proved the most important features to identify hot spots. A variant classifier was trained and validated for proteins where only the amino acid sequence is available, augmented by secondary structure assignment. This version of hotspotter requiring fewer features is almost as robust as the structure-based classifier. Application to the ACE2 (angiotensin converting enzyme 2) receptor, which mediates COVID-19 virus entry into human cells, identified the critical hot spot triad of ACE2 residues at the center of the small interface with the CoV-2 spike protein. Hotspotter results can be used to guide the strategic design of protein interfaces and ligands and also to identify likely interfacial residues for protein:protein docking.

Artificial Intelligence in ADME Property Prediction.

Absorption, distribution, metabolism, excretion (ADME) are key properties of a small molecule that govern pharmacokinetic profiles and impact its efficacy and safety. Computational methods such as machine learning and artificial intelligence have gained significant interest in both academic and industrial settings to predict pharmacokinetic properties of small molecules. These methods are applied in drug discovery to optimize chemical libraries, prioritize hits from biological screens, and optimize ADME properties of lead molecules. In the recent years, the drug discovery community witnessed the use of a range of neural network architectures such as deep neural networks, recurrent neural networks, graph neural networks, and transformer neural networks, which marked a paradigm shift in computer-aided drug design and development. This chapter discusses recent developments with an emphasis on their application to predict ADME properties.

Exploring the Role of Chemoinformatics in Accelerating Drug Discovery: A Computational Approach.

Cheminformatics and its role in drug discovery is expected to be the privileged approach in handling large number of chemical datasets. This approach contributes toward the pharmaceutical development and assessment of chemical compounds at a faster rate efficiently. Additionally, as technological advancement impacts research, cheminformatics is being used more and more in the field of health science. This chapter describes the concepts of cheminformatics along with its involvement in drug discovery with a case study.

Accelerating the Discovery and Design of Antimicrobial Peptides with Artificial Intelligence.

Peptides modulate many processes of human physiology targeting ion channels, protein receptors, or enzymes. They represent valuable starting points for the development of new biologics against communicable and non-communicable disorders. However, turning native peptide ligands into druggable materials requires high selectivity and efficacy, predictable metabolism, and good safety profiles. Machine learning models have gradually emerged as cost-effective and time-saving solutions to predict and generate new proteins with optimal properties. In this chapter, we will discuss the evolution and applications of predictive modeling and generative modeling to discover and design safe and effective antimicrobial peptides. We will also present their current limitations and suggest future research directions, applicable to peptide drug design campaigns.

Accelerating COVID-19 Drug Discovery with High-Performance Computing.

The recent COVID-19 pandemic has served as a timely reminder that the existing drug discovery is a laborious, expensive, and slow process. Never has there been such global demand for a therapeutic treatment to be identified as a matter of such urgency. Unfortunately, this is a scenario likely to repeat itself in future, so it is of interest to explore ways in which to accelerate drug discovery at pandemic speed. Computational methods naturally lend themselves to this because they can be performed rapidly if sufficient computational resources are available. Recently, high-performance computing (HPC) technologies have led to remarkable achievements in computational drug discovery and yielded a series of new platforms, algorithms, and workflows. The application of artificial intelligence (AI) and machine learning (ML) approaches is also a promising and relatively new avenue to revolutionize the drug design process and therefore reduce costs. In this review, I describe how molecular dynamics simulations (MD) were successfully integrated with ML and adapted to HPC to form a powerful tool to study inhibitors for four of the COVID-19 target proteins. The emphasis of this review is on the strategy that was used with an explanation of each of the steps in the accelerated drug discovery workflow. For specific technical details, the reader is directed to the relevant research publications.

Efficient and Reliable Data Management for Biomedical Applications.

This chapter discusses the challenges and requirements of modern Research Data Management (RDM), particularly for biomedical applications in the context of high-performance computing (HPC). The FAIR data principles (Findable, Accessible, Interoperable, Reusable) are of special importance. Data formats, publication platforms, annotation schemata, automated data management and staging, the data infrastructure in HPC centers, file transfer and staging methods in HPC, and the EUDAT components are discussed. Tools and approaches for automated data movement and replication in cross-center workflows are explained, as well as the development of ontologies for structuring and quality-checking of metadata in computational biomedicine. The CompBioMed project is used as a real-world example of implementing these principles and tools in practice. The LEXIS project has built a workflow-execution and data management platform that follows the paradigm of HPC-Cloud convergence for demanding Big Data applications. It is used for orchestrating workflows with YORC, utilizing the data documentation initiative (DDI) and distributed computing resources (DCI). The platform is accessed by a user-friendly LEXIS portal for workflow and data management, making HPC and Cloud Computing significantly more accessible. Checkpointing, duplicate runs, and spare images of the data are used to create resilient workflows. The CompBioMed project is completing the implementation of such a workflow, using data replication and brokering, which will enable urgent computing on exascale platforms.

