Poor transcript-protein correlation in the brain: negatively correlating gene products reveal neuronal polarity as a potential cause

International audienceTranscription, translation, and turnover of transcripts and proteins are essential for cellular function. The contribution of those factors to protein levels is under debate, as transcript levels and cognate protein levels do not necessarily correlate due to regulation of translation and protein turnover. Here we propose neuronal polarity as a third factor that is particularly evident in the CNS, leading to considerable distances between somata and axon terminals. Consequently, transcript levels may negatively correlate with cognate protein levels in CNS regions, i.e., transcript and protein levels behave reciprocally. To test this hypothesis, we performed an integrative inter‐omics study and analyzed three interconnected rat auditory brainstem regions (cochlear nuclear complex, CN; superior olivary complex, SOC; inferior colliculus, IC) and the rest of the brain as a reference. We obtained transcript and protein sets in these regions of interest (ROIs) by DNA microarrays and label‐free mass spectrometry, and performed principal component and correlation analyses. We found 508 transcript|protein pairs and detected poor to moderate transcript|protein correlation in all ROIs, as evidenced by coefficients of determination from 0.34 to 0.54. We identified 57‐80 negatively correlating gene products in the ROIs and intensively analyzed four of them for which the correlation was poorest. Three cognate proteins (Slc6a11, Syngr1, Tppp) were synaptic and hence candidates for a negative correlation because of protein transport into axon terminals. Thus, we systematically analyzed the negatively correlating gene products. Gene ontology analyses revealed overrepresented transport/synapse‐related proteins, supporting our hypothesis. We present 30 synapse/transport‐related proteins with poor transcript|protein correlation. In conclusion, our analyses support that protein transport in polar cells is a third factor that influences the protein level and, thereby, the transcript|protein correlation

Absolute Quantification of Major Photosynthetic Protein Complexes in Chlamydomonas reinhardtii Using Quantification Concatamers (QconCATs)

For modeling approaches in systems biology, knowledge of the absolute abundances of cellular proteins is essential. One way to gain this knowledge is the use of quantification concatamers (QconCATs), which are synthetic proteins consisting of proteotypic peptides derived from the target proteins to be quantified. The QconCAT protein is labeled with a heavy isotope upon expression in E. coli and known amounts of the purified protein are spiked into a whole cell protein extract. Upon tryptic digestion, labeled and unlabeled peptides are released from the QconCAT protein and the native proteins, respectively, and both are quantified by LC-MS/MS. The labeled Q-peptides then serve as standards for determining the absolute quantity of the native peptides/proteins. Here, we have applied the QconCAT approach to Chlamydomonas reinhardtii for the absolute quantification of the major proteins and protein complexes driving photosynthetic light reactions in the thylakoid membranes and carbon fixation in the pyrenoid. We found that with 25.2 attomol/cell the Rubisco large subunit makes up 6.6% of all proteins in a Chlamydomonas cell and with this exceeds the amount of the small subunit by a factor of 1.56. EPYC1, which links Rubisco to form the pyrenoid, is eight times less abundant than RBCS, and Rubisco activase is 32-times less abundant than RBCS. With 5.2 attomol/cell, photosystem II is the most abundant complex involved in the photosynthetic light reactions, followed by plastocyanin, photosystem I and the cytochrome b6/f complex, which range between 2.9 and 3.5 attomol/cell. The least abundant complex is the ATP synthase with 2 attomol/cell. While applying the QconCAT approach, we have been able to identify many potential pitfalls associated with this technique. We analyze and discuss these pitfalls in detail and provide an optimized workflow for future applications of this technique

Artificial Intelligence Understands Peptide Observability and Assists With Absolute Protein Quantification

Targeted mass spectrometry has become the method of choice to gain absolute quantification information of high quality, which is essential for a quantitative understanding of biological systems. However, the design of absolute protein quantification assays remains challenging due to variations in peptide observability and incomplete knowledge about factors influencing peptide detectability. Here, we present a deep learning algorithm for peptide detectability prediction, d::pPop, which allows the informed selection of synthetic proteotypic peptides for the successful design of targeted proteomics quantification assays. The deep neural network is able to learn a regression model that relates the physicochemical properties of a peptide to its ion intensity detected by mass spectrometry. The approach makes use of experimentally detected deviations from the assumed equimolar abundance of all peptides derived from a given protein. Trained on extensive proteomics datasets, d::pPop's plant and non-plant specific models can predict the quality of proteotypic peptides for not yet experimentally identified proteins. Interrogating the deep neural network after learning from ~76,000 peptides per model organism allows to investigate the impact of different physicochemical properties on the observability of a peptide, thus providing insights into peptide observability as a multifaceted process. Empirical evaluation with rank accuracy metrics showed that our prediction approach outperforms existing algorithms. We circumvent the delicate step of selecting positive and negative training sets and at the same time also more closely reflect the need for selecting the top most promising peptides for targeting a protein of interest. Further, we used an artificial QconCAT protein to experimentally validate the observability prediction. Our proteotypic peptide prediction approach not only facilitates the design of absolute protein quantification assays via a user-friendly web interface but also enables the selection of proteotypic peptides for not yet observed proteins, hence rendering the tool especially useful for plant research

