The exact cause of autism spectrum disorder (ASD) is yet unknown, although possible causes include early childhood, foetal development, gestation, delivery mode, genetics, and environmental variables. Approximately 1% of children worldwide have ASD, and this percentage is rising. The immunological, endocrine, gut microbiota and brain-gut axis quality influence the intensity of ASD symptoms. Deficits in the composition and diversity of gut microbiota are common in children with ASD, accounting for 9-90% of these illnesses, including elevated inflammatory cytokines, inflammation, leaky gut syndrome, and pathological microflora growth. Dysbiosis can be made worse by eating issues that are prevalent in ASD. B vitamins, such as cobalamin and folate, which are essential methyl donors for DNA epigenetic changes, are usually produced by a healthy gut microbiota. 50% of people with ASD have a vitamin B deficit. This work summarises research on the impact of gut microbiota on DNA methylation and B vitamin synthesis in ASD, as well as etiological variables connected to dysbiosis. Probiotics, postbiotics, and vitamin B therapies in kids with ASD should be investigated in future studies.

Understanding the etiological complexity of diseases requires identifying biomarkers longitudinally associated with specific phenotypes. Advanced sequencing tools generate dynamic microbiome data, providing insights into microbial community functions and their impact on health. This review aims to explore the current roles and future visionary endeavors of dynamic methods for integrating longitudinal microbiome multi-omics data in personalized and precision medicine. This work seeks to synthesize existing research, propose best practices, and highlight innovative techniques. The development and application of advanced dynamic methods, including the unified analytical frameworks and deep learning tools in artificial intelligence, are critically examined. Aggregating data on microbes, metabolites, genes, and other entities offers profound insights into the interactions among microorganisms, host physiology, and external stimuli. Despite progress, the absence of gold standards for validating analytical protocols and data resources of various longitudinal multi-omics studies remains a significant challenge. The interdependence of workflow steps critically affects overall outcomes. This work provides a comprehensive roadmap for best practices, addressing current challenges with advanced dynamic methods. The review underscores the biological effects of clinical, experimental, and analytical protocol settings on outcomes. Establishing consensus on dynamic microbiome inter-studies and advancing reliable analytical protocols are pivotal for the future of personalized and precision medicine.

Autism spectrum disorder (ASD) is a neurodevelopmental abnormality that impairs social communication. The human gut microbiome (GM) influences a variety of local processes, including dysbiosis and the defense against pathogenic microorganisms. The aim of the present study was to categorize and identify molecular biomarkers for ASD. In the present study, metagenomics whole genome shotgun sequencing was used to identify the gut microbiota in autistic individuals. Fecal samples from four children with ASD and four healthy control siblings, aged 3-10 years old, were examined using bioinformatics analysis. A total of 673,091 genes were cataloged, encompassing 25 phyla and 2 kingdoms based on the taxonomy analysis. The results revealed 257 families, 34 classes, 84 orders, and 1,314 genera among 4,339 species. The top 10 most abundant genes and corresponding functional genes for each group were determined after the abundance profile was screened. The results showed that children with ASD had a higher abundance of certain gut microbiomes than their normal siblings and vice versa. The phyla Firmicutes and Proteobacteria were the most abundant in ASD. The Thermoanaerobacteria class was also restricted to younger healthy individuals. Moreover, the Lactobacillaceae family was more abundant in children with ASD. Additionally, it was discovered that children with ASD had a higher abundance of the Bacteroides genus and a lower abundance of the Bifidobacterium and Prevotella genera. In conclusion, there were more pathogenic genera and species and higher levels of biomass, diversity and richness in the GM of children with ASD.

