Functional Classification Of Proteins Research Articles

DeepPPAPredMut: deep ensemble method for predicting the binding affinity change in protein-protein complexes upon mutation.

Protein-protein interactions underpin many cellular processes and their disruption due to mutations can lead to diseases. With the evolution of protein structure prediction methods like AlphaFold2 and the availability of extensive experimental affinity data, there is a pressing need for updated computational tools that can efficiently predict changes in binding affinity caused by mutations in protein-protein complexes. We developed a deep ensemble model that leverages protein sequences, predicted structure-based features, and protein functional classes to accurately predict the change in binding affinity due to mutations. The model achieved a correlation of 0.97 and a mean absolute error (MAE) of 0.35 kcal/mol on the training dataset, and maintained robust performance on the test set with a correlation of 0.72 and a MAE of 0.83 kcal/mol. Further validation using Leave-One-Out Complex (LOOC) cross-validation exhibited a correlation of 0.83 and a MAE of 0.51 kcal/mol, indicating consistent performance. https://web.iitm.ac.in/bioinfo2/DeepPPAPredMut/index.html.

Effect of tokenization on transformers for biological sequences.

Deep-learning models are transforming biological research, including many bioinformatics and comparative genomics algorithms, such as sequence alignments, phylogenetic tree inference, and automatic classification of protein functions. Among these deep-learning algorithms, models for processing natural languages, developed in the natural language processing (NLP) community, were recently applied to biological sequences. However, biological sequences are different from natural languages, such as English, and French, in which segmentation of the text to separate words is relatively straightforward. Moreover, biological sequences are characterized by extremely long sentences, which hamper their processing by current machine-learning models, notably the transformer architecture. In NLP, one of the first processing steps is to transform the raw text to a list of tokens. Deep-learning applications to biological sequence data mostly segment proteins and DNA to single characters. In this work, we study the effect of alternative tokenization algorithms on eight different tasks in biology, from predicting the function of proteins and their stability, through nucleotide sequence alignment, to classifying proteins to specific families. We demonstrate that applying alternative tokenization algorithms can increase accuracy and at the same time, substantially reduce the input length compared to the trivial tokenizer in which each character is a token. Furthermore, applying these tokenization algorithms allows interpreting trained models, taking into account dependencies among positions. Finally, we trained these tokenizers on a large dataset of protein sequences containing more than 400 billion amino acids, which resulted in over a 3-fold decrease in the number of tokens. We then tested these tokenizers trained on large-scale data on the above specific tasks and showed that for some tasks it is highly beneficial to train database-specific tokenizers. Our study suggests that tokenizers are likely to be a critical component in future deep-network analysis of biological sequence data. Code, data, and trained tokenizers are available on https://github.com/technion-cs-nlp/BiologicalTokenizers.

Proteomic analysis of baicalin intervention on protein expression and modification in the hippocampus of Alzheimer’s disease model rat

Objective We aimed to explore the treatment effect and therapeutic mechanisms of baicalin in Alzheimer’s disease (AD). Methods The AD rat model was established by intracerebroventricular injection of Aβ1-40, with rats in the baicalin group receiving baicalin intraventricular injections. Morris Water Maze and Hematoxylin-eosin (H&E) Staining were employed to detect the successful model construction and baicalin treatment effect. The proteins extracted from the hippocampus were subjected to proteomics analysis. Bioinformatics technology was employed for differential protein screening, functional classification, and enrichment. Western Blot was employed to validate the expressions of differentially expressed proteins (DEPs) and the protein modification alternations. Results Water maze test confirmed the successful AD model construction and baicalin can improve learning and memory abilities. A total of 26 DEPs associated with 28 Gene Ontology (GO) functions were identified in the model and 32 DEPs were obtained between the baicalin group and the model. Bioinformatics analysis demonstrated that AD occurrence resulted in neuronal dysfunction and was associated with immune responses. The baicalin therapeutic effect on AD may be associated with metabolic processes, vitamin response, angiogenesis regulation, and fatty acid response. Immunoglobulin heavy constant mu (Ighm) and Immunoglobulin G2a (IgG2a) exhibited significant increases in AD and baicalin attenuated their expressions, while Fatty acid desaturase 1 (Fads1) exhibited a significantly diminished expression and baicalin could reverse the trend. Succinylation detection exhibited the differentially expressed at 35 kD between the model and baicalin group. Conclusion Baicalin intervention may ameliorate cognitive impairment in AD rats by modulating the expressions of proteins and the succinylation modifications.

