Sequence variations of phase-separating proteins and resources for studying biomolecular condensates.

Phase separation (PS) is an important mechanism underlying the formation of biomolecular condensates. Physiological condensates are associated with numerous biological processes, such as transcription, immunity, signaling, and synaptic transmission. Changes in particular amino acids or segments can disturb the protein's phase behavior and interactions with other biomolecules in condensates. It is thus presumed that variations in the phase-separating-prone domains can significantly impact the properties and functions of condensates. The dysfunction of condensates contributes to a number of pathological processes. Pharmacological perturbation of these condensates is proposed as a promising way to restore physiological states. In this review, we characterize the variations observed in PS proteins that lead to aberrant biomolecular compartmentalization. We also showcase recent advancements in bioinformatics of membraneless organelles (MLOs), focusing on available databases useful for screening PS proteins and describing endogenous condensates, guiding researchers to seek the underlying pathogenic mechanisms of biomolecular condensates.

A reference peptide database for proteome quantification based on experimental mass spectrum response curves.

Motivation: Mass spectrometry (MS) based quantification of proteins/peptides has become a powerful tool in biological research with high sensitivity and throughput. The accuracy of quantification, however, has been problematic as not all peptides are suitable for quantification. Several methods and tools have been developed to identify peptides that response well in mass spectrometry and they are mainly based on predictive models, and rarely consider the linearity of the response curve, limiting the accuracy and applicability of the methods. An alternative solution is to select empirically superior peptides that offer satisfactory MS response intensity and linearity in a wide dynamic range of peptide concentration. Results: We constructed a reference database for proteome quantification based on experimental mass spectrum response curves. The intensity and dynamic range of over 2 647 773 transitions from 121 318 peptides were obtained from a set of dilution experiments, covering 11 040 gene products. These transitions and peptides were evaluated and presented in a database named SCRIPT-MAP. We showed that the best-responder (BR) peptide approach for quantification based on SCRIPT-MAP database is robust, repeatable and accurate in proteome-scale protein quantification. This study provides a reference database as well as a peptides/transitions selection method for quantitative proteomics. Availability and implementation: SCRIPT-MAP database is available at http://www.firmiana.org/responders/. Supplementary information: Supplementary data are available at Bioinformatics online.

Phase separation as a possible mechanism for dosage sensitivity.

BACKGROUND: Deletion of haploinsufficient genes or duplication of triplosensitive ones results in phenotypic effects in a concentration-dependent manner, and the mechanisms underlying these dosage-sensitive effects remain elusive. Phase separation drives functional compartmentalization of biomolecules in a concentration-dependent manner as well, which suggests a potential link between these two processes, and warrants further systematic investigation. RESULTS: Here we provide bioinformatic and experimental evidence to show a close link between phase separation and dosage sensitivity. We first demonstrate that haploinsufficient or triplosensitive gene products exhibit a higher tendency to undergo phase separation. Assessing the well-established dosage-sensitive genes HNRNPK, PAX6, and PQBP1 with experiments, we show that these proteins undergo phase separation. Critically, pathogenic variations in dosage-sensitive genes disturb the phase separation process either through reduced protein levels, or loss of phase-separation-prone regions. Analysis of multi-omics data further demonstrates that loss-of-function genetic perturbations on phase-separating genes cause similar dysfunction phenotypes as dosage-sensitive gene perturbations. In addition, dosage-sensitive scores derived from population genetics data predict phase-separating proteins with much better performance than available sequence-based predictors, further illustrating close ties between these two parameters. CONCLUSIONS: Together, our study shows that phase separation is functionally linked to dosage sensitivity and provides novel insights for phase-separating protein prediction from the perspective of population genetics data.

NQO1 is a determinant for cellular sensitivity to anti-tumor agent Napabucasin.

Napabucasin (NAPA) is thought to be a potent cancer stemness inhibitor in different types of cancer cell lines. While it has shown promising activity in early phase clinical trials, two recent phase III NAPA clinical trials failed to meet the primary endpoint of overall survival. The reason for the failure is not clear, but a possible way to revive the clinical trial is to stratify patients with biomarkers that could predict NAPA response. Here, we report the identification of NAD(P)H dehydrogenase 1 (NQO1) as a major determinant of NAPA efficacy. A proteomic profiling of cancer cell lines revealed that NQO1 abundance is negatively correlated with IC50; in vitro assays showed that NAPA is a substrate for NQO1, which mediates the generation of ROS that leads to cell death. Furthermore, activation of an NQO1 transcription factor NRF2 by chemicals, including an FDA approved drug, can increase the NAPA cytotoxicity. Our findings suggest a potential use of NQO1 expression as a companion diagnostic test to identify patients in future NAPA trials and a combination strategy to expand the application of NAPA-based regimens for cancer therapy.

Author Correction: A proteomic landscape of diffuse-type gastric cancer.

The original version of this Article contained an error in the email address of the corresponding author Jun Qin. The correct email is jqin1965@126.com. The error has been corrected in the HTML and PDF versions of the Article.

A proteomic landscape of diffuse-type gastric cancer.

The diffuse-type gastric cancer (DGC) is a subtype of gastric cancer with the worst prognosis and few treatment options. Here we present a dataset from 84 DGC patients, composed of a proteome of 11,340 gene products and mutation information of 274 cancer driver genes covering paired tumor and nearby tissue. DGC can be classified into three subtypes (PX1-3) based on the altered proteome alone. PX1 and PX2 exhibit dysregulation in the cell cycle and PX2 features an additional EMT process; PX3 is enriched in immune response proteins, has the worst survival, and is insensitive to chemotherapy. Data analysis revealed four major vulnerabilities in DGC that may be targeted for treatment, and allowed the nomination of potential immunotherapy targets for DGC patients, particularly for those in PX3. This dataset provides a rich resource for information and knowledge mining toward altered signaling pathways in DGC and demonstrates the benefit of proteomic analysis in cancer molecular subtyping.

[Differential proteome analysis of carbon tetrachloride-induced mouse liver fibrosis].

To explore the differential proteome pattern in mouse fibrosis liver in comparison to wild type. Mice were fed with carbon tetrachloride or olive oil vehicle for 15 weeks. Mouse livers from both groups were collected and submitted to MS platform for proteome screening. GO (Gene Ontology) biological process and KEGG (Kyoto Enyoolpedia of Genes and Genomes) pathway enrichment analysis were used to analyze differentially expressed proteins. As the results, we identified 17 382 and 20 486 unique peptides in control and carbon tetrachloride-induced groups, respectively. A total of 4 991 proteins (at least 1 unique peptide matched) were identified, of which 2 135 were differentially expressed (> or = 2 fold). In fibrosis mouse liver 1 264 proteins were up regulated and 871 proteins were down regulated. Proteins associated with DNA replication, cell cycle, ECM-receptor interaction, and splicesome were significantly increased in carbon tetrachloride-induced group. Proteins associated with small molecule metabolic process, protein transport, organonitrogen compound metabolic process, and tetrapyrrole biosynthetic processes were down regulated in carbon tetrachloride-induced mouse liver fibrosis tissue. Bioinformatics findings showed that fibrosis was closely related to the regulation of VEGF and T cell receptor signaling pathway, and further suggested that liver fibrosis was a complex signal transduction process that many biological processes such as liver metabolism, inflammation, and immune response are involved. Based this study, we can envision that protection of protein metabolism in liver parenchymal cells and blocking of inflammatory signaling transduction may be beneficial for liver fibrosis therapy.