Abstract: The identification of chemical-protein interactions described in the literature is an important task with applications in drug design, precision medicine and biotechnology. Manual extraction of such relationships from the biomedical literature is costly and often prohibitively time-consuming. The BioCreative VII DrugProt shared task provides a benchmark for methods for the automated extraction of chemical-protein relations from scientific text. Here we describe our contribution to the shared task and report on the achieved results. We define the task as a relation classification problem, which we approach with pretrained transformer language models. Upon this basic architecture, we experiment with utilizing textual and embedded side information from knowledge bases as well as additional training data to improve extraction performance. We perform a comprehensive evaluation of the proposed model and the individual extensions including an extensive hyperparameter search leading to 2647 different runs. We find that ensembling and choosing the right pretrained language model are crucial for optimal performance, whereas adding additional data and embedded side information did not improve results. Our best model is based on an ensemble of 10 pretrained transformers and additional textual descriptions of chemicals taken from the Comparative Toxicogenomics Database. The model reaches an F1 score of 79.73% on the hidden DrugProt test set and achieves the first rank out of 107 submitted runs in the official evaluation. Database URL: https://github.com/leonweber/drugprot.

Abstract: Protein-protein interaction (PPI) inhibitors have an increasing role in drug discovery. It is hypothesized that machine learning (ML) algorithms can classify or identify PPI inhibitors. This work describes the performance of different algorithms and molecular fingerprints used in chemoinformatics to develop a classification model to identify PPI inhibitors making the codes freely available to the community, particularly the medicinal chemistry research groups working with PPI inhibitors. We found that classification algorithms have different performances according to various features employed in the training process. Random forest (RF) models with the extended connectivity fingerprint radius 2 (ECFP4) had the best classification abilities compared to those models trained with ECFP6 o MACCS keys (166-bits). In general, logistic regression (LR) models had lower performance metrics than RF models, but ECFP4 was the representation most appropriate for LR. ECFP4 also generated models with high-performance metrics with support vector machines (SVM). We also constructed ensemble models based on the top-performing models. As part of this work and to help non-computational experts, we developed a pipeline code freely available.

Abstract: BACKGROUND: Psychiatric disorders are characterized by alteration in emotions, mood and behavior. Genetics is known to play a significant role in the development of psychiatric disorders. Genome-wide association studies have identified several loci associated with psychiatric illnesses. We hypothesize the existence of rare variants following Mendelian recessive mode of inheritance. These variants can be identified in families with multiple affected individuals born to unaffected consanguineous parents. METHODS: We visited psychiatric outpatient departments of multiple hospitals in Lahore, Pakistan. We focused on psychosis, as it can occur in several DSM disorders such as schizophrenia, dementia and bipolar disorder. After clinical diagnosis by an American trained psychiatrist, detailed clinical assessments using Diagnostic Interview for Genetic Studies (DIGS), Diagnostic Interview for Psychosis and Affective Disorders (DI-PAD), Positive and Negative Syndrome Scale (PANSS), Hamilton Depression and Anxiety Rating Scale (HAM-D; HAM-A) were administered to all willing affected and unaffected participants. RESULTS: We identified eight pedigrees with two or more psychotic individuals in each family. Clinical diagnoses determined by their psychiatrists included ten individuals with schizophrenia; four individuals with psychosis and bipolar disorder; and two patients with "unspecified psychosis." The rating instruments rigorously confirmed the diagnosis of psychosis in the affected patients from the six families as well as the absence of psychotic disorders in unaffected individuals from the six families. We obtained DNA samples from willing members of all eight families for future genetic analyses. CONCLUSION: Our research highlights an alternative approach to discovery of rare recessively inherited genetic variants causing psychiatric disorders that have remained unidentified to date. These findings could illuminate underlying biological mechanisms leading toward development of targeted therapies in future.