Comparative proteomic analysis of vancomycin-sensitive and vancomycin-intermediate resistant Staphylococcus aureus.

The extensive use of vancomycin has led to the development of Staphylococcus aureus strains with varying degrees of resistance to vancomycin. The present study aimed to explore the molecular causes of vancomycin resistance by conducting a proteomics analysis of subcellular fractions isolated from vancomycin-intermediate resistant S. aureus (VISA) and vancomycin-sensitive S. aureus (VSSA) strains. We conducted proteomics analysis of subcellular fractions isolated from 2 isogenic S. aureus strains: strain 11 (VSSA) and strain 11Y (VISA). We used an integrated quantitative proteomics approach assisted by bioinformatics analysis, and comprehensively investigated the proteome profile. Intensive bioinformatics analysis, including protein annotation, functional classification, functional enrichment, and functional enrichment-based cluster analysis, was used to annotate quantifiable targets. We identified 128 upregulated proteins and 21 downregulated proteins in strain 11Y as compared to strain 11. The largest group of differentially expressed proteins was composed of enzymatic proteins associated with metabolic and catalytic activity, which accounted for 32.1% and 50% of the total proteins, respectively. Some proteins were indispensable parts of the regulatory networks of S. aureus that were altered with vancomycin treatment, and these proteins were related to cell wall metabolism, cell adhesion, proteolysis, and pressure response. Our proteomics study revealed regulatory proteins associated with vancomycin resistance in S. aureus. Some of these proteins were involved in the regulation of cell metabolism and function, which provides potential targets for the development of strategies to manage vancomycin resistance in S. aureus.

A region-resolved proteomic map of the human brain enabled by high-throughput proteomics.

Substantial efforts are underway to deepen our understanding of human brain morphology, structure, and function using high-resolution imaging as well as high-content molecular profiling technologies. The current work adds to these approaches by providing a comprehensive and quantitative protein expression map of 13 anatomically distinct brain regions covering more than 11,000 proteins. This was enabled by the optimization, characterization, and implementation of a high-sensitivity and high-throughput microflow liquid chromatography timsTOF tandem mass spectrometry system (LC-MS/MS) capable of analyzing more than 2,000 consecutive samples prepared from formalin-fixed paraffin embedded (FFPE) material. Analysis of this proteomic resource highlighted brain region-enriched protein expression patterns and functional protein classes, protein localization differences between brain regions and individual markers for specific areas. To facilitate access to and ease further mining of the data by the scientific community, all data can be explored online in a purpose-built R Shiny app (https://brain-region-atlas.proteomics.ls.tum.de).

Relative Abundance of Spermadhesin-1 in the Seminal Plasma of Young Nellore Bulls Is in Agreement with Reproductive Parameters.

