Bioinformatics Methods Research Articles

Differentially Expressed Genes in Atopic Dermatitis: Bioinformatics analysis of pooled microarray gene expression datasets in Gene Expression Omnibus

Introduction:
 Atopic dermatitis (AD) is a chronic and refractory inflammatory skin disease characterized by relapsing eczematous and pruritic skin lesions. Understanding the specific gene expression patterns associated with AD is crucial for advancing diagnosis and targeted treatment development. Using bioinformatics methods, candidate genes and biological pathways involved in AD pathogenesis were identified based on gene expression profiles in the Gene Expression Omnibus (GEO) database.
 Materials and Methods:
 A comprehensive analysis of four pooled transcriptomic datasets obtained from the Gene Expression Omnibus (GEO) database were conducted. Differential gene expression analysis was performed using the GEO2R. The differentially expressed genes (DEGs) between lesion skin of AD patients and normal skin of individuals were analyzed using the Gene Ontology (GO) term enrichment analysis, Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway, and protein-protein interaction (PPI) network to explore the functional roles of these genes.
 Results:
 Among the patient-level gene expression datasets, we identified 133 shared DEGs, consisting of 48 upregulated genes and 85 downregulated genes. GO analyses revealed these DEGs to be significantly enriched in biological processes including inflammatory responses, cytokine-mediated signaling pathway. These DEGs were also enriched in the KEGG pathway, including viral protein interaction with cytokine and cytokine receptor, C-type lectin receptor signaling pathway, cytokine-cytokine receptor interaction, JAK-STAT signaling pathway, and Adipocytokine signaling pathway.
 Conclusion:
 By comparing with other studies using the same method, we found that in addition to the already confirmed pathways such as inflammatory response, different studies have found changes in different hub genes and metabolic pathways, which prompts us to develop individualized treatments for AD.

CD146+CAFs promote progression of endometrial cancer by inducing angiogenesis and vasculogenic mimicry via IL-10/JAK1/STAT3 pathway

Heterogeneous cancer-associated fibroblasts (CAFs) play important roles in cancer progression. However, the specific biological functions and regulatory mechanisms involved in endometrial cancer have yet to be elucidated. We aimed to explore the potential mechanisms of heterogeneous CAFs in promoting endometrial cancer progression. The presence of melanoma cell adhesion molecule (MCAM; CD146) positive CAFs was confirmed by tissue multi-immunofluorescence (mIF), and fluorescence activated cell sorting (FACS). The biological functions were determined by wound healing assays, tuber formation assays and cord formation assays. The effects of CD146+CAFs on endometrial cancer cells were studied in vitro and in vivo. The expression level of interleukin 10 (IL-10) was measured by quantitative real time polymerase chain reaction (qRT-PCR), western boltting and enzyme linked immunosorbent assays (ELISAs). In addition, the transcription factor STAT3 was identified by bioinformatics methods and chromatin immunoprecipitation (ChIP). A subtype of CAFs marked with CD146 was found in endometrial cancer and correlated with poor prognosis. CD146+CAFs promoted angiogenesis and vasculogenic mimicry (VM) in vitro. A xenograft tumour model also showed that CD146+CAFs can facilitate tumour progression. The expression of IL-10 was elevated in CD146+CAFs. IL-10 promoted epithelial-endothelial transformation (EET) and further VM formation in endometrial cancer cells via the janus kinase 1/signal transducer and activator of transcription 3 (JAK1/STAT3) signalling pathway. This process could be blocked by the JAK1/STAT3 inhibitor niclosamide. Mechanically, STAT3 can bind to the promoter of cadherin5 (CDH5) to promote its transcription which may be stimulated by IL-10. We concluded that CD146+CAFs could promote angiogenesis and VM formation via the IL-10/JAK1/STAT3 signalling pathway. These findings may lead to the identification of potential targets for antiangiogenic therapeutic strategies for endometrial cancers.

Cisplatin and Starvation Differently Sensitize Autophagy in Renal Carcinoma: A Potential Therapeutic Pathway to Target Variegated Drugs Resistant Cancerous Cells.

