Marek Gierliński

A Quantitative Spatial Proteomics Analysis of Proteome Turnover in Human Cells

Measuring the properties of endogenous cell proteins, such as expression level, subcellular localization, and turnover rates, on a whole proteome level remains a major challenge in the postgenome era. Quantitative methods for measuring mRNA expression do not reliably predict corresponding protein levels and provide little or no information on other protein properties. Here we describe a combined pulse-labeling, spatial proteomics and data analysis strategy to characterize the expression, localization, synthesis, degradation, and turnover rates of endogenously expressed, untagged human proteins in different subcellular compartments. Using quantitative mass spectrometry and stable isotope labeling with amino acids in cell culture, a total of 80,098 peptides from 8,041 HeLa proteins were quantified, and their spatial distribution between the cytoplasm, nucleus and nucleolus determined and visualized using specialized software tools developed in PepTracker. Using information from ion intensities and rates of change in isotope ratios, protein abundance levels and protein synthesis, degradation and turnover rates were calculated for the whole cell and for the respective cytoplasmic, nuclear, and nucleolar compartments. Expression levels of endogenous HeLa proteins varied by up to seven orders of magnitude. The average turnover rate for HeLa proteins was ∼20 h. Turnover rate did not correlate with either molecular weight or net charge, but did correlate with abundance, with highly abundant proteins showing longer than average half-lives. Fast turnover proteins had overall a higher frequency of PEST motifs than slow turnover proteins but no general correlation was observed between amino or carboxyl terminal amino acid identities and turnover rates. A subset of proteins was identified that exist in pools with different turnover rates depending on their subcellular localization. This strongly correlated with subunits of large, multiprotein complexes, suggesting a general mechanism whereby their assembly is controlled in a different subcellular location to their main site of function.

High-Resolution Whole-Genome Sequencing Reveals That Specific Chromatin Domains from Most Human Chromosomes Associate with Nucleoli

We present a genomewide, high-resolution study of nucleolar-associated chromatin using comparative genome hybridization, deep sequencing, and photoactivation microscopy. We show specific regions from most chromosomes associate with nucleoli. These regions are AT-rich, enriched in repressed genes, and overlap with nuclear lamina-associated loci.

How many biological replicates are needed in an RNA-seq experiment and which differential expression tool should you use?

RNA-seq is now the technology of choice for genome-wide differential gene expression experiments, but it is not clear how many biological replicates are needed to ensure valid biological interpretation of the results or which statistical tools are best for analyzing the data. An RNA-seq experiment with 48 biological replicates in each of two conditions was performed to answer these questions and provide guidelines for experimental design. With three biological replicates, nine of the 11 tools evaluated found only 20%-40% of the significantly differentially expressed (SDE) genes identified with the full set of 42 clean replicates. This rises to >85% for the subset of SDE genes changing in expression by more than fourfold. To achieve >85% for all SDE genes regardless of fold change requires more than 20 biological replicates. The same nine tools successfully control their false discovery rate at ≲5% for all numbers of replicates, while the remaining two tools fail to control their FDR adequately, particularly for low numbers of replicates. For future RNA-seq experiments, these results suggest that at least six biological replicates should be used, rising to at least 12 when it is important to identify SDE genes for all fold changes. If fewer than 12 replicates are used, a superior combination of true positive and false positive performances makes edgeR and DESeq2 the leading tools. For higher replicate numbers, minimizing false positives is more important and DESeq marginally outperforms the other tools.

Tmem79/Matt is the matted mouse gene and is a predisposing gene for atopic dermatitis in human subjects

BACKGROUND Atopic dermatitis (AD) is a major inflammatory condition of the skin caused by inherited skin barrier deficiency, with mutations in the filaggrin gene predisposing to development of AD. Support for barrier deficiency initiating AD came from flaky tail mice, which have a frameshift mutation in Flg and also carry an unknown gene, matted, causing a matted hair phenotype. OBJECTIVE We sought to identify the matted mutant gene in mice and further define whether mutations in the human gene were associated with AD. METHODS A mouse genetics approach was used to separate the matted and Flg mutations to produce congenic single-mutant strains for genetic and immunologic analysis. Next-generation sequencing was used to identify the matted gene. Five independently recruited AD case collections were analyzed to define associations between single nucleotide polymorphisms (SNPs) in the human gene and AD. RESULTS The matted phenotype in flaky tail mice is due to a mutation in the Tmem79/Matt gene, with no expression of the encoded protein mattrin in the skin of mutant mice. Matt(ft) mice spontaneously have dermatitis and atopy caused by a defective skin barrier, with mutant mice having systemic sensitization after cutaneous challenge with house dust mite allergens. Meta-analysis of 4,245 AD cases and 10,558 population-matched control subjects showed that a missense SNP, rs6684514, [corrected] in the human MATT gene has a small but significant association with AD. CONCLUSION In mice mutations in Matt cause a defective skin barrier and spontaneous dermatitis and atopy. A common SNP in MATT has an association with AD in human subjects.

