Welcome Back!
Thanks for tuning in to the FAIR-CF newsletter - where the goal is to show how CF researchers (and other biomedical scientists) can make the most of public data.
Read the FAIR-CF manifesto here.
This month, I have made some updates to the newsletter format…
The ultimate goal of this science communication project is to boost the legitimacy of data re-use as a scientific project. We want to show that data re-use efforts can work in tandem with wet bench experiments and even provide the basis for publications on their own.
To accomplish this goal, my approach is to distill down data re-use studies to their essential parts - and use this information to build a model of research in the field.
What does this mean in practice? Each featured study has been summarized in such a way that you can glean the key information very quickly and draw inspiration for your own scientific projects.
The ‘data’ (research goals, data sources, methods, results, future aims) from these research summaries has also been used to create a map of research in the field. This research map shows how individual publications really belong to more general research frameworks that build to/from one another and ultimately lead to translational progress. As future editions of this newsletter are created, this research map will be updated. Over time - like a computational model fed more and more data - the map will grow increasingly more sophisticated and represent the field of data re-use research efforts more accurately.
In the long term, we want to build an audience of researchers (and translational stakeholders - clinicians, drug developers, government officials, etc.) around this newsletter to discuss current approaches to research. And we want scientists involved in this research space to consider how its research approaches may be innovated to answer new biological questions and create novel types of translational products. Knowing how this research space tends to operate and what scientific problems it is able to tackle is just the first step…
But for now, our goal is simply to get this newsletter out to as many readers as possible. Please feel free to share with any colleagues you think would be interested, and if you do happen to know any of the ‘related authors’ featured in this edition, go ahead and share with them as well. (If you were forwarded this newsletter by a colleague, you should subscribe now by entering your email in the box below).
News from the Field
Cystic Fibrosis
A. Structural Comparative Modeling of Multi-Domain F508del CFTR (McDonald et al.) [Vanderbilt University 🇺🇸, Leipzig University 🇩🇪]
📋 Goal: Build better computational models of the CFTR protein - including all CFTR domains and considering how CFTR mutation changes protein structure
💾 Public Data: CryoEM Density Data [See Here]
🛠 Methods: Comparative Modeling [See Here] → Develop computational protein model
➡️ Output: Multi-domain computational model of Delta-F508 CFTR
🔮 Future: 🚥 Lateral Advance: Develop multi-domain computational models for other CFTR mutations ; 🚦Vertical Advance: Screen compounds against Delta-F508 CFTR (and other mutant CFTR protein models) to identify mutation-specific drug candidates
✍️ Related Authors:
Gergely L. Lukacs (McGill University 🇨🇦): ΔF508-CFTR Modulator Screen Based on Cell Surface Targeting of a Chimeric Nucleotide Binding Domain 1 Reporter
Alan Verkman (University of California SF 🇺🇸): ΔF508-CFTR Modulator Screen Based on Cell Surface Targeting of a Chimeric Nucleotide Binding Domain 1 Reporter
John Riordan (UNC Chapel Hill 🇺🇸): Restoration of NBD1 thermal stability is necessary and sufficient to correct ∆F508 CFTR folding and assembly
B. Investigation of Direct and Retro Chromone-2-Carboxamides Based Analogs of Pseudomonas aeruginosa Quorum Sensing Signal as New Anti-Biofilm Agents (Trognon et al.) [University of Toulouse 🇫🇷]
📋 Goal: Screen compounds that bind PqsR (a Pseudomonas aeruginosa quorum sensing protein) and uncover potential drug candidates to combat drug-resistant P. aeruginosa biofilms
💾 Public Data: PqsR computational model [See Here]
🛠 Methods: Molecular Docking [See Here] → Run series of synthetic compounds in screen against PqsR Computational Model ; wet bench biofilm + cytotoxicity assays → verify computational predictions
➡️ Output: Screened series of chromone carboxamide compounds and found several potent inhibitors of P. aeruginosa biofilm formation that were not cytotoxic to human cells.