Cisplatin, a powerful chemotherapy medication, has long been a cornerstone in the fight against cancer due to chemotherapeutic failure. The mechanism of cisplatin resistance/failure is a multifaceted and complex issue that consists mainly of apoptosis inhibition through autophagy sensitization. Currently, researchers are exploring ways to regulate autophagy in order to tip the balance in favor of effective chemotherapy. Based on this notion, the current study primarily identifies the differentially expressed genes (DEGs) in cisplatin-treated autophagic ACHN cells through the Illumina Hi-seq platform. A protein-protein interaction network was constructed using the STRING database and KEGG. GO classifiers were implicated to identify genes and their participating biological pathways. ClueGO, David, and MCODE detected ontological enrichment and sub-networking. The network topology was further examined using 12 different algorithms to identify top-ranked hub genes through the Cytoscape plugin Cytohubba to identify potential targets, which established profound drug efficacy under an autophagic environment. Considerable upregulation of genes related to autophagy and apoptosis suggests that autophagy boosts cisplatin efficacy in malignant ACHN cells with minimal harm to normal HEK-293 growth. Furthermore, the determination of cellular viability and apoptosis by AnnexinV/FITC-PI assay corroborates with in silico data, indicating the reliability of the bioinformatics method followed by qRT-PCR. Altogether, our data provide a clear molecular insight into drug efficacy under starved conditions to improve chemotherapy and will likely prompt more clinical trials on this aspect.

Evaluation of serum and urine biomarkers for severe COVID-19.

The new coronavirus disease, COVID-19, poses complex challenges exacerbated by several factors, with respiratory tissue lesions being notably significant among them. Consequently, there is a pressing need to identify informative biological markers that can indicate the severity of the disease. Several studies have highlighted the involvement of proteins such as APOA1, XPNPEP2, ORP150, CUBN, HCII, and CREB3L3 in these respiratory tissue lesions. However, there is a lack of information regarding antibodies to these proteins in the human body, which could potentially serve as valuable diagnostic markers for COVID-19. Simultaneously, it is relevant to select biological fluids that can be obtained without invasive procedures. Urine is one such fluid, but its effect on clinical laboratory analysis is not yet fully understood due to lack of study on its composition. Methods used in this study are as follows: total serum protein analysis; ELISA on moderate and severe COVID-19 patients' serum and urine; bioinformatic methods: ROC analysis, PCA, SVM. The levels of antiAPOA1, antiXPNPEP2, antiORP150, antiCUBN, antiHCII, and antiCREB3L3 exhibit gradual fluctuations ranging from moderate to severe in both the serum and urine of COVID-19 patients. However, the diagnostic value of individual anti-protein antibodies is low, in both blood serum and urine. On the contrary, joint detection of these antibodies in patients' serum significantly increases the diagnostic value as demonstrated by the results of principal component analysis (PCA) and support vector machine (SVM). The non-linear regression model achieved an accuracy of 0.833. Furthermore, PCA aided in identifying serum protein markers that have the greatest impact on patient group discrimination. The study revealed that serum serves as a superior analyte for describing protein quantification due to its consistent composition and lack of organic salts and drug residues, which can otherwise affect protein stability.

Utilizing systematic Mendelian randomization to identify potential therapeutic targets for mania.

Mania has caused incalculable economic losses for patients, their families, and even society, but there is currently no effective treatment plan for this disease without side effects. Using bioinformatics and Mendelian randomization methods, potential drug target genes and key substances associated with mania were explored at the mRNA level. We used the chip expression profile from the GEO database to screen differential genes and used the eQTL and mania GWAS data from the IEU database for two-sample Mendelian randomization (MR) to determine core genes by colocalization. Next, we utilized bioinformatics analysis to identify key substances involved in the mechanism of action and determined related gene targets as drug targets. After differential expression analysis and MR, a causal relationship between the expression of 46 genes and mania was found. Colocalization analysis yielded six core genes. Five key substances were identified via enrichment analysis, immune-related analysis, and single-gene GSVA analysis of the core genes. MR revealed phenylalanine to be the only key substance that has a unidirectional causal relationship with mania. In the end, SBNO2, PBX2, RAMP3, and QPCT, which are significantly associated with the phenylalanine metabolism pathway, were identified as drug target genes. SBNO2, PBX2, RAMP3, and QPCT could serve as potential target genes for mania treatment and deserve further basic and clinical research. Medicinal target genes regulate the phenylalanine metabolism pathway to achieve the treatment of mania. Phenylalanine is an important intermediate substance in the treatment of mania that is regulated by drug target genes.

