Molecular Discovery Research Articles

VissE: a versatile tool to identify and visualise higher-order molecular phenotypes from functional enrichment analysis

Functional analysis of high throughput experiments using pathway analysis is now ubiquitous. Though powerful, these methods often produce thousands of redundant results owing to knowledgebase redundancies upstream. This scale of results hinders extensive exploration by biologists and can lead to investigator biases due to previous knowledge and expectations. To address this issue, we present vissE, a flexible network-based analysis and visualisation tool that organises information into semantic categories and provides various visualisation modules to characterise them with respect to the underlying data, thus providing a comprehensive view of the biological system. We demonstrate vissE’s versatility by applying it to three different technologies: bulk, single-cell and spatial transcriptomics. Applying vissE to a factor analysis of a breast cancer spatial transcriptomic data, we identified stromal phenotypes that support tumour dissemination. Its adaptability allows vissE to enhance all existing gene-set enrichment and pathway analysis workflows, empowering biologists during molecular discovery.

Progress and opportunities of pseudo-natural-product design in molecular discovery

Progress and opportunities of pseudo-natural-product design in molecular discovery

Tracing back primed resistance in cancer via sister cells

Exploring non-genetic evolution of cell states during cancer treatments has become attainable by recent advances in lineage-tracing methods. However, transcriptional changes that drive cells into resistant fates may be subtle, necessitating high resolution analysis. Here, we present ReSisTrace that uses shared transcriptomic features of sister cells to predict the states priming treatment resistance. Applying ReSisTrace in ovarian cancer cells perturbed with olaparib, carboplatin or natural killer (NK) cells reveals pre-resistant phenotypes defined by proteostatic and mRNA surveillance features, reflecting traits enriched in the upcoming subclonal selection. Furthermore, we show that DNA repair deficiency renders cells susceptible to both DNA damaging agents and NK killing in a context-dependent manner. Finally, we leverage the obtained pre-resistance profiles to predict and validate small molecules driving cells to sensitive states prior to treatment. In summary, ReSisTrace resolves pre-existing transcriptional features of treatment vulnerability, facilitating both molecular patient stratification and discovery of synergistic pre-sensitizing therapies.

Anti-psoriasis molecular targets and active components discovery of Optimized Yinxieling Formula via affinity-purified strategy

Psoriasis, a prevalent inherited skin condition, involves an inflammatory response as a key pathogenic mechanism. The Optimized Yinxieling Formula (OYF), rooted in traditional Chinese medicine, is extensively utilized in clinical settings to treat psoriasis. Although previous studies have demonstrated OYF's significant anti-inflammatory effects in psoriasis, its potential molecular targets and active components remain unexplored. This study aimed to unveil the anti-psoriasis molecular targets and active components of OYF. Our findings indicated that OYF extract markedly reduced the production of several inflammatory mediators, including IL-23, nitric oxide, TNF-α, and IL-1β, in LPS-induced RAW264.7 cells. We synthesized OYF extract-crosslinked beads to isolate pharmacological targets from RAW264.7 lysates using an affinity purification strategy, known as Target Fishing. The enriched target proteins were subsequently identified via LC-MS/MS, followed by bioinformatics analysis to map the psoriasis-associated pathway-gene network. We identified a total of 76 potential target proteins, which were highly associated with mRNA transcription mechanisms. In particular, pathway-gene network analysis revealed that the IL-23 inflammatory pathway was involved in the anti-psoriasis effect of OYF extract. We further utilized a target protein-based affinity capture strategy, combined with LC-MS and SPR analysis, to globally screen OYF's active components, focusing on the mRNA transcription regulator, fused in sarcoma (FUS). This process led to the identification of umbelliferone, vanillic acid, protocatechuic acid, gentisic acid, and echinacoside as key compounds targeting FUS to inhibit IL-23 expression. Additionally, we formulated a compound cocktail (CpdC), which significantly reduced psoriasis area and severity index (PASI) scores and the expressions of IL-23 and Ki67 in an imiquimod (IMQ)-induced psoriasis mouse model. Collectively, our study elucidates the primary molecular targets and active components of OYF, offering novel insights for psoriasis treatment.

