Modeling biomarker variability in joint analysis of longitudinal and time-to-event data.

The role of visit-to-visit variability of a biomarker in predicting related disease has been recognized in medical science. Existing measures of biological variability are criticized for being entangled with random variability resulted from measurement error or being unreliable due to limited measurements per individual. In this article, we propose a new measure to quantify the biological variability of a biomarker by evaluating the fluctuation of each individual-specific trajectory behind longitudinal measurements. Given a mixed-effects model for longitudinal data with the mean function over time specified by cubic splines, our proposed variability measure can be mathematically expressed as a quadratic form of random effects. A Cox model is assumed for time-to-event data by incorporating the defined variability as well as the current level of the underlying longitudinal trajectory as covariates, which, together with the longitudinal model, constitutes the joint modeling framework in this article. Asymptotic properties of maximum likelihood estimators are established for the present joint model. Estimation is implemented via an Expectation-Maximization (EM) algorithm with fully exponential Laplace approximation used in E-step to reduce the computation burden due to the increase of the random effects dimension. Simulation studies are conducted to reveal the advantage of the proposed method over the two-stage method, as well as a simpler joint modeling approach which does not take into account biomarker variability. Finally, we apply our model to investigate the effect of systolic blood pressure variability on cardiovascular events in the Medical Research Council elderly trial, which is also the motivating example for this article.

A Bayesian multistage spatio-temporally dependent model for spatial clustering and variable selection.

In spatio-temporal epidemiological analysis, it is of critical importance to identify the significant covariates and estimate the associated time-varying effects on the health outcome. Due to the heterogeneity of spatio-temporal data, the subsets of important covariates may vary across space and the temporal trends of covariate effects could be locally different. However, many spatial models neglected the potential local variation patterns, leading to inappropriate inference. Thus, this article proposes a flexible Bayesian hierarchical model to simultaneously identify spatial clusters of regression coefficients with common temporal trends, select significant covariates for each spatial group by introducing binary entry parameters and estimate spatio-temporally varying disease risks. A multistage strategy is employed to reduce the confounding bias caused by spatially structured random components. A simulation study demonstrates the outperformance of the proposed method, compared with several alternatives based on different assessment criteria. The methodology is motivated by two important case studies. The first concerns the low birth weight incidence data in 159 counties of Georgia, USA, for the years 2007 to 2018 and investigates the time-varying effects of potential contributing covariates in different cluster regions. The second concerns the circulatory disease risks across 323 local authorities in England over 10 years and explores the underlying spatial clusters and associated important risk factors.

Performance, comparison and utilization of reduced sulfur (-2) compounds (S2-, FeS and SCN-) in autotrophic denitrification process by thiosulfate-driven autotrophic denitrifier.

The coexistence of reduced sulfur (-2) compounds (S2-, FeS and SCN-) are found in some industrial wastewaters due to pre-treatment of Fe(II) salts. These compounds as electron donors have attracted increasing interest in autotrophic denitrification process. However, the difference of their functions still remain unknown, which limit efficient utilization in autotrophic denitrification process. The study aimed to investigate and compare utilization behavior of these reduced sulfur (-2) compounds in autotrophic denitrification process activated by thiosulfate-driven autotrophic denitrifiers (TAD). Results showed that the best denitrification performance was observed in SCN-; while the reduction of nitrate was significantly inhibited in S2- system and the efficient accumulation of nitrite was observed in FeS system with cycle experiments continuing. Additionally, intermediates containing sulfur were produced rarely in SCN- system. However, the utilization of SCN- was limited obviously in comparison with S2- in coexistence systems. Moreover, the presence of S2- increased the accumulation peak of nitrite in coexistence systems. The biological results indicated that the TAD utilized rapidly these sulfur (-2) compounds, in which genus of Thiobacillus, Magnetospirillum and Azoarcus might play main roles. Moreover, Cupriavidus might also participate in sulfur oxidation in SCN- system. In conclusion, these might be attributed to the characteristics of sulfur (-2) compounds including the toxicity, solubility and reaction process. These findings provide theoretical basis for regulation and utilization of these reduced sulfur (-2) compounds in autotrophic denitrification process.

The efficient utilization of thiocyanate on simultaneous removal of ammonium and nitrate through thiosulfate-driven autotrophic denitrifiers and anammox.