MAPK14 as a key gene for regulating inflammatory response and macrophage M1 polarization induced by ferroptotic keratinocyte in psoriasis.

Psoriasis is a prevalent condition characterized by chronic inflammation, immune dysregulation, and genetic alterations, significantly impacting the well-being of affected individuals. Recently, a novel aspect of programmed cell death, ferroptosis, linked to iron metabolism, has come to light. This research endeavors to unveil novel diagnostic genes associated with ferroptosis in psoriasis, employing bioinformatic methods and experimental validation. Diverse analytical strategies, including "limma," Weighted Gene Co-expression Network Analysis (WGCNA), Least Absolute Shrinkage and Selection Operator (LASSO), Support Vector Machine Recursive Feature Elimination (SVM-RFE), and Random Forest (RF), were employed to pinpoint pivotal ferroptosis-related diagnostic genes (FRDGs) in the training datasets GSE30999, testing dataset GSE41662 and GSE14905. The discriminative potential of FRDGs in distinguishing between normal and psoriatic patients was gauged using Receiver Operating Characteristic (ROC) curves, while the functional pathways of FRDGs were scrutinized through Gene Set Enrichment Analysis (GSEA). Spearman correlation and ssGSEA analysis were applied to explore correlations between FRDGs and immune cell infiltration or oxidative stress-related pathways. The study identified six robust FRDGs - PPARD, MAPK14, PARP9, POR, CDCA3, and PDK4 - which collectively formed a model boasting an exceptional AUC value of 0.994. GSEA analysis uncovered their active involvement in psoriasis-related pathways, and substantial correlations with immune cells and oxidative stress were noted. In vivo, experiments confirmed the consistency of the six FRDGs in the psoriasis model with microarray results. In vitro, genetic knockdown or inhibition of MAPK14 using SW203580 in keratinocytes attenuated ferroptosis and reduced the expression of inflammatory cytokines. Furthermore, the study revealed that intercellular communication between keratinocytes and macrophages was augmented by ferroptotic keratinocytes, increased M1 polarization, and recruitment of macrophage was regulated by MAPK14. In summary, our findings unveil novel ferroptosis-related targets and enhance the understanding of inflammatory responses in psoriasis. Targeting MAPK14 signaling in keratinocytes emerges as a promising therapeutic approach for managing psoriasis.

Novel Diagnostic Biomarker BST2 Identified by Integrated Transcriptomics Promotes the Development of Endometriosis via the TNF-α/NF-κB Signaling Pathway.

Endometriosis (EMS) is a common gynecological condition with apparent heterogeneity, lack of diagnostic markers, and unclear pathogenesis. A series of bioinformatics methods were employed to explore EMS's pathological mechanisms and potential biomarkers by analyzing the combined datasets of EMS (GSE7305, GSE7307, GSE58198, E-MTAB-694), which included 34 normal, 127 eutopic, and 46 ectopic endometrium samples. Then, wet-laboratory experiments (including Western blot, qRT-PCR, and Immunohistochemistry, Immunofluorescence, CCK-8, EdU, Wound healing, Transwell, and Adhesion assays) were applied to examine the biomarkers' expression and function in primary endometrial stromal cells. Bioinformatic analysis indicated that the core pathogenesis of EMS was dysregulated immune-inflammation and tissue remolding processes. Among the upregulated DEGs, BST2 was screened as a potential diagnostic biomarker in EMS, which associated with the revised American Fertility Society (r-AFS) stage and immune-inflammation processes of EMS. Moreover, BST2's overexpression was affirmed in the RNA and protein levels in EMS tissues. In vitro experiments demonstrated that TNF-α promoted the expression of BST2 in ESCs. And BST2 knockdown inhibited migration, invasion, adhesion, and inflammation except for the proliferation of ESCs, probably via the TNF-α/NF-κB pathway. Through a combination of wet and dry studies, we concluded that the core pathogenesis of endometriosis was dysregulated immune-inflammation and tissue remolding, and BST2 might be a potential diagnostic and therapeutic target in endometriosis.

Analyses of hypoxia-related risk factors and clinical relevance in breast cancer.

