Global NMR Discussion Meetings

Biological NMR

  • Expanding Solid-State NMR Frontiers: The 1.2 GHz MAS System at CERM

    Rebecca Calamandrei (CERM, Italy)

    Abstract: The ITACA.SB project (https://www.itaca-sb.it/about/) is dedicated to potentiate the Italian Instruct-ERIC center, CERM/CIRMMP (https://www.cerm.unifi.it/) and significantly enhance structural biology (SB) services at selected laboratories of CNR. By enhancing service capacity and overcoming key access barriers, the project supports high-level life sciences research in Italy, boosts international visibility, and fosters stronger integration with European research infrastructures.
    Within this embodiment, a significant enhancement of the instrumentation at CERM/CIRMMP has enabled the expansion of both solution and solid-state NMR research as well as the biotechnologies instrumentation ranging a broad spectrum of experiment set-up and characterization techniques.
    As part of the infrastructure upgrades supported by ITACA.SB, the 1.2 GHz NMR spectrometer at CERM has been equipped with a 0.7 mm solid-state MAS probe. This high-field system offers exceptional performance for the investigation of solid-phase materials, including protein crystals and, more critically, non-crystalline systems such as amyloid fibrils, membrane proteins, and complex sediments. The implementation of ultra-fast magic angle spinning at 1.2 GHz enables the acquisition of high-resolution, proton-detected spectra, comparable in quality to those obtained in solution-state NMR. This advancement significantly expands the capabilities of solid-state NMR for probing molecular dynamics and intermolecular interactions in challenging biological and material samples.
    As the result of the synergic integration of upgraded infrastructure, targeted user support, and strategic collaboration, ITACA.SB not only strengthens Italy’s contribution to the structural biology landscape but also ensures that CERM/CIRMMP operates as a competitive hub for research, facilitating the alignment within the European Research area.

    1. Marco Schiavina Avatar
      Marco Schiavina

      Hello Rebecca! Nice presentation!
      Among all these beautiful instruments and applications presented here, I was intrigued by the performances of the 1.2 GHz equipped with the 0.7 mm MAS probe.
      Could you please comment about the resolution that can be obtained? How fast can you spin and what nuclear spins can be detected?

      Reply
      1. Rebecca Calamandrei Avatar
        Rebecca Calamandrei

        Hi Marco,
        Thanks for your comment! The 0.7 mm MAS probe is capable of spinning up to 111 kHz and features three channels dedicated to the detection of ¹H, ¹³C, and ¹⁵N. The major benefits of using this type of probe at ultra-high magnetic fields are particularly evident in ¹H-detected spectra, which can achieve a level of resolution comparable to that of solution-state NMR. This enables detailed studies of residue-specific dynamics and protein–ligand interactions. These findings highlight the crucial importance of combining ultra-high magnetic fields with ultra-fast magic angle spinning for the structural and dynamic characterization of biomolecular systems in the solid state.

        Reply
    2. Zainab Mustapha Avatar
      Zainab Mustapha

      Nice presentation. I am curious about the NEO console. Does this mean one can set up two different experiments and both run simultaneously instead of queuing experiments?

      Reply
      1. Rebecca Calamandrei Avatar
        Rebecca Calamandrei

        Thank you for your kind and relevant question. In the novel NEO console, each radiofrequency (RF) channel is equipped with both transmission and reception capabilities. This design effectively allows each channel to operate as an independent spectrometer, with its own RF generation, transmission, and receiver architecture.
        In practice, this enables the implementation of multi-receiver experiments in a user-friendly way. The multiple receiver approach developed at our research infrastructure exploits the recovery delay of one experiment to acquire additional experiments simultaneously (see: [Biophys. J. 2019, 10.1016/j.bpj.2019.05.017]).

        For example, ¹³C- and ¹H-detected experiments can be combined to obtain complementary information on multidomain proteins ([Biomolecules 2022, 10.3390/biom12070929]) or to monitor complex protein–protein interactions in real time ([J. Am. Chem. Soc. 2024, 10.1021/jacs.4c09176]).

        This simultaneous acquisition strategy is a key advantage of the NEO architecture, going beyond traditional queuing of experiments.

        Reply
    3. Nicolas Bolik-Coulon Avatar
      Nicolas Bolik-Coulon

      Nice presentation of the facility!
      Is there any plans to use the 1.2 GHz with a liquid state probe?
      Smaller rotor means less materials. How does the sensitivity of the 0.7mm rotor compares with 1.3/1.9 mm rotors on a GHz for example?

