Vata, Pitta and Kapha, based on their physical and mental constitution, referred to as Prakriti. We hypothesised that conditioning association studies on prior risk, predictable in Ayurveda, will uncover much more variance and potentially open up more predictive health.Inconsistent results across association studies including Genome-wide association, have posed a major challenge in complex disease genetics. Of the several factors which contribute to this, phenotypic heterogeneity is a serious limitation encountered in modern medicine. On the other hand, Ayurveda, a holistic Indian traditional system of medicine, enables subgrouping of individuals into three major categories namely prakriti based subgrouping of individuals with genetic analysis tools was attempted in a Rheumatoid arthritis (RA) cohort. Association of 21 markers from commonly implicated inflammatory and oxidative stress pathways was tested using a case-control approach in a total cohort comprising 325 cases and 356 controls and in the three subgroups separately. We also tested few postulates of Ayurveda on the disease characteristics in different prakriti groups using clinico-genetic data.Identification of genetic susceptibility markers by combining the IL1β  and CD40  seem to be the determinants in Vata subgroup whereas oxidative stress pathway genes are observed in Pitta  and Kapha  subgroups. Fixed effect analysis of the associated markers from CD40, SOD3 and TNFα with genotype X prakriti interaction terms suggests heterogeneity of effects within the subgroups. Further, disease characteristics such as severity was most pronounced in Vata group.Inflammatory genes like prakriti based subgroups, thereby, validating concepts of prakriti and personalized medicine in Ayurveda. Ayurgenomics approach holds promise for biomarker discovery in complex diseases.This exploratory study suggests discrete causal pathways for RA etiology in  Vata predominant, Pitta predominant and Kapha predominant. This classification is based on the body constitution termed as prakriti in Ayurveda lexicon prakriti. Prakriti is determined by proportions of the five basic elements namely Space, Air, Fire, Water and Earth which constitutes the Tridoshas  which are present in each and every cell of the body. Together these three doshas determine the physiological balance and constitution of the individual. The equilibrium of doshas is called health and imbalance is called disease prakriti of an individual. Therefore, in the practice of Ayurveda, determination of prakriti is necessary not only for examination of the person/patient for understanding the physical and mental nature but also for recommendation of a suitable diet and life style regime for maintenance of health, prevention and cure of diseases. Prakriti also plays an important role in the pathogenesis, treatment and prognosis of various diseases .Challenges in identifying clinically relevant genetic determinants underlying complex diseases include phenotypic and genetic heterogeneity; gene-gene interactions and allelic spectrum which include both common and rare genetic variants. Of these, clinical heterogeneity is the primary limitation and necessitates deployment of alternate or combinatorial approaches. Variation, in particular, clinical phenotypic covariates may reflect differences in the underlying genetic etiology of disease. Therefore, stratifying the cohort and conducting genetic analysis or functional studies within strata may facilitate the biomedical researchers to search for “the needle” in a haystack. It is at this juncture that the insights from Ayurveda, the traditional holistic Indian system of medicine, seem promising Prakriti can be used as a tool to sub-group both healthy and diseased individuals and hence overcome the contemporary limitation of clinical heterogeneity in molecular genetic analysis of complex traits. Such an attempt has been made and a correlation between different prakritis and their biochemical and transcriptomic profiles has been previously reported prakriti groups in RA cohort and a significant difference has been reported prakriti groups was used to establish association of high-altitude pulmonary edema with EGLN1 polymorphism prakriti based subgrouping may help overcome the limitation of phenotypic heterogeneity commonly encountered in case-control based genetic association studies and thus delineate a possible involvement of different pathways underlining the disease in different subgroups. RA is referred to as Amavata in Ayurveda and according to Ayurveda text, its etiology may be briefly described as follows: With excessive intake of heavy, incompatible, unwholesome and untimely food, Agni (digestive mechanism) gets impaired and results in indigestion and produces Ama . If this Ama combines with vitiated Vata dosha and while circulating all over the body, it gets lodged in joints and results in pain, swelling, feverishness and stiffness in joints, loss of appetite, indigestion and body ache. If the disease progresses, severity of pain, stiffness and other symptoms increase leading to deformities and disabilities Vata prakriti people are more prone to get joint diseases with severe pain Vata prakriti. This suggests that Ayurvedic categories may be a suitable platform for assessing prior risk of illness. Further, conditioning association studies on this prior risk may uncover much more variance and potentially open up more predictive health.In the present exploratory study, we revisited the Ayurveda system of medicine and attempted to subgroup both Rheumatoid Arthritis (RA) patients and healthy controls to test the hypothesis that prakriti and prakriti based subgrouping of individuals as an alternate strategy to sort the issue of clinical heterogeneity in genetic association studies of complex traits. For this we selected reported markers/genes from inflammatory pathway and a few oxidative stress pathway genes involved in inflammation. Inflammatory pathway genes were selected as most of the candidate gene studies SOD3, PON1 and PON2 with joint diseases/RA Nrf2, a transcription factor, that maintains the cellular defense against oxidative stress , Department of AYUSH, Ministry of health and family welfare, Government of India, New Delhi. The questionnaire consists of 47 questions related to physiological, physical and psychological characteristics of the person . Intensity of prakriti assessment signs and symptoms as well as the objective questionnaire together were considered for determining the prakriti of the study subjects. An excess of at least five prakriti features (∼11%) were considered for estimating the predominant prakriti of an individual. Objective parameters like height, weight, BMI, blood pressure, swelling, blood/serum examination, X-rays and MRI were used in the clinical assessment. Height, weight, colour and texture of skin and hair etc., are assessed in comparison to normal healthy individuals from matched geographical region/race. Visual analogue scale was used for most of the subjective features like pain, burning sensation, heaviness etc. Blood was drawn and used for analysis of Hb%, ESR, RA factor (quantitative), Anti CCP (quantitative) and for DNA extraction according to routine protocol. Age, gender and ethnicity matched individuals not having RA were recruited as controls. These individuals were then subjected to prakriti analysis using the same prakriti determination questionnaire that was used for RA patients. Peripheral blood sample from 350 cases and 376 matched controls was collected using standard phlebotomy procedures following ethical guidelines of Indian Council of Medical Research, Government of India. Control individuals were also subjected to genetic analysis similar to the cases.Patients of both sexes, aged between 18 to 50 years diagnosed with IL10, IL6, TNFα, IL1β, PTPN22, TRAF1, CD40 and a gene PADI102, and a locus (6q23) from Inflammatory pathways previously reported to be associated with RA, were selected. In addition, six SNPs from four genes namely PON1, PON2, CYP1A2 and SOD3 from oxidative stress pathways were also selected. Thus, a total of 21 SNPs from 12 genes and a locus (6q23) were analyzed in this study. Genotyping of these SNPs were done using the PCR-RFLP method .These parameters were compared across different 2) test. Allelic and genotypic associations of markers found significant (p<0.05) by Pearson’s X2 test were evaluated by computing odds ratio (OR) and 95% confidence intervals (CI). Haplotypes were reconstructed using PHASE ver 2.0.2 (http://stephenslab.uchicago.edu/software.html) prakriti was carried out to see heterogeneity of effects between the prakritis for the associated markers (p<0.1) This was done using General Linear Model with sex and disease status as dependent variables, prakriti as fixed factor, genotype as covariate and with genotype and prakriti as interaction terms (GxP). P value <0.05 was considered to be significantly different in GxP interaction.Hardy–Weinberg equilibrium was tested for each marker using Chi square . Power estimates were calculated for small (w = 0.10) and medium effect sizes (w = 0.25). Analysis was done for a total case-control cohort as well as for three individual prakriti groups separately.All the statistical analyses were performed using Statistical Package for Social Sciences  version 15.0 software for windows. Power calculations were carried out using the Power and Precision Software  as dependent variables. P-values <0.05 were considered as significant. Statistical analyses were done using Statistical Package for Social Sciences ver.15.0 .To identify the independent contribution of clinico-genetic variables, data was adjusted for confounding factors like age, gender, BMI, ESR and Hb. A regression model was fitted for the total case-control cohort as well as for each of the three Amavata (RA) patients and 376 control subjects were recruited for this study. A comparative presentation of demographic details and all quantitative clinical parameters among the patient and control groups are provided in A total of 350 prakriti analysis, 13 cases and 14 controls did not fit into the classification criteria of the three predominant subgroups, and were therefore, eliminated from the study. DNA from 12 cases and 6 controls could not be obtained and therefore could not be included in genetic analysis. A total of 325 cases and 356 controls were taken further for the genetic analysis. Over 95% genotyping success was observed in this study. Allelic, genotypic and haplotypic tests of association was carried out for all markers analyzed in this study for the total cohort and for each of the three prakriti based subgroups of cases and controls separately. Results of all these tests are presented in detail in Following SOD3 (rs699473 and rs2536512) and IL1β (rs1143627) was observed in total RA cohort and a trend of association was observed with PON1 (rs662)  . Test of markers .PON2  was associated with Vata cohort and an association trend was observed at CD40 (rs4810485)  in IL1β was found to be over-represented in RA patients in this sub-group (4810485) . Of the ub-group .SOD3 (rs699473 and rs2536512) markers and a trend of PON1 (rs662) were seen in this subgroup similar to that observed in the total cohort  as well as haplotypic (in order of rs13306703-rs699473-rs2536512) association (No significant association was observed in ociation .prakriti showed a significant difference in interaction among the prakriti groups. CD40 and PON2 interaction with Vata was different from that with Kapha (p = 0.03 and p = 