New Developments and Possibilities in Reanalysis and Reinterpretation of Whole Exome Sequencing Datasets for Unsolved Rare Diseases Using Machine Learning Approaches

Impact of dietary fiber on gut microbiota composition, function and gut-brain-modules in healthy adults - a systematic review protocol

Background

The gut microbiota has been extensively implicated in health and disease. The functional outputs of the gut microbiota, such as microbial metabolites, are considered particularly important in this regard. Significant associations exist between alterations in the relative abundance of specific microbial taxa and mental health disorders. Dietary fiber has the potential to alter gut microbiota composition and function, modifying bacterial enzymatic function and the production of metabolites. As many taxa of microorganisms have enzymes capable of producing or degrading neurochemicals i.e. neuroactive gut brain modules, new predictive tools can be applied to existing datasets such as those harvested from dietary fiber interventions. We endeavor to perform a systematic review in order to identify studies reporting compositional gut microbiota alterations after interventions with dietary fiber in healthy individuals. We aim to also extract from the selected studies publicly available microbial genomic sequence datasets for reanalysis with a consistent bioinformatics pipeline, with the ultimate intention of identifying altered gut brain modules following dietary fiber interventions.

Methods

Interventional trials and randomized controlled studies that are originally published, including cross-over and non-crossover design and involving healthy adult humans will be included. A systematic search of PubMed/MEDLINE and EMBASE, two electronic databases, will be completed.

Discussion

Various types of dietary fiber have an impact on the gut microbiota composition, with some promoting the growth of particular taxa while others are reduced in relative abundance. Our search focuses on the impact of this food component on the microbiota of healthy individuals. Compositional gut microbial changes have been reported and our review will compile and update these observations after reanalysis of their datasets with a consistent bioinformatic pipeline. From this it may be possible to predict more detailed functional consequences in terms of neuroactive gut brain modules, of the compositional alterations in gut microbial taxa.

International Undiagnosed Diseases Programs (UDPs): components and outcomes

Over the last 15 years, Undiagnosed Diseases Programs have emerged to address the significant number of individuals with suspected but undiagnosed rare genetic diseases, integrating research and clinical care to optimize diagnostic outcomes. This narrative review summarizes the published literature surrounding Undiagnosed Diseases Programs worldwide, including thirteen studies that evaluate outcomes and two commentary papers. Commonalities in the diagnostic and research process of Undiagnosed Diseases Programs are explored through an appraisal of available literature. This exploration allowed for an assessment of the strengths and limitations of each of the six common steps, namely enrollment, comprehensive clinical phenotyping, research diagnostics, data sharing and matchmaking, results, and follow-up. Current literature highlights the potential utility of Undiagnosed Diseases Programs in research diagnostics. Since participants have often had extensive previous genetic studies, research pipelines allow for diagnostic approaches beyond exome or whole genome sequencing, through reanalysis using research-grade bioinformatics tools and multi-omics technologies. The overall diagnostic yield is presented by study, since different selection criteria at enrollment and reporting processes make comparisons challenging and not particularly informative. Nonetheless, diagnostic yield in an undiagnosed cohort reflects the potential of an Undiagnosed Diseases Program. Further comparisons and exploration of the outcomes of Undiagnosed Diseases Programs worldwide will allow for the development and improvement of the diagnostic and research process and in turn improve the value and utility of an Undiagnosed Diseases Program.

The genetics and pathogenesis of CAKUT

Congenital anomalies of the kidney and urinary tract (CAKUT) comprise a large variety of malformations that arise from defective kidney or urinary tract development and frequently lead to kidney failure. The clinical spectrum ranges from severe malformations, such as renal agenesis, to potentially milder manifestations, such as vesicoureteral reflux. Almost 50% of cases of chronic kidney disease that manifest within the first three decades of life are caused by CAKUT. Evidence suggests that a large number of CAKUT are genetic in origin. To date, mutations in ~54 genes have been identified as monogenic causes of CAKUT, contributing to 12-20% of the aetiology of the disease. Pathogenic copy number variants have also been shown to cause CAKUT and can be detected in 4-11% of patients. Furthermore, environmental and epigenetic factors can increase the risk of CAKUT. The discovery of novel CAKUT-causing genes is challenging owing to variable expressivity, incomplete penetrance and variable genotype-phenotype correlation. However, such a discovery could ultimately lead to improvements in the accurate molecular genetic diagnosis, assessment of prognosis and multidisciplinary clinical management of patients with CAKUT, potentially including personalized therapeutic approaches.

