A Generalized Kruskal–Wallis Test Incorporating Group Uncertainty with Application to Genetic Association Studies

Biocompatible PANI-Encapsulated Chemically Modified Nano-TiO2 Particles for Visible-Light Photocatalytic Applications

Polyaniline (PANI) constitutes a very propitious conductive polymer utilized in several biomedical, as well as environmental applications, including tissue engineering, catalysis, and photocatalysis, due to its unique properties. In this study, nano-PANI/N-TiO2 and nano-PANI/Ag-TiO2 photocatalytic composites were fabricated via aniline's oxidative polymerization, while the Ag-and N-chemically modified TiO2 nanopowders were synthesized through the sol-gel approach. All produced materials were fully characterized. Through micro-Raman and FT-IR analysis, the co-existence of PANI and chemically modified TiO2 particles was confirmed, while via XRD analysis the composites' average crystallite size was determined as ≈20 nm. The semi-crystal structure of polyaniline exhibits higher photocatalytic efficiency compared to that of other less crystalline forms. The spherical-shaped developed materials are innovative, stable (zeta potential in the range from -26 to -37 mV), and cost-effective, characterized by enhanced photocatalytic efficiency under visible light (energy band gaps ≈ 2 eV), and synthesized with relatively simple methods, with the possibility of recycling and reusing them in potential future applications in industry, in wastewater treatment as well as in biomedicine. Thus, the PANI-encapsulated Ag and N chemically modified TiO2 nanocomposites exhibit high degradation efficiency towards Rhodamine B dye upon visible-light irradiation, presenting simultaneously high biocompatibility in different normal cell lines.

Study of genes polymorphisms in RANK/RANKL/OPG and WNT signaling pathways and their associations with bone parameters in broiler chicken

Limb problems are one of the most common problems with fast-growing meat-type chickens. Various bone abnormalities, which can lead to limping, bone weakness, or even fractures, bring overall discomfort to birds and a loss of production. Genetic aspects are often associated with these side effects on bone stability and are also cited as the dominant cause. These points to a close negative relationship of genetic selection for rapid growth with traits involved in bone integrity. Due to the assumption of an additive genetic background, improvements through genetic tools can be used. Our study is focused on selected genes of important signaling pathways for bone metabolism. We tried to detect polymorphisms that would show associations with selected bone parameters in a total of 48 broilers. Those were fast-growing Ross 308 hybrids and slow-growing Hubbard M22BxJA87A hybrids. The TNFRSF11A and WISP1 genes were tested. A total of fourteen polymorphisms were found, three of them were synonymous and five in the intron. In the case of four polymorphisms found in exons of the TNFRSF11A gene (c.11G > T, c.31G > A, c.37C > G, c.514G > A), associations with the observed bone parameters (bone strength, bone dimensions and bone mass) were demonstrated. The genetic architecture of bone traits is not fully understood, therefore the present study and the knowledge gained can help to increase the potential in poultry breeding processes and thus reduce the death of individuals.

Technical violations and infractions are drivers of disengagement from methadone treatment among people with opioid use disorder discharged from Connecticut jails 2014-2018

BACKGROUND

We investigated the interaction between arrests for technical violations vs. receiving new charges with receiving community-based methadone treatment on time-to reincarceration (TTR) in a cohort of men with opioid use disorder (OUD) released from custody from two Connecticut jails from 2014 to 2018.

METHODS

Hazard ratios (HR) were estimated for time to reincarceration for technical violations/infractions, misdemeanors only, felonies only, and both misdemeanors and felonies after adjusting for age, race/ethnicity, and receiving methadone treatment during incarceration or in the community following release. Moderation analyses tested the hypotheses that the benefits of receiving methadone in jail or the community on TTR were significantly different for people with only technical violations and infractions compared to misdemeanor and felony charges.

RESULTS

In the sample of 788 men who were reincarcerated, 29.4% received technical violations with no new charges (n = 232) with the remainder of the sample receiving new charges consisting of 26.9% new misdemeanor charges, 6.5% felony charges, and 37.2% both felony and misdemeanor charges. Compared to men who received new misdemeanor charges, TTR was significantly shorter among those who received technical violations and infractions with no new charges amounting to a 50% increase in TTR (334.5 days, SD = 321.3 vs. 228.1 days, SD = 308.0, p < 0.001; aHR = 1.5, 95% CI = 1.3, 1.8, p < 0.001). TTR of men who resumed methadone and were charged with a new crime was 50% longer than those who resumed methadone and received technical violations/infractions with no new charges. (230.2 days, SD = 340.2 vs. 402.3 days, SD = 231.3; aHR = 1.5, 95%CI = 1.0, 2.2, p = 0.038).

CONCLUSIONS

Reducing technical violations may enhance the benefits of providing community-based methadone following release from incarceration on extending the time between incarcerations during the vulnerable time post-incarceration and reduce the burden on correctional systems.

Comparative analysis of endophyte diversity of Dendrobium officinale lived on rock and tree

Dendrobium officinale usually lives on rock or tree, but their endophyte diversity has not yet been fully revealed? In this study, high-throughput sequencing technology was used to investigate the endophyte diversity of the roots of D. officinale lived on tree (Group 1-3, arboreal type) and rock (Group 4, lithophytic type). The results showed that their composition of endophytic fungi and bacteria were similar at phylum level, while their relative abundance were different. Their taxa composition and abundance of endophytes differed significantly among groups at the genus level. Alpha diversity of endophytic fungi of lithophytic type was higher than those from arboreal type, while there was no advantage in endophytic bacteria. Beta diversity revealed that the endophytic fungi tended to cluster in each group, but the endophytic bacteria were dispersed among the groups. LEfSe analysis found that the numbers of predicted endophyte biomarkers of lithophytic type were more than arboreal types at genus level, and the biomarkers varied among groups. Microbial network analysis revealed similarities and differences in the taxa composition and abundance of shared and special endophytes in each group. These results suggested that the root endophytes of lithophytic and arboreal D. officinale differed in diversity.

