Rationale and design of the prognostic transcriptomic signature in fibrotic hypersensitivity pneumonitis (PREDICT) study.

Hypersensitivity pneumonitis is an immunologically mediated form of lung disease, resulting from inhalational exposure to a large variety of antigens. A subgroup of patients with fibrotic hypersensitivity pneumonitis (FHP) develop symptomatic, functional and radiographic disease progression. Mortality occurs primarily from respiratory failure as a result of progressive and self-sustaining lung injury that often occurs despite immunosuppression and removal of the inciting antigen. The development and validation of a prognostic transcriptomic signature for FHP (PREDICT-HP) is an observational multicentre cohort study designed to explore a transcriptomic signature from peripheral blood mononuclear cells in patients with FHP that is predictive of disease progression. This article describes the design and rationale of the PREDICT-HP study. This study will enrol ∼135 patients with FHP at approximately seven academic medical sites. Participants with a confirmed diagnosis of FHP are followed over 24 months and undergo physical examinations, self-administered questionnaires, chest computed tomography, pulmonary function tests, a 6-min walk test and blood testing for transcriptomic analyses. At each 6-month follow-up visit the study will assess the participants' clinical course and clinical events including hospitalisations and respiratory exacerbations. The PREDICT study has the potential to enhance our ability to predict disease progression and fundamentally advance our understanding of the pathobiology of FHP disease progression.

Compartment-specific protein interactions in beryllium lung disease.

The study provides insights into proteins that may be relevant in BeS and CBD. It provides a framework to investigate the global changes in lung compartment-specific inflammatory cells to better understand the potential interplay of proteins in CBD. https://bit.ly/3PLNTXC.

Systems serology in cystic fibrosis: Anti-Pseudomonas IgG1 responses and reduced lung function.

Nearly one-half of patients with cystic fibrosis (CF) carry the homozygous F508del mutation in the cystic fibrosis transmembrane conductance regulator (CFTR) gene but exhibit variable lung function phenotypes. How adaptive immunity influences their lung function remains unclear, particularly the serological antibody responses to antigens from mucoid Pseudomonas in sera from patients with CF with varying lung function. Sera from patients with CF with reduced lung function show higher anti-outer membrane protein I (OprI) immunoglobulin G1 (IgG1) titers and greater antibody-mediated complement deposition. Induction of anti-OprI antibody isotypes with complement activity enhances lung inflammation in preclinical mouse models. This enhanced inflammation is absent in immunized Rag2-/- mice and is transferrable to unimmunized mice through sera. In a CF cohort undergoing treatment with elexacaftor-tezacaftor-ivacaftor, the declination in anti-OprI IgG1 titers is associated with lung function improvement and reduced hospitalizations. These findings suggest that antibody responses to specific Pseudomonas aeruginosa (PA) antigens worsen lung function in patients with CF.

Quantifying the spatial clustering characteristics of radiographic emphysema explains variability in pulmonary function.

Quantitative assessment of emphysema in CT scans has mostly focused on calculating the percentage of lung tissue that is deemed abnormal based on a density thresholding strategy. However, this overall measure of disease burden discards virtually all the spatial information encoded in the scan that is implicitly utilized in a visual assessment. This simplification is likely grouping heterogenous disease patterns and is potentially obscuring clinical phenotypes and variable disease outcomes. To overcome this, several methods that attempt to quantify heterogeneity in emphysema distribution have been proposed. Here, we compare three of those: one based on estimating a power law for the size distribution of contiguous emphysema clusters, a second that looks at the number of emphysema-to-emphysema voxel adjacencies, and a third that applies a parametric spatial point process model to the emphysema voxel locations. This was done using data from 587 individuals from Phase 1 of COPDGene that had an inspiratory CT scan and plasma protein abundance measurements. The associations between these imaging metrics and visual assessment with clinical measures (FEV[Formula: see text], FEV[Formula: see text]-FVC ratio, etc.) and plasma protein biomarker levels were evaluated using a variety of regression models. Our results showed that a selection of spatial measures had the ability to discern heterogeneous patterns among CTs that had similar emphysema burdens. The most informative quantitative measure, average cluster size from the point process model, showed much stronger associations with nearly every clinical outcome examined than existing CT-derived emphysema metrics and visual assessment. Moreover, approximately 75% more plasma biomarkers were found to be associated with an emphysema heterogeneity phenotype when accounting for spatial clustering measures than when they were excluded.

lmerSeq: an R package for analyzing transformed RNA-Seq data with linear mixed effects models.