The efficient utilization of thiocyanate remain be an important bottleneck in the low-cost nitrogen removal for wastewaters containing thiocyanate. The study aimed to investigate the feasibility of thiocyanate in removal of nitrate and ammonium through anammox (AN) and thiosulfate-driven autotrophic denitrifiers (TSAD). The results showed that removal of nitrate and ammonium were achieved rapidly utilizing thiocyanate, which was attributed to degradation of thiocyanate by TSAD and cooperation with AN. The utilization efficiency of thiocyanate in nitrogen removal was increased by 250% due to the microbial cooperation. Excess thiocyanate and ammonium did not influence the nitrogen removal amount. However, the nitrogen removal were affected obviously by the biomass ratio (XAN/XTSAD) between AN and TSAD Moreover, the dynamics related to removal of pollutants was described successfully by a modified Monod model with time constraints. These findings offer an insight for efficient utilization of thiocyanate in nitrogen removal via microbial cooperation.

A Simple Schiff Base as Fluorescent Probe for Detection of Al3+ in Aqueous Media and its Application in Cells Imaging.

A novel fluorescence probe for the detection of Al3+ was developed based on methionine protected gold nanoclusters (Met-AuNCs). A fluorescent Schiff base (an aldimine) is formed between the aldehyde group of salicylaldehyde (SA) and the amino groups of Met on the AuNCs, and developed for selective detection of Al3+ in aqueous solution. Al3+ can strongly bind with the Schiff base ligands, accompanied by the blue-shift and an obvious fluorescence emission enhancement at 455 nm. The limits of detection (LODs) of the probe are 2 pmol L-1 for Al3+. Moreover, the probe can successfully be used in fluorescence imaging of Al3+ in living cells (SHSY5Y cells), suggesting that the simple fluorescent probe has great potential use in biological imaging.

Multistate modeling and structure selection for multitype recurrent events and terminal event data.

In cardiovascular disease studies, a large number of risk factors are measured but it often remains unknown whether all of them are relevant variables and whether the impact of these variables is changing with time or remains constant. In addition, more than one kind of cardiovascular disease events can be observed in the same patient and events of different types are possibly correlated. It is expected that different kinds of events are associated with different covariates and the forms of covariate effects also vary between event types. To tackle these problems, we proposed a multistate modeling framework for the joint analysis of multitype recurrent events and terminal event. Model structure selection is performed to identify covariates with time-varying coefficients, time-independent coefficients, and null effects. This helps in understanding the disease process as it can detect relevant covariates and identify the temporal dynamics of the covariate effects. It also provides a more parsimonious model to achieve better risk prediction. The performance of the proposed model and selection method is evaluated in numerical studies and illustrated on a real dataset from the Atherosclerosis Risk in Communities study.

Mitochondrial aggregation caused by cytochalasin B compromises the efficiency and safety of three-parent embryo.

It is widely accepted that cytochalasin B (CB) is required in enucleation of the oocyte in order to stabilize the cytoplasm. However, CB treatment results in the uneven distribution of mitochondria, with aggregation towards the nucleus, which might compromise the efficiency and safety of a three-parent embryo. Here, we demonstrated that CB treatment affected mitochondrial dynamics, spindle morphology and mitochondrial DNA carryover in a concentration-dependent manner. Our results showed that mouse oocytes treated with over 1 µg/ml CB exhibited a more aggregated pattern of mitochondria and diminished filamentous actin expression. Abnormal fission of mitochondria together with changes in spindle morphology increased as CB concentration escalated. Based on the results of mouse experiments, we further revealed the practical value of these findings in human oocytes. Chip-based digital PCR and pyrosequencing revealed that the mitochondrial carryover in reconstituted human embryos was significantly reduced by modifying the concentration of CB from the standard 5 µg/ml to 1 µg/ml before spindle transfer and pronuclear transfer. In conclusion, our findings provide an optimal manipulation for improving the efficiency and safety of mitochondrial replacement therapy.

A feasibility study of metal sulfide (FeS and MnS) on simultaneous denitrification and chromate reduction.

Metal sulfide-based biological process is considered as a promising biotechnology for next-generation wastewater treatment. However, it is not clear if simultaneous bio-reduction of nitrate and chromate was achievable in this process. This study aimed to evaluate the feasibility of metal sulfides (FeS and MnS) on simultaneous denitrification and chromate reduction in autotrophic denitrifying column bioreactors. Results showed that simultaneous reduction of nitrate and chromate was achieved using metal sulfides (FeS and MnS) as electron donors, in which sulfate was the sole soluble end-product. Apart from the sulfur element in the metal sulfides, Fe(II) and Mn(II) were also involved in nitrate and chromate reduction as indicative by the formation of their oxidative states compounds. In microbial communities, SHD-231 and Thiobacillus were the most predominant bacteria, which might have played important roles in simultaneous denitrification and chromate reduction. Compared to FeS, MnS showed a higher performance on nitrate and chromate removal, which could also reduce the toxic inhibition of chromate on nitrate reduction. According to results of XRD and XPS, as well as a lower sulfate production in the FeS system, FeS might have been covered easily to hydroxides due to its bio-oxidation, which limited mass transfer efficiency and bio-availability of FeS. The findings in this study offered insights in the development of promising approaches for the treatment of toxic and hazardous compounds using metal sulfide.

