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Abstract Baicalin is a flavonoid extracted from Scutellaria baicalensis Georgi. As it has significant antitumor and apoptosis-inducing effects, baicalin may be useful as a lead compound in new antitumor drug development. However, as the pharmacological actions of baicalin have yet to be elucidated, we isolated its target protein, which was successfully identified as Annexin A2. Annexin A2 forms a heterotetramer with S100A10 protein, which plays an important role in the plasminogen activator system. The heterotetramer bound to tissue plasminogen activator (tPA) activates the conversion of plasminogen to plasmin and promotes the expression of STAT-3 and NF-κB, which are target genes involved in the development of cancer. Moreover, NF-κB and STAT-3 induce the expression of cell inhibitors of apoptotic proteins and inhibit apoptosis. To examine whether these antitumor and apoptosis-inducing effects of baicalin are mediated by Annexin A2, we prepared Annexin A2 knockdown HepG2 cells. We compared mRNA expression by RT-qPCR and apoptosis by caspase-3 activity assays in Annexin A2 knockdown HepG2 cells. The results showed that the antitumor and apoptosis-inducing effects of baicalin are mediated by Annexin A2. The results of this study suggest that agents capable of inhibiting Annexin A2 may be useful candidates for the development of novel antitumor agents.

Wind power is crucial for achieving carbon neutrality, but its output can vary due to local wind conditions. The spatio-temporal behavior of wind power generation connected to the power grid can have a significant impact on system operations. To assess this impact, the use of long-term reanalysis results of wind data based on a numerical weather prediction (NWP) model is considered valid. However, in Japan, the behavior of on-shore wind power generation is influenced by diverse topographical and meteorological features (TMFs) of the installation site, making it challenging to assess possible operational impacts based solely on power curve-based estimates using a popular conversion equation. In this study, a nonparametric machine learning-based post-processing model that learns the statistical relationship between the TMFs at the target location and the actual wind farm (WF) output was developed to represent the expected per-unit output at each location. Focusing on historical reconstruction results and using this post-processing model to reproduce the real-world WF output behavior created a set of expected wind power generation profiles. The dataset includes hourly long term (1958–2012) wind power generation profiles expected under the WF installation assumptions at various on-shore locations in Japan with a 5 km spatial resolution and is expected to contribute to an accurate understanding of the impact of spatio-temporal wind power behavior. The dataset is publicly accessible at https://doi.org/10.5281/zenodo.11496867 (Fujimoto et al., 2024).

Objective: This study aimed to examine the appropriate body position and anatomical site for DUS through comparisons with maximal inspiratory pressure (MIP). Methods: Thirty healthy adults were included in the study. MIP was measured using a spirometer. DUS was performed in both the right and left intercostal spaces in both sitting and supine positions. The diaphragm thickness at functional residual volume (TDFRC) and total lung capacity (TDTLC) were measured, and the fractional change in diaphragm thickness (TF) was calculated under each condition. To assess interobserver reproducibility, DUS was performed by an experienced examiner and a novice examiner. Results: Among the DUS parameters, the TDFRC measured at the right intercostal space in the sitting position showed the strongest correlation with MIP (r = 0.68, p

The recent COVID-19 pandemic has precipitated drastic changes in economic and lifestyle conditions, significantly altering residual electricity demand behavior. This alteration has expanded the demand gap between actual and forecasted electricity usage based on pre-pandemic data, highlighting a critical global issue. Many studies in the pandemic have explored the features of this widening gap, which is impacted by major social events like fast virus spread and lockdowns. However, the influence of factors like economic shifts and lifestyle changes on this demand remains largely unexplored, primarily due to the pandemic's significant effects in these areas. Understanding the essential factors affecting the demand gap is crucial for stakeholders in the electricity sector to develop effective strategies. This study examines the hourly electricity consumption and related factors during the specified period. We present a method combining time-series forecasting and sparse modeling. This helps identify critical factors affecting the electricity demand gap during the pandemic, highlighting the most crucial variables. Utilizing this method, we identify the variables that have undergone significant changes during the pandemic and evaluate their effects on the electricity demand gap. The effectiveness is proven by applying it to the dataset collected in German.