This study aimed to evaluate the proteomic profile of seminal plasma from young Nellore bulls. We used 20 bulls aged between 19.8 and 22.7 months, divided into two groups according to the results of the Breeding Soundness Evaluation (BSE): approved (FIT n = 10) and not approved (UNFIT n = 10). The scrotal perimeter was measured and a semen collection was performed through electroejaculation. The percentage of sperm motility, mass motility, and sperm vigor were calculated using conventional microscopy, and the percentage of sperm abnormalities was calculated using phase-contrast microscopy of all ejaculates. Seminal plasma was separated from spermatozoa using centrifugation and processed for proteomic analysis by LC-MS/MS. Seminal plasma proteins were identified using MASCOT Daemon software v.2.4.0 and label-free quantification analysis was carried out by SCAFFOLD Q+ software v.4.0 using the Exponentially Modified Protein Abundance Index (emPAI) method. Functional classification of proteins was performed based on their genetic ontology terms using KOG. Functional cluster analysis was performed on DAVID. There were no differences in scrotal perimeter and physical semen characteristics between FIT and UNFIT groups of bulls. The percentage of sperm abnormalities was higher (p < 0.05) in the UNFIT group of bulls. A total of 297 proteins were identified for the two groups. There were a total of 11 differentially abundant proteins (p < 0.05), two of them more abundant in FIT bulls (Spermadhesin-1 and Ig gamma-1 chain C region) and nine in UNFIT bulls (Vasoactive intestinal peptide, Metalloproteinase inhibitor 2, Ig lambda-1 chain C regions, Protein FAM3C, Hemoglobin beta, Seminal ribonuclease, Spermadhesin 2, Seminal plasma protein BSP-30kDa, and Spermadhesin Z13). Spermadhesin-1 was the protein with the highest relative abundance (36.7%) in the seminal plasma among all bulls, corresponding to 47.7% for the FIT bulls and 25,7% for the UNFIT bulls. Posttranslational modification, protein turnover, and chaperones were the functional categories with the highest number of classified proteins. Protein functional annotation clusters were related to Phospholipid efflux, ATP binding, and chaperonin-containing T-complex. The differentially abundant proteins in the group of FIT bulls were related to sperm capacitation and protection against reactive species of oxygen. In contrast, differentially expressed proteins in the group of UNFIT bulls were related to motility inhibition, intramembrane cholesterol removal and oxidative stress. In conclusion, the proteomic profile of the seminal plasma of FIT bulls presents proteins with participation in several biological processes favorable to fertilization, while the proteins of the seminal plasma of UNFIT bulls indicate a series of alterations that can compromise the fertilizing capacity of the spermatozoa. In addition, the relative abundance of spermadhesin-1 found in the seminal plasma of young Nellore bulls could be studied as a reproductive parameter for selection.

Multi-omic characterisation as a tool to improve knowledge, valorisation and conservation of wild fruit genetic resources: the case of Arbutus unedo L.

The valorisation and conservation of plant genetic resources (PGRs) and wild fruit PGRs are critical to ensure the maintenance of genetic and cultural heritage and to promote new perspectives on resource use. New strategies to characterize PGRs are needed, and the omics approach can provide information that is still largely unknown. The Strawberry tree (Arbutus unedo L.) is an underutilized, drought and fire-resistant species distributed in the Mediterranean area and its berries have large ethnobotanical use. Although their phenolic profile and antioxidant capacity are known, they are not well characterised, particularly from a proteomic perspective. The aim of this work is the characterisation of two ecotypes of A. unedo (Campania and Sicily) from a molecular viewpoint to valorise and encourage the preservation of this wild fruit. Samples were collected from two different geographical areas to assess whether different geographical conditions could influence the characteristics of leaves and fruits at the three stages of ripening (green, veraison, red). Proteomic analysis identified 904 proteins, of which 122 showed significance along the ripening. Some of these differentially abundant proteins, such as chalcone synthase, show a marked increase during ripening. The protein functional classes with the highest representation are involved in protein and amino acid metabolism, glycolysis and in secondary metabolism. From a proteomic perspective, there are no differences between the fruits from the two regions compared by the ripening stage. However, the pedoclimatic metabolic imprinting allowed the observation of good diversity in the metabolomic profiles between the two ecotypes, especially for anthocyanins, 4 times more abundant in the Sicilian veraisoned fruit than in the Campania one, and catechins, with double the abundance in the Campania ecotype compared to the Sicilian ecotype in the green phase, but more abundant (3x) in the Sicilian veraisoned fruit. Phenolic compounds show a 20% greater abundance in the Campania green arbutus fruit than in the Sicilian one, values that then equalise as ripening progresses. Multi-omic characterisation enhanced the knowledge on a wild fruit plant species which shows specific adaptations and responses to the environment to be considered when addressing the issue of local agrobiodiversity.

The Phosphoproteomic Response of Pepper (Capsicum annuum L.) Seedlings to Selenium Stress

Excessive selenium has gradually become a potential environmentally hazardous element for all organisms. Limited knowledge is available regarding the toxic mechanism of selenium in pepper, so the quantitative proteomics of phosphorylation was studied by Tandem Mass Tag approaches. A total of 4434 phosphorylation sites were identified on 2058 proteins, of which 3749 sites of 1919 proteins contained quantitative information. In the Se/mock (seedlings without Se treatment) comparison group, the number of upregulated phosphoproteins (658) was significantly higher than that of the downregulated ones (61). Systematic bioinformatics analysis, including protein annotation, functional classification, subcellular localization, and cluster analysis was performed. A total of 33 over-represented motifs were found in serine phosphorylation, and the most frequent motif was ‘sP’ (308 occurrences). According to KEGG enrichment analysis, the upregulated phosphorylated proteins (DPPs) were most strongly associated with the ’phenylpropanoid biosynthesis’ and ’nicotinate and nicotinamide metabolism’ pathways, while those that were downregulated were associated with the ’ABC transporters’ and ‘plant hormone signal transduction’ pathways. Our data can provide new insights for evaluating the response mechanism of plants to selenium pollution and improving their resistance to selenium.

