Proteomic Features Research Articles

The Comparable Microenvironment Shared by Colorectal Adenoma and Carcinoma: An Evidence of Stromal Proteomics.

Tumor microenvironment (TME) is a key factor involved in cancer development and metastasis. In the TME of colorectal cancer (CRC), the gene expression status of stromal tissues could influence the CRC process from normal to adenoma then carcinoma; however, the expression status at the protein level has not yet been well evaluated. A total of 22 CRC patients were recruited for this study, and the tissue regions corresponding with adjacent, adenoma, and carcinoma were carefully excised by laser capture microdissection (LCM), including a patient with adenoma and carcinoma. The individual proteomes of this cohort were implemented by high-resolution mass spectrometer under data-independent acquisition (DIA) mode. A series of informatic analysis was employed to statistically seek the proteomic characteristics related with the stroma at different stages of CRC. The identified proteins in the colorectal stromal tissues were much less than and almost overlapped with that in the corresponding epithelial tissues; however, the patterns of protein abundance in the stroma were very distinct from those in the epithelium. Although qualitative and quantitative analysis delineated the epithelial proteins specifically typified in the adjacent, adenoma, and carcinoma, the informatics in the stroma led to another deduction that such proteomes were only divided into two patterns, adjacent- and adenoma/carcinoma-dependent. The comparable proteomes of colorectal adenoma and carcinoma were further confirmed by the bulk preparation- or individual LCM-proteomics. The biochemical features of the tumor stromal proteomes were characterized as enrichment of CD4+ and CD8+ T cells, upregulated pathways of antigen presentation, and enhancement of immune signal interactions. Finally, the features of lymphoid lineages in tumor stroma were verified by tissue microarray (TMA). Based on the proteomic evidence, a hypothesis was raised that in the colorectal tissue, the TME of adenoma and carcinoma were comparable, whereas the key elements driving an epithelium from benign to malignant were likely decided by the changes of genomic mutations or/and expression within it.

Development of cognition decline in non-acute symptomatic patients with cerebral small vessel disease: Non-Acute Symptomatic Cerebral Ischemia Registration study (NASCIR)—rationale and protocol for a prospective multicentre observational study

IntroductionHeadaches, dizziness and memory loss of unspecific causes are the most common non-acute ischemia symptoms in the ageing population, which are often associated with cerebral small vessel disease (CSVD) imaging...

Precision Medicine in Lung Cancer Theranostics: Paving the Way from Traditional Technology to Advance Era

Precision medicine for lung cancer theranostics is an advanced model combining prevention, diagnosis, and treatment for individual or specific population diseases to match individual patient differences. It involves collection and integration of genome, transcriptome, proteome, and metabolome features of lung cancer patients, combined with clinical characteristics. Subsequently, large data and artificial intelligence (AI) analysis have emerged to identify the most suitable therapeutic targets and personal treatment strategies for treatment of patients with lung cancer. We review the development and challenges associated with diagnosis and therapy of lung cancer from traditional technology, including immunotherapy prediction markers, liquid biopsy, surgery, and tumor immune microenvironment and patient-derived xenograft models, to AI in the era of precision medicine. AI has improved precision medicine and the predictive ability and accuracy of patient outcomes. Finally, we discuss some opportunities and challenges for lung cancer theranostics. Precision medicine in lung cancer can help us find the optimum treatment dose and time for a specific patient, which can advance the development of lung cancer therapeutics.

Reiterative Modelling of Combined Transcriptomic and Proteomic Features Refines and Improves the Prediction of Early Recurrence in Squamous Cell Carcinoma of Head and Neck

Reiterative Modelling of Combined Transcriptomic and Proteomic Features Refines and Improves the Prediction of Early Recurrence in Squamous Cell Carcinoma of Head and Neck