Abstract Local energy self‐sufficiency, in which the supply and demand of electricity are controlled such that the generated power from distributed energy resources (DERs) is consumed locally based on a power supply‐and‐demand forecast, mitigates the burden on the power system and contributes to the efficient use of DERs in smart cities. However, widely available smart metres cannot measure behind‐the‐metre pure demand and generation from prosumers. Pure demand and generation forecasts without additional metering contribute to advanced supply‐and‐demand control in smart cities, including demand response. This study proposes a method of forecasting spatio‐temporal behaviours of behind‐the‐metre pure demand and generation by focussing on the information of net demand distribution observable from the smart metres; the proposed method initially predicts the spatio‐temporal net demand distribution with a combined forecaster based on the persistence and non‐parametric regression models, and then separately estimates the behind‐the‐metre pure demand and generation by using demand forecast result of neighbouring pure‐consumers extracted by considering the area‐scale behaviours of the smart metering data. The simulation results demonstrate that the proposed method provides accuracy comparable to forecasts conducted by directly measuring pure demand and generation, without requiring the installation of additional metres.

To use energy generated in wind farms (WFs), which contain uncertainty in their output, as a primary power source in a power system, a sophisticated balancing operation scheme is required. This study focuses on a balancing group (BG) scheme combining WFs and a variable-speed pumped-storage hydro generator (PSHG), which has a large capacity to compensate for the WF output. The proposed BG operational scheduling approach aims to maximize the expected revenue obtained in the day-ahead power market under the uncertainty in WF output and market price by considering the operational constraints of the PSHG, i.e., the water storage capacity and frequency of operation for switching pump-up/-down. In such a BG scheme, it is crucial to consider time-varying and time-dependent uncertainties in WF output to manage PSHG capacity constraints, as well as to derive a reasonable plan in practical computation time. The proposed BG operational scheduling scheme derives a set of WF output scenarios that represents the heterogeneous and time-dependent characteristics of real-world WF output behavior from probability density distributions derived by a cutting-edge prediction approach and implements the expected revenue maximization problem with scenario-based chance constraints of water storage transition by introducing computationally effective iterative optimization algorithm based on surrogate functions. Simulation results suggest that the proposed scheme provides an effective BG operation by considering the uncertainty in the WF output.

The use of electric vehicles (EVs) and photovoltaics (PV) is increasing worldwide. Transportation networks require the effective use of renewable energy (RE) for EVs, whereas power networks require local consumption of PV energy, mainly at the initiative of local governments. Although many previous studies have addressed these requirements using private EVs as mobile storages, their uncertainty and uncontrollability remain highly problematic. Therefore, our previous studies focused on electric buses because of their high controllability and certainty. These studies involved the development and evaluation of two independent minimization problems—kilowatts (KW) and kilowatt-hours (KWH) of surplus RE—as a charging schedule optimization method using mixed integer linear programming. However, the feasibility of simultaneously minimizing KW and KWH still presented technical problems. This study aims to extend and generalize the method to the simultaneous minimization of KW and KWH of the PV-derived reverse power flow (RPF). With this multiobjective optimization, the KW peak-cut of the RPF improves the hosting capacity and increases the availability of connectable RE resources, whereas the minimization of KWH promotes the local consumption of RE and decarbonization of public transportation. Two simulations using detailed data of actual bus operations and actual power flow data for a real city confirmed the feasibility of simultaneously optimizing KW and KWH and the relationship between these indicators and number of EV chargers. The optimized charging of the 17 electric buses achieved a maximum of 211.4 kW peak-cut and 1318.4 kWh RE-RPF absorption, reducing CO2 emissions by 495.7 kg/day.

