Galectin-10 in serum extracellular vesicles reflects asthma pathophysiology.

BACKGROUND: Novel biomarkers (BMs) are urgently needed for bronchial asthma (BA) with various phenotypes and endotypes. OBJECTIVE: We sought to identify novel BMs reflecting tissue pathology from serum extracellular vesicles (EVs). METHODS: We performed data-independent acquisition of serum EVs from 4 healthy controls, 4 noneosinophilic asthma (NEA) patients, and 4 eosinophilic asthma (EA) patients to identify novel BMs for BA. We confirmed EA-specific BMs via data-independent acquisition validation in 61 BA patients and 23 controls. To further validate these findings, we performed data-independent acquisition for 6 patients with chronic rhinosinusitis without nasal polyps and 7 patients with chronic rhinosinusitis with nasal polyps. RESULTS: We identified 3032 proteins, 23 of which exhibited differential expression in EA. Ingenuity pathway analysis revealed that protein signatures from each phenotype reflected disease characteristics. Validation revealed 5 EA-specific BMs, including galectin-10 (Gal10), eosinophil peroxidase, major basic protein, eosinophil-derived neurotoxin, and arachidonate 15-lipoxygenase. The potential of Gal10 in EVs was superior to that of eosinophils in terms of diagnostic capability and detection of airway obstruction. In rhinosinusitis patients, 1752 and 8413 proteins were identified from EVs and tissues, respectively. Among 11 BMs identified in EVs and tissues from patients with chronic rhinosinusitis with nasal polyps, 5 (including Gal10 and eosinophil peroxidase) showed significant correlations between EVs and tissues. Gal10 release from EVs was implicated in eosinophil extracellular trapped cell death in vitro and in vivo. CONCLUSION: Novel BMs such as Gal10 from serum EVs reflect disease pathophysiology in BA and may represent a new target for liquid biopsy approaches.

Idiopathic pulmonary fibrosis-specific Bayesian network integrating extracellular vesicle proteome and clinical information.

Idiopathic pulmonary fibrosis (IPF) is a progressive disease characterized by severe lung fibrosis and a poor prognosis. Although the biomolecules related to IPF have been extensively studied, molecular mechanisms of the pathogenesis and their association with serum biomarkers and clinical findings have not been fully elucidated. We constructed a Bayesian network using multimodal data consisting of a proteome dataset from serum extracellular vesicles, laboratory examinations, and clinical findings from 206 patients with IPF and 36 controls. Differential protein expression analysis was also performed by edgeR and incorporated into the constructed network. We have successfully visualized the relationship between biomolecules and clinical findings with this approach. The IPF-specific network included modules associated with TGF-ß signaling (TGFB1 and LRC32), fibrosis-related (A2MG and PZP), myofibroblast and inflammation (LRP1 and ITIH4), complement-related (SAA1 and SAA2), as well as serum markers, and clinical symptoms (KL-6, SP-D and fine crackles). Notably, it identified SAA2 associated with lymphocyte counts and PSPB connected with the serum markers KL-6 and SP-D, along with fine crackles as clinical manifestations. These results contribute to the elucidation of the pathogenesis of IPF and potential therapeutic targets.

Structural and accessibility studies highlight the differential binding of clemizole to TRPC5 and TRPC6.

Transient Receptor Potential Canonical 5 (T RP C5) and T RP C6 channels play critical physiological roles in various cell types. Their involvement in numerous disease progression mechanisms has led to extensive searches for their inhibitors. Although several potent T RP C inhibitors have been developed and the structure of their binding sites were mapped using cryo electron microscopy, a comprehensive understanding of the molecular interactions within the inhibitor binding site of T RP Cs remains elusive. This study aimed to decipher the structural determinants and molecular mechanisms contributing to the differential binding of clemizole to T RP C5 and T RP C6, with a particular focus on the accessibility of binding site residues. This information can help better understand what molecular features allow for selective binding, which is a key characteristic of clinically effective pharmacological agents. Using computational methodologies, we conducted an in-depth molecular docking analysis of clemizole with T RP C5 and T RP C6 channels. The protein structures were retrieved from publicly accessible protein databases. Discovery Studio 2020 Client Visualizer and Chimera software facilitated our in-silico mutation experiments and enabled us to identify the critical structural elements influencing clemizole binding. Our study reveals key molecular determinants at the clemizole binding site, specifically outlining the role of residues' Accessible Surface Area (ASA) and Relative Accessible Surface Area (RASA) in differential binding. We found that lower accessibility of T RP C6 binding site residues, compared to those in T RP C5, could account for the lower affinity binding of clemizole to T RP C6. This work illuminates the pivotal role of binding site residue accessibility in determining the affinity of clemizole to T RP C5 and T RP C6. A nuanced understanding of the distinct binding properties between these homologous proteins may pave the way for the development of more selective inhibitors, promising improved therapeutic efficacy and fewer off-target effects. By demystifying the structural and molecular subtleties of T RP C inhibitors, this research could significantly accelerate the drug discovery process, offering hope to patients afflicted with T RP C-related diseases.

