Effect of Nitric Oxide on Anammox Bacteria

The effects of nitrogen oxides on anammox bacteria are not well known. Therefore, anammox bacteria were exposed to 3,500 ppm nitric oxide (NO) in the gas phase. The anammox bacteria were not inhibited by the high NO concentration but rather used it to oxidize additional ammonium to dinitrogen gas under conditions relevant to wastewater treatment.Nitric oxide (NO) has several different roles in bacteria, fungi, and mammals (24). In nitrogen cycle bacteria, it acts as an intermediate and cell communication/signal transduction molecule. On the other hand, NO is a highly reactive and toxic compound that contributes to ozone depletion and air pollution (5). Due to its reactive nature, many bacteria employ an arsenal of proteins (those encoded by norVW, as well as bacterial globins, heme proteins, etc.) that are used to detoxify NO to the less-reactive and more-stable nitrous oxide (N₂O) (24). Still, N₂O is a very effective greenhouse gas and an unfavorable constituent in the off-gases from nitrification/denitrification nitrogen removal systems (4). The presence of gene(s) encoding cytochrome cd₁ nitrite reductase (EMBL accession no. {"type":"entrez-protein","attrs":{"text":"CAJ74898","term_id":"91201838","term_text":"CAJ74898"}}CAJ74898), flavorubredoxin NorVW (accession no. {"type":"entrez-protein","attrs":{"text":"CAJ73918","term_id":"91200863","term_text":"CAJ73918"}}CAJ73918 and {"type":"entrez-protein","attrs":{"text":"CAJ73688","term_id":"91200638","term_text":"CAJ73688"}}CAJ73688), and bacterial hemoglobin (accession no. {"type":"entrez-protein","attrs":{"text":"CAJ72702","term_id":"91203063","term_text":"CAJ72702"}}CAJ72702) in the genome of Kuenenia stuttgartiensis led to the proposal that NO also plays this dual role (metabolic versus toxic) in anammox bacteria (Fig. ​(Fig.1)1) (10, 20). This has ramifications for both application and metabolism of anammox bacteria. The source of NO in an anammox reactor could be the activity of other community members (ammonium-oxidizing or denitrifying bacteria) or high concentrations of nitrite in the influent wastewater stream. Full-scale anammox reactors typically contain a significant population of ammonium-oxidizing bacteria (AOB). In the single nitritation-anammox reactors, these carry out the conversion of 50% of the ammonium in the wastewater to nitrite (6). It has been shown that AOB may produce significant amounts of NO (2, 7), and recently it was reported that NO and N₂O could be emitted from these reactors up to 0.005 and 1.2% of the total nitrogen load to the reactor, respectively (6, 23). NO may inhibit the anammox bacteria and could also be further reduced to N₂O in these reactors (6, 23). It is presently unknown whether anammox bacteria contribute to the NO or N₂O emissions, although it has been suggested previously that anammox bacteria do not produce N₂O under physiologically relevant conditions (10). Nevertheless, if conversion of NO could be coupled to anaerobic ammonium oxidation, the toxic air pollutant NO would facilitate further removal of ammonium in full-scale anammox bioreactors. In the present study, we investigated the effect of very high NO fluxes on anammox bacteria.Open in a separate windowFIG. 1.The hypothetical anammox pathway with possible routes of NO removal. Solid black arrows: anammox pathway, including nitrite oxidation to nitrate; gray arrow, possible detoxification pathway to N₂O (not observed in the bioreactor); dashed gray arrow, NO oxidation to nitrite/nitrate (not possible under anoxic conditions).NO has been described many times as a potent inhibitor of nitrogen cycle bacteria; aerobic ammonium oxidizers, nitrite oxidizers, and denitrifiers were all inhibited by concentrations as low as a few micromolar units (1, 18, 24). In a previous study, it was suggested that “Candidatus Brocadia anammoxidans” could tolerate up to 600 ppm NO (approximately 1 mg NO·day⁻¹ NO load) (16). In the reported experiments, without direct measurement of nitrous oxide (N₂O) in the effluent gas stream, it was postulated that NO was reduced to N₂O (16). In the present study, we used a carefully monitored sequencing batch reactor (SBR) to further our understanding of the effect and fate of NO in a laboratory-scale anammox reactor under conditions which are relevant in wastewater treatment plants.An SBR (working volume, 3.5 liters) consisting of approximately 80% of the anammox bacterium “Candidatus Brocadia fulgida” and no detectable aerobic ammonium oxidizers (determined by fluorescence in situ hybridization (FISH) as described previously [15]) was used in the present study. Before the first introduction of NO into the reactor, the influent (synthetic wastewater) (21) was supplied to the reactor at a flow rate of 1.4 ml·min⁻¹ with nitrite and ammonium concentrations (assayed as previously described [9]) at 45 and 39 mM, respectively (corresponding to a total of 2,370 mg N·day⁻¹). All nitrite was consumed in the reactor, while 2 mM ammonium was still present in the effluent. For every 1 mol of ammonium, 1.22 mol of nitrite was consumed, similar to the previously determined anammox stoichiometry (19). NO was first introduced at a concentration of 400 to 600 ppm in the gas phase at a flow rate of 10 ml/min (CLD 700EL chemiluminescence NOx analyzer, detection limit of 0.1 ppm NO, with 15 ml/min Ar/CO₂ as the dilution gas [a load of 25 to 28 mg NO·day⁻¹]; EcoPhysics, Michigan). During this period, 45% (±6%) of the supplied NO was removed from the system. Initially, there was no detectable change in the ammonium and nitrite removal efficiencies and no detectable nitrous oxide (N₂O) in the flue gas (analyzed with an Agilent 6890 gas chromatograph). It is most likely that NO was converted