Empirical Study on Exploring the Role of CD180 and MD-1 Prognostic Indicators for the Chronic Lymphocytic Leukaemia (CLL) Disease

&lt;b&gt;Background and Objective:&lt;/b&gt; Chronic Lymphocytic Leukaemia (CLL) is a frequent type of leukaemia disease. This study was focused on investigating the role of prognostic indicators, such as CD180 and MD-1 for Chronic Lymphocytic Leukaemia (CLL) pathogenesis because they involve cell signalling and proliferation. &lt;b&gt;Materials and Methods:&lt;/b&gt; A total of 12 normal controls and 52 patients were taken to determine the expressions of CD180 and MD-1 with different variations in comparison with the IgVH (Immunoglobulin Heavy Chain variable region gene) mutational status, FISH (fluorescence &lt;i&gt;in situ&lt;/i&gt; hybridization) and Rai staging. &lt;b&gt;Results:&lt;/b&gt; The quantitative data findings were evident that CD180 and MD-1 expressions showed insignificant differences among CLL patients at the protein level based on SPSS results. On the contrary, they resulted in significant differences for subgroups of established biomarkers like Rai staging (stages 0, I, II and III), FISH (13q and non-13q deletions) and IgVH (mutated and unmutated). &lt;b&gt;Conclusion:&lt;/b&gt; The CD180 and MD-1 have been used as prognostic indicators to evaluate the outcomes relevant to the cell cycle and survival rate of CLL cells.

Composite Nanoarchitectonics of Photoactivated Titania-Based Materials with Anticancer Properties

The synthesis of titania-based composite materials with anticancer potential under visible-light irradiation is the aim of this study. In specific, titanium dioxide (TiO₂) nanoparticles (NPs) chemically modified with silver were embedded in a stimuli-responsive microgel (a crosslinked interpenetrating network (IP) network that was synthesized by poly (N-Isopropylacrylamide) and linear chains of polyacrylic acid sodium salt, forming composite particles. The ultimate goal of this research, and for our future plans, is to develop a drug-delivery system that uses optical fibers that could efficiently photoactivate NPs, targeting cancer cells. The produced Ag-TiO₂ NPs, the microgel and the composite materials were characterized through X-ray diffraction (XRD), Fourier transform infrared spectroscopy (FT-IR), micro-Raman spectroscopy, ultraviolet-visible spectroscopy (UV-Vis), dynamic light scattering (DLS) and transmission electron microscopy (TEM). Our results indicated that Ag-TiO₂ NPs were successfully embedded within the thermoresponsive microgel. Either Ag-TiO₂ NPs or the composite materials exhibited high photocatalytic degradation efficiency on the pollutant rhodamine B and significant anticancer potential under visible-light irradiation.

In situ analysis of variations of arsenicals, microbiome and transcriptome profiles along murine intestinal tract

In situ-based studies on microbiome-host interactions after arsenic exposure are few. In this study, the variations in arsenics, microbiota, and host genes along murine intestinal tracts were determined after arsenic exposure for two months. There was a gradual increase in the concentration of total As (C_tAs) in feces from ileum to colon, whereas C_tAs in the corresponding tissues were relatively stable. Differences in arsenic levels between feces and tissues were significantly different. The proportion of arsenite (iAs^Ⅲ) in feces gradually decreased, however, it gradually increased in tissues. After arsenic exposure, the diversity and abundance of microbial community and networks in each segment were significantly dysregulated. Notably, 328, 579 and 90 differently expressed genes were detected in ileum, cecum, and colon, respectively. In addition, microbiome and transcriptome analyses showed a significant correlation between the abundance of Faecalibaculum and expressions of Plb1, Hspa1b, Areg and Duoxa2 genes. This implies that they may be involved in arsenic biotransformation. In vitro experiments using Biofidobactrium and Lactobacillus showed that probiotics have arsenic transformation abilities. Therefore, gut microbiome may modulate arsenic accumulation, excretion and detoxification along the digestive tract. Moreover, the abundance and diversity of gut microbiome may be related to the changes in host health.

Kernel density estimation in mixture models with known mixture proportions

In this article, we consider the density estimation for data with a mixture structure, where the component densities are assumed unknown, but for each observation, the probabilities of its membership to the subpopulations are known or estimable from other resources. Data of this kind arise from practice and have wide applications. Motivated from the classical kernel density estimation method for a single population, we propose a weighted kernel density estimation method to estimate the component density functions nonparametrically. Within the framework of the EM algorithm, we derive an algorithm that computes our proposed estimates effectively. Via extensive simulation studies, we demonstrate that our methods outperform the existing methods in most occasions. We further compare our methods with existing methods by real data examples.

GPC3 affects the prognosis of lung adenocarcinoma and lung squamous cell carcinoma

Background

Glypican 3 (GPC3) is a heparin sulphate proteoglycan whose expression is associated with several malignancies. However, its expression in non-small-cell lung carcinoma (NSCLC) is limited and ambiguous. This study aimed to comprehensively evaluate the expression of GPC3 in NSCLC and develop a risk-score model for predicting the prognosis of NSCLC.

Methods

The gene expression profiles of lung adenocarcinoma (LUAD) and lung squamous cell carcinoma (LUSC) were downloaded from the UCSC Xena database. Using the limma package, the differentially expressed genes (DEGs) between different comparison groups were analysed and the differential expression of GPC3 was calculated. A functional enrichment analysis was conducted for GPC3-associated genes using the DAVID tool. For the GPC3-associated genes shared by the four comparison groups, a protein–protein interaction network was built using the Cytoscape software. After conducting a survival analysis and a Cox regression analysis, the genes found to be significantly correlated with prognosis were selected to construct a risk-score model. Besides, the gene and protein levels of GPC3 were examined by quantitative reverse transcriptase-PCR (qRT-PCR) and immunohistochemistry (IHC) in LUSC tissues and paracancer tissues.

