Journal of Proteome Research最新文献

Extracellular chemical cues constitute much of the language of life among marine organisms, from microbes to mammals. Changes in this chemical pool serve as invisible signals of overall ecosystem health and disruption to this finely tuned equilibrium. In coral reefs, the scope and magnitude of the chemicals involved in maintaining reef equilibria are largely unknown. Processes involving small, polar molecules, which form the majority components of labile dissolved organic carbon, are often poorly captured using traditional techniques. We employed chemical derivatization with mass spectrometry-based targeted exometabolomics to quantify polar dissolved phase metabolites on five coral reefs in the U.S. Virgin Islands. We quantified 45 polar exometabolites, demonstrated their spatial variability, and contextualized these findings in terms of geographic and benthic cover differences. By comparing our results to previously published coral reef exometabolomes, we show the novel quantification of 23 metabolites, including central carbon metabolism compounds (e.g., glutamate) and novel metabolites such as homoserine betaine. We highlight the immense potential of chemical derivatization-based exometabolomics for quantifying labile chemical cues on coral reefs and measuring molecular level responses to environmental stressors. Overall, improving our understanding of the composition and dynamics of reef exometabolites is vital for effective ecosystem monitoring and management strategies.

Engineered macromolecules offer compelling means for the therapy of conventionally undruggable interactions in human disease. However, their efficacy is limited by barriers to tissue and intracellular delivery. Inspired by recent advances in molecular barcoding and evolution, we developed BarcodeBabel, a generalized method for the design of libraries of peptide barcodes suitable for high-throughput mass spectrometry proteomics. Combined with PeptideBabel, a Monte Carlo sampling algorithm for the design of peptides with evolvable physicochemical properties and sequence complexity, we developed a barcoded library of cell penetrating peptides (CPPs) with distinct physicochemical features. Using quantitative targeted mass spectrometry, we identified CPPS with improved nuclear and cytoplasmic delivery exceeding hundreds of millions of molecules per human cell while maintaining minimal membrane disruption and negligible toxicity in vitro. These studies provide a proof of concept for peptide barcoding as a homogeneous high-throughput method for macromolecular screening and delivery. BarcodeBabel and PeptideBabel are available open-source from https://github.com/kentsisresearchgroup/.

The metabolites and microbiota in tongue coating display distinct characteristics in certain digestive disorders, yet their relationship with colorectal cancer (CRC) remains unexplored. Here, we employed liquid chromatography coupled with tandem mass spectrometry to analyze the lipid composition of tongue coating using a nontargeted approach in 30 individuals with colorectal adenomas (CRA), 32 with CRC, and 30 healthy controls (HC). We identified 21 tongue coating lipids that effectively distinguished CRC from HC (AUC = 0.89), and 9 lipids that differentiated CRC from CRA (AUC = 0.9). Furthermore, we observed significant alterations in the tongue coating lipid composition in the CRC group compared to HC/CRA groups. As the adenoma-cancer sequence progressed, there was an increase in long-chain unsaturated triglycerides (TG) levels and a decrease in phosphatidylethanolamine plasmalogen (PE-P) levels. Furthermore, we noted a positive correlation between N-acyl ornithine (NAOrn), sphingomyelin (SM), and ceramide phosphoethanolamine (PE-Cer), potentially produced by members of the Bacteroidetes phylum. The levels of inflammatory lipid metabolite 12-HETE showed a decreasing trend with colorectal tumor progression, indicating the potential involvement of tongue coating microbiota and tumor immune regulation in early CRC development. Our findings highlight the potential utility of tongue coating lipid analysis as a noninvasive tool for CRC diagnosis.