🔮 Future: 🚥 Lateral Advance: Apply the study’s drug screening pipeline to other P. aeruginosa proteins involved in biofilm formation / quorum sensing / virulence factor production ;🚦Vertical Advance: Further in vitro + in vivo testing of potential lead compounds (i.e., potent inhibitors of biofilm formation identified through screening)
✍️ Related Authors:
Paul Williams (University of Nottingham 🇬🇧): Investigation of Direct and Retro Chromone-2-Carboxamides Based Analogs of Pseudomonas aeruginosa Quorum Sensing Signal as New Anti-Biofilm Agents
Naresh Kumar (University of New South Wales 🇦🇺): Thioether-linked dihydropyrrol-2-one analogues as PqsR antagonists against antibiotic resistant Pseudomonas aeruginosa
Rolf Hartmann (Hemholtz Center for Infection Research 🇩🇪): Combining in silico and biophysical methods for the development of Pseudomonas aeruginosa quorum sensing inhibitors: an alternative approach for structure-based drug design
C. The Use of Comparative Genomic Analysis for the Development of Subspecies-Specific PCR Assays for Mycobacterium abscessus (Akwani et al.) [Multiple Institutions: Germany 🇩🇪 + UK 🇬🇧]
📋 Goal: Better diagnose M. abscessus complex (MABC) infections - with resolution at the sub-species level - so that patients can be prescribed more effective antibiotic regimens.
💾 Public Data: MABC genomic data [See Here, location of specific datasets cited in the paper]
🛠 Methods: SNP Analysis [See Here] + Core Genome Multi-locus Sequence Typing [See Here] → Identify MABC subspecies ; Pangenome Analysis [See Here and Here] → Identify sub-species specific genes
➡️ Output: Identified subspecies (M. abscessus, M. bolletii, M. massiliense) + sub-species specific genes ; designed sub-species specific PCR assays
🔮 Future: 🚥 Lateral Advance: Plan similar investigations of other microbial species (e.g., B. cepacia complex) and design species-specific PCR assays ;🚦Vertical Advance: Validate PCR assays to the extent that they can be applied in the clinic.
✍️ Related Authors:
Po-Ren Hsueh (National Taiwan University College of Medicine 🇹🇼): A novel DNA chromatography method to discriminate Mycobacterium abscessus subspecies and macrolide susceptibility
Su-Young Kim (Sungkyunkwan University School of Medicine 🇰🇷): Subspecies-specific sequence detection for differentiation of Mycobacterium abscessus complex
Michel Drancourt (Aix-Marseille University 🇫🇷): Reinstating Mycobacterium massiliense and Mycobacterium bolletii as species of the Mycobacterium abscessus complex
D. Machine learning from Pseudomonas aeruginosa transcriptomes identifies independently modulated sets of genes associated with known transcriptional regulators (Rajput et al.) [UC San Diego 🇺🇸, Technical University of Denmark 🇩🇰]
📋 Goal: Develop a better understanding of the P. aeruginosa transcriptional regulatory network
💾 Public Data: Gene Expression data [See Here, location of specific datasets cited in the paper] (combined in-house + public data)
🛠 Methods: Independent Component Analysis [See Here and Here] → identify co-regulated genes ; wet-bench experiments → verify existence of co-regulated gene modules
➡️ Output: Identified 104 co-regulated gene modules (‘iModulons’), 81 of which reflect the effects of known transcription factors ; Showed that the iModulons discovered by the computational analysis were in fact operating in real, lab-grown Pseudomonas in the context of the nutrient response, secretion systems, and metabolic pathways.
🔮 Future: 🚥 Lateral Advance: Apply a similar computational approach to other relevant CF pathogens ; 🚦Vertical Advance: Further identify the biological role of individual genes in co-regulated modules, then determine how manipulation of these individual genes disrupts the activity of the module at large (with the ultimate goal of informing antimicrobial drug development)
✍️ Related Authors:
Saugat Poudel (UC San Diego 🇺🇸): iModulonDB: a knowledgebase of microbial transcriptional regulation derived from machine learning
Yuan Yuan (UC San Diego 🇺🇸): Machine Learning of All Mycobacterium tuberculosis H37Rv RNA-seq Data Reveals a Structured Interplay between Metabolism, Stress Response, and Infection
Robert EW Hancock (University of British Columbia 🇨🇦): The Stringent Stress Response Controls Proteases and Global Regulators under Optimal Growth Conditions in Pseudomonas aeruginosa
Where are the ‘lung biology’ and ‘gut biology’ sections featured in the previous edition? From now on, a new edition of this newsletter will be released every month, alternating between cystic fibrosis studies, general lung biology studies, and general gut biology studies. Stay tuned for the next ‘lung biology’ edition in August.
Research Map
Research Community
This month’s featured research and related studies involve researchers in 9 countries, including 3 US states
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