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Summary: The gut microbiota influences physiological functions of the host, ranging from the maintenance of local gut homeostasis to systemic immunity and metabolism. Secreted phospholipase A2 group X (sPLA2-X) is abundantly expressed in colonic epithelial cells but is barely detectable in metabolic and immune tissues. Despite this distribution, sPLA2-X-deficient (Pla2g10−/−) mice displayed variable obesity-related phenotypes that were abrogated after treatment with antibiotics or cohousing with Pla2g10+/+ mice, suggesting the involvement of the gut microbiota. Under housing conditions where Pla2g10−/− mice showed aggravation of diet-induced obesity and insulin resistance, they displayed increased colonic inflammation and epithelial damage, reduced production of polyunsaturated fatty acids (PUFAs) and lysophospholipids, decreased abundance of several Clostridium species, and reduced levels of short-chain fatty acids (SCFAs). These obesity-related phenotypes in Pla2g10−/− mice were reversed by dietary supplementation with ω3 PUFAs or SCFAs. Thus, colonic sPLA2-X orchestrates ω3 PUFA-SCFA interplay via modulation of the gut microbiota, thereby secondarily affecting systemic metabolism.

Abstract This study investigated the effects of white meat, such as chicken, intake combined with resistance training on muscle mass and strength in the elderly women, and whether the underlying mechanism involves changes in the gut microbiota. Ninety‐three volunteers (age 59–79 years) were randomly allocated to sedentary control with placebo (Sed + PL) or chicken meat (Sed + HP) and resistance training with placebo (RT + PL) or chicken meat (RT + HP). Resistance training sessions were performed 3 d/week for 12 weeks using leg extensions and curls. Boiled chicken meat (110 g, containing 22.5 g protein) was ingested 3 d/week for 12 weeks. Maximal muscle strength and whole‐body lean mass increased significantly in the RT + PL group compared to the Sed + HP group, and the RT + HP group showed a significantly greater increase than the Sed + HP and RT + PL groups. Additionally, the gut microbiota composition did not change before or after the interventions in any of the four groups. Moreover, the individual comparison of gut bacteria using false discovery rate‐based statistical analysis showed no alterations before or after the interventions in the four groups. Resistance training combined with chicken meat intake may effective have increased muscle mass and strength without drastically modifying the gut microbiota composition in elderly women.

Abstract 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-β 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.

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.

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.

Here, the authors inversely associate Blautia wexlerae with obesity and type 2 diabetes mellitus in humans and further show that administration of B. wexlerae to mice decrease both high-fat diet–induced obesity and diabetes via modulating gut microbial metabolism.

IntroductionThe 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.MethodsIn 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.ResultsThe 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.DiscussionThese 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.

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.

Abstract Corona virus disease 2019 (COVID-19) increases the risk of cardiovascular occlusive/thrombotic events and is linked to poor outcomes. The underlying pathophysiological processes are complex, and remain poorly understood. To this end, platelets play important roles in regulating the cardiovascular system, including via contributions to coagulation and inflammation. There is ample evidence that circulating platelets are activated in COVID-19 patients, which is a primary driver of the observed thrombotic outcome. However, the comprehensive molecular basis of platelet activation in COVID-19 disease remains elusive, which warrants more investigation. Hence, we employed gene co-expression network analysis combined with pathways enrichment analysis to further investigate the aforementioned issues. Our study revealed three important gene clusters/modules that were closely related to COVID-19. These cluster of genes successfully identify COVID-19 cases, relative to healthy in a separate validation data set using machine learning, thereby validating our findings. Furthermore, enrichment analysis showed that these three modules were mostly related to platelet metabolism, protein translation, mitochondrial activity, and oxidative phosphorylation, as well as regulation of megakaryocyte differentiation, and apoptosis, suggesting a hyperactivation status of platelets in COVID-19. We identified the three hub genes from each of three key modules according to their intramodular connectivity value ranking, namely: COPE, CDC37, CAPNS1, AURKAIP1, LAMTOR2, GABARAP MT-ND1, MT-ND5, and MTRNR2L12. Collectively, our results offer a new and interesting insight into platelet involvement in COVID-19 disease at the molecular level, which might aid in defining new targets for treatment of COVID-19–induced thrombosis.

Abstract Background The pathogenesis of chronic thromboembolic pulmonary hypertension (CTEPH) is considered to be associated with chronic inflammation; however, the underlying mechanism remains unclear. Recently, altered gut microbiota were found in patients with pulmonary arterial hypertension (PAH) and in experimental PAH models. The aim of this study was to characterize the gut microbiota in patients with CTEPH and assess the relationship between gut dysbiosis and inflammation in CTEPH. Methods In this observational study, fecal samples were collected from 11 patients with CTEPH and 22 healthy participants. The abundance of gut microbiota in these fecal samples was assessed using 16S ribosomal ribonucleic acid (rRNA) gene sequencing. Inflammatory cytokine and endotoxin levels were also assessed in patients with CTEPH and control participants. Results The levels of serum tumor necrosis factor-α (TNF-α), interleukin (IL)-6, IL-8, and macrophage inflammatory protein (MIP)-1α were elevated in patients with CTEPH. Plasma endotoxin levels were significantly increased in patients with CTEPH (P

