Intra-Individual Paired Mass Spectrometry Dataset for Decoding Solar-Induced Proteomic Changes in Facial Skin.

Photoaging is the premature aging of the skin caused by prolonged exposure to solar radiation. The visual alterations manifest as wrinkles, reduced skin elasticity, uneven skin tone, as well as other signs that surpass the expected outcomes of natural aging. Beyond these surface changes, there is a complex interplay of molecular alterations, encompassing shifts in cellular function, DNA damage, and protein composition disruptions. This data descriptor introduces a unique dataset derived from ten individuals, each with a minimum of 18 years of professional experience as a driver, who are asymmetrically and chronically exposed to solar radiation due to their driving orientation. Skin samples were independently collected from each side of the face using a microdermabrasion-like procedure and analyzed on an Exploris 240 mass spectrometer. Our adapted proteomic statistical framework leverages the sample pairing to provide robust insights. This dataset delves into the molecular differences in exposed skin and serves as a foundational resource for interdisciplinary research in photodermatology, targeted skincare treatments, and computational modelling of skin health.

Proteomic Analysis of a Rat Streptozotocin Model Shows Dysregulated Biological Pathways Implicated in Alzheimer's Disease.

Alzheimer's Disease (AD) is an age-related neurodegenerative disorder characterized by progressive memory loss and cognitive impairment, affecting 35 million individuals worldwide. Intracerebroventricular (ICV) injection of low to moderate doses of streptozotocin (STZ) in adult male Wistar rats can reproduce classical physiopathological hallmarks of AD. This biological model is known as ICV-STZ. Most studies are focused on the description of behavioral and morphological aspects of the ICV-STZ model. However, knowledge regarding the molecular aspects of the ICV-STZ model is still incipient. Therefore, this work is a first attempt to provide a wide proteome description of the ICV-STZ model based on mass spectrometry (MS). To achieve that, samples from the pre-frontal cortex (PFC) and hippocampus (HPC) of the ICV-STZ model and control (wild-type) were used. Differential protein abundance, pathway, and network analysis were performed based on the protein identification and quantification of the samples. Our analysis revealed dysregulated biological pathways implicated in the early stages of late-onset Alzheimer's disease (LOAD), based on differentially abundant proteins (DAPs). Some of these DAPs had their mRNA expression further investigated through qRT-PCR. Our results shed light on the AD onset and demonstrate the ICV-STZ as a valid model for LOAD proteome description.

Urinary proteomics reveals biological processes related to acute kidney injury in Bothrops atrox envenomings.

Acute kidney injury (AKI) is a critical systemic complication caused by Bothrops envenoming, a neglected health problem in the Brazilian Amazon. Understanding the underlying mechanisms leading to AKI is crucial for effectively mitigating the burden of this complication. This study aimed to characterize the urinary protein profile of Bothrops atrox snakebite victims who developed AKI. We analyzed three groups of samples collected on admission: healthy subjects (controls, n = 10), snakebite victims who developed AKI (AKI, n = 10), and those who did not evolve to AKI (No-AKI, n = 10). Using liquid-chromatography tandem mass spectrometry, we identified and quantified (label-free) 1190 proteins. A panel of 65 proteins was identified exclusively in the urine of snakebite victims, with 32 exclusives to the AKI condition. Proteins more abundant or exclusive in AKI's urine were associated with acute phase response, endopeptidase inhibition, complement cascade, and inflammation. Notable proteins include serotransferrin, SERPINA-1, alpha-1B-glycoprotein, and NHL repeat-containing protein 3. Furthermore, evaluating previously reported biomarkers candidates for AKI and renal injury, we found retinol-binding protein, beta-2-microglobulin, cystatin-C, and hepcidin to be significant in cases of AKI induced by Bothrops envenoming. This work sheds light on physiological disturbances caused by Bothrops envenoming, highlighting potential biological processes contributing to AKI. Such insights may aid in better understanding and managing this life-threatening complication.

Proteomics reveals differentially regulated pathways when comparing grade 2 and 4 astrocytomas.

