Citations

AbstractPurposeTo demonstrate a novel MR elastography (MRE) technique, termed here wavelet MRE. With this technique, broadband motion sensitivity is achievable. Moreover, the true tissue displacement can be reconstructed with a simple inverse transform.MethodsA wavelet MRE sequence was developed with motion‐encoding gradients based on Haar wavelets. From the phase images' displacement was estimated using an inverse transform. Simulations were performed using a frequency sweep and a transient as ground‐truth motions. A PVC phantom was scanned using wavelet MRE and standard MRE with both transient (one and 10 cycles of 90‐Hz motion) and steady‐state dual‐frequency motion (30 and 60 Hz) for comparison. The technique was tested in a human brain, and motion trajectories were estimated for each voxel.ResultsIn simulation, the displacement information estimated from wavelet MRE closely matched the true motion. In the phantom test, the MRE phase data generated from the displacement information derived from wavelet MRE agreed well with standard MRE data. Testing of wavelet MRE to assess transient motion waveforms in the brain was successful, and the tissue motion observed was consistent with a previous study.ConclusionThe uniform and broadband frequency response of wavelet MRE makes it a promising method for imaging transient, multifrequency motion, or motion with unknown frequency content. One potential application is measuring the response of brain tissue undergoing low‐amplitude, transient vibrations as a model for the study of traumatic brain injury.

Abstract Background Staphylococcus aureus (SA) infections, both methicillin-susceptible and methicillin-resistant (MSSA, MRSA), cause significant morbidity among infants in neonatal intensive care units (NICU). Colonization usually precedes infection. We studied the molecular epidemiology of SA colonization in NICU infants using whole genome sequencing (WGS) and in relation to the opening of a new NICU with single-family rooms. Methods From 2019-22, a prospective cohort of infants admitted to the Level IV NICU at Mayo Clinic, Rochester, MN was screened twice weekly for SA colonization using cultures of nares/axilla/groin swabs. Infection control procedures were consistent throughout, including use of Contact Precautions for infants colonized with MRSA but not for MSSA. The study was divided into period 1 (1/1/2019-11/30/2020) and period 2 (12/1/2020-12/31/2022) due to a move from a 34-bed unit (26 beds in four open-bay rooms and 8 beds in single-family rooms) to a new 34-bed unit (all single-family rooms). WGS was performed using Illumina MiSeq instrumentation and chemistry with Illumina Nextera XT library chemistry. Assembly and core genome multilocus sequence typing analysis were performed with Ridom SeqSphere+ software. Groups of related isolates were identified based on the number of allelic differences (related 0-8 differences, possibly related 9-29 differences, unrelated ≥ 30 differences). Groups of MRSA-related isolates were compared with reference USA genotypes. Chi square tests did not adjust for clustering. Results There were 663 admissions in period 1 and 730 in period 2. The prevalence of MSSA and MRSA colonization was unchanged in the two periods (MSSA, 18.6% vs. 16.7%, p=0.37; MRSA 1.8% vs. 3.3%, p=0.08). By WGS, most MSSA and MRSA isolates were unrelated to another isolate in both periods (Table). Groups of related MSSA and MRSA isolates differed between the two periods and the number of isolates/group was small (< 6 isolates). Groups of related MRSA isolates were associated with reference genotypes USA100 (period 1), USA1000 (periods 1 & 2), and USA300 (period 2). WGS Results for MSSA and MRSA in Periods 1 and 2 Conclusion NICU infants were usually colonized with unrelated strains of MSSA and MRSA, with only small clusters of related strains. The new NICU was not associated with a change in MSSA or MRSA colonization. Disclosures Priya Sampathkumar, MD, Merck Vaccines: Advisor/Consultant Robin Patel, MD, Abbott Laboratories: Advisor/Consultant|Adaptive Phage Therapeutics: Grant/Research Support|Adaptive Phage Therapeutics: Mayo Clinic has a royalty-bearing know-how agreement and equity in Adaptive Phage Therapeutics.|BIOFIRE: Grant/Research Support|CARB-X: Advisor/Consultant|ContraFect: Grant/Research Support|Day Zero Diagnostics: Advisor/Consultant|HealthTrackRx: Advisor/Consultant|Mammoth Biosciences: Advisor/Consultant|Netflix: Advisor/Consultant|Oxford Nanopore Technologies: Advisor/Consultant|PhAST: Advisor/Consultant|See details: Patent on Bordetella pertussis/parapertussis PCR issued, a patent on a device/method for sonication with royalties paid by Samsung to Mayo Clinic|See details: continued, patent on an anti-biofilm substance issued|TenNor Therapeutics Limited: Grant/Research Support|Torus Biosystems: Advisor/Consultant|Trellis Bioscience, Inc.: Advisor/Consultant W. Charles Huskins, MD, MSc, ADMA Biologics: Advisor/Consultant|Bristol Myers Squibb: Stocks/Bonds|Pfizer: Advisor/Consultant|Pfizer: Stocks/Bonds|Zimmer Biomet: Stocks/Bonds

