GSTP1, GSTT1 and GSTCD, may contribute to determining individual susceptibility to air pollution as a promoter of coronary vulnerability.Experimental and epidemiological studies have reported associations between air pollution exposure, in particular related to vehicle exhaust, and cardiovascular disease. A potential pathophysiological pathway is pollution-induced pulmonary oxidative stress, with secondary systemic inflammation. Genetic polymorphisms in genes implicated in oxidative stress, such as GSTP1, GSTT1 and GSTCD, on risk of acute myocardial infarction (AMI) and hypertension. In addition, we studied whether air pollution effects were modified by the investigated genetic variants.We aimed to investigate effects of long-term traffic-related air pollution exposure, as well as variants in GSTP1 gene, and one in each of the GSTT1 and GSTCD genes, as well as air pollution exposure estimates, were available for 119 AMI cases and 1310 randomly selected population controls. Population control individuals with systolic blood pressure ≥140 mmHg, diastolic blood pressure ≥90 mmHg or on daily antihypertensive medication were defined as hypertensive (n = 468). Individual air pollution exposure levels were modeled as annual means of NO2 (marker of vehicle exhaust pollutants) using central monitoring data and dispersion models, linking to participants' home addresses.Genotype data at 7 single nucleotide polymorphisms (SNPs) in the 3 of long-term NO2 exposure. Three GSTP1 SNPs were significantly associated with hypertension. The effect of air pollution on risk of AMI varied by genotype strata, although the suggested interaction was not significant. We saw no obvious interaction between genetic variants in the GST genes and air pollution exposure for hypertension.Air pollution was significantly associated with risk of AMI: OR 1.78 (95%CI 1.04–3.03) per 10 µg/mGSTP1, GSTT1 and GSTCD genes, albeit not statistically-significantly.Air pollution exposure entails an increased risk of AMI, and this risk differed over genotype strata for variants in the  Exposure to air pollution has been associated with several cardiovascular (CV) outcomes such as atherosclerosis, progression of CV disease (CVD) and CV mortality. 2.5) and cardiovascular disease. 2.5 until 2010 and the limit value does not enter into force until 2015, and to date longitudinal studies measuring population exposure to PM and associations with CVD are lacking. Nitrogen dioxide (NO2) is a gaseous air pollutant which is highly correlated with traffic intensity, and often used as indicator for vehicle exhaust in general. 2 in the European Union has been set to 40 µg/m3 and has been in force since 2010. Several European studies have found an association between NO2 exposure and cardiovascular mortality, well below 40 µg/m3A recent review by the American Heart Association concludes that the evidence is consistent with a causal relationship between exposure to particulate matter (PM) air pollution <2.5 µm in diameter , a result which was supported by an extended meta-analysis of several GWASs. GSTCD and cardiovascular disease have been reported. The polymorphism in the GSTT1 gene is caused by a gene deletion, resulting in almost no enzymatic activity in individuals with the null genotype; hypothetically putting the individual at increased risk of oxidative stress and downstream effects e.g. atherosclerosis and coronary heart disease. Variants in the GSTP1, GSTCD and GSTT1 with risk of AMI and hypertension, and c) if these genetic variants modify the associations between air pollution exposure and the two outcomes.Hence, a probable pathway between air pollution and cardiovascular effects is oxidative stress, but the relationship between variants in antioxidant genes and cardiovascular disease as well as potential interaction between such genetic variants and air pollution exposure in cardiovascular disease is incompletely explored. We hypothesized that there is an interaction between air pollution exposure and genetic variants in the GST genes on risk of acute myocardial infarction (AMI) and thus we aimed to investigate, in a population-based study, a) the association of long-term traffic-related air pollution exposure with risk of acute myocardial infarction (AMI) and hypertension, b) the associations of genetic variants in The study was approved by the local ethical committee and all participants provided written informed consent.The INTERGENE/ADONIX  study has been described in detail elsewhere. 2. Blood pressure measurement was carried out in a sitting position after 5 minutes rest, using a validated blood pressure device . The blood pressure was measured twice and then the mean of the two measurements was used. Blood samples were collected after 4 hours of fasting for immediate serum lipid  analysis and storage for DNA extraction.Study participants received questionnaires and were invited to a medical examination. During the medical examination, height and body weight was measured to the nearest 1 cm and 0.1 kg with the subjects in light clothing and without shoes. Body mass index (BMI) was calculated from weight (kg) and height (m) using the formula BMI = weight/heightBased on the questionnaire, the individuals' level of education was classed as primary if highest achieved education was elementary school, lower secondary school, vocational school or girls' school. Secondary meant that the individual indicated having attended upper secondary school or high school and tertiary signified having attended university or college.Individuals with a systolic blood pressure (SBP) ≥140 mmHg, or diastolic blood pressure (DBP) ≥90 mmHg or who were taking daily antihypertensive medication were defined as ‘hypertensive’. Individuals of non-European birth (5%) were excluded from the current genetic analyses. Of those reporting European origin and included, 90% reported being of Swedish origin.2 outside each participant's baseline home address were used for exposure assessment. Each participant's home address was translated into geographical coordinates and combined with modeled levels of NO2 in a geographical information system (GIS). The dispersion model, which is hosted by the local authorities, contains both emission data and meteorological information and has been previously validated against actual measurements, showing good agreement.x values with high spatial resolution (20×20 meters), which were then converted to estimated NO2 using local empirical relationships. Due to the availability of concentration grids, the calculated exposure levels represented the years 2006 and 2007 and not the exact years of inclusion (2001–2004). For individuals with air pollution data for both years, we used the 2007 value because the geographical area covered was increased from 2006. For individuals with exposure data from only one year, this value was used. Correlation between values for individuals with values from both years was 0.98 for NO2. This high degree of stability over years indicates that 2007 is a good indicator also for the long-term spatial distribution of exposure levels during 2001–2004.Modeled annual average levels of NOGSTP1 gene and one SNP from each of the GSTT1 and GSTCD genes were selected from the literature. SNPs were genotyped using a Sequenom MassARRAY platform  or a competitive allele-specific PCR system KASPar , all with a call rate ≥90%. SNPs with a Hardy-Weinberg Equilibrium (HWE) p-value ≤0.05 were excluded, as were individuals missing results for more than two out of seven GSTP1 SNPs. The GSTT1 SNP rs2266637 (as a marker of the null genotype) was genotyped twice and the resulting concordant pairs of null/non-null genotype individuals from the two assays were used for analysis.7 SNPs from the Methods are analogous for AMI and hypertension, except that for hypertension the analysis was restricted to the population control sample. All analyses used logistic regression models adjusted for age, age squared and gender. Potential selection bias for cases and controls due to geographical clustering of cases' home addresses in areas closer to the two source hospitals was accounted for by adjusting for residential area based on the postal code, since within these smaller areas the distribution of cases and controls was representative. Consequently, all analyses concerning air pollution were also adjusted for residential area and educational level , while for all analyses involving genotypes, BMI was included in the model .2 (as a marker for vehicle exhaust pollutants) on risk of AMI or hypertension were analyzed separately. Thereafter, effects of genetic variants on risk of AMI and hypertension were studied. For GSTP1, each of the 7 SNPs was analyzed coded to the dominant genetic model . GSTCD, a single variant (rs10516526) was studied, coded to the dominant genetic model. The GSTT1 null genotype was studied using the two genotypes captured by the SNP rs2266637. Finally, interaction between air pollution and genetic variants was investigated by estimating effects of air pollution in analyses stratified by genotype in a common regression model. For this model, we also assessed whether smoking might modify the interaction between air pollution and genetic variants on AMI.First, effects of NO2 exposure across all subjects was 15.6 with standard deviation 6.1.In total, 1429 individuals had both valid genotype and air pollution exposure data and could be included in the analyses; 119 of the 192 first-time AMI cases, and 1310 of the 3614 control individuals. The reduction in numbers was mainly due to the geographical area covered by the dispersion model . As expected, AMI cases were significantly different from controls on several characteristics; in particular, cases were more often male and older than controls . In the 3 of NO2  1.78  1.04–3.03) per 10 µg/m3 of NO2 .GSTP1 SNPs showed a significant association with AMI; the most strongly associated with AMI was rs596603, with OR 0.77 (95% CI 0.51–1.16)  remained significant after Bonferroni-correction  . In cont2 exposure. , one genotype stratum has an effect estimate for air pollution exposure on risk of AMI that is significant and almost twice that of the other stratum, suggesting that the genotype may affect the adverse effect of NOxposure. GSTP1 gene on risk of hypertension. Further, this study of interaction between long-term vehicle exhaust exposure and genetic susceptibility in CVD further suggests that cardiovascular response to air pollution exposure may be modified by genetic variants in the GST genes, although the power for interaction analysis was limited.This study's main findings are an association between indicators of long-term vehicle exhaust exposure and acute myocardial infarction, and an effect for several variants in the 2 as a marker for vehicle air pollution exposure in the association analyses. The finding of an association between long-term traffic-related air pollution exposure and AMI is consistent with some earlier studies. 