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Podosphaera xanthii is the main causal agent of powdery mildew (PM) disease for Cucurbita pepo. Disease control is attained principally by applications of chemical fungicides, along with parallel use of tolerant crop varieties and alternate application of elicitors to control development of disease resistance. To get insight into C. pepo molecular responses to P. xanthii infection and elicitor treatment we studied the proteomic profile differences at the phyllosphere of a zucchini cultivar susceptible to PM, at the onset of P. xanthii (PX) infection and after application of Reynoutria sachalinensis (RS) plant extract, respectively, using a nano-LC-HRMS/MS, Q-Exactive-Orbitrap approach. Analysis of peptide sequences regarding four treatment groups (Control; PX; RS; and RSPX (PX-infected priorly treated with RS)) resulted in 2070 CuGenDB annotations. Three comparisons (treatments vs. Control) encompassed most of the Differentially Expressed Proteins (DEPs). In these three comparisons, KEGG and Gene Ontology functional analyses highlighted unique differentially enriched pathways—some of which included highly expressed proteins—in PX-related (proteasome, pentose phosphate pathway, and carbon fixation), RS-related (biosynthesis of secondary metabolites, flavonoids, and starch and sucrose metabolism), and RSPX-related (pyruvate metabolism and polycomb repressive complex) comparisons, respectively, suggesting distinct mechanisms of early plant responses modulated by PX and RS. Furthermore, in four out of six comparisons the thiamine metabolism pathway was found to be enriched, suggesting a pivotal role in PX-induced responses. [ABSTRACT FROM AUTHOR]

We are approaching the third decade since the establishment of the very first proteomics repositories back in the mid‐'00s. New experimental approaches and technologies continuously enrich the field while producing vast amounts of mass spectrometry data. Together with initiatives to establish standard terminology and file formats, proteomics is rapidly transforming into a mature component of systems biology. Here we describe the ProteomeXchange consortium repositories. We specifically search, collect and evaluate public human tissue datasets (categorized as "complete" by the repository) submitted in 2015–2022, to both map the existing information and assess the data set reusability. Human tissue data are variably represented in the repositories reviewed, ranging between 10% and 25% of the total data submitted, with cancers being the most represented, followed by neuronal and cardiovascular diseases. About half of the retrieved data sets were found to lack annotations or metadata necessary to directly replicate the analysis. This poses a rough challenge to data reusability and highlights the need to increase awareness of the mage‐tab file format for metadata in the community. Overall, proteomics repositories have evolved greatly over the past 7 years, as they have grown in size and become equipped with various powerful applications and tools that enable data searching and analytical tasks. However, to make the most of this potential, priority must be given to finding ways to secure detailed metadata for each submission, which is likely the next major milestone for proteomics repositories. [ABSTRACT FROM AUTHOR]

The aim of the current study was to investigate the tear proteome in children and adolescents with type 1 diabetes (T1D) compared to healthy controls, and to identify differences in the tear proteome of children with T1D depending on different characteristics of the disease. Fifty-six children with T1D at least one year after diagnosis, aged 6–17 years old, and fifty-six healthy age- and sex-matched controls were enrolled in this cross-sectional study. The proteomic analysis was based on liquid chromatography tandem mass spectrometry (LC-MS/MS) enabling the identification and quantification of the protein content via Data-Independent Acquisition by Neural Networks (DIA-NN). Data are available via ProteomeXchange with the identifier PXD052994. In total, 3302 proteins were identified from tear samples. Two hundred thirty-nine tear proteins were differentially expressed in children with T1D compared to healthy controls. Most of them were involved in the immune response, tissue homeostasis and inflammation. The presence of diabetic ketoacidosis at diagnosis and the level of glycemic control of children with T1D influenced the tear proteome. Tear proteomics analysis revealed a different proteome pattern in children with T1D compared to healthy controls offering insights on deregulated biological processes underlying the pathogenesis of T1D. Differences within the T1D group could unravel biomarkers for early detection of long-term complications of T1D. [ABSTRACT FROM AUTHOR]

(1) Background: This study aimed to develop a machine learning model based on radiomics of pretreatment magnetic resonance imaging (MRI) 3D T2W contrast sequence scans combined with clinical parameters (CP) to predict neoadjuvant chemoradiotherapy (nCRT) response in patients with locally advanced rectal carcinoma (LARC). The study also assessed the impact of radiomics dimensionality on predictive performance. (2) Methods: Seventy-five patients were prospectively enrolled with clinicopathologically confirmed LARC and nCRT before surgery. Tumor properties were assessed by calculating 2141 radiomics features. Least absolute shrinkage selection operator (LASSO) and multivariate regression were used for feature selection. (3) Results: Two predictive models were constructed, one starting from 72 CP and 107 radiomics features, and the other from 72 CP and 1862 radiomics features. The models revealed moderately advantageous impact of increased dimensionality, with their predictive respective AUCs of 0.86 and 0.90 in the entire cohort and 0.84 within validation folds. Both models outperformed the CP-only model (AUC = 0.80) which served as the benchmark for predictive performance without radiomics. (4) Conclusions: Predictive models developed in this study combining pretreatment MRI radiomics and clinicopathological features may potentially provide a routine clinical predictor of chemoradiotherapy responders, enabling clinicians to personalize treatment strategies for rectal carcinoma. [ABSTRACT FROM AUTHOR]

tRNA fragments (tRFs) are small non-coding RNAs generated through specific cleavage of tRNAs and involved in various biological processes. Among the different types of tRFs, the 3′-tRFs have attracted scientific interest due to their regulatory role in gene expression. In this study, we investigated the role of 3′-tRF-CysGCA, a tRF deriving from cleavage in the T-loop of tRNACysGCA, in the regulation of gene expression in HEK-293 cells. Previous studies have shown that 3′-tRF-CysGCA is incorporated into the RISC complex and interacts with Argonaute proteins, suggesting its involvement in the regulation of gene expression. However, the general role and effect of the deregulation of 3′-tRF-CysGCA levels in human cells have not been investigated so far. To fill this gap, we stably overexpressed 3′-tRF-CysGCA in HEK-293 cells and performed transcriptomic and proteomic analyses. Moreover, we validated the interaction of this tRF with putative targets, the levels of which were found to be affected by 3′-tRF-CysGCA overexpression. Lastly, we investigated the implication of 3′-tRF-CysGCA in various pathways using extensive bioinformatics analysis. Our results indicate that 3′-tRF-CysGCA overexpression led to changes in the global gene expression profile of HEK-293 cells and that multiple cellular pathways were affected by the deregulation of the levels of this tRF. Additionally, we demonstrated that 3′-tRF-CysGCA directly interacts with thymopoietin (TMPO) transcript variant 1 (also known as LAP2α), leading to modulation of its levels. In conclusion, our findings suggest that 3′-tRF-CysGCA plays a significant role in gene expression regulation and highlight the importance of this tRF in cellular processes. [ABSTRACT FROM AUTHOR]