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24 Mar 2020

High CO2 Associated with Downregulation of Skeletal Muscle Synthesis and Ribosomal Biogenesis

By nickwiecien

High levels of CO2 downregulates skeletal muscle protein anabolism, according to a recent paper by TC Korponay et al (2020)  in the American Journal of American Journal of Respiratory Cell and Molecular Biology.

Two independent conditions are associated with worse outcomes in patients with chronic pulmonary diseases; the retention of a high amount of Co2 (also called “hypercapnia”) and skeletal muscle wasting (also associated with worse outcomes in acute pulmonary disease).
Due to the current lack of research on the role of high CO2 levels in regulating skeletal muscle anabolism, researchers sought to investigate the role of high CO2 levels in weakening skeletal muscle protein synthesis in both patients and mice.

Researchers found that locomotor muscles from patients with chronic CO2 retention had decreased ribosomal gene expression in comparison to patients who did not retain CO2 to the same extent. Additionally, researchers found that mice in a high-CO2 environment had downregulated ribosomal biogenesis in their skeletal muscles, as well as decreased protein synthesis.

Evidently, researchers found that there was an impact of high CO2 levels on skeletal muscle synthesis and anabolism. Researchers suggested future studies focusing on ribosomal biogensis and protein synthesis to counteract the impacts of high CO2 on skeletal muscles.

17 Mar 2020

MS-Helios allows for compact data visualization of multi-omic datasets

By nickwiecien

MS-Helios is an easy-to-use command line tool which works to solve the challenge of data analysis and visualization in the face of high-resolution mass spectrometery.

Though high-resolution mass spectrometry can identify hundreds of metabolites and thousands of proteins, this can make data analysis and visualization hard to do.

MS-Helios is a solution, allowing for compact data representation and reduced dimensionality. This tool also allows non-experts and experts alike to generate data and configuration files and publish high-quality, circular plots with Circos.

This software is available for download here.
The manuscript for MS-Helios can be viewed here.

10 Mar 2020

Investigating Abnormal Chromosome Tolerance in Wild Yeast Cells

By nickwiecien

Hose et al (2020) researched the genetic basis of aneuploidy tolerance in wild yeast in a recent edition of Elife.

Aneuploidy, or the abnormal chromosome numbers in a cell, is a harmful condition in developmental stages of wild yeast, yet is also common in plant cancers and pathogenic fungi. It is interesting to note that aneuploidy tolerance varies; for instance, researchers found that some wild isolates of baker’s yeast can tolerate chromosome amplification, while laboratory strains cannot. 

To study the genetic basis of how well wild yeast can tolerate aneuploidy, researchers mapped the genetic basis to Ssd1, an RNA-binding translational regulator that functions in wild strains but is defective in a laboratory strain “W303.” 

Researchers found that aneuploidy tolerance is enabled via a role for Ssd1 in mitochondrial physiology, such as binding and regulating nuclear-encoded mitochondrial mRNAs.


27 Feb 2020

Transgenic animal models demonstrate that mice have similar COPD-related skeletal muscle dysfunction as humans

By nickwiecien

Balnis et al (2020) recently studied the mechanisms of COPD-related skeletal muscle dysfunction using an established transgenic animal model of COPD in a recently published paper in the Journal of Applied Physiology. 

Patients with chronic obstructive pulmonary disease (COPD), a disease that is characterized by obstructed airflow to the lungs and is associated with long-term exposure to cigarette smoke, often also develop skeletal muscle dysfunction. Overall, the comorbidity of COPD and skeletal muscle dysfunction is associated with outcomes such as poor health and mortality.

While some research has suggested that skeletal muscle dysfunction may be the result of COPD-related conditions, such as protein degradation and metabolic disruption, there is still poor understanding on what mechanisms would regulate these processes, as there is little to no research on a validated animal model of pulmonary emphysema. Therefore, Balnis et al (2020) sought to use such a model based on inducible UL-13-driven pulmonary emphysema (IL-13TG) to study the mechanisms of skeletal muscle dysfunction.

