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

Molecular and functional profiling of cell diversity and identity in the lateral superior olive, an auditory brainstem center with ascending and descending projections

The lateral superior olive (LSO), a prominent integration center in the auditory brainstem, contains a remarkably heterogeneous population of neurons. Ascending neurons, predominantly principal neurons (pLSOs), process interaural level differences for sound localization. Descending neurons (lateral olivocochlear neurons, LOCs) provide feedback into the cochlea and are thought to protect against acoustic overload. The molecular determinants of the neuronal diversity in the LSO are largely unknown. Here, we used patch-seq analysis in mice at postnatal days P10-12 to classify developing LSO neurons according to their functional and molecular profiles. Across the entire sample (n = 86 neurons), genes involved in ATP synthesis were particularly highly expressed, confirming the energy expenditure of auditory neurons. Two clusters were identified, pLSOs and LOCs. They were distinguished by 353 differentially expressed genes (DEGs), most of which were novel for the LSO. Electrophysiological analysis confirmed the transcriptomic clustering. We focused on genes affecting neuronal input–output properties and validated some of them by immunohistochemistry, electrophysiology, and pharmacology. These genes encode proteins such as osteopontin, Kv11.3, and Kvβ3 (pLSO-specific), calcitonin-gene-related peptide (LOC-specific), or Kv7.2 and Kv7.3 (no DEGs). We identified 12 “Super DEGs” and 12 genes showing “Cluster similarity.” Collectively, we provide fundamental and comprehensive insights into the molecular composition of individual ascending and descending neurons in the juvenile auditory brainstem and how this may relate to their specific functions, including developmental aspects

Outlier detection at the transcriptome-proteome interface

BACKGROUND:In high-throughput experimental biology, it is widely acknowledged that while expression levels measured at the levels of transcriptome and the corresponding proteome do not, in general, correlate well, messenger RNA levels are used as convenient proxies for protein levels. Our interest is in developing data-driven computational models that can bridge the gap between these two levels of measurement at which different mechanisms of regulation may act on different molecular species causing any observed lack of correlations. To this end, we build data-driven predictors of protein levels using mRNA levels and known proxies of translation efficiencies as covariates. Previous work showed that in such a setting, outliers with respect to the model are reliable candidates for post-translational regulation.RESULTS:Here, we present and compare two novel formulations of deriving a protein concentration predictor from which outliers may be extracted in a systematic manner. The first approach, outlier rejecting regression, allows explicit specification of a certain fraction of the data as outliers. In a regression setting, this is a non-convex optimization problem which we solve by deriving a difference of convex functions algorithm (DCA). With post-translationally regulated proteins, one expects their concentrations to be affected primarily by disruption of protein stability. Our second algorithm exploits this observation by minimizing an asymmetric loss using quantile regression and extracts outlier proteins whose measured concentrations are lower than what a genome-wide regression would predict. We validate the two approaches on a dataset of yeast transcriptome and proteome. Functional annotation check on detected outliers demonstrate that the methods are able to identify post-translationally regulated genes with high statistical confidence