Fractional Amplitude of Low-Frequency Fluctuation and Voxel-Mirrored Homotopic Connectivity in Patients with Persistent Postural-Perceptual Dizziness: Resting-State Functional Magnetic Resonance Imaging Study.

Purpose: Persistent postural-perception dizziness (PPPD) is a chronic subjective form of dizziness characterized by the exacerbation of dizziness with active or passive movement, complex visual stimuli, and upright posture. Therefore, we aimed to analyze the resting-state functional magnetic resonance imaging (fMRI) in patients with PPPD using fractional amplitude of low-frequency fluctuation (fALFF) and voxel-mirrored homotopic connectivity (VMHC) and evaluate the correlation between abnormal regions in the brain and clinical features to investigate the pathogenesis of PPPD. Methods: Thirty patients with PPPD (19 females and 11 males) and 30 healthy controls (HCs; 18 females and 12 males) were closely matched for age and sex. The fALFF and VMHC methods were used to investigate differences in fMRI (BOLD sequences) between the PPPD and HC groups and to explore the associations between areas of functional abnormality and clinical characteristics (dizziness, anxiety, depression, and duration). Result: Compared to the HC group, patients with PPPD displayed different functional change patterns, with increased fALFF in the right precuneus and decreased VMHC in the bilateral precuneus. In addition, patients with PPPD had a positive correlation between precuneus fALFF values and dizziness handicap inventory (DHI) scores, and a negative correlation between VMHC values and the disease duration. Conclusions: Precuneus dysfunction was observed in patients with PPPD. The fALFF values correlated with the degree of dizziness in PPPD, and changes in VMHC values were associated with the duration of dizziness, suggesting that fMRI changes in the precuneus of patients could be used as a potential imaging marker for PPPD.

Integration of peripheral blood- and tissue-based biomarkers of response to immune checkpoint blockade in urothelial carcinoma.

As predictive biomarkers of response to immune checkpoint inhibitors (ICIs) remain a major unmet clinical need in patients with urothelial carcinoma (UC), we sought to identify tissue-based immune biomarkers of clinical benefit to ICIs using multiplex immunofluorescence and to integrate these findings with previously identified peripheral blood biomarkers of response. Fifty-five pretreatment and 12 paired on-treatment UC specimens were identified from patients treated with nivolumab with or without ipilimumab. Whole tissue sections were stained with a 12-plex mIF panel, including CD8, PD-1/CD279, PD-L1/CD274, CD68, CD3, CD4, FoxP3, TCF1/7, Ki67, LAG-3, MHC-II/HLA-DR, and pancytokeratin+SOX10 to identify over three million cells. Immune tissue densities were compared to progression-free survival (PFS) and best overall response (BOR) by RECIST version 1.1. Correlation coefficients were calculated between tissue-based and circulating immune populations. The frequency of intratumoral CD3+ LAG-3+ cells was higher in responders compared to nonresponders (p = 0.0001). LAG-3+ cellular aggregates were associated with response, including CD3+ LAG-3+ in proximity to CD3+ (p = 0.01). Exploratory multivariate modeling showed an association between intratumoral CD3+ LAG-3+ cells and improved PFS independent of prognostic clinical factors (log HR -7.0; 95% confidence interval [CI] -12.7 to -1.4), as well as established biomarkers predictive of ICI response (log HR -5.0; 95% CI -9.8 to -0.2). Intratumoral LAG-3+ immune cell populations warrant further study as a predictive biomarker of clinical benefit to ICIs. Differences in LAG-3+ lymphocyte populations across the intratumoral and peripheral compartments may provide complementary information that could inform the future development of multimodal composite biomarkers of ICI response. © 2023 The Authors. The Journal of Pathology published by John Wiley & Sons Ltd on behalf of The Pathological Society of Great Britain and Ireland.

A topic modeling approach reveals the dynamic T cell composition of peripheral blood during cancer immunotherapy.

We present TopicFlow, a computational framework for flow cytometry data analysis of patient blood samples for the identification of functional and dynamic topics in circulating T cell population. This framework applies a Latent Dirichlet Allocation (LDA) model, adapting the concept of topic modeling in text mining to flow cytometry. To demonstrate the utility of our method, we conducted an analysis of ∼17 million T cells collected from 138 peripheral blood samples in 51 patients with melanoma undergoing treatment with immune checkpoint inhibitors (ICIs). Our study highlights three latent dynamic topics identified by LDA: a T cell exhaustion topic that independently recapitulates the previously identified LAG-3+ immunotype associated with ICI resistance, a naive topic and its association with immune-related toxicity, and a T cell activation topic that emerges upon ICI treatment. Our approach can be broadly applied to mine high-parameter flow cytometry data for insights into mechanisms of treatment response and toxicity.

Uncovering the hidden structure of dynamic T cell composition in peripheral blood during cancer immunotherapy: a topic modeling approach.

Immune checkpoint inhibitors (ICIs), now mainstays in the treatment of cancer treatment, show great potential but only benefit a subset of patients. A more complete understanding of the immunological mechanisms and pharmacodynamics of ICI in cancer patients will help identify the patients most likely to benefit and will generate knowledge for the development of next-generation ICI regimens. We set out to interrogate the early temporal evolution of T cell populations from longitudinal single-cell flow cytometry data. We developed an innovative statistical and computational approach using a Latent Dirichlet Allocation (LDA) model that extends the concept of topic modeling used in text mining. This powerful unsupervised learning tool allows us to discover compositional topics within immune cell populations that have distinct functional and differentiation states and are biologically and clinically relevant. To illustrate the model's utility, we analyzed ∼17 million T cells obtained from 138 pre- and on-treatment peripheral blood samples from a cohort of melanoma patients treated with ICIs. We identified three latent dynamic topics: a T-cell exhaustion topic that recapitulates a LAG3+ predominant patient subgroup with poor clinical outcome; a naive topic that shows association with immune-related toxicity; and an immune activation topic that emerges upon ICI treatment. We identified that a patient subgroup with a high baseline of the naïve topic has a higher toxicity grade. While the current application is demonstrated using flow cytometry data, our approach has broader utility and creates a new direction for translating single-cell data into biological and clinical insights.