Astrocytic tumors are known for their high progression capacity and high mortality rates; in this regard, proteins correlated to prognosis can aid medical conduct. Although several genetic changes related to progression from grade 2 to grade 4 astrocytoma are already known, mRNA copies do not necessarily correlate with protein abundance and therefore could shadow further comprehension about this tumor's biology. This motivates us to seek for complementary strategies to study tumor progression at the protein level. Here we compare the proteomic profile of biopsies from patients with grade 2 (diffuse, n = 6) versus grade 4 astrocytomas (glioblastomas, n = 10) using shotgun proteomics. Data analysis performed with PatternLab for proteomics identified 5,206 and 6,004 proteins in the 2- and 4-grade groups, respectively. Our results revealed seventy-four differentially abundant proteins (p < 0.01); we then shortlist those related to greater malignancy. We also describe molecular pathways distinctly activated in the two groups, such as differences in the organization of the extracellular matrix, decisive both in tumor invasiveness and in signaling for cell division, which, together with marked contrasts in energy metabolism, are determining factors in the speed of growth and dissemination of these neoplasms. The degradation pathways of GABA, enriched in the grade 2 group, is consistent with a favorable prognosis. Other functions such as platelet degranulation, apoptosis, and activation of the MAPK pathway were correlated to grade 4 tumors and, consequently, unfavorable prognoses. Our results provide an important survey of molecular pathways involved in glioma pathogenesis for these histopathological groups.

Beyond the identifiable proteome: Delving into the proteomics of polymyxin-resistant and non-resistant Acinetobacter baumannii from Brazilian hospitals.

This work discloses a unique, comprehensive proteomic dataset of Acinetobacter baumannii strains, both resistant and non-resistant to polymyxin B, isolated in Brazil generated using Orbitrap Fusion Lumos. From nearly 4 million tandem mass spectra, the software DiagnoMass produced 240,685 quality-filtered mass spectral clusters, of which PatternLab for proteomics identified 44,553 peptides mapping to 3479 proteins. Crucially, DiagnoMass shortlisted 3550 and 1408 unique mass spectral clusters for the resistant and non-resistant strains, respectively, with only about a third with sequences (and PTMs) identified by PatternLab. Further open-search attempts via FragPipe yielded an additional ∼20% identifications, suggesting the remaining unidentified spectra likely arise from complex combinations of post-translational modifications and amino-acid substitutions. This highlights the untapped potential of the dataset for future discoveries, particularly given the importance of PTMs, which remain elusive to nucleotide sequencing approaches but are crucial for understanding biological mechanisms. Our innovative approach extends beyond the identifications that are typically subjected to the bias of a search engine; we discern which spectral clusters are differential and subject them to increased scrutiny, akin to spectral library matching by comparing captured spectra to themselves. Our analysis reveals adaptations in the resistant strain, including enhanced detoxification, altered protein synthesis, and metabolic adjustments. SIGNIFICANCE: We present comprehensive proteomic profiles of non-resistant and resistant Acinetobacter baumannii from Brazilian Hospitals strains, and highlight the presence of discriminative and yet unidentified mass spectral clusters. Our work emphasizes the importance of exploring this overlooked data, as it could hold the key to understanding the complex dynamics of antibiotic resistance. This approach not only informs antimicrobial stewardship efforts but also paves the way for the development of innovative diagnostic tools. Thus, our findings have profound implications for the field, as far as methods for providing a new perspective on diagnosing antibiotic resistance as well as classifying proteomes in general.

PatternLab V Handles Multiplex Spectra in Shotgun Proteomic Searches and Increases Identification.

Complex protein mixtures typically generate many tandem mass spectra produced by different peptides coisolated in the gas phase. Widely adopted proteomic data analysis environments usually fail to identify most of these spectra, succeeding at best in identifying only one of the multiple cofragmenting peptides. We present PatternLab V (PLV), an updated version of PatternLab that integrates the YADA 3 deconvolution algorithm to handle such cases efficiently. In general, we expect an increase of 10% in spectral identifications when dealing with complex proteomic samples. PLV is freely available at http://patternlabforproteomics.org.

DiagnoMass: A proteomics hub for pinpointing discriminative spectral clusters.

MOTIVATION: There are several well-established paradigms for identifying and pinpointing discriminative peptides/proteins using shotgun proteomic data; examples are peptide-spectrum matching, de novo sequencing, open searches, and even hybrid approaches. Such an arsenal of complementary paradigms can provide deep data coverage, albeit some unidentified discriminative peptides remain. RESULTS: We present DiagnoMass, software tool that groups similar spectra into spectral clusters and then shortlists those clusters that are discriminative for biological conditions. DiagnoMass then communicates with proteomic tools to attempt the identification of such clusters. We demonstrate the effectiveness of DiagnoMass by analyzing proteomic data from Escherichia coli, Salmonella, and Shigella, listing many high-quality discriminative spectral clusters that had thus far remained unidentified by widely adopted proteomic tools. DiagnoMass can also classify proteomic profiles. We anticipate the use of DiagnoMass as a vital tool for pinpointing biomarkers. AVAILABILITY: DiagnoMass and related documentation, including a usage protocol, are available at http://www.diagnomass.com.