Effect of Muscle Forces on Femur During Level Walking Using a Virtual Population of Older Women.

Aging is associated with a greater risk of muscle and bone disorders such as sarcopenia and osteoporosis. These conditions substantially affect one's mobility and quality of life. In the past, muscles and bones are often studied separately using generic or scaled information that are not personal-specific, nor are they representative of the large variations seen in the elderly population. Consequently, the mechanical interaction between the aged muscle and bone is not well understood, especially when carrying out daily activities. This study presents a coupling approach across the body and the organ level, using fully personal-specific musculoskeletal and finite element models in order to study femoral loading during level walking. Variations in lower limb muscle volume/force were examined using a virtual population. These muscle forces were then applied to the finite element model of the femur to study the variations in predicted strains. The study shows that effective coupling across two scales can be carried out to study the muscle-bone interaction in elderly women. The generation of a virtual population is a feasible approach to augment anatomical variations based on a small population that could mimic variations seen in a larger cohort. This is a valuable alternative to overcome the limitation or the need to collect dataset from a large population, which is both time and resource consuming.

HPC Framework for Performing in Silico Trials Using a 3D Virtual Human Cardiac Population as Means to Assess Drug-Induced Arrhythmic Risk.

Following the 3 R's principles of animal research-replacement, reduction, and refinement-a high-performance computational framework was produced to generate a platform to perform human cardiac in-silico clinical trials as means to assess the pro-arrhythmic risk after the administrations of one or combination of two potentially arrhythmic drugs. The drugs assessed in this study were hydroxychloroquine and azithromycin. The framework employs electrophysiology simulations on high-resolution three-dimensional, biventricular human heart anatomies including phenotypic variabilities, so as to determine if differential QT-prolongation responds to drugs as observed clinically. These simulations also reproduce sex-specific ionic channel characteristics. The derived changes in the pseudo-electrocardiograms, calcium concentrations, as well as activation patterns within 3D geometries were evaluated for signs of induced arrhythmia. The virtual subjects could be evaluated at two different cycle lengths: at a normal heart rate and at a heart rate associated with stress as means to analyze the proarrhythmic risks after the administrations of hydroxychloroquine and azithromycin. Additionally, a series of experiments performed on reanimated swine hearts utilizing Visible Heart® methodologies in a four-chamber working heart model were performed to verify the arrhythmic behaviors observed in the in silico trials.The obtained results indicated similar pro-arrhythmic risk assessments within the virtual population as compared to published clinical trials (21% clinical risk vs 21.8% in silico trial risk). Evidence of transmurally heterogeneous action potential prolongations after providing a large dose of hydroxychloroquine was found as the observed mechanisms for elicited arrhythmias, both in the in vitro and the in silico models. The proposed workflow for in silico clinical drug cardiotoxicity trials allows for reproducing the complex behavior of cardiac electrophysiology in a varied population, in a matter of a few days as compared to the months or years it requires for most in vivo human clinical trials. Importantly, our results provided evidence of the common phenotype variants that produce distinct drug-induced arrhythmogenic outcomes.

Clinical Usefulness of a Cell-based Assay for Detecting Myelin Oligodendrocyte Glycoprotein Antibodies in Central Nervous System Inflammatory Disorders.

