Machine learning has shown utility in detecting patterns within large, unstructured, and complex datasets. One of the promising applications of machine learning is in precision medicine, where disease risk is predicted using patient genetic data. However, creating an accurate prediction model based on genotype data remains challenging due to the so-called “curse of dimensionality” (i.e., extensively larger number of features compared to the number of samples). Therefore, the generalizability of machine learning models benefits from feature selection, which aims to extract only the most “informative” features and remove noisy “non-informative,” irrelevant and redundant features. In this article, we provide a general overview of the different feature selection methods, their advantages, disadvantages, and use cases, focusing on the detection of relevant features (i.e., SNPs) for disease risk prediction.

Fecal microbiota transplantation (FMT) has become a highly effective bacteriotherapy for recurrent Clostridium difficile infection. Meanwhile the efficacy of FMT for treating chronic diseases associated with microbial dysbiosis has so far been modest with a much higher variability in patient response. Notably, a number of studies suggest that FMT success is dependent on the microbial diversity and composition of the stool donor, leading to the proposition of the existence of FMT super-donors. The identification and subsequent characterization of super-donor gut microbiomes will inevitably advance our understanding of the microbial component of chronic diseases and allow for more targeted bacteriotherapy approaches in the future. Here, we review the evidence for super-donors in FMT and explore the concept of keystone species as predictors of FMT success. Possible effects of host-genetics and diet on FMT engraftment and maintenance are also considered. Finally, we discuss the potential long-term applicability of FMT for chronic disease and highlight how super-donors could provide the basis for dysbiosis-matched FMTs.

Reconstruction of a gene with its introns removed results in reduced levels of cytoplasmic mRNA. This is partly explained by introns promoting the export of mRNA through coupling splicing to nuclear export processes. However, we show here that splicing signals can have a direct role in enhancing gene transcription. Removal of promoter proximal splice signals from a mammalian gene or the excision of introns from two different yeast genes results in a marked reduction in levels of nascent transcription, based on both nuclear run-on and direct image analysis. This further establishes that mRNA processing and transcription are tightly coupled mechanisms.

The gut microbiome is established in the newborn period and is recognised to interact with the host to influence metabolism. Different environmental factors that are encountered during this critical period may influence the gut microbial composition, potentially impacting upon later disease risk, such as asthma, metabolic disorder, and inflammatory bowel disease. The sterility dogma of the foetus in utero is challenged by studies that identified bacteria, bacterial DNA, or bacterial products in meconium, amniotic fluid, and the placenta; indicating the initiation of maternal-to-offspring microbial colonisation in utero. This narrative review aims to provide a better understanding of factors that affect the development of the gastrointestinal (GI) microbiome during prenatal, perinatal to postnatal life, and their reciprocal relationship with GI tract development in neonates.

In the past decade, precision genomics based medicine has emerged to provide tailored and effective healthcare for patients depending upon their genetic features. Genome Wide Association Studies have also identified population based risk genetic variants for common and complex diseases. In order to meet the full promise of precision medicine, research is attempting to leverage our increasing genomic understanding and further develop personalized medical healthcare through ever more accurate disease risk prediction models. Polygenic risk scoring and machine learning are two primary approaches for disease risk prediction. Despite recent improvements, the results of polygenic risk scoring remain limited due to the approaches that are currently used. By contrast, machine learning algorithms have increased predictive abilities for complex disease risk. This increase in predictive abilities results from the ability of machine learning algorithms to handle multi-dimensional data. Here, we provide an overview of polygenic risk scoring and machine learning in complex disease risk prediction. We highlight recent machine learning application developments and describe how machine learning approaches can lead to improved complex disease prediction, which will help to incorporate genetic features into future personalized healthcare. Finally, we discuss how the future application of machine learning prediction models might help manage complex disease by providing tissue-specific targets for customized, preventive interventions.