DDID: a comprehensive resource for visualization and analysis of diet-drug interactions.

Diet-drug interactions (DDIs) are pivotal in drug discovery and pharmacovigilance. DDIs can modify the systemic bioavailability/pharmacokinetics of drugs, posing a threat to public health and patient safety. Therefore, it is crucial to establish a platform to reveal the correlation between diets and drugs. Accordingly, we have established a publicly accessible online platform, known as Diet-Drug Interactions Database (DDID, https://bddg.hznu.edu.cn/ddid/), to systematically detail the correlation and corresponding mechanisms of DDIs. The platform comprises 1338 foods/herbs, encompassing flora and fauna, alongside 1516 widely used drugs and 23 950 interaction records. All interactions are meticulously scrutinized and segmented into five categories, thereby resulting in evaluations (positive, negative, no effect, harmful and possible). Besides, cross-linkages between foods/herbs, drugs and other databases are furnished. In conclusion, DDID is a useful resource for comprehending the correlation between foods, herbs and drugs and holds a promise to enhance drug utilization and research on drug combinations.

Integrating multiple 'omics' analyses identifies serological protein biomarkers for preeclampsia.

BACKGROUND: Preeclampsia (PE) is a pregnancy-related vascular disorder which is the leading cause of maternal morbidity and mortality. We sought to identify novel serological protein markers to diagnose PE with a multi-'omics' based discovery approach. METHODS: Seven previous placental expression studies were combined for a multiplex analysis, and in parallel, two-dimensional gel electrophoresis was performed to compare serum proteomes in PE and control subjects. The combined biomarker candidates were validated with available ELISA assays using gestational age-matched PE (n=32) and control (n=32) samples. With the validated biomarkers, a genetic algorithm was then used to construct and optimize biomarker panels in PE assessment. RESULTS: In addition to the previously identified biomarkers, the angiogenic and antiangiogenic factors (soluble fms-like tyrosine kinase (sFlt-1) and placental growth factor (PIGF)), we found 3 up-regulated and 6 down-regulated biomakers in PE sera. Two optimal biomarker panels were developed for early and late onset PE assessment, respectively. CONCLUSIONS: Both early and late onset PE diagnostic panels, constructed with our PE biomarkers, were superior over sFlt-1/PIGF ratio in PE discrimination. The functional significance of these PE biomarkers and their associated pathways were analyzed which may provide new insights into the pathogenesis of PE.

Relationship Between Negative Life Events and Depressive Symptoms for Chinese College Students: The Mediating Role of Rumination and Moderating Role of Perceived Social Support and Psychological Capital.

Objective: External events affect individuals through their cognitive process, a model on how and when negative life events are associated with depressive symptoms was tested by considering individuals' internal and external factors based on the conservation of resource theory (COR). Methods: We conducted a survey to test our hypotheses. Participants were college students who were selected with the cluster sampling method and were asked to complete the scales measuring negative life events, perceived social support, psychological capital (PsyCap), rumination, and depressive symptoms in the classroom with a unit of class. A total of 764 questionnaires were distributed and returned, and 703 valid data were obtained finally. Results: The present study found that (1) the relationship between negative life events and depressive symptoms was moderated by perceived social support negatively, such that the relationship was stronger with low perceived social support; (2) the relationship between negative life events and depressive symptoms was mediated by rumination; (3) the relationship between rumination and depressive symptoms was moderated by PsyCap negatively, such that the relationship was stronger with low PsyCap; (4) the indirect relationship between negative life events and depressive symptoms through rumination was moderated by PsyCap negatively, such that the indirect relationship got stronger with low PsyCap. Conclusion: Rumination is an essential process for negative life events to affect depressive symptoms, PsyCap and perceived social support help alleviate the detrimental effect of negative life events from internal and external perspectives, respectively. Our research conclusion has a theoretical and practical implementation for reducing depressive symptoms in college students.

A systematic review on the state-of-the-art strategies for protein representation.

