Analyses of pig genomes provide insight into porcine demography and evolution

For 10,000 years pigs and humans have shared a close and complex relationship. From domestication to modern breeding practices, humans have shaped the genomes of domestic pigs. Here we present the assembly and analysis of the genome sequence of a female domestic Duroc pig (Sus scrofa) and a comparison with the genomes of wild and domestic pigs from Europe and Asia. Wild pigs emerged in South East Asia and subsequently spread across Eurasia. Our results reveal a deep phylogenetic split between European and Asian wild boars ∼1 million years ago, and a selective sweep analysis indicates selection on genes involved in RNA processing and regulation. Genes associated with immune response and olfaction exhibit fast evolution. Pigs have the largest repertoire of functional olfactory receptor genes, reflecting the importance of smell in this scavenging animal. The pig genome sequence provides an important resource for further improvements of this important livestock species, and our identification of many putative disease-causing variants extends the potential of the pig as a biomedical model

Multidisciplinary evaluation of plant growth promoting rhizobacteria on soil microbiome and strawberry quality

AbstractThe natural soil environment is considered one of the most diverse habitats containing numerous bacteria, fungi, and larger organisms such as nematodes, insects, or rodents. Rhizosphere bacteria play vital roles in plant nutrition and the growth promotion of their host plant. The aim of this study was to evaluate the effects of three plant growth-promoting rhizobacteria (PGPR), Bacillus subtilis, Bacillus amyloliquefaciens, and Pseudomonas monteilii for their potential role as a biofertilizer. The effect of the PGPR was examined at a commercial strawberry farm in Dayton, Oregon. The PGPR were applied to the soil of the strawberry (Fragaria × ananassa cultivar Hood) plants in two different concentrations of PGPR, T1 (0.24% PGPR) and T2 (0.48% PGPR), and C (no PGPR). A total of 450 samples from August 2020 to May 2021 were collected, and microbiome sequencing based on the V4 region of the 16S rRNA gene was conducted. The strawberry quality was measured by sensory evaluation, total acidity (TA), total soluble solids (TSS), color (lightness and chroma), and volatile compounds. Application of the PGPR significantly increased the populations of Bacillus and Pseudomonas and promoted the growth of nitrogen-fixing bacteria. The TSS and color evaluation showed that the PGPR presumptively behaved as a ripening enhancer. The PGPR contributed to the production of fruit-related volatile compounds, while the sensory evaluation did not show significant differences among the three groups. The major finding of this study suggests that the consortium of the three PGPR have a potential role as a biofertilizer by supporting the growth of other microorganisms (nitrogen-fixing bacteria) as part of a synergetic effect and strawberry quality such as sweetness and volatile compounds.</jats:p

Gene expression profiles in adenosine-treated human mast cells

The role of mast cells in allergic diseases and innate immunity has been widely researched and much is known about the expression profiles of immune-related genes in mast cells after bacterial challenges. However, little is known about the gene expression profiles of mast cells in response to adenosine. Herein, we profiled the transcriptome changes of human mast cells treated with adenosine. To perform comparative transcriptome analysis between adenosine-untreated control mast cells (MN) and adenosine-treated mast cells (MT), two independent cDNA libraries were constructed using the 5'-oligocapping method. Analysis of the 3,968 (MN, 1,994; MT, 1,974) expression sequence tags (ESTs) generated from these libraries identified 369 contigs (MN, 189; MT, 180) and 2,655 singletons (MN, 1,289; MT, 1,366) with average lengths of 668 and 655 bp, respectively. Furthermore, comparison of our EST sequences against the eukaryotic orthologous group (KOG) database showed that 2,134 (52.92%) out of 4,032 sequences could be grouped into three major functional categories. As a result of analysis at the individual level of the genes, we found that the expression of genes encoding Pdia (protein disulfide isomerase-associated), adaptor-related protein complex, ATP-dependent DNA helicase II, cyclin M4, reticulon 3 isoform, CD37 antigen isoform A, glutamine synthetase, WD repeat domain, programmed cell death and proliferating cell nuclear antigen increased by 4-fold. In contrast, the expression of genes encoding thymosin beta 4, ring finger protein, high-mobility group, calmodulin 2, RAN binding protein, solute carrier family 25, tubulin alpha and peroxiredoxin decreased by 4-fold. Information obtained from our study will enhance the understanding of defense mechanisms associated with innate immune responses by human mast cells, for which identification of immune regulators of those genes is required.open

Personalized Antiviral Drug Selection in Patients With Chronic Hepatitis B Using a Machine Learning Model: A Multinational Study

Introduction: Tenofovir disoproxil fumarate (TDF) is reportedly superior or at least comparable to entecavir (ETV) for the prevention of hepatocellular carcinoma (HCC) in patients with chronic hepatitis B; however, it has distinct long-term renal and bone toxicities. This study aimed to develop and validate a machine learning model (designated as Prediction of Liver cancer using Artificial intelligence-driven model for Network-antiviral Selection for hepatitis B [PLAN-S]) to predict an individualized risk of HCC during ETV or TDF therapy. Methods: This multinational study included 13,970 patients with chronic hepatitis B. The derivation (n = 6,790), Korean validation (n = 4,543), and Hong Kong-Taiwan validation cohorts (n = 2,637) were established. Patients were classified as the TDF-superior group when a PLAN-S-predicted HCC risk under ETV treatment is greater than under TDF treatment, and the others were defined as the TDF-nonsuperior group. Results: The PLAN-S model was derived using 8 variables and generated a c-index between 0.67 and 0.78 for each cohort. The TDF-superior group included a higher proportion of male patients and patients with cirrhosis than the TDF-nonsuperior group. In the derivation, Korean validation, and Hong Kong-Taiwan validation cohorts, 65.3%, 63.5%, and 76.4% of patients were classified as the TDF-superior group, respectively. In the TDF-superior group of each cohort, TDF was associated with a significantly lower risk of HCC than ETV (hazard ratio = 0.60-0.73, all P 0.1). Discussion: Considering the individual HCC risk predicted by PLAN-S and the potential TDF-related toxicities, TDF and ETV treatment may be recommended for the TDF-superior and TDF-nonsuperior groups, respectively

Deep Learning of EEG Data in the NeuCube Brain-Inspired Spiking Neural Network Architecture for a Better Understanding of Depression

In the recent years, machine learning and deep learning techniques are being applied on brain data to study mental health. The activation of neurons in these models is static and continuous-valued. However, a biological neuron processes the information in the form of discrete spikes based on the spike time and the firing rate. Understanding brain activities is vital to understand the mechanisms underlying mental health. Spiking Neural Networks are offering a computational modelling solution to understand complex dynamic brain processes related to mental disorders, including depression. The objective of this research is modeling and visualizing brain activity of people experiencing symptoms of depression using the SNN NeuCube architecture. Resting EEG data was collected from 22 participants and further divided into groups as healthy and mild-depressed. NeuCube models have been developed along with the connections across different brain regions using Synaptic Time Dependent plasticity (STDP) learning rule for healthy and depressed individuals. This unsupervised learning revealed some distinguishable patterns in the models related to the frontal, central and parietal areas of the depressed versus the control subjects that suggests potential markers for early depression prediction. Traditional machine learning techniques, including MLP methods have been also employed for classification and prediction tasks on the same data, but with lower accuracy and fewer new information gained