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The December HUPOST is now available – See the latest Congress information, news from ECR and MOC, a Humans of HUPO profile, research article, and much, much more!

Written by Dr. Edward Lau and Dr. Rob Beynon

"A mouse has a new liver every few days, but the lifespan of a mouse liver cell is hundreds of days” - Dr. Rob Beynon

In 1965, Robert Schimke led a team that examined the response of rat liver tryptophan oxygenase (then called tryptophan pyrrolase, the first enzyme in the breakdown of the gluconeogenic amino acid tryptophan) to glucocorticoids and to feeding of the substrate, tryptophan [ref 1]. – both treatments led to elevated enzyme levels in the liver. They concluded that glucocorticoids induced synthesis of the enzyme, whereas dietary tryptophan prevented degradation - clear evidence of the importance of the two ‘opposing’ processes of synthesis and degradation controlling the intracellular abundance of an enzyme. This was, parenthetically, one of the first examples of a connection between the transcriptome (controlling synthesis) and the metabolome (controlling degradation).

This example serves to illustrate that despite the high energetic cost of making and degrading proteins, the proteome is in a dynamic state of renewal. Even in the steady state, where the protein abundance is constant, new proteins are synthesised and at the same time, the pool is commensurately depleted through degradation. This process of protein turnover can account for a sizeable proportion of the energy budget. The rate at which any protein is replaced must have evolved through natural selection; some proteins are replaced with minute time scales, others are essentially static through the life of the individual. Moreover, the rate of protein replacement is not the same in different tissues, nor is it the same in different species – smaller mammals, with a higher basal metabolic rate replace their proteins at much higher rates than larger mammals. Indeed, the high rate of protein turnover in small mammals may be a way to elicit thermogenesis.

Although the phenomenon of protein turnover has been described since the pioneering work of Schoenheimer eight decades ago, our knowledge of how it is regulated in homeostasis and disease and how it contributes to the anatomy and physiology of the proteome has remained lagging. Early studies, mostly using radioisotopes, could only measure turnover of total protein, and the goal of measurement of individual protein turnover rates seems unattainable

In recent years this has changed. Advances in separation science and mass spectrometry delivered the ability to resolve proteins, peptides or amino acids labelled with stable isotopes.

We and others would argue that we need to understand the scope and scale of intracellular protein turnover at the level of the proteome – it is likely that the subtlety seen by Schimke and colleagues is manifest in many other biological systems. Many protein turnover studies have been conducted with mammalian cells in culture. With this experimental system, it is relatively straightforward to introduce label isotopologs of essential amino acids into the cell culture media to trace their incorporation into proteins (dynamic SILAC approaches).

Although many insights have been gained from such studies in vitro, it is increasingly evident that turnover of proteins in cell culture is very different from that in large, intact adult animals. In rapidly growing cells protein synthesis is driven by high rates of cell proliferation, with doubling times of a day or less. By contrast, in intact animals the doubling rate of cells is measured in hundreds of days (for example, the mouse hepatocyte). At such a low proliferation rate, protein abundance cannot be adjusted by cell number expansion (with commensurate dilution), and individual proteins can be expected to be replaced in time frames that sit within the lifespan of the cell, ranging from minutes to that lifespan. To avoid proteotoxic aggregates, damaged proteins will need to be removed by carefully regulated degradation, rather than simply diluted to daughter cells. These two differences between cells in culture and in tissues require us to adopt different approaches and analytical strategies.

To properly understand the dynamics of the proteome, whether in steady state or in flux states, we need to measure protein turnover rates in intact animals. But, the convenience of a simple, instantaneous medium change no longer exists, and whole animal labeling studies require a different approach, confounded by difficulties of isotope administration and reutilisation of labeled amino acids for new protein synthesis. Moreover, turnover can only feasibly be accessed through measurement of synthesis by tracking incorporation of stable isotope labels. It is almost obligatory to measure the rate of synthesis of a protein through isotope incorporation, administered over days, months or years.

Quantification of protein turnover in animals is further complicated by the slow precursor availability in the tissue of interest after the isotopic label is administered. For example, labeled proteins or amino acids supplied in the diet have to move through the digestive system, cross the intestinal mucosal barrier, pass through the hepatic system and be transported to peripheral tissues in the blood. Any delay in appearance of labeled amino acids in the precursor pool of a peripheral tissue would interfere with measurement of the true turnover rate. By contrast, heavy water ([²H]₂O) crosses tissue barriers much more quickly. Thus, these two labelling methods could yield different apparent turnover rates for the same protein. But can the two approaches be brought to convergence? Intuitively, one would expect high turnover proteins to be most affected by slow precursor equilibration, and this is indeed the case.