Results

The differential expression of GPC3 was significant (adjusted P < 0.05) in the NSCLC vs. normal, LUAD vs. normal, LUSC versus normal, and LUAD versus. LUSC comparison groups. GPC3 directly interacted with SERPINA1, MFI2, and FOXM1. Moreover, GPC3 expression was significantly correlated with pathologic N, pathologic T, gender, and tumour stage in LUAD samples. Finally, the risk-score model (involving MFI2, FOXM1, and GPC3) for LUAD and that (involving SERPINA1 and FOXM1) for LUSC were established separately. The qRT-PCR result showed that GPC3 expression was much higher in the LUSC tissues than that in the normal group. The IHC results further showed that GPC3 is highly expressed in LUSC tissues, but low in paracancer tissues.

Conclusion

The three-gene risk-score model for LUAD and the two-gene risk-score model for LUSC might be valuable in improving the prognosis of these carcinomas.

Biological Effect of Silver-modified Nanostructured Titanium Dioxide in Cancer

BACKGROUND/AIM

Nanomedicine is a promising scientific field that exploits the unique properties of innovative nanomaterials, providing alternative solutions in diagnostics, prevention and therapeutics. Titanium dioxide nanoparticles (TiO₂ NPs) have a great spectrum of photocatalytic antibacterial and anticancer applications. The chemical modification of TiO₂ optimizes its bioactive performance. The aim of this study was the development of silver modified NPs (Ag/TiO₂ NPs) with anticancer potential.

MATERIALS AND METHODS

Ag/TiO₂ NPs were prepared through the sol-gel method, were fully characterized and were tested on cultured breast cancer epithelial cells (MCF-7 and MDA-MB-231). The MTT colorimetric assay was used to estimate cellular viability. Western blot analysis of protein expression along with a DNA-laddering assay were employed for apoptosis detection.

RESULTS AND CONCLUSION

We show that photo-activated Ag/TiO₂ NPs exhibited significant cytotoxicity on the highly malignant MDA-MB-231 cancer cells, inducing apoptosis, while MCF-7 cells that are characterized by low invasive properties were unaffected under the same conditions.

A generalized robust allele-based genetic association test

The allele-based association test, comparing allele frequency difference between case and control groups, is locally most powerful. However, application of the classical allelic test is limited in practice, because the method is sensitive to the Hardy-Weinberg equilibrium (HWE) assumption, not applicable to continuous traits, and not easy to account for covariate effect or sample correlation. To develop a generalized robust allelic test, we propose a new allele-based regression model with individual allele as the response variable. We show that the score test statistic derived from this robust and unifying regression framework contains a correction factor that explicitly adjusts for potential departure from HWE and encompasses the classical allelic test as a special case. When the trait of interest is continuous, the corresponding allelic test evaluates a weighted difference between individual-level allele frequency estimate and sample estimate where the weight is proportional to an individual's trait value, and the test remains valid under Y-dependent sampling. Finally, the proposed allele-based method can analyze multiple (continuous or binary) phenotypes simultaneously and multiallelic genetic markers, while accounting for covariate effect, sample correlation, and population heterogeneity. To support our analytical findings, we provide empirical evidence from both simulation and application studies.

Comparative Analysis of the Gut Microbial Communities of the Eurasian Kestrel (Falco tinnunculus) at Different Developmental Stages

The gut microflora play a very important role in the life of animals. Although an increasing number of studies have investigated the gut microbiota of birds in recent years, there is a lack of research work on the gut microbiota of wild birds, especially carnivorous raptors, which are thought to be pathogen vectors. There are also a lack of studies focused on the dynamics of the gut microbiota during development in raptors. In this study, 16S rRNA gene amplicon high-throughput sequencing was used to analyze the gut microbiota community composition of a medium-sized raptor, the Eurasian Kestrel (Falco tinnunculus), and to reveal stage-specific signatures in the gut microbiota of nestlings during the pre-fledging period. Moreover, differences in the gut microbiota between adults and nestlings in the same habitat were explored. The results indicated that the Eurasian Kestrel hosts a diverse assemblage of gut microbiota. Proteobacteria, Firmicutes, Actinobacteria, and Bacteroidetes were the primary phyla shared within the guts of adults and chicks. However, adults harbored higher abundances of Proteobacteria while nestlings exhibited higher abundances of Firmicutes and Actinobacteria, and consequently the majority of dominant genera observed in chicks differed from those in adults. Although no significant differences in diversity were observed across the age groups during nestling ontogeny, chicks from all growth stages harbored richer and more diverse bacterial communities than adults. In contrast, the differences in gut microbial communities between adults and younger nestlings were more pronounced. The gut microbes of the nestlings in the last growth stage were converged with those of the adults. This study provides basic reference data for investigations of the gut microbiota community structure of wild birds and deepens our understanding of the dynamics of the gut microflora during raptor development.

Peri-operative antibiotics acutely and significantly impact intestinal microbiota following bariatric surgery

Bariatric surgery is the most effective treatment for weight loss. Vertical sleeve gastrectomy (VSG) involves the resection of ~ 80% of the stomach and was conceived to purely restrict oral intake. However, evidence suggests more complex mechanisms, particularly postoperative changes in gut microbiota, in facilitating weight loss and resolving associated comorbidities. VSG in humans is a complex procedure and includes peri-operative antibiotics and caloric restriction in addition to the altered anatomy. The impact of each of these factors on the intestinal microbiota have not been evaluated. The aim of this study was to determine the relative contributions of each of these factors on intestinal microbiota composition following VSG prior to substantial weight loss. Thirty-two obese patients underwent one of three treatments: (1) VSG plus routine intravenous peri-operative antibiotics (n = 12), (2) VSG with intravenous vancomycin chosen for its low intestinal penetrance (n = 12), and (3) caloric restriction (n = 8). Fecal samples were evaluated for bacterial composition prior to and 7 days following each intervention. Only patients undergoing VSG with routine peri-operative antibiotics showed a significant shift in community composition. Our data support the single dose of routine peri-operative antibiotics as the most influential factor of intestinal microbial composition acutely following VSG.

