Structural characterization of suppressor lipids by high-resolution mass spectrometry

Rationale: Suppressor lipids were originally identified in 1993 and reported to encompass six lipid classes that enable Saccharomyces cerevisiae to live without sphingolipids. Structural characterization, using non-mass spectrometric approaches, revealed that these suppressor lipids are very long chain fatty acid (VLCFA)- containing glycerophospholipids with polar head groups that are typically incorporated into sphingolipids. Here we report, for the first time, the structural characterization of the yeast suppressor lipids using high-resolution mass spectrometry.Methods: Suppressor lipids were isolated by preparative chromatography and subjected to structural characterization using hybrid quadrupole time-of-flight and ion trap-orbitrap mass spectrometry.Results: Our investigation recapitulates the overall structural features of the suppressor lipids and provides an in-depth characterization of their fragmentation pathways. Tandem mass analysis identified the positionally defined molecular lipid species phosphatidylinositol (PI) 26:0/16:1, PI mannoside (PIM) 16:0/26:0 and PIM inositol-phosphate (PIMIP) 16:0/26:0 as abundant suppressor lipids. This finding differs from the original study that only inferred the positional isomer PI 16:0/26:0 and prompts new insight into the biosynthesis of suppressor lipids. Moreover, we also report the identification of a novel suppressor lipid featuring an amino sugar residue linked to a VLCFA-containing PI molecule.Conclusions: Fragmentation pathways of yeast suppressor lipids have been delineated. In addition, the fragmentation information has been added to our open source ALEX lipid database to support automated identification and quantitative monitoring of suppressor lipids in yeast and bacteria that produce similar lipid molecules

CoryneRegNet 7, the reference database and analysis platform for corynebacterial gene regulatory networks.

Parise MTD, Parise D, Kato RB, et al. CoryneRegNet 7, the reference database and analysis platform for corynebacterial gene regulatory networks. Scientific data. 2020;7(1): 142.We present the newest version of CoryneRegNet, the reference database for corynebacterial regulatory interactions, available at www.exbio.wzw.tum.de/coryneregnet/. The exponential growth of next-generation sequencing data in recent years has allowed a better understanding of bacterial molecular mechanisms. Transcriptional regulation is one of the most important mechanisms for bacterial adaptation and survival. These mechanisms may be understood via an organism's network of regulatory interactions. Although the Corynebacterium genus is important in medical, veterinary and biotechnological research, little is known concerning the transcriptional regulation of these bacteria. Here, we unravel transcriptional regulatory networks (TRNs) for 224 corynebacterial strains by utilizing genome-scale transfer of TRNs from four model organisms and assigning statistical significance values to all predicted regulations. As a result, the number of corynebacterial strains with TRNs increased twenty times and the back-end and front-end were reimplemented to support new features as well as future database growth. CoryneRegNet 7 is the largest TRN database forthe Corynebacterium genus and aids in elucidating transcriptional mechanisms enabling adaptation, survival and infection