Mycoplasma tauri sp. nov. isolated from the bovine genital tract

Since 2006, a Mycoplasma species unidentifiable to the species level has been regularly isolated from the semen and prepuce of apparently healthy bulls, and occasionally from cattle displaying inflammatory disease of the genital tract. Seven of these Mycoplasma isolates were subjected to a comprehensive taxonomic study. The strains investigated grew well in modified Hayflick’s medium and colonies on agar exhibited typical fried egg morphology and produced ‘film and spots’. Transmission electron microscopy revealed a cell morphology characteristic of mycoplasmas with spherically shaped cells bounded by a bi-layered cell membrane. The strains studied neither produced acid from sugar carbon sources nor did hydrolyse arginine or urea, and genome annotation indicated that organic acids (pyruvate, lactate) are used as energy sources. Phylogenetic analyses of 16S rRNA gene sequences, the 16S-23S intergenic spacer region, and partial rpoB gene and protein sequences placed the strains within the Mycoplasma (M.) bovis cluster of the Hominis group with M. primatum, M. agalactiae, and M. bovis being their closest relatives. Genomic information including whole-genome similarity metrics (ANIb, ANIm, TETRA, dDDH, AAI) and phylogenomics, proteomic features revealed by matrix-assisted laser desorption ionization time of flight (MALDI-ToF) mass spectrometry as well as serological reactions and polar lipid profiling strongly indicated that the strains examined were representatives of a hitherto unclassified species of genus Mycoplasma, for which the name Mycoplasma tauri sp. nov. with type strain Zaradi2T (=ATCC BAA-1891T = DSM 22451T) is proposed.

From Molecules to Behavior in Long-Term Inorganic Mercury Intoxication: Unraveling Proteomic Features in Cerebellar Neurodegeneration of Rats.

Mercury is a severe environmental pollutant with neurotoxic effects, especially when exposed for long periods. Although there are several evidences regarding mercury toxicity, little is known about inorganic mercury (IHg) species and cerebellum, one of the main targets of mercury associated with the neurological symptomatology of mercurial poisoning. Besides that, the global proteomic profile assessment is a valuable tool to screen possible biomarkers and elucidate molecular targets of mercury neurotoxicity; however, the literature is still scarce. Thus, this study aimed to investigate the effects of long-term exposure to IHg in adult rats’ cerebellum and explore the modulation of the cerebellar proteome associated with biochemical and functional outcomes, providing evidence, in a translational perspective, of new mercury toxicity targets and possible biomarkers. Fifty-four adult rats were exposed to 0.375 mg/kg of HgCl2 or distilled water for 45 days using intragastric gavage. Then, the motor functions were evaluated by rotarod and inclined plane. The cerebellum was collected to quantify mercury levels, to assess the antioxidant activity against peroxyl radicals (ACAPs), the lipid peroxidation (LPO), the proteomic profile, the cell death nature by cytotoxicity and apoptosis, and the Purkinje cells density. The IHg exposure increased mercury levels in the cerebellum, reducing ACAP and increasing LPO. The proteomic approach revealed a total 419 proteins with different statuses of regulation, associated with different biological processes, such as synaptic signaling, energy metabolism and nervous system development, e.g., all these molecular changes are associated with increased cytotoxicity and apoptosis, with a neurodegenerative pattern on Purkinje cells layer and poor motor coordination and balance. In conclusion, all these findings feature a neurodegenerative process triggered by IHg in the cerebellum that culminated into motor functions deficits, which are associated with several molecular features and may be related to the clinical outcomes of people exposed to the toxicant.

Urine proteomics identifies biomarkers for diabetic kidney disease at different stages

BackgroundType 2 diabetic kidney disease is the most common cause of chronic kidney diseases (CKD) and end-stage renal diseases (ESRD). Although kidney biopsy is considered as the ‘gold standard’ for diabetic kidney disease (DKD) diagnosis, it is an invasive procedure, and the diagnosis can be influenced by sampling bias and personal judgement. It is desirable to establish a non-invasive procedure that can complement kidney biopsy in diagnosis and tracking the DKD progress.MethodsIn this cross-sectional study, we collected 252 urine samples, including 134 uncomplicated diabetes, 65 DKD, 40 CKD without diabetes and 13 follow-up diabetic samples, and analyzed the urine proteomes with liquid chromatography coupled with tandem mass spectrometry (LC–MS/MS). We built logistic regression models to distinguish uncomplicated diabetes, DKD and other CKDs.ResultsWe quantified 559 ± 202 gene products (GPs) (Mean ± SD) on a single sample and 2946 GPs in total. Based on logistic regression models, DKD patients could be differentiated from the uncomplicated diabetic patients with 2 urinary proteins (AUC = 0.928), and the stage 3 (DKD3) and stage 4 (DKD4) DKD patients with 3 urinary proteins (AUC = 0.949). These results were validated in an independent dataset. Finally, a 4-protein classifier identified putative pre-DKD3 patients, who showed DKD3 proteomic features but were not diagnosed by clinical standards. Follow-up studies on 11 patients indicated that 2 putative pre-DKD patients have progressed to DKD3.ConclusionsOur study demonstrated the potential for urinary proteomics as a noninvasive method for DKD diagnosis and identifying high-risk patients for progression monitoring.