Nonspecific Orbital Inflammation (NSOI): Unraveling the Molecular Pathogenesis, Diagnostic Modalities, and Therapeutic Interventions.

Nonspecific orbital inflammation (NSOI), colloquially known as orbital pseudotumor, sometimes presents a diagnostic and therapeutic challenge in ophthalmology. This review aims to dissect NSOI through a molecular lens, offering a comprehensive overview of its pathogenesis, clinical presentation, diagnostic methods, and management strategies. The article delves into the underpinnings of NSOI, examining immunological and environmental factors alongside intricate molecular mechanisms involving signaling pathways, cytokines, and mediators. Special emphasis is placed on emerging molecular discoveries and approaches, highlighting the significance of understanding molecular mechanisms in NSOI for the development of novel diagnostic and therapeutic tools. Various diagnostic modalities are scrutinized for their utility and limitations. Therapeutic interventions encompass medical treatments with corticosteroids and immunomodulatory agents, all discussed in light of current molecular understanding. More importantly, this review offers a novel molecular perspective on NSOI, dissecting its pathogenesis and management with an emphasis on the latest molecular discoveries. It introduces an integrated approach combining advanced molecular diagnostics with current clinical assessments and explores emerging targeted therapies. By synthesizing these facets, the review aims to inform clinicians and researchers alike, paving the way for molecularly informed, precision-based strategies for managing NSOI.

Genetic Insights into Prepubertal Gynecomastia: A Comprehensive Analysis of a Rare 45,X[2]/ 46,X, + mar[28] Karyotype

Introduction: This case study delves into the genetic underpinnings of prepubertal gynecomastia by conducting an exhaustive analysis of a rare 45,X[2]/46,X,+mar[28] karyotype. Enlargement of male breast tissue, known as Gynecomastia, is generally considered a natural occurrence during adolescence but requires comprehensive assessment in severe cases. Prepubertal gynecomastia is exceedingly uncommon, and the sparse literature results in a lack of comprehensive understanding regarding its causes and management. Case Presentation: This study focuses on a male child aged eight years and six months who presented with bilateral gynecomastia before reaching puberty and also had short stature. The analysis of the patient's karyotype unveiled an unusual chromosomal arrangement of 45,X[2]/46,X,+mar[28], with the extra marker chromosome stemming from the Y chromosome, revealing Yq microdeletions. The clinical evolution, chromosomal examination, and molecular discoveries concerning the patient are detailed, emphasizing the pivotal importance of karyotyping in instances of prepubertal gynecomastia. Conclusion: Karyotyping plays a pivotal role in identifying potential chromosomal anomalies contributing to prepubertal gynecomastia. Specifically, cases involving Yq deletion warrant vigilant monitoring, particularly concerning short stature.

Deep Characterisation of the sn‐Isomer Lipidome Using High‐Throughput Data‐Independent Acquisition and Ozone‐Induced Dissociation**

AbstractIn recent years there has been a significant interest in the development of innovative lipidomics techniques capable of resolving lipid isomers. To date, methods applied to resolving sn‐isomers have resolved only a limited number of species. We report a workflow based on ozone‐induced dissociation for untargeted characterisation of hundreds of sn‐resolved glycerophospholipid isomers from biological extracts in under 20 min, coupled with an automated data analysis pipeline. It provides an order of magnitude increase in the number of sn‐isomer pairs identified as compared to previous reports and reveals that sn‐isomer populations are tightly regulated and significantly different between cell lines. The sensitivity of this method and potential for de novo molecular discovery is further demonstrated by the identification of unexpected lipids containing ultra‐long monounsaturated acyl chains at the sn‐1 position.

Deep Characterisation of the sn-Isomer Lipidome Using High-Throughput Data-Independent Acquisition and Ozone-Induced Dissociation.

