scholarly journals Quantitative behavior of protein complexes in human cells

2018 ◽  
Author(s):  
Morteza H Chalabi ◽  
Vasileios Tsiamis ◽  
Lukas Käll ◽  
Fabio Vandin ◽  
Veit Schwämmle
Keyword(s):  
Web Service ◽  
Human Cell ◽  
Protein Complexes ◽  
Complex Function ◽  
Cell Types ◽  
Human Cells ◽  
Control Mechanisms ◽  
Complex Behavior ◽  
Link Type

AbstractTranslational and post-translational control mechanisms in the cell result in widely observable differences between measured gene transcription and protein abundances. Herein, protein complexes are among the most tightly controlled entities by selective degradation of their individual proteins. They furthermore act as control hubs that regulate highly important processes in the cell and exhibit a high functional diversity due to their ability to change their composition and their structure. To better understand and predict these functional states, extensive characterization of complex composition, behavior, and abundance is necessary. Mass spectrometry provides an unbiased approach to directly determine protein abundances across cell populations and thus to profile a comprehensive abundance map of proteins. We investigated the behavior of protein subunits in known complexes by comparing their abundance profiles across up to 140 cell types available in ProteomicsDB. After thorough assessment of different randomization methods and statistical scoring algorithms, we developed a computational tool to quantify the significance of concurrent profiles within a complex, therefore providing insights into the conservation of their composition across human cell types. We identified the intrinsic structures in complex behavior that allow to determine which proteins orchestrate complex function. This analysis can be extended to investigate common profiles within arbitrary protein groups. With the CoExpresso web service, we offer a potent scoring scheme to assess proteins for their co-regulation and thereby offer insight into their potential for forming functional groups like protein complexes. CoExpresso can be accessed through http://computproteomics/Apps/CoExpresso. Source code and R scripts for database generation are available at https://bitbucket.org/veitveit/coexpresso.Author summaryMany proteins form multi-functional assemblies called protein complexes instead of working as singly units. These complexes control most processes in the cell making the full characterization of their behavior inevitable to understand cellular control mechanisms. Detailed knowledge about complex behavior will elucidate biomarkers and drug targets that exhibit and correct aberrant cell states, respectively. We investigated abundance changes of the protein complex components over more than 100 different human cell types. By using statistical scoring models, we estimated the evidence for the co-regulation of the proteins and revealed which proteins form subunits with impact on complex function and composition. By providing the interactive web service CoExpresso, any combination of proteins can be tested for their co-regulation in human cells.

Interactions between Neisseria meningitidis and human cells that promote colonisation and disease

2004 ◽  
Vol 6 (14) ◽  
pp. 1-14 ◽  
Author(s):  
Anne Corbett ◽  
Rachel Exley ◽  
Sandrine Bourdoulous ◽  
Christoph M. Tang
Keyword(s):  
Cerebrospinal Fluid ◽  
Bacterial Meningitis ◽  
Neisseria Meningitidis ◽  
Human Cell ◽  
Molecular Basis ◽  
Cell Types ◽  
Cellular Level ◽  
Human Cells ◽  
Healthy Individuals ◽  
Host Pathogen

Neisseria meningitidis is the leading cause of bacterial meningitis, a potentially fatal condition that particularly affects children. Multiple steps are involved during the pathogenesis of infection, including the colonisation of healthy individuals and invasion of the bacterium into the cerebrospinal fluid. The bacterium is capable of adhering to, and entering into, a range of human cell types, which facilitates its ability to cause disease. This article summarises the molecular basis of host–pathogen interactions at the cellular level during meningococcal carriage and disease.

scholarly journals Experimental and computational framework for a dynamic protein atlas of human cell division

2017 ◽  
Author(s):  
Yin Cai ◽  
M. Julius Hossain ◽  
Jean-Karim Hériché ◽  
Antonio Z. Politi ◽  
Nike Walther ◽  
...  
Keyword(s):  
Cell Division ◽  
Human Cell ◽  
Protein Complexes ◽  
Morphological Changes ◽  
Protein Localization ◽  
Image Data ◽  
Cell Types ◽  
Correlation Spectroscopy ◽  
Concentration Data ◽  
Dynamic Interactions