0.02 respectively) and Pitta (p = 0.04 and p = 0.02 respectively). On the other hand, TNFα (rs1800630) genotype suggested significant interaction difference between Vata and Kapha (p = 0.02); and SOD3 (rs2536512) genotype between Kapha and Pitta  when compared for gender  were considered as independent variables. Only age, gender and ESR but none of the genotypes emerged as significant variables in the total cohort. On the other hand, ESR along with TNFα SNP (rs1800629) and CD40 (rs4810485) were seen to be significant in Vata category. Notably, PON2 association was lost on adjusting for gender. In Pitta group, BMI, ESR, & SOD3 (rs2536512) emerged significant. Age, Hb and TNFα (rs1800630) were identified in the Kapha category (PTPN22 (rs2476601) was not used in the regression analysis due to its low MAF (<0.05) and absence of one of the genotypes  as dependent variable and all associated clinical variables and genotypes with p<0.2 from association analysis as independent variables. In the Vata-Pitta comparison, BMI, TNFα (rs1800630) & SOD3 (rs2536512) SNPs were significant and the non-overlapping contribution of these markers seems to best explain the differences in disease susceptibility between these two groups. Similarly, BMI, ESR and TNFα (rs1800630) SNP emerged in the Vata-Kapha comparison; and only BMI and none of the SNPs was seen to contribute to differences in the Kapha-Pitta comparison . It was also observed that even with a small sample size of Vata subgroup, proportion of rarer genotype of CD40  and of TNFα  was found to be significantly higher in Vata cases compared to other subgroups (It is clear from the results that a higher number of RA patients recruited in the study were with the prakriti . Howeverubgroups . In addiubgroups .prakriti as described in Ayurveda Amavata (RA) patients and 300 healthy controls, with a 100 each of Vata, Pitta and Kapha predominant prakriti in this exploratory study on Ayurgenomics. However, it was observed by the clinical team and supported by demographic data , reasonable association of SNPs in IL1β, PON1 and SOD3 markers was observed  and TNFα (rs1800630) rare genotype is significantly interacting with Vata subgroup than with other subgroups . These observations are in line with Ayurveda documentation which states that Vata prakriti patients are more severely affected by Amavata than the other prakriti groups.A plethora of candidate gene association studies and more recently GWAS have been carried out in the last decade to identify genes associated with susceptibility to RA. In the first GWAS which was carried out in Caucasian population with 1860 RA cases and 2938 controls, a strong association of analysis ; this waanalysis ; and alsprakriti . Other dprakriti with the susceptibility genes/markers also provides promising leads in this study. As there were significant differences in clinical features among Vata, Pitta and Kapha groups (Vata subgroup patients is unique in its association with CD40 and RA in Pitta subgroup with PON1 and SOD3 (rs699473) (data not shown). These observations reiterate that subgrouping of individuals, based on prakriti, may be useful in underpinning some of the masked associations , by overcoming the major limitation of sample heterogeneity.Results from regression analysis with total cases and the subgroups showed distinct genetic associations in the different subgroups which were not seen in the total cohort . This sua groups , we checPrakriti groups demonstrated by fixed effect analysis clearly shows that there exists prakriti specific disease susceptibility pathways. It also rules out the possibility that finding evidence for an association in one subset but not in another in this study is due to the suboptimal power of the sample set. As expected the genotype term remained significant and remarkably also GxP  RA cases and controls; b) the three Prakriti subgroups of RA cases.(DOC)Click here for additional data file.Figure S2Histogram depicting BMI comparision across Prakriti subgroups of a) RA cases; b) control group.(DOC)Click here for additional data file.Figure S3Histogram depicting comparison of clinical characteristics a) pain across Prakriti subgroups in RA cases b) swelling across Prakriti subgroups in RA cases c) stiffness across Prakriti subgroups in RA cases d) Hemoglobin across Prakriti subgroups in i) RA cases ii) controls; e) ESR across Prakriti subgroups in i) RA cases ii) controls; f) RA-factor i) between RA cases and controls, ii) across Prakriti subgroups in RA cases; g) anti-CCP antibodies in RA cases across Prakriti subgroups.(DOC)Click here for additional data file.Figure S4Histogram depicting genotypic distribution of CD40 (rs4810485) and TNF-α (rs1800630) among Prakriti subgroups of RA cases and controls.(DOC)Click here for additional data file.Table S1Showing Primers and conditions used for PCR-RFLP.(DOC)Click here for additional data file.Table S2Showing genotypic  and alle(DOC)Click here for additional data file.Table S3Showing genotypic  and alleVata RA cohort.(DOC)Click here for additional data file.Table S4Showing genotypic  and allePitta RA cohort.(DOC)Click here for additional data file.Table S5Showing genotypic  and alleKapha RA cohort.(DOC)Click here for additional data file.Table S6Showing significant Genotype x Prakriti interaction differences.(DOC)Click here for additional data file.Table S7Showing regression analysis of clinico-genetic variables across prakriti groups.(DOC)Click here for additional data file.Material S1An overview of principles of Ayurveda and concepts of Prakriti.(DOC)Click here for additional data file.Material S2Questionnaire used for assessment of Prakriti subgroups in RA cases and controls.(DOC)Click here for additional data file.


The conversions of complex organic compounds to CH4 and CO2 are possible due to the cooperation of four different groups of microorganisms, that is, fermentative, syntrophic, acetogenic, and methanogenic bacteria. Microbes adopt various pathways to evade from the unfavorable conditions in the anaerobic digester like competition between sulfate reducing bacteria (SRB) and methane forming bacteria for the same substrate. Methanosarcina are able to use both acetoclastic and hydrogenotrophic pathways for methane production. This review highlights the cellulosic microorganisms, structure of cellulose, inoculum to substrate ratio, and source of inoculum and its effect on methanogenesis. The molecular techniques such as DGGE (denaturing gradient gel electrophoresis) utilized for dynamic changes in microbial communities and FISH (fluorescent in situ hybridization) that deal with taxonomy and interaction and distribution of tropic groups used are also discussed.Anaerobic digestion is the method of wastes treatment aimed at a reduction of their hazardous effects on the biosphere. The mutualistic behavior of various anaerobic microorganisms results in the decomposition of complex organic substances into simple, chemically stabilized compounds, mainly methane and CO Methanogenesis is complex, redox biochemical reactions occurring under anaerobic conditions. Under symbiotic effects of various anaerobic and relatively anaerobic bacteria, multimolecular organic substances are decomposed into simple, chemically stabilized compounds—methane and carbon dioxide . GeneralDigestion connected with biogas production may play a triple part. First, it is a method of converting the energy contained in biomass into a useful fuel (biogas) which may be stored and transported. Second, it is a method of recycling of organic wastes into stable soil additives, that is, valuable liquid fertilizer and energy. Third, it is a method of wastes treatment aimed at a reduction of their hazardous effects on the environment .4 (50–75%), carbon dioxide CO2 (25–45%), hydrogen sulfide H2S (0-1%), hydrogen H2 (0-1%), carbon monoxide CO (0–2%), nitrogen N2 (0–2%), ammonia NH3 (0-1%), oxygen O2 (0–2%), and water H2O (2–7%) [3 of biogas in associated production of energy, 2.1 kWh of electrical energy and 2.9 kWh of heat are obtained);Production of thermal energy in gas boilers and production of thermal and electrical energy in associated units  , 9, mainic value .2 and CH4 as the main products of the digestion process [Microbiology of anaerobic transformation of organic wastes is a process which involves many different bacterial species, such as hydrolytic, acid forming, acetogenic, and methanogenic bacteria which produce CO process , 12. A sDuring hydrolysis of the polymerized, mostly insoluble organic compounds, like carbohydrates and proteins, fats are decomposed into soluble monomers and dimers, that is, monosaccharides, amino acids, and fatty acids. This stage of the methanogenesis passes through extracellular enzymes from the group of hydrolases  produced by appropriate strains of hydrolytic bacteria. Hydrolysis of hardly decomposable polymers, that is, cellulose and cellulo-cottons is considered to be a stage which limits the rate of wastes digestion. During solid wastes digestion, only 50% of organic compounds undergo biodegradation. The remaining part of the compounds remains in their primary state because of the lack of enzymes participating in their degradation , 14.The rate of hydrolysis process depends on parameters such as size of particles, pH, production of enzymes, diffusion, and adsorption of enzymes on the particles of wastes subjected to the digestion process. Hydrolysis is carried out by bacteria from the group of relative anaerobes of genera like Streptococcus and Enterobacterium , 16.2, and H2, whereas other acidogenesis products play an insignificant role. As a result of these transformations, methanogens may directly use the new products as substrates and energy source. Accumulation of electrons by compounds such as lactate, ethanol, propionate, butyrate, and higher volatile fatty acids is the bacterial response to an increase in hydrogen concentration in the solution. The new products may not be used directly by methanogenic bacteria and must be converted by obligatory bacteria producing hydrogen in the process called acetogenesis. Among the products of acidogenesis, ammonia and hydrogen sulfide which give an intense unpleasant smell to this phase of the process should also be mentioned [During this stage, the acidifying bacteria convert water-soluble chemical substances, including hydrolysis products, to short-chain organic acids , alcohols , aldehydes, carbon dioxide, and hydrogen. From decomposition of proteins, amino acids and peptides arise, which may be a source of energy for anaerobic microorganisms. Acidogenesis may be two-directional due to the effects of various populations of microorganisms. This process may be divided into two types: hydrogenation and dehydrogenation. The basic pathway of transformations passes through acetates, COentioned , 13, 17.Syntrophomonas and Syntrophobacter convert the acid phase products into acetates and hydrogen which may be used by methanogenic bacteria [Methanobacterium suboxydans account for decomposition of pentanoic acid to propionic acid, whereas Methanobacterium propionicum accounts for decomposition of propionic acid to acetic acid. As a result of acetogenesis, hydrogen is released, which exhibits toxic effects on the microorganisms which carry out this process. Therefore, a symbiosis is necessary for acetogenic bacteria with autotrophic methane bacteria using hydrogen, hereinafter referred to as syntrophy [In this process, the acetate bacteria including those of the genera of bacteria . Bacteriyntrophy , 18. Ace2, CO2, and formate and