Background: The clinical implications of myelin oligodendrocyte glycoprotein autoantibodies (MOG-Abs) are increasing. Establishing MOG-Ab assays is essential for effectively treating patients with MOG-Abs. We established an in-house cell-based assay (CBA) to detect MOG-Abs to identify correlations with patients' clinical characteristics. Methods: We established the CBA using HEK 293 cells transiently overexpressing full-length human MOG, tested it against 166 samples from a multicenter registry of central nervous system (CNS) inflammatory disorders, and compared the results with those of the Oxford MOG-Ab-based CBA and a commercial MOG-Ab CBA kit. We recruited additional patients with MOG-Abs and compared the clinical characteristics of MOG-Ab-associated disease (MOGAD) with those of neuromyelitis optica spectrum disorder (NMOSD). Results: Of 166 samples tested, 10 tested positive for MOG-Abs, with optic neuritis (ON) being the most common manifestation (4/15, 26.7%). The in-house and Oxford MOG-Ab CBAs agreed for 164/166 (98.8%) samples (κ=0.883, P<0.001); two patients (2/166, 1.2%) were only positive in our in-house CBA, and the CBA scores of the two laboratories correlated well (r=0.663, P<0.001). The commercial MOG-Ab CBA kit showed one false-negative and three false-positive results. The clinical presentation at disease onset differed between MOGAD and NMOSD; ON was the most frequent manifestation in MOGAD, and transverse myelitis was most frequent in NMOSD. Conclusions: The in-house CBA for MOG-Abs demonstrated reliable results and can potentially be used to evaluate CNS inflammatory disorders. A comprehensive, long-term study with a large patient population would clarify the clinical significance of MOG-Abs.

Trophoblast organoids with physiological polarity model placental structure and function.

Human trophoblast organoids (TOs) are a three-dimensional ex vivo culture model that can be used to study various aspects of placental development, physiology and pathology. However, standard culturing of TOs does not recapitulate the cellular orientation of chorionic villi in vivo given that the multi-nucleated syncytiotrophoblast (STB) develops largely within the inner facing surfaces of these organoids (STBin). Here, we developed a method to culture TOs under conditions that recapitulate the cellular orientation of chorionic villi in vivo. We show that culturing STBin TOs in suspension with gentle agitation leads to the development of TOs containing the STB on the outer surface (STBout). Using membrane capacitance measurements, we determined that the outermost surface of STBout organoids contain large syncytia comprising >50 nuclei, whereas STBin organoids contain small syncytia (<10 nuclei) and mononuclear cells. The growth of TOs under conditions that mimic the cellular orientation of chorionic villi in vivo thus allows for the study of a variety of aspects of placental biology under physiological conditions.

The effect of Ding-kun-dan comparing with Marvelon on primary dysmenorrhea: A prospective, double-blind, multicenter, randomized controlled trial.

ETHNOPHARMACOLOGICAL RELEVANCE: Ding-kun-dan (DKD), as one of well-known traditional Chinese medicine (TCM), is considered as an effective prescription to regulate menstruation, benefit Qi and nourish the blood. Previous studies had showed that DKD could improve sex hormone levels, insulin resistance, metabolism abnormalities and regulate immunity in animal models with polycystic ovary syndrome or endometriosis, however, little study conducted to reveal its clinical efficacy in Primary Dysmenorrhea (PD). AIM OF THE STUDY: To compare the effect of Ding-kun-dan (DKD) with Marvelon on relief of symptoms and change of serum pain-related factors in patients with primary dysmenorrhea. MATERIALS AND METHOD: 136 patients with primary dysmenorrhea were randomly assigned to the DKD group (n = 73, take one tablet per day from 5th day of the menstrual cycle for 10 days every 28 days) and the Marvelon group (n = 63, take one tablet per day from 5th day of the menstrual cycle for 21 days every 28 days), the therapeutic effects were analyzed through evaluating the change of VAS scores, CMSS scores and the level of PGF2a, PGE2, PGF2a/PGE2 and NO during the 12 weeks intervention. RESULTS: Both DKD and Marvelon could effectively relief pain and other associated symptoms at each visit (Baseline, 4th week, 8th week and 12th week). Although Marvelon was significantly superior to DKD in reducing VAS scores, the total CMSS, CMSS severity and duration scores at each posttreatment follow-up (P < 0.01), VAS scores in the DKD group decreased significantly over time while scores in the COC group only dropped rapidly after the first two months of treatment. Efficacy gap between two interventions continuously narrowed over time and the efficacy of DKD became non-inferior at the 12th week compared to that of Marvelon (the difference between groups, - 0.78%; 95% confidence interval (CI), -13.67%-12.75%; non-inferiority margin, 15%). DKD group had better efficacy on mild pain compared to that of the COC group with no statistical difference (75% VS 61.9%, P > 0.05). DKD and Marvelon could effectively reduce PGF2a, PGE2 and higher PGF2a/PGE2 in patients with PD. There was no statistical difference in the level of PGF2a, PGE2, PGF2a/PGE2 and NO between DKD and Marvelon group at each follow-up. No serious adverse effect was observed. CONCLUSION: Ding-kun-dan is another available, effective and safe method for patients with primary dysmenorrhea to choose, especially for those who are suffered from mild pain and/or contraindicated to hormonal agents.