In a recent study [ref 2], we compared two common methods of measuring protein turnover in animals either using heavy labeled amino acids in diet or provision of [²H]₂O in drinking water. The two strategies differ in precursor availability and metabolism, as well as the mass spectral features of peptides following label incorporation. Our question was very simple: what were the optimal data analysis strategies and when applied, do the two methods yield comparable turnover rate results?

We set up mouse labelling studies in which the only significant variable was the labeling protocol. Two groups of adult mice of identical strain, sex, age, maintained with identical husbandry, were labeled either with [¹³C₆]lysine or heavy water ([²H]₂O) over about a month. Animals were sampled at different times over this period, and the proteomes of the heart, liver, kidney, and skeletal muscle were analyzed by mass spectrometry. These tissues differ in their median turnover rate, allowing our analysis to extend over a broader range (liver and kidney are higher than heart, and in turn all three are higher than skeletal muscle). To ensure consistency in data processing, one of us (EL; https://ed-lau.github.io/riana) wrote Riana, new Python software that quantitates peptide labelling, recovers isotope abundance as a function of labelling time and fits these data to recover the first order rate of replacement, equivalent to steady state half-life.

Because heavy water is known to rapidly equilibrate across tissues and compartments, it would give data closest to the ground truth at least where label utilization is concerned. This experimental design therefore allows us to use water data as a reference for optimizing the analysis of lysine labelling data.

As anticipated, with [¹³C₆]lysine labeling there was a delay in precursor equilibration in all tissues. Simple exponential models of protein turnover are compromised by this delay, and the rate of turnover of high turnover proteins is underestimated. This can be corrected by using a suitable two-compartment kinetic model that also models the delay in the precursor pool, the protein turnover rate constant (k_d) and the precursor availability rate constant (k­_d).

Surprisingly, finding suitable k_p values to adjust for labeling delay was not straightforward. Although the ratio of [¹³C₆]lysine vs. [¹²C₆]lysine can be measured in a tissue by LC-MS, an empirical sampling of tissue lysine isotope enrichment over time led to an apparent underestimation of the true precursor pool. Moreover, the best strategy for finding k_p is dependent on the tissue being examined. Global parameter optimization method can effectively find the best k_p value that explains the data sets in slow turnover tissues but is less effective in high turnover tissues. Therefore although the complications of slow precursor equilibration can be overcome with the proper strategy, careful considerations must be given based on the tissue and animal under study.

Such complications make a compelling case for heavy water as a turnover label in intact animals. Heavy water is inexpensive, virtually all peptides demonstrate isotopic incorporation, and the speed with which water equilibrates in the body mitigates complications due to precursor pool delay and reutilisation. That being said, heavy water is not without drawbacks. The pathways of heavy water labeling of different essential and non-essential amino acids is incompletely understood, and measurement of isotope incorporation in precursor spectra is more prone to errors and isobaric contamination in the mass spectra.

Accurate measurement of whole animal, proteome-wide protein turnover is still difficult, and  there are several largely unresolved issues.

●     What are the specific considerations in data analysis strategy when using different labeling protocols?

●     Do existing analytical workflows and software packages give comparable turnover rates and profiles when analyzing a common data set?

●     Each protein yields multiple peptides, and the rate of labeling of each peptide yields a measure of turnover. How are these data aggregated - is it better to combine all peptide data and fit once curve, or fit each peptide data individually? If the latter, are there objective criteria that can be applied to eliminate outlier turnover values?

●     What are the optimal practices for error estimation in turnover measurements, and statistics in comparing turnover across conditions (e.g., homeostasis vs. disease)?

●     Is it possible to establish a set of 'gold standard' turnover rates in different tissues and in different species?

 

We’d like to propose that those who are interested in this challenge bring together their knowledge and expertise in a community effort to resolve some of these questions. Should anyone be interested they are invited to contact us for further information.

