STRING v9.1: protein-protein interaction networks, with increased coverage and integration

Identification of new hub- ferroptosis-related genes in Lupus Nephritis

Background: Lupus Nephritis (LN) is the primary causation of kidney injury in systemic lupus erythematosus (SLE). Ferroptosis is a programmed cell death. Therefore, understanding the crosstalk between LN and ferroptosis is still a significant challenge. Methods: We obtained the expression profile of LN kidney biopsy samples from the Gene Expression Omnibus database and utilised the R-project software to identify differentially expressed genes (DEGs). Then, we conducted a functional correlation analysis. Ferroptosis-related genes (FRGs) and differentially expressed genes (DEGs) crossover to select FRGs with LN. Afterwards, we used CIBERSORT to assess the infiltration of immune cells in both LN tissues and healthy control samples. Finally, we performed immunohistochemistry on LN human renal tissue. Results: 10619 DEGs screened from the LN biopsy tissue were identified. 22 hub-ferroptosis-related genes with LN (FRGs-LN) were screened out. The CIBERSORT findings revealed that there were significant statistical differences in immune cells between healthy control samples and LN tissues. Immunohistochemistry further demonstrated a significant difference in HRAS, TFRC, ATM, and SRC expression in renal tissue between normal and control groups. Conclusion: We developed a signature that allowed us to identify 22 new biomarkers associated with FRGs-LN. These findings suggest new insights into the pathology and therapeutic potential of LN ferroptosis inhibitors and iron chelators.

Bioinformatics and systems-biology approach to identify common pathogenic mechanisms for COVID-19 and systemic lupus erythematosus

BACKGROUND

The Coronavirus disease 2019 (COVID-19) pandemic has brought a heavy burden to the world, interestingly, it shares many clinical symptoms with systemic lupus erythematosus (SLE). It is unclear whether there is a similar pathological process between COVID-9 and SLE. In addition, we don't know how to treat SLE patients with COVID-19. In this study, we analyse the potential similar pathogenesis between SLE and COVID-19 and explore their possible drug regimens using bioinformatics and systems biology approaches.

METHODS

The common differentially expressed genes (DEGs) were extracted from the COVID-19 datasets and the SLE datasets for functional enrichment, pathway analysis and candidate drug analysis.

RESULT

Based on the two transcriptome datasets between COVID-19 and SLE, 325 common DEGs were selected. Hub genes were identified by protein-protein interaction (PPI) analysis. few found a variety of similar functional changes between COVID-19 and SLE, which may be related to the pathogenesis of COVID-19. Besides, we explored the related regulatory networks. Then, through drug target matching, we found many candidate drugs for patients with COVID-19 only or COVID-19 combined with SLE.

CONCLUSION

COVID-19 and SLE patients share many common hub genes, related pathways and regulatory networks. Based on these common targets, we found many potential drugs that could be used in treating patient with COVID-19 or COVID-19 combined with SLE.

Transcriptomic analysis of epicardial adipose tissue reveals the potential crosstalk genes and immune relationship between type 2 diabetes mellitus and atrial fibrillation

BACKGROUND

The complex relationship between atrial fibrillation (AF) and type 2 diabetes mellitus (T2DM) suggests a potential role for epicardial adipose tissue (EAT) that requires further investigation. This study employs bioinformatics and experimental approaches to clarify EAT's role in linking T2DM and AF, aiming to unravel the biological mechanisms involved.

METHOD

Bioinformatics analysis initially identified common differentially expressed genes (DEGs) in EAT from T2DM and AF datasets. Pathway enrichment and network analyses were then performed to determine the biological significance and network connections of these DEGs. Hub genes were identified through six CytoHubba algorithms and subsequently validated biologically, with further in-depth analyses confirming their roles and interactions. Experimentally, db/db mice were utilized to establish a T2DM model. AF induction was executed via programmed transesophageal electrical stimulation and burst pacing, focusing on comparing the incidence and duration of AF. Frozen sections and Hematoxylin and Eosin (H&E) staining illuminated the structures of the heart and EAT. Moreover, quantitative PCR (qPCR) measured the expression of hub genes.

RESULTS

The study identified 106 DEGs in EAT from T2DM and AF datasets, underscoring significant pathways in energy metabolism and immune regulation. Three hub genes, CEBPZ, PAK1IP1, and BCCIP, emerged as pivotal in this context. In db/db mice, a marked predisposition towards AF induction and extended duration was observed, with HE staining verifying the presence of EAT. Additionally, qPCR validated significant changes in hub genes expression in db/db mice EAT. In-depth analysis identified 299 miRNAs and 33 TFs as potential regulators, notably GRHL1 and MYC. GeneMANIA analysis highlighted the hub genes' critical roles in stress responses and leukocyte differentiation, while immune profile correlations highlighted their impact on mast cells and neutrophils, emphasizing the genes' significant influence on immune regulation within the context of T2DM and AF.

CONCLUSION

This investigation reveals the molecular links between T2DM and AF with a focus on EAT. Targeting these pathways, especially EAT-related ones, may enable personalized treatments and improved outcomes.

Site-specific identification and quantitation of endogenous SUMOylation based on SUMO-specific protease and strong anion exchange chromatography

Small ubiquitin-like modifier (SUMO) modification regulates various eukaryotic cellular processes and plays a pivotal role in interferon (IFN)-mediated antiviral defense. While immunoprecipitation enrichment method is widely used for proteome-wide analysis of endogenous SUMOylation, the inability to target all SUMO forms and high cost of antibodies limited its further application. Herein, we proposed an antibody-free enrichment method based on SUMO-specific protease and strong anion exchange chromatography (SPAX) to globally profile the endogenous SUMOylation. The SUMO1/2/3-modified peptides could be simultaneously enriched by SAX chromatography by utilizing its electrostatic interaction with SUMO1/2/3 remnants, which contained multiple aspartic acids (D) and glutamic acids (E). To remove the co-enriched D/E-containing peptides which might interfere with the detection of low-abundance SUMOylated peptides, SUMO-specific protease was used to cleave the SUMO1/2/3 remnants from enriched SUMOylated peptides. As the deSUMOylated peptides lost SUMO remnants, their interaction with SAX materials became weaker, and the D/E-containing peptides could thus be depleted through the second SAX separation. The SPAX method identified over twice the SUMOylated sites than using SAX method only, greatly improving the identification coverage of endogenous SUMOylated sites. Our strategy was then applied to the site-specific identification and quantification of endogenous SUMOylation in A549 cells stimulated by IFN-γ for the first time. A total of 226 SUMOylated sites on 146 proteins were confidently identified, among which multiple up-regulated sites were involved in IFN-mediated antiviral defense, demonstrating the great promise of SPAX to globally profile and discover endogenous SUMOylation with significant biological functions.

Revealing Dynamics of Protein Phosphorylation: A Study on the Cashmere Fineness Disparities in Liaoning Cashmere Goats

Exploring the landscape of protein phosphorylation, this investigation focuses on skin samples from LCG (Liaoning Cashmere Goats), characterized by different levels of cashmere fineness. Employing LC-MS/MS technology, we meticulously scrutinized FT-LCG (fine-type Liaoning Cashmere Goats) and CT-LCG (coarse-type Liaoning Cashmere Goats). Identifying 512 modified proteins, encompassing 1368 phosphorylated peptide segments and 1376 quantifiable phosphorylation sites, our exploration further revealed consistent phosphorylation sites in both groups. Analysis of phosphorylated peptides unveiled kinase substrates, prominently featuring Protein Kinase C, Protein Kinase B and MAPK3-MAPK1-MAPK7-NLK-group. Differential analysis spotlighted 28 disparate proteins, comprising six upregulated and twenty-two downregulated. Cluster analysis showcased the robust clustering efficacy of the two sample groups. GO (Gene Ontology) and KEGG (Kyoto Encyclopedia of Genes and Genomes) enrichment analyses underscored the significance of the purine metabolism pathway, suggesting its pivotal role in modulating cashmere fineness in LCG. Notably, through differential protein analysis, two crucial proteins were identified: HSL-X (hormone-sensitive lipase isoform X1) and KPRP (keratinocyte proline-rich protein). Further evidence supports LIPE and KPRP as key genes regulating cashmere fineness, paving the way for promising avenues in further research. These findings not only contribute to a nuanced understanding of protein-level dynamics in cashmere but also provide a theoretical foundation for the selective breeding of superior Liaoning Cashmere Goat strands.

Biomedical literature mining: graph kernel-based learning for gene-gene interaction extraction

The supervised machine learning method is often used for biomedical relationship extraction. The disadvantage is that it requires much time and money to manually establish an annotated dataset. Based on distant supervision, the knowledge base is combined with the corpus, thus, the training corpus can be automatically annotated. As many biomedical databases provide knowledge bases for study with a limited number of annotated corpora, this method is practical in biomedicine. The clinical significance of each patient's genetic makeup can be understood based on the healthcare provider's genetic database. Unfortunately, the lack of previous biomedical relationship extraction studies focuses on gene-gene interaction. The main purpose of this study is to develop extraction methods for gene-gene interactions that can help explain the heritability of human complex diseases. This study referred to the information on gene-gene interactions in the KEGG PATHWAY database, the abstracts in PubMed were adopted to generate the training sample set, and the graph kernel method was adopted to extract gene-gene interactions. The best assessment result was an F1-score of 0.79. Our developed distant supervision method automatically finds sentences through the corpus without manual labeling for extracting gene-gene interactions, which can effectively reduce the time cost for manual annotation data; moreover, the relationship extraction method based on a graph kernel can be successfully applied to extract gene-gene interactions. In this way, the results of this study are expected to help achieve precision medicine.

