Evidence for enzymatic backbone methylation of the main membrane lipids in the archaeon Methanomassiliicoccus luminyensis

2021 ◽  
Author(s):  
Sarah Coffinet ◽  
Lukas Mühlena ◽  
Julius S. Lipp ◽  
Micha Weil ◽  
Cajetan Neubauer ◽  
...  
Keyword(s):  
Mediterranean Sea ◽  
Isotope Labeling ◽  
Membrane Lipids ◽  
Physiological Role ◽  
Mass Spectrometry Analysis ◽  
Radical Mechanism ◽  
Glycerol Backbone ◽  
Spectrometry Analysis ◽  
Methylation Index ◽  
The Mediterranean

Butanetriol and pentanetriol dibiphytanyl glycerol tetraethers (BDGTs and PDGTs, respectively) are recently identified classes of archaeal membrane lipids that are prominent constituents in anoxic subseafloor sediments. These lipids are intriguing as they possess unusual backbones with four or five carbon atoms instead of the canonical three-carbon glycerol backbone. In this study, we examined the biosynthesis of BDGTs and PDGTs by the methanogen Methanomassiliicoccus luminyensis , the only available isolate known to produce these compounds, via stable isotope labeling with [ methyl - 13 C] methionine followed by mass spectrometry analysis. We show that their biosynthesis proceeds from transfer(s) of the terminal methyl group of methionine to the more common archaeal membrane lipids, i.e., glycerol dibiphytanyl glycerol tetraethers (GDGTs). As this methylation targets a methylene group, a radical mechanism involving a radical S-adenosylmethionine (SAM) enzyme is probable. Over the course of the incubation, the abundance of PDGTs relative to BDGTs, expressed as backbone methylation index, increased, implying that backbone methylation may be related to the growth shift to stationary conditions, possibly due to limited energy and/or substrate availability. The increase of the backbone methylation index with increasing sediment age in a sample set from the Mediterranean Sea adds support for such a relationship. Importance Butanetriol and pentanetriol dibiphytanyl glycerol tetraethers are membrane lipids recently discovered in anoxic environments. These lipids differ from typical membrane-spanning tetraether lipids because they possess a non-glycerol backbone. The biosynthetic pathway and physiological role of these unique lipids are currently unknown. Here, we show that in the strain Methanomassiliicoccus luminyensis these lipids are the result of methyl transfer(s) from a S-adenosyl methionine (SAM) intermediate. We observed a relative increase of the doubly methylated compound, pentanetriol dibiphytanyl glycerol tetraether, in the stationary phase of M. luminyensis as well as in the subseafloor of the Mediterranean Sea and thus introduced a backbone methylation index, which could be used to further explore microbial activity in natural settings.

Phosphorylation of BACH1 switches its function from transcription factor to mitotic chromosome regulator and promotes its interaction with HMMR

2018 ◽  
Vol 475 (5) ◽  
pp. 981-1002 ◽  
Author(s):  
Jie Li ◽  
Hiroki Shima ◽  
Hironari Nishizawa ◽  
Masatoshi Ikeda ◽  
Andrey Brydun ◽  
...  
Keyword(s):  
Mitotic Spindle ◽  
Isotope Labeling ◽  
Binding Activity ◽  
Mass Spectrometry Analysis ◽  
Spindle Orientation ◽  
Mitotic Spindles ◽  
Spectrometry Analysis ◽  
Dna Binding Activity ◽  
Ejection Force ◽  
M Phase

The transcription repressor BACH1 performs mutually independent dual roles in transcription regulation and chromosome alignment during mitosis by supporting polar ejection force of mitotic spindle. We now found that the mitotic spindles became oblique relative to the adhesion surface following endogenous BACH1 depletion in HeLa cells. This spindle orientation rearrangement was rescued by re-expression of BACH1 depending on its interactions with HMMR and CRM1, both of which are required for the positioning of mitotic spindle, but independently of its DNA-binding activity. A mass spectrometry analysis of BACH1 complexes in interphase and M phase revealed that BACH1 lost during mitosis interactions with proteins involved in chromatin and gene expression but retained interactions with HMMR and its known partners including CHICA. By analyzing BACH1 modification using stable isotope labeling with amino acids in cell culture, mitosis-specific phosphorylations of BACH1 were observed, and mutations of these residues abolished the activity of BACH1 to restore mitotic spindle orientation in knockdown cells and to interact with HMMR. Detailed histological analysis of Bach1-deficient mice revealed lengthening of the epithelial fold structures of the intestine. These observations suggest that BACH1 performs stabilization of mitotic spindle orientation together with HMMR and CRM1 in mitosis, and that the cell cycle-specific phosphorylation switches the transcriptional and mitotic functions of BACH1.

scholarly journals Microdomain protein Nce102 is a local sensor of plasma membrane sphingolipid balance

2021 ◽  
Author(s):  
Jakub Zahumensky ◽  
Caroline Mota Fernandes ◽  
Petra Vesela ◽  
Maurizio Del Poeta ◽  
James Bernard Konopka ◽  
...  
Keyword(s):  
Plasma Membrane ◽  
Physiological Role ◽  
Building Blocks ◽  
Mass Spectrometry Analysis ◽  
Spectrometry Analysis ◽  
Signalling Molecules ◽  
Human Fungal Pathogen ◽  
Sphingolipid Biosynthesis ◽  
Complex Sphingolipids