Abstract Background Barley contains abundant soluble beta-glucan fibers, which have established health benefits. In addition, the health benefits conferred by the gut bacteria have attracted considerable interest. However, few studies have focused on the barley consumption and gut bacteria of the Japanese population. In this study, we aimed to identify the relationship between the barley consumption and gut bacteria composition of the Japanese population. Methods In total, 236 participants were recruited in Japan, and 94 participants with no complications of diabetes, hypertension, or dyslipidemia were selected for the study. We analyzed fecal samples from the participants, their medical check-up results, and responses to questionnaires about dietary habits. The participants were grouped according to their median barley consumption. Then, we assessed the relative abundance of 50 genera. Characteristic bacteria were evaluated for their relationship with barley consumption by multiple regression analysis, adjusted for disease and dietary habits, in all participants. We also analyzed the networks and clustering of the 20 selected genera. Results According to the comparison between barley groups, Bifidobacterium, Butyricicoccus, Collinsella, Ruminococcus 2, and Dialister were characteristic candidate bacterias of the group that consumed large amounts of barley (P

Abstract Phosphoproteomic analysis expands our understanding of cancer biology. However, the feasibility of phosphoproteomic analysis using endoscopically collected tumor samples, especially with regards to dynamic changes upon drug treatment, remains unknown in stage IV gastric cancer. Here, we conducted a phosphoproteomic analysis using paired endoscopic biopsy specimens of pre- and post-treatment tumors (Ts) and non-tumor adjacent tissues (NATs) obtained from 4 HER2-positive gastric cancer patients who received trastuzumab-based treatment and from pre-treatment Ts and NATs of 4 HER2-negative gastric cancer patients. Our analysis identified 14,622 class 1 phosphosites with 12,749 quantified phosphosites and revealed molecular changes by HER2 positivity and treatment. An inhibitory signature of the ErbB signaling was observed in the post-treatment HER2-positive T group compared with the pre-treatment HER2-positive T group. Phosphoproteomic profiles obtained by a case-by-case review using paired pre- and post-treatment HER2-positive T could be utilized to discover predictive or resistant biomarkers. Furthermore, these data nominated therapeutic kinase targets which were exclusively activated in the patient unresponded to the treatment. The present study suggests that a phosphoproteomic analysis of endoscopic biopsy specimens provides information on dynamic molecular changes which can individually characterize biologic features upon drug treatment and identify therapeutic targets in stage IV gastric cancer.

Protein–protein interactions (PPIs) are recognized as important targets in drug discovery. The characteristics of molecules that inhibit PPIs differ from those of small-molecule compounds. We developed a novel chemical library database system (DLiP) to design PPI inhibitors. A total of 32,647 PPI-related compounds are registered in the DLiP. It contains 15,214 newly synthesized compounds, with molecular weight ranging from 450 to 650, and 17,433 active and inactive compounds registered by extracting and integrating known compound data related to 105 PPI targets from public databases and published literature. Our analysis revealed that the compounds in this database contain unique chemical structures and have physicochemical properties suitable for binding to the protein–protein interface. In addition, advanced functions have been integrated with the web interface, which allows users to search for potential PPI inhibitor compounds based on types of protein–protein interfaces, filter results by drug-likeness indicators important for PPI targeting such as rule-of-4, and display known active and inactive compounds for each PPI target. The DLiP aids the search for new candidate molecules for PPI drug discovery and is available online (https://skb-insilico.com/dlip).

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 (β = −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.

Background: Barley, a grain rich in soluble dietary fiber β-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.

Abstract Background Inter-individual variations in gut microbiota composition are observed even among healthy populations. The gut microbiota may exhibit a unique composition depending on the country of origin and race of individuals. To comprehensively understand the link between healthy gut microbiota and host state, it is beneficial to conduct large-scale cohort studies. The aim of the present study was to elucidate the integrated and non-redundant factors associated with gut microbiota composition within the Japanese population by 16S rRNA sequencing of fecal samples and questionnaire-based covariate analysis. Results A total of 1596 healthy Japanese individuals participated in this study via two independent cohorts, NIBIOHN cohort (n = 954) and MORINAGA cohort (n = 642). Gut microbiota composition was described and the interaction of these microorganisms with metadata parameters such as anthropometric measurements, bowel habits, medical history, and lifestyle were obtained. Thirteen genera, including Alistipes, Anaerostipes, Bacteroides, Bifidobacterium, Blautia, Eubacterium halli group, Faecalibacterium, Fusicatenibacter, Lachnoclostridium, Parabacteroides, Prevotella_9, Roseburia, and Subdoligranulum were predominant among the two cohorts. On the basis of univariate analysis for overall microbiome variation, 18 matching variables exhibited significant association in both cohorts. A stepwise redundancy analysis revealed that there were four common covariates, Bristol Stool Scale (BSS) scores, gender, age, and defecation frequency, displaying non-redundant association with gut microbial variance. Conclusions We conducted a comprehensive analysis of gut microbiota in healthy Japanese individuals, based on two independent cohorts, and obtained reliable evidence that questionnaire-based covariates such as frequency of bowel movement and specific dietary habit affects the microbial composition of the gut. To our knowledge, this was the first study to investigate integrated and non-redundant factors associated with gut microbiota among Japanese populations.

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.

Idiopathic pulmonary fibrosis (IPF) is a severe lung disease with poor survival that warrants early and precise diagnosis for timely therapeutic intervention. Despite accumulating genomic, transcriptomic, proteomic, and lipidomic data on IPF, evidence from water‐soluble metabolomics is limited. To identify biomarkers for IPF from water‐soluble metabolomic data, we measured the levels of various metabolites in bronchoalveolar lavage fluid (BALF) and serum samples from a bleomycin‐induced murine pulmonary fibrotic model using gas chromatography/mass spectrometry. Thirty‐two of 73 BALF metabolites and 29 of 74 serum metabolites were annotated. We observed that the levels of proline and methionine were higher in BALF but lower in serum than those in the control. Furthermore, analysis of public RNA‐Seq data from the lungs of patients with IPF revealed that proline‐ and methionine‐related genes were significantly upregulated compared to those in the lungs of healthy controls. These results suggest that proline and methionine may be potential biomarkers for IPF and may help to deepen our understanding of the pathophysiology of the disease. Based on our results, we propose a model capable of recapitulating the proline and methionine metabolism of fibrotic lungs, thereby providing better means for studying the disease and developing novel therapeutic strategies for IPF.