Analytical strategies to include the X-chromosome in variance heterogeneity analyses: Evidence for trait-specific polygenic variance structure

Genotype-stratified variance of a quantitative trait could differ in the presence of gene-gene or gene-environment interactions. Genetic markers associated with phenotypic variance are thus considered promising candidates for follow-up interaction or joint location-scale analyses. However, as in studies of main effects, the X-chromosome is routinely excluded from "whole-genome" scans due to analytical challenges. Specifically, as males carry only one copy of the X-chromosome, the inherent sex-genotype dependency could bias the trait-genotype association, through sexual dimorphism in quantitative traits with sex-specific means or variances. Here we investigate phenotypic variance heterogeneity associated with X-chromosome single nucleotide polymorphisms (SNPs) and propose valid and powerful strategies. Among those, a generalized Levene's test has adequate power and remains robust to sexual dimorphism. An alternative approach is a sex-stratified analysis but at the cost of slightly reduced power and modeling flexibility. We applied both methods to an Estonian study of gene expression quantitative trait loci (eQTL; n = 841), and two complex trait studies of height, hip, and waist circumferences, and body mass index from Multi-Ethnic Study of Atherosclerosis (MESA; n = 2,073) and UK Biobank (UKB; n = 327,393). Consistent with previous eQTL findings on mean, we found some but no conclusive evidence for cis regulators being enriched for variance association. SNP rs2681646 is associated with variance of waist circumference (p = 9.5E-07) at X-chromosome-wide significance in UKB, with a suggestive female-specific effect in MESA (p = 0.048). Collectively, an enrichment analysis using permutated UKB (p < 0.1) and MESA (p < 0.01) datasets, suggests a possible polygenic structure for the variance of human height.

The Gut Microbiota in Women Suffering from Gestational Diabetes Mellitus with the Failure of Glycemic Control by Lifestyle Modification

Gestational diabetes mellitus (GDM) is prevalent worldwide, leading to a high risk of significant morbidity for both the mother and offspring with complications. Increasing evidences suggest that gut microbiota plays a role in the pathogenesis of GDM. Lifestyle modification is the cornerstones of GDM treatment. However, a number of patients whose blood glucose is not controlled by lifestyle modification still require exogenous insulin to control blood glucose. No observational study is available about the relationship between the gut microbiota in GDM patients and lifestyle modifications. Thus, we investigated the differences in gut microbiota between GDM patients with successful glycemic control (GDM1) and failure of glycemic control (GDM2) by lifestyle modifications. We sequenced the V3-V4 regions of 16S ribosomal ribonucleic acid (rRNA) gene from stool samples of 52 singleton pregnant women with 24-28 weeks of gestation. Our results showed that Blautia, Eubacterium_hallii_group, and Faecalibacterium in the gut microbiota showed significant differences among the normoglycemic mother, GDM1, and GDM2 groups, respectively. The combined diagnostic performance of Blautia, Eubacterium_hallii_group, and Faecalibacterium in differentiating GDM2 from GDM was considered as the most reasonable identification indicator. Gut bacteria may participate in the pathological development of GDM2 through the peroxisome proliferator-activated receptor (PPAR) signaling pathway. These results indicated that Blautia, Eubacterium_hallii_group, and Faecalibacterium had important characteristic changes in the gut microbiota of women with GDM2.

Practical considerations for sampling and data analysis in contemporary metagenomics-based environmental studies

Recent advancements in metagenomic-based studies, especially analyses of amplicon-based DNA sequencing targeting taxonomic marker genes, has led to an unprecedented characterization of microbial communities from diverse ecosystems around the world. While originally constrained by a lack of appropriate analytical tools and sequencing depth, new technologies and computational and statistical algorithms have been developed to handle highly dimensional, next-generation sequencing datasets. Both these tools allow for the robust analysis of structural and distributional patterns of microbiota essential for the understanding of microbial ecology and biogeography. Furthermore, consortia of individual laboratories working on large interdisciplinary research programs, like the Human and Earth Microbiome Projects, have developed standardized protocols for DNA extraction, sequencing pipelines, and bioinformatics. These approaches provide large repositories of publicly available data to serve as references for on-going and future, hypothesis-driven studies to better characterize the roles of microbial communities in diverse ecosystems. In this review, we outline the currently available statistical approaches and tools to aid in statistically powered study designs and analyses. Given what is now known about the enormous diversity and variability of the microbial communities in aquatic and terrestrial habitats, we also discuss practical considerations for sample collection. Due to the extensive advances made in the field of metagenomics over the last decade, rigorous, well replicated, hypothesis-driven studies are: 1) needed, 2) now possible, and 3) essential to make best use of sequencing-based technologies to characterize the roles of microbial communities in the structure and function of diverse ecosystems.

pKWmEB: integration of Kruskal-Wallis test with empirical Bayes under polygenic background control for multi-locus genome-wide association study

Although nonparametric methods in genome-wide association studies (GWAS) are robust in quantitative trait nucleotide (QTN) detection, the absence of polygenic background control in single-marker association in genome-wide scans results in a high false positive rate. To overcome this issue, we proposed an integrated nonparametric method for multi-locus GWAS. First, a new model transformation was used to whiten the covariance matrix of polygenic matrix K and environmental noise. Using the transferred model, Kruskal-Wallis test along with least angle regression was then used to select all the markers that were potentially associated with the trait. Finally, all the selected markers were placed into multi-locus model, these effects were estimated by empirical Bayes, and all the nonzero effects were further identified by a likelihood ratio test for true QTN detection. This method, named pKWmEB, was validated by a series of Monte Carlo simulation studies. As a result, pKWmEB effectively controlled false positive rate, although a less stringent significance criterion was adopted. More importantly, pKWmEB retained the high power of Kruskal-Wallis test, and provided QTN effect estimates. To further validate pKWmEB, we re-analyzed four flowering time related traits in Arabidopsis thaliana, and detected some previously reported genes that were not identified by the other methods.

