GLDADec: marker-gene guided LDA modeling for bulk gene expression deconvolution.

Inferring cell type proportions from bulk transcriptome data is crucial in immunology and oncology. Here, we introduce guided LDA deconvolution (GLDADec), a bulk deconvolution method that guides topics using cell type-specific marker gene names to estimate topic distributions for each sample. Through benchmarking using blood-derived datasets, we demonstrate its high estimation performance and robustness. Moreover, we apply GLDADec to heterogeneous tissue bulk data and perform comprehensive cell type analysis in a data-driven manner. We show that GLDADec outperforms existing methods in estimation performance and evaluate its biological interpretability by examining enrichment of biological processes for topics. Finally, we apply GLDADec to The Cancer Genome Atlas tumor samples, enabling subtype stratification and survival analysis based on estimated cell type proportions, thus proving its practical utility in clinical settings. This approach, utilizing marker gene names as partial prior information, can be applied to various scenarios for bulk data deconvolution. GLDADec is available as an open-source Python package at https://github.com/mizuno-group/GLDADec.

NRBdMF: A Recommendation Algorithm for Predicting Drug Effects Considering Directionality.

Predicting the novel effects of drugs based on information about approved drugs can be regarded as a recommendation system. Matrix factorization is one of the most used recommendation systems, and various algorithms have been devised for it. A literature survey and summary of existing algorithms for predicting drug effects demonstrated that most such methods, including neighborhood regularized logistic matrix factorization, which was the best performer in benchmark tests, used a binary matrix that considers only the presence or absence of interactions. However, drug effects are known to have two opposite aspects, such as side effects and therapeutic effects. In the present study, we proposed using neighborhood regularized bidirectional matrix factorization (NRBdMF) to predict drug effects by incorporating bidirectionality, which is a characteristic property of drug effects. We used this proposed method for predicting side effects using a matrix that considered the bidirectionality of drug effects, in which known side effects were assigned a positive (+1) label and known treatment effects were assigned a negative (-1) label. The NRBdMF model, which utilizes drug bidirectional information, achieved enrichment of side effects at the top and indications at the bottom of the prediction list. This first attempt to consider the bidirectional nature of drug effects using NRBdMF showed that it reduced false positives and produced a highly interpretable output.

Investigation of a Data Split Strategy Involving the Time Axis in Adverse Event Prediction Using Machine Learning.

Adverse events are a serious issue in drug development, and many prediction methods using machine learning have been developed. The random split cross-validation is the de facto standard for model building and evaluation in machine learning, but care should be taken in adverse event prediction because this approach does not strictly match the real-world situation. The time split, which uses the time axis, is considered suitable for real-world prediction. However, the differences in model performance obtained using the time and random splits are not clear due to the lack of comparable studies. To understand the differences, we compared the model performance between the time and random splits using nine types of compound information as input, eight adverse events as targets, and six machine learning algorithms. The random split showed higher area under the curve values than did the time split for six of eight targets. The chemical spaces of the training and test datasets of the time split were similar, suggesting that the concept of applicability domain is insufficient to explain the differences derived from the splitting. The area under the curve differences were smaller for the protein interaction than for the other datasets. Subsequent detailed analyses suggested the danger of confounding in the use of knowledge-based information in the time split. These findings indicate the importance of understanding the differences between the time and random splits in adverse event prediction and suggest that appropriate use of the splitting strategies and interpretation of results are necessary for the real-world prediction of adverse events. We provide the analysis code and datasets used in the present study at https://github.com/mizuno-group/AE_prediction.

A randomized trial to examine the impact of food on pharmacokinetics of 4-phenylbutyrate and change in amino acid availability after a single oral administration of sodium 4-phenylbutyrarte in healthy volunteers.