Deep learning-based method for predicting and classifying the binding affinity of protein-protein complexes

Protein-protein interactions (PPIs) play a critical role in various biological processes. Accurately estimating the binding affinity of PPIs is essential for understanding the underlying molecular recognition mechanisms. In this study, we employed a deep learning approach to predict the binding affinity (ΔG) of protein-protein complexes. To this end, we compiled a dataset of 903 protein-protein complexes, each with its corresponding experimental binding affinity, which belong to six functional classes. We extracted 8 to 20 non-redundant features from the sequence information as well as the predicted three-dimensional structures using feature selection methods for each protein functional class. Our method showed an overall mean absolute error of 1.05 kcal/mol and a correlation of 0.79 between experimental and predicted ΔG values. Additionally, we evaluated our model for discriminating high and low affinity protein-protein complexes and it achieved an accuracy of 87% with an F1 score of 0.86 using 10-fold cross-validation on the selected features. Our approach presents an efficient tool for studying PPIs and provides crucial insights into the underlying mechanisms of the molecular recognition process. The web server can be freely accessed at https://web.iitm.ac.in/bioinfo2/DeepPPAPred/index.html

Differential expression of lysine acetylation proteins in gastric cancer treated with a new antitumor agent bioactive peptide chelate selenium.

The method of anticancer bioactive peptide (ACBP) functionalized selenium particle (Se), which has enhanced anticancer activity, inhibited the growth of gastric cancer (GC) cells, and increased the ability of apoptosis in vitro, has been reported in previous studies. We used tandem mass spectrometry (TMT) labeling to construct a complete atlas of the acetylation-modified proteome in GC MKN-45 cells treated with ACBP-Se. The proteomics data database was searched and analyzed by bioinformatics: Kyoto Encyclopedia of Genes and Genomes (KEGG), Gene Ontology (GO), functional enrichment, and protein-protein interaction network. Finally, we conducted a quantitative PRM analysis of the selected target-modified peptides. We identified 4,958 acetylation sites from 1,926 proteins in this research. Among these, 4,467 acetylation sites corresponding to 1,777 proteins were quantified. Based on the above data and standards, we found that in the ACBP-Se group vs. the control group, 297 sites were upregulated, and 665 sites were downregulated. We systematically assessed the proteins containing quantitative information sites, including protein annotation, functional classification, and functional enrichment, cluster analysis supported by functional enrichment, domain structures, and protein interaction networks. Finally, we evaluated differentially expressed lysine acetylation sites. We revealed that SHMT2 K200 and PGK1 K97 were the most critical acetylated non-histone proteins, which may have an essential role in ACBP-Se treatment. Here, we identified and quantified the lysine acetylation proteins in GC cells treated with ACBP-Se. The characterization of acetylation indicates that acetylated proteins might be pivotal in the biological process, molecular binding, and metabolic pathways of ACBP-Se treatment progress. Our findings provide a broad understanding of acetylation ACBP-Se treatment of GC, suggesting a potential application for molecular targeted therapy.

Abstract 1917: Protein function prediction and classification using deep learning models and knowledge graph mining

Abstract 1917: Protein function prediction and classification using deep learning models and knowledge graph mining