to N₂, but the increase in the N₂ concentrations in the off-gas was below the detection limit (1,000 ppm).At day 49, the influent NO concentration was increased to 3,500 ppm (640 mg NO·day⁻¹ load). Simultaneously, the stirring speed of the reactor was increased from 200 to 600 rpm to enable better mass transfer to the flocculent anammox biomass. The increase in the stirring speed did not result in any disturbance in the floc size and settling ability of the biomass but did lead to a much higher level of NO removal (128 mg NO·day⁻¹) by the anammox bacteria. The converted NO could theoretically be converted to N₂O via detoxification enzymes or coupled to ammonium oxidation (Fig. ​(Fig.1).1). Surprisingly, there was no change in the nitrite removal capacity of the bioreactor, suggesting that NO was not a substrate preferred over nitrite. Nitrate concentrations (assayed according to the method in reference 9) were stable around 7.2 mM (±0.7 mM). Theoretically, as anammox bacteria reduce NO, they could oxidize a larger proportion of nitrite to nitrate (Fig. ​(Fig.1)1) to increase their capacity for CO₂ fixation; however, such an increase in nitrate production was not observed (or could not be discriminated by the method used [sensitivity, 100 μM]). During this phase of the experiment, the effluent ammonium concentration gradually decreased to below the detection limit (Fig. ​(Fig.2).2). There was only a minimal N₂O (0.6 ppm) emission from the system, and the total N₂ production increased from 3,060 to 3,680 mg N₂·day⁻¹. This indicated that NO reduction was coupled to the catabolism of the anammox bacteria rather than being detoxified by anammox or other community members. To the best of our knowledge, this was the first time that such a high load of NO was not found to be toxic to the nitrogen cycle bacteria. In a previous study, an NO load of 1 mg NO·day⁻¹ was reported to be toxic to anammox bacteria, most probably due to the fact that the experiments were conducted with biomass that had a 100-fold lower cell density and 10-fold lower activity compared to the current enrichment cultures. Furthermore, the NO conversion in the current experiments was stoichiometrically coupled to ammonium oxidation and not converted to N₂O, indicating that the previously reported N₂O emissions from full-scale anammox bioreactors originated not with the anammox bacteria but rather with other community members as hypothesized previously (8).Open in a separate windowFIG. 2.Ammonium concentration in the effluent of the anammox bioreactor. Dashed lines indicate the trend of effluent ammonium concentration during different phases of the reactor operation. Black arrows indicate the manipulations to influent NO stream, and the gray arrow points to an increase in the influent ammonium concentration. d, day.To determine if there could be more NO-dependent ammonium removal, the influent ammonium concentration was first increased to 41 mM (day 80) and then to 43 mM (day 81). This resulted in a slow but gradual increase in the effluent ammonium concentration, and additional ammonium did not appear to be completely converted, most probably due to NO mass transfer limitations. As a result of the higher level of ammonium removal, the observed anammox stoichiometry in the reactor decreased from 1.22 to 0.91 (nitrite/ammonium). Between days 95 and 131, the NO supply to the reactor was turned off, which resulted in an average ammonium concentration of 3.3 mM (±0.9 mM) in the effluent. Following this period, on day 132, the NO load on the reactor was increased back to 640 mg NO·day⁻¹ (Fig. ​(Fig.2).2). As a result, the effluent ammonium concentration gradually decreased again to an average of 1.5 mM (±0.36 mM). The highest level of NO removal achieved in this period was 371 mg NO·day⁻¹. When the NO supply was turned off on day 165, ammonium concentrations increased back to 3.5 mM (±0.71 mM).During the course of the experiment, the biodiversity of the reactor was monitored using FISH and 16S rRNA gene sequence analysis as described previously (15) with probes specific to eubacteria (3), Planctomycetes (13), anammox bacteria (15), “Ca. Brocadia fulgida” (11), and a variety of aerobic ammonium-oxidizing bacteria (12, 22). Before the experiments started and throughout the cultivation of the anammox bacteria with NO, the only detectable anammox species (with FISH and 16S rRNA gene sequence analysis) was “Candidatus Brocadia fulgida.”In the present study, we showed that 2 mM ammonium (4.5% of the influent concentration) could be removed by anammox bacteria via direct coupling to NO reduction. These observations support the proposal of NO as an intermediate of the anammox reaction and have two consequences for application of the anammox process for nitrogen removal. First, we obtained strong indications that previously reported N₂O emissions (6, 8) from full-scale anammox reactors were not generated by anammox bacteria. In our experiments, even under a very high load of NO, there was hardly any detectable N₂O in the effluent gas stream. The competition for nitrogen oxides by denitrifying and anammox bacteria needs further study but may ultimately be used to design operational conditions that would reduce or even prevent NO and N₂O emissions from full-scale nitritation-anammox reactors. Second, by implementing the results of this study, in the future the anammox process could be designed to remove NO from flue gases. Since NO is mostly emitted together with O₂, this could be achieved by the combination of anammox and aerobic ammonium-oxidizing bacteria, for example, with CANON (completely autotrophic nitrogen removal over nitrite)- or OLAND (oxygen-limited autotrophic nitrification-denitrification)-type reactor systems (14, 17).     