In recent years there has been a significant interest in the development of innovative lipidomics techniques capable of resolving lipid isomers. To date, methods applied to resolving sn-isomers have resolved only a limited number of species. We report a workflow based on ozone-induced dissociation for untargeted characterisation of hundreds of sn-resolved glycerophospholipid isomers from biological extracts in under 20 min, coupled with an automated data analysis pipeline. It provides an order of magnitude increase in the number of sn-isomer pairs identified as compared to previous reports and reveals that sn-isomer populations are tightly regulated and significantly different between cell lines. The sensitivity of this method and potential for de novo molecular discovery is further demonstrated by the identification of unexpected lipids containing ultra-long monounsaturated acyl chains at the sn-1 position.

Challenges in distinguishing functional proteins from polyproteins in databases: implications for drug discovery.

This opinion article addresses a major issue in molecular biology and drug discovery by highlighting the complications that arise from combining polyproteins and their functional products within the same database entry. This problem, exemplified by the discovery of novel inhibitors for the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) main protease, has an influence on our ability to retrieve precise data and hinders the development of targeted therapies. It also emphasizes the need for improved database practices and underscores their significance in advancing scientific research. Furthermore, it emphasizes the need of learning from the SARS-CoV-2 pandemic in order to improve global preparedness for future health crises.

IN SILICO STUDIES FOR ANTI-BREAST CANCER ACMELLA OLERACEA (L.) FLOWERS

The study of the efficacy of Acmella oleracea (L.) flowers on breast cancer is still in its early stages. The molecular interaction mechanisms underlying Acmella oleracea's anti-breast cancer activity will be elucidated using in-silico analysis. For this study, seventeen bioactive compounds were used: spilanthol, alpha- and beta-amyrin ester, stigmasterol, beta-sitosterol, alpha-1-sitosterol, 3-acetylaleuritic acid, scopoletin, vanillic acid, trans-ferulic, (72,9E)-2-oxo-undeca-7,9-dienyl 3-methylbut-2-enoate, beta-caryophyllene, beta-pinene, myrcene, caryophyllene oxide, and limone Canonical smiles were obtained from PubChem and inserted into the PASS server to determine biological activity. Several compounds were docked with protein targets, such as ESR1, MAP2K2, and PGR. We used Pyrx 0.8 software for anchoring molecular interaction and Discovery Studio software to visualize the complex binding. In terms of Antineoplastic, apoptosis agonist, caspase-3, caspase-8 stimulant, ovulation inhibitor, steroid synthesis inhibitor, and TP53 expression enhancer, all the compounds tested positive for anticancer activity. According to Swiss ADME and protox analysis, Acmella oleracea flowers have the potential to modulate apoptosis and cell growth. More research is required to confirm the role of Acmella oleracea bioactive compounds in developing target cancers. The study reveals that Acmella oleracea has numerous bioactive chemicals advantageous for cancer therapy by inducing apoptosis through interaction with ESR1, MAPK2, and PGR protein.

Deep kernel methods learn better: from cards to process optimization

The ability of deep learning methods to perform classification and regression tasks relies heavily on their capacity to uncover manifolds in high-dimensional data spaces and project them into low-dimensional representation spaces. In this study, we investigate the structure and character of the manifolds generated by classical variational autoencoder (VAE) approaches and deep kernel learning (DKL). In the former case, the structure of the latent space is determined by the properties of the input data alone, while in the latter, the latent manifold forms as a result of an active learning process that balances the data distribution and target functionalities. We show that DKL with active learning can produce a more compact and smooth latent space which is more conducive to optimization compared to previously reported methods, such as the VAE. We demonstrate this behavior using a simple cards dataset and extend it to the optimization of domain-generated trajectories in physical systems. Our findings suggest that latent manifolds constructed through active learning have a more beneficial structure for optimization problems, especially in feature-rich target-poor scenarios that are common in domain sciences, such as materials synthesis, energy storage, and molecular discovery. The Jupyter Notebooks that encapsulate the complete analysis accompany the article.