Barley is a grain rich in β-glucan, a soluble dietary fiber, and its consumption can help maintain good health and reduce the risk of metabolic disorders, such as dyslipidemia. However, the effect of barley intake on the risk of dyslipidemia has been found to vary among individuals. Differences in gut bacteria among individuals may be a determining factor since dietary fiber is metabolized by gut bacteria and then converted into short-chain fatty acids with physiological functions that reduce the risk of dyslipidemia. This study examined whether gut bacteria explained individual differences in the effects of barley intake on dyslipidemia using data from a cross-sectional study. In this study, participants with high barley intake and no dyslipidemia were labeled as “responders” to the reduced risk of dyslipidemia based on their barley intake and their gut bacteria. The results of the 16S rRNA gene sequencing showed that the fecal samples of responders (n = 22) were richer in Bifidobacterium, Faecalibacterium, Ruminococcus 1, Subdoligranulum, Ruminococcaceae UCG-013, and Lachnospira than those of non-responders (n = 43), who had high barley intake but symptoms of dyslipidemia. These results indicate the presence of certain gut bacteria that define barley responders. Therefore, we attempted to generate a gut bacteria-based responder classification model through machine learning using random forest. The area under the curve value of the classification model in estimating the effect of barley on the occurrence of dyslipidemia in the host was 0.792 and the Matthews correlation coefficient was 0.56. Our findings connect gut bacteria to individual differences in the effects of barley on lipid metabolism, which could assist in developing personalized dietary strategies.

Besides promoting inflammation by mobilizing lipid mediators, group IIA secreted phospholipase A2 (sPLA2-IIA) prevents bacterial infection by degrading bacterial membranes. Here, we show that, despite the restricted intestinal expression of sPLA2-IIA in BALB/c mice, its genetic deletion leads to amelioration of cancer and exacerbation of psoriasis in distal skin. Intestinal expression of sPLA2-IIA is reduced after treatment with antibiotics or under germ-free conditions, suggesting its upregulation by gut microbiota. Metagenome, transcriptome, and metabolome analyses have revealed that sPLA2-IIA deficiency alters the gut microbiota, accompanied by notable changes in the intestinal expression of genes related to immunity and metabolism, as well as in the levels of various blood metabolites and fecal bacterial lipids, suggesting that sPLA2-IIA contributes to shaping of the gut microbiota. The skin phenotypes in Pla2g2a–/– mice are lost (a) when they are cohoused with littermate WT mice, resulting in the mixing of the microbiota between the genotypes, or (b) when they are housed in a more stringent pathogen-free facility, where Pla2g2a expression in WT mice is low and the gut microbial compositions in both genotypes are nearly identical. Thus, our results highlight a potentially new aspect of sPLA2-IIA as a modulator of gut microbiota, perturbation of which affects distal skin responses.

Abstract Background To increase the accuracy of microbiome data analysis, solving the technical limitations of the existing sequencing machines is required. Quality trimming is suggested to reduce the effect of the progressive decrease in sequencing quality with the increased length of the sequenced library. In this study, we examined the effect of the trimming thresholds (0–20 for QIIME1 and 0–30 for QIIME2) on the number of reads that remained after the quality control and chimera removal (the good reads). We also examined the distance of the analysis results to the gold standard using simulated samples. Results Quality trimming increased the number of good reads and abundance measurement accuracy in Illumina paired-end reads of the V3-V4 hypervariable region. Conclusions Our results suggest that the pre-analysis trimming step should be included before the application of QIIME1 or QIIME2.

The commensal microbiota is involved in a variety of diseases. Our group has noticed that patients with Stevens–Johnson syndrome (SJS)/toxic epidermal necrolysis (TEN) often present with persistent inflammation of the ocular surface, even in the chronic stage, and that this inflammation is exacerbated by colonization of the mucosa by certain bacteria. However, the changes in the composition of the ocular microbiome in SJS/TEN patients with severe ocular complications (SOCs) remain to be fully investigated. Here, we conducted a cross-sectional study of 46 Japanese subjects comprising 9 healthy control subjects and 37 SJS/TEN patients with SOC. The 16S rRNA-based genetic analyses revealed that the diversity of the ocular microbiome was reduced in SJS/TEN patients with SOC compared with that in healthy control subjects. Principal coordinate analysis based on Bray–Curtis distance at the genus level revealed that the relative composition of the ocular microbiome was different in healthy control subjects and SJS/TEN patients with SOC, and that the SJS/TEN patients with SOC could be divided into four groups based on whether their microbiome was characterized by enrichment of species in genus Corynebacterium 1, Neisseriaceae uncultured, or Staphylococcus or by simultaneous enrichment in species in genera Propionibacterium, Streptococcus, Fusobacterium, Lawsonella, and Serratia. Collectively, our findings indicate that enrichment of certain bacteria at the ocular surface could be associated with ocular surface inflammation in SJS/TEN patients with SOC.

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.

