Visualization of the landscape of the read alignment shape of ATAC-seq data using Hellinger distance metric.

Assay for Transposase-Accessible Chromatin using high-throughput sequencing (ATAC-seq) is the popular technique using next-generation sequencing to measure chromatin accessibility and identify open chromatin regions. While read alignment shape information of next-generation sequencing data with intensity information has been used in various bioinformatics methods, few studies have focused on pure shape information alone. In this study, we investigated what types of ATAC-seq read alignment shapes are observed for the promoter region and whether the pure shape information was related or unrelated to other gene features. We introduced a novel concept and pipeline for handling the pure shape information of NGS data as probability distributions and quantifying their dissimilarities by information theory. Based on this concept, we demonstrate that the pure shape information of ATAC-seq data is correlated with chromatin openness and some gene characteristics. On the other hand, it is suggested that the pure information of ATAC-seq read alignment shape is unlikely to contain additional information to explain differences in RNA expression. Our study suggests that viewing the read alignment shape of NGS data as probability distributions enables us to capture the characteristics of the genome-wide landscape of such data in a non-parametric manner.

Data-driven identification and classification of nonlinear aging patterns reveals the landscape of associations between DNA methylation and aging.

BACKGROUND: Aging affects the incidence of diseases such as cancer and dementia, so the development of biomarkers for aging is an important research topic in medical science. While such biomarkers have been mainly identified based on the assumption of a linear relationship between phenotypic parameters, including molecular markers, and chronological age, numerous nonlinear changes between markers and aging have been identified. However, the overall landscape of the patterns in nonlinear changes that exist in aging is unknown. RESULT: We propose a novel computational method, Data-driven Identification and Classification of Nonlinear Aging Patterns (DICNAP), that is based on functional data analysis to identify biomarkers for aging and potential patterns of change during aging in a data-driven manner. We applied the proposed method to large-scale, public DNA methylation data to explore the potential patterns of age-related changes in methylation intensity. The results showed that not only linear, but also nonlinear changes in DNA methylation patterns exist. A monotonous demethylation pattern during aging, with its rate decreasing at around age 60, was identified as the candidate stable nonlinear pattern. We also analyzed the age-related changes in methylation variability. The results showed that the variability of methylation intensity tends to increase with age at age-associated sites. The representative variability pattern is a monotonically increasing pattern that accelerates after middle age. CONCLUSION: DICNAP was able to identify the potential patterns of the changes in the landscape of DNA methylation during aging. It contributes to an improvement in our theoretical understanding of the aging process.

Plasma proteins as potential biomarkers of aging of single tissue and cell type.

Plasma proteins serve as biomarkers of aging and various age-related diseases. While a number of plasma proteins have been identified that increase or decrease with age, the interpretation of each protein is challenging. This is due to the nature of plasma, which is a mixture of factors secreted by many different tissues and cells. Therefore, the catalog of age-related proteins secreted by a single cell type in a single tissue would be useful for understanding tissue-specific aging patterns. In this study, the author addressed this challenge by integrative data mining of the Human Protein Atlas and the recently published result of large-scale aging proteomics research. Finally, we identified the 17 age-related proteins produced by a single tissue and a single cell type: MBL2 and HP in the liver (hepatocytes), SFTPC in the lung (type II alveolar cells), PRL and POMC in the pituitary (anterior cells), GCG, CUZD1 and CPA2 in the pancreas (pancreatic cells), MYBPC1 in skeletal muscle (myocytes), PTH in the parathyroid gland (glandular cells), LPO and AMY1A in the salivary gland (glandular cells), INSL3 in the male testis (Leydig cells), KLK3 and KLK4 in the male prostate (glandular cells), MPO and ACP5 in immune cells. This list of proteins would be potentially useful for understanding age-related changes in the plasma proteome and inter-tissue networks.

Cell population-based framework of genetic epidemiology in the single-cell omics era.