GRELinker: A Graph-Based Generative Model for Molecular Linker Design with Reinforcement and Curriculum Learning.

Fragment-based drug discovery (FBDD) is widely used in drug design. One useful strategy in FBDD is designing linkers for linking fragments to optimize their molecular properties. In the current study, we present a novel generative fragment linking model, GRELinker, which utilizes a gated-graph neural network combined with reinforcement and curriculum learning to generate molecules with desirable attributes. The model has been shown to be efficient in multiple tasks, including controlling log P, optimizing synthesizability or predicted bioactivity of compounds, and generating molecules with high 3D similarity but low 2D similarity to the lead compound. Specifically, our model outperforms the previously reported reinforcement learning (RL) built-in method DRlinker on these benchmark tasks. Moreover, GRELinker has been successfully used in an actual FBDD case to generate optimized molecules with enhanced affinities by employing the docking score as the scoring function in RL. Besides, the implementation of curriculum learning in our framework enables the generation of structurally complex linkers more efficiently. These results demonstrate the benefits and feasibility of GRELinker in linker design for molecular optimization and drug discovery.

Diagnosing Cutaneous Melanocytic Tumors in the Molecular Era: Updates and Review of Literature.

Over the past decade, molecular and genomic discoveries have experienced unprecedented growth, fundamentally reshaping our comprehension of melanocytic tumors. This review comprises three main sections. The first part gives an overview of the current genomic landscape of cutaneous melanocytic tumors. The second part provides an update on the associated molecular tests and immunohistochemical stains that are helpful for diagnostic purposes. The third section briefly outlines the diverse molecular pathways now utilized for the classification of cutaneous melanomas. The primary goal of this review is to provide a succinct overview of the molecular pathways involved in melanocytic tumors and demonstrate their practical integration into the realm of diagnostic aids. As the molecular and genomic knowledge base continues to expand, this review hopes to serve as a valuable resource for healthcare professionals, offering insight into the evolving molecular landscape of cutaneous melanocytic tumors and its implications for patient care.

Vulture: cloud-enabled scalable mining of microbial reads in public scRNA-seq data.

The rapidly growing collection of public single-cell sequencing data has become a valuable resource for molecular, cellular, and microbial discovery. Previous studies mostly overlooked detecting pathogens in human single-cell sequencing data. Moreover, existing bioinformatics tools lack the scalability to deal with big public data. We introduce Vulture, a scalable cloud-based pipeline that performs microbial calling for single-cell RNA sequencing (scRNA-seq) data, enabling meta-analysis of host-microbial studies from the public domain. In our benchmarking experiments, Vulture is 66% to 88% faster than local tools (PathogenTrack and Venus) and 41% faster than the state-of-the-art cloud-based tool Cumulus, while achieving comparable microbial read identification. In terms of the cost on cloud computing systems, Vulture also shows a cost reduction of 83% ($12vs. ${\$}$70). We applied Vulture to 2 coronavirus disease 2019, 3 hepatocellular carcinoma (HCC), and 2 gastric cancer human patient cohorts with public sequencing reads data from scRNA-seq experiments and discovered cell type-specific enrichment of severe acute respiratory syndrome coronavirus 2, hepatitis B virus (HBV), and Helicobacter pylori-positive cells, respectively. In the HCC analysis, all cohorts showed hepatocyte-only enrichment of HBV, with cell subtype-associated HBV enrichment based on inferred copy number variations. In summary, Vulture presents a scalable and economical framework to mine unknown host-microbial interactions from large-scale public scRNA-seq data. Vulture is available via an open-source license at https://github.com/holab-hku/Vulture.

Design, synthesis and in vitro anti-proliferative evaluation of new pyridine-2,3-dihydrothiazole/thiazolidin-4-one hybrids as dual CDK2/GSK3β kinase inhibitors.