Abstract Background When visually comparing the results of hierarchical clustering, the differences in the arrangements of components are of special interest. However, in a biological setting, identifying such differences becomes less straightforward, as the changes in the dendrogram structure caused by permuting biological replicates, do not necessarily imply a different biological interpretation. Here, we introduce a visualization tool to help identify biologically similar topologies across different clustering results, even in the presence of replicates. Results Here we introduce CLINE, an open-access web application that allows users to visualize and compare multiple dendrogram structures, by visually displaying the links between areas of similarity across multiple structures. Through the use of a single page and a simple user interface, the user is able to load and remove structures form the visualization, change some aspects of their display and set the parameters used to match cluster topology across consecutive pairs of dendrograms. Conclusions We have implemented a web-tool that allows the users to visualize different dendrogram structures, showing not only the structures themselves, but also linking areas of similarity across multiple structures. The software is freely available at http://mizuguchilab.org/tools/cline/. Also, the source code, documentation and installation instructions are available on GitHub at https://github.com/RodolfoAllendes/cline/.

The gut microbiota is closely related to good health; thus, there have been extensive efforts dedicated to improving health by controlling the gut microbial environment. Probiotics and prebiotics are being developed to support a healthier intestinal environment. However, much work remains to be performed to provide effective solutions to overcome individual differences in the gut microbial community. This study examined the importance of nutrients, other than dietary fiber, on the survival of gut bacteria in high-health-conscious populations. We found that vitamin B1, which is an essential nutrient for humans, had a significant effect on the survival and competition of bacteria in the symbiotic gut microbiota. In particular, sufficient dietary vitamin B1 intake affects the relative abundance of Ruminococcaceae, and these bacteria have proven to require dietary vitamin B1 because they lack the de novo vitamin B1 synthetic pathway. Moreover, we demonstrated that vitamin B1 is involved in the production of butyrate, along with the amount of acetate in the intestinal environment. We established the causality of possible associations and obtained mechanical insight, through in vivo murine experiments and in silico pathway analyses. These findings serve as a reference to support the development of methods to establish optimal intestinal environment conditions for healthy lifestyles.

There is an unmet need for novel biomarkers in the diagnosis of multifactorial COPD. We applied next-generation proteomics to serum extracellular vesicles (EVs) to discover novel COPD biomarkers. EVs from 10 patients with COPD and six healthy controls were analysed by tandem mass tag-based non-targeted proteomics, and those from elastase-treated mouse models of emphysema were also analysed by non-targeted proteomics. For validation, EVs from 23 patients with COPD and 20 healthy controls were validated by targeted proteomics. Using non-targeted proteomics, we identified 406 proteins, 34 of which were significantly upregulated in patients with COPD. Of note, the EV protein signature from patients with COPD reflected inflammation and remodelling. We also identified 63 upregulated candidates from 1956 proteins by analysing EVs isolated from mouse models. Combining human and mouse biomarker candidates, we validated 45 proteins by targeted proteomics, selected reaction monitoring. Notably, levels of fibulin-3, tripeptidyl-peptidase 2, fibulin-1, and soluble scavenger receptor cysteine-rich domain-containing protein were significantly higher in patients with COPD. Moreover, six proteins; fibulin-3, tripeptidyl-peptidase 2, UTP-glucose-1-phosphate uridylyl transferase, CD81, CD177, and oncoprotein-induced transcript 3, were correlated with emphysema. Upregulation of fibulin-3 was confirmed by immunoblotting of EVs and immunohistochemistry in lungs. Strikingly, fibulin-3 knockout mice spontaneously developed emphysema with age, as evidenced by alveolar enlargement and elastin destruction. We discovered potential pathogenic biomarkers for COPD using next-generation proteomics of EVs. This is a novel strategy for biomarker discovery and precision medicine.

While both chronic obstructive pulmonary disease (COPD) and idiopathic pulmonary fibrosis (IPF) are multifactorial disorders characterized by distinct clinical and pathological features, their commonalities and differences have not been fully elucidated. We sought to investigate the preventive roles of tetraspanins Cd151 and Cd9 -that are involved in diverse cellular processes in lung pathophysiology- in pulmonary fibrosis and emphysema, respectively, and to obtain a deeper understanding of their underlying molecular mechanisms toward facilitating improved therapeutic outcomes. Using an integrative approach, we examined the transcriptomic changes in the lungs of Cd151- and Cd9-deficient mice using functional-enrichment-analysis, pathway-perturbation-analysis and protein-protein-interaction (PPI) network analysis. Circadian-rhythm, extracellular-matrix (ECM), cell-adhesion and inflammatory responses and associated factors were prominently influenced by Cd151-deletion. Conversely, cellular-junctions, focal-adhesion, vascular-remodeling, and TNF-signaling were deeply impacted by Cd9-deletion. We also highlighted a “common core” of factors and signaling cascades that underlie the functions of both Cd151 and Cd9 in lung pathology. Circadian dysregulation following Cd151-deletion seemingly facilitated progressive fibrotic lung phenotype. Conversely, TGF-β signaling attenuation and TNF-signaling activation emerged as potentially novel functionaries of Cd9-deletion-induced emphysema. Our findings offer promising avenues for developing novel therapeutic treatments for pulmonary fibrosis and emphysema.