Diurnal cycling of rhizosphere bacterial communities is associated with shifts in carbon metabolism

BACKGROUND

The circadian clock regulates plant metabolic functions and is an important component in plant health and productivity. Rhizosphere bacteria play critical roles in plant growth, health, and development and are shaped primarily by soil communities. Using Illumina next-generation sequencing and high-resolution mass spectrometry, we characterized bacterial communities of wild-type (Col-0) Arabidopsis thaliana and an acyclic line (OX34) ectopically expressing the circadian clock-associated cca1 transcription factor, relative to a soil control, to determine how cycling dynamics affected the microbial community. Microbial communities associated with Brachypodium distachyon (BD21) were also evaluated.

RESULTS

Significantly different bacterial community structures (P = 0.031) were observed in the rhizosphere of wild-type plants between light and dark cycle samples. Furthermore, 13% of the community showed cycling, with abundances of several families, including Burkholderiaceae, Rhodospirillaceae, Planctomycetaceae, and Gaiellaceae, exhibiting fluctuation in abundances relative to the light cycle. However, limited-to-no cycling was observed in the acyclic CCAox34 line or in soil controls. Significant cycling was also observed, to a lesser extent, in Brachypodium. Functional gene inference revealed that genes involved in carbohydrate metabolism were likely more abundant in near-dawn, dark samples. Additionally, the composition of organic matter in the rhizosphere showed a significant variation between dark and light cycles.

CONCLUSIONS

The results of this study suggest that the rhizosphere bacterial community is regulated, to some extent, by the circadian clock and is likely influenced by, and exerts influences, on plant metabolism and productivity. The timing of bacterial cycling in relation to that of Arabidopsis further suggests that diurnal dynamics influence plant-microbe carbon metabolism and exchange. Equally important, our results suggest that previous studies done without relevance to time of day may need to be reevaluated with regard to the impact of diurnal cycles on the rhizosphere microbial community.

Differential Impacts of Land-Based Sources of Pollution on the Microbiota of Southeast Florida Coral Reefs

Coral reefs are dynamic ecosystems known for decades to be endangered due, in large part, to anthropogenic impacts from land-based sources of pollution (LBSP). In this study, we utilized an Illumina-based next-generation sequencing approach to characterize prokaryotic and fungal communities from samples collected off the southeast coast of Florida. Water samples from coastal inlet discharges, oceanic outfalls of municipal wastewater treatment plants, treated wastewater effluent before discharge, open ocean samples, and coral tissue samples (mucus and polyps) were characterized to determine the relationships between microbial communities in these matrices and those in reef water and coral tissues. Significant differences in microbial communities were noted among all sample types but varied between sampling areas. Contamination from outfalls was found to be the greatest potential source of LBSP influencing native microbial community structure among all reef samples, although pollution from inlets was also noted. Notably, reef water and coral tissue communities were found to be more greatly impacted by LBSP at southern reefs, which also experienced the most degradation during the course of the study. The results of this study provide new insights into how microbial communities from LBSP can impact coral reefs in southeast Florida and suggest that wastewater outfalls may have a greater influence on the microbial diversity and structure of these reef communities than do contaminants carried in runoff, although the influences of runoff and coastal inlet discharge on coral reefs are still substantial.

IMPORTANCE Coral reefs are known to be endangered due to sewage discharge and to runoff of nutrients, pesticides, and other substances associated with anthropogenic activity. Here, we used next-generation sequencing to characterize the microbial communities of potential contaminant sources in order to determine how environmental discharges of microbiota and their genetic material may influence the microbiomes of coral reef communities and coastal receiving waters. Runoff delivered through inlet discharges impacted coral microbial communities, but impacts from oceanic outfalls carrying treated wastewater were greater. Geographic differences in the degree of impact suggest that coral microbiomes may be influenced by the microbiological quality of treated wastewater.

Amplicon-based taxonomic characterization of bacteria in urban and peri-urban roof-harvested rainwater stored in tanks

Overall, 26% of Australian households use rainwater tanks as a source of potable and nonpotable water. Limited information is available on the total bacterial communities in tank water. Therefore, identification of dominant bacterial communities, diversity, and their distribution is important in understanding the microbial quality of tank water. In this study, the abundance and diversity of bacterial communities in 88 tank water samples collected from the urban areas of Brisbane (n=44) and the peri-urban center of Currumbin (n=44) in Southeast Queensland, Australia were determined using amplicon-based Illumina next-generation sequencing. In addition, the SourceTracker program was used to identify the sources of fecal contamination in tank water samples. Sequence reads were also analyzed to detect potential bacterial pathogenic genera in the tank water samples collected. Differences in sample coverage, alpha diversity, and richness did not differ significantly between the Brisbane and Currumbin tank water samples. Comamonadaceae and Planctomycetaceae were the most abundant families in all tank water samples. Curvibacter was the most abundant genus in all tank water samples. SourceTracker revealed that around 34% (Brisbane) and 43% (Currumbin) of tank water samples had a signature for bird fecal contamination. The potential opportunistic pathogenic genera including Burkholderia, Chromobacterium, Clostridium, Legionella, Mycobacterium, Nocardia, and Pseudomonas were most prevalent in tank water samples. Next-generation sequencing can be used as an initial screening tool to identify a wide array of potential pathogenic genera in tank water samples followed by quantifying specific pathogen(s) of interest using more sensitive molecular assays such as quantitative PCR (qPCR).

Sleeve gastrectomy drives persistent shifts in the gut microbiome

BACKGROUND

Changes in the gut microbiome following bariatric surgery have been causally linked to metabolic benefits.

OBJECTIVES

We sought to characterize and assess the stability of gut microbiome shifts following sleeve gastrectomy (SG).

SETTING

University laboratories.