Penalized joint generalized estimating equations for longitudinal binary data.

In statistical research, variable selection and feature extraction are a typical issue. Variable selection in linear models has been fully developed, while it has received relatively little attention for longitudinal data. Since a longitudinal study involves within-subject correlations, the likelihood function of discrete longitudinal responses generally cannot be expressed in analytically closed form, and standard variable selection methods cannot be directly applied. As an alternative, the penalized generalized estimating equation (PGEE) is helpful but very likely results in incorrect variable selection if the working correlation matrix is misspecified. In many circumstances, the within-subject correlations are of interest and need to be modeled together with the mean. For longitudinal binary data, it becomes more challenging because the within-subject correlation coefficients have the so-called Fréchet-Hoeffding upper bound. In this paper, we proposed smoothly clipped absolute deviation (SCAD)-based and least absolute shrinkage and selection operator (LASSO)-based penalized joint generalized estimating equation (PJGEE) methods to simultaneously model the mean and correlations for longitudinal binary data, together with variable selection in the mean model. The estimated correlation coefficients satisfy the upper bound constraints. Simulation studies under different scenarios are made to assess the performance of the proposed method. Compared to existing PGEE methods that specify a working correlation matrix for longitudinal binary data, the proposed PJGEE method works much better in terms of variable selection consistency and parameter estimation accuracy. A real data set on Clinical Global Impression is analyzed for illustration.

Preimplantation genetic diagnosis for a carrier with m.3697G > A mitochondrial DNA mutation.

OBJECTIVE: To explore inheritance of the m.3697G > A mitochondrial DNA (mtDNA) mutation and the effectiveness of preimplantation genetic diagnosis (PGD) for the carrier. METHODS: The study encompassed a pedigree of m.3697G > A mtDNA mutation, including one asymptomatic patient who pursued for PGD treatment. Twelve cumulus oocyte complexes (COCs) were collected in the first PGD cycle and 11 COCs in the second cycle. The efficiency of cumulus cells, polar bodies, and trophectoderm (TE) in predicting the m.3697G > A heteroplasmy of embryos was analyzed. RESULTS: From 23 COCs, 20 oocytes were fertilized successfully. On day 5 and 6 post-fertilization, 15 blastocysts were biopsied. The m.3697G > A mutation load of TE biopsies ranged from 15.2 to 100%. In the first cycle, a blastocyst with mutation load of 31.7% and chromosomal mosaicism was transferred, but failed to yield a clinical pregnancy. In the second cycle, a euploid blastocyst with mutation load of 53.9% was transferred, which gave rise to a clinical pregnancy. However, the pregnancy was terminated due to fetal cleft lip and palate. The mutation loads of different tissues (47.7 ± 1.8%) from the induced fetus were comparable to that of the biopsied TE and amniotic fluid cell (49.7%). The mutation load of neither cumulus cells (R2 = 0.02, p = 0.58) nor polar bodies (R2 = 0.33, p = 0.13) correlated with TE mutation load which was regarded as a gold standard. CONCLUSIONS: The m.3697G > A mutation showed a random pattern of inheritance. PGD could be used to reduce the risk of inheritance of a high mutation load. Cumulus cells are not a suitable predictor of blastocyst mutation load.

Estimating the sample mean and standard deviation from order statistics and sample size in meta-analysis.

In recent years, a growing number of researchers have attempted to overcome the constraints of size and scope in different medical studies to find out the overall treatment effects. As a widespread technique to combine results of multiple studies, commonly used meta-analytic approaches for continuous outcomes demand sample means and standard deviations of primary studies, which are absent sometimes, especially when the outcome is skewed. Instead, the median, the extrema, and/or the quartiles are reported. One feasible solution is to convert the preceding order statistics to demanded statistics to keep effect measures consistent. In this article, we propose new methods based on maximum likelihood estimation for known distributions with unknown parameters. For unknown underlying distributions, the Box-Cox transformation is applied to the reported order statistics so that the techniques for normal distribution can be utilized. Two approaches for estimating the power parameter in Box-Cox transformation are provided. Both simulation studies and real data analysis indicate that in most cases, the proposed methods outperform the existing methods in estimation accuracy.