BACKGROUND: Studies that utilize RNA Sequencing (RNA-Seq) in conjunction with designs that introduce dependence between observations (e.g. longitudinal sampling) require specialized analysis tools to accommodate this additional complexity. This R package contains a set of utilities to fit linear mixed effects models to transformed RNA-Seq counts that properly account for this dependence when performing statistical analyses. RESULTS: In a simulation study comparing lmerSeq and two existing methodologies that also work with transformed RNA-Seq counts, we found that lmerSeq was comprehensively better in terms of nominal error rate control and statistical power. CONCLUSIONS: Existing R packages for analyzing transformed RNA-Seq data with linear mixed models are limited in the variance structures they allow and/or the transformation methods they support. The lmerSeq package offers more flexibility in both of these areas and gave substantially better results in our simulations.

Autoantibody profile in sarcoidosis, analysis from the GRADS sarcoidosis cohort.

BACKGROUND: Sarcoidosis, a multi-systemic granulomatous disease, is a predominantly T-cell disease but evidence for a role for humoral immunity in disease pathogenesis is growing. Utilizing samples from the Genomic Research in Alpha-1 anti-trypsin Deficiency and Sarcoidosis (GRADS) study, we examined the prevalence of autoantibodies in sarcoidosis patients with pulmonary-only and extra-pulmonary organ involvement compared to normal controls. STUDY DESIGN AND METHODS: We analyzed serum samples from sarcoidosis patients who participated in the GRADS study utilizing an autoantigen microarray platform for both IgM and IgG antibodies. The cohort included sarcoidosis patients with pulmonary-only disease (POS, n = 106), sarcoidosis patients with extra-pulmonary disease (EPS, n = 120) and a normal control cohort (NC, n = 101). Organ involvement was assessed following a standardized format across all GRADS participating centers. RESULTS: Sarcoidosis patients overall had increased levels of IgM and IgG autoantibodies compared to normal controls. In addition, several autoantibodies were elevated in the POS and EPS cohorts compared to the NC cohort. Differences in autoantibody levels were also noted between the POS and the EPS cohorts. When comparing organ involvement with sarcoidosis, bone, spleen and ear, nose and throat involvement had higher IgM expression than other organs. CONCLUSION: Sarcoidosis patients have elevated IgM and IgG autoantibody levels compared to normal controls. In addition, individuals with pulmonary as well as additional organ involvement had higher IgM expression. Further research is needed focusing on specific organ-autoantibody pairs and role of autoantibodies in disease pathogenesis.

A comparison of methods for multiple degree of freedom testing in repeated measures RNA-sequencing experiments.

BACKGROUND: As the cost of RNA-sequencing decreases, complex study designs, including paired, longitudinal, and other correlated designs, become increasingly feasible. These studies often include multiple hypotheses and thus multiple degree of freedom tests, or tests that evaluate multiple hypotheses jointly, are often useful for filtering the gene list to a set of interesting features for further exploration while controlling the false discovery rate. Though there are several methods which have been proposed for analyzing correlated RNA-sequencing data, there has been little research evaluating and comparing the performance of multiple degree of freedom tests across methods. METHODS: We evaluated 11 different methods for modelling correlated RNA-sequencing data by performing a simulation study to compare the false discovery rate, power, and model convergence rate across several hypothesis tests and sample size scenarios. We also applied each method to a real longitudinal RNA-sequencing dataset. RESULTS: Linear mixed modelling using transformed data had the best false discovery rate control while maintaining relatively high power. However, this method had high model non-convergence, particularly at small sample sizes. No method had high power at the lowest sample size. We found a mix of conservative and anti-conservative behavior across the other methods, which was influenced by the sample size and the hypothesis being evaluated. The patterns observed in the simulation study were largely replicated in the analysis of a longitudinal study including data from intensive care unit patients experiencing cardiogenic or septic shock. CONCLUSIONS: Multiple degree of freedom testing is a valuable tool in longitudinal and other correlated RNA-sequencing experiments. Of the methods that we investigated, linear mixed modelling had the best overall combination of power and false discovery rate control. Other methods may also be appropriate in some scenarios.