Simple, efficient and thorough shotgun proteomic analysis with PatternLab V.

Shotgun proteomics aims to identify and quantify the thousands of proteins in complex mixtures such as cell and tissue lysates and biological fluids. This approach uses liquid chromatography coupled with tandem mass spectrometry and typically generates hundreds of thousands of mass spectra that require specialized computational environments for data analysis. PatternLab for proteomics is a unified computational environment for analyzing shotgun proteomic data. PatternLab V (PLV) is the most comprehensive and crucial update so far, the result of intensive interaction with the proteomics community over several years. All PLV modules have been optimized and its graphical user interface has been completely updated for improved user experience. Major improvements were made to all aspects of the software, ranging from boosting the number of protein identifications to faster extraction of ion chromatograms. PLV provides modules for preparing sequence databases, protein identification, statistical filtering and in-depth result browsing for both labeled and label-free quantitation. The PepExplorer module can even pinpoint de novo sequenced peptides not already present in the database. PLV is of broad applicability and therefore suitable for challenging experimental setups, such as time-course experiments and data handling from unsequenced organisms. PLV interfaces with widely adopted software and community initiatives, e.g., Comet, Skyline, PEAKS and PRIDE. It is freely available at http://www.patternlabforproteomics.org .

Proteogenomic Analysis Reveals Proteins Involved in the First Step of Adipogenesis in Human Adipose-Derived Stem Cells.

BACKGROUND: Obesity is characterized as a disease that directly affects the whole-body metabolism and is associated with excess fat mass and several related comorbidities. Dynamics of adipocyte hypertrophy and hyperplasia play an important role in health and disease, especially in obesity. Human adipose-derived stem cells (hASC) represent an important source for understanding the entire adipogenic differentiation process. However, little is known about the triggering step of adipogenesis in hASC. Here, we performed a proteogenomic approach for understanding the protein abundance alterations during the initiation of the adipogenic differentiation process. METHODS: hASC were isolated from adipose tissue of three donors and were then characterized and expanded. Cells were cultured for 24 hours in adipogenic differentiation medium followed by protein extraction. We used shotgun proteomics to compare the proteomic profile of 24 h-adipogenic, differentiated, and undifferentiated hASC. We also used our previous next-generation sequencing data (RNA-seq) of the total and polysomal mRNA fractions of hASC to study posttranscriptional regulation during the initial steps of adipogenesis. RESULTS: We identified 3420 proteins out of 48,336 peptides, of which 92 proteins were exclusively identified in undifferentiated hASC and 53 proteins were exclusively found in 24 h-differentiated cells. Using a stringent criterion, we identified 33 differentially abundant proteins when comparing 24 h-differentiated and undifferentiated hASC (14 upregulated and 19 downregulated, respectively). Among the upregulated proteins, we shortlisted several adipogenesis-related proteins. A combined analysis of the proteome and the transcriptome allowed the identification of positive correlation coefficients between proteins and mRNAs. CONCLUSIONS: These results demonstrate a specific proteome profile related to adipogenesis at the beginning (24 hours) of the differentiation process in hASC, which advances the understanding of human adipogenesis and obesity. Adipogenic differentiation is finely regulated at the transcriptional, posttranscriptional, and posttranslational levels.

Proteomic changes in Trypanosoma cruzi epimastigotes treated with the proapoptotic compound PAC-1.