METHODS

Diet-induced obese mice were randomized to SG or sham surgery. Mice were housed individually or cohoused such that one SG mouse was housed with one weight-matched, sham-operated mouse. Fecal samples were collected before and on postoperative days 7 and 28. Bacterial composition in feces was characterized by using next-generation Illumina sequencing of 16 S rRNA.

RESULTS

SG mice lost more weight and were more insulin sensitive than sham mice independent of housing status (P<.05). One week following surgery, fecal samples from all mice showed shifts in the microbiome that only persisted in SG-operated mice. Cohousing did not alter the microbial composition of SG-operated mice. Cohoused sham-operated mice showed a unique shift in microbial composition on postoperative day 28 that differed from individually housed, sham-operated mice (P<.001). Cohousing did not affect metabolic outcomes of either SG or sham surgeries.

CONCLUSION

SG results in acute and sustained shifts in the gut microbiome. SG associated shifts are not altered by reexposure to obesity-associated gut microbiota.

Complete Microbiota Engraftment Is Not Essential for Recovery from Recurrent Clostridium difficile Infection following Fecal Microbiota Transplantation

Bacterial communities from subjects treated for recurrent Clostridium difficile infection (rCDI) by fecal microbiota transplantation (FMT), using either heterologous donor stool samples or autologous stool samples, were characterized by Illumina next-generation sequencing. As previously reported, the success of heterologous FMT (90%) was superior to that of autologous FMT (43%) (P = 0.019), and post-FMT intestinal bacterial communities differed significantly between treatment arms (P < 0.001). Subjects cured by autologous FMT typically had greater abundances of the Clostridium XIVa clade and Holdemania bacteria prior to treatment, and the relative abundances of these groups increased significantly after FMT compared to heterologous FMT and pre-FMT samples. The typical shift to post-FMT, donor-like assemblages, featuring high relative abundances of genera within the Bacteroidetes and Firmicutes phyla, was not observed in the autologous FMT subjects. Autologous FMT patient bacterial communities were significantly different in composition than those for heterologous FMT patients and donors (P < 0.001). The SourceTracker program, which employs a Bayesian algorithm to determine source contributions to sink communities, showed that patients initially treated by heterologous FMT had significantly higher percentages of engraftment (i.e., similarity to donor communities, mean value of 74%) compared to those who suffered recurrence following autologous FMT (1%) (P ≤ 0.013). The findings of this study suggest that complete donor engraftment may be not necessary if functionally critical taxa are present in subjects following antibiotic therapy.

This study provides a detailed characterization of fecal bacterial communities in subjects who participated in a previously published randomized clinical trial to treat recurrent C. difficile infection (rCDI). Bacterial communities were characterized to determine differences between subjects who received fecal bacteria either from healthy donor stool samples or their own stool samples as “placebo” in order to determine which groups of bacteria were most important in achieving a cure. The results of this study suggested that bacteria associated with secondary bile acid metabolism could potentially provide resistance to infection and that complete transfer of healthy donor microorganisms was not necessary to resolve CDI following unsuccessful antibiotic treatment.

Effect of Fecal Microbiota Transplantation on Recurrence in Multiply Recurrent Clostridium difficile Infection: A Randomized Trial

BACKGROUND

To date, evidence for the efficacy of fecal microbiota transplantation (FMT) in recurrent Clostridium difficile infection (CDI) has been limited to case series and open-label clinical trials.

OBJECTIVE

To determine the efficacy and safety of FMT for treatment of recurrent CDI.

DESIGN

Randomized, controlled, double-blind clinical trial. (ClinicalTrials.gov: NCT01703494).

SETTING

Two academic medical centers.

PATIENTS

46 patients who had 3 or more recurrences of CDI and received a full course of vancomycin for their most recent acute episode.

INTERVENTION

Fecal microbiota transplantation with donor stool (heterologous) or patient's own stool (autologous) administered by colonoscopy.

MEASUREMENTS

The primary end point was resolution of diarrhea without the need for further anti-CDI therapy during the 8-week follow-up. Safety data were compared between treatment groups via review of adverse events (AEs), serious AEs (SAEs), and new medical conditions for 6 months after FMT. Fecal microbiota analyses were performed on patients' stool before and after FMT and also on donors' stool.

RESULTS

In the intention-to-treat analysis, 20 of 22 patients (90.9%) in the donor FMT group achieved clinical cure compared with 15 of 24 (62.5%) in the autologous FMT group (P = 0.042). Resolution after autologous FMT differed by site (9 of 10 vs. 6 of 14 [P = 0.033]). All 9 patients who developed recurrent CDI after autologous FMT were free of further CDI after subsequent donor FMT. There were no SAEs related to FMT. Donor FMT restored gut bacterial community diversity and composition to resemble that of healthy donors.

LIMITATION

The study included only patients who had 3 or more recurrences and excluded those who were immunocompromised and aged 75 years or older.

CONCLUSION

Donor stool administered via colonoscopy seemed safe and was more efficacious than autologous FMT in preventing further CDI episodes.

PRIMARY FUNDING SOURCE

National Institute of Diabetes and Digestive and Kidney Diseases.

Bias Characterization in Probabilistic Genotype Data and Improved Signal Detection with Multiple Imputation

Missing data are an unavoidable component of modern statistical genetics. Different array or sequencing technologies cover different single nucleotide polymorphisms (SNPs), leading to a complicated mosaic pattern of missingness where both individual genotypes and entire SNPs are sporadically absent. Such missing data patterns cannot be ignored without introducing bias, yet cannot be inferred exclusively from nonmissing data. In genome-wide association studies, the accepted solution to missingness is to impute missing data using external reference haplotypes. The resulting probabilistic genotypes may be analyzed in the place of genotype calls. A general-purpose paradigm, called Multiple Imputation (MI), is known to model uncertainty in many contexts, yet it is not widely used in association studies. Here, we undertake a systematic evaluation of existing imputed data analysis methods and MI. We characterize biases related to uncertainty in association studies, and find that bias is introduced both at the imputation level, when imputation algorithms generate inconsistent genotype probabilities, and at the association level, when analysis methods inadequately model genotype uncertainty. We find that MI performs at least as well as existing methods or in some cases much better, and provides a straightforward paradigm for adapting existing genotype association methods to uncertain data.

