Tracer Analysis Research Articles

Biocatalyst developed with recovered iron-rich minerals enhances the biotransformation of SARS-CoV-2 antiviral drugs in anaerobic bioreactors

The biotransformation of the SARS-CoV-2 antiviral drugs, ribavirin and tenofovir, was studied in methanogenic bioreactors. The role of iron-rich minerals, recovered from a metallurgic effluent, on the biotransformation process was also assessed. Enrichment of anaerobic sludge with recovered minerals promoted superior removal efficiency for both antivirals (97.4 % and 94.7 % for ribavirin and tenofovir, respectively) as compared to the control bioreactor lacking minerals, which achieved 58.5 % and 37.9 % removal for the same drugs, respectively. Further analysis conducted by liquid chromatography coupled to mass spectroscopy revealed several metabolites derived from the biotransformation of both antivirals. Interestingly, tracer analysis with 13CH4 revealed that anaerobic methane oxidation coupled to Fe(III) reduction occurred in the enriched bioreactor, which was reflected in a lower content of methane in the biogas produced from this system, as compared to the control bioreactor. This treatment proposal is suitable within the circular economy concept, in which recovered metals from an industrial wastewater are applied in bioreactors to create a biocatalyst for promoting the biotransformation of emerging pollutants. This strategy may be appropriate for the anaerobic treatment of wastewaters originated from hospitals, as well as from the pharmaceutical and chemical sectors.

Leading-Digit Bias Influences the Decision to Transfuse Hospitalized Patients

Leading-digit bias is a heuristic whereby people overemphasize the left-most digit when evaluating numbers (eg 19.99 vs 20). We hypothesized that leading-digit bias influences the interpretation of hemoglobin (Hb) values and therefore red cell transfusion decisions among hospitalized patients. To test our hypothesis, we examined all inpatient red cell transfusions at the University Health Network from January 2016 to September 2021 (n=226,378 units; 4 months missing). We performed a retrospective analysis of pre-transfusion Hb levels compared the frequency of transfusion at 79 vs. 80 g/L in a quadratic regression discontinuity design. Secondary analysis explored other leading-digit thresholds and Benford numbers served as tracer analyses. All analyses performed using R-Studio Version 4.0.2. Our primary analysis included 9,585 red cell units, of which 1,150 were transfused at Hb=79 and 610 at Hb=80. Regression discontinuity analysis showed the threshold accounted for 572 additional red cell units transfused (p=0.006) (Figure 1). The effect was consistent across all subgroups larger than 5000, present in data with and without exclusions, and absent for Benford numbers. Our study is the first to establish leading-digit bias in red cell transfusion. Our findings might be translated into research utilizing quasi-experimental methodology and inform guidelines for transfusion thresholds. Figure 1. Unadjusted regression discontinuity analysis of pre-transfusion hemoglobin (g/L) comparing transfusion frequency above and below bias threshold Figure 1View largeDownload PPTFigure 1View largeDownload PPT Close modal

Effects of 6-O-α-maltosyl-β cyclodextrin on lipid metabolism in Npc1-deficient Chinese hamster ovary cells.

Niemann-Pick disease Type C (NPC) is a lysosomal storage disorder caused by mutation of the NPC1/NPC2 genes, which ultimately results in the accumulation of unesterified cholesterol (UEC) in lysosomes, thereby inducing symptoms such as progressive neurodegeneration and hepatosplenomegaly. This study determines the effects of 6-O-α-maltosyl-β cyclodextrin (Mal-βCD) on lipid levels and synthesis in Npc1-deficient (Npc1-KO cells) and vehicle CHO cells. Compared to vehicle cells, Npc1-KO cells exhibited high level of UEC, and low levels of esterified cholesterols (ECs) and long-chain fatty acids (LCFAs). The difference in lipid levels between Npc1-KO and CHO cells was largely ameliorated by Mal-βCD administration. Moreover, the effects of Mal-βCD were reproduced in the lysosomes prepared from Npc1-KO cells. Stable isotope tracer analysis with extracellular addition of D4-deuterated palmitic acid (D4-PA) to Npc1-KO cells increased the synthesis of D4-deuterated LCFAs (D4-LCFAs) and D4-deuterated ECs (D4-ECs) in a Mal-βCD-dependent manner. Simultaneous addition of D6-deuterated UEC (D6-UEC) and D4-PA promoted the Mal-βCD-dependent synthesis of D6-/D4-ECs, consisting of D6-UEC and D4-PA, D4-deuterated stearic acid, or D4-deuterated myristic acid, in Npc1-KO cells. These results suggest that Mal-βCD helps to maintain normal lipid metabolism by restoring balance among UEC, ECs, and LCFAs through acting on behalf of NPC1 in Npc1-KO cells and may therefore be useful in designing effective therapies for NPC.