Integrative Analysis of Lactylome and Proteome of Hypertrophic Scar To Identify Pathways or Proteins Associated with Disease Development

Lactylation (Kla), a recently discovered post-translational modification derived from lactate, plays crucial roles in various cellular processes. However, the specific influence of lactylation on the biological processes underlying hypertrophic scar formation remains unclear. In this study, we present a comprehensive profiling of the lactylome and proteome in both hypertrophic scars and adjacent normal skin tissues. A total of 1023 Kla sites originating from 338 nonhistone proteins were identified based on lactylome analysis. Proteome analysis in hypertrophic scar and adjacent skin samples revealed the identification of 2008 proteins. It is worth noting that Kla exhibits a preference for genes associated with ribosome function as well as glycolysis/gluconeogenesis in both normal skin and hypertrophic scar tissues. Furthermore, the functional enrichment analysis demonstrated that differentially lactyled proteins are primarily involved in proteoglycans, HIF-1, and AMPK signaling pathways. The combined analysis of the lactylome and proteome data highlighted a significant upregulation of 14 lactylation sites in hypertrophic scar tissues. Overall, our investigation unveiled the significant involvement of protein lactylation in the regulation of ribosome function as well as glycolysis/gluconeogenesis, potentially contributing to the formation of hypertrophic scars.

Proteome-wide analysis identifies plasma immune regulators of amyloid-beta progression

While immune function is known to play a mechanistic role in Alzheimer's disease (AD), whether immune proteins in peripheral circulation influence the rate of amyloid-β (Aβ) progression - a central feature of AD - remains unknown. In the Baltimore Longitudinal Study of Aging, we quantified 942 immunological proteins in plasma and identified 32 (including CAT [catalase], CD36 [CD36 antigen], and KRT19 [keratin 19]) associated with rates of cortical Aβ accumulation measured with positron emission tomography (PET). Longitudinal changes in a subset of candidate proteins also predicted Aβ progression, and the mid- to late-life (20-year) trajectory of one protein, CAT, was associated with late-life Aβ-positive status in the Atherosclerosis Risk in Communities (ARIC) study. Genetic variation that influenced plasma levels of CAT, CD36 and KRT19 predicted rates of Aβ accumulation, including causal relationships with Aβ PET levels identified with two-sample Mendelian randomization. In addition to associations with tau PET and plasma AD biomarker changes, as well as expression patterns in human microglia subtypes and neurovascular cells in AD brain tissue, we showed that 31 % of candidate proteins were related to mid-life (20-year) or late-life (8-year) dementia risk in ARIC. Our findings reveal plasma proteins associated with longitudinal Aβ accumulation, and identify specific peripheral immune mediators that may contribute to the progression of AD pathophysiology.

Whole-genome DNA methylation profiling revealed epigenetic regulation of NF-κB signaling pathway involved in response to Vibrio alginolyticus infection in the Pacific oyster, Crassostrea gigas

DNA methylation, an essential epigenetic alteration, is tightly linked to a variety of biological processes, such as immune response. To identify the epigenetic regulatory mechanism in Pacific oyster (Crassostrea gigas), whole-genome bisulfite sequencing (WGBS) was conducted on C. gigas at 0 h, 6 h, and 48 h after infection with Vibrio alginolyticus. At 6 h and 48 h, a total of 11,502 and 14,196 differentially methylated regions (DMRs) were identified (p<0.05, FDR<0.001) compared to 0 h, respectively. Gene ontology (GO) analysis showed that differentially methylated genes (DMGs) were significantly enriched in various biological pathways including immunity, cytoskeleton, epigenetic modification, and metabolic processes. Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis showed that transcription machinery (ko03021) is one of the most important pathways. Integrated transcriptome and methylome analyses allowed the identification of 167 and 379 DMG-related DEGs at 6 h and 48 h, respectively. These genes were significantly enriched in immune-related pathways, including nuclear factor kappa B (NF-κB) signaling pathway (ko04064) and tumor necrosis factor (TNF) signaling pathway (ko04668). Interestingly, it's observed that the NF-κB pathway could be activated jointly by TNF Receptor Associated Factor 2 (TRAF2) and Baculoviral IAP Repeat Containing 3 (BIRC3, the homolog of human BIRC2) which were regulated by DNA methylation in response to the challenge posed by V. alginolyticus infection. Through this study, we provided insightful information about the epigenetic regulation of immunity-related genes in the C. gigas, which will be valuable for the understanding of the innate immune system modulation and defense mechanism against bacterial infection in invertebrates.

Machine learning-based screening and validation of liver metastasis-specific genes in colorectal cancer

Colorectal liver metastasis (CRLM) is challenging in the clinical treatment of colorectal cancer. Limited research has been conducted on how CRLM develops. RNA sequencing data were obtained from the Gene Expression Omnibus (GEO) and The Cancer Genome Atlas (TCGA). Four machine learning algorithms were used to screen the hub CRLM-specific genes, including Least Absolute Shrinkage and Selection Operator (Lasso), Random forest, SVM-RFE, and XGboost. The model for identifying CRLM was developed using stepwise logistic regression and was validated using internal and independent datasets. The prognostic value of hub CRLM-specific genes was assessed using the Lasso-Cox method. The in vitro experiments were performed using SW620 cells. The CRLM identification model was developed based on four CRLM-specific genes (SPP1, ZG16, P2RY14, and PRKAR2B), and the model efficacy was validated using GSE41258 and three external cohorts. Five CRLM-specific prognostic hub genes, SPP1, ZG16, P2RY14, CYP2E1, and C5, were identified using the Lasso-Cox algorithm, and a risk score was constructed. The risk score was validated using the GSE39582 cohort. Three genes have both efficacy in identifying CRLM and prognostic value: ZG16, P2RY14, and SPP1. Immune infiltration and enrichment analyses demonstrated that SPP1 was associated with M2 macrophage polarization and extracellular matrix remodeling. In vitro experiments indicated that SPP1 may act as a cancer-promoting factor. The hub CRLM-specific gene SPP1 can help determine the diagnosis, prognosis, and immune infiltration of patients with CRLM.

Comprehensive analysis of aberrantly methylated differentially expressed genes and validation of CDC6 in melanoma

BACKGROUND

Skin Cutaneous Melanoma (SKCM) is a highly aggressive malignant tumor with a significant increase in mortality upon metastasis. The molecular mechanisms driving melanoma progression remain largely unclear. Recent studies have highlighted the importance of epigenetic alterations, especially DNA methylation, in melanoma development. This study aims to identify and analyze methylation-regulated differentially expressed genes (MeDEGs) in genome-wide profiles between primary and metastatic melanoma.

METHODS

Gene expression profiling datasets GSE8401 and gene methylation profiling datasets GSE86355 were collected from the GEO database. Differentially expressed genes (DEGs) and differentially methylated genes (DMGs) were systematically identified. Integration of DEGs and DMGs yielded a set of MeDEGs, which subsequently underwent functional enrichment analysis. The protein-protein interaction (PPI) network was constructed using STRING and visualized using Cytoscape software. Survival analysis was used to select prognostic hub genes. In addition, 37 SKCM and 37 normal skin tissues from the First Affiliated Hospital of Soochow University (FAHSU) were collected for immunohistochemical (IHC) staining and evaluation. Furthermore, DNA methylation patterns of CDC6 were analyzed. To validate these findings, SKCM cell cultures were utilized to elucidate the expression and behavioral characteristics of CDC6. Additionally, gene set enrichment analysis (GSEA) and immune infiltration analysis were conducted for CDC6.

RESULTS

In our study, we discovered 120 hypomethylated-upregulated genes and 212 hypermethylated-downregulated genes. The hypomethylated-upregulated genes were notably associated with biological processes such as spindle assembly checkpoint signaling, mitotic spindle assembly, and negative regulation of mitotic metaphase/anaphase transition. Our pathway analysis revealed significant enrichment in pathways related to dilated cardiomyopathy, amino sugar metabolism, progesterone-mediated oocyte maturation, and chemical carcinogenesis. Conversely, hypermethylated-downregulated genes were found to be enriched in processes like epidermis development, keratinocyte differentiation, and skin development. Additionally, pathway analysis highlighted associations with estrogen signaling, Staphylococcus aureus infection, axon guidance, and arachidonic acid metabolism. Following the establishment of PPI networks and survival analysis, we identified 11 prognostic hub genes: CCNA2, CDC6, CDCA3, CKS2, DTL, HJURP, KRT5, KRT14, KRT15, KRT16, and NEK2. Notably, among the 11 hub genes, our findings indicate that CDC6 plays a pivotal role in enhancing the proliferation, migration, and invasion capabilities of melanoma cells in vitro.

CONCLUSIONS

Our comprehensive genomic analyses reveal that genes with aberrant methylation exhibit differential expression during the transition from primary to metastatic melanoma. The identified genes, especially CDC6, which plays a crucial role in enhancing melanoma cell proliferation, migration, and invasion, provide valuable insights into potential methylation-based biomarkers. These findings could contribute significantly to advancing precision medicine in SKCM.

A Bioinformatic Assay of Quercetin in Gastric Cancer

Gastric cancer (GC) remains a significant global health challenge, with high mortality rates, especially in developing countries. Current treatments are invasive and have considerable risks, necessitating the exploration of safer alternatives. Quercetin (QRC), a flavonoid present in various plants and foods, has demonstrated multiple health benefits, including anticancer properties. This study investigated the therapeutic potential of QRC in the treatment of GC. We utilized advanced molecular techniques to assess the impact of QRC on GC cells, examining its effects on cellular pathways and gene expression. Our findings indicate that QRC significantly inhibits GC cell proliferation and induces apoptosis, suggesting its potential as a safer therapeutic option for GC treatment. Further research is required to validate these results and explore the clinical applications of QRC in cancer therapy.