The pathogenesis of pulmonary arterial hypertension is closely associated with dysregulated inflammation. Recently, abnormal alterations in gut microbiome composition and function were reported in a pulmonary arterial hypertension experimental animal model. However, it remains unclear whether these alterations are a result or the cause of pulmonary arterial hypertension. The purpose of this study was to investigate whether alterations in the gut microbiome affected the hemodynamics in SU5416/hypoxia rats. We used the SU5416/hypoxia rat model in our study. SU5416/hypoxia rats were treated with a single SU5416 injection (30 mg/kg) and a three-week hypoxia exposure (10% O 2 ). Three SU5416/hypoxia rats were treated with a combination of four antibiotics (SU5416/hypoxia + ABx group) for four weeks. Another group was exposed to hypoxia (10% O 2 ) without the SU5416 treatment, and control rats received no treatment. Fecal samples were collected from each animal, and the gut microbiota composition was analyzed by 16S rRNA sequencing. The antibiotic treatment significantly suppressed the vascular remodeling, right ventricular hypertrophy, and increase in the right ventricular systolic pressure in SU5416/hypoxia rats. 16S rRNA sequencing analysis revealed gut microbiota modification in SU5416/hypoxia + ABx group. The Firmicutes-to-Bacteroidetes ratio in SU5416/hypoxia rats was significantly higher than that in control and hypoxia rats. Compared with the control microbiota, 14 bacterial genera, including Bacteroides and Akkermansia , increased, whereas seven bacteria, including Rothia and Prevotellaceae , decreased in abundance in SU5416/hypoxia rats. Antibiotic-induced modification of the gut microbiota suppresses the development of pulmonary arterial hypertension. Dysbiosis may play a causal role in the development and progression of pulmonary arterial hypertension.

Multi-omics analyses, combining transcriptomics, genomics, proteomics, and so on, have led to important insights in many areas of biology and medicine. To support these analyses, software that can handle the difficulties associated with multi-omics datasets is crucial. Here, we describe Panomicon, a web-based, interactive analysis environment for multi-omics data. Building on Toxygates, a tool previously created to study single-omics data that features interactive clustering, heatmaps, and user data uploads, Panomicon introduces improvements for the storage and handling of additional omics types, as well as tools for the generation and visualization of interaction networks between different types of omics data. Panomicon is a new type of environment for the collaborative study of multi-omics data, both for users uploading data to our server and for groups wishing to host their own deployment of Panomicon. We demonstrate Panomicon's capabilities by revisiting a microRNA-mRNA interaction networks study in a non-small cell lung cancer dataset.

With an ever-increasing interest in understanding the relationships between the microbiota and the host, more tools to map, analyze and interpret these relationships have been developed. Most of these tools, however, focus on taxonomic profiling and comparative analysis among groups, with very few analytical tools designed to correlate microbiota and the host phenotypic data. We have developed a software program for creating a web-based integrative database and analysis platform called MANTA (Microbiota And pheNoType correlation Analysis platform). In addition to storing the data, MANTA is equipped with an intuitive user interface that can be used to correlate the microbial composition with phenotypic parameters. Using a case study, we demonstrated that MANTA was able to quickly identify the significant correlations between microbial abundances and phenotypes that are supported by previous studies. Moreover, MANTA enabled the users to quick access locally stored data that can help interpret microbiota-phenotype relations. MANTA is available at https://mizuguchilab.org/manta/ for download and the source code can be found at https://github.com/chenyian-nibio/manta.

Aptamers have a spectrum of applications in biotechnology and drug design, because of the relative simplicity of experimental protocols and advantages of stability and specificity associated with their structural properties. However, to understand the structure-function relationships of aptamers, robust structure modeling tools are necessary. Several such tools have been developed and extensively tested, although most of them target various forms of biological RNA. In this study, we tested the performance of three tools in application to DNA aptamers, since DNA aptamers are the focus of many studies, particularly in drug discovery. We demonstrated that in most cases, the secondary structure of DNA can be reconstructed with acceptable accuracy by at least one of the three tools tested (Mfold, RNAfold, and CentroidFold), although the G-quadruplex motif found in many of the DNA aptamer structures complicates the prediction, as well as the pseudoknot interaction. This problem should be addressed more carefully to improve prediction accuracy.

Abstract Chronic obstructive pulmonary disease (COPD) has been recently characterized as a disease of accelerated lung aging, but the mechanism remains unclear. Tetraspanins have emerged as key players in malignancy and inflammatory diseases. Here, we found that CD9/CD81 double knockout (DKO) mice with a COPD-like phenotype progressively developed a syndrome resembling human aging, including cataracts, hair loss, and atrophy of various organs, including thymus, muscle, and testis, resulting in shorter survival than wild-type (WT) mice. Consistent with this, DNA microarray analysis of DKO mouse lungs revealed differential expression of genes involved in cell death, inflammation, and the sirtuin-1 (SIRT1) pathway. Accordingly, expression of SIRT1 was reduced in DKO mouse lungs. Importantly, siRNA knockdown of CD9 and CD81 in lung epithelial cells additively decreased SIRT1 and Foxo3a expression, but reciprocally upregulated the expression of p21 and p53, leading to reduced cell proliferation and elevated apoptosis. Furthermore, deletion of these tetraspanins increased the expression of pro-inflammatory genes and IL-8. Hence, CD9 and CD81 might coordinately prevent senescence and inflammation, partly by maintaining SIRT1 expression. Altogether, CD9/CD81 DKO mice represent a novel model for both COPD and accelerated senescence.

Constipation is a frequent complication in patients with severe motor and intellectual disabilities (SMID). The aim of this study was to investigate changes in constipation symptoms and gut microbiota associated with the intake of malted rice amazake, a fermented food in Japan, in patients with SMID. Ten patients consumed the test food for six weeks, and their physical condition, dietary and medication status, and constipation assessment scale (CAS) were investigated. Comprehensive fecal microbiome analysis using the 16S rRNA sequence method was performed. The results showed a significant decrease in CAS, and a significant increase in Lactobacillales and decrease in Escherichia-Shigella after consuming malted rice amazake. To investigate the difference in the effects of malted rice amazake consumption, based on the characteristics of the original gut microbiota, the patients were grouped according to the similarity of their gut microbiota before the intervention; Firmicutes-rich Group 1 (n = 5), Actinobacteria-rich Group 2 (n = 4), and Proteobacteria-rich Group 3 (n = 1). The CAS decreased in Groups 1 and 2. The relative abundance of Bifidobacterium showed an increasing tendency both overall and in Group 1, but it was originally higher in Group 2. Our results suggest that malted rice amazake consumption reduces constipation symptoms and simultaneously changes the gut microbiota, but the changes may vary depending on the original composition of the gut microbiota.