Abstract: The joint use of multiple drugs may cause unintended drug-drug interactions (DDIs) and result in adverse consequence to the patients. Accurate identification of DDI types can not only provide hints to avoid these accidental events, but also elaborate the underlying mechanisms by how DDIs occur. Several computational methods have been proposed for multi-type DDI prediction, but room remains for improvement in prediction performance. In this study, we propose a supervised contrastive learning based method, MDDI-SCL, implemented by three-level loss functions, to predict multi-type DDIs. MDDI-SCL is mainly composed of three modules: drug feature encoder and mean squared error loss module, drug latent feature fusion and supervised contrastive loss module, multi-type DDI prediction and classification loss module. The drug feature encoder and mean squared error loss module uses self-attention mechanism and autoencoder to learn drug-level latent features. The drug latent feature fusion and supervised contrastive loss module uses multi-scale feature fusion to learn drug pair-level latent features. The prediction and classification loss module predicts DDI types of each drug pair. We evaluate MDDI-SCL on three different tasks of two datasets. Experimental results demonstrate that MDDI-SCL achieves better or comparable performance as the state-of-the-art methods. Furthermore, the effectiveness of supervised contrastive learning is validated by ablation experiment, and the feasibility of MDDI-SCL is supported by case studies. The source codes are available at https://github.com/ShenggengLin/MDDI-SCL .

Abstract: Abundant substituted catechols are emitted to, and created in, the atmosphere during wildfires and anthropogenic combustion and agro-industrial processes. While ozone (O(3)) and hydroxyl radicals (HO(*)) efficiently react in a 1 mus contact time with catechols at the air-water interface, the nighttime reactivity dominated by nitrate radicals (NO(3)) remains unexplored. Herein, online electrospray ionization mass spectrometry (OESI-MS) is used to explore the reaction of NO(3)(g) with a series of representative catechols (catechol, pyrogallol, 3-methylcatechol, 4-methylcatechol, and 3-methoxycatechol) on the surface of aqueous microdroplets. The work detects the ultrafast generation of nitrocatechol (aromatic) compounds, which are major constituents of atmospheric brown carbon. Two mechanisms are proposed to produce nitrocatechols, one (equivalent to H atom abstraction) following fast electron transfer from the catechols (QH(2)) to NO(3), forming NO(3)(-) and QH(2)(*+) that quickly deprotonates into a semiquinone radical (QH(*)). The second mechanism proceeds via cyclohexadienyl radical intermediates from NO(3) attack to the ring. Experiments in the pH range from 4 to 8 showed that the production of nitrocatechols was favored under the most acidic conditions. Mechanistically, the results explain the interfacial production of chromophoric nitrocatechols that modify the absorption properties of tropospheric particles, making them more susceptible to photooxidation, and alter the Earth's radiative forcing.

Abstract: The chance of survival rate and autophagy of smooth muscle cells under calcium stress were drastically improved with a prolonged inclusion of Lycopene in the media. The results showed an improved viability from 41% to 69% and a reduction in overall autophagic bodies from 7% to 3%, which was well in agreement with the LC3II and III mRNA levels. However, the proliferation was slow compared to the controls. The fall in the major inflammatory marker TNF-alpha and improved antioxidant enzyme GPx were regarded as significant restoration markers of cell survival. The reactive oxygen species (ROS) were reduced from 8 fold to 3 fold post addition of lycopene for 24 h. Further, the docking studies revealed binding of lycopene molecules with 7SK snRNA at 7.6 kcal/mol docking energy with 300 ns stability under physiological conditions. Together, these results suggest that Lycopene administration during ischemic heart disease might improve the functions of the smooth muscle cells and 7SK snRNA might be involved in the binding of lycopene and its antioxidant protective effects.