IDENTIFYING MOLECULAR FUNCTIONS OF DYNEIN MOTOR PROTEINS USING EXTREME GRADIENT BOOSTING ALGORITHM WITH MACHINE LEARNING

The majority of cytoplasmic proteins and vesicles move actively primarily to dynein motor proteins, which are the cause of muscle contraction. Moreover, identifying how dynein are used in cells will rely on structural knowledge. Cytoskeletal motor proteins have different molecular roles and structures, and they belong to three superfamilies of dynamin, actin and myosin. Loss of function of specific molecular motor proteins can be attributed to a number of human diseases, such as Charcot-Charcot-Dystrophy and kidney disease. It is crucial to create a precise model to identify dynein motor proteins in order to aid scientists in understanding their molecular role and designing therapeutic targets based on their influence on human disease. Therefore, we develop an accurate and efficient computational methodology is highly desired, especially when using cutting-edge machine learning methods. In this article, we proposed a machine learning-based superfamily of cytoskeletal motor protein locations prediction method called extreme gradient boosting (XGBoost). We get the initial feature set All by extraction the protein features from the sequence and evolutionary data of the amino acid residues named BLOUSM62. Through our successful eXtreme gradient boosting (XGBoost), accuracy score 0.8676%, Precision score 0.8768%, Sensitivity score 0.760%, Specificity score 0.9752% and MCC score 0.7536%. Our method has demonstrated substantial improvements in the performance of many of the evaluation parameters compared to other state-of-the-art methods. This study offers an effective model for the classification of dynein proteins and lays a foundation for further research to improve the efficiency of protein functional classification.

Combined transcriptome and proteome analysis of yak PASMCs under hypoxic and normoxic conditions.

Yaks are animals that have lived in plateau environments for generations. Yaks can adapt to the hypoxic plateau environment and also pass this adaptability on to the next generation. The lungs are the most important respiratory organs for mammals to adapt to their environment. Pulmonary artery smooth muscle cells play an important role in vascular remodeling under hypoxia, but the genetic mechanism underpinning the yak's ability to adapt to challenging plateau conditions is still unknown. A tandem mass tag (TMT) proteomics study together with an RNA-seq transcriptome analysis were carried out on pulmonary artery smooth muscle cells (PASMCs) that had been grown for 72 hours in both normoxic (20% O2) and hypoxic (1% O2) environments. RNA and TP (total protein) were collected from the hypoxic and normoxic groups for RNA-seq transcriptome sequencing and TMT marker protein quantification, and RT-qPCR validation was performed. A total of 17,711 genes and 6,859 proteins were identified. Further, 5,969 differentially expressed genes (DEGs) and 531 differentially expressed proteins (DEPs) were identified in the comparison group, including 2,924 and 186 upregulated genes and proteins and 3,045 and 345 down-regulated genes and proteins, respectively. The transcriptomic and proteomic analyses revealed that 109 DEGs and DEPs were highly positively correlated, with 77 genes showing the same expression trend. Nine overlapping genes were identified in the HIF-1 signaling pathway, glycolysis / gluconeogenesis, central carbon metabolism in cancer, PPAR signaling pathway, AMPK signaling pathway, and cholesterol metabolism (PGAM1, PGK1, TPI1, HMOX1, IGF1R, OLR1, SCD, FABP4 and LDLR), suggesting that these differentially expressed genes and protein functional classifications are related to the hypoxia-adaptive pathways. Overall, our study offers abundant data for further analysis of the molecular mechanisms in yak PASMCs and their adaptability to different oxygen concentrations.

Predictive modeling of moonlighting DNA-binding proteins.

Moonlighting proteins are multifunctional, single-polypeptide chains capable of performing multiple autonomous functions. Most moonlighting proteins have been discovered through work unrelated to their multifunctionality. We believe that prediction of moonlighting proteins from first principles, that is, using sequence, predicted structure, evolutionary profiles, and global gene expression profiles, for only one functional class of proteins in a single organism at a time will significantly advance our understanding of multifunctional proteins. In this work, we investigated human moonlighting DNA-binding proteins (mDBPs) in terms of properties that distinguish them from other (non-moonlighting) proteins with the same DNA-binding protein (DBP) function. Following a careful and comprehensive analysis of discriminatory features, a machine learning model was developed to assess the predictability of mDBPs from other DBPs (oDBPs). We observed that mDBPs can be discriminated from oDBPs with high accuracy of 74% AUC of ROC using these first principles features. A number of novel predicted mDBPs were found to have literature support for their being moonlighting and others are proposed as candidates, for which the moonlighting function is currently unknown. We believe that this work will help in deciphering and annotating novel moonlighting DBPs and scale up other functions. The source codes and data sets used for this work are freely available at https://zenodo.org/record/7299265#.Y2pO3ctBxPY.

Reverse vaccinology approach to identify novel and immunogenic targets against Porphyromonas gingivalis: An in silico study.

