RESUMEN
Recent developments in sequencing technology and analytical approaches have allowed researchers to show that the healthy gut microbiome is very varied and capable of performing a wide range of tasks. The importance of gut microbiota in controlling immunological, neurological, and endocrine function is becoming well-recognized. Thereby, numerous inflammatory diseases, including those that impact the gastrointestinal system, as well as less obvious ones, including Rheumatoid arthritis (RA), cancer, gestational diabetes (GD), type 1 diabetes (T1D), and type 2 diabetes (T2D), have been linked to dysbiotic gut microbiota. Microbiome engineering is a rapidly evolving frontier for solutions to improve human health. Microbiome engineering seeks to improve the function of an ecosystem by manipulating the composition of microbes. Thereby, generating potential therapies against metabolic, inflammatory, and immunological diseases will be possible through microbiome engineering. This essay first provides an overview of the traditional technological instruments that might be used for microbiome engineering, such as Fecal Microbiota Transplantation (FMT), prebiotics, and probiotics. Moreover, we will also discuss experimental genetic methods such as Metagenomic Alteration of Gut microbiome by In situ Conjugation (MAGIC), Bacteriophage, and Conjugative plasmids in manipulating intestinal microbiota.
RESUMEN
BACKGROUND: Growth associated protein-43 (GAP-43) and neurofilaments light (NFL) are biomarkers of synaptic and axonal injury, and are associated with cognitive decline in Alzheimer's disease (AD) contiuum. We investigated whether Polygenic Hazard Score (PHS) is associated with specific biomarkers and cognitive measures, and if it can predict the relationship between GAP-43, NFL, and cognitive decline in AD. METHOD: We enrolled 646 subjects: 93 with AD, 350 with mild cognitive impairment (MCI), and 203 cognitively normal controls. Variables included GAP-43, plasma NFL, and PHS. A PHS of 0.21 or higher was considered high risk while a PHS below this threshold was considered low risk. A subsample of 190 patients with MCI with four years of follow-up cognitive assessments were selected for longitudinal analysis . We assessed the association of the PHS with AD biomarkers and cognitive measures, as well as the predictive power of PHS on cognitive decline and the conversion of MCI to AD. RESULTS: PHS showed high diagnostic accuracy in distinguishing AD, MCI, and controls. At each follow-up point, high risk MCI patients showed higher level of cognitive impairment compared to the low risk group. GAP-43 correlated with all follow-up cognitive tests in high risk MCI patients which was not detected in low risk MCI patients. Moreover, high risk MCI patients progressed to dementia more rapidly compared to low risk patients. CONCLUSION: PHS can predict cognitive decline and impacts the relationship between neurodegenerative biomarkers and cognitive impairment in AD contiuum. Categorizing patients based on PHS can improve the prediction of cognitive outcomes and disease progression.
RESUMEN
Humans have a complicated symbiotic relationship with their gut microbiome, which is postulated to impact host health and disease broadly. Epigenetic alterations allow host cells to regulate gene expression without altering the DNA sequence. The gut microbiome, offering environmental hints, can influence responses to stimuli by host cells with modifications on their epigenome and gene expression. Recent increasing data suggest that regulatory non-coding RNAs (miRNAs, circular RNAs, and long lncRNA) may affect host-microbe interactions. These RNAs have been suggested as potential host response biomarkers in microbiome-associated disorders, including diabetes and cancer. This article reviews the current understanding of the interplay between gut microbiota and non-coding RNA, including lncRNA, miRNA, and circular RNA. This can lead to a profound understanding of human disease and influence therapy. Furthermore, microbiome engineering as a mainstream strategy for improving human health has been discussed and confirms the hypothesis about a direct cross-talk between microbiome composition and non-coding RNA.