The telomere length landscape of prostate cancer

Replicative immortality is a hallmark of cancer, and can be achieved through telomere lengthening and maintenance. Although the role of telomere length in cancer has been well studied, its association to genomic features is less well known. Here, we report the telomere lengths of 392 localized prostate cancer tumours and characterize their relationship to genomic, transcriptomic and proteomic features. Shorter tumour telomere lengths are associated with elevated genomic instability, including single-nucleotide variants, indels and structural variants. Genes involved in cell proliferation and signaling are correlated with tumour telomere length at all levels of the central dogma. Telomere length is also associated with multiple clinical features of a tumour. Longer telomere lengths in non-tumour samples are associated with a lower rate of biochemical relapse. In summary, we describe the multi-level integration of telomere length, genomics, transcriptomics and proteomics in localized prostate cancer.

Proteomic profiling identifies signatures associated with progression of precancerous gastric lesions and risk of early gastric cancer.

BackgroundMolecular features underlining the multistage progression of gastric lesions and development of early gastric cancer (GC) are poorly understood, restricting the ability to GC prevention and management.MethodsWe portrayed proteomic landscape and explored proteomic signatures associated with progression of gastric lesions and risk of early GC. Tissue proteomic profiling was conducted for a total of 324 subjects. A case-control study was performed in the discovery stage (n=169) based on populations from Linqu, a known high-risk area for GC in China. We then conducted two-stage validation, including a cohort study from Linqu (n = 56), with prospective follow-up for progression of gastric lesions (280–473 days), and an independent case-control study from Beijing (n = 99).FindingsThere was a clear distinction in proteomic features for precancerous gastric lesions and GC. We derived four molecular subtypes of gastric lesions and identified subtype-S4 with the highest progression risk. We found 104 positively-associated and 113 inversely-associated proteins for early GC, with APOA1BP, PGC, HPX and DDT associated with the risk of gastric lesion progression. Integrating these proteomic signatures, the ability to predict progression of gastric lesions was significantly strengthened (areas-under-the-curve=0.88 (95%CI: 0.78–0.99) vs. 0.56 (0.36–0.76), Delong's P = 0.002). Immunohistochemistry assays and examination at mRNA level validated the findings for four proteins.InterpretationWe defined proteomic signatures for progression of gastric lesions and risk of early GC, which may have translational significance for identifying particularly high-risk population and detecting GC at an early stage, improving potential for targeted GC prevention.FundingThe funders are listed in the Acknowledgement.

Diagnostic and prognostic implications of a three-antibody molecular subtyping algorithm for non-muscle invasive bladder cancer.