Urea cycle disorders (UCDs), inborn errors of hepatocyte metabolism, result in the systemic accumulation of ammonia to toxic levels. Sodium 4-phenylbutyrate (NaPB), a standard therapy for UCDs for over 20 years, generates an alternative pathway of nitrogen deposition through glutamine consumption. Administration during or immediately after a meal is the accepted use of NaPB. However, this regimen is not based on clinical evidence. Here, an open-label, single-dose, five-period crossover study was conducted in healthy adults to investigate the effect of food on the pharmacokinetics of NaPB and determine any subsequent change in amino acid availability. Twenty subjects were randomized to one of four treatment groups. Following an overnight fast, NaPB was administered orally at 4.3 g/m2 (high dose, HD) or 1.4 g/m2 (low dose, LD) either 30 min before or just after breakfast. At both doses, compared with post-breakfast administration, pre-breakfast administration significantly increased systemic exposure of PB and decreased plasma glutamine availability. Pre-breakfast LD administration attenuated plasma glutamine availability to the same extent as post-breakfast HD administration. Regardless of the regimen, plasma levels of branched-chain amino acids (BCAA) were decreased below baseline in a dose-dependent manner. In conclusion, preprandial oral administration of NaPB maximized systemic exposure of the drug and thereby its potency to consume plasma glutamine. This finding may improve poor medication compliance because of the issues with odor, taste, and pill burden of NaPB and reduce the risk of BCAA deficiency in NaPB therapy.

Functional Investigation of Solute Carrier Family 35, Member F2, in Three Cellular Models of the Primate Blood-Brain Barrier.

Understanding the mechanisms of drug transport across the blood-brain barrier (BBB) is an important issue for regulating the pharmacokinetics of drugs in the central nervous system. In this study, we focused on solute carrier family 35, member F2 (SLC35F2), whose mRNA is highly expressed in the BBB. SLC35F2 protein was enriched in isolated mouse and monkey brain capillaries relative to brain homogenates and was localized exclusively on the apical membrane of MDCKII cells and brain microvascular endothelial cells (BMECs) differentiated from human induced pluripotent stem cells (hiPS-BMECs). SLC35F2 activity was assessed using its substrate, YM155, and pharmacological experiments revealed SLC35F2 inhibitors, such as famotidine (half-maximal inhibitory concentration, 160 µM). Uptake of YM155 was decreased by famotidine or SLC35F2 knockdown in immortalized human BMECs (human cerebral microvascular endothelial cell/D3 cells). Furthermore, famotidine significantly inhibited the apical (A)-to-basal (B) transport of YM155 in primary cultured monkey BMECs and hiPS-BMECs. Crucially, SLC35F2 knockout diminished the A-to-B transport and intracellular accumulation of YM155 in hiPS-BMECs. By contrast, in studies using an in situ brain perfusion technique, neither deletion of Slc35f2 nor famotidine reduced brain uptake of YM155, even though YM155 is a substrate of mouse SLC35F2. YM155 uptake was decreased significantly by losartan and naringin, inhibitors for the organic anion transporting polypeptide (OATP) 1A4. These findings suggest SLC35F2 is a functional transporter in various cellular models of the primate BBB that delivers its substrates to the brain and that its relative importance in the BBB is modified by differences in the expression of OATPs between primates and rodents. SIGNIFICANCE STATEMENT: This study demonstrated that SLC35F2 is a functional drug influx transporter in three different cellular models of the primate blood-brain barrier (i.e., human cerebral microvascular endothelial cell/D3 cells, primary cultured monkey BMECs, and human induced pluripotent stem-BMECs) but has limited roles in mouse brain. SLC35F2 facilitates apical-to-basal transport across the tight cell monolayer. These findings will contribute to the development of improved strategies for targeting drugs to the central nervous system.

Decomposition Profile Data Analysis for Deep Understanding of Multiple Effects of Natural Products.

It is difficult to understand the entire effect of a natural product because such products generally have multiple effects. We propose a strategy to understand these effects effectively by decomposing them with a profile data analysis method we developed. A transcriptome profile data set was obtained from a public database and analyzed. Considering their high similarity in structure and transcriptome profile, we focused on rescinnamine and syrosingopine. Decomposed effects predicted clear differences between the compounds. Two of the decomposed effects, SREBF1 activation and HDAC inhibition, were investigated experimentally because the relationship between these effects and the compounds had not yet been reported. Analyses in vitro validated these effects, and their strength was consistent with predicted scores. Moreover, the number of outliers in decomposed effects per compound was higher in natural products than in drugs in the data set, which is consistent with the nature of the effects of natural products.

Alteration in the Plasma Concentrations of Endogenous Organic Anion-Transporting Polypeptide 1B Biomarkers in Patients with Non-Small Cell Lung Cancer Treated with Paclitaxel.