Aggregate Size and Architecture Determine Microbial Activity Balance for One-Stage Partial Nitritation and Anammox

Aerobic ammonium-oxidizing bacteria (AerAOB) and anoxic ammonium-oxidizing bacteria (AnAOB) cooperate in partial nitritation/anammox systems to remove ammonium from wastewater. In this process, large granular microbial aggregates enhance the performance, but little is known about granulation so far. In this study, three suspended-growth oxygen-limited autotrophic nitrification-denitrification (OLAND) reactors with different inoculation and operation (mixing and aeration) conditions, designated reactors A, B, and C, were used. The test objectives were (i) to quantify the AerAOB and AnAOB abundance and the activity balance for the different aggregate sizes and (ii) to relate aggregate morphology, size distribution, and architecture putatively to the inoculation and operation of the three reactors. A nitrite accumulation rate ratio (NARR) was defined as the net aerobic nitrite production rate divided by the anoxic nitrite consumption rate. The smallest reactor A, B, and C aggregates were nitrite sources (NARR, >1.7). Large reactor A and C aggregates were granules capable of autonomous nitrogen removal (NARR, 0.6 to 1.1) with internal AnAOB zones surrounded by an AerAOB rim. Around 50% of the autotrophic space in these granules consisted of AerAOB- and AnAOB-specific extracellular polymeric substances. Large reactor B aggregates were thin film-like nitrite sinks (NARR, <0.5) in which AnAOB were not shielded by an AerAOB layer. Voids and channels occupied 13 to 17% of the anoxic zone of AnAOB-rich aggregates (reactors B and C). The hypothesized granulation pathways include granule replication by division and budding and are driven by growth and/or decay based on species-specific physiology and by hydrodynamic shear and mixing.In the last few years, autotrophic nitrogen removal via partial nitritation and anoxic ammonium oxidation (anammox) has evolved from lab- to full-scale treatment of nitrogenous wastewaters with a low biodegradable organic compound content, and this evolution has been driven mainly by a significant decrease in the operational costs compared to the costs of conventional nitrification and heterotrophic denitrification (11, 23). Oxygen-limited autotrophic nitrification and denitrification (OLAND) is one of the autotrophic processes used and is a one-stage procedure; i.e., partial nitritation and anammox occur in the same reactor (30). The “functional” autotrophic microorganisms in OLAND include aerobic ammonium-oxidizing bacteria (AerAOB) and anoxic ammonium-oxidizing bacteria (AnAOB). With oxygen, AerAOB oxidize ammonium to nitrite (nitritation), and with the nitrite AnAOB oxidize the residual ammonium to form dinitrogen gas and some nitrate (anammox). Additional aerobic nitrite oxidation to nitrate (nitratation) by nitrite-oxidizing bacteria (NOB) lowers the nitrogen removal efficiency, but it can, for instance, be prevented at low dissolved oxygen (DO) levels because the oxygen affinity of AerAOB is higher than that of NOB (16). Reactor configurations for the OLAND process can be based on suspended biomass growing in aggregates, like that in a sequencing batch reactor (SBR) (37) or a gas lift or upflow reactor (32). For suspended-growth systems there are two important challenges: biomass retention and equilibrated microbial activities.High biomass retention efficiency is a prerequisite in anammox technologies because of the slow growth of AnAOB (33). In suspended biomass systems, settling properties determine the retention of biomass and are related to the microbial aggregate morphology (floc or granule) and size. Granules can be defined as compact and dense aggregates with an approximately spherical external appearance that do not coagulate under decreased hydrodynamic shear conditions and settle significantly faster than flocs (18). Toh and coworkers calculated a lower sludge volume index for aerobic granules than for aerobic flocs and also showed that there was an increase in the settling velocity with increasing granule size (35). Hence, in terms