Intrinsic molecular subtypes may explain marked variation between bladder cancer patients in prognosis and response to therapy. Complex testing algorithms and little attention to more prevalent, early‐stage (non‐muscle invasive) bladder cancers (NMIBCs) have hindered implementation of subtyping in clinical practice. Here, using a three‐antibody immunohistochemistry (IHC) algorithm, we identify the diagnostic and prognostic associations of well‐validated proteomic features of basal and luminal subtypes in NMIBC. By IHC, we divided 481 NMIBCs into basal (GATA3−/KRT5+) and luminal (GATA3+/KRT5 variable) subtypes. We further divided the luminal subtype into URO (p16 low), URO‐KRT5+ (KRT5+), and genomically unstable (GU) (p16 high) subtypes. Expression thresholds were confirmed using unsupervised hierarchical clustering. Subtypes were correlated with pathology and outcomes. All NMIBC cases clustered into the basal/squamous (basal) or one of the three luminal (URO, URO‐KRT5+, and GU) subtypes. Although uncommon in this NMIBC cohort, basal tumors (3%, n = 16) had dramatically higher grade (100%, n = 16, odds ratio [OR] = 13, relative risk = 3.25) and stage, and rapid progression to muscle invasion (median progression‐free survival = 35.4 months, p = 0.0001). URO, the most common subtype (46%, n = 220), showed rapid recurrence (median recurrence‐free survival [RFS] = 11.5 months, p = 0.039) compared to its GU counterpart (29%, n = 137, median RFS = 16.9 months), even in patients who received intravesical immunotherapy (p = 0.049). URO‐KRT5+ tumors (22%, n = 108) were typically low grade (66%, n = 71, OR = 3.7) and recurred slowly (median RFS = 38.7 months). Therefore, a simple immunohistochemical algorithm can identify clinically relevant molecular subtypes of NMIBC. In routine clinical practice, this three‐antibody algorithm may help clarify diagnostic dilemmas and optimize surveillance and treatment strategies for patients.

Effects of hydrogen peroxide on Scenedesmus obliquus: Cell growth, antioxidant enzyme activity and intracellular protein fingerprinting

Hydrogen peroxide (H2O2) is an environmental-friendly algicide and it is widely used to control algal blooms in aquatic ecosystems. However, the response of algal cell metabolic characteristics and intracellular protein profile under H2O2 stress is still not well understood. In the present study, the green alga Scenedesmus obliquus was exposed to different concentrations of H2O2 (0, 2, 6, 8 and 10 mg L−1) to evaluate the changes in algal morphological, physiological, and proteomic features to H2O2 exposure. The results showed that 8 mg L−1 of H2O2 could effectively inhibit the cell growth and photosynthetic activity of S. obliquus including chlorophyll-a content and chlorophyll fluorescence parameters. The increased activities of superoxide dismutase (SOD) and catalase (CAT) observed in this study indicate that cells exposure to H2O2 caused oxidative stress. The metabolic activity of S. obliquus was significantly decreased by H2O2 treatment. In terms of proteomic analysis, 251 differentially expressed proteins (DEPs) were successfully identified. The Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis revealed significant protein enrichment in the metabolic pathways, photosynthesis, ascorbic acid, and alginate metabolism and phenylpropane biosynthesis of S. obliquus. The analysis of protein-protein interaction system shows that the pathways of photosynthesis and metabolic pathways of S. obliquus were essential to resist oxidative stress. Taking together, these results shed new lights on exploring the cell physiological metabolism and intracellular protein mechanisms of H2O2 inhibition on algal blooms.

Next-generation cancer organoids.

Organotypic models of patient-specific tumours are revolutionizing our understanding of cancer heterogeneity and its implications for personalized medicine. These advancements are, in part, attributed to the ability of organoid models to stably preserve genetic, proteomic, morphological and pharmacotypic features of the parent tumour in vitro, while also offering unprecedented genomic and environmental manipulation. Despite recent innovations in organoid protocols, current techniques for cancer organoid culture are inherently uncontrolled and irreproducible, owing to several non-standardized facets including cancer tissue sources and subsequent processing, medium formulations, and animal-derived three-dimensional matrices. Given the potential for cancer organoids to accurately recapitulate the intra- and intertumoral biological heterogeneity associated with patient-specific cancers, eliminating the undesirable technical variability accompanying cancer organoid culture is necessary to establish reproducible platforms that accelerate translatable insights into patient care. Here we describe the current challenges and recent multidisciplinary advancements and opportunities for standardizing next-generation cancer organoid systems.

Immunophenotyping assessment in a COVID-19 cohort (IMPACC): A prospective longitudinal study.

The IMmunoPhenotyping Assessment in a COVID-19 Cohort (IMPACC) is a prospective longitudinal study designed to enroll 1000 hospitalized patients with COVID-19 (NCT04378777). IMPACC collects detailed clinical, laboratory and radiographic data along with longitudinal biologic sampling of blood and respiratory secretions for in depth testing. Clinical and lab data are integrated to identify immunologic, virologic, proteomic, metabolomic and genomic features of COVID-19-related susceptibility, severity and disease progression. The goals of IMPACC are to better understand the contributions of pathogen dynamics and host immune responses to the severity and course of COVID-19 and to generate hypotheses for identification of biomarkers and effective therapeutics, including optimal timing of such interventions. In this report we summarize the IMPACC study design and protocols including clinical criteria and recruitment, multi-site standardized sample collection and processing, virologic and immunologic assays, harmonization of assay protocols, high-level analyses and the data sharing plans.