2 and cardiovascular mortality, with estimates ranging from 1.05 to 1.39 per 10 µg/m3.th to 95th percentile difference in 30-year average exposure to NO2) in Stockholm, but none for non-fatal MI, which gives a null effect overall (OR 0.99 95% CI 0.76–1.30 for 5th to 95th percentile difference in 30-year average exposure to NO2).Which constituents in the heterogeneous traffic-related air pollution mix are actually responsible for the adverse effect is unknown, and for comparability with other European studies, we have used NO2 exposure at residential addresses found that NO2 at the baseline address showed weaker associations than when using all addresses from 1 January 1971 until death or 31 December 2009 – presumably due to non-differential misclassification of exposure when using one address as a proxy for longer-term exposure. Since we have used NO2 data from the baseline address only, our estimate may similarly be more likely to underestimate the association than overestimate it.While American studies have focused more on between-city studies, European studies have mostly investigated within-city contrasts. This makes the comparability with American studies poor. It has been suggested that within-city contrasts are more strongly associated with cardiovascular events than between-city differences.GSTT1, GSTCD and GSTP1 genes code for metabolizing enzymes that are involved in counteracting the effects of oxidative stress. Due to the properties of the gases and particles constituting vehicle exhaust, inhalation of the exhaust may cause oxidative stress GSTP1 SNPs' association with hypertension, our results also show a significant adverse effect of air pollution exposure on AMI for the subjects with AA-genotype in GSTCD SNP rs10516526, OR 2.25 (95% CI 1.25–4.06) per 10 µg/m3 of NO2, and a near null effect estimate for subjects with GA or GG genotypes combined . To our knowledge, nothing has previously been reported regarding an association of variants in the GSTCD gene with cardiovascular outcomes. What has been reported is a positive association between the G-allele in the same GSTCD SNP and forced expiratory volume in the first second (FEV1). In addition to the GSTT1 gene is present in approximately 20% of Caucasians and has been associated with lower levels of high-density lipoprotein (HDL) cholesterol, elevated triglyceride-HDL ratio and hypertriglyceridemia, all risk factors for CVD. GSTT1 null polymorphism. GSTT1 null genotype and coronary atherosclerosis (defined as >20% obstructive lesion) was seen. GSTT1, i.e. the presence of a coding gene, compared to subjects with the null genotype . The latter result is somewhat counterintuitive; however, the system which counteracts the effects of oxidative stress is complex, and the balance between involved enzymes remains to be explored. Neither of these suggested interactions were however statistically significant, reflecting the limited power of interaction testing.The null polymorphism of the The INTERGENE/ADONIX study is a well characterized, population-based study with high-quality genotyping data. Unfortunately, calculated long-term air pollution data were not available for the entire geographical study area, thus excluding many individuals with otherwise valid data from the present analysis. Modeling air pollution levels for a larger geographical area would enable inclusion of more of the participants and increased power, but such models remain to be developed.Starting with a list of potential covariates and confounders from literature, we assessed confounding for each variable. Smoking was also investigated for potential effect modification of the gene-environment interaction, but none was found. Residential area and educational level are important for reducing any geographical or socio-economic selection bias. BMI seemed to vary by genotype and was thus included in the genotype analyses.A potential complication from adjusting for residential area in the air pollution analyses is some loss of exposure contrast, since this means that effects are essentially estimated within each residential area and then pooled across areas. However, the adjustment addresses potential confounding from area-related subject characteristics (e.g. socio-economic status and lifestyle characteristics), or geographical selection bias. In addition, sufficient contrast generally remains even within smaller geographical areas, as indicated also in the current analysis by the significance of the association between air pollution exposure and AMI that we observed.and high-risk environment), and potentially to give them targeted prevention, health advice and care. This study shows significant association of vehicle air pollution exposure with risk of AMI; effects of variants in the GSTP1 gene on risk of hypertension, and suggests interactions between variants in the GSTP1, GSTT1 and GSTCD genes and vehicle air pollution exposure on risk of AMI.In addition to giving insight into the specific pathways of disease pathology, results from gene-environment interaction studies can provide additional possibilities of identifying subjects at particularly high risk  geographical area covered by the dispersion model used to calculate annual average NO2 exposure in 2007.a) Geographical area covered by the dispersion model used to calculate annual average NO(GIF)Click here for additional data file.Figure S2Linkage disequilibrium (LD) plot for GSTP12 between pairs of SNPs and coloring representing D′. gene SNPs, with values in each cell representing R(GIF)Click here for additional data file.


In septic shock, pulse pressure or cardiac output variation during passive leg raising are preload dependence indices reliable at predicting fluid responsiveness. Therefore, they may help to identify those patients who need intravascular volume expansion, while avoiding unnecessary fluid administration in the other patients. However, whether their use improves septic shock prognosis remains unknown. The aim of this study was to assess the clinical benefits of using preload dependence indices to titrate intravascular fluids during septic shock.In a single-center randomized controlled trial, 60 septic shock patients were allocated to preload dependence indices-guided (preload dependence group) or central venous pressure-guided (control group) intravascular volume expansion with 30 patients in each group. The primary end point was time to shock resolution, defined by vasopressor weaning.P = 0.01), and the same held true for red cell transfusions (178 (82 to 304) versus 103 (0 to 183) mL, P = 0.04). Physiologic variable values did not change over time between groups, except for plasma lactate . Mortality was not significantly different between groups . Intravascular volume expansion was lower in the preload dependence group for patients with lower simplified acute physiology score II (SAPS II), and the opposite was found for patients in the upper two SAPS II quartiles. The amount of intravascular volume expansion did not change across the quartiles of severity in the control group, but steadily increased with severity in the preload dependence group.There was no significant difference in time to shock resolution between groups (median (interquartile range) 2.0 (1.2 to 3.1) versus 2.3 (1.4 to 5.6) days in control and preload dependence groups, respectively). The daily amount of fluids administered for intravascular volume expansion was higher in the control than in the preload dependence group  versus 383 (211 to 604) mL, In patients with septic shock, titrating intravascular volume expansion with preload dependence indices did not change time to shock resolution, but resulted in less daily fluids intake, including red blood cells, without worsening patient outcome.NCT01972828. Registered 11 October 2013.Clinicaltrials.gov The online version of this article (doi:10.1186/s13054-014-0734-3) contains supplementary material, which is available to authorized users. Fluid administration is the first-line component of hemodynamic support in septic shock treatment . PreloadWe conducted an exploratory randomized controlled study to explore whether preload dependence-driven fluid management in ICU patients with septic shock would reduce the duration of cardiovascular failure, as compared to CVP-driven fluid management.This trial was an open-label, controlled, parallel-group study, with balanced randomization, performed in a 15-bed medical ICU. The trial was registered at ClinicalTrials.gov under the number NCT01972828 and the study protocol was approved by the local ethics committee (Comité de Protection des Personnes Sud-Est III). Patients were enrolled between 1 July 2007 and 30 July 2013. The attending physician was responsible for enrolling the patients in the study, and following the protocol. Written informed consent was obtained from the patients themselves or their closest relatives, prior to their inclusion.-1 body weight, with hypotension onset less than 6 hours before inclusion. An amendment to the original protocol extended the time from hypotension onset to 12 hours after inclusion of the first three patients.Eligible participants were adults aged 18 years or older with septic shock , who hadExclusion criteria were pregnancy, acute coronary syndrome, acute cerebral event during the previous 30 days, cardiogenic pulmonary edema, contraindication to central venous or femoral artery catheterization, uncontrolled hemorrhage, burn injury on more than 20% of the body surface, trauma, requirement for immediate surgery or radiological procedure, previous inclusion in present study, inclusion in another randomized controlled trial during the same ICU stay, advance directives to withhold or withdraw life-sustaining treatment, lack of written informed consent by patient or next of kin, lack of affiliation to social security as required by French regulation, patient under a legal protective measure.Patients were randomized into a control group, and a preload dependence group, with a computer-generated list using a 1:1 ratio. The allocation sequence was generated and concealed from the enrolling physician in sequentially numbered, opaque, and sealed envelopes, by a co-author (CG) who did not participate in the assessment of patient eligibility for the study.T) greater than 7 ml.kg-1 of predicted body weight (PBW), and absence of evidence for acute cor pulmonale (ACP)), or stroke volume change (ΔSV) during PLR in the preload dependence group. PLR was performed from the supine position, for 1 minute, and preload dependence was deemed present if stroke volume increased by at least 10% during the procedure. Intravascular volume expansion was administered by aliquots of 500 ml over 15 minutes, to achieve a CVP of at least 8 cm H2O in the control group, and a PPV below 13% or a stroke volume increase below 10% during PLR in the preload dependence group. The type of fluid used for intravascular volume expansion was left at the discretion of the attending physician. In both groups, MAP was maintained between 65 and 75 mm Hg using standardized vasopressors management. The same hematocrit (Ht) and cardiac index (CI) targets were used in both groups.Jugular central venous and femoral arterial lines were connected to the Picco plus device  for the first 31 patients, or an Intellivue MP40 monitor equipped with the Picco-Technology module thereafter . Calibration of cardiac output was performed by intravenous infusion of 15 mL serum saline in triplicate, every hour during the first 6 hours of the study, and every 4 hours thereafter. Hemodynamic treatments were administered according to an hemodynamic algorithm  and inspired oxygen fraction (FiO2) titrated using a PEEP-FIO2 table to achieve 88 to 95% pulse oximetry or 55 to 80 mm Hg partial pressure of arterial oxygen (PaO2) [-1. Diuretics use or fluid removal by hemodialysis was performed in case of fluid overload, and not recommended in the first 48 hours of septic shock.Concomitant treatments were administered according to international guidelines at the time of study design  (antibion (PaO2) ). SedatiThe following variables were recorded at inclusion: demographic and anthropometric data, time of hospital and ICU admission, context for admission to the ICU, immunodeficiency, Charlson comorbidity score , vital s2 and VT if applicable, and other relevant therapeutic interventions.The following variables were recorded at time of inclusion, 6 and 12 hours later and daily during