Abstract As the number of photovoltaic (PV) power generators connected to the distribution grid increases, applications of on-load tap changers (OLTCs), power conditioning systems, and static reactive power compensators are being considered to mitigate the problem of voltage violation in low voltage distribution systems. The reactive power control by power conditioning systems and static reactive power compensators can mitigate steep voltage fluctuations. However, it creates losses in generation opportunities. On the other hand, OLTCs are installed at the bases of distribution lines and can collectively manage the entire system. However, the conventional voltage control method, i.e., the line drop compensation (LDC) method, is not designed for the case in which a large number of PV systems are installed in the distribution network, which results in voltage violations above the limit of the acceptable range. This study proposes a method to determine the optimal LDC control parameters of the voltage regulator, considering the power factor of PV systems to minimize the magnitude of voltage violations based on the voltage profile analysis of low-voltage (LV) distribution networks. Specifically, during a measurement period of several days, the voltages at some LV consumers and pole transformers were measured, and the optimal parameters were determined by analyzing the collected data. The effectiveness of the proposed method was verified through a numerical simulation study using the actual distribution system model under several scenarios of PV penetration rates. Additionally, the difference in the effectiveness of voltage violation reduction was verified in the case where all the LV consumer’s consumer voltage data measured per minute were used as well as in the case where only the maximum and minimum values of the data within the measurement period were used. The results reveal that the proposed method, which operates within the parameters determined by the voltage analysis of the LV distribution network, is superior to the conventional method. Furthermore, it was found that even if only the maximum and minimum values of the measurement data were used, an effective voltage violation reduction could be expected.

Abstract The use of distributed energy resources (DERs) in a city contributes to the net zero CO2 of a city. However, the spatially uneven distribution of power demand and surplus electricity causes congestion in the grid system, making wide‐area operation difficult. The concept of local energy self‐sufficiency via energy management, in which batteries or electric vehicles are charged using power generated by DERs and discharged to neighbouring consumers, is expected to be a way to avoid grid conjunction while maximizing the use of DERs. For efficient local energy self‐sufficiency, it is necessary to identify where and when future power surpluses and shortages will occur within a city and optimize battery operation according to demand. Forecasts that focus only on representative points of a city may be less reproducible in diversity in the power demand transition for individual consumers in local parts of cities. Electricity smart meters that monitor power demand every 30 min from each consumer are expected to help predict the spatiotemporal distribution of power demand to achieve efficient local energy self‐sufficiency. The significance of reflecting regional characteristics in forecasting spatiotemporal distribution of power demand is demonstrated using actual data obtained by smart meters installed in Japanese cities. The results suggest that the forecast approach, which considers the daily periodicity of power demand and weather conditions, obtains high prediction accuracy in predicting power demand in meshed local areas in the city and derives results precisely reproducing the spatiotemporal behaviours of power demand.

The introduction of variable renewable energies, such as photovoltaic systems (PV), into electric power systems will increase the amount of resources necessary to balance supply and demand in terms of both quality and quantity. Thus, a planned power generation that matches the planned values submitted in advance with the actual values of PV power generation is required by installing battery energy storage systems (BESS). Currently, since the cost of BESS is still high, eliminating the imbalance which equals to the difference between planned and actual values of PV power generation by BESS is not economically feasible, and appropriate cooperation is required to share the role of balancing with regulating generators. However, variable renewable energies can cause steep imbalances which cannot be compensated by regulating generators due to their relatively slow response times. To address this problem, this paper proposes a planned power generation method that can reduce the burden on regulating generators by introducing a strategy to mitigate the changing rate of imbalance, (hereinafter called “imbalance leveling”) by determining the optimal scheduling of BESS using the short-term forecast of PV power generation. Numerical simulations were conducted using a model based on Japanese frequency control to compare the proposed method with the conventional method without imbalance leveling. The results confirmed that the proposed method can improve the capacity value per unit of BESS in supply and demand adjustment owing to the effect of leveling the imbalance. Additionally, it can improve the performance of supply and demand adjustment with the same capacity.

Active and reactive power control using smart inverters (SI) is highly effective in mitigating voltage rise in distribution systems, which is caused by the high penetration of photovoltaic (PV) power generation. However, the voltage control performance depends on the SI settings. We propose a new approach that uniquely determines the parameter settings for volt-VAR-watt control based on the active and reactive power-voltage sensitivity matrix of SIs. Because the voltage sensitivity matrix is calculated based solely on the impedance of the distribution system and it does not vary with time or the number of SIs, the proposed method can determine the individual SI parameter settings theoretically and efficiently without the need for optimization problem formulation, power flow calculation, or communication between the SIs. To evaluate the proposed method, the voltage control performance in a real distribution system model with a large number of PV installations is compared with that of volt-VAR-watt control using default parameters and optimized parameters in case that the load demand and PV generation are given in advance. The results show that the proposed method achieves better control performance than other conventional methods in terms of all the evaluation indices; in particular, it realizes effective control in the case of voltage rise. Furthermore, the proposed method can also achieve the same level of voltage control performance as the optimization results, even though it uses only the voltage sensitivity matrix and SI rating capacities for parameters determination, and the accuracy of the proposed voltage control can be ensured.