methanol, methylamine, or dimethyl sulfide. Despite the fact that only few bacteria are able to produce methane from acetic acid, a vast majority of CH4 arising in the methane digestion process results from acetic acid conversions by heterotrophic methane bacteria [2 reduction carried out by autotrophic methane bacteria. During this process. H2 is used up, which creates good conditions for the development of acid bacteria which give rise to short-chain organic acids in acidification phase and consequently to too low production of H2 in acetogenic phase. A consequence of such conversions may be gas rich in CO2, because only its insignificant part will be converted into methane [This phase consists in the production of methane by methanogenic bacteria. Methane in this phase of the process is produced from substrates which are the products of previous phases, that is, acetic acid, Hbacteria . Only 30 methane , 214 and CO2 are possible owing to the cooperation of four different groups of microorganisms and are presented in Conversions of complex organic compounds to CHCooperation of the population of microorganisms enables synthesis of certain products which are then used by another group of bacteria. The bacteria belonging to domain Archaea, which are involved in the production of methane, exhibit synergistic relationships with other populations of microorganisms. They may develop only when hydrogen is used by hydrogenotrophs. Such cooperation between microorganisms producing hydrogen and using hydrogen was defined as interspecific transfer of hydrogen . SyntropSyntrophomonas, Syntrophospora, and Syntrophobacter) which oxidise such compounds as propionate and butyrate and thereby obligatorily use hydrogen as electron acceptor. The reaction of butyrate oxidation, carried out by these microorganisms, may occur only when low pressure of H2 is maintained [3COOH and H2. Species belonging to this genus oxidise also pentanoic and enanthic acids to CH3COOH, CO2, and H2. Oxidation of propionate is also an important stage of methanogenesis process in which syntrophic consortia of acetogenic bacteria and methanogenic archeons obligatorily participated [Syntrophobacter genus from δ-proteobacteria group. CH3COOH and H2 produced by the mentioned bacteria are used by methanogenic archeons to produce methane [Syntrophobacter genus are able to use sulfates as electron acceptor in the process of propionate oxidation. Syntrophic oxidation of propionate is not limited to gram-negative bacteria but also to gram-positive bacteria in which a similar phenomenon was observed. Two thermophilic, gram-positive species of syntrophic bacteria oxidising propionate have been described as follows: Desulfotomaculum thermobenzoicum subsp. thermosyntrophicum and Desulfovibrio [Desulfotomaculum, similarly to Syntrophobacter may use sulfates as electron acceptors. Desulfovibrio may participate, along with Methanobacterium genus, in forming CH3COOH and H2 in the process of anaerobic respiration and digestion, using sulfate and lactate to make these products. The Desulphovibrio compete with methane forming bacteria by using the same substrate and produce H2S and hinder the methane formation as shown in The phenomenon of interspecific transfer of hydrogen enables the growth of the so-called syntrophic bacteria  has been shown to affect the methane yield, methane production rate, and the consumption of VFAs. ISR is generally presented on VS basis. ISR affects the occurrence and duration of lag phase (extracellular hydrolysis), methanogenesis, VS/COD reduction, and susceptibility of the microorganisms of the inhibitory effects , 24. ForThe amount of inoculum used per tonne of waste for starting the anaerobic process was shown to have a relevant effect on both biogas and biomethane production. For a waste-to-inoculum ratio ranging from 1 : 1 to 1 : 3 (w/w), the energy production increased from about 100 kWh/tonne to about 380 kWh/tonne of waste organic fraction. Consequently, the investment costs also rise, going from about 180 € to more than 370 €/tonne of treated municipal solid waste organic fraction. The economic optimization analysis showed that the waste-to-inoculum ratio that minimized the treatment cost ranged from 1 : 1.5 to 1 : 2.5, being strongly influenced by the plant size  and by the Green Certificate value. Considering the cost of producing electrical energy, the optimum ratio was 1 : 2, leading to a cost of 0.26 €/kWh, during the investment period, and of 0.14 €/kWh, after the investment period .Inoculation will not be of much use for the fermentation of easily biodegradable waste. For initiating methanogenesis of such wastes, it is strongly recommended not to homogenize inoculums and waste, which will dissipate and degrade the active methanogens. Multiple loading of the waste to the inoculum is superior to single shot loading if the total organic loading rate is the same. It is suggested that an appropriate inoculation method could initiate methanogenesis rapidly even at a low inoculation rate .Within days of incubation of MSW at 55°C, methane was produced at a high rate. In an attempt to narrow down which components of typical MSW contained the thermophilic methanogens, vacuum cleaner dust, banana peel, kitchen waste, and garden waste were tested as inoculum for thermophilic methanogenesis with acetate as the substrate. Results singled out grass turf as the key source of thermophilic acetate degrading methanogenic consortia. Within 4 days of anaerobic incubation (55°C), anaerobically incubated grass turf samples produced methane accompanied by acetate degradation enabling successful start-up of thermophilic anaerobic digestion. Stirring of the culture was not conducive for successful start-up as it resulted specifically in propionate accumulation .Methanogens as absolutely anaerobic microorganisms inhabit anaerobic environment ecosystems, such as tundras, marshlands, rice fields, bottom deposit, swamps, sandy lagoons, tanks where wastewater is decomposed, sewage sludge, solid wastes landfills, and ruminants' stomach , 16, 34.Among methanogenic microorganisms, we can distinguish psychro-, meso- and thermophilic microorganisms. Mesophilic and thermophilic bacteria described in literature exhibit high activity within temperatures, respectively, 28 to 42°C and 55 to 72°C. So far, no anaerobic psychrophilic bacterium has been found which would exhibit activity at a temperature lower than 25°C. Temperature is very important for methanogenic bacteria, due to a limited temperature resistance of their enzymatic structures.Methanosarcina barkeri and Methanosarcina sp.) were isolated from environments of approximately pH 5, while methylotrophic and hydrogen-oxidising methanogens were found in strongly alkaline ecosystems [2 as a source of carbon [Methanogenic bacteria usually develop in inert conditions, with environmental pH from 6.8 to 7.2. This, however, does not mean that methanogenesis does not occur in environments of acid or alkaline reaction. Methanogens which decompose acetates , rods (Methanobacterium), short rods (Methanobrevibacter), Spirillaceae (Methanospirillum), sarcina (Methanosarcina), and filiforms (Methanothrix). The size of these microorganisms ranges from 0.3 to 7.4 μm [All alive organisms, based on analysis and comparison of conservative phylogenetic features, through analysis of 16S and 18S rRNA, were classified to three main taxonomic units of the living world. Three phylogenetic domains may be distinguished: 6S rRNA,  4133 metta group . Methanota group . As mentta group . Methanoo 7.4 μm . The pro3COOH, H2, and CO2 [2 and CO2 for their growth  and various types of wastes .This group of microbe uses only HMethanosarcinales genus which use simple compounds for their growth, that is, acetate. Acetate is one of the most important substrates for methanogenic bacteria, because over 70% of biomethane comes from processing of acetic acid. Acetotrophic methanogens are obligatory anaerobes which process acetate to methane and carbon dioxide. The activity and efficiency of this group of microorganisms are ic processing of biomass of complex organic compounds [Methanobacteriaceae [It reduces methyl groups, such as species of ompounds . Methanoeriaceae .Methanoculleus thermophilicus (hydrogenotrophic) and Methanosarcina thermophila (acetotrophic). The main hydrogenotrophic microorganisms participating in anaerobic processing of fruit and vegetable wastes comprise Methanosphaera stadtmanae and Methanobrevibacterwolinii [Manure is a complex type of substrate composed of hydrocarbons, proteins, and acids. Characteristics of the population of bacteria and archaeans in anaerobic thermophilic processing of manure indicated a dominance of two species: rwolinii .Methanosarcina. It was found out that during anaerobic processing of sewage sludge and manure, the number of microorganisms of Methanosaeta genus decreased with increasing acetate in the environment, with simultaneous intensive growth of the bacteria belonging to Methanosarcina genus which are acetotrophic methanogens [Methanosaeta concilii was a dominant species among acetotrophic methanogens [Counted among acetotrophic methanogens should be the species belonging to genus hanogens . The stuhanogens .The rate of the development of digestive bacteria depends on the type of applied substrate. If the substrates are carbohydrates, the rate of bacteria generation in acid phase amounts to 5 h; whereas in the case when fats are the substrate, the time is prolonged to approximately 72 h. The generation time, in acetogenesis phase, for bacteria using propionic and fatty acids reaches, 84 and 131 h, respectively. In the methanogenesis phase, the generation time ranges between 15 and 85 h .Methanosarcina sp. have high growth rates  and are tolerant to sudden changes in pH of around 0.8–1.0 units caused by overloading, compared to the other methanogens, which have doubling times of minimum 4–6 days and tend to be affected by a pH shock of 0.5 units or even less [Methanosarcina sp. are able to use both the acetoclastic and the hydrogenotrophic methanogenesis pathway, making them more tolerant to specific inhibitors of the acetoclastic pathway, such as fluoroacetate and methyl fluoride, compared to Methanosaeta sp. [−1  as well [Methanosarcina sp. are therefore able to achieve stable growth at low retention times (even as low as 4 days), high organic loading rates, and high levels of, for example, ammonium [The methanogenic Archaea are responsible for the final and critical step of anaerobic digestion, as they produce valuable methane. One of the major drawbacks of anaerobic digestion is however the sensitivity of the methanogenic consortium to different environmental factors. An abrupt change in pH, an increase in salt or organic matter concentration, an alteration of the loading rate, or the introduction of a toxic compound often causes system failure –49. Overven less –54. Methaeta sp. , 55. The as well , 57. Theammonium , 57.Methanosarcina sp. can offer perspectives to tackle the problem. The specific features of the Methanosarcina sp.  make them suitable for establishing the regeneration of the anaerobic digester if methanogenesis has ceased due to environmental factors. The disadvantage of the addition of Methanosarcina sp. rich anaerobic sludge lies within the limited removal of the residual acetate, which has accumulated most likely due to the uncoupling of the acetogenic bacteria and the methanogens, because of the high Ks value of Methanosarcina sp. for acetate [Methanosarcina sp. rich inoculum can initiate two