of physical properties, large granules are preferable for suspended-growth applications.OLAND aggregate size not only influences settling properties but also affects the proportion of microbial nitrite production and consumption; lower AerAOB activity and higher AnAOB activity were observed with larger aggregates (25, 37). Theoretically, a microbial aggregate with equal nitrite production and nitrite consumption can remove ammonium autonomously, because of its independence from other aggregates for acquisition and conversion of nitrite. Hence, with an increasing aggregate size and thus with a decreasing ratio of nitrite production to nitrite consumption, three functional categories of aggregates can be distinguished: nitrite sources, autonomous nitrogen removers, and nitrite sinks. Because minimal nitrite accumulation is one of the prerequisites for high nitrogen removal efficiency in OLAND reactors, the presence of excess small aggregates is undesirable (9, 37).Although large granular aggregates are desirable for biomass retention and activity balance, so far no formation mechanisms have been proposed for OLAND granules, in contrast to the well-studied anaerobic (13) and aerobic (1) granules. In order to determine general and environment-specific determinants for aggregate size and architecture, three suspended-growth OLAND reactors with different inoculation and operation (mixing and aeration) parameters were selected, and these reactors were designated reactors A, B, and C (Table ​(Table1).1). The first objective of this study was to gain more insight into the relationship between OLAND aggregate size, AerAOB and AnAOB abundance, and the activity balance. The second objective was to propose pathways for aggregation and granulation by relating (dis)similarities in aggregate size distribution, morphology, and architecture to differences in reactor inoculation and operation.

TABLE 1.

Overview of the three OLAND reactor systems from which suspended biomass samples were obtained

Dynamics of Epidemiological Models

We study the SIS and SIRI epidemic models discussing different approaches to compute the thresholds that determine the appearance of an epidemic disease. The stochastic SIS model is a well known mathematical model, studied in several contexts. Here, we present recursively derivations of the dynamic equations for all the moments and we derive the stationary states of the state variables using the moment closure method. We observe that the steady states give a good approximation of the quasi-stationary states of the SIS model. We present the relation between the SIS stochastic model and the contact process introducing creation and annihilation operators. For the spatial stochastic epidemic reinfection model SIRI, where susceptibles S can become infected I, then recover and remain only partial immune against reinfection R, we present the phase transition lines using the mean field and the pair approximation for the moments. We use a scaling argument that allow us to determine analytically an explicit formula for the phase transition lines in pair approximation.     

A spatially stochastic epidemic model with partial immunization shows in mean field approximation the reinfection threshold

Recently, the notion of a reinfection threshold in epidemiological models of only partial immunity has been debated in the literature. We present a rigorous analysis of a model of reinfection which shows a clear threshold behaviour at the parameter point where the reinfection threshold was originally described. Furthermore, we demonstrate that this threshold is the mean field version of a transition in corresponding spatial models of immunization. The reinfection threshold corresponds to the transition between annular growth of an epidemics spreading into a susceptible area leaving recovered behind and compact growth of a susceptible-infected-susceptible region growing into a susceptible area. This transition between annular growth and compact growth was described in the physics literature long before the reinfection threshold debate broke out in the theoretical biology literature.     