Live imaging of nascent RNA dynamics reveals distinct types of transcriptional pulse regulation

Transcription of genes can be discontinuous, occurring in pulses or bursts. It is not clear how properties of transcriptional pulses vary between different genes. We compared the pulsing of five housekeeping and five developmentally induced genes by direct imaging of single gene transcriptional events in individual living Dictyostelium cells. Each gene displayed its own transcriptional signature, differing in probability of firing and pulse duration, frequency, and intensity. In contrast to the prevailing view from both prokaryotes and eukaryotes that transcription displays binary behavior, strongly expressed housekeeping genes altered the magnitude of their transcriptional pulses during development. These nonbinary “tunable” responses may be better suited than stochastic switch behavior for housekeeping functions. Analysis of RNA synthesis kinetics using fluorescence recovery after photobleaching implied modulation of housekeeping-gene pulse strength occurs at the level of transcription initiation rather than elongation. In addition, disparities between single cell and population measures of transcript production suggested differences in RNA stability between gene classes. Analysis of stability using RNAseq revealed no major global differences in stability between developmental and housekeeping transcripts, although strongly induced RNAs showed unusually rapid decay, indicating tight regulation of expression.

Kinetochores coordinate pericentromeric cohesion and early DNA replication by Cdc7-Dbf4 kinase recruitment.

Summary Centromeres play several important roles in ensuring proper chromosome segregation. Not only do they promote kinetochore assembly for microtubule attachment, but they also support robust sister chromatid cohesion at pericentromeres and facilitate replication of centromeric DNA early in S phase. However, it is still elusive how centromeres orchestrate all these functions at the same site. Here, we show that the budding yeast Dbf4-dependent kinase (DDK) accumulates at kinetochores in telophase, facilitated by the Ctf19 kinetochore complex. This promptly recruits Sld3–Sld7 replication initiator proteins to pericentromeric replication origins so that they initiate replication early in S phase. Furthermore, DDK at kinetochores independently recruits the Scc2–Scc4 cohesin loader to centromeres in G1 phase. This enhances cohesin loading and facilitates robust pericentromeric cohesion in S phase. Thus, we have found the central mechanism by which kinetochores orchestrate early S phase DNA replication and robust sister chromatid cohesion at microtubule attachment sites.

Kinetochore-Dependent Microtubule Rescue Ensures Their Efficient and Sustained Interactions in Early Mitosis

Summary How kinetochores regulate microtubule dynamics to ensure proper kinetochore-microtubule interactions is unknown. Here, we studied this during early mitosis in Saccharomyces cerevisiae. When a microtubule shrinks and its plus end reaches a kinetochore bound to its lateral surface, the microtubule end attempts to tether the kinetochore. This process often fails and, responding to this failure, microtubule rescue (conversion from shrinkage to growth) occurs, preventing kinetochore detachment from the microtubule end. This rescue is promoted by Stu2 transfer (ortholog of vertebrate XMAP215/ch-TOG) from the kinetochore to the microtubule end. Meanwhile, microtubule rescue distal to the kinetochore is also promoted by Stu2, which is transported by a kinesin-8 motor Kip3 along the microtubule from the kinetochore. Microtubule extension following rescue facilitates interaction with other widely scattered kinetochores, diminishing long delays in collecting the complete set of kinetochores by microtubules. Thus, kinetochore-dependent microtubule rescue ensures efficient and sustained kinetochore-microtubule interactions in early mitosis.

Quantitative Proteome Analysis of Temporally Resolved Phagosomes Following Uptake Via Key Phagocytic Receptors

Macrophages operate at the forefront of innate immunity and their discrimination of foreign versus “self” particles is critical for a number of responses including efficient pathogen killing, antigen presentation, and cytokine induction. In order to efficiently destroy the particles and detect potential threats, macrophages express an array of receptors to sense and phagocytose prey particles. In this study, we accurately quantified a proteomic time-course of isolated phagosomes from murine bone marrow-derived macrophages induced by particles conjugated to seven different ligands representing pathogen-associated molecular patterns, immune opsonins or apoptotic cell markers. We identified a clear functional differentiation over the three timepoints and detected subtle differences between certain ligand-phagosomes, indicating that triggering of receptors through a single ligand type has mild, but distinct, effects on phagosome proteome and function. Moreover, our data shows that uptake of phosphatidylserine-coated beads induces an active repression of NF-κB immune responses upon Toll-like receptor (TLR)-activation by recruitment of anti-inflammatory regulators to the phagosome. This data shows for the first time a systematic time-course analysis of bone marrow-derived macrophages phagosomes and how phagosome fate is regulated by the receptors triggered for phagocytosis.

Stochastic association of neighboring replicons creates replication factories in budding yeast

Single-cell analyses in budding yeast reveal that neighboring replicons are assembled stochastically and stay associated to maintain stable replication factories.

Statistical models for RNA-seq data derived from a two-condition 48-replicate experiment.

Motivation: High-throughput RNA sequencing (RNA-seq) is now the standard method to determine differential gene expression. Identifying differentially expressed genes crucially depends on estimates of read-count variability. These estimates are typically based on statistical models such as the negative binomial distribution, which is employed by the tools edgeR, DESeq and cuffdiff. Until now, the validity of these models has usually been tested on either low-replicate RNA-seq data or simulations. Results: A 48-replicate RNA-seq experiment in yeast was performed and data tested against theoretical models. The observed gene read counts were consistent with both log-normal and negative binomial distributions, while the mean-variance relation followed the line of constant dispersion parameter of ∼0.01. The high-replicate data also allowed for strict quality control and screening of ‘bad’ replicates, which can drastically affect the gene read-count distribution. Availability and implementation: RNA-seq data have been submitted to ENA archive with project ID PRJEB5348. Contact: g.j.barton@dundee.ac.uk