Host and pathogen response to bacteriophage engineered against Mycobacterium abscessus lung infection.

Two mycobacteriophages were administered intravenously to a male with treatment-refractory Mycobacterium abscessus pulmonary infection and severe cystic fibrosis lung disease. The phages were engineered to enhance their capacity to lyse M. abscessus and were selected specifically as the most effective against the subject's bacterial isolate. In the setting of compassionate use, the evidence of phage-induced lysis was observed using molecular and metabolic assays combined with clinical assessments. M. abscessus isolates pre and post-phage treatment demonstrated genetic stability, with a general decline in diversity and no increased resistance to phage or antibiotics. The anti-phage neutralizing antibody titers to one phage increased with time but did not prevent clinical improvement throughout the course of treatment. The subject received lung transplantation on day 379, and systematic culturing of the explanted lung did not detect M. abscessus. This study describes the course and associated markers of a successful phage treatment of M. abscessus in advanced lung disease.

Prognostic accuracy of a peripheral blood transcriptome signature in chronic hypersensitivity pneumonitis.

The prognostic value of peripheral blood mononuclear cell (PBMC) expression profiles, when used in patients with chronic hypersensitivity pneumonitis (CHP), as an adjunct to traditional clinical assessment is unknown. RNA-seq analysis on PBMC from 37 patients with CHP at initial presentation determined that (1) 74 differentially expressed transcripts at a 10% false discovery rate distinguished those with (n=10) and without (n=27) disease progression, defined as absolute FVC and/or diffusing capacity of the lungs for carbon monoxide (DLCO) decline of ≥10% and increased fibrosis on chest CT images within 24 months, and (2) classification models based on gene expression and clinical factors strongly outperform models based solely on clinical factors (baseline FVC%, DLCO% and chest CT fibrosis).

Heightened turnover and failed maturation of monocyte-derived macrophages in murine chronic granulomatous disease.

Loss of NADPH oxidase activity leads to altered phagocyte responses and exaggerated inflammation in chronic granulomatous disease (CGD). We sought to assess the effects of Nox2 absence on monocyte-derived macrophages (MoMacs) in gp91phox-/y mice during zymosan-induced peritonitis. MoMacs from CGD and wild-type (WT) peritonea were characterized over time after zymosan injection. Although numbers lavaged from both genotypes were virtually identical, there were marked differences in maturation: newly recruited WT MoMacs rapidly enlarged and matured, losing Ly6C and gaining MHCII, CD206, and CD36, whereas CGD MoMacs remained small and were mostly Ly6C+MHCII-. RNA-sequencing analyses showed few intrinsic differences between genotypes in newly recruited MoMacs but significant differences with time. WT MoMacs displayed changes in metabolism, adhesion, and reparative functions, whereas CGD MoMacs remained inflammatory. PKH dye labeling revealed that although WT MoMacs were mostly recruited within the first 24 hours and remained in the peritoneum while maturing and enlarging, CGD monocytes streamed into the peritoneum for days, with many migrating to the diaphragm where they were found in fibrin(ogen) clots surrounding clusters of neutrophils in nascent pyogranulomata. Importantly, these observations seemed to be driven by milieu: adoptive transfer of CGD MoMacs into inflamed peritonea of WT mice resulted in immunophenotypic maturation and normal behavior, whereas altered maturation/behavior of WT MoMacs resulted from transfer into inflamed peritonea of CGD mice. In addition, Nox2-deficient MoMacs behaved similarly to their Nox2-sufficient counterparts within the largely WT milieu of mixed bone marrow chimeras. These data show persistent recruitment with fundamental failure of MoMac maturation in CGD.

Single cell analysis of host response to helminth infection reveals the clonal breadth, heterogeneity, and tissue-specific programming of the responding CD4+ T cell repertoire.