Decimal Place Slope,A Fast and Precise Method for Quantifying 13C Incorporation Levels for Detecting the Metabolic Activity of Microbial Species

The metabolic incorporation of stable isotopes such as ¹³C or ¹⁵N into proteins has become a powerful tool for qualitative and quantitative proteome studies. We recently introduced a method that monitors heavy isotope incorporation into proteins and presented data revealing the metabolic activity of various species in a microbial consortium using this technique. To further develop our method using an liquid chromatography (LC)-mass spectrometry (MS)-based approach, we present here a novel approach for calculating the incorporation level of ¹³C into peptides by using the information given in the decimal places of peptide masses obtained by modern high-resolution MS. In the present study, the applicability of this approach is demonstrated using Pseudomonas putida ML2 proteins uniformly labeled via the consumption of [¹³C₆]benzene present in the medium at concentrations of 0, 10, 25, 50, and 100 atom %. The incorporation of ¹³C was calculated on the basis of several labeled peptides derived from one band on an SDS-PAGE gel. The accuracy of the calculated incorporation level depended upon the number of peptide masses included in the analysis, and it was observed that at least 100 peptide masses were required to reduce the deviation below 4 atom %. This accuracy was comparable with calculations of incorporation based on the isotope envelope. Furthermore, this method can be extended to the calculation of the labeling efficiency for a wide range of biomolecules, including RNA and DNA. The technique will therefore allow a highly accurate determination of the carbon flux in microbial consortia with a direct approach based solely on LC-MS.The metabolic incorporation of stable isotopes such as ¹³C or ¹⁵N into proteins has become a powerful component of qualitative and quantitative proteome studies (1). Incorporation of heavy isotopes can be used to analyze microbial processes such as turnover rates and also to help to establish structure-function relationships within microbial communities. Stable isotope probing (SIP¹) techniques based on DNA-SIP (2) and RNA-SIP (3) have been used for this purpose previously. With the introduction of protein-SIP (4), the need for an accurate alternative method for calculating label incorporation into biomolecules arose. Protein-SIP has several advantages compared with DNA/RNA-SIP, the most important being its capacity to detect dynamic levels of incorporation, whereas only labeled or unlabeled states can be categorized by means of DNA/RNA-SIP because of the need to separate ¹³C-DNA/RNA by density gradient centrifugation. Quantitative analysis of ¹³C incorporation levels is of the utmost importance, especially when unraveling carbon fluxes through either microbial communities or food webs with different trophic levels.In contrast to the incorporation of isotopically labeled amino acids, which is often used in quantitative proteomics (5), metabolic labeling by growth substrates and nutrients (e.g. salts) is often imperfect and makes the processing of mass spectrometry (MS) data difficult. For example, when the incorporation of ¹³C exceeds ∼2 atom %, common database search algorithms fail to identify peptides and proteins. The problem can only be managed successfully if a stable, known degree of ¹³C incorporation can be achieved during the experiment (6). Using a low labeling efficiency of roughly 5 atom %, Huttlin et al. (6) chose the altered envelope chain for calculating the incorporation and simultaneously used the signal intensity for a quantitative comparison with the sample that had a natural abundance of ¹³C. Database approaches for peptide identification can cope only with the natural abundance of carbon isotopes; they fail if the incorporation of ¹³C significantly exceeds the natural isotope abundance or if incorporation patterns occur in unpredictable ways (7).The simplest method for determining the incorporation level is to compare the unlabeled average mass of the monoisotopic peptide with the mass of the labeled protein, as estimated by matrix-assisted laser desorption/ionization or electrospray ionization MS (8, 9). A more advanced approach for determining the isotopic mass distribution of peptides is based on the isotopic distribution of the peaks of a peptide envelope (10, 11). Here, for a given isotopomer, the incorporation efficiency is defined as the percentage of incorporated ¹³C atoms with relation to the total number of carbon atoms with the natural isotope abundance (approximately 1.01 atom % ¹³C). As a reference, the theoretical isotopic distribution of a peptide is calculated based upon an algorithm described elsewhere (12). The isotope distribution of both unlabeled and labeled peptides can subsequently be used to calculate the incorporation level. For this method, an Excel spreadsheet (ProSIPQuant.xls) was developed (4). A similar approach, also based on the calculation of isotopic distributions, has been used in other studies (7). In these studies, however, the identification of the peptides is limited to those that have unlabeled counterparts; in addition, an exact calculation can be hampered by overlapping signals coming from additional peaks with similar masses.In the present study, we describe a new way of determining the isotope incorporation level. Our method makes use of characteristic patterns in the digits after the decimal point of the peptide masses generated by high-accuracy instruments such as the linear ion trap LTQ-Orbitrap (Thermo Fisher Scientific, Bremen, Germany). For tryptic peptides, typical regularities in the decimal places of the monoisotopic masses have been observed (13, 14). These observations have been explored in detail for theoretical and experimental data of proteins originating from Helicobacter pylori (15). As a result, a rule called the “half decimal place rule” (HDPR) was defined; it states that the decimal place is nearly half of the first digit for tryptic peptides with masses in the range of 500–1,000 Da. In other words, the exact mass of a peptide is equal to its nominal mass times ∼1.005. Because the difference between ¹²C and ¹³C is slightly greater than 1 Da, exactly 1.0033548378, the decimal places of a tryptic peptide''s mass are shifted in a regular manner by the incorporation level and lead to a significantly increased slope for the digits in the third and fourth place after the decimal point. This shift can be used to estimate the incorporation level of heavy isotopes into the protein. Detecting such shifts requires the highly accurate measurement possible with modern mass spectrometers such as the LTQ-Orbitrap, the Fourier transform ion cyclotron resonance, or the quadrupole time of flight. In this communication, we demonstrate the applicability of this approach using Pseudomonas putida ML2 proteins labeled uniformly via the consumption of [¹³C₆]benzene with five different substrate concentrations (0, 10, 25, 50, and 100 atom % of ¹³C). The ¹³C incorporation was calculated based on several labeled peptides derived from different proteins in one SDS-PAGE band. By these means, we have established a method that allows the determination of ¹³C incorporation into proteins and can be used to assess the metabolic activity of a given species within a mixed community.     

