PECAplus: statistical analysis of time-dependent regulatory changes in dynamic single-omics and dual-omics experiments

Integration of a multi-omics stem cell differentiation dataset using a dynamical model

Stem cell differentiation is a highly dynamic process involving pervasive changes in gene expression. The large majority of existing studies has characterized differentiation at the level of individual molecular profiles, such as the transcriptome or the proteome. To obtain a more comprehensive view, we measured protein, mRNA and microRNA abundance during retinoic acid-driven differentiation of mouse embryonic stem cells. We found that mRNA and protein abundance are typically only weakly correlated across time. To understand this finding, we developed a hierarchical dynamical model that allowed us to integrate all data sets. This model was able to explain mRNA-protein discordance for most genes and identified instances of potential microRNA-mediated regulation. Overexpression or depletion of microRNAs identified by the model, followed by RNA sequencing and protein quantification, were used to follow up on the predictions of the model. Overall, our study shows how multi-omics integration by a dynamical model could be used to nominate candidate regulators.

BoxCarmax: A High-Selectivity Data-Independent Acquisition Mass Spectrometry Method for the Analysis of Protein Turnover and Complex Samples

The data-independent acquisition (DIA) performed in the latest high-resolution, high-speed mass spectrometers offers a powerful analytical tool for biological investigations. The DIA mass spectrometry (DIA-MS) combined with the isotopic labeling approach holds a particular promise for increasing the multiplexity of DIA-MS analysis, which could assist the relative protein quantification and the proteome-wide turnover profiling. However, the wide MS1 isolation windows employed in conventional DIA methods lead to a limited efficiency in identifying and quantifying isotope-labeled peptide pairs through peptide fragment ions. Here, we optimized a high-selectivity DIA-MS named BoxCarmax that supports the analysis of complex samples, such as those generated from Stable isotope labeling by amino acids in cell culture (SILAC) and pulse SILAC (pSILAC) experiments. BoxCarmax enables multiplexed acquisition at both MS1 and MS2 levels, through the integration of BoxCar and MSX features, as well as a gas-phase separation strategy. We found BoxCarmax significantly improved the quantitative accuracy in SILAC and pSILAC samples by mitigating the ratio suppression of isotope-peptide pairs. We further applied BoxCarmax to measure protein degradation regulation during serum starvation stress in cultured cells, revealing valuable biological insights. Our study offered an alternative and accurate approach for the MS analysis of protein turnover and complex samples.

Proteome Turnover in the Spotlight: Approaches, Applications, and Perspectives

In all cells, proteins are continuously synthesized and degraded to maintain protein homeostasis and modify gene expression levels in response to stimuli. Collectively, the processes of protein synthesis and degradation are referred to as protein turnover. At a steady state, protein turnover is constant to maintain protein homeostasis, but in dynamic responses, proteins change their rates of synthesis and degradation to adjust their proteomes to internal or external stimuli. Thus, probing the kinetics and dynamics of protein turnover lends insight into how cells regulate essential processes such as growth, differentiation, and stress response. Here, we outline historical and current approaches to measuring the kinetics of protein turnover on a proteome-wide scale in both steady-state and dynamic systems, with an emphasis on metabolic tracing using stable isotope-labeled amino acids. We highlight important considerations for designing proteome turnover experiments, key biological findings regarding the conserved principles of proteome turnover regulation, and future perspectives for both technological and biological investigation.

Chemical chaperones reverse early suppression of regulatory circuits during unfolded protein response in B cells from common variable immunodeficiency patients

B cells orchestrate pro-survival and pro-apoptotic inputs during unfolded protein response (UPR) to translate, fold, sort, secrete and recycle immunoglobulins. In common variable immunodeficiency (CVID) patients, activated B cells are predisposed to an overload of abnormally processed, misfolded immunoglobulins. Using highly accurate transcript measurements, we show that expression of UPR genes and immunoglobulin chains differs qualitatively and quantitatively during the first 4 h of chemically induced UPR in B cells from CVID patients and a healthy subject. We tested thapsigargin or tunicamycin as stressors and 4-phenylbutyrate, dimethyl sulfoxide and tauroursodeoxycholic acid as chemical chaperones. We found an early and robust decrease of the UPR upon endoplasmic reticulum (ER) stress in CVID patient cells compared to the healthy control consistent with the disease phenotype. The chemical chaperones increased the UPR in the CVID patient cells in response to the stressors, suggesting that misfolded immunoglobulins were stabilized. We suggest that the AMP-dependent transcription factor alpha branch of the UPR is disturbed in CVID patients, underlying the observed expression behavior.

Exploiting Interdata Relationships in Next-generation Proteomics Analysis

Mass spectrometry based proteomics and other technologies have matured to enable routine quantitative, system-wide analysis of concentrations, modifications, and interactions of proteins, mRNAs, and other molecules. These studies have allowed us to move toward a new field concerned with mining information from the combination of these orthogonal data sets, perhaps called "integromics." We highlight examples of recent studies and tools that aim at relating proteomic information to mRNAs, genetic associations, and changes in small molecules and lipids. We argue that productive data integration differs from parallel acquisition and interpretation and should move toward quantitative modeling of the relationships between the data. These relationships might be expressed by temporal information retrieved from time series experiments, rate equations to model synthesis and degradation, or networks of causal, evolutionary, physical, and other interactions. We outline steps and considerations toward such integromic studies to exploit the synergy between data sets.

New insights into the cellular temporal response to proteostatic stress

Maintaining a healthy proteome involves all layers of gene expression regulation. By quantifying temporal changes of the transcriptome, translatome, proteome, and RNA-protein interactome in cervical cancer cells, we systematically characterize the molecular landscape in response to proteostatic challenges. We identify shared and specific responses to misfolded proteins and to oxidative stress, two conditions that are tightly linked. We reveal new aspects of the unfolded protein response, including many genes that escape global translation shutdown. A subset of these genes supports rerouting of energy production in the mitochondria. We also find that many genes change at multiple levels, in either the same or opposing directions, and at different time points. We highlight a variety of putative regulatory pathways, including the stress-dependent alternative splicing of aminoacyl-tRNA synthetases, and protein-RNA binding within the 3’ untranslated region of molecular chaperones. These results illustrate the potential of this information-rich resource.

Integration of large-scale multi-omic datasets: a protein-centric view

Innovative mass spectrometry-based proteomics has enabled routine measurements of protein abundance, localization, interactions, and modifications, covering unique aspects of gene expression regulation and function. It is now time to move from isolated analyses of these datasets toward true integration of proteomics with other data types to gain insights from the interactions and interdependencies of biomolecules. When combined with genomic or transcriptomic data, proteomics expands genome annotation to identify variant or missing genes. Dynamic proteomic measurements can move analysis from predominantly concentration-based framework to that of synthesis and degradation of proteins. Proteomic data from thousands of cancer patients can foster identification of novel pathogenic mutations via detection of protein sequence changes that lead to dysregulated pathways in various tumors. Such comprehensive efforts can exploit the synergy arising from large and complex datasets to advance virtually every field of biology.