Translating insights from the seed metabolome into improved prediction for lipid-composition traits in oat (Avena sativa L.)

Oat (Avena sativa L.) seed is a rich resource of beneficial lipids, soluble fiber, protein, and antioxidants, and is considered a healthful food for humans. Little is known regarding the genetic controllers of variation for these compounds in oat seed. We characterized natural variation in the mature seed metabolome using untargeted metabolomics on 367 diverse lines and leveraged this information to improve prediction for seed quality traits. We used a latent factor approach to define unobserved variables that may drive covariance among metabolites. One hundred latent factors were identified, of which 21% were enriched for compounds associated with lipid metabolism. Through a combination of whole-genome regression and association mapping, we show that latent factors that generate covariance for many metabolites tend to have a complex genetic architecture. Nonetheless, we recovered significant associations for 23% of the latent factors. These associations were used to inform a multi-kernel genomic prediction model, which was used to predict seed lipid and protein traits in two independent studies. Predictions for 8 of the 12 traits were significantly improved compared to genomic best linear unbiased prediction when this prediction model was informed using associations from lipid-enriched factors. This study provides new insights into variation in the oat seed metabolome and provides genomic resources for breeders to improve selection for health-promoting seed quality traits. More broadly, we outline an approach to distill high-dimensional “omics” data to a set of biologically meaningful variables and translate inferences on these data into improved breeding decisions.     

Genomic regions associated with microdeletion/microduplication syndromes exhibit extreme diversity of structural variation

Segmental duplications (SDs) are a class of long, repetitive DNA elements whose paralogs share a high level of sequence similarity with each other. SDs mediate chromosomal rearrangements that lead to structural variation in the general population as well as genomic disorders associated with multiple congenital anomalies, including the 7q11.23 (Williams–Beuren Syndrome, WBS), 15q13.3, and 16p12.2 microdeletion syndromes. Population-level characterization of SDs has generally been lacking because most techniques used for analyzing these complex regions are both labor and cost intensive. In this study, we have used a high-throughput technique to genotype complex structural variation with a single molecule, long-range optical mapping approach. We characterized SDs and identified novel structural variants (SVs) at 7q11.23, 15q13.3, and 16p12.2 using optical mapping data from 154 phenotypically normal individuals from 26 populations comprising five super-populations. We detected several novel SVs for each locus, some of which had significantly different prevalence between populations. Additionally, we localized the microdeletion breakpoints to specific paralogous duplicons located within complex SDs in two patients with WBS, one patient with 15q13.3, and one patient with 16p12.2 microdeletion syndromes. The population-level data presented here highlights the extreme diversity of large and complex SVs within SD-containing regions. The approach we outline will greatly facilitate the investigation of the role of inter-SD structural variation as a driver of chromosomal rearrangements and genomic disorders.     

南亚热带马尾松-红椎混交林及其纯林土壤细菌群落结构与功能

营建乡土阔叶树种人工纯林和针阔混交林是我国亚热带地区森林经营的发展趋势,但对人工纯林和针阔混交林土壤细菌群落结构及功能知之甚少。本研究以南亚热带乡土针叶树种马尾松、阔叶树种红椎人工纯林及二者的混交林为对象,运用细菌16S rRNA基因高通量测序技术和PICRUSt基因功能预测,分析了3种人工林不同土层(0～20、20～40和40～60 cm)土壤细菌群落的结构与功能。结果表明: 混交林和马尾松林土壤细菌群落结构相似,但与红椎林差异显著,红椎林土壤细菌群落多样性、生物通路代谢功能和氮循环功能低于马尾松林和混交林;土壤全氮、硝态氮和C/N是导致红椎林与马尾松林和混交林土壤细菌群落结构及功能差异的主要土壤理化因子。就土壤细菌群落结构与功能而言,在该地区营造红椎和马尾松针阔混交林要优于红椎纯林。     

In silico annotation of unreviewed acetylcholinesterase (AChE) in some lepidopteran insect pest species reveals the causes of insecticide resistance

Lepidoptera is the second most diverse insect order outnumbered only by the Coeleptera. Acetylcholinesterase (AChE) is the major target site for insecticides. Extensive use of insecticides, to inhibit the function of this enzyme, have resulted in the development of insecticide resistance. Complete knowledge of the target proteins is very important to know the cause of resistance. Computational annotation of insect acetylcholinesterase can be helpful for the characterization of this important protein. Acetylcholinesterase of fourteen lepidopteran insect pest species was annotated by using different bioinformatics tools. AChE in all the species was hydrophilic and thermostable. All the species showed lower values for instability index except L. orbonalis, S. exigua and T. absoluta. Highest percentage of Arg, Asp, Asn, Gln and Cys were recorded in P. rapae. High percentage of Cys and Gln might be reason for insecticide resistance development in P. rapae. Phylogenetic analysis revealed the AChE in T. absoluta, L. orbonalis and S. exigua are closely related and emerged from same primary branch. Three functional motifs were predicted in eleven species while only two were found in L. orbonalis, S. exigua and T. absoluta. AChE in eleven species followed secretory pathway and have signal peptides. No signal peptides were predicted for S. exigua, L. orbonalis and T. absoluta and follow non secretory pathway. Arginine methylation and cysteine palmotylation was found in all species except S. exigua, L. orbonalis and T. absoluta. Glycosylphosphatidylinositol (GPI) anchor was predicted in only nine species.     