Identification of small RNAs during cold acclimation in Arabidopsis thaliana

Background: Cold stress causes dynamic changes in gene expression that are partially caused by small non-coding RNAs since they regulate protein coding transcripts and act in epigenetic gene silencing pathways. Thus, a detailed analysis of transcriptional changes of small RNAs (sRNAs) belonging to all known sRNA classes such as microRNAs (miRNA) and small interfering RNA (siRNAs) in response to cold contributes to an understanding of cold-related transcriptome changes. Result: We subjected A. thaliana plants to cold acclimation conditions (4 °C) and analyzed the sRNA transcriptomes after 3 h, 6 h and 2 d. We found 93 cold responsive differentially expressed miRNAs and only 14 of these were previously shown to be cold responsive. We performed miRNA target prediction for all differentially expressed miRNAs and a GO analysis revealed the overrepresentation of miRNA-targeted transcripts that code for proteins acting in transcriptional regulation. We also identified a large number of differentially expressed cis- and trans-nat-siRNAs, as well as sRNAs that are derived from long non-coding RNAs. By combining the results of sRNA and mRNA profiling with miRNA target predictions and publicly available information on transcription factors, we reconstructed a cold-specific, miRNA and transcription factor dependent gene regulatory network. We verified the validity of links in the network by testing its ability to predict target gene expression under cold acclimation. Conclusion: In A. thaliana, miRNAs and sRNAs derived from cis- and trans-NAT gene pairs and sRNAs derived from lncRNAs play an important role in regulating gene expression in cold acclimation conditions. This study provides a fundamental database to deepen our knowledge and understanding of regulatory networks in cold acclimation

DataPLANT – Ein NFDI-Konsortium der Pflanzen-Grundlagenforschung

In der modernen hypothesen-basierten Forschung sind Forschende zwingend auf Dienste und Infrastrukturen für Forschungsdatenmanagement (FDM) angewiesen, welche die Erfassung, die Verarbeitung, den Austausch und die Archivierung von Forschungsdatensätzen erleichtern. Dabei schafft ein modernes FDM erst die Verknüpfung von interdisziplinärer Expertise, sowie Vergleich und Integration verschiedener Analyseergebnisse mit dem darauf beruhenden immensen zusätzlichen Erkenntnisgewinn. Das Ziel des Projektes DataPLANT[1] besteht darin, diesen Mehrwert für die Pflanzen-Grundlagenforschung zu schaffen. Auf diesem Fachgebiet werden die (molekularen) Prinzipien des pflanzlichen Lebens untersucht, welche beispielsweise das Pflanzenwachstum, den Ernteertrag oder die Biomasseproduktion bestimmen. Die hierzu eingesetzten Methoden von Transkriptomik, Proteomik und Metabolomik bis hin zu bildgebenden Verfahren erzeugen hochdimensionale, polymorphe Daten, die verarbeitet, fusioniert und interpretiert werden müssen. Eine erfolgreiche Nutzung von Daten unterschiedlicher Modalitäten – aus vielen Quellen und Experimenten, vorverarbeitet oder analysiert mit einer Vielzahl von Algorithmen – erfordert eine Kontextualisierung der Daten. Hierzu zählt die Annotation mit detaillierten Metadaten ebenso wie ein eindeutiges Referenzieren der jeweiligen Daten mit ihren Abhängigkeiten. Die FAIR Data[2] and Linked Open Data[3]-Prinzipien bieten entscheidende Richtlinien für den verantwortungsvollen Umgang mit Forschungsdaten.   [1] Homepage und Community-Portal von DataPLANT, https://www.nfdi4plants.de, aufgerufen am 31.01.2021. [2] Vgl. Wilkinson, Mark D. et al. „The FAIR Guiding Principles for scientific data management and stewardship“. Scientific Data 3, Nr. 160018 (2016): 1-9. https://doi.org/10.1038/sdata.2016.18 [3] Vgl. Bizer, Christian et al. „Linked Data on the web (LDOW2008).” Proceedings of the 17th international conference on World Wide Web, (2008): 1265-1266. https://doi.org/10.1145/1367497.1367760 rschungsdaten

A longer isoform of Stim1 is a negative SOCE regulator but increases cAMP-modulated NFAT signaling