Paclitaxel has been considered to cause OATP1B-mediated drug-drug interactions at therapeutic doses; however, its clinical relevance has not been demonstrated. This study aimed to elucidate in vivo inhibition potency of paclitaxel against OATP1B1 and OATP1B3 using endogenous OATP1B biomarkers. Paclitaxel is an inhibitor of OATP1B1 and OATP1B3, with Ki of 0.579 ± 0.107 and 5.29 ± 3.87 µM, respectively. Preincubation potentiated its inhibitory effect on both OATP1B1 and OATP1B3, with Ki of 0.154 ± 0.031 and 0.624 ± 0.183 µM, respectively. Ten patients with non-small cell lung cancer who received 200 mg/m2 of paclitaxel by a 3-hour infusion were recruited. Plasma concentrations of 10 endogenous OATP1B biomarkers-namely, coproporphyrin I, coproporphyrin III, glycochenodeoxycholate-3-sulfate, glycochenodeoxycholate-3-glucuronide, glycodeoxycholate-3-sulfate, glycodeoxycholate-3-glucuronide, lithocholate-3-sulfate, glycolithocholate-3-sulfate, taurolithocholate-3-sulfate, and chenodeoxycholate-24-glucuronide-were determined in the patients with non-small cell lung cancer on the day before paclitaxel administration and after the end of paclitaxel infusion for 7 hours. Paclitaxel increased the area under the plasma concentration-time curve (AUC) of the endogenous biomarkers 2- to 4-fold, although a few patients did not show any increment in the AUC ratios of lithocholate-3-sulfate, glycolithocholate-3-sulfate, and taurolithocholate-3-sulfate. Therapeutic doses of paclitaxel for the treatment of non-small cell lung cancer (200 mg/m2) will cause significant OATP1B1 inhibition during and at the end of the infusion. This is the first demonstration that endogenous OATP1B biomarkers could serve as surrogate biomarkers in patients. SIGNIFICANCE STATEMENT: Endogenous biomarkers can address practical and ethical issues in elucidating transporter-mediated drug-drug interaction (DDI) risks of anticancer drugs clinically. We could elucidate a significant increment of the plasma concentrations of endogenous OATP1B biomarkers after a 3-hour infusion (200 mg/m2) of paclitaxel, a time-dependent inhibitor of OATP1B, in patients with non-small cell lung cancer. The endogenous OATP1B biomarkers are useful to assess the possibility of OATP1B-mediated DDIs in patients and help in appropriately designing a dosing schedule to avoid the DDIs.

Interesting Properties of Profile Data Analysis in the Understanding and Utilization of the Effects of Drugs.

Profile data is defined as data which describes the properties of an object. Omics data of a specimen is profile data because its comprehensiveness supports the idea that omics data is numeric information which reflects biological information of the specimen. In general, omics data analysis utilizes an existing body of biological knowledge, while some profile data analysis methods are independent of existing knowledge, which is suitable for uncovering unidentified aspects of a specimen of interest. The effects of a small compound, such as drugs, are multiple, and include unrecognized effects, even by the developers. To uncover such unrecognized effects, it is useful to employ profile data analysis independent of existing knowledge. In this review, we summarize what profile data is, properties of profile data analysis, and current applications of profile data in order to understand and utilize the effects of small compounds, in particular, in a recently developed method to decompose multiple effects of a drug.

Elucidation of N 1-methyladenosine as a Potential Surrogate Biomarker for Drug Interaction Studies Involving Renal Organic Cation Transporters.

