PepExplainer: An explainable deep learning model for selection-based macrocyclic peptide bioactivity prediction and optimization.

Macrocyclic peptides possess unique features, making them highly promising as a drug modality. However, evaluating their bioactivity through wet lab experiments is generally resource-intensive and time-consuming. Despite advancements in artificial intelligence (AI) for bioactivity prediction, challenges remain due to limited data availability and the interpretability issues in deep learning models, often leading to less-than-ideal predictions. To address these challenges, we developed PepExplainer, an explainable graph neural network based on substructure mask explanation (SME). This model excels at deciphering amino acid substructures, translating macrocyclic peptides into detailed molecular graphs at the atomic level, and efficiently handling non-canonical amino acids and complex macrocyclic peptide structures. PepExplainer's effectiveness is enhanced by utilizing the correlation between peptide enrichment data from selection-based focused library and bioactivity data, and employing transfer learning to improve bioactivity predictions of macrocyclic peptides against IL-17C/IL-17 RE interaction. Additionally, PepExplainer underwent further validation for bioactivity prediction using an additional set of thirteen newly synthesized macrocyclic peptides. Moreover, it enabled the optimization of the IC50 of a macrocyclic peptide, reducing it from 15 nM to 5.6 nM based on the contribution score provided by PepExplainer. This achievement underscores PepExplainer's skill in deciphering complex molecular patterns, highlighting its potential to accelerate the discovery and optimization of macrocyclic peptides.

Comparative metabolomics profiling reveals the unique bioactive compounds and astringent taste formation of rosehips.

Rosehips are a prominent source of numerous bioactive compounds. However, despite their extensive potential, the metabolic profiles among different rosehip species have not been fully elucidated. In this study, 523 secondary metabolites from rosehips of 12 Rosa species were identified using ultra-high-performance liquid chromatography-tandem mass spectrometry. They were primarily composed of flavonoids and phenolic acids. A K-means analysis revealed the characteristic metabolites in different rosehips. For example, R. persica contained a more abundant supply of phenolic acids, while R. roxburghii harbored a richer array of terpenoids. A total of 73 key active ingredients were screened from traditional Chinese medicine databases, and they indicated that R. persica is more promising for use in functional foods or health supplements compared with the other fruits. Moreover, a differential analysis identified 47 compounds as potential contributors to the astringent taste of rosehips, including ellagic acid 4-O-glucoside and cadaverine. This study provides valuable information to develop new functional foods of rosehips and improve the quality of their fruits.

Understanding the role of ursodeoxycholic acid and gut microbiome in non-alcoholic fatty liver disease: current evidence and perspectives.

Non-alcoholic fatty liver disease (NAFLD) is the most common chronic liver disease, resulting in a huge medical burden worldwide. Accumulating evidence suggests that the gut microbiome and bile acids play pivotal roles during the development of NAFLD. Patients with NAFLD exhibit unique signatures of the intestinal microbiome marked by the priority of Gram-negative bacteria, decreased ratio of Firmicutes/Bacteroidetes (F/B), and increased Prevotella and Lachnospiraceae. The intestinal microbiota is involved in the metabolism of bile acids. Ursodeoxycholic acid (UDCA) is a key determinant in maintaining the dynamic communication between the host and gut microbiota. It generally shows surprising therapeutic potential in NAFLD with several mechanisms, such as improving cellular autophagy, apoptosis, and mitochondrial functions. This action is based on its direct or indirect effect, targeting the farnesoid X receptor (FXR) and various other nuclear receptors. This review aims to discuss the current studies on the involvement of the microbiome-UDCA interface in NAFLD therapy and provide prospective insights into future preventative and therapeutic approaches for NAFLD.

Efficacy of probiotic supplementation and impact on fecal microbiota in patients with inflammatory bowel disease: a systematic review and meta-analysis of randomized controlled trials.

Context: Research regarding the treatment of inflammatory bowel disease (IBD) with probiotics has not yielded consistent results. OBJECTIVE: The aim of this meta-analysis was to evaluate the efficacy of probiotics supplementation in patients with IBD. DATA SOURCES: Randomized controlled trials (RCTs) evaluating the efficacy of probiotics in patients with IBD were searched in PubMed, the Google Scholar database, Web of Science, and CrossRef for the period July 2003 to June 2023. DATA EXTRACTION: The RCTs were extracted, independently by 2 authors, according to the PICOS criteria. DATA ANALYSIS: Seven studies, including a total of 795 patients, met the study criteria. Five end points were selected to evaluate the efficacy. Of these, 3 indicators showed a statistically significant difference in efficacy: C-reactive protein (odds ratio [OR]: -2.45, 95% confidence interval [CI]: -3.16, -1.73, P < .01), the number of fecal Bifidobacterium (OR: 3.37, 95% CI: 3.28, 3.47, P < .01), and Lactobacillus(OR: 2.00, 95% CI: 1.91, 2.09, P < .01). The other 2 indicators (disease activity for Crohn's disease and for ulcerative colitis) showed no statistically significant difference, while the OR reflected a positive correlation. CONCLUSION: Probiotics supplementation may have a positive effect on IBD by reducing clinical symptoms, reducing the serological inflammatory markers, and increasing favorable gut flora in patients with IBD. Additional RCTs are needed to evaluate the therapeutic effect of probiotics in IBD.