Anti-PlGF inhibits growth of VEGF(R)-inhibitor-resistant tumors without affecting healthy vessels

Novel antiangiogenic strategies with complementary mechanisms are needed to maximize efficacy and minimize resistance to current angiogenesis inhibitors. We explored the therapeutic potential and mechanisms of alphaPlGF, an antibody against placental growth factor (PlGF), a VEGF homolog, which regulates the angiogenic switch in disease, but not in health. alphaPlGF inhibited growth and metastasis of various tumors, including those resistant to VEGF(R) inhibitors (VEGF(R)Is), and enhanced the efficacy of chemotherapy and VEGF(R)Is. alphaPlGF inhibited angiogenesis, lymphangiogenesis, and tumor cell motility. Distinct from VEGF(R)Is, alphaPlGF prevented infiltration of angiogenic macrophages and severe tumor hypoxia, and thus, did not switch on the angiogenic rescue program responsible for resistance to VEGF(R)Is. Moreover, it did not cause or enhance VEGF(R)I-related side effects. The efficacy and safety of alphaPlGF, its pleiotropic and complementary mechanism to VEGF(R)Is, and the negligible induction of an angiogenic rescue program suggest that alphaPlGF may constitute a novel approach for cancer treatment.     

Genotyping and segregation analyses indicate the presence of only two functional MIC genes in rhesus macaques

MIC molecules are stress-inducible ligands of the activating receptor NKG2D, which is expressed on natural killer cells and subsets of T lymphocytes. In rhesus macaques (Macaca mulatta), three different MIC sequences (MIC1, MIC2, MIC3) have been described that are closely related to but, according to phylogenetic analysis, do not represent orthologues of the human MICA and MICB genes. Although a single haplotype of the rhesus macaque Mhc (Mamu) has been completely sequenced, it remained unknown so far whether these three sequences are derived from two or three Mamu-MIC genes. We genotyped a cohort of 115 rhesus macaque individuals for the presence of MIC1, MIC2, and MIC3 sequences and analysed the segregation in families. All individuals were positive for MIC2, whereas only 66.1 and 80.9 % were positive for MIC1 and MIC3, respectively. MIC1 and MIC3 sequences segregated in offspring, indicating that they behave as alleles. Thus, we conclude that two MIC genes are present in the rhesus macaque Mhc, which we propose to designate as Mamu-MICA (MIC1 and MIC3) and Mamu-MICB (MIC2). “MIC1” and “MIC3” are regarded as divergent allelic lineages of the Mamu-MICA gene. Mamu-MIC genotyping of DNA of a cohort of 68 experimentally simian immunodeficiency virus (SIV)-infected rhesus macaques revealed no significant association of either of the two Mamu-MICA allelic lineages with differences in progression to AIDS-like symptoms. Electronic supplementary material Supplementary material is available in the online version of this article at and is accessible for authorize users. Anne Averdam and Sandra Seelke contributed equally.