A practical guide to interpreting and generating bottom-up proteomics data visualizations

Mass-spectrometry based bottom-up proteomics is the main method to analyze proteomes comprehensively and the rapid evolution of instrumentation and data analysis has made the technology widely available. Data visualization is an integral part of the analysis process and it is crucial for the communication of results. This is a major challenge due to the immense complexity of MS data. In this review, we provide an overview of commonly used visualizations, starting with raw data of traditional and novel MS technologies, then basic peptide and protein level analyses, and finally visualization of highly complex datasets and networks. We specifically provide guidance on how to critically interpret and discuss the multitude of different proteomics data visualizations. Furthermore, we highlight Python-based libraries and other open science tools that can be applied for independent and transparent generation of customized visualizations. To further encourage programmatic data visualization, we provide the Python code used to generate all data figures in this review on GitHub ().DATA AVAILABILITY STATEMENT Proteomics data from the following ProteomeExchange repositories were reused to generate Figures in this study: PXD012867, PXD017703, PXD010697, PXD010103

Phosphoproteome profiling uncovers a key role for CDKs in TNF signaling

Tumor necrosis factor (TNF) has various effects on phosphorylation-mediated cellular signaling. Combining phosphoproteomics, subcellular localization analyses and kinase inhibitor assays, the authors provide systems level insights into TNF signaling and identify modulators of TNF-induced cell death. Tumor necrosis factor (TNF) is one of the few cytokines successfully targeted by therapies against inflammatory diseases. However, blocking this well studied and pleiotropic ligand can cause dramatic side-effects. Here, we reason that a systems-level proteomic analysis of TNF signaling could dissect its diverse functions and offer a base for developing more targeted therapies. Therefore, we combine phosphoproteomics time course experiments with subcellular localization and kinase inhibitor analysis to identify functional modules of protein phosphorylation. The majority of regulated phosphorylation events can be assigned to an upstream kinase by inhibiting master kinases. Spatial proteomics reveals phosphorylation-dependent translocations of hundreds of proteins upon TNF stimulation. Phosphoproteome analysis of TNF-induced apoptosis and necroptosis uncovers a key role for transcriptional cyclin-dependent kinase activity to promote cytokine production and prevent excessive cell death downstream of the TNF signaling receptor. This resource of TNF-induced pathways and sites can be explored at

Oxygen adsorption on the Ru (10 bar 1 0) surface: Anomalous coverage dependence

Oxygen adsorption onto Ru (10 bar 1 0) results in the formation of two ordered overlayers, i.e. a c(2 times 4)-2O and a (2 times 1)pg-2O phase, which were analyzed by low-energy electron diffraction (LEED) and density functional theory (DFT) calculation. In addition, the vibrational properties of these overlayers were studied by high-resolution electron loss spectroscopy. In both phases, oxygen occupies the threefold coordinated hcp site along the densely packed rows on an otherwise unreconstructed surface, i.e. the O atoms are attached to two atoms in the first Ru layer Ru(1) and to one Ru atom in the second layer Ru(2), forming zigzag chains along the troughs. While in the low-coverage c(2 times 4)-O phase, the bond lengths of O to Ru(1) and Ru(2) are 2.08 A and 2.03 A, respectively, corresponding bond lengths in the high-coverage (2 times 1)-2O phase are 2.01 A and 2.04 A (LEED). Although the adsorption energy decreases by 220 meV with O coverage (DFT calculations), we observe experimentally a shortening of the Ru(1)-O bond length with O coverage. This effect could not be reconciled with the present DFT-GGA calculations. The nu(Ru-O) stretch mode is found at 67 meV [c(2 times 4)-2O] and 64 meV [(2 times 1)pg-2O].Comment: 10 pages, figures are available as hardcopies on request by mailing [email protected], submitted to Phys. Rev. B (8. Aug. 97), other related publications can be found at http://www.rz-berlin.mpg.de/th/paper.htm

Detection of a novel human coronavirus by real-time reverse-transcription polymerase chain reaction

We present two real-time reverse-transcription polymerase chain reaction assays for a novel human coronavirus (CoV), targeting regions upstream of the E gene (upE) or within open reading frame (ORF)1b, respectively. Sensitivity for upE is 3.4 copies per reaction (95% confidence interval (CI): 2.5-6.9 copies) or 291 copies/mL of sample. No cross-reactivity was observed with coronaviruses OC43, NL63, 229E, SARS-CoV, nor with 92 clinical specimens containing common human respiratory viruses. We recommend using upE for screening and ORF1b for confirmation

Connectivity-based parcellation of the human frontal polar cortex

The frontal pole corresponds to Brodmann area (BA) 10, the largest single architectonic area in the human frontal lobe. Generally, BA10 is thought to contain two or three subregions that subserve broad functions such as multitasking, social cognition, attention, and episodic memory. However, there is a substantial debate about the functional and structural heterogeneity of this large frontal region. Previous connectivity-based parcellation studies have identified two or three subregions in the human frontal pole. Here, we used diffusion tensor imaging to assess structural connectivity of BA10 in 35 healthy subjects and delineated subregions based on this connectivity. This allowed us to determine the correspondence of structurally based subregions with the scheme previously defined functionally. Three subregions could be defined in each subject. However, these three subregions were not spatially consistent between subjects. Therefore, we accepted a solution with two subregions that encompassed the lateral and medial frontal pole. We then examined resting-state functional connectivity of the two subregions and found significant differences between their connectivities. The medial cluster was connected to nodes of the default-mode network, which is implicated in internally focused, self-related thought, and social cognition. The lateral cluster was connected to nodes of the executive control network, associated with directed attention and working memory. These findings support the concept that there are two major anatomical subregions of the frontal pole related to differences in functional connectivity

Characteristics of Early-Onset vs Late-Onset Colorectal Cancer: A Review.