      Reply
      1. Rebecca Calamandrei Avatar
        Rebecca Calamandrei

        Dear Nicolas Bolik-Coulon,
        Thank you for your question. Indeed, we have a 5 mm CP-TXO probe for 13C direct detection that is also routinely used at the 1.2 GHz instrument. The gain in resolution at ultra-high fields is significant not only for solid-state but also for solution-state NMR experiments. This is particularly beneficial when working with biomolecules whose spectra display extensive peak overlap. The combination of ultra-high field and 13C detection helps to partially overcome the spectral crowding typically observed in IDPs and IDRs, as demonstrated in this study: [doi: 10.1038/s41596-023-00921-9]. Moreover, although the amount of sample decreases when moving from larger to smaller rotors, the linewidth also narrows due to a greater averaging of dipolar couplings, resulting in more intense signals. Additionally, the sensitivity loss caused by the reduced sample volume is partially compensated by improved inductive coupling between the coil and the sample, which becomes more efficient as the coil size decreases, as illustrated in this review [doi:10.1021/acs.chemrev.1c00918].

        Reply

    Leave a Reply

    Your email address will not be published. Required fields are marked *

  • Folding, Stability, and Oligomerization of HSPB8: An NMR-Based Investigation of Its α-Crystallin Domain

    Zainab Amin (IISER Pune, India)

    LinkedIn: @Zainab Khan; X: @ZAINAB_KHAN_7

    Abstract: HSPB8 (Heat Shock Protein B8) is an important chaperone that acts independently of ATP. Perturbations in HSPB8 function have thus been implicated in various protein aggregation disorders. Despite its biological importance, the structural and dynamic behaviour of HSPB8 under different stress conditions remains poorly understood. Understanding these perturbations is a key to elucidating the role of HSPB8 in protein quality control mechanisms. In this study, we performed a biophysical characterization of the α-crystallin domain (ACD) of HSPB8, involved in dimer formation, using solution-state nuclear magnetic resonance spectroscopy under different environmental perturbations. The effect on the structural integrity was characterized by monitoring changes in chemical shifts and linewidths of ACD in response to stressors. The results suggest that the ACD domain of HSPB8 is highly sensitive to environmental perturbations. In parallel, an initial investigation into the folding process of the protein has been carried out using multidimensional NMR spectroscopy. The backbone amide resonances of the unfolded protein were assigned through a combination of 3D NMR experiments, allowing mapping of amino acid residues to their respective peaks in the 2D 15N-1H HSQC spectrum. With the unfolded state characterized, this study aims to further elucidate the conformational landscape of the protein during refolding by gradually reducing the denaturant concentration and monitoring changes using the dynamic NMR techniques. These experiments are expected to yield mechanistic insights into the folding pathway of HSPB8, including the identification of transient, low-population intermediate states that may play critical roles in its chaperone activity and cellular function under stress.

    1. Chandan Singh Avatar
      Chandan Singh

      Thanks for a nice presentation. I have following questions regarding the same:
      What happens if you go in reverse order i.e. If the protein is denatured slowly with the help of urea and HSQC is recorded?

      Reply
    2. Zainab Amin Avatar
      Zainab Amin

      Thanks for asking! We have not tried that as it is difficult to assign the protein its folded monomeric form as of now. I think the protein may or may not follow the same folding pathway as we slowly unfold the protein from the folded form. To have an exact answer ,we may need to perform the experiments.

      Reply
    3. Nicolas Bolik-Coulon Avatar
      Nicolas Bolik-Coulon

      Thank you for this nice presentation!
      Your CEST profiles are quite pretty. I was wondering though if you had any information about the intermediate state, i.e. what is its nature? I thought that if it is was a folding intermediate, its chemical shift would be closer to the folded state but it seems like the major peak is moving away as you decrease the urea concentration.
      In addition, as you progress toward folding, do you expect to form oligomeric species? How do the R2 of the ground and excited states compare?

      Reply
      1. Zainab Amin Avatar
        Zainab Amin

        The observation that the chemical shifts of the minor (excited) state are distinct from both the unfolded and native conformations suggests that the intermediate represents a unique conformational ensemble. While it may involve local structure formation, it remains structurally distinct from the final folded state, a point further supported by our HSQC spectra. Although the fully folded state has not yet been assigned, overlay analysis shows that the intermediate does not fully converge with it, particularly at 2 M urea, where the HSQC profile deviates from the native-like pattern. The minor-state chemical shifts remain relatively consistent across decreasing urea concentrations, which indicates that the intermediate is structurally persistent. Regarding oligomerization, our concentration-dependent HSQC experiments for the folded construct showed only subtle line shape changes, consistent with weak self-association. This suggests a tendency towards dimer formation, though not strong enough to classify as higher-order oligomerization under the conditions tested. At 2 M urea, we do observe increased R₂ values and modest peak broadening, yet not to the extent typically seen with large oligomeric assemblies. Further validation is required to confirm this behavior.
        I hope this helps clarify some of your questions!
        Thank you!