follow-up: Sequential Organ Failure Assessment (SOFA) score , vital sin vitro activity against one or more pathogens that were judged to be responsible for the infection), and time to successful extubation (defined as no reintubation within the 48 hours following extubation).The following variables were recorded at the time of availability: source of infection, results of microbiological culture, adequacy of initial empirical antibiotherapy  or organ system failure (that is SOFA ≥6) from inclusion to end of study, ICU length of stay, and mortality at day 28.The primary outcome was time to shock resolution, defined by vasopressor weaning. Secondary outcomes were the followings: ventilator-free days at day 28, number of days with greater than normal plasma lactate, pulmonary edema (that is extravascular lung water index (ELWI) >10 ml.kgAll individual data were independently checked for accuracy by the Delegation à la Recherche Clinique des Hospices Civils de Lyon. Quality audits included validity control of the informed consent, compliance to the protocol, validity of data recorded in the case report form compared with the medical charts, validity of outcome assessment and accuracy of serious adverse events reporting. One of the co-authors (JCR) checked the adherence to hemodynamic and associated treatments protocols on a daily basis, and reported protocol violation if any.t test, ANOVA or Mann-Whitney U test for continuous or ordinal variables if indicated. The bias corrected and accelerated bootstrap method was used to compute confidence intervals for the difference in median times between groups [The expected duration of shock in the control group was 9 ± 2 days . We calcn groups . Repeaten groups , with pan groups , simplebn groups , survivan groups  and prodn groups .During the study inclusion period, 589 patients were admitted with septic shock, and 61 randomized Figure . One patP = 0.40).Both treatment arms were well balanced at both admission and inclusion Tables  and 2. T2) and vasopressor dose significantly decreased, without any significant difference between groups , while hemoglobin levels were not different between groups . A multivariate analysis of variables associated with the daily amount of intravascular volume expansion was performed using treatment arm, SAPS II, and the following variables at time of inclusion as predictors: SOFA score, lactates, body weight). SAPS II, treatment arm and their interaction were the only significant variables retained in the final model. As shown on Figure In the preload dependence group, intravascular volume expansion was indicated from PPV and PLR criteria in 4% and 96% of the cases, respectively. The reason for this difference is that those criteria required for PPV use were present in only 9% of the total number of hemodynamic algorithm sessions. The daily amount of fluids administered per protocol for intravascular volume expansion was significantly higher in the control group . Use of other associated treatments for septic shock was similar in both groups.Norepinephrine was used as a first-line vasopressor agent in all patients . Number of days with plasma lactate values higher than normal, pulmonary edema, or organ system failure and ICU length of stay were similar in both groups. Mortality was reduced by approximately 50% in the preload dependence group (23% vs. 47%), without reaching statistical significance (P = 0.10). Probability of survival until day 28  between groups. There was no significant difference in cause of death or end-of-life decisions between groups . Nine (30%) patients in the control and 13 (43%) patients in the preload dependence group were exposed to more than one protocol violation between inclusion and end of study (P = 0.42).There was no significant difference between treatment arms regarding protocol violations related to intravascular volume expansion  significantly reduces the daily amount of fluids intake and RBC transfusion, without adverse effect on outcome; (2) results in higher fluid administration in those patients with the most general severity score.2, tachycardia, mottling, metabolic acidosis, increased capillary refill time, low cardiac output, among others) and explicit implementation of these variables into an hemodynamic protocol to avoid co-intervention bias in a non-blinded study raises issues such as interobserver variability , or explicit definition of cutoff values . Nevertheless, the lack of tissue perfusion evaluation before fluid bolus was applied in both study arms and may not interfere with the effect (or lack of effect) on outcome. Eight, the study was erroneously registered after the inclusion of the last patient, ending up in a theoretical selective outcome reporting bias. Finally, we observed a relatively high mortality in the control group. However, mortality of the present study was in the range of recently published randomized controlled studies on severe sepsis  was not statistically significant. Therefore, these results should be addressed with caution and only viewed as exploratory for future studies.The beneficial effect on lactate decrease, that is the faster rate of decline observed in the preload dependence group, is in line with previous studies using dynamic preload indices performed in the perioperative setting -15. In cAn important finding of the present study is that driving intravascular fluid administration with preload dependance indices reduced the daily amount of administered fluids with, at most, no detrimental effect on outcome. The significant decrease in RBC transfusion requirement in the preload dependence arm may be a consequence of the lower amount of fluid administered in this group since hemoglobin levels were not significantly different between groups. Another important finding of this study is that preload dependence-driven fluid resuscitation resulted in a stepwise increase in the amount of administered fluids as patient severity increased Figure , as oppoThe present study did not find any significant effect of the intervention arm on shock duration, or any end point related to outcome. The interaction between patient severity and fluid administration may explain this finding since half of the intervention arm population was exposed to a more restrictive fluid resuscitation with no expected effect on shock duration and an expected beneficial effect on outcome criteria related to volume overload (the less severe patients), while the opposite was true in the more severe patients Figure , making T ventilation (see Additional file While we originally planned a combined use of PPV and PLR test to trigger fluid administration since many clinical scenarios preclude the use of PPV to test fluid responsiveness -33, we oTo sum up, in patients with septic shock titrating intravascular volume expansion with preload dependence indices did not change time to shock resolution but resulted in less daily fluids intake, including red blood cells, without worsening patient outcome.In septic shock patients, titrating intravascular volume expansion with preload dependence indices may have no effect on time to shock resolution.This strategy is, however, associated with a decrease in both the daily amount of intravascular fluids and red blood cell transfusion, with no outcome penalty.


Here, the fate of siRNAs was monitored by FRET-based live cell imaging. Subsequent to in situ observation of uptake and release processes, this approach allowed the observation of a number of hitherto uncharacterized intracellular distribution and degradation processes, commencing with a burst of endosomal releases, followed, in some cases, by fast siRNA influx into the nucleus. The continued observation of intact siRNA against a background of free fluorophores resulting from advanced degradation was possible by a specifically developed imaging algorithm, which identified populations of intact siRNA in pixels based on FRET. This proved to be essential in the end point definition of siRNA distribution, which typically featured partially degraded siRNA pools in perinuclear structures. Our results depict the initial 4 h as a critical time window, characterized by fast initial burst release into the cytosol, which lay the foundations for subsequent intracellular distribution of siRNA. Combination with a subsequent slower, but sustained release from endosomal reservoirs may contribute to the efficiency and duration of RNAi, and explain the success of lipopolyplexes in RNAi experiments in cell culture.Intracellular distribution of siRNA after  Furthers (EMSA) ,27. Howes (EMSA) ,28–29. Ts (EMSA) –32 or tos (EMSA) ,33–37. Fs (EMSA) ,34–35,3750 somewhat, the resulting siRNAs still show IC50 values of 700 pM, which is excellent activity and clearly validating the FRET-labeled siRNA as biologically active in the entire RNAi pathway. Knockdown was confirmed in different cell lines and with different dye labels  using FRET settings, and an algorithm was applied to color pixels according to the integrity levels of the siRNA contained. We observed several waves of release events into the cytosol and by determining averaged persistence times of fluorescent labeled intact siRNA in the cytosol, were able to establish a time frame for the availability of large quantities of intact siRNA for RNAi. This suggests that, at least in cell culture, the molecular basis for efficient RNAi relies on intracellular distribution events taking place during the initial 4 h after Fluorescent labeled siRNA was purchased from IBA GmbH . siRNA is directed against the mRNA of EGFP (GenBank accession U55762). Passenger or sense strand (s): 5′-GCA AGC UGA CCC UGA AGU UCA U-3′ and guiding or antisense strand (as): 5′-GAA CUU CAG GGU CAG CUU GCC G-3′. siRNA is 3′-labeled with Atto 488 or Fluorescein (FL) dye on the sense strand and 5′-labeled with Atto 590 dye or Tetramethylrhodamine (TMR) on the antisense strand.2PO4 and 8.06 mM Na2HPO4) and 5 μM sense strand. To account for imprecision of siRNA concentration the optimal hybridization ratio of Atto 488/Atto 590 was experimentally determined to be 1:1.2 (s : as) as earlier reported by non-denaturing PAGE analysis . FluoreFluorescence emission of siRNA samples was measured on a Jasco FP-6500 fluorimeter  equipped with a peltier element for heating and cooling. For routine emission scans, siRNA was diluted to final concentration of 0.1 μM in 1× PBS, and 17 μl were analyzed in 15 μl Suprasil quartz glass cuvettes  with 1.5 mm path length. Emission scans were performed at 25°C with the following parameters: 3 nm bandwidth of excitation and emission, data pitch of 1 nm, response of 0.5 s, scanning speed of 1000 nm/min, and spectral correction of photomultiplier and illumination lamp. Samples were excited at 488 nm, corresponding to donor excitation (Atto 488 and FL) and emission was recorded from 500 nm to 700 nm.siRNA samples simulating different integrity states were obtained by mixing double labeled siRNA in different ratios with an equal mixture of donor only and acceptor only labeled siRNA Figure . A 100% Emission profiles were recorded, background corrected and the R/G ratio was calculated as ratio of donor intensity at 520 nm and FRET emission at 625 nm.R/G values of different integrity levels were calculated and normalized to the 100% intact siRNA sample. For calibration of the system the degradation level was calculated.On the basis of the R/G ratio to degradation level relation a model of second-order exponential decay was fitted (for details see Supplementary Table S1).RBE4  cells weOne day prior to transfection 20 000 cells were seeded in antibiotic free medium in 8 well μ-slides . For transfection 8 pmol (1.6 μl) siRNA was diluted with 29 μl of