SUMMARYEssential biological functions, such as mitosis, require tight coordination of hundreds of proteins in space and time. Localization, timing of interactions and changes in cellular structure are all crucial to ensure correct assembly, function and regulation of protein complexes1-4. Live cell imaging can reveal protein distributions and dynamics but experimental and theoretical challenges prevented its use to produce quantitative data and a model of mitosis that comprehensively integrates information and enables analysis of the dynamic interactions between the molecular parts of the mitotic machinery within changing cellular boundaries.To address this, we generated a 4D image data-driven, canonical model of the morphological changes during mitotic progression of human cells. We used this model to integrate dynamic 3D concentration data of many fluorescently knocked-in mitotic proteins, imaged by fluorescence correlation spectroscopy-calibrated microscopy5. The approach taken here in the context of the MitoSys consortium to generate a dynamic protein atlas of human cell division is generic. It can be applied to systematically map and mine dynamic protein localization networks that drive cell division in different cell types and can be conceptually transferred to other cellular functions.

scholarly journals Towards a systematic characterization of protein complex function: a natural language processing and machine-learning framework

2021 ◽  
Author(s):  
Varun S. Sharma ◽  
Andrea Fossati ◽  
Rodolfo Ciuffa ◽  
Marija Buljan ◽  
Evan G. Williams ◽  
...  
Keyword(s):  
Natural Language ◽  
Language Processing ◽  
Protein Complex ◽  
Large Scale ◽  
Protein Complexes ◽  
Complex Function ◽  
Word Embedding ◽  
Biological Processes ◽  
Go Terms

SummaryIt is a general assumption of molecular biology that the ensemble of expressed molecules, their activities and interactions determine biological processes, cellular states and phenotypes. Quantitative abundance of transcripts, proteins and metabolites are now routinely measured with considerable depth via an array of “OMICS” technologies, and recently a number of methods have also been introduced for the parallel analysis of the abundance, subunit composition and cell state specific changes of protein complexes. In comparison to the measurement of the molecular entities in a cell, the determination of their function remains experimentally challenging and labor-intensive. This holds particularly true for determining the function of protein complexes, which constitute the core functional assemblies of the cell. Therefore, the tremendous progress in multi-layer molecular profiling has been slow to translate into increased functional understanding of biological processes, cellular states and phenotypes. In this study we describe PCfun, a computational framework for the systematic annotation of protein complex function using Gene Ontology (GO) terms. This work is built upon the use of word embedding— natural language text embedded into continuous vector space that preserves semantic relationships— generated from the machine reading of 1 million open access PubMed Central articles. PCfun leverages the embedding for rapid annotation of protein complex function by integrating two approaches: (1) an unsupervised approach that obtains the nearest neighbor (NN) GO term word vectors for a protein complex query vector, and (2) a supervised approach using Random Forest (RF) models trained specifically for recovering the GO terms of protein complex queries described in the CORUM protein complex database. PCfun consolidates both approaches by performing the statistical test for the enrichment of the top NN GO terms within the child terms of the predicted GO terms by RF models. Thus, PCfun amalgamates information learned from the gold-standard protein-complex database, CORUM, with the unbiased predictions obtained directly from the word embedding, thereby enabling PCfun to identify the potential functions of putative protein complexes. The documentation and examples of the PCfun package are available at https://github.com/sharmavaruns/PCfun. We anticipate that PCfun will serve as a useful tool and novel paradigm for the large-scale characterization of protein complex function.

scholarly journals Characterization of four novel ras-like genes expressed in a human teratocarcinoma cell line.

1990 ◽  
Vol 10 (4) ◽  
pp. 1793-1798 ◽  
Author(s):  
G T Drivas ◽  
A Shih ◽  
E Coutavas ◽  
M G Rush ◽  
P D'Eustachio
Keyword(s):  
Cell Line ◽  
Cdna Library ◽  
Human Cell ◽  
Genomic Dna ◽  
Oligonucleotide Probe ◽  
Cell Types ◽  
Ras Gene ◽  
Teratocarcinoma Cell ◽  
Coding Sequences

A mixed-oligonucleotide probe was used to identify four ras-like coding sequences in a human teratocarcinoma cDNA library. Two of these sequences resembled the rho genes, one was closely related to H-, K-, and N-ras, and one shared only the four sequence domains that define the ras gene superfamily. Homologs of the four genes were found in genomic DNA from a variety of mammals and from chicken. The genes were transcriptionally active in a range of human cell types.

scholarly journals Automated analysis of invadopodia dynamics in live cells

2014 ◽  
Author(s):  
Matthew E Berginski ◽  
Sarah J Creed ◽  
Shelly Cochran ◽  
David W Roadcap ◽  
James E Bear ◽  
...  
Keyword(s):  
Protein Complexes ◽  
Metastatic Cancer ◽  
Automated Analysis ◽  
Time Lapse ◽  
Cell Types ◽  
Live Cells ◽  
Imaging Data ◽  
The Matrix ◽  
Invadopodia Formation