Lactylation is a novel post-translational modification of proteins. Although the histone lactylation modification has been reported to be involved in glucose metabolism, its role and molecular pathways in gestational diabetes mellitus (GDM) are still unclear. This study aims to elucidate the histone lactylation modification landscapes of GDM patients and explore lactylation-modification-related genes involved in GDM. We employed a combination of RNA-seq analysis and chromatin immunoprecipitation sequencing (ChIP-seq) analysis to identify upregulated differentially expressed genes (DEGs) with hyperhistone lactylation modification in GDM. We demonstrated that the levels of lactate and histone lactylation were significantly elevated in GDM patients. DEGs were involved in diabetes-related pathways, such as the PI3K-Akt signaling pathway, Jak-STAT signaling pathway, and mTOR signaling pathway. ChIP-seq analysis indicated that histone lactylation modification in the promoter regions of the GDM group was significantly changed. By integrating the results of RNA-seq and ChIP-seq analysis, we found that CACNA2D1 is a key gene for histone lactylation modification and is involved in the progression of GDM by promoting cell vitality and proliferation. In conclusion, we identified the key gene CACNA2D1, which upregulated and exhibited hypermodification of histone lactylation in GDM. These findings establish a theoretical groundwork for the targeted therapy of GDM.

Peptide identification is important in bottom-up proteomics. Post-translational modifications (PTMs) are crucial in regulating cellular activities. Many database search methods have been developed to identify peptides with PTMs and characterize the PTM patterns. However, the PTMs on peptides hinder the peptide identification rate and the PTM characterization precision, especially for peptides with multiple PTMs. To address this issue, we present a sensitive open search engine, PIPI2, with much better performance on peptides with multiple PTMs than other methods. With a greedy approach, we simplify the PTM characterization problem into a linear one, which enables characterizing multiple PTMs on one peptide. On the simulation data sets with up to four PTMs per peptide, PIPI2 identified over 90% of the spectra, at least 56% more than five other competitors. PIPI2 also characterized these PTM patterns with the highest precision of 77%, demonstrating a significant advantage in handling peptides with multiple PTMs. In the real applications, PIPI2 identified 30% to 88% more peptides with PTMs than its competitors.

Resistance is a major problem with effective cancer treatment and the stroma forms a significant portion of the tumor mass but traditional drug screens involve cancer cells alone. Cancer-associated fibroblasts (CAFs) are a major tumor stroma component and its secreted proteins may influence the function of cancer cells. The majority of secretome studies compare different cancer or CAF cell lines exclusively. Here, we present the direct characterization of the secreted protein profiles between CAFs and KRAS mutant-cancer cell lines from colorectal, lung, and pancreatic tissues using multiplexed mass spectrometry. 2573 secreted proteins were annotated, and differential analysis highlighted understudied CAF-enriched secreted proteins, including Wnt family member 5B (WNT5B), in addition to established CAF markers, such as collagens. The functional role of CAF secreted proteins was explored by assessing its effect on the response to 97 anticancer drugs since stromal cells may cause a differing cancer drug response, which may be missed on routine drug screening using cancer cells alone. CAF secreted proteins caused specific effects on each of the cancer cell lines, which highlights the complexity and challenges in cancer treatment and so the importance to consider stromal elements.

Biological interpretation of untargeted LC-MS-based metabolomics data depends on accurate compound identification, but current techniques fall short of identifying most features that can be detected. The human fecal metabolome is complex, variable, incompletely annotated, and serves as an ideal matrix to evaluate novel compound identification methods. We devised an experimental strategy for compound annotation using multidimensional chromatography and semiautomated feature alignment and applied these methods to study the fecal metabolome in the context of fecal microbiota transplantation (FMT) for recurrent C. difficile infection. Pooled fecal samples were fractionated using semipreparative liquid chromatography and analyzed by an orthogonal LC-MS/MS method. The resulting spectra were searched against commercial, public, and local spectral libraries, and annotations were vetted using retention time alignment and prediction. Multidimensional chromatography yielded more than a 2-fold improvement in identified compounds compared to conventional LC-MS/MS and successfully identified several rare and previously unreported compounds, including novel fatty-acid conjugated bile acid species. Using an automated software-based feature alignment strategy, most metabolites identified by the new approach could be matched to features that were detected but not identified in single-dimensional LC-MS/MS data. Overall, our approach represents a powerful strategy to enhance compound identification and biological insight from untargeted metabolomics data.