Genetic epidemiology is a rapidly advancing field due to the recent availability of large amounts of omics data. In recent years, it has become possible to obtain omics information at the single-cell level, so genetic epidemiological models need to be updated to integrate with single-cell expression data. In this perspective paper, we propose a cell population-based framework for genetic epidemiology in the single-cell era. In this framework, genetic diversity influences phenotypic diversity through the diversity of cell population profiles, which are defined as high-dimensional probability distributions of the state spaces of biomolecules of each omics layer. We discuss how biomolecular experimental measurement data can capture the different properties of this distribution. In particular, single-cell data constitute a sample from this population distribution where only some coordinate values are observable. From a data analysis standpoint, we introduce methodology for feature extraction from cell population profiles. Finally, we discuss how this framework can be applied not only to genetic epidemiology but also to systems biology.

The medical cost and outcome of desensitization protocol in kidney transplantation recipients with high immunological risks.

BACKGROUND: Kidney transplantation is a well-established alternative in renal replacement therapy. Compared with hemodialysis, low-immunological-risk kidney transplantation can reduce the medical treatment costs associated with end-stage renal disease. However, there are few reports on whether high-immunological-risk kidney transplantation reduces the financial burden on governments. We investigated the medical costs of high-immunological-risk kidney transplantation in comparison with the cost of hemodialysis in Japan. METHODS: We compared the medical costs of high-immunological-risk kidney transplantation with those of hemodialysis. 15 patients who underwent crossmatch-positive and/or donor-specific antibody-positive kidney transplantations between 2020 and 2021 were enrolled in this study. The patients received intravenous immunoglobulin, plasmapheresis, and rituximab as desensitizing therapy. RESULTS: Acute antibody-mediated rejection was detected in nine (60%) recipients, while there were no indications of graft function deterioration during the follow-up. For each patient, the transplant hospitalization cost was 38 428 ± 8789 USD. However, the cumulative costs were 59 758 ± 10 006 USD and 79 781 ± 16 366 USD, at 12 and 24 months, respectively. Compared with hemodialysis (34 286 USD per year), high-immunological-risk kidney transplantation tends to be expensive in the first year, but the cost is likely to be lower than that of hemodialysis after 3 years. CONCLUSIONS: Although kidney transplantation is initially expensive compared with hemodialysis, the medical cost becomes advantageous after 3 years even in kidney transplant recipients with high immunological risk.

Bioinformatic Identification of Potential RNA Alterations on the Atrial Fibrillation Remodeling from Human Pulmonary Veins.

Atrial fibrillation (AF) is the most frequent persistent arrhythmia. Many genes have been reported as a genetic background for AF. However, most transcriptome analyses of AF are limited to the atrial samples and have not been evaluated by multiple cardiac regions. In this study, we analyzed the expression levels of protein-coding and long noncoding RNAs (lncRNAs) in six cardiac regions by RNA-seq. Samples were donated from six subjects with or without persistent AF for left atria, left atrial appendages, right atria, sinoatrial nodes, left ventricles, right ventricles, and pulmonary veins (PVs), and additional four right atrial appendages samples were collected from patients undergoing mitral valve replacement. In total, 23 AF samples were compared to 23 non-AF samples. Surprisingly, the most influenced heart region in gene expression by AF was the PV, not the atria. The ion channel-related gene set was significantly enriched upon analysis of these significant genes. In addition, some significant genes are cancer-related lncRNAs in PV in AF. A co-expression network analysis could detect the functional gene clusters. In particular, the cancer-related lncRNA, such as SAMMSON and FOXCUT, belong to the gene network with the cancer-related transcription factor FOXC1. Thus, they may also play an aggravating role in the pathogenesis of AF, similar to carcinogenesis. In the least, this study suggests that (1) RNA alteration is most intense in PVs and (2) post-transcriptional gene regulation by lncRNA may contribute to the progression of AF. Through the screening analysis across the six cardiac regions, the possibility that the PV region can play a role other than paroxysmal triggering in the pathogenesis of AF was demonstrated for the first time. Future research with an increase in the number of PV samples will lead to a novel understanding of the pathophysiology of AF.