Porphyromonas gingivalis is a primary causative agent of chronic periodontitis. Moreover, it leads to several systemic diseases, including rheumatoid arthritis, cardiovascular, neurodegenerative, and Alzheimer’s diseases. It seems that the development of a vaccine against this bacterium is necessary. Thus, this study decided to identify novel immunogenic targets and developed multiple epitope-based vaccines against P. gingivalis. For this purpose, the pan/core-proteome of this bacterium was studied, and the suitable vaccine targets were selected based on different properties, including exposed localization of proteins, antigenicity, non-allergenicity, non-similarity to host proteome, stability, B-cell epitopes and MHC II binding sites, sequence conservation, molecular docking, and immune simulation. Through the quartile scoring method, 12 proteins with ≥ 20 scores were considered as suitable immunogenic targets. The results of the protein domain and functional class search showed that most of the immunogenic proteins were involved in the transport and metabolism of inorganic ions and lipids. In addition, two unknown function proteins, including WP_004584259.1 and WP_099780539.1 were detected as immunogenic targets. Three constructions carrying multi-epitopes were generated including Naked, LCL, and as chimeric structures. Among them, FliC chimeric protein had the strongest affinity to the human TLR2, 4, and 6, while the LCL platform represented the highest level of immune stimulation response. The obtained results from this study revealed new insights into prophylactic routes against P. gingivalis by introducing novel immunogenic targets. However, further investigations, including site-directed mutation and immunoassay are needed to confirm the pathogenic role and protectivity of these novel proteins.

Proteomic Analysis of Serum Lysine Acetylation in Uyghur Patients With T2DM

Lysine acetylation is a reversible modification process after protein translation, which plays a key regulatory role in various metabolic diseases such as diabetes. The prevalence of type 2 diabetes mellitus (T2DM) in the Uyghur population is high, but the acetylation status of proteomics in Uyghur with T2DM is still unclear. Herein, we performed a quantitative proteomic study of lysine acetylation in T2DM patients using Tandem Mass Tags (TMTs) labeling, acetylation enrichment techniques, and high-resolution liquid chromatography-tandem mass spectrometry. We quantified 422 acetylation sites on 120 proteins, of which 347 sites of 103 proteins contained quantitative information. Compared with the control, we found that a total of eight acetylated sites within proteins were significantly differentially expressed with three upregulated and five downregulated, including histones H4 and H3.3C. Meanwhile, we completed bioinformatics analysis, including protein annotation, functional classification, functional enrichment, and cluster analysis, based on functional enrichment. In addition, the mRNA (ApoB-100, histones H4 and H3.3C) and protein (histones H4 and H3.3C) levels were verified through 60 samples. Besides, we also performed histone H4 chromatin immunoprecipitation analysis at the level of INS-1 cells. These could be potentially useful markers for the prediction of prediabetes and also provided a basis for the pathogenesis of T2DM.

Hierarchical representation for PPI sites prediction

BackgroundProtein–protein interactions have pivotal roles in life processes, and aberrant interactions are associated with various disorders. Interaction site identification is key for understanding disease mechanisms and design new drugs. Effective and efficient computational methods for the PPI prediction are of great value due to the overall cost of experimental methods. Promising results have been obtained using machine learning methods and deep learning techniques, but their effectiveness depends on protein representation and feature selection.ResultsWe define a new abstraction of the protein structure, called hierarchical representations, considering and quantifying spatial and sequential neighboring among amino acids. We also investigate the effect of molecular abstractions using the Graph Convolutional Networks technique to classify amino acids as interface and no-interface ones. Our study takes into account three abstractions, hierarchical representations, contact map, and the residue sequence, and considers the eight functional classes of proteins extracted from the Protein–Protein Docking Benchmark 5.0. The performance of our method, evaluated using standard metrics, is compared to the ones obtained with some state-of-the-art protein interface predictors. The analysis of the performance values shows that our method outperforms the considered competitors when the considered molecules are structurally similar.ConclusionsThe hierarchical representation can capture the structural properties that promote the interactions and can be used to represent proteins with unknown structures by codifying only their sequential neighboring. Analyzing the results, we conclude that classes should be arranged according to their architectures rather than functions.

