Prediction analysis for microbiome sequencing data

One goal of human microbiome studies is to relate host traits with human microbiome compositions. The analysis of microbial community sequencing data presents great statistical challenges, especially when the samples have different library sizes and the data are overdispersed with many zeros. To address these challenges, we introduce a new statistical framework, called predictive analysis in metagenomics via inverse regression (PAMIR), to analyze microbiome sequencing data. Within this framework, an inverse regression model is developed for overdispersed microbiota counts given the trait, and then a prediction rule is constructed by taking advantage of the dimension‐reduction structure in the model. An efficient Monte Carlo expectation‐maximization algorithm is proposed for maximum likelihood estimation. The method is further generalized to accommodate other types of covariates. We demonstrate the advantages of PAMIR through simulations and two real data examples.     

Characteristics of sugarcane white leaf phytoplasma transmission by the leafhopper Matsumuratettix hiroglyphicus

The leafhopper Matsumuratettix hiroglyphicus (Matsumura) (Hemiptera: Cicadellidae) is the most important vector of sugarcane white leaf (SCWL) phytoplasma that significantly affects the sugarcane crop in Asia. Here, we aimed to study the characteristics of SCWL phytoplasma transmission by M. hiroglyphicus. To this end, the stylet penetration activities performed during the acquisition access period (AAP) and inoculation access period (IAP) were investigated by the direct current electrical penetration graph technique and confirmed by quantitative polymerase chain reaction (qPCR). Additionally, the latent period (LP) of SCWL phytoplasma in the vector was determined by qPCR and localised by fluorescent in situ hybridisation. The results indicated that the acquisition of SCWL phytoplasma occurred during phloem ingestion (waveform D), whereas its inoculation was associated with salivation into the phloem sieve element (waveform C). The minimum AAP was 15 min and the minimum duration of phloem ingestion was 2.35 min. The minimum LP of SCWL phytoplasma in the vector was at least 14 days; then, SCWL phytoplasma moved to the salivary glands of the insect, enabling the transmission of the pathogen to the host plants. The minimum IAP for a successful transmission of SCWL phytoplasma to the host plants was 11–12 min, with a minimum duration of salivation into phloem of 1.35 min. The female vectors had higher SCWL phytoplasma copy numbers than the male vectors, and displayed faster AAP, IAP, and LP. Overall, our findings provide important information related to the feeding behaviour of M. hiroglyphicus and its effect on the transmission of SCWL phytoplasma.     

AoP-LSE: Antioxidant Proteins Classification Using Deep Latent Space Encoding of Sequence Features

It is of utmost importance to develop a computational method for accurate prediction of antioxidants, as they play a vital role in the prevention of several diseases caused by oxidative stress. In this correspondence, we present an effective computational methodology based on the notion of deep latent space encoding. A deep neural network classifier fused with an auto-encoder learns class labels in a pruned latent space. This strategy has eliminated the need to separately develop classifier and the feature selection model, allowing the standalone model to effectively harness discriminating feature space and perform improved predictions. A thorough analytical study has been presented alongwith the PCA/tSNE visualization and PCA-GCNR scores to show the discriminating power of the proposed method. The proposed method showed a high MCC value of 0.43 and a balanced accuracy of 76.2%, which is superior to the existing models. The model has been evaluated on an independent dataset during which it outperformed the contemporary methods by correctly identifying the novel proteins with an accuracy of 95%.     

Maximum likelihood estimation of two-level latent variable models with mixed continuous and polytomous data

Two-level data with hierarchical structure and mixed continuous and polytomous data are very common in biomedical research. In this article, we propose a maximum likelihood approach for analyzing a latent variable model with these data. The maximum likelihood estimates are obtained by a Monte Carlo EM algorithm that involves the Gibbs sampler for approximating the E-step and the M-step and the bridge sampling for monitoring the convergence. The approach is illustrated by a two-level data set concerning the development and preliminary findings from an AIDS preventative intervention for Filipina commercial sex workers where the relationship between some latent quantities is investigated.     

PCR-Based Detection of Artificial Latent Infections of Geranium by Xanthomonas campestris pv. pelargonii

A procedure entailing biological enrichment and PCR amplification was developed to detect small populations of Xanthomonas campestris pv. pelargonii (X.c, pv. pelargonii ) in tissues of geranium. Known numbers of colony forming units (CFU) of X.c. pv. pelargonii were introduced into 'Red Elite' geraniums through wounding of petioles and stems. Immediately after inoculation, sections of the petioles and stems were harvested and incubated for 24 or 48 h in nutrient broth (biological enrichment). After enrichment, bacterial cells were collected by centrifugation, followed by rapid extraction of total nucleic acid from the cells with GeneReleaser™, PCR amplification of DNA with pathovar-specific primers, and ethidium bromide-stained agarose gel electrophoretic analysis of the PCR products. After 48 h biological enrichment, it was possible to detect as few as 1 CFU of Xc. pv. pelargonii in stems and petioles collected immediately after inoculation, with the detection limit ranging between 1 and 120 CFU during multiple experiments. It also was possible to detect systemic movement of the bacterium in intact plants sampled 24 h after inoculation with a minimal inoculum (4 CFU). This procedure may have application in geranium certification programs concerned with the detection of latent infections associated with low levels of X.c .pv. pelargonii.     