Data-driven comparison of multiple high-dimensional single-cell expression profiles.

Comparing multiple single-cell expression datasets such as cytometry and scRNA-seq data between case and control donors provides information to elucidate the mechanisms of disease. We propose a completely data-driven computational biological method for this task. This overcomes the challenges of conventional cellular subset-based comparisons and facilitates further analyses such as machine learning and gene set analysis of single-cell expression datasets.

Novel insight into the correlation between hernia orifice of cystocele and lower urinary tract function: a pilot study.

BACKGROUND: It has been hypothesized that women with significant pelvic organ prolapse (POP), particularly of the anterior vaginal wall, may have voiding dysfunction (VD). Although the VD mechanism due to cystocele is not fully understood, different vaginal compartments have rarely been closely examined. This study attempted to further elucidate the correlation between POP and VD through a new subgroup classification using cystoscopy. METHODS: This study reviewed clinical records of 49 women who underwent cystocele repair. All patients were scheduled for laparoscopic sacrocolpopexy, preoperatively underwent uroflowmetry and postvoid residual urine volume (PVR) measurement, and completed pelvic floor function questionnaires. Bladder examination by cystoscopy was additionally performed using the lithotomy position with the Valsalva maneuver. RESULTS: Subjects were divided into four groups according to hernia orifice presence determined by cystoscopy, which included the trigone type, posterior wall type, trigone and urethra type, and trigone and posterior wall type. The posterior wall type had statistically higher PVR values versus the trigone and posterior wall type (P = 0.013). The posterior wall type had statistically lower values for average urine flow rate versus the urethra and trigone type (P = 0.020). There were no significant differences noted in the pelvic floor function questionnaires among the four groups. CONCLUSIONS: A new bladder defect classification based upon hernia orifice location was associated with lower urinary tract function. Posterior wall hernia presence caused significant voiding function deterioration. This new subgroup classification, which can more clearly identify and indicate bladder function, is also comparable among patients.

Interpretation of omics data analyses.

Omics studies attempt to extract meaningful messages from large-scale and high-dimensional data sets by treating the data sets as a whole. The concept of treating data sets as a whole is important in every step of the data-handling procedures: the pre-processing step of data records, the step of statistical analyses and machine learning, translation of the outputs into human natural perceptions, and acceptance of the messages with uncertainty. In the pre-processing, the method by which to control the data quality and batch effects are discussed. For the main analyses, the approaches are divided into two types and their basic concepts are discussed. The first type is the evaluation of many items individually, followed by interpretation of individual items in the context of multiple testing and combination. The second type is the extraction of fewer important aspects from the whole data records. The outputs of the main analyses are translated into natural languages with techniques, such as annotation and ontology. The other technique for making the outputs perceptible is visualization. At the end of this review, one of the most important issues in the interpretation of omics data analyses is discussed. Omics studies have a large amount of information in their data sets, and every approach reveals only a very restricted aspect of the whole data sets. The understandable messages from these studies have unavoidable uncertainty.

Genome-wide association study of individual differences of human lymphocyte profiles using large-scale cytometry data.

Human immune systems are very complex, and the basis for individual differences in immune phenotypes is largely unclear. One reason is that the phenotype of the immune system is so complex that it is very difficult to describe its features and quantify differences between samples. To identify the genetic factors that cause individual differences in whole lymphocyte profiles and their changes after vaccination without having to rely on biological assumptions, we performed a genome-wide association study (GWAS), using cytometry data. Here, we applied computational analysis to the cytometry data of 301 people before receiving an influenza vaccine, and 1, 7, and 90 days after the vaccination to extract the feature statistics of the lymphocyte profiles in a nonparametric and data-driven manner. We analyzed two types of cytometry data: measurements of six markers for B cell classification and seven markers for T cell classification. The coordinate values calculated by this method can be treated as feature statistics of the lymphocyte profile. Next, we examined the genetic basis of individual differences in human immune phenotypes with a GWAS for the feature statistics, and we newly identified seven significant and 36 suggestive single-nucleotide polymorphisms associated with the individual differences in lymphocyte profiles and their change after vaccination. This study provides a new workflow for performing combined analyses of cytometry data and other types of genomics data.