An Optimized Miniaturized Filter-Aided Sample Preparation Method for Sensitive Cross-Linking Mass Spectrometry Analysis of Microscale Samples

Cross-linking mass spectrometry (XL-MS) is a powerful tool for elucidating protein structures and protein-protein interactions (PPIs) at the global scale. However, sensitive XL-MS analysis of mass-limited samples remains challenging, due to serious sample loss during sample preparation of the low-abundance cross-linked peptides. Herein, an optimized miniaturized filter-aided sample preparation (O-MICROFASP) method was presented for sensitive XL-MS analysis of microscale samples. By systematically investigating and optimizing crucial experimental factors, this approach dramatically improves the XL identification of low and submicrogram samples. Compared with the conventional FASP method, more than 7.4 times cross-linked peptides were identified from single-shot analysis of 1 μg DSS cross-linked HeLa cell lysates (440 vs 59). The number of cross-linked peptides identified from 0.5 μg HeLa cell lysates was increased by 58% when further reducing the surface area of the filter to 0.058 mm2 in the microreactor. To deepen the identification coverage of XL-proteome, five different types of cross-linkers were used and each μg of cross-linked HeLa cell lysates was processed by O-MICROFASP integrated with tip-based strong cation exchange (SCX) fractionation. Up to 2741 unique cross-linked peptides were identified from the 5 μg HeLa cell lysates, representing 2579 unique K-K linkages on 1092 proteins. About 96% of intraprotein cross-links were within the maximal distance restraints of 26 Å, and 75% of the identified PPIs reported by the STRING database were with high confidence (scores ≥0.9), confirming the high validity of the identified cross-links for protein structural mapping and PPI analysis. This study demonstrates that O-MICROFASP is a universal and efficient method for proteome-wide XL-MS analysis of microscale samples with high sensitivity and reliability.

PGD: Shared gene linking polycystic ovary syndrome and endometrial cancer, influencing proliferation and migration through glycometabolism

The relationship among polycystic ovary syndrome (PCOS), endometrial cancer (EC), and glycometabolism remains unclear. We explored shared genes between PCOS and EC, using bioinformatics to unveil their pathogenic connection and influence on EC prognosis. Gene Expression Omnibus datasets GSE226146 (PCOS) and GSE196033 (EC) were used. A protein-protein interaction (PPI) network was constructed to identify the central genes. Candidate markers were screened using dataset GSE54250. Differences in marker expression were confirmed in mouse PCOS and human EC tissues using RT-PCR and immunohistochemistry. The effect of PGD on EC proliferation and migration was explored using Ki-67 and Transwell assays. PGD's impact on the glycometabolic pathway within carbon metabolism was assessed by quantifying glucose content and lactic acid production. R software identified 31 common genes in GSE226146 and GSE196033. Gene Ontology functional classification revealed enrichment in the "purine nucleoside triphosphate metabolism process," with key Kyoto Encyclopedia of Genes and Genomes pathways related to "carbon metabolism." The PPI network identified 15 hub genes. HK2, NDUFS8, PHGDH, PGD, and SMAD3 were confirmed as candidate markers. The RT-PCR analysis validated distinct HK2 and PGD expression patterns in mouse PCOS ovarian tissue and human EC tissue, as well as in normal and EC cells. Transfection experiments with Ishikawa cells further confirmed PGD's influence on cell proliferation and migration. Suppression of PGD expression impeded glycometabolism within the carbon metabolism of EC cells, suggesting PGD as a significant PCOS risk factor impacting EC proliferation and migration through modulation of single carbon metabolism. These findings highlight PGD's pivotal role in EC onset and prognosis.

Identification of common genetic factors and immune-related pathways associating more than two autoimmune disorders: implications on risk, diagnosis, and treatment

Autoimmune disorders (ADs) are chronic conditions resulting from failure or breakdown of immunological tolerance, resulting in the host immune system attacking its cells or tissues. Recent studies report shared effects, mechanisms, and evolutionary origins among ADs; however, the possible factors connecting them are unknown. This study attempts to identify gene signatures commonly shared between different autoimmune disorders and elucidate their molecular pathways linking the pathogenesis of these ADs using an integrated gene expression approach. We employed differential gene expression analysis across 19 datasets of whole blood/peripheral blood cell samples with five different autoimmune disorders (rheumatoid arthritis, multiple sclerosis, systemic lupus erythematosus, Crohn's disease, and type 1 diabetes) to get nine key genes-EGR1, RUNX3, SMAD7, NAMPT, S100A9, S100A8, CYBB, GATA2, and MCEMP1 that were primarily involved in cell and leukocyte activation, leukocyte mediated immunity, IL-17, AGE-RAGE signaling in diabetic complications, prion disease, and NOD-like receptor signaling confirming its role in immune-related pathways. Combined with biological interpretations such as gene ontology (GO), pathway enrichment, and protein-protein interaction (PPI) network, our current study sheds light on the in-depth research on early detection, diagnosis, and prognosis of different ADs.

Identification and role of differentially expressed genes/proteins between pulmonary tuberculosis patients and controls across lung tissues and blood samples

BACKGROUND

Differentially expressed genes/proteins (DEGs/DEPs) play critical roles in pulmonary tuberculosis (PTB) diagnosis and treatment. However, there is a scarcity of reports on DEGs/DEPs in lung tissues and blood samples in PTB patients.

OBJECTIVE

We aim to identify the DEGs/DEPs in lung tissues and blood samples of PTB patients and investigate their roles in PTB.

MATERIALS AND METHODS

The lung granulomas and normal tissues were collected from PTB patients for proteomic and transcriptomic analyses. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses annotated the functions of DEGs/DEPs. The GSE107994 data set was downloaded to identify the DEGs/DEPs in peripheral blood. The common DEGs and DEPs were identified. A nomogram was established. Pearson correlation analysis was conducted.

RESULTS

Eighty-three DEGs/DEPs were identified. These DEGs/DEPs were mainly enriched in the movement of cell or subcellular components, regulation of cellular component biogenesis, and actin filament-based process as well as in the pathways of inositol phosphate metabolism, adherens junction, phosphatidylinositol signaling system, leukocyte transendothelial migration, regulation of actin cytoskeleton, and tight junction. There were eight common DEGs/DEPs (TYMP, LAP3, ADGRL2, SIL1, LMO7, SULF 1, ANXA3, and PACSIN3) between the lung tissues and blood samples. They were effective in predicting tuberculosis. Moreover, the activated dendritic cells, macrophages, monocytes, neutrophils, and regulatory T cells were significantly positively correlated with TYMP (r > .50), LAP3 (r > .50), SIL1 (r > .50), ANXA3 (r > .5), and PACSIN3 (r < .50), while negatively correlated with LMO7 (r < -0.50) (p < .05). ADGRL2 and SULF1 did not have a significant correlation (p > .05).

LIMITATIONS

The sample size was small.

CONCLUSIONS

Eight common DEGs/DEPs of lung tissues and blood samples were identified. They were correlated with immune cells and demonstrated predictive value for PTB. Our data may facilitate the diagnosis and treatment of PTB.

Integrating physiological and multi-omics methods to elucidate heat stress tolerance for sustainable rice production

Heat stress presents unique challenges compared to other environmental stressors, as predicting crop responses and understanding the mechanisms for heat tolerance are complex tasks. The escalating impact of devastating climate changes heightens the frequency and intensity of heat stresses, posing a noteworthy threat to global agricultural productivity, especially in rice-dependent regions of the developing world. Humidity has been demonstrated to negatively affect rice yields worldwide. Plants have evolved intricate biochemical adaptations, involving intricate interactions among genes, proteins, and metabolites, to counter diverse external signals and ensure their survival. Modern-omics technologies, encompassing transcriptomics, metabolomics, and proteomics, have revolutionized our comprehension of the intricate biochemical and cellular shifts that occur in stressed agricultural plants. Integrating these multi-omics approaches offers a comprehensive view of cellular responses to heat stress and other challenges, surpassing the insights gained from multi-omics analyses. This integration becomes vital in developing heat-tolerant crop varieties, which is crucial in the face of increasingly unpredictable weather patterns. To expedite the development of heat-resistant rice varieties, aiming at sustainability in terms of food production and food security globally, this review consolidates the latest peer-reviewed research highlighting the application of multi-omics strategies.

Bioinformatics analysis of the association between obesity and gastric cancer

Background

Obesity and gastric cancer (GC) are prevalent diseases worldwide. In particular, the number of patients with obesity is increasing annually, while the incidence and mortality rates of GC are ranked high. Consequently, these conditions seriously affect the quality of life of individuals. While evidence suggests a strong association between these two conditions, the underlying mechanisms of this comorbidity remain unclear.

Methods

We obtained the gene expression profiles of GSE94752 and GSE54129 from the Gene Expression Omnibus database. To investigate the associated biological processes, pathway enrichment analyses were conducted using Gene Ontology and Kyoto Encyclopedia of Genes and Genomes for the shared differentially expressed genes in obesity and GC. A protein-protein interaction (PPI) network was subsequently established based on the Search Tool for the Retrieval of Interacting Genes (STRING) database, followed by the screening of the core modules and central genes in this network using Cytoscape plug-in MCODE. Furthermore, we scrutinized the co-expression network and the interplay network of transcription factors (TFs), miRNAs, and mRNAs linked to these central genes. Finally, we conducted further analyses using different datasets to validate the significance of the hub genes.

Results

A total of 246 shared differentially expressed genes (209 upregulated and 37 downregulated) were selected for ensuing analyses. Functional analysis emphasized the pivotal role of inflammation and immune-associated pathways in these two diseases. Using the Cytoscape plug-in CytoHubba, nine hub genes were identified, namely, CXCR4, CXCL8, CXCL10, IL6, TNF, CCL4, CXCL2, CD4, and CCL2. IL6 and CCL4 were confirmed as the final hub genes through validation using different datasets. The TF-miRNA-mRNA regulatory network showed that the TFs primarily associated with the hub genes included RELA and NFKB1, while the predominantly associated miRNAs included has-miR-195-5p and has-miR-106a-5p.

Conclusion

Using bioinformatics methods, we identified two hub genes from the Gene Expression Omnibus datasets for obesity and GC. In addition, we constructed a network of hub genes, TFs, and miRNAs, and identified the major related TFs and miRNAs. These factors may be involved in the common molecular mechanisms of obesity and GC.