Effects of energy loads on energy and nutrient absorption rates and gut microbiome in humans: A randomized crossover trial.

OBJECTIVE: This study aimed to determine the effects of different energy loads on the gut microbiota composition and the rates of energy and nutrient excretion via feces and urine. METHODS: A randomized crossover dietary intervention study was conducted with three dietary conditions: overfeeding (OF), control (CON), and underfeeding (UF). Ten healthy men were subjected to each condition for 8 days (4 days and 3 nights in nonlaboratory and laboratory settings each). The effects of dietary conditions on energy excretion rates via feces and urine were assessed using a bomb calorimeter. RESULTS: Short-term energy loads dynamically altered the gut microbiota at the α-diversity (Shannon index), phylum, and genus levels (p < 0.05). Energy excretion rates via urine and urine plus feces decreased under OF more than under CON (urine -0.7%; p < 0.001, urine plus feces -1.9%; p = 0.049) and UF (urine -1.0%; p < 0.001, urine plus feces -2.1%; p = 0.031). However, energy excretion rates via feces did not differ between conditions. CONCLUSIONS: Although short-term overfeeding dynamically altered the gut microbiota composition, the energy excretion rate via feces was unaffected. Energy excretion rates via urine and urine plus feces were lower under OF than under CON and UF conditions.

Association of skeletal muscle function, quantity, and quality with gut microbiota in Japanese adults: A cross-sectional study.

AIM: The gut microbiota has emerged as a new intervention target for sarcopenia. Prior studies in humans have focused on the association between gut microbiota and skeletal muscle quantity, while the evidence on muscle function and quality is lacking. This study aimed to identify gut microbiota genera associated with skeletal muscle function, quantity, and quality in a general population of Japanese adults. METHODS: This cross-sectional study included 164 participants aged 35-80 years, women and men recruited from urban areas of Japan. Fecal samples were collected and analyzed using 16S rRNA gene amplicon sequencing. Skeletal muscle function was measured using handgrip strength and leg extension power (LEP), while skeletal muscle mass was estimated using bioelectrical impedance analysis. Phase angle was used as a measure of skeletal muscle quality. Multivariate linear regression analysis stratified by age group was used to examine the association between the dominant genera of the gut microbiota and skeletal muscle variables. RESULTS: A significant association was found between Bacteroides and Prevotella 9 with LEP only in the ≥60 years group. When both Bacteroides and Prevotella 9 were included in the same regression model, only Bacteroides remained consistently and significantly associated with LEP. No significant associations were observed between skeletal muscle mass, handgrip strength, and phase angle and major gut microbiota genera. CONCLUSIONS: In this study, we observed a significant positive association between Bacteroides and leg muscle function in older adults. Further studies are required to elucidate the underlying mechanisms linking Bacteroides to lower-extremity muscle function. Geriatr Gerontol Int 2024; 24: 53-60.

The Trends and Future Prospective of In Silico Models from the Viewpoint of ADME Evaluation in Drug Discovery.

Drug discovery and development are aimed at identifying new chemical molecular entities (NCEs) with desirable pharmacokinetic profiles for high therapeutic efficacy. The plasma concentrations of NCEs are a biomarker of their efficacy and are governed by pharmacokinetic processes such as absorption, distribution, metabolism, and excretion (ADME). Poor ADME properties of NCEs are a major cause of attrition in drug development. ADME screening is used to identify and optimize lead compounds in the drug discovery process. Computational models predicting ADME properties have been developed with evolving model-building technologies from a simplified relationship between ADME endpoints and physicochemical properties to machine learning, including support vector machines, random forests, and convolution neural networks. Recently, in the field of in silico ADME research, there has been a shift toward evaluating the in vivo parameters or plasma concentrations of NCEs instead of using predictive results to guide chemical structure design. Another research hotspot is the establishment of a computational prediction platform to strengthen academic drug discovery. Bioinformatics projects have produced a series of in silico ADME models using free software and open-access databases. In this review, we introduce prediction models for various ADME parameters and discuss the currently available academic drug discovery platforms.