Endogenous substrates are emerging biomarkers for drug transporters, which serve as surrogate probes in drug-drug interaction (DDI) studies. In this study, the results of metabolome analysis using wild-type and Oct1/2 double knockout mice suggested that N 1-methyladenosine (m1A) was a novel organic cation transporter (OCT) 2 substrate. An in vitro transport study revealed that m1A is a substrate of mouse Oct1, Oct2, Mate1, human OCT1, OCT2, and multidrug and toxin exclusion protein (MATE) 2-K, but not human MATE1. Urinary excretion accounted for 77% of the systemic elimination of m1A in mice. The renal clearance (46.9 ± 4.9 ml/min per kilogram) of exogenously given m1A was decreased to near the glomerular filtration rates by Oct1/2 double knockout or Mate1 inhibition by pyrimethamine (16.6 ± 2.6 and 24.3 ± 0.6 ml/min per kilogram, respectively), accompanied by significantly higher plasma concentrations. In vivo inhibition of OCT2/MATE2-K by a single dose of 7-[(3R)-3-(1-aminocyclopropyl)pyrrolidin-1-yl]-1-[(1R,2S)-2-fluorocyclopropyl]-8-methoxy-4-oxoquinoline-3-carboxylic acid in cynomolgus monkeys resulted in the elevation of the area under the curve of m1A (1.72-fold) as well as metformin (2.18-fold). The plasma m1A concentration profile showed low diurnal and interindividual variation in healthy volunteers. The renal clearance of m1A in younger (21-45 year old) and older (65-79 year old) volunteers (244 ± 58 and 169 ± 22 ml/min per kilogram, respectively) was about 2-fold higher than the creatinine clearance. The renal clearances of m1A and creatinine were 31% and 17% smaller in older than in younger volunteers. Thus, m1A could be a surrogate probe for the evaluation of DDIs involving OCT2/MATE2-K. SIGNIFICANCE STATEMENT: Endogenous substrates can serve as surrogate probes for clinical drug-drug interaction studies involving drug transporters or enzymes. In this study, m1A was found to be a novel substrate of renal cationic drug transporters OCT2 and MATE2-K. N 1-methyladenosine was revealed to have some advantages compared to other OCT2/MATE substrates (creatinine and N 1-methylnicotinamide). The genetic or chemical impairment of OCT2 or MATE2-K caused a significant increase in the plasma m1A concentration in mice and cynomolgus monkeys due to the high contribution of tubular secretion to the net elimination of m1A. The plasma m1A concentration profile showed low diurnal and interindividual variation in healthy volunteers. Thus, m1A could be a better biomarker of variations in OCT2/MATE2-K activity caused by inhibitory drugs.

Development of a Novel Platform of Proteome Profiling Based on an Easy-to-Handle and Informative 2D-DIGE System.

Proteome profiling based on two-dimensional (2D)-DIGE might be a useful tool for investigating drug-like compounds and the mode of action of drugs. However, obtaining data for profiling requires high labor costs, and it is difficult to control the reproducibility of spot positions because 2D-DIGE usually requires large-size glass plates and spot alignments are greatly affected by the quality of DryStrips and polyacrylamide gels (PAGs). Therefore, we have developed a novel platform by employing small size DryStrips and PAGs, and an image analysis strategy based on dual correction of spot alignment and volume. Our system can automatically detect a large number of consistent spots through all images. Cytosol fractions of HeLa cells treated with dimethyl sulfoxide (DMSO) or bortezomib were analyzed, 1697 consistent spots were detected, and 775 of them were significantly changed with the treatment. Deviations between different days and lot sets of DryStrips and PAGs were investigated by calculating the correlation coefficients. The mean values of the correlation between days and lot sets were 0.96 and 0.94, respectively. Clustering analysis of all the treatment data clearly separated the DMSO or bortezomib treated groups beyond day deviations. Thus, we have succeeded in developing an easy-to-handle 2D-DIGE system that can be a novel proteome profiling platform.

Evaluation of Organic Anion Transporter 1A2-knock-in Mice as a Model of Human Blood-brain Barrier.

The present study aimed to establish a humanized mouse model with which to explore OATP1A2-mediated transcellular transport of drug substrates across the blood-brain barrier (BBB) and to evaluate the usefulness of the humanized mice in preclinical studies. Sulpiride, amisulpride, sultopride, and triptans were used as probes to discriminate OATP1A2 and Oatp1a4. We generated a mouse line humanized for OATP1A2 by introducing the coding region downstream of the Oatp1a4 promoter using the CRISPR/Cas9 technique. In the mice generated, OATP1A2 mRNA in the brain was increased corresponding to disappearance of Oatp1a4. OATP1A2 was localized on both the luminal and abluminal sides of the BBB. Unfortunately, study in vivo employing sulpiride, sumatriptan, and zolmitriptan as probes did not indicate any difference in their brain-to-plasma ratio between the control and humanized mice. Quantitative targeted absolute proteomic analysis of the BBB fraction from the humanized mice revealed that almost all analyzed transporters and membrane proteins were expressed at similar levels to those in control mice. The quantitative levels of OATP1A2 differed depending on the peptide quantified, which suggests that incomplete translation or posttranslational modification may occur. The blood-to-brain transport of zolmitriptan, determined by brain perfusion in situ, was 1.6-fold higher in the humanized mice than in the controls, whereas that of sulpiride was not significantly changed. To our knowledge, we established a mouse line humanized for a BBB uptake transporter for the first time. Unfortunately, because of limited impact, there is still room for improvement of the model system.