 

Reference 1: Schimke et al. 1965 PMID 14253432

Reference 2: Hammond et al. 2022 PMID 35636728

The goal of the “Stylish Academic Writing” professional development webinar series is to help students and trainees improve their scientific writing skills. The inaugural webinar was presented by Professor John Yates III from the Scripps Research Institute. Professor Yates is Editor-in-chief at the Journal of Proteome Research (JPR). This webinar covered different aspects of scientific writing, including Professor Yates’ personal approach to writing, how to make a perfect figure, and even how to manage disputes over authorship. In China alone, the webinar attracted nearly 3,000 online viewers even though it was aired close to midnight.

The title of Professor Yates’ presentation was "Write, Write, Write." Throughout the 45-minute presentation, Professor Yates shared several important tips including:
•    Develop a daily writing habit and try to publish often
•    Write manuscripts in small sections and paragraphs a little at a time
•    Don’t blindly accept changes. Instead, improve your writing by reviewing the stylistic and grammatical reasons behind each edit
•    Focus on the discussion section, which shows how your results advance the current understanding in the field
•    When choosing a journal to submit to, aim high, but choose appropriately
•    Seriously consider reviewer criticism to improve the quality of your paper

Professor Yates noted that writing is like working out in the gym. The more you do it, the stronger you get. As such, trainees should try to write often and publish everything they accomplish, no matter how small. Professor Yates discussed three pillars of academic writing: mechanics, style, and content.

With regards to mechanics and style, Professor Yates suggested that trainees read and pay attention to the writing of others. Proper grammar is essential to getting your message across and incorrect grammar can detract from your scientific content and can cause challenges to the review process. He recommends taking classes in English technical writing, using a copyeditor or software such as Grammarly, and to write frequently. He recommended “The Elements of Style” by Strunk and White as an excellent writing handbook.

Professor Yates discussed scientific content with respect to publishing in JPR. JPR prioritizes exciting, groundbreaking science, where studies are welcome from a wide swath of proteomic and metabolomic research. Publishable research requires proper statistical design for quantitative experiments. For this, he suggested reviewing a review article in JPR by Oberg and Vitek from 2009 (DOI: 10.1021/pr8010099). Studies should include enough samples and sufficient controls to properly power the experiment. Validation of biological experiments (including computational modeling) should be performed using experimental methods on independent patient or biological samples.

This session included an extended Q&A session with the audience, guided by Drs. Justyna Fert-Bober (Cedars Sinai, USA), Tiannan Guo (Westlake University, China) and Brian Searle (Ohio State University, USA). A full video recording of the session including the Q&A session is available on the HUPO Proteomics Knowledge Source website (https://pkr.hupo.org/).

The November HUPOST is now available - there's a ton of news and information in this issue including HPP Day, B/D-HPP article and Twitter Poll, ECR events and activities, Humans of HUPO profile, job opportunities and much, much more!

Authors:

Yun-En Chung¹ and Mathieu Lavallée-Adam¹

¹Department of Biochemistry, Microbiology and Immunology and Ottawa Institute of Systems Biology, University of Ottawa, Ottawa, Ontario, Canada

Mass spectrometry-based proteomics data analysis has never been more exciting. The combination of computational hardware improvements and a wide diversity of instruments and experimental techniques has created a gigantic playground for computational researchers and software developers. In recent years, one attraction at this playground has gained a lot of attention from both academia and industry: real-time analysis of proteomics data.

In a traditional mass spectrometry-based proteomics experiment, tens of thousands of mass spectra are collected for a biological sample. After the conclusion of the experiment these mass spectra are then inputted into software packages for peptide and protein identification and quantification. Hence, since biological information is inferred solely through post-hoc analysis, the mass spectrometry experiment is mostly running blind and does not adapt in real-time based on the biological data that is being acquired.

Improvements in computational hardware and the recent availability of Application Programming Interfaces (APIs) enabling mass spectrometry data analysis during proteomics experiments have paved the way to the design of a new family of algorithms and software packages performing the real-time analysis of mass spectrometry data. Tools such as the Thermo Fisher Scientific Instrument API (IAPI)¹ and the Bruker Parallel database Search Engine in Real-time (PaSER)² are enabling the design of analyses that accelerate data processing, help diagnose problems with instrumentation and enhance the characterization capabilities of mass spectrometry.

One of the early modern applications of real-time mass spectrometry data analysis is the on-the-fly quality assessment of mass spectrometry experiments. Instrument performance drop or malfunction are often only identified after post-hoc data analysis. Such a late discovery results in a waste of time and resources that are used to acquire unusable or subpar data. The QC-ART approach³ has been developed to evaluate instrument performance in near real-time and allow for immediate intervention. QC-ART ensures consistent high-quality data collection and the rapid detection of instrumentation problems.