Unraveling Metabolic and Proteomic Features in Soybean Plants in Response to Copper Hydroxide Nanowires Compared to a Commercial Fertilizer.

Mechanistic understanding of the interaction of copper-based nanomaterials with crops is crucial for exploring their application in precision agriculture and their implications on plant health. We investigated the biological response of soybean (Glycine max) plants to the foliar application of copper hydroxide nanowires (CNWs) at realistic exposure concentrations. A commercial copper based-fungicide (Kocide), dissolved copper ions, and untreated controls were used for comparison to identify unique features at physiological, cellular, and molecular levels. After 32 d of exposure to CNW (0.36, 1.8, and 9 mg CNW/plant), the newly developed tissues accumulated significantly high levels of Cu (18-60 μg/g) compared to Kocide (10 μg/g); however, the rate of Cu translocation from the site of CNW treatment to other tissues was slower compared to other Cu treatments. Like Kocide, CNW exposure at medium and high doses altered Co, Mn, Zn, and Fe accumulation in the tissues and enhanced photosynthetic activities. The proteomic and metabolomic analyses of leaves from CNW-treated soybean plants suggest a dose-dependent response, resulting in the activation of major biological processes, including photosynthesis, energy production, fatty acid metabolism, lignin biosynthesis, and carbohydrate metabolism. In contrast to CNW treatments, Kocide exposure resulted in increased oxidative stress response and amino acid metabolism activation.

Abstract 3113: Pan-cancer stemness defined by CPTAC proteogenomics guides personalized therapies

Abstract Cancer progression involves the gradual loss of a differentiated phenotype and acquisition of progenitor and stem cell-like features. Here, we provide new stemness indices for assessing the degree of oncogenic dedifferentiation. We used machine learning approaches to extract epigenetic, transcriptomic, and proteomic features from human stem cells and applied the computed result to index the Clinical Proteomic Tumor Analysis Consortium (CPTAC) tumor samples by their proteogenomic hallmarks of stemness. Leveraging the resource generated by Human Induced Pluripotent Stem Cells Consortium (HipSci), we extracted stemness signatures from the coherent epigenetic, transcriptomic, and proteomic datasets, using one-class logistic regression machine learning algorithm. The newly obtained stemness scores based on the DNA methylation and gene expression are significantly more robust compared to our previous published work based on the dataset obtained from a smaller number of samples with only genomics data. The stemness score computed by machine learning algorithms using proteomic datasets is novel and original. The obtained proteomic score is able to classify stem cells and non-stem cell classes. Indexing of CPTAC tumors with proteomic stemness score brought us with previously unappreciated findings. We have used the stemness scores computed using gene expression, DNA methylation, and proteins and their modifications to interrogate the coherent proteogenomic CPTAC datasets. The initial analysis of over 2000 tumor samples obtained from twelve types of primary carcinomas of breast, ovary, lung, kidney, uterus, brain (pediatric and adult), head and neck, liver, stomach, colon, and pancreas has confirmed our previously published results. Importantly, our original proteomic-based score brought the analyses to a novel dimension, far beyond previously described results. The initial findings of our work identified proteins and phospho-proteins as active nodes of signaling pathways and transcriptional networks that drive aggressiveness of the primary tumors that cause resistance to existing therapies. Our results indicate that cancer stemness is associated with the engagement of developmental pathways shaping tumor plasticity. Progressive oncogenic de-differentiation of cancer cells impacts tumor microenvironment and is associated with “cold” tumors thus impairing anti-tumor immune response and limiting the efficacy of immunotherapies. Hallmarks of cancer stemness correlate with increased tumor pathology grade and clinical stage and results in worse survival of the cancer patients across analyzed tumor types. Targeting identified proteins and cellular mechanisms that drive lethal phenotype of de-differentiated tumors with existing or novel drugs may pave the way for clinical development of effective cures for cancer patients. Citation Format: Maciej Wiznerowicz, Antono Colaprico, Erik Storrs, Francesca Petralia, Iga Kołodziejczak, Felipe da Veiga Leprevost, Weiping Ma, Daniel Cui Zhou, Bo Wen, Alexander Lazar, Pietro Pugliese, Michele Ceccarelli, Bożena Kamińska, Jan Lubiński, Alexey Nesvizhskii, Bing Zhang, Henry Rodriguez, Ana L. Robles, Mehdi Mesri. Pan-cancer stemness defined by CPTAC proteogenomics guides personalized therapies [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2021; 2021 Apr 10-15 and May 17-21. Philadelphia (PA): AACR; Cancer Res 2021;81(13_Suppl):Abstract nr 3113.