X-linked adrenoleukodystrophy (X-ALD) is an inherited disorder caused by deleterious mutations in the ABCD1 gene. The ABCD1 protein transports very long-chain FAs (VLCFAs) from the cytosol into the peroxisome where the VLCFAs are degraded through β-oxidation. ABCD1 dysfunction leads to VLCFA accumulation in individuals with X-ALD. FAs are activated by esterification to CoA before metabolic utilization. However, the intracellular pools and metabolic profiles of individual acyl-CoA esters have not been fully analyzed. In this study, we profiled the acyl-CoA species in fibroblasts from X-ALD patients and in ABCD1-deficient HeLa cells. We found that hexacosenoyl (26:1)-CoA, but not hexacosanoyl (26:0)-CoA, was the most abundantly concentrated among the VLCFA-CoA species in these cells. We also show that 26:1-CoA is mainly synthesized from oleoyl-CoA, and the metabolic turnover rate of 26:1-CoA was almost identical to that of oleoyl-CoA in both WT and ABCD1-deficient HeLa cells. The findings of our study provide precise quantitative and metabolic information of each acyl-CoA species in living cells. Our results suggest that VLCFA is endogenously synthesized as VLCFA-CoA through a FA elongation pathway and is then efficiently converted to other metabolites, such as phospholipids, in the absence of ABCD1.

This study proposes a novel operational planning method for polymer electrolyte fuel cell cogeneration systems (PEFC-CGSs). PEFC-CGSs provide hot water by utilizing waste heat produced in the electricity generation process, and hot water is stored in an attached tank. Generating and storing hot water based on an optimal operational plan according to household demand leads to further energy saving; therefore, operational planning methods based on household demand prediction have received significant attention. However, the improvement in the demand prediction accuracy does not necessarily lead to efficient PEFC-CGS operation in terms of operational costs; in other words, the accuracy in the demand prediction does not directly indicate the resulting operational efficiency. In this study, the authors propose a novel approach based on a surrogate model for deriving an appropriate plan that minimizes the expected operational costs among the operational plan candidates. In the proposed scheme, the error between expected and actual operational costs explicitly represents the relevance of the operational plan, so that the optimal operational plan can be selected directly from the perspective of the resulting operational efficiency. The practicality of the proposed approach is evaluated with the existing demand prediction-based approach via numerical simulations using real-world measurements of multiple customers in Japan. The proposed method reveals 30% reduction of the excessive operational costs by avoiding the inefficient operation of the auxiliary gas-heater in the experiments and will further enhance the value of introducing highly efficient residential fuel cell system that contributes to a low-carbon society.

This paper proposes a method to determine an optimal radial-loop configuration to minimize power loss in a distribution network with photovoltaic (PV) systems and evaluates the effectiveness of this configuration. Due to the disaggregation of transmission and distribution in a deregulated power system, stable and efficient operations in terms of voltage regulation and power loss reduction utilizing existing equipment is becoming more important for distribution system operators (DSOs). Japanese DSOs operate distribution networks in a radial configuration while supplying power to customers with high reliability. One method to reduce further power loss is to upgrade the network topology to a radial-loop configuration, which achieves partial loop structures by closing the tie-switches of the radial configuration. To implement a radial-loop configuration, DSOs must first evaluate the reliability, as a loop configuration can cause a feeder circuit breaker (FCB) malfunction and an expansion of the nonsupplied area during fault conditions. However, the impact of a radial-loop configuration on reliability and effectiveness has not been verified. Therefore, this paper proposes a method to determine an optimal radial-loop configuration that minimizes active power loss and maintains high reliability. In a numerical simulation, the reliability of radial-loop configurations is verified, and the effectiveness of the configuration is analyzed in terms of active power loss and voltage regulation under several PV system penetration conditions. A 6.6 kV distribution network consisting of 6 feeders and 11 tie-switches is modeled based on an existing Japanese distribution network and used for numerical simulation.

