Diseases prediction has been performed by machine learning approaches with various biological data. One of the representative data is the gut microbial community, which interacts with the host’s immune system. The abundance of a few microorganisms has been used as markers to predict diverse diseases. In this study, we hypothesized that multi-classification using machine learning approach could distinguish the gut microbiome from following six diseases: multiple sclerosis, juvenile idiopathic arthritis, myalgic encephalomyelitis/chronic fatigue syndrome, acquired immune deficiency syndrome, stroke and colorectal cancer. We used the abundance of microorganisms at five taxonomy levels as features in 696 samples collected from different studies to establish the best prediction model. We built classification models based on four multi-class classifiers and two feature selection methods including a forward selection and a backward elimination. As a result, we found that the performance of classification is improved as we use the lower taxonomy levels of features; the highest performance was observed at the genus level. Among four classifiers, LogitBoost–based prediction model outperformed other classifiers. Also, we suggested the optimal feature subsets at the genus-level obtained by backward elimination. We believe the selected feature subsets could be used as markers to distinguish various diseases simultaneously. The finding in this study suggests the potential use of selected features for the diagnosis of several diseases.

The first wave of transcriptional activation occurs after fertilisation in a species-specific pattern. Despite its importance to initial embryonic development, the characteristics of transcription following fertilisation are poorly understood in Aves. Here, we report detailed insights into the onset of genome activation in chickens. We established that two waves of transcriptional activation occurred, one shortly after fertilisation and another at Eyal-Giladi and Kochav Stage V. We found 1544 single nucleotide polymorphisms across 424 transcripts derived from parents that were expressed in offspring during the early embryonic stages. Surprisingly, only the maternal genome was activated in the zygote, and the paternal genome remained silent until the second-wave, regardless of the presence of a paternal pronucleus or supernumerary sperm in the egg. The identified maternal genes involved in cleavage that were replaced by bi-allelic expression. The results demonstrate that only maternal alleles are activated in the chicken zygote upon fertilisation, which could be essential for early embryogenesis and evolutionary outcomes in birds.

Healthy food promotes beneficial bacteria in the gut microbiome. A few prebiotics act as food supplements to increase fermentation by beneficial bacteria, which enhance the host immune system and health. Allium hookeri is a healthy food with antioxidant and anti-inflammatory activities. A. hookeri is used as a feed supplement for broiler chickens to improve growth performance. Although the underlying mechanism is unknown, A. hookeri may alter the gut microbiome. In the current study, 16S rRNA sequencing has been carried out using samples obtained from the cecum of broiler chickens exposed to diets comprising different tissue types (leaf and root) and varying amounts (0.3% and 0.5%) of A. hookeri to investigate their impact on gut microbiome. The microbiome composition in the groups supplemented with A. hookeri leaf varied from that of the control group. Especially, exposure to 0.5% amounts of leaf resulted in differences in the abundance of genera compared with diets comprising 0.3% leaf. Exposure to a diet containing 0.5% A. hookeri leaf decreased the abundance of the following bacteria: Eubacterium nodatum, Marvinbryantia, Oscillospira, and Gelria. The modulation of gut microbiome by leaf supplement correlated with growth traits including body weight, bone strength, and infectious bursal disease antibody. The results demonstrate that A. hookeri may improve the health benefits of broiler chickens by altering the gut microbiome.

Maternal-to-zygotic transition (MZT) is the critical process for the establishment of embryonic identity across vertebrates. During this period, the massive transcriptional activation, called zygotic genome activation (ZGA), is mediated by maternally stored factors, and maternal mRNA clearance by conserved zygotic microRNAs (miRNAs) occurs; however, the important transition in avian species was identified by morphologic perspectives only. In this study, we performed transcriptome analysis to examine the molecular transitions of intrauterine development in chickens. On the basis of coexpression analyses on RNA sequencing data, 2 waves of ZGA-mediated MZT were observed across the early embryonic stages and were associated with transcriptional and translational dynamics. Furthermore, definite transitions were observed according to the distinct developmental characteristics between cleavage and the area pellucida formation period in the functional analysis. Finally, epigenetic modification and the evolutionarily conserved miRNA expression suggest that certain MZT proceeds from Eyal-Giladi and Kochav stage VIII in early chicken development. We expect our study to provide an evolutionary link among vertebrates from the perspective of MZT regulation.-Hwang, Y. S., Seo, M., Bang, S., Kim, H., Han, J. Y. Transcriptional and translational dynamics during maternal-to-zygotic transition in early chicken development.

Machine learning approaches have been applied to identify transcription factor (TF)-DNA interaction important for gene regulation and expression. However, due to the enormous search space of the genome, it is challenging to build models capable of surveying entire reference genomes, especially in species where models were not trained. In this study, we surveyed a variety of methods for classification of epigenomics data in an attempt to improve the detection for 12 members of the Auxin Response Factor (ARF) binding DNAs from maize and soybean as assessed by DNA Affinity Purification and sequencing (DAP-seq). We used the classification for prediction by minimizing the genome search space by only surveying unmethylated regions (UMRs). For identification of DAP-seq binding events within the UMRs, we achieved 78.72% accuracy rate across 12 members of ARFs of maize on average by encoding DNA with count vectorization for k-mer with a logistic regression classifier with up-sampling and feature selection. Importantly, feature selection helps to uncover known and potentially novel ARF binding motifs. This demonstrates an independent method for identification of transcription factor binding sites. Finally, we tested the model built with maize DAP-seq data and applied it directly to the soybean genome and found high false negative rates, which accounted for more than 40% across the ARF TFs tested. The findings in this study suggest the potential use of various methods to predict TF-DNA interactions within and between species with varying degrees of success.