An affordable label-free ultrasensitive immunosensor based on gold nanoparticles deposited on glassy carbon electrode for the transferrin receptor detection

In recent decades, there has been a concerning and consistent rise in the incidence of cancer, posing a significant threat to human health and overall quality of life. The transferrin receptor (TfR) is one of the most crucial protein biomarkers observed to be overexpressed in various cancers. This study reports on the development of a novel voltammetric immunosensor for TfR detection. The electrochemical platform was made up of a glassy carbon electrode (GCE) functionalized with gold nanoparticles (AuNPs), on which anti-TfR was immobilized. The surface characteristics and electrochemical behaviors of the modified electrodes were comprehensively investigated through scanning electron microscopy, XPS, Raman spectroscopy FT-IR, electrochemical cyclic voltammetry and impedance spectroscopy. The developed immunosensor exhibited robust analytical performance with TfR fortified buffer solution, showing a linear range (LR) response from 0.01 to 3000 μg/mL, with a limit of detection (LOD) of 0.01 μg/mL and reproducibility (RSD <4 %). The fabricated sensor demonstrated high reproducibility and selectivity when subjected to testing with various types of interfering proteins. The immunosensor designed for TfR detection demonstrated several advantageous features, such as being cost-effective and requiring a small volume of test sample making it highly suitable for point-of-care applications.

Jejunal transcriptomic profiling of carnosine synthesis precursor-related genes and pathways in slow-growing Korat chicken

Carnosine is a physiologically important molecule in normal human body functions. Chicken meat is an excellent source of carnosine; especially slow-growing Korat chicken (KR) females have a high carnosine content in their meat. The carnosine content of chicken meat can be increased by dietary supplementation of β-alanine (βA) and L-histidine (L-His). Our objective was to reveal the pathways and genes through jejunal transcriptomic profiling related to βA and L-His absorption and transportation. We collected whole jejunum samples from 5 control and 5 experimental KR chicken, fed with 1% βA and 0.5% L-His supplementation. A total of 407 differentially expressed genes (P < 0.05, log2 fold change ≥2) were identified, 272 of which were down-regulated and 135 up-regulated in the group with dietary supplementation compared to the control group. Based on the integrated analysis of the protein-protein interaction network and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway maps, 87 gene ontology terms were identified and 6 KEGG pathways were significantly (P < 0.05) enriched in the jejunum. The analyses revealed 6 key genes, KCND3, OPRM1, CCK, GCG, TRH, and GABBR2, that are related to neuroactive ligand-receptor interaction and the calcium signaling pathway. These findings give insight regarding the molecular mechanism related to carnosine precursor absorption and transportation in the jejunum and help to identify useful molecular markers for improving the carnosine content in slow-growing KR chicken meat.

Single-cell transcriptomic reveals a cell atlas and diversity of chicken amygdala responded to social hierarchy

Amygdala serves as a highly cellular, heterogeneous brain region containing excitatory and inhibitory neurons and is involved in the dopamine and serotoninergic neuron systems. An increasing number of studies have revealed the underpinned mechanism mediating social hierarchy in mammal and vertebrate, however, there are rare studies conducted on how amygdala on social hierarchy in poultry. In this study, we conducted food competition tests and determined the social hierarchy of the rooster. We performed cross-species analysis with mammalian amygdala, and found that cell types of human and rhesus monkeys were more closely related and that of chickens were more distant. We identified 26 clusters and divided them into 10 main clusters, of which GABAergic and glutamatergic neurons were associated with social behaviors. In conclusion, our results provide to serve the developmental studies of the amygdala neuron system and new insights into the underpinned mechanism of social hierarchy in roosters.

Advances in Single-Cell Transcriptome Sequencing and Spatial Transcriptome Sequencing in Plants

"Omics" typically involves exploration of the structure and function of the entire composition of a biological system at a specific level using high-throughput analytical methods to probe and analyze large amounts of data, including genomics, transcriptomics, proteomics, and metabolomics, among other types. Genomics characterizes and quantifies all genes of an organism collectively, studying their interrelationships and their impacts on the organism. However, conventional transcriptomic sequencing techniques target population cells, and their results only reflect the average expression levels of genes in population cells, as they are unable to reveal the gene expression heterogeneity and spatial heterogeneity among individual cells, thus masking the expression specificity between different cells. Single-cell transcriptomic sequencing and spatial transcriptomic sequencing techniques analyze the transcriptome of individual cells in plant or animal tissues, enabling the understanding of each cell's metabolites and expressed genes. Consequently, statistical analysis of the corresponding tissues can be performed, with the purpose of achieving cell classification, evolutionary growth, and physiological and pathological analyses. This article provides an overview of the research progress in plant single-cell and spatial transcriptomics, as well as their applications and challenges in plants. Furthermore, prospects for the development of single-cell and spatial transcriptomics are proposed.

Peptidomics-based study of antihypertensive activity: discovery of novel ACE inhibiting peptides from peanut yogurt

Studies have confirmed that yogurt has the activity of regulating blood pressure because it is rich in probiotic-fermented food-derived active peptides. There are also studies on angiotensin-converting enzyme inhibition (ACEI) peptide milk, but the bioactive molecules in it are still unclear. Therefore, in this study, we developed a peanut yogurt with ACEI activity, analyzed 1877 differential peptides and their antihypertensive pathways before and after fermentation using peptidomics, and identified three peptides (FLPYPY, QPPPSPPPFL and APFPEVFGK) with potential antihypertensive activity using molecular docking and chemical synthesis techniques. These results first elucidated the relationship between peanut yogurt peptides and antihypertensive function, demonstrated the benefits of peanut yogurt, and provided a theoretical basis for the application of probiotic fermented plant yogurt in health care.

Progression from healthy periodontium to gingivitis and periodontitis: Insights from bioinformatics-driven proteomics - A systematic review with meta-analysis

AIM

The current study aimed to: (1) systematically review the published literature regarding the proteomics analyses of saliva and gingival crevicular fluid (GCF) in healthy humans and gingivitis and/or periodontitis patients; and (2) to identify the differentially expressed proteins (DEPs) based on the systematic review, and comprehensively conduct meta-analyses and bioinformatics analyses.

METHODS

An online search of Web of Science, Scopus, and PubMed was performed without any restriction on the year and language of publication. After the identification of the DEPs reported by the included human primary studies, gene ontology (GO), the Kyoto encyclopedia of genes and genomes pathway (KEGG), protein-protein interaction (PPI), and meta-analyses were conducted. The risk of bias among the included studies was evaluated using the modified Newcastle-Ottawa quality assessment scale.

RESULTS

The review identified significant differences in protein expression between healthy individuals and those with gingivitis and periodontitis. In GCF, 247 proteins were upregulated and 128 downregulated in periodontal diseases. Saliva analysis revealed 79 upregulated and 70 downregulated proteins. There were distinct protein profiles between gingivitis and periodontitis, with 159 and 31 unique upregulated proteins in GCF, respectively. Meta-analyses confirmed significant upregulation of various proteins in periodontitis, including ALB and MMP9, while CSTB and GSTP1 were downregulated. AMY1A and SERPINA1 were upregulated in periodontitis saliva. HBD was upregulated in gingivitis GCF, while DEFA3 was downregulated. PPI analysis revealed complex networks of interactions among DEPs. GO and KEGG pathway analyses provided insights into biological processes and pathways associated with periodontal diseases.

CONCLUSION

The ongoing MS-based proteomics studies emphasize the need for a highly sensitive and specific diagnostic tool for periodontal diseases. Clinician acceptance of the eventual diagnostic method relies on its ability to provide superior or complementary information to current clinical assessment procedures. Future research should prioritize the multiplex measurement of multiple biomarkers simultaneously to enhance diagnostic accuracy and large study cohorts are necessary to ensure the validity and reliability of research findings.

Construction of a prognostic model for breast cancer based on moonlighting genes

Breast cancer (BRCA) is a highly heterogeneous disease, with significant differences in prognosis among patients. Existing biomarkers and prognostic models have limited ability to predict BRCA prognosis. Moonlighting genes regulate tumor progression and are associated with cancer prognosis. This study aimed to construct a moonlighting gene-based prognostic model for BRCA. We obtained differentially expressed genes (DEGs) in BRCA from The Cancer Genome Atlas and intersected them with moonlighting genes from MoonProt to acquire differential moonlighting genes. GO and KEGG results showed main enrichment of these genes in the response of BRCA cells to environmental stimuli and pentose phosphate pathway. Based on moonlighting genes, we conducted drug prediction and validated results through cellular experiments. After ABCB1 knockdown, viability and proliferation of BRCA cells were significantly enhanced. Based on differential moonlighting genes, BRCA was divided into three subgroups, among which cluster2 had the highest survival rate and immunophenoscore and relatively low tumor mutation burden. TP53 had the highest mutation frequency in cluster2 and cluster3, while PIK3CA had a higher mutation frequency in cluster1, with the majority being missense mutations. Subsequently, we established an 11-gene prognostic model in the training set based on DEGs among subgroups using univariate Cox regression, LASSO regression, and multivariable Cox regression analyses. Model prognostic performance was verified in GEO, METABRIC and ICGC validation sets. In summary, this study obtained three BRCA moonlighting gene-related subtypes and constructed an 11-gene prognostic model. The 11-gene BRCA prognostic model has good predictive performance, guiding BRCA prognosis for clinical doctors.

Integrative bioinformatics analysis of miRNA and mRNA expression profiles and identification of associated miRNA-mRNA network in intracranial aneurysms

Background

Intracranial aneurysms (IAs) represent protrusions in the vascular wall, with their growth and wall thinning influenced by various factors. These processes can culminate in the rupture of the aneurysm, leading to subarachnoid hemorrhage (SAH). Unfortunately, over half of the patients prove unable to withstand SAH, succumbing to adverse outcomes despite intensive therapeutic interventions, even in premier medical facilities. This study seeks to discern the pivotal microRNAs (miRNAs) and genes associated with the formation and progression of IAs.