Classification Analysis of Waiting Period for Telkom University Alumni to Get Jobs Using Decision Tree and Support Vector Machine

Tracer analysis is one of the ways to increase a university's accreditation. Tracer studies, also known as graduate surveys, are beneficial for enhancing learning and developing university curricula. The period it takes graduates to secure employment is a measure of their quality. The sooner graduates obtain a job, the higher their perceived quality. Conversely, if it takes graduates longer to find employment, their quality is deemed lower. To gain new knowledge from the tracer study dataset regarding the relationship between university contribution and alumni capability in the job market, in this study, data mining techniques are used to determine what factors influence the length of time it takes college graduates to find employment. This classification model contains a total of 2288 data instances from the categorical type of dataset. The features are selected using chi-square. Two classification algorithms, Decision Tree and Support Vector Machine, are compared for the best model. This study also used hyperparameter tuning to improve accuracy. The results show decision tree produces higher accuracy compared to the support vector machine. The accuracy obtained from the decision tree model is 55.02% and increased to 65.06% after hyperparameter tuning. Meanwhile, the support vector machine brought an accuracy of 60.40% and increased to 62.15% after hyperparameter tuning. Factors that affect the classification of the alumni waiting period in getting a job in this study are sex, faculty of the study field, department of the study field, study period, company specification, company category, and work location.

Methane Production by Facultative Anaerobic Wood-Rot Fungi via a New Halomethane-Dependent Pathway.

ABSTRACTThe greenhouse gas methane (CH4) is of pivotal importance for Earth’s climate system and as a human energy source. A significant fraction of this CH4 is produced by anaerobic Archaea. Here, we describe the first CH4 production by facultative anaerobic wood-rot fungi during growth on hydroxylated/carboxylated aromatic compounds, including lignin and lignite. The amount of CH4 produced by fungi is positively correlated with the amount of CH3Cl produced during the rapid growth period of the fungus. Biochemical, genetic, and stable isotopic tracer analyses reveal the existence of a novel halomethane-dependent fungal CH4 production pathway during the degradation of phenol and benzoic acid monomers and polymers and utilization of cyclic sugars. Even though this halomethane-dependent pathway may only play a side role in anaerobic fungal activity, it could represent a globally significant, previously overlooked source of biogenic CH4 in natural ecosystems.IMPORTANCE Here, we demonstrate that wood-rot fungi produce methane anaerobically without the involvement of methanogenic archaea via a new, halomethane-dependent pathway. These findings of an anaerobic fungal methane formation pathway open another avenue in methane research and will further assist with current efforts in the identification of the processes involved and their ecological implications.

Integrated application of a Bayesian mixing model, numerical model, and environmental tracers to characterize groundwater recharge sources in a mountainous area

In this study, the combined use of a Bayesian mixing model (BMM), numerical model (random walk particle tracking—RWPT), and environmental tracers (δ18O–δD, 3H, and CFC) was applied to elucidate the probabilistic contribution of the recharge sources, flow path, and residence time of groundwater across the mountainous area of Jeju Island, South Korea. Especially, the BMM ability to estimate the variable recharge contributions to the aquifer by different elevations and seasons was investigated. The δ18O–δD isotopes showed that groundwater in the study area was primarily fed by precipitation during the wet season, and the BMM estimated that wet season recharge contributed to approximately 64% of the total. The BMM-based probabilistic estimation of recharge sources revealed a mixed contribution of source waters from different elevations. A notable difference in recharge flow path was observed between highland (>450 masl) and lowland (<400 masl) wells, where the inflow of source water from the regional flow was dominant in the former and both regional and local recharges served as significant groundwater sources in the latter. Evidence from age tracers (3H and CFC-12) also supported different recharge mechanisms between highland and lowland wells. A reasonable match between the BMM- and RWPT-derived recharge contributions (RMSE 0.02–0.06) was achieved within the uncertainty ranges, with RWPT being particularly useful for capturing different flow paths between highland and lowland wells. The dynamics revealed here provide important information for establishing an improved and informed groundwater management plan for the mountainous area of Jeju Island. Ultimately, this study highlights the advantageous integrated analysis of BMM, RWPT, and environmental tracer analyses to enhance the reliability of recharge area estimation and increase the collective understanding of complex hydrogeological systems in mountainous areas.