Genetic research has been focused at analysis of datapoints that are assumed to be deterministically known. However, the majority of current, high throughput data is only probabilistically known, and proper methods for handing such uncertain genotypes are limited. Here, we build on existing theory from the field of statistics to introduce a general framework for handling probabilistic genotype data obtained through genotype imputation. This framework, called Multiple Imputation, matches or improves upon existing methods for handling uncertainty in basic analysis of genetic association. As opposed to such methods, our work furthermore extends to more advanced analysis, such as mixed-effects models, with no additional complication. Importantly, it generates posterior probabilities of association that are intrinsically weighted by the certainty of the underlying data, a feature unmatched by other existing methods. Multiple Imputation is also fully compatible with meta-analysis. Finally, our analysis of probabilistic genotype data brings into focus the accuracy and unreliability of imputation’s estimated probabilities. Taken together, these results substantially increase the utility of imputed genotypes in statistical genetics, and may have strong implications for analysis of sequencing data moving forward.

Regional Similarities and Consistent Patterns of Local Variation in Beach Sand Bacterial Communities throughout the Northern Hemisphere

Recent characterization of the bacterial community structure in beach sands has revealed patterns of biogeography similar to those observed in aquatic environments. Studies to date, however, have mainly focused on subtidal sediments from marine beaches. Here, we investigate the bacterial diversity, using Illumina-based sequencing of the V5-V6 region of the 16S rRNA gene, at 11 beaches representing those next to the Great Lakes, Florida, and the Pacific Ocean. The alpha diversity differed significantly among regions (P< 0.0001), while the within-region diversity was more similar. The beta diversity also differed by region (P< 0.001), where freshwater sands had significantly higher abundances of taxa within the Actinobacteria, Betaproteobacteria, and Verrucomicrobia than marine environments. In contrast, marine sands harbored greater abundances of Gamma proteobacteria and Planctomycetes, and those from Florida had more Delta proteobacteria and Firmicutes Marine beaches had significantly different phylogenetic community structures (P ≤ 0.018), but freshwater and Florida beaches showed fewer within-region phylogenetic differences. Furthermore, regionally distinct patterns in taxonomic variation were observed in backshore sands, which had communities distinct from those in nearshore sands (P< 0.001). Sample depth minimally influenced the community composition. The results of this study reveal distinct bacterial community structures in sand on a broad geographic scale but moderate regional similarity and suggest that local variation is primarily related to the distance from the shoreline. This study offers a novel comparison of the bacterial communities in freshwater and marine beach sands and provides an important basis for future comparisons and analyses to elucidate factors affecting microbial ecology in this underexplored environment.

IMPORTANCE

This study presents a large-scale geographic characterization of the bacterial communities present in beach sands. While previous studies have evaluated how environmental factors influence bacterial community composition, few have evaluated bacterial communities in freshwater sands. Furthermore, the use of a consistent methodology to characterize bacterial communities here allowed a novel comparison of communities across geographic regions. We reveal that while the community composition in sands at individual beaches is distinct, beach sands within the same region harbor similar assemblages of bacteria and these assemblages differ greatly between regions. In addition, moisture, associated with distance from the shoreline, strongly influences the bacteria present in sands and more strongly influences the bacteria present than sample depth does. Thus, the data presented here offer an important basis for a broader characterization of the ecology of bacteria in sands, which may also be relevant to public health and resource management initiatives.

A Joint Location-Scale Test Improves Power to Detect Associated SNPs, Gene Sets, and Pathways

Gene-based, pathway, and other multivariate association methods are motivated by the possibility of GxG and GxE interactions; however, accounting for such interactions is limited by the challenges associated with adequate modeling information. Here we propose an easy-to-implement joint location-scale (JLS) association testing framework for single-variant and multivariate analysis that accounts for interactions without explicitly modeling them. We apply the JLS method to a gene-set analysis of cystic fibrosis (CF) lung disease, which is influenced by multiple environmental and genetic factors. We identify and replicate an association between the constituents of the apical plasma membrane and CF lung disease (p = 0.0099 and p = 0.0180, respectively) and highlight a role for the SLC9A3-SLC9A3R1/2-EZR complex in contributing to CF lung disease. Many association studies could benefit from re-analysis with the JLS method that leverages complex genetic architecture for SNP, gene, and pathway identification. Analytical verification, simulation, and additional proof-of-principle applications support our approach.

Evaluation of water sampling methodologies for amplicon-based characterization of bacterial community structure

Reduction in costs of next-generation sequencing technologies has allowed unprecedented characterization of bacterial communities from environmental samples including aquatic ecosystems. However, the extent to which extrinsic factors including sampling volume, sample replication, DNA extraction kits, and sequencing target affect the community structure inferred are poorly explored. Here, triplicate 1, 2, and 6L volume water samples from the Upper Mississippi River were processed to determine variation among replicates and sample volumes. Replicate variability significantly influenced differences in the community α-diversity (P=0.046), while volume significantly changed β-diversity (P=0.037). Differences in phylogenetic and taxonomic community structure differed both among triplicate samples and among the volumes filtered. Communities from 2L and 6L water samples showed similar clustering via discriminant analysis. To assess variation due to DNA extraction method, DNA was extracted from triplicate cell pellets from four sites along the Upper Mississippi River using the Epicentre Metagenomic DNA Isolation Kit for Water and MoBio PowerSoil kit. Operational taxonomic units representing ≤14% of sequence reads differed significantly among all sites and extraction kits used, although differences in diversity and community coverage were not significant (P≥0.057). Samples characterized using only the V6 region had significantly higher coverage and lower richness and α-diversity than those characterized using V4-V6 regions (P<0.001). Triplicate sampling of at least 2L of water provides robust representation of community variability, and these results indicate that DNA extraction kit and sequencing target displayed taxonomic biases that did not affect the overall biological conclusions drawn.

