Data-Independent Acquisition Proteomics Unravels the Effects of Iron Ions on Coronatine Synthesis in Pseudomonas syringae pv. tomato DC3000

6:2 Cl-PFESA, a proposed safe alternative for PFOS, diminishes the gemcitabine effectiveness in the treatment of pancreatic cancer

Pancreatic ductal adenocarcinoma (PDAC)/pancreatic cancer, is a highly aggressive malignancy with poor prognosis. Gemcitabine-based chemotherapy remains the cornerstone of PDAC treatment. Nonetheless, the development of resistance to gemcitabine among patients is a major factor contributing to unfavorable prognostic outcomes. The resistance exhibited by tumors is modulated by a constellation of factors such as genetic mutations, tumor microenvironment transforms, environmental contaminants exposure. Currently, comprehension of the relationship between environmental pollutants and tumor drug resistance remains inadequate. Our study found that PFOS/6:2 Cl-PFESA exposure increases resistance to gemcitabine in PDAC. Subsequent in vivo trials confirmed that exposure to PFOS/6:2 Cl-PFESA reduces gemcitabine's efficacy in suppressing PDAC, with the inhibition rate decreasing from 79.5 % to 56.7 %/38.7 %, respectively. Integrative multi-omics sequencing and molecular biology analyses have identified the upregulation of ribonucleotide reductase catalytic subunit M1 (RRM1) as a critical factor in gemcitabine resistance. Subsequent research has demonstrated that exposure to PFOS and 6:2 Cl-PFESA results in the upregulation of the RRM1 pathway, consequently enhancing chemotherapy resistance. Remarkably, the influence exerted by 6:2 Cl-PFESA exceeds that of PFOS. Despite 6:2 Cl-PFESA being regarded as a safer substitute for PFOS, its pronounced effect on chemotherapeutic resistance in PDAC necessitates a thorough evaluation of its potential risks related to gastrointestinal toxicity.

Aird: a computation-oriented mass spectrometry data format enables a higher compression ratio and less decoding time

Background

With the precision of the mass spectrometry (MS) going higher, the MS file size increases rapidly. Beyond the widely-used open format mzML, near-lossless or lossless compression algorithms and formats emerged in scenarios with different precision requirements. The data precision is often related to the instrument and subsequent processing algorithms. Unlike storage-oriented formats, which focus more on lossless compression rate, computation-oriented formats concentrate as much on decoding speed as the compression rate.

Results

Here we introduce “Aird”, an opensource and computation-oriented format with controllable precision, flexible indexing strategies, and high compression rate. Aird provides a novel compressor called Zlib-Diff-PforDelta (ZDPD) for m/z data. Compared with Zlib only, m/z data size is about 55% lower in Aird average. With the high-speed decoding and encoding performance of the single instruction multiple data technology used in the ZDPD, Aird merely takes 33% decoding time compared with Zlib. We have downloaded seven datasets from ProteomeXchange and Metabolights. They are from different SCIEX, Thermo, and Agilent instruments. Then we convert the raw data into mzML, mgf, and mz5 file formats by MSConvert and compare them with Aird format. Aird uses JavaScript Object Notation for metadata storage. Aird-SDK is written in Java, and AirdPro is a GUI client for vendor file converting written in C#. They are freely available at https://github.com/CSi-Studio/Aird-SDK and https://github.com/CSi-Studio/AirdPro.

Conclusions

With the innovation of MS acquisition mode, MS data characteristics are also constantly changing. New data features can bring more effective compression methods and new index modes to achieve high search performance. The MS data storage mode will also become professional and customized. ZDPD uses multiple MS digital features, and researchers also can use it in other formats like mzML. Aird is designed to become a computing-oriented data format with high scalability, compression rate, and fast decoding speed.

Data-Independent Acquisition-Based Proteome and Phosphoproteome Profiling Reveals Early Protein Phosphorylation and Dephosphorylation Events in Arabidopsis Seedlings upon Cold Exposure

Protein phosphorylation plays an important role in mediating signal transduction in cold response in plants. To better understand how plants sense and respond to the early temperature drop, we performed data-independent acquisition (DIA) method-based mass spectrometry analysis to profile the proteome and phosphoproteome of Arabidopsis seedlings upon cold stress in a time-course manner (10, 30 and 120 min of cold treatments). Our results showed the rapid and extensive changes at the phosphopeptide levels, but not at the protein abundance levels, indicating cold-mediated protein phosphorylation and dephosphorylation events. Alteration of over 1200 proteins at phosphopeptide levels were observed within 2 h of cold treatment, including over 140 kinases, over 40 transcriptional factors and over 40 E3 ligases, revealing the complexity of regulation of cold adaption. We summarized cold responsive phosphoproteins involved in phospholipid signaling, cytoskeleton reorganization, calcium signaling, and MAPK cascades. Cold-altered levels of 73 phosphopeptides (mostly novel cold-responsive) representing 62 proteins were validated by parallel reaction monitoring (PRM). In summary, this study furthers our understanding of the molecular mechanisms of cold adaption in plants and strongly supports that DIA coupled with PRM are valuable tools in uncovering early signaling events in plants.

Modelling and predicting population of core fungi through processing parameters in spontaneous starter (Daqu) fermentation

Traditional fermented foods are usually produced by spontaneous fermentation with multiple microorganisms. Environmental factors play important roles in microbial succession. However, it is still unclear how the processing parameters regulate the microbiota during fermentation. Here, we reveal the effects of processing parameters on the core microbiota in spontaneous fermentation of Chinese liquor starter. Rhizopus, Pichia, Wickerhamomyces, Saccharomycopsis, Aspergillus and Saccharomyces were identified as core microbiota using amplicon sequencing and metaproteomics analysis. Fermentation moisture gradually decreased from 34.8% to 14.2%, and fermentation temperature varied between 17.0 °C and 35.3 °C during the fermentation. Mantel test showed that fermentation moisture (P < 0.001) and fermentation temperature (P < 0.05) significantly affected the core microbiota. Moreover, structural equation modelling analysis indicated that fermentation moisture (P < 0.001) and fermentation temperature (P < 0.001) were respectively influenced by the processing parameters, room humidity and room temperature. The succession of Rhizopus, Pichia, Wickerhamomyces, Saccharomycopsis and Aspergillus were significantly affected by room humidity (P < 0.05), and the succession of Saccharomyces was significantly affected by room temperature (P < 0.001). Further, models were constructed to predict the population of core microbiota by room humidity and room temperature, using Gaussian process regression and linear regression (P < 0.05). This work would be beneficial for regulating microorganisms via controlling processing parameters in spontaneous food fermentations.