Chen, Jun

(1) Background: Mammographic breast density (MBD) is a well-established predictor of breast cancer risk, yet the biological mechanisms underlying this association remain incompletely understood. MBD is characterized by alterations in breast stromal architecture, including increased collagen deposition and changes in immune cell composition. Given emerging evidence that the breast harbors a resident microbiome, we investigated whether the breast tissue microbiome correlates with MBD. (2) Methods: Adjacent normal breast tissue was collected under sterile conditions from 33 women undergoing surgery for benign or malignant breast disease. DNA was extracted and subjected to 16S rRNA gene sequencing (Illumina MiSeq). (3) Results: We observed a non-significant trend toward lower α-diversity in high-MBD samples compared to low-MBD samples, p = 0.13. β-Diversity analyses identified a modest association between MBD and microbial community composition (MiRKAT p = 0.049). A random forest-based model incorporating genus-level relative abundances improved prediction of MBD over clinical characteristics alone, identifying Corynebacterium (Actinobacteria) and other genera as key predictors. (4) Conclusions: Breast tissue microbial features vary with mammographic breast density, suggesting a potential association with density-associated breast cancer risk. These exploratory findings warrant validation in larger cohorts to better elucidate biological mechanisms and clinical relevance.

Abstract Motivation Compositional data are frequently encountered in many disciplines, such as in next-generation sequencing experiments widely used in biomedical studies. Regression analysis with compositional data as either responses or predictors has been well studied. However, when both responses and predictors are compositional, the inventory of analysis tools is surprisingly limited, especially in the high-dimensional setting. Among the few existing methods, most of them rely on a log-ratio transformation to move compositional data from the simplex to real numbers. Yet, a serious weakness of these methods is their failure to handle the substantial fraction of zeroes observed in data collected from next-generation sequencing experiments. Results To investigate associations between two high-dimensional multi-omics compositions, we propose a composition-on-composition (COC) regression analysis method which does not require log-ratio transformations and hence can handle zeroes in the data. To account for high dimensionality, we estimate regression coefficients using a penalized estimation equation approach. Finally, inference procedures for COC regression are also proposed. Superior performance of COC is demonstrated through both comprehensive numerical simulations and case studies. Availability and implementation Source R codes to implement COC method is available at https://github.com/nrios4/COC.

Abstract Motivation Single-cell RNA sequencing (scRNA-seq) has become an important method for characterizing cellular heterogeneity, revealing more biological insights than the bulk RNA-seq. The surge in scRNA-seq data across multiple individuals calls for efficient and statistically powerful methods for differential expression (DE) analysis that addresses individual-level biological variability. Results We introduced DiSC, a method for conducting individual-level DE analysis by extracting multiple distributional characteristics, jointly testing their association with a variable of interest, and using a flexible permutation testing framework to control the false discovery rate (FDR). Our simulation studies demonstrated that DiSC effectively controlled the FDR across various settings and exhibited high statistical power in detecting different types of gene expression changes. Moreover, DiSC is computationally efficient and scalable to the rapidly increasing sample sizes in scRNA-seq studies. When applying DiSC to identify DE genes potentially associated with COVID-19 severity and Alzheimer’s disease across various types of peripheral blood mononuclear cells and neural cells, we found that our method was approximately 100 times faster than other state-of-the-art methods and the results were consistent and supported by existing literature. While DiSC was developed for scRNA-seq data, its robust testing framework can also be applied to other types of single-cell data. We applied DiSC to cytometry by time-of-flight data, DiSC identified significantly more DE markers than traditional methods. Availability and implementation The R software package “SingleCellStat” is freely available on CRAN (https://cran.r-project.org/web/packages/SingleCellStat/index.html) and GitHub (https://github.com/Lujun995/DiSC). The replication code for reproducing the analyses in this study is publicly accessible at https://github.com/Lujun995/DiSC_Replication_Code. The scRNA-seq expression matrix and metadata utilized in our simulations and analyses can be retrieved from https://cells.ucsc.edu/autism/rawMatrix.zip, https://cellxgene.cziscience.com/collections/1ca90a2d-2943-483d-b678-b809bf464c30, and https://covid19.cog.sanger.ac.uk/submissions/release1/haniffa21.processed.h5ad.