different scenarios in the anaerobic digester. In the first case, the Methanosarcina sp. could start to grow fast, because of a high Umax and cause the methanogenic community to evolve towards a Methanosarcina sp. dominated community with enhanced stability. In the other case, the Methanosarcina sp. could temporarily grow, ameliorating the conditions which initially caused the methanogenesis to cease and then decline again in abundance or disappear totally from the system, because of the regained dominance of Methanosaeta sp. at concentrations of acetate below 100 mgL−1 [Severe impairment of the methanogenic community terminates the methane production –49. Rege acetate , 58, 59.00 mgL−1 , 59. BotMethanosarcina sp. are however able to convert both acetate and CO2 with H2 to methane [Methanosarcina sp., as mentioned above [Methanosarcina sp. to convert these compounds to methane thus makes the presence of other methanogens in the anaerobic digester redundant.Anaerobic digesters normally host a diverse methanogenic community, containing both acetoclastic and hydrogenotrophic methanogens, thus enhancing reactor stability , 61. Met methane . Other ced above , 62, 63.Methanosarcina sp., would still be able to achieve stable methanogenesis. The creation of an anaerobic digester entirely based on Methanosarcina, under normal nonstressed growth conditions, would require a decrease of the SRT to a value as low as 3–5 days, since Methanosarcina sp. have low doubling times, compared to Methanosaeta sp. [Methanosarcina and beable to handle severe impairments and organic loading rates [Methanosarcina sp. based anaerobic digester would be able to handle higher amounts (Methanosarcina for intensive energy production reactor systems. The SAB consist mostly of Clostridium sp. at both mesophilic and thermophilic conditions [Clostridium ultunense and Syntrophaceticus schinkii) and thermophilic (Thermacetogenium phaeum and Thermotoga lettingae) conditions [Methanosarcina and Methanobacteriales are postulated to be the dominant hydrogenotrophic methanogens at both mesophilic and thermophilic conditions of organic waste, because of the low sludge retention time of 3–5 days, compared to conventional anaerobic digesters, which have an SRT in the order of 20–30 days [Methanosarcina sp. for acetate, imposing the need.It can therefore be stated that an anaerobic digester, entirely based on aeta sp. , 59. Yetng rates , 64–66.  amounts . Proposenditions . Syntropnditions , 67–69. –30 days . The dis4 and CO2 (acetoclastic methanogenesis) and the reduction of CO2 with hydrogen gas (hydrogenotrophic methanogenesis). The contribution of each pathway to the methanogenesis depends on the inoculum sludge, the organic substrate, and the process conditions [2, formed during hydrolysis, acidogenesis and acetogenesis, will be converted to methane by hydrogenotrophic methanogens  bacteria, instead of being directly converted to methane by the acetoclastic methanogens [Clostridium ultunense and Syntrophaceticus schinkii) and thermophilic (Thermacetogenium phaeum and Thermotoga lettingae) conditions [Clostridium sp., that is, potential acetate oxidizing partners in the syntrophic relationship, are however still able to grow at a pH of 5.0, thus making syntrophic acetate oxidizing bacteria able to remain active at slightly or even heavily deteriorated conditions in anaerobic digestion [2 and CO2 is however thermodynamically very unfavorable (DG00 = 104.6 kJ mol−1). As a result, syntrophic acetate oxidation can only take place at very low values of H2 partial pressure, that is, between 2.6 and 74 Pa [2, thus questioning the benefit of a two-organism system. The answer lies in the sensitivity of the acetoclastic Methanosaeta sp. towards different impairments causing a shift in acetate degradation from direct acetate cleavage towards syntrophic acetate oxidation coupled with hydrogenotrophic methanogenesis [Methanosaeta sp. dominated system towards a hydrogenotrophic methanogens dominated methanogenic community has been reported at elevated ammonium concentrations in the range of 3000 mg TAN L−1 [−1 can be considered as the threshold for the shift in the methanogenesis pathway [−1 at 25°C and 42 kJ mol−1 at 58°C, thus explaining the dominance of syntrophic acetate oxidation at elevated temperatures [Methanosaeta sp. and therefore enhance the SAO-HM. Yet, Methanosarcina is also able to tolerate these enhanced concentrations and is both acetoclastic and hydrogenotrophic [Methanosarcina sp. may enhance reactor stability. Elevated concentrations of fatty acids and ammonium lead to a rise in the abundance of both SAO and Methanosarcina sp., although at the cost of a severe decline in the abundance of Methanosaeta sp., demonstrating the tolerance of this partnership to several environmental factors [Methanosarcina. It has already been reported that nanowires can be formed between Geobacter sp. and methanogens in anaerobic digesters and it seems therefore not unlikely that other species may succeed in achieving direct interspecies electron transfer in anaerobic digesters [Methanosarcina sp. together with the possibility of energy conservation by means of direct electron transfer makes the SAO-HM partnership very suitable for DIET [Methanosarcina sp. with acetoclastic methanogenesis by Methanosarcina sp. at low OLR and ammonium concentrations and syntrophic acetate oxidation coupled with hydrogenotrophic methanogenesis by Methanosarcina sp. at elevated OLR and ammonium concentrations  are found in high numbers in the rumen and are capable of rapid growth on cellulose [−1). These rates exceed those of most cellulolytic microbes, except for some thermophilic bacteria  [C. thermocellum [F. succinogenes represents something of an outlier among anaerobic cellulose degraders. This species can produce more than 100 polysaccharide hydrolases, most of which are retained on the cell surface. However, it does not produce cellulosomal structures, and its genome lacks certain essential cellulosomal components, particularly the cipA gene that encodes the Scaffolding protein, the m primary structural feature of the cellulosome [Because plant cell walls are relatively complex structures, a great variety of enzymes are needed for extensive enzymatic hydrolysis. Only three readily cultivable species of ruminal bacteria (ellulose . The mosocellum) . In the mocellum . F. succlulosome . A key olulosome ; no smallulosome .2  linked together by hydrogen bonds and van der Waals forces. Cellulose is usually present as a crystalline form and a small amount of nonorganized cellulose chains forms amorphous cellulose. In the latter conformation, cellulose is more susceptible to enzymatic degradation [β-1,4- and sometimes by β-1,3-glycosidic bonds. The main difference between cellulose and hemicellulose is that hemicellulose has branches with short lateral chains consisting of different sugars and cellulose consists of easily hydrolyzable oligomers. Lignin is linked to both hemicellulose and cellulose, forming a physical seal that is an impenetrable barrier in the plant cell wall. It is present in the cellular wall to give structural support, impermeability, and resistance against microbial attack and oxidative stress. It is an amorphous heteropolymer, non-water soluble and optically inactive that is formed from phenylpropane units joined together by non-hydrolyzable linkages. This polymer is synthesized by the generation of free radicals, which are released in the peroxidase-mediated dehydrogenation of three phenyl propionic alcohols: coniferyl alcohol (guaiacyl propanol), coumaryl alcohol (p-hydroxyphenyl propanol), and sinapyl alcohol (syringyl propanol). This heterogeneous structure is linked by C-C and aryl-ether linkages, with aryl-glycerol and β-aryl ether being the predominant structures [Cellulose is a linear polymer that is composed of D-glucose subunits linked by radation . Celluloructures . The metDifferent stages of anaerobic digestion contain considerably varied species of symbiotic microorganisms, which can be broadly classified as two groups: acidogens and methanogens. Since the methanogenic reaction is believed to be the rate-limiting step in the overall anaerobic digestion, proper control of the methanogenic phase has been a key factor in the successful operation of most anaerobic processes . SpecifiThe inoculum used for anaerobic digestion plays a vital role in the way it will degrade the organic matter and produce biogas and also affect the amount of biogas in some cases . The ino4/day) reaching a methane yield of 0.53 L CH4/g VS. Likewise, SWINE/SLUDGE and SWINE were also good inocula [The effect of inoculum source on anaerobic thermophilic digestion of separately collected organic fraction of municipal solid wastes was investigated . Laborat inocula .Inocula play an important role in anaerobic reactor startup by balancing the populations of syntrobacter and methanogens. Such balance makes syntrophic metabolism thermodynamically feasible in anaerobic digestion. The effect of inocula on performance of dairy manure digestion after the examinations of microcolonies in the granular, nongranular, and dairy manure suggests that syntrophic occurrence of methanogens and syntrophobacter in granular inocula was common while it was less visible in nongranular and completely absent in dairy manure. The scanning electron microscope and transmission electron microscope images show the variations in the juxtaposition and bacterial population level in different inocula and these were linked with biogas production in different treatments, granular sludge, nongranular sludge, and mixed culture from anaerobic lagoon and the control did not receive exogenous inocula, demonstrating the importance of syntrophic association and population level of different groups of bacteria in anaerobic digester performance .Fibrobacter succinogenes, Ruminococcus albus, and Ruminococcus flavefaciens are dominant cellulose degrading bacteria in the rumen [Clostridium species tend to be dominant in landfills and anaerobic digesters [Cellulose solubilization is the rate determining step in the anaerobic digestion of organic solid waste , 77, 108he rumen , while Cigesters , 123.Cellulose solubilization kinetics from controlled batch digestions were investigated . SeparatMethanoculleus sp. regarded as hydrogenotrophic and Methanosarcina sp. regarded as acetoclastic methanogens were present in turf tested. However, active acetoclastic methanogens were present in turf soil only. The thermophilic methanogens were present in various turf grass species: Stenotaphrum secundatum, Cynodon dactylon, and Zoysia japonica. A dried and pulverized grass extract that could be generated can serve as a readily storable methanogenic inocula for thermophilic anaerobic digestion. The methanogens could also be physically extracted into an aqueous suspension, suitable for inocula [The results of thermophilic methanogens in turf used as inoculums show that  inocula .4 g−1 volatile solid) computed from sea wrack proximate values was comparable to other feedstocks, the highest methane yield was low (MS = 94.33 mL CH4 g−1 VS). Among the microbial seeds, MS proved to be the best microbial source in utilizing sea wrack biomass and seawater. However, salinity (MS = 42%) observed exceeded average seawater salinity (3). Hence, methanogenic activity could have been inhibited. This is the first report on sea wrack biomass utilization for thalassic biogas production [Sea wrack (dislodged sea grasses and seaweeds) was used in biogas production. Fresh water scarcity