Higher immunoglobulin A nephropathy recurrence in related-donor kidney transplants: The Japan Academic Consortium of Kidney Transplantation study.

OBJECTIVE: To investigate the 10-year biopsy-proven recurrence rates and risk factors for immunoglobulin A nephropathy recurrence in kidney transplant recipients. METHODS: We included 299 kidney transplant recipients from 1995 to 2015, who had biopsy-proven underlying immunoglobulin A nephropathy and underwent zero-hour biopsy. The primary end-point was recurrence of immunoglobulin A nephropathy. We compared clinical, treatment and graft failure among those with and without recurrent immunoglobulin A nephropathy. A time-to-recurrence analysis was carried out using the competing risk analysis and time-dependent Cox model. RESULTS: Of 299 recipients, 80 had recurrent immunoglobulin A nephropathy (66.3% with clinical biopsy and 33.7% with protocol biopsy, post-transplant biopsy rate: 90.6%). The 10-year recurrence rate was 34.3% (95% confidence interval 27.6-41.1). Related-donor transplantation (hazard ratio 2.28, P = 0.009) and post-transplant increased proteinuria (hazard ratio 1.59, P < 0.001) were identified as potential risk factors for immunoglobulin A nephropathy recurrence. The 10-year rates were 41.5% in related donor recipients and 16.3% in unrelated donor recipients. There was no conclusive evidence that the calcineurin inhibitor, antimetabolites, basiliximab and rituximab reduce immunoglobulin A nephropathy recurrence. Immunoglobulin A nephropathy recurrence was associated with an increased risk of death-censored graft failure (hazard ratio 5.29, 95% confidence interval 1.39-20.17, P = 0.015). However, related donor itself was not associated with an increased risk of graft failure. CONCLUSIONS: The present results have clinical implications in that the signs of recurrent immunoglobulin A nephropathy should be evaluated carefully in recipients receiving related-donor transplants. There is a need for further studies related to genetic and/or familial interactions in kidney transplant recipients with immunoglobulin A nephropathy and related donors.

Outcome of the risk-stratified desensitization protocol in donor-specific antibody-positive living kidney transplant recipients: a retrospective study.

Acceptable outcomes of donor-specific antibody (DSA)-positive living kidney transplantation (LKT) have recently been reported. However, LKT in crossmatch (XM)-positive patients remains at high-risk and requires an optimal desensitization protocol. We report our intermediate-term outcomes of XM-positive LKT vs. XM-negative LKT in patients who underwent LKT between January 2012 and June 2015 in our institution. The rate of acute antibody-mediated rejection (ABMR) within 90 days postoperation, graft function, and patient, and graft survival rates at 4 years were investigated. Patients were divided into three groups: XM-DSA- (n = 229), XM-DSA+ (n = 36), and XM + DSA+ (n = 15). The XM + DSA+ group patients underwent desensitization with high-dose intravenous immunoglobulin, plasmapheresis, and rituximab. The rates of ABMR within 90 days in the XM-DSA-, XM-DSA+, and XM + DSA+ groups were 1.3%, 9.4%, and 60.0%, respectively (P < 0.001). There were no significant differences in the graft function throughout the observational period, the 4-year patient or graft survival rates among three groups. This study showed that intermediate-term outcomes of XM-positive LKT were comparable to XM-negative LKT. However, our current desensitization protocol cannot avert ABMR within 90 days, and XM positivity is still a significant risk factor for ABMR. Further refinement of the current desensitization regimen is required.

Application of a mathematical model to clarify the statistical characteristics of a pan-tissue DNA methylation clock.