Abstract DNA shape is emerging as an important determinant of transcription factor binding beyond just the DNA sequence. The only tool for large scale DNA shape estimates, DNAshape was derived from Monte-Carlo simulations and predicts four broad and static DNA shape features, Propeller twist, Helical twist, Minor groove width and Roll. The contributions of other shape features e.g. Shift, Slide and Opening cannot be evaluated using DNAshape. Here, we report a novel method DynaSeq, which predicts molecular dynamics-derived ensembles of a more exhaustive set of DNA shape features. We compared the DNAshape and DynaSeq predictions for the common features and applied both to predict the genome-wide binding sites of 1312 TFs available from protein interaction quantification (PIQ) data. The results indicate a good agreement between the two methods for the common shape features and point to advantages in using DynaSeq. Predictive models employing ensembles from individual conformational parameters revealed that base-pair opening - known to be important in strand separation - was the best predictor of transcription factor-binding sites (TFBS) followed by features employed by DNAshape. Of note, TFBS could be predicted not only from the features at the target motif sites, but also from those as far as 200 nucleotides away from the motif.

Abstract Metagenomic analysis based on the 16S rRNA gene is generally performed to examine the diversity and abundance of commensal bacteria in feces, which is now recognized to be associated with human health and diseases. Guanidine thiocyanate (GuSCN) solution is used as a less onerous way compared with a frozen method to transport and stock fecal samples at room temperature for DNA analysis; however, optimal methods to measure fecal bacterial composition in GuSCN solution remain to be investigated. Here, we examined the influence of various factors such as pretreatment (e.g., removing GuSCN solution and washing feces with phosphate-buffered saline (PBS) before mechanical lysis), fecal concentration in the GuSCN solution, storage time, and position of fecal subsampling on the 16S rRNA-based analysis of fecal bacteria in GuSCN solution. We found that pretreatment and fecal concentration affected the bacterial composition, and a little change was noted with subsampling position. Based on these results, we propose a basic protocol, including fecal sampling, sample storage, and DNA extraction, for the 16S rRNA-based analysis of bacterial composition in feces suspended in GuSCN solution.

BIG3 is highly expressed in breast cancers and its interaction with PHB2 results in constitutive activation of E2/ERa signalling. Here the authors unveil the mechanistic details of this regulation showing that BIG3 binds PKA and regulates PP1Ca activity in an oestrogen-dependent manner.

Abstract Toxygates was originally released as a user-friendly interface to enhance the accessibility of the large-scale toxicogenomics database, Open TG-GATEs, generated by the Japanese Toxicogenomics Project. Since the original release, significant new functionality has been added to enable users to perform sophisticated computational analysis with only modest bioinformatics skills. The new features include an orthologous mode for data comparison among different species, interactive clustering and heatmap visualisation, enrichment analysis of gene sets, and user data uploading. In a case study, we use these new functions to study the hepatotoxicity of peroxisome proliferator-activated receptor alpha (PPARα) agonist WY-14643. Our findings suggest that WY-14643 caused hypertrophy in the bile duct by intracellular Ca2+ dysregulation, which resulted in the induction of genes in a non-canonical WNT/Ca2+ signalling pathway. With this new release of Toxygates, we provide a suite of tools that allow anyone to carry out in-depth analysis of toxicogenomics in Open TG-GATEs, and of any other dataset that is uploaded.

Biological data analysis is the key to new discoveries in disease biology and drug discovery. The rapid proliferation of high-throughput ‘omics’ data has necessitated a need for tools and platforms that allow the researchers to combine and analyse different types of biological data and obtain biologically relevant knowledge. We had previously developed TargetMine, an integrative data analysis platform for target prioritisation and broad-based biological knowledge discovery. Here, we describe the newly modelled biological data types and the enhanced visual and analytical features of TargetMine. These enhancements have included: an enhanced coverage of gene–gene relations, small molecule metabolite to pathway mappings, an improved literature survey feature, and in silico prediction of gene functional associations such as protein–protein interactions and global gene co-expression. We have also described two usage examples on trans-omics data analysis and extraction of gene-disease associations using MeSH term descriptors. These examples have demonstrated how the newer enhancements in TargetMine have contributed to a more expansive coverage of the biological data space and can help interpret genotype–phenotype relations. TargetMine with its auxiliary toolkit is available at https://targetmine.mizuguchilab.org. The TargetMine source code is available at https://github.com/chenyian-nibio/targetmine-gradle.

In selecting drug target candidates for pharmaceutical research, the linkage to disease and the tractability of the target are two important factors that can ultimately determine the drug efficacy. Several existing resources can provide gene-disease associations, but determining whether such a list of genes are attractive drug targets often requires further information gathering and analysis. In addition, few resources provide the information required to evaluate the tractability of a target. To address these issues, we have updated TargetMine, a data warehouse for assisting target prioritization, by integrating new data sources for gene-disease associations and enhancing functionalities for target assessment. As a data mining platform that integrates a variety of data sources, including protein structures and chemical compounds, TargetMine now offers a powerful and flexible interface for constructing queries to check genetic evidence, tractability and other relevant features for the candidate genes. We demonstrate these features by using several specific examples.