Since the early beginnings of mass spectrometry-based proteomics, data acquisition remained an extremely active research topic. Still today, new acquisition techniques are being developed to supplement the current families of approaches including data-dependent acquisition⁴, data-independent acquisition⁵ and targeted methods^6,7. Traditionally, an instrument would apply the same acquisition strategy (precursor ion selection algorithm, scan window size, …) for the entirety of an experiment. This standard acquisition method works reasonably well in common proteomics use cases. However, since the instrument does not consider the biological relevance of the data it is acquiring, a significant proportion of this data does not translate into meaningful biological discoveries.

An excellent example of this is how real-time database search for peptide identification can support the selection of peptides for quantification with isobaric labeling. It was previously shown that MS3 spectra lead to more accurate quantification using tandem mass tag reporter ions than MS2 spectra⁸. However, acquiring MS3 spectra is resource intensive. It is therefore important to acquire MS3 spectra for data that is biologically relevant. Orbiter was therefore developed to identify peptides in real-time from MS2 spectra with a database search method⁹. Orbiter then only acquires MS3 spectra from MS2 spectra that yield a confident peptide identification and therefore optimizes resource usage for protein quantification.

Other groups developed software packages to identify peptides in real-time^10,11, while McQueen et al. presented a pseudo real-time approach that paused the experiment to adjust future data acquisition based on such peptide identifications. Inspired by these methods, our team proposed that the real-time identification of peptides and proteins can be used to guide mass spectrometry data acquisition in order to optimize resource usage and maximize protein identifications. Indeed, our computational approach, named MealTime-MS¹², uses real-time database search to identify peptides and supervised learning to assess the confidence of protein identifications. MealTime-MS then uses confident protein identifications to generate an exclusion list preventing the acquisition of tandem mass spectra for peptide ions that are expected to belong to proteins that were already identified in the mass spectrometry run. MealTime-MS showed that up to 33% of the mass spectra collected in traditional experiments could be safely ignored with minimal losses of proteins identified compared to standard experiments and that these mass spectra could be repurposed for the identification of additional proteins.

Alternatively, real-time analysis of mass spectrometry data has demonstrated its utility in targeted proteomics. In a typical targeted proteomics experiment, specific elution time windows need to be determined for targeted peptides. Due to run-to-run technical variation, the size of these scheduled windows must be kept relatively large to ensure the instrument encounters these peptides, thereby limiting the number of possible targets. MaxQuant.Live presented a solution via real-time recognition of precursor ions¹³. The algorithm uses the retention time, mass-to-charge ratio, and intensity of the precursor ions encountered to predict and therefore select those that should be targeted for quantification. This approach enabled the targeting of over 25,000 peptides in a single mass spectrometry run.

Real-time analysis of mass spectrometry-based proteomics data has also demonstrated its clinical applications. Devices such as the MasSpec Pen demonstrated how a small handheld device can be used to rapidly detect features including lipids, metabolites and proteins in human tissue. Such features can be used as biomarkers to diagnose in real-time whether tissues are cancerous or healthy.

After reading about these applications, we would like you to join the conversation on Twitter by answering our poll question here and letting us know where the future of real-time proteomics data analysis sits:  

Twitter Poll:

In which area do you think real-time analysis of mass spectrometry-based proteomics data will have the greatest impact in the future:

1. Protein ID
2. Protein Quantification
3. Clinical Applications
4. Other (write in replies)

Figure 1. Graphical representation of the traditional mass spectrometry-based proteomics pipeline, where acquired data is analyzed after the completion of the experiment and of a pipeline integrating real-time data analysis to adjust mass spectrometry data acquisition during the experiment.

Computer Icon created by Freepik - Flaticon: https://www.flaticon.com/free-icons/course.

Bios:

Yun-En Chung:

Yun-En Chung is an undergraduate student in Translational and Molecular Medicine and researcher in Dr. Mathieu Lavallée-Adam’s lab at the University of Ottawa. His research focuses on the development of software packages to guide mass spectrometry experiments in real-time to improve data acquisition efficiency. His publication on the real-time identification of proteins in mass spectrometry data was recognized as the best paper from a Master’s or Undergraduate student at the Ottawa Institute of Systems Biology in 2020. He also received several awards for his presentations, including a 2^nd place for his oral presentation at the Undergraduate Research Opportunities Program Seminar day at the University of Ottawa and an honorable mention for his poster at the American Society for Mass Spectrometry annual conference in 2022. Yun-En’s research is funded by awards from the Natural Sciences and Engineering Research Council of Canada and Mitacs.

