Gut microbes on the risk of advanced adenomas.

BACKGROUND: More than 90% of colorectal cancer (CRC) arises from advanced adenomas (AA) and gut microbes are closely associated with the initiation and progression of both AA and CRC. OBJECTIVE: To analyze the characteristic microbes in AA. METHODS: Fecal samples were collected from 92 AA and 184 negative control (NC). Illumina HiSeq X sequencing platform was used for high-throughput sequencing of microbial populations. The sequencing results were annotated and compared with NCBI RefSeq database to find the microbial characteristics of AA. R-vegan package was used to analyze α diversity and ß diversity. α diversity included box diagram, and ß diversity included Principal Component Analysis (PCA), principal co-ordinates analysis (PCoA), and non-metric multidimensional scaling (NMDS). The AA risk prediction models were constructed based on six kinds of machine learning algorithms. In addition, unsupervised clustering methods were used to classify bacteria and viruses. Finally, the characteristics of bacteria and viruses in different subtypes were analyzed. RESULTS: The abundance of Prevotella sp900557255, Alistipes putredinis, and Megamonas funiformis were higher in AA, while the abundance of Lilyvirus, Felixounavirus, and Drulisvirus were also higher in AA. The Catboost based model for predicting the risk of AA has the highest accuracy (bacteria test set: 87.27%; virus test set: 83.33%). In addition, 4 subtypes (B1V1, B1V2, B2V1, and B2V2) were distinguished based on the abundance of gut bacteria and enteroviruses (EVs). Escherichia coli D, Prevotella sp900557255, CAG-180 sp000432435, Phocaeicola plebeiuA, Teseptimavirus, Svunavirus, Felixounavirus, and Jiaodavirus are the characteristic bacteria and viruses of 4 subtypes. The results of Catboost model indicated that the accuracy of prediction improved after incorporating subtypes. The accuracy of discovery sets was 100%, 96.34%, 100%, and 98.46% in 4 subtypes, respectively. CONCLUSION: Prevotella sp900557255 and Felixounavirus have high value in early warning of AA. As promising non-invasive biomarkers, gut microbes can become potential diagnostic targets for AA, and the accuracy of predicting AA can be improved by typing.

Advances in bacteria-based drug delivery systems for anti-tumor therapy.

In recent years, bacteria have gained considerable attention as a promising drug carrier that is critical in improving the effectiveness and reducing the side effects of anti-tumor drugs. Drug carriers can be utilised in various forms, including magnetotactic bacteria, bacterial biohybrids, minicells, bacterial ghosts and bacterial spores. Additionally, functionalised and engineered bacteria obtained through gene engineering and surface modification could provide enhanced capabilities for drug delivery. This review summarises the current studies on bacteria-based drug delivery systems for anti-tumor therapy and discusses the prospects and challenges of bacteria as drug carriers. Furthermore, our findings aim to provide new directions and guidance for the research on bacteria-based drug systems.

Age and aging process alter the gut microbes.

BACKGROUND: Gut microbes and age are both factors that influence the development of disease. The community structure of gut microbes is affected by age. OBJECTIVE: To plot time-dependent gut microbe profiles in individuals over 45 years old and explore the correlation between age and gut microbes. METHODS: Fecal samples were collected from 510 healthy individuals over 45 years old. Shannon index, Simpson index, Ace index, etc. were used to analyze the diversity of gut microbes. The beta diversity analysis, including non-metric multidimensional scaling (NMDS), was used to analyze community distribution. Linear discriminant analysis (LDA) and random forest (RF) algorithm were used to analyze the differences of gut microbes. Trend analysis was used to plot the abundances of characteristic gut microbes in different ages. RESULTS: The individuals aged 45-49 had the highest richness of gut bacteria. Fifteen characteristic gut microbes, including Siphoviridae and Bifidobacterium breve, were screened by RF algorithm. The abundance of Ligiactobacillus and Microviridae were higher in individuals older than 65 years. Moreover, the abundance of Blautia_A massiliensis, Lubbockvirus and Enterocloster clostridioformis decreased with age and the abundance of Klebsiella variicola and Prevotella increased with age. The functional genes, such as human diseases and aging, were significantly different among different aged individuals. CONCLUSIONS: The individuals in different ages have characteristic gut microbes. The changes in community structure of gut microbes may be related to age-induced diseases.