        Reply
    4. Zainab Amin Avatar
      Zainab Amin

      The observation that the chemical shifts of the minor (excited) state are distinct from both the unfolded and native conformations suggests that the intermediate represents a unique conformational ensemble. While it may involve local structure formation, it remains structurally distinct from the final folded state, a point further supported by our HSQC spectra. Although the fully folded state has not yet been assigned, overlay analysis shows that the intermediate does not fully converge with it, particularly at 2 M urea, where the HSQC profile deviates from the native-like pattern. The minor-state chemical shifts remain relatively consistent across decreasing urea concentrations, which indicates that the intermediate is structurally persistent. Regarding oligomerization, our concentration-dependent HSQC experiments for the folded construct showed only subtle line shape changes, consistent with weak self-association. This suggests a tendency towards dimer formation, though not strong enough to classify as higher-order oligomerization under the conditions tested. At 2 M urea, we do observe increased R₂ values and modest peak broadening, yet not to the extent typically seen with large oligomeric assemblies. Further validation is required to confirm this behavior.
      I hope this helps clarify some of your questions!
      Thank you!

      Reply
    5. Raj Chaklashiya Avatar
      Raj Chaklashiya

      Hi Zainab, nice talk! I am curious about how you can use this technique to distinguish between different possible outcomes–like for example, if there were 2 or 3 intermediate states, how would that look like as compared to having one (which is what is shown)? I ask because I can imagine this method could be applied also to other proteins as well, which may have more than one intermediate state. Thanks!

      Reply
      1. Zainab Amin Avatar
        Zainab Amin

        Thank you!
        In case of multiple intermediates, you will see multiple minor dips and you can fit them to other models rather than two state model. Also, you can record the experiment at different B1 fields to check if you are missing any hidden or merged minor dips.
        I hope that answers the query to some extend!

        Reply
        1. Raj Chaklashiya Avatar
          Raj Chaklashiya

          Yes it does! Thank you for your response!

          Reply

    Leave a Reply

    Your email address will not be published. Required fields are marked *

  • Advancing GHz-class NMR: High sensitivity through larger volume cryoprobe and optimal control sequences

    David Joseph (Max Planck Institute for Multidisciplinary Sciences, Germany)

    X: @DaJo_1729

    Abstract: Improving the sensitivity of nuclear magnetic resonance (NMR) spectroscopy requires advancements in both instrument technology and experimental methodology. In this study, we introduce the first proton-detected large volume cryoprobe designed for 1.2 GHz instruments, leveraging optimal control pulse sequences to enhance performance (Sci. Adv. 9,eadj1133, 2023). Our results demonstrate up to a 56% increase in sensitivity and more than a twofold reduction in experimental time compared to the small volume cryoprobes in use at the moment. Additionally, we systematically optimized the experimental conditions to fully exploit the capabilities of GHz-class magnets. To further extend the benefits of our approach, we developed a library of optimal control triple resonance experiments, enabling boosted sensitivity for advanced NMR applications.

    1. Cory Widdifield Avatar
      Cory Widdifield

      When comparing the results from the 5 mm TCI probe at 1.2 GHz with the 5 mm TCI probe at 950 MHz, what is the most surprising/interesting/useful insight that you have personally encountered? In the future, what do you think might be the most useful/interesting insights enabled by performing experiments at 1.2 GHz?

      Reply
      1. David Joseph Avatar
        David Joseph

        The most useful insight is that bio-NMR experiments perform much better using optimal control pulses. A 5 mm TCI at 950 MHz approaches the power availability limit for broadband pulses, particularly for the 13C and 15N channels. At 1.2 GHz, a 5 mm TCI can only be used with optimal control pulses. However, using optimal control pulses with fields starting from 800 MHz would provide free signal enhancement and save valuable experimental time.

        The most interesting insights would come from performing experiments at 1.2 GHz to study biomolecular dynamics. All B₀-dependent parameters, such as CSA and alignment, reach their maximum values at this frequency, enabling access to data on motions that would otherwise be impossible to observe with lower field magnets. Increased resolution at 1.2 GHz would also be useful for studying larger proteins and intrinsically disordered proteins.

        Reply
        1. Cory Widdifield Avatar
          Cory Widdifield

          Thank you for your response, David.

          Reply
    2. Gottfried Otting Avatar
      Gottfried Otting

      These are important reference data.
      1) Wouldn’t one expect that the sensitivity obtained with a Shigemi tube is either the same or less than that obtained with a conventional 5 mm tube?
      2) Which compound and signal did you use to measure the sensitivities in the presence of different salt concentrations – ubiquitin or sucrose?
      3) Does CSA relaxation of ubiquitin amide protons broaden their 1H NMR signals noticeably more than at, say, 950 MHz?