Changes in the fecal gut microbiome of home healthcare patients with disabilities through consumption of malted rice amazake.

The aim of the present study was to investigate changes in the gut microbiome both during and after consumption of malted rice amazake (MR-Amazake), a fermented food from Japan, in-home healthcare patients with disabilities, including patients with severe motor and intellectual disabilities. We monitored 12 patients who consumed MR-Amazake for 6 wk and investigated them before and after the intervention as well as 6 wk after the end of intake to compare their physical condition, diet, type of their medication, constipation assessment scale, and analysis of their comprehensive fecal microbiome using 16S rRNA sequencing. Their constipation symptoms were significantly alleviated, and principal coordinate analysis revealed that 30% of patients showed significant changes in the gut microbiome after MR-Amazake ingestion. Furthermore, Bifidobacterium was strongly associated with these changes. These changes were observed only during MR-Amazake intake; the original gut microbiome was restored when MR-Amazake intake was discontinued. These results suggest that 6 wk is a reasonable period of time for MR-Amazake to change the human gut microbiome and that continuous consumption of MR-Amazake is required to sustain such changes.NEW & NOTEWORTHY The consumption of malted rice amazake (MR-Amazake) showed significant changes in the gut microbiome according to principal coordinate analysis in some home healthcare patients with disabilities, including those with severe motor and intellectual disabilities. After discontinuation of intake, the gut microbiome returned to its original state. This is the first pilot study to examine both the changes in the gut microbiome and their sustainability after MR-Amazake intake.

Ramen Consumption and Gut Microbiota Diversity in Japanese Women: Cross-Sectional Data from the NEXIS Cohort Study.

A cross-sectional study involving 224 healthy Japanese adult females explored the relationship between ramen intake, gut microbiota diversity, and blood biochemistry. Using a stepwise regression model, ramen intake was inversely associated with gut microbiome alpha diversity after adjusting for related factors, including diets, Age, BMI, and stool habits (ß = -0.018; r = -0.15 for Shannon index). The intake group of ramen was inversely associated with dietary nutrients and dietary fiber compared with the no-intake group of ramen. Sugar intake, Dorea as a short-chain fatty acid (SCFA)-producing gut microbiota, and γ-glutamyl transferase as a liver function marker were directly associated with ramen intake after adjustment for related factors including diets, gut microbiota, and blood chemistry using a stepwise logistic regression model, whereas Dorea is inconsistently less abundant in the ramen group. In conclusion, the increased ramen was associated with decreased gut bacterial diversity accompanying a perturbation of Dorea through the dietary nutrients, gut microbiota, and blood chemistry, while the methodological limitations existed in a cross-sectional study. People with frequent ramen eating habits need to take measures to consume various nutrients to maintain and improve their health, and dietary management can be applied to the dietary feature in ramen consumption.

DruMAP: A Novel Drug Metabolism and Pharmacokinetics Analysis Platform.

We developed a novel drug metabolism and pharmacokinetics (DMPK) analysis platform named DruMAP. This platform consists of a database for DMPK parameters and programs that can predict many DMPK parameters based on the chemical structure of a compound. The DruMAP database includes curated DMPK parameters from public sources and in-house experimental data obtained under standardized conditions; it also stores predicted DMPK parameters produced by our prediction programs. Users can predict several DMPK parameters simultaneously for novel compounds not found in the database. Furthermore, the highly flexible search system enables users to search for compounds as they desire. The current version of DruMAP comprises more than 30,000 chemical compounds, about 40,000 activity values (collected from public databases and in-house data), and about 600,000 predicted values. Our platform provides a simple tool for searching and predicting DMPK parameters and is expected to contribute to the acceleration of new drug development. DruMAP can be freely accessed at: https://drumap.nibiohn.go.jp/.

Gut microbiota alternation with training periodization and physical fitness in Japanese elite athletes.