The CD4+ T cell response is critical to host protection against helminth infection. How this response varies across different hosts and tissues remains an important gap in our understanding. Using IL-4-reporter mice to identify responding CD4+ T cells to Nippostrongylus brasiliensis infection, T cell receptor sequencing paired with novel clustering algorithms revealed a broadly reactive and clonally diverse CD4+ T cell response. While the most prevalent clones and clonotypes exhibited some tissue selectivity, most were observed to reside in both the lung and lung-draining lymph nodes. Antigen-reactivity of the broader repertoires was predicted to be shared across both tissues and individual mice. Transcriptome, trajectory, and chromatin accessibility analysis of lung and lymph-node repertoires revealed three unique but related populations of responding IL-4+ CD4+ T cells consistent with T follicular helper, T helper 2, and a transitional population sharing similarity with both populations. The shared antigen reactivity of lymph node and lung repertoires combined with the adoption of tissue-specific gene programs allows for the pairing of cellular and humoral responses critical to the orchestration of anti-helminth immunity.

Filtering Spatial Point Patterns Using Kernel Densities.

Understanding spatial inhomogeneity and clustering in point patterns arises in many contexts, ranging from disease outbreak monitoring to analyzing radiologically-based emphysema in biomedical images. This can often involve classifying individual points as being part of a feature/cluster or as being part of a background noise process. Existing methods for this task can struggle when there are differences in the size and/or density of individual clusters. In this work, we propose employing kernel density estimates of the underlying point process intensity function, using an existing data-driven approach to bandwidth selection, to separate feature points from noise. This is achieved by constructing a null distribution, either through asymptotic properties or Monte Carlo simulation, and comparing kernel density estimates to a given quantile of this distribution. We demonstrate that our method, termed Kernel Density and Simulation based Filtering (KDS-Filt), showed superior performance to existing alternative approaches, especially when there is inhomogeneity in cluster sizes and density. We also show the utility of KDS-Filt for identifying clinically relevant information about the spatial distribution of emphysema in lung computed tomography scans. The KDS-Filt methodology is available as part of the sncp R package, which can be downloaded at https://github.com/stop-pre16/sncp.

Human and Mouse Transcriptome Profiling Identifies Cross-Species Homology in Pulmonary and Lymph Node Mononuclear Phagocytes.

The mononuclear phagocyte (MP) system consists of macrophages, monocytes, and dendritic cells (DCs). MP subtypes play distinct functional roles in steady-state and inflammatory conditions. Although murine MPs are well characterized, their pulmonary and lymph node (LN) human homologs remain poorly understood. To address this gap, we have created a gene expression compendium across 24 distinct human and murine lung and LN MPs, along with human blood and murine spleen MPs, to serve as validation datasets. In-depth RNA sequencing identifies corresponding human-mouse MP subtypes and determines marker genes shared and divergent across species. Unexpectedly, only 13%-23% of the top 1,000 marker genes (i.e., genes not shared across species-specific MP subtypes) overlap in corresponding human-mouse MP counterparts. Lastly, CD88 in both species helps distinguish monocytes/macrophages from DCs. Our cross-species expression compendium serves as a resource for future translational studies to investigate beforehand whether pursuing specific MP subtypes or genes will prove fruitful.

MCMSeq: Bayesian hierarchical modeling of clustered and repeated measures RNA sequencing experiments.

BACKGROUND: As the barriers to incorporating RNA sequencing (RNA-Seq) into biomedical studies continue to decrease, the complexity and size of RNA-Seq experiments are rapidly growing. Paired, longitudinal, and other correlated designs are becoming commonplace, and these studies offer immense potential for understanding how transcriptional changes within an individual over time differ depending on treatment or environmental conditions. While several methods have been proposed for dealing with repeated measures within RNA-Seq analyses, they are either restricted to handling only paired measurements, can only test for differences between two groups, and/or have issues with maintaining nominal false positive and false discovery rates. In this work, we propose a Bayesian hierarchical negative binomial generalized linear mixed model framework that can flexibly model RNA-Seq counts from studies with arbitrarily many repeated observations, can include covariates, and also maintains nominal false positive and false discovery rates in its posterior inference. RESULTS: In simulation studies, we showed that our proposed method (MCMSeq) best combines high statistical power (i.e. sensitivity or recall) with maintenance of nominal false positive and false discovery rates compared the other available strategies, especially at the smaller sample sizes investigated. This behavior was then replicated in an application to real RNA-Seq data where MCMSeq was able to find previously reported genes associated with tuberculosis infection in a cohort with longitudinal measurements. CONCLUSIONS: Failing to account for repeated measurements when analyzing RNA-Seq experiments can result in significantly inflated false positive and false discovery rates. Of the methods we investigated, whether they model RNA-Seq counts directly or worked on transformed values, the Bayesian hierarchical model implemented in the mcmseq R package (available at https://github.com/stop-pre16/mcmseq ) best combined sensitivity and nominal error rate control.