Abstract Microbiome sequencing data are inherently sparse and compositional, with excessive zeros arising from biological absence or insufficient sampling. These zeros pose significant challenges for downstream analyses, particularly those that require log-transformation. We introduce BMDD (BiModal Dirichlet Distribution), a novel probabilistic modeling framework for accurate imputation of microbiome sequencing data. Unlike existing imputation approaches that assume unimodal abundance, BMDD captures the bimodal abundance distribution of the taxa via a mixture of Dirichlet priors. It uses variational inference and a scalable expectation-maximization algorithm for efficient imputation. Through simulations and real microbiome datasets, we demonstrate that BMDD outperforms competing methods in reconstructing true abundances and improves the performance of differential abundance analysis. Through multiple posterior samples, BMDD enables robust inference by accounting for uncertainty in zero imputation. Our method offers a principled and computationally efficient solution for analyzing high-dimensional, zero-inflated microbiome sequencing data and is broadly applicable in microbial biomarker discovery and host-microbiome interaction studies. BMDD is available at: https://github.com/zhouhj1994/BMDD . Author Summary Understanding the microbes living in and on our bodies—the microbiome—relies on analyzing complex sequencing data. However, these data often contain many zeros, either because a microbe is truly absent or simply missed due to insufficient sampling. These missing values make it hard to accurately analyze microbial patterns and identify important differences between groups, especially for methods that work on a log scale. To address this, we developed a new method called BMDD that uses a more realistic model to impute the zeros. Unlike existing tools that assume each microbe follows an unimodal abundance distribution, BMDD allows for microbes to follow a bimodal distribution, so they could behave differently in different conditions. It provides not just a single guess, but a range of possible values to better reflect the uncertainty. Our testing shows that BMDD more accurately recovers the true microbial profiles and improves the ability to detect meaningful differences between groups. This method can help researchers gain clearer insights into how the microbiome affects health and disease.

Background Neoadjuvant treatment has become standard for patients with high-risk operable stage III melanoma, but the optimal regimen is unknown. Targeted therapy approaches yield high pathological response rates, while immunotherapy regimens show favorable recurrence-free survival (RFS). NeoACTIVATE was designed to address whether a neoadjuvant combination of both targeted therapy and immunotherapy might leverage the benefits of each. Methods We tested neoadjuvant treatment with 12 weeks of vemurafenib, cobimetinib, and atezolizumab for patients with BRAF-mutated (BRAFm) melanoma (cohort A) and cobimetinib and atezolizumab for patients with BRAF-wild-type (BRAFwt) melanoma (cohort B), regimens which we have shown generate a substantial major pathological response. After therapeutic lymph node dissection, patients received 24 weeks of adjuvant atezolizumab. Here, we report survival outcomes and their association with biomarkers assayed among the gut microbiome and peripheral blood immune subsets. Results With 49 months median follow-up, the median RFS was not reached for cohort A and was 40.8 months for cohort B. At 24 months after operation, 2 of 14 cohort A patients and 4 of 13 cohort B patients had experienced distant relapse. Key findings from correlative analyses included diversity, taxonomic and functional metagenomic gut microbiome signals associated with distant metastasis-free survival at 2 years. Notably, we observed a strong correlation between low microbial arginine biosynthesis (required for T-cell activation and effector function) and early distant recurrence (p=0.0005), which correlated with taxonomic differential abundance findings. Peripheral blood immune monitoring revealed increased double-positive (CD4+CD8+) T cells in patients with early recurrence. Conclusions Neoadjuvant treatment with cobimetinib and atezolizumab±vemurafenib was associated with a low rate of distant metastasis in patients with high-risk stage III melanoma. Freedom from early distant metastasis was highly associated with taxonomic differences in gut microbiome structure and with functional pathway alterations known to modulate T cell immunity. Identification of predictive biomarkers will permit optimization of neoadjuvant therapy regimens for individual patients. Trial registration number NCT03554083.

Abstract Motivation Emerging omics technologies have introduced a two-way grouping structure in multiple testing, as seen in single-cell omics data, where the features can be grouped by either genes or cell types. Traditional multiple testing methods have limited ability to exploit such two-way grouping structure, leading to potential power loss. Results We propose a new 2D Group Benjamini–Hochberg (2dGBH) procedure to harness the two-way grouping structure in omics data, extending the traditional one-way adaptive GBH procedure. Using both simulated and real datasets, we show that 2dGBH effectively controls the false discovery rate across biologically relevant settings, and it is more powerful than the BH or q-value procedure and more robust than the one-way adaptive GBH procedure. Availability and implementation 2dGBH is available as an R package at: https://github.com/chloelulu/tdGBH. The analysis code and data are available at: https://github.com/chloelulu/tdGBH-paper.

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.