Protein bioinformatics databases and resources

Construction of an immune-related gene prognostic model with experimental validation and analysis of immune cell infiltration in lung adenocarcinoma

There is a correlation between tumors and immunity with the degree of immune cell infiltration in tumors being closely related to tumor growth and progression. Therefore, the present study identified immune-related prognostic genes and evaluated the immune infiltration level in lung adenocarcinoma (LUAD). This study performed Kyoto Encyclopedia of Genes and Genomes, Gene Ontology, and Gene Set Enrichment Analysis (GSEA) enrichment analyses on differential immune-associated genes. A risk model was created and validated using six immune-related prognostic genes. Reverse transcription-quantitative PCR was used to assess the prognostic gene expression in non-small cell lung cancer cells. Immune cell infiltration in LUAD was analyzed using the CIBERSORT method. Single sample GSEA was used to compare Tumor Immune Dysfunction and Exclusion (TIDE) scores between high and low-risk groups and to assess the activation of thirteen immune-related pathways. Multifactor Cox proportional hazards model analysis identified six prognostic risk genes (S100A16, FURIN, FGF2, LGR4, TNFRSF11A and VIPR1) to construct a risk model. The survival and receiver operating characteristic curves indicated that patients with higher risk scores had lower overall survival rates. The expression levels of prognostic genes S100A16, FURIN, LGR4, TNFRSF11A and VIPR1 were significantly increased in LUAD. B cells naive, plasma cells, T cells CD4 memory activated, T cells follicular helper, T cells regulatory, NK cells activated, macrophages M1, macrophages M2, and Dendritic cells resting cells showed elevated expression in LUAD. The prognostic genes were differentially associated with individual immune cells. Immune-related function scores, such as those for antigen presenting cell (APC) co-stimulation, APC co-inhibition, check-point, Cytolytic-activity, chemokine receptor, parainflammation, major histocompatibility complex-class-I, type-I-IFN-reponse and T-cell-co-inhibition, were higher in the high-risk group compared with the low-risk group. Furthermore, the TIDE score of the high-risk group was significantly lower than the low-risk group. This immune-related gene prognostic model has the potential to predict the prognosis of LUAD patients, supporting the development of a personalized clinical diagnosis and treatment plan.

Efficacy and Mechanism of Quercetin in the Treatment of Experimental Colitis Using Network Pharmacology Analysis

Quercetin, a flavonoid that is present in vegetables and fruits, has been found to have anti-inflammatory effects. However, the mechanism by which it inhibits colitis is uncertain. This study aimed to explore the effect and pharmacological mechanism of quercetin on dextran sodium sulfate (DSS)-induced ulcerative colitis (UC). Mice were given a 4% (w/v) DSS solution to drink for 7 days, followed by regular water for the following 5 days. Pharmacological mechanisms were predicted by network pharmacology. High-throughput 16S rDNA sequencing was performed to detect changes in the intestinal microbiota composition. Enzyme-linked immunosorbent assay and western blotting were performed to examine the anti-inflammatory role of quercetin in the colon. Quercetin attenuated DSS-induced body weight loss, colon length shortening, and pathological damage to the colon. Quercetin administration modulated the composition of the intestinal microbiota in DSS-induced mice and inhibited the growth of harmful bacteria. Network pharmacology revealed that quercetin target genes were enriched in inflammatory and neoplastic processes. Quercetin dramatically inhibited the expression of phosphorylated protein kinase B (AKT) and phosphatidylinositol 3-kinase (PI3K). Quercetin has a role in the treatment of UC, with pharmacological mechanisms that involve regulation of the intestinal microbiota, re-establishment of healthy microbiomes that favor mucosal healing, and the inhibition of PI3K/AKT signaling.