in island communities where sea wrack could accumulate led to seawater utilization as liquid substrate. Three microbial seeds cow manure (CM), marine sediment (MS), and sea wrack-associated microflora (SWA) were explored for biogas production. The average biogas produced was 2172 ± 156 mL (MS), 1223 ± 308 mL (SWA), and 551 ± 126 mL (CM). Though methane potential  with two different inocula percentages (20–30%) of mesophilic municipal sludge. The best performance for food waste biodegradation and methane generation was the reactor with 20% of total solids and 30% of inocula gives rise to an acclimation stage with acetogenic/acetogenic activity between 20 and 60 days and methane yields of 0.49 L CH4/g VS .The conducted BMP test explored the influence of using two different anaerobic inocula sources on the digestion of food waste: digested sludge from a municipal wastewater treatment plant and from a digester treating the organic fraction of municipal solid wastes. The inoculum from an anaerobic digester treating municipal wastewater sludge was superior to the inocula from anaerobic digester treating food waste in digesting food waste .Fish waste (FW) obtained from a fish processor was ensiled for biogas production. The FW silages were prepared by mixing FW with bread waste (BW) and brewery grain waste (BGW), and the quality of the prepared silages was evaluated. The biogas and methane yield for FW silages after 96 days was calculated to be 671–763 mL/g VS and 441–482 mL/g VS, respectively .A mesophilic lignocellulolytic microbial consortium BYND-5, established by successive subcultivation, was applied to enhance the biogas production. The degradation efficiency of BYND-5 for rice straw was more than 49.0 ± 1.8% after 7 days of cultivation at 30°C. Various organic compounds, including acetic acid, propionic acid, butyric acid, and glycerin, were detected during biodegradation. The diversity analysis of BYND-5 was conducted by ARDRA  of the 16S rDNA clone library. Results indicated that bacterial groups represented in the clone library were the Firmicutes (5.96%), the Bacteroidetes (40.0%), Deferribacteres (8.94%), Proteobacteria (16.17%), Lentisphaerae (2.13%), Fibrobacteraceae (1.7%), and uncultured bacterium (25.1%). Additionally, the enhancement of biogas yield and methane content was directly related to the pretreatment with BYND-5. The microbial community identified herein is a potential candidate consortium for the degradation of waste lignocelluloses and enhancement of biogas production under the mesophilic temperature conditions .−1 VSS d−1) and methanogenic (0.01–0.84 g COD g−1 VSS d−1) activities depended on the type of biomass, whereas no significant differences were observed among the acidogenic activities (1.5–2.2 g COD g−1 VSS d−1). In most cases, the higher the Bacteroidetes and Archaea percentages the higher the hydrolytic and the methanogenic activity, respectively, in the biomasses. Hydrogenotrophic methanogenic activity was always higher than acetoclastic methanogenic activity, and the highest values were achieved in those biomasses with lower percentages of Methanosaeta. In sum, the combination of molecular tools with activity tests seems to be essential for a better characterization of anaerobic biomasses [High activity levels and balanced anaerobic microbial communities are necessary to attain proper anaerobic digestion performance. Therefore, this work was focused on the kinetic performance and the microbial community structure of six full-scale anaerobic digesters and one lab-scale codigester. Hydrolytic  is reflected by the increasing number of studies that use the technique. It is based on the differing mobility on a gel of denatured DNA-fragments of the same size but with different nucleic acid sequences, thus generating band patterns that directly reflect the genetic biodiversity of the sample. The number of bands corresponds to the number of dominant species .The most important application of DGGE is monitoring dynamic changes in microbial communities, especially when many samples have to be processed. There are multiple applications of DGGE related to anaerobic digestion processes: studies on differences between mesophilic and thermophilic reactors, demonstrating the lower biodiversity in thermophilic reactors used for the treatment of residual waters generated by the pharmaceutical industry , and anaAlthough DGGE (or TGGE: temperature gradient gel electrophoresis) is by far the most widely used genetic fingerprinting technique in molecular ecology, it is not the only method based on PCR amplification of nucleic acids and separation of the product mixture by electrophoresis. An alternative approach is the generation of SSCP (single strand conformation polymorphism) patterns; a technique that has been employed to study anaerobic digesters both on laboratory and industrial scales , 139 invReactor performance and microbial community dynamics were investigated during solid state anaerobic digestion (SS-AD) of corn stover at mesophilic and thermophilic conditions. Thermophilic SS-AD led to faster and greater reductions of cellulose and hemicelluloses during the first 12 days compared to mesophilic SS-AD. However, accumulation of volatile fatty acids (VFAs) was 5-fold higher at thermophilic than mesophilic temperatures, resulting in a large pH drop during days 6 to 12 in the thermophilic reactors. Culture-based enumeration revealed 10–50 times greater populations of cellulolytic and xylanolytic microbes during thermophilic SS-AD than mesophilic SS-AD. DGGE analysis of PCR amplified 16S rRNA genes showed dynamic shifts, especially during the thermophilic SS-AD, of bacterial and archaeal populations over the 38 days of SS-AD as a result of acclimation of the initial seed microbial consortia to the lignocellulosic feedstock. The findings of this study can guide future studies to improve efficiency and stability of SS-AD.Pseudomonas mendocina, Bacillus halodurans, Clostridium hastiforme, Gracilibacter thermotolerans and Thermonas haemolytica. Among these B. halodurans, G. thermotolerans, and T. haemolytica are reported to ferment carbohydrates thermotolerantly. In contrast, P. mendocina disappeared in the acidogenesis process because of its mesophilicity. The most thermophiles detected as DGGE bands could grow catalyzing carbohydrates metabolism in swine wastewater to produce volatile fatty acids thermotolerantly [The microbial community dynamics were investigated in thermal acidogenesis using mesophilic sludge through DGGE (denaturing gradient gel electrophoresis) used for the monitoring of the microbial community. The bacterial community consists of lerantly . The comlerantly .in situ (DNA-RNA matching)  labeled with a fluorescent dye. These sequences recognize 16S rRNA sequences in fixed cells and hybridize with them atching) . The speThe study led by Stahl with pure cultures of methanogenic Archaea  formed tMethanosaeta and Methanosarcina [in situ hybridization based survey of the distribution of these microorganisms in thermophilic, as well as mesophilic UASB sludge granules. The authors [Publications that further illustrate the application of FISH in anaerobic digestion have dealt with the interaction and distribution of trophic groups, such as sulfate reducing bacteria and methanogenic Archaea in methanogenic/sulfidogenic reactors , or diffosarcina , 151. Thosarcina . Other wosarcina . A recen authors  operated authors  in a subIn situ hybridization has been also used as a molecular tool to describe microbial communities in other anaerobic wastewater treatment systems beside UASB reactors. A few noteworthy publications include Plumb's analysis of the microbial composition of the biomass inside an anaerobic baffled reactor [The most recent approaches combine complementary techniques. The authors  have stu reactor , 156.We will bring this section to a close with two final comments. First, we should remember that FISH is exclusively a taxonomic method that is most commonly used to examine whether members of a specific phylogenetic affiliation are present in a sample. It cannot, however, reveal information about the function or metabolic features of the microorganisms, although these characteristics can sometimes be deduced from comparison of the microorganisms detected with phylogenetically related bacteria. With a view to overcoming this limitation,  have preThe second closing remark refers to the quantitative aspects of FISH. The possibility of quantitative results represents a big advantage over the other molecular techniques, but this only applies to homogeneous and evenly distributed samples. The bacterial count per region of the microscopic grid should lie between 30 and 150. Between 10 and 20 regions should be counted to ensure statistically significant cell counts. Nonideal samples and fluorescent background  can make cell counting by fluorescence microscopy a tedious and time consuming process that can be influenced by the judgment of the operator and his or her experience. Truth in this case is in the eye of the beholder, and a standardized and automatic procedure would be preferred. This is possible with a laser confocal microscope or an epifluorescence microscope coupled to a digital camera and a computer workstation to analyze the pictures. The system usually requires expensive software, which makes it less accessible, although free simple software can be found on the Internet . Confocal microscopy on its own is of course already an extraordinarily powerful tool for examining the three-dimensional structure and texture of the microbial aggregates  that develop in wastewater treatment systems .Concurrent utilization of multiple molecular techniques is a common practice for studying microbial community structure, function, and dynamics. For example, a combination of FISH and microautoradiography (FISH-MAR), FISH-SIP, and microarrays can be used to evaluate microbial structure and function. 3COOH, H2, and CO2. Methanogenesis occurs through two pathways: the direct cleavage of acetate to CH4 and CO2 known as acetoclastic methanogenesis and hydrogenotrophic methanogenesis reducing the CO2 with hydrogen gas. The share of each pathway to the methanogenesis depends on the inoculum sludge, the organic substrate, and the process conditions.Anaerobic digestion is a complicated reduction process involving a number of biochemical reactions under anaerobic conditions. This digestion processes convert biomass to energy, used in recycling of organic wastes and reduction of hazardous effects on the environment. Hydrolysis, acidogenesis, acetogenesis, and methanogenesis are different stages of anaerobic digestion. Biogas is possible due to the different groups of microorganisms, namely, fermentative, syntrophic, acetogenic, and methanogenic bacteria. Methanogens are the vital microbes and have decisive role for biomethane production and consist of hydrogenotrophic and acetotrophic and the substrates such as CHRuminococcus albus, Ruminococcus flavefaciens, and Fibrobacter succinogenes) are found at high numbers in the rumen and are capable of rapid growth on cellulose.Cellulose is the most abundant biopolymer on earth and is also a significant part of solid wastes. The cellulosic waste can be digested anaerobically to methane which is a main source of energy. Plant cell walls are relatively complex structures; a great variety of enzymes are needed for extensive enzymatic hydrolysis. Three cultivable species of ruminal bacteria , FISH-SIP and microarrays can be used to evaluate microbial structure and function.