A systematic review on machine learning approaches in the diagnosis and prognosis of rare genetic diseases

BACKGROUND

The diagnosis of rare genetic diseases is often challenging due to the complexity of the genetic underpinnings of these conditions and the limited availability of diagnostic tools. Machine learning (ML) algorithms have the potential to improve the accuracy and speed of diagnosis by analyzing large amounts of genomic data and identifying complex multiallelic patterns that may be associated with specific diseases. In this systematic review, we aimed to identify the methodological trends and the ML application areas in rare genetic diseases.

METHODS

We performed a systematic review of the literature following the PRISMA guidelines to search studies that used ML approaches to enhance the diagnosis of rare genetic diseases. Studies that used DNA-based sequencing data and a variety of ML algorithms were included, summarized, and analyzed using bibliometric methods, visualization tools, and a feature co-occurrence analysis.

FINDINGS

Our search identified 22 studies that met the inclusion criteria. We found that exome sequencing was the most frequently used sequencing technology (59%), and rare neoplastic diseases were the most prevalent disease scenario (59%). In rare neoplasms, the most frequent applications of ML models were the differential diagnosis or stratification of patients (38.5%) and the identification of somatic mutations (30.8%). In other rare diseases, the most frequent goals were the prioritization of rare variants or genes (55.5%) and the identification of biallelic or digenic inheritance (33.3%). The most employed method was the random forest algorithm (54.5%). In addition, the features of the datasets needed for training these algorithms were distinctive depending on the goal pursued, including the mutational load in each gene for the differential diagnosis of patients, or the combination of genotype features and sequence-derived features (such as GC-content) for the identification of somatic mutations.

CONCLUSIONS

ML algorithms based on sequencing data are mainly used for the diagnosis of rare neoplastic diseases, with random forest being the most common approach. We identified key features in the datasets used for training these ML models according to the objective pursued. These features can support the development of future ML models in the diagnosis of rare genetic diseases.

Resources and tools for rare disease variant interpretation

Collectively, rare genetic disorders affect a substantial portion of the world's population. In most cases, those affected face difficulties in receiving a clinical diagnosis and genetic characterization. The understanding of the molecular mechanisms of these diseases and the development of therapeutic treatments for patients are also challenging. However, the application of recent advancements in genome sequencing/analysis technologies and computer-aided tools for predicting phenotype-genotype associations can bring significant benefits to this field. In this review, we highlight the most relevant online resources and computational tools for genome interpretation that can enhance the diagnosis, clinical management, and development of treatments for rare disorders. Our focus is on resources for interpreting single nucleotide variants. Additionally, we present use cases for interpreting genetic variants in clinical settings and review the limitations of these results and prediction tools. Finally, we have compiled a curated set of core resources and tools for analyzing rare disease genomes. Such resources and tools can be utilized to develop standardized protocols that will enhance the accuracy and effectiveness of rare disease diagnosis.

Identification and validation of cuproptosis related genes and signature markers in bronchopulmonary dysplasia disease using bioinformatics analysis and machine learning

BACKGROUND

Bronchopulmonary Dysplasia (BPD) has a high incidence and affects the health of preterm infants. Cuproptosis is a novel form of cell death, but its mechanism of action in the disease is not yet clear. Machine learning, the latest tool for the analysis of biological samples, is still relatively rarely used for in-depth analysis and prediction of diseases.

METHODS AND RESULTS

First, the differential expression of cuproptosis-related genes (CRGs) in the GSE108754 dataset was extracted and the heat map showed that the expression of NFE2L2 gene was significantly higher in the control group whereas the expression of GLS gene was significantly higher in the treatment group. Chromosome location analysis showed that both the genes were positively correlated and associated with chromosome 2. The results of immune infiltration and immune cell differential analysis showed differences in the four immune cells, significantly in Monocytes cells. Five new pathways were analyzed through two subgroups based on consistent clustering of CRG expression. Weighted correlation network analysis (WGCNA) set the screening condition to the top 25% to obtain the disease signature genes. Four machine learning algorithms: Generalized Linear Models (GLM), Random Forest (RF), Support Vector Machine (SVM), and Extreme Gradient Boosting (XGB) were used to screen the disease signature genes, and the final five marker genes for disease prediction. The models constructed by GLM method were proved to be more accurate in the validation of two datasets, GSE190215 and GSE188944.

CONCLUSION

We eventually identified two copper death-associated genes, NFE2L2 and GLS. A machine learning model-GLM was constructed to predict the prevalence of BPD disease, and five disease signature genes NFATC3, ERMN, PLA2G4A, MTMR9LP and LOC440700 were identified. These genes that were bioinformatics analyzed could be potential targets for identifying BPD disease and treatment.