Methods

The investigation gathered expression data of miRNAs (from GSE66240) and mRNAs (from GSE158558) within human aneurysm tissue and superficial temporal artery (STA) samples, categorizing them into IA and normal groups. This classification was based on the Gene Expression Omnibus (GEO) database.

Results

A total of 70 differentially expressed microRNAs (DEMs) and 815 differentially expressed mRNAs (DEGs) were pinpointed concerning IA. Subsequently, a miRNA-mRNA network was constructed, incorporating 9 significantly upregulated DEMs and 211 significantly downregulated DEGs. Simultaneously, functional enrichment and pathway analyses were conducted on both DEMs and DEGs. Through protein-protein interaction (PPI) network analysis and functional enrichment, 9 significantly upregulated DEMs (hsa-miR-188-5p, hsa-miR-590-5p, hsa-miR-320b, hsa-miR-423-5p, hsa-miR-140-5p, hsa-miR-486-5p, hsa-miR-320a, hsa-miR-342-3p, and hsa-miR-532-5p) and 50 key genes (such as ATP6V1G1, KBTBD6, VIM, PA2G4, DYNLL1, METTL21A, MDH2, etc.) were identified, suggesting their potential significant role in IA. Among these genes, ten were notably negatively regulated by at least two key miRNAs.

Conclusions

The findings of this study provide valuable insights into the potential pathogenic mechanisms underlying IA by elucidating a miRNA-mRNA network. This comprehensive approach sheds light on the intricate interplay between miRNAs and genes, offering a deeper understanding of the molecular dynamics involved in IA development and progression.

Identification of Protein-Protein Interaction Associated Functions Based on Gene Ontology

Protein-protein interactions (PPIs) involve the physical or functional contact between two or more proteins. Generally, proteins that can interact with each other always have special relationships. Some previous studies have reported that gene ontology (GO) terms are related to the determination of PPIs, suggesting the special patterns on the GO terms of proteins in PPIs. In this study, we explored the special GO term patterns on human PPIs, trying to uncover the underlying functional mechanism of PPIs. The experimental validated human PPIs were retrieved from STRING database, which were termed as positive samples. Additionally, we randomly paired proteins occurring in positive samples, yielding lots of negative samples. A simple calculation was conducted to count the number of positive samples for each GO term pair, where proteins in samples were annotated by GO terms in the pair individually. The similar number for negative samples was also counted and further adjusted due to the great gap between the numbers of positive and negative samples. The difference of the above two numbers and the relative ratio compared with the number on positive samples were calculated. This ratio provided a precise evaluation of the occurrence of GO term pairs for positive samples and negative samples, indicating the latent GO term patterns for PPIs. Our analysis unveiled several nuclear biological processes, including gene transcription, cell proliferation, and nutrient metabolism, as key biological functions. Interactions between major proliferative or metabolic GO terms consistently correspond with significantly reported PPIs in recent literature.

Exploring the microRNA-mRNA regulatory network associated with solasonine in bladder cancer

Background

Solasonine has been demonstrated to exert an inhibitory effect on bladder cancer (BC), but the potential mechanisms remain unclear. Therefore, the aim of this study is to explore the association between microRNAs (miRNAs)-mediated regulation and the anti-tumor activities of solasonine in BC.

Methods

MiRNA sequencing was performed to identify the differentially expressed microRNAs (DE-miRNAs) associated with solasonine in BC cells. Functional enrichment analyses of the DE-miRNAs activated and inhibited by solasonine were then conducted. The DE-miRNAs with prognostic value for BC and those differentially expressed in the BC samples were subsequently identified as the hub DE-miRNAs. After identifying the messenger RNAs (mRNAs) that were targeted by the hub DE-miRNAs and those differentially expressed in the BC samples, a protein-protein interaction analysis was performed to identify the core downstream genes, which were then used to construct a solasonine-miRNA-mRNA regulatory network.

Results

A total of 27 activated and 19 inhibited solasonine-mediated DE-miRNAs were identified that were found to be associated with several tumor-related biological functions and pathways. After integrating the results of the survival analysis and expression assessment, the following nine hub DE-miRNAs were identified: hsa-miR-127-3p, hsa-miR-450b-5p, hsa-miR-99a-5p, hsa-miR-197-3p, hsa-miR-423-3p, hsa-miR-4326, hsa-miR-625-3p, hsa-miR-625-5p, and hsa-miR-92a-3p. The DE-mRNAs targeted by the hub DE-miRNAs were predicted, and 30 core downstream genes were used to construct the solasonine-miRNA-mRNA regulatory network. miR-450b-5p was shown to be associated with the most mRNAs in this network, which suggests that it plays a crucial role in the solasonine-mediated anti-BC effect.

Conclusions

A regulatory network, including solasonine, miRNAs, and mRNAs related to BC, was constructed. This network provides extensive insights into the molecular regulatory mechanisms that underlie the anti-cancer efficacy of solasonine in BC.

A computational approach for structural and functional analyses of disease-associated mutations in the human CYLD gene

Tumor suppressor cylindromatosis protein (CYLD) regulates NF-κB and JNK signaling pathways by cleaving K63-linked poly-ubiquitin chain from its substrate molecules and thus preventing the progression of tumorigenesis and metastasis of the cancer cells. Mutations in CYLD can cause aberrant structure and abnormal functionality leading to tumor formation. In this study, we utilized several computational tools such as PANTHER, PROVEAN, PredictSNP, PolyPhen-2, PhD-SNP, PON-P2, and SIFT to find out deleterious nsSNPs. We also highlighted the damaging impact of those deleterious nsSNPs on the structure and function of the CYLD utilizing ConSurf, I-Mutant, SDM, Phyre2, HOPE, Swiss-PdbViewer, and Mutation 3D. We shortlisted 18 high-risk nsSNPs from a total of 446 nsSNPs recorded in the NCBI database. Based on the conservation profile, stability status, and structural impact analysis, we finalized 13 nsSNPs. Molecular docking analysis and molecular dynamic simulation concluded the study with the findings of two significant nsSNPs (R830K, H827R) which have a remarkable impact on binding affinity, RMSD, RMSF, radius of gyration, and hydrogen bond formation during CYLD-ubiquitin interaction. The principal component analysis compared native and two mutants R830K and H827R of CYLD that signify structural and energy profile fluctuations during molecular dynamic (MD) simulation. Finally, the protein-protein interaction network showed CYLD interacts with 20 proteins involved in several biological pathways that mutations can impair. Considering all these in silico analyses, our study recommended conducting large-scale association studies of nsSNPs of CYLD with cancer as well as designing precise medications against diseases associated with these polymorphisms.

Berberine alleviates neuroinflammation by downregulating NFκB/LCN2 pathway in sepsis-associated encephalopathy: network pharmacology, bioinformatics, and experimental validation

BACKGROUND

Sepsis refers to a systemic inflammatory response caused by infection, involving multiple organs. Sepsis-associated encephalopathy (SAE), as one of the most common complications in patients with severe sepsis, refers to the diffuse brain dysfunction caused by sepsis without central nervous system infection. However, there is no clear diagnostic criteria and lack of specific diagnostic markers.

METHODS

The main active ingredients of coptidis rhizoma(CR) were identified from TCMSP and SwissADME databases. SwissTargetPrediction and PharmMapper databases were used to obtain targets of CR. OMIM, DisGeNET and Genecards databases were used to explore targets of SAE. Limma differential analysis was used to identify the differential expressed genes(DEGs) in GSE167610 and GSE198861 datasets. WGCNA was used to identify feature module. GO and KEGG enrichment analysis were performed using Metascape, DAVID and STRING databases. The PPI network was constructed by STRING database and analyzed by Cytoscape software. AutoDock and PyMOL software were used for molecular docking and visualization. Cecal ligation and puncture(CLP) was used to construct a mouse model of SAE, and the core targets were verified in vivo experiments.

RESULTS

277 common targets were identified by taking the intersection of 4730 targets related to SAE and 509 targets of 9 main active ingredients of CR. 52 common DEGs were mined from GSE167610 and GSE198861 datasets. Among the 25,864 DEGs in GSE198861, LCN2 showed the most significant difference (logFC = 6.9). GO and KEGG enrichment analysis showed that these 52 DEGs were closely related to "inflammatory response" and "innate immunity". A network containing 38 genes was obtained by PPI analysis, among which LCN2 ranked the first in Degree value. Molecular docking results showed that berberine had a well binding affinity with LCN2. Animal experiments results showed that berberine could inhibit the high expression of LCN2,S100A9 and TGM2 induced by CLP in the hippocampus of mice, as well as the high expression of inflammatory factors (TNFα, IL-6 and IL-1β). In addition, berberine might reduce inflammation and neuronal cell death by partially inhibiting NFκB/LCN2 pathway in the hippocampus of CLP models, thereby alleviating SAE.

CONCLUSION

Overall, Berberine may exert anti-inflammatory effects through multi-ingredients, multi-targets and multi-pathways to partially rescue neuronal death and alleviate SAE.

Proteomic analysis of the urothelial cancer landscape

Urothelial bladder cancer (UC) has a wide tumor biological spectrum with challenging prognostic stratification and relevant therapy-associated morbidity. Most molecular classifications relate only indirectly to the therapeutically relevant protein level. We improve the pre-analytics of clinical samples for proteome analyses and characterize a cohort of 434 samples with 242 tumors and 192 paired normal mucosae covering the full range of UC. We evaluate sample-wise tumor specificity and rank biomarkers by target relevance. We identify robust proteomic subtypes with prognostic information independent from histopathological groups. In silico drug prediction suggests efficacy of several compounds hitherto not in clinical use. Both in silico and in vitro data indicate predictive value of the proteomic clusters for these drugs. We underline that proteomics is relevant for personalized oncology and provide abundance and tumor specificity data for a large part of the UC proteome ( www.cancerproteins.org ).