The main functional components of green tea, such as epigallocatechin gallate (EGCG), epigallocatechin (EGC), epicatechin gallate (ECG) and epicatechin (EC), are found to have a broad antineoplastic activity. The discovery of their targets plays an important role in revealing the antineoplastic mechanism. Therefore, to identify potential target proteins for tea polyphenols, we have taken a comparative virtual screening approach using two reverse docking systems, one based on Autodock software and the other on Tarfisdock. Two separate in silico workflows were implemented to derive a set of target proteins related to human diseases and ranked by the binding energy score. Several conventional clinically important proteins with anti-tumor effects are screened out from the PDTD protein database as the potential receptors by both procedures. To further analyze the validity of docking results, we study the binding mode of EGCG and the potential target protein Leukotriene A4 hydrolase in detail. We indicate that interactions mediated by electrostatic and hydrogen bond play a key role in ligand binding. EGCG binds to the enzyme with certain orientation and conformation that is suitable for nucleophilic attacks by several electrical residues inside the enzyme’s activity cavity. This study provides useful information for studying the antitumor mechanism of tea’s functional components. The comparative reverse docking strategy presented generates a tractable set of antineoplastic proteins for future experimental validation as drug targets against tumors. The main active components of green tea are tea polyphenols composed of multiple catechins, such as EGC, ECG, EC and mainly EGCG. Numerous studies have shown that ECGC has a strong, complicated anti-tumor effect, including inhibiting tumor growth and inducing apoptosis, as well as inhibiting multi-targets growth, AKT/protein kinase B signaling pathway and so on. But the detailed mechanism is still unclear. Recent researches have indicated that cell signal transduction is bound up with tumor transformation and development . In signet al. indicate that EGCG may not directly bind to the cell surface receptor [However, although researchers have been looking for EGCG receptors over the years, possible receptors remain a big controversy nowadays. Recent studies have reported that EGCG can bind to the 67 kDa laminin receptor (67LR) related to cancerometastasis ,3, but Areceptor . In contet al. successfully developed INVDOCK [et al. developed a reverse docking web server, Tarfisdock [Therefore, how to search for the molecular targets of tea polyphenols has effectively become an important and challenging task. Using proteomic profile and pharmacokinetic approach to identify the drug targets among thousands of candidate macromolecules has been proved to be laborious and time-consuming –8. There INVDOCK , a reverrfisdock , and userfisdock . Yet anorfisdock , which erfisdock –16. So Arfisdock , a comprIn this research, we take a comparative virtual screening approach which uses both the reverse docking system based on Autodock and the Tarfisdock system, to identify potential target proteins for tea polyphenols. Two separate in silico workflows are implemented to derive a set of protein targets ranked by the energy score from PDTD database, and all of the derived targets are related to human diseases, including tumors. To analyze the validity of docking results, we further explore the binding mode between tea polyphenols and their potential targets. This study may provide useful information for studying the antitumor mechanism of tea functional components. Our work also helps to design new targeted drugs with strong antineoplastic effects by providing the information about the binding mechanism of tea polyphenols to key putative receptor proteins.2.Tea functional components studied in this work include epigallocatechin gallate (EGCG), epicatechin gallate (ECG), epigallocatechin (EGC) and epicatechin (EC). Their chemical structures are displayed in 2.1.Potential protein receptors for EGCG identified by our virtual screening procedures compared with experiment data are listed in in vivo [Fourteen potential receptors whose binding energies rank in the top 3%–2% by Autodock and Tarfisdock, respectively are listed in in vivo  and the in vivo . HoweverSome proteins which might react with EGCG according to experimental data, such as 67 kD laminin receptor, ZAP-70 and Fyn kinase, are not found in the top 3% rank by our screening system, but they also have weak binding-active with EGCG see . The mai2.2.The structures of tea functional components other than EGCG, such as ECG, EGC and EC are similar to EGCG. They may also possess certain biological activity. Therefore, virtual screening of their potential protein receptors is also performed. Co-receptors of these four tea polyphenols are listed in 2.3.Autodock and DOCK are both famous free software widely used in drug design field. Based on DOCK, Tarfisdock now works as a web server which is more convenient for screening drug targets than Autodock. As mentioned above, the two molecular docking procedure Autodock and DOCK have their respective characteristics. Autodock uses Lamarckian genetic algorithm  and a moetc. EGC most likely combines with FKBP tacrolimus protein, glutamate carboxypeptidase II, etc. Squalene oxide cyclase and Leukotriene A4 hydrolase are the most potential protein targets for ECG. Squalene oxide cyclase is the only receptor for EC recognized by both Autodock and Tarfisdock.Potential receptors for tea polyphenols identified by both procedures are listed in The screening results of the 4 types of catechins by two different software are different. EGC has the highest match rate, the number of common receptors divide by total number of receptors found, with match rate of 27.30%, EGCG followed by 18.20%, EC only by 4.50%. The difference is mainly due to the different receptor pockets-determining methods, conformational search algorithms and energy scoring functions used by these two software.The binding free energies between protein and ligand are calculated by an energy scoring function in Autodock as shown by In the above equation, the total binding energy is composed of van der Waals energy, hydrogen bond energy, electrostatic energy, ligand rotary energy and solvation free energy. In contrast, Tarfisdock only focuses on the interaction energies whose energy function is shown by i and receptor atoms j; ijr is the distance between atom i and atom j; ijA, ijB, ijC and ijD are van der Waals repulsion and attraction parameters, respectively; a and b are the van der Waals repulsion and attraction exponents, respectively; iq and jq are point charges on atoms i and j; D and ɛ(r) are dielectric constant; and 332.0 is the factor that converts the electrostatic energy into kcal/mol.In Comparing these two docking procedures’ results, we find that EGCG and ECG share a common best receptor: Leukotriene A4 hydrolase, for its lower binding energy get by both procedures. Therefore, we tried to explore the binding mode between these two kinds of catechins and leukotriene A4 hydrolase.2.4.et al. indicated that mutants correlating with two genes in leukotriene pathway are associated with the risk of myocardial infarction [Myocardial infarction is recognized as the world's leading cause of death. Hakonarson, H. farction . 13% patLTA4 hydrolase is a kind of zincic metalloprotease with intrinsic aminopeptidase activity, containing 610 amino acid residues, 43 α-helix, 7 β hairpin and 22 β sheet. Although numerous applications have demonstrated the success of Autodock in docking simulations for reproducing experimental binding structures, it remains a question whether it works for the particular biological system of interest. To examine the reliability of the docking method, the original ligand BES from a ligand-LTA4 hydrolase complex structure is removed from the PDB file and docks back to the ligand binding site using the same docking strategy as EGCG. The superposition of the structures of the docking simulation results and that of the crystallographic structure complex is shown in 2.5.etc. On the other hand, polar residues GLU348, TYR383, GLU296 and ARG568, colored in marine, form 3 hydrogen bonds O…O—H and 1 hydrogen bond N…O—H with four hydroxyl groups of EGCG, with bond distance of 2.58 Å no bold, 2.11 Å, 2.86 Å and 3.14 Å, respectively. The interaction between Leukotriene A4 hydrolase and its original ligand is shown in Å, 1.84 Å, and 1.70 Å, respectively.The docking conformations of BES/EGCG and Leukotriene A4 hydrolase complexes predicted by Autodock are given in Our analysis shows that the interactions mediated by electrostatic and hydrogen bonds play a key role in the binding of EGCG and Leukotriene A4 hydrolase. It suggests that EGCG may bind to leukotriene A4 hydrolase catalytic site through electrostatic and hydrogen bonds interactions, thus prevents the exposure of the catalytic site to the substrate BES and acts as a competitive inhibitor of the enzyme.3.3.1.http://mms.dsfarm.unipd.it/MMsINC/search/ligandsearch.php). Crystal structure of receptors: 1044 kinds of potential drug target molecules are selected from PDTD database (http://www.dddc.ac.cn/pdtd/). Polar hydrogen atoms and Kollman charges are added to all mol2 files of the receptors. All of the potential drug targets in PDTD database are related to cancer, diabetes and other diseases, including 79.8% of the enzymes, 4.6% of the receptor, 3.5% of the monoclonal antibody, 3.3% of the regulatory factors and hormones. Three experimental implicated targets of EGCG that are absent in PDTD are obtained from PDB database.Crystal structure of the four kinds of tea polyphenols:  come from MMsINC database  are screened by this system using PDTD as its protein database. Gasteiger charge and all the hydrogen atoms (polar and non-polar) are added to receptors initially. For docking, the grid box size of the receptor molecules is 81 * 81 * 81 Åhttp://www.dddc.ac.cn/tarfisdock/) is used. Virtual screening for potential receptors of tea polyphenols  is carried out by invoking DOCK4.0 to search PDTD database from this web interface.Reverse docking based on DOCK4: Tarfisdock web service  Several receptors identified by two different docking procedures are proposed as best putative targets for four types of catechin; (3) We further explore the binding mode between the most potential receptor Leukotriene A4 hydrolase and EGCG. We propose that EGCG binds to the Leukotriene A4 hydrolase active site with certain orientation and conformation so that it may acts as a competitive inhibitor of that enzyme. Structure analysis shows that electrostatic interaction and hydrogen bonding play an important role in their binding process. This study provides important information for studying EGCG and its derivatives’ antitumor mechanism and new targeted drugs design.In this study, a comparative reverse docking approach using Autodock and Tarfisdock is taken to identify the potential receptors for green tea bioactive substances such as EGCG, EGC, ECG and EC. The results show that: (1) a number of receptors of EGCG identified by our method such as Glutathione reductase, Catalase and eEF1-α