Ultrasensitive colorimetric strategy for Hg2+ detection based on T-Hg2+-T configuration and target recycling amplification.

A novelty aptasensor for ultrasensitive detection of Hg2+ is developed, exploiting the combination of plasmonic properties of gold nanoparticles (AuNPs) and exonuclease III (Exo III)-assisted target recycling for signal amplification. In the presence of Hg2+, a DNA duplex can be formed due to the strong coordination of Hg2+ and T bases of single-stranded DNA (ssDNA) probe. Exo III digests the DNA duplex from the 3' to 5' direction, resulting in the releasing of Hg2+. Then, the released Hg2+ binds with another ssDNA probe through T-Hg2+-T coordination. After Exo III-assisted Hg2+ cycles, numerous ssDNA probes are exhausted, which promotes poly(diallyldimethylammonium chloride) (PDDA)-induced AuNP aggregation, leading to an obvious color change and aggregation-induced plasmon red shift of AuNPs (from 520 to 610 nm). Therefore, this biosensor is ultrasensitive, which is applicable to the detection of trace level of Hg2+ with a linear range from 5 pM to 0.6 nM and an ultralow detection limit of 0.2 pM. Furthermore, it enables visual detection of Hg2+ as low as 50 pM by the naked eye. More importantly, the assay can be applied to the reliable determination of spiked Hg2+ in sea water samples with good recovery.

Synergy between autotrophic denitrification and Anammox driven by FeS in a fluidized bed bioreactor for advanced nitrogen removal.

On the basis of the metabolic synergy between autotrophic denitrification (AuDen) and anaerobic ammonium oxidation (Anammox), the feasibility of a novel ferrous sulfide (FeS)-driven AuDen and Anammox coupled system (FS-DADAS) was investigated. The nitrogen removal performance of FS-DADAS was investigated in a lab-scale fluidized bed bioreactor fed with synthetic wastewater containing NH4+-N and NO3--N. The results of long-term operation (120 days) demonstrated the promising performance of the system with 100% NO3--N removal and NH4+-N concentrations lower than 8.11 mg L-1 in the effluent at a nitrogen loading rate of 0.20 g-N·(L·d)-1. Sufficient NO2--N was provided by the AuDen for Anammox where a high removal rate of total nitrogen (TN) was achieved. The contribution of Anammox to TN removal was at >80%. The reactor could maintain a stable pH with less SO42- production owing to the fact that Fe(II) and S acted as electron donors. FeS gradually transformed into a sheet-like secondary mineral, FeOOH. AuDen (Thiobacillus) and Anammox bacteria (Candidatus Kuenenia) were successfully retained in the bioreactor, with relative abundance values of 18.82%-23.64% and 3.52%-8.67%, respectively. FS-DADAS is a promising technology for the complete removal of TN from wastewaters with low C/N ratios at low energy consumption.

D-optimal designs of mean-covariance models for longitudinal data.

Longitudinal data analysis has been very common in various fields. It is important in longitudinal studies to choose appropriate numbers of subjects and repeated measurements and allocation of time points as well. Therefore, existing studies proposed many criteria to select the optimal designs. However, most of them focused on the precision of the mean estimation based on some specific models and certain structures of the covariance matrix. In this paper, we focus on both the mean and the marginal covariance matrix. Based on the mean-covariance models, it is shown that the trick of symmetrization can generate better designs under a Bayesian D-optimality criterion over a given prior parameter space. Then, we propose a novel criterion to select the optimal designs. The goal of the proposed criterion is to make the estimates of both the mean vector and the covariance matrix more accurate, and the total cost is as low as possible. Further, we develop an algorithm to solve the corresponding optimization problem. Based on the algorithm, the criterion is illustrated by an application to a real dataset and some simulation studies. We show the superiority of the symmetric optimal design and the symmetrized optimal design in terms of the relative efficiency and parameter estimation. Moreover, we also demonstrate that the proposed criterion is more effective than the previous criteria, and it is suitable for both maximum likelihood estimation and restricted maximum likelihood estimation procedures.

Colorimetric determination of DNA using an aptamer and plasmonic nanoplatform.