Fat2 polarizes Lar and Sema5c to coordinate the motility of collectively migrating epithelial cells.

Migrating epithelial cells globally align their migration machinery to achieve tissue-level movement. Biochemical signaling across leading-trailing cell-cell interfaces can promote this alignment by partitioning migratory behaviors like protrusion and retraction to opposite sides of the interface. However, how signaling proteins become organized at interfaces to accomplish this is poorly understood. The follicular epithelial cells of Drosophila melanogaster have two signaling modules at their leading-trailing interfaces - one composed of the atypical cadherin Fat2 (also known as Kugelei) and the receptor tyrosine phosphatase Lar, and one composed of Semaphorin5c and its receptor Plexin A. Here, we show that these modules form one interface signaling system with Fat2 at its core. Trailing edge-enriched Fat2 concentrates both Lar and Semaphorin5c at leading edges of cells, but Lar and Semaphorin5c play little role in the localization of Fat2. Fat2 is also more stable at interfaces than Lar or Semaphorin5c. Once localized, Lar and Semaphorin5c act in parallel to promote collective migration. We propose that Fat2 serves as the organizer of this interface signaling system by coupling and polarizing the distributions of multiple effectors that work together to align the migration machinery of neighboring cells.

In-depth mass spectrometry analysis of rhGH administration altered energy metabolism and steroidogenesis.

Growth hormone, as a proteohormone, is primarily known of its dramatic effect on longitudinal growth. Recombinant DNA technology has provided a safe, abundant and comparatively cheap supply of human GH for growth hormone-deficient individuals. However, many healthy subjects, especially athletics, administrate GH for enhanced athletic performance or strength. A better and more comprehensive understanding of rhGH effect in healthy individuals is urgent and essential. In this study, we recruited 14 healthy young male and injected rhGH once. Untargeted LC-MS metabolomics profiling of serum and urine was performed before and after the rhGH injection. The GH-induced dysregulation of energy related pathways, such as amino acid metabolism, nucleotide metabolism, glycolysis and TCA cycle, was revealed. Moreover, individuals supplemented with micro-doses of rhGH exhibited significantly changed urinary steroidal profiles, suggesting a role of rhGH in both energy metabolism and steroidogenesis. We expect that our results will be helpful to provide new evidence on the effects of rhGH injection and provide potential biomarkers for rhGH administration.

Liquid crystal biosensor based on AuNPs signal amplification for detection of human chorionic gonadotropin.

The detection of human chorionic gonadotropin (HCG) allows for the determination of pregnancy and is thus crucial during early pregnancy testing. This study introduces a novel liquid crystal (LC) biosensor that employs Au nanoparticles (AuNPs) for signal amplification, thus enabling the detection of the HCG antigen in a micro, efficient, and cost-effective manner. The sensor design capitalizes on the unique properties of LC to facilitate the detection of HCG. In this research, the surface of the base substrate was first modified with material from DMOAP and APTES, and EDC/sulfo-NHS was used to couple AuNPs and ß-hCG to form an AuNP-ß-hCG conjugate that improves the coupling rate. The carboxyl group of the antibody was reacted with the aldehyde group of glutaraldehyde, which helps to fix the ß-hCG antibody to the surface of the substrate. The HCG sample is immobilized on the surface of the substrate via antigen-antibody immunobinding. As signal amplifiers, the AuNPs can have a significant effect on the topology of the interface and the vertical order of the LC molecules, thus reducing the limit of detection. Finally, the limit of detection was calculated using the SPSS system, and the relationship between grey values and concentrations was also obtained. The detection limit for HCG can be as low as 1.916 × 10-3 mIU·mL-1 under ideal conditions. Compared to other detection methods for HCG, this structure provides a detection pathway with excellent sensitivity, low detection limits, and better specificity, thus offering a new idea for HCG or any other target requiring trace detection.

A volatilomics analytical protocol employing solid phase microextraction coupled to GC × GC-MS analysis and combined with multivariate chemometrics for the detection of pomegranate juice adulteration.