The revegetation of abandoned farmland significantly influences soil organic C (SOC) and total N (TN). However, the dynamics of both soil OC and N storage following the abandonment of farmland are not well understood. To learn more about soil C and N storages dynamics 30 years after the conversion of farmland to grassland, we measured SOC and TN content in paired grassland and farmland sites in the Zhifanggou watershed on the Loess Plateau, China. The grassland sites were established on farmland abandoned for 1, 7, 13, 20, and 30 years. Top soil OC and TN were higher in older grassland, especially in the 0–5 cm soil depths; deeper soil OC and TN was lower in younger grasslands (<20 yr), and higher in older grasslands (30 yr). Soil OC and N storage (0–100 cm) was significantly lower in the younger grasslands (<20 yr), had increased in the older grasslands (30 yr), and at 30 years SOC had increased to pre-abandonment levels. For a thirty year period following abandonment the soil C/N value remained at 10. Our results indicate that soil C and TN were significantly and positively correlated, indicating that studies on the storage of soil OC and TN needs to focus on deeper soil and not be restricted to the uppermost (0–30 cm) soil levels. Changes in land use have important effects on regional ecological processes and global climate change 2 if the total N content in an ecosystem does not change The revegetation of degraded land is one of the principal strategies for the control of both soil erosion and ecosystem recovery in fragile regions where either anthropogenic activities or severe environmental conditions can lead to disturbance Large-scale monocropping and over-grazing Previous studies report that the revegetation of degraded land can increase OC and N storages in soil The objectives of this work were to investigate changes in SOC and TN concentration, soil OC and N storage and the relationship between SOC and TN with time since abandonment of farmland with depth in the soil profile.2)  . The stu2) , in summ2) , of whicA common method used to study vegetation restoration is to monitor plants and soils under similar climatic conditions following the sequence of vegetation development Zea mays) had been widely seeded. The average amount of fertilizer applied was 225–300 kg ha−1 of sheep manure as the base fertilizer in April and 300–450 kg ha−1 of urea which was applied in June as topdressing. We state clearly that no specific permissions were required for the location. We confirm that the location is not privately-owned or protected in any way. We confirm that the field studies do not involve endangered or protected species.An abandoned farmland chronosequence in the watershed was selected for study after the history of the sites was determined through interviews with local farmers . Five age classes, 1, 7, 13, 20, and 30 years were selected. In August 2011, when the grassland community biomass peaked, five sites were established in each of the age classes, and the sites were separated by 0.5–1.5 km apart. At each site we set up a plot of 20 m×20 m. In each plot, five quadrats (1 m×1 m) were separately chosen in each of the four corners and center of the plot. In total, we surveyed five plots with twenty-five quadrats in each age class, and twenty-five plots with one hundred and twenty-five quadrats for our study. In each quadrat, the coverage and height, above- and belowground biomass, litter biomass, and soil samples in 0–100 cm soil cores were observed. The morphological traits of the herbage in each age group are listed in In each quadrat, all the aboveground parts of the green plants were cut, collected from the ground and put into envelops and tagged, as was all litter. Because the biomass samples were large, they were weighed fresh and then a part of each sample was dried and weighed. The aboveground biomass of the samples was calculated by multiplying the ratio of the dry weight/fresh weight ratio by the fresh weight.−3) of the different soil layers was measured using a soil bulk sampler with a 5 cm diameter and 5 cm high stainless steel cutting ring (5 replicates) at points adjacent to the soil sampling quadrats. The original volume of each soil core and its dry mass after oven-drying at 105°C were measured.Soil samples were taken at five points in the quadrats of each plot. These were the four corners and center of the biomass sampling sites as described above. Litter horizons were removed before soil sampling. Soil sampling, using a soil drilling sampler (9 cm inner diameter), was done in seven soil layers, 0–5, 5–10, 10–20, 20–30, 30–50, 50–70, and 70–100 cm. We then mixed the same layers together to make one sample. All samples were sieved through a 2 mm screen, and roots and other debris were removed. Each sample was air-dried and stored at room temperature for the determination of soil physical and chemical properties. The soil bulk density  was calculated depending on the inner diameter of the core sampler, sampling depth and the oven dried weight of the composite soil samples −1); BD is soil bulk density (g cm−3); SOC is soil organic carbon concentration (g kg−1); and D is soil thickness (cm).Our sample soils did not have any coarse fraction (>2 mm). Therefore, the study used the following equation to calculate soil organic carbon storage (Cs) −1); BD is soil bulk density (g cm−3); TN is soil TN concentration (g kg−1); and D is soil thickness (cm).The following equation was used to calculate soil N storage (Ns) P<0.05 level, Tukey's post hoc test was used to carry out the multiple comparisons. All statistical analyses were performed using the software program SPSS, ver. 17.0 .One-way ANOVA was used to analyze the means of the same soil layers among the different abandoned years. Differences were evaluated at the 0.05 significance level. When significance was observed at the SOC in 0–5 cm soil was higher in older grassland following abandonment . In the Soil TN in 0–5 cm soil was also higher in older grassland following abandonment . In the Soil OC storage in the 0–10 cm soil was also higher in older grassland since abandonment . After hSimilar to the accumulative soil OC storage, soil N storage had changed mainly in the top 10 cm soil layer 30 years after having been abandoned. The conversion of farmland into grassland significantly increased the soil N storage in the 0–10 cm soil . After hP<0.01). In the process of the revegetation, soil OC and TN, soil C storage and N storage approximately represents SOC = 10TN  were lower in younger grasslands and higher in older grasslands , this is3− -N and NO2− -N are the readily available forms of soil N for root uptake. Liu et al. 3− -N and NO2− -N were lower in grassland than in cropland with the addition of chemical fertilizer N, because the conversion of farmland into grassland increased the C/N ratio and reduced soil mineral N by enhancing soil N immobilization.We observed that soil C and N were significantly and positively correlated . In the Grasslands play an important role in the global C and N cycles The results of this study indicate that plant succession after land has been abandoned resulted in a significant improvement in the physico-chemical properties of soil. Thirty years following abandonment, soil OC and N storage had increased primarily in the top 10 cm of the soil depth. After 30 years of restoration, deeper soil C and N storage had increased to pre-abandonment levels. This finding indicates that deeper soil has a higher potential to fix both C and N in the future (>30 yr). Thus, in the semi-arid environment of the Loess Plateau, vegetation recovery following abandonment is slow and the improvement of soil properties is likely to require a considerably long period of time (>30 yr). Therefore, the findings are important for assessing the resilience of these degraded ecosystems and developing a more effective strategy of vegetation restoration for the management of degraded grassland from a long-term perspective. More research, for example, on soil physico-chemical properties, soil enzyme activities, soil microbial, animal and plant function and composition, is required to better understand the mechanism behind how the soil fixes C and N in the deeper soil profile of sub-arid regions, for example, the Loess Plateau, China.