Novel artificial intelligence machine learning approaches to precisely predict survival and site-specific recurrence in cervical cancer: A multi-institutional study

BackgroundMachine learning (ML) has been gradually integrated into oncologic research but seldom applied to predict cervical cancer (CC), and no model has been reported to predict survival and site-specific recurrence simultaneously. Thus, we aimed to develop ML models to predict survival and site-specific recurrence in CC and to guide individual surveillance.MethodsWe retrospectively collected data on CC patients from 2006 to 2017 in four hospitals. The survival or recurrence predictive value of the variables was analyzed using multivariate Cox, principal component, and K-means clustering analyses. The predictive performances of eight ML models were compared with logistic or Cox models. A novel web-based predictive calculator was developed based on the ML algorithms.ResultsThis study included 5112 women for analysis (268 deaths, 343 recurrences): (1) For site-specific recurrence, larger tumor size was associated with local recurrence, while positive lymph nodes were associated with distant recurrence. (2) The ML models exhibited better prognostic predictive performance than traditional models. (3) The ML models were superior to traditional models when multiple variables were used. (4) A novel predictive web-based calculator was developed and externally validated to predict survival and site-specific recurrence.ConclusionML models might be a better analytic approach in CC prognostic prediction than traditional models as they can predict survival and site-specific recurrence simultaneously, especially when using multiple variables. Moreover, our novel web-based calculator may provide clinicians with useful information and help them make individual postoperative follow-up plans and further treatment strategies.     

A method for 3D detection of symmetry line in asymmetric postures

The methods for symmetry line detection presented in the literature are typically suited to analyse symmetric upright postures, both standing and seated. The proposed method focuses on the symmetry line detection in subjects assuming asymmetric postures in which this line falls far outside the sagittal plane. The proposed approach evaluates the symmetry line by means of an autoregressive process in order to determine the set of planes suited to slice the back coherently with its geometric spatial configuration. The method is analysed assuming the cutaneous marking as reference and it is compared with a previous one, also developed by these authors. Results are analysed and critically discussed.     

Assessment of the association between the frequency of micronucleus and p16INK4a/Ki-67 co-expression in patients with cervical intraepithelial lesions

Human papilloma virus (HPV) infection is the main etiological factor for cervical intraepithelial lesions (CIN). An important characteristic of this process is the loss of genome stability. Therefore, it is imperative to use biomarkers of DNA damage caused by genomic instability to identify high risk individuals. We investigated the frequency of micronuclei (MN) in peripheral blood lymphocytes (PBL) of 20 patients, diagnosed as histologically CIN 1 and 10 healthy controls. We also examined the frequency of other nuclear anomalies including nucleoplasmic bridges (NPBs) and nuclear buds (NBUDs) in PBL of patients with CIN 1 and healthy controls, and evaluated the benefits of p16^INK4a and Ki-67 (p16^INK4a/Ki-67) immunohistochemical double staining for identifying cervical squamous cells that express HPV E6/E7 oncogenes. We analyzed the association between the frequency of MN in PBL and the amount of p16^INK4a/Ki-67 co-expression in CIN 1 patients to establish genomic instability. Among CIN 1 subjects, 15% exhibited diffuse p16^INK4a/Ki-67 co-expression and were considered high positive, 25% of the CIN 1 cases exhibited p16^INK4a/Ki-67 co-expression restricted to the lower part of the epithelium and were considered low positive and the remaining 60% of cases were negative. The frequency of MN, NPBs and NBUDs differed significantly among groups. We found a statistically significant positive correlation between p16^INK4a/Ki-67 co-expression and the frequency of MN, NPBs and NBUDs in PBL. Our findings demonstrate the efficacy of p16^INK4a/Ki-67 double immunostaining for histological samples with CIN 1. MN frequency in PBL might be useful for detecting genomic instability in cases of HPV infection and CIN.     

Mitochondrial oxidative stress elicits chromosomal instability after exposure to isocyanates in human kidney epithelial cells

The role of oxidative stress is often attributed in environmental renal diseases. Isocyanates, a ubiquitous chemical group with diverse industrial applications, are known to undergo bio-transformation reactions upon accidental and occupational exposure. This study delineates the role of isocyanate-mediated mitochondrial oxidative stress in eliciting chromosomal instability in cultured human kidney epithelial cells. Cells treated with 0.005 µM concentration of methyl isocyanate displayed morphological transformation and stress-induced senescence. Along the time course, an increase in DCF fluorescence indicative of oxidative stress, depletion of superoxide dismutase (SOD) and glutathione reductase (GR) and consistent accumulation of 8-oxo-dG were noticed. Thus, endogenous oxidative stress resulted in aberrant expression of p53, p21, cyclin E and CDK2 proteins, suggestive of deregulated cell cycle, chromosomal aberrations, centromeric amplification, aneuploidy and genomic instability.