The World Health Organization (WHO) describes antibiotic resistance as "one of the biggest threats to global health, food security, and development today", as the number of multi- and pan-resistant bacteria is rising dangerously. Acquired resistance phenomena also impair antifungals, antivirals, anti-cancer drug therapy, while herbicide resistance in weeds threatens the crop industry. On the positive side, it is likely that the chemical space of natural products goes far beyond what has currently been discovered. This idea is fueled by genome sequencing of microorganisms which unveiled numerous so-called cryptic biosynthetic gene clusters (BGCs), many of which are transcriptionally silent under laboratory culture conditions, and by the fact that most bacteria cannot yet be cultivated in the laboratory. However, brute force antibiotic discovery does not yield the same results as it did in the past, and researchers have had to develop creative strategies in order to unravel the hidden potential of microorganisms such as Streptomyces and other antibiotic-producing microorganisms. Identifying the cis elements and their corresponding transcription factors(s) involved in the control of BGCs through bioinformatic approaches is a promising strategy. Theoretically, we are a few 'clicks' away from unveiling the culturing conditions or genetic changes needed to activate the production of cryptic metabolites or increase the production yield of known compounds to make them economically viable. In this opinion article, we describe and illustrate the idea beyond 'cracking' the regulatory code for natural product discovery, by presenting a series of proofs of concept, and discuss what still should be achieved to increase the rate of success of this strategy.
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http://dx.doi.org/10.1016/j.bcp.2018.01.007 | DOI Listing |
Bioinformatics
January 2025
School of Artificial Intelligence, Jilin University, Jilin, China.
Motivation: Predicting RNA-binding proteins (RBPs) is central to understanding post-transcriptional regulatory mechanisms. Here, we introduce EnrichRBP, an automated and interpretable computational platform specifically designed for the comprehensive analysis of RBP interactions with RNA.
Results: EnrichRBP is a web service that enables researchers to develop original deep learning and machine learning architectures to explore the complex dynamics of RNA-binding proteins.
J Inflamm Res
January 2025
Department of Orthopaedics, The First Affiliated Hospital of Nanjing Medical University, Nanjing, Jiangsu, People's Republic of China.
Background: Osteoarthritis (OA) is a leading cause of pain, disability, and reduced mobility worldwide, characterized by metabolic imbalances in chondrocytes, extracellular matrix (ECM), and subchondral bone. Emerging evidence highlights the critical role of long non-coding RNAs (lncRNAs) in OA pathogenesis. This study focuses on lncRNA PTS-1, a novel lncRNA, to explore its function and regulatory mechanisms in OA progression.
View Article and Find Full Text PDFJ Inflamm Res
January 2025
Department of Shandong Trauma Center, Shandong Provincial Hospital affiliated to Shandong First Medical University, Jinan, Shandong, 250014, People's Republic of China.
Background: Posttraumatic elbow stiffness is a complex complication with two characteristics of capsular contracture and heterotopic ossification. Currently, genomic mechanisms and pathogenesis of posttraumatic elbow stiffness remain inadequately understood. This study aims to identify differentially expressed genes (DEGs) and elucidate molecular networks of posttraumatic elbow stiffness, providing novel insights into disease mechanisms at transcriptome level.
View Article and Find Full Text PDFJ Prev Alzheimers Dis
January 2025
Alzheimer's Therapeutic Research Institute, University of Southern California, San Diego, CA, United States.
Background: Investigators conducting clinical trials have an ethical, scientific, and regulatory obligation to protect the safety of trial participants. Traditionally, safety monitoring includes manual review and coding of adverse event data by expert clinicians.
Objectives: Our study explores the use of natural language processing (NLP) and artificial intelligence (AI) methods to streamline and standardize clinician coding of adverse event data in Alzheimer's disease (AD) clinical trials.
Neurosciences (Riyadh)
January 2025
From the School of Clinical Medicine (Liang, Luo, Jia), Shandong Second Medical University, Weifang, from the Department of Neurology (Liang, Zhao, Lin, Li, Luo, Jia) , Beijing Shijingshan Hospital, Shijingshan Teaching Hospital of Capital Medical University, Beijing, and from the Department of Neurology (Li), Affiliated Hospital of Weifang Medical University, Weifang, China.
Objectives: To identify a key Long chain non-coding RNAs (lncRNAs) related to PD and provide a new perspective on the role of LncRNAs in Parkinson's disease (PD) pathophysiology.
Methods: Our study involved analyzing gene chips from the substantia nigra and white blood cells, both normal and PD-inclusive, in the Gene Expression Omnibus (GEO) database, utilizing a weighted gene co-expression network analysis (WGCNA). The technique of WGCNA facilitated the examination of differentially expressed genes (DEGs) in the substantia nigra and the white blood cells of individuals with PD.
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