Biology subjects

Ritter, K.

Publications and source records attributed to Ritter, K..

3 recordsLinked to original sources

bioRxiv2022

Capillary electrophoresis mass spectrometry identifies new isomers of inositol pyrophosphates in mammalian tissues

Technical challenges have to date prevented a complete profiling of the levels of myo-inositol phosphates (InsPs) and pyrophosphates (PP-InsPs) in mammalian tissues. Here, we have deployed capillary electrophoresis mass spectrometry to identify and record the levels of InsPs and PP-InsPs in several tissues obtained from wild type mice and a newly-created PPIP5K2 knockout strain. We observe that the mouse colon harbours unusually high levels of InsPs and PP-InsPs. Additionally, the PP-InsP profile is considerably more complex than previously reported for animal cells: using chemically synthesized internal stable isotope references, and high-resolution mass spectra, we characterize two new PP-InsP isomers as 4/6-PP-InsP5 and 2-PP-InsP5. The latter has not previously been described in Nature. Analysis of feces and the commercial mouse diet suggest the latter is one potential source of noncanonical isomers in the colon. However, we also identify both molecules in the heart, indicating unknown synthesis pathways in mammals. We also demonstrate that the CE-MS method is sensitive enough to measure PP-InsPs from patient samples such as colon biopsies and peripheral blood mononuclear cells (PBMCs). Strikingly, PBMCs also contain 4/6-PP-InsP5 and 2-PP-InsP5. In summary, our study substantially expands PP-InsP biology in mammals.

biochemistry↗

bioRxiv2022

Arabidopsis PFA-DSP-type phosphohydrolases target specific inositol pyrophosphate messengers

Inositol pyrophosphates are signaling molecules containing at least one phosphoanhydride bond that regulate a wide range of cellular processes in eukaryotes. With a cyclic array of phosphate esters and diphosphate groups around myo-inositol, these molecular messengers possess the highest charge density found in nature. Recent work deciphering inositol pyrophosphate biosynthesis in Arabidopsis revealed important functions of these messengers in nutrient sensing, hormone signaling and plant immunity. However, despite the rapid hydrolysis of these molecules in plant extracts, very little is known about the molecular identity of the phosphohydrolases that convert these messengers back to their inositol polyphosphate precursors. Here, we investigate whether Arabidopsis Plant and Fungi Atypical Dual Specificity Phosphatases (PFA-DSP1-5) catalyze inositol pyrophosphate phosphohydrolase activity. We find that recombinant proteins of all five Arabidopsis PFA-DSP homologs display phosphohydrolase activity with a high specificity for the 5-{beta}-phosphate of inositol pyrophosphates. We further show that heterologous expression of Arabidopsis PFA-DSP1-5 rescues wortmannin-sensitivity and deranged inositol pyrophosphate homeostasis caused by the deficiency of the PFA-DSP-type inositol pyrophosphate phosphohydrolase Siw14 in yeast. Heterologous expression in Nicotiana benthamiana leaves provided evidence that Arabidopsis PFA-DSP1 also displays 5-{beta}-phosphate specific inositol pyrophosphate phosphohydrolase activity in planta. Our findings lay the biochemical basis and provide the genetic tools to uncover the roles of inositol pyrophosphates in plant physiology and plant development.

plant biology↗

bioRxiv2022

Performance reserves in brain-imaging-based phenotype prediction

Machine learning studies have shown that various phenotypes can be predicted from structural and functional brain images. However, in most such studies, prediction performance ranged from moderate to disappointing. It is unclear whether prediction performance will substantially improve with larger sample sizes or whether insufficient predictive information in brain images impedes further progress. Here, we systematically assess the effect of sample size on prediction performance using sample sizes far beyond what is possible in common neuroimaging studies. We project 3-9 fold improvements in prediction performance for behavioral and mental health phenotypes when moving from one thousand to one million samples. Moreover, we find that moving from single imaging modalities to multimodal input data can lead to further improvements in prediction performance, often on par with doubling the sample size. Our analyses reveal considerable performance reserves for neuroimaging-based phenotype prediction. Machine learning models may benefit much more from extremely large neuroimaging datasets than currently believed.

neuroscience↗