Mathieu Lavallée-Adam:

Mathieu Lavallée-Adam is an Associate Professor at the University of Ottawa in the Department of Biochemistry, Microbiology and Immunology and is affiliated to the Ottawa Institute of Systems Biology. He obtained a B.Sc. in Computer Science and a Ph.D. in Computer Science, Bioinformatics option, from McGill University and performed his postdoctoral research at The Scripps Research Institute. His research focuses on the development of statistical and machine learning algorithms for the analysis of mass spectrometry-based proteomics data and protein-protein interaction networks. Dr. Lavallée-Adam is a recipient of the John Charles Polanyi Prize in Chemistry, rewarding the impact of his bioinformatics algorithms on the mass spectrometry community and was named Early Career Researcher of the Year by the Ottawa Institute for Systems Biology in 2021. He is also Co-Chair of the HUPO Early Career Researcher Initiative and a member of the HUPO Executive Committee, in which he develops training activities and advocates for junior investigators in proteomics and organize events highlighting their research on the international stage.

References:

1.        Scientific, T. F. Thermo Fisher Scientific IAPI GitHub. (2022). Available at: https://github.com/thermofisherlsms/iapi.

2.        Bruker. PaSER 2022. (2022).

3.        Stanfill, B. A., Nakayasu, E. S., Bramer, L. M., Thompson, A. M., Ansong, C. K., Clauss, T. R., Gritsenko, M. A., Monroe, M. E., Moore, R. J., Orton, D. J., Piehowski, P. D., Schepmoes, A. A., Smith, R. D., Webb-Robertson, B.-J. M., Metz, T. O. & TEDDY Study Group. Quality Control Analysis in Real-time (QC-ART): A Tool for Real-time Quality Control Assessment of Mass Spectrometry-based Proteomics Data. Mol. Cell. Proteomics 17, 1824–1836 (2018).

4.        Liu, H., Sadygov, R. G. & Yates, J. R. A model for random sampling and estimation of relative protein abundance in shotgun proteomics. Anal. Chem. 76, 4193–201 (2004).

5.        Venable, J. D., Dong, M.-Q., Wohlschlegel, J., Dillin, A. & Yates, J. R. Automated approach for quantitative analysis of complex peptide mixtures from tandem mass spectra. Nat. Methods 1, 39–45 (2004).

6.        Kuhn, E., Wu, J., Karl, J., Liao, H., Zolg, W. & Guild, B. Quantification of C-reactive protein in the serum of patients with rheumatoid arthritis using multiple reaction monitoring mass spectrometry and 13C-labeled peptide standards. Proteomics 4, 1175–86 (2004).

7.        Anderson, L. & Hunter, C. L. Quantitative mass spectrometric multiple reaction monitoring assays for major plasma proteins. Mol. Cell. Proteomics 5, 573–88 (2006).

8.        Ting, L., Rad, R., Gygi, S. P. & Haas, W. MS3 eliminates ratio distortion in isobaric multiplexed quantitative proteomics. Nat. Methods 8, 937–40 (2011).

9.        Schweppe, D. K., Eng, J. K., Yu, Q., Bailey, D., Rad, R., Navarrete-Perea, J., Huttlin, E. L., Erickson, B. K., Paulo, J. A. & Gygi, S. P. Full-Featured, Real-Time Database Searching Platform Enables Fast and Accurate Multiplexed Quantitative Proteomics. J. Proteome Res. 19, 2026–2034 (2020).

10.      Bailey, D. J., Rose, C. M., McAlister, G. C., Brumbaugh, J., Yu, P., Wenger, C. D., Westphall, M. S., Thomson, J. A. & Coon, J. J. Instant spectral assignment for advanced decision tree-driven mass spectrometry. Proc. Natl. Acad. Sci. U. S. A. 109, 8411–6 (2012).

11.      Graumann, J., Scheltema, R. A., Zhang, Y., Cox, J. & Mann, M. A framework for intelligent data acquisition and real-time database searching for shotgun proteomics. Mol. Cell. Proteomics 11, M111.013185 (2012).