Proteomic analysis of japonica sorghum following microwave intermittent drying based on label-free technology

The aim of this study was to investigate the influence of microwave drying on the protein quality of japonica sorghum following an intermittent drying test. Using label-free technology and liquid chromatography-tandem mass spectrometry for proteomic analysis, the effects of microwave drying on sorghum differential protein expression, functional classification, and metabolic pathways were analyzed at the molecular level. After sorghum was dried using a microwave, 85 differential proteins were identified. Among them, 51 showed up-regulated expression while 34 had down-regulated expression. The up-regulation and down-regulation of differential protein expressions significantly changed them, and proteins with larger up-regulated and down-regulated expressions were postulated to affect biological and metabolic processes of sorghum during subsequent processing. Differential proteins were significantly (P ˂ 0.01) involved in metabolic pathways, such as carbon metabolism, glycolysis/gluconeogenesis, carbon fixation in photosynthetic organisms, biosynthesis of amino acids, amino sugar and nucleotide sugar metabolism, and the TCA cycle. For the protein interaction network, glyceraldehyde-3-phosphate dehydrogenase of the down-regulated proteins was postulated to be the key factor affecting the entire metabolic system or signal transduction pathway. Up-regulated proteins, including phosphoglycerate mutase and phosphopyruvate hydratase, as well as down-regulated proteins such as glyceraldehyde-3-phosphate dehydrogenase and fructose-bisphosphate aldolase, not only directly or indirectly affected a variety of metabolic processes, but were specifically closely related to glycolysis and glycometabolism. Overall, this study showed that among the related metabolic pathways, differential protein changes in glycometabolism pathways may have the greatest impact on metabolic processes. The research results discussed herein can provide theoretical support for the industrial application of microwave drying and deep processing of sorghum.

Lake charr (Salvelinus namaycush) clotting response may act as a plasma biomarker of sea lamprey (Petromyzon marinus) parasitism: Implications for management and wound assessment

Sea lampreys (Petromyzon marinus) have decimated the Great Lakes fisheries over the past century, and their control is central to protecting native fish populations. Wounding data collected from host fish, including classifying wounds as Type A (penetrating the integument) or Type B (superficial), is an integral part of sea lamprey monitoring efforts and helps inform management decisions; however, wound assessment is subjective and error-prone. This study aimed to determine if protein biomarkers of parasitism could be quantified in host fish plasma to serve as a potential objective aide in current wound assessment practices. Male siscowet lake charr (Salvelinus namaycush) were parasitized in a lab setting for four days, after which the sea lamprey was removed, host blood was collected, and the wound Type recorded. A second blood sample was collected from host fish with Type A wounds 7 months later. The plasma proteome was quantified using iTRAQ, and the relative abundances of 169 proteins were compared between parasitized and non-parasitized control fish. Three functional classes of proteins were modified by sea lamprey parasitism: immune response, lipid transport, and blood coagulation. A major finding was evidence of a concerted anticoagulation response in fish with Type A wounds, including changes in protein components and regulators of fibrin clot formation, some of which did not fully recover within 7 months. A modified clotting assay yielded a smaller thrombin-induced fibrin clot from parasitized fish, supporting the proteomic results. Therefore, measuring blood clottability could improve sea lamprey damage estimates by providing a more objective and quantitative index of parasitism than is offered by wounding data alone.

Chiral Carbon Dots-Enzyme Nanoreactors with Enhanced Catalytic Activity for Cancer Therapy.

As a class of functional proteins, enzymes possess inherent insignificant features, for instance, mediocre stability and membrane impermeability and reduced enzymatic activity after modification, which partly limit their biomedical applications. Thus, it is indispensable to exploit robust nanoreactors with high enzymatic activity and good stability and cell permeability. Here, the chiral carbon dots (CDs)-glucose oxidase (GOx) nanoreactors named LGOx and DGOx were constructed by the coassembly of GOx with L/D-CDs, respectively. L/DGOx can significantly enhance the activity of GOx and improve the efficient delivery of GOx to cancer cells. Moreover, these nanoreactors can generate hydrogen peroxide to efficaciously kill cancer cells and restrain tumor growth, and DGOx exhibits higher enzymatic activity than LGOx. According to our understanding, this is the first report about utilizing chiral CDs as vectors to construct effective CDs-enzyme nanohybrids for cancer therapy, which is envisioned to be a versatile strategy for multitudinous biomedical applications.