Alternative splicing is a potent modifier of protein function. Stro mal interaction molecule 1 (Stim1) is the essential activator of store-operated Ca2+ entry (SOCE) triggering activation of transcrip tion factors. Here, we characterize Stim1A, a splice variant with an additional 31 amino acid domain inserted in frame within its cytosolic domain. Prominent expression of exon A is found in astro cytes, heart, kidney, and testes. Full-length Stim1A functions as a dominant-negative regulator of SOCE and ICRAC, facilitating sequence-specific fast calcium-dependent inactivation and desta bilizing gating of Orai channels. Downregulation or absence of native Stim1A results in increased SOCE. Despite reducing SOCE, Stim1A leads to increased NFAT translocation. Differential proteo mics revealed an interference of Stim1A with the cAMP-SOCE crosstalk by altered modulation of phosphodiesterase 8 (PDE8), resulting in reduced cAMP degradation and increased PIP5K activ ity, facilitating NFAT activation. Our study uncovers a hitherto unknown mechanism regulating NFAT activation and indicates that cell-type-specific splicing of Stim1 is a potent means to regu late the NFAT signalosome and cAMP-SOCE crosstalk

A repeat protein links Rubisco to form the eukaryotic carbon-concentrating organelle.

Biological carbon fixation is a key step in the global carbon cycle that regulates the atmosphere's composition while producing the food we eat and the fuels we burn. Approximately one-third of global carbon fixation occurs in an overlooked algal organelle called the pyrenoid. The pyrenoid contains the CO2-fixing enzyme Rubisco and enhances carbon fixation by supplying Rubisco with a high concentration of CO2 Since the discovery of the pyrenoid more that 130 y ago, the molecular structure and biogenesis of this ecologically fundamental organelle have remained enigmatic. Here we use the model green alga Chlamydomonas reinhardtii to discover that a low-complexity repeat protein, Essential Pyrenoid Component 1 (EPYC1), links Rubisco to form the pyrenoid. We find that EPYC1 is of comparable abundance to Rubisco and colocalizes with Rubisco throughout the pyrenoid. We show that EPYC1 is essential for normal pyrenoid size, number, morphology, Rubisco content, and efficient carbon fixation at low CO2 We explain the central role of EPYC1 in pyrenoid biogenesis by the finding that EPYC1 binds Rubisco to form the pyrenoid matrix. We propose two models in which EPYC1's four repeats could produce the observed lattice arrangement of Rubisco in the Chlamydomonas pyrenoid. Our results suggest a surprisingly simple molecular mechanism for how Rubisco can be packaged to form the pyrenoid matrix, potentially explaining how Rubisco packaging into a pyrenoid could have evolved across a broad range of photosynthetic eukaryotes through convergent evolution. In addition, our findings represent a key step toward engineering a pyrenoid into crops to enhance their carbon fixation efficiency

Acclimation in plants – the Green Hub consortium

Acclimation is the capacity to adapt to environmental changes within the lifetime of an individual. This ability allows plants to cope with the continuous variation in ambient conditions to which they are exposed as sessile organisms. Because environmental changes and extremes are becoming even more pronounced due to the current period of climate change, enhancing the efficacy of plant acclimation is a promising strategy for mitigating the consequences of global warming on crop yields. At the cellular level, the chloroplast plays a central role in many acclimation responses, acting both as a sensor of environmental change and as a target of cellular acclimation responses. In this Perspective article, we outline the activities of the Green Hub consortium funded by the German Science Foundation. The main aim of this research collaboration is to understand and strategically modify the cellular networks that mediate plant acclimation to adverse environments, employing Arabidopsis, tobacco (Nicotiana tabacum) and Chlamydomonas as model organisms. These efforts will contribute to ‘smart breeding’ methods designed to create crop plants with improved acclimation properties. To this end, the model oilseed crop Camelina sativa is being used to test modulators of acclimation for their potential to enhance crop yield under adverse environmental conditions. Here we highlight the current state of research on the role of gene expression, metabolism and signalling in acclimation, with a focus on chloroplast‐related processes. In addition, further approaches to uncovering acclimation mechanisms derived from systems and computational biology, as well as adaptive laboratory evolution with photosynthetic microbes, are highlighted.Deutsche Forschungsgemeinschaft http://dx.doi.org/10.13039/501100001659Peer Reviewe

CSBiology/TempClass: Release 0.0.1

<h4>Nuget</h4><ul><li>This release it available on <a href="https://www.nuget.org/packages/TempClass">nuget.org</a></li></ul><h4>Release Notes</h4><p>0.0.1 (Released 2023-10-25)</p><ul><li>Additions:<ul><li>Initial release</li></ul></li></ul&gt