The widespread introduction of functionally-smart inverters will be an indispensable factor for the large-scale penetration of distributed energy resources (DERs) via the power system. On the other hand, further smartization based on the data-centric operation of smart inverters (S-INVs) is required to cost-effectively achieve the same level of power system operational performance as before under circumstances where the spatio-temporal behavior of power flow is becoming significantly complex due to the penetration of DERs. This review provides an overview of current ambitious efforts toward smartization of operational management of DER inverters, clarifies the expected contribution of machine learning technology to the smart operation of DER inverters, and attempts to identify the issues currently open and areas where research is expected to be promoted in the future.

In recent decades, there has been a gradual penetration of plant factories achieving semiautomated crop cultivation. However, efficient energy utilization, as well as quality control of crops, are very important factors with regard to sustainable operation. Operating parameters, such as room temperature, affect not only the quality of crops but also the electric power required to realize the target operation while being influenced by the environment outside the plant. Therefore, a methodology is needed to analyze and interpret the relationships among these manipulated variables, exogenous variables, crop quality, and the amount of required electric power. Constructing a directed acyclic graph composed of regression models is an attractive approach for such analysis; however, the relationships can possibly be nonlinear, so the direct application of existing analytic approaches will not be appropriate. In this paper, we propose a methodology for relationship analysis among variables based on the directed acyclic graphs while identifying the linearity/nonlinearity in their relationships. In general, the construction of such a graphical model has computational issues, especially when the number of variables is large, and the risk of overfitting. The proposed method utilizes the idea of sparse regularization, which has been actively discussed in the field of machine learning, for realizing the automatic identification of linearity/nonlinearity between variables and screening redundant candidate structures; this approach relaxes the computational complexity issue and controls the risk of overfitting. As a case study, the proposed method is applied to a dataset collected from a real-world cultivation system in a plant factory to discuss its usefulness.

Mycobacterium leprae (M. leprae) is the etiological agent of leprosy, and the skin lesions of lepromatous leprosy are filled with numerous foamy or xanthomatous histiocytes that are parasitized by M. leprae. Lipids are an important nutrient for the intracellular survival of M. leprae. In this study, we attempted to determine the intracellular lipid composition and underlying mechanisms for changes in host cell lipid metabolism induced by M. leprae infection. Using high-performance thin-layer chromatography (HPTLC), we demonstrated specific induction of triacylglycerol (TAG) production in human macrophage THP-1 cells following M. leprae infection. We then used [14C] stearic acid tracing to show incorporation of this newly synthesized host cell TAG into M. leprae. In parallel with TAG accumulation, expression of host glycerol-3-phosphate acyltransferase 3 (GPAT3), a key enzyme in de novo TAG synthesis, was significantly increased in M. leprae-infected cells. CRISPR/Cas9 genome editing of GPAT3 in THP-1 cells (GPAT3 KO) dramatically reduced accumulation of TAG following M. leprae infection, intracellular mycobacterial load, and bacteria viability. These results together suggest that M. leprae induces host GPAT3 expression to facilitate TAG accumulation within macrophages to maintain a suitable environment that is crucial for intracellular survival of these bacilli.

Leaves of Morinda citrifolia (noni) have been used in Polynesian folk medicine for the treatment of pain and inflammation, and their juice is very popular worldwide as a functional food supplement. This study aimed to demonstrate that M. citrifolia seed extract exerts anti-inflammatory effects on RAW264 cells stimulated by lipopolysaccharide. To confirm the inhibitory effect of M. citrifolia seed extract, we assessed the production of nitric oxide (NO) and inflammatory cytokines. The M. citrifolia seed extract showed a significant inhibition of NO production, with no effect on cell viability, and was more active than M. citrifolia seed oil, leaf extract, and fruit extract. The M. citrifolia seed extract was found to reduce the expression of inducible NO synthase and tumor necrosis factor-alpha of pro-inflammatory cytokines. These results suggest that the anti-inflammatory effect of M. citrifolia seed extract is related to a reduction in the expression of inflammatory mediators and support its potential therapeutic use.

The preservation district of groups of traditional buildings is an important category of cultural properties in Japan. Japan has entered an earthquake active period. However, conventional culturally and socially oriented site investigation methods hardly satisfy the requirements of risk management of district preservation. In this study, the authors introduce an investigation method from the structural and security perspectives. They explain the study procedure, methods and results. Results of the investigation of two coastal districts, namely, Ine and Yuasa, are comparatively studied, and regional disparities are highlighted. The study concludes that district vulnerability and house structural performance should be considered as important indices when making decisions regarding district preservation.