indicator free DMEM:F12 medium  and mixed with 1.7 μl Oligofectamine® diluted with 7 μl DMEM:F12 indicator free medium. After 15 min of incubation 19.5 μl of the transfection mixture was added to the cells, now in 124.5 μl of indicator free medium. In case of a second transfection after 4–6 h, medium was replaced by fresh medium and transfection was performed as described.2 containing air flow. Images were acquired at 512 × 512 pixel resolution with a 63x oil immersion objective and as z-stacks of five sections at 1μm thickness with a pinhole diameter of 131 μm (1.4 AU). Resulting stacks have a dimension of 246 μm x 246 μm x 5μm X x Y x Z and were acquired in 2 × 2 to 3 × 3 tile scan with an average interval length of 1 to 2 min. For each position, two sequential images were acquired, I1 at a low HyD detector intensity of 10% and I2 at high detector intensity of 100% combined with a transmission image of the 488 nm laser at 25% hardware and 15% software setting. Donor signal between 510 and 540 nm and FRET signal between 605 and 635 nm were recorded simultaneously for a period of up to 8.5 h, consisting of up to 500 scan cycles. For Atto 590 detection, excitation was performed at 561 nm at 5% laser power and the emission was recorded between 605 and 635 nm, similar to the FRET channel.For live cell transfection μ-slides were placed on a Leica TCS STED CW , equipped with an incubation box, 488 nm, 561 nm and 633 laser lines, 2 HyD detectors and an adaptive focus control system. Image acquisition was started within 1 min after the addition of the transfection mixture. To guarantee live cell conditions, microscope and incubation box were preincubated at 37°C for at least 1 h and cells were incubated in a humidified 5% CODonor and FRET images of Atto 488/Atto 590 at high (I2) and low (I1) intensity were administered to R/G ratio processing algorithm. Thereby a pixel wise analysis, including thresholding, background correction and color coding of R/G values, of confocal data was performed as earlier published ,37 with On the basis of the transmission and fluorescence data, the cell outline and nucleus were manually segmented with ImageJ ROI manager.The raw data file  was The fraction of intact siRNA was obtained as a sum of the products of the pixel fraction and the corresponding integrity level. The integrity level of image R/G values was obtained by dividing the R/G by 5  and using the R/G calibration data.y-axis and time scale on x-axis. The length of a horizontal line represents the observed fluorescence time inside the cell. For further analysis only single release events and events showing a depletion within the observation time (full length events) were taken into account.Release rates were obtained by fitting a piecewise linear function to the start point of release and event number relation.Event duration times were converted into histogram data with a bin size of 5 min and average duration times were obtained by fitting Gaussian populations to local maxima of the distribution.The time of fluorescence persisting inside the cytosol was determined manually. Release events were sorted according to their time point of release and data plotted in a diagram with sorted event numbers on Each cell displaying a release event was segmented into cell and nucleus and mean fluorescence intensity (MFI) values for donor and FRET signal and mean R/G value was measured (ImageJ). An average of all z-slices resulted in the donor, FRET or R/G trace for the observed time point.50 value (700 pM) and produced a near complete knockdown in an EGFP-based reportergene assay .Extensive scrutiny of 5000 min of R/G imaging data obtained during 11 transfection experiments of a total of around 2500 rat brain endothelial cells (RBE4) , revealeThe different classes of events observed are (A) uptake of lipoplex structures containing R/G labeled siRNA, (B) release events characterized by rapid distribution of siRNA in the cytosol, (C) depletion of siRNA fluorescence from the cytosol upon continued incubation, (D) secondary (i.e. repeated) release events in the same cell, (E) accumulation of partially degraded siRNA in perinuclear structures, (F) transport of siRNA into nuclei and (G) persistence of siRNA reservoirs in small, presumably endosomal structures, accompanied by slow decay of the R/G index.In initial experiments longer imaging periods of up to 3 days were conducted that show releases occur at later time points, too, and that intact siRNA can be found inside cells even after 3 days of incubation .Fluorescent dots of high R/G ratio corresponding to lipoplex siRNA particles were settled onto the surface of the observations slide, as well as directly onto the cell surfaces, immediately at the beginning of the live cell experiment. In the latter case, internalization events could not be directly observed for lack of a clear discrimination of the inside versus the outside of a cell. However, already at the start of the observation window, fluorescent dots of intact siRNA were also in evidence inside cells, showing internalization to be a fast process. At least some, presumably most of the particles, were internalized by endosomal uptake or related mechanisms, as verified by colocalization experiments of single or double labeled siRNA and LysoTracker® .Direct observation of uptake events (class A event) was possible in those rare cases where moving cells came across a particle on the slide surface. Figure While spreading within the cytoplasm, the siRNA R/G index decayed from red toward green, signifying siRNA degradation to a certain degree. Progressive decay of the R/G signal upon continued dwelling in the cytosol was commonly observed in similar events, although the time scale of degradation varied . The observation implies that the cytoplasmic concentration of fluorophores dropped below the detection threshold, but it cannot be ruled out that low concentrations of siRNA persist for longer times. Of note, even at the time of depletion from the cytoplasm, most populations displayed an R/G value corresponding to populations of >80% intact siRNA . As a typical example, Figure In an attempt to further identify these perinuclear structures we performed a staining of mitochondria, lysosomes, ER and Golgi apparatus . While signals clearly do not colocalize with ER and mitochondria, some colocalization is observed with lysosomal compartments, and some more with the Golgi apparatus. The signals colocalizing with lysosomal markers are clearly more remote from the nucleus than the observed perinuclear structures, and presumably reflect early stages of continuously ongoing uptake events. Hence, the colocalization signals with the Golgi apparatus correspond at least in part to the perinuclear accumulation we observed. However, colocalization is still only partial and deeper investigations of this ‘compartment’ are necessary to identify their complete structure.Prompted by the observation of nuclear FRET signals in fixed cells during an earlier phase of our investigations  Supplem–D, F. HeIn > 85% of all cells, no release events were observed during up to 6 h of live cell imaging, yet most of the observed cells contained fluorescent lipoplex particles. At least some of these resided in endosomal structures of varying degrees of maturation , which reportedly develop substantial amounts of lytic enzymes, thought to degrade the siRNA payload to a significant degree. Examples of visualization of largely intact siRNA populations from such structures after several hours of live cell imaging implied, that the siRNA was protected against degradation at least for a certain time. Figure A cursory interpretation of class B events suggested that the cells do not display synchronized release behavior at determined time points after transfection, but it was observed that release events clustered during the first hour after addition of the siRNA lipoplexes. For a numerical assessment, all release events occurring during the total of ∼5000 observation minutes were quantified with respect to onset and duration Figure . In totaFigure A histogram of fluorescence persistence times in the cytosol (length of bars in Figure These observations point out a typical feature of single cell analysis, namely the heterogeneity of a cell population otherwise assumed to respond uniformly to a treatment. Of note, from permutations of early and late onset of release, and different persistence times alone, half a dozen different fates for siRNA in a cell arise.The optimization of RNAi efficiency and duration will be of continued interest for decades to come. While siRNA application is already extremely efficient and comparatively easy to implement, further improvements will open new fields in life science and therapy. While much of the initial progress in siRNA delivery was owed to try and error with RNAi efficiency as readout, further improvement is likely to come from a detailed understanding of the various events encountered by siRNA during internalization and release in a cell. Using live cell imaging, we observed and identified a number of distinct events that determine the intracellular fate of siRNA molecules after transfection. An improved understanding of the flow of a transfected siRNA population to either degradation or RISC incorporation conceivably might open avenues to manipulate those pathways. The present work was able to significantly advance our understanding of siRNA intracellular distribution because of an extended time frame accessible to live cell observation. This extended time frame was made possible by using FRET-labeled siRNA, which permits an assessment of the integrity/degradation status of the siRNA population in a given pixel of a CLSM image. While single labeled nucleic acids, including siRNA, are routinely observed , nucleolSome of the observed events, such as endosomal uptake and endosomal rupture, have previously been described by other techniques –47. In pSnapshots after 1, 2 and 3 days did not reveal any changes with respect to the state between 4 and 5 h after transfection. Thus, by being able to observe the degradation state of an extended timespan, we were able to determine that the most dynamic changes to intracellular distribution occur in the initial ∼60 min after transfection. These therefore constitute a crucial time window, during which the cytosol is flooded with large amounts of siRNA. Possibly, the high concentration of siRNA in many cells during this phase is conductive to chasing endogenous small RNAs from the assembled RISCs, which would make the fast burst release in phase 1 (Figure Other long-term events observed by us, such as siRNA degradation in lysosomes, have been inferred from lysosomal degradation of other biopolymers or merely been speculated upon ,49–53. HImportantly, the FRET-based approach used here does not allow to identify the precise mode of degradation, and can thus not discriminate siRNA incorporation into RISC from degradation by cellular nucleases. As a cautionary note, we point out that Argonaute loading is insufficient to explain the observed dynamics of integrity changes and cellular localization of siRNA in the light of the following numbers reported: approximately 300 siRNA molecules are required for efficient gene silencing , an estiClearly, the most intriguing and novel observations reported here occurred at advanced time points, and they too, are only made possible by R/G imaging. These other events, including in particular siRNA clearance from the cytosol, and accumulation of partially intact siRNA populations in perinuclear bodies, may be related to events described for other classes of RNAs in cell biology ,56–58, oSupplementary Data are available at NAR Online.