The effect of the parental environment on offspring through non-DNA sequence-based mechanisms, such as DNA methylation, chromatin modifications, noncoding RNAs, and proteins, could only be established after the conception of “epigenetics”. These effects are now broadly referred to as multigenerational epigenetic effects. Despite accumulating evidence of male gamete-mediated multigenerational epigenetic inheritance, little is known about the factors that underlie heat stress-induced multigenerational epigenetic inheritance via the male germline in Drosophila. In this study, we address this gap by utilizing an established heat stress paradigm in Drosophila and investigating its multigenerational effect on the sperm proteome. Our findings indicate that multigenerational heat stress during the early embryonic stage significantly influences proteins in the sperm associated with translation, chromatin organization, microtubule-based processes, and the generation of metabolites and energy. Assessment of life-history traits revealed that reproductive fitness and stress tolerance remained unaffected by multigenerational heat stress. Our study offers initial insights into the chromatin-based epigenetic mechanisms as a plausible means of transmitting heat stress memory through the male germline in Drosophila. Furthermore, it sheds light on the repercussions of early embryonic heat stress on male reproductive potential. The data sets from this study are available at the ProteomeXchange Consortium under the identifier PXD037488.

The human relaxins belong to the Insulin/IGF/Relaxin superfamily of peptide hormones, and their physiological function is primarily associated with reproduction. In this study, we focused on a prostate tissue-specific relaxin RLN1 (REL1_HUMAN protein) and a broader tissue specificity RLN2 (REL2_HUMAN protein). Due to their structural similarity, REL1 and REL2 proteins were collectively named a ‘human relaxin protein’ in previous studies and were exclusively measured by immunoassays. We hypothesized that the highly selective and sensitive immunoaffinity-selected reaction monitoring (IA-SRM) assays would reveal the identity and abundance of the endogenous REL1 and REL2 in biological samples and facilitate the evaluation of these proteins for diagnostic applications. High levels of RLN1 and RLN2 transcripts were found in prostate and breast cancer cell lines by RT-PCR. However, no endogenous prorelaxin-1 or mature REL1 were detected by IA-SRM in cell lines, seminal plasma, or blood serum. The IA-SRM assay of REL2 demonstrated its undetectable levels (<9.4 pg/mL) in healthy control female and male sera and relatively high levels of REL2 in maternal sera across different gestational weeks (median 331 pg/mL; N = 120). IA-SRM assays uncovered potential cross-reactivity and nonspecific binding for relaxin immunoassays. The developed IA-SRM assays will facilitate the investigation of the physiological and pathological roles of REL1 and REL2 proteins.

Enzymes are indispensable in many biological processes, and with biomedical literature growing exponentially, effective literature review becomes increasingly challenging. Natural language processing methods offer solutions to streamline this process. This study aims to develop an annotated enzyme corpus for training and evaluating enzyme named entity recognition (NER) models. A novel pipeline, combining dictionary matching and rule-based keyword searching, automatically annotated enzyme entities in >4800 full-text publications. Four deep learning NER models were created with different vocabularies (BioBERT/SciBERT) and architectures (BiLSTM/transformer) and evaluated on 526 manually annotated full-text publications. The annotation pipeline achieved an F1-score of 0.86 (precision = 1.00, recall = 0.76), surpassed by fine-tuned transformers for F1-score (BioBERT: 0.89, SciBERT: 0.88) and recall (0.86) with BiLSTM models having higher precision (0.94) than transformers (0.92). The annotation pipeline runs in seconds on standard laptops with almost perfect precision, but was outperformed by fine-tuned transformers in terms of F1-score and recall, demonstrating generalizability beyond the training data. In comparison, SciBERT-based models exhibited higher precision, and BioBERT-based models exhibited higher recall, highlighting the importance of vocabulary and architecture. These models, representing the first enzyme NER algorithms, enable more effective enzyme text mining and information extraction. Codes for automated annotation and model generation are available from https://github.com/omicsNLP/enzymeNER and https://zenodo.org/doi/10.5281/zenodo.10581586.