In selecting drug target candidates for pharmaceutical research, the linkage to disease and the tractability of the target are two important factors that can ultimately determine the drug efficacy. Several existing resources can provide gene-disease associations, but determining whether such a list of genes are attractive drug targets often requires further information gathering and analysis. In addition, few resources provide the information required to evaluate the tractability of a target. To address these issues, we have updated TargetMine, a data warehouse for assisting target prioritization, by integrating new data sources for gene-disease associations and enhancing functionalities for target assessment. As a data mining platform that integrates a variety of data sources, including protein structures and chemical compounds, TargetMine now offers a powerful and flexible interface for constructing queries to check genetic evidence, tractability and other relevant features for the candidate genes. We demonstrate these features by using several specific examples.

The intestinal epithelium serves as a dynamic barrier to protect the host tissue from exposure to a myriad of inflammatory stimuli in the luminal environment. Intestinal epithelial cells (IECs) encompass differentiated and specialized cell types that are equipped with regulatory genes, which allow for sensing of the luminal environment. Potential inflammatory cues can instruct IECs to undergo a diverse set of phenotypic alterations. Aging is a primary risk factor for a variety of diseases; it is now well-documented that aging itself reduces the barrier function and turnover of the intestinal epithelium, resulting in pathogen translocation and immune priming with increased systemic inflammation. In this study, we aimed to provide an effective epigenetic and regulatory outlook that examines age-associated alterations in the intestines through the profiling of microRNAs (miRNAs) on isolated mouse IECs. Our microarray analysis revealed that with aging, there is dysregulation of distinct clusters of miRNAs that was present to a greater degree in small IECs (22 miRNAs) compared to large IECs (three miRNAs). Further, miRNA–mRNA interaction network and pathway analyses indicated that aging differentially regulates key pathways between small IECs (e.g., toll-like receptor-related cascades) and large IECs (e.g., cell cycle, Notch signaling and small ubiquitin-related modifier pathway). Taken together, current findings suggest novel gene regulation pathways by epithelial miRNAs in aging within the gastrointestinal tissues.

Background and Objectives: The gut microbiota is associated with human health and dietary nutrition. Various studies have been reported in this regard, but it is difficult to clearly analyze human gut microbiota as individual differences are significant. The causes of these individual differences in intestinal microflora are genetic and/or environmental. In this study, we focused on differences between identical twins in Japan to clarify the effects of nutrients consumed on the entire gut microbiome, while excluding genetic differences. Materials and Methods: We selected healthy Japanese monozygotic twins for the study and confirmed their zygosity by matching 15 short tandem repeat loci. Their fecal samples were subjected to 16S rRNA sequencing and bioinformatics analyses to identify and compare the fluctuations in intestinal bacteria. Results: We identified 12 genera sensitive to environmental factors, and found that Lactobacillus was relatively unaffected by environmental factors. Moreover, we identified protein, fat, and some nutrient intake that can affect 12 genera, which have been identified to be more sensitive to environmental factors. Among the 12 genera, Bacteroides had a positive correlation with retinol equivalent intake (rs = 0.38), Lachnospira had a significantly negative correlation with protein, sodium, iron, vitamin D, vitamin B6, and vitamin B12 intake (rs = −0.38, −0.41, −0.39, −0.63, −0.42, −0.49, respectively), Lachnospiraceae ND3007 group had a positive correlation with fat intake (rs = 0.39), and Lachnospiraceae UCG-008 group had a negative correlation with the saturated fatty acid intake (rs = −0.45). Conclusions: Our study is the first to focus on the relationship between human gut microbiota and nutrient intake using samples from Japanese twins to exclude the effects of genetic factors. These findings will broaden our understanding of the more intuitive relationship between nutrient intake and the gut microbiota and can be a useful basis for finding useful biomarkers that contribute to human health.

The innate immune protein Stimulator of interferon genes (STING) promotes the induction of interferon beta (IFN-β) production via the phosphorylation of its C-terminal tail (CTT) by TANK-binding kinase 1 (TBK1). Potent ligands of STING are, therefore, promising candidates for novel anti-cancer drugs or vaccine adjuvants. However, the intrinsically flexible CTT poses serious problems in in silico drug discovery. Here, we performed molecular dynamics simulations of the STING fragment containing the CTT in ligand-bound and unbound forms and observed that the binding of a potent ligand cyclic GMP-AMP (cGAMP) induced a local structure in the CTT, reminiscent of the known structure of a TBK1 substrate. The subsequent molecular biological experiments confirmed the observed dynamics of the CTT and identified essential residues for the activation of the IFN-β promoter, leading us to propose a new mechanism of STING activation.

We recently reported that brefeldin A-inhibited guanine nucleotide-exchange protein 3 (BIG3) binds Prohibitin 2 (PHB2) in cytoplasm, thereby causing a loss of function of the PHB2 tumor suppressor in the nuclei of breast cancer cells. However, little is known regarding the mechanism by which BIG3 inhibits the nuclear translocation of PHB2 into breast cancer cells. Here, we report that BIG3 blocks the estrogen (E2)-dependent nuclear import of PHB2 via the karyopherin alpha (KPNA) family in breast cancer cells. We found that overexpressed PHB2 interacted with KPNA1, KPNA5, and KPNA6, thereby leading to the E2-dependent translocation of PHB2 into the nuclei of breast cancer cells. More importantly, knockdown of each endogenous KPNA by siRNA caused a significant inhibition of E2-dependent translocation of PHB2 in BIG3-depleted breast cancer cells, thereby enhancing activation of estrogen receptor alpha (ERα). These data indicated that BIG3 may block the KPNAs (KPNA1, KPNA5, and KPNA6) binding region(s) of PHB2, thereby leading to inhibition of KPNAs-mediated PHB2 nuclear translocation in the presence of E2 in breast cancer cells. Understanding this regulation of PHB2 nuclear import may provide therapeutic strategies for controlling E2/ERα signals in breast cancer cells.