Cancer cells exhibit remarkable alterations in cellular metabolism, particularly in their nutrient substrate preference. We have devised several experimental methods that rapidly analyze the metabolic substrate flux in cancer cells: glycolysis and the oxidation of major fuel substrates glucose, glutamine, and fatty acids. Using the XF Extracellular Flux analyzer, these methods measure, in real-time, the oxygen consumption rate (OCR) and extracellular acidification rate (ECAR) of living cells in a microplate as they respond to substrates and metabolic perturbation agents. In proof-of-principle experiments, we analyzed substrate flux and mitochondrial bioenergetics of two human glioblastoma cell lines, SF188s and SF188f, which were derived from the same parental cell line but proliferate at slow and fast rates, respectively. These analyses led to three interesting observations: 1) both cell lines respired effectively with substantial endogenous substrate respiration; 2) SF188f cells underwent a significant shift from glycolytic to oxidative metabolism, along with a high rate of glutamine oxidation relative to SF188s cells; and 3) the mitochondrial proton leak-linked respiration of SF188f cells increased significantly compared to SF188s cells. It is plausible that the proton leak of SF188f cells may play a role in allowing continuous glutamine-fueled anaplerotic TCA cycle flux by partially uncoupling the TCA cycle from oxidative phosphorylation. Taken together, these rapid, sensitive and high-throughput substrate flux analysis methods introduce highly valuable approaches for developing a greater understanding of genetic and epigenetic pathways that regulate cellular metabolism, and the development of therapies that target cancer metabolism. Cancer cells significantly reprogram their metabolism to drive tumor growth and survival. Otto Warburg first observed that under aerobic conditions, tumors had high rates of glycolysis compared to the surrounding tissue, a phenomenon known as the Warburg effect, or aerobic glycolysis In view of numerous types of genetically and metabolically diverse cancer cells, a rapid, informative, relatively easy-to-perform and higher-throughput substrate flux analysis can facilitate greater understanding of the genetic and epigenetic pathways that regulate cancer cell metabolism, determining whether there is a finite number of metabolic phenotypes among all type of cancer cells, independent of tissue origin, and discovering agents that target specific metabolic pathways for cancer treatment.2 and H2O via the tricarboxylic acid cycle (TCA cycle) which requires the electron transport chain (ETC) in the mitochondria using oxygen as a terminal electron acceptor, and which is coupled to ATP production by oxidative phosphorylation. The CO2 produced can be converted to bicarbonate and protons as catalyzed by carbolic anhydrase Cells produce ATP via two major energy-producing pathways: glycolysis and oxidative phosphorylation. The glycolytic pathway converts glucose to pyruvate. One fate of the pyruvate is reduction to lactate in the cytosol in an oxygen-independent biochemical reaction resulting in ATP production and net proton production. Protons are pumped out of the cell by various mechanisms to maintain the intracellular pH 2 to the ETC. As the electrons flow down the ETC, the energy released is used to pump protons from the matrix into the intramembrane space, forming a transmembrane electrochemical proton gradient across the mitochondrial inner membrane. At the end of the ETC, the electrons are transferred to molecular oxygen, reducing it to water via the terminal cytochrome C oxidase. As protons return to the mitochondrial matrix through the ATP synthase complex, the energy stored in the proton gradient then drives the phosphorylation of ADP to ATP coupling respiration (electron transport) with ATP production. Oxidative phosphorylation, however, is incompletely coupled to respiration. Protons can also re-enter the matrix via proton channels such as uncoupling proteins (UCP), which are located on the inner membrane, bypassing ATP synthase and dissipating the energy gradient without producing ATP in a process known as proton leak. Partially reduced oxygen species (ROS) such as the superoxide anion can be produced at different sites in the ETC depending on conditions Since living cells do not store ATP, they produce it continuously and on demand, and therefore constantly consume oxygen and fuel substrates. Thus, the demand for ATP in cells (i.e. ADP availability) controls the rate of oxygen consumption. Electrons (energy) stored in nutrient substrates are extracted via the mitochondrial TCA cycle reactions and carried by reduced electron carriers NADH and FADHGiven the connection between oxygen consumption and extracellular acidification with nutrient substrate metabolism, increased oxygen consumption is a measure of substrate oxidation when a substrate is added to cells. Likewise, an increase in the rate of extracellular acidification upon glucose addition is a measure of glycolytic flux.2O and CO2 metabolized from substrates such as 3H labeled glucose and 14C labeled fatty acids. Other previously used (and more recently developed) methods to quantify metabolites are stable isotope tracers coupled with mass-spectrometry and NMR analysis, both of which have yielded detailed information on substrate metabolism. These techniques, however, can nevertheless be either labor intensive and cumbersome, and/or relatively inaccessible for many laboratories.Various traditional experimental methods that analyze substrate metabolism have contributed to the current understanding of cellular metabolism. These include measuring the accumulation of radio-labeled end products such as HThis study had two main objectives. The first was to apply the principles described above to establish a series of rapid and easy methods to analyze glycolytic flux and oxidative substrate flux of cancer cells. This was achieved by measuring cellular oxygen consumption rate and extracellular acidification using the XF Extracellular Flux analyzer Carbonyl cyanide 4-(trifluoromethoxy)phenylhydrazone (FCCP), myxothiazol, antimycin A, rotenone, 2-deoxyglucose, oxamate, aminooxyacetate, glucose, sodium pyruvate and sodium palmitate were obtained from Sigma . L-glutamine was obtained from Invitrogen . Oligomycin was obtained from EMD . Bovine Serum Albumin fraction V (fatty acid ultra-free) was obtained from Roche Diagnostics . All compounds and medium were prepared according to the manufacturers' instructions unless indicated otherwise.2 and 100% humidity at ∼80% confluence in 175 cm2 T-flasks (Corning). Human prostate cancer PC-3 and cervical cancer HeLa cells were purchased from American Type Culture Collection , and were maintained in RMPI1640. All cell culture media were supplemented with 10% fetal bovine albumin .Human glioblastoma SF188 cells were obtained from the University of California at San Francisco Brain Tissue Bank. These cells were originally maintained, as recommended by the originator, in an MEM medium containing 5.5 mM glucose and 2 mM L-glutamine. They were adapted step-wise to DMEM medium containing 25 mM glucose and 6 mM L-glutamine as a model system for the study of glutamine metabolism as reported A base medium was used for the assays described in this study directly or supplemented with substrates and cofactors as specified in each of the specific assays (see below) and for each experiment. The base assay medium was prepared as follows. Dulbecco's Modified Eagle's Medium (DMEM) powder  was the starting material. It contains no glucose, L-glutamine, sodium pyruvate, sodium bicarbonate, or phenol red, and has a low phosphate  or 5% CO2 (RMPI1640 and MEM medium), respectively. Because the two cell lines proliferate at different rates during the 24–28 hour incubation period, SF188s and SF188f cells were treated with trypsin and then counted to determine the cell number in each well after an assay. These cell counts were used to normalize either OCR or ECAR. Their viabilities, as determined after assays, were nearly indistinguishable regardless of the presence or absence of exogenous substrates or metabolic inhibitors in the assay medium. Prior to performing an assay, growth medium in the wells of an XF cell plate was exchanged with the appropriate assay medium to achieve a minimum of 1∶1000 dilution of growth medium. 600 µL (XF24) or 150 µL (XF96) of the assay medium was added to cells for an XF assay. While sensor cartridges were calibrated, cell plates were incubated in a 37°C/non-CO2 incubator for 60 minutes prior to the start of an assay. All experiments were performed at 37°C. Each measurement cycle consisted of a mixing time of 3 minutes and a data acquisition period of 3 minutes (13 data points) for the XF24, and 2 min and 4 min for the XF96. OCR and ECAR data points refer to the average rates during the measurement cycles. All compounds were prepared at appropriate concentrations in desired assay medium and adjusted to pH 7.4. A volume of 75 µL for XF24 (25 µL for XF96) of compound was added to each injection port. In a typical experiment, 3 baseline measurements were taken prior to the addition of any compound, and 3 response measurements were taken after the addition of each compound. OCR and ECAR were reported as absolute rates (pmoles/min for OCR and mpH/min for ECAR) or normalized against cell counts, or expressed as a percentage of the baseline oxygen consumption. In this study, baseline OCR or ECAR  refers to the starting rates prior to the addition of an agent, which can be used for comparisons with those rates after the addition. In contrast, basal OCR or ECAR  refers to OCR or ECAR which occur in cells at rest in order to maintain basic cell function. Unless otherwise specified, the third measurement of baseline or after addition of each substrate or compound was used to generate absolute OCR or ECAR values. As well, percentage of baseline OCR values was calculated as OCR at the third measurement after an agent injection divided by the OCR immediately before the injection. Each datum was determined minimally in triplicate.OCR and ECAR measurements were performed using the XF24 or XF96 Extracellular Flux analyzer  as described The assay medium consists of the base medium supplemented with 2 mM L-glutamine. Glutamine is required to achieve the maximal glycolysis rate for some, but not all cell lines. The same medium was used to determine glycolytic capacity. The concentration of glucose added to initiate glycolysis and measure glycolytic capacity was 10 mM, greater than the saturation point under both conditions.The assay medium was the base medium without any exogenous fuel substrate supplementation. The concentration of glucose added to initiate glucose oxidation was 10 mM, which was determined in preliminary experiments to be above saturation.The assay medium was the base medium without any exogenous fuel substrate. The concentration of glutamine added to the cells to initiate glutamine oxidation was 4 mM, which was also pre-determined to be above saturation.The assay medium was the base medium (or KHB ) supplemented with 5.5 mM glucose and 50 µM carnitine (required to transport long chain fatty acid into the mitochondria). Fatty acids tested include long chain fatty acid palmitate, medium chain fatty acid octanoate, and short chain fatty acid butyrate. They were titrated for concentrations stimulating maximal OCR response. The working concentration of palmitate conjugated with BSA was 150 µM, and octanoate 1 mM, which were also above saturation.It is critical that the above assay conditions are strictly adhered to. Any variation in the assay medium composition may result in different interpretations and insights into cellular metabolic network. Saturating substrate concentrations and optimal compound concentrations were determined by performing titration experiments as described in Materials S4 and S5 in SF188s and SF188f cells were detached with trypsin-EDTA and harvested immediately following an XF assay. The number of cells in each well was determined using a ViCell automated trypan blue counter , and was used to normalize OCR and ECAR as indicated in the figure legends.t test was performed for each experimental group to assess the statistical significance against respective controls.The data were presented as mean ± standard deviation, with at least three replicates used for each data point. Unless otherwise indicated, a paired Student's 2 evolution. In order to measure glycolysis using ECAR more accurately and easily, we took the following approach. Glucose is added to cells that are incubated in a glucose-free medium, but supplemented with glutamine (see 2 release (from any substrate but glucose) is detected as ECAR prior to glucose addition. The OCR response to glucose, monitored concurrently with ECAR, serves as an indicator of whether glucose is also catabolized through mitochondrial respiration  removing glucose from the assay medium and b) adding glycolytic pathway inhibitors such as