Cellular Cholesterol Accumulation Facilitates Ubiquitination and Lysosomal Degradation of Cell Surface-Resident ABCA1.

OBJECTIVE: By excreting cellular cholesterol to apolipoprotein A-I, ATP-binding cassette transporter A1 (ABCA1) mediates the biogenesis of high-density lipoprotein in hepatocytes and prevents foam cell formation from macrophages. We recently showed that cell surface-resident ABCA1 (csABCA1) undergoes ubiquitination and later lysosomal degradation through the endosomal sorting complex required for transport system. Herein, we investigated the relevance of this degradation pathway to the turnover of csABCA1 in hypercholesterolemia. APPROACH AND RESULTS: Immunoprecipitation and cell surface-biotinylation studies with HepG2 cells and mouse peritoneal macrophages showed that the ubiquitination level and degradation of csABCA1 were facilitated by treatment with a liver X receptor (LXR) agonist and acetylated low-density lipoprotein. The effects of an LXR agonist and acetylated low-density lipoprotein on the degradation of csABCA1 were repressed completely by treatment with bafilomycin, an inhibitor of lysosomal degradation, and by depletion of tumor susceptibility gene 101, a major component of endosomal sorting complex required for transport-I. RNAi analysis indicated that LXRß inhibited the accelerated lysosomal degradation of csABCA1 by the LXR agonist, regardless of its transcriptional activity. Cell surface coimmunoprecipitation with COS1 cells expressing extracellularly hemagglutinin-tagged ABCA1 showed that LXRß interacted with csABCA1 and inhibited the ubiquitination of csABCA1. Immunoprecipitates with anti-ABCA1 antibodies from the liver plasma membranes showed less LXRß and a higher ubiquitination level of ABCA1 in high-fat diet-fed mice than in normal chow-fed mice. CONCLUSIONS: Under conditions of high cellular cholesterol content, csABCA1 became susceptible to ubiquitination by dissociation of LXRß from csABCA1, which facilitated the lysosomal degradation of csABCA1 through the endosomal sorting complex required for transport system.

Differential roles of ubiquitination in the degradation mechanism of cell surface-resident bile salt export pump and multidrug resistance-associated protein 2.

We previously showed that ubiquitination, a reversible post-translational modification, facilitates degradation of cell surface-resident bile salt export pump (BSEP) and multidrug resistance-associated protein 2 (MRP2), ABC transporters that are expressed at the canalicular membrane (CM) of hepatocytes. In the current study, its underlying mechanism was investigated by evaluating the role of ubiquitination in the processes of internalization and subsequent degradation of cell surface-resident BSEP and MRP2. Cell surface biotinylation analysis using Flp-In T-REx 293 cells showed that ectopic expression of Ub(Δ)(GG), which is ubiquitin (Ub) lacking the two C-terminal glycines essential for the Ub conjugation reaction, inhibited the internalization of 3× FLAG-BSEP, but not of MRP2, and the degradation of the internalized MRP2, but not of the internalized 3× FLAG-BSEP. Its inhibitory effect on BSEP internalization was also indicated by a time-lapse imaging analysis using the rat hepatoma cell line McA-RH7777 in which Ub(Δ)(GG) delayed the loss of fluorescence from photoactivated Dronpa-BSEP on the CM. The effect of Ub(Δ)(GG) on BSEP internalization in these experiments was abrogated by treatment with chlorpromazine, an inhibitor of clathrin-mediated endocytosis, and the introduction of a Y1311A mutation into BSEP. This mutation eliminates the ability of BSEP to interact with the AP2 adaptor complex, an adaptor protein required for cargo selection in clathrin-mediated endocytosis. In conclusion, our data suggest that ubiquitination facilitates clathrin-mediated endocytosis of BSEP and the degradation of internalized MRP2, leading to the degradation of the cell surface-resident form of both transporters.