Introduction: The gut microbiome plays a fundamental role in host homeostasis through regulating immune functions, enzyme activity, and hormone secretion. Exercise is associated with changes in gut microbiome composition and function. However, few studies have investigated the gut microbiome during training periodization. The present study aimed to investigate the relationship between training periodization and the gut microbiome in elite athletes. Methods: In total, 84 elite athletes participated in the cross-sectional study; and gut microbiome was determined during their transition or preparation season period. Further, 10 short-track speed skate athletes participated in the longitudinal study, which assessed the gut microbiome and physical fitness such as aerobic capacity and anaerobic power in the general and specific preparation phase of training periodization. The gut microbiome was analyzed using 16S rRNA sequencing. Results: The cross-sectional study revealed significant differences in Prevotella, Bifidobacterium, Parabacteroides, and Alistipes genera and in enterotype distribution between transition and preparation season phase periodization. In the longitudinal study, training phase periodization altered the level of Bacteroides, Blautia, and Bifidobacterium in the microbiome. Such changes in the microbiome were significantly correlated with alternations in aerobic capacity and tended to correlate with the anaerobic power. Discussion: These findings suggest that periodization alters the gut microbiome abundance related to energy metabolism and trainability of physical fitness. Athlete's condition may thus be mediated to some extent by the microbiota in the intestinal environment.

Characteristic Gut Bacteria in High Barley Consuming Japanese Individuals without Hypertension.

BACKGROUND: Barley, a grain rich in soluble dietary fiber ß-glucan, is expected to lower blood pressure. Conversely, individual differences in its effects on the host might be an issue, and gut bacterial composition may be a determinant. METHODS: Using data from a cross-sectional study, we examined whether the gut bacterial composition could explain the classification of a population with hypertension risks despite their high barley consumption. Participants with high barley intake and no occurrence of hypertension were defined as "responders" (n = 26), whereas participants with high barley intake and hypertension risks were defined as "non-responders" (n = 39). RESULTS: 16S rRNA gene sequencing revealed that feces from the responders presented higher levels of Faecalibacterium, Ruminococcaceae UCG-013, Lachnospira, and Subdoligranulum and lower levels of Lachnoclostridium and Prevotella 9 than that from non-responders. We further created a machine-learning responder classification model using random forest based on gut bacteria with an area under the curve value of 0.75 for estimating the effect of barley on the development of hypertension. CONCLUSIONS: Our findings establish a link between the gut bacteria characteristics and the predicted control of blood pressure provided by barley intake, thereby providing a framework for the future development of personalized dietary strategies.

Evidence-Based Prediction of Cellular Toxicity for Amorphous Silica Nanoparticles.

Developing a generalized model for a robust prediction of nanotoxicity is critical for designing safe nanoparticles. However, complex toxicity mechanisms of nanoparticles in biological environments, such as biomolecular corona formation, prevent a reliable nanotoxicity prediction. This is exacerbated by the potential evaluation bias caused by internal validation, which is not fully appreciated. Herein, we propose an evidence-based prediction method for distinguishing between cytotoxic and noncytotoxic nanoparticles at a given condition by uniting literature data mining and machine learning. We illustrate the proposed method for amorphous silica nanoparticles (SiO2-NPs). SiO2-NPs are currently considered a safety concern; however, they are still widely produced and used in various consumer products. We generated the most diverse attributes of SiO2-NP cellular toxicity to date, using >100 publications, and built predictive models, with algorithms ranging from linear to nonlinear (deep neural network, kernel, and tree-based) classifiers. These models were validated using internal (4124-sample) and external (905-sample) data sets. The resultant categorical boosting (CatBoost) model outperformed other algorithms. We then identified 13 key attributes, including concentration, serum, cell, size, time, surface, and assay type, which can explain SiO2-NP toxicity, using the Shapley Additive exPlanation values in the CatBoost model. The serum attribute underscores the importance of nanoparticle-corona complexes for nanotoxicity prediction. We further show that internal validation does not guarantee generalizability. In general, safe SiO2-NPs can be obtained by modifying their surfaces and using low concentrations. Our work provides a strategy for predicting and explaining the toxicity of any type of engineered nanoparticles in real-world practice.

Predicting thrombotic cardiovascular outcomes induced by waterpipe-associated chemicals using comparative toxicogenomic database: Genes, phenotypes, and pathways.