The study of drug-target protein interaction is a key step in drug research. In recent years, machine learning techniques have become attractive for research, including drug research, due to their automated nature, predictive power, and expected efficiency. Protein representation is a key step in the study of drug-target protein interaction by machine learning, which plays a fundamental role in the ultimate accomplishment of accurate research. With the progress of machine learning, protein representation methods have gradually attracted attention and have consequently developed rapidly. Therefore, in this review, we systematically classify current protein representation methods, comprehensively review them, and discuss the latest advances of interest. According to the information extraction methods and information sources, these representation methods are generally divided into structure and sequence-based representation methods. Each primary class can be further divided into specific subcategories. As for the particular representation methods involve both traditional and the latest approaches. This review contains a comprehensive assessment of the various methods which researchers can use as a reference for their specific protein-related research requirements, including drug research.

A systematic review of state-of-the-art strategies for machine learning-based protein function prediction.

New drug discovery is inseparable from the discovery of drug targets, and the vast majority of the known targets are proteins. At the same time, proteins are essential structural and functional elements of living cells necessary for the maintenance of all forms of life. Therefore, protein functions have become the focus of many pharmacological and biological studies. Traditional experimental techniques are no longer adequate for rapidly growing annotation of protein sequences, and approaches to protein function prediction using computational methods have emerged and flourished. A significant trend has been to use machine learning to achieve this goal. In this review, approaches to protein function prediction based on the sequence, structure, protein-protein interaction (PPI) networks, and fusion of multi-information sources are discussed. The current status of research on protein function prediction using machine learning is considered, and existing challenges and prominent breakthroughs are discussed to provide ideas and methods for future studies.

The roles and mechanism of m6A RNA methylation regulators in cancer immunity.

N6-methyladenosine (m6A), the most common internal modification in RNA, can be regulated by three types of regulators, including methyltransferases (writers), demethylases (erasers), and m6A binding proteins (readers). Recently, immunotherapy represented by immune checkpoint blocking has increasingly become an effective cancer treatment, and increasing shreds of evidence show that m6A RNA methylation affects cancer immunity in various cancers. Until now, there have been few reviews about the role and mechanism of m6A modification in cancer immunity. Here, we first summarized the regulation of m6A regulators on the expression of target messenger RNAs (mRNA) and their corresponding roles in inflammation, immunity response, immune process and immunotherapy in various cancer cells. Meanwhile, we described the roles and mechanisms of m6A RNA modification in tumor microenvironment and immune response by affecting the stability of non-coding RNA (ncRNA). Moreover, we also discussed the m6A regulators or its target RNAs which might be used as predictor of cancer diagnosis and prognosis, and shed light on the potentiality of m6A methylation regulators as therapeutic targets in cancer immunity.

EMNPD: a comprehensive endophytic microorganism natural products database for prompt the discovery of new bioactive substances.

The discovery and utilization of natural products derived from endophytic microorganisms have garnered significant attention in pharmaceutical research. While remarkable progress has been made in this field each year, the absence of dedicated open-access databases for endophytic microorganism natural products research is evident. To address the increasing demand for mining and sharing of data resources related to endophytic microorganism natural products, this study introduces EMNPD, a comprehensive endophytic microorganism natural products database comprising manually curated data. Currently, EMNPD offers 6632 natural products from 1017 endophytic microorganisms, targeting 1286 entities (including 94 proteins, 282 cell lines, and 910 species) with 91 diverse bioactivities. It encompasses the physico-chemical properties of natural products, ADMET information, quantitative activity data with their potency, natural products contents with diverse fermentation conditions, systematic taxonomy, and links to various well-established databases. EMNPD aims to function as an open-access knowledge repository for the study of endophytic microorganisms and their natural products, thereby facilitating drug discovery research and exploration of bioactive substances. The database can be accessed at http://emnpd.idrblab.cn/ without the need for registration, enabling researchers to freely download the data. EMNPD is expected to become a valuable resource in the field of endophytic microorganism natural products and contribute to future drug development endeavors.

Gender role attitudes and work-family conflict: A multiple mediating model including moderated mediation analysis.