Using a transgenic mouse model, researchers found that the skeletal muscles of emphysematous mice are similar to those developed by human patients with COPD. For instance, both groups develop muscular atrophy and have decreased oxygen consumption. Within skeletal muscles, both groups also had downregulated ATP binding and bioenergetics.

Researchers concluded that transgenic animal models of COPD are useful to understand skeletal-muscle dysfunction in humans.

06 Feb 2020

A Strategy for Increasing Analytical Throughput in Quantitative Proteomics

By nickwiecien

Zhong et al (2019) developed a novel strategy aimed towards solving challenges in absolute quantification, and detailed these efforts in a recent issue of Analytical Chemistry.

Absolute quantification is both an effective technique– which allows for robust results in proteomics research– and a challenging one. Problems that absolute quantification presents include low specificity in complex backgrounds, limited analytical throughput and wide dynamic range.

To solve these issues, Zhong et al (2019) developed hybrid offset-triggered multiplex absolute quantification (HOTMAQ), a strategy which increases the analytical throughput (the increase in analysis production rate) of targeted quantitative proteomics by up to 12 times. This technique accomplishes this by using mass-difference and isobaric tags to create an internal standard curve in the MS1 precursor scan, identify peptides at the MS2 level, and mass offset-trigger the quantification of target proteins in synchronous precursor selection at the MS3 level. All of this is accomplished at the same time. 

Because HOTMAQ results in greater quantitative performance, higher flexibility and quicker analysis rate, HOTMAQ is a strategy that can easily be applied to target peptidomics, proteomics, and phosphoproteomics.

Graphical Abstract, demonstrating the technique of hybrid offset-triggered multiplex absolute quantification (HOTMAQ). "Zhong, X., Q. Yu, F. Ma, D.C. Frost, L. Lu, Z. Chen, H. Zetterberg, C. Carlsson, O. Okonkwo, and L. Li, Hotmaq: A multiplexed absolute quantification method for targeted proteomics. Analytical Chemistry, 2019. 91(3): p. 2112-2119. PMCID: PMC6379083"
22 Jan 2020

Software Highlight: LipiDex

By nickwiecien

LipiDex is a free and open-source software package offered by NCQBCS. This software package unifies all stages of the LC-MS/MS lipid identification process, and also utilizes intelligent data filtering to reduce manual result curation while increasing identification confidence.

One can use LipiDex to accomplish a variety of functions. For instance, one can create and manage custom in-silico lipid spectral libraries; model complex lipid MS/MS fragmentation using intuitive fragmentation templates; generate high-confidence MS/MS lipid identifications; annotate chromatographic peak tables with lipid identifications; and automatically filter peak tables for adduct peaks, in-source fragments and dimers.

Information on both LipiDex and Library Forge can be found here, and the software download is located here. Additionally, information on other software that the National Center for Quantitative Biology of Complex Systems offers can be found here.

Graphical Abstract depicting the software package lipidex accomplishing a variety of functions, such as the modeling of complex lipid ms/ms fragmentation, generation of high-confidence ms/ms lipid identifications, annotation of chromatographic peak tables, and the creation of in-silico lipid spectral libraries.
18 Dec 2019

Proteomic and Transcriptomic analyses of Toxoplasma gondii infection

By nickwiecien

Garfoot et al recently published a paper in BMC Genomics on the proteomic and transcriptiomic analyses of early and late-stage Toxoplasma gondii infection found in mice brains.

Toxoplasma gondii is a protozoan pathogen responsible for the infectious disease “toxoplasmosi,” and this pathogen is of researcher utility as it is capable of infecting a host’s brain, transitioning from fast-growing to latent morphology morphology life stages (from “tachysoite” to “bradyzoite”), and eventually creating neuronal cysts which are largely invisible to the host, as well as resilient against the host’s immune response and modern therapeutics.