Novel six-gene prognostic signature based on colon adenocarcinoma immune-related genes

Background

Colon adenocarcinoma (COAD) is one of the most common gastrointestinal tumors worldwide, and immunotherapy is one of the most promising treatments for it. Identifying immune genes involved in the development and maintenance of cancer is key to the use of tumor immunotherapy. This study aimed to determine the prognostic value of immune genes in patients with COAD and to establish an immune-related gene signature. Differentially expressed genes, immune-related genes (DEIGs), and transcription factors (DETFs) were screened using the following databases: Cistrome, The Cancer Genome Atlas (TCGA), the Immunology Database and Analysis Portal, and InnateDB. We constructed a network showing the regulation of DEIGs by DETFs. Using weighted gene co-expression network analysis, we prepared 5 co-expressed gene modules; 6 hub genes (CD1A, CD1B, FGF9, GRP, SERPINE1, and F2RL2) obtained using univariate and multivariate regression analysis were used to construct a risk model. Patients from TCGA database were divided into high- and low-risk groups based on whether their risk score was greater or less than the mean; the public dataset GSE40967, which contains gene expression profiles of 566 colon cancer patients, was used for validation.

Results

Survival analysis, somatic gene mutations, and tumor-infiltrating immune cells differed significantly between the high- and low-risk groups.

Conclusions

This immune-related gene signature could play an important role in guiding treatment, making prognoses, and potentially developing future clinical applications.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12859-022-04909-2.

Molecular evolutionary analysis of the SHI/STY gene family in land plants: A focus on the Brassica species

The plant-specific SHORT INTERNODES/STYLISH (SHI/STY) proteins belong to a family of transcription factors that are involved in the formation and development of early lateral roots. However, the molecular evolution of this family is rarely reported. Here, a total of 195 SHI/STY genes were identified in 21 terrestrial plants, and the Brassica species is the focus of our research. Their physicochemical properties, chromosome location and duplication, motif distribution, exon-intron structures, genetic evolution, and expression patterns were systematically analyzed. These genes are divided into four clades (Clade 1/2/3/4) based on phylogenetic analysis. Motif distribution and gene structure are similar in each clade. SHI/STY proteins are localized in the nucleus by the prediction of subcellular localization. Collinearity analysis indicates that the SHI/STYs are relatively conserved in evolution. Whole-genome duplication is the main factor for their expansion. SHI/STYs have undergone intense purifying selection, but several positive selection sites are also identified. Most promoters of SHI/STY genes contain different types of cis-elements, such as light, stress, and hormone-responsive elements, suggesting that they may be involved in many biological processes. Protein-protein interaction predicted some important SHI/STY interacting proteins, such as LPAT4, MBOATs, PPR, and UBQ3. In addition, the RNA-seq and qRT-PCR analysis were studied in detail in rape. As a result, SHI/STYs are highly expressed in root and bud, and can be affected by Sclerotinia sclerotiorum, drought, cold, and heat stresses. Moreover, quantitative real-time PCR (qRT-PCR) analyses indicates that expression levels of BnSHI/STYs are significantly altered in different treatments (cold, salt, drought, IAA, auxin; ABA, abscisic acid; 6-BA, cytokinin). It provides a new understanding of the evolution and expansion of the SHI/STY family in land plants and lays a foundation for further research on their functions.

Identification of key genes involved in the recurrence of glioblastoma multiforme using weighted gene co-expression network analysis and differential expression analysis

Glioblastoma multiforme (GBM) is the most fatal malignancy, and despite extensive treatment, tumors inevitably recur. This study aimed to identify recurrence-associated molecules in GBM. The gene expression profile GSE139533, containing 70 primary and 47 recurrent GBM tissues and their corresponding clinical traits, was downloaded from the Gene Expression Omnibus (GEO) database and used for weighted gene co-expression network analysis (WGCNA) and differentially expressed gene (DEG) analysis. After identifying the hub genes which differentially expressed in recurrent GBM tissues and in the gene modules correlated with recurrence, data from the Chinese Glioma Genome Atlas (CCGA) and The Cancer Genome Atlas (TCGA) databases were analyzed with GSE43378 to determine the relationship between hub genes and patient prognosis. The diagnostic value of the identified hub genes was verified using 52 GBM tissues. Three gene modules were correlated with recurrence and 2623 genes were clustered in these clinically significant modules. Among these, 13 genes - EHF, TRPM1, FXYD4, CDH15, LHX5, TP73, FBN3, TLX1, C1QL4, COL2A, SEC61G, NEUROD4 and GPR139 - were differentially expressed in recurrent GBM samples; low LHX5 and TLX1 expression predicted poor outcomes. LHX5 and TLX1 expression showed weak positive relationships with Karnofsky performance scale scores. Additionally, LHX5 and TLX1 expression was found to be decreased in our recurrent GBM samples compared with that in primary samples; these genes exhibited high diagnostic value in distinguishing recurrent samples from primary samples. Our findings indicate that LHX5 and TLX1 might be involved in GBM recurrence and act as potential biomarkers for this condition.