Template Creation for High-Resolution Computed Tomography Scans of the Lung in R Software.

RATIONALE AND OBJECTIVES: A standard lung template could improve population-level analyses for computed tomography (CT) scans of the lung. We develop a fully automated preprocessing pipeline for image analysis of the lungs using updated methodologies and R software that results in the creation of a standard lung template. We apply this pipeline to CT scans from a sarcoidosis population, exploring the influence of registration on radiomic analyses. MATERIALS AND METHODS: Using 65 high-resolution CT scans from healthy adults, we create a standard lung template by segmenting the left and right lungs, nonlinearly registering lung masks to an initial template mask, and using an unbiased, iterative procedure to converge to a standard lung shape (Dice similarity coefficient ≥0.99). We compare three-dimensional radiomic features between control and sarcoidosis patients, before and after registration to a study-specific lung template. RESULTS: The final lung template had a right lung volume of 2967 cm3 and left lung volume of 2623 cm3, with a median HU = -862. Registration significantly affected radiomic features, shifting the HU distribution to the left, decreasing variability, and increasing smoothness (p < 0.0001). The registration improved detective ability of radiomics; for contrast, autocorrelation, energy, and homogeneity, the group effect was significant postregistration (p < 0.05), but was not significant preregistration. CONCLUSION: The final lung template and software used for its creation are publicly available via the lungct R package to facilitate its use in practice. This study advances lung imaging by developing tools to improve population-level analyses for various lung diseases.

Using a spatial point process framework to characterize lung computed tomography scans.

Pulmonary emphysema is a destructive disease of the lungs that is currently diagnosed via visual assessment of lung Computed Tomography (CT) scans by a radiologist. Visual assessment can have poor inter-rater reliability, is time consuming, and requires access to trained assessors. Quantitative methods that reliably summarize the biologically relevant characteristics of an image are needed to improve the way lung diseases are characterized. The goal of this work was to show how spatial point process models can be used to create a set of radiologically derived quantitative lung biomarkers of emphysema. We formalized a general framework for applying spatial point processes to lung CT scans, and developed a Shot Noise Cox Process to quantify how radiologically based emphysematous tissue clusters into larger structures. Bayesian estimation of model parameters was done using spatial Birth-Death MCMC (BD-MCMC). In simulations, we showed the BD-MCMC estimation algorithm is able to accurately recover model parameters. In an application to real lung CT scans from the COPDGene cohort, we showed variability in the clustering characteristics of emphysematous tissue across disease subtypes that were based on visual assessments of the CT scans.

Unsupervised discovery of phenotype-specific multi-omics networks.

MOTIVATION: Complex diseases often involve a wide spectrum of phenotypic traits. Better understanding of the biological mechanisms relevant to each trait promotes understanding of the etiology of the disease and the potential for targeted and effective treatment plans. There have been many efforts towards omics data integration and network reconstruction, but limited work has examined the incorporation of relevant (quantitative) phenotypic traits. RESULTS: We propose a novel technique, sparse multiple canonical correlation network analysis (SmCCNet), for integrating multiple omics data types along with a quantitative phenotype of interest, and for constructing multi-omics networks that are specific to the phenotype. As a case study, we focus on miRNA-mRNA networks. Through simulations, we demonstrate that SmCCNet has better overall prediction performance compared to popular gene expression network construction and integration approaches under realistic settings. Applying SmCCNet to studies on chronic obstructive pulmonary disease (COPD) and breast cancer, we found enrichment of known relevant pathways (e.g. the Cadherin pathway for COPD and the interferon-gamma signaling pathway for breast cancer) as well as less known omics features that may be important to the diseases. Although those applications focus on miRNA-mRNA co-expression networks, SmCCNet is applicable to a variety of omics and other data types. It can also be easily generalized to incorporate multiple quantitative phenotype simultaneously. The versatility of SmCCNet suggests great potential of the approach in many areas. AVAILABILITY AND IMPLEMENTATION: The SmCCNet algorithm is written in R, and is freely available on the web at https://cran.r-project.org/web/packages/SmCCNet/index.html. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Resequencing Study Confirms That Host Defense and Cell Senescence Gene Variants Contribute to the Risk of Idiopathic Pulmonary Fibrosis.