Garfoot et al analyzed results from transcriptional and proteomic analyses of fast-growing (bradyzoite) fractions of the infection from mouse brains over a period of 21-150 days and, through deep sequencing of expressed transcripts found that one third of the transcripts were more enriched compared to the slow-growing tachysoites. Furthermore, researchers found that the transcript which grew the most over the course of the infection was the sporoAMA1 transcript.

As a result of this work, researchers have expanded the transcriptional profile of in vivo toxoplasmosis bradyzoites.

Graphical abstract for Garfoot et al's recently published a paper in BMC Genomics on the proteomic and transcriptiomic analyses of early and late-stage Toxoplasma gondii infection found in mice brains.
04 Dec 2019

Metandem, a free and online software for MS-based isobaric labeling metabolomics

By nickwiecien

Hao et al. (2019) recently published a paper in Analytica Chimica Acta detailing the utility of Metandem, a data analysis software which is aids in isobaric labeling-based metabolomics.

While mass spectrometry-based stable isotope labeling is advantageous compared to other methods of isotope labeling due to its multiplexing and accurate quantification capabilities, its data analysis requires specifically customized bioinformatic tools. However, Metandem, a free, unique and online software, can aid in the analysis of stable isotope labeling-based metabolomics data.

Metandem has a number of different features that assist in MS-based isobaric labeling, such as integrating feature extraction, metabolite quantification and identification, batch processing of multiple data files, online parameter optimization for custom datasets, data normalization and statistical analysis.

Metatandem is available free and online at http://metandem.com/web/. 

Graphical abstract for Metandem paper published by Hao et al in Analtica Chimica Acta demonstrating the software's utility in isobaric labeling, integrating feature extraction, and metabolite quantification.
20 Nov 2019

Selecting a Labeling Strategy for Quantitative Proteomics of Multiple Samples

By nickwiecien

Buchberger AR et al (2019) recently published a chapter reviewing various labelling strategies for quantitative proteomic analysis in Mass Spectrometry-Based Chemical Proteomics.

Specifically, the chapter reviews strategies such as label-free quantitation, metabolic labeling, and chemical stable isotope labeling, and also discusses which labeling approach is best for various types of proteomic analyses. The chapter also provides an explanation on how to use N,N‐dimethyl alanine (DiAla) and N,N‐ dimethyl valine (DiVal) isobaric labeling strategies for quantitative analyses in ways which are economic and effective.

The chapter states that quantitative proteomics is crucial for biomarker discovery in studying and understanding various diseases and biological research, as proteins are crucial in all biological processes. Because biomarker studies can be time-consuming, heavily reliant on instruments and vary depending on the strategy used, selecting the appropriate labeling strategy is important in quantitative analysis.

A graphical abstract from Buchberger et al (2019) which depicts labeling strategy options for a type of sample. The sample in the picture is a mouse, the labeling strategies include 5-plex iDiLeu, 12-Plex DiLeu, 4-Plex DiLeu, 2-Plex mDiLeu, and 4-Plex DiAla.
06 Nov 2019

Increasing MS lipidomics power through parameter optimization and In Silico Simulation

By nickwiecien

Hutchins et al (2019) recently published a paper in Analytical Chemistry presenting an algorithm which identifies parameter sets in a way that is quicker and more accurate than typical methods.

The issue of effectively profiling the diversity and range of biomolecules is an important one to consider in Mass Spectrometry, and relies on well-sought out selection of acquisition parameters. However, acquisition parameters are generally selected in a way that is time-consuming and tends to produce lacking results.

By creating an algorithm which simulates LC-MS/MS lipidomic data acquisition performance in a benchtop quadrupole-Orbitrap Mass Spectrometer system and pairing it with an algorithm that defines constrained parameter optimization, researchers were able to efficiently identify LC-MS/MS method parameter sets for specific sample matrices. Additionally, researchers used a simulation called in silico to demonstrate how developments in mass spectrometer speed and sensitivity will result in even more effective biomolecule identification.

Graphical abstract from Hutchins et al (2019) which details the parameter optimization and in silico simulation methods. Instrument parameters --> model MS acquisition --> simulate lipid IDs.
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