Generation of an Escherichia coli strain growing on methanol via the ribulose monophosphate cycle

Methanol is a liquid with high energy storage capacity that holds promise as an alternative substrate to replace sugars in the biotechnology industry. It can be produced from CO2 or methane and its use does not compete with food and animal feed production. However, there are currently only limited biotechnological options for the valorization of methanol, which hinders its widespread adoption. Here, we report the conversion of the industrial platform organism Escherichia coli into a synthetic methylotroph that assimilates methanol via the energy efficient ribulose monophosphate cycle. Methylotrophy is achieved after evolution of a methanol-dependent E. coli strain over 250 generations in continuous chemostat culture. We demonstrate growth on methanol and biomass formation exclusively from the one-carbon source by 13C isotopic tracer analysis. In line with computational modeling, the methylotrophic E. coli strain optimizes methanol oxidation by upregulation of an improved methanol dehydrogenase, increasing ribulose monophosphate cycle activity, channeling carbon flux through the Entner-Doudoroff pathway and downregulating tricarboxylic acid cycle enzymes. En route towards sustainable bioproduction processes, our work lays the foundation for the efficient utilization of methanol as the dominant carbon and energy resource.

Anaerobic ammonium oxidation coupled to arsenate reduction, a novel biogeochemical process observed in arsenic-contaminated paddy soil

Anaerobic ammonium oxidation represents an important pathway of N loss, which can be coupled with reduction of nitrite and metal(loid)s (e.g., Fe(III) and Mn(IV)). Similar to Fe(III) and Mn(IV), As(V) is also an active metal(loid) and ammonium oxidation coupled with As(V) reduction is thermodynamically feasible. However, little is known about this potential process. In this study, anaerobic ammonium oxidation coupled with As(V) reduction, designated as Asammox, was observed in cultures inoculated from As-contaminated paddy soil using 15N isotope tracer analysis. Compared with the treatment amended with 15N-urea only, the production of 15N-labeled N2 (i.e., 30N2 and 29N2) was significantly greater in the treatment amended with As(V) and 15N-urea. Furthermore, the abundances of the genes encoding for arsenate reductase (arrA) and hydrazine synthase (hzsB) were significantly higher in the treatment amended with As(V) and 15N-urea than those in the treatment amended with 15N-urea only. In addition, putative Asammox bacteria affiliated with Halomonas, Pelagibacterium, and Chelativorans were identified by DNA-stable isotope probing. Members of Ca. Brocadia were the most dominant Anammox bacteria in the soil cultures and may interact with Asammox bacteria in ammonium oxidation, suggesting that the N loss may be attributed to the contribution of Asammox and Anammox in the As-contaminated soil. The observation of Asammox, a novel biogeochemical process, and identification of bacteria responsible for this biogeochemical process expands the fundamental understanding of both N and As biogeochemical cycling. In addition, this study provides a proof-of-concept for investigating anaerobic ammonium oxidation coupled with metal(loid)s reduction by combining stable isotope probing and isotope tracer microcosm incubations.

Exploring the Functions of Efficient Canonical Denitrifying Bacteria as N2O Sinks: Implications from 15N Tracer and Transcriptome Analyses.

In denitrifying reactors, canonical complete denitrifying bacteria reduce nitrate (NO3-) to nitrogen via N2O. However, they can also produce N2O under certain conditions. We used a 15N tracer method, in which 15N-labeled NO3-/nitrite (NO2-) and nonlabeled N2O were simultaneously supplied with organic electron donors to five canonical complete denitrifying bacteria affiliated with either Clade I or Clade II nosZ. We calculated their NO3-, NO2-, and N2O consumption rates. The Clade II nosZ bacterium Azospira sp. strain I13 had the highest N2O consumption rate (3.47 ± 0.07 fmol/cell/h) and the second lowest NO3- consumption rate (0.20 ± 0.03 fmol/cell/h); hence, it is a N2O sink. A change from peptone- to acetate/citrate-based organic electron donors increased the NO3- consumption rate by 4.8 fold but barely affected the N2O consumption rate. Electron flow was directed to N2O rather than NO3- in Azospira sp. strain I13 and Az. oryzae strain PS only exerting a N2O sink but to NO3- in the Clade I nosZ N2O-reducing bacteria Pseudomonas stutzeri strain JCM 5965 and Alicycliphilus denitrificans strain I51. Transcriptome analyses revealed that the genotype could not fully describe the phenotype. The results show that N2O production and consumption differ among canonical denitrifying bacteria and will be useful for developing N2O mitigation strategies.