Accurate estimation of molecular counts from amplicon sequence data with unique molecular identifiers.

MOTIVATION: Amplicon sequencing is widely applied to explore heterogeneity and rare variants in genetic populations. Resolving true biological variants and quantifying their abundance is crucial for downstream analyses, but measured abundances are distorted by stochasticity and bias in amplification, plus errors during polymerase chain reaction (PCR) and sequencing. One solution attaches unique molecular identifiers (UMIs) to sample sequences before amplification. Counting UMIs instead of sequences provides unbiased estimates of abundance. While modern methods improve over naïve counting by UMI identity, most do not account for UMI reuse or collision, and they do not adequately model PCR and sequencing errors in the UMIs and sample sequences. RESULTS: We introduce Deduplication and Abundance estimation with UMIs (DAUMI), a probabilistic framework to detect true biological amplicon sequences and accurately estimate their deduplicated abundance. DAUMI recognizes UMI collision, even on highly similar sequences, and detects and corrects most PCR and sequencing errors in the UMI and sampled sequences. DAUMI performs better on simulated and real data compared to other UMI-aware clustering methods. AVAILABILITY AND IMPLEMENTATION: Source code is available at https://github.com/DormanLab/AmpliCI. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

AmpliCI: a high-resolution model-based approach for denoising Illumina amplicon data.

MOTIVATION: Next-generation amplicon sequencing is a powerful tool for investigating microbial communities. A main challenge is to distinguish true biological variants from errors caused by amplification and sequencing. In traditional analyses, such errors are eliminated by clustering reads within a sequence similarity threshold, usually 97%, and constructing operational taxonomic units, but the arbitrary threshold leads to low resolution and high false-positive rates. Recently developed 'denoising' methods have proven able to resolve single-nucleotide amplicon variants, but they still miss low-frequency sequences, especially those near more frequent sequences, because they ignore the sequencing quality information. RESULTS: We introduce AmpliCI, a reference-free, model-based method for rapidly resolving the number, abundance and identity of error-free sequences in massive Illumina amplicon datasets. AmpliCI considers the quality information and allows the data, not an arbitrary threshold or an external database, to drive conclusions. AmpliCI estimates a finite mixture model, using a greedy strategy to gradually select error-free sequences and approximately maximize the likelihood. AmpliCI has better performance than three popular denoising methods, with acceptable computation time and memory usage. AVAILABILITY AND IMPLEMENTATION: Source code is available at https://github.com/DormanLab/AmpliCI. SUPPLEMENTARY INFORMATION: Supplementary material are available at Bioinformatics online.

Dietary Soluble and Insoluble Fiber With or Without Enzymes Altered the Intestinal Microbiota in Weaned Pigs Challenged With Enterotoxigenic E. coli F18.

Post-weaning diarrhea caused by enterotoxigenic E. coli (ETEC) causes significant economic losses for pig producers. This study was to test the hypotheses that an ETEC challenge disrupts intestinal microbial homeostasis and the inclusion of dietary soluble (10% sugar beet pulp) or insoluble fiber (15% corn distillers dried grains with solubles) with or without exogenous carbohydrases will protect or restore the gut microbial homeostasis in weaned pigs. Sixty crossbred piglets (6.9 ± 0.1 kg) were blocked by body weight and randomly assigned to one of six treatments (n = 10), including a non-challenged control (NC), ETEC F18-challenged positive control (PC), ETEC-challenged soluble fiber without (SF-) or with carbohydrases (SF+), and ETEC-challenged insoluble fiber without (IF-) or with carbohydrases (IF+). Pigs were housed individually and orally received either ETEC inoculum or PBS-sham inoculum on day 7 post-weaning. Intestinal contents were collected on day 14 or 15. The V4 region of the bacterial 16S rRNA was amplified and sequenced. High-quality reads (total 6,671,739) were selected and clustered into 3,330 OTUs. No differences were observed in α-diversity among treatments. The ileal microbiota in NC and PC had modest separation in the weighted PCoA plot; the microbial structures were slightly altered by SF+ and IF- compared with PC. The PC increased ileal Escherichia-Shigella (P < 0.01) and numerically decreased Lactobacillus compared to NC. Predicted functional pathways enriched in the ileal microbiota of PC pigs indicated enhanced activity of Gram-negative bacteria, in agreement with increased Escherichia-Shigella. The SF+ tended to decrease (P < 0.10) ileal Escherichia-Shigella compared to PC. Greater abundance of ileal Streptococcus, Turicibacter, and Roseburia and colonic Prevotella were observed in SF- and SF+ than PC (P < 0.05). Pigs fed IF + had greater Lactobacillus and Roseburia than PC pigs (P < 0.05). The ETEC challenge reduced total volatile fatty acid (VFA) compared with NC (P < 0.05). The SF+ tended to increase (P < 0.10) and SF- significantly increased (P < 0.05) colonic total VFA compared with PC. Collectively, ETEC challenge disrupted gut microbial homeostasis and impaired microbial fermentation capacity. Soluble fiber improved VFA production. Dietary fiber and carbohydrases altered microbiota composition to maintain or restore microbial homeostasis.