A 1-month-old girl with feeding difficulties, failure to thrive and stridor following birth was referred to our department. Milder laryngomalacia was diagnosed by endoscopic laryngoscopic examination and followed continuously. At 2 months old, the stridor and feeding difficulties suddenly worsened after Haemophilus influenzae type b vaccination. The patient was admitted to our hospital because of severe respiratory distress, at which point a tongue base cyst was identified. The cystic mass was resected transorally. The severe respiratory distress and feeding difficulties improved, but symptoms of mild stridor remained for 8 months. No recurrence has been observed up to a year post-operatively.

Acyl-CoA:glycerol-3-phosphate acyltransferase (GPAT) and acyl-CoA: 1-acyl-glycerol-3-phosphate acyltransferase (AGPAT) are involved in the de novo synthesis of triacylglycerol (TAG) and glycerophospholipids. Many enzymes belonging to the GPAT/AGPAT family have recently been identified and their physiological or pathophysiological roles have been proposed. The roles of GPAT/AGPAT in the synthesis of TAG and obesity-related diseases were revealed through the identification of causative genes of these diseases or analyses of genetically manipulated animals. Recent studies have suggested that some isoforms of GPAT/AGPAT family enzymes are involved in the fatty acid remodeling of phospholipids. The enzymology of GPAT/AGPAT and their physiological/ pathological roles in the metabolism of glycerolipids have been described and discussed in this review.

Ancient chlamydiae diverged into pathogenic and environmental chlamydiae 0.7-1.4 billion years ago. However, how pathogenic chlamydiae adapted to mammalian cells that provide a stable niche at approximately 37 °C, remains unknown, although environmental chlamydiae have evolved as endosymbionts of lower eukaryotes in harsh niches of relatively low temperatures. Hence, we assessed whether an environmental chlamydia, Parachlamydia Bn9, could grow in human HEp-2 cells at a low culture temperature of 30 °C. The assessment of inclusion formation by quantitative RT-PCR revealed that the numbers of bacterial inclusion bodies and the transcription level of 16SrRNA significantly increased after culture at 30 °C compared to at 37 °C. Confocal microscopy showed that the bacteria were located close to HEp-2 nuclei and were actively replicative. Transmission electron microscopy also revealed replicating bacteria consisting of reticular bodies, but with a few elementary bodies. Cytochalasin D and rifampicin inhibited inclusion formation. Lactacystin slightly inhibited bacterial inclusion formation. KEGG analysis using a draft genome sequence of the bacteria revealed that it possesses metabolic pathways almost identical to those of pathogenic chlamydia. Interestingly, comparative genomic analysis with pathogenic chlamydia revealed that the Parachlamydia similarly possess the genes encoding Type III secretion system, but lacking genes encoding inclusion membrane proteins (IncA to G) required for inclusion maturation. Taken together, we conclude that ancient chlamydiae had the potential to grow in human cells, but overcoming the thermal gap was a critical event for chlamydial adaptation to human cells.

Over the past decade, considerable advances have been made in the discovery of gene targets in metabolic diseases. However, in vivo studies based on molecular biological technologies such as the generation of knockout mice and the construction of short hairpin RNA vectors require considerable effort and time, which is a major limitation for in vivo functional analysis. Here, we introduce a liver-specific nonviral small interfering RNA (siRNA) delivery system into rapid and efficient characterization of hepatic gene targets in metabolic disease mice. The comparative transcriptome analysis in liver between KKAy diabetic and normal control mice demonstrated that the expression of monoacylglycerol O-acyltransferase 1 (Mogat1), an enzyme involved in triglyceride synthesis and storage, was highly elevated during the disease progression. The upregulation of Mogat1 expression in liver was also found in other genetic (db/db) and diet-induced obese mice. The silencing of hepatic Mogat1 via a liver-specific siRNA delivery system resulted in a dramatic improvement in blood glucose levels and hepatic steatosis as well as overweight with no apparent overall toxicities, indicating that hepatic Mogat1 is a promising therapeutic target for metabolic diseases. The integrated approach with transcriptomics and nonviral siRNA delivery system provides a blueprint for rapid drug discovery and development.

