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      The sulfur-related metabolic status of Aspergillus fumigatus during infection reveals cytosolic serine hydroxymethyltransferase as a promising antifungal target

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          ABSTRACT

          Sulfur metabolism is an essential aspect of fungal physiology and pathogenicity. Fungal sulfur metabolism comprises anabolic and catabolic routes that are not well conserved in mammals, therefore is considered a promising source of prospective novel antifungal targets. To gain insight into Aspergillus fumigatus sulfur-related metabolism during infection, we used a NanoString custom nCounter-TagSet and compared the expression of 68 key metabolic genes in different murine models of invasive pulmonary aspergillosis, at 3 time-points, and under a variety of in vitro conditions. We identified a set of 15 genes that were consistently expressed at higher levels in vivo than in vitro, suggesting that they may be particularly relevant for intrapulmonary growth and thus constitute promising drug targets. Indeed, the role of 5 of the 15 genes has previously been empirically validated, supporting the likelihood that the remaining candidates are relevant. In addition, the analysis of gene expression dynamics at early (16 h), mid (24 h), and late (72 h) time-points uncovered potential disease initiation and progression factors. We further characterized one of the identified genes, encoding the cytosolic serine hydroxymethyltransferase ShmB, and demonstrated that it is an essential gene of A. fumigatus, also required for virulence in a murine model of established pulmonary infection. We further showed that the structure of the ligand-binding pocket of the fungal enzyme differs significantly from its human counterpart, suggesting that specific inhibitors can be designed. Therefore, in vivo transcriptomics is a powerful tool for identifying genes crucial for fungal pathogenicity that may encode promising antifungal target candidates.

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          Most cited references95

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          Highly accurate protein structure prediction with AlphaFold

          Proteins are essential to life, and understanding their structure can facilitate a mechanistic understanding of their function. Through an enormous experimental effort 1 – 4 , the structures of around 100,000 unique proteins have been determined 5 , but this represents a small fraction of the billions of known protein sequences 6 , 7 . Structural coverage is bottlenecked by the months to years of painstaking effort required to determine a single protein structure. Accurate computational approaches are needed to address this gap and to enable large-scale structural bioinformatics. Predicting the three-dimensional structure that a protein will adopt based solely on its amino acid sequence—the structure prediction component of the ‘protein folding problem’ 8 —has been an important open research problem for more than 50 years 9 . Despite recent progress 10 – 14 , existing methods fall far short of atomic accuracy, especially when no homologous structure is available. Here we provide the first computational method that can regularly predict protein structures with atomic accuracy even in cases in which no similar structure is known. We validated an entirely redesigned version of our neural network-based model, AlphaFold, in the challenging 14th Critical Assessment of protein Structure Prediction (CASP14) 15 , demonstrating accuracy competitive with experimental structures in a majority of cases and greatly outperforming other methods. Underpinning the latest version of AlphaFold is a novel machine learning approach that incorporates physical and biological knowledge about protein structure, leveraging multi-sequence alignments, into the design of the deep learning algorithm. AlphaFold predicts protein structures with an accuracy competitive with experimental structures in the majority of cases using a novel deep learning architecture.
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            Hidden killers: human fungal infections.

            Although fungal infections contribute substantially to human morbidity and mortality, the impact of these diseases on human health is not widely appreciated. Moreover, despite the urgent need for efficient diagnostic tests and safe and effective new drugs and vaccines, research into the pathophysiology of human fungal infections lags behind that of diseases caused by other pathogens. In this Review, we highlight the importance of fungi as human pathogens and discuss the challenges we face in combating the devastating invasive infections caused by these microorganisms, in particular in immunocompromised individuals.
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              Direct multiplexed measurement of gene expression with color-coded probe pairs.

              We describe a technology, the NanoString nCounter gene expression system, which captures and counts individual mRNA transcripts. Advantages over existing platforms include direct measurement of mRNA expression levels without enzymatic reactions or bias, sensitivity coupled with high multiplex capability, and digital readout. Experiments performed on 509 human genes yielded a replicate correlation coefficient of 0.999, a detection limit between 0.1 fM and 0.5 fM, and a linear dynamic range of over 500-fold. Comparison of the NanoString nCounter gene expression system with microarrays and TaqMan PCR demonstrated that the nCounter system is more sensitive than microarrays and similar in sensitivity to real-time PCR. Finally, a comparison of transcript levels for 21 genes across seven samples measured by the nCounter system and SYBR Green real-time PCR demonstrated similar patterns of gene expression at all transcript levels.
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                Author and article information

                Journal
                Virulence
                Virulence
                Virulence
                Taylor & Francis
                2150-5594
                2150-5608
                17 January 2025
                2025
                17 January 2025
                : 16
                : 1
                : 2449075
                Affiliations
                [a ]Manchester Fungal Infection Group (MFIG), Division of Evolution, Infection, and Genomics, Faculty of Biology, Medicine and Health, University of Manchester; , Manchester, UK
                [b ]Lydia Becker Institute for Immunology and Inflammation, School of Biological Sciences, Faculty of Biology Medicine and Health, Manchester Academic Health Science Centre, University of Manchester; , Manchester, UK
                [c ]MRC Centre for Medical Mycology, University of Exeter; , Exeter, UK
                [d ]Department of Life Sciences, Manchester Metropolitan University; , Manchester, UK
                [e ]Mycology Reference Laboratory (Laboratorio deReferencia e Investigación en Micología LRIM), National Centre for Microbiology, Instituto de Salud Carlos III (ISCIII); , Madrid, Spain
                [f ]CiberInfec ISCIII, CIBER en Enfermedades Infecciosas, Instituto de Salud Carlos III; , Madrid, Spain
                Author notes
                CONTACT Jorge Amich jamich@ 123456isciii.es
                [¥]

                Current affiliation: Department of ClinicalLaboratory Sciences, College of Applied Medical Sciences, King Saud Bin Abdulaziz University for Health Sciences, P.O. Box 3660, Riyadh 11,481, Saudi Arabia. King Abdullah International Medical Research Center, Riyadh, Saudi Arabia.

                Author information
                https://orcid.org/0000-0002-7611-0201
                https://orcid.org/0000-0002-8987-5115
                Article
                2449075
                10.1080/21505594.2024.2449075
                11749473
                39825596
                e4eb6d2d-04d8-4f55-bd3c-6f27c1917a42
                © 2025 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group.

                This is an Open Access article distributed under the terms of the Creative Commons Attribution License ( http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. The terms on which this article has been published allow the posting of the Accepted Manuscript in a repository by the author(s) or with their consent.

                History
                Page count
                Figures: 5, Tables: 2, References: 95, Pages: 1, Words: 9936
                Categories
                Research Article
                Research Article

                Infectious disease & Microbiology
                aspergillus fumigatus,sulfur metabolism,antifungal targets,in vivo transcriptomics,fungal virulence,hydroxymethyltransferase

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