Sphingolipids are essential building blocks of eukaryotic membranes and important signalling molecules, tightly regulated in response to environmental and physiological inputs. Mechanism of sphingolipid level perception at the plasma membrane remains unclear. In Saccharomyces cerevisiae, Nce102 protein has been proposed to function as sphingolipid sensor as it changes its plasma membrane distribution in response to sphingolipid biosynthesis inhibition. We show that Nce102 redistributes specifically in regions of increased sphingolipid demand, e.g., membranes of nascent buds. Furthermore, we report that production of Nce102 increases following sphingolipid biosynthesis inhibition and Nce102 is internalized when excess sphingolipid precursors are supplied. This suggests that the total amount of Nce102 in the plasma membrane is a measure of the current need for sphingolipids, whereas its local distribution marks sites of high sphingolipid demand. Physiological role of Nce102 in regulation of sphingolipid synthesis is demonstrated by mass spectrometry analysis showing reduced levels of complex sphingolipids and long-chain bases in nce102? deletion mutant. Nce102 behaves analogously in human fungal pathogen Candida albicans, suggesting a conserved principle of local sphingolipid control across species.

scholarly journals Harnessing Machine Learning To Unravel Protein Degradation in Escherichia coli

mSystems ◽  
2021 ◽  
Vol 6 (1) ◽  
Author(s):  
Natan Nagar ◽  
Noa Ecker ◽  
Gil Loewenthal ◽  
Oren Avram ◽  
Daniella Ben-Meir ◽  
...  
Keyword(s):  
Machine Learning ◽  
Escherichia Coli ◽  
Protein Degradation ◽  
Isotope Labeling ◽  
Characteristic Curve ◽  
Mass Spectrometry Analysis ◽  
Antibacterial Drug ◽  
Content Type ◽  
Spectrometry Analysis ◽  
E Coli

ABSTRACT Degradation of intracellular proteins in Gram-negative bacteria regulates various cellular processes and serves as a quality control mechanism by eliminating damaged proteins. To understand what causes the proteolytic machinery of the cell to degrade some proteins while sparing others, we employed a quantitative pulsed-SILAC (stable isotope labeling with amino acids in cell culture) method followed by mass spectrometry analysis to determine the half-lives for the proteome of exponentially growing Escherichia coli, under standard conditions. We developed a likelihood-based statistical test to find actively degraded proteins and identified dozens of fast-degrading novel proteins. Finally, we used structural, physicochemical, and protein-protein interaction network descriptors to train a machine learning classifier to discriminate fast-degrading proteins from the rest of the proteome, achieving an area under the receiver operating characteristic curve (AUC) of 0.72. IMPORTANCE Bacteria use protein degradation to control proliferation, dispose of misfolded proteins, and adapt to physiological and environmental shifts, but the factors that dictate which proteins are prone to degradation are mostly unknown. In this study, we have used a combined computational-experimental approach to explore protein degradation in E. coli. We discovered that the proteome of E. coli is composed of three protein populations that are distinct in terms of stability and functionality, and we show that fast-degrading proteins can be identified using a combination of various protein properties. Our findings expand the understanding of protein degradation in bacteria and have implications for protein engineering. Moreover, as rapidly degraded proteins may play an important role in pathogenesis, our findings may help to identify new potential antibacterial drug targets.

Comprehensive Profiling of Fecal Metabolome of Mice by Integrated Chemical Isotope Labeling-Mass Spectrometry Analysis

2018 ◽  
Vol 90 (5) ◽  
pp. 3512-3520 ◽  
Author(s):  
Bi-Feng Yuan ◽  
Quan-Fei Zhu ◽  
Ning Guo ◽  
Shu-Jian Zheng ◽  
Ya-Lan Wang ◽  
...  
Keyword(s):  
Mass Spectrometry ◽  
Isotope Labeling ◽  
Mass Spectrometry Analysis ◽  
Spectrometry Analysis ◽  
Chemical Isotope Labeling

scholarly journals Harnessing machine learning to unravel protein degradation in Escherichia coli

2020 ◽  
Author(s):  
Natan Nagar ◽  
Noa Ecker ◽  
Gil Loewenthal ◽  
Oren Avram ◽  
Daniella Ben-Meir ◽  
...  
Keyword(s):  
Machine Learning ◽  
Escherichia Coli ◽  
Drug Targets ◽  
Isotope Labeling ◽  
Interaction Network ◽  
Culture Method ◽  
Mass Spectrometry Analysis ◽  
Antibacterial Drug ◽  
Spectrometry Analysis ◽  
Quality Control Mechanism

AbstractDegradation of intracellular proteins in Gram-negative bacteria regulates various cellular processes and serves as a quality control mechanism by eliminating damaged proteins. To understand what causes the proteolytic machinery of the cell to degrade some proteins while sparing others, we employed a quantitative pulsed-SILAC (Stable Isotope Labeling with Amino acids in Cell culture) method followed by mass spectrometry analysis to determine the half-lives for the proteome of exponentially growing Escherichia coli, under standard conditions. We developed a likelihood-based statistical test to find actively degraded proteins, and identified dozens of novel proteins that are fast-degrading. Finally, we used structural, physicochemical and protein-protein interaction network descriptors to train a machine-learning classifier to discriminate fast-degrading proteins from the rest of the proteome. Our combined computational-experimental approach provides means for proteomic-based discovery of fast degrading proteins in bacteria and the elucidation of the factors determining protein half-lives and have implications for protein engineering. Moreover, as rapidly degraded proteins may play an important role in pathogenesis, our findings could identify new potential antibacterial drug targets.

Sign in / Sign up

Export Citation Format

Share Document