Rationale: Several common and rare genetic variants have been associated with idiopathic pulmonary fibrosis, a progressive fibrotic condition that is localized to the lung. Objectives: To develop an integrated understanding of the rare and common variants located in multiple loci that have been reported to contribute to the risk of disease. Methods: We performed deep targeted resequencing (3.69 Mb of DNA) in cases (n = 3,624) and control subjects (n = 4,442) across genes and regions previously associated with disease. We tested for associations between disease and 1) individual common variants via logistic regression and 2) groups of rare variants via sequence kernel association tests. Measurements and Main Results: Statistically significant common variant association signals occurred in all 10 of the regions chosen based on genome-wide association studies. The strongest risk variant is the MUC5B promoter variant rs35705950, with an odds ratio of 5.45 (95% confidence interval, 4.91-6.06) for one copy of the risk allele and 18.68 (95% confidence interval, 13.34-26.17) for two copies of the risk allele (P = 9.60 × 10-295). In addition to identifying for the first time that rare variation in FAM13A is associated with disease, we confirmed the role of rare variation in the TERT and RTEL1 gene regions in the risk of IPF, and found that the FAM13A and TERT regions have independent common and rare variant signals. Conclusions: A limited number of common and rare variants contribute to the risk of idiopathic pulmonary fibrosis in each of the resequencing regions, and these genetic variants focus on biological mechanisms of host defense and cell senescence.

Predictive modeling of miRNA-mediated predisposition to alcohol-related phenotypes in mouse.

BACKGROUND: MicroRNAs (miRNAs) are small non-coding RNAs that bind messenger RNAs and promote their degradation or repress their translation. There is increasing evidence of miRNAs playing an important role in alcohol related disorders. However, the role of miRNAs as mediators of the genetic effect on alcohol phenotypes is not fully understood. We conducted a high-throughput sequencing study to measure miRNA expression levels in alcohol naïve animals in the LXS panel of recombinant inbred (RI) mouse strains. We then combined the sequencing data with genotype data, microarry gene expression data, and data on alcohol-related behavioral phenotypes such as 'Drinking in the dark', 'Sleep time', and 'Low dose activation' from the same RI panel. SNP-miRNA-gene triplets with strong association within the triplet that were also associated with one of the 4 alcohol phenotypes were selected and a Bayesian network analysis was used to aggregate results into a directed network model. RESULTS: We found several triplets with strong association within the triplet that were also associated with one of the alcohol phenotypes. The Bayesian network analysis found two networks where a miRNA mediates the genetic effect on the alcohol phenotype. The miRNAs were found to influence the expression of protein-coding genes, which in turn influences the quantitative phenotypes. The pathways in which these genes are enriched have been previously associated with alcohol-related traits. CONCLUSION: This work enhances association studies by identifying miRNAs that may be mediating the association between genetic markers (SNPs) and the alcohol phenotypes. It suggests a mechanism of how genetic variants are affecting traits of interest through the modification of miRNA expression.

miR-MaGiC improves quantification accuracy for small RNA-seq.

OBJECTIVE: Many tools have been developed to profile microRNA (miRNA) expression from small RNA-seq data. These tools must contend with several issues: the small size of miRNAs, the small number of unique miRNAs, the fact that similar miRNAs can be transcribed from multiple loci, and the presence of miRNA isoforms known as isomiRs. Methods failing to address these issues can return misleading information. We propose a novel quantification method designed to address these concerns. RESULTS: We present miR-MaGiC, a novel miRNA quantification method, implemented as a cross-platform tool in Java. miR-MaGiC performs stringent mapping to a core region of each miRNA and defines a meaningful set of target miRNA sequences by collapsing the miRNA space to "functional groups". We hypothesize that these two features, mapping stringency and collapsing, provide more optimal quantification to a more meaningful unit (i.e., miRNA family). We test miR-MaGiC and several published methods on 210 small RNA-seq libraries, evaluating each method's ability to accurately reflect global miRNA expression profiles. We define accuracy as total counts close to the total number of input reads originating from miRNAs. We find that miR-MaGiC, which incorporates both stringency and collapsing, provides the most accurate counts.