      Reply
      1. David Joseph Avatar
        David Joseph

        1) The sensitivity of a Shigemi depends on the amount of sample available. It is especially sensitive when a lower volume of sample is available. There is also an optimal height that provides the best signal-to-noise ratio when using a Shigemi tube. Our concern here was B_1 inhomogeneity, which is lower with a Shigemi tube. However, since the pulses also compensate for ±20% inhomogeneity, we only see only a slight improvement in sensitivity when using a Shigemi tube.

        2) It was p53 1-73, a disordered protein, in a Tris-Bis buffer, using optimal control HNCA sequence.

        3) Thanks for the question! I just looked it up, and for an HNCO experiment, the difference is around 3 Hz, while for an HSQC, it’s around 1 Hz (along the proton dimension). It is broader at 1.2 GHz.

        Reply
    3. David Joseph Avatar
      David Joseph

      1) The sensitivity of a Shigemi depends on the amount of sample available. It is especially sensitive when a lower volume of sample is available. There is also an optimal height that provides the best signal-to-noise ratio when using a Shigemi tube. Our concern here was B_1 inhomogeneity, which is lower with a Shigemi tube. However, since the pulses also compensate for ±20% inhomogeneity, we only see only a slight improvement in sensitivity when using a Shigemi tube.

      2) It was p53 1-73, a disordered protein, in a Tris-Bis buffer, using optimal control HNCA sequence.

      3) Thanks for the question! I just looked it up, and for an HNCO experiment, the difference is around 3 Hz, while for an HSQC, it’s around 1 Hz (along the proton dimension). It is broader at 1.2 GHz.

      Reply
    4. Bijaylaxmi Patra Avatar
      Bijaylaxmi Patra

      Hi David, brilliant presentation. Clear, concise, and insightful.
      You mentioned a useful tip about using buffers with lower conductivity and larger ions. Could you please elaborate on why this is beneficial and how exactly it helps in practice?

      Reply
      1. David Joseph Avatar
        David Joseph

        Hi, thank you! This has to do with noise contribution from the sample, which is especially problematic for the cryoprobe. The noise from the sample is proportional to its conductivity and dielectric properties. Using a buffer with larger ions will lower the mobility, thus lowering the conductivity of the buffer and reducing the noise from the sample. This increases the signal-to-noise ratio of the spectrum.

        Reply
    5. David Joseph Avatar
      David Joseph

      Hi, thank you! This has to do with noise contribution from the sample, which is especially problematic for the cryoprobe. The noise from the sample is proportional to its conductivity and dielectric properties. Using a buffer with larger ions will lower the mobility, thus lowering the conductivity of the buffer and reducing the noise from the sample. This increases the signal-to-noise ratio of the spectrum.

      Reply

    Leave a Reply

    Your email address will not be published. Required fields are marked *

  • Understanding the differential RNA-binding of HuD isoforms through conformational dynamics using solution NMR spectroscopy

    Nikhil Sunny (IISER Pune, India)

    LinkedIn: @Nikhil Sunny; X: @Nikhil__Sunny__

    Abstract: HuD is an RNA-binding protein (RBP) essential for neuronal development and glucose homeostasis. It has tandemly arranged three RNA recognition motifs (RRMs). Multiple isoforms, namely, HuD A, HuD B, and HuD D—have been reported that are differentially expressed in various tissues. The A and B isoforms both feature an unstructured N-terminal region; however, the A isoform has five additional amino acids when compared to the B isoform. This difference significantly impacts the RNA-binding and translation of insulin 2 mRNA. While the structure of HuD RRM12 is known, it does not include the unstructured N-terminal region, creating a gap in understanding its role in RNA targeting. In this study, we focus on understanding the role of the unstructured N-terminal region of A and B isoforms in the RNA-binding activity of the RRM1 domain by using NMR-based dynamics experiments and other biophysical techniques. FOur preliminary results show that the presence of the N-terminal leads to line-broadening and the disappearance of peaks in the 2D 15N-1H HSQC spectra. The disappeared peaks are mainly from the N-terminal region and the possible site of intra- and/or intermolecular interactions. In the presence of the N-terminal region, CSP is found in or near the RNP motifs of RRM1, which are the sites for RNA binding. We believe that this study will provide insights into how intrinsically disordered regions affect the intrinsic dynamics and RNA-binding activity of the RRM.

    1. Nicolas Bolik-Coulon Avatar
      Nicolas Bolik-Coulon

      Interesting!
      Although I believe you are planning on further experiments to confirm the interaction of the N-ter with the folded domain, can you comment on the effect it could have on the binding to RNA?
      Also, how do the R1 and R2 of the N-ter tail evolve as a function of residue number?