A facile plasmonic nanoplatform was developed for rapid and sensitive determination of nucleic acid. Hg2+-regulated molecular beacon (MB, hairpin) containing rich thymine (T) bases at both ends is used as the probe. A hairpin structure can be formed in the MB probe due to the strong binding of Hg2+ to T. However, in the presence of target DNA, the hairpin structure is opened owing to target DNA-specific hybridization with the aptamer. Simultaneously, the opened MB interacts with poly(diallyldimethylammonium chloride) (PDDA) and hinders PDDA-induced aggregation of AuNPs, accompanied by a color change from blue to red and a decrease in absorption ratio (A620/A520). Hence, a good linear relationship was observed between the decreased absorption ratio (A620/A520) and DNA concentration ranging from 0.02 to 2 nmol/L with a low detection limit of 4.42 pmol/L. Moreover, this nanoplatform has been successfully utilized to discriminate between perfect target and mismatch sequences. More importantly, the bioassay is simple, versatile, rapid, and cost-effective compared with other common methods, which holds great promise for clinical diagnosis and biomedical application. Graphical abstract.

Integrative analysis of Mendelian randomization and Bayesian colocalization highlights four genes with putative BMI-mediated causal pathways to diabetes.

Genome-wide association studies have identified hundreds of single nucleotide polymorphisms (SNPs) that are associated with BMI and diabetes. However, lack of adequate data has for long time prevented investigations on the pathogenesis of diabetes where BMI was a mediator of the genetic causal effects on this disease. Of our particular interest is the underlying causal mechanisms of diabetes. We leveraged the summary statistics reported in two studies: UK Biobank (N = 336,473) and Genetic Investigation of ANthropometric Traits (GIANT, N = 339,224) to investigate BMI-mediated genetic causal pathways to diabetes. We first estimated the causal effect of BMI on diabetes by using four Mendelian randomization methods, where a total of 76 independent BMI-associated SNPs (R2 ≤ 0.001, P < 5 × 10-8) were used as instrumental variables. It was consistently shown that higher level of BMI (kg/m2) led to increased risk of diabetes. We then applied two Bayesian colocalization methods and identified shared causal SNPs of BMI and diabetes in genes TFAP2B, TCF7L2, FTO and ZC3H4. This study utilized integrative analysis of Mendelian randomization and colocalization to uncover causal relationships between genetic variants, BMI and diabetes. It highlighted putative causal pathways to diabetes mediated by BMI for four genes.

Immobilization of Phosphatidylserine by Ethanol and Lysozyme on the Cell Surface for Evaluation of Apoptosis-Like Decay in Activated-Sludge Bacteria.

Accurate determination of microbial viability can be crucial in microbe-dominated biosystems. However, the identification of metabolic decay in bacterial cells can be elaborate and difficult. We sought to identify apoptosis-like bacterial processes by using annexin V-fluorescein isothiocyanate (FITC) (AVF), a probe typically used to stain phosphatidylserine (PS) on exposed cell membranes. The bacterial cell wall provides a barrier that is responsible for low efficiency of direct PS staining of decayed bacterial cells. This can be overcome by pretreatment of the bacteria with 70% ethanol, which fixates the bacteria and preserves the PS status, combined with lysozyme treatment to hydrolyze the cell wall. That treatment improved the efficiency of AVF staining considerably, as shown for pure strains of an Ochrobactrum sp. and a Micrococcus sp. Using this method, decayed bacterial cells (induced by starvation) were more strongly stained, indicating externalization of PS to a greater extent than seen for cells harvested at logarithmic growth. A multispecies microbial sludge was artificially decayed by heat treatment or alternating anoxic-oxic treatment, which also induced increased AVF staining, again presumably via decay-related PS externalization. The method developed proved to be efficient for identification of bacterial decay and has potential for the evaluation of multispecies bacterial samples from sources like soil matrix, bioaerosol, and activated sludge.IMPORTANCE Since the externalization of phosphatidylserine (PS) is considered a crucial characteristic of apoptosis, we sought to identify apoptosis-like decay in bacterial cells by PS staining using AVF. We show that this is possible, provided the bacteria are pretreated with ethanol plus lysozyme to remove a physical staining barrier and preserve the original, decay-related externalization of PS. Our work suggests that PS externalization occurs in starved bacteria and this can be quantified with AVF staining, providing a measure of bacterial decay. Since PS is the common component of the lipid bilayer in bacterial cell membranes, this approach also has potential for evaluation of cell decay of other bacterial species.

Hellinger distance-based stable sparse feature selection for high-dimensional class-imbalanced data.

