Microbiota and parasite relationship

Sickle Cell Disease and Gut Health: The Influence of Intestinal Parasites and the Microbiome on Angolan Children

Parasitic infections are a common problem in developing countries and can intensify morbidity in patients with sickle cell disease (SCD), increasing the severity of anemia and the need for transfusions. It has been demonstrated that both helminths and protozoa can affect gut microbiome composition. On the other hand, the presence of specific bacterial communities can also influence parasite establishment. Considering this, our aim was to associate the presence of intestinal parasites with the results of hematological analyses and microbiome composition evaluations in a population of Angolan children with and without SCD. A total of 113 stool samples were collected, and gut microbiome analysis was performed using 16S sequencing and real-time PCR to detect eight different intestinal parasites. In our population, more than half of children (55%) had at least one parasitic infection, and of these, 43% were co-infected. Giardia intestinalis and Ascaris lumbricoides were more frequently found in children from the rural area of Bengo. Moreover, SCD children with ascariasis exhibited higher values of leukocytes and neutrophils, whereas the total hemoglobin levels were lower. In regards to the gut microbiome, the presence of intestinal parasites lowered the prevalence of some beneficial bacteria, namely: Lactobacillus, Bifidobacterium, Cuneatibacter, Bacteroides uniformis, Roseburia, and Shuttleworthia. This study presents the prevalence of several intestinal parasites in a high-risk transmission area with scarce information and opens new perspectives for understanding the interaction between parasites, the microbiome, and SCD.

Causal effect between gut microbiota and metabolic syndrome in European population: a bidirectional mendelian randomization study

BACKGROUND

Observational studies have reported that gut microbiota composition is associated with metabolic syndrome. However, the causal effect of gut microbiota on metabolic syndrome has yet to be confirmed.

METHODS

We performed a bidirectional Mendelian randomization study to investigate the causal effect between gut microbiota and metabolic syndrome in European population. Summary statistics of gut microbiota were from the largest available genome-wide association study meta-analysis (n = 13,266) conducted by the MiBioGen consortium. The summary statistics of outcome were obtained from the most comprehensive genome-wide association studies of metabolic syndrome (n = 291,107). The inverse-variance weighted method was applied as the primary method, and the robustness of the results was assessed by a series of sensitivity analyses.

RESULTS

In the primary causal estimates, Actinobacteria (OR = 0.935, 95% CI = 0.878-0.996, P = 0.037), Bifidobacteriales (OR = 0.928, 95% CI = 0.868-0.992, P = 0.028), Bifidobacteriaceae (OR = 0.928, 95% CI = 0.868-0.992, P = 0.028), Desulfovibrio (OR = 0.920, 95% CI = 0.869-0.975, P = 0.005), and RuminococcaceaeUCG010 (OR = 0.882, 95% CI = 0.803-0.969, P = 0.009) may be associated with a lower risk of metabolic syndrome, while Lachnospiraceae (OR = 1.130, 95% CI = 1.016-1.257, P = 0.025), Veillonellaceae (OR = 1.055, 95% CI = 1.004-1.108, P = 0.034) and Olsenella (OR = 1.046, 95% CI = 1.009-1.085, P = 0.015) may be linked to a higher risk for metabolic syndrome. Reverse MR analysis demonstrated that abundance of RuminococcaceaeUCG010 (OR = 0.938, 95% CI = 0.886-0.994, P = 0.030) may be downregulated by metabolic syndrome. Sensitivity analyses indicated no heterogeneity or horizontal pleiotropy.

CONCLUSIONS

Our Mendelian randomization study provided causal relationship between specific gut microbiota and metabolic syndrome, which might provide new insights into the potential pathogenic mechanisms of gut microbiota in metabolic syndrome and the assignment of effective therapeutic strategies.