      Reply
      1. Nikhil Sunny Avatar
        Nikhil Sunny

        1) The RNA-binding region can either be masked, reducing overall binding affinity, or it can function as an auxiliary region that enhances binding affinity. Since this RNA recognition motif (RRM) is a weak binder, we are working on optimizing the experimental parameters for the binding studies. I suspect it will act as an auxiliary region facilitating RNA binding, as shown from EMSA studies on Isoforms of HuD (full-length)(https://doi.org/10.1371/journal.pone.0194482)
        2) “R1 and R2 of the N-ter tail evolve as a function of residue number.” I haven’t done those experiments. That’s the next part of my project.

        Reply
    2. Chandan Singh Avatar
      Chandan Singh

      Interesting work:

      What type of experiments you are planning to look for exact functional role of this protein?

      Reply
      1. Nikhil Sunny Avatar
        Nikhil Sunny

        I will be doing in vitro studies only; majorly binding studies with RNA using ITC, NMR, fluorescence, etc.

        Reply

    Leave a Reply

    Your email address will not be published. Required fields are marked *

  • Characterizing Metabolic Dysregulation in Early-Stage Chronic Kidney Disease for Diagnostic Insight

    Upasna Gupta (Centre of Biomedical Research (CBMR) & Lucknow and Academy of Scientific and Innovative Research(AcSIR), India)

    LinkedIn: @Upasna Gupta; X: @Upasnagupta30

    Abstract: The progressive illness known as chronic kidney disease (CKD) can often be challenging to diagnose in its early stages with conventional diagnostic approaches such as serum creatinine and albumin assessment. Identifying possible biomarkers for early detection and personalized treatment, as well as physiological changes linked to early CKD—an area that hasn’t been fully investigated before—is the goal of this study to address this gap.
    We performed a metabolomic analysis using ¹H NMR on 115 human serum samples (24 healthy controls, 91 patients with early-stage CKD). MetaboAnalyst 6.0 was used for data pre-processing and statistical analyses (PCA, PLS-DA, OPLS-DA, ANOVA, and Wilcoxon Mann-Whitney test). Strong differentiation between CKD stages was shown by random forest modelling. The KEGG database was used to perform pathway enrichment, and ROC analysis evaluated the diagnostic value of important metabolites.
    Across CKD stages, significant changes in ten different metabolites: myo-inositol, glycerol, pyruvate, carnitine, phenylalanine, tyrosine, histidine, TMAO, 2-hydroxyisobutyrate, and 3-hydroxyisobutyrate (p 1). AUC values > 0.7 from ROC curves demonstrated its potential for diagnosis. Pathway analysis revealed significant dysregulation in metabolism of inositol phosphate, tyrosine, histidine, pyruvate, and biosynthesis of phenylalanine, tryptophan and tyrosine.
    This comprehensive metabolomics investigation identified potential early-stage CKD biomarkers in addition to significant metabolic abnormalities. These findings could help provide individualized care for CKD early management.

    1. Chandan Singh Avatar
      Chandan Singh

      Thanks for a nice presentation. I have following questions regarding the same:
      1. In the stack plot showing the 1D NMR spectra shown gradual variation of creatinine and format in different groups but these two do not show up in the contributing metabolic factors of group deafferentation. What can be possible explanation?

      2. Similarly, my-inositol does not seem to vary much in the 1D plots but its there in contributing factors of group differentiations. What can be the reason?
      Thanks again.

      Reply
      1. Upasna Gupta Avatar
        Upasna Gupta

        Thank you, sir.
        1. Although creatinine was found to be significantly altered when comparing G3a and G3b groups, indicating that its changes become more prominent in later stages of CKD. However, since our primary aim was to identify early-stage biomarkers beyond conventional markers like creatinine, we did not include it in the final list of contributing factors for group deafferentation, though detailed results are provided in the manuscript.

        Formate, on the other hand, showed significant differences when comparing early-stage CKD patients to controls. However, it may not have contributed strongly to the variance specifically within the deafferentation group, and thus was not highlighted in the final metabolic signature for that group.

        2. Great observation, sir, although myo-inositol does not display a marked shift in the 1D NMR stack plots, it was identified as a significant contributor in the multivariate analysis. This suggests that its variation across groups is subtle yet consistent, not readily apparent to the eye but statistically relevant when analysed in the context of the full metabolic profile.

        Reply
    2. Marco Schiavina Avatar
      Marco Schiavina

      Hey, very interesting work, I was wondering how did you handle the large lipo protein signals arising from the blood samples. Did you filter them out? what kind of NMR pulse sequences did you use? Is there any evidence of these proteins to be a biomarker of the disease?