In this work, a solid-phase microextraction (SPME) method combined with two-dimensional gas chromatography coupled to mass spectrometry (GC × GC-MS) was optimized and used to assess the authenticity of pomegranate juice to prevent fraudulent practices. A divinylbenzene/carboxen/polydimethylsiloxane (DVB/CAR/PDMS) fiber was used for the extraction of the volatiles. The critical parameters that affect the extraction process, such as the sample volume, and the extraction time were studied. The optimized protocol involved the addition of 15 mL of juice in 50 mL vial and saturation with 30% w/v NaCl.The extraction was carried out within 45 min under 1000 rpm stirring and was applied in the analysis of real juice samples to assess authenticity and detect low levels of pomegranate juice adulteration with grape and apple juice down to 1%. Commercially available pomegranate juice samples were acquired (n1 = 6) and adulterated with 1% of apple juice (n2 = 6), 1% of grape juice (n3 = 6), and a mixture of 1% apple juice and 1% grape juice (n4 = 6). Authentic pomegranate juice samples and adulterated mixtures were analyzed by SPME-GC × GC-MS. The analysis resulted in the identification of 123 volatile compounds that were further processed with chemometric tools. Principal component analysis (PCA) was employed to visualize the clustering of the samples, and a two-way orthogonal partial least squares discriminant analysis (O2PLS-DA) chemometric model was developed and successfully classified the samples to authentic pomegranate juice or adulterated with an explained total variance of 87.4%. The O2PLS-DA prediction model revealed characteristic volatile markers that could be used to detect pomegranate juice fraud.

Molecularly imprinted polymer hydrogel sheets with metalloporphyrin-incorporated molecular recognition sites for protein capture.

Metalloporphyrins are often found in nature as coordination recognition sites within biological process, and synthetically offer the potential for use in therapeutic, catalytic and diagnostic applications. While porphyrin containing biological recognition elements have stability limitations, molecularly imprinted polymers bearing these structures offer an alternative with excellent robustness and the ability to work in extreme conditions. In this work, we synthesised a polymerizable porphyrin and metalloporphyrin and have incorporated these as co-monomers within a hydrogel thin-sheet MIP for the specific recognition of bovine haemoglobin (BHb). The hydrogels were evaluated using Scatchard analysis, with Kd values of 10.13 × 10-7, 5.30 × 10-7, and 3.40 × 10-7 M, for the control MIP, porphyrin incorporated MIP and the iron-porphyrin incorporated MIP, respectively. The MIPs also observed good selectivity towards the target protein with 73.8%, 77.4%, and 81.2% rebinding of the BHb target for the control MIP, porphyrin incorporated MIP and the iron-porphyrin incorporated MIP, respectively, compared with the non-imprinted (NIP) counterparts. Specificity was determined against a non-target protein, Bovine Serum Albumin (BSA). The results indicate that the introduction of the metalloporphyrin as a functional co-monomer is significantly beneficial to the recognition of a MIP, further enhancing MIP capabilities at targeting proteins.

Illustrate the distribution and metabolic regulatory effects of pterostilbene in cerebral ischemia-reperfusion rat brain by mass spectrometry imaging and spatial metabolomics.

Pterostilbene is a promising molecule with superior pharmacological activities and pharmacokinetic characteristics compared to its structural analogue resveratrol, which could be used to treat ischemic stroke. However, its mechanism is still unclear. The cutting-edge air flow-assisted desorption electrospray ionization mass spectrometry imaging (AFADESI-MSI) and spatial metabolomics analysis were applied to investigate the distribution of pterostilbene in ischemic rat brain and the changes of related small molecule metabolic pathways to further explore the potential mechanisms of pterostilbene against cerebral ischemia-reperfusion injury. This research found that pterostilbene could significantly restore cerebral microcirculation blood flow, reduce infarct volume, improve neurological function and ameliorate neuronal damage in ischemic rats. Moreover, pterostilbene was widely and abundantly distributed in ischemic brain tissue, laying a solid foundation for the rescue of ischemic penumbra. Further study revealed that pterostilbene played a therapeutic role in restoring energy supply, rebalancing neurotransmitters, reducing abnormal polyamine accumulation and phospholipid metabolism. These findings offer an opportunity to illustrate novel mechanisms of pterostilbene in the treatment of cerebral ischemia/reperfusion injury resulting from ischemic stroke.