Proteomics in systems toxicology

Proteins are the ultimate product of gene expression. As they hinge between gene transcription and phenotype, they offer a more realistic perspective of toxicopathic effects, responses and even susceptibility to insult than targeting genes and mRNAs while dodging some inter-individual variability that hinders measuring downstream endpoints like metabolites or enzyme activity. Toxicologists have long focused on proteins as biomarkers but the advent of proteomics shifted risk assessment from narrow single-endpoint analyses to whole-proteome screening, enabling deriving protein-centric adverse outcome pathways (AOPs), which are pivotal for the derivation of Systems Biology informally named Systems Toxicology. Especially if coupled pathology, the identification of molecular initiating events (MIEs) and AOPs allow predictive modeling of toxicological pathways, which now stands as the frontier for the next generation of toxicologists. Advances in mass spectrometry, bioinformatics, protein databases and top-down proteomics create new opportunities for mechanistic and effects-oriented research in all fields, from ecotoxicology to pharmacotoxicology.

Genome-wide identification of the aspartic protease gene family and their response under powdery mildew stress in wheat

Aspartic proteases (APs) are one of the four main protease super families. In plants, they are involved in many biological processes, such as biotic and abiotic stress resistance, protein processing and degradation, senescence, and programmed cell death. By performing a database (TGACv1) search and domain prediction, we identified 263 wheat AP (TaAP) proteins and observed 38 TaAP genes exhibiting alternative splicing. Moreover, by constructing a phylogenetic tree, we found that the TaAP proteins can be divided into three families and have a certain close evolutionary relationship to Arabidopsis thaliana and rice AP proteins. Transcriptome analysis showed that 29 genes in the TaAP family were up-regulated after being induced by powdery mildew. The expression of TaAP224 showed the most significant difference in transcriptome and qRT-PCR analyses. Subsequently, the promoters of these 29 genes were analysed, and we found that they contained multiple disease resistance and hormone elements, such as WRKY71OS, a common disease resistance element that is also involved in the GA signalling pathway and inhibits starch hydrolysis. The comprehensive annotation and expression profiling performed in this study increased our understanding of the TaAP family genes in wheat growth and development, and the results can be used as a basis for further study of candidate TaAP genes involved in powdery mildew resistance mechanisms.

Nutritional component analyses of kiwifruit in different development stages by metabolomic and transcriptomic approaches

BACKGROUND

Metabolites in kiwifruit greatly influence nutritional values; however, the dynamic changes in nutrient composition and the gene expression level of yellow kiwifruit have not been studied so far. To investigate the types and accumulation patterns of metabolites, a metabolomics approach utilizing liquid chromatography-electrospray ionization mass spectrometry and transcriptomics were used to analyze the yellow flesh of kiwifruit cultivar 'jinshi 1' collected at different stages of days after full bloom.

RESULTS

In total, 285 metabolites were identified over the kiwifruit developmental stages. The composition of the metabolites of kiwifruit at different stages of development was different. The organic acids contents and their derivatives were higher at the initial stage of development and then gradually decreased. The lipids and amino acids contents fluctuated at different stages of development but did not change significantly. Transcript profiles throughout yellow kiwifruit development were constructed and analyzed, with a focus on the biosynthesis and metabolism of compounds such as sugars, organic acids and ascorbic acid, which are indispensable for the development and formation of quality fruit. The transcript levels of genes involved in sucrose and starch metabolism were consistent with the change in soluble sugar and starch content throughout kiwifruit development. The metabolism of ascorbic acid was primarily through the l-galactose pathway.

CONCLUSION

Our metabolome and transcriptome approach identified dynamic changes in five types of nutrient metabolite levels, and correlations among such levels, in developing fruit. The results provide information that can be used by metabolic engineers and molecular breeders to improve kiwifruit quality. © 2020 Society of Chemical Industry.