Obligate amoebal endosymbiotic bacterium Protochlamydia with ancestral pathogenic chlamydial features evolved to survive within protist hosts, such as Acanthamoba, 0.7-1.4 billion years ago, but not within vertebrates including humans. This observation raises the possibility that interactions between Protochlamydia and human cells may result in a novel cytopathic effect, leading to new insights into host-parasite relationships. Previously, we reported that Protochlamydia induces apoptosis of the immortalized human cell line, HEp-2. In this study, we attempted to elucidate the molecular mechanism underlying this apoptosis. We first confirmed that, upon stimulation with the bacteria, poly (ADP-ribose) polymerase (PARP) was cleaved at an early stage in HEp-2 cells, which was dependent on the amount of bacteria. A pan-caspase inhibitor and both caspase-3 and -9 inhibitors similarly inhibited the apoptosis of HEp-2 cells. A decrease of the mitochondrial membrane potential was also confirmed. Furthermore, lactacystin, an inhibitor of chlamydial protease-like activity factor (CPAF), blocked the apoptosis. Cytochalasin D also inhibited the apoptosis, which was dependent on the drug concentration, indicating that bacterial entry into cells was required to induce apoptosis. Interestingly, Yersinia type III inhibitors (ME0052, ME0053, and ME0054) did not have any effect on the apoptosis. We also confirmed that the Protochlamydia used in this study possessed a homologue of the cpaf gene and that two critical residues, histidine-101 and serine-499 of C. trachomatis CPAF in the active center, were conserved. Thus, our results indicate that after entry, Protochlamydia-secreted CPAF induces mitochondrial dysfunction with a decrease of the membrane potential, followed by caspase-9, caspase-3 and PARP cleavages for apoptosis. More interestingly, because C. trachomatis infection can block the apoptosis, our finding implies unique features of CPAF between pathogenic and primitive chlamydiae.

Inflammation is a fundamental defensive response to harmful stimuli. However, it can cause damage if it does not subside. To avoid such damage, organisms have developed a mechanism called resolution of inflammation. Here we applied an untargeted metabolomics approach to a sterile and self-resolving animal model of acute inflammation, namely zymosan-induced peritonitis in mice, to examine the effect of inflammation and resolution on the metabolomic profiles. Significant and time-dependent changes in metabolite profiles after zymosan administration were observed in both peritoneal wash fluid (PWF) and plasma. These metabolomic changes correlated well with inflammatory chemokine or cytokine production. In PWF, most of metabolites that could detected increased in zymosan-treated mice, which is suggestive of inflammation, oxidative stress and increased energy demands. In plasma, most metabolites in the central metabolic pathway (glycolysis and TCA cycle) were significantly downregulated after zymosan administration. The concentration of the ketone body 3-hydroxybutyric acid (3-HB) in plasma and PWF increased in zymosan-injected animals indicating upregulation of fatty acid β-oxidation. Increased 3-HB level was observed in the cells that infiltrated into the peritoneal cavity and these infiltrated cells might contribute, at least in part, to the production of 3-HB in the peritoneal cavity.

Protochlamydia, an environmental chlamydia and obligate amoebal endosymbiotic bacterium, evolved to survive within protist hosts, such as Acanthamobae, 700 million years ago. However, these bacteria do not live in vertebrates, including humans. This raises the possibility that interactions between Protochlamydia and human cells could induce a novel cytopathic effect, leading to new insights into host-parasite relationships. Therefore, we studied the effect of Protochlamydia on the survival of human immortal cell line, HEp-2 cells and primary peripheral blood mononuclear cells (PBMC). Using mainly 4',6-diamidino-2-phenylindole staining, fluorescent in situ hybridization, transmission electron microscopy, and also TUNEL and Transwell assays, we demonstrated that the Protochlamydia induced apoptosis in HEp-2 cells. The attachment of viable bacterial cells, but not an increase of bacterial infectious progenies within the cells, was required for the apoptosis. Other chlamydiae [Parachlamydia acanthamoebae and Chlamydia trachomatis (serovars D and L2)] did not induce the same phenomena, indicating that the observed apoptosis may be specific to the Protochlamydia. Furthermore, the bacteria had no effect on the survival of primary PBMCs collected from five volunteers, regardless of activation. We concluded that Protochlamydia induces apoptosis in human-immortal HEp-2 cells and that this endosymbiont could potentially be used as a biological tool for the elucidation of novel host-parasite relationships.