DNA methylation clocks estimate biological age based on DNA methylation profiles. This study developed a mathematical model to describe DNA methylation aging and the establishment of a pan-tissue DNA methylation clock. The model simulates the aging dynamics of DNA methylation profiles based on passive demethylation as well as the process of cross-sectional bulk data acquisition. As a result, this study identified two conditions under which the pan-tissue DNA methylation clock can successfully predict biological age: one condition is that the target tissues are sufficiently well represented in the training dataset, and the other condition is that the target sample contains cell subsets that are common among different tissues. When either of these conditions is met, the clock performs well. It is also suggested that the epigenetic age of all samples in the target tissue tends to be either over or underestimated when biological age prediction fails. The model can reveal the statistical characteristics of DNA methylation clocks.

Data-driven detection of age-related arbitrary monotonic changes in single-cell gene expression distributions.

Identification of genes whose expression increases or decreases with age is central to understanding the mechanisms behind aging. Recent scRNA-seq studies have shown that changes in single-cell expression profiles with aging are complex and diverse. In this study, we introduce a novel workflow to detect changes in the distribution of arbitrary monotonic age-related changes in single-cell expression profiles. Since single-cell gene expression profiles can be analyzed as probability distributions, our approach uses information theory to quantify the differences between distributions and employs distance matrices for association analysis. We tested this technique on simulated data and confirmed that potential parameter changes could be detected in a set of probability distributions. Application of the technique to a public scRNA-seq dataset demonstrated its potential utility as a straightforward screening method for identifying aging-related cellular features.

Delving into gene-set multiplex networks facilitated by a k-nearest neighbor-based measure of similarity.

Gene sets are functional units for living cells. Previously, limited studies investigated the complex relations among gene sets, but documents about their altering patterns across biological conditions still need to be prepared. In this study, we adopted and modified a classical k-nearest neighbor-based association function to detect inter-gene-set similarities. Based on this method, we built multiplex networks of gene sets for the first time; these networks contain layers of gene sets corresponding to different populations of cells. The context-based multiplex networks can capture meaningful biological variation and have considerable differences from knowledge-based networks of gene sets built on Jaccard similarity, as demonstrated in this study. Furthermore, at the scale of individual gene sets, the structural coefficients of gene sets (multiplex PageRank centrality, clustering coefficient, and participation coefficient) disclose the diversity of gene sets from the perspective of structural properties and make it easier to identify unique gene sets. In gene set enrichment analysis (GSEA), each gene set is treated independently, and its contextual and relational attributes are ignored. The structural coefficients of gene sets can supplement GSEA with information about the overall picture of gene sets, promoting the constructive reorganization of the enriched terms and helping researchers better prioritize and select gene sets.

Mathematical model for the relationship between single-cell and bulk gene expression to clarify the interpretation of bulk gene expression data.

BACKGROUND: Differential expression analysis is a standard approach in molecular biology. For example, genes whose expression levels differ between diseased and non-diseased samples are considered to be associated with that disease. On the other hand, differential variability analysis focuses on the differences of the variances of gene expression between sample groups. Although differential variability is also known to capture biological information, its interpretation remains unclear and controversial. Recent single-cell analyses have revealed that differences between sample groups can affect gene expression in a cellular subset-specific manner or by altering the proportion of a particular cellular subset. The aim of this study is to clarify the interpretation of mean and variance of bulk gene expression data. METHOD: We developed a mathematical model in which the bulk gene expression value is proportional to the mean value of the single-cell gene expression profile. Based on this model, we performed theoretical, simulated and real single-cell RNA-seq data analyses. RESULT AND CONCLUSION: We identified how differences in single-cell gene expression profiles affect the differences in the mean and the variance of bulk gene expression. It is shown that differential expression analysis of bulk expression data can overlook significant changes in gene expression at the single-cell level. Further, differential variability analysis capture the complex feature affected by different gene expression shifts for each subset, changes in the proportions of cellular subsets, and variation in single-cell distribution parameters among samples.

Comparative Study of Transcriptome in the Hearts Isolated from Mice, Rats, and Humans.