Raltegravir-Induced Adaptations of the HIV-1 Integrase: Analysis of Structure, Variability, and Mutation Co-occurrence

The human immunodeficiency virus type 1 (HIV-1) has several proteins of therapeutic importance, many of which are currently used as drug targets in antiretroviral therapy. Among these proteins is the integrase, which is responsible for the integration of the viral DNA into the host genome - a crucial step for HIV-1 replication. Given the importance of this protein in the replication process, three integrase inhibitors are currently used as an option for antiretroviral therapy: Raltegravir, Elvitegravir, and Dolutegravir. However, the crescent emergence of mutations that cause resistance to these drugs has become a worldwide health problem. In this study, we compared the variability of each position of the HIV-1 integrase sequence in clinical isolates of Raltegravir-treated and drug-naïve patients by calculating their Shannon entropies. A co-occurrence network was created to explore how mutations co-occur in patients treated with Raltegravir. Then, by building tridimensional models of the HIV-1 integrase intasomes, we investigated the relationship between variability, architecture, and co-occurrence. We observed that positions bearing some of the major resistance pathways are highly conserved among non-treated patients and variable among the treated ones. The residues involved in the three main resistance-related mutations could be identified in the same group when the positions were clustered according to their entropies. Analysis of the integrase architecture showed that the high-entropy residues S119, T124, and T125, are in contact with the host DNA, and their variations may have impacts in the protein-DNA recognition. The co-occurrence network revealed that the major resistance pathways N155H and Q148HR share more mutations with each other than with the Y143R pathway, this observation corroborates the fact that the N155H pathway is most commonly converted into Q148HRK than into Y143RCH pathway in patients' isolates. The network and the structure analysis also support the hypothesis that the resistance-related E138K mutation may be a mechanism to compensate for mutations in neighbor lysine residues to maintain DNA binding. The present study reveals patterns by which the HIV-1 integrase adapts during Raltegravir therapy. This information can be useful to comprehend the impacts of the drug in the enzyme, as well as help planning new therapeutic approaches.

Niemann-Pick proteins type C2 are identified as olfactory related genes of Pardosa pseudoannulata by transcriptome and expression profile analysis

In arthropods, the large majority of studies on olfaction have been mainly focused on insects, whereas little on Arachnida, even though olfaction is very important in arachnid behavior. Pardosa pseudoannulata is one of the most common wandering spiders in rice fields, as the important natural enemy against a range of pests. However, little is known about the potential chemosensory proteins involved in olfactory behavior of these spiders. Niemann-Pick proteins type C2 (NPC2) as a new class of binding and transport proteins for semiochemicals in arthropods especially ticks and mites has received more attention in recent years. In this study, six NPC2s namely PpseNPC1-6 were newly identified in the appendages of P. pseudoannulata based on transcriptome data. A phylogenetic analysis indicated that all of P. pseudoannulata NPC2s were clustered together forming one clade with high posterior probability values. In addition, the sequences shared the same subclade with the NPC2 sequences of ticks and scorpion. The motif-patterns indicated that PpseNPC2-5 had the common pattern with the two-spotted spider mite Tetranychus urticae and the ant Trachymyrmex cornetzi. Furthermore, quantitative real-time PCR (qPCR) measurements were conducted to evaluate the expression profile of these genes in various tissues of P. pseudoannulata. It was found that most NPC2s (PpseNPC2-1, PpseNPC2-2, PpseNPC2-5 and PpseNPC2-6) were highly expressed in adult pedipalps and chelicerae. Owing to the functional olfactory organs in Chelicerata of pedipalps, our results supported a putative role of NPC2s as new odorant carriers in P. pseudoannulata.

Influenza and sudden unexpected death: the possible role of peptide cross-reactivity

This study investigates the hypothesis that cross-reactions may occur between human cardiac proteins and influenza antigens, thus possibly representing the molecular mechanism underlying influenzaassociated sudden unexpected death (SUD). Using titin protein as a research model, data were obtained on (1) the occurrence of the titin octapeptide AELLVLLE or its mimic AELLVALE in influenza A virus hemagglutinin (HA) sequences; (2) the immunological potential of AELLVLLE and its mimic AELLVALE; (3) the possible role of the flanking amino acid aa) context of the two octapeptide determinants in eliciting cross-reactivity between the human cardiac titin protein and HA antigens.