Globally, neurodegeneration and cerebrovascular disease are common and growing causes of morbidity and mortality. Pathophysiology of this group of diseases encompasses various factors from oxidative stress to gut microbial dysbiosis. The study of the etiology and mechanisms of oxidative stress as well as gut dysbiosis-induced neurodegeneration in Alzheimer's disease, Parkinson's disease, multiple sclerosis, amyotrophic lateral sclerosis, autism spectrum disorder, and Huntington's disease has recently received a lot of attention. Numerous studies lend credence to the notion that changes in the intestinal microbiota and enteric neuroimmune system have an impact on the initiation and severity of these diseases. The prebiotic role of polyphenols can influence the makeup of the gut microbiota in neurodegenerative disorders by modulating intracellular signalling pathways. Metabolites of polyphenols function directly as neurotransmitters by crossing the blood-brain barrier or indirectly via influencing the cerebrovascular system. This assessment aims to bring forth an interlink between the consumption of polyphenols biotransformed by gut microbiota which in turn modulate the gut microbial diversity and biochemical changes in the brain. This systematic review will further augment research towards the association of dietary polyphenols in the management of gut dysbiosis-associated neurodegenerative diseases.

Both the epigenetic changes and gut microbiota (GM) have attracted a growing interest in establishing effective diagnostics and potential therapeutic strategies for a number of diseases. These disorders include metabolic, central nervous system-related diseases, autoimmune, and gastrointestinal infections (GI). Despite the number of studies, there is no extensive review that connects the epigenetics modifications and GM as biomarkers that could confer effective diagnostics and confer treatment options. To this end, this review hopes to give detailed information on connecting the modifications in epigenetic and GM. An updated and detailed information on the connection between the epigenetics factors and GM that influence diseases are given. In addition, the review showed some associations between the epigenetics to the maternal GM and offspring health. Finally, the limitations of the concept and prospects into this new emerging discipline were also looked into. Although this review elucidated on the maternal diet and response to offspring health with respect to GM and epigenetic modifications, there still exist various limitations to this newly emerging discipline. In addition to integrating complementary multi-omics data, longitudinal sampling will aid with the identification of functional mechanisms that may serve as therapeutic targets. To this end, this review gave a detailed perspective into harnessing disease diagnostics, prevention and treatment options through epigenetics and GM.

The etiology of Autism Spectrum Disorders (ASD) is a result of the interaction between genes and the environment. The study of epigenetic factors that affect gene expression, such as DNA methylation, has become an important area of research in ASD. In recent years, there has been an increasing body of evidence pointing to epigenetic mechanisms that influence brain development, as in the case of ASD, when gene methylation dysregulation is present. Our analysis revealed 853 differentially methylated CpG in ASD patients, affecting 509 genes across the genome. Enrichment analysis showed five related diseases, including autistic disorder and mental disorders, which are particularly significant. In this work, we identified 64 genes that were previously reported in the SFARI gene database, classified according to their impact index. Additionally, we identified new genes that have not been previously reported as candidates with differences in the methylation patterns of Mexican children with ASD.

The composition, viability and metabolic functionality of intestinal microbiota play an important role in human health and disease. Studies on intestinal microbiota are often based on fecal samples, because these can be sampled in a non-invasive way, although procedures for sampling, processing and storage vary. This review presents factors to consider when developing an automated protocol for sampling, processing and storing fecal samples: donor inclusion criteria, urine-feces separation in smart toilets, homogenization, aliquoting, usage or type of buffer to dissolve and store fecal material, temperature and time for processing and storage and quality control. The lack of standardization and low-throughput of state-of-the-art fecal collection procedures promote a more automated protocol. Based on this review, an automated protocol is proposed. Fecal samples should be collected and immediately processed under anaerobic conditions at either room temperature (RT) for a maximum of 4 h or at 4 °C for no more than 24 h. Upon homogenization, preferably in the absence of added solvent to allow addition of a buffer of choice at a later stage, aliquots obtained should be stored at either -20 °C for up to a few months or -80 °C for a longer period-up to 2 years. Protocols for quality control should characterize microbial composition and viability as well as metabolic functionality.

Despite inconsistent results across studies, emerging evidence suggests that the microbial micro-environment may be associated with autism spectrum disorder (ASD). Geographical and cultural factors highly impact microbial profiles, and there is a shortage of data from East Asian populations. This study aimed to comprehensively characterize microbial profiles in an East Asian sample and explore whether gut microbiota contributes to clinical symptoms, emotional/behavioral problems, and GI symptoms in ASD.