Investigation of normalization procedures for transcriptome profiles of compounds oriented toward practical study design.

The transcriptome profile is a representative phenotype-based descriptor of compounds, widely acknowledged for its ability to effectively capture compound effects. However, the presence of batch differences is inevitable. Despite the existence of sophisticated statistical methods, many of them presume a substantial sample size. How should we design a transcriptome analysis to obtain robust compound profiles, particularly in the context of small datasets frequently encountered in practical scenarios? This study addresses this question by investigating the normalization procedures for transcriptome profiles, focusing on the baseline distribution employed in deriving biological responses as profiles. Firstly, we investigated two large GeneChip datasets, comparing the impact of different normalization procedures. Through an evaluation of the similarity between response profiles of biological replicates within each dataset and the similarity between response profiles of the same compound across datasets, we revealed that the baseline distribution defined by all samples within each batch under batch-corrected condition is a good choice for large datasets. Subsequently, we conducted a simulation to explore the influence of the number of control samples on the robustness of response profiles across datasets. The results offer insights into determining the suitable quantity of control samples for diminutive datasets. It is crucial to acknowledge that these conclusions stem from constrained datasets. Nevertheless, we believe that this study enhances our understanding of how to effectively leverage transcriptome profiles of compounds and promotes the accumulation of essential knowledge for the practical application of such profiles.

Investigation of latent representation of toxicopathological images extracted by CNN model for understanding compound properties in vivo.

Toxicopathological images acquired during safety assessment elucidate an individual's biological responses to a given compound, and their numerization can yield valuable insights contributing to the assessment of compound properties. Currently, toxicopathological images are mainly encoded as pathological findings, evaluated by pathologists, which introduces challenges when used as input for modeling, specifically in terms of representation capability and comparability. In this study, we assessed the usefulness of latent representations extracted from toxicopathological images using Convolutional Neural Network (CNN) in estimating compound properties in vivo. Special emphasis was placed on examining the impact of learning pathological findings, the depth of frozen layers during learning, and the selection of the layer for latent representation. Our findings demonstrate that a machine learning model fed with the latent representation as input surpassed the performance of a model directly employing pathological findings as input, particularly in the classification of a compound's Mechanism of Action and in predicting late-phase findings from early-phase images in repeated-dose tests. While learning pathological findings did improve accuracy, the magnitude of improvement was relatively modest. Similarly, the effect of freezing layers during learning was also limited. Notably, the selection of the layer for latent representation had a substantial impact on the accurate estimation of compound properties in vivo.

Difficulty in chirality recognition for Transformer architectures learning chemical structures from string representations.

Recent years have seen rapid development of descriptor generation based on representation learning of extremely diverse molecules, especially those that apply natural language processing (NLP) models to SMILES, a literal representation of molecular structure. However, little research has been done on how these models understand chemical structure. To address this black box, we investigated the relationship between the learning progress of SMILES and chemical structure using a representative NLP model, the Transformer. We show that while the Transformer learns partial structures of molecules quickly, it requires extended training to understand overall structures. Consistently, the accuracy of molecular property predictions using descriptors generated from models at different learning steps was similar from the beginning to the end of training. Furthermore, we found that the Transformer requires particularly long training to learn chirality and sometimes stagnates with low performance due to misunderstanding of enantiomers. These findings are expected to deepen the understanding of NLP models in chemistry.

Investigation of the usefulness of liver-specific deconvolution method by establishing a liver benchmark dataset.