Hypoxia plays an important role in the heterogeneity, relapse, metastasis, and drug resistance of breast cancer. In this study, we explored the hypoxia-related biological signatures in different subtypes of breast cancer and identified the key prognostic factors by bioinformatics methods. Based on The Cancer Genome Atlas (TCGA) Breast Cancer datasets, we divided the samples into immune-activated/suppressed populations by single-sample gene set enrichment analysis (ssGSEA) and then used hierarchical clustering to further identify hypoxic/non-hypoxic populations from the immune-suppressed samples. A hypoxia related risk model of breast cancer was constructed. Nuclear factor interleukin-3 regulated (NFIL3), serpin family E member 1 (SERPINE1), FOS, biglycan (BGN), epidermal growth factor receptor (EGFR), and sushi-repeat-containing protein, X-linked (SRPX) were identified as key hypoxia-related genes. Margin status, American Joint Committee on Cancer (AJCC) stage, hypoxia status, estrogen receptor/progesterone receptor (ER/PR) status, NFIL3, SERPINE1, EGFR, and risk score were identified as independent prognostic indicators for breast cancer patients. The 3- and 5-year survival curves of the model and immunohistochemical staining on the breast cancer microarray verified the statistical significance and feasibility of our model. Among the different molecular types of breast cancer, ER/PR+ and HER2+ patients might have higher hypoxia-related risk scores. ER/PR-negative samples demonstrated more activated immune-related pathways and better response to most anticancer agents. Our study revealed a novel risk model and potential feasible prognostic factors for breast cancer and might provide new perspectives for individual breast cancer treatment.

The prognostic value of LAYN in HPV-related head and neck squamous cell carcinoma and its influence on immune cell infiltration

BackgroundHPV-positive head and neck squamous cell carcinoma (HNSCC) exhibits different characteristics from HPV-negative tumors in terms of tumor development, clinical features, treatment response, and prognosis. Layilin (LAYN), which contains homology with C-type lectins, plays a critical role in tumorigenesis and cancer progression. However, the prognostic value of LAYN and the relationship between LAYN and immune infiltration levels in HPV-related HNSCC patients still require a comprehensive understanding. Herein, we aimed to assess the prognostic value of LAYN and to investigate its underlying immunological function in HPV-related HNSCC.MethodsThrough various bioinformatics methods, we analyzed the data from The Cancer Genome Atlas (TCGA), Tumor Immune Estimation Resource (TIMER) and Gene Expression Profiling Interactive Analysis (GEPIA) databases to explore the potential underlying oncogenic impression of LAYN, including the relevance of LAYN to survival outcomes, clinicopathological factors, immune cell infiltration, and immune marker sets in HPV-related HNSCC. The expression levels of LAYN and HPV were also verified in HNSCC patient tissues.ResultsLAYN was differentially expressed in a variety of tumors. The expression of LAYN in HNSCC was significantly higher than that in adjacent normal tissues (P < 0.0001), and high expression of LAYN was correlated with poor overall survival (OS) in HNSCC patients (Hazard Ratio (HR) = 1.3, P = 0.035). Moreover, LAYN expression level in HPV-positive HNSCC patients was significantly lower than that in HPV-negative patients, with HPV-positive HNSCC patients displaying a trend of favorable prognosis. In addition, the relationship between LAYN expression and immune infiltration levels in HPV-positive HNSCC group was less tightly correlated than that in HPV-negative HNSCC group, and there was a strong relationship between LAYN expression and markers of M2 macrophage (P < 0.001) and exhausted T cells (P < 0.05) in HPV-negative HNSCC. Kyoto encyclopedia of genes and genomes (KEGG) enrichment analysis suggested that LAYN potentially influenced tumor progression through HPV infection and other cancer-related pathways.ConclusionsLAYN might contribute to tumorigenesis via its positive correlation with immune checkpoint molecules and tumor-associated macrophages (TAMs). Our study might provide a novel prognostic biomarker and latent therapeutic target for the treatment of HPV-related HNSCC.

Downregulation of INPP4B is Associated with Poor Prognosis in Epithelial Ovarian Carcinoma.