Genome-Wide Admixture and Association Study of Serum Selenium Deficiency to Identify Genetic Variants Indirectly Linked to Selenium Regulation in Brazilian Adults

Blood selenium (Se) concentrations differ substantially by population and could be influenced by genetic variants, increasing Se deficiency-related diseases. We conducted a genome-wide association study (GWAS) to identify single nucleotide polymorphisms (SNPs) associated with serum Se deficiency in 382 adults with admixed ancestry. Genotyping arrays were combined to yield 90,937 SNPs. R packages were applied to quality control and imputation. We also performed the ancestral proportion analysis. The Search Tool for the Retrieval of Interacting Genes was used to interrogate known protein-protein interaction networks (PPIs). Our ancestral proportion analysis estimated 71% of the genome was from Caucasians, 22% was from Africans, and 8% was from East Asians. We identified the SNP rs1561573 in the TraB domain containing 2B (TRABD2B), rs425664 in MAF bZIP transcription factor (MAF), rs10444656 in spermatogenesis-associated 13 (SPATA13), and rs6592284 in heat shock protein nuclear import factor (HIKESHI) genes. The PPI analysis showed functional associations of Se deficiency, thyroid hormone metabolism, NRF2-ARE and the Wnt pathway, and heat stress. Our findings show evidence of a genetic association between Se deficiency and metabolic pathways indirectly linked to Se regulation, reinforcing the complex relationship between Se intake and the endogenous factors affecting the Se requirements for optimal health.

Bioinformatics analysis and experimental verification of the cancer-promoting effect of DHODH in clear cell renal cell carcinoma

Clear cell renal cell carcinoma (ccRCC) is a malignant tumor of the urinary system. To explore the potential mechanisms of DHODH in ccRCC, we analyzed its molecular characteristics using public databases. TCGA pan-cancer dataset was used to analyze DHODH expression in different cancer types and TCGA ccRCC dataset was used to assess differential expression, prognosis correlation, immune infiltration, single-gene, and functional enrichment due to DHODH. The GSCALite and CellMiner databases were employed to explore drugs and perform molecular docking analysis with DHODH. Protein-protein interaction networks and ceRNA regulatory networks of DHODH were constructed using multiple databases. The effect of DHODH on ccRCC was confirmed in vitro. DHODH was highly expressed in ccRCC. Immune infiltration analysis revealed that DHODH may be involved in regulating the infiltration of immunosuppressive cells such as Tregs. Notably, DHODH influenced ccRCC progression by forming regulatory networks with molecules, such as hsa-miR-26b-5p and UMPS and significantly enhanced the malignant characteristics of ccRCC cells. Several drugs, such as lapatinib, silmitasertib, itraconazole, and dasatinib, were sensitive to DHODH expression and exhibited strong molecular binding with it. Thus, DHODH may promote ccRCC progression and is a candidate effective therapeutic target for ccRCC.

Proteome Analysis Related to Unsaturated Fatty Acid Synthesis by Interfering with Bovine Adipocyte ACSL1 Gene

Unsaturated fatty acids (UFAs) in beef play a vital role in promoting human health. Long-chain fatty acyl-CoA synthase 1 (ACSL1) is a crucial gene for UFA synthesis in bovine adipocytes. To investigate the protein expression profile during UFA synthesis, we performed a proteomic analysis of bovine adipocytes by RNA interference and non-interference with ACSL1 using label-free techniques. A total of 3558 proteins were identified in both the NC and si-treated groups, of which 1428 were differentially expressed proteins (DEPs; fold change ≥ 1.2 or ≤ 0.83 and p-value < 0.05). The enrichment analysis of the DEPs revealed signaling pathways related to UFA synthesis or metabolism, including cAMP, oxytocin, fatty acid degradation, glycerol metabolism, insulin, and the regulation of lipolysis in adipocytes (p-value < 0.05). Furthermore, based on the enrichment analysis of the DEPs, we screened 50 DEPs that potentially influence the synthesis of UFAs and constructed an interaction network. Moreover, by integrating our previously published transcriptome data, this study established a regulatory network involving differentially expressed long non-coding RNAs (DELs), highlighting 21 DEPs and 13 DELs as key genes involved in UFA synthesis. These findings present potential candidate genes for further investigation into the molecular mechanisms underlying UFA synthesis in bovines, thereby offering insights to enhance the quality of beef and contribute to consumer health in future studies.

Aqueous humor proteomics analyzed by bioinformatics and machine learning in PDR cases versus controls

BACKGROUND

To comprehend the complexities of pathophysiological mechanisms and molecular events that contribute to proliferative diabetic retinopathy (PDR) and evaluate the diagnostic value of aqueous humor (AH) in monitoring the onset of PDR.

METHODS

A cohort containing 16 PDR and 10 cataract patients and another validation cohort containing 8 PDR and 4 cataract patients were studied. AH was collected and subjected to proteomics analyses. Bioinformatics analysis and a machine learning-based pipeline called inference of biomolecular combinations with minimal bias were used to explore the functional relevance, hub proteins, and biomarkers.

RESULTS

Deep profiling of AH proteomes revealed several insights. First, the combination of SIAE, SEMA7A, GNS, and IGKV3D-15 and the combination of ATP6AP1, SPARCL1, and SERPINA7 could serve as surrogate protein biomarkers for monitoring PDR progression. Second, ALB, FN1, ACTB, SERPINA1, C3, and VTN acted as hub proteins in the profiling of AH proteomes. SERPINA1 was the protein with the highest correlation coefficient not only for BCVA but also for the duration of diabetes. Third, "Complement and coagulation cascades" was an important pathway for PDR development.

CONCLUSIONS

AH proteomics provides stable and accurate biomarkers for early warning and diagnosis of PDR. This study provides a deep understanding of the molecular mechanisms of PDR and a rich resource for optimizing PDR management.

Subtractive Proteomics and Reverse-Vaccinology Approaches for Novel Drug Target Identification and Chimeric Vaccine Development against Bartonella henselae Strain Houston-1

Bartonella henselae is a Gram-negative bacterium causing a variety of clinical symptoms, ranging from cat-scratch disease to severe systemic infections, and it is primarily transmitted by infected fleas. Its status as an emerging zoonotic pathogen and its capacity to persist within host erythrocytes and endothelial cells emphasize its clinical significance. Despite progress in understanding its pathogenesis, limited knowledge exists about the virulence factors and regulatory mechanisms specific to the B. henselae strain Houston-1. Exploring these aspects is crucial for targeted therapeutic strategies against this versatile pathogen. Using reverse-vaccinology-based subtractive proteomics, this research aimed to identify the most antigenic proteins for formulating a multi-epitope vaccine against the B. henselae strain Houston-1. One crucial virulent and antigenic protein, the PAS domain-containing sensor histidine kinase protein, was identified. Subsequently, the identification of B-cell and T-cell epitopes for the specified protein was carried out and the evaluated epitopes were checked for their antigenicity, allergenicity, solubility, MHC binding capability, and toxicity. The filtered epitopes were merged using linkers and an adjuvant to create a multi-epitope vaccine construct. The structure was then refined, with 92.3% of amino acids falling within the allowed regions. Docking of the human receptor (TLR4) with the vaccine construct was performed and demonstrated a binding energy of -1047.2 Kcal/mol with more interactions. Molecular dynamic simulations confirmed the stability of this docked complex, emphasizing the conformation and interactions between the molecules. Further experimental validation is necessary to evaluate its effectiveness against B. henselae.

Identification and Pharmacological Targeting of Treatment-Resistant, Stem-like Breast Cancer Cells for Combination Therapy

Tumors frequently harbor isogenic yet epigenetically distinct subpopulations of multi-potent cells with high tumor-initiating potential-often called Cancer Stem-Like Cells (CSLCs). These can display preferential resistance to standard-of-care chemotherapy. Single-cell analyses can help elucidate Master Regulator (MR) proteins responsible for governing the transcriptional state of these cells, thus revealing complementary dependencies that may be leveraged via combination therapy. Interrogation of single-cell RNA sequencing profiles from seven metastatic breast cancer patients, using perturbational profiles of clinically relevant drugs, identified drugs predicted to invert the activity of MR proteins governing the transcriptional state of chemoresistant CSLCs, which were then validated by CROP-seq assays. The top drug, the anthelmintic albendazole, depleted this subpopulation in vivo without noticeable cytotoxicity. Moreover, sequential cycles of albendazole and paclitaxel-a commonly used chemotherapeutic -displayed significant synergy in a patient-derived xenograft (PDX) from a TNBC patient, suggesting that network-based approaches can help develop mechanism-based combinatorial therapies targeting complementary subpopulations.

White Isthmus Transcriptome Analysis Reveals the Mechanism of Translucent Eggshell Formation

The presence of translucent eggshells is a type of egg quality issue that impacts egg sales. While many researchers have studied them, the exact mechanisms behind their formation remain unclear. In this study, we conducted a transcriptomic differential expression analysis of the isthmus region of the oviduct in both normal egg- and translucent egg-laying hens. The analysis revealed that differentially expressed gene pathways were predominantly concentrated in the synthesis, modification, and transport of eggshell membrane proteins, particularly collagen proteins, which provide structural support. These findings suggest that variations in the physical structure of the eggshell membrane, resulting from changes in its chemical composition, are the fundamental cause of translucent eggshell formation. This research provides a theoretical reference for reducing the occurrence of translucent eggs.