Multiple cell types form specialized protein complexes, podosomes or invadopodia and collectively referred to as invadosomes, which are used by the cell to actively degrade the surrounding extracellular matrix. Due to their potential importance in both healthy physiology as well as in pathological conditions such as cancer, the characterization of these structures has been of increasing interest. Following early descriptions of invadopodia, assays were developed which labelled the matrix underneath metastatic cancer cells allowing for the assessment of invadopodia activity in motile cells. However, characterization of invadopodia using these methods has traditionally been done manually with time-consuming and potentially biased quantification methods, limiting the number of experiments and the quantity of data that can be analysed. We have developed a system to automate the segmentation, tracking and quantification of invadopodia in time-lapse fluorescence image sets at both the single invadopodia level and whole cell level. We rigorously tested the ability of the method to detect changes in invadopodia formation and dynamics through the use of well-characterized small molecule inhibitors, with known effects on invadopodia. Our results demonstrate the ability of this analysis method to quantify changes in invadopodia formation from live cell imaging data in a high throughput, automated manner.

scholarly journals Epigenetic regulation in the tumorigenesis of MEN1-associated endocrine cell types

2018 ◽  
Vol 61 (1) ◽  
pp. R13-R24 ◽  
Author(s):  
Sucharitha Iyer ◽  
Sunita K Agarwal
Keyword(s):  
Epigenetic Regulation ◽  
Endocrine Cell ◽  
Protein Complexes ◽  
Cell Types ◽  
Endocrine Tumor ◽  
Molecular Characteristics ◽  
Endocrine Tumors ◽  
Control Mechanisms ◽  
Epigenetic Changes ◽  
Epigenetic Control

Epigenetic regulation is emerging as a key feature in the molecular characteristics of various human diseases. Epigenetic aberrations can occur from mutations in genes associated with epigenetic regulation, improper deposition, removal or reading of histone modifications, DNA methylation/demethylation and impaired non-coding RNA interactions in chromatin. Menin, the protein product of the gene causative for the multiple endocrine neoplasia type 1 (MEN1) syndrome, interacts with chromatin-associated protein complexes and also regulates some non-coding RNAs, thus participating in epigenetic control mechanisms. Germline inactivating mutations in theMEN1gene that encodes menin predispose patients to develop endocrine tumors of the parathyroids, anterior pituitary and the duodenopancreatic neuroendocrine tissues. Therefore, functional loss of menin in the various MEN1-associated endocrine cell types can result in epigenetic changes that promote tumorigenesis. Because epigenetic changes are reversible, they can be targeted to develop therapeutics for restoring the tumor epigenome to the normal state. Irrespective of whether epigenetic alterations are the cause or consequence of the tumorigenesis process, targeting the endocrine tumor-associated epigenome offers opportunities for exploring therapeutic options. This review presents epigenetic control mechanisms relevant to the interactions and targets of menin, and the contribution of epigenetics in the tumorigenesis of endocrine cell types from menin loss.

Characterization of four novel ras-like genes expressed in a human teratocarcinoma cell line

1990 ◽  
Vol 10 (4) ◽  
pp. 1793-1798
Author(s):  
G T Drivas ◽  
A Shih ◽  
E Coutavas ◽  
M G Rush ◽  
P D'Eustachio
Keyword(s):  
Cell Line ◽  
Cdna Library ◽  
Human Cell ◽  
Genomic Dna ◽  
Oligonucleotide Probe ◽  
Cell Types ◽  
Ras Gene ◽  
Teratocarcinoma Cell ◽  
Coding Sequences

A mixed-oligonucleotide probe was used to identify four ras-like coding sequences in a human teratocarcinoma cDNA library. Two of these sequences resembled the rho genes, one was closely related to H-, K-, and N-ras, and one shared only the four sequence domains that define the ras gene superfamily. Homologs of the four genes were found in genomic DNA from a variety of mammals and from chicken. The genes were transcriptionally active in a range of human cell types.

scholarly journals Reprogramming of human cells to pluripotency induces CENP-A chromatin depletion