Apoptosis is a highly regulated process of cell death in metazoans. Therefore, understanding the biochemical changes associated with apoptosis-like death in Trypanosoma cruzi is key to drug development. PAC-1 was recently shown to induce apoptosis in T. cruzi; with this as motivation, we used quantitative proteomics to unveil alterations of PAC-1-treated versus untreated epimastigotes. The PAC-1 treatment reduced the abundance of putative vesicle-associated membrane protein, putative eukaryotic translation initiation factor 1 eIF1, coatomer subunit beta, putative amastin, and a putative cytoskeleton-associated protein. Apoptosis-like signaling also increases the abundance of proteins associated with actin cytoskeleton remodeling, cell polarization, apoptotic signaling, phosphorylation, methylation, ergosterol biosynthesis, vacuolar proteins associated with autophagy, and flagellum motility. We shortlist seventeen protein targets for possible use in chemotherapy for Chagas disease. Almost all differentially abundant proteins belong to a family of proteins previously associated with apoptosis in metazoans, suggesting that the apoptotic pathway's key functions have been preserved from trypanosomatids and metazoans. SIGNIFICANCE: Approximately 8 million people worldwide are infected with Trypanosoma cruzi. The treatment of Chagas disease comprises drugs with severe side effects, thus limiting their application. Thus, developing new pharmaceutical solutions is relevant, and several molecules targeting apoptosis are therapeutically efficient for parasitic, cardiac, and neurological diseases. Apoptotic processes lead to specific morphological features that have been previously observed in T. cruzi. Here, we investigate changes in epimastigotes' proteomic profile treated with the proapoptotic compound PAC-1, providing data concerning the regulation of both metabolic and cellular processes in nonmetazoan apoptotic cells. We shortlist seventeen protein target candidates for use in chemotherapy for Chagas disease.

The impact of blood-processing time on the proteome of human peripheral blood mononuclear cells.

Human peripheral blood mononuclear cells (PBMC) are key to several diagnostics assays and basic science research. Blood pre-analytical variations that occur before obtaining the PBMC fraction can significantly impact the assays results, including viability, composition, integrity, and gene expression changes of immune cells. With this as motivation, we performed a quantitative shotgun proteomics analysis using Isobaric Tag for Relative and Absolute Quantitation (iTRAQ 8plex) labeling to compare PBMC obtained from 24 h-stored blood at room temperature versus freshly isolated. We identified a total of 3195 proteins, of which 245 were differentially abundant (101 upregulated and 144 downregulated). Our results revealed enriched pathways of downregulated proteins related to exocytosis, localization, vesicle-mediated transport, cell activation, and secretion. In contrast, pathways related to exocytosis, neutrophil degranulation and activation, granulocyte activation, leukocyte degranulation, and myeloid leukocyte activation involved in immune response were enriched in upregulated proteins, which may indicate probable granulocyte contamination and activation due to blood storage time and temperature. Examples of upregulated proteins in the 24 h-PBMC samples are CAMP, S100A8, LTA4H, RASAL3, and S100A6, which are involved in an adaptive immune system and antimicrobial activity, proinflammatory mediation, aminopeptidase activities, and naïve T cells survival. Moreover, examples of downregulated proteins are NDUFA5, TAGLN2, H3C1, TUBA8, and CCT2 that are related to the cytoskeleton, cell junction, mitochondrial respiratory chain. In conclusion, the delay in blood-processing time directly impacts the proteomic profile of human PBMC, possibly through granulocyte contamination and activation.

Proteomics reveals that quinoa bioester promotes replenishing effects in epidermal tissue.

The continuous search for natural products that attenuate age-related losses has increasingly gained notice; among them, those applicable for skin care have drawn significant attention. The bioester generated from the Chenopodium quinoa's oil is a natural-origin ingredient described to produce replenishing skin effects. With this as motivation, we used shotgun proteomics to study the effects of quinoa bioester on human reconstructed epidermis tridimensional cell cultures after 0, 3, 6, 12, 24, and 48 h of exposure. Our experimental setup employed reversed-phase nano-chromatography coupled online with an Orbitrap-XL and PatternLab for proteomics as the data analysis tool. Extracted ion chromatograms were obtained as surrogates for relative peptide quantitation. Our findings spotlight proteins with increased abundance, as compared to the untreated cell culture counterparts at the same timepoints, that were related to preventing premature aging, homeostasis, tissue regeneration, protection against ultraviolet radiation and oxidative damage.

Proteomics pinpoints alterations in grade I meningiomas of male versus female patients.