The progressive aggregation of Amyloid-β (Aβ) in the brain is a major trait of Alzheimer's Disease (AD). Aβ is produced as a result of proteolytic processing of the β-amyloid precursor protein (APP). Processing of APP is mediated by multiple enzymes, resulting in the production of distinct peptide products: the non-amyloidogenic peptide sAPPα and the amyloidogenic peptides sAPPβ, Aβ40, and Aβ42. Using a pathway-based approach, we analyzed a large-scale siRNA screen that measured the production of different APP proteolytic products. Our analysis identified many of the biological processes/pathways that are known to regulate APP processing and have been implicated in AD pathogenesis, as well as revealing novel regulatory mechanisms. Furthermore, we also demonstrate that some of these processes differentially regulate APP processing, with some mechanisms favouring production of certain peptide species over others. For example, synaptic transmission having a bias towards regulating Aβ40 production over Aβ42 as well as processes involved in insulin and pancreatic biology having a bias for sAPPβ production over sAPPα. In addition, some of the pathways identified as regulators of APP processing contain genes (CLU, BIN1, CR1, PICALM, TREM2, SORL1, MEF2C, DSG2, EPH1A) recently implicated with AD through genome wide association studies (GWAS) and associated meta-analysis. In addition, we provide supporting evidence and a deeper mechanistic understanding of the role of diabetes in AD. The identification of these processes/pathways, their differential impact on APP processing, and their relationships to each other, provide a comprehensive systems biology view of the "regulatory landscape" of APP.

Determining enzyme functions is essential for a thorough understanding of cellular processes. Although many prediction methods have been developed, it remains a significant challenge to predict enzyme functions at the fourth-digit level of the Enzyme Commission numbers. Functional specificity of enzymes often changes drastically by mutations of a small number of residues and therefore, information about these critical residues can potentially help discriminate detailed functions. However, because these residues must be identified by mutagenesis experiments, the available information is limited, and the lack of experimentally verified specificity determining residues (SDRs) has hindered the development of detailed function prediction methods and computational identification of SDRs. Here we present a novel method for predicting enzyme functions by random forests, EFPrf, along with a set of putative SDRs, the random forests derived SDRs (rf-SDRs). EFPrf consists of a set of binary predictors for enzymes in each CATH superfamily and the rf-SDRs are the residue positions corresponding to the most highly contributing attributes obtained from each predictor. EFPrf showed a precision of 0.98 and a recall of 0.89 in a cross-validated benchmark assessment. The rf-SDRs included many residues, whose importance for specificity had been validated experimentally. The analysis of the rf-SDRs revealed both a general tendency that functionally diverged superfamilies tend to include more active site residues in their rf-SDRs than in less diverged superfamilies, and superfamily-specific conservation patterns of each functional residue. EFPrf and the rf-SDRs will be an effective tool for annotating enzyme functions and for understanding how enzyme functions have diverged within each superfamily.

Prioritising candidate genes for further experimental characterisation is an essential, yet challenging task in biomedical research. One way of achieving this goal is to identify specific biological themes that are enriched within the gene set of interest to obtain insights into the biological phenomena under study. Biological pathway data have been particularly useful in identifying functional associations of genes and/or gene sets. However, biological pathway information as compiled in varied repositories often differs in scope and content, preventing a more effective and comprehensive characterisation of gene sets. Here we describe a new approach to constructing biologically coherent gene sets from pathway data in major public repositories and employing them for functional analysis of large gene sets. We first revealed significant overlaps in gene content between different pathways and then defined a clustering method based on the shared gene content and the similarity of gene overlap patterns. We established the biological relevance of the constructed pathway clusters using independent quantitative measures and we finally demonstrated the effectiveness of the constructed pathway clusters in comparative functional enrichment analysis of gene sets associated with diverse human diseases gathered from the literature. The pathway clusters and gene mappings have been integrated into the TargetMine data warehouse and are likely to provide a concise, manageable and biologically relevant means of functional analysis of gene sets and to facilitate candidate gene prioritisation.

5,6-Dimethylxanthenone-4-acetic acid (DMXAA), a potent type I interferon (IFN) inducer, was evaluated as a chemotherapeutic agent in mouse cancer models and proved to be well tolerated in human cancer clinical trials. Despite its multiple biological functions, DMXAA has not been fully characterized for the potential application as a vaccine adjuvant. In this report, we show that DMXAA does act as an adjuvant due to its unique property as a soluble innate immune activator. Using OVA as a model antigen, DMXAA was demonstrated to improve on the antigen specific immune responses and induce a preferential Th2 (Type-2) response. The adjuvant effect was directly dependent on the IRF3-mediated production of type-I-interferon, but not IL-33. DMXAA could also enhance the immunogenicity of influenza split vaccine which led to significant increase in protective responses against live influenza virus challenge in mice compared to split vaccine alone. We propose that DMXAA can be used as an adjuvant that targets a specific innate immune signaling pathway via IRF3 for potential applications including vaccines against influenza which requires a high safety profile.

Prioritising candidate genes for further experimental characterisation is a non-trivial challenge in drug discovery and biomedical research in general. An integrated approach that combines results from multiple data types is best suited for optimal target selection. We developed TargetMine, a data warehouse for efficient target prioritisation. TargetMine utilises the InterMine framework, with new data models such as protein-DNA interactions integrated in a novel way. It enables complicated searches that are difficult to perform with existing tools and it also offers integration of custom annotations and in-house experimental data. We proposed an objective protocol for target prioritisation using TargetMine and set up a benchmarking procedure to evaluate its performance. The results show that the protocol can identify known disease-associated genes with high precision and coverage. A demonstration version of TargetMine is available at http://targetmine.nibio.go.jp/.