hexokinase inhibitor 2-DG and lactate dehydrogenase (LDH) inhibitor oxamate mine see . The ECApiration .We performed the experiment using SF188s and HeLa cells. As shown in 2 evolution, we used a complex III inhibitor antimycin to stop the electron flow and thus the TCA cycle flux in HeLa cells. Glucose was added first to initiate glycolysis, followed by 2-DG to abolish it, leaving behind non-glycolytic ECAR. The final addition of antimycin stopped the TCA cycle from producing CO2. Although a residue remained, 4±1.4 mpH/min, antimycin eliminated about half of the non-glycolytic ECAR , but we did not observe any significant effect of CP91149 on non-glycolytic acidification (data not shown), leading us to conclude that the residual non-glycolytic ECAR had to be accounted for by other metabolic processes, such as decarboxylation reactions catalyzed by glucose-6 phosphate dehydrogenase and/or pyruvate dehydrogenase. Collectively, these results again confirmed that glycolysis accounts for the majority of ECAR observed in cancer cells, and established that TCA-derived CO2 is a primary contributor of non-glycolytic acidification.The two most significant proton sources that can contribute to non-glycolytic acidification are the TCA cycle and breakdown of intracellular glycogen, i.e., glycogenolysis tic ECAR , confirmin vitro at ambient oxygen levels reflects basal glycolysis rate. When cells experience loss of mitochondrial ATP production due to inhibition of oxidative phosphorylation, either at low oxygen tension or by oligomycin, they augment their glycolytic flux and make more ATP from glycolytic pathways to maintain cellular ATP homeostasis Glycolytic flux determined To determine both glycolytic flux and glycolytic capacity of the same cell population in one experiment, we measured ECAR while consecutively injecting glucose, oligomycin, and 2-DG. As shown in Two experimental conditions must be met in the above experiment. First, the oligomycin concentration should maximally inhibit respiration. This was achieved by selecting the oligomycin concentration that resulted in maximal OCR inhibition in a titration experiment. For example, 0.5 µM oligomycin was found to be sufficient to achieve maximal inhibition of OCR in HeLa cells (data not shown). Second, it is critical to ensure that the supply of exogenous glucose is saturating, allowing the glycolytic machinery to be the limiting factor. The saturating concentration of glucose for achieving maximal ECAR response was determined in a glucose concentration titration experiment, in which increasing concentrations of glucose were added to the cells, followed by the addition of control or oligomycin at the concentration that maximally inhibits respiration. In HeLa cells, for instance, we found that ECAR increased continuously until the glucose concentration reached 5 mM, after which there was no further ECAR increase . Similar2 and H2O , occurred in the absence of any exogenously added substrate in SF188f cells prior to glutamine addition . Similar2 and H2O (2 and H2O consuming oxygen is referred to here as fatty acid oxidation (FAO).Fatty acids are an important energy source for meeting a high energy demand and maintaining cellular functions in many cells, such as skeletal and cardio muscle cells. Fatty acid oxidation in cancer cells has also been reported but its relevance remains to be fully understood  and H2O . The proWe used increased OCR following injection of exogenously fatty acids to measure the oxidation of fatty acids of various chain lengths, including long, medium and short chains. SF188f cells, which effectively oxidize fatty acids, were chosen to develop the fatty acid oxidation assay. First, we tested the oxidation of the long-chain fatty acid palmitate. MYC amplification) Having established the methods to analyze glycolysis and oxidation of exogenous substrates, we performed proof-of-principle experiments in SF188s and SF188f cells see . These t4 cells, compared with SF188s cells, at 14.4±0.8 mpH/104 cells  than in SF188s cells (4.6±0.6 pmol/min/104 cells)  . To dist4 cells) , suggestGlucose oxidation of SF188s and SF188f cells was also examined. Glucose addition did not stimulate any increase in OCR in either cell line (data not shown) suggesting neither oxidizes glucose.Having established differential oxidation of glutamine and fatty acids in SF188s and SF188f cells, we proceeded to investigate the mitochondrial bioenergetics machinery In this study, we presented the concept, design, and development of several methods to analyze the glycolytic flux and oxidative flux of glucose, glutamine, and fatty acids in cancer cells. In the proof-of- principle experiments using human glioblastoma SF188s and SF188f cells, we provided initial evidence suggesting a mechanism that may allow continuous anaplerotic glutamine flux by increasing mitochondrial proton leak partially uncoupling the TCA cycle from oxidative phosphorylation in the fast proliferating SF188f cells.2 production  as we observed in HeLa cells . Thus in this experimental setting, glucose-induced ECAR increase is an accurate measurement of glycolytic flux. In any case where glucose increases the OCR, additional experiments such as the addition of a carbonic anhydrase inhibitor will help ascertain whether glucose-derived CO2 is converted to protons at all, and if so, how significant a contribution to ECAR it might be.Extracellular acidification, or ECAR, is comprised of glycolytic and non-glycolytic acidification. The former is the major contributor to total ECAR in cancer cells studied to date, while the latter accounts for significant albeit small fraction, which is mainly due to TCA cycle COoduction . Our expoduction  and 2. GLa cells  and 2A, The glycolytic capacity of cells under basal conditions upon which cells can draw in the face of increased energy demand, for example, a condition imposed by the loss of mitochondrial ATP, allows cells to maintain energy homeostasis and cellular function. By extension, it can serve as a mechanism by which cancer cells adapt to and survive under hypoxic conditions At least three mechanisms can explain glycolytic capacity. In the first, cells can simply augment glucose uptake and/or the activity of glycolytic enzymes. For example, glucose transporters have been shown to relocalize rapidly from the cytoplasm to the cell surface when under energy stress, resulting in an immediate increase in glucose uptake To produce ATP via oxidative phosphorylation, all nutrient substrates taken up by cells are ultimately oxidized via the TCA cycle and the ETC. In order to measure substrate oxidation, we applied this theory using the availability of an exogenous substrate  at above saturation concentration to control oxygen consumption . This principle is generally applicable to any metabolic substrate such as pyruvate, ketone bodies, and succinate (unpublished observations). Although being rapid and with higher throughput, these methods evidently do not yield as detailed information as metabolomics does. The overall metabolic insights gained, however, can either guide and/or be complementary to tracer-based metabolomics analysis to quantitatively follow substrate carbon flux through specific metabolic pathways Two technical points warrant further discussion. First, by selecting an above saturation substrate concentration in the substrate flux assays, the OCR or ECAR values are not governed by the amount of substrate available, but rather by the cells' ability to catabolize each substrate in meeting the demand of cell proliferation and maintaining basic cell functions. This experimental design makes it possible to evaluate substrate metabolism across many cell lines, regardless of the substrate concentrations in their growth media. For example, the growth media for SF188s and SF188f contained 2 and 6 mM glutamine, respectively; however, 4 mM (above saturation) glutamine, was used to compare glutamine oxidation rate of both . Second,4 cells from ∼40 to ∼60 pmoles/min/104 cell following glutamine addition. The glutamine oxidation results obtained here are consistent with previously reported high glutamine utilization in SF188 cells employing classical glutamine consumption method In this study, we not only determined basal respiration but also identified the type of exogenous nutrient substrates cells are able to oxidize and the rates at which they can be oxidized under the experimental conditions. Our results show that SF188f cells possess the ability to oxidize glutamine at a higher rate compared with SF188s cells. As shown in In addition to respiring on exogenously supplied substrates, we observed substantial respiration with endogenous substrates in cancer cells; for example, SF188f and PC-3 cells  and 4. AThe high rate of glutamine oxidation observed in SF188f cells was also accompanied by an unusually large proton leak. 48% of the cells' basal respiration was found to be diverted to proton leak, with a smaller fraction of 28% devoted to ATP production . We suggBy measuring OCR and ECAR responses of living cells to the addition of substrates along with metabolic perturbation agents, the methods presented here have cleared a path for easy and systematic analyses of metabolic substrate flux in wide range of cancer cell lines . These mFigure S1Determining saturating glucose concentration at which maximal ECAR response were achieved under both basal condition and inhibition of oxidative phosphorylation by oligomycin. A. Glucose at concentrations of 1, 5, 10 and 25 mM were injected sequentially to PC-3 cells. PC-3 cells were plated at 25,000/well in XF24 V7 culture plates. The assay medium was the base medium supplemented with 2 mM L-glutamine. The OCR values were not normalized. A representative experiment out of three is shown here. Each data point represents mean ± SD, n = 4. B. Oligomycin (1 µM) was added to PC3 cells in the same assay medium as in A followed by three sequential injections of glucose at concentrations of 5, 10, and 25 mM as indicated.(EPS)Click here for additional data file.Figure S2SF188f cells are able to oxidation short chain fatty acid bytyrate. A. OCR response of SF188s and SF188f cells to butyrate (0.3 mM). SF188s and SF188f cells were plated at 20,000 and 15,000 cells/well, respectively, in XF96 V3 cell culture plates 24–28 hours prior to the assays. The assay medium was the substrate-free base medium supplemented with 5.5 mM glucose and 50 µM carnitine. Fatty acid oxidation was expressed as % OCR and plotted using measurement 3 as the baseline. A representative experiment out of three is shown here. Each data point represents mean ± SD, n = 6.(EPS)Click here for additional data file.File S1Materials S1-S5.(DOCX)Click here for additional data file.


PCR-amplified partial 16S rRNA gene sequences made from soil DNA were subjected to pyrosequencing  were collected in 2009 from untreated (control), low nitrogen-amended (LN; 50 kg NHquencing  and analquencing . Signifi Soils harbor an immense diversity of bacteria . A vast In nature, microbes are vital contributors to biogeochemical transformations and hence examining their response to anthropomorphic activities over long periods is critical for understanding ecological processes . Several-1) than through natural biological fixation processes (110 Tg Y-1)  . These aand Mg2+ . Calciumand Mg2+ . These aand Mg2+ . Other rand Mg2+ , or a neand Mg2+  in micro1, experimental plots were set up in 1989 to study the long-term effects of N addition on above- and belowground communities  toolkit using the latest version available (1.4.0) at that time munities . Past stmunities . Soil frmunities . Restricmunities . Using pmunities . Major chat time  to clustIt has been suggested that there is substantial phylogenetic diversity in marine and soil environments attributable to the occurrence of rare bacterial populations . AlthougWith the aim of analyzing the effects of prolonged N treatment on individual bacterial groups, and identifying additional genera/families whose presence and/or abundance may be correlated with alterations in soil factors, we subjected our pyrosequencing dataset  to the o2, Petersham, MA, USA. The stand is comprised of predominantly red oak (Quercus rubra L) and black oak (Q. vetulina Lam.), mixed with red maple (Acer rubrum L.), American beech (Fagus grandifolia Ehrh.) and black birch (Betula lenta L.). Soil at this site is mostly stony to sandy loam formed from glacial till. For more details on site description including vegetation, climate, site topography, and N amendments refer to The study site is a mixed hardwood stand naturally regenerated after being clear-cut in 1945 and is located on Prospect Hill at the HF4NO3; low N , and high N . Ammonium nitrate solution was applied by a backpack sprayer yearly in six equal doses at 4-week intervals from May to September. Soil samples were collected from five randomly selected subplots within each treatment plot in September 2009 using a soil corer (7.5 cm diameter). The upper Org layer  was separated from the lower Min layer. In a few cases where