Meningiomas are among the most common primary tumors of the central nervous system (CNS) and originate from the arachnoid or meningothelial cells of the meninges. Surgery is the first option of treatment, but depending on the location and invasion patterns, complete removal of the tumor is not always feasible. Reports indicate many differences in meningiomas from male versus female patients; for example, incidence is higher in females, whereas males usually develop the malignant and more aggressive type. With this as motivation, we used shotgun proteomics to compare the proteomic profile of grade I meningioma biopsies of male and female patients. Our results listed several differentially abundant proteins between the two groups; some examples are S100-A4 and proteins involved in RNA splicing events. For males, we identified enriched pathways for cell-matrix organization and for females, pathways related to RNA transporting and processing. We believe our findings contribute to the understanding of the molecular differences between grade I meningiomas of female and male patients.

Mixed-Data Acquisition: Next-Generation Quantitative Proteomics Data Acquisition.

We present the Mixed-Data Acquisition (MDA) strategy for mass spectrometry data acquisition. MDA combines Data-Dependent Acquisition (DDA) and Data-Independent Acquisition (DIA) in the same run, thus doing away with the requirements for separate DDA spectral libraries. MDA is a natural result from advances in mass spectrometry, such as high scan rates and multiple analyzers, and is tailored toward exploiting these features. We demonstrate MDA's effectiveness on a yeast proteome analysis by overcoming a common bottleneck for XIC-based label-free quantitation; namely, the coelution of precursors when m/z values cannot be distinguished. We anticipate that MDA will become the next mainstream data generation approach for proteomics. MDA can also serve as an orthogonal validation approach for DDA experiments. Specialized software for MDA data analysis is made available on the project's website.

Mixed-data acquisition: next-generation quantitative proteomics data acquisition

We present the Mixed-Data Acquisition (MDA) strategy for mass spectrometry data acquisition. MDA combines Data-Dependent Acquisition (DDA) and Data-Independent Acquisition (DIA) in the same run, thus doing away with the requirements for separate DDA spectral libraries. MDA is a natural result from advances in mass spectrometry, such as high scan rates and multiple analyzers, and is tailored toward exploiting these features. We demonstrate MDA's effectiveness on a yeast proteome analysis by overcoming a common bottleneck for XIC-based label-free quantitation; namely, the coelution of precursors when m/z values cannot be distinguished. We anticipate that MDA will become the next mainstream data generation approach for proteomics. MDA can also serve as an orthogonal validation approach for DDA experiments. Specialized software for MDA data analysis is made available on the project's website.

A quantitation module for isotope-labeled peptides integrated into PatternLab for proteomics.

We present a new module integrated into the widely adopted PatternLab for proteomics to enable analysis of isotope-labeled peptides produced using dimethyl or SILAC. The accurate quantitation of proteins lies within the heart of proteomics; dimethylation has shown to be reliable, inexpensive, and applicable to any sample type. We validate our algorithm using an M. tuberculosis dataset obtained from two biological conditions; we used three dimethyl labels, one serving as an internal control for labeling a mixture of samples from both biological conditions. This internal control certified the proper functioning of our software. Availability: http://patternlabforproteomics.org, freely available for academic use.

Characterization of homodimer interfaces with cross-linking mass spectrometry and isotopically labeled proteins.

Cross-linking coupled with mass spectrometry (XL-MS) has emerged as a powerful strategy for the identification of protein-protein interactions, characterization of interaction regions, and obtainment of structural information on proteins and protein complexes. In XL-MS, proteins or complexes are covalently stabilized with cross-linkers and digested, followed by identification of the cross-linked peptides by tandem mass spectrometry (MS/MS). This provides spatial constraints that enable modeling of protein (complex) structures and regions of interaction. However, most XL-MS approaches are not capable of differentiating intramolecular from intermolecular links in multimeric complexes, and therefore they cannot be used to study homodimer interfaces. We have recently developed an approach that overcomes this limitation by stable isotope-labeling of one of the two monomers, thereby creating a homodimer with one 'light' and one 'heavy' monomer. Here, we describe a step-by-step protocol for stable isotope-labeling, followed by controlled denaturation and refolding in the presence of the wild-type protein. The resulting light-heavy dimers are cross-linked, digested, and analyzed by mass spectrometry. We show how to quantitatively analyze the corresponding data with SIM-XL, an XL-MS software with a module tailored toward the MS/MS data from homodimers. In addition, we provide a video tutorial of the data analysis with this protocol. This protocol can be performed in ∼14 d, and requires basic biochemical and mass spectrometry skills.

Comparing intestinal versus diffuse gastric cancer using a PEFF-oriented proteomic pipeline.