We assessed 82 boys and young men with ASD and 31 typically developing controls (TDC), aged 6-25 years. We analyzed the stool sample of all participants with 16S V3-V4 rRNA sequencing and correlated its profile with GI symptoms, autistic symptoms, and emotional/behavioral problems.

Autistic individuals, compared to TDC, had worse GI symptoms. There were no group differences in alpha diversity of species richness estimates (Shannon-wiener and Simpson diversity indices). Participants with ASD had an increased relative abundance of Fusobacterium, Ruminococcus torques group (at the genus level), and Bacteroides plebeius DSM 17135 (at the species level), while a decreased relative abundance of Ruminococcaceae UCG 013, Ervsipelotrichaceae UCG 003, Parasutterella, Clostridium sensu stricto 1, Turicibacter (at the genus level), and Clostridium spiroforme DSM 1552 and Intestinimonas butyriciproducens (at the species level). Altered taxonomic diversity in ASD significantly correlated with autistic symptoms, thought problems, delinquent behaviors, self dysregulation, and somatic complaints. We did not find an association between gut symptoms and gut microbial dysbiosis.

Our findings suggest that altered microbiota are associated with behavioral phenotypes but not GI symptoms in ASD. The function of the identified microbial profiles mainly involves the immune pathway, supporting the hypothesis of a complex relationship between altered microbiome, immune dysregulation, and ASD that may advance the discovery of molecular biomarkers for ASD.

Gut microbiota play many important roles, such as the regulation of immunity and barrier function in the intestine, and are crucial for maintaining homeostasis in living organisms. The disruption in microbiota is called dysbiosis, which has been associated with various chronic inflammatory conditions, food allergies, colorectal cancer, etc. The gut microbiota is also affected by several other factors such as diet, antibiotics and other medications, or bacterial and viral infections. Moreover, there are some reports on the oral-gut-liver axis indicating that the disruption of oral microbiota affects the intestinal biota. Non-alcoholic fatty liver disease (NAFLD) is one of the systemic diseases caused due to the dysregulation of the oral-gut-liver axis. NAFLD is the most common liver disease reported in the developed countries. It includes liver damage ranging from simple steatosis to nonalcoholic steatohepatitis (NASH), cirrhosis, and cancer. Recently, accumulating evidence supports an association between NAFLD and dysbiosis of oral and gut microbiota. Periodontopathic bacteria, especially Porphyromonas gingivalis, have been correlated with the pathogenesis and development of NAFLD based on the clinical and basic research, and immunology. P. gingivalis was detected in the liver, and lipopolysaccharide from this bacteria has been shown to be involved in the progression of NAFLD, thereby indicating a direct role of P. gingivalis in NAFLD. Moreover, P. gingivalis induces dysbiosis of gut microbiota, which promotes the progression of NAFLD, through disrupting both metabolic and immunologic pathways. Here, we review the roles of microbial dysbiosis in NAFLD. Focusing on P. gingivalis, we evaluate and summarize the most recent advances in our understanding of the relationship between oral-gut microbiome symbiosis and the pathogenesis and progression of non-alcoholic fatty liver disease, as well as discuss novel strategies targeting both P. gingivalis and microbial dysbiosis.

Data contamination in meta-approaches where multiple biological samples are combined considerably affects the results of subsequent downstream analyses, such as differential abundance tests comparing multiple groups at a fixed time point. Little has been thoroughly investigated regarding the impact of the lurking variable of various batch sources, such as different days or different laboratories, in more complicated time series experimental designs, for instance, repeatedly measured longitudinal data and metadata. We highlight that the influence of batch factors is significant on subsequent downstream analyses, including longitudinal differential abundance tests, by performing a case study of microbiome time course data with two treatment groups and a simulation study of mimic microbiome longitudinal counts.