Combined effects of biochar and biogas slurry on soil nitrogen transformation rates and N2O emission in a subtropical poplar plantation

It has been widely accepted that biochar has a great potential of mitigating soil nitrous oxide (N2O) emission. However, the underlying mechanism about how biochar affects nitrogen transformation and the pathways of soil N2O production is under discussion. A 15N-tracer incubation experiment was conducted to investigate the short-term effects of biochar on soil N transformation rates and source partitioning of N2O emissions in soils from a poplar plantation system. A two-factor experimental design was adopted using biogas digestate slurry and biochar as soil amendments. In total, there were 12 treatments, including three rates of biochar: B0 (control), B2 (80 t ha−1), and B3 (120 t ha−1), and four rates of biogas digestate slurry: C (0 m3 ha−1), L (125 m3 ha−1), M (250 m3 ha−1), and H (375 m3 ha−1). We observed significantly lower rates of net nitrification (Nn) and mineralization (Mn) in biochar-treated soils. The 15N tracer analysis revealed a significant decrease in gross autotrophic (ONH4), heterotrophic nitrification (ONrec), and mineralization (MNorg) rates while an increase in gross immobilization (INH4 and INO3) rates in biochar amended soils. When biogas slurry was applied, biochar only significantly reduced ONH4 except in the moderate slurry treatment. Regardless of the slurry application, biochar consistently suppressed N2O emission by 58–89 %, and nitrification was the dominant pathway accounting contributing >90 % to cumulative N2O emissions. Moreover, soil cumulative N2O emissions significantly negatively correlated with soil ammonium contents and positively with MNorg, Mn, and Nn, showing that biochar decreased N2O emission via a reducing effect on nitrification rates and associated N2O emissions. Our results also highlight that application of N fertilizer greatly influence the biochar's impacts on soil N transformation rates and N2O emission, calling for further studies on their interactions to develop mitigate options and to improve N use efficiency.

Mitochondrial hyperfusion via metabolic sensing of regulatory amino acids

The relationship between nutrient starvation and mitochondrial dynamics is poorly understood. We find that cells facing amino acid starvation display clear mitochondrial fusion as a means to evade mitophagy. Surprisingly, further supplementation of glutamine (Q), leucine (L), and arginine (R)did not reverse, but produced stronger mitochondrial hyperfusion. Interestingly, the hyperfusion response to Q+ L+ R was dependent upon mitochondrial fusion proteins Mfn1 and Opa1 but was independent of MTORC1. Metabolite profiling indicates that Q+ L+ R addback replenishes amino acid and nucleotide pools. Inhibition of fumarate hydratase, glutaminolysis, or inosine monophosphate dehydrogenase all block Q+ L+ R-dependent mitochondrial hyperfusion, which suggests critical roles for the tricarboxylic acid (TCA) cycle and purine biosynthesis in this response. Metabolic tracer analyses further support the idea that supplemented Q promotes purine biosynthesis by serving as a donor of amine groups. We thus describe a metabolic mechanism for direct sensing of cellular amino acids to control mitochondrial fusion and cell fate.

Nanomechanical and Morphological AFM Mapping of Normal Tissues and Tumors on Live Brain Slices Using Specially Designed Embedding Matrix and Laser-Shaped Cantilevers.

Cell and tissue nanomechanics has been intriguingly introduced into biomedical research, not only complementing traditional immunophenotyping and molecular analysis, but also bringing unexpected new insights for clinical diagnostics and bioengineering. However, despite the progress in the study of individual cells in culture by atomic force microscopy (AFM), its application for mapping live tissues has a number of technical limitations. Here, we elaborate a new technique to study live slices of normal brain tissue and tumors by combining morphological and nanomechanical AFM mapping in high throughput scanning mode, in contrast to the typically utilized force spectroscopy mode based on single-point probe application. This became possible due to the combined use of an appropriate embedding matrix for vibratomy and originally modified AFM probes. The embedding matrix composition was carefully developed by regulating the amounts of agar and collagen I to reach optimal viscoelastic properties for obtaining high-quality live slices that meet AFM requirements. AFM tips were rounded by irradiating them with focused nanosecond laser pulses, while the resulting tip morphology was verified by scanning electron microscopy. Live slices preparation and AFM investigation take only 55 min and could be combined with a vital cell tracer analysis or immunostaining, thus making it promising for biomedical research and clinical diagnostics.