Advances in reprogramming of energy metabolism in tumor T cells.

Cancer is a leading cause of human death worldwide, and the modulation of the metabolic properties of T cells employed in cancer immunotherapy holds great promise for combating cancer. As a crucial factor, energy metabolism influences the activation, proliferation, and function of T cells, and thus metabolic reprogramming of T cells is a unique research perspective in cancer immunology. Special conditions within the tumor microenvironment and high-energy demands lead to alterations in the energy metabolism of T cells. In-depth research on the reprogramming of energy metabolism in T cells can reveal the mechanisms underlying tumor immune tolerance and provide important clues for the development of new tumor immunotherapy strategies as well. Therefore, the study of T cell energy metabolism has important clinical significance and potential applications. In the study, the current achievements in the reprogramming of T cell energy metabolism were reviewed. Then, the influencing factors associated with T cell energy metabolism were introduced. In addition, T cell energy metabolism in cancer immunotherapy was summarized, which highlighted its potential significance in enhancing T cell function and therapeutic outcomes. In summary, energy exhaustion of T cells leads to functional exhaustion, thus resulting in immune evasion by cancer cells. A better understanding of reprogramming of T cell energy metabolism may enable immunotherapy to combat cancer and holds promise for optimizing and enhancing existing therapeutic approaches.

Prediction model of colorectal cancer (CRC) lymph node metastasis based on intestinal bacteria

Background Lymph node metastasis is the main metastatic mode of CRC. Lymph node metastasis affects patient prognosis. Objective To screen differential intestinal bacteria for CRC lymph node metastasis and construct a prediction model. Methods First, fecal samples of 119 CRC patients with lymph node metastasis and 110 CRC patients without lymph node metastasis were included for the detection of intestinal bacterial 16S rRNA. Then, bioinformatics analysis of the sequencing data was performed. Community structure and composition analysis, difference analysis, and intragroup and intergroup correlation analysis were conducted between the two groups. Finally, six machine learning models were used to construct a prediction model for CRC lymph node metastasis. Results The community richness and the community diversity at the genus level of the two groups were basically consistent. A total of 12 differential bacteria (Agathobacter, Catenibacterium, norank_f__Oscillospiraceae, Lachnospiraceae_FCS020_group, Lachnospiraceae_UCG-004, etc.) were screened at the genus level. Differential bacteria, such as Agathobacter, Catenibacterium, norank_f__Oscillospiraceae, and Lachnospiraceae_FCS020_group, were more associated with no lymph node metastasis in CRC. In the discovery set, the RF model had the highest prediction accuracy (AUC = 1.00, 98.89% correct, specificity = 55.21%, sensitivity = 55.95%). In the test set, SVM model had the highest prediction accuracy (AUC = 0.73, 72.92% correct, specificity = 69.23%, sensitivity = 88.89%). Lachnospiraceae_FCS020_group was the most important variable in the RF model. Lachnospiraceae_UCG − 004 was the most important variable in the SVM model. Conclusion CRC lymph node metastasis is closely related to intestinal bacteria. The prediction model based on intestinal bacteria can provide a new evaluation method for CRC lymph node metastasis (AU)

Prediction model of colorectal cancer (CRC) lymph node metastasis based on intestinal bacteria.

BACKGROUND: Lymph node metastasis is the main metastatic mode of CRC. Lymph node metastasis affects patient prognosis. OBJECTIVE: To screen differential intestinal bacteria for CRC lymph node metastasis and construct a prediction model. METHODS: First, fecal samples of 119 CRC patients with lymph node metastasis and 110 CRC patients without lymph node metastasis were included for the detection of intestinal bacterial 16S rRNA. Then, bioinformatics analysis of the sequencing data was performed. Community structure and composition analysis, difference analysis, and intragroup and intergroup correlation analysis were conducted between the two groups. Finally, six machine learning models were used to construct a prediction model for CRC lymph node metastasis. RESULTS: The community richness and the community diversity at the genus level of the two groups were basically consistent. A total of 12 differential bacteria (Agathobacter, Catenibacterium, norank_f__Oscillospiraceae, Lachnospiraceae_FCS020_group, Lachnospiraceae_UCG-004, etc.) were screened at the genus level. Differential bacteria, such as Agathobacter, Catenibacterium, norank_f__Oscillospiraceae, and Lachnospiraceae_FCS020_group, were more associated with no lymph node metastasis in CRC. In the discovery set, the RF model had the highest prediction accuracy (AUC = 1.00, 98.89% correct, specificity = 55.21%, sensitivity = 55.95%). In the test set, SVM model had the highest prediction accuracy (AUC = 0.73, 72.92% correct, specificity = 69.23%, sensitivity = 88.89%). Lachnospiraceae_FCS020_group was the most important variable in the RF model. Lachnospiraceae_UCG - 004 was the most important variable in the SVM model. CONCLUSION: CRC lymph node metastasis is closely related to intestinal bacteria. The prediction model based on intestinal bacteria can provide a new evaluation method for CRC lymph node metastasis.