Proteomic Profiling of BRAFV600E Mutant Colon Cancer Cells Reveals the Involvement of Nucleophosmin/c-Myc Axis in Modulating the Response and Resistance to BRAF Inhibition by Vemurafenib.

BRAFV600E mutations are found in approximately 10% of colorectal cancer patients and are associated with worse prognosis and poor outcomes with systemic therapies. The aim of this study was to identify novel druggable features of BRAFV600E-mutated colon cancer (CC) cells associated with the response and resistance to BRAFV600E inhibitor vemurafenib. Towards this aim, we carried out global proteomic profiling of BRAFV600E mutant vs. KRAS mutant/BRAF wild-type and double wild-type KRAS/BRAF CC cells followed by bioinformatics analyses. Validation of selected proteomic features was performed by immunohistochemistry and in silico using the TCGA database. We reveal an increased abundance and activity of nucleophosmin (NPM1) in BRAFV600E-mutated CC in vitro, in silico and in tumor tissues from colon adenocarcinoma patients and demonstrate the roles of NPM1 and its interaction partner c-Myc in conveying the resistance to vemurafenib. Pharmacological inhibition of NPM1 effectively restored the sensitivity of vemurafenib-resistant BRAF-mutated CC cells by down-regulating c-Myc expression and activity and consequently suppressing its transcriptional targets RanBP1 and phosphoserine phosphatase that regulate centrosome duplication and serine biosynthesis, respectively. Altogether, findings from this study suggest that the NPM1/c-Myc axis could represent a promising therapeutic target to thwart resistance to vemurafenib in BRAF-mutated CC.

Pathophysiology of cardiogenic shock.

We describe the pathophysiology of cardiogenic shock (CS), from the main pathways to the inflammatory mechanisms and the proteomic features. Although the classical pathophysiological pathways underlying CS, namely reduced organ perfusion due to inadequate cardiac output and peripheral vasoconstriction, have been well-established for a long time, the role of macro-and micro-hemodynamics in the magnitude of the disease and its prognosis has been investigated extensively only over the last few years. Moreover, to complete the complex picture of CS pathophysiology, the study of cytokine cascade, inflammation, and proteomic analysis has been addressed recently. Understanding the pathophysiology of CS is important to treat it optimally.

Pathogenic missense protein variants affect different functional pathways and proteomic features than healthy population variants.

Missense variants are present amongst the healthy population, but some of them are causative of human diseases. A classification of variants associated with "healthy" or "diseased" states is therefore not always straightforward. A deeper understanding of the nature of missense variants in health and disease, the cellular processes they may affect, and the general molecular principles which underlie these differences is essential to offer mechanistic explanations of the true impact of pathogenic variants. Here, we have formalised a statistical framework which enables robust probabilistic quantification of variant enrichment across full-length proteins, their domains, and 3D structure-defined regions. Using this framework, we validate and extend previously reported trends of variant enrichment in different protein structural regions (surface/core/interface). By examining the association of variant enrichment with available functional pathways and transcriptomic and proteomic (protein half-life, thermal stability, abundance) data, we have mined a rich set of molecular features which distinguish between pathogenic and population variants: Pathogenic variants mainly affect proteins involved in cell proliferation and nucleotide processing and are enriched in more abundant proteins. Additionally, rare population variants display features closer to common than pathogenic variants. We validate the association between these molecular features and variant pathogenicity by comparing against existing in silico variant impact annotations. This study provides molecular details into how different proteins exhibit resilience and/or sensitivity towards missense variants and provides the rationale to prioritise variant-enriched proteins and protein domains for therapeutic targeting and development. The ZoomVar database, which we created for this study, is available at fraternalilab.kcl.ac.uk/ZoomVar. It allows users to programmatically annotate missense variants with protein structural information and to calculate variant enrichment in different protein structural regions.