Evaluation of reference genes for gene expression analysis by real-time quantitative PCR (qPCR) in different tissues from mice infected by Ascaris suum

Human ascariasis is the most prevalent helminth infection, affecting 445 million people worldwide. To better understand the impact of the immune system on the pathophysiology of individuals infected with Ascaris suum, mice have been used as experimental models. The RT-qPCR technique is a critical auxiliary tool of investigation used to quantify mRNA levels. However, proper normalization using reference genes is essential to ensure reliable outcomes to avoid analytical errors and false results. Despite the importance of reference genes for experimental A. suum infection studies, no specific reference genes have been identified yet. Therefore, we conducted a study to assess five potential reference genes (GAPDH, 18s, ACTB, B2M, and HPRT1) in different tissues (liver, lungs, small and large intestines) affected by A. suum larval migration in C57BL/6j mice. Tissue collection was carried out to analyze parasite burden and confirm the presence of larvae during the peak of migration in each tissue. Upon confirmation, we analyzed different genes in the tissues and found no common gene with stable expression. Our results highlight the importance of analyzing different genes and using different software programs to ensure reliable relative expression results. Based on our findings, B2M was ranked as the ideal reference gene for the liver, while 18S was the most stable gene in the lung and small intestine. ACTB, or a combination of ACTB with GAPDH, was deemed suitable as reference genes for the large intestine due to their stable expression and less variation between the control and infected groups. To further demonstrate the impact of using different reference genes, we normalized the expression of a chemokine gene (CXCL9) in all tissues. Significant differences in CXCL9 expression levels were observed between different groups in all tissues except for the large intestine. This underscores the importance of selecting appropriate reference genes to avoid overestimating target gene expression levels and encountering normalization-related issues that can lead to false results. In conclusion, our study highlights the significance of using reliable reference genes for accurate RT-qPCR analysis, especially in the context of A. suum infection studies in different tissues. Proper normalization is crucial to ensure the validity of gene expression data and avoid potential pitfalls in interpreting results.

Molecular prevalence and subtype distribution of Blastocystis spp. among children who have diarrheia or are asymptomatic in Wenzhou, Zhejiang Province, China

Blastocystis sp., a significant zoonotic parasite with a global distribution, was the focus of this study, which aimed to investigate its prevalence and genetic diversity among diarrheic and asymptomatic children in Wenzhou, China. We collected 1,032 fecal samples from Yuying Children's Hospital, Wenzhou, China, comprising 684 from children with diarrhea and 348 from asymptomatic children. Genomic DNA extracted from these samples was used to detect Blastocystis spp. by PCR, targeting the small subunit ribosomal RNA gene. Subsequently, a phylogenetic tree was constructed, applying the maximum likelihood method. Blastocystis spp. were detected in 67 (6.5%) of the fecal samples. The prevalence rate of Blastocystis spp. in diarrheic children (8.8%; 60/684) was significantly higher than that in asymptomatic children (2.0%; 7/348) (χ 2 = 17.3, p < 0.001). Sequence analysis of the SSU rRNA gene identified five known Blastocystis spp. subtypes, ST1 (n = 12), ST2 (n = 5), ST3 (n = 35), ST4 (n = 12), and ST7 (n = 3). ST1 and ST3 were present in both diarrheic and asymptomatic children, while ST2, ST4, and ST7 were exclusive to diarrheic children. Intra-subtype genetic polymorphisms were identified, comprising four variations in ST1 (ST1-1 to ST1-4), five in ST3 (ST3-1 to ST3-5), two in ST4 (ST4-1 and ST4-2), and two in ST7 (ST7-1 and ST7-2). Notably, ST1-2 to ST1-4, ST3-3 to ST3-5, and ST7-1 and ST7-2 represent newly identified variations. The composition and genetic characteristics of subtypes among children in this region suggest various sources of infection, including human-to-human and animal-to-human transmission.