Proteome Profiling of Sertoli Cells Using a GeLC-MS/MS Strategy

Proteomics is a technology that allows to decipher the molecular networks involved in the regulation of biological processes such as spermatogenesis. Sertoli cells (SCs) are key players in the paracrine control of this process. Envisioning to increase the knowledge on the molecular networks harbored in SCs, we propose a methodology based on GeLC-MS/MS for the characterization of these cells' proteome. Proteins are separated by SDS-PAGE hyphenated to HPLC and identified by mass spectrometry. The integration of data with bioinformatics tools such as ClueGO + CluePedia from Cytoscape allows the identification of the biological pathways more prevalent in SCs, and that might be modulated by pathophysiological conditions. Moreover, the proteome analysis with tools as SignalP/SecretomeP highlights the proteins more prone to be secreted and involved in the paracrine control of germ cells, which might also be deregulated by diseases.

Protein Bioinformatics Databases and Resources

Many publicly available data repositories and resources have been developed to support protein-related information management, data-driven hypothesis generation, and biological knowledge discovery. To help researchers quickly find the appropriate protein-related informatics resources, we present a comprehensive review (with categorization and description) of major protein bioinformatics databases in this chapter. We also discuss the challenges and opportunities for developing next-generation protein bioinformatics databases and resources to support data integration and data analytics in the Big Data era.

Proteomic approaches in biomarker discovery: new perspectives in cancer diagnostics

Despite remarkable progress in proteomic methods, including improved detection limits and sensitivity, these methods have not yet been established in routine clinical practice. The main limitations, which prevent their integration into clinics, are high cost of equipment, the need for highly trained personnel, and last, but not least, the establishment of reliable and accurate protein biomarkers or panels of protein biomarkers for detection of neoplasms. Furthermore, the complexity and heterogeneity of most solid tumours present obstacles in the discovery of specific protein signatures, which could be used for early detection of cancers, for prediction of disease outcome, and for determining the response to specific therapies. However, cancer proteome, as the end-point of pathological processes that underlie cancer development and progression, could represent an important source for the discovery of new biomarkers and molecular targets for tailored therapies.

KB-Rank: efficient protein structure and functional annotation identification via text query

The KB-Rank tool was developed to help determine the functions of proteins. A user provides text query and protein structures are retrieved together with their functional annotation categories. Structures and annotation categories are ranked according to their estimated relevance to the queried text. The algorithm for ranking first retrieves matches between the query text and the text fields associated with the structures. The structures are next ordered by their relative content of annotations that are found to be prevalent across all the structures retrieved. An interactive web interface was implemented to navigate and interpret the relevance of the structures and annotation categories retrieved by a given search. The aim of the KB-Rank tool is to provide a means to quickly identify protein structures of interest and the annotations most relevant to the queries posed by a user. Informational and navigational searches regarding disease topics are described to illustrate the tool's utilities. The tool is available at the URL http://protein.tcmedc.org/KB-Rank.

Environmental proteomics, biodiversity statistics and food-web structure

Pioneering studies in environmental proteomics have revealed links between protein diversity and ecological function in simple ecological communities, such as microbial biofilms. In the near future, high-throughput proteomic methods will be applied to more complex ecological systems in which microbes and macrobes interact. Data structures in biodiversity and protein surveys have many similarities, so the statistical methods that ecologists use for analyzing biodiversity data should be adapted for use with quantitative surveys of protein diversity. However, increasing quantities of protein and bioinformatics data will not, by themselves, reveal the functional significance of proteins. Instead, ecologists should be measuring changes in the abundance of protein cohorts in response to replicated field manipulations, including nutrient enrichment and removal of top predators.

Integration of proteomics into systems biology of cancer

Deciphering the complexity and heterogeneity of cancer, benefits from integration of proteomic level data into systems biology efforts. The opportunities available as a result of advances in proteomic technologies, the successes to date, and the challenges involved in integrating diverse datasets are addressed in this review.