Proteomics reveals commitment to germination in barley seeds is marked by loss of stress response proteins and mobilisation of nutrient reservoirs

Germination is a critical process in the reproduction and propagation of flowering plants, and is also the key stage of industrial grain malting. Germination commences when seeds are steeped in water, followed by degradation of the endosperm cell walls, enzymatic digestion of starch and proteins to provide nutrients for the growing plant, and emergence of the radicle from the seed. Dormancy is a state where seeds fail to germinate upon steeping, but which prevents inappropriate premature germination of the seeds before harvest from the field. This can result in inefficiencies in industrial malting. We used Sequential Window Acquisition of all THeoretical ions Mass Spectrometry (SWATH-MS) proteomics to measure changes in the barley seed proteome throughout germination. We found a large number of proteins involved in desiccation tolerance and germination inhibition rapidly decreased in abundance after imbibition. This was followed by a decrease in proteins involved in lipid, protein and nutrient reservoir storage, consistent with induction and activation of systems for nutrient mobilisation to provide nutrients to the growing embryo. Dormant seeds that failed to germinate showed substantial biochemical activity distinct from that of seeds undergoing germination, with differences in sulfur metabolic enzymes, endogenous alpha-amylase/trypsin inhibitors, and histone proteins. We verified our findings with analysis of germinating barley seeds from two commercial malting facilities, demonstrating that key features of the dynamic proteome of germinating barley seeds were conserved between laboratory and industrial scales. The results provide a more detailed understanding of the changes in the barley proteome during germination and give possible target proteins for testing or to inform selective breeding to enhance germination or control dormancy. SignificanceGermination is critical to the reproduction and propagation of flowering plants, and in industrial malting. Dormancy, where seeds fail to germinate upon steeping, can result in inefficiencies in industrial malting. Our DIA/SWATH-MS proteomics analyses identified key changes during germination, including an initial loss of proteins involved in desiccation tolerance and germination inhibition, followed by decreases in lipid, protein and nutrient reservoir storage. These changes were consistent between laboratory and industrial malting scales, and therefore demonstrate the utility of laboratory-scale barley germination as a model system for industrial malt house processes. We also showed that dormant seeds that failed to germinate showed substantial biochemical activity distinct from that of seeds undergoing germination, consistent with dormancy being an actively regulated state. Our results provide a more detailed understanding of the changes in the barley proteome during germination and give possible target proteins for testing or to inform selective breeding to enhance germination or control dormancy.

PhyloQuant approach provides insights into Trypanosoma cruzi evolution using a systems-wide mass spectrometry-based quantitative protein profile

The etiological agent of Chagas disease, Trypanosoma cruzi, is a complex of seven genetic subdivisions termed discrete typing units (DTUs), TcI-TcVI and Tcbat. The relevance of T. cruzi genetic diversity to the variable clinical course of the disease, virulence, pathogenicity, drug resistance, transmission cycles and ecological distribution requires understanding the parasite origin and population structure. In this study, we introduce the PhyloQuant approach to infer the evolutionary relationships between organisms based on differential mass spectrometry-based quantitative features. In particular, large scale quantitative bottom-up proteomics features (MS1, iBAQ and LFQ) were analyzed using maximum parsimony, showing a correlation between T. cruzi DTUs and closely related trypanosomes’ protein expression and sequence-based clustering. Character mapping enabled the identification of synapomorphies, herein the proteins and their respective expression profiles that differentiate T. cruzi DTUs and trypanosome species. The distance matrices based on phylogenetics and PhyloQuant clustering showed statistically significant correlation highlighting the complementarity between the two strategies. Moreover, PhyloQuant allows the identification of differentially regulated and strain/DTU/species-specific proteins, and has potential application in the identification of specific biomarkers and candidate therapeutic targets.