Particle phase-state variability in the North Atlantic free troposphere during summertime is determined by atmospheric transport patterns and sources

Abstract. Free tropospheric aerosol particles have important but poorly constrained climate effects due to transformations of their physicochemical properties during long-range transport. In this study, we investigate the chemical composition and provide an overview of the phase states of individual particles that have undergone long-range transport over the North Atlantic Ocean in June and July 2014, 2015, and 2017 to the Observatory of Mount Pico (OMP) in the Azores. The OMP is an ideal site for studying long-range-transported free tropospheric particles because local emissions have a negligible influence and contributions from the boundary layer are rare. We used the FLEXible PARTicle Lagrangian particle dispersion model (FLEXPART) to determine the origins and transport trajectories of sampled air masses and found that most of them originated from North America and recirculated over the North Atlantic Ocean. The FLEXPART analysis showed that the sampled air masses were highly aged (average plume age &gt;10 d). Size-resolved chemical compositions of individual particles were probed using computer-controlled scanning electron microscopy with an energy-dispersive X-ray spectrometer (CCSEM-EDX) and scanning transmission X-ray microscopy with near-edge X-ray absorption fine structure spectroscopy (STXM-NEXAFS). CCSEM-EDX results showed that the most abundant particle types were carbonaceous (∼ 29.9 % to 82.0 %), sea salt (∼ 0.3 % to 31.6 %), and sea salt with sulfate (∼ 2.4 % to 31.5 %). We used a tilted stage interfaced within an environmental scanning electron microscope (ESEM) to determine the phase states of individual submicron particles. We found that most particles (∼ 47 % to 99 %) were in the liquid state at the time of collection due to inorganic inclusions. Moreover, we also observed substantial fractions of solid and semisolid particles (∼ 0 % to 30 % and ∼ 1 % to 42 %, respectively) during different transport patterns and events, reflecting the particles' phase-state variability for different atmospheric transport events and sources. Combining phase state measurements with FLEXPART CO tracer analysis, we found that wildfire-influenced plumes can result in particles with a wide range of viscosities after long-range transport in the free troposphere. We also used temperature and RH values extracted from the Global Forecast System (GFS) along the FLEXPART-simulated path to predict the phase state of the particles during transport and found that neglecting internal mixing with inorganics would lead to an overestimation of the viscosity of free tropospheric particles. Our findings warrant future investigation aiming at the quantitative assessment of the influence of internal mixing on the phase states of the individual particles. This study also provides insights into the chemical composition and phase state of free tropospheric particles, which can help models to reduce uncertainties about the effects of ambient aerosol particles on climate.

Particle tracer analysis for PIV experiments in a closed loop transonic wind tunnel

A performance study of several tracer particles for PIV has been conducted. The study included particle response time analysis of an oblique shock wave generated by a conical nose of a store model in a closed loop transonic wind tunnel, and in-situ measurements of the particle size distribution in a low-speed wind tunnel facility. From the oblique shock wave analysis, the estimated particle response time for different smoke materials and generator settings was of the order of 1.4 - 2.2 μs and the effective particle size was 0.58 - 0.85 μm, which was much larger than the measured geometric mean particle size. This larger size was mainly attributed to the correlation bias towards larger particles in PIV measurements. The effective particle size from the particle response time analysis accounts for the holistic performance of the PIV system. Additionally, particle response analysis was performed on ambient water droplets resident in the tunnel. The estimated particle response time for the water droplets was in the order of 3.4 μs and the effective particle size was 0.97 μm, revealing inferior performance compared to the smoke particles.