      Reply
      1. Upasna Gupta Avatar
        Upasna Gupta

        Thank You, Dr. Marco Schiavina
        Yes, we filtered the serum samples using a 3 kDa Amicon filter to remove larger proteins and lipoproteins. However, as reported in earlier studies, small lipid fragments can still appear in the aliphatic region (δ 0.75–2.5 ppm) due to aggregation or interactions with other macromolecules. To suppress these broad signals and focus on low-molecular-weight metabolites, we used the CPMG pulse sequence, which attenuates macromolecular signals. This approach enhanced the spectral resolution and improved our ability to reliably detect metabolites associated with CKD-related metabolic dysregulation.
        While we didn’t focus on lipoproteins as biomarkers in this study, there’s growing evidence supporting their relevance, and it’s a great direction for future research.

        Reply
    3. Upasna Gupta Avatar
      Upasna Gupta

      Thank you, sir.
      1. Although creatinine was found to be significantly altered when comparing G3a and G3b groups, indicating that its changes become more prominent in later stages of CKD. However, since our primary aim was to identify early-stage biomarkers beyond conventional markers like creatinine, we did not include it in the final list of contributing factors for group deafferentation, though detailed results are provided in the manuscript.

      Formate, on the other hand, showed significant differences when comparing early-stage CKD patients to controls. However, it may not have contributed strongly to the variance specifically within the deafferentation group, and thus was not highlighted in the final metabolic signature for that group.

      2. Great observation, sir, although myo-inositol does not display a marked shift in the 1D NMR stack plots, it was identified as a significant contributor in the multivariate analysis. This suggests that its variation across groups is subtle yet consistent, not readily apparent to the eye but statistically relevant when analysed in the context of the full metabolic profile.

      Reply
    4. Upasna Gupta Avatar
      Upasna Gupta

      Thank You, Dr. Marco Schiavina
      Yes, we filtered the serum samples using a 3 kDa Amicon filter to remove larger proteins and lipoproteins. However, as reported in earlier studies, small lipid fragments can still appear in the aliphatic region (δ 0.75–2.5 ppm) due to aggregation or interactions with other macromolecules. To suppress these broad signals and focus on low-molecular-weight metabolites, we used the CPMG pulse sequence, which attenuates macromolecular signals. This approach enhanced the spectral resolution and improved our ability to reliably detect metabolites associated with CKD-related metabolic dysregulation.
      While we didn’t focus on lipoproteins as biomarkers in this study, there’s growing evidence supporting their relevance, and it’s a great direction for future research.

      Reply

    Leave a Reply

    Your email address will not be published. Required fields are marked *

  • Fibrosis Under the Lens: NMR Metabolomics and Machine Learning Illuminate Hidden Pathways and Offer a Non-Invasive Alternative to Liver Biopsy”

    Shreya Pandey (Centre of Biomedical Research, India)

    LinkedIn: Shreya Pandey; X: @shreyapandey171

    Abstract: The landscape of chronic liver disease has changed significantly, with metabolic dysfunction-associated steatotic liver disease(MASLD) now emerging as the most widespread form worldwide. In Asia, particularly in India, the prevalence of MASLD is increasing, largely driven by poor dietary habits and a sedentary way of life. MASLD spans from fat deposition to inflammation and fibrosis. Fibrosis stands out as the most critical indicator of liver-related complications and overall risk of death in MASLD. Early identification of fibrosis is critical, but current tests are often invasive or unreliable. While studies have explored metabolic changes in MASLD, few have focused on distinguishing early-stage fibrosis from steatosis.
    In this study, we used NMR-based metabolomics to analyse serum samples from n = 103 MASLD patients, divided into fibrosis (n = 44) and non-fibrosis (n = 59) groups based on standard non-invasive scoring systems. We identified seven metabolites—arginine, glycerol, aspartate, glucose, phenylalanine, histidine, and citrate—that significantly differed between the two groups and showed good diagnostic potential (AUROC> 0.70). Pathway analysis revealed disruptions in arginine and nitrogen metabolism, associated with liver scarring processes, and in energy and lipid metabolism, pointing to mitochondrial dysfunction and lipotoxic stress. Reduced aspartate levels also suggested loss of natural protection against fibrosis.
    This is the first study of MASLD cohort to differentiate early-stage fibrosis from steatosis using metabolomics. Our findings highlight the potential of a simple NMR based blood test to aid early diagnosis, guide treatment decisions, and personalize care—offering a non-invasive alternative to improve MASLD management.

    1. Chandan Singh Avatar
      Chandan Singh

      Thanks for the nice presentation. I have following questions related to the presentation:

      1. As per the study enhanced level of arginine leads to increased proline synthesis. Is this enhanced proline level reflected in metabolic profile?
      2. How is the lipid profile? Are there specific lipids which are changing?
      3. How does enhanced level of collagen synthesis lead to fibrosis?
      4. What portion of the result was used in machine learning?