Analysis of COL1A1 and COL1A2 gene variants in two fetuses with osteogenesis imperfecta / 中华医学遗传学杂志

OBJECTIVE@#To explore the genetic basis of two fetuses with an osteogenesis imperfecta (OI) phenotype.@*METHODS@#Two fetuses diagnosed at the Affiliated Hospital of Weifang Medical College respectively on June 11, 2021 and October 16, 2021 were selected as the study subjects. Clinical data of the fetuses were collected. Amniotic fluid samples of the fetuses and peripheral blood samples of their pedigree members were collected for the extraction of genomic DNA. Whole exome sequencing (WES) and Sanger sequencing were carried out to identify the candidate variants. Minigene splicing reporter analysis was used to validate the variant which may affect the pre-mRNA splicing.@*RESULTS@#For fetus 1, ultrasonography at 17+6 weeks of gestation had revealed shortening of bilateral humerus and femurs by more than two weeks, in addition with multiple fractures and angular deformities of long bones. WES revealed that fetus 1 had harbored a heterozygous c.3949_3950insGGCATGT (p.N1317Rfs*114) variant in exon 49 of the COL1A1 gene (NM_000088.4). Based on the guidelines from the American College of Medical Genetics and Genomics (ACMG), it was classified as a pathogenic variant (PVS1+PS2+PM2_Supporting) for disrupting the downstream open reading frame resulting in premature translational termination, being de novo in origin, and lacking records in the population and disease databases.For fetus 2, ultrasonography at 23 weeks of gestation also revealed shortening of bilateral humerus and femurs by one and four weeks, respectively, in addition with bending of bilateral femurs, tibias and fibulas. Fetus 2 had harbored a heterozygous c.1557+3A>G variant in intron 26 of the COL1A2 gene (NM_000089.4). Minigene experiment showed that it has induced skipping of exon 26 from the COL1A2 mRNA transcript, resulting in an in-frame deletion (c.1504_1557del) of the COL1A2 mRNA transcript. The variant was inherited from its father and had been previously reported in a family with OI type 4. It was therefore classified as a pathogenic variant (PS3+PM1+PM2_Supporting+PP3+PP5).@*CONCLUSION@#The c.3949_3950insGGCATGT (p.N1317Rfs*114) variant in the COL1A1 gene and c.1557+3A>G variant in the COL1A2 gene probably underlay the disease in the two fetuses. Above findings not only have enriched the mutational spectrum of OI, but also shed light on the correlation between its genotype and phenotype and provided a basis for genetic counseling and prenatal diagnosis for the affected pedigrees.

A review of the clinical progress on helminths and their derivative products in autoimmune disease / 细胞与分子免疫学杂志

Traditional medications used for treating autoimmune diseases often come with a wide range of adverse effects. Current treatments focus mainly on symptom management, resulting in significant health issues and financial burdens for patients. Recently, clinical research has demonstrated the potential of helminths and their derivatives as effective therapies for autoimmune disorders. Helminths, being a near-natural immunomodulator, exhibit milder effects than broad-spectrum immunosuppressants and corticosteroids, thereby presenting a promising alternative for the treatment of autoimmune diseases. However, different helminths' therapeutic efficacy and mechanisms and their derivatives in treating autoimmune diseases may vary. Therefore, we aim to review recent clinical advancements in the use of helminths and their derivatives for treating inflammatory bowel disease, multiple sclerosis, and autism spectrum disorder, with a view to offering novel clinical treatment approaches.