Gastric cancer is the fifth most common malignant neoplasia and the third leading cause of cancer death worldwide. Mac-Cormick et al. recently showed the importance of considering the anatomical region of the tumor in proteomic gastric cancer studies; more differences were found between distinct anatomical regions than when comparing healthy versus diseased tissue. Thus, failing to consider the anatomical region could lead to differential proteins that are not disease specific. With this as motivation, we compared the proteomic profiles of intestinal and diffuse adenocarcinoma from the same anatomical region, the corpus. To achieve this, we used isobaric labeling (iTRAQ) of peptides, a 10-step HILIC fractionation, and reversed-phase nano-chromatography coupled online with a Q-Exactive Plus mass spectrometer. We updated PatternLab to take advantage of the new Comet-PEFF search engine that enables identifying post-translational modifications and mutations included in neXtProt's PSI Extended FASTA Format (PEFF) metadata. Our pipeline then uses a text-mining tool that automatically extracts PubMed IDs from the proteomic result metadata and drills down keywords from manuscripts related with the biological processes at hand. Our results disclose important proteins such as apolipoprotein B-100, S100 and 14-3-3 proteins, among many others, highlighting the different pathways enriched by each cancer type. SIGNIFICANCE: Gastric cancer is a heterogeneous and multifactorial disease responsible for a significant number of deaths every year. Despite the constant improvement of surgical techniques and multimodal treatments, survival rates are low, mostly due to limited diagnostic techniques and late symptoms. Intestinal and diffuse types of gastric cancer have distinct clinical and pathological characteristics; yet little is known about the molecular mechanisms regulating these two types of gastric tumors. Here we compared the proteomic profile of diffuse and intestinal types of gastric cancer from the same anatomical location, the corpus, from four male patients. This methodological design aimed to eliminate proteomic variations resulting from comparison of tumors from distinct anatomical regions. Our PEFF-tailored proteomic pipeline significantly increased the identifications as when compared to previous versions of PatternLab.

A multi-protease, multi-dissociation, bottom-up-to-top-down proteomic view of the Loxosceles intermedia venom.

Venoms are a rich source for the discovery of molecules with biotechnological applications, but their analysis is challenging even for state-of-the-art proteomics. Here we report on a large-scale proteomic assessment of the venom of Loxosceles intermedia, the so-called brown spider. Venom was extracted from 200 spiders and fractioned into two aliquots relative to a 10 kDa cutoff mass. Each of these was further fractioned and digested with trypsin (4 h), trypsin (18 h), pepsin (18 h), and chymotrypsin (18 h), then analyzed by MudPIT on an LTQ-Orbitrap XL ETD mass spectrometer fragmenting precursors by CID, HCD, and ETD. Aliquots of undigested samples were also analyzed. Our experimental design allowed us to apply spectral networks, thus enabling us to obtain meta-contig assemblies, and consequently de novo sequencing of practically complete proteins, culminating in a deep proteome assessment of the venom. Data are available via ProteomeXchange, with identifier PXD005523.

The Point Mutation G461S in the MfCYP51 Gene is Associated with Tebuconazole Resistance in Monilinia fructicola Populations in Brazil.

The ascomycete Monilinia fructicola is the causal agent of brown rot of stone fruit in Brazil, causing major pre- and postharvest losses. For many years, the demethylation inhibitor (DMI) fungicide tebuconazole has been used as the most effective active ingredient for controlling brown rot and, as a result, strains of M. fructicola resistant to this ingredient have emerged in many Brazilian states producing stone fruit. The aim of this study was to investigate the mechanisms associated with the resistance of M. fructicola to DMI tebuconazole. By sequencing the M. fructicola CYP51 (MfCYP51) gene, encoding the azole target sterol 14α-demethylase, a mutation was identified at the nucleotide position 1,492, causing the amino acid substitution from glycine to serine at the codon position 461, associated with reduced tebuconazole sensitivity. In addition, it was observed that MfCYP51 gene expression could play a secondary role in DMI fungicide resistance of M. fructicola strains in Brazil. However, for the specific isolate found to exhibit elevated expression levels of MfCYP51, no insertions that would trigger gene expression were found. Based on the point mutation associated with tebuconazole resistance, an allele-specific polymerase chain reaction method was developed to quickly identify resistant genotypes within the Brazilian population. This is the first report determining molecular mechanisms for DMI resistance identification for M. fructicola isolates from Brazil. This information provides an important advancement for risk assessment of DMI fungicides used to manage brown rot of stone fruit.