With the fierce labor market competition, the family population's size continues to expand, and the conflict between work and family requirements for individual roles becomes increasingly intense. Most studies focus on work-family conflict as an antecedent variable, and few studies use work-family conflict as an outcome variable. This study aimed to explore the underlying mechanism of the relationship between gender role attitudes and work-family conflict. Two models were tested using conditional process analysis for testing direct and indirect effects on a sample of 324 employees: A serial multiple mediation model, and the multiple mediation model including the moderating role of education level and subjective socioeconomic status. The results suggested that (1) gender role attitudes significantly and positively predicted work-family conflict. (2) Parental sacrifice and subjective well-being played multiple mediating roles between gender role attitudes and work-family conflict. (3) Education level moderated the relationship between gender role attitudes and parental sacrifice, as evidenced by the fact that low education level amplified the positive predictive effect of gender role attitudes on parental sacrifice. (4) Subjective socioeconomic status moderated the relationship between gender role attitudes and subjective well-being, suggesting that high subjective socioeconomic status amplified the negative predictive effect of gender role attitudes on subjective well-being. This work contributes to the understanding of the process underlying the relationship between gender role attitudes and work-family conflict, and to the literature reporting the possible moderated role of education level and subjective socioeconomic status on the influence outcomes of gender role attitudes. Theoretical and practical implications are also discussed.

The Biological Functions and Regulatory Mechanisms of Fatty Acid Binding Protein 5 in Various Diseases.

In recent years, fatty acid binding protein 5 (FABP5), also known as fatty acid transporter, has been widely researched with the help of modern genetic technology. Emerging evidence suggests its critical role in regulating lipid transport, homeostasis, and metabolism. Its involvement in the pathogenesis of various diseases such as metabolic syndrome, skin diseases, cancer, and neurological diseases is the key to understanding the true nature of the protein. This makes FABP5 be a promising component for numerous clinical applications. This review has summarized the most recent advances in the research of FABP5 in modulating cellular processes, providing an in-depth analysis of the protein's biological properties, biological functions, and mechanisms involved in various diseases. In addition, we have discussed the possibility of using FABP5 as a new diagnostic biomarker and therapeutic target for human diseases, shedding light on challenges facing future research.

The aberrant upregulation of exon 10-inclusive SREK1 through SRSF10 acts as an oncogenic driver in human hepatocellular carcinoma.

Deregulation of alternative splicing is implicated as a relevant source of molecular heterogeneity in cancer. However, the targets and intrinsic mechanisms of splicing in hepatocarcinogenesis are largely unknown. Here, we report a functional impact of a Splicing Regulatory Glutamine/Lysine-Rich Protein 1 (SREK1) variant and its regulator, Serine/arginine-rich splicing factor 10 (SRSF10). HCC patients with poor prognosis express higher levels of exon 10-inclusive SREK1 (SREK1L). SREK1L can sustain BLOC1S5-TXNDC5 (B-T) expression, a targeted gene of nonsense-mediated mRNA decay through inhibiting exon-exon junction complex binding with B-T to exert its oncogenic role. B-T plays its competing endogenous RNA role by inhibiting miR-30c-5p and miR-30e-5p, and further promoting the expression of downstream oncogenic targets SRSF10 and TXNDC5. Interestingly, SRSF10 can act as a splicing regulator for SREK1L to promote hepatocarcinogenesis via the formation of a SRSF10-associated complex. In summary, we demonstrate a SRSF10/SREK1L/B-T signalling loop to accelerate the hepatocarcinogenesis.

Identification and validation of a prognostic 9-genes expression signature for gastric cancer.