12.      Pelletier, A. R., Chung, Y.-E., Ning, Z., Wong, N., Figeys, D. & Lavallée-Adam, M. MealTime-MS: A Machine Learning-Guided Real-Time Mass Spectrometry Analysis for Protein Identification and Efficient Dynamic Exclusion. J. Am. Soc. Mass Spectrom. 31, 1459–1472 (2020).

13.      Wichmann, C., Meier, F., Winter, S. V., Brunner, A.-D., Cox, J. & Mann, M. MaxQuant.Live enables global targeting of more than 25,000 peptides. bioRxiv 443838 (2018). doi:10.1101/443838

Join the next online panel hosted by the HUPO Early Career Researcher (ECR) Initiative on November 16th at 4PM GMT for a discussion on 'Science communication - who, how, where, and why?'.  We have three wonderful panelists:

• Dr. David Tabb, Institut Pasteur
• Dr. Ann Van der Jeugd, Leuven Brain Institute
• Dr. Ben Orsburn, Johns Hopkins University
  

Effective science communication has ripple effects through all aspects of society. But how do you convey clear and concise messages? What is the best way to get these messages across a wide range of platforms? Get the answers to all these questions and more!

REGISTER HERE in advance for this webinar.

After registering, you will receive a confirmation email containing information about joining the webinar.

The HUPO Education and Training Committee (ETC) webinar series is intended to help you organize and write a quality academic research paper. We will present how to polish academic style, how to manage data presentation, and how to learn from critical reading. Our mission is to provide academic support to strengthen student learning and empower every student to develop as self-academic writer whose work is admire.

Date: November 2, 2022
Time: 8:00 am PST

WEBINAR LINK:  https://us06web.zoom.us/j/84142263041

Speaker: John Yates III, PhD, Professor, Department of Molecular Medicine, Editor of Journal of Proteome Research. Dr. Yates research interests include development of integrated methods for tandem mass spectrometry analysis of protein mixtures, bioinformatics using mass spectrometry data, and biological studies involving proteomics. Additional to thousands of publications, international and national awards, and recognitions, Dr. Yates served as an Associate Editor at Analytical Chemistry for 15 years and is currently the Editor in Chief at the Journal of Proteome Research.

See our exciting program information below.  REGISTER TODAY!

All times are US Eastern Time. 

10:00 am Matthias Mann, "Ultra-high sensitivity for precision oncology"

10:30 am Erwin Schoof, "Leveraging advanced latest-generation acquisition and MS instrument architecture for improving single cell proteomics experiments"

11:00 am Roman Zubarev, "Since cell proteomics - some considerations from the chemical proteomics point of view"

11:30 am Claudia Ctortecka, "Variations of the proteoCHIP - a high-throughput sample preparation approach for single-cell proteomics"

12:00 pm Ying Zhu, "Enhancing single cell proteomics and tissue proteome mapping with ion mobility filtering"

12:30 pm Ryan Kelly, "Advanced separations and data acquisitions strategies for in-depth single-cell proteomics"

13:00 pm Closing remarks

REGISTER HERE!

Organized by the B/D-HPP Single Cell Initiative. Please contact Initiative Chair Dr. Bogdan Budnik (Bogdan.Budnik@wyss.harvard.edu) with any questions or if you want more info!  

Check out the October issue of HUPOST here, full of the latest news and updates, including HUPO's Strategic Plan!

Dr. Seyedmohammad started off his career in immunology where he Investigated Inhibitory signals at the T-cell Immune Synapse. During his master’s degree at Imperial College London, he developed important immune assays that helped in the identification of prominent signaling molecules involved in T-cell activation. He then obtained his doctoral degree in biochemistry at the University of Cambridge, where he studied the characterization of a bacterial iron transport protein from Pseudomonas aeruginosa. In that thesis, Dr. Seyedmohammad successfully proved the trimeric conformation of the protein and identified key binding motifs driving the acquisition of iron into the bacterial cell. After venturing into several start-ups, he began a post-doctoral fellowship under the supervision of Dr. Van Eyk at the Advanced Clinical Biosciences Research Institute in Cedars Sinai Medical Research Division, where he is currently using novel proteomic approaches to study heart failure. Dr. Seyedmohammad is responsible for identifying newly synthesized proteins in human cardiomyocytes using AHA-labeling and is applying this approach to characterize important protein pathways involved in heart disease as a consequence of ischemia and reperfusion. He is also responsible for developing high-throughput assays, using a COVARIS sonication system to scale-up sample processing for a robust workflow.