BACKGROUND: Feature selection in class-imbalance learning has gained increasing attention in recent years due to the massive growth of high-dimensional class-imbalanced data across many scientific fields. In addition to reducing model complexity and discovering key biomarkers, feature selection is also an effective method of combating overlapping which may arise in such data and become a crucial aspect for determining classification performance. However, ordinary feature selection techniques for classification can not be simply used for addressing class-imbalanced data without any adjustment. Thus, more efficient feature selection technique must be developed for complicated class-imbalanced data, especially in the context of high-dimensionality. RESULTS: We proposed an algorithm called sssHD to achieve stable sparse feature selection applied it to complicated class-imbalanced data. sssHD is based on the Hellinger distance (HD) coupled with sparse regularization techniques. We stated that Hellinger distance is not only class-insensitive but also translation-invariant. Simulation result indicates that HD-based selection algorithm is effective in recognizing key features and control false discoveries for class-imbalance learning. Five gene expression datasets are also employed to test the performance of the sssHD algorithm, and a comparison with several existing selection procedures is performed. The result shows that sssHD is highly competitive in terms of five assessment metrics. In addition, sssHD presents limited differences between performing and not performing re-balance preprocessing. CONCLUSIONS: sssHD is a practical feature selection method for high-dimensional class-imbalanced data, which is simple and can be an alternative for performing feature selection in class-imbalanced data. sssHD can be easily extended by connecting it with different re-balance preprocessing, different sparse regularization structures as well as different classifiers. As such, the algorithm is extremely general and has a wide range of applicability.

Preventive use of non-invasive ventilation is associated with reduced risk of extubation failure in patients on mechanical ventilation for more than 7 days: a propensity-matched cohort study.

BACKGROUND: Extubation failure (EF) is high in patients on mechanical ventilation for more than 7 days. However, strategies to prevent EF in this population are lacking. AIMS: To evaluate the efficacy of preventive use of noninvasive ventilation in patients on mechanical ventilation for more than 7 days. METHODS: We performed a retrospective study in an intensive care unit of a teaching hospital. We enrolled patients who received mechanical ventilation for more than 7 days and successfully completed a weaning trial. After extubation, patients who immediately received non-invasive ventilation (NIV) were classified as the NIV group, and those who received conventional oxygenation therapy only were classified as the usual care group. RESULTS: We enrolled 95 patients in the NIV group and 61 patients in the usual care group. NIV is associated with reduced risk of EF compared to usual care both 72 h following extubation (11.6% vs 32.8%, P < 0.01, for the overall cohort; 8.6% vs 42.9%, P < 0.01, for the propensity-matched cohort) and 7 days following extubation (25.3% vs 45.9%, P < 0.01, for the overall cohort; 28.6% vs 51.4%, P = 0.09, for the propensity-matched cohort). Within 7 days of extubation, the NIV group had a lower proportion of EF than the controls (log rank test: P < 0.01 and P = 0.02 for the overall and propensity-matched cohorts, respectively). CONCLUSIONS: In patients on mechanical ventilation for more than 7 days, preventive use of NIV is associated with a reduction in EF.

[Performance and Mechanisms of Advanced Nitrogen Removal via FeS-driven Autotrophic Denitrification Coupled with ANAMMOX].

Synergy among members of complex microbial communities in the transformation of elements is a key ecological regulation strategy in nature. Making full use of this phenomenon and achieving functional combinations of different microorganisms may have a significant effect on developing new wastewater treatment processes. In this study, nitrogen-containing pollutants were applied in a static batch experiment. The dosage of FeS, the ratio of NO3--N/NO2--N, and the ratio of ANAMMOX (AN) to autotrophic denitrification (AD) biomass were the controlled reaction conditions. The cooperation mechanism resulting from the metabolic complementation of AN and AD is discussed, and the concept of a (AN+AD)TN 0 nitrogen removal process is proposed. This study showed that the excessive dosage of FeS could ensure the more thorough reaction of AD without significantly affecting the metabolic activity of AN bacteria. A complex microbial community was involved in the competition for metabolic substrates when the proportion of NO2--N in the electron acceptor was increased, resulting in a negative impact on the removal of TN. The increase of AN biomass contributed to the strengthening of the cooperation between AN and AD. When the stoichiometric ratio of NH4+-N to NO3--N was less than 0.85, TN could be completely removed. The results showed that a more effective wastewater treatment process may be established by understanding the interactions between microorganisms, and by manipulating or regulating complex microbial communities. This could achieve the efficient removal of pollutants under low material consumption conditions.