      Thanks again

      Reply
      1. Shreya Pandey Avatar
        Shreya Pandey

        Thank you sir.
        1- Yes sir, even the proline level was enhanced in the NMR profiling , however it didnot match the criteria to be considered as significant metabolite ( despite having p value 1 , the AUC value was 0.65 so we had to exclude it).
        2- The few lipids that we obtained using diffusion edited pulse program also profile had significant difference. -CO-CH2-CH2- (corresponding to cholesterol and FA{TAG and Phospholipids}) was found to be increased in fibrotic cohort, similarly PUFA was found to be decreased in fibrotic cohort. As we did using NMR we have limited data corresponding to lipids. Once we use LC we might get broader insights which we will be starting soon
        3- When there is continuous injury or inflammation to liver , Hepatic stellate cells gets activated due to cascade of events. These HSCs are major contributers for collagen. When the level of collagen increases , it starts accumulating in the liver , distorting the normal structure and function and liver and eventually forming scarred tissue. This condition is called fibrosis.
        4- Only the data from bins of significant metabolites was used. We excluded the water region and the regions that were not significant

        Reply
    2. Chandan Singh Avatar
      Chandan Singh

      Thanks for a nice presentation. I have following questions:

      1. As shown in the presentation the enhanced level of arginine leads to increased collagen production via increased proline level. Is increased proline reflected in the NMR profiling?
      2. How is does lipid profile look? Any specific lipids which are enhanced?
      3. How does increased collagen lead to liver fibrosis?
      4. What exact data was used in machine learning?

      Thanks again

      Reply
      1. Shreya Pandey Avatar
        Shreya Pandey

        Thank you sir.
        1- Yes sir, even the proline level was enhanced in the NMR profiling , however it didnot match the criteria to be considered as significant metabolite ( despite having p value 1 , the AUC value was 0.65 so we had to exclude it).
        2- The few lipids that we obtained using diffusion edited pulse program also profile had significant difference. -CO-CH2-CH2- (corresponding to cholesterol and FA{TAG and Phospholipids}) was found to be increased in fibrotic cohort, similarly PUFA was found to be decreased in fibrotic cohort. As we did using NMR we have limited data corresponding to lipids. Once we use LC we might get broader insights which we will be starting soon
        3- When there is continuous injury or inflammation to liver , Hepatic stellate cells gets activated due to cascade of events. These HSCs are major contributers for collagen. When the level of collagen increases , it starts accumulating in the liver , distorting the normal structure and function and liver and eventually forming scarred tissue. This condition is called fibrosis.
        4- Sir, the data from the binned sheet that we obtained from chenomx was used in machine learning learning.

        Reply
    3. Daniel Vincent Avatar
      Daniel Vincent

      Interesting work! Could you tell the pulse program used? What data did you use for building the model ? Is NMETA available online

      Reply
      1. Shreya Pandey Avatar
        Shreya Pandey

        Thank you Daniel, we have used CPMG pulse program which is basically used to suppress large molecules. We have used the binned sheet generated using chenomx for creating model. As far as NMETA is concerned, it is not available online we are still working on it.

        Reply
    4. Ch s karthik Avatar
      Ch s karthik

      The presentation looks very informative but can you answer me the following question: What is NMeta? What all information does it require?

      Reply
      1. Shreya Pandey Avatar
        Shreya Pandey

        Thank you, Karthik,
        NMETA is a web-based application we are currently working on as part of our effort to develop a non-invasive alternative to liver biopsy.
        The process is simple: perform a 1D NMR experiment on a serum sample and upload the resulting spectrum to our webpage. The tool will then provide the probability of the individual having liver fibrosis.

        Reply
    5. Marco Schiavina Avatar
      Marco Schiavina

      Hello Shreya!
      Interesting work, I was wondering how did you handle the large lipo protein signals arising from the blood samples. Did you filter them out? Is there any evidence of these proteins to be a biomarker of the disease?

      Reply
      1. Shreya Pandey Avatar
        Shreya Pandey

        Thank you, Dr. Schiavina,
        We have not filtered the serum as we had to perform diffusion edited experiment on the same serum sample. Instead, we have used the CPMG pulse sequence to suppress signals from large molecules, particularly lipoproteins and lipid fragments, thereby minimizing their interference.
        While previous studies have compared MASLD (formerly NAFLD) with hepatocellular carcinoma (reference: ref:-https://www.thelancet.com/journals/ebiom/article/PIIS2352-3964(21)00455-2/fulltext), none have specifically examined non-fibrotic MASH versus early fibrotic MASH within the MASLD cohort. We are currently investigating this comparison and expect to share promising results soon.