Abstract: Genetic and environmental exposures cause variability in gene expression. Although most genes are affected in a population, their effect sizes vary greatly, indicating the existence of regulatory mechanisms that could amplify or attenuate expression variability. Here, we investigate the relationship between the sequence and transcription start site architectures of promoters and their expression variability across human individuals. We find that expression variability can be largely explained by a promoter's DNA sequence and its binding sites for specific transcription factors. We show that promoter expression variability reflects the biological process of a gene, demonstrating a selective trade-off between stability for metabolic genes and plasticity for responsive genes and those involved in signaling. Promoters with a rigid transcription start site architecture are more prone to have variable expression and to be associated with genetic variants with large effect sizes, while a flexible usage of transcription start sites within a promoter attenuates expression variability and limits genotypic effects. Our work provides insights into the variable nature of responsive genes and reveals a novel mechanism for supplying transcriptional and mutational robustness to essential genes through multiple transcription start site regions within a promoter.

Abstract: BACKGROUND: Single sample pathway analysis (ssPA) transforms molecular level omics data to the pathway level, enabling the discovery of patient-specific pathway signatures. Compared to conventional pathway analysis, ssPA overcomes the limitations by enabling multi-group comparisons, alongside facilitating numerous downstream analyses such as pathway-based machine learning. While in transcriptomics ssPA is a widely used technique, there is little literature evaluating its suitability for metabolomics. Here we provide a benchmark of established ssPA methods (ssGSEA, GSVA, SVD (PLAGE), and z-score) alongside the evaluation of two novel methods we propose: ssClustPA and kPCA, using semi-synthetic metabolomics data. We then demonstrate how ssPA can facilitate pathway-based interpretation of metabolomics data by performing a case-study on inflammatory bowel disease mass spectrometry data, using clustering to determine subtype-specific pathway signatures. RESULTS: While GSEA-based and z-score methods outperformed the others in terms of recall, clustering/dimensionality reduction-based methods provided higher precision at moderate-to-high effect sizes. A case study applying ssPA to inflammatory bowel disease data demonstrates how these methods yield a much richer depth of interpretation than conventional approaches, for example by clustering pathway scores to visualise a pathway-based patient subtype-specific correlation network. We also developed the sspa python package (freely available at https://pypi.org/project/sspa/ ), providing implementations of all the methods benchmarked in this study. CONCLUSION: This work underscores the value ssPA methods can add to metabolomic studies and provides a useful reference for those wishing to apply ssPA methods to metabolomics data.

Abstract: BACKGROUND: A new viral outbreak caused by monkeypox has appeared after COVID-19. As of yet, no specific drug has been found for its treatment. Shengma-Gegen decoction (SMGGD), a pathogen-eliminating and detoxifying agent composed of four kinds of Chinese herbs, has been demonstrated to be effective against several viruses in China, suggesting that it may be effective in treating monkeypox, however, the precise role and mechanisms are still unknown. METHODS: Network pharmacology was used to investigate the monkeypox-specific SMGGD targets. These targets were analyzed via String for protein-to-protein interaction (PPI), followed by identification of hub genes with Cytoscape software. Function enrichment analysis of the hub targets was performed. The interactions between hub targets and corresponding ligands were validated via molecular docking. RESULTS: Through screening and analysis, a total of 94 active components and 8 hub targets were identified in the TCM-bioactive compound-hub gene network. Molecular docking results showed that the active components of SMGGD have strong binding affinity for their corresponding targets. According to functional analysis, these hub genes are mainly involved in the TNF, AGE-RAGE, IL-17, and MAPK pathways, which are linked to the host inflammatory response to infection and viral replication. Therefore, SMGGD might suppress the replication of monkeypox virus through the MAPK signaling pathway while also reducing inflammatory damage caused by viral infection. CONCLUSION: SMGGD may have positive therapeutic effects on monkeypox by reducing inflammatory damage and limiting virus replication.

Abstract: Mast cells play a critical role in inflammatory diseases and tumor growth. The versatility of mast cells is reflected in their ability to secrete a wide range of biologically active cytokines, including interleukins, chemokines, lipid mediators, proteases, and biogenic amines. The aim of this review article is to analyze the complex involvement of mast cells in the secretion of interleukins and the role of interleukins in the regulation of biological activities of mast cells.