Abstract Background Patients with ulcerative colitis and total abdominal proctocolectomy with ileal pouch-anal anastomosis have a 50% risk of pouchitis and a 5% to 10% risk of chronic pouchitis. Aims The goal of the study was to compare pouch microbiota and stool bile acid composition in patients with chronic pouchitis, chronic pouchitis and primary sclerosing cholangitis, and normal pouch. Methods Patients with ulcerative colitis and ileal pouch-anal anastomosis were recruited from March 20, 2014, to August 6, 2019, and categorized into normal pouch, chronic pouchitis, and chronic pouchitis/primary sclerosing cholangitis groups. Stool samples were subjected to bile acid quantification and 16S rRNA gene sequencing. Statistical comparisons of absolute bile acid abundance and pouch microbiota α-diversity, β-diversity, and taxa abundance were performed among the patient groups. Results A total of 51 samples were analyzed. Both α-diversity (P = .01, species richness) and β-diversity (P = .001) significantly differed among groups. Lithocholic acid was significantly lower in patients with chronic pouchitis/primary sclerosing cholangitis than in those with chronic pouchitis (P = .01) or normal pouch (P = .03). Decreased α-diversity was associated with an increased primary to secondary bile acid ratio (P = .002), which was also associated with changes in β-diversity (P = .006). Conclusions Pouch microbiota α- and β-diversity differed among patients with normal pouch, chronic pouchitis, and chronic pouchitis/primary sclerosing cholangitis. Lithocholic acid level and primary to secondary bile acid ratio were highly associated with pouch microbiota richness, structure, and composition. These findings emphasize the associations between pouch microbiota and bile acid composition in dysbiosis and altered metabolism, suggesting that secondary bile acids are decreased in chronic pouchitis.

It is well known that the microbiome data are ridden with outliers and have heavy distribution tails, but the impact of outliers and heavy-tailedness has yet to be examined systematically. This paper investigates the impact of outliers and heavy-tailedness on differential abundance analysis (DAA) using the linear models for the differential abundance analysis (LinDA) method and proposes effective strategies to mitigate their influence. The presence of outliers and heavy-tailedness can significantly decrease the power of LinDA. We investigate various techniques to address outliers and heavy-tailedness, including generalizing LinDA into a more flexible framework that allows for the use of robust regression and winsorizing the data before applying LinDA. Our extensive numerical experiments and real-data analyses demonstrate that robust Huber regression has overall the best performance in addressing outliers and heavy-tailedness.

AbstractMotivationGenomic data are subject to various sources of confounding, such as demographic variables, biological heterogeneity, and batch effects. To identify genomic features associated with a variable of interest in the presence of confounders, the traditional approach involves fitting a confounder-adjusted regression model to each genomic feature, followed by multiplicity correction.ResultsThis study shows that the traditional approach is suboptimal and proposes a new two-dimensional false discovery rate control framework (2DFDR+) that provides significant power improvement over the conventional method and applies to a wide range of settings. 2DFDR+ uses marginal independence test statistics as auxiliary information to filter out less promising features, and FDR control is performed based on conditional independence test statistics in the remaining features. 2DFDR+ provides (asymptotically) valid inference from samples in settings where the conditional distribution of the genomic variables given the covariate of interest and the confounders is arbitrary and completely unknown. Promising finite sample performance is demonstrated via extensive simulations and real data applications.Availability and implementationR codes and vignettes are available at https://github.com/asmita112358/tdfdr.np.

Although the etiology of rheumatoid arthritis (RA) is unknown, a strong genetic predisposition and the presence of preclinical antibodies before the onset of symptoms is documented. An expansion of Eggerthella lenta is associated with severe disease in RA. Here, using a humanized mouse model of collagen-induced arthritis, we determined the impact of E. lenta abundance on RA severity. Naïve mice gavaged with E. lenta produce preclinical rheumatoid factor and, when induced for arthritis, develop severe disease. The augmented antibody response was much higher in female mice, and among patients with RA, women had higher average load of E. lenta . Expansion of E. lenta increased CXCL5 and CD4 T cells, and both interleukin-17– and interferon-γ–producing B cells. Further, E. lenta gavage caused gut dysbiosis and decline in amino acids and nicotinamide adenine dinucleotide with an increase in microbe-dependent bile acids and succinyl carnitine causing systemic senescent-like inflammation.

In this article, we present a flexible model for microbiome count data. We consider a quasi‐likelihood framework, in which we do not make any assumptions on the distribution of the microbiome count except that its variance is an unknown but smooth function of the mean. By comparing our model to the negative binomial generalized linear model (GLM) and Poisson GLM in simulation studies, we show that our flexible quasi‐likelihood method yields valid inferential results. Using a real microbiome study, we demonstrate the utility of our method by examining the relationship between adenomas and microbiota. We also provide an R package “fql” for the application of our method.

Abstract Differential abundance analysis (DAA) is one central statistical task in microbiome data analysis. A robust and powerful DAA tool can help identify highly confident microbial candidates for further biological validation. Current microbiome studies frequently generate correlated samples from different microbiome sampling schemes such as spatial and temporal sampling. In the past decade, a number of DAA tools for correlated microbiome data (DAA-c) have been proposed. Disturbingly, different DAA-c tools could sometimes produce quite discordant results. To recommend the best practice to the field, we performed the first comprehensive evaluation of existing DAA-c tools using real data-based simulations. Overall, the linear model-based methods LinDA, MaAsLin2 and LDM are more robust than methods based on generalized linear models. The LinDA method is the only method that maintains reasonable performance in the presence of strong compositional effects.

Abstract Summary Due to the sparsity and high dimensionality, microbiome data are routinely summarized into pairwise distances capturing the compositional differences. Many biological insights can be gained by analyzing the distance matrix in relation to some covariates. A microbiome sampling method that characterizes the inter-sample relationship more reproducibly is expected to yield higher statistical power. Traditionally, the intraclass correlation coefficient (ICC) has been used to quantify the degree of reproducibility for a univariate measurement using technical replicates. In this work, we extend the traditional ICC to distance measures and propose a distance-based ICC (dICC). We derive the asymptotic distribution of the sample-based dICC to facilitate statistical inference. We illustrate dICC using a real dataset from a metagenomic reproducibility study. Availability and implementation dICC is implemented in the R CRAN package GUniFrac. Supplementary information Supplementary data are available at Bioinformatics online.