Herein, the molecular hybridization drug discovery approach was used in the design and synthesis of twelve novel pyridine-2,3-dihydrothiazole hybrids (2a,b-5a,b and 13a,b-14a,b) and fourteen pyridine-thiazolidin-4-one hybrids (6a,b-12a,b) as anti-proliferative analogues targeting CDK2 and GSK3β kinase inhibition. Almost all of the newly synthesized hybrids, including their precursors (1a,b), were evaluated for their anti-proliferative activity against three human cancer cell lines-MCF-7, HepG2 and HEp-2-as well as normal Vero cell lines. Both compounds 1a (pyridine-thiourea precursor) and 8a (pyridine-5-acetyl-thiazolidin-4-one hybrid) exhibited excellent anti-proliferative activity against HEp-2 (IC50 = 7.5 μg mL-1, 5.9 μg mL-1, respectively). Additionally, 13a (pyridine-5-(p-tolyldiazenyl-2,3-dihydrothiazole)) hybrid demonstrated excellent anti-proliferative activity against HepG2 (IC50 = 9.5 μg mL-1), with an acceptable safety profile against Vero (<45% inhibition at 100 μg mL-1) in the cases of 8a and 13a alone. The three promising anti-proliferative hybrids (1a, 8a, 13a) were selected for the assessment of their in vitro inhibitory kinase activity against CDK2/GSK3β using roscovitine (IC50 = 0.88 μg mL-1) and CHIR-99021 (IC50 = 0.07 μg mL-1) as references, respectively. Compound 13a was the most potent dual CDK2/GSK3β inhibitor (IC50 = 0.396 μg mL-1, 0.118 μg mL-1, respectively) followed by 8a (IC50 = 0.675 μg mL-1, 0.134 μg mL-1, respectively), and the weakest was 1a. To elucidate the mechanism of the most potent anti-proliferative 13a hybrid, further cell cycle analysis was performed revealing that it caused G1 cell cycle arrest and induced apoptosis. Moreover, it resulted in an increase in Bax and caspase-3 with a decrease in Bcl-2 levels in HepG2 cells compared with untreated cells. Finally, in silico drug likeness/ADME prediction for the three potent compounds as well as a molecular docking simulation study were conducted in order to explore the binding affinity and interactions in the binding site of each enzyme, which inspired their usage as anti-proliferative leads for further modification.

Systematic review of metabolomic alterations in ulcerative colitis: unveiling key metabolic signatures and pathways.

Despite numerous metabolomic studies on ulcerative colitis (UC), the results have been highly variable, making it challenging to identify key metabolic abnormalities in UC. This study aims to uncover key metabolites and metabolic pathways in UC by analyzing existing metabolomics data. A systematic review. We conducted a comprehensive search in databases (PubMed, Cochrane Library, Embase, and Web of Science) and relevant study references for metabolomic research on UC up to 28 December 2022. Significant metabolite differences between UC patients and controls were identified, followed by an analysis of relevant metabolic pathways. This review incorporated 78 studies, identifying 2868 differentially expressed metabolites between UC patients and controls. The metabolites were predominantly from 'lipids and lipid-like molecules' and 'organic acids and derivatives' superclasses. We found 101 metabolites consistently altered in multiple datasets within the same sample type and 78 metabolites common across different sample types. Of these, 62 metabolites exhibited consistent regulatory trends across various datasets or sample types. Pathway analysis revealed 22 significantly altered metabolic pathways, with 6 pathways being recurrently enriched across different sample types. This study elucidates key metabolic characteristics in UC, offering insights into molecular mechanisms and biomarker discovery for the disease. Future research could focus on validating these findings and exploring their clinical applications.

Efficient GPU computation of large protein Solvent-Excluded Surface.