Immune responses in the liver are related to the development and progression of liver failure, and precise prediction of their behavior is important. Deconvolution is a methodology for estimating the immune cell proportions from the transcriptome, and it is mainly applied to blood-derived samples and tumor tissues. However, the influence of tissue-specific modeling on the estimation results has rarely been investigated. Here, we constructed a system to evaluate the performance of the deconvolution method on liver transcriptome data. We prepared seven mouse liver injury models using small-molecule compounds and established a benchmark dataset with corresponding liver bulk RNA-Seq and immune cell proportions. RNA-Seq expression for nine leukocyte subsets and four liver-associated cell types were obtained from the Gene Expression Omnibus to provide a reference. We found that the combination of reference cell sets affects the estimation results of reference-based deconvolution methods and established a liver-specific deconvolution by optimizing the reference cell set for each cell to be estimated. We applied this model to independent datasets and showed that liver-specific modeling is highly extrapolatable. We expect that this approach will enable sophisticated estimation from rich tissue data accumulated in public databases and to obtain information on aggregated immune cell trafficking.

Clinical symptoms, biochemistry, and liver histology during the native liver period of progressive familial intrahepatic cholestasis type 2.

BACKGROUND: Progressive familial intrahepatic cholestasis type 2 (PFIC2) is an ultra-rare disease caused by mutations in the ABCB11 gene. This study aimed to understand the course of PFIC2 during the native liver period. METHODS: From November 2014 to October 2015, a survey to identify PFIC2 patients was conducted in 207 hospitals registered with the Japanese Society of Pediatric Gastroenterology, Hepatology, and Nutrition. Investigators retrospectively collected clinical data at each facility in November 2018 using pre-specified forms. RESULTS: Based on the biallelic pathogenic variants in ABCB11 and/or no hepatic immunohistochemical detection of BSEP, 14 Japanese PFIC2 patients were enrolled at seven facilities. The median follow-up was 63.2 [47.7-123.3] months. The median age of disease onset was 2.5 [1-4] months. Twelve patients underwent living donor liver transplantation (LDLT), with a median age at LDLT of 9 [4-57] months. Two other patients received sodium 4-phenylbutyrate (NaPB) therapy and survived over 60 months with the native liver. No patients received biliary diversion. The cases that resulted in LDLT had gradually deteriorated growth retardation, biochemical tests, and liver histology since the initial visit. In the other two patients, jaundice, growth retardation, and most of the biochemical tests improved after NaPB therapy was started, but pruritus and liver fibrosis did not. CONCLUSIONS: Japanese PFIC2 patients had gradually worsening clinical findings since the initial visit, resulting in LDLT during infancy. NaPB therapy improved jaundice and growth retardation but was insufficient to treat pruritus and liver fibrosis.

Thioester-Based Coupled Fluorogenic Assays in Microdevice for the Detection of Single-Molecule Enzyme Activities of Esterases with Specified Substrate Recognition.

Single-molecule enzyme activity assay is a platform that enables the analysis of enzyme activities at single proteoform level. The limitation of the targetable enzymes is the major drawback of the assay, but the general assay platform is reported to study single-molecule enzyme activities of esterases based on the coupled assay using thioesters as substrate analogues. The coupled assay is realized by developing highly water-soluble thiol-reacting probes based on phosphonate-substituted boron dipyrromethene (BODIPY). The system enables the detection of cholinesterase activities in blood samples at single-molecule level, and it is shown that the dissecting alterations of single-molecule esterase activities can serve as an informative platform for activity-based diagnosis.

Identification of activity-based biomarkers for early-stage pancreatic tumors in blood using single-molecule enzyme activity screening.

Single-molecule enzyme activity-based enzyme profiling (SEAP) is a methodology to globally analyze protein functions in living samples at the single-molecule level. It has been previously applied to detect functional alterations in phosphatases and glycosidases. Here, we expand the potential for activity-based biomarker discovery by developing a semi-automated synthesis platform for fluorogenic probes that can detect various peptidases and protease activities at the single-molecule level. The peptidase/protease probes were prepared on the basis of a 7-amino-4-methylcoumarin fluorophore. The introduction of a phosphonic acid to the core scaffold made the probe suitable for use in a microdevice-based assay, while phosphonic acid served as the handle for the affinity separation of the probe using Phos-tag. Using this semi-automated scheme, 48 fluorogenic probes for the single-molecule peptidase/protease activity analysis were prepared. Activity-based screening using blood samples revealed altered single-molecule activity profiles of CD13 and DPP4 in blood samples of patients with early-stage pancreatic tumors. The study shows the power of single-molecule enzyme activity screening to discover biomarkers on the basis of the functional alterations of proteins.