the Org horizon was 10–12 cm deep, deeper coring was needed to get to the Min soil. Thirty samples (5 cores per plot × 2 horizons × 3 treatments) were collected in polyethylene bags and brought to the laboratory on ice. Samples were sieved (2 mm pore size) to remove roots, debris and stones, and then stored at -20∘C for further use.As described in our previous report , three 33 for analyses. Nitrate and NH4 -N were extracted in potassium chloride and determined colorimetrically by Ion Analyzer in 2012. The rest of the analyses were carried out in 2010 as described in Air-dried soil samples (20–40 g) were sent to the Soil Testing Service Laboratory at the University of Maine, Orono, ME, USA′AA CGC GAA GAACCT TAC3′ and R1401-1a 5′CGG TGT GTA CAA GGC CCG GGA ACG3′) as described in ∘C for 5 min, followed by 20 cycles of denaturation at 95∘C for 30 s, annealing at 61∘C for 30 s, and extension at 72∘C for 45 s, with a final extension at 72∘C for 10 min. The triplicate reaction products (amplicons) from each soil sample were pooled for sequencing. DNA purification kit  was used to purify the pooled PCR products which were then subjected to further cleaning via the Agencourt®; AMPure®; XP Bead Purification method  to remove fragments <100 bp. The quality of the PCR products were evaluated in an Agilent 2100 Bioanalyzer using the DNA 1000 LabChip . The 30 bar-coded samples were pooled in equimolar quantities . The sequences corresponding only to these IDs (selected at CT ≥0.8) were then extracted out by using the command filter.fasta.py (available in QIIME) and were further processed for oligotyping analyses. Selected sequences corresponding to a phylum or a subgroup/class were aligned using PyNAST  pyrosequencing data were quality filtered in QIIME (version 1.4.0) with default settings for most steps as described in g PyNAST . PyNAST g PyNAST  in QIIMEProteobacteria and Acidobacteria where oligotyping had to be conducted individually for each class or subgroup because among some of the subgroups there were several-fold differences in the number of sequences ; these required different M values (the minimum substantive abundance of the most abundant unique sequence in an oligotype) for analyses. To begin with all sequences were 430 bp in size; after alignment and filtering the gaps, the sizes of the aligned reads were different for different phyla  for relatively smaller datasets in the range of 200–5000 reads. In order to correct for technical errors due to pyrosequencing, the value for parameter ‘s’ (‘s’ is the minimum number of samples in which an oligotype is expected to be present) was set to two with the assumption that any sequence that occurs in two biological samples represents an element of a microbiome and is not an error. 6. For determining percent alignment scores, the sequences corresponding to individual oligotypes were extracted from the oligo-representatives directory and aligned using the ClustalW2 tool7.For supervised oligotyping, In one small comparative study with a subset of data, oligotype representative sequences were classified at CT ≥0.5 as well as ≥0.8 using the RDP classifier to determine if additional genera could be identified using a lower CT value.The oligotype representative sequences have been deposited in the NCBI short read archive. The accession numbers are presented in Table Actinobacteria, Bacteroidetes, Firmicutes, and Proteobacteria) were selected and clustered into OTUs using UCLUST (n size) for each OTU using the same value that was used for M in the corresponding oligotyping analyses. In addition, in order to match the parameter set for oligotyping (s = 2), OTUs that were not present in at least two samples were removed from the OTU table using python scripts . The OTU representative sequences were classified using RDP version 2.2 (currently used version) in QIIME.To compare the oligotyping method with OTU clustering, quality-filtered reads of four phyla , on the soil NH4 and NO3 data. Non-metric dimensional scaling (NMS) analyses were conducted using PC-ORD . To normalize the data, digit one was added to all data before log10 transformation. Briefly, following settings were used for NMS: number of axes = 4, maximum number of iterations = 500; stability criterion (the standard deviation in stress over the last 10 iterations) = 10-6; number of runs with real data = 100; and the number of runs with randomized data = 250. Random numbers were chosen as a source of starting ordinations. The tie handling was done by penalizing unequal ordination distance . The following were chosen as output options: varimax, randomization test, plot stress vs. iterations and calculate scores for OTUs by weighted averages. Dimensionality of solutions was selected for these analyses based on the assessment using a graph of stress as a function of dimensionality (scree plot). A Monte Carlo test was used to examine the stress and the strength of NMS results. Two-way permutational MANOVA was conducted using the Bray-Curtis distances to evaluate the effect of the horizons and the treatment, and the interaction between them. Mantel tests were conducted to evaluate the significance of correlations among Bray-Curtis distance scores and soil chemistry and soil Org N metabolites.SYSTAT  was used for standard statistical tests, including paired 4 concentration in the Org soil was significantly higher than that in the Min soil for all treatments (Table S3), there was no difference in NO3 levels between the two horizons. Long-term N treatment did not significantly alter either NH4 or NO3 concentrations of either soil horizon (Table S3). Other details on soil analyses are described in While NHSoils are highly diverse and harbor an abundance of rare microbes. Rare microbes are more prone to primer-PCR amplification and sequencing biases thus making it harder to identify such individuals. In addition, it is well known that soil replicates have high microsite variability in chemistry as well as bacterial populations. This combination of rarity and microsite variability perhaps is the reason that the same microbes are not present in all replicate samples, and why the guidelines suggested for other biomes in M is the value that is used to filter out potential noise in a sample. For this reason it is generally kept at a reasonably high number. It is likely that with forest soils, sequences representing rare taxa may be filtered out as noise due to their low abundance at high M values. In addition, high M values in such cases filtered out more than 50% of all sequences. The more diverse the phyla are (in terms of number of entropy components) more are the sequences that are filtered out with high M values . With the goal of retaining maximum sequences and diversity in terms of the number of oligotypes, several M values were tested for most phyla before final analyses. We tested different M values using α- and β-Proteobacteria, two well-known bacterial classes that are highly diverse and varied in the size of data for our soil samples with 38,858 and 3,340 sequences, respectively. The s value was set at two for both analyses. Lowering M values resulted in the identification of more genera at CT ≥0.8 in both classes (Table 1). For example at CT of ≥0.8, the total number of genera identified with an M = 75 for α-Proteobacteria was 5, but with M = 15, 11 genera were identified. Similarly, for β-Proteobacteria, M = 25 identified only two genera while M = 3 identified eight genera (Table 1) In α-Proteobacteria, genera such as Labrys and Acidisoma were identified with M = 15; they would be missed at higher M values. Similar results were obtained within β-Proteobacteria (Table 1). Therefore, final data analyses in this study were conducted using an M value that was based on two criteria, namely, the retention of maximum sequences, and maximum diversity (in terms of number of oligotypes) with taxonomic assignment at CT values no less than 0.8. Data presented here show that using high M values for groups that have low abundance would not identify genera with high confidence limits .Table 2). However, even when the oligotyping analysis appeared not fully resolved, the range of % identities was still within 99.76–100%. Table 2 shows two such examples: in one case there are two peaks with entropy values of <0.25, and in another case there is only one large peak with an entropy value at 0.65. In such cases, the small peaks were deliberately disregarded because their further decomposition did not result in additional oligotypes. The sequence identities within OTUs clustered using the same dataset ranged from 97 to 100% (results not shown).We compared the sequence identities within OTUs clustered at ≥97% similarity  in QIIMETable 3). Although the same data set was used for both analyses, genus level information for phyla such as Acidobacteria, Bacteroidetes, and Chloroflexi was generated in the present study. It should be pointed out that the use of an updated version (2.7) of RDP also revealed four previously unidentified phyla .Major motivation for examining the use of oligotyping as a tool was to reveal concealed microbial population diversity that could not be seen using the OTU clustering approach  with theTable 4). More genera were discernible when CT ≥0.5 was used (vs. ≥0.8) with oligotype representative sequences, (Table S4). One such example is the genus Terriglobus (Acidobacteria-Gp1), which was identified only at CT ≥0.5 .Whereas OTU clustering resulted in 2% of the OTUs containing ∼50% of the total sequences , for oliTable 3). Oligotyping revealed that sequences corresponding to some genera were present in control but absent in N-treated soils. In other instances, those present in N-amended plots were not found in control plots.Details on the various steps of oligotyping analysis and the number of oligotypes identified for each phylum are given in Table S2. In general, no direct correlation was observed between the numbers of sequences and the oligotypes. In all, sequences affiliated with 73 known genera were identified from these soils as compared to 27 genera observed in our previous study . In general, all five replicate soil samples from within the same treatment plot clustered together and displayed stronger similarities among their oligotypes as compared to those from other treatment plots. For the two largest phyla, Acidobacteria and Proteobacteria, NMS and Permanova analyses were conducted for individual subgroups and classes, respectively . With the exception of Bacteroidetes and subgroup Gp10 in Acidobacteria, the bacterial communities of the two horizons were significantly different. Additionally, within each soil horizon significant treatment effects on the structure of the bacterial community were observed for all phyla except for Chloroflexi, Firmicutes, TM7, Bacteroidetes, and subgroups Gp6 and Gp10 of Acidobacteria .Non-metric multidimensional scaling (NMS) followed by permutational MANOVA (Permanova) with ordination scores, i.e., the Bray-Curtis distance, obtained from normalized total oligotype data revealed significant differences among bacterial communities based on treatment and soil horizon  of the variation among samples, and treatment accounted for about 21% (P ≤ 0.0004) of the variation . Most of this variation was due to subgroups Gp1, Gp2, and Gp3 . In the second most diverse phylum, Proteobacteria, horizon explained 36% (P ≤ 0.0002) of variation among the samples and the treatment accounted for 16.5% (P ≤ 0.0006) of the variation; a major part of this variation was seen in α-and γ-Proteobacteria . Often there was an overlap between the LN- and HN-amended soil communities. Treatment effects were generally more pronounced in the Org soil horizon.The highest variation in partitioning was observed in the phylum 4, NO3, and total C (Table 5) as compared to Min soil. When data for each phylum were analyzed separately, significant correlations were observed for Acidobacteria, Proteobacteria, Actinobacteria, Verrucomicrobia, WPS-2, and AD3. Exceptions included Proteobacteria which showed no correlation with Ca and P, nor did Chlamydiae with NH4 and total C. The remaining phyla had correlations with fewer elements, specifics of which varied (Table 5).Mantel test results on soil chemistry and the bacterial community revealed strong correlations for the Org soil. Pooled oligotyping data from all phyla found in the Org soil horizon showed a stronger positive correlation between the entire bacterial community and soil pH, Ca, P, K, Zn, Mg, NH4, and NO3. With few exceptions, this was true for analyses of each individual phylum. There was a positive correlation of Al with Acidobacteria and WPS-2, and Ca with Proteobacteria (Table 5).Oligotyping data pooled for all analyzed phyla from the Min horizon community showed strong positive correlations only with soil pH, acidity, NHAmmoniphilus, Clostridium X1, Solibacillus, Sporosarcina, and Viridibacillus) were found in Min soils but were absent in Org soils, and one genus (Conexibacter) was identified in Org soils but not in Min soils . Overall, oligotypes corresponding