The Solvent-Excluded Surface (SES) is an essential representation of molecules which is massively used in molecular modeling and drug discovery since it represents the interacting surface between molecules. Based on its properties, it supports the visualization of both large scale shapes and details of molecules. While several methods targeted its computation, the ability to process large molecular structures to address the introduction of big complex analysis while leveraging the massively parallel architecture of GPUs has remained a challenge. This is mostly caused by the need for consequent memory allocation or by the complexity of the parallelization of its processing. In this paper, we leverage the last theoretical advances made for the depiction of the SES to provide fast analytical computation with low impact on memory. We show that our method is able to compute the complete surface while handling large molecular complexes with competitive computation time costs compared to previous works.

Knowledge Distillation Guided Interpretable Brain Subgraph Neural Networks for Brain Disorder Exploration.

The human brain is a highly complex neurological system that has been the subject of continuous exploration by scientists. With the help of modern neuroimaging techniques, there has been significant progress made in brain disorder analysis. There is an increasing interest about utilizing artificial intelligence techniques to improve the efficiency of disorder diagnosis in recent years. However, these methods rely only on neuroimaging data for disorder diagnosis and do not explore the pathogenic mechanism behind the disorder or provide an interpretable result toward the diagnosis decision. Furthermore, the scarcity of medical data limits the performance of existing methods. As the hot application of graph neural networks (GNNs) in molecular graphs and drug discovery due to its strong graph-structured data learning ability, whether GNNs can also play a huge role in the field of brain disorder analysis. Thus, in this work, we innovatively model brain neuroimaging data into graph-structured data and propose knowledge distillation (KD) guided brain subgraph neural networks to extract discriminative subgraphs between patient and healthy brain graphs to explain which brain regions and abnormal functional connectivities cause the disorder. Specifically, we introduce the KD technique to transfer the knowledge of pretrained teacher model to guide brain subgraph neural networks training and alleviate the problem of insufficient training data. And these discriminative subgraphs are conducive to learn better brain graph-level representations for disorder prediction. We conduct abundant experiments on two functional magnetic resonance imaging datasets, i.e., Parkinson's disease (PD) and attention-deficit/hyperactivity disorder (ADHD), and experimental results well demonstrate the superiority of our method over other brain graph analysis methods for disorder prediction accuracy. The interpretable experimental results given by our method are consistent with corresponding medical research, which is encouraging to provide a potential for deeper brain disorder study.

Proteomics and Metabolomics in Congenital Zika Syndrome: A Review of Molecular Insights and Biomarker Discovery.

Zika virus (ZIKV) infection can be transmitted vertically, leading to the development of congenital Zika syndrome (CZS) in infected fetuses. During the early stages of gestation, the fetuses face an elevated risk of developing CZS. However, it is important to note that late-stage infections can also result in adverse outcomes. The differences between CZS and non-CZS phenotypes remain poorly understood. In this review, we provide a summary of the molecular mechanisms underlying ZIKV infection and placental and blood-brain barriers trespassing. Also, we have included molecular alterations that elucidate the progression of CZS by proteomics and metabolomics studies. Lastly, this review comprises investigations into body fluid samples, which have aided to identify potential biomarkers associated with CZS.

Controlled generation of ortho-quinone methides and (4+3) cyclization with 2-indolylalcohols by dual photoredox/Brønsted acid relay catalysis

Given the significance of oxacyclic frameworks in molecular scaffolds and drug discovery, it is intriguing to precisely construct and manipulate such ring systems in chemical research. In this area, the intermolecular, multicomponent cyclization for the synthesis of diversely substituted seven-membered ring oxacycles under simple conditions is still a challenge. Here, we report a dual photoredox/Brønsted acid relay catalytic strategy for in situ generation of ortho-quinone methides and subsequent (4 ​+ ​3) cyclization with 2-indolylalcohols. In this one-pot multicomponent reaction, two C–C and one C–O bonds are formed, providing de novo access to various biologically important indole-fused, oxygen-containing seven-membered heterocycles. By virtue of a chiral phosphoric acid, an asymmetric version can also be achieved with good to excellent levels of enantioselectivity (up to 96:4 er).