13C tracer analysis suggests extensive recycling of endogenous CO2 in vivo

Background13C tracer analysis is increasingly used to monitor cellular metabolism in vivo and in intact cells, but data interpretation is still the key element to unveil the complexity of metabolic activities. The distinct 13C labeling patterns (e.g., M + 1 species in vivo but not in vitro) of metabolites from [U-13C]-glucose or [U-13C]-glutamine tracing in vivo and in vitro have been previously reported by multiple groups. However, the reason for the difference in the M + 1 species between in vivo and in vitro experiments remains poorly understood.MethodsWe have performed [U-13C]-glucose and [U-13C]-glutamine tracing in sarcoma-bearing mice (in vivo) and in cancer cell lines (in vitro). 13C enrichment of metabolites in cultured cells and tissues was determined by LC coupled with high-resolution mass spectrometry (LC-HRMS). All p-values are obtained from the Student’s t-test two-tailed using GraphPad Prism 8 unless otherwise noted.ResultsWe observed distinct enrichment patterns of tricarboxylic acid cycle intermediates in vivo and in vitro. As expected, citrate M + 2 or M + 4 was the dominant mass isotopologue in vitro. However, citrate M + 1 was unexpectedly the dominant isotopologue in mice receiving [U-13C]-glucose or [U-13C]-glutamine infusion, but not in cultured cells. Our results are consistent with a model where the difference in M + 1 species is due to the different sources of CO2 in vivo and in vitro, which was largely overlooked in the past. In addition, a time course study shows the generation of high abundance citrate M + 1 in plasma of mice as early as few minutes after [U-13C]-glucose infusion.ConclusionsAltogether, our results show that recycling of endogenous CO2 is substantial in vivo. The production and recycling of 13CO2 from the decarboxylation of [U-13C]-glucose or [U-13C]-glutamine is negligible in vitro partially due to dilution by the exogenous HCO3−/CO2 source, but in vivo incorporation of endogenous 13CO2 into M + 1 metabolites is substantial and should be considered. These findings provide a new paradigm to understand carbon atom transformations in vivo and should be taken into account when developing mathematical models to better reflect carbon flux.

SAT104 - The effect of glucagon on rates of hepatic mitochondrial oxidation and pyruvate carboxylase flux in man assessed by positional isotopomer tracer analysis (PINTA)

SAT104 - The effect of glucagon on rates of hepatic mitochondrial oxidation and pyruvate carboxylase flux in man assessed by positional isotopomer tracer analysis (PINTA)

Abstract 3030: Integrated in vitro and in silico screening of breast cancer reveals serine metabolism as a precision oncology target

Abstract Cellular metabolism sits at the forefront of growth and has been identified as an essential element for the progression of cancer. Large-scale chromosomal alterations and mutations incurred by cancerous cells can predispose cancer cells to dysfunctional metabolic programming independent of microenvironmental pressure. Cancer cells are adaptable to this reprogramming and find alternative measures to sustain elevated metabolic demands. However, this process reduces the number of functional metabolic pathways. Dysregulated metabolic pathways within cancerous cells create an opportunity for therapeutic intervention by targeting genetic chokepoints within these pathways. We analyzed metabolic landscape of breast cancers by combining genomic and transcriptomic data from cancer patient datasets, to identify metabolic pathways prone to dysregulation due to genomic influence. Our platform identified disruption in the serine biosynthesis, which some cancerous cells rely upon to sustain elevated growth in a subset of breast carcinoma. We utilized gain- and loss-of-function techniques in multiple breast cancer cell lines to recapitulate metabolic disruption observed in tumors. These in vitro screens are complemented by metabolic flux analysis and stable-isotope tracer analysis that reveal the vulnerable metabolic pathways in breast cancers with dysregulated serine metabolism. Our data demonstrate that specific metabolic targets can be identified through this integrated screening and validated empirically to confirm their significance as avenues for potential therapeutics. Citation Format: Mark Daniel Slayton, Abhinav Achreja, Jin Heon Jeon, Liwei Bao, Alisa Liu, Mason Collard, Deepak Nagrath, Sofia Merajver. Integrated in vitro and in silico screening of breast cancer reveals serine metabolism as a precision oncology target [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2022; 2022 Apr 8-13. Philadelphia (PA): AACR; Cancer Res 2022;82(12_Suppl):Abstract nr 3030.

