Comparative Analysis of Cell Mixtures Deconvolution and Gene Signatures Generated for Blood, Immune and Cancer Cells.

In the last two decades, many detailed full transcriptomic studies on complex biological samples have been published and included in large gene expression repositories. These studies primarily provide a bulk expression signal for each sample, including multiple cell-types mixed within the global signal. The cellular heterogeneity in these mixtures does not allow the activity of specific genes in specific cell types to be identified. Therefore, inferring relative cellular composition is a very powerful tool to achieve a more accurate molecular profiling of complex biological samples. In recent decades, computational techniques have been developed to solve this problem by applying deconvolution methods, designed to decompose cell mixtures into their cellular components and calculate the relative proportions of these elements. Some of them only calculate the cell proportions (supervised methods), while other deconvolution algorithms can also identify the gene signatures specific for each cell type (unsupervised methods). In these work, five deconvolution methods (CIBERSORT, FARDEEP, DECONICA, LINSEED and ABIS) were implemented and used to analyze blood and immune cells, and also cancer cells, in complex mixture samples (using three bulk expression datasets). Our study provides three analytical tools (corrplots, cell-signature plots and bar-mixture plots) that allow a thorough comparative analysis of the cell mixture data. The work indicates that CIBERSORT is a robust method optimized for the identification of immune cell-types, but not as efficient in the identification of cancer cells. We also found that LINSEED is a very powerful unsupervised method that provides precise and specific gene signatures for each of the main immune cell types tested: neutrophils and monocytes (of the myeloid lineage), B-cells, NK cells and T-cells (of the lymphoid lineage), and also for cancer cells.

Integrated in silico MS-based phosphoproteomics and network enrichment analysis of RASopathy proteins.

BACKGROUND: RASopathies are a group of syndromes showing clinical overlap caused by mutations in genes affecting the RAS-MAPK pathway. Consequent disruption on cellular signaling leads and is driven by phosphoproteome remodeling. However, we still lack a comprehensive picture of the different key players and altered downstream effectors. METHODS: An in silico interactome of RASopathy proteins was generated using pathway enrichment analysis/STRING tool, including identification of main hub proteins. We also integrated phosphoproteomic and immunoblotting studies using previous published information on RASopathy proteins and their neighbors in the context of RASopathy syndromes. Data from Phosphosite database ( www.phosphosite.org ) was collected in order to obtain the potential phosphosites subjected to regulation in the 27 causative RASopathy proteins. We compiled a dataset of dysregulated phosphosites in RASopathies, searched for commonalities between syndromes in harmonized data, and analyzed the role of phosphorylation in the syndromes by the identification of key players between the causative RASopathy proteins and the associated interactome. RESULTS: In this study, we provide a curated data set of 27 causative RASopathy genes, identify up to 511 protein-protein associations using pathway enrichment analysis/STRING tool, and identify 12 nodes as main hub proteins. We found that a large group of proteins contain tyrosine residues and their biological processes include but are not limited to the nervous system. Harmonizing published RASopathy phosphoproteomic and immunoblotting studies we identified a total of 147 phosphosites with increased phosphorylation, whereas 47 have reduced phosphorylation. The PKB signaling pathway is the most represented among the dysregulated phosphoproteins within the RASopathy proteins and their neighbors, followed by phosphoproteins implicated in the regulation of cell proliferation and the MAPK pathway. CONCLUSIONS: This work illustrates the complex network underlying the RASopathies and the potential of phosphoproteomics for dissecting the molecular mechanisms in these syndromes. A combined study of associated genes, their interactome and phosphorylation events in RASopathies, elucidates key players and mechanisms to direct future research, diagnosis and therapeutic windows.

Transcriptomic landscape, gene signatures and regulatory profile of aging in the human brain.

The molecular characteristics of aging that lead to increased disease susceptibility remain poorly understood. Here we present a transcriptomic profile of the human brain associated with age and aging, derived from a systematic integrative analysis of four independent cohorts of genome-wide expression data from 2202 brain samples (cortex, hippocampus and cerebellum) of individuals of different ages (from young infants, 5-10 years old, to elderly people, up to 100 years old) categorized in age stages by decades. The study provides a signature of 1148 genes detected in cortex, 874 genes in hippocampus and 657 genes in cerebellum, that present significant differential expression changes with age according to a robust gamma rank correlation profiling. The signatures show a significant large overlap of 258 genes between cortex and hippocampus, and 63 common genes between the three brain regions. Focusing on cortex, functional enrichment analysis and cell-type analysis provided biological insight about the aging signature. Response to stress and immune response were up-regulated functions. Synapse, neurotransmission and calcium signaling were down-regulated functions. Cell analysis, derived from single-cell data, disclosed an increase of neuronal activity in the young stages of life and a decline of such activity in the old stages. A regulatory analysis identified the transcription factors (TF) associated with the signature of 258 genes, common to cortex and hippocampus; revealing the role of MEF2(A,D), PDX1, FOSL(1,2) and RFX(5,1) as candidate regulators of the signature. Finally, a deep-learning neural network algorithm was used to build a biological age predictor based on the aging signature. This article is part of a Special Issue entitled: Transcriptional Profiles and Regulatory Gene Networks edited by Dr. Federico Manuel Giorgi and Dr. Shaun Mahony.

Proteomic and Transcriptomic Profiling Identifies Early Developmentally Regulated Proteins in Dictyostelium Discoideum.

Cyclic AMP acts as a secondary messenger involving different cellular functions in eukaryotes. Here, proteomic and transcriptomic profiling has been combined to identify novel early developmentally regulated proteins in eukaryote cells. These proteomic and transcriptomic experiments were performed in Dictyostelium discoideum given the unique advantages that this organism offers as a eukaryotic model for cell motility and as a nonmammalian model of human disease. By comparing whole-cell proteome analysis of developed (cAMP-pulsed) wild-type AX2 cells and an independent transcriptomic analysis of developed wild-type AX4 cells, our results show that up to 70% of the identified proteins overlap in the two independent studies. Among them, we have found 26 proteins previously related to cAMP signaling and identified 110 novel proteins involved in calcium signaling, adhesion, actin cytoskeleton, the ubiquitin-proteasome pathway, metabolism, and proteins that previously lacked any annotation. Our study validates previous findings, mostly for the canonical cAMP-pathway, and also generates further insight into the complexity of the transcriptomic changes during early development. This article also compares proteomic data between parental and cells lacking glkA, a GSK-3 kinase implicated in substrate adhesion and chemotaxis in Dictyostelium. This analysis reveals a set of proteins that show differences in expression in the two strains as well as overlapping protein level changes independent of GlkA.