Gastric cancer (GC) is a common malignant tumor with high incidence and mortality. Reasonable assessment of prognosis is essential to improve the outcomes of patients. In this study, we constructed and validated a prognostic gene model to evaluate the risks of GC patients. To identify the differentially expressed genes between GC patients and controls, we extracted Gene expression profiles of GC patients (N=432) from Gene Expression Omnibus database and then stable signature genes by using Robust likelihood-based modeling with 1000 iterations. Unsupervised hierarchical clustering of all samples was performed basing on the characteristics of gene expressions. Meanwhile, the differences between the clusters were analyzed by Kaplan Meier survival analysis. A 9-genes model was obtained (frequency = 999; p=1.333628e-18), including two negative impact factors (NR1I2 and LGALSL) and 7 positive ones (C1ORF198, CST2, LAMP5, FOXS1, CES1P1, MMP7 and COL8A1). This model was verified in single factor survival analysis (p=0.004447558) and significant analysis with recurrence time (p=0.001474831) by using independent datasets from TCGA. The constructed 9-genes model was stable and effective, which might serve as prognostic signature to predict the survival of GC patients and monitor the long-term treatment of GC.

[Knockdown of PLK1 mRNA by special siRNA].

To study knockdown effect of small interfering RNA (siRNA) to PLK1 (Polo-like kinase 1) mRNA in colorectal cancer cell line SW480 and its mitosis and growth was changed. Ten special siRNA molecules were designed targeting different sites of PLK1 mRNA sequence and chemically synthesized. The siRNA molecules were transfected into SW480 by Oligofectamine. The gene mRNA level was assayed by Real-Time PCR. The changes of PLK1 protein, SW480 cell cycle and survival percentage was checked by Western-blot, Flow cytometry and Cell counter assays respectively. All 10 siRNA molecules knocked PLK1 mRNA down in different level. Of them P1, P4 and P9 showed over 80% knockdown efficiency and the others had more than 20% knockdown efficiency to PLK1 mRNA. The best knockdown effect over 95% of all groups was at 25 nmol/L of a mixture with P1, P4 and P9 siRNA equally. In this situation the protein was very less and the cells were blocked at G2 phase of cell cycle. After 72 h cell survival percentages were consistent with PKL1 mRNA level change by siRNA gradient concentration. The results showed that siRNA targeting PLK1 mRNA had effectively knocked PLK1 mRNA down in SW480 cell line. And a blended siRNAs held the best knockdown effect. The cell was blocked on the mitosis and growth.

Evaluation of ST13 gene expression in colorectal cancer patients.

We identified a novel gene ST13 from a subtractive cDNA library of normal intestinal mucosa in 1993, more studies showed that ST13 was a co-chaperone of Hsp70s. Recently we detected the ST13 gene expression in tumor tissue and adjacent normal tissue of the same colorectal cancer patient and investigated if the ST13 gene expression might have any prognostic value. Analysis was performed at molecular level by reverse transcription-PCR using real-time detection method. We measured two genes simultaneously, ST13 as the target gene and glyceraldehydes-3-phosphate dehydrogenase as a reference gene, in primary colorectal tumor specimens and tumor-adjacent normal mucosa specimens from 50 colorectal cancer patients. The expression levels of the ST13 gene were significantly decreased in primary tumors compared with adjacent mucosa (P<0.05). But there were no significant differences in the expression of ST13 as compared with different Dukes' stage, tumor differentiation grade, invasion depth, lymph node metastasis and disease-specific survival.

[Study of an enhancer in promoter region of human colorectal cancer related ST13 gene].

OBJECTIVE: To study the base sequence of an enhancer in up-stream 5'-flank near regulation region (from -595 to +74) of human colorectal cancer related gene ST13. METHODS: Several deletion PCR primers were designed. Amplified DNA fragments of ST13 gene 5'-flank near region were cloned with pGEMT-EASY vector and sequenced; then subcloned into several pGL2 report vectors respectively. Equal quantitative recombined DNA was transfected into SW620 cell lines and the luciferase activity was checked. RESULTS: Several amplified base sequence fragments (669 bp,263 bp,163 bp) in pGL2-Basic all enhanced and promoted luciferase gene expression strongly. The 47 bp and 101 bp fragments didn't promote luciferase gene expression. 101 bp fragment recombined with pGL2-Promoter enhanced luciferase gene expression distinctly (P<0.01), but the effect was less strong than the positive pGL2-Control(P<0.05). CONCLUSION: The base sequence 101 bp (from -595 to -494) in up-stream 5'-flank near regulation region of colorectal cancer related gene ST13 is an enhancer regulating gene transcript.