The incidence of early-onset colorectal cancer (younger than 50 years) is rising globally, the reasons for which are unclear. It appears to represent a unique disease process with different clinical, pathological, and molecular characteristics compared with late-onset colorectal cancer. Data on oncological outcomes are limited, and sensitivity to conventional neoadjuvant and adjuvant therapy regimens appear to be unknown. The purpose of this review is to summarize the available literature on early-onset colorectal cancer. Within the next decade, it is estimated that 1 in 10 colon cancers and 1 in 4 rectal cancers will be diagnosed in adults younger than 50 years. Potential risk factors include a Westernized diet, obesity, antibiotic usage, and alterations in the gut microbiome. Although genetic predisposition plays a role, most cases are sporadic. The full spectrum of germline and somatic sequence variations implicated remains unknown. Younger patients typically present with descending colonic or rectal cancer, advanced disease stage, and unfavorable histopathological features. Despite being more likely to receive neoadjuvant and adjuvant therapy, patients with early-onset disease demonstrate comparable oncological outcomes with their older counterparts. The clinicopathological features, underlying molecular profiles, and drivers of early-onset colorectal cancer differ from those of late-onset disease. Standardized, age-specific preventive, screening, diagnostic, and therapeutic strategies are required to optimize outcomes

Reciprocal priming between receptor tyrosine kinases at recycling endosomes orchestrates cellular signalling outputs

From Wiley via Jisc Publications RouterHistory: received 2020-10-29, rev-recd 2021-04-27, accepted 2021-04-28, pub-electronic 2021-06-04Article version: VoRPublication status: PublishedFunder: Wellcome Trust; Grant(s): 107636/Z/15/Z, 210002/Z/17/ZFunder: UKRI | Biotechnology and Biological Sciences Research Council (BBSRC); Id: http://dx.doi.org/10.13039/501100000268; Grant(s): BB/R015864/1, BB/M011208/1Funder: UKRI | Medical Research Council (MRC); Id: http://dx.doi.org/10.13039/501100000265; Grant(s): MR/T016043/1Funder: Cancer Research UK (CRUK); Id: http://dx.doi.org/10.13039/501100000289; Grant(s): A27445Funder: NIHR Manchester Biomedical Research Centre; Grant(s): IS‐BRC‐1215‐20007Funder: Breast Cancer Now; Grant(s): MAN‐Q2‐Y4/5Abstract: Integration of signalling downstream of individual receptor tyrosine kinases (RTKs) is crucial to fine‐tune cellular homeostasis during development and in pathological conditions, including breast cancer. However, how signalling integration is regulated and whether the endocytic fate of single receptors controls such signalling integration remains poorly elucidated. Combining quantitative phosphoproteomics and targeted assays, we generated a detailed picture of recycling‐dependent fibroblast growth factor (FGF) signalling in breast cancer cells, with a focus on distinct FGF receptors (FGFRs). We discovered reciprocal priming between FGFRs and epidermal growth factor (EGF) receptor (EGFR) that is coordinated at recycling endosomes. FGFR recycling ligands induce EGFR phosphorylation on threonine 693. This phosphorylation event alters both FGFR and EGFR trafficking and primes FGFR‐mediated proliferation but not cell invasion. In turn, FGFR signalling primes EGF‐mediated outputs via EGFR threonine 693 phosphorylation. This reciprocal priming between distinct families of RTKs from recycling endosomes exemplifies a novel signalling integration hub where recycling endosomes orchestrate cellular behaviour. Therefore, targeting reciprocal priming over individual receptors may improve personalized therapies in breast and other cancers

Discovery-Versus Hypothesis-Driven Detection of Protein-Protein Interactions and Complexes

Protein complexes are the main functional modules in the cell that coordinate and perform the vast majority of molecular functions. The main approaches to identify and quantify the interactome to date are based on mass spectrometry (MS). Here I summarize the benefits and limitations of different MS-based interactome screens, with a focus on untargeted interactome acquisition, such as co-fractionation MS. Specific emphasis is given to the discussion of discovery- versus hypothesis-driven data analysis concepts and their applicability to large, proteome-wide interactome screens. Hypothesis-driven analysis approaches, i.e., complex- or network-centric, are highlighted as promising strategies for comparative studies. While these approaches require prior information from public databases, also reviewed herein, the available wealth of interactomic data continuously increases, thereby providing more exhaustive information for future studies. Finally, guidance on the selection of interactome acquisition and analysis methods is provided to aid the reader in the design of protein-protein interaction studies

Author Correction: Proteomic and interactomic insights into the molecular basis of cell functional diversity

The ability of living systems to adapt to changing conditions originates from their capacity to change their molecular constitution. This is achieved by multiple mechanisms that modulate the quantitative composition and the diversity of the molecular inventory. Molecular diversification is particularly pronounced on the proteome level, at which multiple proteoforms derived from the same gene can in turn combinatorially form different protein complexes, thus expanding the repertoire of functional modules in the cell. The study of molecular and modular diversity and their involvement in responses to changing conditions has only recently become possible through the development of new 'omics'-based screening technologies. This Review explores our current knowledge of the mechanisms regulating functional diversification along the axis of gene expression, with a focus on the proteome and interactome. We explore the interdependence between different molecular levels and how this contributes to functional diversity. Finally, we highlight several recent techniques for studying molecular diversity, with specific focus on mass spectrometry-based analysis of the proteome and its organization into functional modules, and examine future directions for this rapidly growing field. Cells maximize the repertoire of functions produced from their genome through introducing diversity at each stage of the gene expression process, including at the post-translational level. New advances in proteomics and interactomics have begun to shed light on the extent to which diversity is introduced on the proteome level and by the organization of proteins into modular interaction networks