Parasitosis by Fasciola hepatica and Variations in Gut Microbiota in School-Aged Children from Peru

(1) Background: Human fascioliasis is considered an endemic and hyper-endemic disease in the Peruvian Andean valleys. Our objective was to determine variations in the composition of the gut microbiota among children with Fasciola hepatica and children who do not have this parasitosis. (2) Method: A secondary analysis was performed using fecal samples stored in our biobank. The samples were collected as part of an epidemiological Fasciola hepatica cross-sectional study in children from 4 through 14 years old from a community in Cajamarca, Peru. (3) Results: In a comparison of the bacterial genera that make up the intestinal microbiota between the F. hepatica positive and negative groups, it was found that there are significant differences in the determination of Lactobacillus (p = 0.010, CI: 8.5-61.4), Bacteroides (p = 0.020, CI: 18.5-61.4), Clostridium (p < 0.001, CI: 3.5-36.0), and Bifidobacterium (p = 0.018, CI: 1.1-28.3), with each of these genera being less frequent in children parasitized with F. hepatica. (4) Conclusions: These results show that F. hepatica may be associated with direct or indirect changes in the bacterial population of the intestinal microbiota, particularly affecting three bacterial genera.

Bioinformatics analysis of the diversity of gut microbiota and different microbiota on insulin resistance in diabetes mellitus patients

It aimed to explore the diversity of gut microbiota (GM) and the effect of different microbiota on insulin resistance in diabetes mellitus (DM) patients through bioinformatics analysis.

MATERIAL AND METHOD

Microarray data were obtained from GEO database. GM samples from DM patients and healthy controls were collected, and 16S rRNA gene sequencing was carried out adopting high-throughput sequencing technology. The differential expression genes were screened using the Qlucore Omics Explorer 3.0 software. Subsequently, online tools such as STRING and DAVID were utilized for bioinformatics analysis of the differential expression genes. The differences in bacterial diversity between DM patients and healthy controls were evaluated by analyzing the diversity indicators of the microbiota, such as Shannon and Chao1 indexes. Differential abundance and functional prediction analysis were adopted to explore the different microbiota and its possible metabolic pathways between DM patients and controls. And differences in insulin resistance in specific bacterial taxa were analyzed.

RESULT

GM diversity between DM patients and controls had significant differences. GM diversity was lower in DM patients compared with controls, as indicated by a decrease in Shannon and Chao1 indexes. The differential abundance analysis showed that there were multiple different bacterial communities between DM patients and controls, including some bacterial communities at the genus-level. Functional prediction analysis also revealed potential metabolic pathways related to GM and insulin resistance in DM patients. HEXB, ZC3H12A, CCR, CXCR3, GBR10, CDK9, TXN, IGFBP3, PDHA1, and NDUFB3 genes may be potential targets for treatment.

CONCLUSION

There are differences in GM diversity between DM patients and healthy controls, and the different microbiota may be related to the occurrence and development of insulin resistance.

Therapeutic Approach Targeting Gut Microbiome in Gastrointestinal Infectious Diseases

While emerging evidence highlights the significance of gut microbiome in gastrointestinal infectious diseases, treatments like Fecal Microbiota Transplantation (FMT) and probiotics are gaining popularity, especially for diarrhea patients. However, the specific role of the gut microbiome in different gastrointestinal infectious diseases remains uncertain. There is no consensus on whether gut modulation therapy is universally effective for all such infections. In this comprehensive review, we examine recent developments of the gut microbiome's involvement in several gastrointestinal infectious diseases, including infection of Helicobacter pylori, Clostridium difficile, Vibrio cholerae, enteric viruses, Salmonella enterica serovar Typhimurium, Pseudomonas aeruginosa Staphylococcus aureus, Candida albicans, and Giardia duodenalis. We have also incorporated information about fungi and engineered bacteria in gastrointestinal infectious diseases, aiming for a more comprehensive overview of the role of the gut microbiome. This review will provide insights into the pathogenic mechanisms of the gut microbiome while exploring the microbiome's potential in the prevention, diagnosis, prediction, and treatment of gastrointestinal infections.