Hookah, or waterpipe, is a tobacco smoking device that has gained popularity in the United States. A growing body of evidence demonstrates that waterpipe smoke (WPS) is associated with various adverse effects on human health, including infectious diseases, cancer, and cardiovascular diseases (CVDs), particularly thrombotic events. However, the molecular mechanisms through which WPS contributes to disease development remain unclear. In this study, we utilized an analytical approach based on the Comparative Toxicogenomics Database (CTD) to integrate chemical, gene, phenotype, and disease data to predict potential molecular mechanisms underlying the effects of WPS, based on its chemical and toxicant profile. Our analysis revealed that CVDs were among the top disease categories with regard to the number of curated interactions with WPS chemicals. We identified 5674 genes common between those modulated by WPS chemicals and traditional tobacco smoking. The CVDs with the most curated interactions with WPS chemicals were hypertension, atherosclerosis, and myocardial infarction, whereas "particulate matter", "heavy metals", and "nicotine" showed the highest number of curated interactions with CVDs. Our analysis predicted that the potential mechanisms underlying WPS-induced thrombotic diseases involve common phenotypes, such as inflammation, apoptosis, and cell proliferation, which are shared across all thrombotic diseases and the three aforementioned chemicals. In terms of enriched signaling pathways, we identified several, including chemokine and MAPK signaling, with particulate matter exhibiting the most statistically significant association with all 12 significant signaling pathways related to WPS chemicals. Collectively, our predictive comprehensive analysis provides evidence that WPS negatively impacts health and offers insights into the potential mechanisms through which it exerts these effects. This information should guide further research to explore and better understand the WPS and other tobacco product-related health consequences.

Integration of pharmacoproteomic and computational approaches reveals the cellular signal transduction pathways affected by apatinib in gastric cancer cell lines.

Apatinib is known to be a highly selective vascular endothelial growth factor receptor 2 (VEGFR2) inhibitor with anti-angiogenic and anti-tumor properties. In a phase III study, the objective response rate to apatinib was low. It remains unclear why the effectivity of apatinib varies among patients and what type of patients are candidates for the treatment. In this study, we investigated the anti-tumor efficacy of apatinib against 13 gastric cancer cell lines and found that it differed depending on the cell line. Using integrated wet and dry approaches, we showed that apatinib was a multi-kinase inhibitor of c-Kit, RAF1, VEGFR1, VEGFR2, and VEGFR3, predominantly inhibiting c-Kit. Notably, KATO-III, which was the most apatinib-sensitive among the gastric cancer cell lines investigated, was the only cell line expressing c-Kit, RAF1, VEGFR1, and VEGFR3 but not VEGFR2. Furthermore, we identified SNW1 as a molecule affected by apatinib that plays an important role in cell survival. Finally, we identified the molecular network related to SNW1 that was affected by treatment with apatinib. These results suggest that the mechanism of action of apatinib in KATO-III cells is independent of VEGFR2 and that the differential efficacy of apatinib was due to differences in expression patterns of receptor tyrosine kinases. Furthermore, our results suggest that the differential efficacy of apatinib in gastric cell lines may be attributed to SNW1 phosphorylation levels at a steady state. These findings contribute to a deeper understanding of the mechanism of action of apatinib in gastric cancer cells.

Prediction of the Contribution Ratio of a Target Metabolic Enzyme to Clearance from Chemical Structure Information.

The contribution ratio of metabolic enzymes such as cytochrome P450 to in vivo clearance (fraction metabolized: fm) is a pharmacokinetic index that is particularly important for the quantitative evaluation of drug-drug interactions. Since obtaining experimental in vivo fm values is challenging, those derived from in vitro experiments have often been used alternatively. This study aimed to explore the possibility of constructing machine learning models for predicting in vivo fm using chemical structure information alone. We collected in vivo fm values and chemical structures of 319 compounds from a public database with careful manual curation and constructed predictive models using several machine learning methods. The results showed that in vivo fm values can be obtained from structural information alone with a performance comparable to that based on in vitro experimental values and that the prediction accuracy for the compounds involved in CYP induction or inhibition is significantly higher than that by using in vitro values. Our new approach to predicting in vivo fm values in the early stages of drug discovery should help improve the efficiency of the drug optimization process.

Structural dynamics of the N-terminal SH2 domain of PI3K in its free and CD28-bound states.