The ability of stroke volume variation (SVV), pulse pressure variation (PPV) and global end-diastolic volume (GEDV) for prediction of fluid responsiveness in presence of pleural effusion is unknown. The aim of the present study was to challenge the ability of SVV, PPV and GEDV to predict fluid responsiveness in a porcine model with pleural effusions.−1 6% hydroxyethyl starch. After withdrawal of 8 ml kg−1 blood and induction of pleural effusion up to 50 ml kg−1 on either side, measurements at baseline and after fluid loading were repeated. Cardiac output, stroke volume, central venous pressure (CVP) and pulmonary occlusion pressure (PAOP) were obtained by pulmonary thermodilution, whereas GEDV was determined by transpulmonary thermodilution. SVV and PPV were monitored continuously by pulse contour analysis.Pigs were studied at baseline and after fluid loading with 8 ml kgPleural effusion was associated with significant changes in lung compliance, peak airway pressure and stroke volume in both responders and non-responders. At baseline, SVV, PPV and GEDV reliably predicted fluid responsiveness (area under the curve 0.85 (p<0.001), 0.88 (p<0.001), 0.77 (p = 0.007). After induction of pleural effusion the ability of SVV, PPV and GEDV to predict fluid responsiveness was well preserved and also PAOP was predictive. Threshold values for SVV and PPV increased in presence of pleural effusion.In this porcine model, bilateral pleural effusion did not affect the ability of SVV, PPV and GEDV to predict fluid responsiveness. Several studies demonstrated that an individually tailored fluid therapy during major surgery was associated with reduced morbidity and length of stay on the intensive care unit by avoiding both fluid overloading and inappropriate application of vasoactive agents Recently, SVV and PPV have been incorporated into advanced algorithms for performing goal-directed therapy PAC) in a porcine model with pleural effusion. We hypothesized that SVV, PPV and GEDV are still able to reliably predict fluid responsiveness under these circumstances, but that threshold values are affected by pleural effusion.The aim of our prospective animal study was to determine the ability of SVV, PPV and GEDV to predict a percentage change ≥15% in stroke volume by pulmonary thermodilution , ranging from 12 to 16 weeks of age, weighing 34±2 kg and of either gender. The animals originate from the Institution for animal breeding, Christian-Albrechts University Kiel, Olshausen- straße 40, 24098 Kiel.After approval by the Animal Investigation Committee, Christian-Albrechts University Kiel (Permit Number: V 312-72241.121-39), the study was conducted in compliance with the recommendations in the −1) 1 hour before surgery, and each animal was transferred directly to the operating theatre with the requirements of animal welfare during transport. Anesthesia was induced with a bolus dose of intramuscular ketamine (20 mg kg−1). After establishing venous access, propofol (2 mg kg−1) and sufentanil (0.5 µg kg−1) were administered via an ear vein. Airway management implied endotracheal intubation and pigs were ventilated with the Viasys Avea ventilator  in a volume-controlled mode with a tidal volume of 10 ml kg−1, a positive end-expiratory pressure of 5 cm H2O, an I:E ratio of 1∶1.5 and a FiO2 of 0.4. This ventilator is able to assess esophageal pressure (Pes) by a balloon-tipped 8.0 Fr catheter which was placed into the esophagus posterior to the heart. Before application of muscle relaxants, a dynamic occlusion test was performed during spontaneous inspiratory efforts to assure that the esophageal catheter was in the correct position and changes of Pes reflected changes in airway pressure. Calculation of lung compliance (CL) was performed according to the following equation: CL = (CTRS • CCW)/(CCW • CTRS); where CTRS represents the compliance of the total respiratory system and CCW represents the chest wall compliance calculated as the ratio of the exhaled tidal volume to the delta esophageal pressure (dPes) tp,es), as the distending pressure of the lung, was measured during an end-inspiratory manoeuvre, reflecting the theoretical pressure difference between airway (Paw) and pleural pressure (Ppl) using the following formula: . Pes was considered as a surrogate for pleural pressure (Ppl) between the lung and the chest wall 2 35–40 mmHg) was achieved by adjusting the respiratory rate and end-tidal carbon dioxid was measured with an infrared absorption analyzer . For monitoring oxygen saturation, a pulse oximeter was placed on the ear . Maintenance of anesthesia was performed by using propofol (4–6 mg kg−1 h−1) and sufentanil (0.3 µg kg−1 h−1). For the ensurance that changes in SVV and PPV reflected only the effects of positive pressure ventilation, muscle relaxation was provided by pancuronium (0.2 mg kg−1 h−1) to avoid spontaneous breathing efforts. Depth of anesthesia was monitored by Bispectral Index . We repeatedly performed pain stimuli like tail clamping to detect an inadequate depth of anesthesia and focused on the corneal reflex and lacrimation. If assessment suggested inadequate level of anesthesia, additional sufentanil and propofol was injected. During instrumentation, the pigs received an infusion of Ringer solution (6 ml kg−1 h−1). A heating blanket was used to avoid a drop in body temperature and to maintain temperature between 38.0 and 39.0°C. Cardiac rhythm was monitored by a standard lead II electrocardiogram.All surgery was performed under general anesthesia with the aim of avoiding pain and minimizing distress or suffering for the animals. The animals were fasted overnight, but had free access to water. At test day, premedication was performed with the neuropleptic azaperone (4 mg kgPAC) and stroke volume (SVPAC) derived from pulmonary artery catheter. This catheter was advanced under continous pressure recording into wedge position and then connected to a CO monitor . A 5.0 Fr thermistor tipped catheter for thermodilution and pulse contour analysis was inserted percutaneously into the femoral artery  and was connected to the PiCCOplus Monitoring system . This system allows discontinuous measurement of GEDV by transpulmonary thermodilution and continous measurement of SVV and PPV by pulse contour analysis. Thermodilution measurements were obtained by injecting 10 ml ice cold saline (≤8°C) through the central venous port of the pulmonary artery catheter to assess GEDV, COPAC and SVPAC simultaneously. Regardless of the respiratory cycle, injections were performed at least three times. With respect to the preceding measurement, a difference of COPAC ≥15% was discarded and calibration repeated.For hemodynamic monitoring, a 7.5 Fr pulmonary artery catheter  was inserted percutaneously in the right internal jugular vein via an 8.5 Fr introducer for measurement of CVP, PAOP, pulmonary artery thermodilution cardiac output  was calculated by transpulmonary thermodilution according to the following formula:where GEDV represents the sum of the right- and left-heart end-diastolic volumes. GEDV consists of the product of CO and the difference between mean transit time (mtt) and down-slope time (dst) measured by transpulmonary thermodilution. An 8.5 Fr introducer for volume infusion or blood withdrawal was placed into the femoral vein and a 22 Fr thoracic drainage tube  was inserted at each side.−1) over 15 minutes into either side via the thoracic drainage tube and the magnitude of pleural effusions was repeatedly assessed by ultrasound . With respect to estimation of pleural effusion, the maximum end-expiratory distance between the parietal and visceral pleura was measured at the end of expiration and the estimated volume of the pleural effusion was then obtained by the following formula With the pig placed in the supine position pleural effusion was induced by intrapleural infusion of approximately 1500 ml warmed normal saline  blood bag. Thereafter, normal saline was infused between the parietal and visceral pleura for induction of bilateral pleural effusion (approximately 1500 ml each side) and respiratory and hemodynamic variables were recorded. Subsequently, the withdrawn blood was given back to the animal, followed by data collection. Animals with an increase in stroke volume derived by pulmonary thermodilution of at least 15% after fluid challenge were considered to be fluid responsive (Responder) and those with less increase in stroke volume were considered to be Non-Responders. Before and after each fluid loading, SVV, PPV, GEDV, PAOP as well as SVPAC, COPAC, arterial pressure and CVP were recorded simultaneously. Measurements were performed with the animal in supine position and in absence of heart rhythm disturbances. After completion of the trial period, animals were killed by an overdose of sufentanil, propofol and potassium chloride  at baseline and during pleural effusion was performed. CVP, PAOP, SVV, PPV and GEDV at the different experimental stages were analyzed using one way analysis of variance. Paired t - test was used for comparison before and after fluid administration and unpaired t-test was used for comparison between responders and non-responders. P<0.05 was considered significant.All data are given as mean±SD. Statistical comparisons were performed using commercially available statistics software . A Kolmogorov-Smirnov test was used to test for Gaussian distribution. One way analysis of variance (ANOVA) was used for comparison of hemodynamic data at baseline and during pleural effusion, respectively. Receiver operator characteristic (ROC) curves were generated to investigate the ability of a variable to identify responders and non-responders. The optimal threshold value indicating maximum sensitivity and specificity was determined. Areas under the ROC curves were calculated and Pearson correlation for preload variables and subsequent changes in SV2O−1 to 18±4 ml cmH2O−1 (p<0.05) and stroke volume by pulmonary thermodilution from 45.2±4.7 ml to 30.8±8.3 ml (p<0.05). There were 6 responders and 5 non-responders. Fluid loading increased stroke volume at baseline by 15±11% (p<0.05) and during pleural effusion by 23±17% (p<0.05). Hemodynamic and respiratory variables at baseline and during pleural effusion are presented in PAC ≥15%. During pleural effusion, AUC was 0.92 for PPV , 0.89 for SVV , 0.92 for GEDV  and 0.69 for PAOP . Again, CVP  was not able to reliably predict fluid responsiveness (PAC%) are shown in Data of all 11 pigs were included into final analysis. Mean weight was 34±2 kg and we observed no hemodynamic instability requiring pharmacologic support during the measurements. Pleural effusion decreased lung compliance from 41±10 ml cmHsiveness . ROC anasiveness . CorrelaMain findings of our experimental animal investigation are as follows:In presence of bilateral pleural effusion and during different loading conditions, the dynamic and volumetric variables SVV, PPV and GEDV were able to predict a percentage change in stroke volume. The changed threshold values, however, indicate that for proper interpretation of these variables, information regarding the presence of pleural effusion is important.Appropriate perioperative fluid loading according to the patient’s individual needs has been shown to reduce mortality and length of stay on the intensive care unit Accordingly, also in our study CVP failed to reliably predict an increase of stroke volume during fluid loading at baseline and in presence of pleural effusion. Interestingly, in presence of pleural effusion, PAOP as a static cardiac filling pressure achieved statistical significance in predicting fluid responsiveness. Although several studies have repeatedly demonstrated poor reliability of filling pressures to predict fluid responsiveness Changes in intrathoracic pressure and consecutive changes in venous return and left ventricular preload were identified as the physiological background underlying both SVV and PPV 2O) and pleural effusion especially in non-responder. With respect to the hemodynamic effect of pleural effusion, recent animal investigations could demonstrate that induction of a pleural effusion up to 40 ml kg−1 did not affect hemodynamic variables such as cardiac output and arterial pressure but increased PAOP and CVP. In contrast, pleural effusion up to 80 ml kg−1 was associated with severe reduction in cardiac output and arterial pressure, thereby causing half of the animals to die −1, as well as a significant increase in static cardiac filling pressures, but none of the animals died after initiation of pleural effusion (Recently, an animal study investigated the influence of an unilateral pleural effusion on respiratory mechanics and chest wall compliance effusion . InteresSeveral investigations demonstrated transpulmonary pressure as a useful tool to estimate and improve respiratory mechanics in critically ill patients Interestingly, in presence of pleural effusion, ROC analysis yielded reliable prediction of fluid responsiveness by SVV and PPV compared with baseline measurements in responders. An explanation for these results might be the underlying ventilation induced variation in intrathoracic pressure, which accounts for SVV and PPV and may be increased by pleural fluids. In this context, a recent animal study, investigating hemodynamic changes in presence of pericardial and pleural effusions, obtained better toleration of elevated intrapericardial pressure in presence of pleural effusion With respect to the reliability of the static volumetric variable GEDV to predict fluid responsiveness, recent literature remains controversial Several limitations of our study should be noted. First, we investigated healthy pigs with normal cardiac and pulmonary function certainly not exhibiting lung injuries like pneumonia compared to critically ill patients. Therefore our results cannot directly transferred to critically ill patients, suffering from diseases causing pleural effusion. Chronic diseases associated with pleural effusions may interact by stiffening the lung and the chest wall with consecutive reduction of chest wall compliance. Therefore, influence of pleural effusion on variables of fluid responsiveness should be investigated in humans.In conclusion, the present experimental animal study demonstrated reliable indication of fluid responsiveness by GEDV, SVV and PPV even in presence of large pleural effusions. However, ROC-analysis yielded increased threshold values for SVV and PPV to discriminate between responder and non-responder in presence of pleural effusion. As only healthy pigs with normal cardiac and pulmonary function were investigated, the present results cannot be generalized and extrapolated to critical ill patients.