INPP4B is a tyrosine-specific phosphatase in the human body, which plays an important role in the developing process of carcinogenesis. However, The correlation between INPP4B and epithelial ovarian cancer is rarely explored. In this study, the expression of INPP4B in human epithelial ovarian carcinoma and normal ovaries was detected, to explore the correlation between INPP4B expression and clinicopathological risk factors of epithelial ovarian carcinoma and to clarify its significance in the developing process of and prognosis of epithelial ovarian carcinoma. The expression of INPP4B in various tumors was detected by bioinformatics method, and the expression in epithelial ovarian cancer and normal control group was detected by Elisa. The immunohistochemical method was used in this experiment to analyze the expression of INPP4B in specimens of 100 cases of epithelial ovarian carcinoma and 20 cases of normal ovaries. Analysis of clinicopathological risk factors and related survival analysis was carried out on the expression of INPP4B in 100 cases of epithelial ovarian carcinoma. The results showed that the positive expressed INPP4B protein in epithelial ovarian carcinoma was significantly less, compared with that in normal ovaries (P < 0.05). The expression of INPP4B was significantly associated with many clinicopathologic factors, such as tumor differentiation (P < 0.001), FIGO stage (P < 0.001), lymph node metastasis (P < 0.001) and distant metastasis at recurrence (P=0. 009), but not with age, pathologic type of tumor, serum CA125 at recurrence and chemotherapy sensitivity. In epithelial ovarian carcinoma, there is a downregulation of INPP4B expression, which may be related to poor tumor differentiation, late FIGO stage, lymph node metastasis, distant metastasis at recurrence and insensitivity to chemotherapy. Under-expression of INPP4B, lymph node metastasis, FIGO stage, and distant metastasis at recurrence are factors of poor prognostic. The under-expression level of INPP4B may be involved in the progression of epithelial ovarian carcinoma.

AlphaFold for a medicinal chemist: tool or toy?

The development of novel small drug molecules is a complex and important cross-disciplinary task. In the early stages of development, chemoinformatics and bioinformatics methods are routinely used to reduce the cost of finding a lead compound. Among the tools of medicinal chemistry, docking and molecular dynamics occupy a special place. These methods are used to predict the possible mechanism of binding of a potential ligand to a protein target. However, in order to perform a docking study, it is necessary to know the spatial structure of the protein under investigation. Although databases of crystallographic structures are available, the three-dimensional representations of many protein molecules have not been reported. There is therefore a need to model such three-dimensional conformations. Several computer algorithms have been published to solve this problem. AlphaFold is considered by the scientific community to be the most effective approach to predicting the three-dimensional structure of proteins. However, the scope of its application in medicinal chemistry, especially for virtual screening, remains unclear. This review describes methods for predicting the three-dimensional structure of a protein and provides representative examples of the use of AlphaFold for the design and rational selection of potential ligands. Special attention is given to publications presenting the results of experimental validation of the approach. On the basis of performed analysis, the main problems in the field and possible ways to solve them are formulated.&lt;br&gt; The bibliography includes 154 references.

The first complete genome sequence and genetic evolution analysis of bovine norovirus in Xinjiang, China

Abstract Introduction Viruses are among the main pathogens causing diarrhoea in calves. The current study found that bovine norovirus (BNoV) is one of the principal viruses causing diarrhoea in calves in Xinjiang, China. Material and Methods A total of 974 calf faecal samples from six regions in Xinjiang were tested for BNoV using reverse-transcriptase PCR. The genomic characteristics of BNoV and the genetic evolution of the VP1 gene, protein three-dimensional structure characteristics and amino acid variation were analysed using bioinformatics methods. Results Epidemiological survey results showed that the infection rate of BNoV was 19.82%, and all samples tested positive in five regions. The results of the genetic evolution analysis showed that BNoV strains from Tacheng of northern Xinjiang and Kashgar of southern Xinjiang both belonged to the GIII.2 genotype of BNoV but were not on the same cluster of evolutionary branches. Additionally, the amino acid variation of the VP1 protein was not observed to significantly affect its spatial structure. Conclusion This study is the first to report the genetic characteristics of the BNoV complete genome sequence in Xinjiang and provides a scientific basis for BNoV vaccine development and pathogenesis research.

Expression and clinical role of PRDX6 in lung adenocarcinoma.

To explore the levels of expression and clinical role of peroxiredoxin 6 (PRDX6) in lung adenocarcinoma. This retrospective study used a series of bioinformatics methods to detect the levels of expression of and mutations in the PRDX6 gene in a range of cancers and lung adenocarcinoma. Immunohistochemistry was used to verify the levels of expression of PRDX6 protein in samples of lung adenocarcinoma compared with normal adjacent tissue. The effect of PRDX6 gene knockdown on the in vitro proliferation of a lung adenocarcinoma cell line was measured. Bioinformatics methods were used to determine the diagnostic value and impact on survival of the PRDX6 gene in patients with lung adenocarcinoma. The results showed that the PRDX6 gene was highly expressed in lung adenocarcinoma and there were five mutations at different sites on the gene. PRDX6 promoted the proliferation of the lung adenocarcinoma cell line. The survival duration of lung adenocarcinoma patients with high levels of PRDX6 gene expression was significantly shorter than that of patients with low PRDX6 gene expression. PRDX6 is highly expressed in lung adenocarcinoma and higher levels of expression of the PRDX6 gene were associated with a poorer prognosis.