重组含有Epstein-Barr病毒潜伏膜蛋白1(LMP1)基因的杆状病毒在昆虫细胞中的表达

用杆状病毒表达系统重组病毒,在昆虫细胞中表达了完整的含有ＥＢＶ－ＬＭＰ１基因３个外显子开放读码框架的长２．３ｋｂ的ｃＤＮＡ片段。用重组病毒感染Ｓｆ９细胞,用免疫荧光染色,结果表明：４８小时表达重组蛋白,７２小时细胞较完整,免疫荧光染色强阳性,９６小时后细胞出现破碎。我们采集７２小时的组织培养上清和细胞破碎裂解液,分别采用ＳＤＳ－ＰＡＧＥ、ＨＰＬＣ分子筛法,用免疫蛋白印迹法实验证明,表达的蛋白能被抗ＬＭＰ１的单克隆抗体所识别,测定表达蛋白的分子量为６０ｋＤ。经蛋白含量扫描图分析,采用Ｓｅｐｈａｄｅｘ－７５柱初步纯化表达的ＬＭＰ１蛋白,将后者进行裸鼠体内致瘤实验,未见肿瘤生长。     

A plasma global metabolic profiling approach applied to an exercise study monitoring the effects of glucose, galactose and fructose drinks during post-exercise recovery

A global metabolic profiling methodology based on gas chromatography coupled to time-of-flight mass spectrometry (GC-TOFMS) for human plasma was applied to a human exercise study focused on the effects of beverages containing glucose, galactose, or fructose taken after exercise and throughout a recovery period of 6 h and 45 min. One group of 10 well trained male cyclists performed 3 experimental sessions on separate days (randomized, single center). After performing a standardized depletion protocol on a bicycle, subjects consumed one of three different beverages: maltodextrin (MD)+glucose (2:1 ratio), MD+galactose (2:1), and MD+fructose (2:1), consumed at an average of ～1.25 g of carbohydrate (CHO) ingested per minute. Blood was taken straight after exercise and every 45 min within the recovery phase. With the resulting blood plasma, insulin, free fatty acid (FFA) profile, glucose, and GC-TOFMS global metabolic profiling measurements were performed. The resulting profiling data was able to match the results obtained from the other clinical measurements with the addition of being able to follow many different metabolites throughout the recovery period. The data quality was assessed, with all the labelled internal standards yielding values of <15% CV for all samples (n=335), apart from the labelled sucrose which gave a value of 15.19%. Differences between recovery treatments including the appearance of galactonic acid from the galactose based beverage were also highlighted.     

Genetic loss of D-amino acid oxidase activity reverses schizophrenia-like phenotypes in mice

Reduced function of the N -methyl- d -aspartate receptor (NMDAR) has been implicated in the pathophysiology of schizophrenia. The NMDAR contains a glycine binding site in its NR1 subunit that may be a useful target for the treatment of schizophrenia. In this study, we assessed the therapeutic potential of long-term increases in the brain levels of the endogenous NMDAR glycine site agonist D-serine, through the genetic inactivation of its catabolic enzyme D-amino acid oxidase (DAO) in mice. The effects of eliminating DAO function were investigated in mice that display schizophrenia-related behavioral deficits due to a mutation ( Grin 1 ^D481N ) in the NR1 subunit that results in a reduction in NMDAR glycine affinity. Grin 1 ^D481N mice show deficits in sociability, prolonged latent inhibition, enhanced startle reactivity and impaired spatial memory. The hypofunctional Dao 1 ^G181R mutation elevated brain levels of D-serine, but alone it did not affect performance in the behavioral measures. Compared to animals with only the Grin 1 ^D481N mutation, mice with both the Dao1 ^G181R and Grin 1 ^D481N mutations displayed an improvement in social approach and spatial memory retention, as well as a reversal of abnormally persistent latent inhibition and a partial normalization of startle responses. Thus, an increased level of D-serine resulting from decreased catalysis corrected the performance of mice with deficient NMDAR glycine site activation in behavioral tasks relevant to the negative and cognitive symptoms of schizophrenia. Diminished DAO activity and elevations in D-serine may serve as an effective therapeutic intervention for the treatment of psychiatric symptoms.