A Carbon 21 Steroidal Glycoside with Pregnane Skeleton from Cynanchum atratum Bunge Promotes Megakaryocytic and Erythroid Differentiation in Erythroleukemia HEL Cells through Regulating Platelet-Derived Growth Factor Receptor Beta and JAK2/STAT3 Pathway

Erythroleukemia is a rare form of acute myeloid leukemia (AML). Its molecular pathogenesis remains vague, and this disease has no specific therapeutic treatments. Previously, our group isolated a series of Carbon 21 (C-21) steroidal glycosides with pregnane skeleton from the root of Cynanchum atratum Bunge. Among them, we found that a compound, named BW18, can induce S-phase cell cycle arrest and apoptosis via the mitogen-activated protein kinase (MAPK) pathway in human chronic myeloid leukemia K562 cells. However, its anti-tumor activity against erythroleukemia remains largely unknown. In this study, we aimed to investigate the anti-erythroleukemia activity of BW18 and the underlying molecular mechanisms. Our results demonstrated that BW18 exhibited a good anti-erythroleukemia activity in the human erythroleukemia cell line HEL and an in vivo xenograft mouse model. In addition, BW18 induced cell cycle arrest at the G2/M phase and promoted megakaryocytic and erythroid differentiation in HEL cells. Furthermore, RNA sequencing (RNA-seq) and rescue assay demonstrated that overexpression of platelet-derived growth factor receptor beta (PDGFRB) reversed BW18-induced megakaryocytic differentiation in HEL cells, but not erythroid differentiation. In addition, the network pharmacology analysis, the molecular docking and cellular thermal shift assay (CETSA) revealed that BW18 could inactivate Janus tyrosine kinase 2 (JAK2)/signal transducer and activator of transcription 3 (STAT3) pathway, which might mediate BW18-induced erythroid differentiation. Taken together, our findings elucidated a novel role of PDGFRB in regulating erythroleukemia differentiation and highlighted BW18 as an attractive lead compound for erythroleukemia treatment.

In Silico Analysis of Protein-Protein Interactions of Putative Endoplasmic Reticulum Metallopeptidase 1 in Schizosaccharomyces pombe

Ermp1 is a putative metalloprotease from Schizosaccharomyces pombe and a member of the Fxna peptidases. Although their function is unknown, orthologous proteins from rats and humans have been associated with the maturation of ovarian follicles and increased ER stress. This study focuses on proposing the first prediction of PPI by comparison of the interologues between humans and yeasts, as well as the molecular docking and dynamics of the M28 domain of Ermp1 with possible target proteins. As results, 45 proteins are proposed that could interact with the metalloprotease. Most of these proteins are related to the transport of Ca2+ and the metabolism of amino acids and proteins. Docking and molecular dynamics suggest that the M28 domain of Ermp1 could hydrolyze leucine and methionine residues of Amk2, Ypt5 and Pex12. These results could support future experimental investigations of other Fxna peptidases, such as human ERMP1.

Molecular evolution of Phytocyanin gene and analysis of expression at different coloring periods in apple (Malus domestica)

BACKGROUND

PC (phytocyanin) is a class of copper-containing electron transfer proteins closely related to plant photosynthesis, abiotic stress responses growth and development in plants, and regulation of the expression of some flavonoids and phenylpropanoids, etc., however, compared with other plants, the PC gene family has not been systematically characterized in apple.

RESULTS

A total of 59 MdPC gene members unevenly distributed across 12 chromosomes were identified at the genome-wide level. The proteins of the MdPC family were classified into four subfamilies based on differences in copper binding sites and glycosylation sites: Apple Early nodulin-like proteins (MdENODLs), Apple Uclacyanin-like proteins (MdUCLs), Apple Stellacyanin-like proteins (MdSCLs), and Apple Plantacyanin-like proteins (MdPLCLs). Some MdPC members with similar gene structures and conserved motifs belong to the same group or subfamily. The internal collinearity analysis revealed 14 collinearity gene pairs among members of the apple MdPC gene. Interspecific collinearity analysis showed that apple had 31 and 35 homologous gene pairs with strawberry and grape, respectively. Selection pressure analysis indicated that the MdPC gene was under purifying selection. Prediction of protein interactions showed that MdPC family members interacted strongly with the Nad3 protein. GO annotation results indicated that the MdPC gene also regulated the biosynthesis of phenylpropanoids. Chip data analysis showed that (MdSCL3, MdSCL7 and MdENODL27) were highly expressed in mature fruits and peels. Many cis-regulatory elements related to light response, phytohormones, abiotic stresses and flavonoid biosynthetic genes regulation were identified 2000 bp upstream of the promoter of the MdPC gene, and qRT-PCR results showed that gene members in Group IV (MdSCL1/3, MdENODL27) were up-regulated at all five stages of apple coloring, but the highest expression was observed at the DAF13 (day after fruit bag removal) stage. The gene members in Group II (MdUCL9, MdPLCL3) showed down-regulated or lower expression in the first four stages of apple coloring but up-regulated and highest expression in the DAF 21 stage.

CONCLUSION

Herein, one objective of these findings is to provide valuable information for understanding the structure, molecular evolution, and expression pattern of the MdPC gene, another major objective in this study was designed to lay the groundwork for further research on the molecular mechanism of PC gene regulation of apple fruit coloration.

Identification of Susceptibility Genes Underlying Bovine Respiratory Disease in Xinjiang Brown Cattle Based on DNA Methylation

DNA methylation is a form of epigenetic regulation, having pivotal parts in controlling cellular expansion and expression levels within genes. Although blood DNA methylation has been studied in humans and other species, its prominence in cattle is largely unknown. This study aimed to methodically probe the genomic methylation map of Xinjiang brown (XJB) cattle suffering from bovine respiratory disease (BRD), consequently widening cattle blood methylome ranges. Genome-wide DNA methylation profiling of the XJB blood was investigated through whole-genome bisulfite sequencing (WGBS). Many differentially methylated regions (DMRs) obtained by comparing the cases and controls groups were found within the CG, CHG, and CHH (where H is A, T, or C) sequences (16,765, 7502, and 2656, respectively), encompassing 4334 differentially methylated genes (DMGs). Furthermore, GO/KEGG analyses showed that some DMGs were involved within immune response pathways. Combining WGBS-Seq data and existing RNA-Seq data, we identified 71 significantly differentially methylated (DMGs) and expressed (DEGs) genes (p < 0.05). Next, complementary analyses identified nine DMGs (LTA, STAT3, IKBKG, IRAK1, NOD2, TLR2, TNFRSF1A, and IKBKB) that might be involved in the immune response of XJB cattle infected with respiratory diseases. Although further investigations are needed to confirm their exact implication in the involved immune processes, these genes could potentially be used for a marker-assisted selection of animals resistant to BRD. This study also provides new knowledge regarding epigenetic control for the bovine respiratory immune process.

Identification of MAD2L1 and BUB1B as Potential Biomarkers Associated with Progression and Prognosis of Ovarian Cancer

Ovarian cancer develops insidiously and is frequently diagnosed at advanced stages. Screening for ovarian cancer is an effective strategy for reducing mortality. This study aimed to investigate the molecular mechanisms underlying the development of ovarian cancer and identify novel tumor biomarkers for the diagnosis and prognosis of ovarian cancer. Three databases containing gene expression profiles specific to serous ovarian cancer (GSE18520, GSE12470, and GSE26712) were acquired. Gene Ontology and Kyoto Encyclopedia of Genes and Genomes were analyzed for the differentially expressed gene (DEGs). The protein-protein interaction (PPI) network was constructed using the STRING database. The pivotal genes in the PPI network were screened using the Cytoscape software. Survival curve analysis was performed using a Kaplan-Meier Plotter. The cancer genome atlas and Gene Expression Omnibus databases were used to find the relationship between Hub gene and serous ovarian cancer. PCR and immunohistochemistry were used to detect the expression of Hub gene in serous ovarian cancer tissues and cells. Downstream pathways of the candidate tumor marker genes were predicted using Gene Set Enrichment Analysis. In this study, 252 DEGs were screened for pathway enrichment. 20 Hub genes were identified. Survival analysis suggested that Aurka, Bub1b, Cenpf, Cks1b, Kif20a, Mad2l1, Racgap1, and Ube2c were associated with the survival of patients with serous ovarian cancer. MAD2L1 and BUB1B levels were significantly different in serous ovarian cancer at different stages. Finally, Mad2l1 was found to play a role in the cell cycle, oocyte meiosis, and ubiquitin-mediated proteolysis. Meanwhile, Bub1b may play a role in the cell cycle, ubiquitin-mediated proteolysis, and spliceosome processes. Mad2l1 and Bub1b could be used as markers to predict ovarian carcinogenesis and prognosis, providing candidate targets for the diagnosis and treatment of serous ovarian cancer.

Identification of potential targetable genes in papillary, follicular, and anaplastic thyroid carcinoma using bioinformatics analysis

PURPOSE

To perform an extensive exploratory analysis to build a deeper insight into clinically relevant molecular biomarkers in Papillary, Follicular, and Anaplastic thyroid carcinomas (PTC, FTC, ATC).

METHODS

Thirteen Thyroid Cancer (THCA) datasets incorporating PTC, FTC, and ATC were derived from the Gene Expression Omnibus. Genes differentially expressed (DEGs) between THCA and normal were identified and subjected to GO and KEGG analyses. Multiple topological properties were harnessed and protein-protein interaction (PPI) networks were constructed to identify the hub genes followed by survival analysis and validation.

RESULTS

There were 70, 87, and 377 DEGs, and 23, 27, and 53 hub genes for PTC, FTC, and ATC samples, respectively. Survival analysis detected 39 overall and 49 relapse-free survival-relevant hub genes. Six hub genes, BCL2, FN1, ITPR1, LYVE1, NTRK2, TBC1D4, were found common to more than one THCA type. The most significant hub genes found in the study were: BCL2, CD44, DCN, FN1, IRS1, ITPR1, MFAP4, MKI67, NTRK2, PCLO, TGFA. The most enriched and significant GO terms were Melanocyte differentiation for PTC, Extracellular region for FTC, and Extracellular exosome for ATC. Prostate cancer for PTC was the most significantly enriched KEGG pathway. The results were validated using TCGA data.

CONCLUSIONS

The findings unravel potential biomarkers and therapeutic targets of thyroid carcinomas.