MADS-Box Family Genes in Lagerstroemia indica and Their Involvement in Flower Development.

MADS-box is a key transcription factor regulating the transition to flowering and flower development. Lagerstroemia indica 'Xiang Yun' is a new cultivar of crape myrtle characterized by its non-fruiting nature. To study the molecular mechanism underlying the non-fruiting characteristics of 'Xiang Yun', 82 MADS-box genes were identified from the genome of L. indica. The physicochemical properties of these genes were examined using bioinformatics methods, and their expression as well as endogenous hormone levels at various stages of flower development were analyzed. The results showed that LiMADS genes were primarily classified into two types: type I and type II, with the majority being type II that contained an abundance of cis-acting elements in their promoters. By screening nine core proteins by predicted protein interactions and performing qRT-PCR analysis as well as in combination with transcriptome data, we found that the expression levels of most MADS genes involved in flower development were significantly lower in 'Xiang Yun' than in the wild type 'Hong Ye'. Hormonal analysis indicated that 'Xiang Yun' had higher levels of iP, IPR, TZR, and zeatin during its early stages of flower development than 'Hong Ye', whereas the MeJA content was substantially lower at the late stage of flower development of 'Hong Ye'. Finally, correlation analysis showed that JA, IAA, SA, and TZR were positively correlated with the expression levels of most type II genes. Based on these analyses, a working model for the non-fruiting 'Xiang Yun' was proposed. During the course of flower development, plant hormone response pathways may affect the expression of MADS genes, resulting in their low expression in flower development, which led to the abnormal development of the stamen and embryo sac and ultimately affected the fruiting process of 'Xiang Yun'.

Spatial and Temporal Disparity Analyses of Glycosylated Benzaldehyde and Identification and Expression Pattern Analyses of Uridine Diphosphate Glycosyltransferase Genes in Prunus mume.

The species Prunus mume consists of uniquely aromatic woody perennials with large amounts of free aromatic substances in the flower cells. Uridine diphosphate glycosyltransferase (UGT) modifies these free aromatic substances into water-soluble glycoside-bound volatiles (GBVs) which play an important role in regulating the use of volatiles by plants for information exchange, defense, and stress tolerance. To investigate the changes in the glycosidic state of aromatic substances during the flowering period of P. mume and discern the location and expression of glycoside synthesis genes, we extracted and enzymatically hydrolyzed GBVs of P. mume and then utilized gas chromatography-mass spectrometry (GC-MS) to characterize and analyze the types and contents of GBV glycosides. Further, we identified and classified the members of the UGT gene family of P. mume using the bioinformatic method and analyzed the correlation between the expression of the UGT family genes in P. mume and the changes in glycosidic content. The results showed that the benzenoids were the main aromatic substance that was glycosylated during flowering in P. mume and that glycosidic benzaldehyde was the most prevalent compound in different flower parts and at different flowering stages. The titer of glycoside benzaldehyde gradually increased during the bud stage and reached the highest level at the big bud stage (999.6 μg·g-1). Significantly, titers of glycoside benzaldehyde significantly decreased and stabilized after flowering while the level of free benzaldehyde, in contrast, significantly increased and then reached a plateau after the flowering process was completed. A total of 155 UGT family genes were identified in the P. mume genome, which were divided into 13 subfamilies (A-E, G-N); according to the classification of Arabidopsis thaliana UGT gene subfamilies, the L subfamily contains 17 genes. The transcriptome analysis showed that PmUGTL9 and PmUGTL13 were highly expressed in the bud stage and were strongly correlated with the content of the glycosidic form of benzaldehyde at all stages of flowering. This study provides a theoretical basis to elucidate the function of UGT family genes in P. mume during flower development, to explore the mechanism of the storage and transportation of aromatic compounds in flower tissues, and to exploit industrial applications of aromatic products from P. mume.