Protein conformational changes with fluctuations are fundamental aspects of protein-protein interactions (PPIs); understanding these motions is required for the rational design of PPI-regulating compounds. Src homology 2 (SH2) domains are commonly found in adapter proteins involved in signal transduction and specifically bind to consensus motifs of proteins containing phosphorylated tyrosine (pY). Here, we analysed the interaction between the N-terminal SH2 domain (nSH2) of the regulatory subunit in phosphoinositide 3-kinase (PI3K) and the cytoplasmic region of the T-cell co-receptor, CD28, using NMR and molecular dynamics (MD) simulations. First, we assigned the backbone signals of nSH2 on 1 H-15 N heteronuclear single quantum coherence spectra in the absence or presence of the CD28 phosphopeptide, SDpYMNMTPRRPG. Chemical shift perturbation experiments revealed allosteric changes at the BC loop and the C-terminal region of nSH2 upon CD28 binding. NMR relaxation experiments showed a conformational exchange associated with CD28 binding in these regions. The conformational stabilisation of the C-terminal region correlated with the regulation of PI3K catalytic function. Further, using 19 F- and 31 P-labelled CD28 phosphopeptide, we analysed the structural dynamics of CD28 and demonstrated that the aromatic ring of the pY residue fluctuated between multiple conformations upon nSH2 binding. Our MD simulations largely explained the NMR results and the structural dynamics of nSH2 and CD28 in both bound and unbound states. Notably, in addition to its major conformation, we detected a minor conformation of nSH2 in the CD28 bound state that may explain the allosteric conformational change in the BC loop.

Attention network for predicting T-cell receptor-peptide binding can associate attention with interpretable protein structural properties.

Understanding how a T-cell receptor (TCR) recognizes its specific ligand peptide is crucial for gaining an insight into biological functions and disease mechanisms. Despite its importance, experimentally determining TCR-peptide-major histocompatibility complex (TCR-pMHC) interactions is expensive and time-consuming. To address this challenge, computational methods have been proposed, but they are typically evaluated by internal retrospective validation only, and few researchers have incorporated and tested an attention layer from language models into structural information. Therefore, in this study, we developed a machine learning model based on a modified version of Transformer, a source-target attention neural network, to predict the TCR-pMHC interaction solely from the amino acid sequences of the TCR complementarity-determining region (CDR) 3 and the peptide. This model achieved competitive performance on a benchmark dataset of the TCR-pMHC interaction, as well as on a truly new external dataset. Additionally, by analyzing the results of binding predictions, we associated the neural network weights with protein structural properties. By classifying the residues into large- and small-attention groups, we identified statistically significant properties associated with the largely attended residues such as hydrogen bonds within CDR3. The dataset that we created and the ability of our model to provide an interpretable prediction of TCR-peptide binding should increase our knowledge about molecular recognition and pave the way for designing new therapeutics.

Identification of Human Gut Microbiome Associated with Enterolignan Production.

Dietary plant lignans are converted inside the gut to enterolignans enterodiol (ED) and enterolactone (EL), which have several biological functions, and health benefits. In this study, we characterized the gut microbiome composition associated with enterolignan production using data from a cross-sectional study in the Japanese population. We identified enterolignan producers by measuring ED and EL levels in subject's serum using liquid chromatography-tandem mass spectrometry. Enterolignan producers show more abundant proportion of Ruminococcaceae and Lachnospiraceae than non-enterolignan producers. In particular, subjects with EL in their serum had a highly diverse gut microbiome that was rich in Ruminococcaceae and Rikenellaceae. Moreover, we built a random forest classification model to classify subjects to either EL producers or not using three characteristic bacteria. In conclusion, our analysis revealed the composition of gut microbiome that is associated with lignan metabolism. We also confirmed that it can be used to classify the microbiome ability to metabolize lignan using machine learning approach.

Snaq: A Dynamic Snakemake Pipeline for Microbiome Data Analysis With QIIME2.

Optimizing and automating a protocol for 16S microbiome data analysis with QIIME2 is a challenging task. It involves a multi-step process, and multiple parameters and options that need to be tested and determined. In this article, we describe Snaq, a snakemake pipeline that helps automate and optimize 16S data analysis using QIIME2. Snaq offers an informative file naming system and automatically performs the analysis of a data set by downloading and installing the required databases and classifiers, all through a single command-line instruction. It works natively on Linux and Mac and on Windows through the use of containers, and is potentially extendable by adding new rules. This pipeline will substantially reduce the efforts in sending commands and prevent the confusion caused by the accumulation of analysis results due to testing multiple parameters.

Utilizing public and private sector data to build better machine learning models for the prediction of pharmacokinetic parameters.

One solution to compensate for the shortage of publicly available data is to collect more quality-controlled data from the private sector through public-private partnerships. However, several issues must be resolved before implementing such a system. Here, we review the technical aspects of public-private partnerships using our initiative in Japan as an example. In particular, we focus on the procedure for collecting data from multiple private sector companies and building prediction models and discuss how merging public and private sector datasets will help to improve the chemical space coverage and prediction performance. Teaser: Japan's first public-private consortium in pharmacokinetics has incorporated data from multiple pharmaceutical companies to create useful predictive models.