Open Biology ◽  
2020 ◽  
Vol 10 (10) ◽  
pp. 200227
Author(s):  
Inês Milagre ◽  
Carolina Pereira ◽  
Raquel A. Oliveira ◽  
Lars E. T. Jansen
Keyword(s):  
Stem Cells ◽  
Stem Cell ◽  
Physiological Role ◽  
Cell Types ◽  
Human Cells ◽  
Early Event ◽  
Human Stem Cells ◽  
Centromere Function ◽  
Underlying Causes

Pluripotent stem cells (PSCs) are central to development as they are the precursors of all cell types in the embryo. Therefore, maintaining a stable karyotype is essential, both for their physiological role as well as for their use in regenerative medicine. Karyotype abnormalities in PSCs in culture are common but the underlying causes remain unknown. To gain insight, we explore the composition of the centromere and kinetochore in human embryonic and induced PSCs. Centromere function depends on CENP-A nucleosome-defined chromatin. We show that while PSCs maintain abundant pools of CENP-A, CENP-C and CENP-T, these essential centromere components are strongly reduced at stem cell centromeres. Outer kinetochore recruitment is also impaired to a lesser extent, indicating an overall weaker kinetochore while the inner centromere protein Aurora B remains unaffected. We further show that, similar to differentiated human cells, CENP-A chromatin assembly in PSCs requires transition into G1 phase. Finally, reprogramming experiments indicate that reduction of centromeric CENP-A levels is an early event during dedifferentiation, coinciding with global chromatin remodelling. Our characterization of centromeres in human stem cells suggests a possible link between impaired centromere function and stem cell aneuploidies.

scholarly journals Quantification of proteins, protein complexes and mRNA in single cells by proximity-sequencing

2020 ◽  
Author(s):  
Luke Vistain ◽  
Hoang Van Phan ◽  
Christian Jordi ◽  
Mengjie Chen ◽  
Sai T. Reddy ◽  
...  
Keyword(s):  
Protein Interactions ◽  
Protein Complexes ◽  
Single Cells ◽  
Cell Types ◽  
Cellular Behaviors ◽  
Functional States ◽  
Drug Studies ◽  
New Protein

Multiplexed analysis of single-cells enables accurate modeling of cellular behaviors, classification of new cell types, and characterization of their functional states. Here we present proximity-sequencing (Prox-seq), a method for simultaneous measurement of an individual cell’s proteins, protein complexes and mRNA. Prox-seq utilizes deep sequencing and barcoded proximity assays to measure proteins and their complexes from all pairwise combinations of targeted proteins, in thousands of single-cells. The number of measured protein complexes scales quadratically with the number of targeted proteins, providing unparalleled multiplexing capacity. We developed a high-throughput experimental and computational pipeline and demonstrated the potential of Prox-Seq for multi-omic analysis with a panel of 13 barcoded proximity probes, enabling the measurement of 91 protein complexes, along with thousands of mRNA molecules in single T-cells and B-cells. Prox-seq provides access to an untapped yet powerful measurement modality for single-cell phenotyping and can discover new protein interactions in signaling and drug studies.

scholarly journals Automated analysis of invadopodia dynamics in live cells

2014 ◽  
Author(s):  
Matthew E Berginski ◽  
Sarah J Creed ◽  
Shelly Cochran ◽  
David W Roadcap ◽  
James E Bear ◽  
...  
Keyword(s):  
Protein Complexes ◽  
Metastatic Cancer ◽  
Automated Analysis ◽  
Time Lapse ◽  
Cell Types ◽  
Live Cells ◽  
Imaging Data ◽  
The Matrix ◽  
Invadopodia Formation

Multiple cell types form specialized protein complexes, podosomes or invadopodia and collectively referred to as invadosomes, which are used by the cell to actively degrade the surrounding extracellular matrix. Due to their potential importance in both healthy physiology as well as in pathological conditions such as cancer, the characterization of these structures has been of increasing interest. Following early descriptions of invadopodia, assays were developed which labelled the matrix underneath metastatic cancer cells allowing for the assessment of invadopodia activity in motile cells. However, characterization of invadopodia using these methods has traditionally been done manually with time-consuming and potentially biased quantification methods, limiting the number of experiments and the quantity of data that can be analysed. We have developed a system to automate the segmentation, tracking and quantification of invadopodia in time-lapse fluorescence image sets at both the single invadopodia level and whole cell level. We rigorously tested the ability of the method to detect changes in invadopodia formation and dynamics through the use of well-characterized small molecule inhibitors, with known effects on invadopodia. Our results demonstrate the ability of this analysis method to quantify changes in invadopodia formation from live cell imaging data in a high throughput, automated manner.

Sign in / Sign up

Export Citation Format

Share Document