Mathematical Modelling and Bioinformatics Analyses of Drug Resistance for Cancer Treatment

Abstract: Cancer is a leading cause of human death worldwide. Drug resistance, mainly caused by gene mutation, is a key obstacle to tumour treatment. Therefore, studying the mechanisms of drug resistance in cancer is extremely valuable for clinical applications. : This paper aims to review bioinformatics approaches and mathematical models for determining the evolutionary mechanisms of drug resistance and investigating their functions in designing therapy schemes for cancer diseases. We focus on the models with drug resistance based on genetic mutations for cancer therapy and bioinformatics approaches to study drug resistance involving gene co-expression networks and machine learning algorithms. : We first review mathematical models with single-drug resistance and multidrug resistance. The resistance probability of a drug is different from the order of drug administration in a multidrug resistance model. Then, we discuss bioinformatics methods and machine learning algorithms that are designed to develop gene co-expression networks and explore the functions of gene mutations in drug resistance using multi-omics datasets of cancer cells, which can be used to predict individual drug response and prognostic biomarkers. : It was found that the resistance probability and expected number of drug-resistant tumour cells increase with the increase in the net reproductive rate of resistant tumour cells. Constrained models, such as logistical growth resistance models, can be used to identify more clinically realistic treatment strategies for cancer therapy. In addition, bioinformatics methods and machine learning algorithms can also lead to the development of effective therapy schemes.

GLA Mutations Suppress Autophagy and Stimulate Lysosome Generation in Fabry Disease.

Fabry disease (FD) is an X-linked recessive inheritance lysosomal storage disorder caused by pathogenic mutations in the GLA gene leading to a deficiency of the enzyme alpha-galactosidase A (α-Gal A). Multiple organ systems are implicated in FD, most notably the kidney, heart, and central nervous system. In our previous study, we identified four GLA mutations from four independent Fabry disease families with kidney disease or neuropathic pain: c.119C>A (p.P40H), c.280T>C (C94R), c.680G>C (p.R227P) and c.801+1G>A (p.L268fsX3). To reveal the molecular mechanism underlying the predisposition to Fabry disease caused by GLA mutations, we analyzed the effects of these four GLA mutations on the protein structure of α-galactosidase A using bioinformatics methods. The results showed that these mutations have a significant impact on the internal dynamics and structures of GLA, and all these altered amino acids are close to the enzyme activity center and lead to significantly reduced enzyme activity. Furthermore, these mutations led to the accumulation of autophagosomes and impairment of autophagy in the cells, which may in turn negatively regulate autophagy by slightly increasing the phosphorylation of mTOR. Moreover, the overexpression of these GLA mutants promoted the expression of lysosome-associated membrane protein 2 (LAMP2), resulting in an increased number of lysosomes. Our study reveals the pathogenesis of these four GLA mutations in FD and provides a scientific foundation for accurate diagnosis and precise medical intervention for FD.

Regulation of endogenous hormone and miRNA in leaves of alfalfa (Medicago sativa L.) seedlings under drought stress by endogenous nitric oxide

BackgroundAlfalfa (Medicago sativa. L) is one of the best leguminous herbage in China and even in the world, with high nutritional and ecological value. However, one of the drawbacks of alfalfa is its sensitivity to dry conditions, which is a global agricultural problem. The objective of this study was to investigate the regulatory effects of endogenous nitric oxide (NO) on endogenous hormones and related miRNAs in alfalfa seedling leaves under drought stress. The effects of endogenous NO on endogenous hormones such as ABA, GA3, SA, and IAA in alfalfa leaves under drought stress were studied. In addition, high-throughput sequencing technology was used to identify drought-related miRNAs and endogenous NO-responsive miRNAs in alfalfa seedling leaves under drought stress.ResultBy measuring the contents of four endogenous hormones in alfalfa leaves, it was found that endogenous NO could regulate plant growth and stress resistance by inducing the metabolism levels of IAA, ABA, GA3, and SA in alfalfa, especially ABA and SA in alfalfa. In addition, small RNA sequencing technology and bioinformatics methods were used to analyze endogenous NO-responsive miRNAs under drought stress. It was found that most miRNAs were enriched in biological pathways and molecular functions related to hormones (ABA, ETH, and JA), phenylpropane metabolism, and plant stress tolerance.ConclusionIn this study, the analysis of endogenous hormone signals and miRNAs in alfalfa leaves under PEG and PEG + cPTIO conditions provided an important basis for endogenous NO to improve the drought resistance of alfalfa at the physiological and molecular levels. It has important scientific value and practical significance for endogenous NO to improve plant drought resistance.