Species sorting and seasonal dynamics primarily shape bacterial communities in the Upper Mississippi River

Bacterial community structure (BCS) in freshwater ecosystems varies seasonally and due to physicochemical gradients, but metacommunity structure of a major river remains understudied. Here we characterize the BCS along the Mississippi River and contributing rivers in Minnesota over three years using Illumina next-generation sequencing, to determine how changes in environmental conditions as well as inputs from surrounding land and confluences impacted community structure. Contributions of sediment to water microbial diversity were also evaluated. Long-term variation in community membership was observed, and significant shifts in relative abundances of major freshwater taxa, including α-Proteobacteria, Burkholderiales, and Actinomycetales, were observed due to temporal and spatial variations. Environmental parameters (e.g. temperature, rainfall, and nutrient concentrations) primarily contributed to differences in phyla abundances (88% of variance), with minimal influence from spatial distance alone (<1% of variance). Furthermore, an annually-recurrent BCS was observed in late summer, further suggesting that seasonal dynamics strongly influence community composition. Sediment communities differed from those in the water, but contributed up to 50% to community composition in the water column. Among water sampling sites, 34% showed significant variability in BCS of replicate samples indicating variability among riverine communities due to heterogeneity in the water column. Results of this study highlight the need for a better understanding of spatial and temporal variations in riverine bacterial diversity associated with physicochemical gradients and reveal how communities in sediments, and potentially other environmental reservoirs, impact waterborne BCS. Techniques used in this study may prove useful to determine sources of microbes from sediments and soils to waterways, which will facilitate best management practices and total maximum daily load determinations.

Association studies with imputed variants using expectation-maximization likelihood-ratio tests

Genotype imputation has become standard practice in modern genetic studies. As sequencing-based reference panels continue to grow, increasingly more markers are being well or better imputed but at the same time, even more markers with relatively low minor allele frequency are being imputed with low imputation quality. Here, we propose new methods that incorporate imputation uncertainty for downstream association analysis, with improved power and/or computational efficiency. We consider two scenarios: I) when posterior probabilities of all potential genotypes are estimated; and II) when only the one-dimensional summary statistic, imputed dosage, is available. For scenario I, we have developed an expectation-maximization likelihood-ratio test for association based on posterior probabilities. When only imputed dosages are available (scenario II), we first sample the genotype probabilities from its posterior distribution given the dosages, and then apply the EM-LRT on the sampled probabilities. Our simulations show that type I error of the proposed EM-LRT methods under both scenarios are protected. Compared with existing methods, EM-LRT-Prob (for scenario I) offers optimal statistical power across a wide spectrum of MAF and imputation quality. EM-LRT-Dose (for scenario II) achieves a similar level of statistical power as EM-LRT-Prob and, outperforms the standard Dosage method, especially for markers with relatively low MAF or imputation quality. Applications to two real data sets, the Cebu Longitudinal Health and Nutrition Survey study and the Women’s Health Initiative Study, provide further support to the validity and efficiency of our proposed methods.

Reader reaction on the generalized Kruskal-Wallis test for genetic association studies incorporating group uncertainty

Acar and Sun (2013, Biometrics 69, 427-435) presented a generalized Kruskal-Wallis (GKW) test for genetic association studies that incorporated the genotype uncertainty and showed its robust and competitive performance compared to existing methods. We present another interesting way to derive the GKW test via a rank linear model.

Re-ranking sequencing variants in the post-GWAS era for accurate causal variant identification

Next generation sequencing has dramatically increased our ability to localize disease-causing variants by providing base-pair level information at costs increasingly feasible for the large sample sizes required to detect complex-trait associations. Yet, identification of causal variants within an established region of association remains a challenge. Counter-intuitively, certain factors that increase power to detect an associated region can decrease power to localize the causal variant. First, combining GWAS with imputation or low coverage sequencing to achieve the large sample sizes required for high power can have the unintended effect of producing differential genotyping error among SNPs. This tends to bias the relative evidence for association toward better genotyped SNPs. Second, re-use of GWAS data for fine-mapping exploits previous findings to ensure genome-wide significance in GWAS-associated regions. However, using GWAS findings to inform fine-mapping analysis can bias evidence away from the causal SNP toward the tag SNP and SNPs in high LD with the tag. Together these factors can reduce power to localize the causal SNP by more than half. Other strategies commonly employed to increase power to detect association, namely increasing sample size and using higher density genotyping arrays, can, in certain common scenarios, actually exacerbate these effects and further decrease power to localize causal variants. We develop a re-ranking procedure that accounts for these adverse effects and substantially improves the accuracy of causal SNP identification, often doubling the probability that the causal SNP is top-ranked. Application to the NCI BPC3 aggressive prostate cancer GWAS with imputation meta-analysis identified a new top SNP at 2 of 3 associated loci and several additional possible causal SNPs at these loci that may have otherwise been overlooked. This method is simple to implement using R scripts provided on the author's website.

As next-generation sequencing (NGS) costs continue to fall and genome-wide association study (GWAS) platform coverage improves, the human genetics community is positioned to identify potentially causal variants. However, current NGS or imputation-based studies of either the whole genome or regions previously identified by GWAS have not yet been very successful in identifying causal variants. A major hurdle is the development of methods to distinguish disease-causing variants from their highly-correlated proxies within an associated region. We show that various common factors, such as differential sequencing or imputation accuracy rates and linkage disequilibrium patterns, with or without GWAS-informed region selection, can substantially decrease the probability of identifying the correct causal SNP, often by more than half. We then describe a novel and easy-to-implement re-ranking procedure that can double the probability that the causal SNP is top-ranked in many settings. Application to the NCI Breast and Prostate Cancer (BPC3) Cohort Consortium aggressive prostate cancer data identified new top SNPs within two associated loci previously established via GWAS, as well as several additional possible causal SNPs that had been previously overlooked.