Metabolomics and proteomics insights into subacute ruminal acidosis etiology and inhibition of proliferation of yak rumen epithelial cells in vitro

BACKGROUND

Untargeted metabolomics and proteomics were employed to investigate the intracellular response of yak rumen epithelial cells (YRECs) to conditions mimicking subacute rumen acidosis (SARA) etiology, including exposure to short-chain fatty acids (SCFA), low pH5.5 (Acid), and lipopolysaccharide (LPS) exposure for 24 h.

RESULTS

These treatments significantly altered the cellular morphology of YRECs. Metabolomic analysis identified significant perturbations with SCFA, Acid and LPS treatment affecting 259, 245 and 196 metabolites (VIP > 1, P < 0.05, and fold change (FC) ≥ 1.5 or FC ≤ 0.667). Proteomic analysis revealed that treatment with SCFA, Acid, and LPS resulted in differential expression of 1251, 1396, and 242 proteins, respectively (FC ≥ 1.2 or ≤ 0.83, P < 0.05, FDR < 1%). Treatment with SCFA induced elevated levels of metabolites involved in purine metabolism, glutathione metabolism, and arginine biosynthesis, and dysregulated proteins associated with actin cytoskeleton organization and ribosome pathways. Furthermore, SCFA reduced the number, morphology, and functionality of mitochondria, leading to oxidative damage and inhibition of cell survival. Gene expression analysis revealed a decrease the genes expression of the cytoskeleton and cell cycle, while the genes expression associated with inflammation and autophagy increased (P < 0.05). Acid exposure altered metabolites related to purine metabolism, and affected proteins associated with complement and coagulation cascades and RNA degradation. Acid also leads to mitochondrial dysfunction, alterations in mitochondrial integrity, and reduced ATP generation. It also causes actin filaments to change from filamentous to punctate, affecting cellular cytoskeletal function, and increases inflammation-related molecules, indicating the promotion of inflammatory responses and cellular damage (P < 0.05). LPS treatment induced differential expression of proteins involved in the TNF signaling pathway and cytokine-cytokine receptor interaction, accompanied by alterations in metabolites associated with arachidonic acid metabolism and MAPK signaling (P < 0.05). The inflammatory response and activation of signaling pathways induced by LPS treatment were also confirmed through protein interaction network analysis. The integrated analysis reveals co-enrichment of proteins and metabolites in cellular signaling and metabolic pathways.

CONCLUSIONS

In summary, this study contributes to a comprehensive understanding of the detrimental effects of SARA-associated factors on YRECs, elucidating their molecular mechanisms and providing potential therapeutic targets for mitigating SARA.

Tumors Established in a Defective Immune Environment Reprogram the Oncogenic Signaling Pathways to Escalate Tumor Antigenicity

Tumors developed in immunocompromised hosts are more immunogenic. However, few studies have addressed the potential mechanisms underlying the high immunogenicity of tumors found in a suppressed immune system. Therefore, we aimed to elucidate the impacts of the immune system on tumor behaviors and immunogenicity sculpting. A murine colorectal adenocarcinoma cell line, CT26wt, was administrated into immunocompetent (BALB/c) and immunocompromised (NOD.SCID) mice, respectively. On day 11, the CT26 cells slowly progressed in the NOD.SCID mice compared to the BALB/c mice. We then performed liquid chromatography-tandem mass spectrometry (LC-MS/MS) and analyzed the differentially expressed proteins (DEPs). The DEPs participated in numerous oncogenic pathways, PI3K/AKT/mTOR cell signaling, and the silencing of several tumor suppressors, such as PTEN and RBL1, during tumorigenesis. On day 34, the CT26/SCID tumors inversely became malignant counterparts; then the CT26/SCID tumors were harvested and re-inoculated into immunocompetent mice (CT26/SCID-Re tumors) to determine the immunogenicity. The CT26/SCID-Re tumor growth rate significantly decreased. Furthermore, increased infiltrations of dendritic cells and tumor-infiltrating T lymphocytes were found in the CT26/SCID-Re tumors. These findings suggest that immunogenic tumors might express multiple tumor rejection antigens, unlike wild-type tumors, and attract more immune cells, therefore decreasing the growth rate. Collectively, our study first revealed that in immunodeficient hosts, tumor suppressors were silenced and oncogenic signaling pathways were changed during the initial phase of tumor development. With tumor progression, the tumor antigens were overexpressed, exhibiting elevated immunogenicity. This study offers a hint on the mechanisms of tumorigenesis and provides a niche for investigating the interaction between host immunity and cancer development.

Leaf gene expression trajectories during the growing season are consistent between sites and years in American beech

Transcriptomics provides a versatile tool for ecological monitoring. Here, through genome-guided profiling of transcripts mapping to 33 042 gene models, expression differences can be discerned among multi-year and seasonal leaf samples collected from American beech trees at two latitudinally separated sites. Despite a bottleneck due to post-Columbian deforestation, the single nucleotide polymorphism-based population genetic background analysis has yielded sufficient variation to account for differences between populations and among individuals. Our expression analyses during spring-summer and summer-autumn transitions for two consecutive years involved 4197 differentially expressed protein coding genes. Using Populus orthologues we reconstructed a protein-protein interactome representing leaf physiological states of trees during the seasonal transitions. Gene set enrichment analysis revealed gene ontology terms that highlight molecular functions and biological processes possibly influenced by abiotic forcings such as recovery from drought and response to excess precipitation. Further, based on 324 co-regulated transcripts, we focused on a subset of GO terms that could be putatively attributed to late spring phenological shifts. Our conservative results indicate that extended transcriptome-based monitoring of forests can capture diverse ranges of responses including air quality, chronic disease, as well as herbivore outbreaks that require activation and/or downregulation of genes collectively tuning reaction norms maintaining the survival of long living trees such as the American beech.

Comprehensive CCM3 Mutational Analysis in Two Patients with Syndromic Cerebral Cavernous Malformation

Cerebral cavernous malformation (CCM) is a vascular disease that affects the central nervous system, which familial form is due to autosomal dominant mutations in the genes KRIT1(CCM1), MGC4607(CCM2), and PDCD10(CCM3). Patients affected by the PDCD10 mutations usually have the onset of symptoms at an early age and a more aggressive phenotype. The aim of this study is to investigate the molecular mechanism involved with CCM3 disease pathogenesis. Herein, we report two typical cases of CCM3 phenotype and compare the clinical and neuroradiological findings with five patients with a familial form of KRIT1 or CCM2 mutations and six patients with a sporadic form. In addition, we evaluated the PDCD10 gene expression by qPCR and developed a bioinformatic pipeline to understand the structural changes of mutations. The two CCM3 patients had an early onset of symptoms and a high lesion burden. Furthermore, the sequencing showed that Patient 1 had a frameshift mutation in c.222delT; p.(Asn75Thrfs*14) that leads to lacking the last 124 C-terminal amino acids on its primary structure and Patient 2 had a variant on the splicing site region c.475-2A > G. The mRNA expression was fourfold lower in both patients with PDCD10 mutation. Using in silico analysis, we identify that the frameshift mutation transcript lacks the C-terminal FAT-homology domain compared to the wild-type PDCD10 and preserves the N-terminal dimerization domain. The two patients studied here allow estimating the potential impact of mutations in clinical interpretation as well as support to better understand the mechanism and pathogenesis of CCM3.

Investigation of two-pore K+ (TPK) channels in Triticum aestivum L. suggests their role in stress response

Two-pore K+ (TPK) channels are voltage-independent and involved in stress response in plants. Herein, we identified 12 TaTPK genes located on nine chromosomes in the Triticum aestivum genome. The majority of TaTPK genes comprised two exons. Each TaTPK channel comprised four transmembrane (TM) helices, N- and C-terminal ion-channel domains, two EF-hand domains and one 14-3-3 binding site. Additionally, highly conserved 'GYGD' motif responsible for K+ ion specificity, was found in between the TMs in both the ion-channel domains. Nine TaTPK channels were predicted to be localised at the plasma membrane, while three were vacuolar. The protein-protein and protein-chemical interactions indicated the coordinated functioning of the TaTPK channels with the other K+ transporters and their possible interaction with the Ca2+-signaling pathway. Expression studies suggested their importance in both vegetative and reproductive tissues development. Significantly modulated expression of various TaTPK genes during heat, drought, combined heat and drought and salt stresses, and after fungal infestation, depicted their function in stress responses. The miRNAs and transcription factors interaction analyses suggested their role in the hormone, light, growth and development-related, and stress-responsive signaling cascades. The current study suggested vital functions of various TaTPK genes, especially in stress response, and would provide an opportunity for their detailed characterization in future studies.

Phosphoregulation in the N-terminus of NRT2.1 affects nitrate uptake by controlling the interaction of NRT2.1 with NAR2.1 and kinase HPCAL1 in Arabidopsis

NRT2.1, the major high affinity nitrate transporter in roots, can be phosphorylated at five different sites within the N- and the C-terminus. Here, we characterized the functional relationship of two N-terminal phosphorylation sites, S21 and S28, in Arabidopsis. Based on a site-specific correlation network, we identified a receptor kinase (HPCAL1, AT5G49770), phosphorylating NRT2.1 at S21 and resulting in active nitrate uptake. HPCAL1 itself was regulated by phosphorylation at S839 and S870 within its kinase domain. In the active state, when S839 was dephosphorylated and S870 was phosphorylated, HPCAL1 was found to interact with the N-terminus of NRT2.1, mainly when S28 was dephosphorylated. Phosphorylation of NRT2.1 at S21 resulted in a reduced interaction of NRT2.1 with its activator NAR2.1, but nitrate transport activity remained. By contrast, phosphorylated NRT2.1 at S28 enhanced the interaction with NAR2.1, but reduced the interaction with HPCAL1. Here we identified HPCAL1 as the kinase affecting this phospho-switch through phosphorylation of NRT2.1 at S21.