The heart is a significant organ in mammalian life, and the heartbeat mechanism has been an essential focus of science. However, few studies have focused on species differences. Accordingly, challenges remain in studying genes that have universal functions across species and genes that determine species differences. Here, we analyzed transcriptome data in mouse, rat, and human atria, ventricles, and sinoatrial nodes (SA) obtained from different platforms and compared them by calculating specificity measure (SPM) values in consideration of species differences. Among the three heart regions, the species differences in SA were the greatest, and we searched for genes that determined the essential characteristics of SA, which was SHOX2 in our criteria. The SPM value of SHOX2 was prominently high across species. Similarly, by calculating SPM values, we identified 3 atrial-specific, 11 ventricular-specific, and 17 SA-specific markers. Ontology analysis identified 70 cardiac region- and species-specific ontologies. These results suggest that reanalyzing existing data by calculating SPM values may identify novel tissue-specific genes and species-dependent gene expression. This study identified the importance of SHOX2 as an SA-specific transcription factor, a novel cardiac regional marker, and species-dependent ontologies.

Recipient myeloperoxidase-producing cells regulate antibody-mediated acute versus chronic kidney allograft rejection.

Antibody-mediated rejection (ABMR) continues to be a major problem undermining the success of kidney transplantation. Acute ABMR of kidney grafts is characterized by neutrophil and monocyte margination in the tubular capillaries and by graft transcripts indicating NK cell activation, but the myeloid cell mechanisms required for acute ABMR have remained unclear. Dysregulated donor-specific antibody (DSA) responses with high antibody titers are induced in B6.CCR5-/- mice transplanted with complete MHC-mismatched A/J kidneys and are required for rejection of the grafts. This study tested the role of recipient myeloid cell production of myeloperoxidase (MPO) in the cellular and molecular components of acute ABMR. Despite induction of equivalent DSA titers, B6.CCR5-/- recipients rejected A/J kidneys between days 18 and 25, with acute ABMR, whereas B6.CCR5-/-MPO-/- recipients rejected the grafts between days 46 and 54, with histopathological features of chronic graft injury. On day 15, myeloid cells infiltrating grafts from B6.CCR5-/- and B6.CCR5-/-MPO-/- recipients expressed marked phenotypic and functional transcript differences that correlated with the development of acute versus chronic allograft injury, respectively. Near the time of peak DSA titers, activation of NK cells to proliferate and express CD107a was decreased within allografts in B6.CCR5-/-MPO-/- recipients. Despite high titers of DSA, depletion of neutrophils reproduced the inhibition of NK cell activation and decreased macrophage infiltration but increased monocytes producing MPO. Overall, recipient myeloid cells producing MPO regulate graft-infiltrating monocyte/macrophage function and NK cell activation that are required for DSA-mediated acute kidney allograft injury, and their absence switches DSA-mediated acute pathology and graft outcomes to chronic ABMR.

Decomposition of a set of distributions in extended exponential family form for distinguishing multiple oligo-dimensional marker expression profiles of single-cell populations and visualizing their dynamics.

Single-cell expression analysis is an effective tool for studying the dynamics of cell population profiles. However, the majority of statistical methods are applied to individual profiles and the methods for comparing multiple profiles simultaneously are limited. In this study, we propose a nonparametric statistical method, called Decomposition into Extended Exponential Family (DEEF), that embeds a set of single-cell expression profiles of several markers into a low-dimensional space and identifies the principal distributions that describe their heterogeneity. We demonstrate that DEEF can appropriately decompose and embed sets of theoretical probability distributions. We then apply DEEF to a cytometry dataset to examine the effects of epidermal growth factor stimulation on an adult human mammary gland. It is shown that DEEF can describe the complex dynamics of cell population profiles using two parameters and visualize them as a trajectory. The two parameters identified the principal patterns of the cell population profile without prior biological assumptions. As a further application, we perform a dimensionality reduction and a time series reconstruction. DEEF can reconstruct the distributions based on the top coordinates, which enables the creation of an artificial dataset based on an actual single-cell expression dataset. Using the coordinate system assigned by DEEF, it is possible to analyze the relationship between the attributes of the distribution sample and the features or shape of the distribution using conventional data mining methods.