Predicting response to immunotherapy in gastric cancer via assessing perineural invasion-mediated inflammation in tumor microenvironment.

BACKGROUND: The perineural invasion (PNI)-mediated inflammation of the tumor microenvironment (TME) varies among gastric cancer (GC) patients and exhibits a close relationship with prognosis and immunotherapy. Assessing the neuroinflammation of TME is important in predicting the response to immunotherapy in GC patients. METHODS: Fifteen independent cohorts were enrolled in this study. An inflammatory score was developed and validated in GC. Based on PNI-related prognostic inflammatory signatures, patients were divided into Clusters A and B using unsupervised clustering. The characteristics of clusters and the potential regulatory mechanism of key genes were verified by RT-PCR, western-blot, immunohistochemistry and immunofluorescence in cell and tumor tissue samples.The neuroinflammation infiltration (NII) scoring system was developed based on principal component analysis (PCA) and visualized in a nomogram together with other clinical characteristics. RESULTS: Inflammatory scores were higher in GC patients with PNI compared with those without PNI (P < 0.001). NII.clusterB patients with PNI had abundant immune cell infiltration in the TME but worse prognosis compared with patients in the NII.clusterA patients with PNI and non-PNI subgroups. Higher immune checkpoint expression was noted in NII.clusterB-PNI. VCAM1 is a specific signature of NII.clusterB-PNI, which regulates PD-L1 expression by affecting the phosphorylation of STAT3 in GC cells. Patients with PNI and high NII scores may benefit from immunotherapy. Patients with low nomogram scores had a better prognosis than those with high nomogram scores. CONCLUSIONS: Inflammation mediated by PNI is one of the results of tumor-nerve crosstalk, but its impact on the tumor immune microenvironment is complex. Assessing the inflammation features of PNI is a potential method in predicting the response of immunotherapy effectively.

Profile of biological characterizations and clinical application of corneal stem/progenitor cells.

Corneal stem/progenitor cells are typical adult stem/progenitor cells. The human cornea covers the front of the eyeball, which protects the eye from the outside environment while allowing vision. The location and function demand the cornea to maintain its transparency and to continuously renew its epithelial surface by replacing injured or aged cells through a rapid turnover process in which corneal stem/progenitor cells play an important role. Corneal stem/progenitor cells include mainly corneal epithelial stem cells, corneal endothelial cell progenitors and corneal stromal stem cells. Since the discovery of corneal epithelial stem cells (also known as limbal stem cells) in 1971, an increasing number of markers for corneal stem/progenitor cells have been proposed, but there is no consensus regarding the definitive markers for them. Therefore, the identification, isolation and cultivation of these cells remain challenging without a unified approach. In this review, we systematically introduce the profile of biological characterizations, such as anatomy, characteristics, isolation, cultivation and molecular markers, and clinical applications of the three categories of corneal stem/progenitor cells.

An artificial intelligence method to assess the tumor microenvironment with treatment outcomes for gastric cancer patients after gastrectomy.

BACKGROUND: The tumor microenvironment (TME) plays an important role in the occurrence and development of gastric cancer (GC) and is widely used to assess the treatment outcomes of GC patients. Immunohistochemistry (IHC) and gene sequencing are the main analysis methods for the TME but are limited due to the subjectivity of observers, the high cost of equipment and the need for professional analysts. METHODS: The ImmunoScore (IS) was developed in the TCGA cohort and validated in GEO cohorts. The Radiomic ImmunoScore (RIS) was developed in the TCGA cohort and validated in the Nanfang cohort. A nomogram was developed and validated in the Nanfang cohort based on RIS and clinical features. RESULTS: For IS, the area under the curves (AUCs) were 0.798 for 2-year overall survival (OS) and 0.873 for 4-year overall survival. For RIS, in the TCGA cohort, the AUCs distinguishing High-IS or Low-IS and predicting prognosis were 0.85 and 0.81, respectively; in the Nanfang cohort, the AUC predicting prognosis was 0.72. The nomogram performed better than the TNM staging system according to the ROC curve (all P < 0.01). Patients with TNM stage II and III in the High-nomogram group were more likely to benefit from adjuvant chemotherapy than Low-nomogram group patients. CONCLUSIONS: The RIS and the nomogram can be used to assess the TME, prognosis and adjuvant chemotherapy benefit of GC patients after radical gastrectomy and are valuable additions to the current TNM staging system. High-nomogram GC patients may benefit more from adjuvant chemotherapy than Low-nomogram GC patients.