to five genera  appeared exclusively in N-treated soils (in both horizons combined), while those representing eight genera  were present in the control but absent from the N-treated soils . A comparison of the presence of oligotypes specific to each genus revealed that some oligotypes were unique to Org soils while others were exclusively present in Min soils . Additionally some oligotypes were present only in the N-amended soils and not in the control plots. Although Figures and Tables show all identified genera, only those that exhibited significant differences with treatment or soil horizon are discussed in this section.Across all three treatments, oligotypes corresponding to five genera . Subgroups Gp1, Gp2, and Gp3 accounted for most of the Acidobacteria sequences and oligotypes. Soil samples from control plots had three times more sequences affiliated with Gp1 as compared to Gp3. However, based on the total number of oligotypes, the Gp3 subgroup was more diverse than Gp1 in both soil horizons . Gp13 was represented by a large number of oligotypes each containing small sets of sequences. Four new genera were identified in the phylum Acidobacteria since our last report with the same soil samples (Table 3). All of the genera identified in the Org horizon were represented by a significantly larger number of sequences as compared to those identified in the Min soil horizon for all three treatments . However, the number of oligotypes did not vary greatly between soil horizons. Sequences of the genus Edaphobacter were significantly higher in HN vs. the control while Acidobacterium, Granulicella, and Bryobacter showed a reverse trend in the Org horizon . Oligotypes for Edaphobacter were more abundant in N treatment plots relative to control in Min soil of both .Proteobacteria was the second most abundant phylum and was highly diverse in these soils. This phylum comprised ∼20% of the total sequences and total oligotypes (Table S2). Classes α-Proteobacteria and γ-Proteobacteria together accounted for >70% of this phylum’s sequences and oligotypes in each horizon . Regardless of the number of sequences, oligotyping data revealed that among all other classes and phyla, α-Proteobacteria were the most diverse in both soil horizons. In general, LN treatment was positively correlated with sequences and oligotype numbers. Altogether, 30 genera were found in Proteobacteria : 11 each in α- and γ-Proteobacteria , and eight in β-Proteobacteria . Fourteen out of these 30 genera were present in relatively high abundance (sequences ≥50). Shifts in community structure were observed among most genera in response to N treatments . An increase in the number of sequences was observed for several genera in soil treated with LN .Actinobacteria constituted only 7% of total sequences and 7.6% of oligotypes (Table S2). Eleven genera were identified from this phylum relative to the prior findings of seven (by OTU clustering) for the same data set (Table 3). The absence of certain genera in N-amended soils was observed in a few cases .Verrucomicrobia was the third most abundant phylum with ∼10% of sequences and ∼10% of oligotypes in these soils (Table S2). Only two genera (Table 3) from this phylum were identified. Genus Opitutus was present in tenfold higher numbers than Alterococcus . The number of sequences for Opitutus was lower in N-treated soils than in Control. For both Actinobacteria and Verrucomicrobia, horizon and treatment explained 44 and 10–13% of the total variation, respectively .Chlamydiae constituted 2.5% of the total sequences and ∼10% of oligotypes (Table S2). Three genera were identified in this phylum (Table 3); the relative abundance of oligotypes corresponding to the genus Parachlamydia were highest in N treatment plots relative to control in Org soil .Chloroflexi constituted 2.7% of the total sequences and 6.5 % of oligotypes (Table S2), with only two genera , which were absent in the control treatment in the Org horizon but present in the Min soils .Firmicutes constituted only about 0.3% of total sequences, with 3.0% oligotypes, 12 genera were identified in this phylum . Most of these were present in very low numbers and were more prevalent in Min soils ; all five of the genera seen in the Min soil horizon were absent in the Org soil horizon ; there was little effect of N treatments .Although Gemmatimonadetes, and Nitrospira comprised ∼1% of the total sequences and 2.4% of oligotypes (Table S2). From these three phyla, only two genera were identified; Gemmatimonas in Gemmatimonadetes and Nitrospira in Nitrospira . The number of sequences representing the genus Nitrospira was greater in Min vs. the Org soil horizon and in LN treatment vs. the control and HN treatment. While both treatment and horizon explained significant variation found in Gemmatimonadetes  only horizon-specific effects were seen for TM7 . NMS generated a four dimensional solution for TM7 (Table S5). Because of a small dataset, no further analyses were conducted on Nitrospira.Together the phyla TM7, , Cyanobacteria, Elusimicrobia, WPS-2, and AD3 were not identified in our previous report with the same data. TM6 was represented by only 36 sequences, most of which were filtered out in the initial oligotyping run and, therefore, were not analyzed further (Table S2). The other five phyla together constituted a relatively small portion of the total sequences  and 17.2% of the total oligotypes (Table S2). Among these phyla, WPS-2 was the most affected by treatment as well as by horizon . Only the genus Elusimicrobium was identified in Elusimicrobia .The phyla TM6Bacteroidetes contained only 259 sequences but 36 oligotypes, and six distinct genera were identified in this phylum; four of which were absent from N-amended soils . NMS analyses yielded only one dimension solution for this phylum, and thus no figure was generated (Table S5).Table 4), the former has greater resolving ability for classifying nearly identical, closely related organisms provided a good reference genomic library is available to compare against. Using entropy analysis, oligotyping simultaneously clusters multiple sequences based on similarity/identity of each nucleotide along the entire length of the read for all sequences within a given group. However, OTU groups sequences at 97% similarity with a representative sequence. The 3% difference in nucleotides may occur anywhere along the entire length of the read, and that location can vary from sequence to sequence within the group. That is to say, 2 sequences within an OTU can differ from the representative sequence at different nucleotide positions. Additionally, once a sequence has been selected and grouped within one OTU it cannot be assigned to another even if it has greater similarity with the representative sequence of the second OTU. It is this difference in the way sequences are clustered by these methods that makes oligotyping more powerful in grouping closely related organisms. While a relationship between oligotypes and most soil chemistry parameters was observed only a few such relationships were observed for OTU data by the earlier report  is the lack of reference genomes . In the present study, high diversity was evident from a large number of entropy peaks with high values for components for most phylum level datasets . This suggests that many organisms are probably present in low abundance (constituting the rare microbiome) in HF soil. Even with much smaller datasets, where M values of two or three were used, genera were identified at CT ≥0.8 . s = 2), then the chance of it being noise or a technical error is almost zero and thus should be considered as a sequence affiliated with a rare organism. Using this same dataset, s = 1 (default). Therefore, the present classification with oligotyping with s = 2 should be more reliable as compared to OTU clustering in eliminating noise.The results presented here demonstrate the applicability of oligotyping to complex microbiomes of forest soils. However, some adjustments may be necessary to the stringency of parameters that s Figure . TherefoTerriglobus  could only be identified at CT ≥0.5; sequences for this genus were found in all 30 samples and were significantly higher in HN-Org soils as compared to control. These observations suggest that the standard CT value of ≥0.8 at the genus level may need to be adjusted when working with the V6-V8 hypervariable regions of the 16S rRNA gene especially in ecosystems for which reference genome libraries are lacking.The primers used in the present study specifically target the V6–V8 region of 16S rRNA genes and were chosen due to the high sequence variability associated with this region . The pooElusimicrobia, Cyanobacteria, TM6, WPS-2 to the sequences that were termed unclassified in our previous study. No genera were identified within these phyla with the exception of Elusimicrobium. Most importantly, the overall unclassified sequences previously constituting 15–20% of the total were reduced to 0.5% in the current analysis.Available analytical tools and public databases, such as RDP, are constantly being updated to meet increasing demand for taxonomic classification arising from high throughput outputs created by next generation sequencing platforms , 2013. MAcidobacteria constituted >50% of total sequences, only four genera were identified in this phylum. The availability of reference genomes would be useful in further classifying this phylum; however, to date only eight genera have been taxonomically described in this phylum . Although HF soils may be nutrient limited, bacteria in the Org horizon are perhaps adapted to relatively nutrient-rich environment compared to those in the Min horizon. Our results demonstrate that with few exceptions, the Org soil communities were more impacted by N-treatment as compared to the Min soil communities. Compared to Min soil horizon, bacteria in the Org soils demonstrated stronger relationships with most of the soil chemistry parameters (Table 5). Although Acidobacteria found in soil from 30 forested and 27 grassland sites. They found either positive or negative correlations of different OTUs or T-RFLPs within respective subgroups of Acidobacteria over a wide pH and nutrient range. Another study involving 87 soil types with pH values ranging from 3.5 to 8.5 reported an overall inverse relationship between soil pH and the relative abundance of Acidobacteria . Our results are in agreement with those of Gp1 ideally requires moderately acidic conditions (pH 4.0–5.5).bacteria . However4 and NO3 concentrations is interesting and apparently contradictory. We speculate that this is due to the combined effects of the fast uptake of the fertilizer (applied only during the growing season) by the macroflora, its rapid conversion into other inorganic N and Org N metabolites , and leaching of a significant amount of applied N.Despite the existence of significant effects on aboveground foliar Org N metabolites , tree physiology and productivity , and chaNitrosospira of phylum Proteobacteria, which include well-known NH3 oxidizers/nitrifiers) appeared mostly in N-treated soils. Using the amplification of a functional N-transformation gene amoA, Nitrosospira sequences in response to N-treatments at a Chinese Agricultural Experimental Station. In our study the sequences and oligotypes corresponding to Nitrospira were higher in LN-treated Min soil. However, using 16S markers, Nitrospira  in response to long term N-fertilization at five forested sites in British Columbia, Canada.Polyamines are present in all living organisms. They are required for growth and are also involved in stress responses . PolyamiM value, we suggest that lowering M values may be appropriate for the complex microbiomes such as forest soils that are comprised of an enormous diversity of bacteria that are often present in low abundance. As suggested by Mantel test results, bacterial communities in the Org soil at HF have high preference for a nutrient rich environment and the communities found in the Min soil are better adapted to nutrient poor conditions. Overall, effects of N-treatment on the microbiome were more evident in the Org soil than the Min soil horizon, perhaps due to the fact that N utilization requires an abundance of C, which three times higher in the Org as compared to Min soil.A total of 46 previously unidentified genera were recognized by oligotyping vs. OTU clustering analysis of PCR-amplified partial 16S rDNA sequences from HF soil DNA. Because of the lack of a reference genome database for forest soils, both clustering approaches yield limited information at the genus and species level; however, oligotyping enables reliable classification of closely related organisms because of the high stringency of this tool. This analytical approach further revealed strong correlations between soil chemistry and oligotypes; no such correlations were discernible with the OTU clustering approach. Based on the fact that we could identify several genera at CT ≥0.98 using a relatively lower The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.