Photocatalytic CO2 reduction by a Z-scheme mechanism in an aqueous suspension of particulate (CuGa)0.3Zn1.4S2, BiVO4 and a Co complex operating dual-functionally as an electron mediator and as a cocatalyst

A visible-light-driven Z-scheme photocatalytic CO2 reduction reaction (CO2RR) to produce CO was demonstrated using an aqueous particulate dispersion containing two bare semiconductors, (CuGa)0.3Zn1.4S2 for CO2RR and BiVO4 for water oxidation. The semiconductors were mixed with a water-soluble cobalt tris(dimethylbipyridine) complex. The CO selectivity was 98% (against H2), and the rate of CO generation was 1–2 orders of magnitude higher than those of previously-reported aqueous suspension photocatalytic systems. O2 was continuously evolved, and isotope tracer analyses confirmed that CO2 was the carbon source for CO. Experimental studies and calculations suggest that the Co complex acts dual-functionally in synergy with (CuGa)0.3Zn1.4S2 and BiVO4: it behaves as an efficient ionic electron mediator, and also acts as a new active CO2RR cocatalyst after a structural change by accepting photoexcited electrons from (CuGa)0.3Zn1.4S2. This simple method, operating in a self-optimizing manner in solution, has great potential to help achieve sustainable, highly active artificial photosynthetic systems.

Multiple metabolic pathways fuel the truncated tricarboxylic acid cycle of the prostate to sustain constant citrate production and secretion

ObjectiveThe prostate is metabolically unique: it produces high levels of citrate for secretion via a truncated tricarboxylic acid (TCA) cycle to maintain male fertility. In prostate cancer (PCa), this phenotype is reprogrammed, making it an interesting therapeutic target. However, how the truncated prostate TCA cycle works is still not completely understood. MethodsWe optimized targeted metabolomics in mouse and human organoid models in ex vivo primary culture. We then used stable isotope tracer analyses to identify the pathways that fuel citrate synthesis. ResultsFirst, mouse and human organoids were shown to recapitulate the unique citrate-secretory program of the prostate, thus representing a novel model that reproduces this unusual metabolic profile. Using stable isotope tracer analysis, several key nutrients were shown to allow the completion of the prostate TCA cycle, revealing a much more complex metabolic profile than originally anticipated. Indeed, along with the known pathway of aspartate replenishing oxaloacetate, glutamine was shown to fuel citrate synthesis through both glutaminolysis and reductive carboxylation in a GLS1-dependent manner. In human organoids, aspartate entered the TCA cycle at the malate entry point, upstream of oxaloacetate. Our results demonstrate that the citrate-secretory phenotype of prostate organoids is supported by the known aspartate–oxaloacetate–citrate pathway, but also by at least three additional pathways: glutaminolysis, reductive carboxylation, and aspartate–malate conversion. ConclusionsOur results add a significant new dimension to the prostate citrate-secretory phenotype, with at least four distinct pathways being involved in citrate synthesis. Better understanding this distinctive citrate metabolic program will have applications in both male fertility as well as in the development of novel targeted anti-metabolic therapies for PCa.

Interwell connectivity inversion method of steam flooding: Based on an analytical model and genetic algorithm

The steam channeling between wells of steam flooding in heavy oil reservoirs is an important factor impacting the development effect. Interwell connectivity is a key indicator for analyzing and predicting steam channeling. At present, only the correlation between injection and production is used to analyze the interwell connectivity, and the variation characteristics of liquid production and bottom hole temperature are not comprehensively considered to achieve the quantitative prediction of interwell connectivity in steam flooding. In this paper, based on the mass-conservation equation and energy-conservation equation, the analytical-calculation model of liquid production and bottom hole temperature of steam flooding in one injection well and one production well model is established. The ‘interwell connectivity coefficients’ are used as the splitting parameter of injected steam to each production well, and the analytical-calculation model of multiwell injection-production is extended. Taking the minimum mean square deviation of the error between the liquid production and bottom hole temperature as the objective function, the genetic algorithm is used to fit the liquid production and bottom hole temperature, and the ‘interwell connectivity coefficients’ of steam flooding are inverted. The innovation of this method lies in the establishment of analytical models of multiwell injection-production fluid production and bottom hole temperature and the establishment of a variable weight function in the objective function, which improves the accuracy of interwell connectivity inversion of steam flooding. Utilizing reservoir numerical simulation technology, the accuracy of the method is verified by synthetic models of homogeneous and high-permeability channels. The method is applied to the steam flooding reservoir of the Ng5 unit in Zhongerbei, Shengli Oilfield, China. The interwell connectivity of the two steam flooding well groups is inverted, and the results are consistent with the tracer analysis results. The findings of this study can help for better understanding of the geological conditions of the steam flooding reservoirs, and provide guidance for the selection of the next development plan.