Prediction of Essential Proteins of Klebsiella pneumoniae using Integrative Bioinformatics and Systems Biology Approach: Unveiling New Avenues for Drug Discovery.

Klebsiella pneumoniae is an opportunistic multidrug-resistant bacterial pathogen responsible for various health care-associated infections. The prediction of proteins that are essential for the survival of bacterial pathogens can greatly facilitate the drug development and discovery pipeline toward target identification. To this end, the present study reports a comprehensive computational approach integrating bioinformatics and systems biology-based methods to identify essential proteins of K. pneumoniae involved in vital processes. From the proteome of this pathogen, we predicted a total of 854 essential proteins based on sequence, protein-protein interaction (PPI) and genome-scale metabolic model methods. These predicted essential proteins are involved in vital processes for cellular regulation such as translation, metabolism, and biosynthesis of essential factors, among others. Cluster analysis of the PPI network revealed the highly connected modules involved in the basic functionality of the organism. Further, the predicted consensus set of essential proteins of K. pneumoniae was evaluated by comparing them with existing resources (NetGenes and PATHOgenex) and literature. The findings of this study offer guidance toward understanding cell functionality, thereby facilitating the understanding of pathogen systems and providing a way forward to shortlist potential therapeutic candidates for developing novel antimicrobial agents against K. pneumoniae. In addition, the research strategy presented herein is a fusion of sequence and systems biology-based approaches that offers prospects as a model to predict essential proteins for other pathogens.

Responses of keratinocytes to Trichophyton mentagrophyte infection based on whole transcriptome analysis.

Dermatophytosis is an intractable superficial mycosis in humans and animals mainly caused by Trichophyton mentagrophytes (T. mentagrophytes), with a global prevalence of about 20%. Keratinocytes are the most abundant participants in skin immunity, and they also play a role in the first-line defence against T. mentagrophytes. However, no studies of keratinocyte responses against T. mentagrophytes infection based on the whole transcriptome have been reported. Here, we systematically analysed changes in keratinocytes infected with T. mentagrophytes using whole transcriptome sequencing technology. The phenotypic changes in keratinocytes after infection with 1 × 105 conidia/mL T. mentagrophytes were observed by light microscopy, scanning electron microscopy, transmission electron microscopy and terminal deoxynucleotidyl transferase dUTP nick end labeling. RNA-sequencing (RNA-seq), small RNA-seq technology and related bioinformatics methods were used to systematically analyse the whole transcriptome changes in keratinocytes upon T. mentagrophytes stimulation. We found that T. mentagrophytes infection caused morphological changes, membrane damage, the formation of irregular organelles and keratinocyte apoptosis. A total of 204 differentially expressed (DE) circular RNAs (circRNAs), 868 DE long noncoding RNAs (lncRNAs), 2973 DE mRNAs and 209 DE micro RNAs (miRNAs) were identified between noninfected and T. mentagrophytes-infected keratinocytes. The expression level of selected RNAs was validated by quantitative real-time polymerase chain reaction (qRT-PCR). Functional enrichment analysis revealed that the parental genes of DE circRNAs were related to cell response, cell death and establishment of the skin barrier. Genes targeted by miRNA were involved in regulating the initiation of the immune response. Based on the expression level of circRNAs, lncRNAs, mRNAs and miRNAs, circRNA-miRNA-mRNA competing endogenous (ceRNA) networks comprised of 159 DE miRNAs, 141 DE circRNAs and 2307 DE mRNAs, and lncRNA-miRNA-mRNA ceRNA networks comprised of 790 DE lncRNAs, 190 DE miRNAs and 2663 DE mRNAs were constructed. The reliability of two selected ceRNA networks was verified using qRT-PCR. Further functional enrichment analysis revealed that the DE mRNAs interacting with circRNAs and lncRNAs in the ceRNA network mainly participated in fungal recognition, inflammation, the innate immune response and the death of keratinocytes. Our findings might provide new evidence on the pathogenesis of T. mentagrophytes-induced dermatophytosis, which is essential for identifying new therapeutic targets for dermatophytosis treatment.