        Reply

    Leave a Reply

    Your email address will not be published. Required fields are marked *

  • Conformational equilibria of the Mg2+-channel CorA

    Tobias Schubeis – @TSchubeis

    The CorA channel is the major Mg2+-influx pathway in prokaryotes. The mechanistic details of the channel gating and the transport of the metal ions are still not entirely understood. Here we investigate the dynamics of CorA in DMPC lipid bilayers with 1H-detected solid-state NMR at 100 kHz MAS.

    Leave a Reply

    Your email address will not be published. Required fields are marked *

  • From Rigid to Flexible: Impact of Macrocycle Loss on Tolaasin’s Backbone Dynamics and Activity

    Durga Prasad

    In this project, we investigate the impact of macrocycle loss on tolaasin activity. Tolaasin, a Cyclic Lipopeptide (CLiP) from Pseudomonas tolaasii, plays a critical role in causing brown blotch disease in mushrooms. Its 18-amino acid sequence features an N-terminal lipid tail and a macrocycle formed via an ester bond. Tolaasin exhibits inhibitory action against fungal and Gram-positive bacteria, underscoring its significance. Studies have shown that hydrolysing the ester bond, potentially opening the macrocycle, can detoxify tolaasin, highlighting the macrocycle’s role in tolaasin’s function. Understanding how specific structural changes alter membrane interactions is crucial for developing novel therapeutics and biocontrol agents.
    Our approach involves studying hydrolysed tolaasin in parallel with the native molecule in SDS micelles using NMR spectroscopy. To enable advanced multidimensional structural analysis, we first produce 15N isotope-enriched tolaasin by cultivating the producing bacterium on a minimal medium supplemented with suitable labeled isotopically enriched precursors. Subsequently, isotopically enriched hydrolyzed form was synthesized through controlled alkaline hydrolysis.
    A comprehensive analysis, including full resonance assignment and 15N R1, R2, and het-nOe experiments, allows us to investigate peptide backbone dynamics. The order parameters (S2) derived from model-free analysis of relaxation data provide insights into molecular motions occurring on a nanosecond to picosecond timescale. By employing reduced spectral density mapping at Jω0, JωN, and Jω0.87H, we distinguish residues with distinct rigidity and flexibility profiles in both forms of tolaasin. Furthermore, NH R1 rates are determined both in the absence and presence of a soluble paramagnetic relaxation agent. This facilitates mapping the PRE wave of tolaasin, extracting tilt and azimuth angles, and enabling the mapping of helix orientations. Our findings indicate that the opening up of the macrocycle results in a partial loss of peptide backbone rigidity, leading to involvement in microsecond dynamics by later exocyclic residues.

    Leave a Reply

    Your email address will not be published. Required fields are marked *

  • Functional insight and Mechanistic Study of UBA domain of E2-25K in Lys-48 Ubiquitin Chain Elongation

    Gajendra Singh

    E2-25K (Ube2K) is a ubiquitin-conjugating enzyme that can only synthesize the K48(Lys-48) ubiquitin chain. All ubiquitin E2s have a conserved catalytic ubiquitin-conjugating domain (UBC), whereas E2-25K is the only enzyme that contains additional C-terminal ubiquitin association domain (UBA). We investigated the function of UBA domain in K48 chain elongation. NMR based results suggest that UBA domain provides the binding surface to ubiquitin during the chain elongation. It also holds the di ubiquitin (Ub 2) with different affinity to proximal and distal Ub units. We showed that the UBA domain expedites the polyubiquitin (K48) chain formation. Fluorescent polarization and mutational studies indicated that the UBA domain increases the K48 chain processivity. However, the activity is dependent upon the Ub chain length. Gel-based kinetics results also manifest the function UBA domain in polyubiquitin chain and the results evident that the rate reduces, drastically with truncated UBA domain.

    Leave a Reply

    Your email address will not be published. Required fields are marked *

  • Ester hydrolysis of calcein-AM in a lipid bilayer demonstrates the concept of lipozyme catalysis

    Kiran Kumar – @KiranKumar86269

    Lipids are not typically thought to catalyze biological reactions, but evidence shows that certain lipid aggregates can speed up chemical reactions in synthetic organic chemistry. We demonstrate the potential for the hydrophobic region of a lipid bilayer to provide an environment suitable for catalysis as lipozyme. We demonstrate this concept by the ester hydrolysis of calcein-AM to produce calcein as a fluorescent product, Which is a widely used assay for esterase activity in cells. The reaction product was characterized by microscopy and 1H-NMR measurements. Overall, we explore the implications of considering lipid aggregates as catalytic entities

    Leave a Reply

    Your email address will not be published. Required fields are marked *